diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..4b998a3c0bd6185161714ff24646753f4e646cf6 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +Step-Audio-AQAA.[[:space:]]A[[:space:]]Fully[[:space:]]End-to-End[[:space:]]Expressive[[:space:]]Large[[:space:]]Audio[[:space:]]Language[[:space:]]Model.pdf filter=lfs diff=lfs merge=lfs -text +Step-Audio-EditX[[:space:]]Technical[[:space:]]Report.pdf filter=lfs diff=lfs merge=lfs -text +Step-Audio.[[:space:]]Unified[[:space:]]Understanding[[:space:]]and[[:space:]]Generation[[:space:]]in[[:space:]]Intelligent[[:space:]]Speech[[:space:]]Interaction.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/Step-Audio-AQAA. A Fully End-to-End Expressive Large Audio Language Model.pdf b/Step-Audio-AQAA. A Fully End-to-End Expressive Large Audio Language Model.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9e236e1ef092f653586d45dc341695ba0b602971 --- /dev/null +++ b/Step-Audio-AQAA. A Fully End-to-End Expressive Large Audio Language Model.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4290ba946aaf9ebc8a1df00a905cbafb19f18ca3bcf9a38389716602ee5f7d7e +size 1203894 diff --git a/Step-Audio-EditX Technical Report.pdf b/Step-Audio-EditX Technical Report.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9d7c013eed3c7dd1ebc0e44a4363806d3b4bea38 --- /dev/null +++ b/Step-Audio-EditX Technical Report.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1ff7493dcedd3e506b8de85860b0c608d06f0392245fb5385b7fa8231234e50 +size 786245 diff --git a/Step-Audio. Unified Understanding and Generation in Intelligent Speech Interaction.pdf b/Step-Audio. Unified Understanding and Generation in Intelligent Speech Interaction.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c0100d2c801316c2a795c8a80181c83fbb8d6e12 --- /dev/null +++ b/Step-Audio. Unified Understanding and Generation in Intelligent Speech Interaction.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce5f6d5b9575f4552c970f118d3191ff49f3e509a847f0fff58c23aa7b510b3f +size 6952309 diff --git a/code/ComfyUI_StepAudioTTS.zip b/code/ComfyUI_StepAudioTTS.zip new file mode 100644 index 0000000000000000000000000000000000000000..b60d71a644beb451835f6efeee5b75fbcbb93d4e --- /dev/null +++ b/code/ComfyUI_StepAudioTTS.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:17e36cf8812529f50c8b80b72d9111e20c476a2694365ad4f9049f019106b38b +size 14201121 diff --git a/code/Step-Audio [intervitens].zip b/code/Step-Audio [intervitens].zip new file mode 100644 index 0000000000000000000000000000000000000000..78744a5dc029ee7feb40d341824baf7ef75de872 --- /dev/null +++ b/code/Step-Audio [intervitens].zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:88549705b04c2bbde00c6e5c67b8966c0aac195c1605a756abd893c53d690e00 +size 37467537 diff --git a/code/Step-Audio-EditX.zip b/code/Step-Audio-EditX.zip new file mode 100644 index 0000000000000000000000000000000000000000..122231ba9c85ba0047c3f13ee464fdbe32520d2b --- /dev/null +++ b/code/Step-Audio-EditX.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6376c6fe2c68201749f7dee3717eab495e5630d3611511e3afaa9b1fe265afcd +size 5979796 diff --git a/code/Step-Audio-tts.zip b/code/Step-Audio-tts.zip new file mode 100644 index 0000000000000000000000000000000000000000..525cc574cb1d6063c27e3ef179a59847f086bf7c --- /dev/null +++ b/code/Step-Audio-tts.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bba78bbc9039e5b3d7df4f77917119bbacc6d8aa9baf4d8114edfceda83fc624 +size 3827854 diff --git a/code/Step-Audio.zip b/code/Step-Audio.zip new file mode 100644 index 0000000000000000000000000000000000000000..5df2dbeb2fb7dff47d2467876fe570ab4d5ceec1 --- /dev/null +++ b/code/Step-Audio.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4aaa4b50011e9c82ac51020de7177b43a07e5f47cbd7e8bb55e80929cac5d7a +size 55625681 diff --git a/code/Step-Audio2.zip b/code/Step-Audio2.zip new file mode 100644 index 0000000000000000000000000000000000000000..62b15fc22455edf43d59f19e8d70998dd2734514 --- /dev/null +++ b/code/Step-Audio2.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:30f63a2dc8c598cc9c968c1a6cca2bc8150beeeec45b8b081843ac2580388dc9 +size 26895459 diff --git a/code/StepAudioInfer.zip b/code/StepAudioInfer.zip new file mode 100644 index 0000000000000000000000000000000000000000..c19517593e43b9bae93c17ed7c682064b79d4715 --- /dev/null +++ b/code/StepAudioInfer.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1bf64260061b9cfb68fc673770b78f561a783a889dc01a3346d5bb12c1f8bf25 +size 39121775 diff --git a/code/astrbot_plugin_tts_Step_Audio.zip b/code/astrbot_plugin_tts_Step_Audio.zip new file mode 100644 index 0000000000000000000000000000000000000000..28379d95f3ff90a893d7ff40844f347bf42e0b68 --- /dev/null +++ b/code/astrbot_plugin_tts_Step_Audio.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b6d563e44b30e27b1c51a05316e262ab6483db0cf6d42a43f5a5407ba9206380 +size 6151313 diff --git a/dataset/StepEval-Audio-360/.gitattributes b/dataset/StepEval-Audio-360/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..1ef325f1b111266a6b26e0196871bd78baa8c2f3 --- /dev/null +++ b/dataset/StepEval-Audio-360/.gitattributes @@ -0,0 +1,59 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mds filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +# Audio files - uncompressed +*.pcm filter=lfs diff=lfs merge=lfs -text +*.sam filter=lfs diff=lfs merge=lfs -text +*.raw filter=lfs diff=lfs merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +# Image files - compressed +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +# Video files - compressed +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.webm filter=lfs diff=lfs merge=lfs -text diff --git a/dataset/StepEval-Audio-360/README.md b/dataset/StepEval-Audio-360/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5513f8423e67a8e04069274d22dc660626f44326 --- /dev/null +++ b/dataset/StepEval-Audio-360/README.md @@ -0,0 +1,79 @@ +--- +license: apache-2.0 +--- +# StepEval-Audio-360 +## Dataset Description +StepEval Audio 360 is a comprehensive dataset that evaluates the ability of multi-modal large language models (MLLMs) in human-AI audio interaction. This audio benchmark dataset, sourced from professional human annotators, covers a full spectrum of capabilities: singing, creativity, role-playing, logical reasoning, voice understanding, voice instruction following, gaming, speech emotion control, and language ability. + +## Languages +StepEval Audio 360 comprises about human voice recorded in different languages and dialects, including Chinese(Szechuan dialect and cantonese), English, and Japanese. It contains both audio and transcription data. + +## Links +- Homepage: [Step-Audio](https://github.com/stepfun-ai/Step-Audio) +- Paper: [Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction +](https://arxiv.org/abs/2502.11946) +- ModelScope: https://modelscope.cn/datasets/stepfun-ai/StepEval-Audio-360 +- Step-Audio Model Suite： + - Step-Audio-Tokenizer： + - Hugging Face：https://huggingface.co/stepfun-ai/Step-Audio-Tokenizer + - ModelScope：https://modelscope.cn/models/stepfun-ai/Step-Audio-Tokenizer + - Step-Audio-Chat : + - HuggingFace: https://huggingface.co/stepfun-ai/Step-Audio-Chat + - ModelScope: https://modelscope.cn/models/stepfun-ai/Step-Audio-Chat + - Step-Audio-TTS-3B： + - Hugging Face: https://huggingface.co/stepfun-ai/Step-Audio-TTS-3B + - ModelScope: https://modelscope.cn/models/stepfun-ai/Step-Audio-TTS-3B + +## User Manual +* Download the dataset +``` +# Make sure you have git-lfs installed (https://git-lfs.com) +git lfs install +git clone https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-360 +cd StepEval-Audio-360 +git lfs pull +``` + +* Decompress audio data +``` +mkdir audios +tar -xvf audios.tar.gz -C audios +``` + +* How to use +``` +from datasets import load_dataset + +dataset = load_dataset("stepfun-ai/StepEval-Audio-360") +dataset = dataset["test"] +for item in dataset: + conversation_id = item["conversation_id"] + category = item["category"] + conversation = item["conversation"] + + # parse multi-turn dialogue data + for turn in conversation: + role = turn["role"] + text = turn["text"] + audio_filename = turn["audio_filename"] # refer to decompressed audio file + if audio_filename is not None: + print(role, text, audio_filename) + else: + print(role, text) +``` + +## Licensing +This dataset project is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). + +## Citation +If you utilize this dataset, please cite it using the BibTeX provided. +``` +@misc {stepfun_2025, + author = { {StepFun} }, + title = { StepEval-Audio-360 (Revision 72a072e) }, + year = 2025, + url = { https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-360 }, + doi = { 10.57967/hf/4528 }, + publisher = { Hugging Face } +} +``` \ No newline at end of file diff --git a/dataset/StepEval-Audio-360/audios.tar.gz b/dataset/StepEval-Audio-360/audios.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..c3b67d27dc511655f4e0aa1fa304a7fb7dcdab9a --- /dev/null +++ b/dataset/StepEval-Audio-360/audios.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f7e9f043765500c6f6940ae58a55cf226ddfdde533099f4765bc40d2710d82d3 +size 166398432 diff --git a/dataset/StepEval-Audio-360/data/test-00000-of-00001.parquet b/dataset/StepEval-Audio-360/data/test-00000-of-00001.parquet new file mode 100644 index 0000000000000000000000000000000000000000..226594d76162f053ebfff493c695c375f12889c1 --- /dev/null +++ b/dataset/StepEval-Audio-360/data/test-00000-of-00001.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d2990e77433b866431bbad8adc27b3aebee77046ceca5d265113994fedf2eaff +size 69065 diff --git a/dataset/StepEval-Audio-360/source.txt b/dataset/StepEval-Audio-360/source.txt new file mode 100644 index 0000000000000000000000000000000000000000..a9f8f0710f0d55a1aae80e5c8cb445bff3561d7a --- /dev/null +++ b/dataset/StepEval-Audio-360/source.txt @@ -0,0 +1 @@ +https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-360 \ No newline at end of file diff --git a/demo/Step-Audio-EditX/.gitattributes b/demo/Step-Audio-EditX/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..0a49b9f652822afab66e97fc574083af28b1c26e --- /dev/null +++ b/demo/Step-Audio-EditX/.gitattributes @@ -0,0 +1,4 @@ +examples filter=lfs diff=lfs merge=lfs -text +speakers/nezha_prompt.wav filter=lfs diff=lfs merge=lfs -text +speakers/nezhaRAP_prompt.wav filter=lfs diff=lfs merge=lfs -text +speakers/nezha哼唱_prompt.wav filter=lfs diff=lfs merge=lfs -text diff --git a/demo/Step-Audio-EditX/.gitignore b/demo/Step-Audio-EditX/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..568d54d7ff558e25ee0c4f1c6d9bf24eedf70144 --- /dev/null +++ b/demo/Step-Audio-EditX/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ +output/ \ No newline at end of file diff --git a/demo/Step-Audio-EditX/LICENSE b/demo/Step-Audio-EditX/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..29f81d812f3e768fa89638d1f72920dbfd1413a8 --- /dev/null +++ b/demo/Step-Audio-EditX/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/demo/Step-Audio-EditX/README.md b/demo/Step-Audio-EditX/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8e8a4be8289d5ba25809c63b4e0447d3e017952c --- /dev/null +++ b/demo/Step-Audio-EditX/README.md @@ -0,0 +1,13 @@ +--- +title: Step-Audio-EditX +emoji: 🚀 +colorFrom: red +colorTo: red +sdk: gradio +sdk_version: 5.49.1 +app_file: app.py +pinned: true +short_description: Try out Step-Audio-EditX +--- + +Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/demo/Step-Audio-EditX/__init__.py b/demo/Step-Audio-EditX/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/app.py b/demo/Step-Audio-EditX/app.py new file mode 100644 index 0000000000000000000000000000000000000000..a99353dbee6bd7b1ea625e6b13ddc54f8f776a50 --- /dev/null +++ b/demo/Step-Audio-EditX/app.py @@ -0,0 +1,505 @@ +import gradio as gr +import os +import argparse +import torch +import logging +import threading +from datetime import datetime +import torchaudio +import librosa +import soundfile as sf + +# ZeroGPU support +try: + import spaces + ZEROGPU_AVAILABLE = True +except ImportError: + ZEROGPU_AVAILABLE = False + # Create a dummy decorator for non-ZeroGPU environments + class spaces: + @staticmethod + def GPU(duration=10): + def decorator(func): + return func + return decorator + +# Project imports +from tokenizer import StepAudioTokenizer +from tts import StepAudioTTS +from model_loader import ModelSource +from config.edit_config import get_supported_edit_types + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# Global variables for ZeroGPU-optimized loading +encoder = None +common_tts_engine = None +args_global = None +_model_lock = threading.Lock() # Thread lock for model initialization + +def initialize_models(): + """Initialize models on first GPU call (ZeroGPU optimization: load inside GPU context)""" + global encoder, common_tts_engine, args_global + + # Fast path: check if already initialized (without lock) + if common_tts_engine is not None: + return # Already initialized + + # Slow path: acquire lock and double-check + with _model_lock: + # Double-check pattern: another thread might have initialized while waiting for lock + if common_tts_engine is not None: + return # Already initialized by another thread + + if args_global is None: + raise RuntimeError("Global args not set. Cannot initialize models.") + + try: + logger.info("🚀 Initializing models inside GPU context (first call)...") + + # Determine model source + source_mapping = { + "auto": ModelSource.AUTO, + "local": ModelSource.LOCAL, + "modelscope": ModelSource.MODELSCOPE, + "huggingface": ModelSource.HUGGINGFACE + } + model_source = source_mapping[args_global.model_source] + + # Load StepAudioTokenizer (avoid CUDA initialization in main process) + encoder = StepAudioTokenizer( + os.path.join(args_global.model_path, "Step-Audio-Tokenizer"), + model_source=model_source, + funasr_model_id=args_global.tokenizer_model_id + ) + logger.info("✓ StepAudioTokenizer loaded") + + # Initialize common TTS engine (avoid CUDA initialization in main process) + common_tts_engine = StepAudioTTS( + os.path.join(args_global.model_path, "Step-Audio-EditX"), + encoder, + model_source=model_source, + tts_model_id=args_global.tts_model_id + ) + logger.info("✓ StepCommonAudioTTS loaded") + print("Models initialized inside GPU context.") + + if ZEROGPU_AVAILABLE: + logger.info("💡 Models loaded inside GPU context - ready for inference") + else: + logger.info("💡 Models loaded - ready for inference") + + except Exception as e: + logger.error(f"❌ Error loading models: {e}") + raise + +def get_model_config(): + """Get model configuration without initializing GPU models""" + if args_global is None: + raise RuntimeError("Global args not set. Cannot get model config.") + + return { + "encoder_path": os.path.join(args_global.model_path, "Step-Audio-Tokenizer"), + "tts_path": os.path.join(args_global.model_path, "Step-Audio-EditX"), + "model_source": args_global.model_source, + "tokenizer_model_id": args_global.tokenizer_model_id, + "tts_model_id": args_global.tts_model_id + } + +def get_gpu_duration(audio_input, text_input, target_text, task_type, task_info): + """Dynamic GPU duration based on whether models need initialization""" + global common_tts_engine + + if common_tts_engine is None: + # First call - need time for model loading (up to 5 minutes) + return 300 # Maximum allowed duration for model initialization + else: + # Subsequent calls - only inference time needed + return 120 # Standard inference duration + +@spaces.GPU(duration=get_gpu_duration) # Dynamic duration based on model state +def process_audio_with_gpu(audio_input, text_input, target_text, task_type, task_info): + """Process audio using GPU (models are loaded inside GPU context to avoid main process errors)""" + global common_tts_engine + + # Initialize models if not already loaded (inside GPU context to avoid main process errors) + if common_tts_engine is None: + print("Initializing common_tts_engine inside GPU context...") + logger.info("🎯 GPU allocated for 300s (first call with model loading)...") + initialize_models() + logger.info("✅ Models loaded successfully inside GPU context") + else: + print("common_tts_engine already initialized.") + logger.info("🎯 GPU allocated for 120s (inference with loaded models)...") + + try: + # Use loaded models (first call may include loading time, subsequent calls are fast) + if task_type == "clone": + output_audio, sr = common_tts_engine.clone(audio_input, text_input, target_text) + else: + output_audio, sr = common_tts_engine.edit(audio_input, text_input, task_type, task_info, target_text) + + logger.info("✅ Audio processing completed") + return output_audio, sr + + except Exception as e: + logger.error(f"❌ Audio processing failed: {e}") + raise + # GPU automatically deallocated when function exits + +# Save audio to temporary directory +def save_audio(audio_type, audio_data, sr, tmp_dir): + """Save audio data to a temporary file with timestamp""" + current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + save_path = os.path.join(tmp_dir, audio_type, f"{current_time}.wav") + os.makedirs(os.path.dirname(save_path), exist_ok=True) + + try: + if isinstance(audio_data, torch.Tensor): + torchaudio.save(save_path, audio_data, sr) + else: + sf.write(save_path, audio_data, sr) + logger.debug(f"Audio saved to: {save_path}") + return save_path + except Exception as e: + logger.error(f"Failed to save audio: {e}") + raise + + +class EditxTab: + """Audio editing and voice cloning interface tab""" + + def __init__(self, args): + self.args = args + self.edit_type_list = list(get_supported_edit_types().keys()) + self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}") + + def history_messages_to_show(self, messages): + """Convert message history to gradio chatbot format""" + show_msgs = [] + for message in messages: + edit_type = message['edit_type'] + edit_info = message['edit_info'] + source_text = message['source_text'] + target_text = message['target_text'] + raw_audio_part = message['raw_wave'] + edit_audio_part = message['edit_wave'] + type_str = f"{edit_type}-{edit_info}" if edit_info is not None else f"{edit_type}" + show_msgs.extend([ + {"role": "user", "content": f"任务类型：{type_str}\n文本：{source_text}"}, + {"role": "user", "content": gr.Audio(value=raw_audio_part, interactive=False)}, + {"role": "assistant", "content": f"输出音频：\n文本：{target_text}"}, + {"role": "assistant", "content": gr.Audio(value=edit_audio_part, interactive=False)} + ]) + return show_msgs + + def generate_clone(self, prompt_text_input, prompt_audio_input, generated_text, edit_type, edit_info, state): + """Generate cloned audio (models are loaded on first GPU call)""" + self.logger.info("Starting voice cloning process") + state['history_audio'] = [] + state['history_messages'] = [] + + # Input validation + if not prompt_text_input or prompt_text_input.strip() == "": + error_msg = "[Error] Uploaded text cannot be empty." + self.logger.error(error_msg) + return [{"role": "user", "content": error_msg}], state + if not prompt_audio_input: + error_msg = "[Error] Uploaded audio cannot be empty." + self.logger.error(error_msg) + return [{"role": "user", "content": error_msg}], state + if not generated_text or generated_text.strip() == "": + error_msg = "[Error] Clone content cannot be empty." + self.logger.error(error_msg) + return [{"role": "user", "content": error_msg}], state + if edit_type != "clone": + error_msg = "[Error] CLONE button must use clone task." + self.logger.error(error_msg) + return [{"role": "user", "content": error_msg}], state + + try: + # Use GPU inference with models loaded inside GPU context + output_audio, output_sr = process_audio_with_gpu( + prompt_audio_input, prompt_text_input, generated_text, "clone", edit_info + ) + + if output_audio is not None and output_sr is not None: + # Convert tensor to numpy if needed + if isinstance(output_audio, torch.Tensor): + audio_numpy = output_audio.cpu().numpy().squeeze() + else: + audio_numpy = output_audio + + # Load original audio for comparison + input_audio_data_numpy, input_sample_rate = librosa.load(prompt_audio_input) + + # Create message for history + cur_assistant_msg = { + "edit_type": edit_type, + "edit_info": edit_info, + "source_text": prompt_text_input, + "target_text": generated_text, + "raw_wave": (input_sample_rate, input_audio_data_numpy), + "edit_wave": (output_sr, audio_numpy), + } + state["history_audio"].append((output_sr, audio_numpy, generated_text)) + state["history_messages"].append(cur_assistant_msg) + + show_msgs = self.history_messages_to_show(state["history_messages"]) + self.logger.info("Voice cloning completed successfully") + return show_msgs, state + else: + error_msg = "[Error] Clone failed" + self.logger.error(error_msg) + return [{"role": "user", "content": error_msg}], state + + except Exception as e: + error_msg = f"[Error] Clone failed: {str(e)}" + self.logger.error(error_msg) + return [{"role": "user", "content": error_msg}], state + + def generate_edit(self, prompt_text_input, prompt_audio_input, generated_text, edit_type, edit_info, state): + """Generate edited audio (models are loaded on first GPU call)""" + self.logger.info("Starting audio editing process") + + # Input validation + if not prompt_audio_input: + error_msg = "[Error] Uploaded audio cannot be empty." + self.logger.error(error_msg) + return [{"role": "user", "content": error_msg}], state + + try: + # Determine which audio to use + if len(state["history_audio"]) == 0: + # First edit - use uploaded audio + audio_to_edit = prompt_audio_input + text_to_use = prompt_text_input + self.logger.debug("Using prompt audio, no history found") + else: + # Use previous edited audio - save it to temp file first + sample_rate, audio_numpy, previous_text = state["history_audio"][-1] + temp_path = save_audio("temp", audio_numpy, sample_rate, self.args.tmp_dir) + audio_to_edit = temp_path + text_to_use = previous_text + self.logger.debug(f"Using previous audio from history, count: {len(state['history_audio'])}") + + # For para-linguistic, use generated_text; otherwise use source text + if edit_type not in {"paralinguistic"}: + generated_text = text_to_use + + # Use GPU inference with models loaded inside GPU context + output_audio, output_sr = process_audio_with_gpu( + audio_to_edit, text_to_use, generated_text, edit_type, edit_info + ) + + if output_audio is not None and output_sr is not None: + # Convert tensor to numpy if needed + if isinstance(output_audio, torch.Tensor): + audio_numpy = output_audio.cpu().numpy().squeeze() + else: + audio_numpy = output_audio + + # Load original audio for comparison + if len(state["history_audio"]) == 0: + input_audio_data_numpy, input_sample_rate = librosa.load(prompt_audio_input) + else: + input_sample_rate, input_audio_data_numpy, _ = state["history_audio"][-1] + + # Create message for history + cur_assistant_msg = { + "edit_type": edit_type, + "edit_info": edit_info, + "source_text": text_to_use, + "target_text": generated_text, + "raw_wave": (input_sample_rate, input_audio_data_numpy), + "edit_wave": (output_sr, audio_numpy), + } + state["history_audio"].append((output_sr, audio_numpy, generated_text)) + state["history_messages"].append(cur_assistant_msg) + + show_msgs = self.history_messages_to_show(state["history_messages"]) + self.logger.info("Audio editing completed successfully") + return show_msgs, state + else: + error_msg = "[Error] Edit failed" + self.logger.error(error_msg) + return [{"role": "user", "content": error_msg}], state + + except Exception as e: + error_msg = f"[Error] Edit failed: {str(e)}" + self.logger.error(error_msg) + return [{"role": "user", "content": error_msg}], state + + def clear_history(self, state): + """Clear conversation history""" + state["history_messages"] = [] + state["history_audio"] = [] + return [], state + + def init_state(self): + """Initialize conversation state""" + return { + "history_messages": [], + "history_audio": [] + } + + def register_components(self): + """Register gradio components - maintaining exact layout from original""" + with gr.Tab("Editx"): + with gr.Row(): + with gr.Column(): + self.model_input = gr.Textbox(label="Model Name", value="Step-Audio-EditX", scale=1) + self.prompt_text_input = gr.Textbox(label="Prompt Text", value="", scale=1) + self.prompt_audio_input = gr.Audio( + sources=["upload", "microphone"], + format="wav", + type="filepath", + label="Input Audio", + ) + self.generated_text = gr.Textbox(label="Target Text", lines=1, max_lines=200, max_length=1000) + with gr.Column(): + with gr.Row(): + self.edit_type = gr.Dropdown(label="Task", choices=self.edit_type_list, value="clone") + self.edit_info = gr.Dropdown(label="Sub-task", choices=[], value=None) + self.chat_box = gr.Chatbot(label="History", type="messages", height=480*1) + with gr.Row(): + with gr.Column(): + with gr.Row(): + self.button_tts = gr.Button("CLONE", variant="primary") + self.button_edit = gr.Button("EDIT", variant="primary") + with gr.Column(): + self.clean_history_submit = gr.Button("Clear History", variant="primary") + + gr.Markdown("---") + gr.Markdown(""" + **Button Description:** + - CLONE: Synthesizes audio based on uploaded audio and text, only used for clone mode, will clear history information when used. + - EDIT: Edits based on uploaded audio, or continues to stack edit effects based on the previous round of generated audio. + """) + gr.Markdown(""" + **Operation Workflow:** + - Upload the audio to be edited on the left side and fill in the corresponding text content of the audio; + - If the task requires modifying text content (such as clone, para-linguistic), fill in the text to be synthesized in the "clone text" field. For all other tasks, keep the uploaded audio text content unchanged; + - Select tasks and subtasks on the right side (some tasks have no subtasks, such as vad, etc.); + - Click the "CLONE" or "EDIT" button on the left side, and audio will be generated in the dialog box on the right side. + """) + gr.Markdown(""" + **Para-linguistic Description:** + - Supported tags include: [Breathing] [Laughter] [Surprise-oh] [Confirmation-en] [Uhm] [Surprise-ah] [Surprise-wa] [Sigh] [Question-ei] [Dissatisfaction-hnn] + - Example: + - Fill in "clone text" field: "Great, the weather is so nice today." Click the "CLONE" button to get audio. + - Change "clone text" field to: "Great[Laughter], the weather is so nice today[Surprise-ah]." Click the "EDIT" button to get para-linguistic audio. + """) + + def register_events(self): + """Register event handlers""" + # Create independent state for each session + state = gr.State(self.init_state()) + + self.button_tts.click(self.generate_clone, + inputs=[self.prompt_text_input, self.prompt_audio_input, self.generated_text, self.edit_type, self.edit_info, state], + outputs=[self.chat_box, state]) + self.button_edit.click(self.generate_edit, + inputs=[self.prompt_text_input, self.prompt_audio_input, self.generated_text, self.edit_type, self.edit_info, state], + outputs=[self.chat_box, state]) + + self.clean_history_submit.click(self.clear_history, inputs=[state], outputs=[self.chat_box, state]) + self.edit_type.change( + fn=self.update_edit_info, + inputs=self.edit_type, + outputs=self.edit_info, + ) + + def update_edit_info(self, category): + """Update sub-task dropdown based on main task selection""" + category_items = get_supported_edit_types() + choices = category_items.get(category, []) + value = None if len(choices) == 0 else choices[0] + return gr.Dropdown(label="Sub-task", choices=choices, value=value) + + +def launch_demo(args, editx_tab): + """Launch the gradio demo""" + with gr.Blocks( + theme=gr.themes.Soft(), + title="🎙️ Step-Audio-EditX", + css=""" + :root { + --font: "Helvetica Neue", Helvetica, Arial, sans-serif; + --font-mono: "SFMono-Regular", Consolas, "Liberation Mono", Menlo, monospace; + } + """) as demo: + gr.Markdown("## 🎙️ Step-Audio-EditX") + gr.Markdown("Audio Editing and Zero-Shot Cloning using Step-Audio-EditX") + + # Register components + editx_tab.register_components() + + # Register events + editx_tab.register_events() + + # Launch demo + demo.queue().launch( + server_name=args.server_name, + server_port=args.server_port, + share=args.share if hasattr(args, 'share') else False + ) + + +if __name__ == "__main__": + # Parse command line arguments + parser = argparse.ArgumentParser(description="Step-Audio Edit Demo") + parser.add_argument("--model-path", type=str, default="stepfun-ai", help="Model path.") + parser.add_argument("--server-name", type=str, default="0.0.0.0", help="Demo server name.") + parser.add_argument("--server-port", type=int, default=7860, help="Demo server port.") + parser.add_argument("--tmp-dir", type=str, default="/tmp/gradio", help="Save path.") + parser.add_argument("--share", action="store_true", help="Share gradio app.") + + # Multi-source loading support parameters + parser.add_argument( + "--model-source", + type=str, + default="huggingface", + choices=["auto", "local", "modelscope", "huggingface"], + help="Model source: auto (detect automatically), local, modelscope, or huggingface" + ) + parser.add_argument( + "--tokenizer-model-id", + type=str, + default="dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online", + help="Tokenizer model ID for online loading" + ) + parser.add_argument( + "--tts-model-id", + type=str, + default=None, + help="TTS model ID for online loading (if different from model-path)" + ) + + args = parser.parse_args() + + # Store args globally for model configuration + args_global = args + + logger.info(f"Configuration loaded:") + logger.info(f"Model source: {args.model_source}") + logger.info(f"Model path: {args.model_path}") + logger.info(f"Tokenizer model ID: {args.tokenizer_model_id}") + if args.tts_model_id: + logger.info(f"TTS model ID: {args.tts_model_id}") + + # Models will be initialized on first GPU call to avoid ZeroGPU main process errors + + if ZEROGPU_AVAILABLE: + logger.info("🎉 ZeroGPU detected - using dynamic GPU duration management!") + logger.info("💡 First call: 300s (model loading), subsequent calls: 120s (inference only)") + else: + logger.info("💻 Running in local mode - models will be loaded on first call") + + # Create EditxTab instance + editx_tab = EditxTab(args) + + # Launch demo + launch_demo(args, editx_tab) \ No newline at end of file diff --git a/demo/Step-Audio-EditX/config/__init__.py b/demo/Step-Audio-EditX/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8316f7ee8c92fc225b936a4afa7a9a3992525175 --- /dev/null +++ b/demo/Step-Audio-EditX/config/__init__.py @@ -0,0 +1,12 @@ +""" +Configuration module for Step-Audio +""" + +from .prompts import AUDIO_EDIT_CLONE_SYSTEM_PROMPT_TPL, AUDIO_EDIT_SYSTEM_PROMPT +from .edit_config import get_supported_edit_types + +__all__ = [ + 'AUDIO_EDIT_CLONE_SYSTEM_PROMPT_TPL', + 'AUDIO_EDIT_SYSTEM_PROMPT', + 'get_supported_edit_types' +] \ No newline at end of file diff --git a/demo/Step-Audio-EditX/config/edit_config.py b/demo/Step-Audio-EditX/config/edit_config.py new file mode 100644 index 0000000000000000000000000000000000000000..5e55649c17fbafe77451d7d8575bcbb1d44d6057 --- /dev/null +++ b/demo/Step-Audio-EditX/config/edit_config.py @@ -0,0 +1,32 @@ +""" +音频编辑配置模块 +包含支持的编辑类型和相关配置 +""" + +def get_supported_edit_types(): + """ + 获取支持的编辑类型和选项 + + Returns: + Dict[str, list]: Dictionary of edit types and their options + """ + return { + "clone": [], + "emotion": [ + 'happy', 'angry', 'sad', 'humour', 'confusion', 'disgusted', + 'empathy', 'embarrass', 'fear', 'surprised', 'excited', + 'depressed', 'coldness', 'admiration', 'remove' + ], + "style": [ + 'serious', 'arrogant', 'child', 'older', 'girl', 'pure', + 'sister', 'sweet', 'ethereal', 'whisper', 'gentle', 'recite', + 'generous', 'act_coy', 'warm', 'shy', 'comfort', 'authority', + 'chat', 'radio', 'soulful', 'story', 'vivid', 'program', + 'news', 'advertising', 'roar', 'murmur', 'shout', 'deeply', 'loudly', + 'remove', 'exaggerated' + ], + "vad": [], + "denoise": [], + "paralinguistic": [], + "speed": ["faster", "slower", "more faster", "more slower"], + } \ No newline at end of file diff --git a/demo/Step-Audio-EditX/config/prompts.py b/demo/Step-Audio-EditX/config/prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..050b2d550ec3527e2286f399046dd50c019c0f81 --- /dev/null +++ b/demo/Step-Audio-EditX/config/prompts.py @@ -0,0 +1,23 @@ +""" +系统提示配置模块 +包含所有TTS和编辑相关的系统提示 +""" + +AUDIO_EDIT_CLONE_SYSTEM_PROMPT_TPL = """Generate audio with the following timbre, prosody and speaking style + +[speaker_start] +speaker name: {speaker} +speaker prompt text: +{prompt_text} +speaker audio tokens: +{prompt_wav_tokens} +[speaker_end] +""" + +AUDIO_EDIT_SYSTEM_PROMPT = """As a highly skilled audio editing and tuning specialist, you excel in interpreting user instructions and applying precise adjustments to meet their needs. Your expertise spans a wide range of enhancement capabilities, including but not limited to: +# Emotional Enhancement +# Speaking Style Transfer +# Non-linguistic Adjustments +# Audio Tuning & Editing +Note: You will receive instructions in natural language and are expected to accurately interpret and execute the most suitable audio edits and enhancements. +""" \ No newline at end of file diff --git a/demo/Step-Audio-EditX/funasr_detach/__init__.py b/demo/Step-Audio-EditX/funasr_detach/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..403e80bce8b65e19f497d662218b00f5a0175946 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/__init__.py @@ -0,0 +1,38 @@ +"""Initialize funasr package.""" + +import os +import pkgutil +import importlib + +dirname = os.path.dirname(__file__) +version_file = os.path.join(dirname, "version.txt") +with open(version_file, "r") as f: + __version__ = f.read().strip() + + +import importlib +import pkgutil + + +def import_submodules(package, recursive=True): + if isinstance(package, str): + package = importlib.import_module(package) + results = {} + for loader, name, is_pkg in pkgutil.walk_packages( + package.__path__, package.__name__ + "." + ): + try: + results[name] = importlib.import_module(name) + except Exception as e: + # 如果想要看到导入错误的具体信息，可以取消注释下面的行 + # print(f"Failed to import {name}: {e}") + pass + if recursive and is_pkg: + results.update(import_submodules(name)) + return results + + +import_submodules(__name__) + +from funasr_detach.auto.auto_model import AutoModel +from funasr_detach.auto.auto_frontend import AutoFrontend diff --git a/demo/Step-Audio-EditX/funasr_detach/auto/__init__.py b/demo/Step-Audio-EditX/funasr_detach/auto/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/auto/auto_frontend.py b/demo/Step-Audio-EditX/funasr_detach/auto/auto_frontend.py new file mode 100644 index 0000000000000000000000000000000000000000..2c779c7e17db40df217ff628593f315e5078adc6 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/auto/auto_frontend.py @@ -0,0 +1,90 @@ +import time +import logging +from tqdm import tqdm + +from funasr_detach.register import tables +from funasr_detach.download.download_from_hub import download_model +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank +from funasr_detach.auto.auto_model import prepare_data_iterator +from funasr_detach.auto.auto_model import prepare_data_iterator + + +class AutoFrontend: + def __init__(self, **kwargs): + assert "model" in kwargs + if "model_conf" not in kwargs: + logging.info( + "download models from model hub: {}".format( + kwargs.get("model_hub", "ms") + ) + ) + kwargs = download_model(**kwargs) + + # build frontend + frontend = kwargs.get("frontend", None) + if frontend is not None: + frontend_class = tables.frontend_classes.get(frontend) + frontend = frontend_class(**kwargs["frontend_conf"]) + + self.frontend = frontend + if "frontend" in kwargs: + del kwargs["frontend"] + self.kwargs = kwargs + + def __call__(self, input, input_len=None, kwargs=None, **cfg): + + kwargs = self.kwargs if kwargs is None else kwargs + kwargs.update(cfg) + + key_list, data_list = prepare_data_iterator(input, input_len=input_len) + batch_size = kwargs.get("batch_size", 1) + device = kwargs.get("device", "cpu") + if device == "cpu": + batch_size = 1 + + meta_data = {} + + result_list = [] + num_samples = len(data_list) + pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True) + + time0 = time.perf_counter() + for beg_idx in range(0, num_samples, batch_size): + end_idx = min(num_samples, beg_idx + batch_size) + data_batch = data_list[beg_idx:end_idx] + key_batch = key_list[beg_idx:end_idx] + + # extract fbank feats + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000) + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths = extract_fbank( + audio_sample_list, + data_type=kwargs.get("data_type", "sound"), + frontend=self.frontend, + **kwargs, + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() + * self.frontend.frame_shift + * self.frontend.lfr_n + / 1000 + ) + + speech.to(device=device), speech_lengths.to(device=device) + batch = {"input": speech, "input_len": speech_lengths, "key": key_batch} + result_list.append(batch) + + pbar.update(1) + description = f"{meta_data}, " + pbar.set_description(description) + + time_end = time.perf_counter() + pbar.set_description(f"time escaped total: {time_end - time0:0.3f}") + + return result_list diff --git a/demo/Step-Audio-EditX/funasr_detach/auto/auto_model.py b/demo/Step-Audio-EditX/funasr_detach/auto/auto_model.py new file mode 100644 index 0000000000000000000000000000000000000000..95fd1c2bbc130bb8b43f040ba00b27f7519896aa --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/auto/auto_model.py @@ -0,0 +1,575 @@ +import json +import time +import copy +import torch +import random +import string +import logging +import os.path +import numpy as np +from tqdm import tqdm + +from funasr_detach.register import tables +from funasr_detach.utils.load_utils import load_bytes +from funasr_detach.download.file import download_from_url +from funasr_detach.download.download_from_hub import download_model +from funasr_detach.utils.vad_utils import slice_padding_audio_samples +from funasr_detach.train_utils.set_all_random_seed import set_all_random_seed +from funasr_detach.train_utils.load_pretrained_model import load_pretrained_model +from funasr_detach.utils.load_utils import load_audio_text_image_video +from funasr_detach.utils.timestamp_tools import timestamp_sentence +from funasr_detach.models.campplus.utils import sv_chunk, postprocess, distribute_spk + +try: + from funasr_detach.models.campplus.cluster_backend import ClusterBackend +except: + print("If you want to use the speaker diarization, please `pip install hdbscan`") + + +def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None): + """ + + :param input: + :param input_len: + :param data_type: + :param frontend: + :return: + """ + data_list = [] + key_list = [] + filelist = [".scp", ".txt", ".json", ".jsonl"] + + chars = string.ascii_letters + string.digits + if isinstance(data_in, str) and data_in.startswith("http"): # url + data_in = download_from_url(data_in) + if isinstance(data_in, str) and os.path.exists( + data_in + ): # wav_path; filelist: wav.scp, file.jsonl;text.txt; + _, file_extension = os.path.splitext(data_in) + file_extension = file_extension.lower() + if file_extension in filelist: # filelist: wav.scp, file.jsonl;text.txt; + with open(data_in, encoding="utf-8") as fin: + for line in fin: + key = "rand_key_" + "".join(random.choice(chars) for _ in range(13)) + if data_in.endswith( + ".jsonl" + ): # file.jsonl: json.dumps({"source": data}) + lines = json.loads(line.strip()) + data = lines["source"] + key = data["key"] if "key" in data else key + else: # filelist, wav.scp, text.txt: id \t data or data + lines = line.strip().split(maxsplit=1) + data = lines[1] if len(lines) > 1 else lines[0] + key = lines[0] if len(lines) > 1 else key + + data_list.append(data) + key_list.append(key) + else: + key = "rand_key_" + "".join(random.choice(chars) for _ in range(13)) + data_list = [data_in] + key_list = [key] + elif isinstance(data_in, (list, tuple)): + if data_type is not None and isinstance( + data_type, (list, tuple) + ): # mutiple inputs + data_list_tmp = [] + for data_in_i, data_type_i in zip(data_in, data_type): + key_list, data_list_i = prepare_data_iterator( + data_in=data_in_i, data_type=data_type_i + ) + data_list_tmp.append(data_list_i) + data_list = [] + for item in zip(*data_list_tmp): + data_list.append(item) + else: + # [audio sample point, fbank, text] + data_list = data_in + key_list = [ + "rand_key_" + "".join(random.choice(chars) for _ in range(13)) + for _ in range(len(data_in)) + ] + else: # raw text; audio sample point, fbank; bytes + if isinstance(data_in, bytes): # audio bytes + data_in = load_bytes(data_in) + if key is None: + key = "rand_key_" + "".join(random.choice(chars) for _ in range(13)) + data_list = [data_in] + key_list = [key] + + return key_list, data_list + + +class AutoModel: + + def __init__(self, **kwargs): + if not kwargs.get("disable_log", False): + tables.print() + + model, kwargs = self.build_model(**kwargs) + + # if vad_model is not None, build vad model else None + vad_model = kwargs.get("vad_model", None) + vad_kwargs = kwargs.get("vad_model_revision", None) + if vad_model is not None: + logging.info("Building VAD model.") + vad_kwargs = { + "model": vad_model, + "model_revision": vad_kwargs, + "device": kwargs["device"], + } + vad_model, vad_kwargs = self.build_model(**vad_kwargs) + + # if punc_model is not None, build punc model else None + punc_model = kwargs.get("punc_model", None) + punc_kwargs = kwargs.get("punc_model_revision", None) + if punc_model is not None: + logging.info("Building punc model.") + punc_kwargs = { + "model": punc_model, + "model_revision": punc_kwargs, + "device": kwargs["device"], + } + punc_model, punc_kwargs = self.build_model(**punc_kwargs) + + # if spk_model is not None, build spk model else None + spk_model = kwargs.get("spk_model", None) + spk_kwargs = kwargs.get("spk_model_revision", None) + if spk_model is not None: + logging.info("Building SPK model.") + spk_kwargs = { + "model": spk_model, + "model_revision": spk_kwargs, + "device": kwargs["device"], + } + spk_model, spk_kwargs = self.build_model(**spk_kwargs) + self.cb_model = ClusterBackend().to(kwargs["device"]) + spk_mode = kwargs.get("spk_mode", "punc_segment") + if spk_mode not in ["default", "vad_segment", "punc_segment"]: + logging.error( + "spk_mode should be one of default, vad_segment and punc_segment." + ) + self.spk_mode = spk_mode + + self.kwargs = kwargs + self.model = model + self.vad_model = vad_model + self.vad_kwargs = vad_kwargs + self.punc_model = punc_model + self.punc_kwargs = punc_kwargs + self.spk_model = spk_model + self.spk_kwargs = spk_kwargs + self.model_path = kwargs.get("model_path") + self.repo_path = kwargs.get("repo_path") + + + def build_model(self, **kwargs): + assert "model" in kwargs + if "model_conf" not in kwargs: + logging.info( + "download models from model hub: {}".format( + kwargs.get("model_hub", "ms") + ) + ) + kwargs = download_model(**kwargs) + + set_all_random_seed(kwargs.get("seed", 0)) + + device = kwargs.get("device", "cuda") + if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0: + device = "cpu" + kwargs["batch_size"] = 1 + kwargs["device"] = device + + if kwargs.get("ncpu", None): + torch.set_num_threads(kwargs.get("ncpu")) + + # build tokenizer + tokenizer = kwargs.get("tokenizer", None) + if tokenizer is not None: + tokenizer_class = tables.tokenizer_classes.get(tokenizer) + tokenizer = tokenizer_class(**kwargs["tokenizer_conf"]) + kwargs["tokenizer"] = tokenizer + kwargs["token_list"] = tokenizer.token_list + vocab_size = len(tokenizer.token_list) + else: + vocab_size = -1 + + # build frontend + frontend = kwargs.get("frontend", None) + if frontend is not None: + frontend_class = tables.frontend_classes.get(frontend) + frontend = frontend_class(**kwargs["frontend_conf"]) + kwargs["frontend"] = frontend + kwargs["input_size"] = frontend.output_size() + + # build model + model_class = tables.model_classes.get(kwargs["model"]) + model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size) + + model.to(device) + + # init_param + init_param = kwargs.get("init_param", None) + if init_param is not None: + logging.info(f"Loading pretrained params from {init_param}") + load_pretrained_model( + model=model, + path=init_param, + ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False), + oss_bucket=kwargs.get("oss_bucket", None), + scope_map=kwargs.get("scope_map", None), + excludes=kwargs.get("excludes", None), + ) + + return model, kwargs + + def __call__(self, *args, **cfg): + kwargs = self.kwargs + kwargs.update(cfg) + res = self.model(*args, kwargs) + return res + + def generate(self, input, input_len=None, **cfg): + if self.vad_model is None: + return self.inference(input, input_len=input_len, **cfg) + + else: + return self.inference_with_vad(input, input_len=input_len, **cfg) + + def inference( + self, input, input_len=None, model=None, kwargs=None, key=None, **cfg + ): + kwargs = self.kwargs if kwargs is None else kwargs + kwargs.update(cfg) + model = self.model if model is None else model + model = model.cuda() + model.eval() + + batch_size = kwargs.get("batch_size", 1) + # if kwargs.get("device", "cpu") == "cpu": + # batch_size = 1 + + key_list, data_list = prepare_data_iterator( + input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key + ) + + speed_stats = {} + asr_result_list = [] + num_samples = len(data_list) + disable_pbar = kwargs.get("disable_pbar", False) + pbar = ( + tqdm(colour="blue", total=num_samples, dynamic_ncols=True) + if not disable_pbar + else None + ) + time_speech_total = 0.0 + time_escape_total = 0.0 + for beg_idx in range(0, num_samples, batch_size): + end_idx = min(num_samples, beg_idx + batch_size) + data_batch = data_list[beg_idx:end_idx] + key_batch = key_list[beg_idx:end_idx] + batch = {"data_in": data_batch, "key": key_batch} + if (end_idx - beg_idx) == 1 and kwargs.get( + "data_type", None + ) == "fbank": # fbank + batch["data_in"] = data_batch[0] + batch["data_lengths"] = input_len + + time1 = time.perf_counter() + with torch.no_grad(): + results, meta_data = model.inference(**batch, **kwargs) + time2 = time.perf_counter() + + asr_result_list.extend(results) + + # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item() + batch_data_time = meta_data.get("batch_data_time", -1) + time_escape = time2 - time1 + speed_stats["load_data"] = meta_data.get("load_data", 0.0) + speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0) + speed_stats["forward"] = f"{time_escape:0.3f}" + speed_stats["batch_size"] = f"{len(results)}" + speed_stats["time_cost"] = f"{(time_escape)}" + speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}" + description = f"{speed_stats}, " + if pbar: + pbar.update(1) + pbar.set_description(description) + time_speech_total += batch_data_time + time_escape_total += time_escape + + if pbar: + # pbar.update(1) + pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}") + torch.cuda.empty_cache() + return asr_result_list + + def inference_with_vad(self, input, input_len=None, **cfg): + + # step.1: compute the vad model + self.vad_kwargs.update(cfg) + beg_vad = time.time() + res = self.inference( + input, + input_len=input_len, + model=self.vad_model, + kwargs=self.vad_kwargs, + **cfg, + ) + end_vad = time.time() + print(f"time cost vad: {end_vad - beg_vad:0.3f}") + + # step.2 compute asr model + model = self.model + kwargs = self.kwargs + kwargs.update(cfg) + batch_size = int(kwargs.get("batch_size_s", 300)) * 1000 + batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60)) * 1000 + kwargs["batch_size"] = batch_size + + key_list, data_list = prepare_data_iterator( + input, input_len=input_len, data_type=kwargs.get("data_type", None) + ) + results_ret_list = [] + time_speech_total_all_samples = 1e-6 + + beg_total = time.time() + pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True) + for i in range(len(res)): + key = res[i]["key"] + vadsegments = res[i]["value"] + input_i = data_list[i] + speech = load_audio_text_image_video( + input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000) + ) + speech_lengths = len(speech) + n = len(vadsegments) + data_with_index = [(vadsegments[i], i) for i in range(n)] + sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0]) + results_sorted = [] + + if not len(sorted_data): + logging.info("decoding, utt: {}, empty speech".format(key)) + continue + + if len(sorted_data) > 0 and len(sorted_data[0]) > 0: + batch_size = max( + batch_size, sorted_data[0][0][1] - sorted_data[0][0][0] + ) + + batch_size_ms_cum = 0 + beg_idx = 0 + beg_asr_total = time.time() + time_speech_total_per_sample = speech_lengths / 16000 + time_speech_total_all_samples += time_speech_total_per_sample + + all_segments = [] + for j, _ in enumerate(range(0, n)): + # pbar_sample.update(1) + batch_size_ms_cum += sorted_data[j][0][1] - sorted_data[j][0][0] + if ( + j < n - 1 + and ( + batch_size_ms_cum + + sorted_data[j + 1][0][1] + - sorted_data[j + 1][0][0] + ) + < batch_size + and (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) + < batch_size_threshold_ms + ): + continue + batch_size_ms_cum = 0 + end_idx = j + 1 + speech_j, speech_lengths_j = slice_padding_audio_samples( + speech, speech_lengths, sorted_data[beg_idx:end_idx] + ) + results = self.inference( + speech_j, + input_len=None, + model=model, + kwargs=kwargs, + disable_pbar=True, + **cfg, + ) + if self.spk_model is not None: + # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]] + for _b in range(len(speech_j)): + vad_segments = [ + [ + sorted_data[beg_idx:end_idx][_b][0][0] / 1000.0, + sorted_data[beg_idx:end_idx][_b][0][1] / 1000.0, + np.array(speech_j[_b]), + ] + ] + segments = sv_chunk(vad_segments) + all_segments.extend(segments) + speech_b = [i[2] for i in segments] + spk_res = self.inference( + speech_b, + input_len=None, + model=self.spk_model, + kwargs=kwargs, + disable_pbar=True, + **cfg, + ) + results[_b]["spk_embedding"] = spk_res[0]["spk_embedding"] + beg_idx = end_idx + if len(results) < 1: + continue + results_sorted.extend(results) + + restored_data = [0] * n + for j in range(n): + index = sorted_data[j][1] + restored_data[index] = results_sorted[j] + result = {} + + # results combine for texts, timestamps, speaker embeddings and others + # TODO: rewrite for clean code + for j in range(n): + for k, v in restored_data[j].items(): + if k.startswith("timestamp"): + if k not in result: + result[k] = [] + for t in restored_data[j][k]: + t[0] += vadsegments[j][0] + t[1] += vadsegments[j][0] + result[k].extend(restored_data[j][k]) + elif k == "spk_embedding": + if k not in result: + result[k] = restored_data[j][k] + else: + result[k] = torch.cat( + [result[k], restored_data[j][k]], dim=0 + ) + elif "text" in k: + if k not in result: + result[k] = restored_data[j][k] + else: + result[k] += " " + restored_data[j][k] + else: + if k not in result: + result[k] = restored_data[j][k] + else: + result[k] += restored_data[j][k] + + return_raw_text = kwargs.get("return_raw_text", False) + # step.3 compute punc model + if self.punc_model is not None: + self.punc_kwargs.update(cfg) + punc_res = self.inference( + result["text"], + model=self.punc_model, + kwargs=self.punc_kwargs, + disable_pbar=True, + **cfg, + ) + raw_text = copy.copy(result["text"]) + if return_raw_text: + result["raw_text"] = raw_text + result["text"] = punc_res[0]["text"] + else: + raw_text = None + + # speaker embedding cluster after resorted + if self.spk_model is not None and kwargs.get("return_spk_res", True): + if raw_text is None: + logging.error("Missing punc_model, which is required by spk_model.") + all_segments = sorted(all_segments, key=lambda x: x[0]) + spk_embedding = result["spk_embedding"] + labels = self.cb_model( + spk_embedding.cpu(), oracle_num=kwargs.get("preset_spk_num", None) + ) + # del result['spk_embedding'] + sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu()) + if self.spk_mode == "vad_segment": # recover sentence_list + sentence_list = [] + for res, vadsegment in zip(restored_data, vadsegments): + if "timestamp" not in res: + logging.error( + "Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \ + and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\ + can predict timestamp, and speaker diarization relies on timestamps." + ) + sentence_list.append( + { + "start": vadsegment[0], + "end": vadsegment[1], + "sentence": res["text"], + "timestamp": res["timestamp"], + } + ) + elif self.spk_mode == "punc_segment": + if "timestamp" not in result: + logging.error( + "Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \ + and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\ + can predict timestamp, and speaker diarization relies on timestamps." + ) + sentence_list = timestamp_sentence( + punc_res[0]["punc_array"], + result["timestamp"], + raw_text, + return_raw_text=return_raw_text, + ) + distribute_spk(sentence_list, sv_output) + result["sentence_info"] = sentence_list + elif kwargs.get("sentence_timestamp", False): + sentence_list = timestamp_sentence( + punc_res[0]["punc_array"], + result["timestamp"], + raw_text, + return_raw_text=return_raw_text, + ) + result["sentence_info"] = sentence_list + if "spk_embedding" in result: + del result["spk_embedding"] + + result["key"] = key + results_ret_list.append(result) + end_asr_total = time.time() + time_escape_total_per_sample = end_asr_total - beg_asr_total + pbar_total.update(1) + pbar_total.set_description( + f"rtf_avg: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, " + f"time_speech: {time_speech_total_per_sample: 0.3f}, " + f"time_escape: {time_escape_total_per_sample:0.3f}" + ) + + return results_ret_list + + def infer_encoder( + self, input, input_len=None, model=None, kwargs=None, key=None, **cfg + ): + kwargs = self.kwargs if kwargs is None else kwargs + kwargs.update(cfg) + model = self.model if model is None else model + model = model.cuda() + model.eval() + + batch_size = kwargs.get("batch_size", 1) + + key_list, data_list = prepare_data_iterator( + input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key + ) + + asr_result_list = [] + num_samples = len(data_list) + for beg_idx in range(0, num_samples, batch_size): + end_idx = min(num_samples, beg_idx + batch_size) + data_batch = data_list[beg_idx:end_idx] + key_batch = key_list[beg_idx:end_idx] + batch = {"data_in": data_batch, "key": key_batch} + if (end_idx - beg_idx) == 1 and kwargs.get( + "data_type", None + ) == "fbank": # fbank + batch["data_in"] = data_batch[0] + batch["data_lengths"] = input_len + + with torch.no_grad(): + results, meta_data, cache = model.infer_encoder(**batch, **kwargs) + asr_result_list.extend(results) + + torch.cuda.empty_cache() + return asr_result_list, cache diff --git a/demo/Step-Audio-EditX/funasr_detach/auto/auto_tokenizer.py b/demo/Step-Audio-EditX/funasr_detach/auto/auto_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..6215a2ecd45e87d3694eef286d209c7f63b338f1 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/auto/auto_tokenizer.py @@ -0,0 +1,7 @@ +class AutoTokenizer: + """ + Undo + """ + + def __init__(self): + pass diff --git a/demo/Step-Audio-EditX/funasr_detach/bin/__init__.py b/demo/Step-Audio-EditX/funasr_detach/bin/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/bin/compute_audio_cmvn.py b/demo/Step-Audio-EditX/funasr_detach/bin/compute_audio_cmvn.py new file mode 100644 index 0000000000000000000000000000000000000000..7e650799521e4271735a1f0699e74ce192d52893 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/bin/compute_audio_cmvn.py @@ -0,0 +1,152 @@ +import os +import json +import numpy as np +import torch +import hydra +import logging +from omegaconf import DictConfig, OmegaConf + +from funasr_detach.register import tables +from funasr_detach.download.download_from_hub import download_model +from funasr_detach.train_utils.set_all_random_seed import set_all_random_seed + + +@hydra.main(config_name=None, version_base=None) +def main_hydra(kwargs: DictConfig): + if kwargs.get("debug", False): + import pdb + + pdb.set_trace() + + assert "model" in kwargs + if "model_conf" not in kwargs: + logging.info( + "download models from model hub: {}".format(kwargs.get("model_hub", "ms")) + ) + kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs) + + main(**kwargs) + + +def main(**kwargs): + print(kwargs) + # set random seed + tables.print() + set_all_random_seed(kwargs.get("seed", 0)) + torch.backends.cudnn.enabled = kwargs.get( + "cudnn_enabled", torch.backends.cudnn.enabled + ) + torch.backends.cudnn.benchmark = kwargs.get( + "cudnn_benchmark", torch.backends.cudnn.benchmark + ) + torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True) + + tokenizer = kwargs.get("tokenizer", None) + + # build frontend if frontend is none None + frontend = kwargs.get("frontend", None) + if frontend is not None: + frontend_class = tables.frontend_classes.get(frontend) + frontend = frontend_class(**kwargs["frontend_conf"]) + kwargs["frontend"] = frontend + kwargs["input_size"] = frontend.output_size() + + # dataset + dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset")) + dataset_train = dataset_class( + kwargs.get("train_data_set_list"), + frontend=frontend, + tokenizer=None, + is_training=False, + **kwargs.get("dataset_conf") + ) + + # dataloader + batch_sampler = kwargs["dataset_conf"].get( + "batch_sampler", "DynamicBatchLocalShuffleSampler" + ) + batch_sampler_train = None + if batch_sampler is not None: + batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler) + dataset_conf = kwargs.get("dataset_conf") + dataset_conf["batch_type"] = "example" + dataset_conf["batch_size"] = 1 + batch_sampler_train = batch_sampler_class( + dataset_train, is_training=False, **dataset_conf + ) + + dataloader_train = torch.utils.data.DataLoader( + dataset_train, + collate_fn=dataset_train.collator, + batch_sampler=batch_sampler_train, + num_workers=int(kwargs.get("dataset_conf").get("num_workers", 4)), + pin_memory=True, + ) + + iter_stop = int(kwargs.get("scale", 1.0) * len(dataloader_train)) + + total_frames = 0 + for batch_idx, batch in enumerate(dataloader_train): + if batch_idx >= iter_stop: + break + + fbank = batch["speech"].numpy()[0, :, :] + if total_frames == 0: + mean_stats = np.sum(fbank, axis=0) + var_stats = np.sum(np.square(fbank), axis=0) + else: + mean_stats += np.sum(fbank, axis=0) + var_stats += np.sum(np.square(fbank), axis=0) + total_frames += fbank.shape[0] + + cmvn_info = { + "mean_stats": list(mean_stats.tolist()), + "var_stats": list(var_stats.tolist()), + "total_frames": total_frames, + } + cmvn_file = kwargs.get("cmvn_file", "cmvn.json") + # import pdb;pdb.set_trace() + with open(cmvn_file, "w") as fout: + fout.write(json.dumps(cmvn_info)) + + mean = -1.0 * mean_stats / total_frames + var = 1.0 / np.sqrt(var_stats / total_frames - mean * mean) + dims = mean.shape[0] + am_mvn = os.path.dirname(cmvn_file) + "/am.mvn" + with open(am_mvn, "w") as fout: + fout.write( + "" + + "\n" + + " " + + str(dims) + + " " + + str(dims) + + "\n" + + "[ 0 ]" + + "\n" + + " " + + str(dims) + + " " + + str(dims) + + "\n" + ) + mean_str = ( + str(list(mean)).replace(",", "").replace("[", "[ ").replace("]", " ]") + ) + fout.write(" 0 " + mean_str + "\n") + fout.write(" " + str(dims) + " " + str(dims) + "\n") + var_str = str(list(var)).replace(",", "").replace("[", "[ ").replace("]", " ]") + fout.write(" 0 " + var_str + "\n") + fout.write("" + "\n") + + +""" +python funasr/bin/compute_audio_cmvn.py \ +--config-path "/Users/zhifu/funasr1.0/examples/aishell/paraformer/conf" \ +--config-name "train_asr_paraformer_conformer_12e_6d_2048_256.yaml" \ +++train_data_set_list="/Users/zhifu/funasr1.0/data/list/audio_datasets.jsonl" \ +++cmvn_file="/Users/zhifu/funasr1.0/data/list/cmvn.json" \ +++dataset_conf.num_workers=0 +""" +if __name__ == "__main__": + main_hydra() diff --git a/demo/Step-Audio-EditX/funasr_detach/bin/inference.py b/demo/Step-Audio-EditX/funasr_detach/bin/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..b864e8f3be50ea8cea4b077af6e9a7549d59d448 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/bin/inference.py @@ -0,0 +1,33 @@ +import hydra +import logging +from omegaconf import DictConfig, OmegaConf, ListConfig + +from funasr_detach.auto.auto_model import AutoModel + + +@hydra.main(config_name=None, version_base=None) +def main_hydra(cfg: DictConfig): + def to_plain_list(cfg_item): + if isinstance(cfg_item, ListConfig): + return OmegaConf.to_container(cfg_item, resolve=True) + elif isinstance(cfg_item, DictConfig): + return {k: to_plain_list(v) for k, v in cfg_item.items()} + else: + return cfg_item + + kwargs = to_plain_list(cfg) + log_level = getattr(logging, kwargs.get("log_level", "INFO").upper()) + + logging.basicConfig(level=log_level) + + if kwargs.get("debug", False): + import pdb + + pdb.set_trace() + model = AutoModel(**kwargs) + res = model.generate(input=kwargs["input"]) + print(res) + + +if __name__ == "__main__": + main_hydra() diff --git a/demo/Step-Audio-EditX/funasr_detach/bin/tokenize_text.py b/demo/Step-Audio-EditX/funasr_detach/bin/tokenize_text.py new file mode 100644 index 0000000000000000000000000000000000000000..5731cef6f4f041d1a7c25102020396bd8a6e6982 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/bin/tokenize_text.py @@ -0,0 +1,281 @@ +#!/usr/bin/env python3 +import argparse +from collections import Counter +import logging +from pathlib import Path +import sys +from typing import List +from typing import Optional + + +from funasr_detach.utils.cli_utils import get_commandline_args +from funasr_detach.tokenizer.build_tokenizer import build_tokenizer +from funasr_detach.tokenizer.cleaner import TextCleaner +from funasr_detach.tokenizer.phoneme_tokenizer import g2p_classes +from funasr_detach.utils.types import str2bool +from funasr_detach.utils.types import str_or_none + + +def field2slice(field: Optional[str]) -> slice: + """Convert field string to slice + + Note that field string accepts 1-based integer. + + Examples: + >>> field2slice("1-") + slice(0, None, None) + >>> field2slice("1-3") + slice(0, 3, None) + >>> field2slice("-3") + slice(None, 3, None) + """ + field = field.strip() + try: + if "-" in field: + # e.g. "2-" or "2-5" or "-7" + s1, s2 = field.split("-", maxsplit=1) + if s1.strip() == "": + s1 = None + else: + s1 = int(s1) + if s1 == 0: + raise ValueError("1-based string") + if s2.strip() == "": + s2 = None + else: + s2 = int(s2) + else: + # e.g. "2" + s1 = int(field) + s2 = s1 + 1 + if s1 == 0: + raise ValueError("must be 1 or more value") + except ValueError: + raise RuntimeError(f"Format error: e.g. '2-', '2-5', or '-5': {field}") + + if s1 is None: + slic = slice(None, s2) + else: + # -1 because of 1-based integer following "cut" command + # e.g "1-3" -> slice(0, 3) + slic = slice(s1 - 1, s2) + return slic + + +def tokenize( + input: str, + output: str, + field: Optional[str], + delimiter: Optional[str], + token_type: str, + space_symbol: str, + non_linguistic_symbols: Optional[str], + bpemodel: Optional[str], + log_level: str, + write_vocabulary: bool, + vocabulary_size: int, + remove_non_linguistic_symbols: bool, + cutoff: int, + add_symbol: List[str], + cleaner: Optional[str], + g2p: Optional[str], +): + + logging.basicConfig( + level=log_level, + format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", + ) + if input == "-": + fin = sys.stdin + else: + fin = Path(input).open("r", encoding="utf-8") + if output == "-": + fout = sys.stdout + else: + p = Path(output) + p.parent.mkdir(parents=True, exist_ok=True) + fout = p.open("w", encoding="utf-8") + + cleaner = TextCleaner(cleaner) + tokenizer = build_tokenizer( + token_type=token_type, + bpemodel=bpemodel, + delimiter=delimiter, + space_symbol=space_symbol, + non_linguistic_symbols=non_linguistic_symbols, + remove_non_linguistic_symbols=remove_non_linguistic_symbols, + g2p_type=g2p, + ) + + counter = Counter() + if field is not None: + field = field2slice(field) + + for line in fin: + line = line.rstrip() + if field is not None: + # e.g. field="2-" + # uttidA hello world!! -> hello world!! + tokens = line.split(delimiter) + tokens = tokens[field] + if delimiter is None: + line = " ".join(tokens) + else: + line = delimiter.join(tokens) + + line = cleaner(line) + tokens = tokenizer.text2tokens(line) + if not write_vocabulary: + fout.write(" ".join(tokens) + "\n") + else: + for t in tokens: + counter[t] += 1 + + if not write_vocabulary: + return + + ## FIXME + ## del duplicate add_symbols in counter + for symbol_and_id in add_symbol: + # e.g symbol=":0" + try: + symbol, idx = symbol_and_id.split(":") + except ValueError: + raise RuntimeError(f"Format error: e.g. ':0': {symbol_and_id}") + symbol = symbol.strip() + if symbol in counter: + del counter[symbol] + + # ======= write_vocabulary mode from here ======= + # Sort by the number of occurrences in descending order + # and filter lower frequency words than cutoff value + words_and_counts = list( + filter(lambda x: x[1] > cutoff, sorted(counter.items(), key=lambda x: -x[1])) + ) + # Restrict the vocabulary size + if vocabulary_size > 0: + if vocabulary_size < len(add_symbol): + raise RuntimeError(f"vocabulary_size is too small: {vocabulary_size}") + words_and_counts = words_and_counts[: vocabulary_size - len(add_symbol)] + + # Parse the values of --add_symbol + for symbol_and_id in add_symbol: + # e.g symbol=":0" + try: + symbol, idx = symbol_and_id.split(":") + idx = int(idx) + except ValueError: + raise RuntimeError(f"Format error: e.g. ':0': {symbol_and_id}") + symbol = symbol.strip() + + # e.g. idx=0 -> append as the first symbol + # e.g. idx=-1 -> append as the last symbol + if idx < 0: + idx = len(words_and_counts) + 1 + idx + words_and_counts.insert(idx, (symbol, None)) + + # Write words + for w, c in words_and_counts: + fout.write(w + "\n") + + # Logging + total_count = sum(counter.values()) + invocab_count = sum(c for w, c in words_and_counts if c is not None) + logging.info(f"OOV rate = {(total_count - invocab_count) / total_count * 100} %") + + +def get_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="Tokenize texts", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--log_level", + type=lambda x: x.upper(), + default="INFO", + choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"), + help="The verbose level of logging", + ) + + parser.add_argument( + "--input", "-i", required=True, help="Input text. - indicates sys.stdin" + ) + parser.add_argument( + "--output", "-o", required=True, help="Output text. - indicates sys.stdout" + ) + parser.add_argument( + "--field", + "-f", + help="The target columns of the input text as 1-based integer. e.g 2-", + ) + parser.add_argument( + "--token_type", + "-t", + default="char", + choices=["char", "bpe", "word", "phn"], + help="Token type", + ) + parser.add_argument("--delimiter", "-d", default=None, help="The delimiter") + parser.add_argument("--space_symbol", default="", help="The space symbol") + parser.add_argument("--bpemodel", default=None, help="The bpemodel file path") + parser.add_argument( + "--non_linguistic_symbols", + type=str_or_none, + help="non_linguistic_symbols file path", + ) + parser.add_argument( + "--remove_non_linguistic_symbols", + type=str2bool, + default=False, + help="Remove non-language-symbols from tokens", + ) + parser.add_argument( + "--cleaner", + type=str_or_none, + choices=[None, "tacotron", "jaconv", "vietnamese", "korean_cleaner"], + default=None, + help="Apply text cleaning", + ) + parser.add_argument( + "--g2p", + type=str_or_none, + choices=g2p_classes, + default=None, + help="Specify g2p method if --token_type=phn", + ) + + group = parser.add_argument_group("write_vocabulary mode related") + group.add_argument( + "--write_vocabulary", + type=str2bool, + default=False, + help="Write tokens list instead of tokenized text per line", + ) + group.add_argument("--vocabulary_size", type=int, default=0, help="Vocabulary size") + group.add_argument( + "--cutoff", + default=0, + type=int, + help="cut-off frequency used for write-vocabulary mode", + ) + group.add_argument( + "--add_symbol", + type=str, + default=[], + action="append", + help="Append symbol e.g. --add_symbol ':0' --add_symbol ':1'", + ) + + return parser + + +def main(cmd=None): + print(get_commandline_args(), file=sys.stderr) + parser = get_parser() + args = parser.parse_args(cmd) + kwargs = vars(args) + tokenize(**kwargs) + + +if __name__ == "__main__": + main() diff --git a/demo/Step-Audio-EditX/funasr_detach/bin/train.py b/demo/Step-Audio-EditX/funasr_detach/bin/train.py new file mode 100644 index 0000000000000000000000000000000000000000..ec40162096116cc86b5c458e32d39d237aa89c5f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/bin/train.py @@ -0,0 +1,227 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- + +import os +import sys +import torch +import hydra +import logging +import argparse +from io import BytesIO +import torch.distributed as dist +from collections.abc import Sequence +from omegaconf import DictConfig, OmegaConf +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.distributed.fsdp import FullyShardedDataParallel as FSDP + +from funasr_detach.register import tables +from funasr_detach.optimizers import optim_classes +from funasr_detach.train_utils.trainer import Trainer +from funasr_detach.schedulers import scheduler_classes +from funasr_detach.train_utils.initialize import initialize +from funasr_detach.download.download_from_hub import download_model +from funasr_detach.models.lora.utils import mark_only_lora_as_trainable +from funasr_detach.train_utils.set_all_random_seed import set_all_random_seed +from funasr_detach.train_utils.load_pretrained_model import load_pretrained_model + +# from funasr_detach.tokenizer.build_tokenizer import build_tokenizer +# from funasr_detach.tokenizer.token_id_converter import TokenIDConverter +# from funasr_detach.tokenizer.funtoken import build_tokenizer + + +@hydra.main(config_name=None, version_base=None) +def main_hydra(kwargs: DictConfig): + if kwargs.get("debug", False): + import pdb + + pdb.set_trace() + + assert "model" in kwargs + if "model_conf" not in kwargs: + logging.info( + "download models from model hub: {}".format(kwargs.get("model_hub", "ms")) + ) + kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs) + + main(**kwargs) + + +def main(**kwargs): + print(kwargs) + + # set random seed + set_all_random_seed(kwargs.get("seed", 0)) + torch.backends.cudnn.enabled = kwargs.get( + "cudnn_enabled", torch.backends.cudnn.enabled + ) + torch.backends.cudnn.benchmark = kwargs.get( + "cudnn_benchmark", torch.backends.cudnn.benchmark + ) + torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True) + + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + if local_rank == 0: + tables.print() + # Check if we are using DDP or FSDP + use_ddp = "WORLD_SIZE" in os.environ and int(os.environ["WORLD_SIZE"]) > 1 + use_fsdp = kwargs.get("use_fsdp", None) + if use_ddp or use_fsdp: + dist.init_process_group( + backend=kwargs.get("backend", "nccl"), init_method="env://" + ) + torch.cuda.set_device(local_rank) + + # save config.yaml + if ( + (use_ddp or use_fsdp) + and dist.get_rank() == 0 + or not (use_ddp or use_fsdp) + and local_rank == 0 + ): + os.makedirs(kwargs.get("output_dir", "./"), exist_ok=True) + yaml_file = os.path.join(kwargs.get("output_dir", "./"), "config.yaml") + OmegaConf.save(config=kwargs, f=yaml_file) + logging.info("config.yaml is saved to: %s", yaml_file) + + tokenizer = kwargs.get("tokenizer", None) + if tokenizer is not None: + tokenizer_class = tables.tokenizer_classes.get(tokenizer) + tokenizer = tokenizer_class(**kwargs["tokenizer_conf"]) + kwargs["tokenizer"] = tokenizer + + # build frontend if frontend is none None + frontend = kwargs.get("frontend", None) + if frontend is not None: + frontend_class = tables.frontend_classes.get(frontend) + frontend = frontend_class(**kwargs["frontend_conf"]) + kwargs["frontend"] = frontend + kwargs["input_size"] = frontend.output_size() + + # build model + model_class = tables.model_classes.get(kwargs["model"]) + model = model_class( + **kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list) + ) + + # init_param + init_param = kwargs.get("init_param", None) + if init_param is not None: + if not isinstance(init_param, (list, tuple)): + init_param = (init_param,) + logging.info("init_param is not None: %s", init_param) + for p in init_param: + logging.info(f"Loading pretrained params from {p}") + load_pretrained_model( + model=model, + path=p, + ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True), + oss_bucket=kwargs.get("oss_bucket", None), + scope_map=kwargs.get("scope_map", None), + excludes=kwargs.get("excludes", None), + ) + else: + initialize(model, kwargs.get("init", "kaiming_normal")) + + # freeze_param + freeze_param = kwargs.get("freeze_param", None) + if freeze_param is not None: + freeze_param = eval(freeze_param) + if isinstance(freeze_param, Sequence): + freeze_param = (freeze_param,) + logging.info("freeze_param is not None: %s", freeze_param) + for t in freeze_param: + for k, p in model.named_parameters(): + if k.startswith(t + ".") or k == t: + logging.info(f"Setting {k}.requires_grad = False") + p.requires_grad = False + + if use_ddp: + model = model.cuda(local_rank) + model = DDP( + model, + device_ids=[local_rank], + find_unused_parameters=kwargs.get("train_conf", {}).get( + "find_unused_parameters", False + ), + ) + elif use_fsdp: + model = FSDP(model).cuda(local_rank) + else: + model = model.to(device=kwargs.get("device", "cuda")) + + # optim + optim = kwargs.get("optim", "adam") + assert optim in optim_classes + optim_class = optim_classes.get(optim) + optim = optim_class(model.parameters(), **kwargs.get("optim_conf")) + + # scheduler + scheduler = kwargs.get("scheduler", "warmuplr") + assert scheduler in scheduler_classes + scheduler_class = scheduler_classes.get(scheduler) + scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf")) + + # dataset + dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset")) + dataset_tr = dataset_class( + kwargs.get("train_data_set_list"), + frontend=frontend, + tokenizer=tokenizer, + is_training=True, + **kwargs.get("dataset_conf"), + ) + dataset_val = dataset_class( + kwargs.get("valid_data_set_list"), + frontend=frontend, + tokenizer=tokenizer, + is_training=False, + **kwargs.get("dataset_conf"), + ) + + # dataloader + batch_sampler = kwargs["dataset_conf"].get( + "batch_sampler", "DynamicBatchLocalShuffleSampler" + ) + batch_sampler_val = None + if batch_sampler is not None: + batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler) + batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf")) + batch_sampler_val = batch_sampler_class( + dataset_val, is_training=False, **kwargs.get("dataset_conf") + ) + dataloader_tr = torch.utils.data.DataLoader( + dataset_tr, + collate_fn=dataset_tr.collator, + batch_sampler=batch_sampler, + num_workers=kwargs.get("dataset_conf").get("num_workers", 4), + pin_memory=True, + ) + + dataloader_val = torch.utils.data.DataLoader( + dataset_val, + collate_fn=dataset_val.collator, + batch_sampler=batch_sampler_val, + num_workers=kwargs.get("dataset_conf").get("num_workers", 4), + pin_memory=True, + ) + trainer = Trainer( + model=model, + optim=optim, + scheduler=scheduler, + dataloader_train=dataloader_tr, + dataloader_val=dataloader_val, + local_rank=local_rank, + use_ddp=use_ddp, + use_fsdp=use_fsdp, + output_dir=kwargs.get("output_dir", "./exp"), + resume=kwargs.get("resume", True), + **kwargs.get("train_conf"), + ) + trainer.run() + + if use_ddp or use_fsdp: + torch.distributed.destroy_process_group() + + +if __name__ == "__main__": + main_hydra() diff --git a/demo/Step-Audio-EditX/funasr_detach/datasets/__init__.py b/demo/Step-Audio-EditX/funasr_detach/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/__init__.py b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/datasets.py b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..36d0915fac1e8892fef3614210ec7a35265b8450 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/datasets.py @@ -0,0 +1,112 @@ +import torch + +from funasr_detach.register import tables +from funasr_detach.utils.load_utils import extract_fbank, load_audio_text_image_video + + +@tables.register("dataset_classes", "AudioDataset") +class AudioDataset(torch.utils.data.Dataset): + """ + AudioDataset + """ + + def __init__( + self, + path, + index_ds: str = None, + frontend=None, + tokenizer=None, + int_pad_value: int = -1, + float_pad_value: float = 0.0, + **kwargs + ): + super().__init__() + index_ds_class = tables.index_ds_classes.get(index_ds) + self.index_ds = index_ds_class(path, **kwargs) + preprocessor_speech = kwargs.get("preprocessor_speech", None) + if preprocessor_speech: + preprocessor_speech_class = tables.preprocessor_classes.get( + preprocessor_speech + ) + preprocessor_speech = preprocessor_speech_class( + **kwargs.get("preprocessor_speech_conf") + ) + self.preprocessor_speech = preprocessor_speech + preprocessor_text = kwargs.get("preprocessor_text", None) + if preprocessor_text: + preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text) + preprocessor_text = preprocessor_text_class( + **kwargs.get("preprocessor_text_conf") + ) + self.preprocessor_text = preprocessor_text + + self.frontend = frontend + self.fs = 16000 if frontend is None else frontend.fs + self.data_type = "sound" + self.tokenizer = tokenizer + + self.int_pad_value = int_pad_value + self.float_pad_value = float_pad_value + + def get_source_len(self, index): + item = self.index_ds[index] + return self.index_ds.get_source_len(item) + + def get_target_len(self, index): + item = self.index_ds[index] + return self.index_ds.get_target_len(item) + + def __len__(self): + return len(self.index_ds) + + def __getitem__(self, index): + item = self.index_ds[index] + # import pdb; + # pdb.set_trace() + source = item["source"] + data_src = load_audio_text_image_video(source, fs=self.fs) + if self.preprocessor_speech: + data_src = self.preprocessor_speech(data_src, fs=self.fs) + speech, speech_lengths = extract_fbank( + data_src, data_type=self.data_type, frontend=self.frontend, is_final=True + ) # speech: [b, T, d] + + target = item["target"] + if self.preprocessor_text: + target = self.preprocessor_text(target) + if self.tokenizer: + ids = self.tokenizer.encode(target) + text = torch.tensor(ids, dtype=torch.int64) + else: + ids = target + text = ids + ids_lengths = len(ids) + text_lengths = torch.tensor([ids_lengths], dtype=torch.int32) + + return { + "speech": speech[0, :, :], + "speech_lengths": speech_lengths, + "text": text, + "text_lengths": text_lengths, + } + + def collator(self, samples: list = None): + outputs = {} + for sample in samples: + for key in sample.keys(): + if key not in outputs: + outputs[key] = [] + outputs[key].append(sample[key]) + + for key, data_list in outputs.items(): + if isinstance(data_list[0], torch.Tensor): + if data_list[0].dtype == torch.int64: + + pad_value = self.int_pad_value + else: + pad_value = self.float_pad_value + + outputs[key] = torch.nn.utils.rnn.pad_sequence( + data_list, batch_first=True, padding_value=pad_value + ) + return outputs diff --git a/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/index_ds.py b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/index_ds.py new file mode 100644 index 0000000000000000000000000000000000000000..8b9312b05c0f3d43336d75b9fe14bf3b15d84b1f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/index_ds.py @@ -0,0 +1,150 @@ +import os +import json +import torch +import logging +import concurrent.futures +import librosa +import torch.distributed as dist + +from funasr_detach.register import tables + + +@tables.register("index_ds_classes", "IndexDSJsonlRankSplit") +class IndexDSJsonlRankSplit(torch.utils.data.Dataset): + + def __init__(self, path): + super().__init__() + + contents = [] + with open(path, encoding="utf-8") as fin: + for line in fin: + data = json.loads(line.strip()) + if "text" in data: # for sft + self.contents.append(data["text"]) + if "source" in data: # for speech lab pretrain + prompt = data["prompt"] + source = data["source"] + target = data["target"] + source_len = data["source_len"] + target_len = data["target_len"] + + contents.append( + { + "source": source, + "prompt": prompt, + "target": target, + "source_len": source_len, + "target_len": target_len, + } + ) + + self.contents = [] + total_num = len(contents) + try: + rank = dist.get_rank() + world_size = dist.get_world_size() + except: + rank = 0 + world_size = 1 + logging.warning("distributed is not initialized, only single shard") + num_per_rank = total_num // world_size + + # rank = 0 + # import ipdb; ipdb.set_trace() + self.contents = contents[rank * num_per_rank : (rank + 1) * num_per_rank] + + logging.info( + "in rank: {}, num of samplers: {}, total_num of samplers across ranks: {}".format( + rank, len(self.contents), len(contents) + ) + ) + + def __len__(self): + return len(self.contents) + + def __getitem__(self, index): + try: + data = self.contents[index] + except: + print(index) + return data + + def get_source_len(self, data_dict): + return data_dict["source_len"] + + def get_target_len(self, data_dict): + + return data_dict["target_len"] if "target_len" in data_dict else 0 + + +@tables.register("index_ds_classes", "IndexDSJsonl") +@tables.register("index_ds_classes", "IndexDSJsonlRankFull") +class IndexDSJsonlRankFull(torch.utils.data.Dataset): + + def __init__(self, path: str, **kwargs): + super().__init__() + + if isinstance(path, (list, tuple)): # wav.scp, text.txt/text.trans + from funasr_detach.datasets.audio_datasets.scp2jsonl import ( + gen_jsonl_from_wav_text_list, + ) + + jsonl_outdir = os.path.dirname(path[0]) + jsonl_name = ( + "datalist_train.jsonl" + if kwargs.get("is_training", True) + else "datalist_val.jsonl" + ) + jsonl_file_out = os.path.join(jsonl_outdir, jsonl_name) + if not os.path.exists(jsonl_file_out): + print(f"datalist is: {path}, generate jsonl from it") + gen_jsonl_from_wav_text_list( + path, jsonl_file_out=jsonl_file_out, **kwargs + ) + path = jsonl_file_out + + contents = [] + with open(path, encoding="utf-8") as fin: + for line in fin: + data = json.loads(line.strip()) + if "text" in data: # for sft + self.contents.append(data["text"]) + if "source" in data: # for speech lab pretrain + prompt = data.get("prompt", "") + source = data["source"] + target = data["target"] + source_len = data.get("source_len", 1) + target_len = data.get("target_len", 0) + + contents.append( + { + "source": source, + "prompt": prompt, + "target": target, + "source_len": source_len, + "target_len": target_len, + } + ) + + self.contents = contents + + logging.info( + "total_num of samplers across ranks: {}".format(len(self.contents)) + ) + + def __len__(self): + return len(self.contents) + + def __getitem__(self, index): + try: + data = self.contents[index] + except: + print(index) + return data + + def get_source_len(self, data_dict): + return data_dict.get("source_len", 1) + + def get_target_len(self, data_dict): + + return data_dict.get("target_len", 0) diff --git a/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/preprocessor.py b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/preprocessor.py new file mode 100644 index 0000000000000000000000000000000000000000..842c48da86e5eb2f690c343696837d85c5052ec6 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/preprocessor.py @@ -0,0 +1,55 @@ +import os +import json +import torch +import logging +import concurrent.futures +import librosa +import torch.distributed as dist +from typing import Collection +import torch +import torchaudio +from torch import nn +import random +import re +from funasr_detach.tokenizer.cleaner import TextCleaner +from funasr_detach.register import tables + + +@tables.register("preprocessor_classes", "SpeechPreprocessSpeedPerturb") +class SpeechPreprocessSpeedPerturb(nn.Module): + def __init__(self, speed_perturb: list = None, **kwargs): + super().__init__() + self.speed_perturb = speed_perturb + + def forward(self, waveform, fs, **kwargs): + if self.speed_perturb is None: + return waveform + speed = random.choice(self.speed_perturb) + if speed != 1.0: + if not isinstance(waveform, torch.Tensor): + waveform = torch.tensor(waveform) + waveform, _ = torchaudio.sox_effects.apply_effects_tensor( + waveform.view(1, -1), fs, [["speed", str(speed)], ["rate", str(fs)]] + ) + waveform = waveform.view(-1) + + return waveform + + +@tables.register("preprocessor_classes", "TextPreprocessSegDict") +class TextPreprocessSegDict(nn.Module): + def __init__( + self, + seg_dict: str = None, + text_cleaner: Collection[str] = None, + split_with_space: bool = False, + **kwargs + ): + super().__init__() + + self.text_cleaner = TextCleaner(text_cleaner) + + def forward(self, text, **kwargs): + text = self.text_cleaner(text) + + return text diff --git a/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/samplers.py b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..daaa5d83a46a70c716c52f7940a67af541023adc --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/samplers.py @@ -0,0 +1,306 @@ +import torch +import numpy as np +import logging +import torch.distributed as dist + +from funasr_detach.register import tables + + +@tables.register("batch_sampler_classes", "DynamicBatchLocalShuffleSampler") +class BatchSampler(torch.utils.data.BatchSampler): + + def __init__( + self, + dataset, + batch_type: str = "example", + batch_size: int = 100, + buffer_size: int = 30, + drop_last: bool = False, + shuffle: bool = True, + is_training: bool = True, + **kwargs + ): + + self.drop_last = drop_last + self.pre_idx = -1 + self.dataset = dataset + self.total_samples = len(dataset) + self.batch_type = batch_type + self.batch_size = int(batch_size) + self.buffer_size = buffer_size + self.max_token_length = kwargs.get("max_token_length", 5000) + self.shuffle_idx = np.arange(self.total_samples) + self.shuffle = shuffle and is_training + self.length_scale_source = kwargs.get("length_scale_source", 1.0) + + def __len__(self): + return (self.total_samples - 1) // self.batch_size + 1 + + def set_epoch(self, epoch): + np.random.seed(epoch) + + def __iter__(self): + + if self.shuffle: + np.random.shuffle(self.shuffle_idx) + + batch = [] + max_token = 0 + num_sample = 0 + + iter_num = (self.total_samples - 1) // self.buffer_size + 1 + # print("iter_num: ", iter_num) + for iter in range(self.pre_idx + 1, iter_num): + datalen_with_index = [] + for i in range(self.buffer_size): + idx = iter * self.buffer_size + i + if idx >= self.total_samples: + continue + + idx_map = self.shuffle_idx[idx] + # prompt = self.dataset.indexed_dataset[idx_map]["prompt"] + target_len = ( + self.dataset.get_target_len(idx_map) + if self.batch_type == "length" + else 0.0 + ) + source_len = ( + self.dataset.get_source_len(idx_map) / self.length_scale_source + ) + sample_len_cur = source_len + target_len + + datalen_with_index.append([idx, sample_len_cur]) + + datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1]) + for item in datalen_with_index_sort: + idx, sample_len_cur_raw = item + if sample_len_cur_raw > self.max_token_length: + continue + + max_token_cur = max(max_token, sample_len_cur_raw) + max_token_padding = 1 + num_sample + if self.batch_type != "example": + max_token_padding *= max_token_cur + if max_token_padding <= self.batch_size: + batch.append(idx) + max_token = max_token_cur + num_sample += 1 + else: + yield batch + batch = [idx] + max_token = sample_len_cur_raw + num_sample = 1 + + +@tables.register("batch_sampler_classes", "BatchSampler") +@tables.register("batch_sampler_classes", "RankFullLocalShuffleBatchSampler") +class RankFullLocalShuffleBatchSampler(torch.utils.data.BatchSampler): + + def __init__( + self, + dataset, + batch_type: str = "example", + batch_size: int = 100, + buffer_size: int = 30, + drop_last: bool = True, + shuffle: bool = True, + is_training: bool = True, + **kwargs + ): + + self.drop_last = drop_last + self.pre_idx = -1 + self.dataset = dataset + self.total_samples = len(dataset) + self.batch_type = batch_type + self.batch_size = int(batch_size) + self.buffer_size = buffer_size + self.max_token_length = kwargs.get("max_token_length", 1500) + self.shuffle_idx = np.arange(self.total_samples) + self.shuffle = shuffle and is_training + self.length_scale_source = kwargs.get("length_scale_source", 1.0) + + try: + rank = dist.get_rank() + world_size = dist.get_world_size() + except: + rank = 0 + world_size = 1 + self.rank = rank + self.world_size = world_size + + def __len__(self): + return (self.total_samples - 1) // (self.batch_size * self.world_size) + 1 + + def set_epoch(self, epoch): + np.random.seed(epoch) + + def __iter__(self): + + batch_size_total = self.batch_size * self.world_size + + if self.shuffle: + np.random.shuffle(self.shuffle_idx) + + batch = [] + max_token = 0 + num_sample = 0 + + iter_num = (self.total_samples - 1) // self.buffer_size + 1 + # print("iter_num: ", iter_num) + for iter in range(self.pre_idx + 1, iter_num): + # if iter == iter_num -1 and self.drop_last: + # continue + datalen_with_index = [] + for i in range(self.buffer_size): + idx = iter * self.buffer_size + i + if idx >= self.total_samples: + continue + + idx_map = self.shuffle_idx[idx] + # prompt = self.dataset.indexed_dataset[idx_map]["prompt"] + + source_len = ( + self.dataset.get_source_len(idx_map) / self.length_scale_source + ) + target_len = ( + self.dataset.get_target_len(idx_map) + if self.batch_type == "length" + else 0.0 + ) + sample_len_cur = source_len + target_len + + datalen_with_index.append([idx, sample_len_cur]) + + datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1]) + for item in datalen_with_index_sort: + idx, sample_len_cur_raw = item + if sample_len_cur_raw > self.max_token_length: + continue + + max_token_cur = max(max_token, sample_len_cur_raw) + max_token_padding = 1 + num_sample + # if self.batch_type != 'example': + # max_token_padding *= max_token_cur + if max_token_padding <= batch_size_total: + batch.append(idx) + max_token = max_token_cur + num_sample += 1 + else: + batch_rank = batch[ + self.rank * self.batch_size : (self.rank + 1) * self.batch_size + ] + yield batch_rank + batch = [idx] + max_token = sample_len_cur_raw + num_sample = 1 + + +@tables.register("batch_sampler_classes", "RankFullLocalShuffleDynamicBatchSampler") +class RankFullLocalShuffleDynamicBatchSampler(torch.utils.data.BatchSampler): + + def __init__( + self, + dataset, + batch_type: str = "example", + batch_size: int = 100, + buffer_size: int = 30, + drop_last: bool = True, + shuffle: bool = True, + is_training: bool = True, + **kwargs + ): + + self.drop_last = drop_last + self.pre_idx = -1 + self.dataset = dataset + self.total_samples = len(dataset) + self.batch_type = batch_type + self.batch_size = int(batch_size) + self.buffer_size = buffer_size + self.max_token_length = kwargs.get("max_token_length", 1500) + self.shuffle_idx = np.arange(self.total_samples) + self.shuffle = shuffle and is_training + self.length_scale_source = kwargs.get("length_scale_source", 1.0) + + try: + rank = dist.get_rank() + world_size = dist.get_world_size() + except: + rank = 0 + world_size = 1 + self.rank = rank + self.world_size = world_size + + def __len__(self): + return (self.total_samples - 1) // (self.batch_size * self.world_size) + 1 + + def set_epoch(self, epoch): + np.random.seed(epoch) + + def __iter__(self): + + batch_size_total = self.batch_size * self.world_size + if self.shuffle: + np.random.shuffle(self.shuffle_idx) + + batch_list_all_rank = [] + batch_list_cur = [] + max_token = 0 + num_sample = 0 + + iter_num = (self.total_samples - 1) // self.buffer_size + 1 + # print("iter_num: ", iter_num) + for iter in range(self.pre_idx + 1, iter_num): + # if iter == iter_num - 1 and self.drop_last: + # continue + datalen_with_index = [] + for i in range(self.buffer_size): + idx = iter * self.buffer_size + i + if idx >= self.total_samples: + continue + + idx_map = self.shuffle_idx[idx] + # prompt = self.dataset.indexed_dataset[idx_map]["prompt"] + + source_len = ( + self.dataset.get_source_len(idx_map) / self.length_scale_source + ) + target_len = ( + self.dataset.get_target_len(idx_map) + if self.batch_type == "length" + else 0.0 + ) + sample_len_cur = source_len + target_len + + datalen_with_index.append([idx, sample_len_cur]) + + datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1]) + for ii, item in enumerate(datalen_with_index_sort): + is_last_batch = iter == iter_num - 1 and ii == len( + datalen_with_index_sort + ) + idx, sample_len_cur_raw = item + if sample_len_cur_raw > self.max_token_length: + continue + + max_token_cur = max(max_token, sample_len_cur_raw) + max_token_padding = 1 + num_sample + + if self.batch_type != "example": + max_token_padding *= max_token_cur + if len(batch_list_all_rank) < self.world_size: + + if max_token_padding <= self.batch_size: + batch_list_cur.append(idx) + max_token = max_token_cur + num_sample += 1 + else: + batch_list_all_rank.append(batch_list_cur) + batch_list_cur = [] + else: + batch_rank = batch_list_all_rank[self.rank] + yield batch_rank + batch_list_all_rank = [idx] + max_token = sample_len_cur_raw + num_sample = 1 diff --git a/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/scp2jsonl.py b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/scp2jsonl.py new file mode 100644 index 0000000000000000000000000000000000000000..36fe8e0921af203c5db6859bfd6d448499006462 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/datasets/audio_datasets/scp2jsonl.py @@ -0,0 +1,116 @@ +import os +import json +import torch +import logging +import hydra +from omegaconf import DictConfig, OmegaConf +import concurrent.futures +import librosa +import torch.distributed as dist + + +def gen_jsonl_from_wav_text_list( + path, data_type_list=("source", "target"), jsonl_file_out: str = None, **kwargs +): + try: + rank = dist.get_rank() + world_size = dist.get_world_size() + except: + rank = 0 + world_size = 1 + + cpu_cores = os.cpu_count() or 1 + print(f"convert wav.scp text to jsonl, ncpu: {cpu_cores}") + if rank == 0: + json_dict = {} + for data_type, data_file in zip(data_type_list, path): + json_dict[data_type] = {} + with open(data_file, "r") as f: + + data_file_lists = f.readlines() + lines_for_each_th = (len(data_file_lists) - 1) // cpu_cores + 1 + task_num = cpu_cores if len(data_file_lists) > cpu_cores else 1 + with concurrent.futures.ThreadPoolExecutor( + max_workers=cpu_cores + ) as executor: + + futures = [ + executor.submit( + parse_context_length, + data_file_lists[ + i * lines_for_each_th : (i + 1) * lines_for_each_th + ], + data_type, + ) + for i in range(task_num) + ] + + for future in concurrent.futures.as_completed(futures): + + json_dict[data_type].update(future.result()) + # print(json_dict) + + with open(jsonl_file_out, "w") as f: + for key in json_dict[data_type_list[0]].keys(): + jsonl_line = {"key": key} + for data_file in data_type_list: + jsonl_line.update(json_dict[data_file][key]) + jsonl_line = json.dumps(jsonl_line, ensure_ascii=False) + f.write(jsonl_line + "\n") + f.flush() + + else: + pass + + if world_size > 1: + dist.barrier() + + +def parse_context_length(data_list: list, data_type: str): + + res = {} + for i, line in enumerate(data_list): + key, line = line.strip().split(maxsplit=1) + line = line.strip() + if os.path.exists(line): + waveform, _ = librosa.load(line, sr=16000) + sample_num = len(waveform) + context_len = int(sample_num // 16000 * 1000 / 10) + else: + context_len = len(line.split()) if " " in line else len(line) + res[key] = {data_type: line, f"{data_type}_len": context_len} + return res + + +@hydra.main(config_name=None, version_base=None) +def main_hydra(cfg: DictConfig): + + kwargs = OmegaConf.to_container(cfg, resolve=True) + + scp_file_list = kwargs.get( + "scp_file_list", + ( + "/Users/zhifu/funasr1.0/test_local/wav.scp", + "/Users/zhifu/funasr1.0/test_local/text.txt", + ), + ) + if isinstance(scp_file_list, str): + scp_file_list = eval(scp_file_list) + data_type_list = kwargs.get("data_type_list", ("source", "target")) + jsonl_file_out = kwargs.get( + "jsonl_file_out", "/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl" + ) + gen_jsonl_from_wav_text_list( + scp_file_list, data_type_list=data_type_list, jsonl_file_out=jsonl_file_out + ) + + +""" +python -m funasr_detach.datasets.audio_datasets.scp2jsonl \ +++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \ +++data_type_list='["source", "target"]' \ +++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl +""" + +if __name__ == "__main__": + main_hydra() diff --git a/demo/Step-Audio-EditX/funasr_detach/download/__init__.py b/demo/Step-Audio-EditX/funasr_detach/download/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/download/download_dataset_from_hub.py b/demo/Step-Audio-EditX/funasr_detach/download/download_dataset_from_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..94e627fd34b580de721b4666c9e0f37eae8dd40b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/download/download_dataset_from_hub.py @@ -0,0 +1,19 @@ +def download_dataset(): + pass + + +def download_dataset_from_ms(**kwargs): + from modelscope.msdatasets import MsDataset + + dataset_name = kwargs.get( + "dataset_name", "speech_asr/speech_asr_aishell1_trainsets" + ) + subset_name = kwargs.get("subset_name", "default") + split = kwargs.get("split", "train") + data_dump_dir = kwargs.get("data_dump_dir", None) + ds = MsDataset.load( + dataset_name=dataset_name, + subset_name=subset_name, + split=split, + cache_dir=data_dump_dir, + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/download/download_from_hub.py b/demo/Step-Audio-EditX/funasr_detach/download/download_from_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..d4e46e0fbefe62a74148fb7faf11d2b769cd9fa5 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/download/download_from_hub.py @@ -0,0 +1,231 @@ +import os +import json +import threading +from omegaconf import OmegaConf + +from funasr_detach.download.name_maps_from_hub import name_maps_ms, name_maps_hf + +# Global cache for downloaded models to avoid repeated downloads +# Key: (repo_id, model_revision, model_hub) +# Value: repo_cache_dir +_model_cache = {} +_cache_lock = threading.Lock() + + +def download_model(**kwargs): + model_hub = kwargs.get("model_hub", "ms") + model_or_path = kwargs.get("model") + repo_path = kwargs.get("repo_path", "") + + # Handle name mapping based on model_hub + if model_hub == "ms" and model_or_path in name_maps_ms: + model_or_path = name_maps_ms[model_or_path] + elif model_hub == "hf" and model_or_path in name_maps_hf: + model_or_path = name_maps_hf[model_or_path] + + model_revision = kwargs.get("model_revision") + + # Download model if it doesn't exist locally + if not os.path.exists(model_or_path): + if model_hub == "local": + # For local models, the path should already exist + raise FileNotFoundError(f"Local model path does not exist: {model_or_path}") + elif model_hub in ["ms", "hf"]: + repo_path, model_or_path = get_or_download_model_dir( + model_or_path, + model_revision, + is_training=kwargs.get("is_training"), + check_latest=kwargs.get("kwargs", True), + model_hub=model_hub, + ) + else: + raise ValueError(f"Unsupported model_hub: {model_hub}") + + print(f"Using model path: {model_or_path}") + kwargs["model_path"] = model_or_path + kwargs["repo_path"] = repo_path + + # Common logic for processing configuration files (same for all model hubs) + if os.path.exists(os.path.join(model_or_path, "configuration.json")): + with open( + os.path.join(model_or_path, "configuration.json"), "r", encoding="utf-8" + ) as f: + conf_json = json.load(f) + cfg = {} + add_file_root_path(model_or_path, conf_json["file_path_metas"], cfg) + cfg.update(kwargs) + config = OmegaConf.load(cfg["config"]) + kwargs = OmegaConf.merge(config, cfg) + kwargs["model"] = config["model"] + elif os.path.exists(os.path.join(model_or_path, "config.yaml")) and os.path.exists( + os.path.join(model_or_path, "model.pt") + ): + config = OmegaConf.load(os.path.join(model_or_path, "config.yaml")) + kwargs = OmegaConf.merge(config, kwargs) + init_param = os.path.join(model_or_path, "model.pb") + kwargs["init_param"] = init_param + if os.path.exists(os.path.join(model_or_path, "tokens.txt")): + kwargs["tokenizer_conf"]["token_list"] = os.path.join( + model_or_path, "tokens.txt" + ) + if os.path.exists(os.path.join(model_or_path, "tokens.json")): + kwargs["tokenizer_conf"]["token_list"] = os.path.join( + model_or_path, "tokens.json" + ) + if os.path.exists(os.path.join(model_or_path, "seg_dict")): + kwargs["tokenizer_conf"]["seg_dict"] = os.path.join( + model_or_path, "seg_dict" + ) + if os.path.exists(os.path.join(model_or_path, "bpe.model")): + kwargs["tokenizer_conf"]["bpemodel"] = os.path.join( + model_or_path, "bpe.model" + ) + kwargs["model"] = config["model"] + if os.path.exists(os.path.join(model_or_path, "am.mvn")): + kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn") + if os.path.exists(os.path.join(model_or_path, "jieba_usr_dict")): + kwargs["jieba_usr_dict"] = os.path.join(model_or_path, "jieba_usr_dict") + + return OmegaConf.to_container(kwargs, resolve=True) + + +def add_file_root_path(model_or_path: str, file_path_metas: dict, cfg={}): + + if isinstance(file_path_metas, dict): + for k, v in file_path_metas.items(): + if isinstance(v, str): + p = os.path.join(model_or_path, v) + if os.path.exists(p): + cfg[k] = p + elif isinstance(v, dict): + if k not in cfg: + cfg[k] = {} + add_file_root_path(model_or_path, v, cfg[k]) + + return cfg + + +def get_or_download_model_dir( + model, + model_revision=None, + is_training=False, + check_latest=True, + model_hub="ms", +): + """Get local model directory or download model if necessary. + + Args: + model (str): model id or path to local model directory. + For HF subfolders, use format: "repo_id/subfolder_path" + model_revision (str, optional): model version number. + is_training (bool): Whether this is for training + check_latest (bool): Whether to check for latest version + model_hub (str): Model hub type ("ms" for ModelScope, "hf" for HuggingFace) + """ + # Extract repo_id for caching (handle subfolder case) + if "/" in model and len(model.split("/")) > 2: + parts = model.split("/") + repo_id = "/".join(parts[:2]) # e.g., "organization/repo" or "stepfun-ai/Step-Audio-EditX" + subfolder = "/".join(parts[2:]) # e.g., "subfolder/model" + else: + repo_id = model + subfolder = None + + # Create cache key + cache_key = (repo_id, model_revision, model_hub) + + # Check cache first + with _cache_lock: + if cache_key in _model_cache: + cached_repo_dir = _model_cache[cache_key] + print(f"Using cached model for {repo_id}: {cached_repo_dir}") + + # For subfolder case, construct the model_cache_dir from cached repo + if subfolder: + model_cache_dir = os.path.join(cached_repo_dir, subfolder) + if not os.path.exists(model_cache_dir): + raise FileNotFoundError(f"Subfolder {subfolder} not found in cached repo {repo_id}") + else: + model_cache_dir = cached_repo_dir + + return cached_repo_dir, model_cache_dir + + # Cache miss, need to download + if model_hub == "ms": + # ModelScope download + from modelscope.hub.snapshot_download import snapshot_download + from modelscope.utils.constant import Invoke, ThirdParty + + key = Invoke.LOCAL_TRAINER if is_training else Invoke.PIPELINE + + # Download the repo (use repo_id, not the full model path with subfolder) + repo_cache_dir = snapshot_download( + repo_id, + revision=model_revision, + user_agent={Invoke.KEY: key, ThirdParty.KEY: "funasr"}, + ) + repo_cache_dir = normalize_cache_path(repo_cache_dir) + + # Construct model_cache_dir + if subfolder: + model_cache_dir = os.path.join(repo_cache_dir, subfolder) + if not os.path.exists(model_cache_dir): + raise FileNotFoundError(f"Subfolder {subfolder} not found in downloaded repo {repo_id}") + else: + model_cache_dir = normalize_cache_path(repo_cache_dir) + + elif model_hub == "hf": + # HuggingFace download + try: + from huggingface_hub import snapshot_download + except ImportError: + raise ImportError( + "huggingface_hub is required for downloading from HuggingFace. " + "Please install it with: pip install huggingface_hub" + ) + + # Download the repo (use repo_id, not the full model path with subfolder) + repo_cache_dir = snapshot_download( + repo_id=repo_id, + revision=model_revision, + allow_patterns=None, # Download all files to ensure resource files are available + ) + repo_cache_dir = normalize_cache_path(repo_cache_dir) + + # Construct model_cache_dir + if subfolder: + model_cache_dir = os.path.join(repo_cache_dir, subfolder) + if not os.path.exists(model_cache_dir): + raise FileNotFoundError(f"Subfolder {subfolder} not found in downloaded repo {repo_id}") + else: + model_cache_dir = normalize_cache_path(repo_cache_dir) + else: + raise ValueError(f"Unsupported model_hub: {model_hub}") + + # Cache the result before returning + with _cache_lock: + _model_cache[cache_key] = repo_cache_dir + + print(f"Model downloaded to: {model_cache_dir}") + return repo_cache_dir, model_cache_dir + +def normalize_cache_path(cache_path): + """Normalize cache path to ensure consistent format with snapshots/{commit_id}.""" + # Check if the cache_path directory contains a snapshots folder + snapshots_dir = os.path.join(cache_path, "snapshots") + if os.path.exists(snapshots_dir) and os.path.isdir(snapshots_dir): + # Find the commit_id subdirectory in snapshots + try: + snapshot_items = os.listdir(snapshots_dir) + # Look for the first directory (should be the commit_id) + for item in snapshot_items: + item_path = os.path.join(snapshots_dir, item) + if os.path.isdir(item_path): + # Found commit_id directory, return the full path + return os.path.join(cache_path, "snapshots", item) + except OSError: + pass + + # If no snapshots directory found or error occurred, return original path + return cache_path + diff --git a/demo/Step-Audio-EditX/funasr_detach/download/file.py b/demo/Step-Audio-EditX/funasr_detach/download/file.py new file mode 100644 index 0000000000000000000000000000000000000000..03d44bf95c69a173fad45ce2fb67069df9581a4e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/download/file.py @@ -0,0 +1,335 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. + +import contextlib +import os +import tempfile +from abc import ABCMeta, abstractmethod +from pathlib import Path +from typing import Generator, Union + +import requests +from urllib.parse import urlparse + + +def download_from_url(url): + result = urlparse(url) + file_path = None + if result.scheme is not None and len(result.scheme) > 0: + storage = HTTPStorage() + # bytes + data = storage.read(url) + work_dir = tempfile.TemporaryDirectory().name + if not os.path.exists(work_dir): + os.makedirs(work_dir) + file_path = os.path.join(work_dir, os.path.basename(url)) + with open(file_path, "wb") as fb: + fb.write(data) + assert file_path is not None, f"failed to download: {url}" + return file_path + + +class Storage(metaclass=ABCMeta): + """Abstract class of storage. + + All backends need to implement two apis: ``read()`` and ``read_text()``. + ``read()`` reads the file as a byte stream and ``read_text()`` reads + the file as texts. + """ + + @abstractmethod + def read(self, filepath: str): + pass + + @abstractmethod + def read_text(self, filepath: str): + pass + + @abstractmethod + def write(self, obj: bytes, filepath: Union[str, Path]) -> None: + pass + + @abstractmethod + def write_text( + self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8" + ) -> None: + pass + + +class LocalStorage(Storage): + """Local hard disk storage""" + + def read(self, filepath: Union[str, Path]) -> bytes: + """Read data from a given ``filepath`` with 'rb' mode. + + Args: + filepath (str or Path): Path to read data. + + Returns: + bytes: Expected bytes object. + """ + with open(filepath, "rb") as f: + content = f.read() + return content + + def read_text(self, filepath: Union[str, Path], encoding: str = "utf-8") -> str: + """Read data from a given ``filepath`` with 'r' mode. + + Args: + filepath (str or Path): Path to read data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + + Returns: + str: Expected text reading from ``filepath``. + """ + with open(filepath, "r", encoding=encoding) as f: + value_buf = f.read() + return value_buf + + def write(self, obj: bytes, filepath: Union[str, Path]) -> None: + """Write data to a given ``filepath`` with 'wb' mode. + + Note: + ``write`` will create a directory if the directory of ``filepath`` + does not exist. + + Args: + obj (bytes): Data to be written. + filepath (str or Path): Path to write data. + """ + dirname = os.path.dirname(filepath) + if dirname and not os.path.exists(dirname): + os.makedirs(dirname, exist_ok=True) + + with open(filepath, "wb") as f: + f.write(obj) + + def write_text( + self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8" + ) -> None: + """Write data to a given ``filepath`` with 'w' mode. + + Note: + ``write_text`` will create a directory if the directory of + ``filepath`` does not exist. + + Args: + obj (str): Data to be written. + filepath (str or Path): Path to write data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + """ + dirname = os.path.dirname(filepath) + if dirname and not os.path.exists(dirname): + os.makedirs(dirname, exist_ok=True) + + with open(filepath, "w", encoding=encoding) as f: + f.write(obj) + + @contextlib.contextmanager + def as_local_path( + self, filepath: Union[str, Path] + ) -> Generator[Union[str, Path], None, None]: + """Only for unified API and do nothing.""" + yield filepath + + +class HTTPStorage(Storage): + """HTTP and HTTPS storage.""" + + def read(self, url): + # TODO @wenmeng.zwm add progress bar if file is too large + r = requests.get(url) + r.raise_for_status() + return r.content + + def read_text(self, url): + r = requests.get(url) + r.raise_for_status() + return r.text + + @contextlib.contextmanager + def as_local_path(self, filepath: str) -> Generator[Union[str, Path], None, None]: + """Download a file from ``filepath``. + + ``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It + can be called with ``with`` statement, and when exists from the + ``with`` statement, the temporary path will be released. + + Args: + filepath (str): Download a file from ``filepath``. + + Examples: + >>> storage = HTTPStorage() + >>> # After existing from the ``with`` clause, + >>> # the path will be removed + >>> with storage.get_local_path('http://path/to/file') as path: + ... # do something here + """ + try: + f = tempfile.NamedTemporaryFile(delete=False) + f.write(self.read(filepath)) + f.close() + yield f.name + finally: + os.remove(f.name) + + def write(self, obj: bytes, url: Union[str, Path]) -> None: + raise NotImplementedError("write is not supported by HTTP Storage") + + def write_text( + self, obj: str, url: Union[str, Path], encoding: str = "utf-8" + ) -> None: + raise NotImplementedError("write_text is not supported by HTTP Storage") + + +class OSSStorage(Storage): + """OSS storage.""" + + def __init__(self, oss_config_file=None): + # read from config file or env var + raise NotImplementedError("OSSStorage.__init__ to be implemented in the future") + + def read(self, filepath): + raise NotImplementedError("OSSStorage.read to be implemented in the future") + + def read_text(self, filepath, encoding="utf-8"): + raise NotImplementedError( + "OSSStorage.read_text to be implemented in the future" + ) + + @contextlib.contextmanager + def as_local_path(self, filepath: str) -> Generator[Union[str, Path], None, None]: + """Download a file from ``filepath``. + + ``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It + can be called with ``with`` statement, and when exists from the + ``with`` statement, the temporary path will be released. + + Args: + filepath (str): Download a file from ``filepath``. + + Examples: + >>> storage = OSSStorage() + >>> # After existing from the ``with`` clause, + >>> # the path will be removed + >>> with storage.get_local_path('http://path/to/file') as path: + ... # do something here + """ + try: + f = tempfile.NamedTemporaryFile(delete=False) + f.write(self.read(filepath)) + f.close() + yield f.name + finally: + os.remove(f.name) + + def write(self, obj: bytes, filepath: Union[str, Path]) -> None: + raise NotImplementedError("OSSStorage.write to be implemented in the future") + + def write_text( + self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8" + ) -> None: + raise NotImplementedError( + "OSSStorage.write_text to be implemented in the future" + ) + + +G_STORAGES = {} + + +class File(object): + _prefix_to_storage: dict = { + "oss": OSSStorage, + "http": HTTPStorage, + "https": HTTPStorage, + "local": LocalStorage, + } + + @staticmethod + def _get_storage(uri): + assert isinstance(uri, str), f"uri should be str type, but got {type(uri)}" + + if "://" not in uri: + # local path + storage_type = "local" + else: + prefix, _ = uri.split("://") + storage_type = prefix + + assert storage_type in File._prefix_to_storage, ( + f"Unsupported uri {uri}, valid prefixs: " + f"{list(File._prefix_to_storage.keys())}" + ) + + if storage_type not in G_STORAGES: + G_STORAGES[storage_type] = File._prefix_to_storage[storage_type]() + + return G_STORAGES[storage_type] + + @staticmethod + def read(uri: str) -> bytes: + """Read data from a given ``filepath`` with 'rb' mode. + + Args: + filepath (str or Path): Path to read data. + + Returns: + bytes: Expected bytes object. + """ + storage = File._get_storage(uri) + return storage.read(uri) + + @staticmethod + def read_text(uri: Union[str, Path], encoding: str = "utf-8") -> str: + """Read data from a given ``filepath`` with 'r' mode. + + Args: + filepath (str or Path): Path to read data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + + Returns: + str: Expected text reading from ``filepath``. + """ + storage = File._get_storage(uri) + return storage.read_text(uri) + + @staticmethod + def write(obj: bytes, uri: Union[str, Path]) -> None: + """Write data to a given ``filepath`` with 'wb' mode. + + Note: + ``write`` will create a directory if the directory of ``filepath`` + does not exist. + + Args: + obj (bytes): Data to be written. + filepath (str or Path): Path to write data. + """ + storage = File._get_storage(uri) + return storage.write(obj, uri) + + @staticmethod + def write_text(obj: str, uri: str, encoding: str = "utf-8") -> None: + """Write data to a given ``filepath`` with 'w' mode. + + Note: + ``write_text`` will create a directory if the directory of + ``filepath`` does not exist. + + Args: + obj (str): Data to be written. + filepath (str or Path): Path to write data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + """ + storage = File._get_storage(uri) + return storage.write_text(obj, uri) + + @contextlib.contextmanager + def as_local_path(uri: str) -> Generator[Union[str, Path], None, None]: + """Only for unified API and do nothing.""" + storage = File._get_storage(uri) + with storage.as_local_path(uri) as local_path: + yield local_path diff --git a/demo/Step-Audio-EditX/funasr_detach/download/name_maps_from_hub.py b/demo/Step-Audio-EditX/funasr_detach/download/name_maps_from_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..b7483f78a0f8286038cc01bae6b96291f7b71b20 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/download/name_maps_from_hub.py @@ -0,0 +1,13 @@ +name_maps_ms = { + "paraformer-zh": "damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", + "paraformer-en": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020", + "paraformer-en-spk": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020", + "paraformer-zh-streaming": "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", + "fsmn-vad": "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", + "ct-punc": "damo/punc_ct-transformer_cn-en-common-vocab471067-large", + "ct-punc-c": "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", + "fa-zh": "damo/speech_timestamp_prediction-v1-16k-offline", + "cam++": "damo/speech_campplus_sv_zh-cn_16k-common", +} + +name_maps_hf = {} diff --git a/demo/Step-Audio-EditX/funasr_detach/download/runtime_sdk_download_tool.py b/demo/Step-Audio-EditX/funasr_detach/download/runtime_sdk_download_tool.py new file mode 100644 index 0000000000000000000000000000000000000000..29c6662adb564f849783859ac4a32e5246dc8402 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/download/runtime_sdk_download_tool.py @@ -0,0 +1,60 @@ +import os +import argparse +from pathlib import Path + +from funasr_detach.utils.types import str2bool + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--model-name", type=str, required=True) + parser.add_argument("--export-dir", type=str, required=True) + parser.add_argument( + "--export", type=str2bool, default=True, help="whether to export model" + ) + parser.add_argument("--type", type=str, default="onnx", help='["onnx", "torch"]') + parser.add_argument("--device", type=str, default="cpu", help='["cpu", "cuda"]') + parser.add_argument( + "--quantize", type=str2bool, default=False, help="export quantized model" + ) + parser.add_argument( + "--fallback-num", type=int, default=0, help="amp fallback number" + ) + parser.add_argument("--audio_in", type=str, default=None, help='["wav", "wav.scp"]') + parser.add_argument( + "--model_revision", type=str, default=None, help="model_revision" + ) + parser.add_argument("--calib_num", type=int, default=200, help="calib max num") + args = parser.parse_args() + + model_dir = args.model_name + if not Path(args.model_name).exists(): + from modelscope.hub.snapshot_download import snapshot_download + + try: + model_dir = snapshot_download( + args.model_name, cache_dir=args.export_dir, revision=args.model_revision + ) + except: + raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format( + model_dir + ) + if args.export: + model_file = os.path.join(model_dir, "model.onnx") + if args.quantize: + model_file = os.path.join(model_dir, "model_quant.onnx") + if not os.path.exists(model_file): + print(".onnx is not exist, begin to export onnx") + from funasr_detach.bin.export_model import ModelExport + + export_model = ModelExport( + cache_dir=args.export_dir, + onnx=True, + device="cpu", + quant=args.quantize, + ) + export_model.export(model_dir) + + +if __name__ == "__main__": + main() diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/__init__.py b/demo/Step-Audio-EditX/funasr_detach/frontends/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/default.py b/demo/Step-Audio-EditX/funasr_detach/frontends/default.py new file mode 100644 index 0000000000000000000000000000000000000000..15159f5b29c783350195b65ba469d6ea65be3f67 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/default.py @@ -0,0 +1,347 @@ +import copy +from typing import Optional +from typing import Tuple +from typing import Union +import logging +import humanfriendly +import numpy as np +import torch +import torch.nn as nn + +try: + from torch_complex.tensor import ComplexTensor +except: + print("Please install torch_complex firstly") + +from funasr_detach.frontends.utils.log_mel import LogMel +from funasr_detach.frontends.utils.stft import Stft +from funasr_detach.frontends.utils.frontend import Frontend +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +class DefaultFrontend(nn.Module): + """Conventional frontend structure for ASR. + Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN + """ + + def __init__( + self, + fs: Union[int, str] = 16000, + n_fft: int = 512, + win_length: int = None, + hop_length: int = 128, + window: Optional[str] = "hann", + center: bool = True, + normalized: bool = False, + onesided: bool = True, + n_mels: int = 80, + fmin: int = None, + fmax: int = None, + htk: bool = False, + frontend_conf: Optional[dict] = None, + apply_stft: bool = True, + use_channel: int = None, + ): + super().__init__() + if isinstance(fs, str): + fs = humanfriendly.parse_size(fs) + + # Deepcopy (In general, dict shouldn't be used as default arg) + frontend_conf = copy.deepcopy(frontend_conf) + self.hop_length = hop_length + + if apply_stft: + self.stft = Stft( + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + center=center, + window=window, + normalized=normalized, + onesided=onesided, + ) + else: + self.stft = None + self.apply_stft = apply_stft + + if frontend_conf is not None: + self.frontend = Frontend(idim=n_fft // 2 + 1, **frontend_conf) + else: + self.frontend = None + + self.logmel = LogMel( + fs=fs, + n_fft=n_fft, + n_mels=n_mels, + fmin=fmin, + fmax=fmax, + htk=htk, + ) + self.n_mels = n_mels + self.use_channel = use_channel + self.frontend_type = "default" + + def output_size(self) -> int: + return self.n_mels + + def forward( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + # 1. Domain-conversion: e.g. Stft: time -> time-freq + if self.stft is not None: + input_stft, feats_lens = self._compute_stft(input, input_lengths) + else: + input_stft = ComplexTensor(input[..., 0], input[..., 1]) + feats_lens = input_lengths + # 2. [Option] Speech enhancement + if self.frontend is not None: + assert isinstance(input_stft, ComplexTensor), type(input_stft) + # input_stft: (Batch, Length, [Channel], Freq) + input_stft, _, mask = self.frontend(input_stft, feats_lens) + + # 3. [Multi channel case]: Select a channel + if input_stft.dim() == 4: + # h: (B, T, C, F) -> h: (B, T, F) + if self.training: + if self.use_channel is not None: + input_stft = input_stft[:, :, self.use_channel, :] + else: + # Select 1ch randomly + ch = np.random.randint(input_stft.size(2)) + input_stft = input_stft[:, :, ch, :] + else: + # Use the first channel + input_stft = input_stft[:, :, 0, :] + + # 4. STFT -> Power spectrum + # h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F) + input_power = input_stft.real**2 + input_stft.imag**2 + + # 5. Feature transform e.g. Stft -> Log-Mel-Fbank + # input_power: (Batch, [Channel,] Length, Freq) + # -> input_feats: (Batch, Length, Dim) + input_feats, _ = self.logmel(input_power, feats_lens) + + return input_feats, feats_lens + + def _compute_stft( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> torch.Tensor: + input_stft, feats_lens = self.stft(input, input_lengths) + + assert input_stft.dim() >= 4, input_stft.shape + # "2" refers to the real/imag parts of Complex + assert input_stft.shape[-1] == 2, input_stft.shape + + # Change torch.Tensor to ComplexTensor + # input_stft: (..., F, 2) -> (..., F) + input_stft = ComplexTensor(input_stft[..., 0], input_stft[..., 1]) + return input_stft, feats_lens + + +class MultiChannelFrontend(nn.Module): + """Conventional frontend structure for ASR. + Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN + """ + + def __init__( + self, + fs: Union[int, str] = 16000, + n_fft: int = 512, + win_length: int = None, + hop_length: int = None, + frame_length: int = None, + frame_shift: int = None, + window: Optional[str] = "hann", + center: bool = True, + normalized: bool = False, + onesided: bool = True, + n_mels: int = 80, + fmin: int = None, + fmax: int = None, + htk: bool = False, + frontend_conf: Optional[dict] = None, + apply_stft: bool = True, + use_channel: int = None, + lfr_m: int = 1, + lfr_n: int = 1, + cmvn_file: str = None, + mc: bool = True, + ): + super().__init__() + if isinstance(fs, str): + fs = humanfriendly.parse_size(fs) + + # Deepcopy (In general, dict shouldn't be used as default arg) + frontend_conf = copy.deepcopy(frontend_conf) + if win_length is None and hop_length is None: + self.win_length = frame_length * 16 + self.hop_length = frame_shift * 16 + elif frame_length is None and frame_shift is None: + self.win_length = self.win_length + self.hop_length = self.hop_length + else: + logging.error( + "Only one of (win_length, hop_length) and (frame_length, frame_shift)" + "can be set." + ) + exit(1) + + if apply_stft: + self.stft = Stft( + n_fft=n_fft, + win_length=self.win_length, + hop_length=self.hop_length, + center=center, + window=window, + normalized=normalized, + onesided=onesided, + ) + else: + self.stft = None + self.apply_stft = apply_stft + + if frontend_conf is not None: + self.frontend = Frontend(idim=n_fft // 2 + 1, **frontend_conf) + else: + self.frontend = None + + self.logmel = LogMel( + fs=fs, + n_fft=n_fft, + n_mels=n_mels, + fmin=fmin, + fmax=fmax, + htk=htk, + ) + self.n_mels = n_mels + self.use_channel = use_channel + self.mc = mc + if not self.mc: + if self.use_channel is not None: + logging.info("use the channel %d" % (self.use_channel)) + else: + logging.info("random select channel") + self.cmvn_file = cmvn_file + if self.cmvn_file is not None: + mean, std = self._load_cmvn(self.cmvn_file) + self.register_buffer("mean", torch.from_numpy(mean)) + self.register_buffer("std", torch.from_numpy(std)) + self.frontend_type = "multichannelfrontend" + + def output_size(self) -> int: + return self.n_mels + + def forward( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + # 1. Domain-conversion: e.g. Stft: time -> time-freq + # import pdb;pdb.set_trace() + if self.stft is not None: + input_stft, feats_lens = self._compute_stft(input, input_lengths) + else: + input_stft = ComplexTensor(input[..., 0], input[..., 1]) + feats_lens = input_lengths + # 2. [Option] Speech enhancement + if self.frontend is not None: + assert isinstance(input_stft, ComplexTensor), type(input_stft) + # input_stft: (Batch, Length, [Channel], Freq) + input_stft, _, mask = self.frontend(input_stft, feats_lens) + + # 3. [Multi channel case]: Select a channel(sa_asr) + if input_stft.dim() == 4 and not self.mc: + # h: (B, T, C, F) -> h: (B, T, F) + if self.training: + if self.use_channel is not None: + input_stft = input_stft[:, :, self.use_channel, :] + + else: + # Select 1ch randomly + ch = np.random.randint(input_stft.size(2)) + input_stft = input_stft[:, :, ch, :] + else: + # Use the first channel + input_stft = input_stft[:, :, 0, :] + + # 4. STFT -> Power spectrum + # h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F) + input_power = input_stft.real**2 + input_stft.imag**2 + + # 5. Feature transform e.g. Stft -> Log-Mel-Fbank + # input_power: (Batch, [Channel,] Length, Freq) + # -> input_feats: (Batch, Length, Dim) + input_feats, _ = self.logmel(input_power, feats_lens) + if self.mc: + # MFCCA + if input_feats.dim() == 4: + bt = input_feats.size(0) + channel_size = input_feats.size(2) + input_feats = ( + input_feats.transpose(1, 2) + .reshape(bt * channel_size, -1, 80) + .contiguous() + ) + feats_lens = feats_lens.repeat(1, channel_size).squeeze() + else: + channel_size = 1 + return input_feats, feats_lens, channel_size + else: + # 6. Apply CMVN + if self.cmvn_file is not None: + if feats_lens is None: + feats_lens = input_feats.new_full( + [input_feats.size(0)], input_feats.size(1) + ) + self.mean = self.mean.to(input_feats.device, input_feats.dtype) + self.std = self.std.to(input_feats.device, input_feats.dtype) + mask = make_pad_mask(feats_lens, input_feats, 1) + + if input_feats.requires_grad: + input_feats = input_feats + self.mean + else: + input_feats += self.mean + if input_feats.requires_grad: + input_feats = input_feats.masked_fill(mask, 0.0) + else: + input_feats.masked_fill_(mask, 0.0) + + input_feats *= self.std + + return input_feats, feats_lens + + def _compute_stft( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> torch.Tensor: + input_stft, feats_lens = self.stft(input, input_lengths) + + assert input_stft.dim() >= 4, input_stft.shape + # "2" refers to the real/imag parts of Complex + assert input_stft.shape[-1] == 2, input_stft.shape + + # Change torch.Tensor to ComplexTensor + # input_stft: (..., F, 2) -> (..., F) + input_stft = ComplexTensor(input_stft[..., 0], input_stft[..., 1]) + return input_stft, feats_lens + + def _load_cmvn(self, cmvn_file): + with open(cmvn_file, "r", encoding="utf-8") as f: + lines = f.readlines() + means_list = [] + vars_list = [] + for i in range(len(lines)): + line_item = lines[i].split() + if line_item[0] == "": + line_item = lines[i + 1].split() + if line_item[0] == "": + add_shift_line = line_item[3 : (len(line_item) - 1)] + means_list = list(add_shift_line) + continue + elif line_item[0] == "": + line_item = lines[i + 1].split() + if line_item[0] == "": + rescale_line = line_item[3 : (len(line_item) - 1)] + vars_list = list(rescale_line) + continue + means = np.array(means_list).astype(np.float) + vars = np.array(vars_list).astype(np.float) + return means, vars diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/eend_ola_feature.py b/demo/Step-Audio-EditX/funasr_detach/frontends/eend_ola_feature.py new file mode 100644 index 0000000000000000000000000000000000000000..f90157fd75a5804c0ac2ec361f45c0fe93cf29cc --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/eend_ola_feature.py @@ -0,0 +1,49 @@ +# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita) +# Licensed under the MIT license. +# +# This module is for computing audio features + +import librosa +import numpy as np + + +def transform(Y, dtype=np.float32): + Y = np.abs(Y) + n_fft = 2 * (Y.shape[1] - 1) + sr = 8000 + n_mels = 23 + mel_basis = librosa.filters.mel(sr, n_fft, n_mels) + Y = np.dot(Y**2, mel_basis.T) + Y = np.log10(np.maximum(Y, 1e-10)) + mean = np.mean(Y, axis=0) + Y = Y - mean + return Y.astype(dtype) + + +def subsample(Y, T, subsampling=1): + Y_ss = Y[::subsampling] + T_ss = T[::subsampling] + return Y_ss, T_ss + + +def splice(Y, context_size=0): + Y_pad = np.pad(Y, [(context_size, context_size), (0, 0)], "constant") + Y_spliced = np.lib.stride_tricks.as_strided( + np.ascontiguousarray(Y_pad), + (Y.shape[0], Y.shape[1] * (2 * context_size + 1)), + (Y.itemsize * Y.shape[1], Y.itemsize), + writeable=False, + ) + return Y_spliced + + +def stft(data, frame_size=1024, frame_shift=256): + fft_size = 1 << (frame_size - 1).bit_length() + if len(data) % frame_shift == 0: + return librosa.stft( + data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift + ).T[:-1] + else: + return librosa.stft( + data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift + ).T diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/fused.py b/demo/Step-Audio-EditX/funasr_detach/frontends/fused.py new file mode 100644 index 0000000000000000000000000000000000000000..69f64bf2143ba4e999d414082d2dcd8655e5fd36 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/fused.py @@ -0,0 +1,144 @@ +from funasr_detach.frontends.default import DefaultFrontend +from funasr_detach.frontends.s3prl import S3prlFrontend +import numpy as np +import torch +import torch.nn as nn +from typing import Tuple + + +class FusedFrontends(nn.Module): + def __init__( + self, frontends=None, align_method="linear_projection", proj_dim=100, fs=16000 + ): + + super().__init__() + self.align_method = ( + align_method # fusing method : linear_projection only for now + ) + self.proj_dim = proj_dim # dim of the projection done on each frontend + self.frontends = [] # list of the frontends to combine + + for i, frontend in enumerate(frontends): + frontend_type = frontend["frontend_type"] + if frontend_type == "default": + n_mels, fs, n_fft, win_length, hop_length = ( + frontend.get("n_mels", 80), + fs, + frontend.get("n_fft", 512), + frontend.get("win_length"), + frontend.get("hop_length", 128), + ) + window, center, normalized, onesided = ( + frontend.get("window", "hann"), + frontend.get("center", True), + frontend.get("normalized", False), + frontend.get("onesided", True), + ) + fmin, fmax, htk, apply_stft = ( + frontend.get("fmin", None), + frontend.get("fmax", None), + frontend.get("htk", False), + frontend.get("apply_stft", True), + ) + + self.frontends.append( + DefaultFrontend( + n_mels=n_mels, + n_fft=n_fft, + fs=fs, + win_length=win_length, + hop_length=hop_length, + window=window, + center=center, + normalized=normalized, + onesided=onesided, + fmin=fmin, + fmax=fmax, + htk=htk, + apply_stft=apply_stft, + ) + ) + elif frontend_type == "s3prl": + frontend_conf, download_dir, multilayer_feature = ( + frontend.get("frontend_conf"), + frontend.get("download_dir"), + frontend.get("multilayer_feature"), + ) + self.frontends.append( + S3prlFrontend( + fs=fs, + frontend_conf=frontend_conf, + download_dir=download_dir, + multilayer_feature=multilayer_feature, + ) + ) + + else: + raise NotImplementedError # frontends are only default or s3prl + + self.frontends = torch.nn.ModuleList(self.frontends) + + self.gcd = np.gcd.reduce([frontend.hop_length for frontend in self.frontends]) + self.factors = [frontend.hop_length // self.gcd for frontend in self.frontends] + if torch.cuda.is_available(): + dev = "cuda" + else: + dev = "cpu" + if self.align_method == "linear_projection": + self.projection_layers = [ + torch.nn.Linear( + in_features=frontend.output_size(), + out_features=self.factors[i] * self.proj_dim, + ) + for i, frontend in enumerate(self.frontends) + ] + self.projection_layers = torch.nn.ModuleList(self.projection_layers) + self.projection_layers = self.projection_layers.to(torch.device(dev)) + + def output_size(self) -> int: + return len(self.frontends) * self.proj_dim + + def forward( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + + # step 0 : get all frontends features + self.feats = [] + for frontend in self.frontends: + with torch.no_grad(): + input_feats, feats_lens = frontend.forward(input, input_lengths) + self.feats.append([input_feats, feats_lens]) + + if ( + self.align_method == "linear_projection" + ): # TODO(Dan): to add other align methods + + # first step : projections + self.feats_proj = [] + for i, frontend in enumerate(self.frontends): + input_feats = self.feats[i][0] + self.feats_proj.append(self.projection_layers[i](input_feats)) + + # 2nd step : reshape + self.feats_reshaped = [] + for i, frontend in enumerate(self.frontends): + input_feats_proj = self.feats_proj[i] + bs, nf, dim = input_feats_proj.shape + input_feats_reshaped = torch.reshape( + input_feats_proj, (bs, nf * self.factors[i], dim // self.factors[i]) + ) + self.feats_reshaped.append(input_feats_reshaped) + + # 3rd step : drop the few last frames + m = min([x.shape[1] for x in self.feats_reshaped]) + self.feats_final = [x[:, :m, :] for x in self.feats_reshaped] + + input_feats = torch.cat( + self.feats_final, dim=-1 + ) # change the input size of the preencoder : proj_dim * n_frontends + feats_lens = torch.ones_like(self.feats[0][1]) * (m) + + else: + raise NotImplementedError + + return input_feats, feats_lens diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/s3prl.py b/demo/Step-Audio-EditX/funasr_detach/frontends/s3prl.py new file mode 100644 index 0000000000000000000000000000000000000000..0e022f6cb9675668a17f4ebc895320eb6f81eb64 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/s3prl.py @@ -0,0 +1,139 @@ +import copy +import logging +import os +from argparse import Namespace +from typing import Optional +from typing import Tuple +from typing import Union + +import humanfriendly +import torch +import torch.nn as nn + +from funasr_detach.frontends.utils.frontend import Frontend +from funasr_detach.models.transformer.utils.nets_utils import pad_list + + +def base_s3prl_setup(args): + args.upstream_feature_selection = getattr(args, "upstream_feature_selection", None) + args.upstream_model_config = getattr(args, "upstream_model_config", None) + args.upstream_refresh = getattr(args, "upstream_refresh", False) + args.upstream_ckpt = getattr(args, "upstream_ckpt", None) + args.init_ckpt = getattr(args, "init_ckpt", None) + args.verbose = getattr(args, "verbose", False) + args.tile_factor = getattr(args, "tile_factor", 1) + return args + + +class S3prlFrontend(nn.Module): + """Speech Pretrained Representation frontend structure for ASR.""" + + def __init__( + self, + fs: Union[int, str] = 16000, + frontend_conf: Optional[dict] = None, + download_dir: str = None, + multilayer_feature: bool = False, + ): + super().__init__() + if isinstance(fs, str): + fs = humanfriendly.parse_size(fs) + + if download_dir is not None: + torch.hub.set_dir(download_dir) + + self.multilayer_feature = multilayer_feature + self.upstream, self.featurizer = self._get_upstream(frontend_conf) + self.pretrained_params = copy.deepcopy(self.upstream.state_dict()) + self.output_dim = self.featurizer.output_dim + self.frontend_type = "s3prl" + self.hop_length = self.upstream.get_downsample_rates("key") + + def _get_upstream(self, frontend_conf): + """Get S3PRL upstream model.""" + s3prl_args = base_s3prl_setup( + Namespace(**frontend_conf, device="cpu"), + ) + self.args = s3prl_args + + s3prl_path = None + python_path_list = os.environ.get("PYTHONPATH", "(None)").split(":") + for p in python_path_list: + if p.endswith("s3prl"): + s3prl_path = p + break + assert s3prl_path is not None + + s3prl_upstream = torch.hub.load( + s3prl_path, + s3prl_args.upstream, + ckpt=s3prl_args.upstream_ckpt, + model_config=s3prl_args.upstream_model_config, + refresh=s3prl_args.upstream_refresh, + source="local", + ).to("cpu") + + if getattr( + s3prl_upstream, "model", None + ) is not None and s3prl_upstream.model.__class__.__name__ in [ + "Wav2Vec2Model", + "HubertModel", + ]: + s3prl_upstream.model.encoder.layerdrop = 0.0 + + from s3prl.upstream.interfaces import Featurizer + + if self.multilayer_feature is None: + feature_selection = "last_hidden_state" + else: + feature_selection = "hidden_states" + s3prl_featurizer = Featurizer( + upstream=s3prl_upstream, + feature_selection=feature_selection, + upstream_device="cpu", + ) + + return s3prl_upstream, s3prl_featurizer + + def _tile_representations(self, feature): + """Tile up the representations by `tile_factor`. + Input - sequence of representations + shape: (batch_size, seq_len, feature_dim) + Output - sequence of tiled representations + shape: (batch_size, seq_len * factor, feature_dim) + """ + assert ( + len(feature.shape) == 3 + ), "Input argument `feature` has invalid shape: {}".format(feature.shape) + tiled_feature = feature.repeat(1, 1, self.args.tile_factor) + tiled_feature = tiled_feature.reshape( + feature.size(0), feature.size(1) * self.args.tile_factor, feature.size(2) + ) + return tiled_feature + + def output_size(self) -> int: + return self.output_dim + + def forward( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + wavs = [wav[: input_lengths[i]] for i, wav in enumerate(input)] + self.upstream.eval() + with torch.no_grad(): + feats = self.upstream(wavs) + feats = self.featurizer(wavs, feats) + + if self.args.tile_factor != 1: + feats = self._tile_representations(feats) + + input_feats = pad_list(feats, 0.0) + feats_lens = torch.tensor([f.shape[0] for f in feats], dtype=torch.long) + + # Saving CUDA Memory + del feats + + return input_feats, feats_lens + + def reload_pretrained_parameters(self): + self.upstream.load_state_dict(self.pretrained_params) + logging.info("Pretrained S3PRL frontend model parameters reloaded!") diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/__init__.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..24134d8b02377f5fd6223a12b470d09f4124cfe7 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/__init__.py @@ -0,0 +1 @@ +"""Initialize sub package.""" diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/beamformer.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/beamformer.py new file mode 100644 index 0000000000000000000000000000000000000000..114461c27b4b3cc35c8cf5de6a25e6c925a4d8cd --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/beamformer.py @@ -0,0 +1,84 @@ +import torch +from torch_complex import functional as FC +from torch_complex.tensor import ComplexTensor + + +def get_power_spectral_density_matrix( + xs: ComplexTensor, mask: torch.Tensor, normalization=True, eps: float = 1e-15 +) -> ComplexTensor: + """Return cross-channel power spectral density (PSD) matrix + + Args: + xs (ComplexTensor): (..., F, C, T) + mask (torch.Tensor): (..., F, C, T) + normalization (bool): + eps (float): + Returns + psd (ComplexTensor): (..., F, C, C) + + """ + # outer product: (..., C_1, T) x (..., C_2, T) -> (..., T, C, C_2) + psd_Y = FC.einsum("...ct,...et->...tce", [xs, xs.conj()]) + + # Averaging mask along C: (..., C, T) -> (..., T) + mask = mask.mean(dim=-2) + + # Normalized mask along T: (..., T) + if normalization: + # If assuming the tensor is padded with zero, the summation along + # the time axis is same regardless of the padding length. + mask = mask / (mask.sum(dim=-1, keepdim=True) + eps) + + # psd: (..., T, C, C) + psd = psd_Y * mask[..., None, None] + # (..., T, C, C) -> (..., C, C) + psd = psd.sum(dim=-3) + + return psd + + +def get_mvdr_vector( + psd_s: ComplexTensor, + psd_n: ComplexTensor, + reference_vector: torch.Tensor, + eps: float = 1e-15, +) -> ComplexTensor: + """Return the MVDR(Minimum Variance Distortionless Response) vector: + + h = (Npsd^-1 @ Spsd) / (Tr(Npsd^-1 @ Spsd)) @ u + + Reference: + On optimal frequency-domain multichannel linear filtering + for noise reduction; M. Souden et al., 2010; + https://ieeexplore.ieee.org/document/5089420 + + Args: + psd_s (ComplexTensor): (..., F, C, C) + psd_n (ComplexTensor): (..., F, C, C) + reference_vector (torch.Tensor): (..., C) + eps (float): + Returns: + beamform_vector (ComplexTensor)r: (..., F, C) + """ + # Add eps + C = psd_n.size(-1) + eye = torch.eye(C, dtype=psd_n.dtype, device=psd_n.device) + shape = [1 for _ in range(psd_n.dim() - 2)] + [C, C] + eye = eye.view(*shape) + psd_n += eps * eye + + # numerator: (..., C_1, C_2) x (..., C_2, C_3) -> (..., C_1, C_3) + numerator = FC.einsum("...ec,...cd->...ed", [psd_n.inverse(), psd_s]) + # ws: (..., C, C) / (...,) -> (..., C, C) + ws = numerator / (FC.trace(numerator)[..., None, None] + eps) + # h: (..., F, C_1, C_2) x (..., C_2) -> (..., F, C_1) + beamform_vector = FC.einsum("...fec,...c->...fe", [ws, reference_vector]) + return beamform_vector + + +def apply_beamforming_vector( + beamform_vector: ComplexTensor, mix: ComplexTensor +) -> ComplexTensor: + # (..., C) x (..., C, T) -> (..., T) + es = FC.einsum("...c,...ct->...t", [beamform_vector.conj(), mix]) + return es diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/complex_utils.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/complex_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e1b3157820cfa5f66e1567e502a3cbc997ea1cf4 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/complex_utils.py @@ -0,0 +1,194 @@ +"""Beamformer module.""" + +from distutils.version import LooseVersion +from typing import Sequence +from typing import Tuple +from typing import Union + +import torch + +try: + from torch_complex import functional as FC + from torch_complex.tensor import ComplexTensor +except: + print("Please install torch_complex firstly") + + +EPS = torch.finfo(torch.double).eps +is_torch_1_8_plus = LooseVersion(torch.__version__) >= LooseVersion("1.8.0") +is_torch_1_9_plus = LooseVersion(torch.__version__) >= LooseVersion("1.9.0") + + +def new_complex_like( + ref: Union[torch.Tensor, ComplexTensor], + real_imag: Tuple[torch.Tensor, torch.Tensor], +): + if isinstance(ref, ComplexTensor): + return ComplexTensor(*real_imag) + elif is_torch_complex_tensor(ref): + return torch.complex(*real_imag) + else: + raise ValueError( + "Please update your PyTorch version to 1.9+ for complex support." + ) + + +def is_torch_complex_tensor(c): + return ( + not isinstance(c, ComplexTensor) and is_torch_1_9_plus and torch.is_complex(c) + ) + + +def is_complex(c): + return isinstance(c, ComplexTensor) or is_torch_complex_tensor(c) + + +def to_double(c): + if not isinstance(c, ComplexTensor) and is_torch_1_9_plus and torch.is_complex(c): + return c.to(dtype=torch.complex128) + else: + return c.double() + + +def to_float(c): + if not isinstance(c, ComplexTensor) and is_torch_1_9_plus and torch.is_complex(c): + return c.to(dtype=torch.complex64) + else: + return c.float() + + +def cat(seq: Sequence[Union[ComplexTensor, torch.Tensor]], *args, **kwargs): + if not isinstance(seq, (list, tuple)): + raise TypeError( + "cat(): argument 'tensors' (position 1) must be tuple of Tensors, " + "not Tensor" + ) + if isinstance(seq[0], ComplexTensor): + return FC.cat(seq, *args, **kwargs) + else: + return torch.cat(seq, *args, **kwargs) + + +def complex_norm( + c: Union[torch.Tensor, ComplexTensor], dim=-1, keepdim=False +) -> torch.Tensor: + if not is_complex(c): + raise TypeError("Input is not a complex tensor.") + if is_torch_complex_tensor(c): + return torch.norm(c, dim=dim, keepdim=keepdim) + else: + return torch.sqrt((c.real**2 + c.imag**2).sum(dim=dim, keepdim=keepdim) + EPS) + + +def einsum(equation, *operands): + # NOTE: Do not mix ComplexTensor and torch.complex in the input! + # NOTE (wangyou): Until PyTorch 1.9.0, torch.einsum does not support + # mixed input with complex and real tensors. + if len(operands) == 1: + if isinstance(operands[0], (tuple, list)): + operands = operands[0] + complex_module = FC if isinstance(operands[0], ComplexTensor) else torch + return complex_module.einsum(equation, *operands) + elif len(operands) != 2: + op0 = operands[0] + same_type = all(op.dtype == op0.dtype for op in operands[1:]) + if same_type: + _einsum = FC.einsum if isinstance(op0, ComplexTensor) else torch.einsum + return _einsum(equation, *operands) + else: + raise ValueError("0 or More than 2 operands are not supported.") + a, b = operands + if isinstance(a, ComplexTensor) or isinstance(b, ComplexTensor): + return FC.einsum(equation, a, b) + elif is_torch_1_9_plus and (torch.is_complex(a) or torch.is_complex(b)): + if not torch.is_complex(a): + o_real = torch.einsum(equation, a, b.real) + o_imag = torch.einsum(equation, a, b.imag) + return torch.complex(o_real, o_imag) + elif not torch.is_complex(b): + o_real = torch.einsum(equation, a.real, b) + o_imag = torch.einsum(equation, a.imag, b) + return torch.complex(o_real, o_imag) + else: + return torch.einsum(equation, a, b) + else: + return torch.einsum(equation, a, b) + + +def inverse( + c: Union[torch.Tensor, ComplexTensor], +) -> Union[torch.Tensor, ComplexTensor]: + if isinstance(c, ComplexTensor): + return c.inverse2() + else: + return c.inverse() + + +def matmul( + a: Union[torch.Tensor, ComplexTensor], b: Union[torch.Tensor, ComplexTensor] +) -> Union[torch.Tensor, ComplexTensor]: + # NOTE: Do not mix ComplexTensor and torch.complex in the input! + # NOTE (wangyou): Until PyTorch 1.9.0, torch.matmul does not support + # multiplication between complex and real tensors. + if isinstance(a, ComplexTensor) or isinstance(b, ComplexTensor): + return FC.matmul(a, b) + elif is_torch_1_9_plus and (torch.is_complex(a) or torch.is_complex(b)): + if not torch.is_complex(a): + o_real = torch.matmul(a, b.real) + o_imag = torch.matmul(a, b.imag) + return torch.complex(o_real, o_imag) + elif not torch.is_complex(b): + o_real = torch.matmul(a.real, b) + o_imag = torch.matmul(a.imag, b) + return torch.complex(o_real, o_imag) + else: + return torch.matmul(a, b) + else: + return torch.matmul(a, b) + + +def trace(a: Union[torch.Tensor, ComplexTensor]): + # NOTE (wangyou): until PyTorch 1.9.0, torch.trace does not + # support bacth processing. Use FC.trace() as fallback. + return FC.trace(a) + + +def reverse(a: Union[torch.Tensor, ComplexTensor], dim=0): + if isinstance(a, ComplexTensor): + return FC.reverse(a, dim=dim) + else: + return torch.flip(a, dims=(dim,)) + + +def solve(b: Union[torch.Tensor, ComplexTensor], a: Union[torch.Tensor, ComplexTensor]): + """Solve the linear equation ax = b.""" + # NOTE: Do not mix ComplexTensor and torch.complex in the input! + # NOTE (wangyou): Until PyTorch 1.9.0, torch.solve does not support + # mixed input with complex and real tensors. + if isinstance(a, ComplexTensor) or isinstance(b, ComplexTensor): + if isinstance(a, ComplexTensor) and isinstance(b, ComplexTensor): + return FC.solve(b, a, return_LU=False) + else: + return matmul(inverse(a), b) + elif is_torch_1_9_plus and (torch.is_complex(a) or torch.is_complex(b)): + if torch.is_complex(a) and torch.is_complex(b): + return torch.linalg.solve(a, b) + else: + return matmul(inverse(a), b) + else: + if is_torch_1_8_plus: + return torch.linalg.solve(a, b) + else: + return torch.solve(b, a)[0] + + +def stack(seq: Sequence[Union[ComplexTensor, torch.Tensor]], *args, **kwargs): + if not isinstance(seq, (list, tuple)): + raise TypeError( + "stack(): argument 'tensors' (position 1) must be tuple of Tensors, " + "not Tensor" + ) + if isinstance(seq[0], ComplexTensor): + return FC.stack(seq, *args, **kwargs) + else: + return torch.stack(seq, *args, **kwargs) diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/dnn_beamformer.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/dnn_beamformer.py new file mode 100644 index 0000000000000000000000000000000000000000..d75eac22f2def2a18fd851ece1c71a338e18d35e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/dnn_beamformer.py @@ -0,0 +1,173 @@ +"""DNN beamformer module.""" + +from typing import Tuple + +import torch +from torch.nn import functional as F + +from funasr_detach.frontends.utils.beamformer import apply_beamforming_vector +from funasr_detach.frontends.utils.beamformer import get_mvdr_vector +from funasr_detach.frontends.utils.beamformer import ( + get_power_spectral_density_matrix, # noqa: H301 +) +from funasr_detach.frontends.utils.mask_estimator import MaskEstimator +from torch_complex.tensor import ComplexTensor + + +class DNN_Beamformer(torch.nn.Module): + """DNN mask based Beamformer + + Citation: + Multichannel End-to-end Speech Recognition; T. Ochiai et al., 2017; + https://arxiv.org/abs/1703.04783 + + """ + + def __init__( + self, + bidim, + btype="blstmp", + blayers=3, + bunits=300, + bprojs=320, + bnmask=2, + dropout_rate=0.0, + badim=320, + ref_channel: int = -1, + beamformer_type="mvdr", + ): + super().__init__() + self.mask = MaskEstimator( + btype, bidim, blayers, bunits, bprojs, dropout_rate, nmask=bnmask + ) + self.ref = AttentionReference(bidim, badim) + self.ref_channel = ref_channel + + self.nmask = bnmask + + if beamformer_type != "mvdr": + raise ValueError( + "Not supporting beamformer_type={}".format(beamformer_type) + ) + self.beamformer_type = beamformer_type + + def forward( + self, data: ComplexTensor, ilens: torch.LongTensor + ) -> Tuple[ComplexTensor, torch.LongTensor, ComplexTensor]: + """The forward function + + Notation: + B: Batch + C: Channel + T: Time or Sequence length + F: Freq + + Args: + data (ComplexTensor): (B, T, C, F) + ilens (torch.Tensor): (B,) + Returns: + enhanced (ComplexTensor): (B, T, F) + ilens (torch.Tensor): (B,) + + """ + + def apply_beamforming(data, ilens, psd_speech, psd_noise): + # u: (B, C) + if self.ref_channel < 0: + u, _ = self.ref(psd_speech, ilens) + else: + # (optional) Create onehot vector for fixed reference microphone + u = torch.zeros( + *(data.size()[:-3] + (data.size(-2),)), device=data.device + ) + u[..., self.ref_channel].fill_(1) + + ws = get_mvdr_vector(psd_speech, psd_noise, u) + enhanced = apply_beamforming_vector(ws, data) + + return enhanced, ws + + # data (B, T, C, F) -> (B, F, C, T) + data = data.permute(0, 3, 2, 1) + + # mask: (B, F, C, T) + masks, _ = self.mask(data, ilens) + assert self.nmask == len(masks) + + if self.nmask == 2: # (mask_speech, mask_noise) + mask_speech, mask_noise = masks + + psd_speech = get_power_spectral_density_matrix(data, mask_speech) + psd_noise = get_power_spectral_density_matrix(data, mask_noise) + + enhanced, ws = apply_beamforming(data, ilens, psd_speech, psd_noise) + + # (..., F, T) -> (..., T, F) + enhanced = enhanced.transpose(-1, -2) + mask_speech = mask_speech.transpose(-1, -3) + else: # multi-speaker case: (mask_speech1, ..., mask_noise) + mask_speech = list(masks[:-1]) + mask_noise = masks[-1] + + psd_speeches = [ + get_power_spectral_density_matrix(data, mask) for mask in mask_speech + ] + psd_noise = get_power_spectral_density_matrix(data, mask_noise) + + enhanced = [] + ws = [] + for i in range(self.nmask - 1): + psd_speech = psd_speeches.pop(i) + # treat all other speakers' psd_speech as noises + enh, w = apply_beamforming( + data, ilens, psd_speech, sum(psd_speeches) + psd_noise + ) + psd_speeches.insert(i, psd_speech) + + # (..., F, T) -> (..., T, F) + enh = enh.transpose(-1, -2) + mask_speech[i] = mask_speech[i].transpose(-1, -3) + + enhanced.append(enh) + ws.append(w) + + return enhanced, ilens, mask_speech + + +class AttentionReference(torch.nn.Module): + def __init__(self, bidim, att_dim): + super().__init__() + self.mlp_psd = torch.nn.Linear(bidim, att_dim) + self.gvec = torch.nn.Linear(att_dim, 1) + + def forward( + self, psd_in: ComplexTensor, ilens: torch.LongTensor, scaling: float = 2.0 + ) -> Tuple[torch.Tensor, torch.LongTensor]: + """The forward function + + Args: + psd_in (ComplexTensor): (B, F, C, C) + ilens (torch.Tensor): (B,) + scaling (float): + Returns: + u (torch.Tensor): (B, C) + ilens (torch.Tensor): (B,) + """ + B, _, C = psd_in.size()[:3] + assert psd_in.size(2) == psd_in.size(3), psd_in.size() + # psd_in: (B, F, C, C) + psd = psd_in.masked_fill( + torch.eye(C, dtype=torch.bool, device=psd_in.device), 0 + ) + # psd: (B, F, C, C) -> (B, C, F) + psd = (psd.sum(dim=-1) / (C - 1)).transpose(-1, -2) + + # Calculate amplitude + psd_feat = (psd.real**2 + psd.imag**2) ** 0.5 + + # (B, C, F) -> (B, C, F2) + mlp_psd = self.mlp_psd(psd_feat) + # (B, C, F2) -> (B, C, 1) -> (B, C) + e = self.gvec(torch.tanh(mlp_psd)).squeeze(-1) + u = F.softmax(scaling * e, dim=-1) + return u, ilens diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/dnn_wpe.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/dnn_wpe.py new file mode 100644 index 0000000000000000000000000000000000000000..6b4d2f59a0fa407f48e701cef8c61e3968242368 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/dnn_wpe.py @@ -0,0 +1,93 @@ +from typing import Tuple + +from pytorch_wpe import wpe_one_iteration +import torch +from torch_complex.tensor import ComplexTensor + +from funasr_detach.frontends.utils.mask_estimator import MaskEstimator +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +class DNN_WPE(torch.nn.Module): + def __init__( + self, + wtype: str = "blstmp", + widim: int = 257, + wlayers: int = 3, + wunits: int = 300, + wprojs: int = 320, + dropout_rate: float = 0.0, + taps: int = 5, + delay: int = 3, + use_dnn_mask: bool = True, + iterations: int = 1, + normalization: bool = False, + ): + super().__init__() + self.iterations = iterations + self.taps = taps + self.delay = delay + + self.normalization = normalization + self.use_dnn_mask = use_dnn_mask + + self.inverse_power = True + + if self.use_dnn_mask: + self.mask_est = MaskEstimator( + wtype, widim, wlayers, wunits, wprojs, dropout_rate, nmask=1 + ) + + def forward( + self, data: ComplexTensor, ilens: torch.LongTensor + ) -> Tuple[ComplexTensor, torch.LongTensor, ComplexTensor]: + """The forward function + + Notation: + B: Batch + C: Channel + T: Time or Sequence length + F: Freq or Some dimension of the feature vector + + Args: + data: (B, C, T, F) + ilens: (B,) + Returns: + data: (B, C, T, F) + ilens: (B,) + """ + # (B, T, C, F) -> (B, F, C, T) + enhanced = data = data.permute(0, 3, 2, 1) + mask = None + + for i in range(self.iterations): + # Calculate power: (..., C, T) + power = enhanced.real**2 + enhanced.imag**2 + if i == 0 and self.use_dnn_mask: + # mask: (B, F, C, T) + (mask,), _ = self.mask_est(enhanced, ilens) + if self.normalization: + # Normalize along T + mask = mask / mask.sum(dim=-1)[..., None] + # (..., C, T) * (..., C, T) -> (..., C, T) + power = power * mask + + # Averaging along the channel axis: (..., C, T) -> (..., T) + power = power.mean(dim=-2) + + # enhanced: (..., C, T) -> (..., C, T) + enhanced = wpe_one_iteration( + data.contiguous(), + power, + taps=self.taps, + delay=self.delay, + inverse_power=self.inverse_power, + ) + + enhanced.masked_fill_(make_pad_mask(ilens, enhanced.real), 0) + + # (B, F, C, T) -> (B, T, C, F) + enhanced = enhanced.permute(0, 3, 2, 1) + if mask is not None: + mask = mask.transpose(-1, -3) + return enhanced, ilens, mask diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/feature_transform.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/feature_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..1b191222f0afd3d4d63ac2d23e59e7e6271c27c9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/feature_transform.py @@ -0,0 +1,263 @@ +from typing import List +from typing import Tuple +from typing import Union + +import librosa +import numpy as np +import torch +from torch_complex.tensor import ComplexTensor + +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +class FeatureTransform(torch.nn.Module): + def __init__( + self, + # Mel options, + fs: int = 16000, + n_fft: int = 512, + n_mels: int = 80, + fmin: float = 0.0, + fmax: float = None, + # Normalization + stats_file: str = None, + apply_uttmvn: bool = True, + uttmvn_norm_means: bool = True, + uttmvn_norm_vars: bool = False, + ): + super().__init__() + self.apply_uttmvn = apply_uttmvn + + self.logmel = LogMel(fs=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax) + self.stats_file = stats_file + if stats_file is not None: + self.global_mvn = GlobalMVN(stats_file) + else: + self.global_mvn = None + + if self.apply_uttmvn is not None: + self.uttmvn = UtteranceMVN( + norm_means=uttmvn_norm_means, norm_vars=uttmvn_norm_vars + ) + else: + self.uttmvn = None + + def forward( + self, x: ComplexTensor, ilens: Union[torch.LongTensor, np.ndarray, List[int]] + ) -> Tuple[torch.Tensor, torch.LongTensor]: + # (B, T, F) or (B, T, C, F) + if x.dim() not in (3, 4): + raise ValueError(f"Input dim must be 3 or 4: {x.dim()}") + if not torch.is_tensor(ilens): + ilens = torch.from_numpy(np.asarray(ilens)).to(x.device) + + if x.dim() == 4: + # h: (B, T, C, F) -> h: (B, T, F) + if self.training: + # Select 1ch randomly + ch = np.random.randint(x.size(2)) + h = x[:, :, ch, :] + else: + # Use the first channel + h = x[:, :, 0, :] + else: + h = x + + # h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F) + h = h.real**2 + h.imag**2 + + h, _ = self.logmel(h, ilens) + if self.stats_file is not None: + h, _ = self.global_mvn(h, ilens) + if self.apply_uttmvn: + h, _ = self.uttmvn(h, ilens) + + return h, ilens + + +class LogMel(torch.nn.Module): + """Convert STFT to fbank feats + + The arguments is same as librosa.filters.mel + + Args: + fs: number > 0 [scalar] sampling rate of the incoming signal + n_fft: int > 0 [scalar] number of FFT components + n_mels: int > 0 [scalar] number of Mel bands to generate + fmin: float >= 0 [scalar] lowest frequency (in Hz) + fmax: float >= 0 [scalar] highest frequency (in Hz). + If `None`, use `fmax = fs / 2.0` + htk: use HTK formula instead of Slaney + norm: {None, 1, np.inf} [scalar] + if 1, divide the triangular mel weights by the width of the mel band + (area normalization). Otherwise, leave all the triangles aiming for + a peak value of 1.0 + + """ + + def __init__( + self, + fs: int = 16000, + n_fft: int = 512, + n_mels: int = 80, + fmin: float = 0.0, + fmax: float = None, + htk: bool = False, + norm=1, + ): + super().__init__() + + _mel_options = dict( + sr=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax, htk=htk, norm=norm + ) + self.mel_options = _mel_options + + # Note(kamo): The mel matrix of librosa is different from kaldi. + melmat = librosa.filters.mel(**_mel_options) + # melmat: (D2, D1) -> (D1, D2) + self.register_buffer("melmat", torch.from_numpy(melmat.T).float()) + + def extra_repr(self): + return ", ".join(f"{k}={v}" for k, v in self.mel_options.items()) + + def forward( + self, feat: torch.Tensor, ilens: torch.LongTensor + ) -> Tuple[torch.Tensor, torch.LongTensor]: + # feat: (B, T, D1) x melmat: (D1, D2) -> mel_feat: (B, T, D2) + mel_feat = torch.matmul(feat, self.melmat) + + logmel_feat = (mel_feat + 1e-20).log() + # Zero padding + logmel_feat = logmel_feat.masked_fill(make_pad_mask(ilens, logmel_feat, 1), 0.0) + return logmel_feat, ilens + + +class GlobalMVN(torch.nn.Module): + """Apply global mean and variance normalization + + Args: + stats_file(str): npy file of 1-dim array or text file. + From the _first element to + the {(len(array) - 1) / 2}th element are treated as + the sum of features, + and the rest excluding the last elements are + treated as the sum of the square value of features, + and the last elements eqauls to the number of samples. + std_floor(float): + """ + + def __init__( + self, + stats_file: str, + norm_means: bool = True, + norm_vars: bool = True, + eps: float = 1.0e-20, + ): + super().__init__() + self.norm_means = norm_means + self.norm_vars = norm_vars + + self.stats_file = stats_file + stats = np.load(stats_file) + + stats = stats.astype(float) + assert (len(stats) - 1) % 2 == 0, stats.shape + + count = stats.flatten()[-1] + mean = stats[: (len(stats) - 1) // 2] / count + var = stats[(len(stats) - 1) // 2 : -1] / count - mean * mean + std = np.maximum(np.sqrt(var), eps) + + self.register_buffer("bias", torch.from_numpy(-mean.astype(np.float32))) + self.register_buffer("scale", torch.from_numpy(1 / std.astype(np.float32))) + + def extra_repr(self): + return ( + f"stats_file={self.stats_file}, " + f"norm_means={self.norm_means}, norm_vars={self.norm_vars}" + ) + + def forward( + self, x: torch.Tensor, ilens: torch.LongTensor + ) -> Tuple[torch.Tensor, torch.LongTensor]: + # feat: (B, T, D) + if self.norm_means: + x += self.bias.type_as(x) + x.masked_fill(make_pad_mask(ilens, x, 1), 0.0) + + if self.norm_vars: + x *= self.scale.type_as(x) + return x, ilens + + +class UtteranceMVN(torch.nn.Module): + def __init__( + self, norm_means: bool = True, norm_vars: bool = False, eps: float = 1.0e-20 + ): + super().__init__() + self.norm_means = norm_means + self.norm_vars = norm_vars + self.eps = eps + + def extra_repr(self): + return f"norm_means={self.norm_means}, norm_vars={self.norm_vars}" + + def forward( + self, x: torch.Tensor, ilens: torch.LongTensor + ) -> Tuple[torch.Tensor, torch.LongTensor]: + return utterance_mvn( + x, ilens, norm_means=self.norm_means, norm_vars=self.norm_vars, eps=self.eps + ) + + +def utterance_mvn( + x: torch.Tensor, + ilens: torch.LongTensor, + norm_means: bool = True, + norm_vars: bool = False, + eps: float = 1.0e-20, +) -> Tuple[torch.Tensor, torch.LongTensor]: + """Apply utterance mean and variance normalization + + Args: + x: (B, T, D), assumed zero padded + ilens: (B, T, D) + norm_means: + norm_vars: + eps: + + """ + ilens_ = ilens.type_as(x) + # mean: (B, D) + mean = x.sum(dim=1) / ilens_[:, None] + + if norm_means: + x -= mean[:, None, :] + x_ = x + else: + x_ = x - mean[:, None, :] + + # Zero padding + x_.masked_fill(make_pad_mask(ilens, x_, 1), 0.0) + if norm_vars: + var = x_.pow(2).sum(dim=1) / ilens_[:, None] + var = torch.clamp(var, min=eps) + x /= var.sqrt()[:, None, :] + x_ = x + return x_, ilens + + +def feature_transform_for(args, n_fft): + return FeatureTransform( + # Mel options, + fs=args.fbank_fs, + n_fft=n_fft, + n_mels=args.n_mels, + fmin=args.fbank_fmin, + fmax=args.fbank_fmax, + # Normalization + stats_file=args.stats_file, + apply_uttmvn=args.apply_uttmvn, + uttmvn_norm_means=args.uttmvn_norm_means, + uttmvn_norm_vars=args.uttmvn_norm_vars, + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/frontend.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/frontend.py new file mode 100644 index 0000000000000000000000000000000000000000..8b0782add1c5ef4c971d66e491cc298d795d2a83 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/frontend.py @@ -0,0 +1,151 @@ +from typing import List +from typing import Optional +from typing import Tuple +from typing import Union + +import numpy +import torch +import torch.nn as nn +from torch_complex.tensor import ComplexTensor + +from funasr_detach.frontends.utils.dnn_beamformer import DNN_Beamformer +from funasr_detach.frontends.utils.dnn_wpe import DNN_WPE + + +class Frontend(nn.Module): + def __init__( + self, + idim: int, + # WPE options + use_wpe: bool = False, + wtype: str = "blstmp", + wlayers: int = 3, + wunits: int = 300, + wprojs: int = 320, + wdropout_rate: float = 0.0, + taps: int = 5, + delay: int = 3, + use_dnn_mask_for_wpe: bool = True, + # Beamformer options + use_beamformer: bool = False, + btype: str = "blstmp", + blayers: int = 3, + bunits: int = 300, + bprojs: int = 320, + bnmask: int = 2, + badim: int = 320, + ref_channel: int = -1, + bdropout_rate=0.0, + ): + super().__init__() + + self.use_beamformer = use_beamformer + self.use_wpe = use_wpe + self.use_dnn_mask_for_wpe = use_dnn_mask_for_wpe + # use frontend for all the data, + # e.g. in the case of multi-speaker speech separation + self.use_frontend_for_all = bnmask > 2 + + if self.use_wpe: + if self.use_dnn_mask_for_wpe: + # Use DNN for power estimation + # (Not observed significant gains) + iterations = 1 + else: + # Performing as conventional WPE, without DNN Estimator + iterations = 2 + + self.wpe = DNN_WPE( + wtype=wtype, + widim=idim, + wunits=wunits, + wprojs=wprojs, + wlayers=wlayers, + taps=taps, + delay=delay, + dropout_rate=wdropout_rate, + iterations=iterations, + use_dnn_mask=use_dnn_mask_for_wpe, + ) + else: + self.wpe = None + + if self.use_beamformer: + self.beamformer = DNN_Beamformer( + btype=btype, + bidim=idim, + bunits=bunits, + bprojs=bprojs, + blayers=blayers, + bnmask=bnmask, + dropout_rate=bdropout_rate, + badim=badim, + ref_channel=ref_channel, + ) + else: + self.beamformer = None + + def forward( + self, x: ComplexTensor, ilens: Union[torch.LongTensor, numpy.ndarray, List[int]] + ) -> Tuple[ComplexTensor, torch.LongTensor, Optional[ComplexTensor]]: + assert len(x) == len(ilens), (len(x), len(ilens)) + # (B, T, F) or (B, T, C, F) + if x.dim() not in (3, 4): + raise ValueError(f"Input dim must be 3 or 4: {x.dim()}") + if not torch.is_tensor(ilens): + ilens = torch.from_numpy(numpy.asarray(ilens)).to(x.device) + + mask = None + h = x + if h.dim() == 4: + if self.training: + choices = [(False, False)] if not self.use_frontend_for_all else [] + if self.use_wpe: + choices.append((True, False)) + + if self.use_beamformer: + choices.append((False, True)) + + use_wpe, use_beamformer = choices[numpy.random.randint(len(choices))] + + else: + use_wpe = self.use_wpe + use_beamformer = self.use_beamformer + + # 1. WPE + if use_wpe: + # h: (B, T, C, F) -> h: (B, T, C, F) + h, ilens, mask = self.wpe(h, ilens) + + # 2. Beamformer + if use_beamformer: + # h: (B, T, C, F) -> h: (B, T, F) + h, ilens, mask = self.beamformer(h, ilens) + + return h, ilens, mask + + +def frontend_for(args, idim): + return Frontend( + idim=idim, + # WPE options + use_wpe=args.use_wpe, + wtype=args.wtype, + wlayers=args.wlayers, + wunits=args.wunits, + wprojs=args.wprojs, + wdropout_rate=args.wdropout_rate, + taps=args.wpe_taps, + delay=args.wpe_delay, + use_dnn_mask_for_wpe=args.use_dnn_mask_for_wpe, + # Beamformer options + use_beamformer=args.use_beamformer, + btype=args.btype, + blayers=args.blayers, + bunits=args.bunits, + bprojs=args.bprojs, + bnmask=args.bnmask, + badim=args.badim, + ref_channel=args.ref_channel, + bdropout_rate=args.bdropout_rate, + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/log_mel.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/log_mel.py new file mode 100644 index 0000000000000000000000000000000000000000..695c9d6ece07aabaf446be4cdd8d77a281256f9f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/log_mel.py @@ -0,0 +1,83 @@ +import librosa +import torch +from typing import Tuple + +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +class LogMel(torch.nn.Module): + """Convert STFT to fbank feats + + The arguments is same as librosa.filters.mel + + Args: + fs: number > 0 [scalar] sampling rate of the incoming signal + n_fft: int > 0 [scalar] number of FFT components + n_mels: int > 0 [scalar] number of Mel bands to generate + fmin: float >= 0 [scalar] lowest frequency (in Hz) + fmax: float >= 0 [scalar] highest frequency (in Hz). + If `None`, use `fmax = fs / 2.0` + htk: use HTK formula instead of Slaney + """ + + def __init__( + self, + fs: int = 16000, + n_fft: int = 512, + n_mels: int = 80, + fmin: float = None, + fmax: float = None, + htk: bool = False, + log_base: float = None, + ): + super().__init__() + + fmin = 0 if fmin is None else fmin + fmax = fs / 2 if fmax is None else fmax + _mel_options = dict( + sr=fs, + n_fft=n_fft, + n_mels=n_mels, + fmin=fmin, + fmax=fmax, + htk=htk, + ) + self.mel_options = _mel_options + self.log_base = log_base + + # Note(kamo): The mel matrix of librosa is different from kaldi. + melmat = librosa.filters.mel(**_mel_options) + # melmat: (D2, D1) -> (D1, D2) + self.register_buffer("melmat", torch.from_numpy(melmat.T).float()) + + def extra_repr(self): + return ", ".join(f"{k}={v}" for k, v in self.mel_options.items()) + + def forward( + self, + feat: torch.Tensor, + ilens: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + # feat: (B, T, D1) x melmat: (D1, D2) -> mel_feat: (B, T, D2) + mel_feat = torch.matmul(feat, self.melmat) + mel_feat = torch.clamp(mel_feat, min=1e-10) + + if self.log_base is None: + logmel_feat = mel_feat.log() + elif self.log_base == 2.0: + logmel_feat = mel_feat.log2() + elif self.log_base == 10.0: + logmel_feat = mel_feat.log10() + else: + logmel_feat = mel_feat.log() / torch.log(self.log_base) + + # Zero padding + if ilens is not None: + logmel_feat = logmel_feat.masked_fill( + make_pad_mask(ilens, logmel_feat, 1), 0.0 + ) + else: + ilens = feat.new_full( + [feat.size(0)], fill_value=feat.size(1), dtype=torch.long + ) + return logmel_feat, ilens diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/mask_estimator.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/mask_estimator.py new file mode 100644 index 0000000000000000000000000000000000000000..8d4fc9caa7a4ecd814f419b746aef2fbe4c48c02 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/mask_estimator.py @@ -0,0 +1,77 @@ +from typing import Tuple + +import numpy as np +import torch +from torch.nn import functional as F +from torch_complex.tensor import ComplexTensor + +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.language_model.rnn.encoders import RNN +from funasr_detach.models.language_model.rnn.encoders import RNNP + + +class MaskEstimator(torch.nn.Module): + def __init__(self, type, idim, layers, units, projs, dropout, nmask=1): + super().__init__() + subsample = np.ones(layers + 1, dtype=np.int32) + + typ = type.lstrip("vgg").rstrip("p") + if type[-1] == "p": + self.brnn = RNNP(idim, layers, units, projs, subsample, dropout, typ=typ) + else: + self.brnn = RNN(idim, layers, units, projs, dropout, typ=typ) + + self.type = type + self.nmask = nmask + self.linears = torch.nn.ModuleList( + [torch.nn.Linear(projs, idim) for _ in range(nmask)] + ) + + def forward( + self, xs: ComplexTensor, ilens: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, ...], torch.LongTensor]: + """The forward function + + Args: + xs: (B, F, C, T) + ilens: (B,) + Returns: + hs (torch.Tensor): The hidden vector (B, F, C, T) + masks: A tuple of the masks. (B, F, C, T) + ilens: (B,) + """ + assert xs.size(0) == ilens.size(0), (xs.size(0), ilens.size(0)) + _, _, C, input_length = xs.size() + # (B, F, C, T) -> (B, C, T, F) + xs = xs.permute(0, 2, 3, 1) + + # Calculate amplitude: (B, C, T, F) -> (B, C, T, F) + xs = (xs.real**2 + xs.imag**2) ** 0.5 + # xs: (B, C, T, F) -> xs: (B * C, T, F) + xs = xs.contiguous().view(-1, xs.size(-2), xs.size(-1)) + # ilens: (B,) -> ilens_: (B * C) + ilens_ = ilens[:, None].expand(-1, C).contiguous().view(-1) + + # xs: (B * C, T, F) -> xs: (B * C, T, D) + xs, _, _ = self.brnn(xs, ilens_) + # xs: (B * C, T, D) -> xs: (B, C, T, D) + xs = xs.view(-1, C, xs.size(-2), xs.size(-1)) + + masks = [] + for linear in self.linears: + # xs: (B, C, T, D) -> mask:(B, C, T, F) + mask = linear(xs) + + mask = torch.sigmoid(mask) + # Zero padding + mask.masked_fill(make_pad_mask(ilens, mask, length_dim=2), 0) + + # (B, C, T, F) -> (B, F, C, T) + mask = mask.permute(0, 3, 1, 2) + + # Take cares of multi gpu cases: If input_length > max(ilens) + if mask.size(-1) < input_length: + mask = F.pad(mask, [0, input_length - mask.size(-1)], value=0) + masks.append(mask) + + return tuple(masks), ilens diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/utils/stft.py b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/stft.py new file mode 100644 index 0000000000000000000000000000000000000000..3967170697d0db48323ba95c931cb9ed921e3ba5 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/utils/stft.py @@ -0,0 +1,239 @@ +from distutils.version import LooseVersion +from typing import Optional +from typing import Tuple +from typing import Union + +import torch + +try: + from torch_complex.tensor import ComplexTensor +except: + print("Please install torch_complex firstly") +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.frontends.utils.complex_utils import is_complex + +import librosa +import numpy as np + +is_torch_1_9_plus = LooseVersion(torch.__version__) >= LooseVersion("1.9.0") + + +is_torch_1_7_plus = LooseVersion(torch.__version__) >= LooseVersion("1.7") + + +class Stft(torch.nn.Module): + def __init__( + self, + n_fft: int = 512, + win_length: int = None, + hop_length: int = 128, + window: Optional[str] = "hann", + center: bool = True, + normalized: bool = False, + onesided: bool = True, + ): + super().__init__() + self.n_fft = n_fft + if win_length is None: + self.win_length = n_fft + else: + self.win_length = win_length + self.hop_length = hop_length + self.center = center + self.normalized = normalized + self.onesided = onesided + if window is not None and not hasattr(torch, f"{window}_window"): + if window.lower() != "povey": + raise ValueError(f"{window} window is not implemented") + self.window = window + + def extra_repr(self): + return ( + f"n_fft={self.n_fft}, " + f"win_length={self.win_length}, " + f"hop_length={self.hop_length}, " + f"center={self.center}, " + f"normalized={self.normalized}, " + f"onesided={self.onesided}" + ) + + def forward( + self, input: torch.Tensor, ilens: torch.Tensor = None + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """STFT forward function. + + Args: + input: (Batch, Nsamples) or (Batch, Nsample, Channels) + ilens: (Batch) + Returns: + output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2) + + """ + bs = input.size(0) + if input.dim() == 3: + multi_channel = True + # input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample) + input = input.transpose(1, 2).reshape(-1, input.size(1)) + else: + multi_channel = False + + # NOTE(kamo): + # The default behaviour of torch.stft is compatible with librosa.stft + # about padding and scaling. + # Note that it's different from scipy.signal.stft + + # output: (Batch, Freq, Frames, 2=real_imag) + # or (Batch, Channel, Freq, Frames, 2=real_imag) + if self.window is not None: + if self.window.lower() == "povey": + window = torch.hann_window( + self.win_length, + periodic=False, + device=input.device, + dtype=input.dtype, + ).pow(0.85) + else: + window_func = getattr(torch, f"{self.window}_window") + window = window_func( + self.win_length, dtype=input.dtype, device=input.device + ) + else: + window = None + + # For the compatibility of ARM devices, which do not support + # torch.stft() due to the lake of MKL. + if input.is_cuda or torch.backends.mkl.is_available(): + stft_kwargs = dict( + n_fft=self.n_fft, + win_length=self.win_length, + hop_length=self.hop_length, + center=self.center, + window=window, + normalized=self.normalized, + onesided=self.onesided, + ) + if is_torch_1_7_plus: + stft_kwargs["return_complex"] = False + output = torch.stft(input, **stft_kwargs) + else: + if self.training: + raise NotImplementedError( + "stft is implemented with librosa on this device, which does not " + "support the training mode." + ) + + # use stft_kwargs to flexibly control different PyTorch versions' kwargs + stft_kwargs = dict( + n_fft=self.n_fft, + win_length=self.win_length, + hop_length=self.hop_length, + center=self.center, + window=window, + ) + + if window is not None: + # pad the given window to n_fft + n_pad_left = (self.n_fft - window.shape[0]) // 2 + n_pad_right = self.n_fft - window.shape[0] - n_pad_left + stft_kwargs["window"] = torch.cat( + [torch.zeros(n_pad_left), window, torch.zeros(n_pad_right)], 0 + ).numpy() + else: + win_length = ( + self.win_length if self.win_length is not None else self.n_fft + ) + stft_kwargs["window"] = torch.ones(win_length) + + output = [] + # iterate over istances in a batch + for i, instance in enumerate(input): + stft = librosa.stft(input[i].numpy(), **stft_kwargs) + output.append(torch.tensor(np.stack([stft.real, stft.imag], -1))) + output = torch.stack(output, 0) + if not self.onesided: + len_conj = self.n_fft - output.shape[1] + conj = output[:, 1 : 1 + len_conj].flip(1) + conj[:, :, :, -1].data *= -1 + output = torch.cat([output, conj], 1) + if self.normalized: + output = output * (stft_kwargs["window"].shape[0] ** (-0.5)) + + # output: (Batch, Freq, Frames, 2=real_imag) + # -> (Batch, Frames, Freq, 2=real_imag) + output = output.transpose(1, 2) + if multi_channel: + # output: (Batch * Channel, Frames, Freq, 2=real_imag) + # -> (Batch, Frame, Channel, Freq, 2=real_imag) + output = output.view(bs, -1, output.size(1), output.size(2), 2).transpose( + 1, 2 + ) + + if ilens is not None: + if self.center: + pad = self.n_fft // 2 + ilens = ilens + 2 * pad + + olens = (ilens - self.n_fft) // self.hop_length + 1 + output.masked_fill_(make_pad_mask(olens, output, 1), 0.0) + else: + olens = None + + return output, olens + + def inverse( + self, input: Union[torch.Tensor, ComplexTensor], ilens: torch.Tensor = None + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Inverse STFT. + + Args: + input: Tensor(batch, T, F, 2) or ComplexTensor(batch, T, F) + ilens: (batch,) + Returns: + wavs: (batch, samples) + ilens: (batch,) + """ + if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + istft = torch.functional.istft + else: + try: + import torchaudio + except ImportError: + raise ImportError( + "Please install torchaudio>=0.3.0 or use torch>=1.6.0" + ) + + if not hasattr(torchaudio.functional, "istft"): + raise ImportError( + "Please install torchaudio>=0.3.0 or use torch>=1.6.0" + ) + istft = torchaudio.functional.istft + + if self.window is not None: + window_func = getattr(torch, f"{self.window}_window") + if is_complex(input): + datatype = input.real.dtype + else: + datatype = input.dtype + window = window_func(self.win_length, dtype=datatype, device=input.device) + else: + window = None + + if is_complex(input): + input = torch.stack([input.real, input.imag], dim=-1) + elif input.shape[-1] != 2: + raise TypeError("Invalid input type") + input = input.transpose(1, 2) + + wavs = istft( + input, + n_fft=self.n_fft, + hop_length=self.hop_length, + win_length=self.win_length, + window=window, + center=self.center, + normalized=self.normalized, + onesided=self.onesided, + length=ilens.max() if ilens is not None else ilens, + ) + + return wavs, ilens diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/wav_frontend.py b/demo/Step-Audio-EditX/funasr_detach/frontends/wav_frontend.py new file mode 100644 index 0000000000000000000000000000000000000000..e6805ec0281061cac35a61d4946893923d928bbe --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/wav_frontend.py @@ -0,0 +1,556 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. +# Part of the implementation is borrowed from espnet/espnet. +from typing import Tuple +import copy +import numpy as np +import torch +import torch.nn as nn +import torchaudio.compliance.kaldi as kaldi +from torch.nn.utils.rnn import pad_sequence + +import funasr_detach.frontends.eend_ola_feature as eend_ola_feature +from funasr_detach.register import tables + + +def load_cmvn(cmvn_file): + with open(cmvn_file, "r", encoding="utf-8") as f: + lines = f.readlines() + means_list = [] + vars_list = [] + for i in range(len(lines)): + line_item = lines[i].split() + if line_item[0] == "": + line_item = lines[i + 1].split() + if line_item[0] == "": + add_shift_line = line_item[3 : (len(line_item) - 1)] + means_list = list(add_shift_line) + continue + elif line_item[0] == "": + line_item = lines[i + 1].split() + if line_item[0] == "": + rescale_line = line_item[3 : (len(line_item) - 1)] + vars_list = list(rescale_line) + continue + means = np.array(means_list).astype(np.float32) + vars = np.array(vars_list).astype(np.float32) + cmvn = np.array([means, vars]) + cmvn = torch.as_tensor(cmvn, dtype=torch.float32) + return cmvn + + +def apply_cmvn(inputs, cmvn): # noqa + """ + Apply CMVN with mvn data + """ + + device = inputs.device + dtype = inputs.dtype + frame, dim = inputs.shape + + means = cmvn[0:1, :dim] + vars = cmvn[1:2, :dim] + inputs += means.to(device) + inputs *= vars.to(device) + + return inputs.type(torch.float32) + + +def apply_lfr(inputs, lfr_m, lfr_n): + LFR_inputs = [] + T = inputs.shape[0] + T_lfr = int(np.ceil(T / lfr_n)) + left_padding = inputs[0].repeat((lfr_m - 1) // 2, 1) + inputs = torch.vstack((left_padding, inputs)) + T = T + (lfr_m - 1) // 2 + for i in range(T_lfr): + if lfr_m <= T - i * lfr_n: + LFR_inputs.append((inputs[i * lfr_n : i * lfr_n + lfr_m]).view(1, -1)) + else: # process last LFR frame + num_padding = lfr_m - (T - i * lfr_n) + frame = (inputs[i * lfr_n :]).view(-1) + for _ in range(num_padding): + frame = torch.hstack((frame, inputs[-1])) + LFR_inputs.append(frame) + LFR_outputs = torch.vstack(LFR_inputs) + return LFR_outputs.type(torch.float32) + + +@tables.register("frontend_classes", "WavFrontend") +class WavFrontend(nn.Module): + """Conventional frontend structure for ASR.""" + + def __init__( + self, + cmvn_file: str = None, + fs: int = 16000, + window: str = "hamming", + n_mels: int = 80, + frame_length: int = 25, + frame_shift: int = 10, + filter_length_min: int = -1, + filter_length_max: int = -1, + lfr_m: int = 1, + lfr_n: int = 1, + dither: float = 1.0, + snip_edges: bool = True, + upsacle_samples: bool = True, + **kwargs, + ): + super().__init__() + self.fs = fs + self.window = window + self.n_mels = n_mels + self.frame_length = frame_length + self.frame_shift = frame_shift + self.filter_length_min = filter_length_min + self.filter_length_max = filter_length_max + self.lfr_m = lfr_m + self.lfr_n = lfr_n + self.cmvn_file = cmvn_file + self.dither = dither + self.snip_edges = snip_edges + self.upsacle_samples = upsacle_samples + self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file) + + def output_size(self) -> int: + return self.n_mels * self.lfr_m + + def forward( + self, + input: torch.Tensor, + input_lengths, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor]: + batch_size = input.size(0) + feats = [] + feats_lens = [] + for i in range(batch_size): + waveform_length = input_lengths[i] + waveform = input[i][:waveform_length] + if self.upsacle_samples: + waveform = waveform * (1 << 15) + waveform = waveform.unsqueeze(0) + mat = kaldi.fbank( + waveform, + num_mel_bins=self.n_mels, + frame_length=self.frame_length, + frame_shift=self.frame_shift, + dither=self.dither, + energy_floor=0.0, + window_type=self.window, + sample_frequency=self.fs, + snip_edges=self.snip_edges, + ) + + if self.lfr_m != 1 or self.lfr_n != 1: + mat = apply_lfr(mat, self.lfr_m, self.lfr_n) + if self.cmvn is not None: + mat = apply_cmvn(mat, self.cmvn) + feat_length = mat.size(0) + feats.append(mat) + feats_lens.append(feat_length) + + feats_lens = torch.as_tensor(feats_lens) + if batch_size == 1: + feats_pad = feats[0][None, :, :] + else: + feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0) + return feats_pad, feats_lens + + def forward_fbank( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + batch_size = input.size(0) + feats = [] + feats_lens = [] + for i in range(batch_size): + waveform_length = input_lengths[i] + waveform = input[i][:waveform_length] + waveform = waveform * (1 << 15) + waveform = waveform.unsqueeze(0) + mat = kaldi.fbank( + waveform, + num_mel_bins=self.n_mels, + frame_length=self.frame_length, + frame_shift=self.frame_shift, + dither=self.dither, + energy_floor=0.0, + window_type=self.window, + sample_frequency=self.fs, + ) + + feat_length = mat.size(0) + feats.append(mat) + feats_lens.append(feat_length) + + feats_lens = torch.as_tensor(feats_lens) + feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0) + return feats_pad, feats_lens + + def forward_lfr_cmvn( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + batch_size = input.size(0) + feats = [] + feats_lens = [] + for i in range(batch_size): + mat = input[i, : input_lengths[i], :] + if self.lfr_m != 1 or self.lfr_n != 1: + mat = apply_lfr(mat, self.lfr_m, self.lfr_n) + if self.cmvn is not None: + mat = apply_cmvn(mat, self.cmvn) + feat_length = mat.size(0) + feats.append(mat) + feats_lens.append(feat_length) + + feats_lens = torch.as_tensor(feats_lens) + feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0) + return feats_pad, feats_lens + + +@tables.register("frontend_classes", "WavFrontendOnline") +class WavFrontendOnline(nn.Module): + """Conventional frontend structure for streaming ASR/VAD.""" + + def __init__( + self, + cmvn_file: str = None, + fs: int = 16000, + window: str = "hamming", + n_mels: int = 80, + frame_length: int = 25, + frame_shift: int = 10, + filter_length_min: int = -1, + filter_length_max: int = -1, + lfr_m: int = 1, + lfr_n: int = 1, + dither: float = 1.0, + snip_edges: bool = True, + upsacle_samples: bool = True, + **kwargs, + ): + super().__init__() + self.fs = fs + self.window = window + self.n_mels = n_mels + self.frame_length = frame_length + self.frame_shift = frame_shift + self.frame_sample_length = int(self.frame_length * self.fs / 1000) + self.frame_shift_sample_length = int(self.frame_shift * self.fs / 1000) + self.filter_length_min = filter_length_min + self.filter_length_max = filter_length_max + self.lfr_m = lfr_m + self.lfr_n = lfr_n + self.cmvn_file = cmvn_file + self.dither = dither + self.snip_edges = snip_edges + self.upsacle_samples = upsacle_samples + # self.waveforms = None + # self.reserve_waveforms = None + # self.fbanks = None + # self.fbanks_lens = None + self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file) + # self.input_cache = None + # self.lfr_splice_cache = [] + + def output_size(self) -> int: + return self.n_mels * self.lfr_m + + @staticmethod + def apply_cmvn(inputs: torch.Tensor, cmvn: torch.Tensor) -> torch.Tensor: + """ + Apply CMVN with mvn data + """ + + device = inputs.device + dtype = inputs.dtype + frame, dim = inputs.shape + + means = np.tile(cmvn[0:1, :dim], (frame, 1)) + vars = np.tile(cmvn[1:2, :dim], (frame, 1)) + inputs += torch.from_numpy(means).type(dtype).to(device) + inputs *= torch.from_numpy(vars).type(dtype).to(device) + + return inputs.type(torch.float32) + + @staticmethod + def apply_lfr( + inputs: torch.Tensor, lfr_m: int, lfr_n: int, is_final: bool = False + ) -> Tuple[torch.Tensor, torch.Tensor, int]: + """ + Apply lfr with data + """ + + LFR_inputs = [] + # inputs = torch.vstack((inputs_lfr_cache, inputs)) + T = inputs.shape[0] # include the right context + T_lfr = int( + np.ceil((T - (lfr_m - 1) // 2) / lfr_n) + ) # minus the right context: (lfr_m - 1) // 2 + splice_idx = T_lfr + for i in range(T_lfr): + if lfr_m <= T - i * lfr_n: + LFR_inputs.append((inputs[i * lfr_n : i * lfr_n + lfr_m]).view(1, -1)) + else: # process last LFR frame + if is_final: + num_padding = lfr_m - (T - i * lfr_n) + frame = (inputs[i * lfr_n :]).view(-1) + for _ in range(num_padding): + frame = torch.hstack((frame, inputs[-1])) + LFR_inputs.append(frame) + else: + # update splice_idx and break the circle + splice_idx = i + break + splice_idx = min(T - 1, splice_idx * lfr_n) + lfr_splice_cache = inputs[splice_idx:, :] + LFR_outputs = torch.vstack(LFR_inputs) + return LFR_outputs.type(torch.float32), lfr_splice_cache, splice_idx + + @staticmethod + def compute_frame_num( + sample_length: int, frame_sample_length: int, frame_shift_sample_length: int + ) -> int: + frame_num = int( + (sample_length - frame_sample_length) / frame_shift_sample_length + 1 + ) + return ( + frame_num if frame_num >= 1 and sample_length >= frame_sample_length else 0 + ) + + def forward_fbank( + self, + input: torch.Tensor, + input_lengths: torch.Tensor, + cache: dict = {}, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + batch_size = input.size(0) + assert batch_size == 1 + input = torch.cat((cache["input_cache"], input), dim=1) + frame_num = self.compute_frame_num( + input.shape[-1], self.frame_sample_length, self.frame_shift_sample_length + ) + # update self.in_cache + cache["input_cache"] = input[ + :, -(input.shape[-1] - frame_num * self.frame_shift_sample_length) : + ] + waveforms = torch.empty(0) + feats_pad = torch.empty(0) + feats_lens = torch.empty(0) + if frame_num: + waveforms = [] + feats = [] + feats_lens = [] + for i in range(batch_size): + waveform = input[i].cuda() + # we need accurate wave samples that used for fbank extracting + waveforms.append( + waveform[ + : ( + (frame_num - 1) * self.frame_shift_sample_length + + self.frame_sample_length + ) + ] + ) + waveform = waveform * (1 << 15) + waveform = waveform.unsqueeze(0) + mat = kaldi.fbank( + waveform, + num_mel_bins=self.n_mels, + frame_length=self.frame_length, + frame_shift=self.frame_shift, + dither=self.dither, + energy_floor=0.0, + window_type=self.window, + sample_frequency=self.fs, + ) + + feat_length = mat.size(0) + feats.append(mat) + feats_lens.append(feat_length) + + waveforms = torch.stack(waveforms) + feats_lens = torch.as_tensor(feats_lens) + feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0) + cache["fbanks"] = feats_pad + cache["fbanks_lens"] = copy.deepcopy(feats_lens) + return waveforms, feats_pad, feats_lens + + def forward_lfr_cmvn( + self, + input: torch.Tensor, + input_lengths: torch.Tensor, + is_final: bool = False, + cache: dict = {}, + **kwargs, + ): + batch_size = input.size(0) + feats = [] + feats_lens = [] + lfr_splice_frame_idxs = [] + for i in range(batch_size): + mat = input[i, : input_lengths[i], :] + if self.lfr_m != 1 or self.lfr_n != 1: + # update self.lfr_splice_cache in self.apply_lfr + # mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n, self.lfr_splice_cache[i], + mat, cache["lfr_splice_cache"][i], lfr_splice_frame_idx = ( + self.apply_lfr(mat, self.lfr_m, self.lfr_n, is_final) + ) + if self.cmvn_file is not None: + mat = self.apply_cmvn(mat, self.cmvn) + feat_length = mat.size(0) + feats.append(mat) + feats_lens.append(feat_length) + lfr_splice_frame_idxs.append(lfr_splice_frame_idx) + feats_lens = torch.as_tensor(feats_lens) + feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0) + lfr_splice_frame_idxs = torch.as_tensor(lfr_splice_frame_idxs) + return feats_pad, feats_lens, lfr_splice_frame_idxs + + def forward(self, input: torch.Tensor, input_lengths: torch.Tensor, **kwargs): + is_final = kwargs.get("is_final", False) + cache = kwargs.get("cache", {}) + if len(cache) == 0: + self.init_cache(cache) + + batch_size = input.shape[0] + assert ( + batch_size == 1 + ), "we support to extract feature online only when the batch size is equal to 1 now" + + waveforms, feats, feats_lengths = self.forward_fbank( + input, input_lengths, cache=cache + ) # input shape: B T D + + if feats.shape[0]: + + cache["waveforms"] = torch.cat( + (cache["reserve_waveforms"], waveforms.cpu()), dim=1 + ) + + if not cache["lfr_splice_cache"]: # 初始化splice_cache + for i in range(batch_size): + cache["lfr_splice_cache"].append( + feats[i][0, :].unsqueeze(dim=0).repeat((self.lfr_m - 1) // 2, 1) + ) + # need the number of the input frames + self.lfr_splice_cache[0].shape[0] is greater than self.lfr_m + if feats_lengths[0] + cache["lfr_splice_cache"][0].shape[0] >= self.lfr_m: + lfr_splice_cache_tensor = torch.stack( + cache["lfr_splice_cache"] + ) # B T D + feats = torch.cat((lfr_splice_cache_tensor, feats), dim=1) + + feats_lengths += lfr_splice_cache_tensor[0].shape[0] + frame_from_waveforms = int( + (cache["waveforms"].shape[1] - self.frame_sample_length) + / self.frame_shift_sample_length + + 1 + ) + minus_frame = ( + (self.lfr_m - 1) // 2 + if cache["reserve_waveforms"].numel() == 0 + else 0 + ) + feats, feats_lengths, lfr_splice_frame_idxs = self.forward_lfr_cmvn( + feats, feats_lengths, is_final, cache=cache + ) + if self.lfr_m == 1: + cache["reserve_waveforms"] = torch.empty(0) + else: + reserve_frame_idx = lfr_splice_frame_idxs[0] - minus_frame + # print('reserve_frame_idx: ' + str(reserve_frame_idx)) + # print('frame_frame: ' + str(frame_from_waveforms)) + cache["reserve_waveforms"] = cache["waveforms"][ + :, + reserve_frame_idx + * self.frame_shift_sample_length : frame_from_waveforms + * self.frame_shift_sample_length, + ] + sample_length = ( + frame_from_waveforms - 1 + ) * self.frame_shift_sample_length + self.frame_sample_length + cache["waveforms"] = cache["waveforms"][:, :sample_length] + else: + # update self.reserve_waveforms and self.lfr_splice_cache + cache["reserve_waveforms"] = cache["waveforms"][ + :, : -(self.frame_sample_length - self.frame_shift_sample_length) + ] + for i in range(batch_size): + cache["lfr_splice_cache"][i] = torch.cat( + (cache["lfr_splice_cache"][i], feats[i]), dim=0 + ) + return torch.empty(0), feats_lengths + else: + if is_final: + cache["waveforms"] = ( + waveforms + if cache["reserve_waveforms"].numel() == 0 + else cache["reserve_waveforms"] + ) + feats = torch.stack(cache["lfr_splice_cache"]) + feats_lengths = ( + torch.zeros(batch_size, dtype=torch.int) + feats.shape[1] + ) + feats, feats_lengths, _ = self.forward_lfr_cmvn( + feats, feats_lengths, is_final, cache=cache + ) + # if is_final: + # self.init_cache(cache) + return feats, feats_lengths + + def init_cache(self, cache: dict = {}): + cache["reserve_waveforms"] = torch.empty(0) + cache["input_cache"] = torch.empty(0) + cache["lfr_splice_cache"] = [] + cache["waveforms"] = None + cache["fbanks"] = None + cache["fbanks_lens"] = None + return cache + + +class WavFrontendMel23(nn.Module): + """Conventional frontend structure for ASR.""" + + def __init__( + self, + fs: int = 16000, + frame_length: int = 25, + frame_shift: int = 10, + lfr_m: int = 1, + lfr_n: int = 1, + **kwargs, + ): + super().__init__() + self.fs = fs + self.frame_length = frame_length + self.frame_shift = frame_shift + self.lfr_m = lfr_m + self.lfr_n = lfr_n + self.n_mels = 23 + + def output_size(self) -> int: + return self.n_mels * (2 * self.lfr_m + 1) + + def forward( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + batch_size = input.size(0) + feats = [] + feats_lens = [] + for i in range(batch_size): + waveform_length = input_lengths[i] + waveform = input[i][:waveform_length] + waveform = waveform.numpy() + mat = eend_ola_feature.stft(waveform, self.frame_length, self.frame_shift) + mat = eend_ola_feature.transform(mat) + mat = eend_ola_feature.splice(mat, context_size=self.lfr_m) + mat = mat[:: self.lfr_n] + mat = torch.from_numpy(mat) + feat_length = mat.size(0) + feats.append(mat) + feats_lens.append(feat_length) + + feats_lens = torch.as_tensor(feats_lens) + feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0) + return feats_pad, feats_lens diff --git a/demo/Step-Audio-EditX/funasr_detach/frontends/windowing.py b/demo/Step-Audio-EditX/funasr_detach/frontends/windowing.py new file mode 100644 index 0000000000000000000000000000000000000000..25fbe59f70b662bbe1986c8318c2a2bd1d0dbd96 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/frontends/windowing.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python3 +# 2020, Technische Universität München; Ludwig Kürzinger +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Sliding Window for raw audio input data.""" + +import torch +import torch.nn as nn +from typing import Tuple + + +class SlidingWindow(nn.Module): + """Sliding Window. + Provides a sliding window over a batched continuous raw audio tensor. + Optionally, provides padding (Currently not implemented). + Combine this module with a pre-encoder compatible with raw audio data, + for example Sinc convolutions. + Known issues: + Output length is calculated incorrectly if audio shorter than win_length. + WARNING: trailing values are discarded - padding not implemented yet. + There is currently no additional window function applied to input values. + """ + + def __init__( + self, + win_length: int = 400, + hop_length: int = 160, + channels: int = 1, + padding: int = None, + fs=None, + ): + """Initialize. + Args: + win_length: Length of frame. + hop_length: Relative starting point of next frame. + channels: Number of input channels. + padding: Padding (placeholder, currently not implemented). + fs: Sampling rate (placeholder for compatibility, not used). + """ + super().__init__() + self.fs = fs + self.win_length = win_length + self.hop_length = hop_length + self.channels = channels + self.padding = padding + + def forward( + self, input: torch.Tensor, input_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Apply a sliding window on the input. + Args: + input: Input (B, T, C*D) or (B, T*C*D), with D=C=1. + input_lengths: Input lengths within batch. + Returns: + Tensor: Output with dimensions (B, T, C, D), with D=win_length. + Tensor: Output lengths within batch. + """ + input_size = input.size() + B = input_size[0] + T = input_size[1] + C = self.channels + D = self.win_length + # (B, T, C) --> (T, B, C) + continuous = input.view(B, T, C).permute(1, 0, 2) + windowed = continuous.unfold(0, D, self.hop_length) + # (T, B, C, D) --> (B, T, C, D) + output = windowed.permute(1, 0, 2, 3).contiguous() + # After unfold(), windowed lengths change: + output_lengths = (input_lengths - self.win_length) // self.hop_length + 1 + return output, output_lengths + + def output_size(self) -> int: + """Return output length of feature dimension D, i.e. the window length.""" + return self.win_length diff --git a/demo/Step-Audio-EditX/funasr_detach/losses/__init__.py b/demo/Step-Audio-EditX/funasr_detach/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/losses/label_smoothing_loss.py b/demo/Step-Audio-EditX/funasr_detach/losses/label_smoothing_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..5c1f22a54c81dc6530f497faf009a36585fedf9a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/losses/label_smoothing_loss.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Label smoothing module.""" + +import torch +from torch import nn +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +class LabelSmoothingLoss(nn.Module): + """Label-smoothing loss. + + :param int size: the number of class + :param int padding_idx: ignored class id + :param float smoothing: smoothing rate (0.0 means the conventional CE) + :param bool normalize_length: normalize loss by sequence length if True + :param torch.nn.Module criterion: loss function to be smoothed + """ + + def __init__( + self, + size, + padding_idx, + smoothing, + normalize_length=False, + criterion=nn.KLDivLoss(reduction="none"), + ): + """Construct an LabelSmoothingLoss object.""" + super(LabelSmoothingLoss, self).__init__() + self.criterion = criterion + self.padding_idx = padding_idx + self.confidence = 1.0 - smoothing + self.smoothing = smoothing + self.size = size + self.true_dist = None + self.normalize_length = normalize_length + + def forward(self, x, target): + """Compute loss between x and target. + + :param torch.Tensor x: prediction (batch, seqlen, class) + :param torch.Tensor target: + target signal masked with self.padding_id (batch, seqlen) + :return: scalar float value + :rtype torch.Tensor + """ + assert x.size(2) == self.size + batch_size = x.size(0) + x = x.view(-1, self.size) + target = target.view(-1) + with torch.no_grad(): + true_dist = x.clone() + true_dist.fill_(self.smoothing / (self.size - 1)) + ignore = target == self.padding_idx # (B,) + total = len(target) - ignore.sum().item() + target = target.masked_fill(ignore, 0) # avoid -1 index + true_dist.scatter_(1, target.unsqueeze(1), self.confidence) + kl = self.criterion(torch.log_softmax(x, dim=1), true_dist) + denom = total if self.normalize_length else batch_size + return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom + + +class SequenceBinaryCrossEntropy(nn.Module): + def __init__( + self, normalize_length=False, criterion=nn.BCEWithLogitsLoss(reduction="none") + ): + super().__init__() + self.normalize_length = normalize_length + self.criterion = criterion + + def forward(self, pred, label, lengths): + pad_mask = make_pad_mask(lengths, maxlen=pred.shape[1]).to(pred.device) + loss = self.criterion(pred, label) + denom = (~pad_mask).sum() if self.normalize_length else pred.shape[0] + return loss.masked_fill(pad_mask.unsqueeze(-1), 0).sum() / denom + + +class NllLoss(nn.Module): + """Nll loss. + + :param int size: the number of class + :param int padding_idx: ignored class id + :param bool normalize_length: normalize loss by sequence length if True + :param torch.nn.Module criterion: loss function + """ + + def __init__( + self, + size, + padding_idx, + normalize_length=False, + criterion=nn.NLLLoss(reduction="none"), + ): + """Construct an NllLoss object.""" + super(NllLoss, self).__init__() + self.criterion = criterion + self.padding_idx = padding_idx + self.size = size + self.true_dist = None + self.normalize_length = normalize_length + + def forward(self, x, target): + """Compute loss between x and target. + + :param torch.Tensor x: prediction (batch, seqlen, class) + :param torch.Tensor target: + target signal masked with self.padding_id (batch, seqlen) + :return: scalar float value + :rtype torch.Tensor + """ + assert x.size(2) == self.size + batch_size = x.size(0) + x = x.view(-1, self.size) + target = target.view(-1) + with torch.no_grad(): + ignore = target == self.padding_idx # (B,) + total = len(target) - ignore.sum().item() + target = target.masked_fill(ignore, 0) # avoid -1 index + kl = self.criterion(x, target) + denom = total if self.normalize_length else batch_size + return kl.masked_fill(ignore, 0).sum() / denom diff --git a/demo/Step-Audio-EditX/funasr_detach/metrics/__init__.py b/demo/Step-Audio-EditX/funasr_detach/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/metrics/common.py b/demo/Step-Audio-EditX/funasr_detach/metrics/common.py new file mode 100644 index 0000000000000000000000000000000000000000..ff779678b4ff546004e2148cab37d2fbb4a6fa97 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/metrics/common.py @@ -0,0 +1,249 @@ +#!/usr/bin/env python3 +# encoding: utf-8 + +# Copyright 2017 Johns Hopkins University (Shinji Watanabe) +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Common functions for ASR.""" + +import json +import logging +import sys + +from itertools import groupby +import numpy as np +import six + + +def end_detect(ended_hyps, i, M=3, D_end=np.log(1 * np.exp(-10))): + """End detection. + + described in Eq. (50) of S. Watanabe et al + "Hybrid CTC/Attention Architecture for End-to-End Speech Recognition" + + :param ended_hyps: + :param i: + :param M: + :param D_end: + :return: + """ + if len(ended_hyps) == 0: + return False + count = 0 + best_hyp = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[0] + for m in six.moves.range(M): + # get ended_hyps with their length is i - m + hyp_length = i - m + hyps_same_length = [x for x in ended_hyps if len(x["yseq"]) == hyp_length] + if len(hyps_same_length) > 0: + best_hyp_same_length = sorted( + hyps_same_length, key=lambda x: x["score"], reverse=True + )[0] + if best_hyp_same_length["score"] - best_hyp["score"] < D_end: + count += 1 + + if count == M: + return True + else: + return False + + +# TODO(takaaki-hori): add different smoothing methods +def label_smoothing_dist(odim, lsm_type, transcript=None, blank=0): + """Obtain label distribution for loss smoothing. + + :param odim: + :param lsm_type: + :param blank: + :param transcript: + :return: + """ + if transcript is not None: + with open(transcript, "rb") as f: + trans_json = json.load(f)["utts"] + + if lsm_type == "unigram": + assert transcript is not None, ( + "transcript is required for %s label smoothing" % lsm_type + ) + labelcount = np.zeros(odim) + for k, v in trans_json.items(): + ids = np.array([int(n) for n in v["output"][0]["tokenid"].split()]) + # to avoid an error when there is no text in an uttrance + if len(ids) > 0: + labelcount[ids] += 1 + labelcount[odim - 1] = len(transcript) # count + labelcount[labelcount == 0] = 1 # flooring + labelcount[blank] = 0 # remove counts for blank + labeldist = labelcount.astype(np.float32) / np.sum(labelcount) + else: + logging.error("Error: unexpected label smoothing type: %s" % lsm_type) + sys.exit() + + return labeldist + + +def get_vgg2l_odim(idim, in_channel=3, out_channel=128): + """Return the output size of the VGG frontend. + + :param in_channel: input channel size + :param out_channel: output channel size + :return: output size + :rtype int + """ + idim = idim / in_channel + idim = np.ceil(np.array(idim, dtype=np.float32) / 2) # 1st max pooling + idim = np.ceil(np.array(idim, dtype=np.float32) / 2) # 2nd max pooling + return int(idim) * out_channel # numer of channels + + +class ErrorCalculator(object): + """Calculate CER and WER for E2E_ASR and CTC models during training. + + :param y_hats: numpy array with predicted text + :param y_pads: numpy array with true (target) text + :param char_list: + :param sym_space: + :param sym_blank: + :return: + """ + + def __init__( + self, char_list, sym_space, sym_blank, report_cer=False, report_wer=False + ): + """Construct an ErrorCalculator object.""" + super(ErrorCalculator, self).__init__() + + self.report_cer = report_cer + self.report_wer = report_wer + + self.char_list = char_list + self.space = sym_space + self.blank = sym_blank + self.idx_blank = self.char_list.index(self.blank) + if self.space in self.char_list: + self.idx_space = self.char_list.index(self.space) + else: + self.idx_space = None + + def __call__(self, ys_hat, ys_pad, is_ctc=False): + """Calculate sentence-level WER/CER score. + + :param torch.Tensor ys_hat: prediction (batch, seqlen) + :param torch.Tensor ys_pad: reference (batch, seqlen) + :param bool is_ctc: calculate CER score for CTC + :return: sentence-level WER score + :rtype float + :return: sentence-level CER score + :rtype float + """ + cer, wer = None, None + if is_ctc: + return self.calculate_cer_ctc(ys_hat, ys_pad) + elif not self.report_cer and not self.report_wer: + return cer, wer + + seqs_hat, seqs_true = self.convert_to_char(ys_hat, ys_pad) + if self.report_cer: + cer = self.calculate_cer(seqs_hat, seqs_true) + + if self.report_wer: + wer = self.calculate_wer(seqs_hat, seqs_true) + return cer, wer + + def calculate_cer_ctc(self, ys_hat, ys_pad): + """Calculate sentence-level CER score for CTC. + + :param torch.Tensor ys_hat: prediction (batch, seqlen) + :param torch.Tensor ys_pad: reference (batch, seqlen) + :return: average sentence-level CER score + :rtype float + """ + import editdistance + + cers, char_ref_lens = [], [] + for i, y in enumerate(ys_hat): + y_hat = [x[0] for x in groupby(y)] + y_true = ys_pad[i] + seq_hat, seq_true = [], [] + for idx in y_hat: + idx = int(idx) + if idx != -1 and idx != self.idx_blank and idx != self.idx_space: + seq_hat.append(self.char_list[int(idx)]) + + for idx in y_true: + idx = int(idx) + if idx != -1 and idx != self.idx_blank and idx != self.idx_space: + seq_true.append(self.char_list[int(idx)]) + + hyp_chars = "".join(seq_hat) + ref_chars = "".join(seq_true) + if len(ref_chars) > 0: + cers.append(editdistance.eval(hyp_chars, ref_chars)) + char_ref_lens.append(len(ref_chars)) + + cer_ctc = float(sum(cers)) / sum(char_ref_lens) if cers else None + return cer_ctc + + def convert_to_char(self, ys_hat, ys_pad): + """Convert index to character. + + :param torch.Tensor seqs_hat: prediction (batch, seqlen) + :param torch.Tensor seqs_true: reference (batch, seqlen) + :return: token list of prediction + :rtype list + :return: token list of reference + :rtype list + """ + seqs_hat, seqs_true = [], [] + for i, y_hat in enumerate(ys_hat): + y_true = ys_pad[i] + eos_true = np.where(y_true == -1)[0] + ymax = eos_true[0] if len(eos_true) > 0 else len(y_true) + # NOTE: padding index (-1) in y_true is used to pad y_hat + seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]] + seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1] + seq_hat_text = "".join(seq_hat).replace(self.space, " ") + seq_hat_text = seq_hat_text.replace(self.blank, "") + seq_true_text = "".join(seq_true).replace(self.space, " ") + seqs_hat.append(seq_hat_text) + seqs_true.append(seq_true_text) + return seqs_hat, seqs_true + + def calculate_cer(self, seqs_hat, seqs_true): + """Calculate sentence-level CER score. + + :param list seqs_hat: prediction + :param list seqs_true: reference + :return: average sentence-level CER score + :rtype float + """ + import editdistance + + char_eds, char_ref_lens = [], [] + for i, seq_hat_text in enumerate(seqs_hat): + seq_true_text = seqs_true[i] + hyp_chars = seq_hat_text.replace(" ", "") + ref_chars = seq_true_text.replace(" ", "") + char_eds.append(editdistance.eval(hyp_chars, ref_chars)) + char_ref_lens.append(len(ref_chars)) + return float(sum(char_eds)) / sum(char_ref_lens) + + def calculate_wer(self, seqs_hat, seqs_true): + """Calculate sentence-level WER score. + + :param list seqs_hat: prediction + :param list seqs_true: reference + :return: average sentence-level WER score + :rtype float + """ + import editdistance + + word_eds, word_ref_lens = [], [] + for i, seq_hat_text in enumerate(seqs_hat): + seq_true_text = seqs_true[i] + hyp_words = seq_hat_text.split() + ref_words = seq_true_text.split() + word_eds.append(editdistance.eval(hyp_words, ref_words)) + word_ref_lens.append(len(ref_words)) + return float(sum(word_eds)) / sum(word_ref_lens) diff --git a/demo/Step-Audio-EditX/funasr_detach/metrics/compute_acc.py b/demo/Step-Audio-EditX/funasr_detach/metrics/compute_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..98b1ee36369091a118a4fad0e9c2c7657eb7f807 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/metrics/compute_acc.py @@ -0,0 +1,24 @@ +import torch + + +def th_accuracy(pad_outputs, pad_targets, ignore_label): + """Calculate accuracy. + + Args: + pad_outputs (Tensor): Prediction tensors (B * Lmax, D). + pad_targets (LongTensor): Target label tensors (B, Lmax, D). + ignore_label (int): Ignore label id. + + Returns: + float: Accuracy value (0.0 - 1.0). + + """ + pad_pred = pad_outputs.view( + pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1) + ).argmax(2) + mask = pad_targets != ignore_label + numerator = torch.sum( + pad_pred.masked_select(mask) == pad_targets.masked_select(mask) + ) + denominator = torch.sum(mask) + return float(numerator) / float(denominator) diff --git a/demo/Step-Audio-EditX/funasr_detach/metrics/compute_eer.py b/demo/Step-Audio-EditX/funasr_detach/metrics/compute_eer.py new file mode 100644 index 0000000000000000000000000000000000000000..80311fe87a80241ce532251f4e5eb527123d2204 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/metrics/compute_eer.py @@ -0,0 +1,59 @@ +import numpy as np +from sklearn.metrics import roc_curve +import argparse + + +def _compute_eer(label, pred, positive_label=1): + """ + Python compute equal error rate (eer) + ONLY tested on binary classification + + :param label: ground-truth label, should be a 1-d list or np.array, each element represents the ground-truth label of one sample + :param pred: model prediction, should be a 1-d list or np.array, each element represents the model prediction of one sample + :param positive_label: the class that is viewed as positive class when computing EER + :return: equal error rate (EER) + """ + + # all fpr, tpr, fnr, fnr, threshold are lists (in the format of np.array) + fpr, tpr, threshold = roc_curve(label, pred, pos_label=positive_label) + fnr = 1 - tpr + + # the threshold of fnr == fpr + eer_threshold = threshold[np.nanargmin(np.absolute((fnr - fpr)))] + + # theoretically eer from fpr and eer from fnr should be identical but they can be slightly differ in reality + eer_1 = fpr[np.nanargmin(np.absolute((fnr - fpr)))] + eer_2 = fnr[np.nanargmin(np.absolute((fnr - fpr)))] + + # return the mean of eer from fpr and from fnr + eer = (eer_1 + eer_2) / 2 + return eer, eer_threshold + + +def compute_eer(trials_path, scores_path): + labels = [] + for one_line in open(trials_path, "r"): + labels.append(one_line.strip().rsplit(" ", 1)[-1] == "target") + labels = np.array(labels, dtype=int) + + scores = [] + for one_line in open(scores_path, "r"): + scores.append(float(one_line.strip().rsplit(" ", 1)[-1])) + scores = np.array(scores, dtype=float) + + eer, threshold = _compute_eer(labels, scores) + return eer, threshold + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("trials", help="trial list") + parser.add_argument("scores", help="score file, normalized to [0, 1]") + args = parser.parse_args() + + eer, threshold = compute_eer(args.trials, args.scores) + print("EER is {:.4f} at threshold {:.4f}".format(eer * 100.0, threshold)) + + +if __name__ == "__main__": + main() diff --git a/demo/Step-Audio-EditX/funasr_detach/metrics/compute_min_dcf.py b/demo/Step-Audio-EditX/funasr_detach/metrics/compute_min_dcf.py new file mode 100644 index 0000000000000000000000000000000000000000..62f3e3c1d5d0bb112d9544203c7c848ebc7ff261 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/metrics/compute_min_dcf.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +# Copyright 2018 David Snyder +# Apache 2.0 + +# This script computes the minimum detection cost function, which is a common +# error metric used in speaker recognition. Compared to equal error-rate, +# which assigns equal weight to false negatives and false positives, this +# error-rate is usually used to assess performance in settings where achieving +# a low false positive rate is more important than achieving a low false +# negative rate. See the NIST 2016 Speaker Recognition Evaluation Plan at +# https://www.nist.gov/sites/default/files/documents/2016/10/07/sre16_eval_plan_v1.3.pdf +# for more details about the metric. +from __future__ import print_function +from operator import itemgetter +import sys, argparse, os + + +def GetArgs(): + parser = argparse.ArgumentParser( + description="Compute the minimum " + "detection cost function along with the threshold at which it occurs. " + "Usage: sid/compute_min_dcf.py [options...] " + " " + "E.g., sid/compute_min_dcf.py --p-target 0.01 --c-miss 1 --c-fa 1 " + "exp/scores/trials data/test/trials", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--p-target", + type=float, + dest="p_target", + default=0.01, + help="The prior probability of the target speaker in a trial.", + ) + parser.add_argument( + "--c-miss", + type=float, + dest="c_miss", + default=1, + help="Cost of a missed detection. This is usually not changed.", + ) + parser.add_argument( + "--c-fa", + type=float, + dest="c_fa", + default=1, + help="Cost of a spurious detection. This is usually not changed.", + ) + parser.add_argument( + "scores_filename", + help="Input scores file, with columns of the form " " ", + ) + parser.add_argument( + "trials_filename", + help="Input trials file, with columns of the form " + " ", + ) + sys.stderr.write(" ".join(sys.argv) + "\n") + args = parser.parse_args() + args = CheckArgs(args) + return args + + +def CheckArgs(args): + if args.c_fa <= 0: + raise Exception("--c-fa must be greater than 0") + if args.c_miss <= 0: + raise Exception("--c-miss must be greater than 0") + if args.p_target <= 0 or args.p_target >= 1: + raise Exception("--p-target must be greater than 0 and less than 1") + return args + + +# Creates a list of false-negative rates, a list of false-positive rates +# and a list of decision thresholds that give those error-rates. +def ComputeErrorRates(scores, labels): + + # Sort the scores from smallest to largest, and also get the corresponding + # indexes of the sorted scores. We will treat the sorted scores as the + # thresholds at which the the error-rates are evaluated. + sorted_indexes, thresholds = zip( + *sorted( + [(index, threshold) for index, threshold in enumerate(scores)], + key=itemgetter(1), + ) + ) + labels = [labels[i] for i in sorted_indexes] + fns = [] + tns = [] + + # At the end of this loop, fns[i] is the number of errors made by + # incorrectly rejecting scores less than thresholds[i]. And, tns[i] + # is the total number of times that we have correctly rejected scores + # less than thresholds[i]. + for i in range(0, len(labels)): + if i == 0: + fns.append(labels[i]) + tns.append(1 - labels[i]) + else: + fns.append(fns[i - 1] + labels[i]) + tns.append(tns[i - 1] + 1 - labels[i]) + positives = sum(labels) + negatives = len(labels) - positives + + # Now divide the false negatives by the total number of + # positives to obtain the false negative rates across + # all thresholds + fnrs = [fn / float(positives) for fn in fns] + + # Divide the true negatives by the total number of + # negatives to get the true negative rate. Subtract these + # quantities from 1 to get the false positive rates. + fprs = [1 - tn / float(negatives) for tn in tns] + return fnrs, fprs, thresholds + + +# Computes the minimum of the detection cost function. The comments refer to +# equations in Section 3 of the NIST 2016 Speaker Recognition Evaluation Plan. +def ComputeMinDcf(fnrs, fprs, thresholds, p_target, c_miss, c_fa): + min_c_det = float("inf") + min_c_det_threshold = thresholds[0] + for i in range(0, len(fnrs)): + # See Equation (2). it is a weighted sum of false negative + # and false positive errors. + c_det = c_miss * fnrs[i] * p_target + c_fa * fprs[i] * (1 - p_target) + if c_det < min_c_det: + min_c_det = c_det + min_c_det_threshold = thresholds[i] + # See Equations (3) and (4). Now we normalize the cost. + c_def = min(c_miss * p_target, c_fa * (1 - p_target)) + min_dcf = min_c_det / c_def + return min_dcf, min_c_det_threshold + + +def compute_min_dcf(scores_filename, trials_filename, c_miss=1, c_fa=1, p_target=0.01): + scores_file = open(scores_filename, "r").readlines() + trials_file = open(trials_filename, "r").readlines() + c_miss = c_miss + c_fa = c_fa + p_target = p_target + + scores = [] + labels = [] + + trials = {} + for line in trials_file: + utt1, utt2, target = line.rstrip().split() + trial = utt1 + " " + utt2 + trials[trial] = target + + for line in scores_file: + utt1, utt2, score = line.rstrip().split() + trial = utt1 + " " + utt2 + if trial in trials: + scores.append(float(score)) + if trials[trial] == "target": + labels.append(1) + else: + labels.append(0) + else: + raise Exception( + "Missing entry for " + utt1 + " and " + utt2 + " " + scores_filename + ) + + fnrs, fprs, thresholds = ComputeErrorRates(scores, labels) + mindcf, threshold = ComputeMinDcf(fnrs, fprs, thresholds, p_target, c_miss, c_fa) + return mindcf, threshold + + +def main(): + args = GetArgs() + mindcf, threshold = compute_min_dcf( + args.scores_filename, + args.trials_filename, + args.c_miss, + args.c_fa, + args.p_target, + ) + sys.stdout.write( + "minDCF is {0:.4f} at threshold {1:.4f} (p-target={2}, c-miss={3}, " + "c-fa={4})\n".format(mindcf, threshold, args.p_target, args.c_miss, args.c_fa) + ) + + +if __name__ == "__main__": + main() diff --git a/demo/Step-Audio-EditX/funasr_detach/metrics/compute_wer.py b/demo/Step-Audio-EditX/funasr_detach/metrics/compute_wer.py new file mode 100644 index 0000000000000000000000000000000000000000..bb8eff21bfe60e4c8863cd42efc3c898a58d669e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/metrics/compute_wer.py @@ -0,0 +1,203 @@ +import os +import numpy as np +import sys + + +def compute_wer(ref_file, hyp_file, cer_detail_file): + rst = { + "Wrd": 0, + "Corr": 0, + "Ins": 0, + "Del": 0, + "Sub": 0, + "Snt": 0, + "Err": 0.0, + "S.Err": 0.0, + "wrong_words": 0, + "wrong_sentences": 0, + } + + hyp_dict = {} + ref_dict = {} + with open(hyp_file, "r") as hyp_reader: + for line in hyp_reader: + key = line.strip().split()[0] + value = line.strip().split()[1:] + hyp_dict[key] = value + with open(ref_file, "r") as ref_reader: + for line in ref_reader: + key = line.strip().split()[0] + value = line.strip().split()[1:] + ref_dict[key] = value + + cer_detail_writer = open(cer_detail_file, "w") + for hyp_key in hyp_dict: + if hyp_key in ref_dict: + out_item = compute_wer_by_line(hyp_dict[hyp_key], ref_dict[hyp_key]) + rst["Wrd"] += out_item["nwords"] + rst["Corr"] += out_item["cor"] + rst["wrong_words"] += out_item["wrong"] + rst["Ins"] += out_item["ins"] + rst["Del"] += out_item["del"] + rst["Sub"] += out_item["sub"] + rst["Snt"] += 1 + if out_item["wrong"] > 0: + rst["wrong_sentences"] += 1 + cer_detail_writer.write(hyp_key + print_cer_detail(out_item) + "\n") + cer_detail_writer.write( + "ref:" + + "\t" + + " ".join(list(map(lambda x: x.lower(), ref_dict[hyp_key]))) + + "\n" + ) + cer_detail_writer.write( + "hyp:" + + "\t" + + " ".join(list(map(lambda x: x.lower(), hyp_dict[hyp_key]))) + + "\n" + ) + + if rst["Wrd"] > 0: + rst["Err"] = round(rst["wrong_words"] * 100 / rst["Wrd"], 2) + if rst["Snt"] > 0: + rst["S.Err"] = round(rst["wrong_sentences"] * 100 / rst["Snt"], 2) + + cer_detail_writer.write("\n") + cer_detail_writer.write( + "%WER " + + str(rst["Err"]) + + " [ " + + str(rst["wrong_words"]) + + " / " + + str(rst["Wrd"]) + + ", " + + str(rst["Ins"]) + + " ins, " + + str(rst["Del"]) + + " del, " + + str(rst["Sub"]) + + " sub ]" + + "\n" + ) + cer_detail_writer.write( + "%SER " + + str(rst["S.Err"]) + + " [ " + + str(rst["wrong_sentences"]) + + " / " + + str(rst["Snt"]) + + " ]" + + "\n" + ) + cer_detail_writer.write( + "Scored " + + str(len(hyp_dict)) + + " sentences, " + + str(len(hyp_dict) - rst["Snt"]) + + " not present in hyp." + + "\n" + ) + + +def compute_wer_by_line(hyp, ref): + hyp = list(map(lambda x: x.lower(), hyp)) + ref = list(map(lambda x: x.lower(), ref)) + + len_hyp = len(hyp) + len_ref = len(ref) + + cost_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int16) + + ops_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int8) + + for i in range(len_hyp + 1): + cost_matrix[i][0] = i + for j in range(len_ref + 1): + cost_matrix[0][j] = j + + for i in range(1, len_hyp + 1): + for j in range(1, len_ref + 1): + if hyp[i - 1] == ref[j - 1]: + cost_matrix[i][j] = cost_matrix[i - 1][j - 1] + else: + substitution = cost_matrix[i - 1][j - 1] + 1 + insertion = cost_matrix[i - 1][j] + 1 + deletion = cost_matrix[i][j - 1] + 1 + + compare_val = [substitution, insertion, deletion] + + min_val = min(compare_val) + operation_idx = compare_val.index(min_val) + 1 + cost_matrix[i][j] = min_val + ops_matrix[i][j] = operation_idx + + match_idx = [] + i = len_hyp + j = len_ref + rst = {"nwords": len_ref, "cor": 0, "wrong": 0, "ins": 0, "del": 0, "sub": 0} + while i >= 0 or j >= 0: + i_idx = max(0, i) + j_idx = max(0, j) + + if ops_matrix[i_idx][j_idx] == 0: # correct + if i - 1 >= 0 and j - 1 >= 0: + match_idx.append((j - 1, i - 1)) + rst["cor"] += 1 + + i -= 1 + j -= 1 + + elif ops_matrix[i_idx][j_idx] == 2: # insert + i -= 1 + rst["ins"] += 1 + + elif ops_matrix[i_idx][j_idx] == 3: # delete + j -= 1 + rst["del"] += 1 + + elif ops_matrix[i_idx][j_idx] == 1: # substitute + i -= 1 + j -= 1 + rst["sub"] += 1 + + if i < 0 and j >= 0: + rst["del"] += 1 + elif j < 0 and i >= 0: + rst["ins"] += 1 + + match_idx.reverse() + wrong_cnt = cost_matrix[len_hyp][len_ref] + rst["wrong"] = wrong_cnt + + return rst + + +def print_cer_detail(rst): + return ( + "(" + + "nwords=" + + str(rst["nwords"]) + + ",cor=" + + str(rst["cor"]) + + ",ins=" + + str(rst["ins"]) + + ",del=" + + str(rst["del"]) + + ",sub=" + + str(rst["sub"]) + + ") corr:" + + "{:.2%}".format(rst["cor"] / rst["nwords"]) + + ",cer:" + + "{:.2%}".format(rst["wrong"] / rst["nwords"]) + ) + + +if __name__ == "__main__": + if len(sys.argv) != 4: + print("usage : python compute-wer.py test.ref test.hyp test.wer") + sys.exit(0) + + ref_file = sys.argv[1] + hyp_file = sys.argv[2] + cer_detail_file = sys.argv[3] + compute_wer(ref_file, hyp_file, cer_detail_file) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/bat/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/bat/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/bat/model.py b/demo/Step-Audio-EditX/funasr_detach/models/bat/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d3528ad48b3801d2e7bacad813d52159b71a0701 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/bat/model.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import time +import torch +import logging +from contextlib import contextmanager +from typing import Dict, Optional, Tuple +from distutils.version import LooseVersion + +from funasr_detach.register import tables +from funasr_detach.utils import postprocess_utils +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.models.transducer.model import Transducer +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.transformer.scorers.ctc import CTCPrefixScorer +from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss +from funasr_detach.models.transformer.scorers.length_bonus import LengthBonus +from funasr_detach.models.transformer.utils.nets_utils import get_transducer_task_io +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank +from funasr_detach.models.transducer.beam_search_transducer import BeamSearchTransducer + + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "BAT") # TODO: BAT training +class BAT(Transducer): + pass diff --git a/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/cif_predictor.py b/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/cif_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..3b0a229769e27c9002c34766c663bdcbb36c8f69 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/cif_predictor.py @@ -0,0 +1,417 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch + +from funasr_detach.register import tables +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +class mae_loss(torch.nn.Module): + + def __init__(self, normalize_length=False): + super(mae_loss, self).__init__() + self.normalize_length = normalize_length + self.criterion = torch.nn.L1Loss(reduction="sum") + + def forward(self, token_length, pre_token_length): + loss_token_normalizer = token_length.size(0) + if self.normalize_length: + loss_token_normalizer = token_length.sum().type(torch.float32) + loss = self.criterion(token_length, pre_token_length) + loss = loss / loss_token_normalizer + return loss + + +def cif(hidden, alphas, threshold): + batch_size, len_time, hidden_size = hidden.size() + + # loop varss + integrate = torch.zeros([batch_size], device=hidden.device) + frame = torch.zeros([batch_size, hidden_size], device=hidden.device) + # intermediate vars along time + list_fires = [] + list_frames = [] + + for t in range(len_time): + alpha = alphas[:, t] + distribution_completion = ( + torch.ones([batch_size], device=hidden.device) - integrate + ) + + integrate += alpha + list_fires.append(integrate) + + fire_place = integrate >= threshold + integrate = torch.where( + fire_place, + integrate - torch.ones([batch_size], device=hidden.device), + integrate, + ) + cur = torch.where(fire_place, distribution_completion, alpha) + remainds = alpha - cur + + frame += cur[:, None] * hidden[:, t, :] + list_frames.append(frame) + frame = torch.where( + fire_place[:, None].repeat(1, hidden_size), + remainds[:, None] * hidden[:, t, :], + frame, + ) + + fires = torch.stack(list_fires, 1) + frames = torch.stack(list_frames, 1) + list_ls = [] + len_labels = torch.round(alphas.sum(-1)).int() + max_label_len = len_labels.max() + for b in range(batch_size): + fire = fires[b, :] + l = torch.index_select( + frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze() + ) + pad_l = torch.zeros( + [max_label_len - l.size(0), hidden_size], device=hidden.device + ) + list_ls.append(torch.cat([l, pad_l], 0)) + return torch.stack(list_ls, 0), fires + + +def cif_wo_hidden(alphas, threshold): + batch_size, len_time = alphas.size() + + # loop varss + integrate = torch.zeros([batch_size], device=alphas.device) + # intermediate vars along time + list_fires = [] + + for t in range(len_time): + alpha = alphas[:, t] + + integrate += alpha + list_fires.append(integrate) + + fire_place = integrate >= threshold + integrate = torch.where( + fire_place, + integrate - torch.ones([batch_size], device=alphas.device) * threshold, + integrate, + ) + + fires = torch.stack(list_fires, 1) + return fires + + +@tables.register("predictor_classes", "CifPredictorV3") +class CifPredictorV3(torch.nn.Module): + def __init__( + self, + idim, + l_order, + r_order, + threshold=1.0, + dropout=0.1, + smooth_factor=1.0, + noise_threshold=0, + tail_threshold=0.0, + tf2torch_tensor_name_prefix_torch="predictor", + tf2torch_tensor_name_prefix_tf="seq2seq/cif", + smooth_factor2=1.0, + noise_threshold2=0, + upsample_times=5, + upsample_type="cnn", + use_cif1_cnn=True, + tail_mask=True, + ): + super(CifPredictorV3, self).__init__() + + self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0) + self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1) + self.cif_output = torch.nn.Linear(idim, 1) + self.dropout = torch.nn.Dropout(p=dropout) + self.threshold = threshold + self.smooth_factor = smooth_factor + self.noise_threshold = noise_threshold + self.tail_threshold = tail_threshold + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + + self.upsample_times = upsample_times + self.upsample_type = upsample_type + self.use_cif1_cnn = use_cif1_cnn + if self.upsample_type == "cnn": + self.upsample_cnn = torch.nn.ConvTranspose1d( + idim, idim, self.upsample_times, self.upsample_times + ) + self.cif_output2 = torch.nn.Linear(idim, 1) + elif self.upsample_type == "cnn_blstm": + self.upsample_cnn = torch.nn.ConvTranspose1d( + idim, idim, self.upsample_times, self.upsample_times + ) + self.blstm = torch.nn.LSTM( + idim, + idim, + 1, + bias=True, + batch_first=True, + dropout=0.0, + bidirectional=True, + ) + self.cif_output2 = torch.nn.Linear(idim * 2, 1) + elif self.upsample_type == "cnn_attn": + self.upsample_cnn = torch.nn.ConvTranspose1d( + idim, idim, self.upsample_times, self.upsample_times + ) + from funasr_detach.models.transformer.encoder import ( + EncoderLayer as TransformerEncoderLayer, + ) + from funasr_detach.models.transformer.attention import MultiHeadedAttention + from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, + ) + + positionwise_layer_args = ( + idim, + idim * 2, + 0.1, + ) + self.self_attn = TransformerEncoderLayer( + idim, + MultiHeadedAttention(4, idim, 0.1), + PositionwiseFeedForward(*positionwise_layer_args), + 0.1, + True, # normalize_before, + False, # concat_after, + ) + self.cif_output2 = torch.nn.Linear(idim, 1) + self.smooth_factor2 = smooth_factor2 + self.noise_threshold2 = noise_threshold2 + + def forward( + self, + hidden, + target_label=None, + mask=None, + ignore_id=-1, + mask_chunk_predictor=None, + target_label_length=None, + ): + h = hidden + context = h.transpose(1, 2) + queries = self.pad(context) + output = torch.relu(self.cif_conv1d(queries)) + + # alphas2 is an extra head for timestamp prediction + if not self.use_cif1_cnn: + _output = context + else: + _output = output + if self.upsample_type == "cnn": + output2 = self.upsample_cnn(_output) + output2 = output2.transpose(1, 2) + elif self.upsample_type == "cnn_blstm": + output2 = self.upsample_cnn(_output) + output2 = output2.transpose(1, 2) + output2, (_, _) = self.blstm(output2) + elif self.upsample_type == "cnn_attn": + output2 = self.upsample_cnn(_output) + output2 = output2.transpose(1, 2) + output2, _ = self.self_attn(output2, mask) + # import pdb; pdb.set_trace() + alphas2 = torch.sigmoid(self.cif_output2(output2)) + alphas2 = torch.nn.functional.relu( + alphas2 * self.smooth_factor2 - self.noise_threshold2 + ) + # repeat the mask in T demension to match the upsampled length + if mask is not None: + mask2 = ( + mask.repeat(1, self.upsample_times, 1) + .transpose(-1, -2) + .reshape(alphas2.shape[0], -1) + ) + mask2 = mask2.unsqueeze(-1) + alphas2 = alphas2 * mask2 + alphas2 = alphas2.squeeze(-1) + token_num2 = alphas2.sum(-1) + + output = output.transpose(1, 2) + + output = self.cif_output(output) + alphas = torch.sigmoid(output) + alphas = torch.nn.functional.relu( + alphas * self.smooth_factor - self.noise_threshold + ) + if mask is not None: + mask = mask.transpose(-1, -2).float() + alphas = alphas * mask + if mask_chunk_predictor is not None: + alphas = alphas * mask_chunk_predictor + alphas = alphas.squeeze(-1) + mask = mask.squeeze(-1) + if target_label_length is not None: + target_length = target_label_length + elif target_label is not None: + target_length = (target_label != ignore_id).float().sum(-1) + else: + target_length = None + token_num = alphas.sum(-1) + + if target_length is not None: + alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) + elif self.tail_threshold > 0.0: + hidden, alphas, token_num = self.tail_process_fn( + hidden, alphas, token_num, mask=mask + ) + + acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) + if target_length is None and self.tail_threshold > 0.0: + token_num_int = torch.max(token_num).type(torch.int32).item() + acoustic_embeds = acoustic_embeds[:, :token_num_int, :] + return acoustic_embeds, token_num, alphas, cif_peak, token_num2 + + def get_upsample_timestamp(self, hidden, mask=None, token_num=None): + h = hidden + b = hidden.shape[0] + context = h.transpose(1, 2) + queries = self.pad(context) + output = torch.relu(self.cif_conv1d(queries)) + + # alphas2 is an extra head for timestamp prediction + if not self.use_cif1_cnn: + _output = context + else: + _output = output + if self.upsample_type == "cnn": + output2 = self.upsample_cnn(_output) + output2 = output2.transpose(1, 2) + elif self.upsample_type == "cnn_blstm": + output2 = self.upsample_cnn(_output) + output2 = output2.transpose(1, 2) + output2, (_, _) = self.blstm(output2) + elif self.upsample_type == "cnn_attn": + output2 = self.upsample_cnn(_output) + output2 = output2.transpose(1, 2) + output2, _ = self.self_attn(output2, mask) + alphas2 = torch.sigmoid(self.cif_output2(output2)) + alphas2 = torch.nn.functional.relu( + alphas2 * self.smooth_factor2 - self.noise_threshold2 + ) + # repeat the mask in T demension to match the upsampled length + if mask is not None: + mask2 = ( + mask.repeat(1, self.upsample_times, 1) + .transpose(-1, -2) + .reshape(alphas2.shape[0], -1) + ) + mask2 = mask2.unsqueeze(-1) + alphas2 = alphas2 * mask2 + alphas2 = alphas2.squeeze(-1) + _token_num = alphas2.sum(-1) + if token_num is not None: + alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1)) + # re-downsample + ds_alphas = alphas2.reshape(b, -1, self.upsample_times).sum(-1) + ds_cif_peak = cif_wo_hidden(ds_alphas, self.threshold - 1e-4) + # upsampled alphas and cif_peak + us_alphas = alphas2 + us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4) + return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak + + def tail_process_fn(self, hidden, alphas, token_num=None, mask=None): + b, t, d = hidden.size() + tail_threshold = self.tail_threshold + if mask is not None: + zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device) + ones_t = torch.ones_like(zeros_t) + mask_1 = torch.cat([mask, zeros_t], dim=1) + mask_2 = torch.cat([ones_t, mask], dim=1) + mask = mask_2 - mask_1 + tail_threshold = mask * tail_threshold + alphas = torch.cat([alphas, zeros_t], dim=1) + alphas = torch.add(alphas, tail_threshold) + else: + tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to( + alphas.device + ) + tail_threshold = torch.reshape(tail_threshold, (1, 1)) + alphas = torch.cat([alphas, tail_threshold], dim=1) + zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device) + hidden = torch.cat([hidden, zeros], dim=1) + token_num = alphas.sum(dim=-1) + token_num_floor = torch.floor(token_num) + + return hidden, alphas, token_num_floor + + def gen_frame_alignments( + self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None + ): + batch_size, maximum_length = alphas.size() + int_type = torch.int32 + + is_training = self.training + if is_training: + token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) + else: + token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) + + max_token_num = torch.max(token_num).item() + + alphas_cumsum = torch.cumsum(alphas, dim=1) + alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) + alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) + + index = torch.ones([batch_size, max_token_num], dtype=int_type) + index = torch.cumsum(index, dim=1) + index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) + + index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) + index_div_bool_zeros = index_div.eq(0) + index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 + index_div_bool_zeros_count = torch.clamp( + index_div_bool_zeros_count, 0, encoder_sequence_length.max() + ) + token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to( + token_num.device + ) + index_div_bool_zeros_count *= token_num_mask + + index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat( + 1, 1, maximum_length + ) + ones = torch.ones_like(index_div_bool_zeros_count_tile) + zeros = torch.zeros_like(index_div_bool_zeros_count_tile) + ones = torch.cumsum(ones, dim=2) + cond = index_div_bool_zeros_count_tile == ones + index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) + + index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type( + torch.bool + ) + index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type( + int_type + ) + index_div_bool_zeros_count_tile_out = torch.sum( + index_div_bool_zeros_count_tile, dim=1 + ) + index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type( + int_type + ) + predictor_mask = ( + ( + ~make_pad_mask( + encoder_sequence_length, maxlen=encoder_sequence_length.max() + ) + ) + .type(int_type) + .to(encoder_sequence_length.device) + ) + index_div_bool_zeros_count_tile_out = ( + index_div_bool_zeros_count_tile_out * predictor_mask + ) + + predictor_alignments = index_div_bool_zeros_count_tile_out + predictor_alignments_length = predictor_alignments.sum(-1).type( + encoder_sequence_length.dtype + ) + return predictor_alignments.detach(), predictor_alignments_length.detach() diff --git a/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..aae64def2674a0cefbf023fde8020f5b172d2619 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/model.py @@ -0,0 +1,395 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import copy +import time +import torch +import logging +from contextlib import contextmanager +from distutils.version import LooseVersion +from typing import Dict, List, Optional, Tuple + +from funasr_detach.register import tables +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.utils import postprocess_utils +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.models.paraformer.model import Paraformer +from funasr_detach.models.paraformer.search import Hypothesis +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.utils.timestamp_tools import ts_prediction_lfr6_standard +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank + + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "BiCifParaformer") +class BiCifParaformer(Paraformer): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Paper1: FunASR: A Fundamental End-to-End Speech Recognition Toolkit + https://arxiv.org/abs/2305.11013 + Paper2: Achieving timestamp prediction while recognizing with non-autoregressive end-to-end ASR model + https://arxiv.org/abs/2301.12343 + """ + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + + def _calc_pre2_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + if self.predictor_bias == 1: + _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_pad_lens = ys_pad_lens + self.predictor_bias + _, _, _, _, pre_token_length2 = self.predictor( + encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id + ) + + # loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length) + loss_pre2 = self.criterion_pre( + ys_pad_lens.type_as(pre_token_length2), pre_token_length2 + ) + + return loss_pre2 + + def _calc_att_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + if self.predictor_bias == 1: + _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_pad_lens = ys_pad_lens + self.predictor_bias + pre_acoustic_embeds, pre_token_length, _, pre_peak_index, _ = self.predictor( + encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id + ) + + # 0. sampler + decoder_out_1st = None + if self.sampling_ratio > 0.0: + sematic_embeds, decoder_out_1st = self.sampler( + encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds + ) + else: + sematic_embeds = pre_acoustic_embeds + + # 1. Forward decoder + decoder_outs = self.decoder( + encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens + ) + decoder_out, _ = decoder_outs[0], decoder_outs[1] + + if decoder_out_1st is None: + decoder_out_1st = decoder_out + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_pad) + acc_att = th_accuracy( + decoder_out_1st.view(-1, self.vocab_size), + ys_pad, + ignore_label=self.ignore_id, + ) + loss_pre = self.criterion_pre( + ys_pad_lens.type_as(pre_token_length), pre_token_length + ) + + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out_1st.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att, loss_pre + + def calc_predictor(self, encoder_out, encoder_out_lens): + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + ( + pre_acoustic_embeds, + pre_token_length, + alphas, + pre_peak_index, + pre_token_length2, + ) = self.predictor( + encoder_out, None, encoder_out_mask, ignore_id=self.ignore_id + ) + return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index + + def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num): + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + ds_alphas, ds_cif_peak, us_alphas, us_peaks = ( + self.predictor.get_upsample_timestamp( + encoder_out, encoder_out_mask, token_num + ) + ) + return ds_alphas, ds_cif_peak, us_alphas, us_peaks + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + if len(text_lengths.size()) > 1: + text_lengths = text_lengths[:, 0] + if len(speech_lengths.size()) > 1: + speech_lengths = speech_lengths[:, 0] + + batch_size = speech.shape[0] + + # Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + + loss_ctc, cer_ctc = None, None + loss_pre = None + stats = dict() + + # decoder: CTC branch + if self.ctc_weight != 0.0: + loss_ctc, cer_ctc = self._calc_ctc_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # Collect CTC branch stats + stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None + stats["cer_ctc"] = cer_ctc + + # decoder: Attention decoder branch + loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + loss_pre2 = self._calc_pre2_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # 3. CTC-Att loss definition + if self.ctc_weight == 0.0: + loss = ( + loss_att + + loss_pre * self.predictor_weight + + loss_pre2 * self.predictor_weight * 0.5 + ) + else: + loss = ( + self.ctc_weight * loss_ctc + + (1 - self.ctc_weight) * loss_att + + loss_pre * self.predictor_weight + + loss_pre2 * self.predictor_weight * 0.5 + ) + + # Collect Attn branch stats + stats["loss_att"] = loss_att.detach() if loss_att is not None else None + stats["acc"] = acc_att + stats["cer"] = cer_att + stats["wer"] = wer_att + stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None + stats["loss_pre2"] = loss_pre2.detach().cpu() + + stats["loss"] = torch.clone(loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + if self.length_normalized_loss: + batch_size = int((text_lengths + self.predictor_bias).sum()) + + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + + # init beamsearch + is_use_ctc = ( + kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None + ) + is_use_lm = ( + kwargs.get("lm_weight", 0.0) > 0.00001 + and kwargs.get("lm_file", None) is not None + ) + if self.beam_search is None and (is_use_lm or is_use_ctc): + logging.info("enable beam_search") + self.init_beam_search(**kwargs) + self.nbest = kwargs.get("nbest", 1) + + meta_data = {} + # if isinstance(data_in, torch.Tensor): # fbank + # speech, speech_lengths = data_in, data_lengths + # if len(speech.shape) < 3: + # speech = speech[None, :, :] + # if speech_lengths is None: + # speech_lengths = speech.shape[1] + # else: + # extract fbank feats + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000) + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths = extract_fbank( + audio_sample_list, + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000 + ) + + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + + # Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + # predictor + predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = ( + predictor_outs[0], + predictor_outs[1], + predictor_outs[2], + predictor_outs[3], + ) + pre_token_length = pre_token_length.round().long() + if torch.max(pre_token_length) < 1: + return [] + decoder_outs = self.cal_decoder_with_predictor( + encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length + ) + decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] + + # BiCifParaformer, test no bias cif2 + _, _, us_alphas, us_peaks = self.calc_predictor_timestamp( + encoder_out, encoder_out_lens, pre_token_length + ) + + results = [] + b, n, d = decoder_out.size() + for i in range(b): + x = encoder_out[i, : encoder_out_lens[i], :] + am_scores = decoder_out[i, : pre_token_length[i], :] + if self.beam_search is not None: + nbest_hyps = self.beam_search( + x=x, + am_scores=am_scores, + maxlenratio=kwargs.get("maxlenratio", 0.0), + minlenratio=kwargs.get("minlenratio", 0.0), + ) + + nbest_hyps = nbest_hyps[: self.nbest] + else: + + yseq = am_scores.argmax(dim=-1) + score = am_scores.max(dim=-1)[0] + score = torch.sum(score, dim=-1) + # pad with mask tokens to ensure compatibility with sos/eos tokens + yseq = torch.tensor( + [self.sos] + yseq.tolist() + [self.eos], device=yseq.device + ) + nbest_hyps = [Hypothesis(yseq=yseq, score=score)] + for nbest_idx, hyp in enumerate(nbest_hyps): + ibest_writer = None + if kwargs.get("output_dir") is not None: + if not hasattr(self, "writer"): + self.writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = self.writer[f"{nbest_idx+1}best_recog"] + + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list( + filter( + lambda x: x != self.eos + and x != self.sos + and x != self.blank_id, + token_int, + ) + ) + + if tokenizer is not None: + # Change integer-ids to tokens + token = tokenizer.ids2tokens(token_int) + text = tokenizer.tokens2text(token) + + _, timestamp = ts_prediction_lfr6_standard( + us_alphas[i][: encoder_out_lens[i] * 3], + us_peaks[i][: encoder_out_lens[i] * 3], + copy.copy(token), + vad_offset=kwargs.get("begin_time", 0), + ) + + text_postprocessed, time_stamp_postprocessed, word_lists = ( + postprocess_utils.sentence_postprocess(token, timestamp) + ) + + result_i = { + "key": key[i], + "text": text_postprocessed, + "timestamp": time_stamp_postprocessed, + } + + if ibest_writer is not None: + ibest_writer["token"][key[i]] = " ".join(token) + # ibest_writer["text"][key[i]] = text + ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed + ibest_writer["text"][key[i]] = text_postprocessed + else: + result_i = {"key": key[i], "token_int": token_int} + results.append(result_i) + + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..243f3ff1c8f9cb444477f31b66af6d521bfa9aa1 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/bicif_paraformer/template.yaml @@ -0,0 +1,134 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +#model: funasr.models.paraformer.model:Paraformer +model: BiCifParaformer +model_conf: + ctc_weight: 0.0 + lsm_weight: 0.1 + length_normalized_loss: true + predictor_weight: 1.0 + predictor_bias: 1 + sampling_ratio: 0.75 + +# encoder +encoder: SANMEncoder +encoder_conf: + output_size: 512 + attention_heads: 4 + linear_units: 2048 + num_blocks: 50 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + +# decoder +decoder: ParaformerSANMDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 16 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + att_layer_num: 16 + kernel_size: 11 + sanm_shfit: 0 + +predictor: CifPredictorV3 +predictor_conf: + idim: 512 + threshold: 1.0 + l_order: 1 + r_order: 1 + tail_threshold: 0.45 + smooth_factor2: 0.25 + noise_threshold2: 0.01 + upsample_times: 3 + use_cif1_cnn: false + upsample_type: cnn_blstm + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 7 + lfr_n: 6 + +specaug: SpecAugLFR +specaug_conf: + apply_time_warp: false + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + lfr_rate: 6 + num_freq_mask: 1 + apply_time_mask: true + time_mask_width_range: + - 0 + - 12 + num_time_mask: 1 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + val_scheduler_criterion: + - valid + - acc + best_model_criterion: + - - valid + - acc + - max + keep_nbest_models: 10 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0005 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/branchformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/branchformer/cgmlp.py b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/cgmlp.py new file mode 100644 index 0000000000000000000000000000000000000000..61e46008f68d952a1e7d0636e51b19f636c4f101 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/cgmlp.py @@ -0,0 +1,124 @@ +"""MLP with convolutional gating (cgMLP) definition. + +References: + https://openreview.net/forum?id=RA-zVvZLYIy + https://arxiv.org/abs/2105.08050 + +""" + +import torch + +from funasr_detach.models.transformer.utils.nets_utils import get_activation +from funasr_detach.models.transformer.layer_norm import LayerNorm + + +class ConvolutionalSpatialGatingUnit(torch.nn.Module): + """Convolutional Spatial Gating Unit (CSGU).""" + + def __init__( + self, + size: int, + kernel_size: int, + dropout_rate: float, + use_linear_after_conv: bool, + gate_activation: str, + ): + super().__init__() + + n_channels = size // 2 # split input channels + self.norm = LayerNorm(n_channels) + self.conv = torch.nn.Conv1d( + n_channels, + n_channels, + kernel_size, + 1, + (kernel_size - 1) // 2, + groups=n_channels, + ) + if use_linear_after_conv: + self.linear = torch.nn.Linear(n_channels, n_channels) + else: + self.linear = None + + if gate_activation == "identity": + self.act = torch.nn.Identity() + else: + self.act = get_activation(gate_activation) + + self.dropout = torch.nn.Dropout(dropout_rate) + + def espnet_initialization_fn(self): + torch.nn.init.normal_(self.conv.weight, std=1e-6) + torch.nn.init.ones_(self.conv.bias) + if self.linear is not None: + torch.nn.init.normal_(self.linear.weight, std=1e-6) + torch.nn.init.ones_(self.linear.bias) + + def forward(self, x, gate_add=None): + """Forward method + + Args: + x (torch.Tensor): (N, T, D) + gate_add (torch.Tensor): (N, T, D/2) + + Returns: + out (torch.Tensor): (N, T, D/2) + """ + + x_r, x_g = x.chunk(2, dim=-1) + + x_g = self.norm(x_g) # (N, T, D/2) + x_g = self.conv(x_g.transpose(1, 2)).transpose(1, 2) # (N, T, D/2) + if self.linear is not None: + x_g = self.linear(x_g) + + if gate_add is not None: + x_g = x_g + gate_add + + x_g = self.act(x_g) + out = x_r * x_g # (N, T, D/2) + out = self.dropout(out) + return out + + +class ConvolutionalGatingMLP(torch.nn.Module): + """Convolutional Gating MLP (cgMLP).""" + + def __init__( + self, + size: int, + linear_units: int, + kernel_size: int, + dropout_rate: float, + use_linear_after_conv: bool, + gate_activation: str, + ): + super().__init__() + + self.channel_proj1 = torch.nn.Sequential( + torch.nn.Linear(size, linear_units), torch.nn.GELU() + ) + self.csgu = ConvolutionalSpatialGatingUnit( + size=linear_units, + kernel_size=kernel_size, + dropout_rate=dropout_rate, + use_linear_after_conv=use_linear_after_conv, + gate_activation=gate_activation, + ) + self.channel_proj2 = torch.nn.Linear(linear_units // 2, size) + + def forward(self, x, mask): + if isinstance(x, tuple): + xs_pad, pos_emb = x + else: + xs_pad, pos_emb = x, None + + xs_pad = self.channel_proj1(xs_pad) # size -> linear_units + xs_pad = self.csgu(xs_pad) # linear_units -> linear_units/2 + xs_pad = self.channel_proj2(xs_pad) # linear_units/2 -> size + + if pos_emb is not None: + out = (xs_pad, pos_emb) + else: + out = xs_pad + return out diff --git a/demo/Step-Audio-EditX/funasr_detach/models/branchformer/encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..250f8e5107cc8216a24175e3b1a0aca55358a5bd --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/encoder.py @@ -0,0 +1,550 @@ +# Copyright 2022 Yifan Peng (Carnegie Mellon University) +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Branchformer encoder definition. + +Reference: + Yifan Peng, Siddharth Dalmia, Ian Lane, and Shinji Watanabe, + “Branchformer: Parallel MLP-Attention Architectures to Capture + Local and Global Context for Speech Recognition and Understanding,” + in Proceedings of ICML, 2022. + +""" + +import logging +from typing import List, Optional, Tuple, Union + +import numpy +import torch +import torch.nn as nn + +from funasr_detach.models.branchformer.cgmlp import ConvolutionalGatingMLP +from funasr_detach.models.branchformer.fastformer import FastSelfAttention +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.attention import ( # noqa: H301 + LegacyRelPositionMultiHeadedAttention, + MultiHeadedAttention, + RelPositionMultiHeadedAttention, +) +from funasr_detach.models.transformer.embedding import ( # noqa: H301 + LegacyRelPositionalEncoding, + PositionalEncoding, + RelPositionalEncoding, + ScaledPositionalEncoding, +) +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.utils.subsampling import ( + Conv2dSubsampling, + Conv2dSubsampling2, + Conv2dSubsampling6, + Conv2dSubsampling8, + TooShortUttError, + check_short_utt, +) + +from funasr_detach.register import tables + + +class BranchformerEncoderLayer(torch.nn.Module): + """Branchformer encoder layer module. + + Args: + size (int): model dimension + attn: standard self-attention or efficient attention, optional + cgmlp: ConvolutionalGatingMLP, optional + dropout_rate (float): dropout probability + merge_method (str): concat, learned_ave, fixed_ave + cgmlp_weight (float): weight of the cgmlp branch, between 0 and 1, + used if merge_method is fixed_ave + attn_branch_drop_rate (float): probability of dropping the attn branch, + used if merge_method is learned_ave + stochastic_depth_rate (float): stochastic depth probability + """ + + def __init__( + self, + size: int, + attn: Optional[torch.nn.Module], + cgmlp: Optional[torch.nn.Module], + dropout_rate: float, + merge_method: str, + cgmlp_weight: float = 0.5, + attn_branch_drop_rate: float = 0.0, + stochastic_depth_rate: float = 0.0, + ): + super().__init__() + assert (attn is not None) or ( + cgmlp is not None + ), "At least one branch should be valid" + + self.size = size + self.attn = attn + self.cgmlp = cgmlp + self.merge_method = merge_method + self.cgmlp_weight = cgmlp_weight + self.attn_branch_drop_rate = attn_branch_drop_rate + self.stochastic_depth_rate = stochastic_depth_rate + self.use_two_branches = (attn is not None) and (cgmlp is not None) + + if attn is not None: + self.norm_mha = LayerNorm(size) # for the MHA module + if cgmlp is not None: + self.norm_mlp = LayerNorm(size) # for the MLP module + self.norm_final = LayerNorm(size) # for the final output of the block + + self.dropout = torch.nn.Dropout(dropout_rate) + + if self.use_two_branches: + if merge_method == "concat": + self.merge_proj = torch.nn.Linear(size + size, size) + + elif merge_method == "learned_ave": + # attention-based pooling for two branches + self.pooling_proj1 = torch.nn.Linear(size, 1) + self.pooling_proj2 = torch.nn.Linear(size, 1) + + # linear projections for calculating merging weights + self.weight_proj1 = torch.nn.Linear(size, 1) + self.weight_proj2 = torch.nn.Linear(size, 1) + + # linear projection after weighted average + self.merge_proj = torch.nn.Linear(size, size) + + elif merge_method == "fixed_ave": + assert ( + 0.0 <= cgmlp_weight <= 1.0 + ), "cgmlp weight should be between 0.0 and 1.0" + + # remove the other branch if only one branch is used + if cgmlp_weight == 0.0: + self.use_two_branches = False + self.cgmlp = None + self.norm_mlp = None + elif cgmlp_weight == 1.0: + self.use_two_branches = False + self.attn = None + self.norm_mha = None + + # linear projection after weighted average + self.merge_proj = torch.nn.Linear(size, size) + + else: + raise ValueError(f"unknown merge method: {merge_method}") + + else: + self.merge_proj = torch.nn.Identity() + + def forward(self, x_input, mask, cache=None): + """Compute encoded features. + + Args: + x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb. + - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)]. + - w/o pos emb: Tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, 1, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + """ + + if cache is not None: + raise NotImplementedError("cache is not None, which is not tested") + + if isinstance(x_input, tuple): + x, pos_emb = x_input[0], x_input[1] + else: + x, pos_emb = x_input, None + + skip_layer = False + # with stochastic depth, residual connection `x + f(x)` becomes + # `x <- x + 1 / (1 - p) * f(x)` at training time. + stoch_layer_coeff = 1.0 + if self.training and self.stochastic_depth_rate > 0: + skip_layer = torch.rand(1).item() < self.stochastic_depth_rate + stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate) + + if skip_layer: + if cache is not None: + x = torch.cat([cache, x], dim=1) + if pos_emb is not None: + return (x, pos_emb), mask + return x, mask + + # Two branches + x1 = x + x2 = x + + # Branch 1: multi-headed attention module + if self.attn is not None: + x1 = self.norm_mha(x1) + + if isinstance(self.attn, FastSelfAttention): + x_att = self.attn(x1, mask) + else: + if pos_emb is not None: + x_att = self.attn(x1, x1, x1, pos_emb, mask) + else: + x_att = self.attn(x1, x1, x1, mask) + + x1 = self.dropout(x_att) + + # Branch 2: convolutional gating mlp + if self.cgmlp is not None: + x2 = self.norm_mlp(x2) + + if pos_emb is not None: + x2 = (x2, pos_emb) + x2 = self.cgmlp(x2, mask) + if isinstance(x2, tuple): + x2 = x2[0] + + x2 = self.dropout(x2) + + # Merge two branches + if self.use_two_branches: + if self.merge_method == "concat": + x = x + stoch_layer_coeff * self.dropout( + self.merge_proj(torch.cat([x1, x2], dim=-1)) + ) + elif self.merge_method == "learned_ave": + if ( + self.training + and self.attn_branch_drop_rate > 0 + and torch.rand(1).item() < self.attn_branch_drop_rate + ): + # Drop the attn branch + w1, w2 = 0.0, 1.0 + else: + # branch1 + score1 = ( + self.pooling_proj1(x1).transpose(1, 2) / self.size**0.5 + ) # (batch, 1, time) + if mask is not None: + min_value = float( + numpy.finfo( + torch.tensor(0, dtype=score1.dtype).numpy().dtype + ).min + ) + score1 = score1.masked_fill(mask.eq(0), min_value) + score1 = torch.softmax(score1, dim=-1).masked_fill( + mask.eq(0), 0.0 + ) + else: + score1 = torch.softmax(score1, dim=-1) + pooled1 = torch.matmul(score1, x1).squeeze(1) # (batch, size) + weight1 = self.weight_proj1(pooled1) # (batch, 1) + + # branch2 + score2 = ( + self.pooling_proj2(x2).transpose(1, 2) / self.size**0.5 + ) # (batch, 1, time) + if mask is not None: + min_value = float( + numpy.finfo( + torch.tensor(0, dtype=score2.dtype).numpy().dtype + ).min + ) + score2 = score2.masked_fill(mask.eq(0), min_value) + score2 = torch.softmax(score2, dim=-1).masked_fill( + mask.eq(0), 0.0 + ) + else: + score2 = torch.softmax(score2, dim=-1) + pooled2 = torch.matmul(score2, x2).squeeze(1) # (batch, size) + weight2 = self.weight_proj2(pooled2) # (batch, 1) + + # normalize weights of two branches + merge_weights = torch.softmax( + torch.cat([weight1, weight2], dim=-1), dim=-1 + ) # (batch, 2) + merge_weights = merge_weights.unsqueeze(-1).unsqueeze( + -1 + ) # (batch, 2, 1, 1) + w1, w2 = merge_weights[:, 0], merge_weights[:, 1] # (batch, 1, 1) + + x = x + stoch_layer_coeff * self.dropout( + self.merge_proj(w1 * x1 + w2 * x2) + ) + elif self.merge_method == "fixed_ave": + x = x + stoch_layer_coeff * self.dropout( + self.merge_proj( + (1.0 - self.cgmlp_weight) * x1 + self.cgmlp_weight * x2 + ) + ) + else: + raise RuntimeError(f"unknown merge method: {self.merge_method}") + else: + if self.attn is None: + x = x + stoch_layer_coeff * self.dropout(self.merge_proj(x2)) + elif self.cgmlp is None: + x = x + stoch_layer_coeff * self.dropout(self.merge_proj(x1)) + else: + # This should not happen + raise RuntimeError("Both branches are not None, which is unexpected.") + + x = self.norm_final(x) + + if pos_emb is not None: + return (x, pos_emb), mask + + return x, mask + + +@tables.register("encoder_classes", "BranchformerEncoder") +class BranchformerEncoder(nn.Module): + """Branchformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 256, + use_attn: bool = True, + attention_heads: int = 4, + attention_layer_type: str = "rel_selfattn", + pos_enc_layer_type: str = "rel_pos", + rel_pos_type: str = "latest", + use_cgmlp: bool = True, + cgmlp_linear_units: int = 2048, + cgmlp_conv_kernel: int = 31, + use_linear_after_conv: bool = False, + gate_activation: str = "identity", + merge_method: str = "concat", + cgmlp_weight: Union[float, List[float]] = 0.5, + attn_branch_drop_rate: Union[float, List[float]] = 0.0, + num_blocks: int = 12, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: Optional[str] = "conv2d", + zero_triu: bool = False, + padding_idx: int = -1, + stochastic_depth_rate: Union[float, List[float]] = 0.0, + ): + super().__init__() + self._output_size = output_size + + if rel_pos_type == "legacy": + if pos_enc_layer_type == "rel_pos": + pos_enc_layer_type = "legacy_rel_pos" + if attention_layer_type == "rel_selfattn": + attention_layer_type = "legacy_rel_selfattn" + elif rel_pos_type == "latest": + assert attention_layer_type != "legacy_rel_selfattn" + assert pos_enc_layer_type != "legacy_rel_pos" + else: + raise ValueError("unknown rel_pos_type: " + rel_pos_type) + + if pos_enc_layer_type == "abs_pos": + pos_enc_class = PositionalEncoding + elif pos_enc_layer_type == "scaled_abs_pos": + pos_enc_class = ScaledPositionalEncoding + elif pos_enc_layer_type == "rel_pos": + assert attention_layer_type == "rel_selfattn" + pos_enc_class = RelPositionalEncoding + elif pos_enc_layer_type == "legacy_rel_pos": + assert attention_layer_type == "legacy_rel_selfattn" + pos_enc_class = LegacyRelPositionalEncoding + logging.warning( + "Using legacy_rel_pos and it will be deprecated in the future." + ) + else: + raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) + + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(input_size, output_size), + torch.nn.LayerNorm(output_size), + torch.nn.Dropout(dropout_rate), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d2": + self.embed = Conv2dSubsampling2( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif isinstance(input_layer, torch.nn.Module): + self.embed = torch.nn.Sequential( + input_layer, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer is None: + if input_size == output_size: + self.embed = None + else: + self.embed = torch.nn.Linear(input_size, output_size) + else: + raise ValueError("unknown input_layer: " + input_layer) + + if attention_layer_type == "selfattn": + encoder_selfattn_layer = MultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + elif attention_layer_type == "legacy_rel_selfattn": + assert pos_enc_layer_type == "legacy_rel_pos" + encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + logging.warning( + "Using legacy_rel_selfattn and it will be deprecated in the future." + ) + elif attention_layer_type == "rel_selfattn": + assert pos_enc_layer_type == "rel_pos" + encoder_selfattn_layer = RelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + zero_triu, + ) + elif attention_layer_type == "fast_selfattn": + assert pos_enc_layer_type in ["abs_pos", "scaled_abs_pos"] + encoder_selfattn_layer = FastSelfAttention + encoder_selfattn_layer_args = ( + output_size, + attention_heads, + attention_dropout_rate, + ) + else: + raise ValueError("unknown encoder_attn_layer: " + attention_layer_type) + + cgmlp_layer = ConvolutionalGatingMLP + cgmlp_layer_args = ( + output_size, + cgmlp_linear_units, + cgmlp_conv_kernel, + dropout_rate, + use_linear_after_conv, + gate_activation, + ) + + if isinstance(stochastic_depth_rate, float): + stochastic_depth_rate = [stochastic_depth_rate] * num_blocks + if len(stochastic_depth_rate) != num_blocks: + raise ValueError( + f"Length of stochastic_depth_rate ({len(stochastic_depth_rate)}) " + f"should be equal to num_blocks ({num_blocks})" + ) + + if isinstance(cgmlp_weight, float): + cgmlp_weight = [cgmlp_weight] * num_blocks + if len(cgmlp_weight) != num_blocks: + raise ValueError( + f"Length of cgmlp_weight ({len(cgmlp_weight)}) should be equal to " + f"num_blocks ({num_blocks})" + ) + + if isinstance(attn_branch_drop_rate, float): + attn_branch_drop_rate = [attn_branch_drop_rate] * num_blocks + if len(attn_branch_drop_rate) != num_blocks: + raise ValueError( + f"Length of attn_branch_drop_rate ({len(attn_branch_drop_rate)}) " + f"should be equal to num_blocks ({num_blocks})" + ) + + self.encoders = repeat( + num_blocks, + lambda lnum: BranchformerEncoderLayer( + output_size, + ( + encoder_selfattn_layer(*encoder_selfattn_layer_args) + if use_attn + else None + ), + cgmlp_layer(*cgmlp_layer_args) if use_cgmlp else None, + dropout_rate, + merge_method, + cgmlp_weight[lnum], + attn_branch_drop_rate[lnum], + stochastic_depth_rate[lnum], + ), + ) + self.after_norm = LayerNorm(output_size) + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Calculate forward propagation. + + Args: + xs_pad (torch.Tensor): Input tensor (#batch, L, input_size). + ilens (torch.Tensor): Input length (#batch). + prev_states (torch.Tensor): Not to be used now. + + Returns: + torch.Tensor: Output tensor (#batch, L, output_size). + torch.Tensor: Output length (#batch). + torch.Tensor: Not to be used now. + + """ + + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + + if ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling2) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + elif self.embed is not None: + xs_pad = self.embed(xs_pad) + + xs_pad, masks = self.encoders(xs_pad, masks) + + if isinstance(xs_pad, tuple): + xs_pad = xs_pad[0] + + xs_pad = self.after_norm(xs_pad) + olens = masks.squeeze(1).sum(1) + return xs_pad, olens, None diff --git a/demo/Step-Audio-EditX/funasr_detach/models/branchformer/fastformer.py b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/fastformer.py new file mode 100644 index 0000000000000000000000000000000000000000..0ed1214a1f8179ed04c6b46d84121b9efe50d63e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/fastformer.py @@ -0,0 +1,153 @@ +"""Fastformer attention definition. + +Reference: + Wu et al., "Fastformer: Additive Attention Can Be All You Need" + https://arxiv.org/abs/2108.09084 + https://github.com/wuch15/Fastformer + +""" + +import numpy +import torch + + +class FastSelfAttention(torch.nn.Module): + """Fast self-attention used in Fastformer.""" + + def __init__( + self, + size, + attention_heads, + dropout_rate, + ): + super().__init__() + if size % attention_heads != 0: + raise ValueError( + f"Hidden size ({size}) is not an integer multiple " + f"of attention heads ({attention_heads})" + ) + self.attention_head_size = size // attention_heads + self.num_attention_heads = attention_heads + + self.query = torch.nn.Linear(size, size) + self.query_att = torch.nn.Linear(size, attention_heads) + self.key = torch.nn.Linear(size, size) + self.key_att = torch.nn.Linear(size, attention_heads) + self.transform = torch.nn.Linear(size, size) + self.dropout = torch.nn.Dropout(dropout_rate) + + def espnet_initialization_fn(self): + self.apply(self.init_weights) + + def init_weights(self, module): + if isinstance(module, torch.nn.Linear): + module.weight.data.normal_(mean=0.0, std=0.02) + if isinstance(module, torch.nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + def transpose_for_scores(self, x): + """Reshape and transpose to compute scores. + + Args: + x: (batch, time, size = n_heads * attn_dim) + + Returns: + (batch, n_heads, time, attn_dim) + """ + + new_x_shape = x.shape[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + return x.reshape(*new_x_shape).transpose(1, 2) + + def forward(self, xs_pad, mask): + """Forward method. + + Args: + xs_pad: (batch, time, size = n_heads * attn_dim) + mask: (batch, 1, time), nonpadding is 1, padding is 0 + + Returns: + torch.Tensor: (batch, time, size) + """ + + batch_size, seq_len, _ = xs_pad.shape + mixed_query_layer = self.query(xs_pad) # (batch, time, size) + mixed_key_layer = self.key(xs_pad) # (batch, time, size) + + if mask is not None: + mask = mask.eq(0) # padding is 1, nonpadding is 0 + + # (batch, n_heads, time) + query_for_score = ( + self.query_att(mixed_query_layer).transpose(1, 2) + / self.attention_head_size**0.5 + ) + if mask is not None: + min_value = float( + numpy.finfo( + torch.tensor(0, dtype=query_for_score.dtype).numpy().dtype + ).min + ) + query_for_score = query_for_score.masked_fill(mask, min_value) + query_weight = torch.softmax(query_for_score, dim=-1).masked_fill(mask, 0.0) + else: + query_weight = torch.softmax(query_for_score, dim=-1) + + query_weight = query_weight.unsqueeze(2) # (batch, n_heads, 1, time) + query_layer = self.transpose_for_scores( + mixed_query_layer + ) # (batch, n_heads, time, attn_dim) + + pooled_query = ( + torch.matmul(query_weight, query_layer) + .transpose(1, 2) + .reshape(-1, 1, self.num_attention_heads * self.attention_head_size) + ) # (batch, 1, size = n_heads * attn_dim) + pooled_query = self.dropout(pooled_query) + pooled_query_repeat = pooled_query.repeat(1, seq_len, 1) # (batch, time, size) + + mixed_query_key_layer = ( + mixed_key_layer * pooled_query_repeat + ) # (batch, time, size) + + # (batch, n_heads, time) + query_key_score = ( + self.key_att(mixed_query_key_layer) / self.attention_head_size**0.5 + ).transpose(1, 2) + if mask is not None: + min_value = float( + numpy.finfo( + torch.tensor(0, dtype=query_key_score.dtype).numpy().dtype + ).min + ) + query_key_score = query_key_score.masked_fill(mask, min_value) + query_key_weight = torch.softmax(query_key_score, dim=-1).masked_fill( + mask, 0.0 + ) + else: + query_key_weight = torch.softmax(query_key_score, dim=-1) + + query_key_weight = query_key_weight.unsqueeze(2) # (batch, n_heads, 1, time) + key_layer = self.transpose_for_scores( + mixed_query_key_layer + ) # (batch, n_heads, time, attn_dim) + pooled_key = torch.matmul( + query_key_weight, key_layer + ) # (batch, n_heads, 1, attn_dim) + pooled_key = self.dropout(pooled_key) + + # NOTE: value = query, due to param sharing + weighted_value = (pooled_key * query_layer).transpose( + 1, 2 + ) # (batch, time, n_heads, attn_dim) + weighted_value = weighted_value.reshape( + weighted_value.shape[:-2] + + (self.num_attention_heads * self.attention_head_size,) + ) # (batch, time, size) + weighted_value = ( + self.dropout(self.transform(weighted_value)) + mixed_query_layer + ) + + return weighted_value diff --git a/demo/Step-Audio-EditX/funasr_detach/models/branchformer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..ddcc3faf2a33ff9f988cd824cc5a148642554eb4 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/model.py @@ -0,0 +1,17 @@ +import logging + +from funasr_detach.models.transformer.model import Transformer +from funasr_detach.register import tables + + +@tables.register("model_classes", "Branchformer") +class Branchformer(Transformer): + """CTC-attention hybrid Encoder-Decoder model""" + + def __init__( + self, + *args, + **kwargs, + ): + + super().__init__(*args, **kwargs) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/branchformer/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..71907e56b116f2432f5cef3393e2703fe9eba068 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/branchformer/template.yaml @@ -0,0 +1,116 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: Branchformer +model_conf: + ctc_weight: 0.3 + lsm_weight: 0.1 # label smoothing option + length_normalized_loss: false + +# encoder +encoder: BranchformerEncoder +encoder_conf: + output_size: 256 + use_attn: true + attention_heads: 4 + attention_layer_type: rel_selfattn + pos_enc_layer_type: rel_pos + rel_pos_type: latest + use_cgmlp: true + cgmlp_linear_units: 2048 + cgmlp_conv_kernel: 31 + use_linear_after_conv: false + gate_activation: identity + merge_method: concat + cgmlp_weight: 0.5 # used only if merge_method is "fixed_ave" + attn_branch_drop_rate: 0.0 # used only if merge_method is "learned_ave" + num_blocks: 24 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: conv2d + stochastic_depth_rate: 0.0 + +# decoder +decoder: TransformerDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 6 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0. + src_attention_dropout_rate: 0. + + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + dither: 0.0 + lfr_m: 1 + lfr_n: 1 + +specaug: SpecAug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_range: + - 0 + - 40 + num_time_mask: 2 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + keep_nbest_models: 10 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.001 + weight_decay: 0.000001 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 35000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 4 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/campplus/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/campplus/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/campplus/cluster_backend.py b/demo/Step-Audio-EditX/funasr_detach/models/campplus/cluster_backend.py new file mode 100644 index 0000000000000000000000000000000000000000..c297daea19cc6dbb6653b4ef6fb82ff787121024 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/campplus/cluster_backend.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) +# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker) + +import scipy +import torch +import sklearn +import hdbscan +import numpy as np + +from sklearn.cluster._kmeans import k_means + + +class SpectralCluster: + r"""A spectral clustering mehtod using unnormalized Laplacian of affinity matrix. + This implementation is adapted from https://github.com/speechbrain/speechbrain. + """ + + def __init__(self, min_num_spks=1, max_num_spks=15, pval=0.022): + self.min_num_spks = min_num_spks + self.max_num_spks = max_num_spks + self.pval = pval + + def __call__(self, X, oracle_num=None): + # Similarity matrix computation + sim_mat = self.get_sim_mat(X) + + # Refining similarity matrix with pval + prunned_sim_mat = self.p_pruning(sim_mat) + + # Symmetrization + sym_prund_sim_mat = 0.5 * (prunned_sim_mat + prunned_sim_mat.T) + + # Laplacian calculation + laplacian = self.get_laplacian(sym_prund_sim_mat) + + # Get Spectral Embeddings + emb, num_of_spk = self.get_spec_embs(laplacian, oracle_num) + + # Perform clustering + labels = self.cluster_embs(emb, num_of_spk) + + return labels + + def get_sim_mat(self, X): + # Cosine similarities + M = sklearn.metrics.pairwise.cosine_similarity(X, X) + return M + + def p_pruning(self, A): + if A.shape[0] * self.pval < 6: + pval = 6.0 / A.shape[0] + else: + pval = self.pval + + n_elems = int((1 - pval) * A.shape[0]) + + # For each row in a affinity matrix + for i in range(A.shape[0]): + low_indexes = np.argsort(A[i, :]) + low_indexes = low_indexes[0:n_elems] + + # Replace smaller similarity values by 0s + A[i, low_indexes] = 0 + return A + + def get_laplacian(self, M): + M[np.diag_indices(M.shape[0])] = 0 + D = np.sum(np.abs(M), axis=1) + D = np.diag(D) + L = D - M + return L + + def get_spec_embs(self, L, k_oracle=None): + lambdas, eig_vecs = scipy.linalg.eigh(L) + + if k_oracle is not None: + num_of_spk = k_oracle + else: + lambda_gap_list = self.getEigenGaps( + lambdas[self.min_num_spks - 1 : self.max_num_spks + 1] + ) + num_of_spk = np.argmax(lambda_gap_list) + self.min_num_spks + + emb = eig_vecs[:, :num_of_spk] + return emb, num_of_spk + + def cluster_embs(self, emb, k): + _, labels, _ = k_means(emb, k) + return labels + + def getEigenGaps(self, eig_vals): + eig_vals_gap_list = [] + for i in range(len(eig_vals) - 1): + gap = float(eig_vals[i + 1]) - float(eig_vals[i]) + eig_vals_gap_list.append(gap) + return eig_vals_gap_list + + +class UmapHdbscan: + r""" + Reference: + - Siqi Zheng, Hongbin Suo. Reformulating Speaker Diarization as Community Detection With + Emphasis On Topological Structure. ICASSP2022 + """ + + def __init__( + self, + n_neighbors=20, + n_components=60, + min_samples=10, + min_cluster_size=10, + metric="cosine", + ): + self.n_neighbors = n_neighbors + self.n_components = n_components + self.min_samples = min_samples + self.min_cluster_size = min_cluster_size + self.metric = metric + + def __call__(self, X): + import umap.umap_ as umap + + umap_X = umap.UMAP( + n_neighbors=self.n_neighbors, + min_dist=0.0, + n_components=min(self.n_components, X.shape[0] - 2), + metric=self.metric, + ).fit_transform(X) + labels = hdbscan.HDBSCAN( + min_samples=self.min_samples, + min_cluster_size=self.min_cluster_size, + allow_single_cluster=True, + ).fit_predict(umap_X) + return labels + + +class ClusterBackend(torch.nn.Module): + r"""Perfom clustering for input embeddings and output the labels. + Args: + model_dir: A model dir. + model_config: The model config. + """ + + def __init__(self): + super().__init__() + self.model_config = {"merge_thr": 0.78} + # self.other_config = kwargs + + self.spectral_cluster = SpectralCluster() + self.umap_hdbscan_cluster = UmapHdbscan() + + def forward(self, X, **params): + # clustering and return the labels + k = params["oracle_num"] if "oracle_num" in params else None + assert ( + len(X.shape) == 2 + ), "modelscope error: the shape of input should be [N, C]" + if X.shape[0] < 20: + return np.zeros(X.shape[0], dtype="int") + if X.shape[0] < 2048 or k is not None: + # unexpected corner case + labels = self.spectral_cluster(X, k) + else: + labels = self.umap_hdbscan_cluster(X) + + if k is None and "merge_thr" in self.model_config: + labels = self.merge_by_cos(labels, X, self.model_config["merge_thr"]) + + return labels + + def merge_by_cos(self, labels, embs, cos_thr): + # merge the similar speakers by cosine similarity + assert cos_thr > 0 and cos_thr <= 1 + while True: + spk_num = labels.max() + 1 + if spk_num == 1: + break + spk_center = [] + for i in range(spk_num): + spk_emb = embs[labels == i].mean(0) + spk_center.append(spk_emb) + assert len(spk_center) > 0 + spk_center = np.stack(spk_center, axis=0) + norm_spk_center = spk_center / np.linalg.norm( + spk_center, axis=1, keepdims=True + ) + affinity = np.matmul(norm_spk_center, norm_spk_center.T) + affinity = np.triu(affinity, 1) + spks = np.unravel_index(np.argmax(affinity), affinity.shape) + if affinity[spks] < cos_thr: + break + for i in range(len(labels)): + if labels[i] == spks[1]: + labels[i] = spks[0] + elif labels[i] > spks[1]: + labels[i] -= 1 + return labels diff --git a/demo/Step-Audio-EditX/funasr_detach/models/campplus/components.py b/demo/Step-Audio-EditX/funasr_detach/models/campplus/components.py new file mode 100644 index 0000000000000000000000000000000000000000..86d66dbd48e406019f8dc55331c4f44bdf63852d --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/campplus/components.py @@ -0,0 +1,310 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) +# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker) + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint as cp + + +class BasicResBlock(torch.nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicResBlock, self).__init__() + self.conv1 = torch.nn.Conv2d( + in_planes, planes, kernel_size=3, stride=(stride, 1), padding=1, bias=False + ) + self.bn1 = torch.nn.BatchNorm2d(planes) + self.conv2 = torch.nn.Conv2d( + planes, planes, kernel_size=3, stride=1, padding=1, bias=False + ) + self.bn2 = torch.nn.BatchNorm2d(planes) + + self.shortcut = torch.nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = torch.nn.Sequential( + torch.nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=(stride, 1), + bias=False, + ), + torch.nn.BatchNorm2d(self.expansion * planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class FCM(torch.nn.Module): + def __init__( + self, block=BasicResBlock, num_blocks=[2, 2], m_channels=32, feat_dim=80 + ): + super(FCM, self).__init__() + self.in_planes = m_channels + self.conv1 = torch.nn.Conv2d( + 1, m_channels, kernel_size=3, stride=1, padding=1, bias=False + ) + self.bn1 = torch.nn.BatchNorm2d(m_channels) + + self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2) + self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2) + + self.conv2 = torch.nn.Conv2d( + m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False + ) + self.bn2 = torch.nn.BatchNorm2d(m_channels) + self.out_channels = m_channels * (feat_dim // 8) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return torch.nn.Sequential(*layers) + + def forward(self, x): + x = x.unsqueeze(1) + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = F.relu(self.bn2(self.conv2(out))) + + shape = out.shape + out = out.reshape(shape[0], shape[1] * shape[2], shape[3]) + return out + + +def get_nonlinear(config_str, channels): + nonlinear = torch.nn.Sequential() + for name in config_str.split("-"): + if name == "relu": + nonlinear.add_module("relu", torch.nn.ReLU(inplace=True)) + elif name == "prelu": + nonlinear.add_module("prelu", torch.nn.PReLU(channels)) + elif name == "batchnorm": + nonlinear.add_module("batchnorm", torch.nn.BatchNorm1d(channels)) + elif name == "batchnorm_": + nonlinear.add_module( + "batchnorm", torch.nn.BatchNorm1d(channels, affine=False) + ) + else: + raise ValueError("Unexpected module ({}).".format(name)) + return nonlinear + + +def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2): + mean = x.mean(dim=dim) + std = x.std(dim=dim, unbiased=unbiased) + stats = torch.cat([mean, std], dim=-1) + if keepdim: + stats = stats.unsqueeze(dim=dim) + return stats + + +class StatsPool(torch.nn.Module): + def forward(self, x): + return statistics_pooling(x) + + +class TDNNLayer(torch.nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + bias=False, + config_str="batchnorm-relu", + ): + super(TDNNLayer, self).__init__() + if padding < 0: + assert ( + kernel_size % 2 == 1 + ), "Expect equal paddings, but got even kernel size ({})".format( + kernel_size + ) + padding = (kernel_size - 1) // 2 * dilation + self.linear = torch.nn.Conv1d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + ) + self.nonlinear = get_nonlinear(config_str, out_channels) + + def forward(self, x): + x = self.linear(x) + x = self.nonlinear(x) + return x + + +class CAMLayer(torch.nn.Module): + def __init__( + self, + bn_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + bias, + reduction=2, + ): + super(CAMLayer, self).__init__() + self.linear_local = torch.nn.Conv1d( + bn_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + ) + self.linear1 = torch.nn.Conv1d(bn_channels, bn_channels // reduction, 1) + self.relu = torch.nn.ReLU(inplace=True) + self.linear2 = torch.nn.Conv1d(bn_channels // reduction, out_channels, 1) + self.sigmoid = torch.nn.Sigmoid() + + def forward(self, x): + y = self.linear_local(x) + context = x.mean(-1, keepdim=True) + self.seg_pooling(x) + context = self.relu(self.linear1(context)) + m = self.sigmoid(self.linear2(context)) + return y * m + + def seg_pooling(self, x, seg_len=100, stype="avg"): + if stype == "avg": + seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True) + elif stype == "max": + seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True) + else: + raise ValueError("Wrong segment pooling type.") + shape = seg.shape + seg = seg.unsqueeze(-1).expand(*shape, seg_len).reshape(*shape[:-1], -1) + seg = seg[..., : x.shape[-1]] + return seg + + +class CAMDenseTDNNLayer(torch.nn.Module): + def __init__( + self, + in_channels, + out_channels, + bn_channels, + kernel_size, + stride=1, + dilation=1, + bias=False, + config_str="batchnorm-relu", + memory_efficient=False, + ): + super(CAMDenseTDNNLayer, self).__init__() + assert ( + kernel_size % 2 == 1 + ), "Expect equal paddings, but got even kernel size ({})".format(kernel_size) + padding = (kernel_size - 1) // 2 * dilation + self.memory_efficient = memory_efficient + self.nonlinear1 = get_nonlinear(config_str, in_channels) + self.linear1 = torch.nn.Conv1d(in_channels, bn_channels, 1, bias=False) + self.nonlinear2 = get_nonlinear(config_str, bn_channels) + self.cam_layer = CAMLayer( + bn_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + ) + + def bn_function(self, x): + return self.linear1(self.nonlinear1(x)) + + def forward(self, x): + if self.training and self.memory_efficient: + x = cp.checkpoint(self.bn_function, x) + else: + x = self.bn_function(x) + x = self.cam_layer(self.nonlinear2(x)) + return x + + +class CAMDenseTDNNBlock(torch.nn.ModuleList): + def __init__( + self, + num_layers, + in_channels, + out_channels, + bn_channels, + kernel_size, + stride=1, + dilation=1, + bias=False, + config_str="batchnorm-relu", + memory_efficient=False, + ): + super(CAMDenseTDNNBlock, self).__init__() + for i in range(num_layers): + layer = CAMDenseTDNNLayer( + in_channels=in_channels + i * out_channels, + out_channels=out_channels, + bn_channels=bn_channels, + kernel_size=kernel_size, + stride=stride, + dilation=dilation, + bias=bias, + config_str=config_str, + memory_efficient=memory_efficient, + ) + self.add_module("tdnnd%d" % (i + 1), layer) + + def forward(self, x): + for layer in self: + x = torch.cat([x, layer(x)], dim=1) + return x + + +class TransitLayer(torch.nn.Module): + def __init__( + self, in_channels, out_channels, bias=True, config_str="batchnorm-relu" + ): + super(TransitLayer, self).__init__() + self.nonlinear = get_nonlinear(config_str, in_channels) + self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias) + + def forward(self, x): + x = self.nonlinear(x) + x = self.linear(x) + return x + + +class DenseLayer(torch.nn.Module): + def __init__( + self, in_channels, out_channels, bias=False, config_str="batchnorm-relu" + ): + super(DenseLayer, self).__init__() + self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias) + self.nonlinear = get_nonlinear(config_str, out_channels) + + def forward(self, x): + if len(x.shape) == 2: + x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1) + else: + x = self.linear(x) + x = self.nonlinear(x) + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/campplus/model.py b/demo/Step-Audio-EditX/funasr_detach/models/campplus/model.py new file mode 100644 index 0000000000000000000000000000000000000000..fa355cbfbc12deb5344e011007447659fd95be95 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/campplus/model.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) +# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker) + +import time +import torch +import numpy as np +from collections import OrderedDict +from contextlib import contextmanager +from distutils.version import LooseVersion + +from funasr_detach.register import tables +from funasr_detach.models.campplus.utils import extract_feature +from funasr_detach.utils.load_utils import load_audio_text_image_video +from funasr_detach.models.campplus.components import ( + DenseLayer, + StatsPool, + TDNNLayer, + CAMDenseTDNNBlock, + TransitLayer, + get_nonlinear, + FCM, +) + + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "CAMPPlus") +class CAMPPlus(torch.nn.Module): + def __init__( + self, + feat_dim=80, + embedding_size=192, + growth_rate=32, + bn_size=4, + init_channels=128, + config_str="batchnorm-relu", + memory_efficient=True, + output_level="segment", + **kwargs, + ): + super().__init__() + + self.head = FCM(feat_dim=feat_dim) + channels = self.head.out_channels + self.output_level = output_level + + self.xvector = torch.nn.Sequential( + OrderedDict( + [ + ( + "tdnn", + TDNNLayer( + channels, + init_channels, + 5, + stride=2, + dilation=1, + padding=-1, + config_str=config_str, + ), + ), + ] + ) + ) + channels = init_channels + for i, (num_layers, kernel_size, dilation) in enumerate( + zip((12, 24, 16), (3, 3, 3), (1, 2, 2)) + ): + block = CAMDenseTDNNBlock( + num_layers=num_layers, + in_channels=channels, + out_channels=growth_rate, + bn_channels=bn_size * growth_rate, + kernel_size=kernel_size, + dilation=dilation, + config_str=config_str, + memory_efficient=memory_efficient, + ) + self.xvector.add_module("block%d" % (i + 1), block) + channels = channels + num_layers * growth_rate + self.xvector.add_module( + "transit%d" % (i + 1), + TransitLayer( + channels, channels // 2, bias=False, config_str=config_str + ), + ) + channels //= 2 + + self.xvector.add_module("out_nonlinear", get_nonlinear(config_str, channels)) + + if self.output_level == "segment": + self.xvector.add_module("stats", StatsPool()) + self.xvector.add_module( + "dense", + DenseLayer(channels * 2, embedding_size, config_str="batchnorm_"), + ) + else: + assert ( + self.output_level == "frame" + ), "`output_level` should be set to 'segment' or 'frame'. " + + for m in self.modules(): + if isinstance(m, (torch.nn.Conv1d, torch.nn.Linear)): + torch.nn.init.kaiming_normal_(m.weight.data) + if m.bias is not None: + torch.nn.init.zeros_(m.bias) + + def forward(self, x): + x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T) + x = self.head(x) + x = self.xvector(x) + if self.output_level == "frame": + x = x.transpose(1, 2) + return x + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + # extract fbank feats + meta_data = {} + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_in, fs=16000, audio_fs=kwargs.get("fs", 16000), data_type="sound" + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths, speech_times = extract_feature(audio_sample_list) + speech = speech.to(device=kwargs["device"]) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = np.array(speech_times).sum().item() / 16000.0 + results = [{"spk_embedding": self.forward(speech.to(torch.float32))}] + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/campplus/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/campplus/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..21f20bad12ac10c4afe4c760b45684edf044c796 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/campplus/template.yaml @@ -0,0 +1,23 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: CAMPPlus +model_conf: + feat_dim: 80 + embedding_size: 192 + growth_rate: 32 + bn_size: 4 + init_channels: 128 + config_str: 'batchnorm-relu' + memory_efficient: True + output_level: 'segment' + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/campplus/utils.py b/demo/Step-Audio-EditX/funasr_detach/models/campplus/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..93f86adc450f1b8f33f8ecdb47a118745d9a5164 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/campplus/utils.py @@ -0,0 +1,520 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) +# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker) + +import io +import os +import torch +import requests +import tempfile +import contextlib +import numpy as np +import librosa as sf +from typing import Union +from pathlib import Path +from typing import Generator, Union +from abc import ABCMeta, abstractmethod +import torchaudio.compliance.kaldi as Kaldi + +from funasr_detach.models.transformer.utils.nets_utils import pad_list + + +def check_audio_list(audio: list): + audio_dur = 0 + for i in range(len(audio)): + seg = audio[i] + assert seg[1] >= seg[0], "modelscope error: Wrong time stamps." + assert isinstance(seg[2], np.ndarray), "modelscope error: Wrong data type." + assert ( + int(seg[1] * 16000) - int(seg[0] * 16000) == seg[2].shape[0] + ), "modelscope error: audio data in list is inconsistent with time length." + if i > 0: + assert seg[0] >= audio[i - 1][1], "modelscope error: Wrong time stamps." + audio_dur += seg[1] - seg[0] + return audio_dur + # assert audio_dur > 5, 'modelscope error: The effective audio duration is too short.' + + +def sv_preprocess(inputs: Union[np.ndarray, list]): + output = [] + for i in range(len(inputs)): + if isinstance(inputs[i], str): + file_bytes = File.read(inputs[i]) + data, fs = sf.load(io.BytesIO(file_bytes), dtype="float32") + if len(data.shape) == 2: + data = data[:, 0] + data = torch.from_numpy(data).unsqueeze(0) + data = data.squeeze(0) + elif isinstance(inputs[i], np.ndarray): + assert ( + len(inputs[i].shape) == 1 + ), "modelscope error: Input array should be [N, T]" + data = inputs[i] + if data.dtype in ["int16", "int32", "int64"]: + data = (data / (1 << 15)).astype("float32") + else: + data = data.astype("float32") + data = torch.from_numpy(data) + else: + raise ValueError( + "modelscope error: The input type is restricted to audio address and nump array." + ) + output.append(data) + return output + + +def sv_chunk(vad_segments: list, fs=16000) -> list: + config = { + "seg_dur": 1.5, + "seg_shift": 0.75, + } + + def seg_chunk(seg_data): + seg_st = seg_data[0] + data = seg_data[2] + chunk_len = int(config["seg_dur"] * fs) + chunk_shift = int(config["seg_shift"] * fs) + last_chunk_ed = 0 + seg_res = [] + for chunk_st in range(0, data.shape[0], chunk_shift): + chunk_ed = min(chunk_st + chunk_len, data.shape[0]) + if chunk_ed <= last_chunk_ed: + break + last_chunk_ed = chunk_ed + chunk_st = max(0, chunk_ed - chunk_len) + chunk_data = data[chunk_st:chunk_ed] + if chunk_data.shape[0] < chunk_len: + chunk_data = np.pad( + chunk_data, (0, chunk_len - chunk_data.shape[0]), "constant" + ) + seg_res.append([chunk_st / fs + seg_st, chunk_ed / fs + seg_st, chunk_data]) + return seg_res + + segs = [] + for i, s in enumerate(vad_segments): + segs.extend(seg_chunk(s)) + + return segs + + +def extract_feature(audio): + features = [] + feature_times = [] + feature_lengths = [] + for au in audio: + feature = Kaldi.fbank(au.unsqueeze(0), num_mel_bins=80) + feature = feature - feature.mean(dim=0, keepdim=True) + features.append(feature) + feature_times.append(au.shape[0]) + feature_lengths.append(feature.shape[0]) + # padding for batch inference + features_padded = pad_list(features, pad_value=0) + # features = torch.cat(features) + return features_padded, feature_lengths, feature_times + + +def postprocess( + segments: list, vad_segments: list, labels: np.ndarray, embeddings: np.ndarray +) -> list: + assert len(segments) == len(labels) + labels = correct_labels(labels) + distribute_res = [] + for i in range(len(segments)): + distribute_res.append([segments[i][0], segments[i][1], labels[i]]) + # merge the same speakers chronologically + distribute_res = merge_seque(distribute_res) + + # accquire speaker center + spk_embs = [] + for i in range(labels.max() + 1): + spk_emb = embeddings[labels == i].mean(0) + spk_embs.append(spk_emb) + spk_embs = np.stack(spk_embs) + + def is_overlapped(t1, t2): + if t1 > t2 + 1e-4: + return True + return False + + # distribute the overlap region + for i in range(1, len(distribute_res)): + if is_overlapped(distribute_res[i - 1][1], distribute_res[i][0]): + p = (distribute_res[i][0] + distribute_res[i - 1][1]) / 2 + distribute_res[i][0] = p + distribute_res[i - 1][1] = p + + # smooth the result + distribute_res = smooth(distribute_res) + + return distribute_res + + +def correct_labels(labels): + labels_id = 0 + id2id = {} + new_labels = [] + for i in labels: + if i not in id2id: + id2id[i] = labels_id + labels_id += 1 + new_labels.append(id2id[i]) + return np.array(new_labels) + + +def merge_seque(distribute_res): + res = [distribute_res[0]] + for i in range(1, len(distribute_res)): + if distribute_res[i][2] != res[-1][2] or distribute_res[i][0] > res[-1][1]: + res.append(distribute_res[i]) + else: + res[-1][1] = distribute_res[i][1] + return res + + +def smooth(res, mindur=1): + # short segments are assigned to nearest speakers. + for i in range(len(res)): + res[i][0] = round(res[i][0], 2) + res[i][1] = round(res[i][1], 2) + if res[i][1] - res[i][0] < mindur: + if i == 0: + res[i][2] = res[i + 1][2] + elif i == len(res) - 1: + res[i][2] = res[i - 1][2] + elif res[i][0] - res[i - 1][1] <= res[i + 1][0] - res[i][1]: + res[i][2] = res[i - 1][2] + else: + res[i][2] = res[i + 1][2] + # merge the speakers + res = merge_seque(res) + + return res + + +def distribute_spk(sentence_list, sd_time_list): + sd_sentence_list = [] + for d in sentence_list: + sentence_start = d["start"] + sentence_end = d["end"] + sentence_spk = 0 + max_overlap = 0 + for sd_time in sd_time_list: + spk_st, spk_ed, spk = sd_time + spk_st = spk_st * 1000 + spk_ed = spk_ed * 1000 + overlap = max(min(sentence_end, spk_ed) - max(sentence_start, spk_st), 0) + if overlap > max_overlap: + max_overlap = overlap + sentence_spk = spk + d["spk"] = int(sentence_spk) + sd_sentence_list.append(d) + return sd_sentence_list + + +class Storage(metaclass=ABCMeta): + """Abstract class of storage. + + All backends need to implement two apis: ``read()`` and ``read_text()``. + ``read()`` reads the file as a byte stream and ``read_text()`` reads + the file as texts. + """ + + @abstractmethod + def read(self, filepath: str): + pass + + @abstractmethod + def read_text(self, filepath: str): + pass + + @abstractmethod + def write(self, obj: bytes, filepath: Union[str, Path]) -> None: + pass + + @abstractmethod + def write_text( + self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8" + ) -> None: + pass + + +class LocalStorage(Storage): + """Local hard disk storage""" + + def read(self, filepath: Union[str, Path]) -> bytes: + """Read data from a given ``filepath`` with 'rb' mode. + + Args: + filepath (str or Path): Path to read data. + + Returns: + bytes: Expected bytes object. + """ + with open(filepath, "rb") as f: + content = f.read() + return content + + def read_text(self, filepath: Union[str, Path], encoding: str = "utf-8") -> str: + """Read data from a given ``filepath`` with 'r' mode. + + Args: + filepath (str or Path): Path to read data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + + Returns: + str: Expected text reading from ``filepath``. + """ + with open(filepath, "r", encoding=encoding) as f: + value_buf = f.read() + return value_buf + + def write(self, obj: bytes, filepath: Union[str, Path]) -> None: + """Write data to a given ``filepath`` with 'wb' mode. + + Note: + ``write`` will create a directory if the directory of ``filepath`` + does not exist. + + Args: + obj (bytes): Data to be written. + filepath (str or Path): Path to write data. + """ + dirname = os.path.dirname(filepath) + if dirname and not os.path.exists(dirname): + os.makedirs(dirname, exist_ok=True) + + with open(filepath, "wb") as f: + f.write(obj) + + def write_text( + self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8" + ) -> None: + """Write data to a given ``filepath`` with 'w' mode. + + Note: + ``write_text`` will create a directory if the directory of + ``filepath`` does not exist. + + Args: + obj (str): Data to be written. + filepath (str or Path): Path to write data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + """ + dirname = os.path.dirname(filepath) + if dirname and not os.path.exists(dirname): + os.makedirs(dirname, exist_ok=True) + + with open(filepath, "w", encoding=encoding) as f: + f.write(obj) + + @contextlib.contextmanager + def as_local_path( + self, filepath: Union[str, Path] + ) -> Generator[Union[str, Path], None, None]: + """Only for unified API and do nothing.""" + yield filepath + + +class HTTPStorage(Storage): + """HTTP and HTTPS storage.""" + + def read(self, url): + # TODO @wenmeng.zwm add progress bar if file is too large + r = requests.get(url) + r.raise_for_status() + return r.content + + def read_text(self, url): + r = requests.get(url) + r.raise_for_status() + return r.text + + @contextlib.contextmanager + def as_local_path(self, filepath: str) -> Generator[Union[str, Path], None, None]: + """Download a file from ``filepath``. + + ``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It + can be called with ``with`` statement, and when exists from the + ``with`` statement, the temporary path will be released. + + Args: + filepath (str): Download a file from ``filepath``. + + Examples: + >>> storage = HTTPStorage() + >>> # After existing from the ``with`` clause, + >>> # the path will be removed + >>> with storage.get_local_path('http://path/to/file') as path: + ... # do something here + """ + try: + f = tempfile.NamedTemporaryFile(delete=False) + f.write(self.read(filepath)) + f.close() + yield f.name + finally: + os.remove(f.name) + + def write(self, obj: bytes, url: Union[str, Path]) -> None: + raise NotImplementedError("write is not supported by HTTP Storage") + + def write_text( + self, obj: str, url: Union[str, Path], encoding: str = "utf-8" + ) -> None: + raise NotImplementedError("write_text is not supported by HTTP Storage") + + +class OSSStorage(Storage): + """OSS storage.""" + + def __init__(self, oss_config_file=None): + # read from config file or env var + raise NotImplementedError("OSSStorage.__init__ to be implemented in the future") + + def read(self, filepath): + raise NotImplementedError("OSSStorage.read to be implemented in the future") + + def read_text(self, filepath, encoding="utf-8"): + raise NotImplementedError( + "OSSStorage.read_text to be implemented in the future" + ) + + @contextlib.contextmanager + def as_local_path(self, filepath: str) -> Generator[Union[str, Path], None, None]: + """Download a file from ``filepath``. + + ``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It + can be called with ``with`` statement, and when exists from the + ``with`` statement, the temporary path will be released. + + Args: + filepath (str): Download a file from ``filepath``. + + Examples: + >>> storage = OSSStorage() + >>> # After existing from the ``with`` clause, + >>> # the path will be removed + >>> with storage.get_local_path('http://path/to/file') as path: + ... # do something here + """ + try: + f = tempfile.NamedTemporaryFile(delete=False) + f.write(self.read(filepath)) + f.close() + yield f.name + finally: + os.remove(f.name) + + def write(self, obj: bytes, filepath: Union[str, Path]) -> None: + raise NotImplementedError("OSSStorage.write to be implemented in the future") + + def write_text( + self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8" + ) -> None: + raise NotImplementedError( + "OSSStorage.write_text to be implemented in the future" + ) + + +G_STORAGES = {} + + +class File(object): + _prefix_to_storage: dict = { + "oss": OSSStorage, + "http": HTTPStorage, + "https": HTTPStorage, + "local": LocalStorage, + } + + @staticmethod + def _get_storage(uri): + assert isinstance(uri, str), f"uri should be str type, but got {type(uri)}" + + if "://" not in uri: + # local path + storage_type = "local" + else: + prefix, _ = uri.split("://") + storage_type = prefix + + assert storage_type in File._prefix_to_storage, ( + f"Unsupported uri {uri}, valid prefixs: " + f"{list(File._prefix_to_storage.keys())}" + ) + + if storage_type not in G_STORAGES: + G_STORAGES[storage_type] = File._prefix_to_storage[storage_type]() + + return G_STORAGES[storage_type] + + @staticmethod + def read(uri: str) -> bytes: + """Read data from a given ``filepath`` with 'rb' mode. + + Args: + filepath (str or Path): Path to read data. + + Returns: + bytes: Expected bytes object. + """ + storage = File._get_storage(uri) + return storage.read(uri) + + @staticmethod + def read_text(uri: Union[str, Path], encoding: str = "utf-8") -> str: + """Read data from a given ``filepath`` with 'r' mode. + + Args: + filepath (str or Path): Path to read data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + + Returns: + str: Expected text reading from ``filepath``. + """ + storage = File._get_storage(uri) + return storage.read_text(uri) + + @staticmethod + def write(obj: bytes, uri: Union[str, Path]) -> None: + """Write data to a given ``filepath`` with 'wb' mode. + + Note: + ``write`` will create a directory if the directory of ``filepath`` + does not exist. + + Args: + obj (bytes): Data to be written. + filepath (str or Path): Path to write data. + """ + storage = File._get_storage(uri) + return storage.write(obj, uri) + + @staticmethod + def write_text(obj: str, uri: str, encoding: str = "utf-8") -> None: + """Write data to a given ``filepath`` with 'w' mode. + + Note: + ``write_text`` will create a directory if the directory of + ``filepath`` does not exist. + + Args: + obj (str): Data to be written. + filepath (str or Path): Path to write data. + encoding (str): The encoding format used to open the ``filepath``. + Default: 'utf-8'. + """ + storage = File._get_storage(uri) + return storage.write_text(obj, uri) + + @contextlib.contextmanager + def as_local_path(uri: str) -> Generator[Union[str, Path], None, None]: + """Only for unified API and do nothing.""" + storage = File._get_storage(uri) + with storage.as_local_path(uri) as local_path: + yield local_path diff --git a/demo/Step-Audio-EditX/funasr_detach/models/conformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/conformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/conformer/encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/conformer/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..7dee6b1dd3b7932bd1ba79696a890f2f9038551a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/conformer/encoder.py @@ -0,0 +1,1281 @@ +# Copyright 2020 Tomoki Hayashi +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Conformer encoder definition.""" + +import logging +from typing import Union, Dict, List, Tuple, Optional + +import torch +from torch import nn + +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.models.transformer.attention import ( + MultiHeadedAttention, # noqa: H301 + RelPositionMultiHeadedAttention, # noqa: H301 + LegacyRelPositionMultiHeadedAttention, # noqa: H301 + RelPositionMultiHeadedAttentionChunk, +) +from funasr_detach.models.transformer.embedding import ( + PositionalEncoding, # noqa: H301 + ScaledPositionalEncoding, # noqa: H301 + RelPositionalEncoding, # noqa: H301 + LegacyRelPositionalEncoding, # noqa: H301 + StreamingRelPositionalEncoding, +) +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear +from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d +from funasr_detach.models.transformer.utils.nets_utils import get_activation +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.utils.nets_utils import ( + TooShortUttError, + check_short_utt, + make_chunk_mask, + make_source_mask, +) +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, # noqa: H301 +) +from funasr_detach.models.transformer.utils.repeat import repeat, MultiBlocks +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling2 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling6 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling8 +from funasr_detach.models.transformer.utils.subsampling import TooShortUttError +from funasr_detach.models.transformer.utils.subsampling import check_short_utt +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsamplingPad +from funasr_detach.models.transformer.utils.subsampling import StreamingConvInput +from funasr_detach.register import tables + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model. + + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernerl size of conv layers. + + """ + + def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True): + """Construct an ConvolutionModule object.""" + super(ConvolutionModule, self).__init__() + # kernerl_size should be a odd number for 'SAME' padding + assert (kernel_size - 1) % 2 == 0 + + self.pointwise_conv1 = nn.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.depthwise_conv = nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + groups=channels, + bias=bias, + ) + self.norm = nn.BatchNorm1d(channels) + self.pointwise_conv2 = nn.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.activation = activation + + def forward(self, x): + """Compute convolution module. + + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + + """ + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + x = self.activation(self.norm(x)) + + x = self.pointwise_conv2(x) + + return x.transpose(1, 2) + + +class EncoderLayer(nn.Module): + """Encoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance + can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + stochastic_depth_rate (float): Proability to skip this layer. + During training, the layer may skip residual computation and return input + as-is with given probability. + """ + + def __init__( + self, + size, + self_attn, + feed_forward, + feed_forward_macaron, + conv_module, + dropout_rate, + normalize_before=True, + concat_after=False, + stochastic_depth_rate=0.0, + ): + """Construct an EncoderLayer object.""" + super(EncoderLayer, self).__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.norm_ff = LayerNorm(size) # for the FNN module + self.norm_mha = LayerNorm(size) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = LayerNorm(size) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = LayerNorm(size) # for the CNN module + self.norm_final = LayerNorm(size) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear = nn.Linear(size + size, size) + self.stochastic_depth_rate = stochastic_depth_rate + + def forward(self, x_input, mask, cache=None): + """Compute encoded features. + + Args: + x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb. + - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)]. + - w/o pos emb: Tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + if isinstance(x_input, tuple): + x, pos_emb = x_input[0], x_input[1] + else: + x, pos_emb = x_input, None + + skip_layer = False + # with stochastic depth, residual connection `x + f(x)` becomes + # `x <- x + 1 / (1 - p) * f(x)` at training time. + stoch_layer_coeff = 1.0 + if self.training and self.stochastic_depth_rate > 0: + skip_layer = torch.rand(1).item() < self.stochastic_depth_rate + stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate) + + if skip_layer: + if cache is not None: + x = torch.cat([cache, x], dim=1) + if pos_emb is not None: + return (x, pos_emb), mask + return x, mask + + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + stoch_layer_coeff * self.ff_scale * self.dropout( + self.feed_forward_macaron(x) + ) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + + if cache is None: + x_q = x + else: + assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size) + x_q = x[:, -1:, :] + residual = residual[:, -1:, :] + mask = None if mask is None else mask[:, -1:, :] + + if pos_emb is not None: + x_att = self.self_attn(x_q, x, x, pos_emb, mask) + else: + x_att = self.self_attn(x_q, x, x, mask) + + if self.concat_after: + x_concat = torch.cat((x, x_att), dim=-1) + x = residual + stoch_layer_coeff * self.concat_linear(x_concat) + else: + x = residual + stoch_layer_coeff * self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x = residual + stoch_layer_coeff * self.dropout(self.conv_module(x)) + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + x = residual + stoch_layer_coeff * self.ff_scale * self.dropout( + self.feed_forward(x) + ) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + + if cache is not None: + x = torch.cat([cache, x], dim=1) + + if pos_emb is not None: + return (x, pos_emb), mask + + return x, mask + + +@tables.register("encoder_classes", "ConformerEncoder") +class ConformerEncoder(nn.Module): + """Conformer encoder module. + + Args: + input_size (int): Input dimension. + output_size (int): Dimension of attention. + attention_heads (int): The number of heads of multi head attention. + linear_units (int): The number of units of position-wise feed forward. + num_blocks (int): The number of decoder blocks. + dropout_rate (float): Dropout rate. + attention_dropout_rate (float): Dropout rate in attention. + positional_dropout_rate (float): Dropout rate after adding positional encoding. + input_layer (Union[str, torch.nn.Module]): Input layer type. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + If True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + If False, no additional linear will be applied. i.e. x -> x + att(x) + positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear". + positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer. + rel_pos_type (str): Whether to use the latest relative positional encoding or + the legacy one. The legacy relative positional encoding will be deprecated + in the future. More Details can be found in + https://github.com/espnet/espnet/pull/2816. + encoder_pos_enc_layer_type (str): Encoder positional encoding layer type. + encoder_attn_layer_type (str): Encoder attention layer type. + activation_type (str): Encoder activation function type. + macaron_style (bool): Whether to use macaron style for positionwise layer. + use_cnn_module (bool): Whether to use convolution module. + zero_triu (bool): Whether to zero the upper triangular part of attention matrix. + cnn_module_kernel (int): Kernerl size of convolution module. + padding_idx (int): Padding idx for input_layer=embed. + + """ + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + normalize_before: bool = True, + concat_after: bool = False, + positionwise_layer_type: str = "linear", + positionwise_conv_kernel_size: int = 3, + macaron_style: bool = False, + rel_pos_type: str = "legacy", + pos_enc_layer_type: str = "rel_pos", + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = True, + zero_triu: bool = False, + cnn_module_kernel: int = 31, + padding_idx: int = -1, + interctc_layer_idx: List[int] = [], + interctc_use_conditioning: bool = False, + stochastic_depth_rate: Union[float, List[float]] = 0.0, + ): + super().__init__() + self._output_size = output_size + + if rel_pos_type == "legacy": + if pos_enc_layer_type == "rel_pos": + pos_enc_layer_type = "legacy_rel_pos" + if selfattention_layer_type == "rel_selfattn": + selfattention_layer_type = "legacy_rel_selfattn" + elif rel_pos_type == "latest": + assert selfattention_layer_type != "legacy_rel_selfattn" + assert pos_enc_layer_type != "legacy_rel_pos" + else: + raise ValueError("unknown rel_pos_type: " + rel_pos_type) + + activation = get_activation(activation_type) + if pos_enc_layer_type == "abs_pos": + pos_enc_class = PositionalEncoding + elif pos_enc_layer_type == "scaled_abs_pos": + pos_enc_class = ScaledPositionalEncoding + elif pos_enc_layer_type == "rel_pos": + assert selfattention_layer_type == "rel_selfattn" + pos_enc_class = RelPositionalEncoding + elif pos_enc_layer_type == "legacy_rel_pos": + assert selfattention_layer_type == "legacy_rel_selfattn" + pos_enc_class = LegacyRelPositionalEncoding + logging.warning( + "Using legacy_rel_pos and it will be deprecated in the future." + ) + else: + raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) + + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(input_size, output_size), + torch.nn.LayerNorm(output_size), + torch.nn.Dropout(dropout_rate), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2dpad": + self.embed = Conv2dSubsamplingPad( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d2": + self.embed = Conv2dSubsampling2( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif isinstance(input_layer, torch.nn.Module): + self.embed = torch.nn.Sequential( + input_layer, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer is None: + self.embed = torch.nn.Sequential( + pos_enc_class(output_size, positional_dropout_rate) + ) + else: + raise ValueError("unknown input_layer: " + input_layer) + self.normalize_before = normalize_before + if positionwise_layer_type == "linear": + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + elif positionwise_layer_type == "conv1d": + positionwise_layer = MultiLayeredConv1d + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d-linear": + positionwise_layer = Conv1dLinear + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + else: + raise NotImplementedError("Support only linear or conv1d.") + + if selfattention_layer_type == "selfattn": + encoder_selfattn_layer = MultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + elif selfattention_layer_type == "legacy_rel_selfattn": + assert pos_enc_layer_type == "legacy_rel_pos" + encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + logging.warning( + "Using legacy_rel_selfattn and it will be deprecated in the future." + ) + elif selfattention_layer_type == "rel_selfattn": + assert pos_enc_layer_type == "rel_pos" + encoder_selfattn_layer = RelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + zero_triu, + ) + else: + raise ValueError("unknown encoder_attn_layer: " + selfattention_layer_type) + + convolution_layer = ConvolutionModule + convolution_layer_args = (output_size, cnn_module_kernel, activation) + + if isinstance(stochastic_depth_rate, float): + stochastic_depth_rate = [stochastic_depth_rate] * num_blocks + + if len(stochastic_depth_rate) != num_blocks: + raise ValueError( + f"Length of stochastic_depth_rate ({len(stochastic_depth_rate)}) " + f"should be equal to num_blocks ({num_blocks})" + ) + + self.encoders = repeat( + num_blocks, + lambda lnum: EncoderLayer( + output_size, + encoder_selfattn_layer(*encoder_selfattn_layer_args), + positionwise_layer(*positionwise_layer_args), + positionwise_layer(*positionwise_layer_args) if macaron_style else None, + convolution_layer(*convolution_layer_args) if use_cnn_module else None, + dropout_rate, + normalize_before, + concat_after, + stochastic_depth_rate[lnum], + ), + ) + if self.normalize_before: + self.after_norm = LayerNorm(output_size) + + self.interctc_layer_idx = interctc_layer_idx + if len(interctc_layer_idx) > 0: + assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks + self.interctc_use_conditioning = interctc_use_conditioning + self.conditioning_layer = None + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ctc: CTC = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Calculate forward propagation. + + Args: + xs_pad (torch.Tensor): Input tensor (#batch, L, input_size). + ilens (torch.Tensor): Input length (#batch). + prev_states (torch.Tensor): Not to be used now. + + Returns: + torch.Tensor: Output tensor (#batch, L, output_size). + torch.Tensor: Output length (#batch). + torch.Tensor: Not to be used now. + + """ + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + + if ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling2) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + or isinstance(self.embed, Conv2dSubsamplingPad) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + else: + xs_pad = self.embed(xs_pad) + + intermediate_outs = [] + if len(self.interctc_layer_idx) == 0: + xs_pad, masks = self.encoders(xs_pad, masks) + else: + for layer_idx, encoder_layer in enumerate(self.encoders): + xs_pad, masks = encoder_layer(xs_pad, masks) + + if layer_idx + 1 in self.interctc_layer_idx: + encoder_out = xs_pad + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + # intermediate outputs are also normalized + if self.normalize_before: + encoder_out = self.after_norm(encoder_out) + + intermediate_outs.append((layer_idx + 1, encoder_out)) + + if self.interctc_use_conditioning: + ctc_out = ctc.softmax(encoder_out) + + if isinstance(xs_pad, tuple): + x, pos_emb = xs_pad + x = x + self.conditioning_layer(ctc_out) + xs_pad = (x, pos_emb) + else: + xs_pad = xs_pad + self.conditioning_layer(ctc_out) + + if isinstance(xs_pad, tuple): + xs_pad = xs_pad[0] + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + + olens = masks.squeeze(1).sum(1) + if len(intermediate_outs) > 0: + return (xs_pad, intermediate_outs), olens, None + return xs_pad, olens, None + + +class CausalConvolution(torch.nn.Module): + """ConformerConvolution module definition. + Args: + channels: The number of channels. + kernel_size: Size of the convolving kernel. + activation: Type of activation function. + norm_args: Normalization module arguments. + causal: Whether to use causal convolution (set to True if streaming). + """ + + def __init__( + self, + channels: int, + kernel_size: int, + activation: torch.nn.Module = torch.nn.ReLU(), + norm_args: Dict = {}, + causal: bool = False, + ) -> None: + """Construct an ConformerConvolution object.""" + super().__init__() + + assert (kernel_size - 1) % 2 == 0 + + self.kernel_size = kernel_size + + self.pointwise_conv1 = torch.nn.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + ) + + if causal: + self.lorder = kernel_size - 1 + padding = 0 + else: + self.lorder = 0 + padding = (kernel_size - 1) // 2 + + self.depthwise_conv = torch.nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=padding, + groups=channels, + ) + self.norm = torch.nn.BatchNorm1d(channels, **norm_args) + self.pointwise_conv2 = torch.nn.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + ) + + self.activation = activation + + def forward( + self, + x: torch.Tensor, + cache: Optional[torch.Tensor] = None, + right_context: int = 0, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x: ConformerConvolution input sequences. (B, T, D_hidden) + cache: ConformerConvolution input cache. (1, conv_kernel, D_hidden) + right_context: Number of frames in right context. + Returns: + x: ConformerConvolution output sequences. (B, T, D_hidden) + cache: ConformerConvolution output cache. (1, conv_kernel, D_hidden) + """ + x = self.pointwise_conv1(x.transpose(1, 2)) + x = torch.nn.functional.glu(x, dim=1) + + if self.lorder > 0: + if cache is None: + x = torch.nn.functional.pad(x, (self.lorder, 0), "constant", 0.0) + else: + x = torch.cat([cache, x], dim=2) + + if right_context > 0: + cache = x[:, :, -(self.lorder + right_context) : -right_context] + else: + cache = x[:, :, -self.lorder :] + + x = self.depthwise_conv(x) + x = self.activation(self.norm(x)) + + x = self.pointwise_conv2(x).transpose(1, 2) + + return x, cache + + +class ChunkEncoderLayer(torch.nn.Module): + """Chunk Conformer module definition. + Args: + block_size: Input/output size. + self_att: Self-attention module instance. + feed_forward: Feed-forward module instance. + feed_forward_macaron: Feed-forward module instance for macaron network. + conv_mod: Convolution module instance. + norm_class: Normalization module class. + norm_args: Normalization module arguments. + dropout_rate: Dropout rate. + """ + + def __init__( + self, + block_size: int, + self_att: torch.nn.Module, + feed_forward: torch.nn.Module, + feed_forward_macaron: torch.nn.Module, + conv_mod: torch.nn.Module, + norm_class: torch.nn.Module = LayerNorm, + norm_args: Dict = {}, + dropout_rate: float = 0.0, + ) -> None: + """Construct a Conformer object.""" + super().__init__() + + self.self_att = self_att + + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.feed_forward_scale = 0.5 + + self.conv_mod = conv_mod + + self.norm_feed_forward = norm_class(block_size, **norm_args) + self.norm_self_att = norm_class(block_size, **norm_args) + + self.norm_macaron = norm_class(block_size, **norm_args) + self.norm_conv = norm_class(block_size, **norm_args) + self.norm_final = norm_class(block_size, **norm_args) + + self.dropout = torch.nn.Dropout(dropout_rate) + + self.block_size = block_size + self.cache = None + + def reset_streaming_cache(self, left_context: int, device: torch.device) -> None: + """Initialize/Reset self-attention and convolution modules cache for streaming. + Args: + left_context: Number of left frames during chunk-by-chunk inference. + device: Device to use for cache tensor. + """ + self.cache = [ + torch.zeros( + (1, left_context, self.block_size), + device=device, + ), + torch.zeros( + ( + 1, + self.block_size, + self.conv_mod.kernel_size - 1, + ), + device=device, + ), + ] + + def forward( + self, + x: torch.Tensor, + pos_enc: torch.Tensor, + mask: torch.Tensor, + chunk_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Encode input sequences. + Args: + x: Conformer input sequences. (B, T, D_block) + pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_block) + mask: Source mask. (B, T) + chunk_mask: Chunk mask. (T_2, T_2) + Returns: + x: Conformer output sequences. (B, T, D_block) + mask: Source mask. (B, T) + pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_block) + """ + residual = x + + x = self.norm_macaron(x) + x = residual + self.feed_forward_scale * self.dropout( + self.feed_forward_macaron(x) + ) + + residual = x + x = self.norm_self_att(x) + x_q = x + x = residual + self.dropout( + self.self_att( + x_q, + x, + x, + pos_enc, + mask, + chunk_mask=chunk_mask, + ) + ) + + residual = x + + x = self.norm_conv(x) + x, _ = self.conv_mod(x) + x = residual + self.dropout(x) + residual = x + + x = self.norm_feed_forward(x) + x = residual + self.feed_forward_scale * self.dropout(self.feed_forward(x)) + + x = self.norm_final(x) + return x, mask, pos_enc + + def chunk_forward( + self, + x: torch.Tensor, + pos_enc: torch.Tensor, + mask: torch.Tensor, + chunk_size: int = 16, + left_context: int = 0, + right_context: int = 0, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Encode chunk of input sequence. + Args: + x: Conformer input sequences. (B, T, D_block) + pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_block) + mask: Source mask. (B, T_2) + left_context: Number of frames in left context. + right_context: Number of frames in right context. + Returns: + x: Conformer output sequences. (B, T, D_block) + pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_block) + """ + residual = x + + x = self.norm_macaron(x) + x = residual + self.feed_forward_scale * self.feed_forward_macaron(x) + + residual = x + x = self.norm_self_att(x) + if left_context > 0: + key = torch.cat([self.cache[0], x], dim=1) + else: + key = x + val = key + + if right_context > 0: + att_cache = key[:, -(left_context + right_context) : -right_context, :] + else: + att_cache = key[:, -left_context:, :] + x = residual + self.self_att( + x, + key, + val, + pos_enc, + mask, + left_context=left_context, + ) + + residual = x + x = self.norm_conv(x) + x, conv_cache = self.conv_mod( + x, cache=self.cache[1], right_context=right_context + ) + x = residual + x + residual = x + + x = self.norm_feed_forward(x) + x = residual + self.feed_forward_scale * self.feed_forward(x) + + x = self.norm_final(x) + self.cache = [att_cache, conv_cache] + + return x, pos_enc + + +@tables.register("encoder_classes", "ChunkConformerEncoder") +class ConformerChunkEncoder(torch.nn.Module): + """Encoder module definition. + Args: + input_size: Input size. + body_conf: Encoder body configuration. + input_conf: Encoder input configuration. + main_conf: Encoder main configuration. + """ + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + embed_vgg_like: bool = False, + normalize_before: bool = True, + concat_after: bool = False, + positionwise_layer_type: str = "linear", + positionwise_conv_kernel_size: int = 3, + macaron_style: bool = False, + rel_pos_type: str = "legacy", + pos_enc_layer_type: str = "rel_pos", + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = True, + zero_triu: bool = False, + norm_type: str = "layer_norm", + cnn_module_kernel: int = 31, + conv_mod_norm_eps: float = 0.00001, + conv_mod_norm_momentum: float = 0.1, + simplified_att_score: bool = False, + dynamic_chunk_training: bool = False, + short_chunk_threshold: float = 0.75, + short_chunk_size: int = 25, + left_chunk_size: int = 0, + time_reduction_factor: int = 1, + unified_model_training: bool = False, + default_chunk_size: int = 16, + jitter_range: int = 4, + subsampling_factor: int = 1, + ) -> None: + """Construct an Encoder object.""" + super().__init__() + + self.embed = StreamingConvInput( + input_size=input_size, + conv_size=output_size, + subsampling_factor=subsampling_factor, + vgg_like=embed_vgg_like, + output_size=output_size, + ) + + self.pos_enc = StreamingRelPositionalEncoding( + output_size, + positional_dropout_rate, + ) + + activation = get_activation(activation_type) + + pos_wise_args = ( + output_size, + linear_units, + positional_dropout_rate, + activation, + ) + + conv_mod_norm_args = { + "eps": conv_mod_norm_eps, + "momentum": conv_mod_norm_momentum, + } + + conv_mod_args = ( + output_size, + cnn_module_kernel, + activation, + conv_mod_norm_args, + dynamic_chunk_training or unified_model_training, + ) + + mult_att_args = ( + attention_heads, + output_size, + attention_dropout_rate, + simplified_att_score, + ) + + fn_modules = [] + for _ in range(num_blocks): + module = lambda: ChunkEncoderLayer( + output_size, + RelPositionMultiHeadedAttentionChunk(*mult_att_args), + PositionwiseFeedForward(*pos_wise_args), + PositionwiseFeedForward(*pos_wise_args), + CausalConvolution(*conv_mod_args), + dropout_rate=dropout_rate, + ) + fn_modules.append(module) + + self.encoders = MultiBlocks( + [fn() for fn in fn_modules], + output_size, + ) + + self._output_size = output_size + + self.dynamic_chunk_training = dynamic_chunk_training + self.short_chunk_threshold = short_chunk_threshold + self.short_chunk_size = short_chunk_size + self.left_chunk_size = left_chunk_size + + self.unified_model_training = unified_model_training + self.default_chunk_size = default_chunk_size + self.jitter_range = jitter_range + + self.time_reduction_factor = time_reduction_factor + + def output_size(self) -> int: + return self._output_size + + def get_encoder_input_raw_size(self, size: int, hop_length: int) -> int: + """Return the corresponding number of sample for a given chunk size, in frames. + Where size is the number of features frames after applying subsampling. + Args: + size: Number of frames after subsampling. + hop_length: Frontend's hop length + Returns: + : Number of raw samples + """ + return self.embed.get_size_before_subsampling(size) * hop_length + + def get_encoder_input_size(self, size: int) -> int: + """Return the corresponding number of sample for a given chunk size, in frames. + Where size is the number of features frames after applying subsampling. + Args: + size: Number of frames after subsampling. + Returns: + : Number of raw samples + """ + return self.embed.get_size_before_subsampling(size) + + def reset_streaming_cache(self, left_context: int, device: torch.device) -> None: + """Initialize/Reset encoder streaming cache. + Args: + left_context: Number of frames in left context. + device: Device ID. + """ + return self.encoders.reset_streaming_cache(left_context, device) + + def forward( + self, + x: torch.Tensor, + x_len: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Encode input sequences. + Args: + x: Encoder input features. (B, T_in, F) + x_len: Encoder input features lengths. (B,) + Returns: + x: Encoder outputs. (B, T_out, D_enc) + x_len: Encoder outputs lenghts. (B,) + """ + short_status, limit_size = check_short_utt( + self.embed.subsampling_factor, x.size(1) + ) + + if short_status: + raise TooShortUttError( + f"has {x.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + x.size(1), + limit_size, + ) + + mask = make_source_mask(x_len).to(x.device) + + if self.unified_model_training: + if self.training: + chunk_size = ( + self.default_chunk_size + + torch.randint( + -self.jitter_range, self.jitter_range + 1, (1,) + ).item() + ) + else: + chunk_size = self.default_chunk_size + x, mask = self.embed(x, mask, chunk_size) + pos_enc = self.pos_enc(x) + chunk_mask = make_chunk_mask( + x.size(1), + chunk_size, + left_chunk_size=self.left_chunk_size, + device=x.device, + ) + x_utt = self.encoders( + x, + pos_enc, + mask, + chunk_mask=None, + ) + x_chunk = self.encoders( + x, + pos_enc, + mask, + chunk_mask=chunk_mask, + ) + + olens = mask.eq(0).sum(1) + if self.time_reduction_factor > 1: + x_utt = x_utt[:, :: self.time_reduction_factor, :] + x_chunk = x_chunk[:, :: self.time_reduction_factor, :] + olens = torch.floor_divide(olens - 1, self.time_reduction_factor) + 1 + + return x_utt, x_chunk, olens + + elif self.dynamic_chunk_training: + max_len = x.size(1) + if self.training: + chunk_size = torch.randint(1, max_len, (1,)).item() + + if chunk_size > (max_len * self.short_chunk_threshold): + chunk_size = max_len + else: + chunk_size = (chunk_size % self.short_chunk_size) + 1 + else: + chunk_size = self.default_chunk_size + + x, mask = self.embed(x, mask, chunk_size) + pos_enc = self.pos_enc(x) + + chunk_mask = make_chunk_mask( + x.size(1), + chunk_size, + left_chunk_size=self.left_chunk_size, + device=x.device, + ) + else: + x, mask = self.embed(x, mask, None) + pos_enc = self.pos_enc(x) + chunk_mask = None + x = self.encoders( + x, + pos_enc, + mask, + chunk_mask=chunk_mask, + ) + + olens = mask.eq(0).sum(1) + if self.time_reduction_factor > 1: + x = x[:, :: self.time_reduction_factor, :] + olens = torch.floor_divide(olens - 1, self.time_reduction_factor) + 1 + + return x, olens, None + + def full_utt_forward( + self, + x: torch.Tensor, + x_len: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Encode input sequences. + Args: + x: Encoder input features. (B, T_in, F) + x_len: Encoder input features lengths. (B,) + Returns: + x: Encoder outputs. (B, T_out, D_enc) + x_len: Encoder outputs lenghts. (B,) + """ + short_status, limit_size = check_short_utt( + self.embed.subsampling_factor, x.size(1) + ) + + if short_status: + raise TooShortUttError( + f"has {x.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + x.size(1), + limit_size, + ) + + mask = make_source_mask(x_len).to(x.device) + x, mask = self.embed(x, mask, None) + pos_enc = self.pos_enc(x) + x_utt = self.encoders( + x, + pos_enc, + mask, + chunk_mask=None, + ) + + if self.time_reduction_factor > 1: + x_utt = x_utt[:, :: self.time_reduction_factor, :] + return x_utt + + def simu_chunk_forward( + self, + x: torch.Tensor, + x_len: torch.Tensor, + chunk_size: int = 16, + left_context: int = 32, + right_context: int = 0, + ) -> torch.Tensor: + short_status, limit_size = check_short_utt( + self.embed.subsampling_factor, x.size(1) + ) + + if short_status: + raise TooShortUttError( + f"has {x.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + x.size(1), + limit_size, + ) + + mask = make_source_mask(x_len) + + x, mask = self.embed(x, mask, chunk_size) + pos_enc = self.pos_enc(x) + chunk_mask = make_chunk_mask( + x.size(1), + chunk_size, + left_chunk_size=self.left_chunk_size, + device=x.device, + ) + + x = self.encoders( + x, + pos_enc, + mask, + chunk_mask=chunk_mask, + ) + olens = mask.eq(0).sum(1) + if self.time_reduction_factor > 1: + x = x[:, :: self.time_reduction_factor, :] + + return x + + def chunk_forward( + self, + x: torch.Tensor, + x_len: torch.Tensor, + processed_frames: torch.tensor, + chunk_size: int = 16, + left_context: int = 32, + right_context: int = 0, + ) -> torch.Tensor: + """Encode input sequences as chunks. + Args: + x: Encoder input features. (1, T_in, F) + x_len: Encoder input features lengths. (1,) + processed_frames: Number of frames already seen. + left_context: Number of frames in left context. + right_context: Number of frames in right context. + Returns: + x: Encoder outputs. (B, T_out, D_enc) + """ + mask = make_source_mask(x_len) + x, mask = self.embed(x, mask, None) + + if left_context > 0: + processed_mask = ( + torch.arange(left_context, device=x.device) + .view(1, left_context) + .flip(1) + ) + processed_mask = processed_mask >= processed_frames + mask = torch.cat([processed_mask, mask], dim=1) + pos_enc = self.pos_enc(x, left_context=left_context) + x = self.encoders.chunk_forward( + x, + pos_enc, + mask, + chunk_size=chunk_size, + left_context=left_context, + right_context=right_context, + ) + + if right_context > 0: + x = x[:, 0:-right_context, :] + + if self.time_reduction_factor > 1: + x = x[:, :: self.time_reduction_factor, :] + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/conformer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/conformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..e18e5482cdd5bb7069c593fae660f6279c8a80af --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/conformer/model.py @@ -0,0 +1,19 @@ +import logging + +import torch + +from funasr_detach.models.transformer.model import Transformer +from funasr_detach.register import tables + + +@tables.register("model_classes", "Conformer") +class Conformer(Transformer): + """CTC-attention hybrid Encoder-Decoder model""" + + def __init__( + self, + *args, + **kwargs, + ): + + super().__init__(*args, **kwargs) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/conformer/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/conformer/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..13773cae6225156ce1022f17d91857bec82cbea5 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/conformer/template.yaml @@ -0,0 +1,117 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: Conformer +model_conf: + ctc_weight: 0.3 + lsm_weight: 0.1 # label smoothing option + length_normalized_loss: false + +# encoder +encoder: ConformerEncoder +encoder_conf: + output_size: 256 + attention_heads: 4 + linear_units: 2048 + num_blocks: 12 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.0 + input_layer: conv2d + normalize_before: true + pos_enc_layer_type: rel_pos + selfattention_layer_type: rel_selfattn + activation_type: swish + macaron_style: true + use_cnn_module: true + cnn_module_kernel: 15 + +# decoder +decoder: TransformerDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 6 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.0 + src_attention_dropout_rate: 0.0 + + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + dither: 0.0 + lfr_m: 1 + lfr_n: 1 + +specaug: SpecAug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_range: + - 0 + - 40 + num_time_mask: 2 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + val_scheduler_criterion: + - valid + - acc + best_model_criterion: + - - valid + - acc + - max + keep_nbest_models: 10 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0005 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..95c888ba280f9ebe9d6c775508b597c55c6a98ec --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/decoder.py @@ -0,0 +1,1023 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +import logging +import numpy as np +from typing import Tuple + +from funasr_detach.register import tables +from funasr_detach.models.scama import utils as myutils +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.embedding import PositionalEncoding +from funasr_detach.models.paraformer.decoder import ( + DecoderLayerSANM, + ParaformerSANMDecoder, +) +from funasr_detach.models.sanm.positionwise_feed_forward import ( + PositionwiseFeedForwardDecoderSANM, +) +from funasr_detach.models.sanm.attention import ( + MultiHeadedAttentionSANMDecoder, + MultiHeadedAttentionCrossAtt, +) + + +class ContextualDecoderLayer(torch.nn.Module): + def __init__( + self, + size, + self_attn, + src_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + ): + """Construct an DecoderLayer object.""" + super(ContextualDecoderLayer, self).__init__() + self.size = size + self.self_attn = self_attn + self.src_attn = src_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(size) + if self_attn is not None: + self.norm2 = LayerNorm(size) + if src_attn is not None: + self.norm3 = LayerNorm(size) + self.dropout = torch.nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear1 = torch.nn.Linear(size + size, size) + self.concat_linear2 = torch.nn.Linear(size + size, size) + + def forward( + self, + tgt, + tgt_mask, + memory, + memory_mask, + cache=None, + ): + # tgt = self.dropout(tgt) + if isinstance(tgt, Tuple): + tgt, _ = tgt + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.normalize_before: + tgt = self.norm2(tgt) + if self.training: + cache = None + x, cache = self.self_attn(tgt, tgt_mask, cache=cache) + x = residual + self.dropout(x) + x_self_attn = x + + residual = x + if self.normalize_before: + x = self.norm3(x) + x = self.src_attn(x, memory, memory_mask) + x_src_attn = x + + x = residual + self.dropout(x) + return x, tgt_mask, x_self_attn, x_src_attn + + +class ContextualBiasDecoder(torch.nn.Module): + def __init__( + self, + size, + src_attn, + dropout_rate, + normalize_before=True, + ): + """Construct an DecoderLayer object.""" + super(ContextualBiasDecoder, self).__init__() + self.size = size + self.src_attn = src_attn + if src_attn is not None: + self.norm3 = LayerNorm(size) + self.dropout = torch.nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + + def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None): + x = tgt + if self.src_attn is not None: + if self.normalize_before: + x = self.norm3(x) + x = self.dropout(self.src_attn(x, memory, memory_mask)) + return x, tgt_mask, memory, memory_mask, cache + + +@tables.register("decoder_classes", "ContextualParaformerDecoder") +class ContextualParaformerDecoder(ParaformerSANMDecoder): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition + https://arxiv.org/abs/2006.01713 + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + att_layer_num: int = 6, + kernel_size: int = 21, + sanm_shfit: int = 0, + ): + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + if input_layer == "none": + self.embed = None + if input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(vocab_size, attention_dim), + # pos_enc_class(attention_dim, positional_dropout_rate), + ) + elif input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(vocab_size, attention_dim), + torch.nn.LayerNorm(attention_dim), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + else: + raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}") + + self.normalize_before = normalize_before + if self.normalize_before: + self.after_norm = LayerNorm(attention_dim) + if use_output_layer: + self.output_layer = torch.nn.Linear(attention_dim, vocab_size) + else: + self.output_layer = None + + self.att_layer_num = att_layer_num + self.num_blocks = num_blocks + if sanm_shfit is None: + sanm_shfit = (kernel_size - 1) // 2 + self.decoders = repeat( + att_layer_num - 1, + lambda lnum: DecoderLayerSANM( + attention_dim, + MultiHeadedAttentionSANMDecoder( + attention_dim, + self_attention_dropout_rate, + kernel_size, + sanm_shfit=sanm_shfit, + ), + MultiHeadedAttentionCrossAtt( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + self.dropout = torch.nn.Dropout(dropout_rate) + self.bias_decoder = ContextualBiasDecoder( + size=attention_dim, + src_attn=MultiHeadedAttentionCrossAtt( + attention_heads, attention_dim, src_attention_dropout_rate + ), + dropout_rate=dropout_rate, + normalize_before=True, + ) + self.bias_output = torch.nn.Conv1d( + attention_dim * 2, attention_dim, 1, bias=False + ) + self.last_decoder = ContextualDecoderLayer( + attention_dim, + MultiHeadedAttentionSANMDecoder( + attention_dim, + self_attention_dropout_rate, + kernel_size, + sanm_shfit=sanm_shfit, + ), + MultiHeadedAttentionCrossAtt( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ) + if num_blocks - att_layer_num <= 0: + self.decoders2 = None + else: + self.decoders2 = repeat( + num_blocks - att_layer_num, + lambda lnum: DecoderLayerSANM( + attention_dim, + MultiHeadedAttentionSANMDecoder( + attention_dim, + self_attention_dropout_rate, + kernel_size, + sanm_shfit=0, + ), + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + self.decoders3 = repeat( + 1, + lambda lnum: DecoderLayerSANM( + attention_dim, + None, + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + contextual_info: torch.Tensor, + clas_scale: float = 1.0, + return_hidden: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward decoder. + + Args: + hs_pad: encoded memory, float32 (batch, maxlen_in, feat) + hlens: (batch) + ys_in_pad: + input token ids, int64 (batch, maxlen_out) + if input_layer == "embed" + input tensor (batch, maxlen_out, #mels) in the other cases + ys_in_lens: (batch) + Returns: + (tuple): tuple containing: + + x: decoded token score before softmax (batch, maxlen_out, token) + if use_output_layer is True, + olens: (batch, ) + """ + tgt = ys_in_pad + tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None] + + memory = hs_pad + memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :] + + x = tgt + x, tgt_mask, memory, memory_mask, _ = self.decoders( + x, tgt_mask, memory, memory_mask + ) + _, _, x_self_attn, x_src_attn = self.last_decoder( + x, tgt_mask, memory, memory_mask + ) + + # contextual paraformer related + contextual_length = ( + torch.Tensor([contextual_info.shape[1]]).int().repeat(hs_pad.shape[0]) + ) + contextual_mask = myutils.sequence_mask( + contextual_length, device=memory.device + )[:, None, :] + cx, tgt_mask, _, _, _ = self.bias_decoder( + x_self_attn, tgt_mask, contextual_info, memory_mask=contextual_mask + ) + + if self.bias_output is not None: + x = torch.cat([x_src_attn, cx * clas_scale], dim=2) + x = self.bias_output(x.transpose(1, 2)).transpose(1, 2) # 2D -> D + x = x_self_attn + self.dropout(x) + + if self.decoders2 is not None: + x, tgt_mask, memory, memory_mask, _ = self.decoders2( + x, tgt_mask, memory, memory_mask + ) + + x, tgt_mask, memory, memory_mask, _ = self.decoders3( + x, tgt_mask, memory, memory_mask + ) + if self.normalize_before: + x = self.after_norm(x) + olens = tgt_mask.sum(1) + if self.output_layer is not None and return_hidden is False: + x = self.output_layer(x) + return x, olens + + def gen_tf2torch_map_dict(self): + + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + map_dict_local = { + ## decoder + # ffn + "{}.decoders.layeridx.norm1.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.norm1.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.feed_forward.w_1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.decoders.layeridx.feed_forward.w_1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders.layeridx.feed_forward.norm.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders.layeridx.feed_forward.norm.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders.layeridx.feed_forward.w_2.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,1024),(1,1024,256) + # fsmn + "{}.decoders.layeridx.norm2.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.norm2.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.self_attn.fsmn_block.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/depth_conv_w".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 2, 0), + }, # (256,1,31),(1,31,256,1) + # src att + "{}.decoders.layeridx.norm3.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.norm3.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.src_attn.linear_q.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,256),(1,256,256) + "{}.decoders.layeridx.src_attn.linear_q.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.src_attn.linear_k_v.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.decoders.layeridx.src_attn.linear_k_v.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders.layeridx.src_attn.linear_out.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,256),(1,256,256) + "{}.decoders.layeridx.src_attn.linear_out.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + # dnn + "{}.decoders3.layeridx.norm1.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_dnn_layer_layeridx/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders3.layeridx.norm1.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_dnn_layer_layeridx/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders3.layeridx.feed_forward.w_1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.decoders3.layeridx.feed_forward.w_1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders3.layeridx.feed_forward.norm.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders3.layeridx.feed_forward.norm.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders3.layeridx.feed_forward.w_2.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,1024),(1,1024,256) + # embed_concat_ffn + "{}.embed_concat_ffn.layeridx.norm1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/LayerNorm/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.embed_concat_ffn.layeridx.norm1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/LayerNorm/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.embed_concat_ffn.layeridx.feed_forward.w_1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/conv1d/kernel".format(tensor_name_prefix_tf), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.embed_concat_ffn.layeridx.feed_forward.w_1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/conv1d/bias".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.embed_concat_ffn.layeridx.feed_forward.norm.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/LayerNorm_1/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.embed_concat_ffn.layeridx.feed_forward.norm.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/LayerNorm_1/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.embed_concat_ffn.layeridx.feed_forward.w_2.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/conv1d_1/kernel".format(tensor_name_prefix_tf), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,1024),(1,1024,256) + # out norm + "{}.after_norm.weight".format(tensor_name_prefix_torch): { + "name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.after_norm.bias".format(tensor_name_prefix_torch): { + "name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + # in embed + "{}.embed.0.weight".format(tensor_name_prefix_torch): { + "name": "{}/w_embs".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (4235,256),(4235,256) + # out layer + "{}.output_layer.weight".format(tensor_name_prefix_torch): { + "name": [ + "{}/dense/kernel".format(tensor_name_prefix_tf), + "{}/w_embs".format(tensor_name_prefix_tf), + ], + "squeeze": [None, None], + "transpose": [(1, 0), None], + }, # (4235,256),(256,4235) + "{}.output_layer.bias".format(tensor_name_prefix_torch): { + "name": [ + "{}/dense/bias".format(tensor_name_prefix_tf), + ( + "seq2seq/2bias" + if tensor_name_prefix_tf == "seq2seq/decoder/inputter_1" + else "seq2seq/bias" + ), + ], + "squeeze": [None, None], + "transpose": [None, None], + }, # (4235,),(4235,) + ## clas decoder + # src att + "{}.bias_decoder.norm3.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_15/multi_head_1/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.bias_decoder.norm3.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_15/multi_head_1/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.bias_decoder.src_attn.linear_q.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,256),(1,256,256) + "{}.bias_decoder.src_attn.linear_q.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.bias_decoder.src_attn.linear_k_v.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.bias_decoder.src_attn.linear_k_v.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_1/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.bias_decoder.src_attn.linear_out.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_2/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,256),(1,256,256) + "{}.bias_decoder.src_attn.linear_out.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_2/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + # dnn + "{}.bias_output.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_15/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": (2, 1, 0), + }, # (1024,256),(1,256,1024) + } + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + map_dict = self.gen_tf2torch_map_dict() + var_dict_torch_update = dict() + decoder_layeridx_sets = set() + for name in sorted(var_dict_torch.keys(), reverse=False): + names = name.split(".") + if names[0] == self.tf2torch_tensor_name_prefix_torch: + if names[1] == "decoders": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + layeridx_bias = 0 + layeridx += layeridx_bias + decoder_layeridx_sets.add(layeridx) + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + elif names[1] == "last_decoder": + layeridx = 15 + name_q = name.replace("last_decoder", "decoders.layeridx") + layeridx_bias = 0 + layeridx += layeridx_bias + decoder_layeridx_sets.add(layeridx) + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + + elif names[1] == "decoders2": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + name_q = name_q.replace("decoders2", "decoders") + layeridx_bias = len(decoder_layeridx_sets) + + layeridx += layeridx_bias + if "decoders." in name: + decoder_layeridx_sets.add(layeridx) + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + + elif names[1] == "decoders3": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + + layeridx_bias = 0 + layeridx += layeridx_bias + if "decoders." in name: + decoder_layeridx_sets.add(layeridx) + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + elif names[1] == "bias_decoder": + name_q = name + + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + + elif ( + names[1] == "embed" + or names[1] == "output_layer" + or names[1] == "bias_output" + ): + name_tf = map_dict[name]["name"] + if isinstance(name_tf, list): + idx_list = 0 + if name_tf[idx_list] in var_dict_tf.keys(): + pass + else: + idx_list = 1 + data_tf = var_dict_tf[name_tf[idx_list]] + if map_dict[name]["squeeze"][idx_list] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name]["squeeze"][idx_list] + ) + if map_dict[name]["transpose"][idx_list] is not None: + data_tf = np.transpose( + data_tf, map_dict[name]["transpose"][idx_list] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf[idx_list], + var_dict_tf[name_tf[idx_list]].shape, + ) + ) + + else: + data_tf = var_dict_tf[name_tf] + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name]["squeeze"] + ) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf, + var_dict_tf[name_tf].shape, + ) + ) + + elif names[1] == "after_norm": + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape + ) + ) + + elif names[1] == "embed_concat_ffn": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + + layeridx_bias = 0 + layeridx += layeridx_bias + if "decoders." in name: + decoder_layeridx_sets.add(layeridx) + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + + return var_dict_torch_update diff --git a/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..68fcd86b599a893ac9b178cd5565bc230fea817a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/model.py @@ -0,0 +1,634 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import os +import re +import time +import torch +import codecs +import logging +import tempfile +import requests +import numpy as np +from typing import Dict, Tuple +from contextlib import contextmanager +from distutils.version import LooseVersion + +from funasr_detach.register import tables +from funasr_detach.losses.label_smoothing_loss import ( + LabelSmoothingLoss, # noqa: H301 +) +from funasr_detach.utils import postprocess_utils +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.models.paraformer.model import Paraformer +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.models.paraformer.search import Hypothesis +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank + + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "ContextualParaformer") +class ContextualParaformer(Paraformer): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + FunASR: A Fundamental End-to-End Speech Recognition Toolkit + https://arxiv.org/abs/2305.11013 + """ + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.target_buffer_length = kwargs.get("target_buffer_length", -1) + inner_dim = kwargs.get("inner_dim", 256) + bias_encoder_type = kwargs.get("bias_encoder_type", "lstm") + use_decoder_embedding = kwargs.get("use_decoder_embedding", False) + crit_attn_weight = kwargs.get("crit_attn_weight", 0.0) + crit_attn_smooth = kwargs.get("crit_attn_smooth", 0.0) + bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0) + + if bias_encoder_type == "lstm": + self.bias_encoder = torch.nn.LSTM( + inner_dim, + inner_dim, + 1, + batch_first=True, + dropout=bias_encoder_dropout_rate, + ) + self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim) + elif bias_encoder_type == "mean": + self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim) + else: + logging.error("Unsupport bias encoder type: {}".format(bias_encoder_type)) + + if self.target_buffer_length > 0: + self.hotword_buffer = None + self.length_record = [] + self.current_buffer_length = 0 + self.use_decoder_embedding = use_decoder_embedding + self.crit_attn_weight = crit_attn_weight + if self.crit_attn_weight > 0: + self.attn_loss = torch.nn.L1Loss() + self.crit_attn_smooth = crit_attn_smooth + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Decoder + Calc loss + + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + if len(text_lengths.size()) > 1: + text_lengths = text_lengths[:, 0] + if len(speech_lengths.size()) > 1: + speech_lengths = speech_lengths[:, 0] + + batch_size = speech.shape[0] + + hotword_pad = kwargs.get("hotword_pad") + hotword_lengths = kwargs.get("hotword_lengths") + dha_pad = kwargs.get("dha_pad") + + # 1. Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + + loss_ctc, cer_ctc = None, None + + stats = dict() + + # 1. CTC branch + if self.ctc_weight != 0.0: + loss_ctc, cer_ctc = self._calc_ctc_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # Collect CTC branch stats + stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None + stats["cer_ctc"] = cer_ctc + + # 2b. Attention decoder branch + loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = ( + self._calc_att_clas_loss( + encoder_out, + encoder_out_lens, + text, + text_lengths, + hotword_pad, + hotword_lengths, + ) + ) + + # 3. CTC-Att loss definition + if self.ctc_weight == 0.0: + loss = loss_att + loss_pre * self.predictor_weight + else: + loss = ( + self.ctc_weight * loss_ctc + + (1 - self.ctc_weight) * loss_att + + loss_pre * self.predictor_weight + ) + + if loss_ideal is not None: + loss = loss + loss_ideal * self.crit_attn_weight + stats["loss_ideal"] = loss_ideal.detach().cpu() + + # Collect Attn branch stats + stats["loss_att"] = loss_att.detach() if loss_att is not None else None + stats["acc"] = acc_att + stats["cer"] = cer_att + stats["wer"] = wer_att + stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None + + stats["loss"] = torch.clone(loss.detach()) + # force_gatherable: to-device and to-tensor if scalar for DataParallel + if self.length_normalized_loss: + batch_size = int((text_lengths + self.predictor_bias).sum()) + + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def _calc_att_clas_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + hotword_pad: torch.Tensor, + hotword_lengths: torch.Tensor, + ): + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + if self.predictor_bias == 1: + _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_pad_lens = ys_pad_lens + self.predictor_bias + pre_acoustic_embeds, pre_token_length, _, _ = self.predictor( + encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id + ) + + # -1. bias encoder + if self.use_decoder_embedding: + hw_embed = self.decoder.embed(hotword_pad) + else: + hw_embed = self.bias_embed(hotword_pad) + hw_embed, (_, _) = self.bias_encoder(hw_embed) + _ind = np.arange(0, hotword_pad.shape[0]).tolist() + selected = hw_embed[ + _ind, [i - 1 for i in hotword_lengths.detach().cpu().tolist()] + ] + contextual_info = ( + selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device) + ) + + # 0. sampler + decoder_out_1st = None + if self.sampling_ratio > 0.0: + if self.step_cur < 2: + logging.info( + "enable sampler in paraformer, sampling_ratio: {}".format( + self.sampling_ratio + ) + ) + sematic_embeds, decoder_out_1st = self.sampler( + encoder_out, + encoder_out_lens, + ys_pad, + ys_pad_lens, + pre_acoustic_embeds, + contextual_info, + ) + else: + if self.step_cur < 2: + logging.info( + "disable sampler in paraformer, sampling_ratio: {}".format( + self.sampling_ratio + ) + ) + sematic_embeds = pre_acoustic_embeds + + # 1. Forward decoder + decoder_outs = self.decoder( + encoder_out, + encoder_out_lens, + sematic_embeds, + ys_pad_lens, + contextual_info=contextual_info, + ) + decoder_out, _ = decoder_outs[0], decoder_outs[1] + """ + if self.crit_attn_weight > 0 and attn.shape[-1] > 1: + ideal_attn = ideal_attn + self.crit_attn_smooth / (self.crit_attn_smooth + 1.0) + attn_non_blank = attn[:,:,:,:-1] + ideal_attn_non_blank = ideal_attn[:,:,:-1] + loss_ideal = self.attn_loss(attn_non_blank.max(1)[0], ideal_attn_non_blank.to(attn.device)) + else: + loss_ideal = None + """ + loss_ideal = None + + if decoder_out_1st is None: + decoder_out_1st = decoder_out + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_pad) + acc_att = th_accuracy( + decoder_out_1st.view(-1, self.vocab_size), + ys_pad, + ignore_label=self.ignore_id, + ) + loss_pre = self.criterion_pre( + ys_pad_lens.type_as(pre_token_length), pre_token_length + ) + + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out_1st.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal + + def sampler( + self, + encoder_out, + encoder_out_lens, + ys_pad, + ys_pad_lens, + pre_acoustic_embeds, + contextual_info, + ): + tgt_mask = ( + ~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None] + ).to(ys_pad.device) + ys_pad = ys_pad * tgt_mask[:, :, 0] + if self.share_embedding: + ys_pad_embed = self.decoder.output_layer.weight[ys_pad] + else: + ys_pad_embed = self.decoder.embed(ys_pad) + with torch.no_grad(): + decoder_outs = self.decoder( + encoder_out, + encoder_out_lens, + pre_acoustic_embeds, + ys_pad_lens, + contextual_info=contextual_info, + ) + decoder_out, _ = decoder_outs[0], decoder_outs[1] + pred_tokens = decoder_out.argmax(-1) + nonpad_positions = ys_pad.ne(self.ignore_id) + seq_lens = (nonpad_positions).sum(1) + same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1) + input_mask = torch.ones_like(nonpad_positions) + bsz, seq_len = ys_pad.size() + for li in range(bsz): + target_num = ( + ((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio + ).long() + if target_num > 0: + input_mask[li].scatter_( + dim=0, + index=torch.randperm(seq_lens[li])[:target_num].to( + pre_acoustic_embeds.device + ), + value=0, + ) + input_mask = input_mask.eq(1) + input_mask = input_mask.masked_fill(~nonpad_positions, False) + input_mask_expand_dim = input_mask.unsqueeze(2).to( + pre_acoustic_embeds.device + ) + + sematic_embeds = pre_acoustic_embeds.masked_fill( + ~input_mask_expand_dim, 0 + ) + ys_pad_embed.masked_fill(input_mask_expand_dim, 0) + return sematic_embeds * tgt_mask, decoder_out * tgt_mask + + def cal_decoder_with_predictor( + self, + encoder_out, + encoder_out_lens, + sematic_embeds, + ys_pad_lens, + hw_list=None, + clas_scale=1.0, + ): + if hw_list is None: + hw_list = [ + torch.Tensor([1]).long().to(encoder_out.device) + ] # empty hotword list + hw_list_pad = pad_list(hw_list, 0) + if self.use_decoder_embedding: + hw_embed = self.decoder.embed(hw_list_pad) + else: + hw_embed = self.bias_embed(hw_list_pad) + hw_embed, (h_n, _) = self.bias_encoder(hw_embed) + hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1) + else: + hw_lengths = [len(i) for i in hw_list] + hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to( + encoder_out.device + ) + if self.use_decoder_embedding: + hw_embed = self.decoder.embed(hw_list_pad) + else: + hw_embed = self.bias_embed(hw_list_pad) + hw_embed = torch.nn.utils.rnn.pack_padded_sequence( + hw_embed, hw_lengths, batch_first=True, enforce_sorted=False + ) + _, (h_n, _) = self.bias_encoder(hw_embed) + hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1) + + decoder_outs = self.decoder( + encoder_out, + encoder_out_lens, + sematic_embeds, + ys_pad_lens, + contextual_info=hw_embed, + clas_scale=clas_scale, + ) + decoder_out = decoder_outs[0] + decoder_out = torch.log_softmax(decoder_out, dim=-1) + return decoder_out, ys_pad_lens + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + # init beamsearch + is_use_ctc = ( + kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None + ) + is_use_lm = ( + kwargs.get("lm_weight", 0.0) > 0.00001 + and kwargs.get("lm_file", None) is not None + ) + if self.beam_search is None and (is_use_lm or is_use_ctc): + logging.info("enable beam_search") + self.init_beam_search(**kwargs) + self.nbest = kwargs.get("nbest", 1) + + meta_data = {} + + # extract fbank feats + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000) + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths = extract_fbank( + audio_sample_list, + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000 + ) + + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + + # hotword + self.hotword_list = self.generate_hotwords_list( + kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend + ) + + # Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + # predictor + predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = ( + predictor_outs[0], + predictor_outs[1], + predictor_outs[2], + predictor_outs[3], + ) + pre_token_length = pre_token_length.round().long() + if torch.max(pre_token_length) < 1: + return [] + + decoder_outs = self.cal_decoder_with_predictor( + encoder_out, + encoder_out_lens, + pre_acoustic_embeds, + pre_token_length, + hw_list=self.hotword_list, + clas_scale=kwargs.get("clas_scale", 1.0), + ) + decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] + + results = [] + b, n, d = decoder_out.size() + for i in range(b): + x = encoder_out[i, : encoder_out_lens[i], :] + am_scores = decoder_out[i, : pre_token_length[i], :] + if self.beam_search is not None: + nbest_hyps = self.beam_search( + x=x, + am_scores=am_scores, + maxlenratio=kwargs.get("maxlenratio", 0.0), + minlenratio=kwargs.get("minlenratio", 0.0), + ) + + nbest_hyps = nbest_hyps[: self.nbest] + else: + + yseq = am_scores.argmax(dim=-1) + score = am_scores.max(dim=-1)[0] + score = torch.sum(score, dim=-1) + # pad with mask tokens to ensure compatibility with sos/eos tokens + yseq = torch.tensor( + [self.sos] + yseq.tolist() + [self.eos], device=yseq.device + ) + nbest_hyps = [Hypothesis(yseq=yseq, score=score)] + for nbest_idx, hyp in enumerate(nbest_hyps): + ibest_writer = None + if kwargs.get("output_dir") is not None: + if not hasattr(self, "writer"): + self.writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"] + + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list( + filter( + lambda x: x != self.eos + and x != self.sos + and x != self.blank_id, + token_int, + ) + ) + + if tokenizer is not None: + # Change integer-ids to tokens + token = tokenizer.ids2tokens(token_int) + text = tokenizer.tokens2text(token) + + text_postprocessed, _ = postprocess_utils.sentence_postprocess( + token + ) + result_i = {"key": key[i], "text": text_postprocessed} + + if ibest_writer is not None: + ibest_writer["token"][key[i]] = " ".join(token) + ibest_writer["text"][key[i]] = text + ibest_writer["text_postprocessed"][key[i]] = text_postprocessed + else: + result_i = {"key": key[i], "token_int": token_int} + results.append(result_i) + + return results, meta_data + + def generate_hotwords_list( + self, hotword_list_or_file, tokenizer=None, frontend=None + ): + def load_seg_dict(seg_dict_file): + seg_dict = {} + assert isinstance(seg_dict_file, str) + with open(seg_dict_file, "r", encoding="utf8") as f: + lines = f.readlines() + for line in lines: + s = line.strip().split() + key = s[0] + value = s[1:] + seg_dict[key] = " ".join(value) + return seg_dict + + def seg_tokenize(txt, seg_dict): + pattern = re.compile(r"^[\u4E00-\u9FA50-9]+$") + out_txt = "" + for word in txt: + word = word.lower() + if word in seg_dict: + out_txt += seg_dict[word] + " " + else: + if pattern.match(word): + for char in word: + if char in seg_dict: + out_txt += seg_dict[char] + " " + else: + out_txt += "" + " " + else: + out_txt += "" + " " + return out_txt.strip().split() + + seg_dict = None + if frontend.cmvn_file is not None: + model_dir = os.path.dirname(frontend.cmvn_file) + seg_dict_file = os.path.join(model_dir, "seg_dict") + if os.path.exists(seg_dict_file): + seg_dict = load_seg_dict(seg_dict_file) + else: + seg_dict = None + # for None + if hotword_list_or_file is None: + hotword_list = None + # for local txt inputs + elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith( + ".txt" + ): + logging.info("Attempting to parse hotwords from local txt...") + hotword_list = [] + hotword_str_list = [] + with codecs.open(hotword_list_or_file, "r") as fin: + for line in fin.readlines(): + hw = line.strip() + hw_list = hw.split() + if seg_dict is not None: + hw_list = seg_tokenize(hw_list, seg_dict) + hotword_str_list.append(hw) + hotword_list.append(tokenizer.tokens2ids(hw_list)) + hotword_list.append([self.sos]) + hotword_str_list.append("") + logging.info( + "Initialized hotword list from file: {}, hotword list: {}.".format( + hotword_list_or_file, hotword_str_list + ) + ) + # for url, download and generate txt + elif hotword_list_or_file.startswith("http"): + logging.info("Attempting to parse hotwords from url...") + work_dir = tempfile.TemporaryDirectory().name + if not os.path.exists(work_dir): + os.makedirs(work_dir) + text_file_path = os.path.join( + work_dir, os.path.basename(hotword_list_or_file) + ) + local_file = requests.get(hotword_list_or_file) + open(text_file_path, "wb").write(local_file.content) + hotword_list_or_file = text_file_path + hotword_list = [] + hotword_str_list = [] + with codecs.open(hotword_list_or_file, "r") as fin: + for line in fin.readlines(): + hw = line.strip() + hw_list = hw.split() + if seg_dict is not None: + hw_list = seg_tokenize(hw_list, seg_dict) + hotword_str_list.append(hw) + hotword_list.append(tokenizer.tokens2ids(hw_list)) + hotword_list.append([self.sos]) + hotword_str_list.append("") + logging.info( + "Initialized hotword list from file: {}, hotword list: {}.".format( + hotword_list_or_file, hotword_str_list + ) + ) + # for text str input + elif not hotword_list_or_file.endswith(".txt"): + logging.info("Attempting to parse hotwords as str...") + hotword_list = [] + hotword_str_list = [] + for hw in hotword_list_or_file.strip().split(): + hotword_str_list.append(hw) + hw_list = hw.strip().split() + if seg_dict is not None: + hw_list = seg_tokenize(hw_list, seg_dict) + hotword_list.append(tokenizer.tokens2ids(hw_list)) + hotword_list.append([self.sos]) + hotword_str_list.append("") + logging.info("Hotword list: {}.".format(hotword_str_list)) + else: + hotword_list = None + return hotword_list diff --git a/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4490b6df8c9c11b380d8ec4ad88ec6a06512ced5 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/contextual_paraformer/template.yaml @@ -0,0 +1,129 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: ContextualParaformer +model_conf: + ctc_weight: 0.0 + lsm_weight: 0.1 + length_normalized_loss: true + predictor_weight: 1.0 + predictor_bias: 1 + sampling_ratio: 0.75 + inner_dim: 512 + +# encoder +encoder: SANMEncoder +encoder_conf: + output_size: 512 + attention_heads: 4 + linear_units: 2048 + num_blocks: 50 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + + +# decoder +decoder: ContextualParaformerDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 16 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + att_layer_num: 16 + kernel_size: 11 + sanm_shfit: 0 + +predictor: CifPredictorV2 +predictor_conf: + idim: 512 + threshold: 1.0 + l_order: 1 + r_order: 1 + tail_threshold: 0.45 + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 7 + lfr_n: 6 + +specaug: SpecAugLFR +specaug_conf: + apply_time_warp: false + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + lfr_rate: 6 + num_freq_mask: 1 + apply_time_mask: true + time_mask_width_range: + - 0 + - 12 + num_time_mask: 1 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + val_scheduler_criterion: + - valid + - acc + best_model_criterion: + - - valid + - acc + - max + keep_nbest_models: 10 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0005 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null \ No newline at end of file diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..36030ac2b694e034cc683699de44c0a6883def85 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/model.py @@ -0,0 +1,425 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +import numpy as np +import torch.nn.functional as F +from contextlib import contextmanager +from distutils.version import LooseVersion +from typing import Any, List, Tuple, Optional + +from funasr_detach.register import tables +from funasr_detach.train_utils.device_funcs import to_device +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.utils.load_utils import load_audio_text_image_video +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.ct_transformer.utils import ( + split_to_mini_sentence, + split_words, +) + +import jieba as jieba + +load_jieba = False + + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "CTTransformer") +class CTTransformer(torch.nn.Module): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection + https://arxiv.org/pdf/2003.01309.pdf + """ + + def __init__( + self, + encoder: str = None, + encoder_conf: dict = None, + vocab_size: int = -1, + punc_list: list = None, + punc_weight: list = None, + embed_unit: int = 128, + att_unit: int = 256, + dropout_rate: float = 0.5, + ignore_id: int = -1, + sos: int = 1, + eos: int = 2, + sentence_end_id: int = 3, + **kwargs, + ): + super().__init__() + + punc_size = len(punc_list) + if punc_weight is None: + punc_weight = [1] * punc_size + + self.embed = torch.nn.Embedding(vocab_size, embed_unit) + encoder_class = tables.encoder_classes.get(encoder) + encoder = encoder_class(**encoder_conf) + + self.decoder = torch.nn.Linear(att_unit, punc_size) + self.encoder = encoder + self.punc_list = punc_list + self.punc_weight = punc_weight + self.ignore_id = ignore_id + self.sos = sos + self.eos = eos + self.sentence_end_id = sentence_end_id + + def punc_forward(self, text: torch.Tensor, text_lengths: torch.Tensor, **kwargs): + """Compute loss value from buffer sequences. + + Args: + input (torch.Tensor): Input ids. (batch, len) + hidden (torch.Tensor): Target ids. (batch, len) + + """ + x = self.embed(text) + # mask = self._target_mask(input) + h, _, _ = self.encoder(x, text_lengths) + y = self.decoder(h) + return y, None + + def with_vad(self): + return False + + def score( + self, y: torch.Tensor, state: Any, x: torch.Tensor + ) -> Tuple[torch.Tensor, Any]: + """Score new token. + + Args: + y (torch.Tensor): 1D torch.int64 prefix tokens. + state: Scorer state for prefix tokens + x (torch.Tensor): encoder feature that generates ys. + + Returns: + tuple[torch.Tensor, Any]: Tuple of + torch.float32 scores for next token (vocab_size) + and next state for ys + + """ + y = y.unsqueeze(0) + h, _, cache = self.encoder.forward_one_step( + self.embed(y), self._target_mask(y), cache=state + ) + h = self.decoder(h[:, -1]) + logp = h.log_softmax(dim=-1).squeeze(0) + return logp, cache + + def batch_score( + self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor + ) -> Tuple[torch.Tensor, List[Any]]: + """Score new token batch. + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, List[Any]]: Tuple of + batchfied scores for next token with shape of `(n_batch, vocab_size)` + and next state list for ys. + + """ + # merge states + n_batch = len(ys) + n_layers = len(self.encoder.encoders) + if states[0] is None: + batch_state = None + else: + # transpose state of [batch, layer] into [layer, batch] + batch_state = [ + torch.stack([states[b][i] for b in range(n_batch)]) + for i in range(n_layers) + ] + + # batch decoding + h, _, states = self.encoder.forward_one_step( + self.embed(ys), self._target_mask(ys), cache=batch_state + ) + h = self.decoder(h[:, -1]) + logp = h.log_softmax(dim=-1) + + # transpose state of [layer, batch] into [batch, layer] + state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)] + return logp, state_list + + def nll( + self, + text: torch.Tensor, + punc: torch.Tensor, + text_lengths: torch.Tensor, + punc_lengths: torch.Tensor, + max_length: Optional[int] = None, + vad_indexes: Optional[torch.Tensor] = None, + vad_indexes_lengths: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute negative log likelihood(nll) + + Normally, this function is called in batchify_nll. + Args: + text: (Batch, Length) + punc: (Batch, Length) + text_lengths: (Batch,) + max_lengths: int + """ + batch_size = text.size(0) + # For data parallel + if max_length is None: + text = text[:, : text_lengths.max()] + punc = punc[:, : text_lengths.max()] + else: + text = text[:, :max_length] + punc = punc[:, :max_length] + + if self.with_vad(): + # Should be VadRealtimeTransformer + assert vad_indexes is not None + y, _ = self.punc_forward(text, text_lengths, vad_indexes) + else: + # Should be TargetDelayTransformer, + y, _ = self.punc_forward(text, text_lengths) + + # Calc negative log likelihood + # nll: (BxL,) + if self.training == False: + _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1) + from sklearn.metrics import f1_score + + f1_score = f1_score( + punc.view(-1).detach().cpu().numpy(), + indices.squeeze(-1).detach().cpu().numpy(), + average="micro", + ) + nll = torch.Tensor([f1_score]).repeat(text_lengths.sum()) + return nll, text_lengths + else: + self.punc_weight = self.punc_weight.to(punc.device) + nll = F.cross_entropy( + y.view(-1, y.shape[-1]), + punc.view(-1), + self.punc_weight, + reduction="none", + ignore_index=self.ignore_id, + ) + # nll: (BxL,) -> (BxL,) + if max_length is None: + nll.masked_fill_(make_pad_mask(text_lengths).to(nll.device).view(-1), 0.0) + else: + nll.masked_fill_( + make_pad_mask(text_lengths, maxlen=max_length + 1) + .to(nll.device) + .view(-1), + 0.0, + ) + # nll: (BxL,) -> (B, L) + nll = nll.view(batch_size, -1) + return nll, text_lengths + + def forward( + self, + text: torch.Tensor, + punc: torch.Tensor, + text_lengths: torch.Tensor, + punc_lengths: torch.Tensor, + vad_indexes: Optional[torch.Tensor] = None, + vad_indexes_lengths: Optional[torch.Tensor] = None, + ): + nll, y_lengths = self.nll( + text, punc, text_lengths, punc_lengths, vad_indexes=vad_indexes + ) + ntokens = y_lengths.sum() + loss = nll.sum() / ntokens + stats = dict(loss=loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + loss, stats, weight = force_gatherable((loss, stats, ntokens), loss.device) + return loss, stats, weight + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + assert len(data_in) == 1 + text = load_audio_text_image_video( + data_in, data_type=kwargs.get("kwargs", "text") + )[0] + vad_indexes = kwargs.get("vad_indexes", None) + # text = data_in[0] + # text_lengths = data_lengths[0] if data_lengths is not None else None + split_size = kwargs.get("split_size", 20) + + jieba_usr_dict = kwargs.get("jieba_usr_dict", None) + global load_jieba + if load_jieba: + jieba_usr_dict = jieba + kwargs["jieba_usr_dict"] = "jieba_usr_dict" + else: + if jieba_usr_dict and isinstance(jieba_usr_dict, str): + # import jieba + jieba.load_userdict(jieba_usr_dict) + jieba_usr_dict = jieba + kwargs["jieba_usr_dict"] = "jieba_usr_dict" + load_jieba = True + tokens = split_words(text, jieba_usr_dict=jieba_usr_dict) + tokens_int = tokenizer.encode(tokens) + + mini_sentences = split_to_mini_sentence(tokens, split_size) + mini_sentences_id = split_to_mini_sentence(tokens_int, split_size) + assert len(mini_sentences) == len(mini_sentences_id) + cache_sent = [] + cache_sent_id = torch.from_numpy(np.array([], dtype="int32")) + new_mini_sentence = "" + new_mini_sentence_punc = [] + cache_pop_trigger_limit = 200 + results = [] + meta_data = {} + punc_array = None + for mini_sentence_i in range(len(mini_sentences)): + mini_sentence = mini_sentences[mini_sentence_i] + mini_sentence_id = mini_sentences_id[mini_sentence_i] + mini_sentence = cache_sent + mini_sentence + mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0) + data = { + "text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0), + "text_lengths": torch.from_numpy( + np.array([len(mini_sentence_id)], dtype="int32") + ), + } + data = to_device(data, kwargs["device"]) + # y, _ = self.wrapped_model(**data) + y, _ = self.punc_forward(**data) + _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1) + punctuations = indices + if indices.size()[0] != 1: + punctuations = torch.squeeze(indices) + assert punctuations.size()[0] == len(mini_sentence) + + # Search for the last Period/QuestionMark as cache + if mini_sentence_i < len(mini_sentences) - 1: + sentenceEnd = -1 + last_comma_index = -1 + for i in range(len(punctuations) - 2, 1, -1): + if ( + self.punc_list[punctuations[i]] == "。" + or self.punc_list[punctuations[i]] == "？" + ): + sentenceEnd = i + break + if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，": + last_comma_index = i + + if ( + sentenceEnd < 0 + and len(mini_sentence) > cache_pop_trigger_limit + and last_comma_index >= 0 + ): + # The sentence it too long, cut off at a comma. + sentenceEnd = last_comma_index + punctuations[sentenceEnd] = self.sentence_end_id + cache_sent = mini_sentence[sentenceEnd + 1 :] + cache_sent_id = mini_sentence_id[sentenceEnd + 1 :] + mini_sentence = mini_sentence[0 : sentenceEnd + 1] + punctuations = punctuations[0 : sentenceEnd + 1] + + # if len(punctuations) == 0: + # continue + + punctuations_np = punctuations.cpu().numpy() + new_mini_sentence_punc += [int(x) for x in punctuations_np] + words_with_punc = [] + for i in range(len(mini_sentence)): + if ( + i == 0 + or self.punc_list[punctuations[i - 1]] == "。" + or self.punc_list[punctuations[i - 1]] == "？" + ) and len(mini_sentence[i][0].encode()) == 1: + mini_sentence[i] = mini_sentence[i].capitalize() + if i == 0: + if len(mini_sentence[i][0].encode()) == 1: + mini_sentence[i] = " " + mini_sentence[i] + if i > 0: + if ( + len(mini_sentence[i][0].encode()) == 1 + and len(mini_sentence[i - 1][0].encode()) == 1 + ): + mini_sentence[i] = " " + mini_sentence[i] + words_with_punc.append(mini_sentence[i]) + if self.punc_list[punctuations[i]] != "_": + punc_res = self.punc_list[punctuations[i]] + if len(mini_sentence[i][0].encode()) == 1: + if punc_res == "，": + punc_res = "," + elif punc_res == "。": + punc_res = "." + elif punc_res == "？": + punc_res = "?" + words_with_punc.append(punc_res) + new_mini_sentence += "".join(words_with_punc) + # Add Period for the end of the sentence + new_mini_sentence_out = new_mini_sentence + new_mini_sentence_punc_out = new_mini_sentence_punc + if mini_sentence_i == len(mini_sentences) - 1: + if new_mini_sentence[-1] == "，" or new_mini_sentence[-1] == "、": + new_mini_sentence_out = new_mini_sentence[:-1] + "。" + new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [ + self.sentence_end_id + ] + elif new_mini_sentence[-1] == ",": + new_mini_sentence_out = new_mini_sentence[:-1] + "." + new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [ + self.sentence_end_id + ] + elif ( + new_mini_sentence[-1] != "。" + and new_mini_sentence[-1] != "？" + and len(new_mini_sentence[-1].encode()) != 1 + ): + new_mini_sentence_out = new_mini_sentence + "。" + new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [ + self.sentence_end_id + ] + if len(punctuations): + punctuations[-1] = 2 + elif ( + new_mini_sentence[-1] != "." + and new_mini_sentence[-1] != "?" + and len(new_mini_sentence[-1].encode()) == 1 + ): + new_mini_sentence_out = new_mini_sentence + "." + new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [ + self.sentence_end_id + ] + if len(punctuations): + punctuations[-1] = 2 + # keep a punctuations array for punc segment + if punc_array is None: + punc_array = punctuations + else: + punc_array = torch.cat([punc_array, punctuations], dim=0) + result_i = { + "key": key[0], + "text": new_mini_sentence_out, + "punc_array": punc_array, + } + results.append(result_i) + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f7a47a3e9ee4b018171d3fdeacc3da8fd738c04a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/template.yaml @@ -0,0 +1,53 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +model: CTTransformer +model_conf: + ignore_id: 0 + embed_unit: 256 + att_unit: 256 + dropout_rate: 0.1 + punc_list: + - + - _ + - ',' + - 。 + - '?' + - 、 + punc_weight: + - 1.0 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + sentence_end_id: 3 + +encoder: SANMEncoder +encoder_conf: + input_size: 256 + output_size: 256 + attention_heads: 8 + linear_units: 1024 + num_blocks: 4 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.0 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + padding_idx: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + + + diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/utils.py b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..66e9950e4968a6a9a2805d1b023e8f4e4529a936 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer/utils.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import re + + +def split_to_mini_sentence(words: list, word_limit: int = 20): + assert word_limit > 1 + if len(words) <= word_limit: + return [words] + sentences = [] + length = len(words) + sentence_len = length // word_limit + for i in range(sentence_len): + sentences.append(words[i * word_limit : (i + 1) * word_limit]) + if length % word_limit > 0: + sentences.append(words[sentence_len * word_limit :]) + return sentences + + +def split_words(text: str, jieba_usr_dict=None, **kwargs): + if jieba_usr_dict: + input_list = text.split() + token_list_all = [] + langauge_list = [] + token_list_tmp = [] + language_flag = None + for token in input_list: + if isEnglish(token) and language_flag == "Chinese": + token_list_all.append(token_list_tmp) + langauge_list.append("Chinese") + token_list_tmp = [] + elif not isEnglish(token) and language_flag == "English": + token_list_all.append(token_list_tmp) + langauge_list.append("English") + token_list_tmp = [] + + token_list_tmp.append(token) + + if isEnglish(token): + language_flag = "English" + else: + language_flag = "Chinese" + + if token_list_tmp: + token_list_all.append(token_list_tmp) + langauge_list.append(language_flag) + + result_list = [] + for token_list_tmp, language_flag in zip(token_list_all, langauge_list): + if language_flag == "English": + result_list.extend(token_list_tmp) + else: + seg_list = jieba_usr_dict.cut( + join_chinese_and_english(token_list_tmp), HMM=False + ) + result_list.extend(seg_list) + + return result_list + + else: + words = [] + segs = text.split() + for seg in segs: + # There is no space in seg. + current_word = "" + for c in seg: + if len(c.encode()) == 1: + # This is an ASCII char. + current_word += c + else: + # This is a Chinese char. + if len(current_word) > 0: + words.append(current_word) + current_word = "" + words.append(c) + if len(current_word) > 0: + words.append(current_word) + return words + + +def isEnglish(text: str): + if re.search("^[a-zA-Z']+$", text): + return True + else: + return False + + +def join_chinese_and_english(input_list): + line = "" + for token in input_list: + if isEnglish(token): + line = line + " " + token + else: + line = line + token + + line = line.strip() + return line diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/attention.py b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..37ce3c372de70d8cc7477139ae5937d1f5edfdd9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/attention.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +from funasr_detach.models.sanm.attention import MultiHeadedAttentionSANM + + +class MultiHeadedAttentionSANMwithMask(MultiHeadedAttentionSANM): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None): + q_h, k_h, v_h, v = self.forward_qkv(x) + fsmn_memory = self.forward_fsmn(v, mask[0], mask_shfit_chunk) + q_h = q_h * self.d_k ** (-0.5) + scores = torch.matmul(q_h, k_h.transpose(-2, -1)) + att_outs = self.forward_attention(v_h, scores, mask[1], mask_att_chunk_encoder) + return att_outs + fsmn_memory diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..86e8cccf013657d1652a6d14ab7c5faac6911ff4 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/encoder.py @@ -0,0 +1,410 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +from typing import List, Optional, Tuple + +from funasr_detach.register import tables +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.sanm.attention import MultiHeadedAttention +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.utils.subsampling import check_short_utt +from funasr_detach.models.transformer.utils.subsampling import TooShortUttError +from funasr_detach.models.transformer.embedding import SinusoidalPositionEncoder +from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear +from funasr_detach.models.transformer.utils.mask import subsequent_mask, vad_mask +from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, +) +from funasr_detach.models.ct_transformer_streaming.attention import ( + MultiHeadedAttentionSANMwithMask, +) +from funasr_detach.models.transformer.utils.subsampling import ( + Conv2dSubsampling, + Conv2dSubsampling2, + Conv2dSubsampling6, + Conv2dSubsampling8, +) + + +class EncoderLayerSANM(torch.nn.Module): + def __init__( + self, + in_size, + size, + self_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + stochastic_depth_rate=0.0, + ): + """Construct an EncoderLayer object.""" + super(EncoderLayerSANM, self).__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(in_size) + self.norm2 = LayerNorm(size) + self.dropout = torch.nn.Dropout(dropout_rate) + self.in_size = in_size + self.size = size + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear = torch.nn.Linear(size + size, size) + self.stochastic_depth_rate = stochastic_depth_rate + self.dropout_rate = dropout_rate + + def forward( + self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None + ): + """Compute encoded features. + + Args: + x_input (torch.Tensor): Input tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + skip_layer = False + # with stochastic depth, residual connection `x + f(x)` becomes + # `x <- x + 1 / (1 - p) * f(x)` at training time. + stoch_layer_coeff = 1.0 + if self.training and self.stochastic_depth_rate > 0: + skip_layer = torch.rand(1).item() < self.stochastic_depth_rate + stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate) + + if skip_layer: + if cache is not None: + x = torch.cat([cache, x], dim=1) + return x, mask + + residual = x + if self.normalize_before: + x = self.norm1(x) + + if self.concat_after: + x_concat = torch.cat( + ( + x, + self.self_attn( + x, + mask, + mask_shfit_chunk=mask_shfit_chunk, + mask_att_chunk_encoder=mask_att_chunk_encoder, + ), + ), + dim=-1, + ) + if self.in_size == self.size: + x = residual + stoch_layer_coeff * self.concat_linear(x_concat) + else: + x = stoch_layer_coeff * self.concat_linear(x_concat) + else: + if self.in_size == self.size: + x = residual + stoch_layer_coeff * self.dropout( + self.self_attn( + x, + mask, + mask_shfit_chunk=mask_shfit_chunk, + mask_att_chunk_encoder=mask_att_chunk_encoder, + ) + ) + else: + x = stoch_layer_coeff * self.dropout( + self.self_attn( + x, + mask, + mask_shfit_chunk=mask_shfit_chunk, + mask_att_chunk_encoder=mask_att_chunk_encoder, + ) + ) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm2(x) + + return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder + + def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0): + """Compute encoded features. + + Args: + x_input (torch.Tensor): Input tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + + residual = x + if self.normalize_before: + x = self.norm1(x) + + if self.in_size == self.size: + attn, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back) + x = residual + attn + else: + x, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back) + + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + self.feed_forward(x) + if not self.normalize_before: + x = self.norm2(x) + + return x, cache + + +@tables.register("encoder_classes", "SANMVadEncoder") +class SANMVadEncoder(torch.nn.Module): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + + """ + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: Optional[str] = "conv2d", + pos_enc_class=SinusoidalPositionEncoder, + normalize_before: bool = True, + concat_after: bool = False, + positionwise_layer_type: str = "linear", + positionwise_conv_kernel_size: int = 1, + padding_idx: int = -1, + interctc_layer_idx: List[int] = [], + interctc_use_conditioning: bool = False, + kernel_size: int = 11, + sanm_shfit: int = 0, + selfattention_layer_type: str = "sanm", + ): + super().__init__() + self._output_size = output_size + + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(input_size, output_size), + torch.nn.LayerNorm(output_size), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate) + elif input_layer == "conv2d2": + self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate) + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate) + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate) + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), + SinusoidalPositionEncoder(), + ) + elif input_layer is None: + if input_size == output_size: + self.embed = None + else: + self.embed = torch.nn.Linear(input_size, output_size) + elif input_layer == "pe": + self.embed = SinusoidalPositionEncoder() + else: + raise ValueError("unknown input_layer: " + input_layer) + self.normalize_before = normalize_before + if positionwise_layer_type == "linear": + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d": + positionwise_layer = MultiLayeredConv1d + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d-linear": + positionwise_layer = Conv1dLinear + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + else: + raise NotImplementedError("Support only linear or conv1d.") + + if selfattention_layer_type == "selfattn": + encoder_selfattn_layer = MultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + + elif selfattention_layer_type == "sanm": + self.encoder_selfattn_layer = MultiHeadedAttentionSANMwithMask + encoder_selfattn_layer_args0 = ( + attention_heads, + input_size, + output_size, + attention_dropout_rate, + kernel_size, + sanm_shfit, + ) + + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + output_size, + attention_dropout_rate, + kernel_size, + sanm_shfit, + ) + + self.encoders0 = repeat( + 1, + lambda lnum: EncoderLayerSANM( + input_size, + output_size, + self.encoder_selfattn_layer(*encoder_selfattn_layer_args0), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + self.encoders = repeat( + num_blocks - 1, + lambda lnum: EncoderLayerSANM( + output_size, + output_size, + self.encoder_selfattn_layer(*encoder_selfattn_layer_args), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + ), + ) + if self.normalize_before: + self.after_norm = LayerNorm(output_size) + + self.interctc_layer_idx = interctc_layer_idx + if len(interctc_layer_idx) > 0: + assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks + self.interctc_use_conditioning = interctc_use_conditioning + self.conditioning_layer = None + self.dropout = torch.nn.Dropout(dropout_rate) + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + vad_indexes: torch.Tensor, + prev_states: torch.Tensor = None, + ctc: CTC = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Embed positions in tensor. + + Args: + xs_pad: input tensor (B, L, D) + ilens: input length (B) + prev_states: Not to be used now. + Returns: + position embedded tensor and mask + """ + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + sub_masks = subsequent_mask(masks.size(-1), device=xs_pad.device).unsqueeze(0) + no_future_masks = masks & sub_masks + xs_pad *= self.output_size() ** 0.5 + if self.embed is None: + xs_pad = xs_pad + elif ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling2) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + else: + xs_pad = self.embed(xs_pad) + + # xs_pad = self.dropout(xs_pad) + mask_tup0 = [masks, no_future_masks] + encoder_outs = self.encoders0(xs_pad, mask_tup0) + xs_pad, _ = encoder_outs[0], encoder_outs[1] + intermediate_outs = [] + + for layer_idx, encoder_layer in enumerate(self.encoders): + if layer_idx + 1 == len(self.encoders): + # This is last layer. + coner_mask = torch.ones( + masks.size(0), + masks.size(-1), + masks.size(-1), + device=xs_pad.device, + dtype=torch.bool, + ) + for word_index, length in enumerate(ilens): + coner_mask[word_index, :, :] = vad_mask( + masks.size(-1), vad_indexes[word_index], device=xs_pad.device + ) + layer_mask = masks & coner_mask + else: + layer_mask = no_future_masks + mask_tup1 = [masks, layer_mask] + encoder_outs = encoder_layer(xs_pad, mask_tup1) + xs_pad, layer_mask = encoder_outs[0], encoder_outs[1] + + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + + olens = masks.squeeze(1).sum(1) + if len(intermediate_outs) > 0: + return (xs_pad, intermediate_outs), olens, None + return xs_pad, olens, None diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/model.py b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/model.py new file mode 100644 index 0000000000000000000000000000000000000000..f0a22a478ca7a2ec92b3b2d03032f622400fcbf4 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/model.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +import numpy as np +from contextlib import contextmanager +from distutils.version import LooseVersion + +from funasr_detach.register import tables +from funasr_detach.train_utils.device_funcs import to_device +from funasr_detach.models.ct_transformer.model import CTTransformer +from funasr_detach.utils.load_utils import load_audio_text_image_video +from funasr_detach.models.ct_transformer.utils import ( + split_to_mini_sentence, + split_words, +) + + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "CTTransformerStreaming") +class CTTransformerStreaming(CTTransformer): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection + https://arxiv.org/pdf/2003.01309.pdf + """ + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + + def punc_forward( + self, + text: torch.Tensor, + text_lengths: torch.Tensor, + vad_indexes: torch.Tensor, + **kwargs, + ): + """Compute loss value from buffer sequences. + + Args: + input (torch.Tensor): Input ids. (batch, len) + hidden (torch.Tensor): Target ids. (batch, len) + + """ + x = self.embed(text) + # mask = self._target_mask(input) + h, _, _ = self.encoder(x, text_lengths, vad_indexes=vad_indexes) + y = self.decoder(h) + return y, None + + def with_vad(self): + return True + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + cache: dict = {}, + **kwargs, + ): + assert len(data_in) == 1 + + if len(cache) == 0: + cache["pre_text"] = [] + text = load_audio_text_image_video( + data_in, data_type=kwargs.get("kwargs", "text") + )[0] + text = "".join(cache["pre_text"]) + " " + text + + split_size = kwargs.get("split_size", 20) + + tokens = split_words(text) + tokens_int = tokenizer.encode(tokens) + + mini_sentences = split_to_mini_sentence(tokens, split_size) + mini_sentences_id = split_to_mini_sentence(tokens_int, split_size) + assert len(mini_sentences) == len(mini_sentences_id) + cache_sent = [] + cache_sent_id = torch.from_numpy(np.array([], dtype="int32")) + skip_num = 0 + sentence_punc_list = [] + sentence_words_list = [] + cache_pop_trigger_limit = 200 + results = [] + meta_data = {} + punc_array = None + for mini_sentence_i in range(len(mini_sentences)): + mini_sentence = mini_sentences[mini_sentence_i] + mini_sentence_id = mini_sentences_id[mini_sentence_i] + mini_sentence = cache_sent + mini_sentence + mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0) + data = { + "text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0), + "text_lengths": torch.from_numpy( + np.array([len(mini_sentence_id)], dtype="int32") + ), + "vad_indexes": torch.from_numpy( + np.array([len(cache["pre_text"])], dtype="int32") + ), + } + data = to_device(data, kwargs["device"]) + # y, _ = self.wrapped_model(**data) + y, _ = self.punc_forward(**data) + _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1) + punctuations = indices + if indices.size()[0] != 1: + punctuations = torch.squeeze(indices) + assert punctuations.size()[0] == len(mini_sentence) + + # Search for the last Period/QuestionMark as cache + if mini_sentence_i < len(mini_sentences) - 1: + sentenceEnd = -1 + last_comma_index = -1 + for i in range(len(punctuations) - 2, 1, -1): + if ( + self.punc_list[punctuations[i]] == "。" + or self.punc_list[punctuations[i]] == "？" + ): + sentenceEnd = i + break + if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，": + last_comma_index = i + + if ( + sentenceEnd < 0 + and len(mini_sentence) > cache_pop_trigger_limit + and last_comma_index >= 0 + ): + # The sentence it too long, cut off at a comma. + sentenceEnd = last_comma_index + punctuations[sentenceEnd] = self.sentence_end_id + cache_sent = mini_sentence[sentenceEnd + 1 :] + cache_sent_id = mini_sentence_id[sentenceEnd + 1 :] + mini_sentence = mini_sentence[0 : sentenceEnd + 1] + punctuations = punctuations[0 : sentenceEnd + 1] + + # if len(punctuations) == 0: + # continue + + punctuations_np = punctuations.cpu().numpy() + sentence_punc_list += [self.punc_list[int(x)] for x in punctuations_np] + sentence_words_list += mini_sentence + + assert len(sentence_punc_list) == len(sentence_words_list) + words_with_punc = [] + sentence_punc_list_out = [] + for i in range(0, len(sentence_words_list)): + if i > 0: + if ( + len(sentence_words_list[i][0].encode()) == 1 + and len(sentence_words_list[i - 1][-1].encode()) == 1 + ): + sentence_words_list[i] = " " + sentence_words_list[i] + if skip_num < len(cache["pre_text"]): + skip_num += 1 + else: + words_with_punc.append(sentence_words_list[i]) + if skip_num >= len(cache["pre_text"]): + sentence_punc_list_out.append(sentence_punc_list[i]) + if sentence_punc_list[i] != "_": + words_with_punc.append(sentence_punc_list[i]) + sentence_out = "".join(words_with_punc) + + sentenceEnd = -1 + for i in range(len(sentence_punc_list) - 2, 1, -1): + if sentence_punc_list[i] == "。" or sentence_punc_list[i] == "？": + sentenceEnd = i + break + cache["pre_text"] = sentence_words_list[sentenceEnd + 1 :] + if sentence_out[-1] in self.punc_list: + sentence_out = sentence_out[:-1] + sentence_punc_list_out[-1] = "_" + # keep a punctuations array for punc segment + if punc_array is None: + punc_array = punctuations + else: + punc_array = torch.cat([punc_array, punctuations], dim=0) + + result_i = {"key": key[0], "text": sentence_out, "punc_array": punc_array} + results.append(result_i) + + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b2126983c4838b0fb5434b315bc8e2715f819ffa --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/ct_transformer_streaming/template.yaml @@ -0,0 +1,50 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +model: CTTransformerStreaming +model_conf: + ignore_id: 0 + embed_unit: 256 + att_unit: 256 + dropout_rate: 0.1 + punc_list: + - + - _ + - ， + - 。 + - ？ + - 、 + punc_weight: + - 1.0 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + - 1.0 + sentence_end_id: 3 + +encoder: SANMVadEncoder +encoder_conf: + input_size: 256 + output_size: 256 + attention_heads: 8 + linear_units: 1024 + num_blocks: 3 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.0 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 5 + selfattention_layer_type: sanm + padding_idx: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: \ No newline at end of file diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ctc/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/ctc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/ctc/ctc.py b/demo/Step-Audio-EditX/funasr_detach/models/ctc/ctc.py new file mode 100644 index 0000000000000000000000000000000000000000..615727fe1b3a31649b2dcaee450f7ec87cf3a7a9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/ctc/ctc.py @@ -0,0 +1,180 @@ +import logging + +import torch +import torch.nn.functional as F + + +class CTC(torch.nn.Module): + """CTC module. + + Args: + odim: dimension of outputs + encoder_output_size: number of encoder projection units + dropout_rate: dropout rate (0.0 ~ 1.0) + ctc_type: builtin or warpctc + reduce: reduce the CTC loss into a scalar + """ + + def __init__( + self, + odim: int, + encoder_output_size: int, + dropout_rate: float = 0.0, + ctc_type: str = "builtin", + reduce: bool = True, + ignore_nan_grad: bool = True, + ): + super().__init__() + eprojs = encoder_output_size + self.dropout_rate = dropout_rate + self.ctc_lo = torch.nn.Linear(eprojs, odim) + self.ctc_type = ctc_type + self.ignore_nan_grad = ignore_nan_grad + + if self.ctc_type == "builtin": + self.ctc_loss = torch.nn.CTCLoss(reduction="none") + elif self.ctc_type == "warpctc": + import warpctc_pytorch as warp_ctc + + if ignore_nan_grad: + logging.warning("ignore_nan_grad option is not supported for warp_ctc") + self.ctc_loss = warp_ctc.CTCLoss(size_average=True, reduce=reduce) + else: + raise ValueError( + f'ctc_type must be "builtin" or "warpctc": {self.ctc_type}' + ) + + self.reduce = reduce + + def loss_fn(self, th_pred, th_target, th_ilen, th_olen) -> torch.Tensor: + if self.ctc_type == "builtin": + th_pred = th_pred.log_softmax(2) + loss = self.ctc_loss(th_pred, th_target, th_ilen, th_olen) + + if loss.requires_grad and self.ignore_nan_grad: + # ctc_grad: (L, B, O) + ctc_grad = loss.grad_fn(torch.ones_like(loss)) + ctc_grad = ctc_grad.sum([0, 2]) + indices = torch.isfinite(ctc_grad) + size = indices.long().sum() + if size == 0: + # Return as is + logging.warning( + "All samples in this mini-batch got nan grad." + " Returning nan value instead of CTC loss" + ) + elif size != th_pred.size(1): + logging.warning( + f"{th_pred.size(1) - size}/{th_pred.size(1)}" + " samples got nan grad." + " These were ignored for CTC loss." + ) + + # Create mask for target + target_mask = torch.full( + [th_target.size(0)], + 1, + dtype=torch.bool, + device=th_target.device, + ) + s = 0 + for ind, le in enumerate(th_olen): + if not indices[ind]: + target_mask[s : s + le] = 0 + s += le + + # Calc loss again using maksed data + loss = self.ctc_loss( + th_pred[:, indices, :], + th_target[target_mask], + th_ilen[indices], + th_olen[indices], + ) + else: + size = th_pred.size(1) + + if self.reduce: + # Batch-size average + loss = loss.sum() / size + else: + loss = loss / size + return loss + + elif self.ctc_type == "warpctc": + # warpctc only supports float32 + th_pred = th_pred.to(dtype=torch.float32) + + th_target = th_target.cpu().int() + th_ilen = th_ilen.cpu().int() + th_olen = th_olen.cpu().int() + loss = self.ctc_loss(th_pred, th_target, th_ilen, th_olen) + if self.reduce: + # NOTE: sum() is needed to keep consistency since warpctc + # return as tensor w/ shape (1,) + # but builtin return as tensor w/o shape (scalar). + loss = loss.sum() + return loss + + elif self.ctc_type == "gtnctc": + log_probs = torch.nn.functional.log_softmax(th_pred, dim=2) + return self.ctc_loss(log_probs, th_target, th_ilen, 0, "none") + + else: + raise NotImplementedError + + def forward(self, hs_pad, hlens, ys_pad, ys_lens): + """Calculate CTC loss. + + Args: + hs_pad: batch of padded hidden state sequences (B, Tmax, D) + hlens: batch of lengths of hidden state sequences (B) + ys_pad: batch of padded character id sequence tensor (B, Lmax) + ys_lens: batch of lengths of character sequence (B) + """ + # hs_pad: (B, L, NProj) -> ys_hat: (B, L, Nvocab) + ys_hat = self.ctc_lo(F.dropout(hs_pad, p=self.dropout_rate)) + + if self.ctc_type == "gtnctc": + # gtn expects list form for ys + ys_true = [y[y != -1] for y in ys_pad] # parse padded ys + else: + # ys_hat: (B, L, D) -> (L, B, D) + ys_hat = ys_hat.transpose(0, 1) + # (B, L) -> (BxL,) + ys_true = torch.cat([ys_pad[i, :l] for i, l in enumerate(ys_lens)]) + + loss = self.loss_fn(ys_hat, ys_true, hlens, ys_lens).to( + device=hs_pad.device, dtype=hs_pad.dtype + ) + + return loss + + def softmax(self, hs_pad): + """softmax of frame activations + + Args: + Tensor hs_pad: 3d tensor (B, Tmax, eprojs) + Returns: + torch.Tensor: softmax applied 3d tensor (B, Tmax, odim) + """ + return F.softmax(self.ctc_lo(hs_pad), dim=2) + + def log_softmax(self, hs_pad): + """log_softmax of frame activations + + Args: + Tensor hs_pad: 3d tensor (B, Tmax, eprojs) + Returns: + torch.Tensor: log softmax applied 3d tensor (B, Tmax, odim) + """ + return F.log_softmax(self.ctc_lo(hs_pad), dim=2) + + def argmax(self, hs_pad): + """argmax of frame activations + + Args: + torch.Tensor hs_pad: 3d tensor (B, Tmax, eprojs) + Returns: + torch.Tensor: argmax applied 2d tensor (B, Tmax) + """ + return torch.argmax(self.ctc_lo(hs_pad), dim=2) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/data2vec.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/data2vec.py new file mode 100644 index 0000000000000000000000000000000000000000..2f0af081d4dc7e29d9ed0454509a8ba546a37d1b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/data2vec.py @@ -0,0 +1,160 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from contextlib import contextmanager +from distutils.version import LooseVersion +from typing import Dict +from typing import Optional +from typing import Tuple + +import torch +import torch.nn as nn + +# from funasr_detach.layers.abs_normalize import AbsNormalize +# from funasr_detach.models.base_model import FunASRModel +# from funasr_detach.models.encoder.abs_encoder import AbsEncoder +from funasr_detach.frontends.abs_frontend import AbsFrontend + +# from funasr_detach.models.preencoder.abs_preencoder import AbsPreEncoder +# from funasr_detach.models.specaug.abs_specaug import AbsSpecAug +from funasr_detach.train_utils.device_funcs import force_gatherable + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +class Data2VecPretrainModel(nn.Module): + """Data2Vec Pretrain model""" + + def __init__( + self, + frontend=None, + specaug=None, + normalize=None, + encoder=None, + preencoder=None, + ): + + super().__init__() + + self.frontend = frontend + self.specaug = specaug + self.normalize = normalize + self.preencoder = preencoder + self.encoder = encoder + self.num_updates = 0 + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + """ + # Check that batch_size is unified + assert speech.shape[0] == speech_lengths.shape[0], ( + speech.shape, + speech_lengths.shape, + ) + + self.encoder.set_num_updates(self.num_updates) + + # 1. Encoder + encoder_out = self.encode(speech, speech_lengths) + + losses = encoder_out["losses"] + loss = sum(losses.values()) + sample_size = encoder_out["sample_size"] + loss = loss.sum() / sample_size + + target_var = float(encoder_out["target_var"]) + pred_var = float(encoder_out["pred_var"]) + ema_decay = float(encoder_out["ema_decay"]) + + stats = dict( + loss=torch.clone(loss.detach()), + target_var=target_var, + pred_var=pred_var, + ema_decay=ema_decay, + ) + + loss, stats, weight = force_gatherable((loss, stats, sample_size), loss.device) + return loss, stats, weight + + def collect_feats( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Dict[str, torch.Tensor]: + feats, feats_lengths = self._extract_feats(speech, speech_lengths) + return {"feats": feats, "feats_lengths": feats_lengths} + + def encode( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + ): + """Frontend + Encoder. + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + """ + with autocast(False): + # 1. Extract feats + feats, feats_lengths = self._extract_feats(speech, speech_lengths) + + # 2. Data augmentation + if self.specaug is not None and self.training: + feats, feats_lengths = self.specaug(feats, feats_lengths) + + # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + feats, feats_lengths = self.normalize(feats, feats_lengths) + + # Pre-encoder, e.g. used for raw input data + if self.preencoder is not None: + feats, feats_lengths = self.preencoder(feats, feats_lengths) + + # 4. Forward encoder + if min(speech_lengths) == max( + speech_lengths + ): # for clipping, set speech_lengths as None + speech_lengths = None + encoder_out = self.encoder( + feats, speech_lengths, mask=True, features_only=False + ) + + return encoder_out + + def _extract_feats( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + assert speech_lengths.dim() == 1, speech_lengths.shape + + # for data-parallel + speech = speech[:, : speech_lengths.max()] + + if self.frontend is not None: + # Frontend + # e.g. STFT and Feature extract + # data_loader may send time-domain signal in this case + # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim) + feats, feats_lengths = self.frontend(speech, speech_lengths) + else: + # No frontend and no feature extract + feats, feats_lengths = speech, speech_lengths + return feats, feats_lengths + + def set_num_updates(self, num_updates): + self.num_updates = num_updates + + def get_num_updates(self): + return self.num_updates diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/data2vec_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/data2vec_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..2377c069706cc003f44fdd2b0ebd87214c215b4a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/data2vec_encoder.py @@ -0,0 +1,578 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F + +from funasr_detach.models.data2vec.data_utils import compute_mask_indices +from funasr_detach.models.data2vec.ema_module import EMAModule +from funasr_detach.models.data2vec.grad_multiply import GradMultiply +from funasr_detach.models.data2vec.wav2vec2 import ( + ConvFeatureExtractionModel, + TransformerEncoder, +) +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +def get_annealed_rate(start, end, curr_step, total_steps): + r = end - start + pct_remaining = 1 - curr_step / total_steps + return end - r * pct_remaining + + +class Data2VecEncoder(nn.Module): + def __init__( + self, + # for ConvFeatureExtractionModel + input_size: int = None, + extractor_mode: str = None, + conv_feature_layers: str = "[(512,2,2)] + [(512,2,2)]", + # for Transformer Encoder + ## model architecture + layer_type: str = "transformer", + layer_norm_first: bool = False, + encoder_layers: int = 12, + encoder_embed_dim: int = 768, + encoder_ffn_embed_dim: int = 3072, + encoder_attention_heads: int = 12, + activation_fn: str = "gelu", + ## dropouts + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.0, + encoder_layerdrop: float = 0.0, + dropout_input: float = 0.0, + dropout_features: float = 0.0, + ## grad settings + feature_grad_mult: float = 1.0, + ## masking + mask_prob: float = 0.65, + mask_length: int = 10, + mask_selection: str = "static", + mask_other: int = 0, + no_mask_overlap: bool = False, + mask_min_space: int = 1, + require_same_masks: bool = True, # if set as True, collate_fn should be clipping + mask_dropout: float = 0.0, + ## channel masking + mask_channel_length: int = 10, + mask_channel_prob: float = 0.0, + mask_channel_before: bool = False, + mask_channel_selection: str = "static", + mask_channel_other: int = 0, + no_mask_channel_overlap: bool = False, + mask_channel_min_space: int = 1, + ## positional embeddings + conv_pos: int = 128, + conv_pos_groups: int = 16, + pos_conv_depth: int = 1, + max_positions: int = 100000, + # EMA module + average_top_k_layers: int = 8, + layer_norm_target_layer: bool = False, + instance_norm_target_layer: bool = False, + instance_norm_targets: bool = False, + layer_norm_targets: bool = False, + batch_norm_target_layer: bool = False, + group_norm_target_layer: bool = False, + ema_decay: float = 0.999, + ema_end_decay: float = 0.9999, + ema_anneal_end_step: int = 100000, + ema_transformer_only: bool = True, + ema_layers_only: bool = True, + min_target_var: float = 0.1, + min_pred_var: float = 0.01, + # Loss + loss_beta: float = 0.0, + loss_scale: float = None, + # FP16 optimization + required_seq_len_multiple: int = 2, + ): + super().__init__() + + # ConvFeatureExtractionModel + self.conv_feature_layers = conv_feature_layers + feature_enc_layers = eval(conv_feature_layers) + self.extractor_embed = feature_enc_layers[-1][0] + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + mode=extractor_mode, + in_d=input_size, + ) + + # Transformer Encoder + ## model architecture + self.layer_type = layer_type + self.layer_norm_first = layer_norm_first + self.encoder_layers = encoder_layers + self.encoder_embed_dim = encoder_embed_dim + self.encoder_ffn_embed_dim = encoder_ffn_embed_dim + self.encoder_attention_heads = encoder_attention_heads + self.activation_fn = activation_fn + ## dropout + self.dropout = dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.encoder_layerdrop = encoder_layerdrop + self.dropout_input = dropout_input + self.dropout_features = dropout_features + ## grad settings + self.feature_grad_mult = feature_grad_mult + ## masking + self.mask_prob = mask_prob + self.mask_length = mask_length + self.mask_selection = mask_selection + self.mask_other = mask_other + self.no_mask_overlap = no_mask_overlap + self.mask_min_space = mask_min_space + self.require_same_masks = ( + require_same_masks # if set as True, collate_fn should be clipping + ) + self.mask_dropout = mask_dropout + ## channel masking + self.mask_channel_length = mask_channel_length + self.mask_channel_prob = mask_channel_prob + self.mask_channel_before = mask_channel_before + self.mask_channel_selection = mask_channel_selection + self.mask_channel_other = mask_channel_other + self.no_mask_channel_overlap = no_mask_channel_overlap + self.mask_channel_min_space = mask_channel_min_space + ## positional embeddings + self.conv_pos = conv_pos + self.conv_pos_groups = conv_pos_groups + self.pos_conv_depth = pos_conv_depth + self.max_positions = max_positions + self.mask_emb = nn.Parameter( + torch.FloatTensor(self.encoder_embed_dim).uniform_() + ) + self.encoder = TransformerEncoder( + dropout=self.dropout, + encoder_embed_dim=self.encoder_embed_dim, + required_seq_len_multiple=required_seq_len_multiple, + pos_conv_depth=self.pos_conv_depth, + conv_pos=self.conv_pos, + conv_pos_groups=self.conv_pos_groups, + # transformer layers + layer_type=self.layer_type, + encoder_layers=self.encoder_layers, + encoder_ffn_embed_dim=self.encoder_ffn_embed_dim, + encoder_attention_heads=self.encoder_attention_heads, + attention_dropout=self.attention_dropout, + activation_dropout=self.activation_dropout, + activation_fn=self.activation_fn, + layer_norm_first=self.layer_norm_first, + encoder_layerdrop=self.encoder_layerdrop, + max_positions=self.max_positions, + ) + ## projections and dropouts + self.post_extract_proj = nn.Linear(self.extractor_embed, self.encoder_embed_dim) + self.dropout_input = nn.Dropout(self.dropout_input) + self.dropout_features = nn.Dropout(self.dropout_features) + self.layer_norm = torch.nn.LayerNorm(self.extractor_embed) + self.final_proj = nn.Linear(self.encoder_embed_dim, self.encoder_embed_dim) + + # EMA module + self.average_top_k_layers = average_top_k_layers + self.layer_norm_target_layer = layer_norm_target_layer + self.instance_norm_target_layer = instance_norm_target_layer + self.instance_norm_targets = instance_norm_targets + self.layer_norm_targets = layer_norm_targets + self.batch_norm_target_layer = batch_norm_target_layer + self.group_norm_target_layer = group_norm_target_layer + self.ema_decay = ema_decay + self.ema_end_decay = ema_end_decay + self.ema_anneal_end_step = ema_anneal_end_step + self.ema_transformer_only = ema_transformer_only + self.ema_layers_only = ema_layers_only + self.min_target_var = min_target_var + self.min_pred_var = min_pred_var + self.ema = None + + # Loss + self.loss_beta = loss_beta + self.loss_scale = loss_scale + + # FP16 optimization + self.required_seq_len_multiple = required_seq_len_multiple + + self.num_updates = 0 + + logging.info("Data2VecEncoder settings: {}".format(self.__dict__)) + + def make_ema_teacher(self): + skip_keys = set() + if self.ema_layers_only: + self.ema_transformer_only = True + for k, _ in self.encoder.pos_conv.named_parameters(): + skip_keys.add(f"pos_conv.{k}") + + self.ema = EMAModule( + self.encoder if self.ema_transformer_only else self, + ema_decay=self.ema_decay, + ema_fp32=True, + skip_keys=skip_keys, + ) + + def set_num_updates(self, num_updates): + if self.ema is None and self.final_proj is not None: + logging.info("Making EMA Teacher") + self.make_ema_teacher() + elif self.training and self.ema is not None: + if self.ema_decay != self.ema_end_decay: + if num_updates >= self.ema_anneal_end_step: + decay = self.ema_end_decay + else: + decay = get_annealed_rate( + self.ema_decay, + self.ema_end_decay, + num_updates, + self.ema_anneal_end_step, + ) + self.ema.set_decay(decay) + if self.ema.get_decay() < 1: + self.ema.step(self.encoder if self.ema_transformer_only else self) + + self.num_updates = num_updates + + def apply_mask( + self, + x, + padding_mask, + mask_indices=None, + mask_channel_indices=None, + ): + B, T, C = x.shape + + if self.mask_channel_prob > 0 and self.mask_channel_before: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + if self.mask_prob > 0: + if mask_indices is None: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=1, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + require_same_masks=self.require_same_masks, + mask_dropout=self.mask_dropout, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x[mask_indices] = self.mask_emb + else: + mask_indices = None + + if self.mask_channel_prob > 0 and not self.mask_channel_before: + if mask_channel_indices is None: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + return x, mask_indices + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + return torch.floor( + (input_length - kernel_size).to(torch.float32) / stride + 1 + ) + + conv_cfg_list = eval(self.conv_feature_layers) + + for i in range(len(conv_cfg_list)): + input_lengths = _conv_out_length( + input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2] + ) + + return input_lengths.to(torch.long) + + def forward( + self, + xs_pad, + ilens=None, + mask=False, + features_only=True, + layer=None, + mask_indices=None, + mask_channel_indices=None, + padding_count=None, + ): + # create padding_mask by ilens + if ilens is not None: + padding_mask = make_pad_mask(lengths=ilens).to(xs_pad.device) + else: + padding_mask = None + + features = xs_pad + + if self.feature_grad_mult > 0: + features = self.feature_extractor(features) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.feature_extractor(features) + + features = features.transpose(1, 2) + + features = self.layer_norm(features) + + orig_padding_mask = padding_mask + + if padding_mask is not None: + input_lengths = (1 - padding_mask.long()).sum(-1) + # apply conv formula to get real output_lengths + output_lengths = self._get_feat_extract_output_lengths(input_lengths) + + padding_mask = torch.zeros( + features.shape[:2], dtype=features.dtype, device=features.device + ) + # these two operations makes sure that all values + # before the output lengths indices are attended to + padding_mask[ + ( + torch.arange(padding_mask.shape[0], device=padding_mask.device), + output_lengths - 1, + ) + ] = 1 + padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool() + else: + padding_mask = None + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + pre_encoder_features = None + if self.ema_transformer_only: + pre_encoder_features = features.clone() + + features = self.dropout_input(features) + + if mask: + x, mask_indices = self.apply_mask( + features, + padding_mask, + mask_indices=mask_indices, + mask_channel_indices=mask_channel_indices, + ) + else: + x = features + mask_indices = None + + x, layer_results = self.encoder( + x, + padding_mask=padding_mask, + layer=layer, + ) + + if features_only: + encoder_out_lens = (1 - padding_mask.long()).sum(1) + return x, encoder_out_lens, None + + result = { + "losses": {}, + "padding_mask": padding_mask, + "x": x, + } + + with torch.no_grad(): + self.ema.model.eval() + + if self.ema_transformer_only: + y, layer_results = self.ema.model.extract_features( + pre_encoder_features, + padding_mask=padding_mask, + min_layer=self.encoder_layers - self.average_top_k_layers, + ) + y = { + "x": y, + "padding_mask": padding_mask, + "layer_results": layer_results, + } + else: + y = self.ema.model.extract_features( + source=xs_pad, + padding_mask=orig_padding_mask, + mask=False, + ) + + target_layer_results = [l[2] for l in y["layer_results"]] + + permuted = False + if self.instance_norm_target_layer or self.batch_norm_target_layer: + target_layer_results = [ + tl.permute(1, 2, 0) for tl in target_layer_results # TBC -> BCT + ] + permuted = True + + if self.batch_norm_target_layer: + target_layer_results = [ + F.batch_norm( + tl.float(), running_mean=None, running_var=None, training=True + ) + for tl in target_layer_results + ] + + if self.instance_norm_target_layer: + target_layer_results = [ + F.instance_norm(tl.float()) for tl in target_layer_results + ] + + if permuted: + target_layer_results = [ + tl.transpose(1, 2) for tl in target_layer_results # BCT -> BTC + ] + + if self.group_norm_target_layer: + target_layer_results = [ + F.layer_norm(tl.float(), tl.shape[-2:]) + for tl in target_layer_results + ] + + if self.layer_norm_target_layer: + target_layer_results = [ + F.layer_norm(tl.float(), tl.shape[-1:]) + for tl in target_layer_results + ] + + y = sum(target_layer_results) / len(target_layer_results) + + if self.layer_norm_targets: + y = F.layer_norm(y.float(), y.shape[-1:]) + + if self.instance_norm_targets: + y = F.instance_norm(y.float().transpose(1, 2)).transpose(1, 2) + + if not permuted: + y = y.transpose(0, 1) + + y = y[mask_indices] + + x = x[mask_indices] + x = self.final_proj(x) + + sz = x.size(-1) + + if self.loss_beta == 0: + loss = F.mse_loss(x.float(), y.float(), reduction="none").sum(dim=-1) + else: + loss = F.smooth_l1_loss( + x.float(), y.float(), reduction="none", beta=self.loss_beta + ).sum(dim=-1) + + if self.loss_scale is not None: + scale = self.loss_scale + else: + scale = 1 / math.sqrt(sz) + + result["losses"]["regression"] = loss.sum() * scale + + if "sample_size" not in result: + result["sample_size"] = loss.numel() + + with torch.no_grad(): + result["target_var"] = self.compute_var(y) + result["pred_var"] = self.compute_var(x.float()) + + if self.num_updates > 5000 and result["target_var"] < self.min_target_var: + logging.error( + f"target var is {result['target_var'].item()} < {self.min_target_var}, exiting" + ) + raise Exception( + f"target var is {result['target_var'].item()} < {self.min_target_var}, exiting" + ) + if self.num_updates > 5000 and result["pred_var"] < self.min_pred_var: + logging.error( + f"pred var is {result['pred_var'].item()} < {self.min_pred_var}, exiting" + ) + raise Exception( + f"pred var is {result['pred_var'].item()} < {self.min_pred_var}, exiting" + ) + + if self.ema is not None: + result["ema_decay"] = self.ema.get_decay() * 1000 + + return result + + @staticmethod + def compute_var(y): + y = y.view(-1, y.size(-1)) + if dist.is_initialized(): + zc = torch.tensor(y.size(0)).cuda() + zs = y.sum(dim=0) + zss = (y**2).sum(dim=0) + + dist.all_reduce(zc) + dist.all_reduce(zs) + dist.all_reduce(zss) + + var = zss / (zc - 1) - (zs**2) / (zc * (zc - 1)) + return torch.sqrt(var + 1e-6).mean() + else: + return torch.sqrt(y.var(dim=0) + 1e-6).mean() + + def extract_features(self, xs_pad, ilens, mask=False, layer=None): + res = self.forward( + xs_pad, + ilens, + mask=mask, + features_only=True, + layer=layer, + ) + return res + + def remove_pretraining_modules(self, last_layer=None): + self.final_proj = None + self.ema = None + if last_layer is not None: + self.encoder.layers = nn.ModuleList( + l for i, l in enumerate(self.encoder.layers) if i <= last_layer + ) + + def output_size(self) -> int: + return self.encoder_embed_dim diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/data_utils.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2ff7d9cea12e5580dbbca7188b5417a4b0b35ef1 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/data_utils.py @@ -0,0 +1,147 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from typing import Optional, Tuple + +import numpy as np +import torch + + +def compute_mask_indices( + shape: Tuple[int, int], + padding_mask: Optional[torch.Tensor], + mask_prob: float, + mask_length: int, + mask_type: str = "static", + mask_other: float = 0.0, + min_masks: int = 0, + no_overlap: bool = False, + min_space: int = 0, + require_same_masks: bool = True, + mask_dropout: float = 0.0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape + + Args: + shape: the the shape for which to compute masks. + should be of size 2 where first element is batch size and 2nd is timesteps + padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements + mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by + number of timesteps divided by length of mask span to mask approximately this percentage of all elements. + however due to overlaps, the actual number will be smaller (unless no_overlap is True) + mask_type: how to compute mask lengths + static = fixed size + uniform = sample from uniform distribution [mask_other, mask_length*2] + normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element + poisson = sample from possion distribution with lambda = mask length + min_masks: minimum number of masked spans + no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping + min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans + require_same_masks: if true, will randomly drop out masks until same amount of masks remains in each sample + mask_dropout: randomly dropout this percentage of masks in each example + """ + + bsz, all_sz = shape + mask = np.full((bsz, all_sz), False) + + all_num_mask = int( + # add a random number for probabilistic rounding + mask_prob * all_sz / float(mask_length) + + np.random.rand() + ) + + all_num_mask = max(min_masks, all_num_mask) + + mask_idcs = [] + for i in range(bsz): + if padding_mask is not None: + sz = all_sz - padding_mask[i].long().sum().item() + num_mask = int( + # add a random number for probabilistic rounding + mask_prob * sz / float(mask_length) + + np.random.rand() + ) + num_mask = max(min_masks, num_mask) + else: + sz = all_sz + num_mask = all_num_mask + + if mask_type == "static": + lengths = np.full(num_mask, mask_length) + elif mask_type == "uniform": + lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask) + elif mask_type == "normal": + lengths = np.random.normal(mask_length, mask_other, size=num_mask) + lengths = [max(1, int(round(x))) for x in lengths] + elif mask_type == "poisson": + lengths = np.random.poisson(mask_length, size=num_mask) + lengths = [int(round(x)) for x in lengths] + else: + raise Exception("unknown mask selection " + mask_type) + + if sum(lengths) == 0: + lengths[0] = min(mask_length, sz - 1) + + if no_overlap: + mask_idc = [] + + def arrange(s, e, length, keep_length): + span_start = np.random.randint(s, e - length) + mask_idc.extend(span_start + i for i in range(length)) + + new_parts = [] + if span_start - s - min_space >= keep_length: + new_parts.append((s, span_start - min_space + 1)) + if e - span_start - length - min_space > keep_length: + new_parts.append((span_start + length + min_space, e)) + return new_parts + + parts = [(0, sz)] + min_length = min(lengths) + for length in sorted(lengths, reverse=True): + lens = np.fromiter( + (e - s if e - s >= length + min_space else 0 for s, e in parts), + np.int32, + ) + l_sum = np.sum(lens) + if l_sum == 0: + break + probs = lens / np.sum(lens) + c = np.random.choice(len(parts), p=probs) + s, e = parts.pop(c) + parts.extend(arrange(s, e, length, min_length)) + mask_idc = np.asarray(mask_idc) + else: + min_len = min(lengths) + if sz - min_len <= num_mask: + min_len = sz - num_mask - 1 + + mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) + + mask_idc = np.asarray( + [ + mask_idc[j] + offset + for j in range(len(mask_idc)) + for offset in range(lengths[j]) + ] + ) + + mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) + + min_len = min([len(m) for m in mask_idcs]) + for i, mask_idc in enumerate(mask_idcs): + if len(mask_idc) > min_len and require_same_masks: + mask_idc = np.random.choice(mask_idc, min_len, replace=False) + if mask_dropout > 0: + num_holes = np.rint(len(mask_idc) * mask_dropout).astype(int) + mask_idc = np.random.choice( + mask_idc, len(mask_idc) - num_holes, replace=False + ) + + mask[i, mask_idc] = True + + return mask diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/ema_module.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/ema_module.py new file mode 100644 index 0000000000000000000000000000000000000000..095d4422909d2164f2183e6823932829459eb710 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/ema_module.py @@ -0,0 +1,134 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Used for EMA tracking a given pytorch module. The user is responsible for calling step() +and setting the appropriate decay +""" + +import copy +import logging + +import torch + + +class EMAModule: + """Exponential Moving Average of Fairseq Models""" + + def __init__( + self, model, ema_decay=0.9999, ema_fp32=False, device=None, skip_keys=None + ): + """ + @param model model to initialize the EMA with + @param config EMAConfig object with configuration like + ema_decay, ema_update_freq, ema_fp32 + @param device If provided, copy EMA to this device (e.g. gpu). + Otherwise EMA is in the same device as the model. + """ + + self.decay = ema_decay + self.ema_fp32 = ema_fp32 + self.model = copy.deepcopy(model) + self.model.requires_grad_(False) + self.skip_keys = skip_keys or set() + self.fp32_params = {} + + if device is not None: + logging.info(f"Copying EMA model to device {device}") + self.model = self.model.to(device=device) + + if self.ema_fp32: + self.build_fp32_params() + + self.update_freq_counter = 0 + + def build_fp32_params(self, state_dict=None): + """ + Store a copy of the EMA params in fp32. + If state dict is passed, the EMA params is copied from + the provided state dict. Otherwise, it is copied from the + current EMA model parameters. + """ + if not self.ema_fp32: + raise RuntimeError( + "build_fp32_params should not be called if ema_fp32=False. " + "Use ema_fp32=True if this is really intended." + ) + + if state_dict is None: + state_dict = self.model.state_dict() + + def _to_float(t): + return t.float() if torch.is_floating_point(t) else t + + for param_key in state_dict: + if param_key in self.fp32_params: + self.fp32_params[param_key].copy_(state_dict[param_key]) + else: + self.fp32_params[param_key] = _to_float(state_dict[param_key]) + + def restore(self, state_dict, build_fp32_params=False): + """Load data from a model spec into EMA model""" + self.model.load_state_dict(state_dict, strict=False) + if build_fp32_params: + self.build_fp32_params(state_dict) + + def set_decay(self, decay): + self.decay = decay + + def get_decay(self): + return self.decay + + def _step_internal(self, new_model): + """One update of the EMA model based on new model weights""" + decay = self.decay + + ema_state_dict = {} + ema_params = self.fp32_params if self.ema_fp32 else self.model.state_dict() + for key, param in new_model.state_dict().items(): + if isinstance(param, dict): + continue + try: + ema_param = ema_params[key] + except KeyError: + ema_param = ( + param.float().clone() if param.ndim == 1 else copy.deepcopy(param) + ) + + if param.shape != ema_param.shape: + raise ValueError( + "incompatible tensor shapes between model param and ema param" + + "{} vs. {}".format(param.shape, ema_param.shape) + ) + + if "version" in key: + # Do not decay a model.version pytorch param + continue + + if key in self.skip_keys or ( + "num_batches_tracked" in key and ema_param.dtype == torch.int64 + ): + ema_param = param.to(dtype=ema_param.dtype).clone() + ema_params[key].copy_(ema_param) + else: + ema_param.mul_(decay) + ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1 - decay) + ema_state_dict[key] = ema_param + self.restore(ema_state_dict, build_fp32_params=False) + + def step(self, new_model): + self._step_internal(new_model) + + def reverse(self, model): + """ + Load the model parameters from EMA model. + Useful for inference or fine-tuning from the EMA model. + """ + d = self.model.state_dict() + if "_ema" in d: + del d["_ema"] + + model.load_state_dict(d, strict=False) + return model diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/grad_multiply.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/grad_multiply.py new file mode 100644 index 0000000000000000000000000000000000000000..1e9a2369b6243045ba8ce9481387680cf08d091e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/grad_multiply.py @@ -0,0 +1,18 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + + +class GradMultiply(torch.autograd.Function): + @staticmethod + def forward(ctx, x, scale): + ctx.scale = scale + res = x.new(x) + return res + + @staticmethod + def backward(ctx, grad): + return grad * ctx.scale, None diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/multihead_attention.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/multihead_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..16c51f78e54bdb3cff1e566a7ef003bab9bdd057 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/multihead_attention.py @@ -0,0 +1,641 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn import Parameter + +from funasr_detach.models.data2vec.quant_noise import quant_noise + + +class FairseqDropout(nn.Module): + def __init__(self, p, module_name=None): + super().__init__() + self.p = p + self.module_name = module_name + self.apply_during_inference = False + + def forward(self, x, inplace: bool = False): + if self.p > 0 and (self.training or self.apply_during_inference): + return F.dropout(x, p=self.p, training=True, inplace=inplace) + else: + return x + + def make_generation_fast_( + self, + name: str, + retain_dropout: bool = False, + retain_dropout_modules: Optional[List[str]] = None, + **kwargs, + ): + if retain_dropout: + if retain_dropout_modules is not None and self.module_name is None: + logging.warning( + "Cannot enable dropout during inference for module {} " + "because module_name was not set".format(name) + ) + elif ( + retain_dropout_modules is None # if None, apply to all modules + or self.module_name in retain_dropout_modules + ): + logging.info( + "Enabling dropout during inference for module: {}".format(name) + ) + self.apply_during_inference = True + else: + logging.info("Disabling dropout for module: {}".format(name)) + + +class MultiheadAttention(nn.Module): + """Multi-headed attention. + + See "Attention Is All You Need" for more details. + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + q_noise=0.0, + qn_block_size=8, + ): + super().__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + self.scaling = self.head_dim**-0.5 + + self.self_attention = self_attention + self.encoder_decoder_attention = encoder_decoder_attention + + assert not self.self_attention or self.qkv_same_dim, ( + "Self-attention requires query, key and " "value to be of the same size" + ) + + self.k_proj = quant_noise( + nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.v_proj = quant_noise( + nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.q_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) + + self.out_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) + + if add_bias_kv: + self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) + self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) + else: + self.bias_k = self.bias_v = None + + self.add_zero_attn = add_zero_attn + + self.reset_parameters() + + self.onnx_trace = False + self.skip_embed_dim_check = False + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def reset_parameters(self): + if self.qkv_same_dim: + # Empirically observed the convergence to be much better with + # the scaled initialization + nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) + else: + nn.init.xavier_uniform_(self.k_proj.weight) + nn.init.xavier_uniform_(self.v_proj.weight) + nn.init.xavier_uniform_(self.q_proj.weight) + + nn.init.xavier_uniform_(self.out_proj.weight) + if self.out_proj.bias is not None: + nn.init.constant_(self.out_proj.bias, 0.0) + if self.bias_k is not None: + nn.init.xavier_normal_(self.bias_k) + if self.bias_v is not None: + nn.init.xavier_normal_(self.bias_v) + + def _get_reserve_head_index(self, num_heads_to_keep: int): + k_proj_heads_norm = [] + q_proj_heads_norm = [] + v_proj_heads_norm = [] + + for i in range(self.num_heads): + start_idx = i * self.head_dim + end_idx = (i + 1) * self.head_dim + k_proj_heads_norm.append( + torch.sum(torch.abs(self.k_proj.weight[start_idx:end_idx,])).tolist() + + torch.sum(torch.abs(self.k_proj.bias[start_idx:end_idx])).tolist() + ) + q_proj_heads_norm.append( + torch.sum(torch.abs(self.q_proj.weight[start_idx:end_idx,])).tolist() + + torch.sum(torch.abs(self.q_proj.bias[start_idx:end_idx])).tolist() + ) + v_proj_heads_norm.append( + torch.sum(torch.abs(self.v_proj.weight[start_idx:end_idx,])).tolist() + + torch.sum(torch.abs(self.v_proj.bias[start_idx:end_idx])).tolist() + ) + + heads_norm = [] + for i in range(self.num_heads): + heads_norm.append( + k_proj_heads_norm[i] + q_proj_heads_norm[i] + v_proj_heads_norm[i] + ) + + sorted_head_index = sorted( + range(self.num_heads), key=lambda k: heads_norm[k], reverse=True + ) + reserve_head_index = [] + for i in range(num_heads_to_keep): + start = sorted_head_index[i] * self.head_dim + end = (sorted_head_index[i] + 1) * self.head_dim + reserve_head_index.append((start, end)) + return reserve_head_index + + def _adaptive_prune_heads(self, reserve_head_index: List[Tuple[int, int]]): + new_q_weight = [] + new_q_bias = [] + new_k_weight = [] + new_k_bias = [] + new_v_weight = [] + new_v_bias = [] + new_out_proj_weight = [] + + for ele in reserve_head_index: + start_idx, end_idx = ele + new_q_weight.append(self.q_proj.weight[start_idx:end_idx,]) + new_q_bias.append(self.q_proj.bias[start_idx:end_idx]) + + new_k_weight.append(self.k_proj.weight[start_idx:end_idx,]) + + new_k_bias.append(self.k_proj.bias[start_idx:end_idx]) + + new_v_weight.append(self.v_proj.weight[start_idx:end_idx,]) + new_v_bias.append(self.v_proj.bias[start_idx:end_idx]) + + new_out_proj_weight.append(self.out_proj.weight[:, start_idx:end_idx]) + + new_q_weight = torch.cat(new_q_weight).detach() + new_k_weight = torch.cat(new_k_weight).detach() + new_v_weight = torch.cat(new_v_weight).detach() + new_out_proj_weight = torch.cat(new_out_proj_weight, dim=-1).detach() + new_q_weight.requires_grad = True + new_k_weight.requires_grad = True + new_v_weight.requires_grad = True + new_out_proj_weight.requires_grad = True + + new_q_bias = torch.cat(new_q_bias).detach() + new_q_bias.requires_grad = True + + new_k_bias = torch.cat(new_k_bias).detach() + new_k_bias.requires_grad = True + + new_v_bias = torch.cat(new_v_bias).detach() + new_v_bias.requires_grad = True + + self.q_proj.weight = torch.nn.Parameter(new_q_weight) + self.q_proj.bias = torch.nn.Parameter(new_q_bias) + + self.k_proj.weight = torch.nn.Parameter(new_k_weight) + self.k_proj.bias = torch.nn.Parameter(new_k_bias) + + self.v_proj.weight = torch.nn.Parameter(new_v_weight) + self.v_proj.bias = torch.nn.Parameter(new_v_bias) + + self.out_proj.weight = torch.nn.Parameter(new_out_proj_weight) + + self.num_heads = len(reserve_head_index) + self.embed_dim = self.head_dim * self.num_heads + self.q_proj.out_features = self.embed_dim + self.k_proj.out_features = self.embed_dim + self.v_proj.out_features = self.embed_dim + + def _set_skip_embed_dim_check(self): + self.skip_embed_dim_check = True + + def forward( + self, + query, + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + need_weights: bool = True, + static_kv: bool = False, + attn_mask: Optional[Tensor] = None, + before_softmax: bool = False, + need_head_weights: bool = False, + ) -> Tuple[Tensor, Optional[Tensor]]: + """Input shape: Time x Batch x Channel + + Args: + key_padding_mask (ByteTensor, optional): mask to exclude + keys that are pads, of shape `(batch, src_len)`, where + padding elements are indicated by 1s. + need_weights (bool, optional): return the attention weights, + averaged over heads (default: False). + attn_mask (ByteTensor, optional): typically used to + implement causal attention, where the mask prevents the + attention from looking forward in time (default: None). + before_softmax (bool, optional): return the raw attention + weights and values before the attention softmax. + need_head_weights (bool, optional): return the attention + weights for each head. Implies *need_weights*. Default: + return the average attention weights over all heads. + """ + if need_head_weights: + need_weights = True + + is_tpu = query.device.type == "xla" + + tgt_len, bsz, embed_dim = query.size() + src_len = tgt_len + if not self.skip_embed_dim_check: + assert ( + embed_dim == self.embed_dim + ), f"query dim {embed_dim} != {self.embed_dim}" + assert list(query.size()) == [tgt_len, bsz, embed_dim] + if key is not None: + src_len, key_bsz, _ = key.size() + if not torch.jit.is_scripting(): + assert key_bsz == bsz + assert value is not None + assert src_len, bsz == value.shape[:2] + + if ( + not self.onnx_trace + and not is_tpu # don't use PyTorch version on TPUs + and incremental_state is None + and not static_kv + # A workaround for quantization to work. Otherwise JIT compilation + # treats bias in linear module as method. + and not torch.jit.is_scripting() + # The Multihead attention implemented in pytorch forces strong dimension check + # for input embedding dimention and K,Q,V projection dimension. + # Since pruning will break the dimension check and it is not easy to modify the pytorch API, + # it is preferred to bypass the pytorch MHA when we need to skip embed_dim_check + and not self.skip_embed_dim_check + ): + assert key is not None and value is not None + return F.multi_head_attention_forward( + query, + key, + value, + self.embed_dim, + self.num_heads, + torch.empty([0]), + torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)), + self.bias_k, + self.bias_v, + self.add_zero_attn, + self.dropout_module.p, + self.out_proj.weight, + self.out_proj.bias, + self.training or self.dropout_module.apply_during_inference, + key_padding_mask, + need_weights, + attn_mask, + use_separate_proj_weight=True, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + ) + + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if saved_state is not None and "prev_key" in saved_state: + # previous time steps are cached - no need to recompute + # key and value if they are static + if static_kv: + assert self.encoder_decoder_attention and not self.self_attention + key = value = None + else: + saved_state = None + + if self.self_attention: + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.q_proj(query) + if key is None: + assert value is None + k = v = None + else: + k = self.k_proj(key) + v = self.v_proj(key) + + else: + assert key is not None and value is not None + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + q *= self.scaling + + if self.bias_k is not None: + assert self.bias_v is not None + k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + key_padding_mask.new_zeros(key_padding_mask.size(0), 1), + ], + dim=1, + ) + + q = ( + q.contiguous() + .view(tgt_len, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + if k is not None: + k = ( + k.contiguous() + .view(-1, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + if v is not None: + v = ( + v.contiguous() + .view(-1, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + if saved_state is not None: + # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) + if "prev_key" in saved_state: + _prev_key = saved_state["prev_key"] + assert _prev_key is not None + prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + k = prev_key + else: + assert k is not None + k = torch.cat([prev_key, k], dim=1) + src_len = k.size(1) + if "prev_value" in saved_state: + _prev_value = saved_state["prev_value"] + assert _prev_value is not None + prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + v = prev_value + else: + assert v is not None + v = torch.cat([prev_value, v], dim=1) + prev_key_padding_mask: Optional[Tensor] = None + if "prev_key_padding_mask" in saved_state: + prev_key_padding_mask = saved_state["prev_key_padding_mask"] + assert k is not None and v is not None + key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( + key_padding_mask=key_padding_mask, + prev_key_padding_mask=prev_key_padding_mask, + batch_size=bsz, + src_len=k.size(1), + static_kv=static_kv, + ) + + saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_key_padding_mask"] = key_padding_mask + # In this branch incremental_state is never None + assert incremental_state is not None + incremental_state = self._set_input_buffer(incremental_state, saved_state) + assert k is not None + assert k.size(1) == src_len + + # This is part of a workaround to get around fork/join parallelism + # not supporting Optional types. + if key_padding_mask is not None and key_padding_mask.dim() == 0: + key_padding_mask = None + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + if self.add_zero_attn: + assert v is not None + src_len += 1 + k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) + v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + torch.zeros(key_padding_mask.size(0), 1).type_as( + key_padding_mask + ), + ], + dim=1, + ) + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) + + assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] + + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(0) + if self.onnx_trace: + attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) + attn_weights += attn_mask + + if key_padding_mask is not None: + # don't attend to padding symbols + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + if not is_tpu: + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + else: + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if before_softmax: + return attn_weights, v + + attn_weights_float = F.softmax(attn_weights, dim=-1, dtype=torch.float32) + attn_weights = attn_weights_float.type_as(attn_weights) + attn_probs = self.dropout_module(attn_weights) + + assert v is not None + attn = torch.bmm(attn_probs, v) + assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] + if self.onnx_trace and attn.size(1) == 1: + # when ONNX tracing a single decoder step (sequence length == 1) + # the transpose is a no-op copy before view, thus unnecessary + attn = attn.contiguous().view(tgt_len, bsz, self.embed_dim) + else: + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim) + attn = self.out_proj(attn) + attn_weights: Optional[Tensor] = None + if need_weights: + attn_weights = attn_weights_float.view( + bsz, self.num_heads, tgt_len, src_len + ).transpose(1, 0) + if not need_head_weights: + # average attention weights over heads + attn_weights = attn_weights.mean(dim=0) + + return attn, attn_weights + + @staticmethod + def _append_prev_key_padding_mask( + key_padding_mask: Optional[Tensor], + prev_key_padding_mask: Optional[Tensor], + batch_size: int, + src_len: int, + static_kv: bool, + ) -> Optional[Tensor]: + # saved key padding masks have shape (bsz, seq_len) + if prev_key_padding_mask is not None and static_kv: + new_key_padding_mask = prev_key_padding_mask + elif prev_key_padding_mask is not None and key_padding_mask is not None: + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 + ) + # During incremental decoding, as the padding token enters and + # leaves the frame, there will be a time when prev or current + # is None + elif prev_key_padding_mask is not None: + if src_len > prev_key_padding_mask.size(1): + filler = torch.zeros( + (batch_size, src_len - prev_key_padding_mask.size(1)), + device=prev_key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), filler.float()], dim=1 + ) + else: + new_key_padding_mask = prev_key_padding_mask.float() + elif key_padding_mask is not None: + if src_len > key_padding_mask.size(1): + filler = torch.zeros( + (batch_size, src_len - key_padding_mask.size(1)), + device=key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [filler.float(), key_padding_mask.float()], dim=1 + ) + else: + new_key_padding_mask = key_padding_mask.float() + else: + new_key_padding_mask = prev_key_padding_mask + return new_key_padding_mask + + @torch.jit.export + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + new_order: Tensor, + ): + """Reorder buffered internal state (for incremental generation).""" + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + for k in input_buffer.keys(): + input_buffer_k = input_buffer[k] + if input_buffer_k is not None: + if self.encoder_decoder_attention and input_buffer_k.size( + 0 + ) == new_order.size(0): + break + input_buffer[k] = input_buffer_k.index_select(0, new_order) + incremental_state = self._set_input_buffer(incremental_state, input_buffer) + return incremental_state + + def _get_input_buffer( + self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ) -> Dict[str, Optional[Tensor]]: + result = self.get_incremental_state(incremental_state, "attn_state") + if result is not None: + return result + else: + empty_result: Dict[str, Optional[Tensor]] = {} + return empty_result + + def _set_input_buffer( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + buffer: Dict[str, Optional[Tensor]], + ): + return self.set_incremental_state(incremental_state, "attn_state", buffer) + + def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int): + return attn_weights + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + items_to_add = {} + keys_to_remove = [] + for k in state_dict.keys(): + if k.endswith(prefix + "in_proj_weight"): + # in_proj_weight used to be q + k + v with same dimensions + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim] + items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim] + items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :] + + keys_to_remove.append(k) + + k_bias = prefix + "in_proj_bias" + if k_bias in state_dict.keys(): + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim] + items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][ + dim : 2 * dim + ] + items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :] + + keys_to_remove.append(prefix + "in_proj_bias") + + for k in keys_to_remove: + del state_dict[k] + + for key, value in items_to_add.items(): + state_dict[key] = value diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/quant_noise.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/quant_noise.py new file mode 100644 index 0000000000000000000000000000000000000000..af798a2cd50d1722338491e29c8bdbf9c6dd339e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/quant_noise.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + + +def quant_noise(module, p, block_size): + """ + Wraps modules and applies quantization noise to the weights for + subsequent quantization with Iterative Product Quantization as + described in "Training with Quantization Noise for Extreme Model Compression" + + Args: + - module: nn.Module + - p: amount of Quantization Noise + - block_size: size of the blocks for subsequent quantization with iPQ + + Remarks: + - Module weights must have the right sizes wrt the block size + - Only Linear, Embedding and Conv2d modules are supported for the moment + - For more detail on how to quantize by blocks with convolutional weights, + see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks" + - We implement the simplest form of noise here as stated in the paper + which consists in randomly dropping blocks + """ + + # if no quantization noise, don't register hook + if p <= 0: + return module + + # supported modules + assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d)) + + # test whether module.weight has the right sizes wrt block_size + is_conv = module.weight.ndim == 4 + + # 2D matrix + if not is_conv: + assert ( + module.weight.size(1) % block_size == 0 + ), "Input features must be a multiple of block sizes" + + # 4D matrix + else: + # 1x1 convolutions + if module.kernel_size == (1, 1): + assert ( + module.in_channels % block_size == 0 + ), "Input channels must be a multiple of block sizes" + # regular convolutions + else: + k = module.kernel_size[0] * module.kernel_size[1] + assert k % block_size == 0, "Kernel size must be a multiple of block size" + + def _forward_pre_hook(mod, input): + # no noise for evaluation + if mod.training: + if not is_conv: + # gather weight and sizes + weight = mod.weight + in_features = weight.size(1) + out_features = weight.size(0) + + # split weight matrix into blocks and randomly drop selected blocks + mask = torch.zeros( + in_features // block_size * out_features, device=weight.device + ) + mask.bernoulli_(p) + mask = mask.repeat_interleave(block_size, -1).view(-1, in_features) + + else: + # gather weight and sizes + weight = mod.weight + in_channels = mod.in_channels + out_channels = mod.out_channels + + # split weight matrix into blocks and randomly drop selected blocks + if mod.kernel_size == (1, 1): + mask = torch.zeros( + int(in_channels // block_size * out_channels), + device=weight.device, + ) + mask.bernoulli_(p) + mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels) + else: + mask = torch.zeros( + weight.size(0), weight.size(1), device=weight.device + ) + mask.bernoulli_(p) + mask = ( + mask.unsqueeze(2) + .unsqueeze(3) + .repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1]) + ) + + # scale weights and apply mask + mask = mask.to( + torch.bool + ) # x.bool() is not currently supported in TorchScript + s = 1 / (1 - p) + mod.weight.data = s * weight.masked_fill(mask, 0) + + module.register_forward_pre_hook(_forward_pre_hook) + return module diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/utils.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..dafbc5a9fdedd7c31990ec52781dfeced2226b04 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/utils.py @@ -0,0 +1,156 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from funasr_detach.models.data2vec.multihead_attention import MultiheadAttention + + +class Fp32LayerNorm(nn.LayerNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.layer_norm( + input.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) + + +class Fp32GroupNorm(nn.GroupNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.group_norm( + input.float(), + self.num_groups, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) + + +class TransposeLast(nn.Module): + def __init__(self, deconstruct_idx=None): + super().__init__() + self.deconstruct_idx = deconstruct_idx + + def forward(self, x): + if self.deconstruct_idx is not None: + x = x[self.deconstruct_idx] + return x.transpose(-2, -1) + + +class SamePad(nn.Module): + def __init__(self, kernel_size, causal=False): + super().__init__() + if causal: + self.remove = kernel_size - 1 + else: + self.remove = 1 if kernel_size % 2 == 0 else 0 + + def forward(self, x): + if self.remove > 0: + x = x[:, :, : -self.remove] + return x + + +def pad_to_multiple(x, multiple, dim=-1, value=0): + # Inspired from https://github.com/lucidrains/local-attention/blob/master/local_attention/local_attention.py#L41 + if x is None: + return None, 0 + tsz = x.size(dim) + m = tsz / multiple + remainder = math.ceil(m) * multiple - tsz + if m.is_integer(): + return x, 0 + pad_offset = (0,) * (-1 - dim) * 2 + + return F.pad(x, (*pad_offset, 0, remainder), value=value), remainder + + +def gelu_accurate(x): + if not hasattr(gelu_accurate, "_a"): + gelu_accurate._a = math.sqrt(2 / math.pi) + return ( + 0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3)))) + ) + + +def gelu(x: torch.Tensor) -> torch.Tensor: + return torch.nn.functional.gelu(x.float()).type_as(x) + + +def get_available_activation_fns(): + return [ + "relu", + "gelu", + "gelu_fast", # deprecated + "gelu_accurate", + "tanh", + "linear", + ] + + +def get_activation_fn(activation: str): + """Returns the activation function corresponding to `activation`""" + + if activation == "relu": + return F.relu + elif activation == "gelu": + return gelu + elif activation == "gelu_accurate": + return gelu_accurate + elif activation == "tanh": + return torch.tanh + elif activation == "linear": + return lambda x: x + elif activation == "swish": + return torch.nn.SiLU + else: + raise RuntimeError("--activation-fn {} not supported".format(activation)) + + +def init_bert_params(module): + """ + Initialize the weights specific to the BERT Model. + This overrides the default initializations depending on the specified arguments. + 1. If normal_init_linear_weights is set then weights of linear + layer will be initialized using the normal distribution and + bais will be set to the specified value. + 2. If normal_init_embed_weights is set then weights of embedding + layer will be initialized using the normal distribution. + 3. If normal_init_proj_weights is set then weights of + in_project_weight for MultiHeadAttention initialized using + the normal distribution (to be validated). + """ + + def normal_(data): + # with FSDP, module params will be on CUDA, so we cast them back to CPU + # so that the RNG is consistent with and without FSDP + data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device)) + + if isinstance(module, nn.Linear): + normal_(module.weight.data) + if module.bias is not None: + module.bias.data.zero_() + if isinstance(module, nn.Embedding): + normal_(module.weight.data) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + if isinstance(module, MultiheadAttention): + normal_(module.q_proj.weight.data) + normal_(module.k_proj.weight.data) + normal_(module.v_proj.weight.data) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/data2vec/wav2vec2.py b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/wav2vec2.py new file mode 100644 index 0000000000000000000000000000000000000000..d9b6af530131bf9b246704c6d474164979c2e7bd --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/data2vec/wav2vec2.py @@ -0,0 +1,407 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +from typing import List, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from funasr_detach.models.data2vec import utils +from funasr_detach.models.data2vec.multihead_attention import MultiheadAttention + + +class ConvFeatureExtractionModel(nn.Module): + def __init__( + self, + conv_layers: List[Tuple[int, int, int]], + dropout: float = 0.0, + mode: str = "default", + conv_bias: bool = False, + in_d: int = 1, + ): + super().__init__() + + assert mode in {"default", "layer_norm"} + + def block( + n_in, + n_out, + k, + stride, + is_layer_norm=False, + is_group_norm=False, + conv_bias=False, + ): + def make_conv(): + conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) + nn.init.kaiming_normal_(conv.weight) + return conv + + assert ( + is_layer_norm and is_group_norm + ) == False, "layer norm and group norm are exclusive" + + if is_layer_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + nn.Sequential( + utils.TransposeLast(), + utils.Fp32LayerNorm(dim, elementwise_affine=True), + utils.TransposeLast(), + ), + nn.GELU(), + ) + elif is_group_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + utils.Fp32GroupNorm(dim, dim, affine=True), + nn.GELU(), + ) + else: + return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU()) + + self.conv_layers = nn.ModuleList() + for i, cl in enumerate(conv_layers): + assert len(cl) == 3, "invalid conv definition: " + str(cl) + (dim, k, stride) = cl + + self.conv_layers.append( + block( + in_d, + dim, + k, + stride, + is_layer_norm=mode == "layer_norm", + is_group_norm=mode == "default" and i == 0, + conv_bias=conv_bias, + ) + ) + in_d = dim + + def forward(self, x): + if len(x.shape) == 2: + x = x.unsqueeze(1) + else: + x = x.transpose(1, 2) + + for conv in self.conv_layers: + x = conv(x) + return x + + +def make_conv_pos(e, k, g): + pos_conv = nn.Conv1d( + e, + e, + kernel_size=k, + padding=k // 2, + groups=g, + ) + dropout = 0 + std = math.sqrt((4 * (1.0 - dropout)) / (k * e)) + nn.init.normal_(pos_conv.weight, mean=0, std=std) + nn.init.constant_(pos_conv.bias, 0) + + pos_conv = nn.utils.weight_norm(pos_conv, name="weight", dim=2) + pos_conv = nn.Sequential(pos_conv, utils.SamePad(k), nn.GELU()) + + return pos_conv + + +class TransformerEncoder(nn.Module): + def build_encoder_layer(self): + if self.layer_type == "transformer": + layer = TransformerSentenceEncoderLayer( + embedding_dim=self.embedding_dim, + ffn_embedding_dim=self.encoder_ffn_embed_dim, + num_attention_heads=self.encoder_attention_heads, + dropout=self.dropout, + attention_dropout=self.attention_dropout, + activation_dropout=self.activation_dropout, + activation_fn=self.activation_fn, + layer_norm_first=self.layer_norm_first, + ) + else: + logging.error("Only transformer is supported for data2vec now") + return layer + + def __init__( + self, + # position + dropout, + encoder_embed_dim, + required_seq_len_multiple, + pos_conv_depth, + conv_pos, + conv_pos_groups, + # transformer layers + layer_type, + encoder_layers, + encoder_ffn_embed_dim, + encoder_attention_heads, + attention_dropout, + activation_dropout, + activation_fn, + layer_norm_first, + encoder_layerdrop, + max_positions, + ): + super().__init__() + + # position + self.dropout = dropout + self.embedding_dim = encoder_embed_dim + self.required_seq_len_multiple = required_seq_len_multiple + if pos_conv_depth > 1: + num_layers = pos_conv_depth + k = max(3, conv_pos // num_layers) + + def make_conv_block(e, k, g, l): + return nn.Sequential( + *[ + nn.Sequential( + nn.Conv1d( + e, + e, + kernel_size=k, + padding=k // 2, + groups=g, + ), + utils.SamePad(k), + utils.TransposeLast(), + torch.nn.LayerNorm(e, elementwise_affine=False), + utils.TransposeLast(), + nn.GELU(), + ) + for _ in range(l) + ] + ) + + self.pos_conv = make_conv_block( + self.embedding_dim, k, conv_pos_groups, num_layers + ) + + else: + self.pos_conv = make_conv_pos( + self.embedding_dim, + conv_pos, + conv_pos_groups, + ) + + # transformer layers + self.layer_type = layer_type + self.encoder_ffn_embed_dim = encoder_ffn_embed_dim + self.encoder_attention_heads = encoder_attention_heads + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.activation_fn = activation_fn + self.layer_norm_first = layer_norm_first + self.layerdrop = encoder_layerdrop + self.max_positions = max_positions + self.layers = nn.ModuleList( + [self.build_encoder_layer() for _ in range(encoder_layers)] + ) + self.layer_norm = torch.nn.LayerNorm(self.embedding_dim) + + self.apply(utils.init_bert_params) + + def forward(self, x, padding_mask=None, layer=None): + x, layer_results = self.extract_features(x, padding_mask, layer) + + if self.layer_norm_first and layer is None: + x = self.layer_norm(x) + + return x, layer_results + + def extract_features( + self, + x, + padding_mask=None, + tgt_layer=None, + min_layer=0, + ): + + if padding_mask is not None: + x[padding_mask] = 0 + + x_conv = self.pos_conv(x.transpose(1, 2)) + x_conv = x_conv.transpose(1, 2) + x = x + x_conv + + if not self.layer_norm_first: + x = self.layer_norm(x) + + # pad to the sequence length dimension + x, pad_length = utils.pad_to_multiple( + x, self.required_seq_len_multiple, dim=-2, value=0 + ) + if pad_length > 0 and padding_mask is None: + padding_mask = x.new_zeros((x.size(0), x.size(1)), dtype=torch.bool) + padding_mask[:, -pad_length:] = True + else: + padding_mask, _ = utils.pad_to_multiple( + padding_mask, self.required_seq_len_multiple, dim=-1, value=True + ) + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + layer_results = [] + r = None + for i, layer in enumerate(self.layers): + dropout_probability = np.random.random() if self.layerdrop > 0 else 1 + if not self.training or (dropout_probability > self.layerdrop): + x, (z, lr) = layer(x, self_attn_padding_mask=padding_mask) + if i >= min_layer: + layer_results.append((x, z, lr)) + if i == tgt_layer: + r = x + break + + if r is not None: + x = r + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + # undo paddding + if pad_length > 0: + x = x[:, :-pad_length] + + def undo_pad(a, b, c): + return ( + a[:-pad_length], + b[:-pad_length] if b is not None else b, + c[:-pad_length], + ) + + layer_results = [undo_pad(*u) for u in layer_results] + + return x, layer_results + + def max_positions(self): + """Maximum output length supported by the encoder.""" + return self.max_positions + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + return state_dict + + +class TransformerSentenceEncoderLayer(nn.Module): + """ + Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained + models. + """ + + def __init__( + self, + embedding_dim: int = 768, + ffn_embedding_dim: int = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + activation_fn: str = "relu", + layer_norm_first: bool = False, + ) -> None: + + super().__init__() + # Initialize parameters + self.embedding_dim = embedding_dim + self.dropout = dropout + self.activation_dropout = activation_dropout + + # Initialize blocks + self.activation_fn = utils.get_activation_fn(activation_fn) + self.self_attn = MultiheadAttention( + self.embedding_dim, + num_attention_heads, + dropout=attention_dropout, + self_attention=True, + ) + + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(self.activation_dropout) + self.dropout3 = nn.Dropout(dropout) + + self.layer_norm_first = layer_norm_first + + # layer norm associated with the self attention layer + self.self_attn_layer_norm = torch.nn.LayerNorm(self.embedding_dim) + self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim) + self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim) + + # layer norm associated with the position wise feed-forward NN + self.final_layer_norm = torch.nn.LayerNorm(self.embedding_dim) + + def forward( + self, + x: torch.Tensor, # (T, B, C) + self_attn_mask: torch.Tensor = None, + self_attn_padding_mask: torch.Tensor = None, + ): + """ + LayerNorm is applied either before or after the self-attention/ffn + modules similar to the original Transformer imlementation. + """ + residual = x + + if self.layer_norm_first: + x = self.self_attn_layer_norm(x) + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + attn_mask=self_attn_mask, + need_weights=False, + ) + x = self.dropout1(x) + x = residual + x + + residual = x + x = self.final_layer_norm(x) + x = self.activation_fn(self.fc1(x)) + x = self.dropout2(x) + x = self.fc2(x) + + layer_result = x + + x = self.dropout3(x) + x = residual + x + else: + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + need_weights=False, + ) + + x = self.dropout1(x) + x = residual + x + + x = self.self_attn_layer_norm(x) + + residual = x + x = self.activation_fn(self.fc1(x)) + x = self.dropout2(x) + x = self.fc2(x) + + layer_result = x + + x = self.dropout3(x) + x = residual + x + x = self.final_layer_norm(x) + + return x, (attn, layer_result) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..20edebef81fc03fe396cdf9dc7e31730441dd8b5 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/encoder.py @@ -0,0 +1,471 @@ +# Copyright 2022 Kwangyoun Kim (ASAPP inc.) +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""E-Branchformer encoder definition. +Reference: + Kwangyoun Kim, Felix Wu, Yifan Peng, Jing Pan, + Prashant Sridhar, Kyu J. Han, Shinji Watanabe, + "E-Branchformer: Branchformer with Enhanced merging + for speech recognition," in SLT 2022. +""" + +import logging +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.models.branchformer.cgmlp import ConvolutionalGatingMLP +from funasr_detach.models.branchformer.fastformer import FastSelfAttention +from funasr_detach.models.transformer.utils.nets_utils import ( + get_activation, + make_pad_mask, +) +from funasr_detach.models.transformer.attention import ( # noqa: H301 + LegacyRelPositionMultiHeadedAttention, + MultiHeadedAttention, + RelPositionMultiHeadedAttention, +) +from funasr_detach.models.transformer.embedding import ( # noqa: H301 + LegacyRelPositionalEncoding, + PositionalEncoding, + RelPositionalEncoding, + ScaledPositionalEncoding, +) +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, +) +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.utils.subsampling import ( + Conv2dSubsampling, + Conv2dSubsampling2, + Conv2dSubsampling6, + Conv2dSubsampling8, + TooShortUttError, + check_short_utt, +) +from funasr_detach.register import tables + + +class EBranchformerEncoderLayer(torch.nn.Module): + """E-Branchformer encoder layer module. + + Args: + size (int): model dimension + attn: standard self-attention or efficient attention + cgmlp: ConvolutionalGatingMLP + feed_forward: feed-forward module, optional + feed_forward: macaron-style feed-forward module, optional + dropout_rate (float): dropout probability + merge_conv_kernel (int): kernel size of the depth-wise conv in merge module + """ + + def __init__( + self, + size: int, + attn: torch.nn.Module, + cgmlp: torch.nn.Module, + feed_forward: Optional[torch.nn.Module], + feed_forward_macaron: Optional[torch.nn.Module], + dropout_rate: float, + merge_conv_kernel: int = 3, + ): + super().__init__() + + self.size = size + self.attn = attn + self.cgmlp = cgmlp + + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.ff_scale = 1.0 + if self.feed_forward is not None: + self.norm_ff = LayerNorm(size) + if self.feed_forward_macaron is not None: + self.ff_scale = 0.5 + self.norm_ff_macaron = LayerNorm(size) + + self.norm_mha = LayerNorm(size) # for the MHA module + self.norm_mlp = LayerNorm(size) # for the MLP module + self.norm_final = LayerNorm(size) # for the final output of the block + + self.dropout = torch.nn.Dropout(dropout_rate) + + self.depthwise_conv_fusion = torch.nn.Conv1d( + size + size, + size + size, + kernel_size=merge_conv_kernel, + stride=1, + padding=(merge_conv_kernel - 1) // 2, + groups=size + size, + bias=True, + ) + self.merge_proj = torch.nn.Linear(size + size, size) + + def forward(self, x_input, mask, cache=None): + """Compute encoded features. + + Args: + x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb. + - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)]. + - w/o pos emb: Tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, 1, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + """ + + if cache is not None: + raise NotImplementedError("cache is not None, which is not tested") + + if isinstance(x_input, tuple): + x, pos_emb = x_input[0], x_input[1] + else: + x, pos_emb = x_input, None + + if self.feed_forward_macaron is not None: + residual = x + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x)) + + # Two branches + x1 = x + x2 = x + + # Branch 1: multi-headed attention module + x1 = self.norm_mha(x1) + + if isinstance(self.attn, FastSelfAttention): + x_att = self.attn(x1, mask) + else: + if pos_emb is not None: + x_att = self.attn(x1, x1, x1, pos_emb, mask) + else: + x_att = self.attn(x1, x1, x1, mask) + + x1 = self.dropout(x_att) + + # Branch 2: convolutional gating mlp + x2 = self.norm_mlp(x2) + + if pos_emb is not None: + x2 = (x2, pos_emb) + x2 = self.cgmlp(x2, mask) + if isinstance(x2, tuple): + x2 = x2[0] + + x2 = self.dropout(x2) + + # Merge two branches + x_concat = torch.cat([x1, x2], dim=-1) + x_tmp = x_concat.transpose(1, 2) + x_tmp = self.depthwise_conv_fusion(x_tmp) + x_tmp = x_tmp.transpose(1, 2) + x = x + self.dropout(self.merge_proj(x_concat + x_tmp)) + + if self.feed_forward is not None: + # feed forward module + residual = x + x = self.norm_ff(x) + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + + x = self.norm_final(x) + + if pos_emb is not None: + return (x, pos_emb), mask + + return x, mask + + +@tables.register("encoder_classes", "EBranchformerEncoder") +class EBranchformerEncoder(nn.Module): + """E-Branchformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + attention_layer_type: str = "rel_selfattn", + pos_enc_layer_type: str = "rel_pos", + rel_pos_type: str = "latest", + cgmlp_linear_units: int = 2048, + cgmlp_conv_kernel: int = 31, + use_linear_after_conv: bool = False, + gate_activation: str = "identity", + num_blocks: int = 12, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: Optional[str] = "conv2d", + zero_triu: bool = False, + padding_idx: int = -1, + layer_drop_rate: float = 0.0, + max_pos_emb_len: int = 5000, + use_ffn: bool = False, + macaron_ffn: bool = False, + ffn_activation_type: str = "swish", + linear_units: int = 2048, + positionwise_layer_type: str = "linear", + merge_conv_kernel: int = 3, + interctc_layer_idx=None, + interctc_use_conditioning: bool = False, + ): + super().__init__() + self._output_size = output_size + + if rel_pos_type == "legacy": + if pos_enc_layer_type == "rel_pos": + pos_enc_layer_type = "legacy_rel_pos" + if attention_layer_type == "rel_selfattn": + attention_layer_type = "legacy_rel_selfattn" + elif rel_pos_type == "latest": + assert attention_layer_type != "legacy_rel_selfattn" + assert pos_enc_layer_type != "legacy_rel_pos" + else: + raise ValueError("unknown rel_pos_type: " + rel_pos_type) + + if pos_enc_layer_type == "abs_pos": + pos_enc_class = PositionalEncoding + elif pos_enc_layer_type == "scaled_abs_pos": + pos_enc_class = ScaledPositionalEncoding + elif pos_enc_layer_type == "rel_pos": + assert attention_layer_type == "rel_selfattn" + pos_enc_class = RelPositionalEncoding + elif pos_enc_layer_type == "legacy_rel_pos": + assert attention_layer_type == "legacy_rel_selfattn" + pos_enc_class = LegacyRelPositionalEncoding + logging.warning( + "Using legacy_rel_pos and it will be deprecated in the future." + ) + else: + raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) + + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(input_size, output_size), + torch.nn.LayerNorm(output_size), + torch.nn.Dropout(dropout_rate), + pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len), + ) + elif input_layer == "conv2d2": + self.embed = Conv2dSubsampling2( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len), + ) + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len), + ) + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len), + ) + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), + pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len), + ) + elif isinstance(input_layer, torch.nn.Module): + self.embed = torch.nn.Sequential( + input_layer, + pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len), + ) + elif input_layer is None: + if input_size == output_size: + self.embed = None + else: + self.embed = torch.nn.Linear(input_size, output_size) + else: + raise ValueError("unknown input_layer: " + input_layer) + + activation = get_activation(ffn_activation_type) + if positionwise_layer_type == "linear": + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + elif positionwise_layer_type is None: + logging.warning("no macaron ffn") + else: + raise ValueError("Support only linear.") + + if attention_layer_type == "selfattn": + encoder_selfattn_layer = MultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + elif attention_layer_type == "legacy_rel_selfattn": + assert pos_enc_layer_type == "legacy_rel_pos" + encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + logging.warning( + "Using legacy_rel_selfattn and it will be deprecated in the future." + ) + elif attention_layer_type == "rel_selfattn": + assert pos_enc_layer_type == "rel_pos" + encoder_selfattn_layer = RelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + zero_triu, + ) + elif attention_layer_type == "fast_selfattn": + assert pos_enc_layer_type in ["abs_pos", "scaled_abs_pos"] + encoder_selfattn_layer = FastSelfAttention + encoder_selfattn_layer_args = ( + output_size, + attention_heads, + attention_dropout_rate, + ) + else: + raise ValueError("unknown encoder_attn_layer: " + attention_layer_type) + + cgmlp_layer = ConvolutionalGatingMLP + cgmlp_layer_args = ( + output_size, + cgmlp_linear_units, + cgmlp_conv_kernel, + dropout_rate, + use_linear_after_conv, + gate_activation, + ) + + self.encoders = repeat( + num_blocks, + lambda lnum: EBranchformerEncoderLayer( + output_size, + encoder_selfattn_layer(*encoder_selfattn_layer_args), + cgmlp_layer(*cgmlp_layer_args), + positionwise_layer(*positionwise_layer_args) if use_ffn else None, + ( + positionwise_layer(*positionwise_layer_args) + if use_ffn and macaron_ffn + else None + ), + dropout_rate, + merge_conv_kernel, + ), + layer_drop_rate, + ) + self.after_norm = LayerNorm(output_size) + + if interctc_layer_idx is None: + interctc_layer_idx = [] + self.interctc_layer_idx = interctc_layer_idx + if len(interctc_layer_idx) > 0: + assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks + self.interctc_use_conditioning = interctc_use_conditioning + self.conditioning_layer = None + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ctc: CTC = None, + max_layer: int = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Calculate forward propagation. + + Args: + xs_pad (torch.Tensor): Input tensor (#batch, L, input_size). + ilens (torch.Tensor): Input length (#batch). + prev_states (torch.Tensor): Not to be used now. + ctc (CTC): Intermediate CTC module. + max_layer (int): Layer depth below which InterCTC is applied. + Returns: + torch.Tensor: Output tensor (#batch, L, output_size). + torch.Tensor: Output length (#batch). + torch.Tensor: Not to be used now. + """ + + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + + if ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling2) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + elif self.embed is not None: + xs_pad = self.embed(xs_pad) + + intermediate_outs = [] + if len(self.interctc_layer_idx) == 0: + if max_layer is not None and 0 <= max_layer < len(self.encoders): + for layer_idx, encoder_layer in enumerate(self.encoders): + xs_pad, masks = encoder_layer(xs_pad, masks) + if layer_idx >= max_layer: + break + else: + xs_pad, masks = self.encoders(xs_pad, masks) + else: + for layer_idx, encoder_layer in enumerate(self.encoders): + xs_pad, masks = encoder_layer(xs_pad, masks) + + if layer_idx + 1 in self.interctc_layer_idx: + encoder_out = xs_pad + + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + intermediate_outs.append((layer_idx + 1, encoder_out)) + + if self.interctc_use_conditioning: + ctc_out = ctc.softmax(encoder_out) + + if isinstance(xs_pad, tuple): + xs_pad = list(xs_pad) + xs_pad[0] = xs_pad[0] + self.conditioning_layer(ctc_out) + xs_pad = tuple(xs_pad) + else: + xs_pad = xs_pad + self.conditioning_layer(ctc_out) + + if isinstance(xs_pad, tuple): + xs_pad = xs_pad[0] + + xs_pad = self.after_norm(xs_pad) + olens = masks.squeeze(1).sum(1) + if len(intermediate_outs) > 0: + return (xs_pad, intermediate_outs), olens, None + return xs_pad, olens, None diff --git a/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..e66530e2da0ddaa3074f584339cdf6f766eafa9c --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/model.py @@ -0,0 +1,17 @@ +import logging + +from funasr_detach.models.transformer.model import Transformer +from funasr_detach.register import tables + + +@tables.register("model_classes", "EBranchformer") +class EBranchformer(Transformer): + """CTC-attention hybrid Encoder-Decoder model""" + + def __init__( + self, + *args, + **kwargs, + ): + + super().__init__(*args, **kwargs) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..420f2e3d914b11cc2234c01b792ecb0eb92a1075 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/e_branchformer/template.yaml @@ -0,0 +1,116 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: Branchformer +model_conf: + ctc_weight: 0.3 + lsm_weight: 0.1 # label smoothing option + length_normalized_loss: false + +# encoder +encoder: EBranchformerEncoder +encoder_conf: + output_size: 256 + attention_heads: 4 + attention_layer_type: rel_selfattn + pos_enc_layer_type: rel_pos + rel_pos_type: latest + cgmlp_linear_units: 1024 + cgmlp_conv_kernel: 31 + use_linear_after_conv: false + gate_activation: identity + num_blocks: 12 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: conv2d + layer_drop_rate: 0.0 + linear_units: 1024 + positionwise_layer_type: linear + use_ffn: true + macaron_ffn: true + merge_conv_kernel: 31 + +# decoder +decoder: TransformerDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 6 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0. + src_attention_dropout_rate: 0. + + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + dither: 0.0 + lfr_m: 1 + lfr_n: 1 + +specaug: SpecAug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_range: + - 0 + - 40 + num_time_mask: 2 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 180 + keep_nbest_models: 10 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.001 + weight_decay: 0.000001 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 35000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 4 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/e2e_diar_eend_ola.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/e2e_diar_eend_ola.py new file mode 100644 index 0000000000000000000000000000000000000000..cfcedb1ff07e91108d1c9576ff0b3049320392c0 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eend/e2e_diar_eend_ola.py @@ -0,0 +1,272 @@ +from contextlib import contextmanager +from distutils.version import LooseVersion +from typing import Dict, List, Tuple, Optional + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from funasr_detach.frontends.wav_frontend import WavFrontendMel23 +from funasr_detach.models.eend.encoder import EENDOLATransformerEncoder +from funasr_detach.models.eend.encoder_decoder_attractor import EncoderDecoderAttractor +from funasr_detach.models.eend.utils.losses import ( + standard_loss, + cal_power_loss, + fast_batch_pit_n_speaker_loss, +) +from funasr_detach.models.eend.utils.power import create_powerlabel +from funasr_detach.models.eend.utils.power import generate_mapping_dict +from funasr_detach.train_utils.device_funcs import force_gatherable + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + pass +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +def pad_attractor(att, max_n_speakers): + C, D = att.shape + if C < max_n_speakers: + att = torch.cat( + [att, torch.zeros(max_n_speakers - C, D).to(torch.float32).to(att.device)], + dim=0, + ) + return att + + +def pad_labels(ts, out_size): + for i, t in enumerate(ts): + if t.shape[1] < out_size: + ts[i] = F.pad( + t, (0, out_size - t.shape[1], 0, 0), mode="constant", value=0.0 + ) + return ts + + +def pad_results(ys, out_size): + ys_padded = [] + for i, y in enumerate(ys): + if y.shape[1] < out_size: + ys_padded.append( + torch.cat( + [ + y, + torch.zeros(y.shape[0], out_size - y.shape[1]) + .to(torch.float32) + .to(y.device), + ], + dim=1, + ) + ) + else: + ys_padded.append(y) + return ys_padded + + +class DiarEENDOLAModel(nn.Module): + """EEND-OLA diarization model""" + + def __init__( + self, + frontend: Optional[WavFrontendMel23], + encoder: EENDOLATransformerEncoder, + encoder_decoder_attractor: EncoderDecoderAttractor, + n_units: int = 256, + max_n_speaker: int = 8, + attractor_loss_weight: float = 1.0, + mapping_dict=None, + **kwargs, + ): + super().__init__() + self.frontend = frontend + self.enc = encoder + self.encoder_decoder_attractor = encoder_decoder_attractor + self.attractor_loss_weight = attractor_loss_weight + self.max_n_speaker = max_n_speaker + if mapping_dict is None: + mapping_dict = generate_mapping_dict(max_speaker_num=self.max_n_speaker) + self.mapping_dict = mapping_dict + # PostNet + self.postnet = nn.LSTM(self.max_n_speaker, n_units, 1, batch_first=True) + self.output_layer = nn.Linear(n_units, mapping_dict["oov"] + 1) + + def forward_encoder(self, xs, ilens): + xs = nn.utils.rnn.pad_sequence(xs, batch_first=True, padding_value=-1) + pad_shape = xs.shape + xs_mask = [torch.ones(ilen).to(xs.device) for ilen in ilens] + xs_mask = torch.nn.utils.rnn.pad_sequence( + xs_mask, batch_first=True, padding_value=0 + ).unsqueeze(-2) + emb = self.enc(xs, xs_mask) + emb = torch.split(emb.view(pad_shape[0], pad_shape[1], -1), 1, dim=0) + emb = [e[0][:ilen] for e, ilen in zip(emb, ilens)] + return emb + + def forward_post_net(self, logits, ilens): + maxlen = torch.max(ilens).to(torch.int).item() + logits = nn.utils.rnn.pad_sequence(logits, batch_first=True, padding_value=-1) + logits = nn.utils.rnn.pack_padded_sequence( + logits, ilens.cpu().to(torch.int64), batch_first=True, enforce_sorted=False + ) + outputs, (_, _) = self.postnet(logits) + outputs = nn.utils.rnn.pad_packed_sequence( + outputs, batch_first=True, padding_value=-1, total_length=maxlen + )[0] + outputs = [ + output[: ilens[i].to(torch.int).item()] for i, output in enumerate(outputs) + ] + outputs = [self.output_layer(output) for output in outputs] + return outputs + + def forward( + self, + speech: List[torch.Tensor], + speaker_labels: List[torch.Tensor], + orders: torch.Tensor, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + + # Check that batch_size is unified + assert len(speech) == len(speaker_labels), (len(speech), len(speaker_labels)) + speech_lengths = torch.tensor([len(sph) for sph in speech]).to(torch.int64) + speaker_labels_lengths = torch.tensor( + [spk.shape[-1] for spk in speaker_labels] + ).to(torch.int64) + batch_size = len(speech) + + # Encoder + encoder_out = self.forward_encoder(speech, speech_lengths) + + # Encoder-decoder attractor + attractor_loss, attractors = self.encoder_decoder_attractor( + [e[order] for e, order in zip(encoder_out, orders)], speaker_labels_lengths + ) + speaker_logits = [ + torch.matmul(e, att.permute(1, 0)) + for e, att in zip(encoder_out, attractors) + ] + + # pit loss + pit_speaker_labels = fast_batch_pit_n_speaker_loss( + speaker_logits, speaker_labels + ) + pit_loss = standard_loss(speaker_logits, pit_speaker_labels) + + # pse loss + with torch.no_grad(): + power_ts = [ + create_powerlabel( + label.cpu().numpy(), self.mapping_dict, self.max_n_speaker + ).to(encoder_out[0].device, non_blocking=True) + for label in pit_speaker_labels + ] + pad_attractors = [pad_attractor(att, self.max_n_speaker) for att in attractors] + pse_speaker_logits = [ + torch.matmul(e, pad_att.permute(1, 0)) + for e, pad_att in zip(encoder_out, pad_attractors) + ] + pse_speaker_logits = self.forward_post_net(pse_speaker_logits, speech_lengths) + pse_loss = cal_power_loss(pse_speaker_logits, power_ts) + + loss = pse_loss + pit_loss + self.attractor_loss_weight * attractor_loss + + stats = dict() + stats["pse_loss"] = pse_loss.detach() + stats["pit_loss"] = pit_loss.detach() + stats["attractor_loss"] = attractor_loss.detach() + stats["batch_size"] = batch_size + + # Collect total loss stats + stats["loss"] = torch.clone(loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def estimate_sequential( + self, + speech: torch.Tensor, + n_speakers: int = None, + shuffle: bool = True, + threshold: float = 0.5, + **kwargs, + ): + speech_lengths = torch.tensor([len(sph) for sph in speech]).to(torch.int64) + emb = self.forward_encoder(speech, speech_lengths) + if shuffle: + orders = [np.arange(e.shape[0]) for e in emb] + for order in orders: + np.random.shuffle(order) + attractors, probs = self.encoder_decoder_attractor.estimate( + [ + e[torch.from_numpy(order).to(torch.long).to(speech[0].device)] + for e, order in zip(emb, orders) + ] + ) + else: + attractors, probs = self.encoder_decoder_attractor.estimate(emb) + attractors_active = [] + for p, att, e in zip(probs, attractors, emb): + if n_speakers and n_speakers >= 0: + att = att[:n_speakers,] + attractors_active.append(att) + elif threshold is not None: + silence = torch.nonzero(p < threshold)[0] + n_spk = silence[0] if silence.size else None + att = att[:n_spk,] + attractors_active.append(att) + else: + NotImplementedError("n_speakers or threshold has to be given.") + raw_n_speakers = [att.shape[0] for att in attractors_active] + attractors = [ + ( + pad_attractor(att, self.max_n_speaker) + if att.shape[0] <= self.max_n_speaker + else att[: self.max_n_speaker] + ) + for att in attractors_active + ] + ys = [torch.matmul(e, att.permute(1, 0)) for e, att in zip(emb, attractors)] + logits = self.forward_post_net(ys, speech_lengths) + ys = [ + self.recover_y_from_powerlabel(logit, raw_n_speaker) + for logit, raw_n_speaker in zip(logits, raw_n_speakers) + ] + + return ys, emb, attractors, raw_n_speakers + + def recover_y_from_powerlabel(self, logit, n_speaker): + pred = torch.argmax(torch.softmax(logit, dim=-1), dim=-1) + oov_index = torch.where(pred == self.mapping_dict["oov"])[0] + for i in oov_index: + if i > 0: + pred[i] = pred[i - 1] + else: + pred[i] = 0 + pred = [self.inv_mapping_func(i) for i in pred] + decisions = [bin(num)[2:].zfill(self.max_n_speaker)[::-1] for num in pred] + decisions = ( + torch.from_numpy( + np.stack([np.array([int(i) for i in dec]) for dec in decisions], axis=0) + ) + .to(logit.device) + .to(torch.float32) + ) + decisions = decisions[:, :n_speaker] + return decisions + + def inv_mapping_func(self, label): + + if not isinstance(label, int): + label = int(label) + if label in self.mapping_dict["label2dec"].keys(): + num = self.mapping_dict["label2dec"][label] + else: + num = -1 + return num + + def collect_feats(self, **batch: torch.Tensor) -> Dict[str, torch.Tensor]: + pass diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/eend_ola_dataloader.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/eend_ola_dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..6e2a6c0c809e573aa732520f5ab4bd8481bffeec --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eend/eend_ola_dataloader.py @@ -0,0 +1,61 @@ +import logging + +import kaldiio +import numpy as np +import torch +from torch.utils.data import DataLoader +from torch.utils.data import Dataset + + +def custom_collate(batch): + keys, speech, speaker_labels, orders = zip(*batch) + speech = [torch.from_numpy(np.copy(sph)).to(torch.float32) for sph in speech] + speaker_labels = [ + torch.from_numpy(np.copy(spk)).to(torch.float32) for spk in speaker_labels + ] + orders = [torch.from_numpy(np.copy(o)).to(torch.int64) for o in orders] + batch = dict(speech=speech, speaker_labels=speaker_labels, orders=orders) + + return keys, batch + + +class EENDOLADataset(Dataset): + def __init__( + self, + data_file, + ): + self.data_file = data_file + with open(data_file) as f: + lines = f.readlines() + self.samples = [line.strip().split() for line in lines] + logging.info("total samples: {}".format(len(self.samples))) + + def __len__(self): + return len(self.samples) + + def __getitem__(self, idx): + key, speech_path, speaker_label_path = self.samples[idx] + speech = kaldiio.load_mat(speech_path) + speaker_label = kaldiio.load_mat(speaker_label_path).reshape( + speech.shape[0], -1 + ) + + order = np.arange(speech.shape[0]) + np.random.shuffle(order) + + return key, speech, speaker_label, order + + +class EENDOLADataLoader: + def __init__(self, data_file, batch_size, shuffle=True, num_workers=8): + dataset = EENDOLADataset(data_file) + self.data_loader = DataLoader( + dataset, + batch_size=batch_size, + collate_fn=custom_collate, + shuffle=shuffle, + num_workers=num_workers, + ) + + def build_iter(self, epoch): + return self.data_loader diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..b77ae4e8099a31c8476315d32f5be5796754f7bf --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eend/encoder.py @@ -0,0 +1,126 @@ +import math + +import torch +import torch.nn.functional as F +from torch import nn + + +class MultiHeadSelfAttention(nn.Module): + def __init__(self, n_units, h=8, dropout_rate=0.1): + super().__init__() + self.linearQ = nn.Linear(n_units, n_units) + self.linearK = nn.Linear(n_units, n_units) + self.linearV = nn.Linear(n_units, n_units) + self.linearO = nn.Linear(n_units, n_units) + self.d_k = n_units // h + self.h = h + self.dropout = nn.Dropout(dropout_rate) + + def __call__(self, x, batch_size, x_mask): + q = self.linearQ(x).view(batch_size, -1, self.h, self.d_k) + k = self.linearK(x).view(batch_size, -1, self.h, self.d_k) + v = self.linearV(x).view(batch_size, -1, self.h, self.d_k) + scores = torch.matmul(q.permute(0, 2, 1, 3), k.permute(0, 2, 3, 1)) / math.sqrt( + self.d_k + ) + if x_mask is not None: + x_mask = x_mask.unsqueeze(1) + scores = scores.masked_fill(x_mask == 0, -1e9) + self.att = F.softmax(scores, dim=3) + p_att = self.dropout(self.att) + x = torch.matmul(p_att, v.permute(0, 2, 1, 3)) + x = x.permute(0, 2, 1, 3).contiguous().view(-1, self.h * self.d_k) + return self.linearO(x) + + +class PositionwiseFeedForward(nn.Module): + def __init__(self, n_units, d_units, dropout_rate): + super(PositionwiseFeedForward, self).__init__() + self.linear1 = nn.Linear(n_units, d_units) + self.linear2 = nn.Linear(d_units, n_units) + self.dropout = nn.Dropout(dropout_rate) + + def __call__(self, x): + return self.linear2(self.dropout(F.relu(self.linear1(x)))) + + +class PositionalEncoding(torch.nn.Module): + def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): + super(PositionalEncoding, self).__init__() + self.d_model = d_model + self.reverse = reverse + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x): + if self.pe is not None: + if self.pe.size(1) >= x.size(1): + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + pe = torch.zeros(x.size(1), self.d_model) + if self.reverse: + position = torch.arange( + x.size(1) - 1, -1, -1.0, dtype=torch.float32 + ).unsqueeze(1) + else: + position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) + self.pe = pe.to(device=x.device, dtype=x.dtype) + + def forward(self, x: torch.Tensor): + self.extend_pe(x) + x = x * self.xscale + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class EENDOLATransformerEncoder(nn.Module): + def __init__( + self, + idim: int, + n_layers: int, + n_units: int, + e_units: int = 2048, + h: int = 4, + dropout_rate: float = 0.1, + use_pos_emb: bool = False, + ): + super(EENDOLATransformerEncoder, self).__init__() + self.linear_in = nn.Linear(idim, n_units) + self.lnorm_in = nn.LayerNorm(n_units) + self.n_layers = n_layers + self.dropout = nn.Dropout(dropout_rate) + for i in range(n_layers): + setattr(self, "{}{:d}".format("lnorm1_", i), nn.LayerNorm(n_units)) + setattr( + self, + "{}{:d}".format("self_att_", i), + MultiHeadSelfAttention(n_units, h), + ) + setattr(self, "{}{:d}".format("lnorm2_", i), nn.LayerNorm(n_units)) + setattr( + self, + "{}{:d}".format("ff_", i), + PositionwiseFeedForward(n_units, e_units, dropout_rate), + ) + self.lnorm_out = nn.LayerNorm(n_units) + + def __call__(self, x, x_mask=None): + BT_size = x.shape[0] * x.shape[1] + e = self.linear_in(x.reshape(BT_size, -1)) + for i in range(self.n_layers): + e = getattr(self, "{}{:d}".format("lnorm1_", i))(e) + s = getattr(self, "{}{:d}".format("self_att_", i))(e, x.shape[0], x_mask) + e = e + self.dropout(s) + e = getattr(self, "{}{:d}".format("lnorm2_", i))(e) + s = getattr(self, "{}{:d}".format("ff_", i))(e) + e = e + self.dropout(s) + return self.lnorm_out(e) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/encoder_decoder_attractor.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/encoder_decoder_attractor.py new file mode 100644 index 0000000000000000000000000000000000000000..425c0930cddd88cce988f2bc4b0838be400a9b7c --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eend/encoder_decoder_attractor.py @@ -0,0 +1,79 @@ +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + + +class EncoderDecoderAttractor(nn.Module): + + def __init__(self, n_units, encoder_dropout=0.1, decoder_dropout=0.1): + super(EncoderDecoderAttractor, self).__init__() + self.enc0_dropout = nn.Dropout(encoder_dropout) + self.encoder = nn.LSTM( + n_units, n_units, 1, batch_first=True, dropout=encoder_dropout + ) + self.dec0_dropout = nn.Dropout(decoder_dropout) + self.decoder = nn.LSTM( + n_units, n_units, 1, batch_first=True, dropout=decoder_dropout + ) + self.counter = nn.Linear(n_units, 1) + self.n_units = n_units + + def forward_core(self, xs, zeros): + ilens = torch.from_numpy(np.array([x.shape[0] for x in xs])).to(torch.int64) + xs = [self.enc0_dropout(x) for x in xs] + xs = nn.utils.rnn.pad_sequence(xs, batch_first=True, padding_value=-1) + xs = nn.utils.rnn.pack_padded_sequence( + xs, ilens, batch_first=True, enforce_sorted=False + ) + _, (hx, cx) = self.encoder(xs) + zlens = torch.from_numpy(np.array([z.shape[0] for z in zeros])).to(torch.int64) + max_zlen = torch.max(zlens).to(torch.int).item() + zeros = [self.enc0_dropout(z) for z in zeros] + zeros = nn.utils.rnn.pad_sequence(zeros, batch_first=True, padding_value=-1) + zeros = nn.utils.rnn.pack_padded_sequence( + zeros, zlens, batch_first=True, enforce_sorted=False + ) + attractors, (_, _) = self.decoder(zeros, (hx, cx)) + attractors = nn.utils.rnn.pad_packed_sequence( + attractors, batch_first=True, padding_value=-1, total_length=max_zlen + )[0] + attractors = [ + att[: zlens[i].to(torch.int).item()] for i, att in enumerate(attractors) + ] + return attractors + + def forward(self, xs, n_speakers): + zeros = [ + torch.zeros(n_spk + 1, self.n_units).to(torch.float32).to(xs[0].device) + for n_spk in n_speakers + ] + attractors = self.forward_core(xs, zeros) + labels = torch.cat( + [ + torch.from_numpy(np.array([[1] * n_spk + [0]], np.float32)) + for n_spk in n_speakers + ], + dim=1, + ) + labels = labels.to(xs[0].device) + logit = torch.cat( + [ + self.counter(att).view(-1, n_spk + 1) + for att, n_spk in zip(attractors, n_speakers) + ], + dim=1, + ) + loss = F.binary_cross_entropy(torch.sigmoid(logit), labels) + + attractors = [att[slice(0, att.shape[0] - 1)] for att in attractors] + return loss, attractors + + def estimate(self, xs, max_n_speakers=15): + zeros = [ + torch.zeros(max_n_speakers, self.n_units).to(torch.float32).to(xs[0].device) + for _ in xs + ] + attractors = self.forward_core(xs, zeros) + probs = [torch.sigmoid(torch.flatten(self.counter(att))) for att in attractors] + return attractors, probs diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/feature.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/feature.py new file mode 100644 index 0000000000000000000000000000000000000000..2ce925203c0d89e884b2701fc7936ebe852eb34f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/feature.py @@ -0,0 +1,275 @@ +# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita) +# Licensed under the MIT license. +# +# This module is for computing audio features + +import numpy as np +import librosa + + +def get_input_dim( + frame_size, + context_size, + transform_type, +): + if transform_type.startswith("logmel23"): + frame_size = 23 + elif transform_type.startswith("logmel"): + frame_size = 40 + else: + fft_size = 1 << (frame_size - 1).bit_length() + frame_size = int(fft_size / 2) + 1 + input_dim = (2 * context_size + 1) * frame_size + return input_dim + + +def transform(Y, transform_type=None, dtype=np.float32): + """Transform STFT feature + + Args: + Y: STFT + (n_frames, n_bins)-shaped np.complex array + transform_type: + None, "log" + dtype: output data type + np.float32 is expected + Returns: + Y (numpy.array): transformed feature + """ + Y = np.abs(Y) + if not transform_type: + pass + elif transform_type == "log": + Y = np.log(np.maximum(Y, 1e-10)) + elif transform_type == "logmel": + n_fft = 2 * (Y.shape[1] - 1) + sr = 16000 + n_mels = 40 + mel_basis = librosa.filters.mel(sr, n_fft, n_mels) + Y = np.dot(Y**2, mel_basis.T) + Y = np.log10(np.maximum(Y, 1e-10)) + elif transform_type == "logmel23": + n_fft = 2 * (Y.shape[1] - 1) + sr = 8000 + n_mels = 23 + mel_basis = librosa.filters.mel(sr, n_fft, n_mels) + Y = np.dot(Y**2, mel_basis.T) + Y = np.log10(np.maximum(Y, 1e-10)) + elif transform_type == "logmel23_mn": + n_fft = 2 * (Y.shape[1] - 1) + sr = 8000 + n_mels = 23 + mel_basis = librosa.filters.mel(sr, n_fft, n_mels) + Y = np.dot(Y**2, mel_basis.T) + Y = np.log10(np.maximum(Y, 1e-10)) + mean = np.mean(Y, axis=0) + Y = Y - mean + elif transform_type == "logmel23_swn": + n_fft = 2 * (Y.shape[1] - 1) + sr = 8000 + n_mels = 23 + mel_basis = librosa.filters.mel(sr, n_fft, n_mels) + Y = np.dot(Y**2, mel_basis.T) + Y = np.log10(np.maximum(Y, 1e-10)) + # b = np.ones(300)/300 + # mean = scipy.signal.convolve2d(Y, b[:, None], mode='same') + + # simple 2-means based threshoding for mean calculation + powers = np.sum(Y, axis=1) + th = (np.max(powers) + np.min(powers)) / 2.0 + for i in range(10): + th = (np.mean(powers[powers >= th]) + np.mean(powers[powers < th])) / 2 + mean = np.mean(Y[powers > th, :], axis=0) + Y = Y - mean + elif transform_type == "logmel23_mvn": + n_fft = 2 * (Y.shape[1] - 1) + sr = 8000 + n_mels = 23 + mel_basis = librosa.filters.mel(sr, n_fft, n_mels) + Y = np.dot(Y**2, mel_basis.T) + Y = np.log10(np.maximum(Y, 1e-10)) + mean = np.mean(Y, axis=0) + Y = Y - mean + std = np.maximum(np.std(Y, axis=0), 1e-10) + Y = Y / std + else: + raise ValueError("Unknown transform_type: %s" % transform_type) + return Y.astype(dtype) + + +def subsample(Y, T, subsampling=1): + """Frame subsampling""" + Y_ss = Y[::subsampling] + T_ss = T[::subsampling] + return Y_ss, T_ss + + +def splice(Y, context_size=0): + """Frame splicing + + Args: + Y: feature + (n_frames, n_featdim)-shaped numpy array + context_size: + number of frames concatenated on left-side + if context_size = 5, 11 frames are concatenated. + + Returns: + Y_spliced: spliced feature + (n_frames, n_featdim * (2 * context_size + 1))-shaped + """ + Y_pad = np.pad(Y, [(context_size, context_size), (0, 0)], "constant") + Y_spliced = np.lib.stride_tricks.as_strided( + np.ascontiguousarray(Y_pad), + (Y.shape[0], Y.shape[1] * (2 * context_size + 1)), + (Y.itemsize * Y.shape[1], Y.itemsize), + writeable=False, + ) + return Y_spliced + + +def stft(data, frame_size=1024, frame_shift=256): + """Compute STFT features + + Args: + data: audio signal + (n_samples,)-shaped np.float32 array + frame_size: number of samples in a frame (must be a power of two) + frame_shift: number of samples between frames + + Returns: + stft: STFT frames + (n_frames, n_bins)-shaped np.complex64 array + """ + # round up to nearest power of 2 + fft_size = 1 << (frame_size - 1).bit_length() + # HACK: The last frame is ommited + # as librosa.stft produces such an excessive frame + if len(data) % frame_shift == 0: + return librosa.stft( + data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift + ).T[:-1] + else: + return librosa.stft( + data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift + ).T + + +def _count_frames(data_len, size, shift): + # HACK: Assuming librosa.stft(..., center=True) + n_frames = 1 + int(data_len / shift) + if data_len % shift == 0: + n_frames = n_frames - 1 + return n_frames + + +def get_frame_labels( + kaldi_obj, rec, start=0, end=None, frame_size=1024, frame_shift=256, n_speakers=None +): + """Get frame-aligned labels of given recording + Args: + kaldi_obj (KaldiData) + rec (str): recording id + start (int): start frame index + end (int): end frame index + None means the last frame of recording + frame_size (int): number of frames in a frame + frame_shift (int): number of shift samples + n_speakers (int): number of speakers + if None, the value is given from data + Returns: + T: label + (n_frames, n_speakers)-shaped np.int32 array + """ + filtered_segments = kaldi_obj.segments[kaldi_obj.segments["rec"] == rec] + speakers = np.unique( + [kaldi_obj.utt2spk[seg["utt"]] for seg in filtered_segments] + ).tolist() + if n_speakers is None: + n_speakers = len(speakers) + es = end * frame_shift if end is not None else None + data, rate = kaldi_obj.load_wav(rec, start * frame_shift, es) + n_frames = _count_frames(len(data), frame_size, frame_shift) + T = np.zeros((n_frames, n_speakers), dtype=np.int32) + if end is None: + end = n_frames + + for seg in filtered_segments: + speaker_index = speakers.index(kaldi_obj.utt2spk[seg["utt"]]) + start_frame = np.rint(seg["st"] * rate / frame_shift).astype(int) + end_frame = np.rint(seg["et"] * rate / frame_shift).astype(int) + rel_start = rel_end = None + if start <= start_frame and start_frame < end: + rel_start = start_frame - start + if start < end_frame and end_frame <= end: + rel_end = end_frame - start + if rel_start is not None or rel_end is not None: + T[rel_start:rel_end, speaker_index] = 1 + return T + + +def get_labeledSTFT( + kaldi_obj, + rec, + start, + end, + frame_size, + frame_shift, + n_speakers=None, + use_speaker_id=False, +): + """Extracts STFT and corresponding labels + + Extracts STFT and corresponding diarization labels for + given recording id and start/end times + + Args: + kaldi_obj (KaldiData) + rec (str): recording id + start (int): start frame index + end (int): end frame index + frame_size (int): number of samples in a frame + frame_shift (int): number of shift samples + n_speakers (int): number of speakers + if None, the value is given from data + Returns: + Y: STFT + (n_frames, n_bins)-shaped np.complex64 array, + T: label + (n_frmaes, n_speakers)-shaped np.int32 array. + """ + data, rate = kaldi_obj.load_wav(rec, start * frame_shift, end * frame_shift) + Y = stft(data, frame_size, frame_shift) + filtered_segments = kaldi_obj.segments[rec] + # filtered_segments = kaldi_obj.segments[kaldi_obj.segments['rec'] == rec] + speakers = np.unique( + [kaldi_obj.utt2spk[seg["utt"]] for seg in filtered_segments] + ).tolist() + if n_speakers is None: + n_speakers = len(speakers) + T = np.zeros((Y.shape[0], n_speakers), dtype=np.int32) + + if use_speaker_id: + all_speakers = sorted(kaldi_obj.spk2utt.keys()) + S = np.zeros((Y.shape[0], len(all_speakers)), dtype=np.int32) + + for seg in filtered_segments: + speaker_index = speakers.index(kaldi_obj.utt2spk[seg["utt"]]) + if use_speaker_id: + all_speaker_index = all_speakers.index(kaldi_obj.utt2spk[seg["utt"]]) + start_frame = np.rint(seg["st"] * rate / frame_shift).astype(int) + end_frame = np.rint(seg["et"] * rate / frame_shift).astype(int) + rel_start = rel_end = None + if start <= start_frame and start_frame < end: + rel_start = start_frame - start + if start < end_frame and end_frame <= end: + rel_end = end_frame - start + if rel_start is not None or rel_end is not None: + T[rel_start:rel_end, speaker_index] = 1 + if use_speaker_id: + S[rel_start:rel_end, all_speaker_index] = 1 + + if use_speaker_id: + return Y, T, S + else: + return Y, T diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/kaldi_data.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/kaldi_data.py new file mode 100644 index 0000000000000000000000000000000000000000..b691407ac5013f39a0568e191d9bb38d9fc4e91c --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/kaldi_data.py @@ -0,0 +1,152 @@ +# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita) +# Licensed under the MIT license. +# +# This library provides utilities for kaldi-style data directory. + + +from __future__ import print_function +import os +import sys +import numpy as np +import subprocess +import librosa as sf +import io +from functools import lru_cache + + +def load_segments(segments_file): + """load segments file as array""" + if not os.path.exists(segments_file): + return None + return np.loadtxt( + segments_file, + dtype=[("utt", "object"), ("rec", "object"), ("st", "f"), ("et", "f")], + ndmin=1, + ) + + +def load_segments_hash(segments_file): + ret = {} + if not os.path.exists(segments_file): + return None + for line in open(segments_file): + utt, rec, st, et = line.strip().split() + ret[utt] = (rec, float(st), float(et)) + return ret + + +def load_segments_rechash(segments_file): + ret = {} + if not os.path.exists(segments_file): + return None + for line in open(segments_file): + utt, rec, st, et = line.strip().split() + if rec not in ret: + ret[rec] = [] + ret[rec].append({"utt": utt, "st": float(st), "et": float(et)}) + return ret + + +def load_wav_scp(wav_scp_file): + """return dictionary { rec: wav_rxfilename }""" + lines = [line.strip().split(None, 1) for line in open(wav_scp_file)] + return {x[0]: x[1] for x in lines} + + +@lru_cache(maxsize=1) +def load_wav(wav_rxfilename, start=0, end=None): + """This function reads audio file and return data in numpy.float32 array. + "lru_cache" holds recently loaded audio so that can be called + many times on the same audio file. + OPTIMIZE: controls lru_cache size for random access, + considering memory size + """ + if wav_rxfilename.endswith("|"): + # input piped command + p = subprocess.Popen(wav_rxfilename[:-1], shell=True, stdout=subprocess.PIPE) + data, samplerate = sf.load(io.BytesIO(p.stdout.read()), dtype="float32") + # cannot seek + data = data[start:end] + elif wav_rxfilename == "-": + # stdin + data, samplerate = sf.load(sys.stdin, dtype="float32") + # cannot seek + data = data[start:end] + else: + # normal wav file + data, samplerate = sf.load(wav_rxfilename, start=start, stop=end) + return data, samplerate + + +def load_utt2spk(utt2spk_file): + """returns dictionary { uttid: spkid }""" + lines = [line.strip().split(None, 1) for line in open(utt2spk_file)] + return {x[0]: x[1] for x in lines} + + +def load_spk2utt(spk2utt_file): + """returns dictionary { spkid: list of uttids }""" + if not os.path.exists(spk2utt_file): + return None + lines = [line.strip().split() for line in open(spk2utt_file)] + return {x[0]: x[1:] for x in lines} + + +def load_reco2dur(reco2dur_file): + """returns dictionary { recid: duration }""" + if not os.path.exists(reco2dur_file): + return None + lines = [line.strip().split(None, 1) for line in open(reco2dur_file)] + return {x[0]: float(x[1]) for x in lines} + + +def process_wav(wav_rxfilename, process): + """This function returns preprocessed wav_rxfilename + Args: + wav_rxfilename: input + process: command which can be connected via pipe, + use stdin and stdout + Returns: + wav_rxfilename: output piped command + """ + if wav_rxfilename.endswith("|"): + # input piped command + return wav_rxfilename + process + "|" + else: + # stdin "-" or normal file + return "cat {} | {} |".format(wav_rxfilename, process) + + +def extract_segments(wavs, segments=None): + """This function returns generator of segmented audio as + (utterance id, numpy.float32 array) + TODO?: sampling rate is not converted. + """ + if segments is not None: + # segments should be sorted by rec-id + for seg in segments: + wav = wavs[seg["rec"]] + data, samplerate = load_wav(wav) + st_sample = np.rint(seg["st"] * samplerate).astype(int) + et_sample = np.rint(seg["et"] * samplerate).astype(int) + yield seg["utt"], data[st_sample:et_sample] + else: + # segments file not found, + # wav.scp is used as segmented audio list + for rec in wavs: + data, samplerate = load_wav(wavs[rec]) + yield rec, data + + +class KaldiData: + def __init__(self, data_dir): + self.data_dir = data_dir + self.segments = load_segments_rechash(os.path.join(self.data_dir, "segments")) + self.utt2spk = load_utt2spk(os.path.join(self.data_dir, "utt2spk")) + self.wavs = load_wav_scp(os.path.join(self.data_dir, "wav.scp")) + self.reco2dur = load_reco2dur(os.path.join(self.data_dir, "reco2dur")) + self.spk2utt = load_spk2utt(os.path.join(self.data_dir, "spk2utt")) + + def load_wav(self, recid, start=0, end=None): + data, rate = load_wav(self.wavs[recid], start, end) + return data, rate diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/losses.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..43bc197ffced852ef6981acebf675e4b032d7870 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/losses.py @@ -0,0 +1,53 @@ +import numpy as np +import torch +import torch.nn.functional as F +from scipy.optimize import linear_sum_assignment + + +def standard_loss(ys, ts): + losses = [ + F.binary_cross_entropy(torch.sigmoid(y), t) * len(y) for y, t in zip(ys, ts) + ] + loss = torch.sum(torch.stack(losses)) + n_frames = ( + torch.from_numpy(np.array(np.sum([t.shape[0] for t in ts]))) + .to(torch.float32) + .to(ys[0].device) + ) + loss = loss / n_frames + return loss + + +def fast_batch_pit_n_speaker_loss(ys, ts): + with torch.no_grad(): + bs = len(ys) + indices = [] + for b in range(bs): + y = ys[b].transpose(0, 1) + t = ts[b].transpose(0, 1) + C, _ = t.shape + y = y[:, None, :].repeat(1, C, 1) + t = t[None, :, :].repeat(C, 1, 1) + bce_loss = F.binary_cross_entropy( + torch.sigmoid(y), t, reduction="none" + ).mean(-1) + C = bce_loss.cpu() + indices.append(linear_sum_assignment(C)) + labels_perm = [t[:, idx[1]] for t, idx in zip(ts, indices)] + + return labels_perm + + +def cal_power_loss(logits, power_ts): + losses = [ + F.cross_entropy(input=logit, target=power_t.to(torch.long)) * len(logit) + for logit, power_t in zip(logits, power_ts) + ] + loss = torch.sum(torch.stack(losses)) + n_frames = ( + torch.from_numpy(np.array(np.sum([power_t.shape[0] for power_t in power_ts]))) + .to(torch.float32) + .to(power_ts[0].device) + ) + loss = loss / n_frames + return loss diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/power.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/power.py new file mode 100644 index 0000000000000000000000000000000000000000..7e65ddbef822aa4c53e5b6df365f84a3e4cffd9f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/power.py @@ -0,0 +1,114 @@ +import numpy as np +import torch +import torch.multiprocessing +import torch.nn.functional as F +from itertools import combinations +from itertools import permutations + + +def generate_mapping_dict(max_speaker_num=6, max_olp_speaker_num=3): + all_kinds = [] + all_kinds.append(0) + for i in range(max_olp_speaker_num): + selected_num = i + 1 + coms = np.array(list(combinations(np.arange(max_speaker_num), selected_num))) + for com in coms: + tmp = np.zeros(max_speaker_num) + tmp[com] = 1 + item = int(raw_dec_trans(tmp.reshape(1, -1), max_speaker_num)[0]) + all_kinds.append(item) + all_kinds_order = sorted(all_kinds) + + mapping_dict = {} + mapping_dict["dec2label"] = {} + mapping_dict["label2dec"] = {} + for i in range(len(all_kinds_order)): + dec = all_kinds_order[i] + mapping_dict["dec2label"][dec] = i + mapping_dict["label2dec"][i] = dec + oov_id = len(all_kinds_order) + mapping_dict["oov"] = oov_id + return mapping_dict + + +def raw_dec_trans(x, max_speaker_num): + num_list = [] + for i in range(max_speaker_num): + num_list.append(x[:, i]) + base = 1 + T = x.shape[0] + res = np.zeros((T)) + for num in num_list: + res += num * base + base = base * 2 + return res + + +def mapping_func(num, mapping_dict): + if num in mapping_dict["dec2label"].keys(): + label = mapping_dict["dec2label"][num] + else: + label = mapping_dict["oov"] + return label + + +def dec_trans(x, max_speaker_num, mapping_dict): + num_list = [] + for i in range(max_speaker_num): + num_list.append(x[:, i]) + base = 1 + T = x.shape[0] + res = np.zeros((T)) + for num in num_list: + res += num * base + base = base * 2 + res = np.array([mapping_func(i, mapping_dict) for i in res]) + return res + + +def create_powerlabel(label, mapping_dict, max_speaker_num=6, max_olp_speaker_num=3): + T, C = label.shape + padding_label = np.zeros((T, max_speaker_num)) + padding_label[:, :C] = label + out_label = dec_trans(padding_label, max_speaker_num, mapping_dict) + out_label = torch.from_numpy(out_label) + return out_label + + +def generate_perm_pse( + label, n_speaker, mapping_dict, max_speaker_num, max_olp_speaker_num=3 +): + perms = np.array(list(permutations(range(n_speaker)))).astype(np.float32) + perms = torch.from_numpy(perms).to(label.device).to(torch.int64) + perm_labels = [label[:, perm] for perm in perms] + perm_pse_labels = [ + create_powerlabel(perm_label.cpu().numpy(), mapping_dict, max_speaker_num).to( + perm_label.device, non_blocking=True + ) + for perm_label in perm_labels + ] + return perm_labels, perm_pse_labels + + +def generate_min_pse( + label, n_speaker, mapping_dict, max_speaker_num, pse_logit, max_olp_speaker_num=3 +): + perm_labels, perm_pse_labels = generate_perm_pse( + label, + n_speaker, + mapping_dict, + max_speaker_num, + max_olp_speaker_num=max_olp_speaker_num, + ) + losses = [ + F.cross_entropy(input=pse_logit, target=perm_pse_label.to(torch.long)) + * len(pse_logit) + for perm_pse_label in perm_pse_labels + ] + loss = torch.stack(losses) + min_index = torch.argmin(loss) + selected_perm_label, selected_pse_label = ( + perm_labels[min_index], + perm_pse_labels[min_index], + ) + return selected_perm_label, selected_pse_label diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/report.py b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/report.py new file mode 100644 index 0000000000000000000000000000000000000000..ba584522d683e2483a960e92b51d30f1679da11e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eend/utils/report.py @@ -0,0 +1,201 @@ +import copy +import numpy as np +import time +import torch +from funasr_detach.models.eend.utils.power import create_powerlabel +from itertools import combinations + +metrics = [ + ("diarization_error", "speaker_scored", "DER"), + ("speech_miss", "speech_scored", "SAD_MR"), + ("speech_falarm", "speech_scored", "SAD_FR"), + ("speaker_miss", "speaker_scored", "MI"), + ("speaker_falarm", "speaker_scored", "FA"), + ("speaker_error", "speaker_scored", "CF"), + ("correct", "frames", "accuracy"), +] + + +def recover_prediction(y, n_speaker): + if n_speaker <= 1: + return y + elif n_speaker == 2: + com_index = torch.from_numpy( + np.array(list(combinations(np.arange(n_speaker), 2))) + ).to(y.dtype) + num_coms = com_index.shape[0] + y_single = y[:, :-num_coms] + y_olp = y[:, -num_coms:] + olp_map_index = torch.where(y_olp > 0.5) + olp_map_index = torch.stack(olp_map_index, dim=1) + com_map_index = com_index[olp_map_index[:, -1]] + speaker_map_index = ( + torch.from_numpy(np.array(com_map_index)).view(-1).to(torch.int64) + ) + frame_map_index = ( + olp_map_index[:, 0][:, None].repeat([1, 2]).view(-1).to(torch.int64) + ) + y_single[frame_map_index] = 0 + y_single[frame_map_index, speaker_map_index] = 1 + return y_single + else: + olp2_com_index = torch.from_numpy( + np.array(list(combinations(np.arange(n_speaker), 2))) + ).to(y.dtype) + olp2_num_coms = olp2_com_index.shape[0] + olp3_com_index = torch.from_numpy( + np.array(list(combinations(np.arange(n_speaker), 3))) + ).to(y.dtype) + olp3_num_coms = olp3_com_index.shape[0] + y_single = y[:, :n_speaker] + y_olp2 = y[:, n_speaker : n_speaker + olp2_num_coms] + y_olp3 = y[:, -olp3_num_coms:] + + olp3_map_index = torch.where(y_olp3 > 0.5) + olp3_map_index = torch.stack(olp3_map_index, dim=1) + olp3_com_map_index = olp3_com_index[olp3_map_index[:, -1]] + olp3_speaker_map_index = ( + torch.from_numpy(np.array(olp3_com_map_index)).view(-1).to(torch.int64) + ) + olp3_frame_map_index = ( + olp3_map_index[:, 0][:, None].repeat([1, 3]).view(-1).to(torch.int64) + ) + y_single[olp3_frame_map_index] = 0 + y_single[olp3_frame_map_index, olp3_speaker_map_index] = 1 + y_olp2[olp3_frame_map_index] = 0 + + olp2_map_index = torch.where(y_olp2 > 0.5) + olp2_map_index = torch.stack(olp2_map_index, dim=1) + olp2_com_map_index = olp2_com_index[olp2_map_index[:, -1]] + olp2_speaker_map_index = ( + torch.from_numpy(np.array(olp2_com_map_index)).view(-1).to(torch.int64) + ) + olp2_frame_map_index = ( + olp2_map_index[:, 0][:, None].repeat([1, 2]).view(-1).to(torch.int64) + ) + y_single[olp2_frame_map_index] = 0 + y_single[olp2_frame_map_index, olp2_speaker_map_index] = 1 + return y_single + + +class PowerReporter: + def __init__(self, valid_data_loader, mapping_dict, max_n_speaker): + valid_data_loader_cp = copy.deepcopy(valid_data_loader) + self.valid_data_loader = valid_data_loader_cp + del valid_data_loader + self.mapping_dict = mapping_dict + self.max_n_speaker = max_n_speaker + + def report(self, model, eidx, device): + self.report_val(model, eidx, device) + + def report_val(self, model, eidx, device): + model.eval() + ud_valid_start = time.time() + valid_res, valid_loss, stats_keys, vad_valid_accuracy = self.report_core( + model, self.valid_data_loader, device + ) + + # Epoch Display + valid_der = valid_res["diarization_error"] / valid_res["speaker_scored"] + valid_accuracy = ( + valid_res["correct"].to(torch.float32) / valid_res["frames"] * 100 + ) + vad_valid_accuracy = vad_valid_accuracy * 100 + print( + "Epoch ", + eidx + 1, + "Valid Loss ", + valid_loss, + "Valid_DER %.5f" % valid_der, + "Valid_Accuracy %.5f%% " % valid_accuracy, + "VAD_Valid_Accuracy %.5f%% " % vad_valid_accuracy, + ) + ud_valid = (time.time() - ud_valid_start) / 60.0 + print("Valid cost time ... ", ud_valid) + + def inv_mapping_func(self, label, mapping_dict): + if not isinstance(label, int): + label = int(label) + if label in mapping_dict["label2dec"].keys(): + num = mapping_dict["label2dec"][label] + else: + num = -1 + return num + + def report_core(self, model, data_loader, device): + res = {} + for item in metrics: + res[item[0]] = 0.0 + res[item[1]] = 0.0 + with torch.no_grad(): + loss_s = 0.0 + uidx = 0 + for xs, ts, orders in data_loader: + xs = [x.to(device) for x in xs] + ts = [t.to(device) for t in ts] + orders = [o.to(device) for o in orders] + loss, pit_loss, mpit_loss, att_loss, ys, logits, labels, attractors = ( + model(xs, ts, orders) + ) + loss_s += loss.item() + uidx += 1 + + for logit, t, att in zip(logits, labels, attractors): + pred = torch.argmax(torch.softmax(logit, dim=-1), dim=-1) # (T, ) + oov_index = torch.where(pred == self.mapping_dict["oov"])[0] + for i in oov_index: + if i > 0: + pred[i] = pred[i - 1] + else: + pred[i] = 0 + pred = [self.inv_mapping_func(i, self.mapping_dict) for i in pred] + decisions = [ + bin(num)[2:].zfill(self.max_n_speaker)[::-1] for num in pred + ] + decisions = ( + torch.from_numpy( + np.stack( + [np.array([int(i) for i in dec]) for dec in decisions], + axis=0, + ) + ) + .to(att.device) + .to(torch.float32) + ) + decisions = decisions[:, : att.shape[0]] + + stats = self.calc_diarization_error(decisions, t) + res["speaker_scored"] += stats["speaker_scored"] + res["speech_scored"] += stats["speech_scored"] + res["frames"] += stats["frames"] + for item in metrics: + res[item[0]] += stats[item[0]] + loss_s /= uidx + vad_acc = 0 + + return res, loss_s, stats.keys(), vad_acc + + def calc_diarization_error(self, decisions, label, label_delay=0): + label = label[: len(label) - label_delay, ...] + n_ref = torch.sum(label, dim=-1) + n_sys = torch.sum(decisions, dim=-1) + res = {} + res["speech_scored"] = torch.sum(n_ref > 0) + res["speech_miss"] = torch.sum((n_ref > 0) & (n_sys == 0)) + res["speech_falarm"] = torch.sum((n_ref == 0) & (n_sys > 0)) + res["speaker_scored"] = torch.sum(n_ref) + res["speaker_miss"] = torch.sum( + torch.max(n_ref - n_sys, torch.zeros_like(n_ref)) + ) + res["speaker_falarm"] = torch.sum( + torch.max(n_sys - n_ref, torch.zeros_like(n_ref)) + ) + n_map = torch.sum(((label == 1) & (decisions == 1)), dim=-1).to(torch.float32) + res["speaker_error"] = torch.sum(torch.min(n_ref, n_sys) - n_map) + res["correct"] = torch.sum(label == decisions) / label.shape[1] + res["diarization_error"] = ( + res["speaker_miss"] + res["speaker_falarm"] + res["speaker_error"] + ) + res["frames"] = len(label) + return res diff --git a/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/audio.py b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..4b44d03a801b3354c68967c2b1c458038e105116 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/audio.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import numpy as np +import torch.nn as nn +from functools import partial +import torch.nn.functional as F +from typing import Callable, Dict + +from funasr_detach.models.emotion2vec.fairseq_modules import ( + LayerNorm, + SamePad, + TransposeLast, + ConvFeatureExtractionModel, +) +from funasr_detach.models.emotion2vec.modules import Modality, BlockEncoder, Decoder1d +from funasr_detach.models.emotion2vec.base import ( + ModalitySpecificEncoder, + get_alibi_bias, +) + + +class AudioEncoder(ModalitySpecificEncoder): + + def __init__( + self, + modality_cfg, + embed_dim: int, + make_block: Callable[[float], nn.ModuleList], + norm_layer: Callable[[int], nn.LayerNorm], + layer_norm_first: bool, + alibi_biases: Dict, + ): + + self.feature_enc_layers = eval(modality_cfg.feature_encoder_spec) + feature_embed_dim = self.feature_enc_layers[-1][0] + + local_encoder = ConvFeatureExtractionModel( + conv_layers=self.feature_enc_layers, + dropout=0.0, + mode=modality_cfg.extractor_mode, + conv_bias=False, + ) + + project_features = nn.Sequential( + TransposeLast(), + nn.LayerNorm(feature_embed_dim), + nn.Linear(feature_embed_dim, embed_dim), + ) + + num_pos_layers = modality_cfg.conv_pos_depth + k = max(3, modality_cfg.conv_pos_width // num_pos_layers) + + positional_encoder = nn.Sequential( + TransposeLast(), + *[ + nn.Sequential( + nn.Conv1d( + embed_dim, + embed_dim, + kernel_size=k, + padding=k // 2, + groups=modality_cfg.conv_pos_groups, + ), + SamePad(k), + TransposeLast(), + LayerNorm(embed_dim, elementwise_affine=False), + TransposeLast(), + nn.GELU(), + ) + for _ in range(num_pos_layers) + ], + TransposeLast(), + ) + + if modality_cfg.conv_pos_pre_ln: + positional_encoder = nn.Sequential(LayerNorm(embed_dim), positional_encoder) + + dpr = np.linspace( + modality_cfg.start_drop_path_rate, + modality_cfg.end_drop_path_rate, + modality_cfg.prenet_depth, + ) + context_encoder = BlockEncoder( + nn.ModuleList(make_block(dpr[i]) for i in range(modality_cfg.prenet_depth)), + norm_layer(embed_dim) if not layer_norm_first else None, + layer_norm_first, + modality_cfg.prenet_layerdrop, + modality_cfg.prenet_dropout, + ) + + decoder = ( + Decoder1d(modality_cfg.decoder, embed_dim) + if modality_cfg.decoder is not None + else None + ) + + alibi_bias_fn = partial(get_alibi_bias, alibi_biases=alibi_biases) + + super().__init__( + modality_cfg=modality_cfg, + embed_dim=embed_dim, + local_encoder=local_encoder, + project_features=project_features, + fixed_positional_encoder=None, + relative_positional_encoder=positional_encoder, + context_encoder=context_encoder, + decoder=decoder, + get_alibi_bias=alibi_bias_fn, + ) + + def convert_padding_mask(self, x, padding_mask): + def get_feat_extract_output_lengths(input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + return torch.floor((input_length - kernel_size) / stride + 1) + + for i in range(len(self.feature_enc_layers)): + input_lengths = _conv_out_length( + input_lengths, + self.feature_enc_layers[i][1], + self.feature_enc_layers[i][2], + ) + + return input_lengths.to(torch.long) + + if padding_mask is not None: + input_lengths = (1 - padding_mask.long()).sum(-1) + # apply conv formula to get real output_lengths + output_lengths = get_feat_extract_output_lengths(input_lengths) + + if padding_mask.any(): + padding_mask = torch.zeros(x.shape[:2], dtype=x.dtype, device=x.device) + + # these two operations makes sure that all values + # before the output lengths indices are attended to + padding_mask[ + ( + torch.arange(padding_mask.shape[0], device=padding_mask.device), + output_lengths - 1, + ) + ] = 1 + padding_mask = ( + 1 - padding_mask.flip([-1]).cumsum(-1).flip([-1]) + ).bool() + else: + padding_mask = torch.zeros( + x.shape[:2], dtype=torch.bool, device=x.device + ) + + return padding_mask + + def reset_parameters(self): + super().reset_parameters() + for mod in self.project_features.children(): + if isinstance(mod, nn.Linear): + mod.reset_parameters() + if self.decoder is not None: + self.decoder.reset_parameters() diff --git a/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/base.py b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/base.py new file mode 100644 index 0000000000000000000000000000000000000000..a154fff046481d174b5ea2cbdbccfed1f134aabb --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/base.py @@ -0,0 +1,646 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from collections import namedtuple +from dataclasses import dataclass +from functools import partial +from omegaconf import MISSING, II +from typing import Optional, Callable +from funasr_detach.models.emotion2vec.fairseq_modules import compute_mask_indices +from funasr_detach.models.emotion2vec.fairseq_modules import GradMultiply +from funasr_detach.models.emotion2vec.fairseq_modules import index_put + + +logger = logging.getLogger(__name__) + + +MaskSeed = namedtuple("MaskSeed", ["seed", "update", "ids"]) +MaskInfo = namedtuple("MaskInfo", ["x_unmasked", "mask", "ids_restore", "ids_keep"]) + + +class ModalitySpecificEncoder(nn.Module): + def __init__( + self, + modality_cfg, + embed_dim: int, + local_encoder: nn.Module, + project_features: nn.Module, + fixed_positional_encoder: Optional[nn.Module], + relative_positional_encoder: Optional[nn.Module], + context_encoder: nn.Module, + decoder: nn.Module, + get_alibi_bias: Optional[Callable[[int, int, str, str], torch.Tensor]], + ): + super().__init__() + + self.modality_cfg = modality_cfg + self.local_encoder = local_encoder + self.project_features = project_features + self.fixed_positional_encoder = fixed_positional_encoder + self.relative_positional_encoder = relative_positional_encoder + self.context_encoder = context_encoder + + self.decoder = decoder + self.get_alibi_bias = get_alibi_bias if modality_cfg.use_alibi_encoder else None + + self.local_grad_mult = self.modality_cfg.local_grad_mult + + self.extra_tokens = None + if modality_cfg.num_extra_tokens > 0: + self.extra_tokens = nn.Parameter( + torch.zeros(1, modality_cfg.num_extra_tokens, embed_dim) + ) + if not modality_cfg.init_extra_token_zero: + nn.init.normal_(self.extra_tokens) + elif self.extra_tokens.size(1) > 1: + nn.init.normal_(self.extra_tokens[:, 1:]) + + self.alibi_scale = None + if self.get_alibi_bias is not None: + self.alibi_scale = nn.Parameter( + torch.full( + ( + ( + (modality_cfg.prenet_depth + modality_cfg.model_depth) + if modality_cfg.learned_alibi_scale_per_layer + else 1 + ), + 1, + ( + self.modality_cfg.num_alibi_heads + if modality_cfg.learned_alibi_scale_per_head + else 1 + ), + 1, + 1, + ), + modality_cfg.alibi_scale, + dtype=torch.float, + ), + requires_grad=modality_cfg.learned_alibi_scale, + ) + + if modality_cfg.learned_alibi and self.get_alibi_bias is not None: + assert modality_cfg.alibi_max_pos is not None + alibi_bias = self.get_alibi_bias( + batch_size=1, + time_steps=modality_cfg.alibi_max_pos, + heads=modality_cfg.num_alibi_heads, + scale=1.0, + dtype=torch.float, + device="cpu", + ) + self.alibi_bias = nn.Parameter(alibi_bias) + self.get_alibi_bias = partial( + _learned_alibi_bias, alibi_bias=self.alibi_bias + ) + + def upgrade_state_dict_named(self, state_dict, name): + k = f"{name}.alibi_scale" + if k in state_dict and state_dict[k].dim() == 4: + state_dict[k] = state_dict[k].unsqueeze(0) + + return state_dict + + def convert_padding_mask(self, x, padding_mask): + return padding_mask + + def decoder_input(self, x, mask_info: MaskInfo): + inp_drop = self.modality_cfg.decoder.input_dropout + if inp_drop > 0: + x = F.dropout(x, inp_drop, training=self.training, inplace=True) + + num_extra = self.modality_cfg.num_extra_tokens + + if mask_info is not None: + num_masked = mask_info.ids_restore.shape[1] - x.shape[1] + num_extra + + mask_tokens = x.new_empty( + x.size(0), + num_masked, + x.size(-1), + ).normal_(0, self.modality_cfg.mask_noise_std) + + x_ = torch.cat([x[:, num_extra:], mask_tokens], dim=1) + x = torch.gather(x_, dim=1, index=mask_info.ids_restore) + + if self.modality_cfg.decoder.add_positions_masked: + assert self.fixed_positional_encoder is not None + pos = self.fixed_positional_encoder(x, None) + x = x + (pos * mask_info.mask.unsqueeze(-1)) + else: + x = x[:, num_extra:] + + if self.modality_cfg.decoder.add_positions_all: + assert self.fixed_positional_encoder is not None + x = x + self.fixed_positional_encoder(x, None) + + return x, mask_info + + def local_features(self, features): + if self.local_grad_mult > 0: + if self.local_grad_mult == 1.0: + x = self.local_encoder(features) + else: + x = GradMultiply.apply( + self.local_encoder(features), self.local_grad_mult + ) + else: + with torch.no_grad(): + x = self.local_encoder(features) + + x = self.project_features(x) + return x + + def contextualized_features( + self, + x, + padding_mask, + mask, + remove_masked, + clone_batch: int = 1, + mask_seeds: Optional[torch.Tensor] = None, + precomputed_mask=None, + ): + + if padding_mask is not None: + padding_mask = self.convert_padding_mask(x, padding_mask) + + local_features = x + if mask and clone_batch == 1: + local_features = local_features.clone() + + orig_B, orig_T, _ = x.shape + pre_mask_B = orig_B + mask_info = None + + x_pos = None + if self.fixed_positional_encoder is not None: + x = x + self.fixed_positional_encoder(x, padding_mask) + + if mask: + if clone_batch > 1: + x = x.repeat_interleave(clone_batch, 0) + if mask_seeds is not None: + clone_hash = [ + int(hash((mask_seeds.seed, ind)) % 1e10) + for ind in range(clone_batch - 1) + ] + clone_hash = torch.tensor([0] + clone_hash).long().view(1, -1) + + id = mask_seeds.ids + id = id.repeat_interleave(clone_batch, 0) + id = id.view(-1, clone_batch) + clone_hash.to(id) + id = id.view(-1) + mask_seeds = MaskSeed( + seed=mask_seeds.seed, update=mask_seeds.update, ids=id + ) + if padding_mask is not None: + padding_mask = padding_mask.repeat_interleave(clone_batch, 0) + + x, mask_info = self.compute_mask( + x, + padding_mask, + mask_seed=mask_seeds, + apply=self.relative_positional_encoder is not None or not remove_masked, + precomputed_mask=precomputed_mask, + ) + + if self.relative_positional_encoder is not None: + x_pos = self.relative_positional_encoder(x) + + masked_padding_mask = padding_mask + if mask and remove_masked: + x = mask_info.x_unmasked + if x_pos is not None: + x = x + gather_unmasked(x_pos, mask_info) + + if padding_mask is not None and padding_mask.any(): + masked_padding_mask = gather_unmasked_mask(padding_mask, mask_info) + if not masked_padding_mask.any(): + masked_padding_mask = None + else: + masked_padding_mask = None + + elif x_pos is not None: + x = x + x_pos + + alibi_bias = None + alibi_scale = self.alibi_scale + + if self.get_alibi_bias is not None: + alibi_bias = self.get_alibi_bias( + batch_size=pre_mask_B, + time_steps=orig_T, + heads=self.modality_cfg.num_alibi_heads, + dtype=torch.float32, + device=x.device, + ) + + if alibi_scale is not None: + alibi_scale = alibi_scale.clamp_min(0) + if alibi_scale.size(0) == 1: + alibi_bias = alibi_bias * alibi_scale.squeeze(0).type_as(alibi_bias) + alibi_scale = None + + if clone_batch > 1: + alibi_bias = alibi_bias.repeat_interleave(clone_batch, 0) + + if mask_info is not None and remove_masked: + alibi_bias = masked_alibi(alibi_bias, mask_info) + + if self.extra_tokens is not None: + num = self.extra_tokens.size(1) + x = torch.cat([self.extra_tokens.expand(x.size(0), -1, -1), x], dim=1) + if masked_padding_mask is not None: + # B x T + masked_padding_mask = F.pad(masked_padding_mask, (num, 0)) + if alibi_bias is not None: + # B x H x T x T + alibi_bias = F.pad(alibi_bias, (num, 0, num, 0)) + + x = self.context_encoder( + x, + masked_padding_mask, + alibi_bias, + ( + alibi_scale[: self.modality_cfg.prenet_depth] + if alibi_scale is not None + else None + ), + ) + + return { + "x": x, + "local_features": local_features, + "padding_mask": masked_padding_mask, + "alibi_bias": alibi_bias, + "alibi_scale": ( + alibi_scale[self.modality_cfg.prenet_depth :] + if alibi_scale is not None and alibi_scale.size(0) > 1 + else alibi_scale + ), + "encoder_mask": mask_info, + } + + def forward( + self, + features, + padding_mask, + mask: bool, + remove_masked: bool, + clone_batch: int = 1, + mask_seeds: Optional[torch.Tensor] = None, + precomputed_mask=None, + ): + x = self.local_features(features) + return self.contextualized_features( + x, + padding_mask, + mask, + remove_masked, + clone_batch, + mask_seeds, + precomputed_mask, + ) + + def reset_parameters(self): + pass + + def compute_mask( + self, + x, + padding_mask, + mask_seed: Optional[MaskSeed], + apply, + precomputed_mask, + ): + if precomputed_mask is not None: + mask = precomputed_mask + mask_info = self.make_maskinfo(x, mask) + else: + B, T, C = x.shape + cfg = self.modality_cfg + + mask_prob = cfg.mask_prob + + if ( + cfg.mask_prob_min is not None + and cfg.mask_prob_min >= 0 + and cfg.mask_prob_min < mask_prob + ): + mask_prob = np.random.uniform(cfg.mask_prob_min, mask_prob) + + if mask_prob > 0: + if cfg.mask_length == 1: + mask_info = random_masking(x, mask_prob, mask_seed) + else: + if self.modality_cfg.inverse_mask: + mask_prob = 1 - mask_prob + + mask = compute_mask_indices( + (B, T), + padding_mask, + mask_prob, + cfg.mask_length, + min_masks=1, + require_same_masks=True, + mask_dropout=cfg.mask_dropout, + add_masks=cfg.add_masks, + seed=mask_seed.seed if mask_seed is not None else None, + epoch=mask_seed.update if mask_seed is not None else None, + indices=mask_seed.ids if mask_seed is not None else None, + ) + + mask = torch.from_numpy(mask).to(device=x.device) + if self.modality_cfg.inverse_mask: + mask = 1 - mask + mask_info = self.make_maskinfo(x, mask) + else: + mask_info = None + + if apply: + x = self.apply_mask(x, mask_info) + + return x, mask_info + + def make_maskinfo(self, x, mask, shape=None): + if shape is None: + B, T, D = x.shape + else: + B, T, D = shape + + mask = mask.to(torch.uint8) + ids_shuffle = mask.argsort(dim=1) + ids_restore = ids_shuffle.argsort(dim=1).unsqueeze(-1).expand(-1, -1, D) + + len_keep = T - mask[0].sum() + if self.modality_cfg.keep_masked_pct > 0: + len_keep += round((T - int(len_keep)) * self.modality_cfg.keep_masked_pct) + + ids_keep = ids_shuffle[:, :len_keep] + + if shape is not None: + x_unmasked = None + else: + ids_keep = ids_keep.unsqueeze(-1).expand(-1, -1, D) + x_unmasked = torch.gather(x, dim=1, index=ids_keep) + + mask_info = MaskInfo( + x_unmasked=x_unmasked, + mask=mask, + ids_restore=ids_restore, + ids_keep=ids_keep, + ) + return mask_info + + def apply_mask(self, x, mask_info): + cfg = self.modality_cfg + B, T, C = x.shape + + if mask_info is not None: + mask = mask_info.mask + if cfg.encoder_zero_mask: + x = x * (1 - mask.type_as(x).unsqueeze(-1)) + else: + num_masks = mask.sum().item() + masks = x.new_empty(num_masks, x.size(-1)).normal_( + 0, cfg.mask_noise_std + ) + x = index_put(x, mask, masks) + if cfg.mask_channel_prob > 0: + mask_channel = compute_mask_indices( + (B, C), + None, + cfg.mask_channel_prob, + cfg.mask_channel_length, + ) + mask_channel = ( + torch.from_numpy(mask_channel) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x = index_put(x, mask_channel, 0) + return x + + def remove_pretraining_modules(self, keep_decoder=False): + if not keep_decoder: + self.decoder = None + + +def get_annealed_rate(start, end, curr_step, total_steps): + if curr_step >= total_steps: + return end + r = end - start + pct_remaining = 1 - curr_step / total_steps + return end - r * pct_remaining + + +# adapted from MAE +def random_masking(x, mask_ratio, mask_seed: Optional[MaskSeed]): + N, L, D = x.shape # batch, length, dim + len_keep = int(L * (1 - mask_ratio)) + + generator = None + if mask_seed is not None: + seed = int( + hash((mask_seed.seed, mask_seed.update, mask_seed.ids.sum().item())) % 1e6 + ) + generator = torch.Generator(device=x.device) + generator.manual_seed(seed) + + noise = torch.rand(N, L, generator=generator, device=x.device) # noise in [0, 1] + + # sort noise for each sample + ids_shuffle = noise.argsort(dim=1) # ascend: small is keep, large is remove + ids_restore = ids_shuffle.argsort(dim=1) + + # keep the first subset + ids_keep = ids_shuffle[:, :len_keep] + ids_keep = ids_keep.unsqueeze(-1).expand(-1, -1, D) + x_unmasked = torch.gather(x, dim=1, index=ids_keep) + + # generate the binary mask: 0 is keep, 1 is remove + mask = torch.ones([N, L], dtype=x.dtype, device=x.device) + mask[:, :len_keep] = 0 + # unshuffle to get the binary mask + mask = torch.gather(mask, dim=1, index=ids_restore) + + ids_restore = ids_restore.unsqueeze(-1).expand(-1, -1, D) + + return MaskInfo( + x_unmasked=x_unmasked, mask=mask, ids_restore=ids_restore, ids_keep=ids_keep + ) + + +def gather_unmasked(x: torch.Tensor, mask_info: MaskInfo) -> torch.Tensor: + return torch.gather( + x, + dim=1, + index=mask_info.ids_keep, + ) + + +def gather_unmasked_mask(x: torch.Tensor, mask_info: MaskInfo) -> torch.Tensor: + return torch.gather( + x, + dim=1, + index=mask_info.ids_keep[..., 0], # ignore the feature dimension + ) + + +def get_alibi( + max_positions: int, + attention_heads: int, + dims: int = 1, + distance: str = "manhattan", +): + def get_slopes(n): + def get_slopes_power_of_2(n): + start = 2 ** (-(2 ** -(math.log2(n) - 3))) + ratio = start + return [start * ratio**i for i in range(n)] + + # In the paper, we only train models that have 2^a heads for some + # a. This function has some good properties that only occur when + # the input is a power of 2. To maintain that even when the number + # of heads is not a power of 2, we use this workaround. + if math.log2(n).is_integer(): + return get_slopes_power_of_2(n) + else: + closest_power_of_2 = 2 ** math.floor(math.log2(n)) + return ( + get_slopes_power_of_2(closest_power_of_2) + + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2] + ) + + maxpos = max_positions + attn_heads = attention_heads + slopes = torch.Tensor(get_slopes(attn_heads)) + + if dims == 1: + # prepare alibi position linear bias. Note that wav2vec2 is non + # autoregressive model so we want a symmetric mask with 0 on the + # diagonal and other wise linear decreasing valuees + pos_bias = ( + torch.abs( + torch.arange(maxpos).unsqueeze(0) - torch.arange(maxpos).unsqueeze(1) + ) + * -1 + ) + elif dims == 2: + if distance == "manhattan": + df = lambda x1, y1, x2, y2: abs(x1 - x2) + abs(y1 - y2) + elif distance == "euclidean": + df = lambda x1, y1, x2, y2: math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) + + n = math.sqrt(max_positions) + assert n.is_integer(), n + n = int(n) + + pos_bias = torch.zeros((max_positions, max_positions)) + + for i in range(n): + for j in range(n): + for k in range(n): + for l in range(n): + new_x = i * n + j + new_y = k * n + l + pos_bias[new_x, new_y] = -df(i, j, k, l) + + else: + raise Exception(f"unsupported number of alibi dims: {dims}") + + alibi_bias = slopes.unsqueeze(1).unsqueeze(1) * pos_bias.unsqueeze(0).expand( + attn_heads, -1, -1 + ) + + return alibi_bias + + +def get_alibi_bias( + alibi_biases, + batch_size, + time_steps, + heads, + dtype, + device, + dims=1, + distance="manhattan", +): + cache_key = f"{dims}_{heads}_{distance}" + + buffered = alibi_biases.get(cache_key, None) + + target_size = heads * batch_size + if ( + buffered is None + or buffered.size(0) < target_size + or buffered.size(1) < time_steps + or buffered.dtype != dtype + or buffered.device != device + ): + bt = max(time_steps, buffered.size(1) if buffered is not None else 0) + bn = max(target_size, buffered.size(0) if buffered is not None else 0) // heads + + buffered = ( + get_alibi(bt, heads, dims=dims, distance=distance) + .to(dtype=dtype, device=device) + .repeat(bn, 1, 1) + ) + + alibi_biases[cache_key] = buffered + + b = buffered[:target_size, :time_steps, :time_steps] + b = b.view(batch_size, heads, time_steps, time_steps) + return b + + +def _learned_alibi_bias( + alibi_bias, + batch_size, + time_steps, + heads, + scale, + dtype, + device, +): + assert alibi_bias.size(1) == heads, alibi_bias.shape + assert alibi_bias.dtype == dtype, alibi_bias.dtype + assert alibi_bias.device == device, alibi_bias.device + + if alibi_bias.size(-1) < time_steps: + psz = math.ceil((time_steps - alibi_bias.size(-1)) / 2) + alibi_bias = F.pad(alibi_bias, (psz, psz, psz, psz), mode="replicate") + + alibi_bias = alibi_bias.expand(batch_size, -1, -1, -1) * scale + return alibi_bias[..., :time_steps, :time_steps] + + +def masked_alibi(alibi_bias, mask_info): + H = alibi_bias.size(1) + + orig_bias = alibi_bias + + index = mask_info.ids_keep.unsqueeze(1)[..., 0].unsqueeze(-1) + alibi_bias = torch.gather( + orig_bias, + dim=-2, + index=index.expand(-1, H, -1, mask_info.ids_restore.size(1)), + ) + alibi_bias = torch.gather( + alibi_bias, + dim=-1, + index=index.transpose(-1, -2).expand(-1, H, alibi_bias.size(-2), -1), + ) + + return alibi_bias diff --git a/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/fairseq_modules.py b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/fairseq_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..aa689367240be012dc6f26219318b2a91d26cc49 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/fairseq_modules.py @@ -0,0 +1,310 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Optional, Tuple, List +import numpy as np + + +def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): + return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine) + + +class SamePad(nn.Module): + def __init__(self, kernel_size, causal=False): + super().__init__() + if causal: + self.remove = kernel_size - 1 + else: + self.remove = 1 if kernel_size % 2 == 0 else 0 + + def forward(self, x): + if self.remove > 0: + x = x[:, :, : -self.remove] + return x + + +class TransposeLast(nn.Module): + def __init__(self, deconstruct_idx=None): + super().__init__() + self.deconstruct_idx = deconstruct_idx + + def forward(self, x): + if self.deconstruct_idx is not None: + x = x[self.deconstruct_idx] + return x.transpose(-2, -1) + + +class Fp32LayerNorm(nn.LayerNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.layer_norm( + input.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) + + +class Fp32GroupNorm(nn.GroupNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.group_norm( + input.float(), + self.num_groups, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) + + +class ConvFeatureExtractionModel(nn.Module): + def __init__( + self, + conv_layers: List[Tuple[int, int, int]], + dropout: float = 0.0, + mode: str = "default", + conv_bias: bool = False, + ): + super().__init__() + + assert mode in {"default", "layer_norm"} + + def block( + n_in, + n_out, + k, + stride, + is_layer_norm=False, + is_group_norm=False, + conv_bias=False, + ): + def make_conv(): + conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) + nn.init.kaiming_normal_(conv.weight) + return conv + + assert ( + is_layer_norm and is_group_norm + ) == False, "layer norm and group norm are exclusive" + + if is_layer_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + nn.Sequential( + TransposeLast(), + Fp32LayerNorm(dim, elementwise_affine=True), + TransposeLast(), + ), + nn.GELU(), + ) + elif is_group_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + Fp32GroupNorm(dim, dim, affine=True), + nn.GELU(), + ) + else: + return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU()) + + in_d = 1 + self.conv_layers = nn.ModuleList() + for i, cl in enumerate(conv_layers): + assert len(cl) == 3, "invalid conv definition: " + str(cl) + (dim, k, stride) = cl + + self.conv_layers.append( + block( + in_d, + dim, + k, + stride, + is_layer_norm=mode == "layer_norm", + is_group_norm=mode == "default" and i == 0, + conv_bias=conv_bias, + ) + ) + in_d = dim + + def forward(self, x): + + # BxT -> BxCxT + x = x.unsqueeze(1) + + for conv in self.conv_layers: + x = conv(x) + + return x + + +def compute_mask_indices( + shape: Tuple[int, int], + padding_mask: Optional[torch.Tensor], + mask_prob: float, + mask_length: int, + mask_type: str = "static", + mask_other: float = 0.0, + min_masks: int = 0, + no_overlap: bool = False, + min_space: int = 0, + require_same_masks: bool = True, + mask_dropout: float = 0.0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape + + Args: + shape: the the shape for which to compute masks. + should be of size 2 where first element is batch size and 2nd is timesteps + padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements + mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by + number of timesteps divided by length of mask span to mask approximately this percentage of all elements. + however due to overlaps, the actual number will be smaller (unless no_overlap is True) + mask_type: how to compute mask lengths + static = fixed size + uniform = sample from uniform distribution [mask_other, mask_length*2] + normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element + poisson = sample from possion distribution with lambda = mask length + min_masks: minimum number of masked spans + no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping + min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans + require_same_masks: if true, will randomly drop out masks until same amount of masks remains in each sample + mask_dropout: randomly dropout this percentage of masks in each example + """ + + bsz, all_sz = shape + mask = np.full((bsz, all_sz), False) + + all_num_mask = int( + # add a random number for probabilistic rounding + mask_prob * all_sz / float(mask_length) + + np.random.rand() + ) + + all_num_mask = max(min_masks, all_num_mask) + + mask_idcs = [] + for i in range(bsz): + if padding_mask is not None: + sz = all_sz - padding_mask[i].long().sum().item() + num_mask = int( + # add a random number for probabilistic rounding + mask_prob * sz / float(mask_length) + + np.random.rand() + ) + num_mask = max(min_masks, num_mask) + else: + sz = all_sz + num_mask = all_num_mask + + if mask_type == "static": + lengths = np.full(num_mask, mask_length) + elif mask_type == "uniform": + lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask) + elif mask_type == "normal": + lengths = np.random.normal(mask_length, mask_other, size=num_mask) + lengths = [max(1, int(round(x))) for x in lengths] + elif mask_type == "poisson": + lengths = np.random.poisson(mask_length, size=num_mask) + lengths = [int(round(x)) for x in lengths] + else: + raise Exception("unknown mask selection " + mask_type) + + if sum(lengths) == 0: + lengths[0] = min(mask_length, sz - 1) + + if no_overlap: + mask_idc = [] + + def arrange(s, e, length, keep_length): + span_start = np.random.randint(s, e - length) + mask_idc.extend(span_start + i for i in range(length)) + + new_parts = [] + if span_start - s - min_space >= keep_length: + new_parts.append((s, span_start - min_space + 1)) + if e - span_start - length - min_space > keep_length: + new_parts.append((span_start + length + min_space, e)) + return new_parts + + parts = [(0, sz)] + min_length = min(lengths) + for length in sorted(lengths, reverse=True): + lens = np.fromiter( + (e - s if e - s >= length + min_space else 0 for s, e in parts), + np.int, + ) + l_sum = np.sum(lens) + if l_sum == 0: + break + probs = lens / np.sum(lens) + c = np.random.choice(len(parts), p=probs) + s, e = parts.pop(c) + parts.extend(arrange(s, e, length, min_length)) + mask_idc = np.asarray(mask_idc) + else: + min_len = min(lengths) + if sz - min_len <= num_mask: + min_len = sz - num_mask - 1 + + mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) + + mask_idc = np.asarray( + [ + mask_idc[j] + offset + for j in range(len(mask_idc)) + for offset in range(lengths[j]) + ] + ) + + mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) + + min_len = min([len(m) for m in mask_idcs]) + for i, mask_idc in enumerate(mask_idcs): + if len(mask_idc) > min_len and require_same_masks: + mask_idc = np.random.choice(mask_idc, min_len, replace=False) + if mask_dropout > 0: + num_holes = np.rint(len(mask_idc) * mask_dropout).astype(int) + mask_idc = np.random.choice( + mask_idc, len(mask_idc) - num_holes, replace=False + ) + + mask[i, mask_idc] = True + + return mask + + +class GradMultiply(torch.autograd.Function): + @staticmethod + def forward(ctx, x, scale): + ctx.scale = scale + res = x.new(x) + return res + + @staticmethod + def backward(ctx, grad): + return grad * ctx.scale, None + + +def is_xla_tensor(tensor): + return torch.is_tensor(tensor) and tensor.device.type == "xla" + + +def index_put(tensor, indices, value): + if is_xla_tensor(tensor): + for _ in range(indices.dim(), tensor.dim()): + indices = indices.unsqueeze(-1) + if indices.size(-1) < tensor.size(-1): + indices = indices.expand_as(tensor) + tensor = torch.mul(tensor, ~indices) + torch.mul(value, indices) + else: + tensor[indices] = value + return tensor diff --git a/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/model.py b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/model.py new file mode 100644 index 0000000000000000000000000000000000000000..b5c776b100e8cd38f98a6863debb2bfdab6e78f7 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/model.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) +# Modified from https://github.com/ddlBoJack/emotion2vec/tree/main + +import os +import time +import torch +import logging +import numpy as np +from functools import partial +from omegaconf import OmegaConf +import torch.nn.functional as F +from contextlib import contextmanager +from distutils.version import LooseVersion + +from funasr_detach.register import tables +from funasr_detach.models.emotion2vec.modules import AltBlock +from funasr_detach.models.emotion2vec.audio import AudioEncoder +from funasr_detach.utils.load_utils import load_audio_text_image_video + + +logger = logging.getLogger(__name__) +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "Emotion2vec") +class Emotion2vec(torch.nn.Module): + """ + Author: Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, Jinchao Li, Zhifu Gao, Shiliang Zhang, Xie Chen + emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation + https://arxiv.org/abs/2312.15185 + """ + + def __init__(self, **kwargs): + super().__init__() + # import pdb; pdb.set_trace() + cfg = OmegaConf.create(kwargs["model_conf"]) + self.cfg = cfg + + make_layer_norm = partial( + torch.nn.LayerNorm, + eps=cfg.get("norm_eps"), + elementwise_affine=cfg.get("norm_affine"), + ) + + def make_block(drop_path, dim=None, heads=None): + return AltBlock( + cfg.get("embed_dim") if dim is None else dim, + cfg.get("num_heads") if heads is None else heads, + cfg.get("mlp_ratio"), + qkv_bias=True, + drop=cfg.get("encoder_dropout"), + attn_drop=cfg.get("attention_dropout"), + mlp_drop=cfg.get("activation_dropout"), + post_mlp_drop=cfg.get("post_mlp_drop"), + drop_path=drop_path, + norm_layer=make_layer_norm, + layer_norm_first=cfg.get("layer_norm_first"), + ffn_targets=not cfg.get("end_of_block_targets"), + ) + + self.alibi_biases = {} + self.modality_encoders = torch.nn.ModuleDict() + + enc = AudioEncoder( + cfg.modalities.audio, + cfg.get("embed_dim"), + make_block, + make_layer_norm, + cfg.get("layer_norm_first"), + self.alibi_biases, + ) + self.modality_encoders["AUDIO"] = enc + + self.ema = None + + self.average_top_k_layers = cfg.get("average_top_k_layers") + self.loss_beta = cfg.get("loss_beta") + self.loss_scale = cfg.get("loss_scale") + + self.dropout_input = torch.nn.Dropout(cfg.get("dropout_input")) + + dpr = np.linspace( + cfg.get("start_drop_path_rate"), + cfg.get("end_drop_path_rate"), + cfg.get("depth"), + ) + + self.blocks = torch.nn.ModuleList( + [make_block(dpr[i]) for i in range(cfg.get("depth"))] + ) + + self.norm = None + if cfg.get("layer_norm_first"): + self.norm = make_layer_norm(cfg.get("embed_dim")) + + vocab_size = kwargs.get("vocab_size", -1) + self.proj = None + if vocab_size > 0: + self.proj = torch.nn.Linear(cfg.get("embed_dim"), vocab_size) + + def forward( + self, + source, + target=None, + id=None, + mode=None, + padding_mask=None, + mask=True, + features_only=False, + force_remove_masked=False, + remove_extra_tokens=True, + precomputed_mask=None, + **kwargs, + ): + + feature_extractor = self.modality_encoders["AUDIO"] + + mask_seeds = None + + extractor_out = feature_extractor( + source, + padding_mask, + mask, + remove_masked=not features_only or force_remove_masked, + clone_batch=self.cfg.get("clone_batch") if not features_only else 1, + mask_seeds=mask_seeds, + precomputed_mask=precomputed_mask, + ) + + x = extractor_out["x"] + encoder_mask = extractor_out["encoder_mask"] + masked_padding_mask = extractor_out["padding_mask"] + masked_alibi_bias = extractor_out.get("alibi_bias", None) + alibi_scale = extractor_out.get("alibi_scale", None) + + if self.dropout_input is not None: + x = self.dropout_input(x) + + layer_results = [] + for i, blk in enumerate(self.blocks): + if ( + not self.training + or self.cfg.get("layerdrop", 0) == 0 + or (np.random.random() > self.cfg.get("layerdrop", 0)) + ): + ab = masked_alibi_bias + if ab is not None and alibi_scale is not None: + scale = ( + alibi_scale[i] + if alibi_scale.size(0) > 1 + else alibi_scale.squeeze(0) + ) + ab = ab * scale.type_as(ab) + + x, lr = blk( + x, + padding_mask=masked_padding_mask, + alibi_bias=ab, + ) + if features_only: + layer_results.append(lr) + + if self.norm is not None: + x = self.norm(x) + + if features_only: + if remove_extra_tokens: + x = x[:, feature_extractor.modality_cfg.num_extra_tokens :] + if masked_padding_mask is not None: + masked_padding_mask = masked_padding_mask[ + :, feature_extractor.modality_cfg.num_extra_tokens : + ] + + return { + "x": x, + "padding_mask": masked_padding_mask, + "layer_results": layer_results, + "mask": encoder_mask, + } + + def extract_features( + self, source, mode=None, padding_mask=None, mask=False, remove_extra_tokens=True + ): + res = self.forward( + source, + mode=mode, + padding_mask=padding_mask, + mask=mask, + features_only=True, + remove_extra_tokens=remove_extra_tokens, + ) + return res + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + + # if source_file.endswith('.wav'): + # wav, sr = sf.read(source_file) + # channel = sf.info(source_file).channels + # assert sr == 16e3, "Sample rate should be 16kHz, but got {}in file {}".format(sr, source_file) + # assert channel == 1, "Channel should be 1, but got {} in file {}".format(channel, source_file) + granularity = kwargs.get("granularity", "utterance") + extract_embedding = kwargs.get("extract_embedding", True) + if self.proj is None: + extract_embedding = True + meta_data = {} + # extract fbank feats + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_in, + fs=16000, + audio_fs=kwargs.get("fs", 16000), + data_type=kwargs.get("data_type", "sound"), + tokenizer=tokenizer, + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + meta_data["batch_data_time"] = len(audio_sample_list[0]) / kwargs.get( + "fs", 16000 + ) + + results = [] + output_dir = kwargs.get("output_dir") + if output_dir: + os.makedirs(output_dir, exist_ok=True) + for i, wav in enumerate(audio_sample_list): + source = wav.to(device=kwargs["device"]) + if self.cfg.normalize: + source = F.layer_norm(source, source.shape) + source = source.view(1, -1) + + feats = self.extract_features(source, padding_mask=None) + x = feats["x"] + feats = feats["x"].squeeze(0).cpu().numpy() + if granularity == "frame": + feats = feats + elif granularity == "utterance": + feats = np.mean(feats, axis=0) + + if output_dir and extract_embedding: + np.save(os.path.join(output_dir, "{}.npy".format(key[i])), feats) + + labels = tokenizer.token_list if tokenizer is not None else [] + scores = [] + if self.proj: + x = x.mean(dim=1) + x = self.proj(x) + x = torch.softmax(x, dim=-1) + scores = x[0].tolist() + + result_i = {"key": key[i], "labels": labels, "scores": scores} + if extract_embedding: + result_i["feats"] = feats + results.append(result_i) + + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/modules.py b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..3ec80b17f76fecf56ec4354de8515b304eceaf9b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/modules.py @@ -0,0 +1,323 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import numpy as np +import torch.nn as nn +from enum import Enum, auto +import torch.nn.functional as F +from dataclasses import dataclass +from funasr_detach.models.emotion2vec.fairseq_modules import ( + LayerNorm, + SamePad, + TransposeLast, +) + + +class Modality(Enum): + AUDIO = auto() + + +@dataclass +class D2vDecoderConfig: + decoder_dim: int = 384 + decoder_groups: int = 16 + decoder_kernel: int = 5 + decoder_layers: int = 5 + input_dropout: float = 0.1 + + add_positions_masked: bool = False + add_positions_all: bool = False + + decoder_residual: bool = True + projection_layers: int = 1 + projection_ratio: float = 2.0 + + +class FixedPositionalEncoder(nn.Module): + def __init__(self, pos_embed): + super().__init__() + self.positions = pos_embed + + def forward(self, x, padding_mask): + return self.positions + + +class TextFeatPositionalEncoder(nn.Module): + """ + Original encoder expects (B, T) long input. This module wraps it to take + local_encoder output which are (B, T, D) float tensors + """ + + def __init__(self, pos_encoder): + super().__init__() + self.pos_encoder = pos_encoder + + def forward(self, x, padding_mask): + # assume padded token embeddings are 0s + # TODO: consider using padding_mask as input + return self.pos_encoder(x[..., 0]) + + +class BlockEncoder(nn.Module): + def __init__(self, blocks, norm_layer, layer_norm_first, layerdrop, dropout): + super().__init__() + self.blocks = blocks + self.norm = norm_layer + self.layer_norm_first = layer_norm_first + self.layerdrop = layerdrop + self.dropout = nn.Dropout(dropout, inplace=True) + + def forward(self, x, padding_mask, alibi_bias, alibi_scale): + if self.norm is not None and not self.layer_norm_first: + x = self.norm(x) + + x = self.dropout(x) + + for i, blk in enumerate(self.blocks): + if ( + not self.training + or self.layerdrop == 0 + or (np.random.random() > self.layerdrop) + ): + ab = alibi_bias + if ab is not None and alibi_scale is not None: + scale = ( + alibi_scale[i] + if alibi_scale.size(0) > 1 + else alibi_scale.squeeze(0) + ) + ab = ab * scale.type_as(ab) + x, _ = blk(x, padding_mask, ab) + + if self.norm is not None and self.layer_norm_first: + x = self.norm(x) + + return x + + +class DecoderBase(nn.Module): + decoder_cfg: D2vDecoderConfig + + def __init__(self, cfg: D2vDecoderConfig): + super().__init__() + + self.decoder_cfg = cfg + + def reset_parameters(self): + for mod in self.proj.modules(): + if isinstance(mod, nn.Linear): + mod.reset_parameters() + + def add_residual(self, x, residual, i, mask_info): + if ( + residual is None + or not self.decoder_cfg.decoder_residual + or residual.size(1) != x.size(1) + ): + return x + + ret = x + residual + + return ret + + +class Decoder1d(DecoderBase): + def __init__(self, cfg: D2vDecoderConfig, input_dim): + super().__init__(cfg) + + def make_block(in_dim): + block = [ + nn.Conv1d( + in_dim, + cfg.decoder_dim, + kernel_size=cfg.decoder_kernel, + padding=cfg.decoder_kernel // 2, + groups=cfg.decoder_groups, + ), + SamePad(cfg.decoder_kernel), + TransposeLast(), + LayerNorm(cfg.decoder_dim, elementwise_affine=False), + TransposeLast(), + nn.GELU(), + ] + + return nn.Sequential(*block) + + self.blocks = nn.Sequential( + *[ + make_block(input_dim if i == 0 else cfg.decoder_dim) + for i in range(cfg.decoder_layers) + ] + ) + + projs = [] + curr_dim = cfg.decoder_dim + for i in range(cfg.projection_layers - 1): + next_dim = int(curr_dim * cfg.projection_ratio) if i == 0 else curr_dim + projs.append(nn.Linear(curr_dim, next_dim)) + projs.append(nn.GELU()) + curr_dim = next_dim + projs.append(nn.Linear(curr_dim, input_dim)) + if len(projs) == 1: + self.proj = projs[0] + else: + self.proj = nn.Sequential(*projs) + + def forward(self, x, mask_info): + + x = x.transpose(1, 2) + + residual = x + + for i, layer in enumerate(self.blocks): + x = layer(x) + x = self.add_residual(x, residual, i, mask_info) + residual = x + + x = x.transpose(1, 2) + x = self.proj(x) + return x + + +class AltBlock(nn.Module): + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + mlp_drop=0.0, + post_mlp_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_norm_first=True, + ffn_targets=False, + cosine_attention=False, + ): + super().__init__() + + self.layer_norm_first = layer_norm_first + self.ffn_targets = ffn_targets + + from funasr_detach.models.emotion2vec.timm_modules import DropPath, Mlp + + self.norm1 = norm_layer(dim) + self.attn = AltAttention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + cosine_attention=cosine_attention, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=mlp_drop, + ) + self.post_mlp_dropout = nn.Dropout(post_mlp_drop, inplace=False) + + def forward(self, x, padding_mask=None, alibi_bias=None): + if self.layer_norm_first: + x = x + self.drop_path(self.attn(self.norm1(x), padding_mask, alibi_bias)) + r = x = self.mlp(self.norm2(x)) + t = x + x = r + self.drop_path(self.post_mlp_dropout(x)) + if not self.ffn_targets: + t = x + else: + x = x + self.drop_path(self.attn(x, padding_mask, alibi_bias)) + r = x = self.norm1(x) + x = self.mlp(x) + t = x + x = self.norm2(r + self.drop_path(self.post_mlp_dropout(x))) + if not self.ffn_targets: + t = x + + return x, t + + +class AltAttention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + cosine_attention=False, + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.cosine_attention = cosine_attention + + if cosine_attention: + self.logit_scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True + ) + + def forward(self, x, padding_mask=None, alibi_bias=None): + B, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) # qkv x B x H x L x D + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + dtype = q.dtype + + if self.cosine_attention: + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp( + self.logit_scale, max=torch.log(torch.tensor(1.0 / 0.01)) + ).exp() + attn = attn * logit_scale + else: + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + if alibi_bias is not None: + attn = attn.type_as(alibi_bias) + attn[:, : alibi_bias.size(1)] += alibi_bias + + if padding_mask is not None and padding_mask.any(): + attn = attn.masked_fill( + padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + + attn = attn.softmax(dim=-1, dtype=torch.float32).to(dtype=dtype) + attn = self.attn_drop(attn) + x = (attn @ v).transpose(1, 2) # + x = x.reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d0ac3c95e3ab200775b1f5e50780699cea687d6f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/template.yaml @@ -0,0 +1,113 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: Emotion2vec +model_conf: + loss_beta: 0.0 + loss_scale: null + depth: 8 + start_drop_path_rate: 0.0 + end_drop_path_rate: 0.0 + num_heads: 12 + norm_eps: 1e-05 + norm_affine: true + encoder_dropout: 0.1 + post_mlp_drop: 0.1 + attention_dropout: 0.1 + activation_dropout: 0.0 + dropout_input: 0.0 + layerdrop: 0.05 + embed_dim: 768 + mlp_ratio: 4.0 + layer_norm_first: false + average_top_k_layers: 8 + end_of_block_targets: false + clone_batch: 8 + layer_norm_target_layer: false + batch_norm_target_layer: false + instance_norm_target_layer: true + instance_norm_targets: false + layer_norm_targets: false + ema_decay: 0.999 + ema_same_dtype: true + log_norms: true + ema_end_decay: 0.99999 + ema_anneal_end_step: 20000 + ema_encoder_only: false + max_update: 100000 + extractor_mode: layer_norm + shared_decoder: null + min_target_var: 0.1 + min_pred_var: 0.01 + supported_modality: AUDIO + mae_init: false + seed: 1 + skip_ema: false + cls_loss: 1.0 + recon_loss: 0.0 + d2v_loss: 1.0 + decoder_group: false + adversarial_training: false + adversarial_hidden_dim: 128 + adversarial_weight: 0.1 + cls_type: chunk + normalize: true + + modalities: + audio: + type: AUDIO + prenet_depth: 4 + prenet_layerdrop: 0.05 + prenet_dropout: 0.1 + start_drop_path_rate: 0.0 + end_drop_path_rate: 0.0 + num_extra_tokens: 10 + init_extra_token_zero: true + mask_noise_std: 0.01 + mask_prob_min: null + mask_prob: 0.5 + inverse_mask: false + mask_prob_adjust: 0.05 + keep_masked_pct: 0.0 + mask_length: 5 + add_masks: false + remove_masks: false + mask_dropout: 0.0 + encoder_zero_mask: true + mask_channel_prob: 0.0 + mask_channel_length: 64 + ema_local_encoder: false + local_grad_mult: 1.0 + use_alibi_encoder: true + alibi_scale: 1.0 + learned_alibi: false + alibi_max_pos: null + learned_alibi_scale: true + learned_alibi_scale_per_head: true + learned_alibi_scale_per_layer: false + num_alibi_heads: 12 + model_depth: 8 + decoder: + decoder_dim: 384 + decoder_groups: 16 + decoder_kernel: 7 + decoder_layers: 4 + input_dropout: 0.1 + add_positions_masked: false + add_positions_all: false + decoder_residual: true + projection_layers: 1 + projection_ratio: 2.0 + extractor_mode: layer_norm + feature_encoder_spec: '[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]' + conv_pos_width: 95 + conv_pos_groups: 16 + conv_pos_depth: 5 + conv_pos_pre_ln: false + + diff --git a/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/timm_modules.py b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/timm_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..5f284d1b76f8389d6230c3965baca2391011dfc6 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/emotion2vec/timm_modules.py @@ -0,0 +1,100 @@ +import torch.nn as nn +import collections.abc +from itertools import repeat +from functools import partial + + +def drop_path( + x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True +): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + def extra_repr(self): + return f"drop_prob={round(self.drop_prob,3):0.3f}" + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return tuple(x) + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple + + +class Mlp(nn.Module): + """MLP as used in Vision Transformer, MLP-Mixer and related networks""" + + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0.0, + use_conv=False, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + bias = to_2tuple(bias) + drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0]) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs[0]) + self.norm = ( + norm_layer(hidden_features) if norm_layer is not None else nn.Identity() + ) + self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1]) + self.drop2 = nn.Dropout(drop_probs[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eres2net/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/eres2net/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e92be5e2c04ead16e243e3a8aa4a3d1e5ac96195 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eres2net/__init__.py @@ -0,0 +1,2 @@ +from .eres2net import ERes2Net +from .eres2net_aug import ERes2NetAug diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eres2net/eres2net.py b/demo/Step-Audio-EditX/funasr_detach/models/eres2net/eres2net.py new file mode 100644 index 0000000000000000000000000000000000000000..d86fe44d0483492f352d48d3e78ccd6e5fb31f41 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eres2net/eres2net.py @@ -0,0 +1,431 @@ +# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved. +# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker. +ERes2Net incorporates both local and global feature fusion techniques to improve the performance. +The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal. +The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal. +ERes2Net-Large is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better +recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance. +""" + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import funasr_detach.models.sond.pooling.pooling_layers as pooling_layers + +from funasr_detach.models.eres2net.fusion import AFF + + +class ReLU(nn.Hardtanh): + + def __init__(self, inplace=False): + super(ReLU, self).__init__(0, 20, inplace) + + def __repr__(self): + inplace_str = "inplace" if self.inplace else "" + return self.__class__.__name__ + " (" + inplace_str + ")" + + +def conv1x1(in_planes, out_planes, stride=1): + "1x1 convolution without padding" + return nn.Conv2d( + in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False + ) + + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d( + in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False + ) + + +class BasicBlockERes2Net(nn.Module): + expansion = 2 + + def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2): + super(BasicBlockERes2Net, self).__init__() + width = int(math.floor(planes * (baseWidth / 64.0))) + self.conv1 = conv1x1(in_planes, width * scale, stride) + self.bn1 = nn.BatchNorm2d(width * scale) + self.nums = scale + + convs = [] + bns = [] + for i in range(self.nums): + convs.append(conv3x3(width, width)) + bns.append(nn.BatchNorm2d(width)) + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + self.relu = ReLU(inplace=True) + + self.conv3 = conv1x1(width * scale, planes * self.expansion) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), + ) + self.stride = stride + self.width = width + self.scale = scale + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + spx = torch.split(out, self.width, 1) + for i in range(self.nums): + if i == 0: + sp = spx[i] + else: + sp = sp + spx[i] + sp = self.convs[i](sp) + sp = self.relu(self.bns[i](sp)) + if i == 0: + out = sp + else: + out = torch.cat((out, sp), 1) + + out = self.conv3(out) + out = self.bn3(out) + + residual = self.shortcut(x) + out += residual + out = self.relu(out) + + return out + + +class BasicBlockERes2Net_diff_AFF(nn.Module): + expansion = 2 + + def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2): + super(BasicBlockERes2Net_diff_AFF, self).__init__() + width = int(math.floor(planes * (baseWidth / 64.0))) + self.conv1 = conv1x1(in_planes, width * scale, stride) + self.bn1 = nn.BatchNorm2d(width * scale) + self.nums = scale + + convs = [] + fuse_models = [] + bns = [] + for i in range(self.nums): + convs.append(conv3x3(width, width)) + bns.append(nn.BatchNorm2d(width)) + for j in range(self.nums - 1): + fuse_models.append(AFF(channels=width)) + + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + self.fuse_models = nn.ModuleList(fuse_models) + self.relu = ReLU(inplace=True) + + self.conv3 = conv1x1(width * scale, planes * self.expansion) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), + ) + self.stride = stride + self.width = width + self.scale = scale + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + spx = torch.split(out, self.width, 1) + for i in range(self.nums): + if i == 0: + sp = spx[i] + else: + sp = self.fuse_models[i - 1](sp, spx[i]) + + sp = self.convs[i](sp) + sp = self.relu(self.bns[i](sp)) + if i == 0: + out = sp + else: + out = torch.cat((out, sp), 1) + + out = self.conv3(out) + out = self.bn3(out) + + residual = self.shortcut(x) + out += residual + out = self.relu(out) + + return out + + +class ERes2Net(nn.Module): + def __init__( + self, + block=BasicBlockERes2Net, + block_fuse=BasicBlockERes2Net_diff_AFF, + num_blocks=[3, 4, 6, 3], + m_channels=32, + feat_dim=80, + embedding_size=192, + pooling_func="TSTP", + two_emb_layer=False, + ): + super(ERes2Net, self).__init__() + self.in_planes = m_channels + self.feat_dim = feat_dim + self.embedding_size = embedding_size + self.stats_dim = int(feat_dim / 8) * m_channels * 8 + self.two_emb_layer = two_emb_layer + + self.conv1 = nn.Conv2d( + 1, m_channels, kernel_size=3, stride=1, padding=1, bias=False + ) + self.bn1 = nn.BatchNorm2d(m_channels) + self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, m_channels * 2, num_blocks[1], stride=2) + self.layer3 = self._make_layer( + block_fuse, m_channels * 4, num_blocks[2], stride=2 + ) + self.layer4 = self._make_layer( + block_fuse, m_channels * 8, num_blocks[3], stride=2 + ) + + # Downsampling module for each layer + self.layer1_downsample = nn.Conv2d( + m_channels * 2, + m_channels * 4, + kernel_size=3, + stride=2, + padding=1, + bias=False, + ) + self.layer2_downsample = nn.Conv2d( + m_channels * 4, + m_channels * 8, + kernel_size=3, + padding=1, + stride=2, + bias=False, + ) + self.layer3_downsample = nn.Conv2d( + m_channels * 8, + m_channels * 16, + kernel_size=3, + padding=1, + stride=2, + bias=False, + ) + + # Bottom-up fusion module + self.fuse_mode12 = AFF(channels=m_channels * 4) + self.fuse_mode123 = AFF(channels=m_channels * 8) + self.fuse_mode1234 = AFF(channels=m_channels * 16) + + self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2 + self.pool = getattr(pooling_layers, pooling_func)( + in_dim=self.stats_dim * block.expansion + ) + self.seg_1 = nn.Linear( + self.stats_dim * block.expansion * self.n_stats, embedding_size + ) + if self.two_emb_layer: + self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False) + self.seg_2 = nn.Linear(embedding_size, embedding_size) + else: + self.seg_bn_1 = nn.Identity() + self.seg_2 = nn.Identity() + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T) + x = x.unsqueeze_(1) + out = F.relu(self.bn1(self.conv1(x))) + out1 = self.layer1(out) + out2 = self.layer2(out1) + out1_downsample = self.layer1_downsample(out1) + fuse_out12 = self.fuse_mode12(out2, out1_downsample) + out3 = self.layer3(out2) + fuse_out12_downsample = self.layer2_downsample(fuse_out12) + fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample) + out4 = self.layer4(out3) + fuse_out123_downsample = self.layer3_downsample(fuse_out123) + fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample) + stats = self.pool(fuse_out1234) + + embed_a = self.seg_1(stats) + if self.two_emb_layer: + out = F.relu(embed_a) + out = self.seg_bn_1(out) + embed_b = self.seg_2(out) + return embed_b + else: + return embed_a + + +class BasicBlockRes2Net(nn.Module): + expansion = 2 + + def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2): + super(BasicBlockRes2Net, self).__init__() + width = int(math.floor(planes * (baseWidth / 64.0))) + self.conv1 = conv1x1(in_planes, width * scale, stride) + self.bn1 = nn.BatchNorm2d(width * scale) + self.nums = scale - 1 + convs = [] + bns = [] + for i in range(self.nums): + convs.append(conv3x3(width, width)) + bns.append(nn.BatchNorm2d(width)) + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + self.relu = ReLU(inplace=True) + + self.conv3 = conv1x1(width * scale, planes * self.expansion) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), + ) + self.stride = stride + self.width = width + self.scale = scale + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + spx = torch.split(out, self.width, 1) + for i in range(self.nums): + if i == 0: + sp = spx[i] + else: + sp = sp + spx[i] + sp = self.convs[i](sp) + sp = self.relu(self.bns[i](sp)) + if i == 0: + out = sp + else: + out = torch.cat((out, sp), 1) + + out = torch.cat((out, spx[self.nums]), 1) + + out = self.conv3(out) + out = self.bn3(out) + + residual = self.shortcut(x) + out += residual + out = self.relu(out) + + return out + + +class Res2Net(nn.Module): + def __init__( + self, + block=BasicBlockRes2Net, + num_blocks=[3, 4, 6, 3], + m_channels=32, + feat_dim=80, + embedding_size=192, + pooling_func="TSTP", + two_emb_layer=False, + ): + super(Res2Net, self).__init__() + self.in_planes = m_channels + self.feat_dim = feat_dim + self.embedding_size = embedding_size + self.stats_dim = int(feat_dim / 8) * m_channels * 8 + self.two_emb_layer = two_emb_layer + + self.conv1 = nn.Conv2d( + 1, m_channels, kernel_size=3, stride=1, padding=1, bias=False + ) + self.bn1 = nn.BatchNorm2d(m_channels) + self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, m_channels * 2, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, m_channels * 4, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, m_channels * 8, num_blocks[3], stride=2) + + self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2 + self.pool = getattr(pooling_layers, pooling_func)( + in_dim=self.stats_dim * block.expansion + ) + self.seg_1 = nn.Linear( + self.stats_dim * block.expansion * self.n_stats, embedding_size + ) + if self.two_emb_layer: + self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False) + self.seg_2 = nn.Linear(embedding_size, embedding_size) + else: + self.seg_bn_1 = nn.Identity() + self.seg_2 = nn.Identity() + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T) + + x = x.unsqueeze_(1) + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + + stats = self.pool(out) + + embed_a = self.seg_1(stats) + if self.two_emb_layer: + out = F.relu(embed_a) + out = self.seg_bn_1(out) + embed_b = self.seg_2(out) + return embed_b + else: + return embed_a diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eres2net/eres2net_aug.py b/demo/Step-Audio-EditX/funasr_detach/models/eres2net/eres2net_aug.py new file mode 100644 index 0000000000000000000000000000000000000000..d2e4e90c5372c0493cb94413491031027791a20c --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eres2net/eres2net_aug.py @@ -0,0 +1,292 @@ +# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved. +# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker. +ERes2Net incorporates both local and global feature fusion techniques to improve the performance. +The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal. +The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal. +ERes2Net-Large is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better +recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance. +""" + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import funasr_detach.models.sond.pooling.pooling_layers as pooling_layers + +from funasr_detach.models.eres2net.fusion import AFF + + +class ReLU(nn.Hardtanh): + + def __init__(self, inplace=False): + super(ReLU, self).__init__(0, 20, inplace) + + def __repr__(self): + inplace_str = "inplace" if self.inplace else "" + return self.__class__.__name__ + " (" + inplace_str + ")" + + +def conv1x1(in_planes, out_planes, stride=1): + "1x1 convolution without padding" + return nn.Conv2d( + in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False + ) + + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d( + in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False + ) + + +class BasicBlockERes2Net(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3): + super(BasicBlockERes2Net, self).__init__() + width = int(math.floor(planes * (baseWidth / 64.0))) + self.conv1 = conv1x1(in_planes, width * scale, stride) + self.bn1 = nn.BatchNorm2d(width * scale) + self.nums = scale + + convs = [] + bns = [] + for i in range(self.nums): + convs.append(conv3x3(width, width)) + bns.append(nn.BatchNorm2d(width)) + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + self.relu = ReLU(inplace=True) + + self.conv3 = conv1x1(width * scale, planes * self.expansion) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), + ) + self.stride = stride + self.width = width + self.scale = scale + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + spx = torch.split(out, self.width, 1) + for i in range(self.nums): + if i == 0: + sp = spx[i] + else: + sp = sp + spx[i] + sp = self.convs[i](sp) + sp = self.relu(self.bns[i](sp)) + if i == 0: + out = sp + else: + out = torch.cat((out, sp), 1) + + out = self.conv3(out) + out = self.bn3(out) + + residual = self.shortcut(x) + out += residual + out = self.relu(out) + + return out + + +class BasicBlockERes2Net_diff_AFF(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3): + super(BasicBlockERes2Net_diff_AFF, self).__init__() + width = int(math.floor(planes * (baseWidth / 64.0))) + self.conv1 = conv1x1(in_planes, width * scale, stride) + self.bn1 = nn.BatchNorm2d(width * scale) + + self.nums = scale + + convs = [] + fuse_models = [] + bns = [] + for i in range(self.nums): + convs.append(conv3x3(width, width)) + bns.append(nn.BatchNorm2d(width)) + for j in range(self.nums - 1): + fuse_models.append(AFF(channels=width)) + + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + self.fuse_models = nn.ModuleList(fuse_models) + self.relu = ReLU(inplace=True) + + self.conv3 = conv1x1(width * scale, planes * self.expansion) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), + ) + self.stride = stride + self.width = width + self.scale = scale + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + spx = torch.split(out, self.width, 1) + for i in range(self.nums): + if i == 0: + sp = spx[i] + else: + sp = self.fuse_models[i - 1](sp, spx[i]) + + sp = self.convs[i](sp) + sp = self.relu(self.bns[i](sp)) + if i == 0: + out = sp + else: + out = torch.cat((out, sp), 1) + + out = self.conv3(out) + out = self.bn3(out) + + residual = self.shortcut(x) + out += residual + out = self.relu(out) + + return out + + +class ERes2NetAug(nn.Module): + def __init__( + self, + block=BasicBlockERes2Net, + block_fuse=BasicBlockERes2Net_diff_AFF, + num_blocks=[3, 4, 6, 3], + m_channels=64, + feat_dim=80, + embedding_size=192, + pooling_func="TSTP", + two_emb_layer=False, + ): + super(ERes2NetAug, self).__init__() + self.in_planes = m_channels + self.feat_dim = feat_dim + self.embedding_size = embedding_size + self.stats_dim = int(feat_dim / 8) * m_channels * 8 + self.two_emb_layer = two_emb_layer + + self.conv1 = nn.Conv2d( + 1, m_channels, kernel_size=3, stride=1, padding=1, bias=False + ) + self.bn1 = nn.BatchNorm2d(m_channels) + self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, m_channels * 2, num_blocks[1], stride=2) + self.layer3 = self._make_layer( + block_fuse, m_channels * 4, num_blocks[2], stride=2 + ) + self.layer4 = self._make_layer( + block_fuse, m_channels * 8, num_blocks[3], stride=2 + ) + + self.layer1_downsample = nn.Conv2d( + m_channels * 4, + m_channels * 8, + kernel_size=3, + padding=1, + stride=2, + bias=False, + ) + self.layer2_downsample = nn.Conv2d( + m_channels * 8, + m_channels * 16, + kernel_size=3, + padding=1, + stride=2, + bias=False, + ) + self.layer3_downsample = nn.Conv2d( + m_channels * 16, + m_channels * 32, + kernel_size=3, + padding=1, + stride=2, + bias=False, + ) + self.fuse_mode12 = AFF(channels=m_channels * 8) + self.fuse_mode123 = AFF(channels=m_channels * 16) + self.fuse_mode1234 = AFF(channels=m_channels * 32) + + self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2 + self.pool = getattr(pooling_layers, pooling_func)( + in_dim=self.stats_dim * block.expansion + ) + self.seg_1 = nn.Linear( + self.stats_dim * block.expansion * self.n_stats, embedding_size + ) + if self.two_emb_layer: + self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False) + self.seg_2 = nn.Linear(embedding_size, embedding_size) + else: + self.seg_bn_1 = nn.Identity() + self.seg_2 = nn.Identity() + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T) + + x = x.unsqueeze_(1) + out = F.relu(self.bn1(self.conv1(x))) + out1 = self.layer1(out) + out2 = self.layer2(out1) + out1_downsample = self.layer1_downsample(out1) + fuse_out12 = self.fuse_mode12(out2, out1_downsample) + out3 = self.layer3(out2) + fuse_out12_downsample = self.layer2_downsample(fuse_out12) + fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample) + out4 = self.layer4(out3) + fuse_out123_downsample = self.layer3_downsample(fuse_out123) + fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample) + stats = self.pool(fuse_out1234) + + embed_a = self.seg_1(stats) + if self.two_emb_layer: + out = F.relu(embed_a) + out = self.seg_bn_1(out) + embed_b = self.seg_2(out) + return embed_b + else: + return embed_a diff --git a/demo/Step-Audio-EditX/funasr_detach/models/eres2net/fusion.py b/demo/Step-Audio-EditX/funasr_detach/models/eres2net/fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..0bd3a1da97061d50602ce6224c63dd9e26a54d32 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/eres2net/fusion.py @@ -0,0 +1,28 @@ +# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved. +# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +import torch +import torch.nn as nn + + +class AFF(nn.Module): + + def __init__(self, channels=64, r=4): + super(AFF, self).__init__() + inter_channels = int(channels // r) + + self.local_att = nn.Sequential( + nn.Conv2d(channels * 2, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(inter_channels), + nn.SiLU(inplace=True), + nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(channels), + ) + + def forward(self, x, ds_y): + xa = torch.cat((x, ds_y), dim=1) + x_att = self.local_att(xa) + x_att = 1.0 + torch.tanh(x_att) + xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0 - x_att) + + return xo diff --git a/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..4288fce5e01a902f089fb0298ecd0b22effa3d2b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/encoder.py @@ -0,0 +1,338 @@ +from typing import Tuple, Dict +import copy + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from funasr_detach.register import tables + + +class LinearTransform(nn.Module): + + def __init__(self, input_dim, output_dim): + super(LinearTransform, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.linear = nn.Linear(input_dim, output_dim, bias=False) + + def forward(self, input): + output = self.linear(input) + + return output + + +class AffineTransform(nn.Module): + + def __init__(self, input_dim, output_dim): + super(AffineTransform, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.linear = nn.Linear(input_dim, output_dim) + + def forward(self, input): + output = self.linear(input) + + return output + + +class RectifiedLinear(nn.Module): + + def __init__(self, input_dim, output_dim): + super(RectifiedLinear, self).__init__() + self.dim = input_dim + self.relu = nn.ReLU() + self.dropout = nn.Dropout(0.1) + + def forward(self, input): + out = self.relu(input) + return out + + +class FSMNBlock(nn.Module): + + def __init__( + self, + input_dim: int, + output_dim: int, + lorder=None, + rorder=None, + lstride=1, + rstride=1, + ): + super(FSMNBlock, self).__init__() + + self.dim = input_dim + + if lorder is None: + return + + self.lorder = lorder + self.rorder = rorder + self.lstride = lstride + self.rstride = rstride + + self.conv_left = nn.Conv2d( + self.dim, + self.dim, + [lorder, 1], + dilation=[lstride, 1], + groups=self.dim, + bias=False, + ) + + if self.rorder > 0: + self.conv_right = nn.Conv2d( + self.dim, + self.dim, + [rorder, 1], + dilation=[rstride, 1], + groups=self.dim, + bias=False, + ) + else: + self.conv_right = None + + def forward(self, input: torch.Tensor, cache: torch.Tensor): + x = torch.unsqueeze(input, 1) + x_per = x.permute(0, 3, 2, 1) # B D T C + + cache = cache.to(x_per.device) + y_left = torch.cat((cache, x_per), dim=2) + cache = y_left[:, :, -(self.lorder - 1) * self.lstride :, :] + y_left = self.conv_left(y_left) + out = x_per + y_left + + if self.conv_right is not None: + # maybe need to check + y_right = F.pad(x_per, [0, 0, 0, self.rorder * self.rstride]) + y_right = y_right[:, :, self.rstride :, :] + y_right = self.conv_right(y_right) + out += y_right + + out_per = out.permute(0, 3, 2, 1) + output = out_per.squeeze(1) + + return output, cache + + +class BasicBlock(nn.Module): + def __init__( + self, + linear_dim: int, + proj_dim: int, + lorder: int, + rorder: int, + lstride: int, + rstride: int, + stack_layer: int, + ): + super(BasicBlock, self).__init__() + self.lorder = lorder + self.rorder = rorder + self.lstride = lstride + self.rstride = rstride + self.stack_layer = stack_layer + self.linear = LinearTransform(linear_dim, proj_dim) + self.fsmn_block = FSMNBlock( + proj_dim, proj_dim, lorder, rorder, lstride, rstride + ) + self.affine = AffineTransform(proj_dim, linear_dim) + self.relu = RectifiedLinear(linear_dim, linear_dim) + + def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]): + x1 = self.linear(input) # B T D + cache_layer_name = "cache_layer_{}".format(self.stack_layer) + if cache_layer_name not in cache: + cache[cache_layer_name] = torch.zeros( + x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1 + ) + x2, cache[cache_layer_name] = self.fsmn_block(x1, cache[cache_layer_name]) + x3 = self.affine(x2) + x4 = self.relu(x3) + return x4 + + +class FsmnStack(nn.Sequential): + def __init__(self, *args): + super(FsmnStack, self).__init__(*args) + + def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]): + x = input + for module in self._modules.values(): + x = module(x, cache) + return x + + +""" +FSMN net for keyword spotting +input_dim: input dimension +linear_dim: fsmn input dimensionll +proj_dim: fsmn projection dimension +lorder: fsmn left order +rorder: fsmn right order +num_syn: output dimension +fsmn_layers: no. of sequential fsmn layers +""" + + +@tables.register("encoder_classes", "FSMN") +class FSMN(nn.Module): + def __init__( + self, + input_dim: int, + input_affine_dim: int, + fsmn_layers: int, + linear_dim: int, + proj_dim: int, + lorder: int, + rorder: int, + lstride: int, + rstride: int, + output_affine_dim: int, + output_dim: int, + ): + super(FSMN, self).__init__() + + self.input_dim = input_dim + self.input_affine_dim = input_affine_dim + self.fsmn_layers = fsmn_layers + self.linear_dim = linear_dim + self.proj_dim = proj_dim + self.output_affine_dim = output_affine_dim + self.output_dim = output_dim + + self.in_linear1 = AffineTransform(input_dim, input_affine_dim) + self.in_linear2 = AffineTransform(input_affine_dim, linear_dim) + self.relu = RectifiedLinear(linear_dim, linear_dim) + self.fsmn = FsmnStack( + *[ + BasicBlock(linear_dim, proj_dim, lorder, rorder, lstride, rstride, i) + for i in range(fsmn_layers) + ] + ) + self.out_linear1 = AffineTransform(linear_dim, output_affine_dim) + self.out_linear2 = AffineTransform(output_affine_dim, output_dim) + self.softmax = nn.Softmax(dim=-1) + + def fuse_modules(self): + pass + + def forward( + self, input: torch.Tensor, cache: Dict[str, torch.Tensor] + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + """ + Args: + input (torch.Tensor): Input tensor (B, T, D) + cache: when cache is not None, the forward is in streaming. The type of cache is a dict, egs, + {'cache_layer_1': torch.Tensor(B, T1, D)}, T1 is equal to self.lorder. It is {} for the 1st frame + """ + + x1 = self.in_linear1(input) + x2 = self.in_linear2(x1) + x3 = self.relu(x2) + x4 = self.fsmn(x3, cache) # self.cache will update automatically in self.fsmn + x5 = self.out_linear1(x4) + x6 = self.out_linear2(x5) + x7 = self.softmax(x6) + + return x7 + + +""" +one deep fsmn layer +dimproj: projection dimension, input and output dimension of memory blocks +dimlinear: dimension of mapping layer +lorder: left order +rorder: right order +lstride: left stride +rstride: right stride +""" + + +@tables.register("encoder_classes", "DFSMN") +class DFSMN(nn.Module): + + def __init__( + self, dimproj=64, dimlinear=128, lorder=20, rorder=1, lstride=1, rstride=1 + ): + super(DFSMN, self).__init__() + + self.lorder = lorder + self.rorder = rorder + self.lstride = lstride + self.rstride = rstride + + self.expand = AffineTransform(dimproj, dimlinear) + self.shrink = LinearTransform(dimlinear, dimproj) + + self.conv_left = nn.Conv2d( + dimproj, + dimproj, + [lorder, 1], + dilation=[lstride, 1], + groups=dimproj, + bias=False, + ) + + if rorder > 0: + self.conv_right = nn.Conv2d( + dimproj, + dimproj, + [rorder, 1], + dilation=[rstride, 1], + groups=dimproj, + bias=False, + ) + else: + self.conv_right = None + + def forward(self, input): + f1 = F.relu(self.expand(input)) + p1 = self.shrink(f1) + + x = torch.unsqueeze(p1, 1) + x_per = x.permute(0, 3, 2, 1) + + y_left = F.pad(x_per, [0, 0, (self.lorder - 1) * self.lstride, 0]) + + if self.conv_right is not None: + y_right = F.pad(x_per, [0, 0, 0, (self.rorder) * self.rstride]) + y_right = y_right[:, :, self.rstride :, :] + out = x_per + self.conv_left(y_left) + self.conv_right(y_right) + else: + out = x_per + self.conv_left(y_left) + + out1 = out.permute(0, 3, 2, 1) + output = input + out1.squeeze(1) + + return output + + +""" +build stacked dfsmn layers +""" + + +def buildDFSMNRepeats(linear_dim=128, proj_dim=64, lorder=20, rorder=1, fsmn_layers=6): + repeats = [ + nn.Sequential(DFSMN(proj_dim, linear_dim, lorder, rorder, 1, 1)) + for i in range(fsmn_layers) + ] + + return nn.Sequential(*repeats) + + +if __name__ == "__main__": + fsmn = FSMN(400, 140, 4, 250, 128, 10, 2, 1, 1, 140, 2599) + print(fsmn) + + num_params = sum(p.numel() for p in fsmn.parameters()) + print("the number of model params: {}".format(num_params)) + x = torch.zeros(128, 200, 400) # batch-size * time * dim + y, _ = fsmn(x) # batch-size * time * dim + print("input shape: {}".format(x.shape)) + print("output shape: {}".format(y.shape)) + + print(fsmn.to_kaldi_net()) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/model.py b/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/model.py new file mode 100644 index 0000000000000000000000000000000000000000..7c485231bfea71cf4d27d7bb78a5a73ff015570b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/model.py @@ -0,0 +1,1048 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import os +import json +import time +import math +import torch +from torch import nn +from enum import Enum +from dataclasses import dataclass +from funasr_detach.register import tables +from typing import List, Tuple, Dict, Any, Optional + +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank + + +class VadStateMachine(Enum): + kVadInStateStartPointNotDetected = 1 + kVadInStateInSpeechSegment = 2 + kVadInStateEndPointDetected = 3 + + +class FrameState(Enum): + kFrameStateInvalid = -1 + kFrameStateSpeech = 1 + kFrameStateSil = 0 + + +# final voice/unvoice state per frame +class AudioChangeState(Enum): + kChangeStateSpeech2Speech = 0 + kChangeStateSpeech2Sil = 1 + kChangeStateSil2Sil = 2 + kChangeStateSil2Speech = 3 + kChangeStateNoBegin = 4 + kChangeStateInvalid = 5 + + +class VadDetectMode(Enum): + kVadSingleUtteranceDetectMode = 0 + kVadMutipleUtteranceDetectMode = 1 + + +class VADXOptions: + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Deep-FSMN for Large Vocabulary Continuous Speech Recognition + https://arxiv.org/abs/1803.05030 + """ + + def __init__( + self, + sample_rate: int = 16000, + detect_mode: int = VadDetectMode.kVadMutipleUtteranceDetectMode.value, + snr_mode: int = 0, + max_end_silence_time: int = 800, + max_start_silence_time: int = 3000, + do_start_point_detection: bool = True, + do_end_point_detection: bool = True, + window_size_ms: int = 200, + sil_to_speech_time_thres: int = 150, + speech_to_sil_time_thres: int = 150, + speech_2_noise_ratio: float = 1.0, + do_extend: int = 1, + lookback_time_start_point: int = 200, + lookahead_time_end_point: int = 100, + max_single_segment_time: int = 60000, + nn_eval_block_size: int = 8, + dcd_block_size: int = 4, + snr_thres: int = -100.0, + noise_frame_num_used_for_snr: int = 100, + decibel_thres: int = -100.0, + speech_noise_thres: float = 0.6, + fe_prior_thres: float = 1e-4, + silence_pdf_num: int = 1, + sil_pdf_ids: List[int] = [0], + speech_noise_thresh_low: float = -0.1, + speech_noise_thresh_high: float = 0.3, + output_frame_probs: bool = False, + frame_in_ms: int = 10, + frame_length_ms: int = 25, + **kwargs, + ): + self.sample_rate = sample_rate + self.detect_mode = detect_mode + self.snr_mode = snr_mode + self.max_end_silence_time = max_end_silence_time + self.max_start_silence_time = max_start_silence_time + self.do_start_point_detection = do_start_point_detection + self.do_end_point_detection = do_end_point_detection + self.window_size_ms = window_size_ms + self.sil_to_speech_time_thres = sil_to_speech_time_thres + self.speech_to_sil_time_thres = speech_to_sil_time_thres + self.speech_2_noise_ratio = speech_2_noise_ratio + self.do_extend = do_extend + self.lookback_time_start_point = lookback_time_start_point + self.lookahead_time_end_point = lookahead_time_end_point + self.max_single_segment_time = max_single_segment_time + self.nn_eval_block_size = nn_eval_block_size + self.dcd_block_size = dcd_block_size + self.snr_thres = snr_thres + self.noise_frame_num_used_for_snr = noise_frame_num_used_for_snr + self.decibel_thres = decibel_thres + self.speech_noise_thres = speech_noise_thres + self.fe_prior_thres = fe_prior_thres + self.silence_pdf_num = silence_pdf_num + self.sil_pdf_ids = sil_pdf_ids + self.speech_noise_thresh_low = speech_noise_thresh_low + self.speech_noise_thresh_high = speech_noise_thresh_high + self.output_frame_probs = output_frame_probs + self.frame_in_ms = frame_in_ms + self.frame_length_ms = frame_length_ms + + +class E2EVadSpeechBufWithDoa(object): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Deep-FSMN for Large Vocabulary Continuous Speech Recognition + https://arxiv.org/abs/1803.05030 + """ + + def __init__(self): + self.start_ms = 0 + self.end_ms = 0 + self.buffer = [] + self.contain_seg_start_point = False + self.contain_seg_end_point = False + self.doa = 0 + + def Reset(self): + self.start_ms = 0 + self.end_ms = 0 + self.buffer = [] + self.contain_seg_start_point = False + self.contain_seg_end_point = False + self.doa = 0 + + +class E2EVadFrameProb(object): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Deep-FSMN for Large Vocabulary Continuous Speech Recognition + https://arxiv.org/abs/1803.05030 + """ + + def __init__(self): + self.noise_prob = 0.0 + self.speech_prob = 0.0 + self.score = 0.0 + self.frame_id = 0 + self.frm_state = 0 + + +class WindowDetector(object): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Deep-FSMN for Large Vocabulary Continuous Speech Recognition + https://arxiv.org/abs/1803.05030 + """ + + def __init__( + self, + window_size_ms: int, + sil_to_speech_time: int, + speech_to_sil_time: int, + frame_size_ms: int, + ): + self.window_size_ms = window_size_ms + self.sil_to_speech_time = sil_to_speech_time + self.speech_to_sil_time = speech_to_sil_time + self.frame_size_ms = frame_size_ms + + self.win_size_frame = int(window_size_ms / frame_size_ms) + self.win_sum = 0 + self.win_state = [0] * self.win_size_frame # 初始化窗 + + self.cur_win_pos = 0 + self.pre_frame_state = FrameState.kFrameStateSil + self.cur_frame_state = FrameState.kFrameStateSil + self.sil_to_speech_frmcnt_thres = int(sil_to_speech_time / frame_size_ms) + self.speech_to_sil_frmcnt_thres = int(speech_to_sil_time / frame_size_ms) + + self.voice_last_frame_count = 0 + self.noise_last_frame_count = 0 + self.hydre_frame_count = 0 + + def Reset(self) -> None: + self.cur_win_pos = 0 + self.win_sum = 0 + self.win_state = [0] * self.win_size_frame + self.pre_frame_state = FrameState.kFrameStateSil + self.cur_frame_state = FrameState.kFrameStateSil + self.voice_last_frame_count = 0 + self.noise_last_frame_count = 0 + self.hydre_frame_count = 0 + + def GetWinSize(self) -> int: + return int(self.win_size_frame) + + def DetectOneFrame( + self, frameState: FrameState, frame_count: int, cache: dict = {} + ) -> AudioChangeState: + cur_frame_state = FrameState.kFrameStateSil + if frameState == FrameState.kFrameStateSpeech: + cur_frame_state = 1 + elif frameState == FrameState.kFrameStateSil: + cur_frame_state = 0 + else: + return AudioChangeState.kChangeStateInvalid + self.win_sum -= self.win_state[self.cur_win_pos] + self.win_sum += cur_frame_state + self.win_state[self.cur_win_pos] = cur_frame_state + self.cur_win_pos = (self.cur_win_pos + 1) % self.win_size_frame + + if ( + self.pre_frame_state == FrameState.kFrameStateSil + and self.win_sum >= self.sil_to_speech_frmcnt_thres + ): + self.pre_frame_state = FrameState.kFrameStateSpeech + return AudioChangeState.kChangeStateSil2Speech + + if ( + self.pre_frame_state == FrameState.kFrameStateSpeech + and self.win_sum <= self.speech_to_sil_frmcnt_thres + ): + self.pre_frame_state = FrameState.kFrameStateSil + return AudioChangeState.kChangeStateSpeech2Sil + + if self.pre_frame_state == FrameState.kFrameStateSil: + return AudioChangeState.kChangeStateSil2Sil + if self.pre_frame_state == FrameState.kFrameStateSpeech: + return AudioChangeState.kChangeStateSpeech2Speech + return AudioChangeState.kChangeStateInvalid + + def FrameSizeMs(self) -> int: + return int(self.frame_size_ms) + + +class Stats(object): + def __init__( + self, + sil_pdf_ids, + max_end_sil_frame_cnt_thresh, + speech_noise_thres, + ): + self.data_buf_start_frame = 0 + self.frm_cnt = 0 + self.latest_confirmed_speech_frame = 0 + self.lastest_confirmed_silence_frame = -1 + self.continous_silence_frame_count = 0 + self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected + self.confirmed_start_frame = -1 + self.confirmed_end_frame = -1 + self.number_end_time_detected = 0 + self.sil_frame = 0 + self.sil_pdf_ids = sil_pdf_ids + self.noise_average_decibel = -100.0 + self.pre_end_silence_detected = False + self.next_seg = True + + self.output_data_buf = [] + self.output_data_buf_offset = 0 + self.frame_probs = [] + self.max_end_sil_frame_cnt_thresh = max_end_sil_frame_cnt_thresh + self.speech_noise_thres = speech_noise_thres + self.scores = None + self.max_time_out = False + self.decibel = [] + self.data_buf = None + self.data_buf_all = None + self.waveform = None + self.last_drop_frames = 0 + + +@tables.register("model_classes", "FsmnVADStreaming") +class FsmnVADStreaming(nn.Module): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Deep-FSMN for Large Vocabulary Continuous Speech Recognition + https://arxiv.org/abs/1803.05030 + """ + + def __init__( + self, + encoder: str = None, + encoder_conf: Optional[Dict] = None, + vad_post_args: Dict[str, Any] = None, + **kwargs, + ): + super().__init__() + self.vad_opts = VADXOptions(**kwargs) + + encoder_class = tables.encoder_classes.get(encoder) + encoder = encoder_class(**encoder_conf) + self.encoder = encoder + + def ResetDetection(self, cache: dict = {}): + cache["stats"].continous_silence_frame_count = 0 + cache["stats"].latest_confirmed_speech_frame = 0 + cache["stats"].lastest_confirmed_silence_frame = -1 + cache["stats"].confirmed_start_frame = -1 + cache["stats"].confirmed_end_frame = -1 + cache["stats"].vad_state_machine = ( + VadStateMachine.kVadInStateStartPointNotDetected + ) + cache["windows_detector"].Reset() + cache["stats"].sil_frame = 0 + cache["stats"].frame_probs = [] + + if cache["stats"].output_data_buf: + assert cache["stats"].output_data_buf[-1].contain_seg_end_point == True + drop_frames = int( + cache["stats"].output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms + ) + real_drop_frames = drop_frames - cache["stats"].last_drop_frames + cache["stats"].last_drop_frames = drop_frames + cache["stats"].data_buf_all = cache["stats"].data_buf_all[ + real_drop_frames + * int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) : + ] + cache["stats"].decibel = cache["stats"].decibel[real_drop_frames:] + cache["stats"].scores = cache["stats"].scores[:, real_drop_frames:, :] + + def ComputeDecibel(self, cache: dict = {}) -> None: + frame_sample_length = int( + self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000 + ) + frame_shift_length = int( + self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000 + ) + if cache["stats"].data_buf_all is None: + cache["stats"].data_buf_all = cache["stats"].waveform[ + 0 + ] # cache["stats"].data_buf is pointed to cache["stats"].waveform[0] + cache["stats"].data_buf = cache["stats"].data_buf_all + else: + cache["stats"].data_buf_all = torch.cat( + (cache["stats"].data_buf_all, cache["stats"].waveform[0]) + ) + for offset in range( + 0, + cache["stats"].waveform.shape[1] - frame_sample_length + 1, + frame_shift_length, + ): + cache["stats"].decibel.append( + 10 + * math.log10( + (cache["stats"].waveform[0][offset : offset + frame_sample_length]) + .square() + .sum() + + 0.000001 + ) + ) + + def ComputeScores(self, feats: torch.Tensor, cache: dict = {}) -> None: + scores = self.encoder(feats, cache=cache["encoder"]).to( + "cpu" + ) # return B * T * D + assert ( + scores.shape[1] == feats.shape[1] + ), "The shape between feats and scores does not match" + self.vad_opts.nn_eval_block_size = scores.shape[1] + cache["stats"].frm_cnt += scores.shape[1] # count total frames + if cache["stats"].scores is None: + cache["stats"].scores = scores # the first calculation + else: + cache["stats"].scores = torch.cat((cache["stats"].scores, scores), dim=1) + + def PopDataBufTillFrame( + self, frame_idx: int, cache: dict = {} + ) -> None: # need check again + while cache["stats"].data_buf_start_frame < frame_idx: + if len(cache["stats"].data_buf) >= int( + self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000 + ): + cache["stats"].data_buf_start_frame += 1 + cache["stats"].data_buf = cache["stats"].data_buf_all[ + ( + cache["stats"].data_buf_start_frame + - cache["stats"].last_drop_frames + ) + * int( + self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000 + ) : + ] + + def PopDataToOutputBuf( + self, + start_frm: int, + frm_cnt: int, + first_frm_is_start_point: bool, + last_frm_is_end_point: bool, + end_point_is_sent_end: bool, + cache: dict = {}, + ) -> None: + self.PopDataBufTillFrame(start_frm, cache=cache) + expected_sample_number = int( + frm_cnt * self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000 + ) + if last_frm_is_end_point: + extra_sample = max( + 0, + int( + self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000 + - self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000 + ), + ) + expected_sample_number += int(extra_sample) + if end_point_is_sent_end: + expected_sample_number = max( + expected_sample_number, len(cache["stats"].data_buf) + ) + if len(cache["stats"].data_buf) < expected_sample_number: + print("error in calling pop data_buf\n") + + if len(cache["stats"].output_data_buf) == 0 or first_frm_is_start_point: + cache["stats"].output_data_buf.append(E2EVadSpeechBufWithDoa()) + cache["stats"].output_data_buf[-1].Reset() + cache["stats"].output_data_buf[-1].start_ms = ( + start_frm * self.vad_opts.frame_in_ms + ) + cache["stats"].output_data_buf[-1].end_ms = ( + cache["stats"].output_data_buf[-1].start_ms + ) + cache["stats"].output_data_buf[-1].doa = 0 + cur_seg = cache["stats"].output_data_buf[-1] + if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms: + print("warning\n") + out_pos = len(cur_seg.buffer) # cur_seg.buff现在没做任何操作 + data_to_pop = 0 + if end_point_is_sent_end: + data_to_pop = expected_sample_number + else: + data_to_pop = int( + frm_cnt * self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000 + ) + if data_to_pop > len(cache["stats"].data_buf): + print('VAD data_to_pop is bigger than cache["stats"].data_buf.size()!!!\n') + data_to_pop = len(cache["stats"].data_buf) + expected_sample_number = len(cache["stats"].data_buf) + + cur_seg.doa = 0 + for sample_cpy_out in range(0, data_to_pop): + # cur_seg.buffer[out_pos ++] = data_buf_.back(); + out_pos += 1 + for sample_cpy_out in range(data_to_pop, expected_sample_number): + # cur_seg.buffer[out_pos++] = data_buf_.back() + out_pos += 1 + if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms: + print("Something wrong with the VAD algorithm\n") + cache["stats"].data_buf_start_frame += frm_cnt + cur_seg.end_ms = (start_frm + frm_cnt) * self.vad_opts.frame_in_ms + if first_frm_is_start_point: + cur_seg.contain_seg_start_point = True + if last_frm_is_end_point: + cur_seg.contain_seg_end_point = True + + def OnSilenceDetected(self, valid_frame: int, cache: dict = {}): + cache["stats"].lastest_confirmed_silence_frame = valid_frame + if ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateStartPointNotDetected + ): + self.PopDataBufTillFrame(valid_frame, cache=cache) + + # silence_detected_callback_ + # pass + + def OnVoiceDetected(self, valid_frame: int, cache: dict = {}) -> None: + cache["stats"].latest_confirmed_speech_frame = valid_frame + self.PopDataToOutputBuf(valid_frame, 1, False, False, False, cache=cache) + + def OnVoiceStart( + self, start_frame: int, fake_result: bool = False, cache: dict = {} + ) -> None: + if self.vad_opts.do_start_point_detection: + pass + if cache["stats"].confirmed_start_frame != -1: + print("not reset vad properly\n") + else: + cache["stats"].confirmed_start_frame = start_frame + + if ( + not fake_result + and cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateStartPointNotDetected + ): + self.PopDataToOutputBuf( + cache["stats"].confirmed_start_frame, 1, True, False, False, cache=cache + ) + + def OnVoiceEnd( + self, end_frame: int, fake_result: bool, is_last_frame: bool, cache: dict = {} + ) -> None: + for t in range(cache["stats"].latest_confirmed_speech_frame + 1, end_frame): + self.OnVoiceDetected(t, cache=cache) + if self.vad_opts.do_end_point_detection: + pass + if cache["stats"].confirmed_end_frame != -1: + print("not reset vad properly\n") + else: + cache["stats"].confirmed_end_frame = end_frame + if not fake_result: + cache["stats"].sil_frame = 0 + self.PopDataToOutputBuf( + cache["stats"].confirmed_end_frame, + 1, + False, + True, + is_last_frame, + cache=cache, + ) + cache["stats"].number_end_time_detected += 1 + + def MaybeOnVoiceEndIfLastFrame( + self, is_final_frame: bool, cur_frm_idx: int, cache: dict = {} + ) -> None: + if is_final_frame: + self.OnVoiceEnd(cur_frm_idx, False, True, cache=cache) + cache["stats"].vad_state_machine = ( + VadStateMachine.kVadInStateEndPointDetected + ) + + def GetLatency(self, cache: dict = {}) -> int: + return int( + self.LatencyFrmNumAtStartPoint(cache=cache) * self.vad_opts.frame_in_ms + ) + + def LatencyFrmNumAtStartPoint(self, cache: dict = {}) -> int: + vad_latency = cache["windows_detector"].GetWinSize() + if self.vad_opts.do_extend: + vad_latency += int( + self.vad_opts.lookback_time_start_point / self.vad_opts.frame_in_ms + ) + return vad_latency + + def GetFrameState(self, t: int, cache: dict = {}): + frame_state = FrameState.kFrameStateInvalid + cur_decibel = cache["stats"].decibel[t] + cur_snr = cur_decibel - cache["stats"].noise_average_decibel + # for each frame, calc log posterior probability of each state + if cur_decibel < self.vad_opts.decibel_thres: + frame_state = FrameState.kFrameStateSil + self.DetectOneFrame(frame_state, t, False, cache=cache) + return frame_state + + sum_score = 0.0 + noise_prob = 0.0 + assert len(cache["stats"].sil_pdf_ids) == self.vad_opts.silence_pdf_num + if len(cache["stats"].sil_pdf_ids) > 0: + assert len(cache["stats"].scores) == 1 # 只支持batch_size = 1的测试 + sil_pdf_scores = [ + cache["stats"].scores[0][t][sil_pdf_id] + for sil_pdf_id in cache["stats"].sil_pdf_ids + ] + sum_score = sum(sil_pdf_scores) + noise_prob = math.log(sum_score) * self.vad_opts.speech_2_noise_ratio + total_score = 1.0 + sum_score = total_score - sum_score + speech_prob = math.log(sum_score) + if self.vad_opts.output_frame_probs: + frame_prob = E2EVadFrameProb() + frame_prob.noise_prob = noise_prob + frame_prob.speech_prob = speech_prob + frame_prob.score = sum_score + frame_prob.frame_id = t + cache["stats"].frame_probs.append(frame_prob) + if ( + math.exp(speech_prob) + >= math.exp(noise_prob) + cache["stats"].speech_noise_thres + ): + if ( + cur_snr >= self.vad_opts.snr_thres + and cur_decibel >= self.vad_opts.decibel_thres + ): + frame_state = FrameState.kFrameStateSpeech + else: + frame_state = FrameState.kFrameStateSil + else: + frame_state = FrameState.kFrameStateSil + if cache["stats"].noise_average_decibel < -99.9: + cache["stats"].noise_average_decibel = cur_decibel + else: + cache["stats"].noise_average_decibel = ( + cur_decibel + + cache["stats"].noise_average_decibel + * (self.vad_opts.noise_frame_num_used_for_snr - 1) + ) / self.vad_opts.noise_frame_num_used_for_snr + + return frame_state + + def forward( + self, + feats: torch.Tensor, + waveform: torch.tensor, + cache: dict = {}, + is_final: bool = False, + **kwargs, + ): + # if len(cache) == 0: + # self.AllResetDetection() + # self.waveform = waveform # compute decibel for each frame + cache["stats"].waveform = waveform + is_streaming_input = kwargs.get("is_streaming_input", True) + self.ComputeDecibel(cache=cache) + self.ComputeScores(feats, cache=cache) + if not is_final: + self.DetectCommonFrames(cache=cache) + else: + self.DetectLastFrames(cache=cache) + segments = [] + for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now + segment_batch = [] + if len(cache["stats"].output_data_buf) > 0: + for i in range( + cache["stats"].output_data_buf_offset, + len(cache["stats"].output_data_buf), + ): + if ( + is_streaming_input + ): # in this case, return [beg, -1], [], [-1, end], [beg, end] + if ( + not cache["stats"] + .output_data_buf[i] + .contain_seg_start_point + ): + continue + if ( + not cache["stats"].next_seg + and not cache["stats"] + .output_data_buf[i] + .contain_seg_end_point + ): + continue + start_ms = ( + cache["stats"].output_data_buf[i].start_ms + if cache["stats"].next_seg + else -1 + ) + if cache["stats"].output_data_buf[i].contain_seg_end_point: + end_ms = cache["stats"].output_data_buf[i].end_ms + cache["stats"].next_seg = True + cache["stats"].output_data_buf_offset += 1 + else: + end_ms = -1 + cache["stats"].next_seg = False + segment = [start_ms, end_ms] + + else: # in this case, return [beg, end] + + if not is_final and ( + not cache["stats"] + .output_data_buf[i] + .contain_seg_start_point + or not cache["stats"] + .output_data_buf[i] + .contain_seg_end_point + ): + continue + segment = [ + cache["stats"].output_data_buf[i].start_ms, + cache["stats"].output_data_buf[i].end_ms, + ] + cache[ + "stats" + ].output_data_buf_offset += 1 # need update this parameter + + segment_batch.append(segment) + + if segment_batch: + segments.append(segment_batch) + # if is_final: + # # reset class variables and clear the dict for the next query + # self.AllResetDetection() + return segments + + def init_cache(self, cache: dict = {}, **kwargs): + + cache["frontend"] = {} + cache["prev_samples"] = torch.empty(0) + cache["encoder"] = {} + windows_detector = WindowDetector( + self.vad_opts.window_size_ms, + self.vad_opts.sil_to_speech_time_thres, + self.vad_opts.speech_to_sil_time_thres, + self.vad_opts.frame_in_ms, + ) + windows_detector.Reset() + + stats = Stats( + sil_pdf_ids=self.vad_opts.sil_pdf_ids, + max_end_sil_frame_cnt_thresh=self.vad_opts.max_end_silence_time + - self.vad_opts.speech_to_sil_time_thres, + speech_noise_thres=self.vad_opts.speech_noise_thres, + ) + cache["windows_detector"] = windows_detector + cache["stats"] = stats + return cache + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + cache: dict = {}, + **kwargs, + ): + + if len(cache) == 0: + self.init_cache(cache, **kwargs) + + meta_data = {} + chunk_size = kwargs.get("chunk_size", 60000) # 50ms + chunk_stride_samples = int(chunk_size * frontend.fs / 1000) + + time1 = time.perf_counter() + is_streaming_input = ( + kwargs.get("is_streaming_input", False) + if chunk_size >= 15000 + else kwargs.get("is_streaming_input", True) + ) + is_final = ( + kwargs.get("is_final", False) + if is_streaming_input + else kwargs.get("is_final", True) + ) + cfg = {"is_final": is_final, "is_streaming_input": is_streaming_input} + audio_sample_list = load_audio_text_image_video( + data_in, + fs=frontend.fs, + audio_fs=kwargs.get("fs", 16000), + data_type=kwargs.get("data_type", "sound"), + tokenizer=tokenizer, + cache=cfg, + ) + _is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True + is_streaming_input = cfg["is_streaming_input"] + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + assert len(audio_sample_list) == 1, "batch_size must be set 1" + + audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0])) + + n = int(len(audio_sample) // chunk_stride_samples + int(_is_final)) + m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final))) + segments = [] + for i in range(n): + kwargs["is_final"] = _is_final and i == n - 1 + audio_sample_i = audio_sample[ + i * chunk_stride_samples : (i + 1) * chunk_stride_samples + ] + + # extract fbank feats + speech, speech_lengths = extract_fbank( + [audio_sample_i], + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + cache=cache["frontend"], + is_final=kwargs["is_final"], + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() + * frontend.frame_shift + * frontend.lfr_n + / 1000 + ) + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + + batch = { + "feats": speech, + "waveform": cache["frontend"]["waveforms"], + "is_final": kwargs["is_final"], + "cache": cache, + "is_streaming_input": is_streaming_input, + } + segments_i = self.forward(**batch) + if len(segments_i) > 0: + segments.extend(*segments_i) + + cache["prev_samples"] = audio_sample[:-m] + if _is_final: + self.init_cache(cache) + + ibest_writer = None + if kwargs.get("output_dir") is not None: + if not hasattr(self, "writer"): + self.writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = self.writer[f"{1}best_recog"] + + results = [] + result_i = {"key": key[0], "value": segments} + if ( + "MODELSCOPE_ENVIRONMENT" in os.environ + and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas" + ): + result_i = json.dumps(result_i) + + results.append(result_i) + + if ibest_writer is not None: + ibest_writer["text"][key[0]] = segments + + return results, meta_data + + def DetectCommonFrames(self, cache: dict = {}) -> int: + if ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateEndPointDetected + ): + return 0 + for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1): + frame_state = FrameState.kFrameStateInvalid + frame_state = self.GetFrameState( + cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, + cache=cache, + ) + self.DetectOneFrame( + frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache + ) + + return 0 + + def DetectLastFrames(self, cache: dict = {}) -> int: + if ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateEndPointDetected + ): + return 0 + for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1): + frame_state = FrameState.kFrameStateInvalid + frame_state = self.GetFrameState( + cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, + cache=cache, + ) + if i != 0: + self.DetectOneFrame( + frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache + ) + else: + self.DetectOneFrame( + frame_state, cache["stats"].frm_cnt - 1, True, cache=cache + ) + + return 0 + + def DetectOneFrame( + self, + cur_frm_state: FrameState, + cur_frm_idx: int, + is_final_frame: bool, + cache: dict = {}, + ) -> None: + tmp_cur_frm_state = FrameState.kFrameStateInvalid + if cur_frm_state == FrameState.kFrameStateSpeech: + if math.fabs(1.0) > self.vad_opts.fe_prior_thres: + tmp_cur_frm_state = FrameState.kFrameStateSpeech + else: + tmp_cur_frm_state = FrameState.kFrameStateSil + elif cur_frm_state == FrameState.kFrameStateSil: + tmp_cur_frm_state = FrameState.kFrameStateSil + state_change = cache["windows_detector"].DetectOneFrame( + tmp_cur_frm_state, cur_frm_idx, cache=cache + ) + frm_shift_in_ms = self.vad_opts.frame_in_ms + if AudioChangeState.kChangeStateSil2Speech == state_change: + silence_frame_count = cache["stats"].continous_silence_frame_count + cache["stats"].continous_silence_frame_count = 0 + cache["stats"].pre_end_silence_detected = False + start_frame = 0 + if ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateStartPointNotDetected + ): + start_frame = max( + cache["stats"].data_buf_start_frame, + cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache), + ) + self.OnVoiceStart(start_frame, cache=cache) + cache["stats"].vad_state_machine = ( + VadStateMachine.kVadInStateInSpeechSegment + ) + for t in range(start_frame + 1, cur_frm_idx + 1): + self.OnVoiceDetected(t, cache=cache) + elif ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateInSpeechSegment + ): + for t in range( + cache["stats"].latest_confirmed_speech_frame + 1, cur_frm_idx + ): + self.OnVoiceDetected(t, cache=cache) + if ( + cur_frm_idx - cache["stats"].confirmed_start_frame + 1 + > self.vad_opts.max_single_segment_time / frm_shift_in_ms + ): + self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache) + cache["stats"].vad_state_machine = ( + VadStateMachine.kVadInStateEndPointDetected + ) + elif not is_final_frame: + self.OnVoiceDetected(cur_frm_idx, cache=cache) + else: + self.MaybeOnVoiceEndIfLastFrame( + is_final_frame, cur_frm_idx, cache=cache + ) + else: + pass + elif AudioChangeState.kChangeStateSpeech2Sil == state_change: + cache["stats"].continous_silence_frame_count = 0 + if ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateStartPointNotDetected + ): + pass + elif ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateInSpeechSegment + ): + if ( + cur_frm_idx - cache["stats"].confirmed_start_frame + 1 + > self.vad_opts.max_single_segment_time / frm_shift_in_ms + ): + self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache) + cache["stats"].vad_state_machine = ( + VadStateMachine.kVadInStateEndPointDetected + ) + elif not is_final_frame: + self.OnVoiceDetected(cur_frm_idx, cache=cache) + else: + self.MaybeOnVoiceEndIfLastFrame( + is_final_frame, cur_frm_idx, cache=cache + ) + else: + pass + elif AudioChangeState.kChangeStateSpeech2Speech == state_change: + cache["stats"].continous_silence_frame_count = 0 + if ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateInSpeechSegment + ): + if ( + cur_frm_idx - cache["stats"].confirmed_start_frame + 1 + > self.vad_opts.max_single_segment_time / frm_shift_in_ms + ): + cache["stats"].max_time_out = True + self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache) + cache["stats"].vad_state_machine = ( + VadStateMachine.kVadInStateEndPointDetected + ) + elif not is_final_frame: + self.OnVoiceDetected(cur_frm_idx, cache=cache) + else: + self.MaybeOnVoiceEndIfLastFrame( + is_final_frame, cur_frm_idx, cache=cache + ) + else: + pass + elif AudioChangeState.kChangeStateSil2Sil == state_change: + cache["stats"].continous_silence_frame_count += 1 + if ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateStartPointNotDetected + ): + # silence timeout, return zero length decision + if ( + ( + self.vad_opts.detect_mode + == VadDetectMode.kVadSingleUtteranceDetectMode.value + ) + and ( + cache["stats"].continous_silence_frame_count * frm_shift_in_ms + > self.vad_opts.max_start_silence_time + ) + ) or (is_final_frame and cache["stats"].number_end_time_detected == 0): + for t in range( + cache["stats"].lastest_confirmed_silence_frame + 1, cur_frm_idx + ): + self.OnSilenceDetected(t, cache=cache) + self.OnVoiceStart(0, True, cache=cache) + self.OnVoiceEnd(0, True, False, cache=cache) + cache["stats"].vad_state_machine = ( + VadStateMachine.kVadInStateEndPointDetected + ) + else: + if cur_frm_idx >= self.LatencyFrmNumAtStartPoint(cache=cache): + self.OnSilenceDetected( + cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache), + cache=cache, + ) + elif ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateInSpeechSegment + ): + if ( + cache["stats"].continous_silence_frame_count * frm_shift_in_ms + >= cache["stats"].max_end_sil_frame_cnt_thresh + ): + lookback_frame = int( + cache["stats"].max_end_sil_frame_cnt_thresh / frm_shift_in_ms + ) + if self.vad_opts.do_extend: + lookback_frame -= int( + self.vad_opts.lookahead_time_end_point / frm_shift_in_ms + ) + lookback_frame -= 1 + lookback_frame = max(0, lookback_frame) + self.OnVoiceEnd( + cur_frm_idx - lookback_frame, False, False, cache=cache + ) + cache["stats"].vad_state_machine = ( + VadStateMachine.kVadInStateEndPointDetected + ) + elif ( + cur_frm_idx - cache["stats"].confirmed_start_frame + 1 + > self.vad_opts.max_single_segment_time / frm_shift_in_ms + ): + self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache) + cache["stats"].vad_state_machine = ( + VadStateMachine.kVadInStateEndPointDetected + ) + elif self.vad_opts.do_extend and not is_final_frame: + if cache["stats"].continous_silence_frame_count <= int( + self.vad_opts.lookahead_time_end_point / frm_shift_in_ms + ): + self.OnVoiceDetected(cur_frm_idx, cache=cache) + else: + self.MaybeOnVoiceEndIfLastFrame( + is_final_frame, cur_frm_idx, cache=cache + ) + else: + pass + + if ( + cache["stats"].vad_state_machine + == VadStateMachine.kVadInStateEndPointDetected + and self.vad_opts.detect_mode + == VadDetectMode.kVadMutipleUtteranceDetectMode.value + ): + self.ResetDetection(cache=cache) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2b47f11c01baef4de865963388b25b4e315a05cc --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/fsmn_vad_streaming/template.yaml @@ -0,0 +1,62 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: FsmnVADStreaming +model_conf: + sample_rate: 16000 + detect_mode: 1 + snr_mode: 0 + max_end_silence_time: 800 + max_start_silence_time: 3000 + do_start_point_detection: True + do_end_point_detection: True + window_size_ms: 200 + sil_to_speech_time_thres: 150 + speech_to_sil_time_thres: 150 + speech_2_noise_ratio: 1.0 + do_extend: 1 + lookback_time_start_point: 200 + lookahead_time_end_point: 100 + max_single_segment_time: 60000 + snr_thres: -100.0 + noise_frame_num_used_for_snr: 100 + decibel_thres: -100.0 + speech_noise_thres: 0.6 + fe_prior_thres: 0.0001 + silence_pdf_num: 1 + sil_pdf_ids: [0] + speech_noise_thresh_low: -0.1 + speech_noise_thresh_high: 0.3 + output_frame_probs: False + frame_in_ms: 10 + frame_length_ms: 25 + +encoder: FSMN +encoder_conf: + input_dim: 400 + input_affine_dim: 140 + fsmn_layers: 4 + linear_dim: 250 + proj_dim: 128 + lorder: 20 + rorder: 0 + lstride: 1 + rstride: 0 + output_affine_dim: 140 + output_dim: 248 + +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + dither: 0.0 + lfr_m: 5 + lfr_n: 1 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/language_model/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/language_model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..24134d8b02377f5fd6223a12b470d09f4124cfe7 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/__init__.py @@ -0,0 +1 @@ +"""Initialize sub package.""" diff --git a/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/argument.py b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/argument.py new file mode 100644 index 0000000000000000000000000000000000000000..4be9b6b902c05e7be2a9ce8b467350b43aeed747 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/argument.py @@ -0,0 +1,156 @@ +# Copyright 2020 Hirofumi Inaguma +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Conformer common arguments.""" + + +def add_arguments_rnn_encoder_common(group): + """Define common arguments for RNN encoder.""" + group.add_argument( + "--etype", + default="blstmp", + type=str, + choices=[ + "lstm", + "blstm", + "lstmp", + "blstmp", + "vgglstmp", + "vggblstmp", + "vgglstm", + "vggblstm", + "gru", + "bgru", + "grup", + "bgrup", + "vgggrup", + "vggbgrup", + "vgggru", + "vggbgru", + ], + help="Type of encoder network architecture", + ) + group.add_argument( + "--elayers", + default=4, + type=int, + help="Number of encoder layers", + ) + group.add_argument( + "--eunits", + "-u", + default=300, + type=int, + help="Number of encoder hidden units", + ) + group.add_argument( + "--eprojs", default=320, type=int, help="Number of encoder projection units" + ) + group.add_argument( + "--subsample", + default="1", + type=str, + help="Subsample input frames x_y_z means " + "subsample every x frame at 1st layer, " + "every y frame at 2nd layer etc.", + ) + return group + + +def add_arguments_rnn_decoder_common(group): + """Define common arguments for RNN decoder.""" + group.add_argument( + "--dtype", + default="lstm", + type=str, + choices=["lstm", "gru"], + help="Type of decoder network architecture", + ) + group.add_argument( + "--dlayers", default=1, type=int, help="Number of decoder layers" + ) + group.add_argument( + "--dunits", default=320, type=int, help="Number of decoder hidden units" + ) + group.add_argument( + "--dropout-rate-decoder", + default=0.0, + type=float, + help="Dropout rate for the decoder", + ) + group.add_argument( + "--sampling-probability", + default=0.0, + type=float, + help="Ratio of predicted labels fed back to decoder", + ) + group.add_argument( + "--lsm-type", + const="", + default="", + type=str, + nargs="?", + choices=["", "unigram"], + help="Apply label smoothing with a specified distribution type", + ) + return group + + +def add_arguments_rnn_attention_common(group): + """Define common arguments for RNN attention.""" + group.add_argument( + "--atype", + default="dot", + type=str, + choices=[ + "noatt", + "dot", + "add", + "location", + "coverage", + "coverage_location", + "location2d", + "location_recurrent", + "multi_head_dot", + "multi_head_add", + "multi_head_loc", + "multi_head_multi_res_loc", + ], + help="Type of attention architecture", + ) + group.add_argument( + "--adim", + default=320, + type=int, + help="Number of attention transformation dimensions", + ) + group.add_argument( + "--awin", default=5, type=int, help="Window size for location2d attention" + ) + group.add_argument( + "--aheads", + default=4, + type=int, + help="Number of heads for multi head attention", + ) + group.add_argument( + "--aconv-chans", + default=-1, + type=int, + help="Number of attention convolution channels \ + (negative value indicates no location-aware attention)", + ) + group.add_argument( + "--aconv-filts", + default=100, + type=int, + help="Number of attention convolution filters \ + (negative value indicates no location-aware attention)", + ) + group.add_argument( + "--dropout-rate", + default=0.0, + type=float, + help="Dropout rate for the encoder", + ) + return group diff --git a/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/attentions.py b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/attentions.py new file mode 100644 index 0000000000000000000000000000000000000000..bd73c3f3c4e7f853c8877d197529bc8c87371d56 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/attentions.py @@ -0,0 +1,1808 @@ +"""Attention modules for RNN.""" + +import math +import six + +import torch +import torch.nn.functional as F + +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.utils.nets_utils import to_device + + +def _apply_attention_constraint( + e, last_attended_idx, backward_window=1, forward_window=3 +): + """Apply monotonic attention constraint. + + This function apply the monotonic attention constraint + introduced in `Deep Voice 3: Scaling + Text-to-Speech with Convolutional Sequence Learning`_. + + Args: + e (Tensor): Attention energy before applying softmax (1, T). + last_attended_idx (int): The index of the inputs of the last attended [0, T]. + backward_window (int, optional): Backward window size in attention constraint. + forward_window (int, optional): Forward window size in attetion constraint. + + Returns: + Tensor: Monotonic constrained attention energy (1, T). + + .. _`Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning`: + https://arxiv.org/abs/1710.07654 + + """ + if e.size(0) != 1: + raise NotImplementedError("Batch attention constraining is not yet supported.") + backward_idx = last_attended_idx - backward_window + forward_idx = last_attended_idx + forward_window + if backward_idx > 0: + e[:, :backward_idx] = -float("inf") + if forward_idx < e.size(1): + e[:, forward_idx:] = -float("inf") + return e + + +class NoAtt(torch.nn.Module): + """No attention""" + + def __init__(self): + super(NoAtt, self).__init__() + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.c = None + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.c = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev): + """NoAtt forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B, T_max, D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: dummy (does not use) + :param torch.Tensor att_prev: dummy (does not use) + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: previous attention weights + :rtype: torch.Tensor + """ + batch = len(enc_hs_pad) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + + # initialize attention weight with uniform dist. + if att_prev is None: + # if no bias, 0 0-pad goes 0 + mask = 1.0 - make_pad_mask(enc_hs_len).float() + att_prev = mask / mask.new(enc_hs_len).unsqueeze(-1) + att_prev = att_prev.to(self.enc_h) + self.c = torch.sum( + self.enc_h * att_prev.view(batch, self.h_length, 1), dim=1 + ) + + return self.c, att_prev + + +class AttDot(torch.nn.Module): + """Dot product attention + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int att_dim: attention dimension + :param bool han_mode: flag to swith on mode of hierarchical attention + and not store pre_compute_enc_h + """ + + def __init__(self, eprojs, dunits, att_dim, han_mode=False): + super(AttDot, self).__init__() + self.mlp_enc = torch.nn.Linear(eprojs, att_dim) + self.mlp_dec = torch.nn.Linear(dunits, att_dim) + + self.dunits = dunits + self.eprojs = eprojs + self.att_dim = att_dim + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev, scaling=2.0): + """AttDot forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: dummy (does not use) + :param torch.Tensor att_prev: dummy (does not use) + :param float scaling: scaling parameter before applying softmax + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: previous attention weight (B x T_max) + :rtype: torch.Tensor + """ + + batch = enc_hs_pad.size(0) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_enc_h = torch.tanh(self.mlp_enc(self.enc_h)) + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + e = torch.sum( + self.pre_compute_enc_h + * torch.tanh(self.mlp_dec(dec_z)).view(batch, 1, self.att_dim), + dim=2, + ) # utt x frame + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w = F.softmax(scaling * e, dim=1) + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1) + return c, w + + +class AttAdd(torch.nn.Module): + """Additive attention + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int att_dim: attention dimension + :param bool han_mode: flag to swith on mode of hierarchical attention + and not store pre_compute_enc_h + """ + + def __init__(self, eprojs, dunits, att_dim, han_mode=False): + super(AttAdd, self).__init__() + self.mlp_enc = torch.nn.Linear(eprojs, att_dim) + self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False) + self.gvec = torch.nn.Linear(att_dim, 1) + self.dunits = dunits + self.eprojs = eprojs + self.att_dim = att_dim + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev, scaling=2.0): + """AttAdd forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param torch.Tensor att_prev: dummy (does not use) + :param float scaling: scaling parameter before applying softmax + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: previous attention weights (B x T_max) + :rtype: torch.Tensor + """ + + batch = len(enc_hs_pad) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_enc_h = self.mlp_enc(self.enc_h) + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + # dec_z_tiled: utt x frame x att_dim + dec_z_tiled = self.mlp_dec(dec_z).view(batch, 1, self.att_dim) + + # dot with gvec + # utt x frame x att_dim -> utt x frame + e = self.gvec(torch.tanh(self.pre_compute_enc_h + dec_z_tiled)).squeeze(2) + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w = F.softmax(scaling * e, dim=1) + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1) + + return c, w + + +class AttLoc(torch.nn.Module): + """location-aware attention module. + + Reference: Attention-Based Models for Speech Recognition + (https://arxiv.org/pdf/1506.07503.pdf) + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int att_dim: attention dimension + :param int aconv_chans: # channels of attention convolution + :param int aconv_filts: filter size of attention convolution + :param bool han_mode: flag to swith on mode of hierarchical attention + and not store pre_compute_enc_h + """ + + def __init__( + self, eprojs, dunits, att_dim, aconv_chans, aconv_filts, han_mode=False + ): + super(AttLoc, self).__init__() + self.mlp_enc = torch.nn.Linear(eprojs, att_dim) + self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False) + self.mlp_att = torch.nn.Linear(aconv_chans, att_dim, bias=False) + self.loc_conv = torch.nn.Conv2d( + 1, + aconv_chans, + (1, 2 * aconv_filts + 1), + padding=(0, aconv_filts), + bias=False, + ) + self.gvec = torch.nn.Linear(att_dim, 1) + + self.dunits = dunits + self.eprojs = eprojs + self.att_dim = att_dim + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + + def forward( + self, + enc_hs_pad, + enc_hs_len, + dec_z, + att_prev, + scaling=2.0, + last_attended_idx=None, + backward_window=1, + forward_window=3, + ): + """Calculate AttLoc forward propagation. + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param torch.Tensor att_prev: previous attention weight (B x T_max) + :param float scaling: scaling parameter before applying softmax + :param torch.Tensor forward_window: + forward window size when constraining attention + :param int last_attended_idx: index of the inputs of the last attended + :param int backward_window: backward window size in attention constraint + :param int forward_window: forward window size in attetion constraint + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: previous attention weights (B x T_max) + :rtype: torch.Tensor + """ + batch = len(enc_hs_pad) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_enc_h = self.mlp_enc(self.enc_h) + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + # initialize attention weight with uniform dist. + if att_prev is None: + # if no bias, 0 0-pad goes 0 + att_prev = 1.0 - make_pad_mask(enc_hs_len).to( + device=dec_z.device, dtype=dec_z.dtype + ) + att_prev = att_prev / att_prev.new(enc_hs_len).unsqueeze(-1) + + # att_prev: utt x frame -> utt x 1 x 1 x frame + # -> utt x att_conv_chans x 1 x frame + att_conv = self.loc_conv(att_prev.view(batch, 1, 1, self.h_length)) + # att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans + att_conv = att_conv.squeeze(2).transpose(1, 2) + # att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim + att_conv = self.mlp_att(att_conv) + + # dec_z_tiled: utt x frame x att_dim + dec_z_tiled = self.mlp_dec(dec_z).view(batch, 1, self.att_dim) + + # dot with gvec + # utt x frame x att_dim -> utt x frame + e = self.gvec( + torch.tanh(att_conv + self.pre_compute_enc_h + dec_z_tiled) + ).squeeze(2) + + # NOTE: consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + + # apply monotonic attention constraint (mainly for TTS) + if last_attended_idx is not None: + e = _apply_attention_constraint( + e, last_attended_idx, backward_window, forward_window + ) + + w = F.softmax(scaling * e, dim=1) + + # weighted sum over flames + # utt x hdim + c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1) + + return c, w + + +class AttCov(torch.nn.Module): + """Coverage mechanism attention + + Reference: Get To The Point: Summarization with Pointer-Generator Network + (https://arxiv.org/abs/1704.04368) + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int att_dim: attention dimension + :param bool han_mode: flag to swith on mode of hierarchical attention + and not store pre_compute_enc_h + """ + + def __init__(self, eprojs, dunits, att_dim, han_mode=False): + super(AttCov, self).__init__() + self.mlp_enc = torch.nn.Linear(eprojs, att_dim) + self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False) + self.wvec = torch.nn.Linear(1, att_dim) + self.gvec = torch.nn.Linear(att_dim, 1) + + self.dunits = dunits + self.eprojs = eprojs + self.att_dim = att_dim + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev_list, scaling=2.0): + """AttCov forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param list att_prev_list: list of previous attention weight + :param float scaling: scaling parameter before applying softmax + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: list of previous attention weights + :rtype: list + """ + + batch = len(enc_hs_pad) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_enc_h = self.mlp_enc(self.enc_h) + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + # initialize attention weight with uniform dist. + if att_prev_list is None: + # if no bias, 0 0-pad goes 0 + att_prev_list = to_device( + enc_hs_pad, (1.0 - make_pad_mask(enc_hs_len).float()) + ) + att_prev_list = [ + att_prev_list / att_prev_list.new(enc_hs_len).unsqueeze(-1) + ] + + # att_prev_list: L' * [B x T] => cov_vec B x T + cov_vec = sum(att_prev_list) + # cov_vec: B x T => B x T x 1 => B x T x att_dim + cov_vec = self.wvec(cov_vec.unsqueeze(-1)) + + # dec_z_tiled: utt x frame x att_dim + dec_z_tiled = self.mlp_dec(dec_z).view(batch, 1, self.att_dim) + + # dot with gvec + # utt x frame x att_dim -> utt x frame + e = self.gvec( + torch.tanh(cov_vec + self.pre_compute_enc_h + dec_z_tiled) + ).squeeze(2) + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w = F.softmax(scaling * e, dim=1) + att_prev_list += [w] + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1) + + return c, att_prev_list + + +class AttLoc2D(torch.nn.Module): + """2D location-aware attention + + This attention is an extended version of location aware attention. + It take not only one frame before attention weights, + but also earlier frames into account. + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int att_dim: attention dimension + :param int aconv_chans: # channels of attention convolution + :param int aconv_filts: filter size of attention convolution + :param int att_win: attention window size (default=5) + :param bool han_mode: + flag to swith on mode of hierarchical attention and not store pre_compute_enc_h + """ + + def __init__( + self, eprojs, dunits, att_dim, att_win, aconv_chans, aconv_filts, han_mode=False + ): + super(AttLoc2D, self).__init__() + self.mlp_enc = torch.nn.Linear(eprojs, att_dim) + self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False) + self.mlp_att = torch.nn.Linear(aconv_chans, att_dim, bias=False) + self.loc_conv = torch.nn.Conv2d( + 1, + aconv_chans, + (att_win, 2 * aconv_filts + 1), + padding=(0, aconv_filts), + bias=False, + ) + self.gvec = torch.nn.Linear(att_dim, 1) + + self.dunits = dunits + self.eprojs = eprojs + self.att_dim = att_dim + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.aconv_chans = aconv_chans + self.att_win = att_win + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev, scaling=2.0): + """AttLoc2D forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param torch.Tensor att_prev: previous attention weight (B x att_win x T_max) + :param float scaling: scaling parameter before applying softmax + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: previous attention weights (B x att_win x T_max) + :rtype: torch.Tensor + """ + + batch = len(enc_hs_pad) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_enc_h = self.mlp_enc(self.enc_h) + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + # initialize attention weight with uniform dist. + if att_prev is None: + # B * [Li x att_win] + # if no bias, 0 0-pad goes 0 + att_prev = to_device(enc_hs_pad, (1.0 - make_pad_mask(enc_hs_len).float())) + att_prev = att_prev / att_prev.new(enc_hs_len).unsqueeze(-1) + att_prev = att_prev.unsqueeze(1).expand(-1, self.att_win, -1) + + # att_prev: B x att_win x Tmax -> B x 1 x att_win x Tmax -> B x C x 1 x Tmax + att_conv = self.loc_conv(att_prev.unsqueeze(1)) + # att_conv: B x C x 1 x Tmax -> B x Tmax x C + att_conv = att_conv.squeeze(2).transpose(1, 2) + # att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim + att_conv = self.mlp_att(att_conv) + + # dec_z_tiled: utt x frame x att_dim + dec_z_tiled = self.mlp_dec(dec_z).view(batch, 1, self.att_dim) + + # dot with gvec + # utt x frame x att_dim -> utt x frame + e = self.gvec( + torch.tanh(att_conv + self.pre_compute_enc_h + dec_z_tiled) + ).squeeze(2) + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w = F.softmax(scaling * e, dim=1) + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1) + + # update att_prev: B x att_win x Tmax -> B x att_win+1 x Tmax + # -> B x att_win x Tmax + att_prev = torch.cat([att_prev, w.unsqueeze(1)], dim=1) + att_prev = att_prev[:, 1:] + + return c, att_prev + + +class AttLocRec(torch.nn.Module): + """location-aware recurrent attention + + This attention is an extended version of location aware attention. + With the use of RNN, + it take the effect of the history of attention weights into account. + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int att_dim: attention dimension + :param int aconv_chans: # channels of attention convolution + :param int aconv_filts: filter size of attention convolution + :param bool han_mode: + flag to swith on mode of hierarchical attention and not store pre_compute_enc_h + """ + + def __init__( + self, eprojs, dunits, att_dim, aconv_chans, aconv_filts, han_mode=False + ): + super(AttLocRec, self).__init__() + self.mlp_enc = torch.nn.Linear(eprojs, att_dim) + self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False) + self.loc_conv = torch.nn.Conv2d( + 1, + aconv_chans, + (1, 2 * aconv_filts + 1), + padding=(0, aconv_filts), + bias=False, + ) + self.att_lstm = torch.nn.LSTMCell(aconv_chans, att_dim, bias=False) + self.gvec = torch.nn.Linear(att_dim, 1) + + self.dunits = dunits + self.eprojs = eprojs + self.att_dim = att_dim + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev_states, scaling=2.0): + """AttLocRec forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param tuple att_prev_states: previous attention weight and lstm states + ((B, T_max), ((B, att_dim), (B, att_dim))) + :param float scaling: scaling parameter before applying softmax + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: previous attention weights and lstm states (w, (hx, cx)) + ((B, T_max), ((B, att_dim), (B, att_dim))) + :rtype: tuple + """ + + batch = len(enc_hs_pad) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_enc_h = self.mlp_enc(self.enc_h) + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + if att_prev_states is None: + # initialize attention weight with uniform dist. + # if no bias, 0 0-pad goes 0 + att_prev = to_device(enc_hs_pad, (1.0 - make_pad_mask(enc_hs_len).float())) + att_prev = att_prev / att_prev.new(enc_hs_len).unsqueeze(-1) + + # initialize lstm states + att_h = enc_hs_pad.new_zeros(batch, self.att_dim) + att_c = enc_hs_pad.new_zeros(batch, self.att_dim) + att_states = (att_h, att_c) + else: + att_prev = att_prev_states[0] + att_states = att_prev_states[1] + + # B x 1 x 1 x T -> B x C x 1 x T + att_conv = self.loc_conv(att_prev.view(batch, 1, 1, self.h_length)) + # apply non-linear + att_conv = F.relu(att_conv) + # B x C x 1 x T -> B x C x 1 x 1 -> B x C + att_conv = F.max_pool2d(att_conv, (1, att_conv.size(3))).view(batch, -1) + + att_h, att_c = self.att_lstm(att_conv, att_states) + + # dec_z_tiled: utt x frame x att_dim + dec_z_tiled = self.mlp_dec(dec_z).view(batch, 1, self.att_dim) + + # dot with gvec + # utt x frame x att_dim -> utt x frame + e = self.gvec( + torch.tanh(att_h.unsqueeze(1) + self.pre_compute_enc_h + dec_z_tiled) + ).squeeze(2) + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w = F.softmax(scaling * e, dim=1) + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1) + + return c, (w, (att_h, att_c)) + + +class AttCovLoc(torch.nn.Module): + """Coverage mechanism location aware attention + + This attention is a combination of coverage and location-aware attentions. + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int att_dim: attention dimension + :param int aconv_chans: # channels of attention convolution + :param int aconv_filts: filter size of attention convolution + :param bool han_mode: + flag to swith on mode of hierarchical attention and not store pre_compute_enc_h + """ + + def __init__( + self, eprojs, dunits, att_dim, aconv_chans, aconv_filts, han_mode=False + ): + super(AttCovLoc, self).__init__() + self.mlp_enc = torch.nn.Linear(eprojs, att_dim) + self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False) + self.mlp_att = torch.nn.Linear(aconv_chans, att_dim, bias=False) + self.loc_conv = torch.nn.Conv2d( + 1, + aconv_chans, + (1, 2 * aconv_filts + 1), + padding=(0, aconv_filts), + bias=False, + ) + self.gvec = torch.nn.Linear(att_dim, 1) + + self.dunits = dunits + self.eprojs = eprojs + self.att_dim = att_dim + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.aconv_chans = aconv_chans + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev_list, scaling=2.0): + """AttCovLoc forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param list att_prev_list: list of previous attention weight + :param float scaling: scaling parameter before applying softmax + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: list of previous attention weights + :rtype: list + """ + + batch = len(enc_hs_pad) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_enc_h = self.mlp_enc(self.enc_h) + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + # initialize attention weight with uniform dist. + if att_prev_list is None: + # if no bias, 0 0-pad goes 0 + mask = 1.0 - make_pad_mask(enc_hs_len).float() + att_prev_list = [ + to_device(enc_hs_pad, mask / mask.new(enc_hs_len).unsqueeze(-1)) + ] + + # att_prev_list: L' * [B x T] => cov_vec B x T + cov_vec = sum(att_prev_list) + + # cov_vec: B x T -> B x 1 x 1 x T -> B x C x 1 x T + att_conv = self.loc_conv(cov_vec.view(batch, 1, 1, self.h_length)) + # att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans + att_conv = att_conv.squeeze(2).transpose(1, 2) + # att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim + att_conv = self.mlp_att(att_conv) + + # dec_z_tiled: utt x frame x att_dim + dec_z_tiled = self.mlp_dec(dec_z).view(batch, 1, self.att_dim) + + # dot with gvec + # utt x frame x att_dim -> utt x frame + e = self.gvec( + torch.tanh(att_conv + self.pre_compute_enc_h + dec_z_tiled) + ).squeeze(2) + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w = F.softmax(scaling * e, dim=1) + att_prev_list += [w] + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1) + + return c, att_prev_list + + +class AttMultiHeadDot(torch.nn.Module): + """Multi head dot product attention + + Reference: Attention is all you need + (https://arxiv.org/abs/1706.03762) + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int aheads: # heads of multi head attention + :param int att_dim_k: dimension k in multi head attention + :param int att_dim_v: dimension v in multi head attention + :param bool han_mode: flag to swith on mode of hierarchical attention + and not store pre_compute_k and pre_compute_v + """ + + def __init__(self, eprojs, dunits, aheads, att_dim_k, att_dim_v, han_mode=False): + super(AttMultiHeadDot, self).__init__() + self.mlp_q = torch.nn.ModuleList() + self.mlp_k = torch.nn.ModuleList() + self.mlp_v = torch.nn.ModuleList() + for _ in six.moves.range(aheads): + self.mlp_q += [torch.nn.Linear(dunits, att_dim_k)] + self.mlp_k += [torch.nn.Linear(eprojs, att_dim_k, bias=False)] + self.mlp_v += [torch.nn.Linear(eprojs, att_dim_v, bias=False)] + self.mlp_o = torch.nn.Linear(aheads * att_dim_v, eprojs, bias=False) + self.dunits = dunits + self.eprojs = eprojs + self.aheads = aheads + self.att_dim_k = att_dim_k + self.att_dim_v = att_dim_v + self.scaling = 1.0 / math.sqrt(att_dim_k) + self.h_length = None + self.enc_h = None + self.pre_compute_k = None + self.pre_compute_v = None + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_k = None + self.pre_compute_v = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev): + """AttMultiHeadDot forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param torch.Tensor att_prev: dummy (does not use) + :return: attention weighted encoder state (B x D_enc) + :rtype: torch.Tensor + :return: list of previous attention weight (B x T_max) * aheads + :rtype: list + """ + + batch = enc_hs_pad.size(0) + # pre-compute all k and v outside the decoder loop + if self.pre_compute_k is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_k = [ + torch.tanh(self.mlp_k[h](self.enc_h)) + for h in six.moves.range(self.aheads) + ] + + if self.pre_compute_v is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_v = [ + self.mlp_v[h](self.enc_h) for h in six.moves.range(self.aheads) + ] + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + c = [] + w = [] + for h in six.moves.range(self.aheads): + e = torch.sum( + self.pre_compute_k[h] + * torch.tanh(self.mlp_q[h](dec_z)).view(batch, 1, self.att_dim_k), + dim=2, + ) # utt x frame + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w += [F.softmax(self.scaling * e, dim=1)] + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c += [ + torch.sum( + self.pre_compute_v[h] * w[h].view(batch, self.h_length, 1), dim=1 + ) + ] + + # concat all of c + c = self.mlp_o(torch.cat(c, dim=1)) + + return c, w + + +class AttMultiHeadAdd(torch.nn.Module): + """Multi head additive attention + + Reference: Attention is all you need + (https://arxiv.org/abs/1706.03762) + + This attention is multi head attention using additive attention for each head. + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int aheads: # heads of multi head attention + :param int att_dim_k: dimension k in multi head attention + :param int att_dim_v: dimension v in multi head attention + :param bool han_mode: flag to swith on mode of hierarchical attention + and not store pre_compute_k and pre_compute_v + """ + + def __init__(self, eprojs, dunits, aheads, att_dim_k, att_dim_v, han_mode=False): + super(AttMultiHeadAdd, self).__init__() + self.mlp_q = torch.nn.ModuleList() + self.mlp_k = torch.nn.ModuleList() + self.mlp_v = torch.nn.ModuleList() + self.gvec = torch.nn.ModuleList() + for _ in six.moves.range(aheads): + self.mlp_q += [torch.nn.Linear(dunits, att_dim_k)] + self.mlp_k += [torch.nn.Linear(eprojs, att_dim_k, bias=False)] + self.mlp_v += [torch.nn.Linear(eprojs, att_dim_v, bias=False)] + self.gvec += [torch.nn.Linear(att_dim_k, 1)] + self.mlp_o = torch.nn.Linear(aheads * att_dim_v, eprojs, bias=False) + self.dunits = dunits + self.eprojs = eprojs + self.aheads = aheads + self.att_dim_k = att_dim_k + self.att_dim_v = att_dim_v + self.scaling = 1.0 / math.sqrt(att_dim_k) + self.h_length = None + self.enc_h = None + self.pre_compute_k = None + self.pre_compute_v = None + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_k = None + self.pre_compute_v = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev): + """AttMultiHeadAdd forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param torch.Tensor att_prev: dummy (does not use) + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: list of previous attention weight (B x T_max) * aheads + :rtype: list + """ + + batch = enc_hs_pad.size(0) + # pre-compute all k and v outside the decoder loop + if self.pre_compute_k is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_k = [ + self.mlp_k[h](self.enc_h) for h in six.moves.range(self.aheads) + ] + + if self.pre_compute_v is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_v = [ + self.mlp_v[h](self.enc_h) for h in six.moves.range(self.aheads) + ] + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + c = [] + w = [] + for h in six.moves.range(self.aheads): + e = self.gvec[h]( + torch.tanh( + self.pre_compute_k[h] + + self.mlp_q[h](dec_z).view(batch, 1, self.att_dim_k) + ) + ).squeeze(2) + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w += [F.softmax(self.scaling * e, dim=1)] + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c += [ + torch.sum( + self.pre_compute_v[h] * w[h].view(batch, self.h_length, 1), dim=1 + ) + ] + + # concat all of c + c = self.mlp_o(torch.cat(c, dim=1)) + + return c, w + + +class AttMultiHeadLoc(torch.nn.Module): + """Multi head location based attention + + Reference: Attention is all you need + (https://arxiv.org/abs/1706.03762) + + This attention is multi head attention using location-aware attention for each head. + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int aheads: # heads of multi head attention + :param int att_dim_k: dimension k in multi head attention + :param int att_dim_v: dimension v in multi head attention + :param int aconv_chans: # channels of attention convolution + :param int aconv_filts: filter size of attention convolution + :param bool han_mode: flag to swith on mode of hierarchical attention + and not store pre_compute_k and pre_compute_v + """ + + def __init__( + self, + eprojs, + dunits, + aheads, + att_dim_k, + att_dim_v, + aconv_chans, + aconv_filts, + han_mode=False, + ): + super(AttMultiHeadLoc, self).__init__() + self.mlp_q = torch.nn.ModuleList() + self.mlp_k = torch.nn.ModuleList() + self.mlp_v = torch.nn.ModuleList() + self.gvec = torch.nn.ModuleList() + self.loc_conv = torch.nn.ModuleList() + self.mlp_att = torch.nn.ModuleList() + for _ in six.moves.range(aheads): + self.mlp_q += [torch.nn.Linear(dunits, att_dim_k)] + self.mlp_k += [torch.nn.Linear(eprojs, att_dim_k, bias=False)] + self.mlp_v += [torch.nn.Linear(eprojs, att_dim_v, bias=False)] + self.gvec += [torch.nn.Linear(att_dim_k, 1)] + self.loc_conv += [ + torch.nn.Conv2d( + 1, + aconv_chans, + (1, 2 * aconv_filts + 1), + padding=(0, aconv_filts), + bias=False, + ) + ] + self.mlp_att += [torch.nn.Linear(aconv_chans, att_dim_k, bias=False)] + self.mlp_o = torch.nn.Linear(aheads * att_dim_v, eprojs, bias=False) + self.dunits = dunits + self.eprojs = eprojs + self.aheads = aheads + self.att_dim_k = att_dim_k + self.att_dim_v = att_dim_v + self.scaling = 1.0 / math.sqrt(att_dim_k) + self.h_length = None + self.enc_h = None + self.pre_compute_k = None + self.pre_compute_v = None + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_k = None + self.pre_compute_v = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev, scaling=2.0): + """AttMultiHeadLoc forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param torch.Tensor att_prev: + list of previous attention weight (B x T_max) * aheads + :param float scaling: scaling parameter before applying softmax + :return: attention weighted encoder state (B x D_enc) + :rtype: torch.Tensor + :return: list of previous attention weight (B x T_max) * aheads + :rtype: list + """ + + batch = enc_hs_pad.size(0) + # pre-compute all k and v outside the decoder loop + if self.pre_compute_k is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_k = [ + self.mlp_k[h](self.enc_h) for h in six.moves.range(self.aheads) + ] + + if self.pre_compute_v is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_v = [ + self.mlp_v[h](self.enc_h) for h in six.moves.range(self.aheads) + ] + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + if att_prev is None: + att_prev = [] + for _ in six.moves.range(self.aheads): + # if no bias, 0 0-pad goes 0 + mask = 1.0 - make_pad_mask(enc_hs_len).float() + att_prev += [ + to_device(enc_hs_pad, mask / mask.new(enc_hs_len).unsqueeze(-1)) + ] + + c = [] + w = [] + for h in six.moves.range(self.aheads): + att_conv = self.loc_conv[h](att_prev[h].view(batch, 1, 1, self.h_length)) + att_conv = att_conv.squeeze(2).transpose(1, 2) + att_conv = self.mlp_att[h](att_conv) + + e = self.gvec[h]( + torch.tanh( + self.pre_compute_k[h] + + att_conv + + self.mlp_q[h](dec_z).view(batch, 1, self.att_dim_k) + ) + ).squeeze(2) + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w += [F.softmax(scaling * e, dim=1)] + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c += [ + torch.sum( + self.pre_compute_v[h] * w[h].view(batch, self.h_length, 1), dim=1 + ) + ] + + # concat all of c + c = self.mlp_o(torch.cat(c, dim=1)) + + return c, w + + +class AttMultiHeadMultiResLoc(torch.nn.Module): + """Multi head multi resolution location based attention + + Reference: Attention is all you need + (https://arxiv.org/abs/1706.03762) + + This attention is multi head attention using location-aware attention for each head. + Furthermore, it uses different filter size for each head. + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int aheads: # heads of multi head attention + :param int att_dim_k: dimension k in multi head attention + :param int att_dim_v: dimension v in multi head attention + :param int aconv_chans: maximum # channels of attention convolution + each head use #ch = aconv_chans * (head + 1) / aheads + e.g. aheads=4, aconv_chans=100 => filter size = 25, 50, 75, 100 + :param int aconv_filts: filter size of attention convolution + :param bool han_mode: flag to swith on mode of hierarchical attention + and not store pre_compute_k and pre_compute_v + """ + + def __init__( + self, + eprojs, + dunits, + aheads, + att_dim_k, + att_dim_v, + aconv_chans, + aconv_filts, + han_mode=False, + ): + super(AttMultiHeadMultiResLoc, self).__init__() + self.mlp_q = torch.nn.ModuleList() + self.mlp_k = torch.nn.ModuleList() + self.mlp_v = torch.nn.ModuleList() + self.gvec = torch.nn.ModuleList() + self.loc_conv = torch.nn.ModuleList() + self.mlp_att = torch.nn.ModuleList() + for h in six.moves.range(aheads): + self.mlp_q += [torch.nn.Linear(dunits, att_dim_k)] + self.mlp_k += [torch.nn.Linear(eprojs, att_dim_k, bias=False)] + self.mlp_v += [torch.nn.Linear(eprojs, att_dim_v, bias=False)] + self.gvec += [torch.nn.Linear(att_dim_k, 1)] + afilts = aconv_filts * (h + 1) // aheads + self.loc_conv += [ + torch.nn.Conv2d( + 1, aconv_chans, (1, 2 * afilts + 1), padding=(0, afilts), bias=False + ) + ] + self.mlp_att += [torch.nn.Linear(aconv_chans, att_dim_k, bias=False)] + self.mlp_o = torch.nn.Linear(aheads * att_dim_v, eprojs, bias=False) + self.dunits = dunits + self.eprojs = eprojs + self.aheads = aheads + self.att_dim_k = att_dim_k + self.att_dim_v = att_dim_v + self.scaling = 1.0 / math.sqrt(att_dim_k) + self.h_length = None + self.enc_h = None + self.pre_compute_k = None + self.pre_compute_v = None + self.mask = None + self.han_mode = han_mode + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_k = None + self.pre_compute_v = None + self.mask = None + + def forward(self, enc_hs_pad, enc_hs_len, dec_z, att_prev): + """AttMultiHeadMultiResLoc forward + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param torch.Tensor att_prev: list of previous attention weight + (B x T_max) * aheads + :return: attention weighted encoder state (B x D_enc) + :rtype: torch.Tensor + :return: list of previous attention weight (B x T_max) * aheads + :rtype: list + """ + + batch = enc_hs_pad.size(0) + # pre-compute all k and v outside the decoder loop + if self.pre_compute_k is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_k = [ + self.mlp_k[h](self.enc_h) for h in six.moves.range(self.aheads) + ] + + if self.pre_compute_v is None or self.han_mode: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_v = [ + self.mlp_v[h](self.enc_h) for h in six.moves.range(self.aheads) + ] + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + if att_prev is None: + att_prev = [] + for _ in six.moves.range(self.aheads): + # if no bias, 0 0-pad goes 0 + mask = 1.0 - make_pad_mask(enc_hs_len).float() + att_prev += [ + to_device(enc_hs_pad, mask / mask.new(enc_hs_len).unsqueeze(-1)) + ] + + c = [] + w = [] + for h in six.moves.range(self.aheads): + att_conv = self.loc_conv[h](att_prev[h].view(batch, 1, 1, self.h_length)) + att_conv = att_conv.squeeze(2).transpose(1, 2) + att_conv = self.mlp_att[h](att_conv) + + e = self.gvec[h]( + torch.tanh( + self.pre_compute_k[h] + + att_conv + + self.mlp_q[h](dec_z).view(batch, 1, self.att_dim_k) + ) + ).squeeze(2) + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + w += [F.softmax(self.scaling * e, dim=1)] + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c += [ + torch.sum( + self.pre_compute_v[h] * w[h].view(batch, self.h_length, 1), dim=1 + ) + ] + + # concat all of c + c = self.mlp_o(torch.cat(c, dim=1)) + + return c, w + + +class AttForward(torch.nn.Module): + """Forward attention module. + + Reference: + Forward attention in sequence-to-sequence acoustic modeling for speech synthesis + (https://arxiv.org/pdf/1807.06736.pdf) + + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int att_dim: attention dimension + :param int aconv_chans: # channels of attention convolution + :param int aconv_filts: filter size of attention convolution + """ + + def __init__(self, eprojs, dunits, att_dim, aconv_chans, aconv_filts): + super(AttForward, self).__init__() + self.mlp_enc = torch.nn.Linear(eprojs, att_dim) + self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False) + self.mlp_att = torch.nn.Linear(aconv_chans, att_dim, bias=False) + self.loc_conv = torch.nn.Conv2d( + 1, + aconv_chans, + (1, 2 * aconv_filts + 1), + padding=(0, aconv_filts), + bias=False, + ) + self.gvec = torch.nn.Linear(att_dim, 1) + self.dunits = dunits + self.eprojs = eprojs + self.att_dim = att_dim + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + + def reset(self): + """reset states""" + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + + def forward( + self, + enc_hs_pad, + enc_hs_len, + dec_z, + att_prev, + scaling=1.0, + last_attended_idx=None, + backward_window=1, + forward_window=3, + ): + """Calculate AttForward forward propagation. + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B x T_max x D_enc) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B x D_dec) + :param torch.Tensor att_prev: attention weights of previous step + :param float scaling: scaling parameter before applying softmax + :param int last_attended_idx: index of the inputs of the last attended + :param int backward_window: backward window size in attention constraint + :param int forward_window: forward window size in attetion constraint + :return: attention weighted encoder state (B, D_enc) + :rtype: torch.Tensor + :return: previous attention weights (B x T_max) + :rtype: torch.Tensor + """ + batch = len(enc_hs_pad) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_enc_h = self.mlp_enc(self.enc_h) + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + if att_prev is None: + # initial attention will be [1, 0, 0, ...] + att_prev = enc_hs_pad.new_zeros(*enc_hs_pad.size()[:2]) + att_prev[:, 0] = 1.0 + + # att_prev: utt x frame -> utt x 1 x 1 x frame + # -> utt x att_conv_chans x 1 x frame + att_conv = self.loc_conv(att_prev.view(batch, 1, 1, self.h_length)) + # att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans + att_conv = att_conv.squeeze(2).transpose(1, 2) + # att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim + att_conv = self.mlp_att(att_conv) + + # dec_z_tiled: utt x frame x att_dim + dec_z_tiled = self.mlp_dec(dec_z).unsqueeze(1) + + # dot with gvec + # utt x frame x att_dim -> utt x frame + e = self.gvec( + torch.tanh(self.pre_compute_enc_h + dec_z_tiled + att_conv) + ).squeeze(2) + + # NOTE: consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + + # apply monotonic attention constraint (mainly for TTS) + if last_attended_idx is not None: + e = _apply_attention_constraint( + e, last_attended_idx, backward_window, forward_window + ) + + w = F.softmax(scaling * e, dim=1) + + # forward attention + att_prev_shift = F.pad(att_prev, (1, 0))[:, :-1] + w = (att_prev + att_prev_shift) * w + # NOTE: clamp is needed to avoid nan gradient + w = F.normalize(torch.clamp(w, 1e-6), p=1, dim=1) + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c = torch.sum(self.enc_h * w.unsqueeze(-1), dim=1) + + return c, w + + +class AttForwardTA(torch.nn.Module): + """Forward attention with transition agent module. + + Reference: + Forward attention in sequence-to-sequence acoustic modeling for speech synthesis + (https://arxiv.org/pdf/1807.06736.pdf) + + :param int eunits: # units of encoder + :param int dunits: # units of decoder + :param int att_dim: attention dimension + :param int aconv_chans: # channels of attention convolution + :param int aconv_filts: filter size of attention convolution + :param int odim: output dimension + """ + + def __init__(self, eunits, dunits, att_dim, aconv_chans, aconv_filts, odim): + super(AttForwardTA, self).__init__() + self.mlp_enc = torch.nn.Linear(eunits, att_dim) + self.mlp_dec = torch.nn.Linear(dunits, att_dim, bias=False) + self.mlp_ta = torch.nn.Linear(eunits + dunits + odim, 1) + self.mlp_att = torch.nn.Linear(aconv_chans, att_dim, bias=False) + self.loc_conv = torch.nn.Conv2d( + 1, + aconv_chans, + (1, 2 * aconv_filts + 1), + padding=(0, aconv_filts), + bias=False, + ) + self.gvec = torch.nn.Linear(att_dim, 1) + self.dunits = dunits + self.eunits = eunits + self.att_dim = att_dim + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + self.trans_agent_prob = 0.5 + + def reset(self): + self.h_length = None + self.enc_h = None + self.pre_compute_enc_h = None + self.mask = None + self.trans_agent_prob = 0.5 + + def forward( + self, + enc_hs_pad, + enc_hs_len, + dec_z, + att_prev, + out_prev, + scaling=1.0, + last_attended_idx=None, + backward_window=1, + forward_window=3, + ): + """Calculate AttForwardTA forward propagation. + + :param torch.Tensor enc_hs_pad: padded encoder hidden state (B, Tmax, eunits) + :param list enc_hs_len: padded encoder hidden state length (B) + :param torch.Tensor dec_z: decoder hidden state (B, dunits) + :param torch.Tensor att_prev: attention weights of previous step + :param torch.Tensor out_prev: decoder outputs of previous step (B, odim) + :param float scaling: scaling parameter before applying softmax + :param int last_attended_idx: index of the inputs of the last attended + :param int backward_window: backward window size in attention constraint + :param int forward_window: forward window size in attetion constraint + :return: attention weighted encoder state (B, dunits) + :rtype: torch.Tensor + :return: previous attention weights (B, Tmax) + :rtype: torch.Tensor + """ + batch = len(enc_hs_pad) + # pre-compute all h outside the decoder loop + if self.pre_compute_enc_h is None: + self.enc_h = enc_hs_pad # utt x frame x hdim + self.h_length = self.enc_h.size(1) + # utt x frame x att_dim + self.pre_compute_enc_h = self.mlp_enc(self.enc_h) + + if dec_z is None: + dec_z = enc_hs_pad.new_zeros(batch, self.dunits) + else: + dec_z = dec_z.view(batch, self.dunits) + + if att_prev is None: + # initial attention will be [1, 0, 0, ...] + att_prev = enc_hs_pad.new_zeros(*enc_hs_pad.size()[:2]) + att_prev[:, 0] = 1.0 + + # att_prev: utt x frame -> utt x 1 x 1 x frame + # -> utt x att_conv_chans x 1 x frame + att_conv = self.loc_conv(att_prev.view(batch, 1, 1, self.h_length)) + # att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans + att_conv = att_conv.squeeze(2).transpose(1, 2) + # att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim + att_conv = self.mlp_att(att_conv) + + # dec_z_tiled: utt x frame x att_dim + dec_z_tiled = self.mlp_dec(dec_z).view(batch, 1, self.att_dim) + + # dot with gvec + # utt x frame x att_dim -> utt x frame + e = self.gvec( + torch.tanh(att_conv + self.pre_compute_enc_h + dec_z_tiled) + ).squeeze(2) + + # NOTE consider zero padding when compute w. + if self.mask is None: + self.mask = to_device(enc_hs_pad, make_pad_mask(enc_hs_len)) + e.masked_fill_(self.mask, -float("inf")) + + # apply monotonic attention constraint (mainly for TTS) + if last_attended_idx is not None: + e = _apply_attention_constraint( + e, last_attended_idx, backward_window, forward_window + ) + + w = F.softmax(scaling * e, dim=1) + + # forward attention + att_prev_shift = F.pad(att_prev, (1, 0))[:, :-1] + w = ( + self.trans_agent_prob * att_prev + + (1 - self.trans_agent_prob) * att_prev_shift + ) * w + # NOTE: clamp is needed to avoid nan gradient + w = F.normalize(torch.clamp(w, 1e-6), p=1, dim=1) + + # weighted sum over flames + # utt x hdim + # NOTE use bmm instead of sum(*) + c = torch.sum(self.enc_h * w.view(batch, self.h_length, 1), dim=1) + + # update transition agent prob + self.trans_agent_prob = torch.sigmoid( + self.mlp_ta(torch.cat([c, out_prev, dec_z], dim=1)) + ) + + return c, w + + +def att_for(args, num_att=1, han_mode=False): + """Instantiates an attention module given the program arguments + + :param Namespace args: The arguments + :param int num_att: number of attention modules + (in multi-speaker case, it can be 2 or more) + :param bool han_mode: switch on/off mode of hierarchical attention network (HAN) + :rtype torch.nn.Module + :return: The attention module + """ + att_list = torch.nn.ModuleList() + num_encs = getattr(args, "num_encs", 1) # use getattr to keep compatibility + aheads = getattr(args, "aheads", None) + awin = getattr(args, "awin", None) + aconv_chans = getattr(args, "aconv_chans", None) + aconv_filts = getattr(args, "aconv_filts", None) + + if num_encs == 1: + for i in range(num_att): + att = initial_att( + args.atype, + args.eprojs, + args.dunits, + aheads, + args.adim, + awin, + aconv_chans, + aconv_filts, + ) + att_list.append(att) + elif num_encs > 1: # no multi-speaker mode + if han_mode: + att = initial_att( + args.han_type, + args.eprojs, + args.dunits, + args.han_heads, + args.han_dim, + args.han_win, + args.han_conv_chans, + args.han_conv_filts, + han_mode=True, + ) + return att + else: + att_list = torch.nn.ModuleList() + for idx in range(num_encs): + att = initial_att( + args.atype[idx], + args.eprojs, + args.dunits, + aheads[idx], + args.adim[idx], + awin[idx], + aconv_chans[idx], + aconv_filts[idx], + ) + att_list.append(att) + else: + raise ValueError( + "Number of encoders needs to be more than one. {}".format(num_encs) + ) + return att_list + + +def initial_att( + atype, eprojs, dunits, aheads, adim, awin, aconv_chans, aconv_filts, han_mode=False +): + """Instantiates a single attention module + + :param str atype: attention type + :param int eprojs: # projection-units of encoder + :param int dunits: # units of decoder + :param int aheads: # heads of multi head attention + :param int adim: attention dimension + :param int awin: attention window size + :param int aconv_chans: # channels of attention convolution + :param int aconv_filts: filter size of attention convolution + :param bool han_mode: flag to swith on mode of hierarchical attention + :return: The attention module + """ + + if atype == "noatt": + att = NoAtt() + elif atype == "dot": + att = AttDot(eprojs, dunits, adim, han_mode) + elif atype == "add": + att = AttAdd(eprojs, dunits, adim, han_mode) + elif atype == "location": + att = AttLoc(eprojs, dunits, adim, aconv_chans, aconv_filts, han_mode) + elif atype == "location2d": + att = AttLoc2D(eprojs, dunits, adim, awin, aconv_chans, aconv_filts, han_mode) + elif atype == "location_recurrent": + att = AttLocRec(eprojs, dunits, adim, aconv_chans, aconv_filts, han_mode) + elif atype == "coverage": + att = AttCov(eprojs, dunits, adim, han_mode) + elif atype == "coverage_location": + att = AttCovLoc(eprojs, dunits, adim, aconv_chans, aconv_filts, han_mode) + elif atype == "multi_head_dot": + att = AttMultiHeadDot(eprojs, dunits, aheads, adim, adim, han_mode) + elif atype == "multi_head_add": + att = AttMultiHeadAdd(eprojs, dunits, aheads, adim, adim, han_mode) + elif atype == "multi_head_loc": + att = AttMultiHeadLoc( + eprojs, dunits, aheads, adim, adim, aconv_chans, aconv_filts, han_mode + ) + elif atype == "multi_head_multi_res_loc": + att = AttMultiHeadMultiResLoc( + eprojs, dunits, aheads, adim, adim, aconv_chans, aconv_filts, han_mode + ) + return att + + +def att_to_numpy(att_ws, att): + """Converts attention weights to a numpy array given the attention + + :param list att_ws: The attention weights + :param torch.nn.Module att: The attention + :rtype: np.ndarray + :return: The numpy array of the attention weights + """ + # convert to numpy array with the shape (B, Lmax, Tmax) + if isinstance(att, AttLoc2D): + # att_ws => list of previous concate attentions + att_ws = torch.stack([aw[:, -1] for aw in att_ws], dim=1).cpu().numpy() + elif isinstance(att, (AttCov, AttCovLoc)): + # att_ws => list of list of previous attentions + att_ws = ( + torch.stack([aw[idx] for idx, aw in enumerate(att_ws)], dim=1).cpu().numpy() + ) + elif isinstance(att, AttLocRec): + # att_ws => list of tuple of attention and hidden states + att_ws = torch.stack([aw[0] for aw in att_ws], dim=1).cpu().numpy() + elif isinstance( + att, + (AttMultiHeadDot, AttMultiHeadAdd, AttMultiHeadLoc, AttMultiHeadMultiResLoc), + ): + # att_ws => list of list of each head attention + n_heads = len(att_ws[0]) + att_ws_sorted_by_head = [] + for h in six.moves.range(n_heads): + att_ws_head = torch.stack([aw[h] for aw in att_ws], dim=1) + att_ws_sorted_by_head += [att_ws_head] + att_ws = torch.stack(att_ws_sorted_by_head, dim=1).cpu().numpy() + else: + # att_ws => list of attentions + att_ws = torch.stack(att_ws, dim=1).cpu().numpy() + return att_ws diff --git a/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/decoders.py b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/decoders.py new file mode 100644 index 0000000000000000000000000000000000000000..bc482a14ead49936104088c3a4da653fac9e3237 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/decoders.py @@ -0,0 +1,1224 @@ +"""RNN decoder module.""" + +import logging +import math +import random +from argparse import Namespace + +import numpy as np +import six +import torch +import torch.nn.functional as F + +from funasr_detach.models.transformer.utils.scorers.ctc_prefix_score import ( + CTCPrefixScore, +) +from funasr_detach.models.transformer.utils.scorers.ctc_prefix_score import ( + CTCPrefixScoreTH, +) +from funasr_detach.models.transformer.utils.scorers.scorer_interface import ( + ScorerInterface, +) +from funasr_detach.metrics import end_detect +from funasr_detach.models.transformer.utils.nets_utils import mask_by_length +from funasr_detach.models.transformer.utils.nets_utils import pad_list +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.models.transformer.utils.nets_utils import to_device +from funasr_detach.models.language_model.rnn.attentions import att_to_numpy + +MAX_DECODER_OUTPUT = 5 +CTC_SCORING_RATIO = 1.5 + + +class Decoder(torch.nn.Module, ScorerInterface): + """Decoder module + + :param int eprojs: encoder projection units + :param int odim: dimension of outputs + :param str dtype: gru or lstm + :param int dlayers: decoder layers + :param int dunits: decoder units + :param int sos: start of sequence symbol id + :param int eos: end of sequence symbol id + :param torch.nn.Module att: attention module + :param int verbose: verbose level + :param list char_list: list of character strings + :param ndarray labeldist: distribution of label smoothing + :param float lsm_weight: label smoothing weight + :param float sampling_probability: scheduled sampling probability + :param float dropout: dropout rate + :param float context_residual: if True, use context vector for token generation + :param float replace_sos: use for multilingual (speech/text) translation + """ + + def __init__( + self, + eprojs, + odim, + dtype, + dlayers, + dunits, + sos, + eos, + att, + verbose=0, + char_list=None, + labeldist=None, + lsm_weight=0.0, + sampling_probability=0.0, + dropout=0.0, + context_residual=False, + replace_sos=False, + num_encs=1, + ): + + torch.nn.Module.__init__(self) + self.dtype = dtype + self.dunits = dunits + self.dlayers = dlayers + self.context_residual = context_residual + self.embed = torch.nn.Embedding(odim, dunits) + self.dropout_emb = torch.nn.Dropout(p=dropout) + + self.decoder = torch.nn.ModuleList() + self.dropout_dec = torch.nn.ModuleList() + self.decoder += [ + ( + torch.nn.LSTMCell(dunits + eprojs, dunits) + if self.dtype == "lstm" + else torch.nn.GRUCell(dunits + eprojs, dunits) + ) + ] + self.dropout_dec += [torch.nn.Dropout(p=dropout)] + for _ in six.moves.range(1, self.dlayers): + self.decoder += [ + ( + torch.nn.LSTMCell(dunits, dunits) + if self.dtype == "lstm" + else torch.nn.GRUCell(dunits, dunits) + ) + ] + self.dropout_dec += [torch.nn.Dropout(p=dropout)] + # NOTE: dropout is applied only for the vertical connections + # see https://arxiv.org/pdf/1409.2329.pdf + self.ignore_id = -1 + + if context_residual: + self.output = torch.nn.Linear(dunits + eprojs, odim) + else: + self.output = torch.nn.Linear(dunits, odim) + + self.loss = None + self.att = att + self.dunits = dunits + self.sos = sos + self.eos = eos + self.odim = odim + self.verbose = verbose + self.char_list = char_list + # for label smoothing + self.labeldist = labeldist + self.vlabeldist = None + self.lsm_weight = lsm_weight + self.sampling_probability = sampling_probability + self.dropout = dropout + self.num_encs = num_encs + + # for multilingual E2E-ST + self.replace_sos = replace_sos + + self.logzero = -10000000000.0 + + def zero_state(self, hs_pad): + return hs_pad.new_zeros(hs_pad.size(0), self.dunits) + + def rnn_forward(self, ey, z_list, c_list, z_prev, c_prev): + if self.dtype == "lstm": + z_list[0], c_list[0] = self.decoder[0](ey, (z_prev[0], c_prev[0])) + for i in six.moves.range(1, self.dlayers): + z_list[i], c_list[i] = self.decoder[i]( + self.dropout_dec[i - 1](z_list[i - 1]), (z_prev[i], c_prev[i]) + ) + else: + z_list[0] = self.decoder[0](ey, z_prev[0]) + for i in six.moves.range(1, self.dlayers): + z_list[i] = self.decoder[i]( + self.dropout_dec[i - 1](z_list[i - 1]), z_prev[i] + ) + return z_list, c_list + + def forward(self, hs_pad, hlens, ys_pad, strm_idx=0, lang_ids=None): + """Decoder forward + + :param torch.Tensor hs_pad: batch of padded hidden state sequences (B, Tmax, D) + [in multi-encoder case, + list of torch.Tensor, + [(B, Tmax_1, D), (B, Tmax_2, D), ..., ] ] + :param torch.Tensor hlens: batch of lengths of hidden state sequences (B) + [in multi-encoder case, list of torch.Tensor, + [(B), (B), ..., ] + :param torch.Tensor ys_pad: batch of padded character id sequence tensor + (B, Lmax) + :param int strm_idx: stream index indicates the index of decoding stream. + :param torch.Tensor lang_ids: batch of target language id tensor (B, 1) + :return: attention loss value + :rtype: torch.Tensor + :return: accuracy + :rtype: float + """ + # to support mutiple encoder asr mode, in single encoder mode, + # convert torch.Tensor to List of torch.Tensor + if self.num_encs == 1: + hs_pad = [hs_pad] + hlens = [hlens] + + # TODO(kan-bayashi): need to make more smart way + ys = [y[y != self.ignore_id] for y in ys_pad] # parse padded ys + # attention index for the attention module + # in SPA (speaker parallel attention), + # att_idx is used to select attention module. In other cases, it is 0. + att_idx = min(strm_idx, len(self.att) - 1) + + # hlens should be list of list of integer + hlens = [list(map(int, hlens[idx])) for idx in range(self.num_encs)] + + self.loss = None + # prepare input and output word sequences with sos/eos IDs + eos = ys[0].new([self.eos]) + sos = ys[0].new([self.sos]) + if self.replace_sos: + ys_in = [torch.cat([idx, y], dim=0) for idx, y in zip(lang_ids, ys)] + else: + ys_in = [torch.cat([sos, y], dim=0) for y in ys] + ys_out = [torch.cat([y, eos], dim=0) for y in ys] + + # padding for ys with -1 + # pys: utt x olen + ys_in_pad = pad_list(ys_in, self.eos) + ys_out_pad = pad_list(ys_out, self.ignore_id) + + # get dim, length info + batch = ys_out_pad.size(0) + olength = ys_out_pad.size(1) + for idx in range(self.num_encs): + logging.info( + self.__class__.__name__ + + "Number of Encoder:{}; enc{}: input lengths: {}.".format( + self.num_encs, idx + 1, hlens[idx] + ) + ) + logging.info( + self.__class__.__name__ + + " output lengths: " + + str([y.size(0) for y in ys_out]) + ) + + # initialization + c_list = [self.zero_state(hs_pad[0])] + z_list = [self.zero_state(hs_pad[0])] + for _ in six.moves.range(1, self.dlayers): + c_list.append(self.zero_state(hs_pad[0])) + z_list.append(self.zero_state(hs_pad[0])) + z_all = [] + if self.num_encs == 1: + att_w = None + self.att[att_idx].reset() # reset pre-computation of h + else: + att_w_list = [None] * (self.num_encs + 1) # atts + han + att_c_list = [None] * (self.num_encs) # atts + for idx in range(self.num_encs + 1): + self.att[idx].reset() # reset pre-computation of h in atts and han + + # pre-computation of embedding + eys = self.dropout_emb(self.embed(ys_in_pad)) # utt x olen x zdim + + # loop for an output sequence + for i in six.moves.range(olength): + if self.num_encs == 1: + att_c, att_w = self.att[att_idx]( + hs_pad[0], hlens[0], self.dropout_dec[0](z_list[0]), att_w + ) + else: + for idx in range(self.num_encs): + att_c_list[idx], att_w_list[idx] = self.att[idx]( + hs_pad[idx], + hlens[idx], + self.dropout_dec[0](z_list[0]), + att_w_list[idx], + ) + hs_pad_han = torch.stack(att_c_list, dim=1) + hlens_han = [self.num_encs] * len(ys_in) + att_c, att_w_list[self.num_encs] = self.att[self.num_encs]( + hs_pad_han, + hlens_han, + self.dropout_dec[0](z_list[0]), + att_w_list[self.num_encs], + ) + if i > 0 and random.random() < self.sampling_probability: + logging.info(" scheduled sampling ") + z_out = self.output(z_all[-1]) + z_out = np.argmax(z_out.detach().cpu(), axis=1) + z_out = self.dropout_emb(self.embed(to_device(hs_pad[0], z_out))) + ey = torch.cat((z_out, att_c), dim=1) # utt x (zdim + hdim) + else: + ey = torch.cat((eys[:, i, :], att_c), dim=1) # utt x (zdim + hdim) + z_list, c_list = self.rnn_forward(ey, z_list, c_list, z_list, c_list) + if self.context_residual: + z_all.append( + torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1) + ) # utt x (zdim + hdim) + else: + z_all.append(self.dropout_dec[-1](z_list[-1])) # utt x (zdim) + + z_all = torch.stack(z_all, dim=1).view(batch * olength, -1) + # compute loss + y_all = self.output(z_all) + self.loss = F.cross_entropy( + y_all, + ys_out_pad.view(-1), + ignore_index=self.ignore_id, + reduction="mean", + ) + # compute perplexity + ppl = math.exp(self.loss.item()) + # -1: eos, which is removed in the loss computation + self.loss *= np.mean([len(x) for x in ys_in]) - 1 + acc = th_accuracy(y_all, ys_out_pad, ignore_label=self.ignore_id) + logging.info("att loss:" + "".join(str(self.loss.item()).split("\n"))) + + # show predicted character sequence for debug + if self.verbose > 0 and self.char_list is not None: + ys_hat = y_all.view(batch, olength, -1) + ys_true = ys_out_pad + for (i, y_hat), y_true in zip( + enumerate(ys_hat.detach().cpu().numpy()), ys_true.detach().cpu().numpy() + ): + if i == MAX_DECODER_OUTPUT: + break + idx_hat = np.argmax(y_hat[y_true != self.ignore_id], axis=1) + idx_true = y_true[y_true != self.ignore_id] + seq_hat = [self.char_list[int(idx)] for idx in idx_hat] + seq_true = [self.char_list[int(idx)] for idx in idx_true] + seq_hat = "".join(seq_hat) + seq_true = "".join(seq_true) + logging.info("groundtruth[%d]: " % i + seq_true) + logging.info("prediction [%d]: " % i + seq_hat) + + if self.labeldist is not None: + if self.vlabeldist is None: + self.vlabeldist = to_device(hs_pad[0], torch.from_numpy(self.labeldist)) + loss_reg = -torch.sum( + (F.log_softmax(y_all, dim=1) * self.vlabeldist).view(-1), dim=0 + ) / len(ys_in) + self.loss = (1.0 - self.lsm_weight) * self.loss + self.lsm_weight * loss_reg + + return self.loss, acc, ppl + + def recognize_beam(self, h, lpz, recog_args, char_list, rnnlm=None, strm_idx=0): + """beam search implementation + + :param torch.Tensor h: encoder hidden state (T, eprojs) + [in multi-encoder case, list of torch.Tensor, + [(T1, eprojs), (T2, eprojs), ...] ] + :param torch.Tensor lpz: ctc log softmax output (T, odim) + [in multi-encoder case, list of torch.Tensor, + [(T1, odim), (T2, odim), ...] ] + :param Namespace recog_args: argument Namespace containing options + :param char_list: list of character strings + :param torch.nn.Module rnnlm: language module + :param int strm_idx: + stream index for speaker parallel attention in multi-speaker case + :return: N-best decoding results + :rtype: list of dicts + """ + # to support mutiple encoder asr mode, in single encoder mode, + # convert torch.Tensor to List of torch.Tensor + if self.num_encs == 1: + h = [h] + lpz = [lpz] + if self.num_encs > 1 and lpz is None: + lpz = [lpz] * self.num_encs + + for idx in range(self.num_encs): + logging.info( + "Number of Encoder:{}; enc{}: input lengths: {}.".format( + self.num_encs, idx + 1, h[0].size(0) + ) + ) + att_idx = min(strm_idx, len(self.att) - 1) + # initialization + c_list = [self.zero_state(h[0].unsqueeze(0))] + z_list = [self.zero_state(h[0].unsqueeze(0))] + for _ in six.moves.range(1, self.dlayers): + c_list.append(self.zero_state(h[0].unsqueeze(0))) + z_list.append(self.zero_state(h[0].unsqueeze(0))) + if self.num_encs == 1: + a = None + self.att[att_idx].reset() # reset pre-computation of h + else: + a = [None] * (self.num_encs + 1) # atts + han + att_w_list = [None] * (self.num_encs + 1) # atts + han + att_c_list = [None] * (self.num_encs) # atts + for idx in range(self.num_encs + 1): + self.att[idx].reset() # reset pre-computation of h in atts and han + + # search parms + beam = recog_args.beam_size + penalty = recog_args.penalty + ctc_weight = getattr(recog_args, "ctc_weight", False) # for NMT + + if lpz[0] is not None and self.num_encs > 1: + # weights-ctc, + # e.g. ctc_loss = w_1*ctc_1_loss + w_2 * ctc_2_loss + w_N * ctc_N_loss + weights_ctc_dec = recog_args.weights_ctc_dec / np.sum( + recog_args.weights_ctc_dec + ) # normalize + logging.info( + "ctc weights (decoding): " + " ".join([str(x) for x in weights_ctc_dec]) + ) + else: + weights_ctc_dec = [1.0] + + # preprate sos + if self.replace_sos and recog_args.tgt_lang: + y = char_list.index(recog_args.tgt_lang) + else: + y = self.sos + logging.info(" index: " + str(y)) + logging.info(" mark: " + char_list[y]) + vy = h[0].new_zeros(1).long() + + maxlen = np.amin([h[idx].size(0) for idx in range(self.num_encs)]) + if recog_args.maxlenratio != 0: + # maxlen >= 1 + maxlen = max(1, int(recog_args.maxlenratio * maxlen)) + minlen = int(recog_args.minlenratio * maxlen) + logging.info("max output length: " + str(maxlen)) + logging.info("min output length: " + str(minlen)) + + # initialize hypothesis + if rnnlm: + hyp = { + "score": 0.0, + "yseq": [y], + "c_prev": c_list, + "z_prev": z_list, + "a_prev": a, + "rnnlm_prev": None, + } + else: + hyp = { + "score": 0.0, + "yseq": [y], + "c_prev": c_list, + "z_prev": z_list, + "a_prev": a, + } + if lpz[0] is not None: + ctc_prefix_score = [ + CTCPrefixScore(lpz[idx].detach().numpy(), 0, self.eos, np) + for idx in range(self.num_encs) + ] + hyp["ctc_state_prev"] = [ + ctc_prefix_score[idx].initial_state() for idx in range(self.num_encs) + ] + hyp["ctc_score_prev"] = [0.0] * self.num_encs + if ctc_weight != 1.0: + # pre-pruning based on attention scores + ctc_beam = min(lpz[0].shape[-1], int(beam * CTC_SCORING_RATIO)) + else: + ctc_beam = lpz[0].shape[-1] + hyps = [hyp] + ended_hyps = [] + + for i in six.moves.range(maxlen): + logging.debug("position " + str(i)) + + hyps_best_kept = [] + for hyp in hyps: + vy[0] = hyp["yseq"][i] + ey = self.dropout_emb(self.embed(vy)) # utt list (1) x zdim + if self.num_encs == 1: + att_c, att_w = self.att[att_idx]( + h[0].unsqueeze(0), + [h[0].size(0)], + self.dropout_dec[0](hyp["z_prev"][0]), + hyp["a_prev"], + ) + else: + for idx in range(self.num_encs): + att_c_list[idx], att_w_list[idx] = self.att[idx]( + h[idx].unsqueeze(0), + [h[idx].size(0)], + self.dropout_dec[0](hyp["z_prev"][0]), + hyp["a_prev"][idx], + ) + h_han = torch.stack(att_c_list, dim=1) + att_c, att_w_list[self.num_encs] = self.att[self.num_encs]( + h_han, + [self.num_encs], + self.dropout_dec[0](hyp["z_prev"][0]), + hyp["a_prev"][self.num_encs], + ) + ey = torch.cat((ey, att_c), dim=1) # utt(1) x (zdim + hdim) + z_list, c_list = self.rnn_forward( + ey, z_list, c_list, hyp["z_prev"], hyp["c_prev"] + ) + + # get nbest local scores and their ids + if self.context_residual: + logits = self.output( + torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1) + ) + else: + logits = self.output(self.dropout_dec[-1](z_list[-1])) + local_att_scores = F.log_softmax(logits, dim=1) + if rnnlm: + rnnlm_state, local_lm_scores = rnnlm.predict(hyp["rnnlm_prev"], vy) + local_scores = ( + local_att_scores + recog_args.lm_weight * local_lm_scores + ) + else: + local_scores = local_att_scores + + if lpz[0] is not None: + local_best_scores, local_best_ids = torch.topk( + local_att_scores, ctc_beam, dim=1 + ) + ctc_scores, ctc_states = ( + [None] * self.num_encs, + [None] * self.num_encs, + ) + for idx in range(self.num_encs): + ctc_scores[idx], ctc_states[idx] = ctc_prefix_score[idx]( + hyp["yseq"], local_best_ids[0], hyp["ctc_state_prev"][idx] + ) + local_scores = (1.0 - ctc_weight) * local_att_scores[ + :, local_best_ids[0] + ] + if self.num_encs == 1: + local_scores += ctc_weight * torch.from_numpy( + ctc_scores[0] - hyp["ctc_score_prev"][0] + ) + else: + for idx in range(self.num_encs): + local_scores += ( + ctc_weight + * weights_ctc_dec[idx] + * torch.from_numpy( + ctc_scores[idx] - hyp["ctc_score_prev"][idx] + ) + ) + if rnnlm: + local_scores += ( + recog_args.lm_weight * local_lm_scores[:, local_best_ids[0]] + ) + local_best_scores, joint_best_ids = torch.topk( + local_scores, beam, dim=1 + ) + local_best_ids = local_best_ids[:, joint_best_ids[0]] + else: + local_best_scores, local_best_ids = torch.topk( + local_scores, beam, dim=1 + ) + + for j in six.moves.range(beam): + new_hyp = {} + # [:] is needed! + new_hyp["z_prev"] = z_list[:] + new_hyp["c_prev"] = c_list[:] + if self.num_encs == 1: + new_hyp["a_prev"] = att_w[:] + else: + new_hyp["a_prev"] = [ + att_w_list[idx][:] for idx in range(self.num_encs + 1) + ] + new_hyp["score"] = hyp["score"] + local_best_scores[0, j] + new_hyp["yseq"] = [0] * (1 + len(hyp["yseq"])) + new_hyp["yseq"][: len(hyp["yseq"])] = hyp["yseq"] + new_hyp["yseq"][len(hyp["yseq"])] = int(local_best_ids[0, j]) + if rnnlm: + new_hyp["rnnlm_prev"] = rnnlm_state + if lpz[0] is not None: + new_hyp["ctc_state_prev"] = [ + ctc_states[idx][joint_best_ids[0, j]] + for idx in range(self.num_encs) + ] + new_hyp["ctc_score_prev"] = [ + ctc_scores[idx][joint_best_ids[0, j]] + for idx in range(self.num_encs) + ] + # will be (2 x beam) hyps at most + hyps_best_kept.append(new_hyp) + + hyps_best_kept = sorted( + hyps_best_kept, key=lambda x: x["score"], reverse=True + )[:beam] + + # sort and get nbest + hyps = hyps_best_kept + logging.debug("number of pruned hypotheses: " + str(len(hyps))) + logging.debug( + "best hypo: " + + "".join([char_list[int(x)] for x in hyps[0]["yseq"][1:]]) + ) + + # add eos in the final loop to avoid that there are no ended hyps + if i == maxlen - 1: + logging.info("adding in the last position in the loop") + for hyp in hyps: + hyp["yseq"].append(self.eos) + + # add ended hypotheses to a final list, + # and removed them from current hypotheses + # (this will be a problem, number of hyps < beam) + remained_hyps = [] + for hyp in hyps: + if hyp["yseq"][-1] == self.eos: + # only store the sequence that has more than minlen outputs + # also add penalty + if len(hyp["yseq"]) > minlen: + hyp["score"] += (i + 1) * penalty + if rnnlm: # Word LM needs to add final score + hyp["score"] += recog_args.lm_weight * rnnlm.final( + hyp["rnnlm_prev"] + ) + ended_hyps.append(hyp) + else: + remained_hyps.append(hyp) + + # end detection + if end_detect(ended_hyps, i) and recog_args.maxlenratio == 0.0: + logging.info("end detected at %d", i) + break + + hyps = remained_hyps + if len(hyps) > 0: + logging.debug("remaining hypotheses: " + str(len(hyps))) + else: + logging.info("no hypothesis. Finish decoding.") + break + + for hyp in hyps: + logging.debug( + "hypo: " + "".join([char_list[int(x)] for x in hyp["yseq"][1:]]) + ) + + logging.debug("number of ended hypotheses: " + str(len(ended_hyps))) + + nbest_hyps = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[ + : min(len(ended_hyps), recog_args.nbest) + ] + + # check number of hypotheses + if len(nbest_hyps) == 0: + logging.warning( + "there is no N-best results, " + "perform recognition again with smaller minlenratio." + ) + # should copy because Namespace will be overwritten globally + recog_args = Namespace(**vars(recog_args)) + recog_args.minlenratio = max(0.0, recog_args.minlenratio - 0.1) + if self.num_encs == 1: + return self.recognize_beam(h[0], lpz[0], recog_args, char_list, rnnlm) + else: + return self.recognize_beam(h, lpz, recog_args, char_list, rnnlm) + + logging.info("total log probability: " + str(nbest_hyps[0]["score"])) + logging.info( + "normalized log probability: " + + str(nbest_hyps[0]["score"] / len(nbest_hyps[0]["yseq"])) + ) + + # remove sos + return nbest_hyps + + def recognize_beam_batch( + self, + h, + hlens, + lpz, + recog_args, + char_list, + rnnlm=None, + normalize_score=True, + strm_idx=0, + lang_ids=None, + ): + # to support mutiple encoder asr mode, in single encoder mode, + # convert torch.Tensor to List of torch.Tensor + if self.num_encs == 1: + h = [h] + hlens = [hlens] + lpz = [lpz] + if self.num_encs > 1 and lpz is None: + lpz = [lpz] * self.num_encs + + att_idx = min(strm_idx, len(self.att) - 1) + for idx in range(self.num_encs): + logging.info( + "Number of Encoder:{}; enc{}: input lengths: {}.".format( + self.num_encs, idx + 1, h[idx].size(1) + ) + ) + h[idx] = mask_by_length(h[idx], hlens[idx], 0.0) + + # search params + batch = len(hlens[0]) + beam = recog_args.beam_size + penalty = recog_args.penalty + ctc_weight = getattr(recog_args, "ctc_weight", 0) # for NMT + att_weight = 1.0 - ctc_weight + ctc_margin = getattr( + recog_args, "ctc_window_margin", 0 + ) # use getattr to keep compatibility + # weights-ctc, + # e.g. ctc_loss = w_1*ctc_1_loss + w_2 * ctc_2_loss + w_N * ctc_N_loss + if lpz[0] is not None and self.num_encs > 1: + weights_ctc_dec = recog_args.weights_ctc_dec / np.sum( + recog_args.weights_ctc_dec + ) # normalize + logging.info( + "ctc weights (decoding): " + " ".join([str(x) for x in weights_ctc_dec]) + ) + else: + weights_ctc_dec = [1.0] + + n_bb = batch * beam + pad_b = to_device(h[0], torch.arange(batch) * beam).view(-1, 1) + + max_hlen = np.amin([max(hlens[idx]) for idx in range(self.num_encs)]) + if recog_args.maxlenratio == 0: + maxlen = max_hlen + else: + maxlen = max(1, int(recog_args.maxlenratio * max_hlen)) + minlen = int(recog_args.minlenratio * max_hlen) + logging.info("max output length: " + str(maxlen)) + logging.info("min output length: " + str(minlen)) + + # initialization + c_prev = [ + to_device(h[0], torch.zeros(n_bb, self.dunits)) for _ in range(self.dlayers) + ] + z_prev = [ + to_device(h[0], torch.zeros(n_bb, self.dunits)) for _ in range(self.dlayers) + ] + c_list = [ + to_device(h[0], torch.zeros(n_bb, self.dunits)) for _ in range(self.dlayers) + ] + z_list = [ + to_device(h[0], torch.zeros(n_bb, self.dunits)) for _ in range(self.dlayers) + ] + vscores = to_device(h[0], torch.zeros(batch, beam)) + + rnnlm_state = None + if self.num_encs == 1: + a_prev = [None] + att_w_list, ctc_scorer, ctc_state = [None], [None], [None] + self.att[att_idx].reset() # reset pre-computation of h + else: + a_prev = [None] * (self.num_encs + 1) # atts + han + att_w_list = [None] * (self.num_encs + 1) # atts + han + att_c_list = [None] * (self.num_encs) # atts + ctc_scorer, ctc_state = [None] * (self.num_encs), [None] * (self.num_encs) + for idx in range(self.num_encs + 1): + self.att[idx].reset() # reset pre-computation of h in atts and han + + if self.replace_sos and recog_args.tgt_lang: + logging.info(" index: " + str(char_list.index(recog_args.tgt_lang))) + logging.info(" mark: " + recog_args.tgt_lang) + yseq = [ + [char_list.index(recog_args.tgt_lang)] for _ in six.moves.range(n_bb) + ] + elif lang_ids is not None: + # NOTE: used for evaluation during training + yseq = [ + [lang_ids[b // recog_args.beam_size]] for b in six.moves.range(n_bb) + ] + else: + logging.info(" index: " + str(self.sos)) + logging.info(" mark: " + char_list[self.sos]) + yseq = [[self.sos] for _ in six.moves.range(n_bb)] + + accum_odim_ids = [self.sos for _ in six.moves.range(n_bb)] + stop_search = [False for _ in six.moves.range(batch)] + nbest_hyps = [[] for _ in six.moves.range(batch)] + ended_hyps = [[] for _ in range(batch)] + + exp_hlens = [ + hlens[idx].repeat(beam).view(beam, batch).transpose(0, 1).contiguous() + for idx in range(self.num_encs) + ] + exp_hlens = [exp_hlens[idx].view(-1).tolist() for idx in range(self.num_encs)] + exp_h = [ + h[idx].unsqueeze(1).repeat(1, beam, 1, 1).contiguous() + for idx in range(self.num_encs) + ] + exp_h = [ + exp_h[idx].view(n_bb, h[idx].size()[1], h[idx].size()[2]) + for idx in range(self.num_encs) + ] + + if lpz[0] is not None: + scoring_num = min( + ( + int(beam * CTC_SCORING_RATIO) + if att_weight > 0.0 and not lpz[0].is_cuda + else 0 + ), + lpz[0].size(-1), + ) + ctc_scorer = [ + CTCPrefixScoreTH( + lpz[idx], + hlens[idx], + 0, + self.eos, + margin=ctc_margin, + ) + for idx in range(self.num_encs) + ] + + for i in six.moves.range(maxlen): + logging.debug("position " + str(i)) + + vy = to_device(h[0], torch.LongTensor(self._get_last_yseq(yseq))) + ey = self.dropout_emb(self.embed(vy)) + if self.num_encs == 1: + att_c, att_w = self.att[att_idx]( + exp_h[0], exp_hlens[0], self.dropout_dec[0](z_prev[0]), a_prev[0] + ) + att_w_list = [att_w] + else: + for idx in range(self.num_encs): + att_c_list[idx], att_w_list[idx] = self.att[idx]( + exp_h[idx], + exp_hlens[idx], + self.dropout_dec[0](z_prev[0]), + a_prev[idx], + ) + exp_h_han = torch.stack(att_c_list, dim=1) + att_c, att_w_list[self.num_encs] = self.att[self.num_encs]( + exp_h_han, + [self.num_encs] * n_bb, + self.dropout_dec[0](z_prev[0]), + a_prev[self.num_encs], + ) + ey = torch.cat((ey, att_c), dim=1) + + # attention decoder + z_list, c_list = self.rnn_forward(ey, z_list, c_list, z_prev, c_prev) + if self.context_residual: + logits = self.output( + torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1) + ) + else: + logits = self.output(self.dropout_dec[-1](z_list[-1])) + local_scores = att_weight * F.log_softmax(logits, dim=1) + + # rnnlm + if rnnlm: + rnnlm_state, local_lm_scores = rnnlm.buff_predict(rnnlm_state, vy, n_bb) + local_scores = local_scores + recog_args.lm_weight * local_lm_scores + + # ctc + if ctc_scorer[0]: + local_scores[:, 0] = self.logzero # avoid choosing blank + part_ids = ( + torch.topk(local_scores, scoring_num, dim=-1)[1] + if scoring_num > 0 + else None + ) + for idx in range(self.num_encs): + att_w = att_w_list[idx] + att_w_ = att_w if isinstance(att_w, torch.Tensor) else att_w[0] + local_ctc_scores, ctc_state[idx] = ctc_scorer[idx]( + yseq, ctc_state[idx], part_ids, att_w_ + ) + local_scores = ( + local_scores + + ctc_weight * weights_ctc_dec[idx] * local_ctc_scores + ) + + local_scores = local_scores.view(batch, beam, self.odim) + if i == 0: + local_scores[:, 1:, :] = self.logzero + + # accumulate scores + eos_vscores = local_scores[:, :, self.eos] + vscores + vscores = vscores.view(batch, beam, 1).repeat(1, 1, self.odim) + vscores[:, :, self.eos] = self.logzero + vscores = (vscores + local_scores).view(batch, -1) + + # global pruning + accum_best_scores, accum_best_ids = torch.topk(vscores, beam, 1) + accum_odim_ids = ( + torch.fmod(accum_best_ids, self.odim).view(-1).data.cpu().tolist() + ) + accum_padded_beam_ids = ( + (accum_best_ids // self.odim + pad_b).view(-1).data.cpu().tolist() + ) + + y_prev = yseq[:][:] + yseq = self._index_select_list(yseq, accum_padded_beam_ids) + yseq = self._append_ids(yseq, accum_odim_ids) + vscores = accum_best_scores + vidx = to_device(h[0], torch.LongTensor(accum_padded_beam_ids)) + + a_prev = [] + num_atts = self.num_encs if self.num_encs == 1 else self.num_encs + 1 + for idx in range(num_atts): + if isinstance(att_w_list[idx], torch.Tensor): + _a_prev = torch.index_select( + att_w_list[idx].view(n_bb, *att_w_list[idx].shape[1:]), 0, vidx + ) + elif isinstance(att_w_list[idx], list): + # handle the case of multi-head attention + _a_prev = [ + torch.index_select(att_w_one.view(n_bb, -1), 0, vidx) + for att_w_one in att_w_list[idx] + ] + else: + # handle the case of location_recurrent when return is a tuple + _a_prev_ = torch.index_select( + att_w_list[idx][0].view(n_bb, -1), 0, vidx + ) + _h_prev_ = torch.index_select( + att_w_list[idx][1][0].view(n_bb, -1), 0, vidx + ) + _c_prev_ = torch.index_select( + att_w_list[idx][1][1].view(n_bb, -1), 0, vidx + ) + _a_prev = (_a_prev_, (_h_prev_, _c_prev_)) + a_prev.append(_a_prev) + z_prev = [ + torch.index_select(z_list[li].view(n_bb, -1), 0, vidx) + for li in range(self.dlayers) + ] + c_prev = [ + torch.index_select(c_list[li].view(n_bb, -1), 0, vidx) + for li in range(self.dlayers) + ] + + # pick ended hyps + if i >= minlen: + k = 0 + penalty_i = (i + 1) * penalty + thr = accum_best_scores[:, -1] + for samp_i in six.moves.range(batch): + if stop_search[samp_i]: + k = k + beam + continue + for beam_j in six.moves.range(beam): + _vscore = None + if eos_vscores[samp_i, beam_j] > thr[samp_i]: + yk = y_prev[k][:] + if len(yk) <= min( + hlens[idx][samp_i] for idx in range(self.num_encs) + ): + _vscore = eos_vscores[samp_i][beam_j] + penalty_i + elif i == maxlen - 1: + yk = yseq[k][:] + _vscore = vscores[samp_i][beam_j] + penalty_i + if _vscore: + yk.append(self.eos) + if rnnlm: + _vscore += recog_args.lm_weight * rnnlm.final( + rnnlm_state, index=k + ) + _score = _vscore.data.cpu().numpy() + ended_hyps[samp_i].append( + {"yseq": yk, "vscore": _vscore, "score": _score} + ) + k = k + 1 + + # end detection + stop_search = [ + stop_search[samp_i] or end_detect(ended_hyps[samp_i], i) + for samp_i in six.moves.range(batch) + ] + stop_search_summary = list(set(stop_search)) + if len(stop_search_summary) == 1 and stop_search_summary[0]: + break + + if rnnlm: + rnnlm_state = self._index_select_lm_state(rnnlm_state, 0, vidx) + if ctc_scorer[0]: + for idx in range(self.num_encs): + ctc_state[idx] = ctc_scorer[idx].index_select_state( + ctc_state[idx], accum_best_ids + ) + + torch.cuda.empty_cache() + + dummy_hyps = [ + {"yseq": [self.sos, self.eos], "score": np.array([-float("inf")])} + ] + ended_hyps = [ + ended_hyps[samp_i] if len(ended_hyps[samp_i]) != 0 else dummy_hyps + for samp_i in six.moves.range(batch) + ] + if normalize_score: + for samp_i in six.moves.range(batch): + for x in ended_hyps[samp_i]: + x["score"] /= len(x["yseq"]) + + nbest_hyps = [ + sorted(ended_hyps[samp_i], key=lambda x: x["score"], reverse=True)[ + : min(len(ended_hyps[samp_i]), recog_args.nbest) + ] + for samp_i in six.moves.range(batch) + ] + + return nbest_hyps + + def calculate_all_attentions(self, hs_pad, hlen, ys_pad, strm_idx=0, lang_ids=None): + """Calculate all of attentions + + :param torch.Tensor hs_pad: batch of padded hidden state sequences + (B, Tmax, D) + in multi-encoder case, list of torch.Tensor, + [(B, Tmax_1, D), (B, Tmax_2, D), ..., ] ] + :param torch.Tensor hlen: batch of lengths of hidden state sequences (B) + [in multi-encoder case, list of torch.Tensor, + [(B), (B), ..., ] + :param torch.Tensor ys_pad: + batch of padded character id sequence tensor (B, Lmax) + :param int strm_idx: + stream index for parallel speaker attention in multi-speaker case + :param torch.Tensor lang_ids: batch of target language id tensor (B, 1) + :return: attention weights with the following shape, + 1) multi-head case => attention weights (B, H, Lmax, Tmax), + 2) multi-encoder case => + [(B, Lmax, Tmax1), (B, Lmax, Tmax2), ..., (B, Lmax, NumEncs)] + 3) other case => attention weights (B, Lmax, Tmax). + :rtype: float ndarray + """ + # to support mutiple encoder asr mode, in single encoder mode, + # convert torch.Tensor to List of torch.Tensor + if self.num_encs == 1: + hs_pad = [hs_pad] + hlen = [hlen] + + # TODO(kan-bayashi): need to make more smart way + ys = [y[y != self.ignore_id] for y in ys_pad] # parse padded ys + att_idx = min(strm_idx, len(self.att) - 1) + + # hlen should be list of list of integer + hlen = [list(map(int, hlen[idx])) for idx in range(self.num_encs)] + + self.loss = None + # prepare input and output word sequences with sos/eos IDs + eos = ys[0].new([self.eos]) + sos = ys[0].new([self.sos]) + if self.replace_sos: + ys_in = [torch.cat([idx, y], dim=0) for idx, y in zip(lang_ids, ys)] + else: + ys_in = [torch.cat([sos, y], dim=0) for y in ys] + ys_out = [torch.cat([y, eos], dim=0) for y in ys] + + # padding for ys with -1 + # pys: utt x olen + ys_in_pad = pad_list(ys_in, self.eos) + ys_out_pad = pad_list(ys_out, self.ignore_id) + + # get length info + olength = ys_out_pad.size(1) + + # initialization + c_list = [self.zero_state(hs_pad[0])] + z_list = [self.zero_state(hs_pad[0])] + for _ in six.moves.range(1, self.dlayers): + c_list.append(self.zero_state(hs_pad[0])) + z_list.append(self.zero_state(hs_pad[0])) + att_ws = [] + if self.num_encs == 1: + att_w = None + self.att[att_idx].reset() # reset pre-computation of h + else: + att_w_list = [None] * (self.num_encs + 1) # atts + han + att_c_list = [None] * (self.num_encs) # atts + for idx in range(self.num_encs + 1): + self.att[idx].reset() # reset pre-computation of h in atts and han + + # pre-computation of embedding + eys = self.dropout_emb(self.embed(ys_in_pad)) # utt x olen x zdim + + # loop for an output sequence + for i in six.moves.range(olength): + if self.num_encs == 1: + att_c, att_w = self.att[att_idx]( + hs_pad[0], hlen[0], self.dropout_dec[0](z_list[0]), att_w + ) + att_ws.append(att_w) + else: + for idx in range(self.num_encs): + att_c_list[idx], att_w_list[idx] = self.att[idx]( + hs_pad[idx], + hlen[idx], + self.dropout_dec[0](z_list[0]), + att_w_list[idx], + ) + hs_pad_han = torch.stack(att_c_list, dim=1) + hlen_han = [self.num_encs] * len(ys_in) + att_c, att_w_list[self.num_encs] = self.att[self.num_encs]( + hs_pad_han, + hlen_han, + self.dropout_dec[0](z_list[0]), + att_w_list[self.num_encs], + ) + att_ws.append(att_w_list.copy()) + ey = torch.cat((eys[:, i, :], att_c), dim=1) # utt x (zdim + hdim) + z_list, c_list = self.rnn_forward(ey, z_list, c_list, z_list, c_list) + + if self.num_encs == 1: + # convert to numpy array with the shape (B, Lmax, Tmax) + att_ws = att_to_numpy(att_ws, self.att[att_idx]) + else: + _att_ws = [] + for idx, ws in enumerate(zip(*att_ws)): + ws = att_to_numpy(ws, self.att[idx]) + _att_ws.append(ws) + att_ws = _att_ws + return att_ws + + @staticmethod + def _get_last_yseq(exp_yseq): + last = [] + for y_seq in exp_yseq: + last.append(y_seq[-1]) + return last + + @staticmethod + def _append_ids(yseq, ids): + if isinstance(ids, list): + for i, j in enumerate(ids): + yseq[i].append(j) + else: + for i in range(len(yseq)): + yseq[i].append(ids) + return yseq + + @staticmethod + def _index_select_list(yseq, lst): + new_yseq = [] + for i in lst: + new_yseq.append(yseq[i][:]) + return new_yseq + + @staticmethod + def _index_select_lm_state(rnnlm_state, dim, vidx): + if isinstance(rnnlm_state, dict): + new_state = {} + for k, v in rnnlm_state.items(): + new_state[k] = [torch.index_select(vi, dim, vidx) for vi in v] + elif isinstance(rnnlm_state, list): + new_state = [] + for i in vidx: + new_state.append(rnnlm_state[int(i)][:]) + return new_state + + # scorer interface methods + def init_state(self, x): + # to support mutiple encoder asr mode, in single encoder mode, + # convert torch.Tensor to List of torch.Tensor + if self.num_encs == 1: + x = [x] + + c_list = [self.zero_state(x[0].unsqueeze(0))] + z_list = [self.zero_state(x[0].unsqueeze(0))] + for _ in six.moves.range(1, self.dlayers): + c_list.append(self.zero_state(x[0].unsqueeze(0))) + z_list.append(self.zero_state(x[0].unsqueeze(0))) + # TODO(karita): support strm_index for `asr_mix` + strm_index = 0 + att_idx = min(strm_index, len(self.att) - 1) + if self.num_encs == 1: + a = None + self.att[att_idx].reset() # reset pre-computation of h + else: + a = [None] * (self.num_encs + 1) # atts + han + for idx in range(self.num_encs + 1): + self.att[idx].reset() # reset pre-computation of h in atts and han + return dict( + c_prev=c_list[:], + z_prev=z_list[:], + a_prev=a, + workspace=(att_idx, z_list, c_list), + ) + + def score(self, yseq, state, x): + # to support mutiple encoder asr mode, in single encoder mode, + # convert torch.Tensor to List of torch.Tensor + if self.num_encs == 1: + x = [x] + + att_idx, z_list, c_list = state["workspace"] + vy = yseq[-1].unsqueeze(0) + ey = self.dropout_emb(self.embed(vy)) # utt list (1) x zdim + if self.num_encs == 1: + att_c, att_w = self.att[att_idx]( + x[0].unsqueeze(0), + [x[0].size(0)], + self.dropout_dec[0](state["z_prev"][0]), + state["a_prev"], + ) + else: + att_w = [None] * (self.num_encs + 1) # atts + han + att_c_list = [None] * (self.num_encs) # atts + for idx in range(self.num_encs): + att_c_list[idx], att_w[idx] = self.att[idx]( + x[idx].unsqueeze(0), + [x[idx].size(0)], + self.dropout_dec[0](state["z_prev"][0]), + state["a_prev"][idx], + ) + h_han = torch.stack(att_c_list, dim=1) + att_c, att_w[self.num_encs] = self.att[self.num_encs]( + h_han, + [self.num_encs], + self.dropout_dec[0](state["z_prev"][0]), + state["a_prev"][self.num_encs], + ) + ey = torch.cat((ey, att_c), dim=1) # utt(1) x (zdim + hdim) + z_list, c_list = self.rnn_forward( + ey, z_list, c_list, state["z_prev"], state["c_prev"] + ) + if self.context_residual: + logits = self.output( + torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1) + ) + else: + logits = self.output(self.dropout_dec[-1](z_list[-1])) + logp = F.log_softmax(logits, dim=1).squeeze(0) + return ( + logp, + dict( + c_prev=c_list[:], + z_prev=z_list[:], + a_prev=att_w, + workspace=(att_idx, z_list, c_list), + ), + ) + + +def decoder_for(args, odim, sos, eos, att, labeldist): + return Decoder( + args.eprojs, + odim, + args.dtype, + args.dlayers, + args.dunits, + sos, + eos, + att, + args.verbose, + args.char_list, + labeldist, + args.lsm_weight, + args.sampling_probability, + args.dropout_rate_decoder, + getattr(args, "context_residual", False), # use getattr to keep compatibility + getattr(args, "replace_sos", False), # use getattr to keep compatibility + getattr(args, "num_encs", 1), + ) # use getattr to keep compatibility diff --git a/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/encoders.py b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/encoders.py new file mode 100644 index 0000000000000000000000000000000000000000..cc7d13e772bb438967f29551a4e5a74c340eb98a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/language_model/rnn/encoders.py @@ -0,0 +1,372 @@ +import logging + +import numpy as np +import six +import torch +import torch.nn.functional as F +from torch.nn.utils.rnn import pack_padded_sequence +from torch.nn.utils.rnn import pad_packed_sequence + +from funasr_detach.metrics.common import get_vgg2l_odim +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.utils.nets_utils import to_device + + +class RNNP(torch.nn.Module): + """RNN with projection layer module + + :param int idim: dimension of inputs + :param int elayers: number of encoder layers + :param int cdim: number of rnn units (resulted in cdim * 2 if bidirectional) + :param int hdim: number of projection units + :param np.ndarray subsample: list of subsampling numbers + :param float dropout: dropout rate + :param str typ: The RNN type + """ + + def __init__(self, idim, elayers, cdim, hdim, subsample, dropout, typ="blstm"): + super(RNNP, self).__init__() + bidir = typ[0] == "b" + for i in six.moves.range(elayers): + if i == 0: + inputdim = idim + else: + inputdim = hdim + + RNN = torch.nn.LSTM if "lstm" in typ else torch.nn.GRU + rnn = RNN( + inputdim, cdim, num_layers=1, bidirectional=bidir, batch_first=True + ) + + setattr(self, "%s%d" % ("birnn" if bidir else "rnn", i), rnn) + + # bottleneck layer to merge + if bidir: + setattr(self, "bt%d" % i, torch.nn.Linear(2 * cdim, hdim)) + else: + setattr(self, "bt%d" % i, torch.nn.Linear(cdim, hdim)) + + self.elayers = elayers + self.cdim = cdim + self.subsample = subsample + self.typ = typ + self.bidir = bidir + self.dropout = dropout + + def forward(self, xs_pad, ilens, prev_state=None): + """RNNP forward + + :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, idim) + :param torch.Tensor ilens: batch of lengths of input sequences (B) + :param torch.Tensor prev_state: batch of previous RNN states + :return: batch of hidden state sequences (B, Tmax, hdim) + :rtype: torch.Tensor + """ + logging.debug(self.__class__.__name__ + " input lengths: " + str(ilens)) + elayer_states = [] + for layer in six.moves.range(self.elayers): + if not isinstance(ilens, torch.Tensor): + ilens = torch.tensor(ilens) + xs_pack = pack_padded_sequence(xs_pad, ilens.cpu(), batch_first=True) + rnn = getattr(self, ("birnn" if self.bidir else "rnn") + str(layer)) + rnn.flatten_parameters() + if prev_state is not None and rnn.bidirectional: + prev_state = reset_backward_rnn_state(prev_state) + ys, states = rnn( + xs_pack, hx=None if prev_state is None else prev_state[layer] + ) + elayer_states.append(states) + # ys: utt list of frame x cdim x 2 (2: means bidirectional) + ys_pad, ilens = pad_packed_sequence(ys, batch_first=True) + sub = self.subsample[layer + 1] + if sub > 1: + ys_pad = ys_pad[:, ::sub] + ilens = torch.tensor([int(i + 1) // sub for i in ilens]) + # (sum _utt frame_utt) x dim + projection_layer = getattr(self, "bt%d" % layer) + projected = projection_layer(ys_pad.contiguous().view(-1, ys_pad.size(2))) + xs_pad = projected.view(ys_pad.size(0), ys_pad.size(1), -1) + if layer < self.elayers - 1: + xs_pad = torch.tanh(F.dropout(xs_pad, p=self.dropout)) + + return xs_pad, ilens, elayer_states # x: utt list of frame x dim + + +class RNN(torch.nn.Module): + """RNN module + + :param int idim: dimension of inputs + :param int elayers: number of encoder layers + :param int cdim: number of rnn units (resulted in cdim * 2 if bidirectional) + :param int hdim: number of final projection units + :param float dropout: dropout rate + :param str typ: The RNN type + """ + + def __init__(self, idim, elayers, cdim, hdim, dropout, typ="blstm"): + super(RNN, self).__init__() + bidir = typ[0] == "b" + self.nbrnn = ( + torch.nn.LSTM( + idim, + cdim, + elayers, + batch_first=True, + dropout=dropout, + bidirectional=bidir, + ) + if "lstm" in typ + else torch.nn.GRU( + idim, + cdim, + elayers, + batch_first=True, + dropout=dropout, + bidirectional=bidir, + ) + ) + if bidir: + self.l_last = torch.nn.Linear(cdim * 2, hdim) + else: + self.l_last = torch.nn.Linear(cdim, hdim) + self.typ = typ + + def forward(self, xs_pad, ilens, prev_state=None): + """RNN forward + + :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D) + :param torch.Tensor ilens: batch of lengths of input sequences (B) + :param torch.Tensor prev_state: batch of previous RNN states + :return: batch of hidden state sequences (B, Tmax, eprojs) + :rtype: torch.Tensor + """ + logging.debug(self.__class__.__name__ + " input lengths: " + str(ilens)) + if not isinstance(ilens, torch.Tensor): + ilens = torch.tensor(ilens) + xs_pack = pack_padded_sequence(xs_pad, ilens.cpu(), batch_first=True) + self.nbrnn.flatten_parameters() + if prev_state is not None and self.nbrnn.bidirectional: + # We assume that when previous state is passed, + # it means that we're streaming the input + # and therefore cannot propagate backward BRNN state + # (otherwise it goes in the wrong direction) + prev_state = reset_backward_rnn_state(prev_state) + ys, states = self.nbrnn(xs_pack, hx=prev_state) + # ys: utt list of frame x cdim x 2 (2: means bidirectional) + ys_pad, ilens = pad_packed_sequence(ys, batch_first=True) + # (sum _utt frame_utt) x dim + projected = torch.tanh( + self.l_last(ys_pad.contiguous().view(-1, ys_pad.size(2))) + ) + xs_pad = projected.view(ys_pad.size(0), ys_pad.size(1), -1) + return xs_pad, ilens, states # x: utt list of frame x dim + + +def reset_backward_rnn_state(states): + """Sets backward BRNN states to zeroes + + Useful in processing of sliding windows over the inputs + """ + if isinstance(states, (list, tuple)): + for state in states: + state[1::2] = 0.0 + else: + states[1::2] = 0.0 + return states + + +class VGG2L(torch.nn.Module): + """VGG-like module + + :param int in_channel: number of input channels + """ + + def __init__(self, in_channel=1): + super(VGG2L, self).__init__() + # CNN layer (VGG motivated) + self.conv1_1 = torch.nn.Conv2d(in_channel, 64, 3, stride=1, padding=1) + self.conv1_2 = torch.nn.Conv2d(64, 64, 3, stride=1, padding=1) + self.conv2_1 = torch.nn.Conv2d(64, 128, 3, stride=1, padding=1) + self.conv2_2 = torch.nn.Conv2d(128, 128, 3, stride=1, padding=1) + + self.in_channel = in_channel + + def forward(self, xs_pad, ilens, **kwargs): + """VGG2L forward + + :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D) + :param torch.Tensor ilens: batch of lengths of input sequences (B) + :return: batch of padded hidden state sequences (B, Tmax // 4, 128 * D // 4) + :rtype: torch.Tensor + """ + logging.debug(self.__class__.__name__ + " input lengths: " + str(ilens)) + + # x: utt x frame x dim + # xs_pad = F.pad_sequence(xs_pad) + + # x: utt x 1 (input channel num) x frame x dim + xs_pad = xs_pad.view( + xs_pad.size(0), + xs_pad.size(1), + self.in_channel, + xs_pad.size(2) // self.in_channel, + ).transpose(1, 2) + + # NOTE: max_pool1d ? + xs_pad = F.relu(self.conv1_1(xs_pad)) + xs_pad = F.relu(self.conv1_2(xs_pad)) + xs_pad = F.max_pool2d(xs_pad, 2, stride=2, ceil_mode=True) + + xs_pad = F.relu(self.conv2_1(xs_pad)) + xs_pad = F.relu(self.conv2_2(xs_pad)) + xs_pad = F.max_pool2d(xs_pad, 2, stride=2, ceil_mode=True) + if torch.is_tensor(ilens): + ilens = ilens.cpu().numpy() + else: + ilens = np.array(ilens, dtype=np.float32) + ilens = np.array(np.ceil(ilens / 2), dtype=np.int64) + ilens = np.array( + np.ceil(np.array(ilens, dtype=np.float32) / 2), dtype=np.int64 + ).tolist() + + # x: utt_list of frame (remove zeropaded frames) x (input channel num x dim) + xs_pad = xs_pad.transpose(1, 2) + xs_pad = xs_pad.contiguous().view( + xs_pad.size(0), xs_pad.size(1), xs_pad.size(2) * xs_pad.size(3) + ) + return xs_pad, ilens, None # no state in this layer + + +class Encoder(torch.nn.Module): + """Encoder module + + :param str etype: type of encoder network + :param int idim: number of dimensions of encoder network + :param int elayers: number of layers of encoder network + :param int eunits: number of lstm units of encoder network + :param int eprojs: number of projection units of encoder network + :param np.ndarray subsample: list of subsampling numbers + :param float dropout: dropout rate + :param int in_channel: number of input channels + """ + + def __init__( + self, etype, idim, elayers, eunits, eprojs, subsample, dropout, in_channel=1 + ): + super(Encoder, self).__init__() + typ = etype.lstrip("vgg").rstrip("p") + if typ not in ["lstm", "gru", "blstm", "bgru"]: + logging.error("Error: need to specify an appropriate encoder architecture") + + if etype.startswith("vgg"): + if etype[-1] == "p": + self.enc = torch.nn.ModuleList( + [ + VGG2L(in_channel), + RNNP( + get_vgg2l_odim(idim, in_channel=in_channel), + elayers, + eunits, + eprojs, + subsample, + dropout, + typ=typ, + ), + ] + ) + logging.info("Use CNN-VGG + " + typ.upper() + "P for encoder") + else: + self.enc = torch.nn.ModuleList( + [ + VGG2L(in_channel), + RNN( + get_vgg2l_odim(idim, in_channel=in_channel), + elayers, + eunits, + eprojs, + dropout, + typ=typ, + ), + ] + ) + logging.info("Use CNN-VGG + " + typ.upper() + " for encoder") + self.conv_subsampling_factor = 4 + else: + if etype[-1] == "p": + self.enc = torch.nn.ModuleList( + [RNNP(idim, elayers, eunits, eprojs, subsample, dropout, typ=typ)] + ) + logging.info(typ.upper() + " with every-layer projection for encoder") + else: + self.enc = torch.nn.ModuleList( + [RNN(idim, elayers, eunits, eprojs, dropout, typ=typ)] + ) + logging.info(typ.upper() + " without projection for encoder") + self.conv_subsampling_factor = 1 + + def forward(self, xs_pad, ilens, prev_states=None): + """Encoder forward + + :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D) + :param torch.Tensor ilens: batch of lengths of input sequences (B) + :param torch.Tensor prev_state: batch of previous encoder hidden states (?, ...) + :return: batch of hidden state sequences (B, Tmax, eprojs) + :rtype: torch.Tensor + """ + if prev_states is None: + prev_states = [None] * len(self.enc) + assert len(prev_states) == len(self.enc) + + current_states = [] + for module, prev_state in zip(self.enc, prev_states): + xs_pad, ilens, states = module(xs_pad, ilens, prev_state=prev_state) + current_states.append(states) + + # make mask to remove bias value in padded part + mask = to_device(xs_pad, make_pad_mask(ilens).unsqueeze(-1)) + + return xs_pad.masked_fill(mask, 0.0), ilens, current_states + + +def encoder_for(args, idim, subsample): + """Instantiates an encoder module given the program arguments + + :param Namespace args: The arguments + :param int or List of integer idim: dimension of input, e.g. 83, or + List of dimensions of inputs, e.g. [83,83] + :param List or List of List subsample: subsample factors, e.g. [1,2,2,1,1], or + List of subsample factors of each encoder. + e.g. [[1,2,2,1,1], [1,2,2,1,1]] + :rtype torch.nn.Module + :return: The encoder module + """ + num_encs = getattr(args, "num_encs", 1) # use getattr to keep compatibility + if num_encs == 1: + # compatible with single encoder asr mode + return Encoder( + args.etype, + idim, + args.elayers, + args.eunits, + args.eprojs, + subsample, + args.dropout_rate, + ) + elif num_encs >= 1: + enc_list = torch.nn.ModuleList() + for idx in range(num_encs): + enc = Encoder( + args.etype[idx], + idim[idx], + args.elayers[idx], + args.eunits[idx], + args.eprojs, + subsample[idx], + args.dropout_rate[idx], + ) + enc_list.append(enc) + return enc_list + else: + raise ValueError( + "Number of encoders needs to be more than one. {}".format(num_encs) + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/language_model/seq_rnn_lm.py b/demo/Step-Audio-EditX/funasr_detach/models/language_model/seq_rnn_lm.py new file mode 100644 index 0000000000000000000000000000000000000000..3683eed656d73acdc4fd5c68ef8d8f4b143039dc --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/language_model/seq_rnn_lm.py @@ -0,0 +1,172 @@ +"""Sequential implementation of Recurrent Neural Network Language Model.""" + +from typing import Tuple +from typing import Union + +import torch +import torch.nn as nn +from funasr_detach.train.abs_model import AbsLM + + +class SequentialRNNLM(AbsLM): + """Sequential RNNLM. + + See also: + https://github.com/pytorch/examples/blob/4581968193699de14b56527296262dd76ab43557/word_language_model/model.py + + """ + + def __init__( + self, + vocab_size: int, + unit: int = 650, + nhid: int = None, + nlayers: int = 2, + dropout_rate: float = 0.0, + tie_weights: bool = False, + rnn_type: str = "lstm", + ignore_id: int = 0, + ): + super().__init__() + + ninp = unit + if nhid is None: + nhid = unit + rnn_type = rnn_type.upper() + + self.drop = nn.Dropout(dropout_rate) + self.encoder = nn.Embedding(vocab_size, ninp, padding_idx=ignore_id) + if rnn_type in ["LSTM", "GRU"]: + rnn_class = getattr(nn, rnn_type) + self.rnn = rnn_class( + ninp, nhid, nlayers, dropout=dropout_rate, batch_first=True + ) + else: + try: + nonlinearity = {"RNN_TANH": "tanh", "RNN_RELU": "relu"}[rnn_type] + except KeyError: + raise ValueError( + """An invalid option for `--model` was supplied, + options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']""" + ) + self.rnn = nn.RNN( + ninp, + nhid, + nlayers, + nonlinearity=nonlinearity, + dropout=dropout_rate, + batch_first=True, + ) + self.decoder = nn.Linear(nhid, vocab_size) + + # Optionally tie weights as in: + # "Using the Output Embedding to Improve Language Models" + # (Press & Wolf 2016) https://arxiv.org/abs/1608.05859 + # and + # "Tying Word Vectors and Word Classifiers: + # A Loss Framework for Language Modeling" (Inan et al. 2016) + # https://arxiv.org/abs/1611.01462 + if tie_weights: + if nhid != ninp: + raise ValueError( + "When using the tied flag, nhid must be equal to emsize" + ) + self.decoder.weight = self.encoder.weight + + self.rnn_type = rnn_type + self.nhid = nhid + self.nlayers = nlayers + + def zero_state(self): + """Initialize LM state filled with zero values.""" + if isinstance(self.rnn, torch.nn.LSTM): + h = torch.zeros((self.nlayers, self.nhid), dtype=torch.float) + c = torch.zeros((self.nlayers, self.nhid), dtype=torch.float) + state = h, c + else: + state = torch.zeros((self.nlayers, self.nhid), dtype=torch.float) + + return state + + def forward( + self, input: torch.Tensor, hidden: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + emb = self.drop(self.encoder(input)) + output, hidden = self.rnn(emb, hidden) + output = self.drop(output) + decoded = self.decoder( + output.contiguous().view(output.size(0) * output.size(1), output.size(2)) + ) + return ( + decoded.view(output.size(0), output.size(1), decoded.size(1)), + hidden, + ) + + def score( + self, + y: torch.Tensor, + state: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], + x: torch.Tensor, + ) -> Tuple[torch.Tensor, Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]]: + """Score new token. + + Args: + y: 1D torch.int64 prefix tokens. + state: Scorer state for prefix tokens + x: 2D encoder feature that generates ys. + + Returns: + Tuple of + torch.float32 scores for next token (n_vocab) + and next state for ys + + """ + y, new_state = self(y[-1].view(1, 1), state) + logp = y.log_softmax(dim=-1).view(-1) + return logp, new_state + + def batch_score( + self, ys: torch.Tensor, states: torch.Tensor, xs: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Score new token batch. + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, List[Any]]: Tuple of + batchfied scores for next token with shape of `(n_batch, n_vocab)` + and next state list for ys. + + """ + if states[0] is None: + states = None + elif isinstance(self.rnn, torch.nn.LSTM): + # states: Batch x 2 x (Nlayers, Dim) -> 2 x (Nlayers, Batch, Dim) + h = torch.stack([h for h, c in states], dim=1) + c = torch.stack([c for h, c in states], dim=1) + states = h, c + else: + # states: Batch x (Nlayers, Dim) -> (Nlayers, Batch, Dim) + states = torch.stack(states, dim=1) + + ys, states = self(ys[:, -1:], states) + # ys: (Batch, 1, Nvocab) -> (Batch, NVocab) + assert ys.size(1) == 1, ys.shape + ys = ys.squeeze(1) + logp = ys.log_softmax(dim=-1) + + # state: Change to batch first + if isinstance(self.rnn, torch.nn.LSTM): + # h, c: (Nlayers, Batch, Dim) + h, c = states + # states: Batch x 2 x (Nlayers, Dim) + states = [(h[:, i], c[:, i]) for i in range(h.size(1))] + else: + # states: (Nlayers, Batch, Dim) -> Batch x (Nlayers, Dim) + states = [states[:, i] for i in range(states.size(1))] + + return logp, states diff --git a/demo/Step-Audio-EditX/funasr_detach/models/language_model/transformer_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/language_model/transformer_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..627491fc930d449c725c381ece9974d8a0b3c11b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/language_model/transformer_encoder.py @@ -0,0 +1,468 @@ +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Transformer encoder definition.""" + +from typing import List +from typing import Optional +from typing import Tuple + +import torch +from torch import nn +import logging + +from funasr_detach.models.transformer.attention import MultiHeadedAttention +from funasr_detach.models.transformer.embedding import PositionalEncoding +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear +from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, # noqa: H301 +) +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.utils.dynamic_conv import DynamicConvolution +from funasr_detach.models.transformer.utils.dynamic_conv2d import DynamicConvolution2D +from funasr_detach.models.transformer.utils.lightconv import LightweightConvolution +from funasr_detach.models.transformer.utils.lightconv2d import LightweightConvolution2D +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling2 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling6 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling8 +from funasr_detach.models.transformer.utils.subsampling import TooShortUttError +from funasr_detach.models.transformer.utils.subsampling import check_short_utt + + +class EncoderLayer(nn.Module): + """Encoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance + can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + stochastic_depth_rate (float): Proability to skip this layer. + During training, the layer may skip residual computation and return input + as-is with given probability. + """ + + def __init__( + self, + size, + self_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + stochastic_depth_rate=0.0, + ): + """Construct an EncoderLayer object.""" + super(EncoderLayer, self).__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(size) + self.norm2 = LayerNorm(size) + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear = nn.Linear(size + size, size) + self.stochastic_depth_rate = stochastic_depth_rate + + def forward(self, x, mask, cache=None): + """Compute encoded features. + + Args: + x_input (torch.Tensor): Input tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + skip_layer = False + # with stochastic depth, residual connection `x + f(x)` becomes + # `x <- x + 1 / (1 - p) * f(x)` at training time. + stoch_layer_coeff = 1.0 + if self.training and self.stochastic_depth_rate > 0: + skip_layer = torch.rand(1).item() < self.stochastic_depth_rate + stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate) + + if skip_layer: + if cache is not None: + x = torch.cat([cache, x], dim=1) + return x, mask + + residual = x + if self.normalize_before: + x = self.norm1(x) + + if cache is None: + x_q = x + else: + assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size) + x_q = x[:, -1:, :] + residual = residual[:, -1:, :] + mask = None if mask is None else mask[:, -1:, :] + + if self.concat_after: + x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1) + x = residual + stoch_layer_coeff * self.concat_linear(x_concat) + else: + x = residual + stoch_layer_coeff * self.dropout( + self.self_attn(x_q, x, x, mask) + ) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm2(x) + + if cache is not None: + x = torch.cat([cache, x], dim=1) + + return x, mask + + +class TransformerEncoder_lm(nn.Module): + """Transformer encoder module. + + Args: + idim (int): Input dimension. + attention_dim (int): Dimension of attention. + attention_heads (int): The number of heads of multi head attention. + conv_wshare (int): The number of kernel of convolution. Only used in + selfattention_layer_type == "lightconv*" or "dynamiconv*". + conv_kernel_length (Union[int, str]): Kernel size str of convolution + (e.g. 71_71_71_71_71_71). Only used in selfattention_layer_type + == "lightconv*" or "dynamiconv*". + conv_usebias (bool): Whether to use bias in convolution. Only used in + selfattention_layer_type == "lightconv*" or "dynamiconv*". + linear_units (int): The number of units of position-wise feed forward. + num_blocks (int): The number of decoder blocks. + dropout_rate (float): Dropout rate. + positional_dropout_rate (float): Dropout rate after adding positional encoding. + attention_dropout_rate (float): Dropout rate in attention. + input_layer (Union[str, torch.nn.Module]): Input layer type. + pos_enc_class (torch.nn.Module): Positional encoding module class. + `PositionalEncoding `or `ScaledPositionalEncoding` + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear". + positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer. + selfattention_layer_type (str): Encoder attention layer type. + padding_idx (int): Padding idx for input_layer=embed. + stochastic_depth_rate (float): Maximum probability to skip the encoder layer. + intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer. + indices start from 1. + if not None, intermediate outputs are returned (which changes return type + signature.) + + """ + + def __init__( + self, + idim, + attention_dim=256, + attention_heads=4, + conv_wshare=4, + conv_kernel_length="11", + conv_usebias=False, + linear_units=2048, + num_blocks=6, + dropout_rate=0.1, + positional_dropout_rate=0.1, + attention_dropout_rate=0.0, + input_layer="conv2d", + pos_enc_class=PositionalEncoding, + normalize_before=True, + concat_after=False, + positionwise_layer_type="linear", + positionwise_conv_kernel_size=1, + selfattention_layer_type="selfattn", + padding_idx=-1, + stochastic_depth_rate=0.0, + intermediate_layers=None, + ctc_softmax=None, + conditioning_layer_dim=None, + ): + """Construct an Encoder object.""" + super().__init__() + + self.conv_subsampling_factor = 1 + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(idim, attention_dim), + torch.nn.LayerNorm(attention_dim), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling(idim, attention_dim, dropout_rate) + self.conv_subsampling_factor = 4 + elif input_layer == "conv2d-scaled-pos-enc": + self.embed = Conv2dSubsampling( + idim, + attention_dim, + dropout_rate, + pos_enc_class(attention_dim, positional_dropout_rate), + ) + self.conv_subsampling_factor = 4 + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6(idim, attention_dim, dropout_rate) + self.conv_subsampling_factor = 6 + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8(idim, attention_dim, dropout_rate) + self.conv_subsampling_factor = 8 + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + elif isinstance(input_layer, torch.nn.Module): + self.embed = torch.nn.Sequential( + input_layer, + pos_enc_class(attention_dim, positional_dropout_rate), + ) + elif input_layer is None: + self.embed = torch.nn.Sequential( + pos_enc_class(attention_dim, positional_dropout_rate) + ) + else: + raise ValueError("unknown input_layer: " + input_layer) + self.normalize_before = normalize_before + positionwise_layer, positionwise_layer_args = self.get_positionwise_layer( + positionwise_layer_type, + attention_dim, + linear_units, + dropout_rate, + positionwise_conv_kernel_size, + ) + if selfattention_layer_type in [ + "selfattn", + "rel_selfattn", + "legacy_rel_selfattn", + ]: + logging.info("encoder self-attention layer type = self-attention") + encoder_selfattn_layer = MultiHeadedAttention + encoder_selfattn_layer_args = [ + ( + attention_heads, + attention_dim, + attention_dropout_rate, + ) + ] * num_blocks + elif selfattention_layer_type == "lightconv": + logging.info("encoder self-attention layer type = lightweight convolution") + encoder_selfattn_layer = LightweightConvolution + encoder_selfattn_layer_args = [ + ( + conv_wshare, + attention_dim, + attention_dropout_rate, + int(conv_kernel_length.split("_")[lnum]), + False, + conv_usebias, + ) + for lnum in range(num_blocks) + ] + elif selfattention_layer_type == "lightconv2d": + logging.info( + "encoder self-attention layer " + "type = lightweight convolution 2-dimensional" + ) + encoder_selfattn_layer = LightweightConvolution2D + encoder_selfattn_layer_args = [ + ( + conv_wshare, + attention_dim, + attention_dropout_rate, + int(conv_kernel_length.split("_")[lnum]), + False, + conv_usebias, + ) + for lnum in range(num_blocks) + ] + elif selfattention_layer_type == "dynamicconv": + logging.info("encoder self-attention layer type = dynamic convolution") + encoder_selfattn_layer = DynamicConvolution + encoder_selfattn_layer_args = [ + ( + conv_wshare, + attention_dim, + attention_dropout_rate, + int(conv_kernel_length.split("_")[lnum]), + False, + conv_usebias, + ) + for lnum in range(num_blocks) + ] + elif selfattention_layer_type == "dynamicconv2d": + logging.info( + "encoder self-attention layer type = dynamic convolution 2-dimensional" + ) + encoder_selfattn_layer = DynamicConvolution2D + encoder_selfattn_layer_args = [ + ( + conv_wshare, + attention_dim, + attention_dropout_rate, + int(conv_kernel_length.split("_")[lnum]), + False, + conv_usebias, + ) + for lnum in range(num_blocks) + ] + else: + raise NotImplementedError(selfattention_layer_type) + + self.encoders = repeat( + num_blocks, + lambda lnum: EncoderLayer( + attention_dim, + encoder_selfattn_layer(*encoder_selfattn_layer_args[lnum]), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + stochastic_depth_rate * float(1 + lnum) / num_blocks, + ), + ) + if self.normalize_before: + self.after_norm = LayerNorm(attention_dim) + + self.intermediate_layers = intermediate_layers + self.use_conditioning = True if ctc_softmax is not None else False + if self.use_conditioning: + self.ctc_softmax = ctc_softmax + self.conditioning_layer = torch.nn.Linear( + conditioning_layer_dim, attention_dim + ) + + def get_positionwise_layer( + self, + positionwise_layer_type="linear", + attention_dim=256, + linear_units=2048, + dropout_rate=0.1, + positionwise_conv_kernel_size=1, + ): + """Define positionwise layer.""" + if positionwise_layer_type == "linear": + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = (attention_dim, linear_units, dropout_rate) + elif positionwise_layer_type == "conv1d": + positionwise_layer = MultiLayeredConv1d + positionwise_layer_args = ( + attention_dim, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d-linear": + positionwise_layer = Conv1dLinear + positionwise_layer_args = ( + attention_dim, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + else: + raise NotImplementedError("Support only linear or conv1d.") + return positionwise_layer, positionwise_layer_args + + def forward(self, xs, masks): + """Encode input sequence. + + Args: + xs (torch.Tensor): Input tensor (#batch, time, idim). + masks (torch.Tensor): Mask tensor (#batch, time). + + Returns: + torch.Tensor: Output tensor (#batch, time, attention_dim). + torch.Tensor: Mask tensor (#batch, time). + + """ + if isinstance( + self.embed, + (Conv2dSubsampling, Conv2dSubsampling6, Conv2dSubsampling8), + ): + xs, masks = self.embed(xs, masks) + else: + xs = self.embed(xs) + + if self.intermediate_layers is None: + xs, masks = self.encoders(xs, masks) + else: + intermediate_outputs = [] + for layer_idx, encoder_layer in enumerate(self.encoders): + xs, masks = encoder_layer(xs, masks) + + if ( + self.intermediate_layers is not None + and layer_idx + 1 in self.intermediate_layers + ): + encoder_output = xs + # intermediate branches also require normalization. + if self.normalize_before: + encoder_output = self.after_norm(encoder_output) + intermediate_outputs.append(encoder_output) + + if self.use_conditioning: + intermediate_result = self.ctc_softmax(encoder_output) + xs = xs + self.conditioning_layer(intermediate_result) + + if self.normalize_before: + xs = self.after_norm(xs) + + if self.intermediate_layers is not None: + return xs, masks, intermediate_outputs + return xs, masks + + def forward_one_step(self, xs, masks, cache=None): + """Encode input frame. + + Args: + xs (torch.Tensor): Input tensor. + masks (torch.Tensor): Mask tensor. + cache (List[torch.Tensor]): List of cache tensors. + + Returns: + torch.Tensor: Output tensor. + torch.Tensor: Mask tensor. + List[torch.Tensor]: List of new cache tensors. + + """ + if isinstance(self.embed, Conv2dSubsampling): + xs, masks = self.embed(xs, masks) + else: + xs = self.embed(xs) + if cache is None: + cache = [None for _ in range(len(self.encoders))] + new_cache = [] + for c, e in zip(cache, self.encoders): + xs, masks = e(xs, masks, cache=c) + new_cache.append(xs) + if self.normalize_before: + xs = self.after_norm(xs) + return xs, masks, new_cache diff --git a/demo/Step-Audio-EditX/funasr_detach/models/language_model/transformer_lm.py b/demo/Step-Audio-EditX/funasr_detach/models/language_model/transformer_lm.py new file mode 100644 index 0000000000000000000000000000000000000000..e9214a620ffc6fa511ad3f0ab583f2cc5757f2a9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/language_model/transformer_lm.py @@ -0,0 +1,133 @@ +from typing import Any +from typing import List +from typing import Tuple + +import torch +import torch.nn as nn + +from funasr_detach.models.transformer.embedding import PositionalEncoding +from funasr_detach.models.encoder.transformer_encoder import ( + TransformerEncoder_s0 as Encoder, +) +from funasr_detach.models.transformer.utils.mask import subsequent_mask +from funasr_detach.train.abs_model import AbsLM + + +class TransformerLM(AbsLM): + def __init__( + self, + vocab_size: int, + pos_enc: str = None, + embed_unit: int = 128, + att_unit: int = 256, + head: int = 2, + unit: int = 1024, + layer: int = 4, + dropout_rate: float = 0.5, + ): + super().__init__() + if pos_enc == "sinusoidal": + pos_enc_class = PositionalEncoding + elif pos_enc is None: + + def pos_enc_class(*args, **kwargs): + return nn.Sequential() # indentity + + else: + raise ValueError(f"unknown pos-enc option: {pos_enc}") + + self.embed = nn.Embedding(vocab_size, embed_unit) + self.encoder = Encoder( + idim=embed_unit, + attention_dim=att_unit, + attention_heads=head, + linear_units=unit, + num_blocks=layer, + dropout_rate=dropout_rate, + input_layer="linear", + pos_enc_class=pos_enc_class, + ) + self.decoder = nn.Linear(att_unit, vocab_size) + + def _target_mask(self, ys_in_pad): + ys_mask = ys_in_pad != 0 + m = subsequent_mask(ys_mask.size(-1), device=ys_mask.device).unsqueeze(0) + return ys_mask.unsqueeze(-2) & m + + def forward(self, input: torch.Tensor, hidden: None) -> Tuple[torch.Tensor, None]: + """Compute LM loss value from buffer sequences. + + Args: + input (torch.Tensor): Input ids. (batch, len) + hidden (torch.Tensor): Target ids. (batch, len) + + """ + x = self.embed(input) + mask = self._target_mask(input) + h, _ = self.encoder(x, mask) + y = self.decoder(h) + return y, None + + def score( + self, y: torch.Tensor, state: Any, x: torch.Tensor + ) -> Tuple[torch.Tensor, Any]: + """Score new token. + + Args: + y (torch.Tensor): 1D torch.int64 prefix tokens. + state: Scorer state for prefix tokens + x (torch.Tensor): encoder feature that generates ys. + + Returns: + tuple[torch.Tensor, Any]: Tuple of + torch.float32 scores for next token (vocab_size) + and next state for ys + + """ + y = y.unsqueeze(0) + h, _, cache = self.encoder.forward_one_step( + self.embed(y), self._target_mask(y), cache=state + ) + h = self.decoder(h[:, -1]) + logp = h.log_softmax(dim=-1).squeeze(0) + return logp, cache + + def batch_score( + self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor + ) -> Tuple[torch.Tensor, List[Any]]: + """Score new token batch. + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, List[Any]]: Tuple of + batchfied scores for next token with shape of `(n_batch, vocab_size)` + and next state list for ys. + + """ + # merge states + n_batch = len(ys) + n_layers = len(self.encoder.encoders) + if states[0] is None: + batch_state = None + else: + # transpose state of [batch, layer] into [layer, batch] + batch_state = [ + torch.stack([states[b][i] for b in range(n_batch)]) + for i in range(n_layers) + ] + + # batch decoding + h, _, states = self.encoder.forward_one_step( + self.embed(ys), self._target_mask(ys), cache=batch_state + ) + h = self.decoder(h[:, -1]) + logp = h.log_softmax(dim=-1) + + # transpose state of [layer, batch] into [batch, layer] + state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)] + return logp, state_list diff --git a/demo/Step-Audio-EditX/funasr_detach/models/lora/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/lora/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/lora/layers.py b/demo/Step-Audio-EditX/funasr_detach/models/lora/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..6e292c5b7700da091b5319efdc6aed3be675d32b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/lora/layers.py @@ -0,0 +1,369 @@ +# ------------------------------------------------------------------------------------------ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. +# ------------------------------------------------------------------------------------------ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import math +from typing import Optional, List + + +class LoRALayer: + def __init__( + self, + r: int, + lora_alpha: int, + lora_dropout: float, + merge_weights: bool, + ): + self.r = r + self.lora_alpha = lora_alpha + # Optional dropout + if lora_dropout > 0.0: + self.lora_dropout = nn.Dropout(p=lora_dropout) + else: + self.lora_dropout = lambda x: x + # Mark the weight as unmerged + self.merged = False + self.merge_weights = merge_weights + + +class Embedding(nn.Embedding, LoRALayer): + # LoRA implemented in a dense layer + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + r: int = 0, + lora_alpha: int = 1, + merge_weights: bool = True, + **kwargs + ): + nn.Embedding.__init__(self, num_embeddings, embedding_dim, **kwargs) + LoRALayer.__init__( + self, + r=r, + lora_alpha=lora_alpha, + lora_dropout=0, + merge_weights=merge_weights, + ) + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Parameter(self.weight.new_zeros((r, num_embeddings))) + self.lora_B = nn.Parameter(self.weight.new_zeros((embedding_dim, r))) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + + def reset_parameters(self): + nn.Embedding.reset_parameters(self) + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.zeros_(self.lora_A) + nn.init.normal_(self.lora_B) + + def train(self, mode: bool = True): + nn.Embedding.train(self, mode) + if self.merge_weights and self.merged: + # Make sure that the weights are not merged + if self.r > 0: + self.weight.data -= (self.lora_B @ self.lora_A).T * self.scaling + self.merged = False + + def eval(self): + nn.Linear.eval(self) + if self.merge_weights and not self.merged: + # Merge the weights and mark it + if self.r > 0: + self.weight.data += (self.lora_B @ self.lora_A) * self.scaling + self.merged = True + + def forward(self, x: torch.Tensor): + if self.r > 0 and not self.merged: + result = nn.Embedding.forward(self, x) + if self.r > 0: + after_A = F.embedding( + x, + self.lora_A.T, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ) + result += (after_A @ self.lora_B.T) * self.scaling + return result + else: + return nn.Embedding.forward(self, x) + + +class Linear(nn.Linear, LoRALayer): + # LoRA implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) + merge_weights: bool = True, + **kwargs + ): + nn.Linear.__init__(self, in_features, out_features, **kwargs) + LoRALayer.__init__( + self, + r=r, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + merge_weights=merge_weights, + ) + + self.fan_in_fan_out = fan_in_fan_out + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Parameter(self.weight.new_zeros((r, in_features))) + self.lora_B = nn.Parameter(self.weight.new_zeros((out_features, r))) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + if fan_in_fan_out: + self.weight.data = self.weight.data.T + + def reset_parameters(self): + nn.Linear.reset_parameters(self) + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B) + + def train(self, mode: bool = True): + def T(w): + return w.T if self.fan_in_fan_out else w + + nn.Linear.train(self, mode) + if self.merge_weights and self.merged: + # Make sure that the weights are not merged + if self.r > 0: + self.weight.data -= T(self.lora_B @ self.lora_A) * self.scaling + self.merged = False + + def eval(self): + def T(w): + return w.T if self.fan_in_fan_out else w + + nn.Linear.eval(self) + if self.merge_weights and not self.merged: + # Merge the weights and mark it + if self.r > 0: + self.weight.data += T(self.lora_B @ self.lora_A) * self.scaling + self.merged = True + + def forward(self, x: torch.Tensor): + def T(w): + return w.T if self.fan_in_fan_out else w + + if self.r > 0 and not self.merged: + result = F.linear(x, T(self.weight), bias=self.bias) + if self.r > 0: + result += ( + self.lora_dropout(x) @ self.lora_A.T @ self.lora_B.T + ) * self.scaling + return result + else: + return F.linear(x, T(self.weight), bias=self.bias) + + +class MergedLinear(nn.Linear, LoRALayer): + # LoRA implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + enable_lora: List[bool] = [False], + fan_in_fan_out: bool = False, + merge_weights: bool = True, + **kwargs + ): + nn.Linear.__init__(self, in_features, out_features, **kwargs) + LoRALayer.__init__( + self, + r=r, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + merge_weights=merge_weights, + ) + assert ( + out_features % len(enable_lora) == 0 + ), "The length of enable_lora must divide out_features" + self.enable_lora = enable_lora + self.fan_in_fan_out = fan_in_fan_out + # Actual trainable parameters + if r > 0 and any(enable_lora): + self.lora_A = nn.Parameter( + self.weight.new_zeros((r * sum(enable_lora), in_features)) + ) + self.lora_B = nn.Parameter( + self.weight.new_zeros( + (out_features // len(enable_lora) * sum(enable_lora), r) + ) + ) # weights for Conv1D with groups=sum(enable_lora) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + # Compute the indices + self.lora_ind = self.weight.new_zeros( + (out_features,), dtype=torch.bool + ).view(len(enable_lora), -1) + self.lora_ind[enable_lora, :] = True + self.lora_ind = self.lora_ind.view(-1) + self.reset_parameters() + if fan_in_fan_out: + self.weight.data = self.weight.data.T + + def reset_parameters(self): + nn.Linear.reset_parameters(self) + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B) + + def zero_pad(self, x): + result = x.new_zeros((*x.shape[:-1], self.out_features)) + result = result.view(-1, self.out_features) + result[:, self.lora_ind] = x.reshape( + -1, self.out_features // len(self.enable_lora) * sum(self.enable_lora) + ) + return result.view((*x.shape[:-1], self.out_features)) + + def train(self, mode: bool = True): + def T(w): + return w.T if self.fan_in_fan_out else w + + nn.Linear.train(self, mode) + if self.merge_weights and self.merged: + # Make sure that the weights are not merged + if self.r > 0 and any(self.enable_lora): + delta_w = F.conv1d( + self.lora_A.data.unsqueeze(0), + self.lora_B.data.unsqueeze(-1), + groups=sum(self.enable_lora), + ).squeeze(0) + self.weight.data -= self.zero_pad(T(delta_w * self.scaling)) + self.merged = False + + def eval(self): + def T(w): + return w.T if self.fan_in_fan_out else w + + nn.Linear.eval(self) + if self.merge_weights and not self.merged: + # Merge the weights and mark it + if self.r > 0 and any(self.enable_lora): + delta_w = F.conv1d( + self.lora_A.data.unsqueeze(0), + self.lora_B.data.unsqueeze(-1), + groups=sum(self.enable_lora), + ).squeeze(0) + self.weight.data += self.zero_pad(T(delta_w * self.scaling)) + self.merged = True + + def forward(self, x: torch.Tensor): + def T(w): + return w.T if self.fan_in_fan_out else w + + if self.merged: + return F.linear(x, T(self.weight), bias=self.bias) + else: + result = F.linear(x, T(self.weight), bias=self.bias) + if self.r > 0: + after_A = F.linear(self.lora_dropout(x), self.lora_A) + after_B = F.conv1d( + after_A.transpose(-2, -1), + self.lora_B.unsqueeze(-1), + groups=sum(self.enable_lora), + ).transpose(-2, -1) + result += self.zero_pad(after_B) * self.scaling + return result + + +class Conv2d(nn.Conv2d, LoRALayer): + # LoRA implemented in a dense layer + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + merge_weights: bool = True, + **kwargs + ): + nn.Conv2d.__init__(self, in_channels, out_channels, kernel_size, **kwargs) + LoRALayer.__init__( + self, + r=r, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + merge_weights=merge_weights, + ) + assert type(kernel_size) is int + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Parameter( + self.weight.new_zeros((r * kernel_size, in_channels * kernel_size)) + ) + self.lora_B = nn.Parameter( + self.weight.new_zeros((out_channels * kernel_size, r * kernel_size)) + ) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + + def reset_parameters(self): + nn.Conv2d.reset_parameters(self) + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B) + + def train(self, mode: bool = True): + nn.Conv2d.train(self, mode) + if self.merge_weights and self.merged: + # Make sure that the weights are not merged + self.weight.data -= (self.lora_B @ self.lora_A).view( + self.weight.shape + ) * self.scaling + self.merged = False + + def eval(self): + nn.Conv2d.eval(self) + if self.merge_weights and not self.merged: + # Merge the weights and mark it + self.weight.data += (self.lora_B @ self.lora_A).view( + self.weight.shape + ) * self.scaling + self.merged = True + + def forward(self, x: torch.Tensor): + if self.r > 0 and not self.merged: + return F.conv2d( + x, + self.weight + + (self.lora_B @ self.lora_A).view(self.weight.shape) * self.scaling, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + ) + return nn.Conv2d.forward(self, x) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/lora/utils.py b/demo/Step-Audio-EditX/funasr_detach/models/lora/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0b28c65398a359e8d733fa84dabbfde322b101dc --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/lora/utils.py @@ -0,0 +1,49 @@ +# ------------------------------------------------------------------------------------------ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. +# ------------------------------------------------------------------------------------------ +import torch +import torch.nn as nn + +from typing import Dict + +from .layers import LoRALayer + + +def mark_only_lora_as_trainable(model: nn.Module, bias: str = "none") -> None: + for n, p in model.named_parameters(): + if "lora_" not in n and "cif" not in n: + p.requires_grad = False + if bias == "none": + return + elif bias == "all": + for n, p in model.named_parameters(): + if "bias" in n: + p.requires_grad = True + elif bias == "lora_only": + for m in model.modules(): + if isinstance(m, LoRALayer) and hasattr(m, "bias") and m.bias is not None: + m.bias.requires_grad = True + else: + raise NotImplementedError + + +def lora_state_dict(model: nn.Module, bias: str = "none") -> Dict[str, torch.Tensor]: + my_state_dict = model.state_dict() + if bias == "none": + return {k: my_state_dict[k] for k in my_state_dict if "lora_" in k} + elif bias == "all": + return { + k: my_state_dict[k] for k in my_state_dict if "lora_" in k or "bias" in k + } + elif bias == "lora_only": + to_return = {} + for k in my_state_dict: + if "lora_" in k: + to_return[k] = my_state_dict[k] + bias_name = k.split("lora_")[0] + "bias" + if bias_name in my_state_dict: + to_return[bias_name] = my_state_dict[bias_name] + return to_return + else: + raise NotImplementedError diff --git a/demo/Step-Audio-EditX/funasr_detach/models/mfcca/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/mfcca/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/mfcca/e2e_asr_mfcca.py b/demo/Step-Audio-EditX/funasr_detach/models/mfcca/e2e_asr_mfcca.py new file mode 100644 index 0000000000000000000000000000000000000000..4660823e40b8d94bf1751a43b965ff90c2e22077 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/mfcca/e2e_asr_mfcca.py @@ -0,0 +1,331 @@ +from contextlib import contextmanager +from distutils.version import LooseVersion +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple +from typing import Union +import logging +import torch + +from funasr_detach.metrics import ErrorCalculator +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.losses.label_smoothing_loss import ( + LabelSmoothingLoss, # noqa: H301 +) +from funasr_detach.models.ctc import CTC +from funasr_detach.models.decoder.abs_decoder import AbsDecoder +from funasr_detach.models.encoder.abs_encoder import AbsEncoder +from funasr_detach.frontends.abs_frontend import AbsFrontend +from funasr_detach.models.preencoder.abs_preencoder import AbsPreEncoder +from funasr_detach.models.specaug.abs_specaug import AbsSpecAug +from funasr_detach.layers.abs_normalize import AbsNormalize +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.base_model import FunASRModel + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +import pdb +import random +import math + + +class MFCCA(FunASRModel): + """ + Author: Audio, Speech and Language Processing Group (ASLP@NPU), Northwestern Polytechnical University + MFCCA:Multi-Frame Cross-Channel attention for multi-speaker ASR in Multi-party meeting scenario + https://arxiv.org/abs/2210.05265 + """ + + def __init__( + self, + vocab_size: int, + token_list: Union[Tuple[str, ...], List[str]], + frontend: Optional[AbsFrontend], + specaug: Optional[AbsSpecAug], + normalize: Optional[AbsNormalize], + encoder: AbsEncoder, + decoder: AbsDecoder, + ctc: CTC, + rnnt_decoder: None = None, + ctc_weight: float = 0.5, + ignore_id: int = -1, + lsm_weight: float = 0.0, + mask_ratio: float = 0.0, + length_normalized_loss: bool = False, + report_cer: bool = True, + report_wer: bool = True, + sym_space: str = "", + sym_blank: str = "", + preencoder: Optional[AbsPreEncoder] = None, + ): + assert 0.0 <= ctc_weight <= 1.0, ctc_weight + assert rnnt_decoder is None, "Not implemented" + + super().__init__() + # note that eos is the same as sos (equivalent ID) + self.sos = vocab_size - 1 + self.eos = vocab_size - 1 + self.vocab_size = vocab_size + self.ignore_id = ignore_id + self.ctc_weight = ctc_weight + self.token_list = token_list.copy() + + self.mask_ratio = mask_ratio + + self.frontend = frontend + self.specaug = specaug + self.normalize = normalize + self.preencoder = preencoder + self.encoder = encoder + # we set self.decoder = None in the CTC mode since + # self.decoder parameters were never used and PyTorch complained + # and threw an Exception in the multi-GPU experiment. + # thanks Jeff Farris for pointing out the issue. + if ctc_weight == 1.0: + self.decoder = None + else: + self.decoder = decoder + if ctc_weight == 0.0: + self.ctc = None + else: + self.ctc = ctc + self.rnnt_decoder = rnnt_decoder + self.criterion_att = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + if report_cer or report_wer: + self.error_calculator = ErrorCalculator( + token_list, sym_space, sym_blank, report_cer, report_wer + ) + else: + self.error_calculator = None + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + assert text_lengths.dim() == 1, text_lengths.shape + # Check that batch_size is unified + assert ( + speech.shape[0] + == speech_lengths.shape[0] + == text.shape[0] + == text_lengths.shape[0] + ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) + # pdb.set_trace() + if speech.dim() == 3 and speech.size(2) == 8 and self.mask_ratio != 0: + rate_num = random.random() + # rate_num = 0.1 + if rate_num <= self.mask_ratio: + retain_channel = math.ceil(random.random() * 8) + if retain_channel > 1: + speech = speech[ + :, :, torch.randperm(8)[0:retain_channel].sort().values + ] + else: + speech = speech[:, :, torch.randperm(8)[0]] + # pdb.set_trace() + batch_size = speech.shape[0] + # for data-parallel + text = text[:, : text_lengths.max()] + + # 1. Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + + # 2a. Attention-decoder branch + if self.ctc_weight == 1.0: + loss_att, acc_att, cer_att, wer_att = None, None, None, None + else: + loss_att, acc_att, cer_att, wer_att = self._calc_att_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # 2b. CTC branch + if self.ctc_weight == 0.0: + loss_ctc, cer_ctc = None, None + else: + loss_ctc, cer_ctc = self._calc_ctc_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # 2c. RNN-T branch + if self.rnnt_decoder is not None: + _ = self._calc_rnnt_loss(encoder_out, encoder_out_lens, text, text_lengths) + + if self.ctc_weight == 0.0: + loss = loss_att + elif self.ctc_weight == 1.0: + loss = loss_ctc + else: + loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + + stats = dict( + loss=loss.detach(), + loss_att=loss_att.detach() if loss_att is not None else None, + loss_ctc=loss_ctc.detach() if loss_ctc is not None else None, + acc=acc_att, + cer=cer_att, + wer=wer_att, + cer_ctc=cer_ctc, + ) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def collect_feats( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Dict[str, torch.Tensor]: + feats, feats_lengths, channel_size = self._extract_feats(speech, speech_lengths) + return {"feats": feats, "feats_lengths": feats_lengths} + + def encode( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Frontend + Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + """ + with autocast(False): + # 1. Extract feats + feats, feats_lengths, channel_size = self._extract_feats( + speech, speech_lengths + ) + # 2. Data augmentation + if self.specaug is not None and self.training: + feats, feats_lengths = self.specaug(feats, feats_lengths) + + # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + feats, feats_lengths = self.normalize(feats, feats_lengths) + + # Pre-encoder, e.g. used for raw input data + if self.preencoder is not None: + feats, feats_lengths = self.preencoder(feats, feats_lengths) + # pdb.set_trace() + encoder_out, encoder_out_lens, _ = self.encoder( + feats, feats_lengths, channel_size + ) + + assert encoder_out.size(0) == speech.size(0), ( + encoder_out.size(), + speech.size(0), + ) + if encoder_out.dim() == 4: + assert encoder_out.size(2) <= encoder_out_lens.max(), ( + encoder_out.size(), + encoder_out_lens.max(), + ) + else: + assert encoder_out.size(1) <= encoder_out_lens.max(), ( + encoder_out.size(), + encoder_out_lens.max(), + ) + + return encoder_out, encoder_out_lens + + def _extract_feats( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + assert speech_lengths.dim() == 1, speech_lengths.shape + # for data-parallel + speech = speech[:, : speech_lengths.max()] + if self.frontend is not None: + # Frontend + # e.g. STFT and Feature extract + # data_loader may send time-domain signal in this case + # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim) + feats, feats_lengths, channel_size = self.frontend(speech, speech_lengths) + else: + # No frontend and no feature extract + feats, feats_lengths = speech, speech_lengths + channel_size = 1 + return feats, feats_lengths, channel_size + + def _calc_att_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + # 1. Forward decoder + decoder_out, _ = self.decoder( + encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens + ) + + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_out_pad) + acc_att = th_accuracy( + decoder_out.view(-1, self.vocab_size), + ys_out_pad, + ignore_label=self.ignore_id, + ) + + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att + + def _calc_ctc_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + # Calc CTC loss + if encoder_out.dim() == 4: + encoder_out = encoder_out.mean(1) + loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens) + + # Calc CER using CTC + cer_ctc = None + if not self.training and self.error_calculator is not None: + ys_hat = self.ctc.argmax(encoder_out).data + cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True) + return loss_ctc, cer_ctc + + def _calc_rnnt_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + raise NotImplementedError diff --git a/demo/Step-Audio-EditX/funasr_detach/models/mfcca/encoder_layer_mfcca.py b/demo/Step-Audio-EditX/funasr_detach/models/mfcca/encoder_layer_mfcca.py new file mode 100644 index 0000000000000000000000000000000000000000..5403fcdbac34935489c035ba85550366824e4348 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/mfcca/encoder_layer_mfcca.py @@ -0,0 +1,275 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2020 Johns Hopkins University (Shinji Watanabe) +# Northwestern Polytechnical University (Pengcheng Guo) +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Encoder self-attention layer definition.""" + +import torch + +from torch import nn + +from funasr_detach.models.transformer.layer_norm import LayerNorm +from torch.autograd import Variable + + +class Encoder_Conformer_Layer(nn.Module): + """Encoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance + can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + + """ + + def __init__( + self, + size, + self_attn, + feed_forward, + feed_forward_macaron, + conv_module, + dropout_rate, + normalize_before=True, + concat_after=False, + cca_pos=0, + ): + """Construct an Encoder_Conformer_Layer object.""" + super(Encoder_Conformer_Layer, self).__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.norm_ff = LayerNorm(size) # for the FNN module + self.norm_mha = LayerNorm(size) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = LayerNorm(size) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = LayerNorm(size) # for the CNN module + self.norm_final = LayerNorm(size) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + self.concat_after = concat_after + self.cca_pos = cca_pos + + if self.concat_after: + self.concat_linear = nn.Linear(size + size, size) + + def forward(self, x_input, mask, cache=None): + """Compute encoded features. + + Args: + x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb. + - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)]. + - w/o pos emb: Tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + if isinstance(x_input, tuple): + x, pos_emb = x_input[0], x_input[1] + else: + x, pos_emb = x_input, None + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x)) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + + if cache is None: + x_q = x + else: + assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size) + x_q = x[:, -1:, :] + residual = residual[:, -1:, :] + mask = None if mask is None else mask[:, -1:, :] + + if self.cca_pos < 2: + if pos_emb is not None: + x_att = self.self_attn(x_q, x, x, pos_emb, mask) + else: + x_att = self.self_attn(x_q, x, x, mask) + else: + x_att = self.self_attn(x_q, x, x, mask) + + if self.concat_after: + x_concat = torch.cat((x, x_att), dim=-1) + x = residual + self.concat_linear(x_concat) + else: + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x = residual + self.dropout(self.conv_module(x)) + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + + if cache is not None: + x = torch.cat([cache, x], dim=1) + + if pos_emb is not None: + return (x, pos_emb), mask + + return x, mask + + +class EncoderLayer(nn.Module): + """Encoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance + can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + + """ + + def __init__( + self, + size, + self_attn_cros_channel, + self_attn_conformer, + feed_forward_csa, + feed_forward_macaron_csa, + conv_module_csa, + dropout_rate, + normalize_before=True, + concat_after=False, + ): + """Construct an EncoderLayer object.""" + super(EncoderLayer, self).__init__() + + self.encoder_cros_channel_atten = self_attn_cros_channel + self.encoder_csa = Encoder_Conformer_Layer( + size, + self_attn_conformer, + feed_forward_csa, + feed_forward_macaron_csa, + conv_module_csa, + dropout_rate, + normalize_before, + concat_after, + cca_pos=0, + ) + self.norm_mha = LayerNorm(size) # for the MHA module + self.dropout = nn.Dropout(dropout_rate) + + def forward(self, x_input, mask, channel_size, cache=None): + """Compute encoded features. + + Args: + x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb. + - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)]. + - w/o pos emb: Tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + if isinstance(x_input, tuple): + x, pos_emb = x_input[0], x_input[1] + else: + x, pos_emb = x_input, None + residual = x + x = self.norm_mha(x) + t_leng = x.size(1) + d_dim = x.size(2) + x_new = x.reshape(-1, channel_size, t_leng, d_dim).transpose( + 1, 2 + ) # x_new B*T * C * D + x_k_v = x_new.new(x_new.size(0), x_new.size(1), 5, x_new.size(2), x_new.size(3)) + pad_before = Variable( + torch.zeros(x_new.size(0), 2, x_new.size(2), x_new.size(3)) + ).type(x_new.type()) + pad_after = Variable( + torch.zeros(x_new.size(0), 2, x_new.size(2), x_new.size(3)) + ).type(x_new.type()) + x_pad = torch.cat([pad_before, x_new, pad_after], 1) + x_k_v[:, :, 0, :, :] = x_pad[:, 0:-4, :, :] + x_k_v[:, :, 1, :, :] = x_pad[:, 1:-3, :, :] + x_k_v[:, :, 2, :, :] = x_pad[:, 2:-2, :, :] + x_k_v[:, :, 3, :, :] = x_pad[:, 3:-1, :, :] + x_k_v[:, :, 4, :, :] = x_pad[:, 4:, :, :] + x_new = x_new.reshape(-1, channel_size, d_dim) + x_k_v = x_k_v.reshape(-1, 5 * channel_size, d_dim) + x_att = self.encoder_cros_channel_atten(x_new, x_k_v, x_k_v, None) + x_att = ( + x_att.reshape(-1, t_leng, channel_size, d_dim) + .transpose(1, 2) + .reshape(-1, t_leng, d_dim) + ) + x = residual + self.dropout(x_att) + if pos_emb is not None: + x_input = (x, pos_emb) + else: + x_input = x + x_input, mask = self.encoder_csa(x_input, mask) + + return x_input, mask, channel_size diff --git a/demo/Step-Audio-EditX/funasr_detach/models/mfcca/mfcca_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/mfcca/mfcca_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..5bd8ba16521f87488d6613592b32f78508b6a4f9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/mfcca/mfcca_encoder.py @@ -0,0 +1,436 @@ +from typing import Optional +from typing import Tuple + +import logging +import torch +from torch import nn + + +from funasr_detach.models.encoder.encoder_layer_mfcca import EncoderLayer +from funasr_detach.models.transformer.utils.nets_utils import get_activation +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.attention import ( + MultiHeadedAttention, # noqa: H301 + RelPositionMultiHeadedAttention, # noqa: H301 + LegacyRelPositionMultiHeadedAttention, # noqa: H301 +) +from funasr_detach.models.transformer.embedding import ( + PositionalEncoding, # noqa: H301 + ScaledPositionalEncoding, # noqa: H301 + RelPositionalEncoding, # noqa: H301 + LegacyRelPositionalEncoding, # noqa: H301 +) +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear +from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, # noqa: H301 +) +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling2 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling6 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling8 +from funasr_detach.models.transformer.utils.subsampling import TooShortUttError +from funasr_detach.models.transformer.utils.subsampling import check_short_utt +from funasr_detach.models.encoder.abs_encoder import AbsEncoder +import pdb +import math + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model. + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernerl size of conv layers. + """ + + def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True): + """Construct an ConvolutionModule object.""" + super(ConvolutionModule, self).__init__() + # kernerl_size should be a odd number for 'SAME' padding + assert (kernel_size - 1) % 2 == 0 + + self.pointwise_conv1 = nn.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.depthwise_conv = nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + groups=channels, + bias=bias, + ) + self.norm = nn.BatchNorm1d(channels) + self.pointwise_conv2 = nn.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.activation = activation + + def forward(self, x): + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + """ + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + x = self.activation(self.norm(x)) + + x = self.pointwise_conv2(x) + + return x.transpose(1, 2) + + +class MFCCAEncoder(AbsEncoder): + """Conformer encoder module. + Args: + input_size (int): Input dimension. + output_size (int): Dimention of attention. + attention_heads (int): The number of heads of multi head attention. + linear_units (int): The number of units of position-wise feed forward. + num_blocks (int): The number of decoder blocks. + dropout_rate (float): Dropout rate. + attention_dropout_rate (float): Dropout rate in attention. + positional_dropout_rate (float): Dropout rate after adding positional encoding. + input_layer (Union[str, torch.nn.Module]): Input layer type. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + If True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + If False, no additional linear will be applied. i.e. x -> x + att(x) + positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear". + positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer. + rel_pos_type (str): Whether to use the latest relative positional encoding or + the legacy one. The legacy relative positional encoding will be deprecated + in the future. More Details can be found in + https://github.com/espnet/espnet/pull/2816. + encoder_pos_enc_layer_type (str): Encoder positional encoding layer type. + encoder_attn_layer_type (str): Encoder attention layer type. + activation_type (str): Encoder activation function type. + macaron_style (bool): Whether to use macaron style for positionwise layer. + use_cnn_module (bool): Whether to use convolution module. + zero_triu (bool): Whether to zero the upper triangular part of attention matrix. + cnn_module_kernel (int): Kernerl size of convolution module. + padding_idx (int): Padding idx for input_layer=embed. + """ + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + normalize_before: bool = True, + concat_after: bool = False, + positionwise_layer_type: str = "linear", + positionwise_conv_kernel_size: int = 3, + macaron_style: bool = False, + rel_pos_type: str = "legacy", + pos_enc_layer_type: str = "rel_pos", + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = True, + zero_triu: bool = False, + cnn_module_kernel: int = 31, + padding_idx: int = -1, + ): + super().__init__() + self._output_size = output_size + + if rel_pos_type == "legacy": + if pos_enc_layer_type == "rel_pos": + pos_enc_layer_type = "legacy_rel_pos" + if selfattention_layer_type == "rel_selfattn": + selfattention_layer_type = "legacy_rel_selfattn" + elif rel_pos_type == "latest": + assert selfattention_layer_type != "legacy_rel_selfattn" + assert pos_enc_layer_type != "legacy_rel_pos" + else: + raise ValueError("unknown rel_pos_type: " + rel_pos_type) + + activation = get_activation(activation_type) + if pos_enc_layer_type == "abs_pos": + pos_enc_class = PositionalEncoding + elif pos_enc_layer_type == "scaled_abs_pos": + pos_enc_class = ScaledPositionalEncoding + elif pos_enc_layer_type == "rel_pos": + assert selfattention_layer_type == "rel_selfattn" + pos_enc_class = RelPositionalEncoding + elif pos_enc_layer_type == "legacy_rel_pos": + assert selfattention_layer_type == "legacy_rel_selfattn" + pos_enc_class = LegacyRelPositionalEncoding + logging.warning( + "Using legacy_rel_pos and it will be deprecated in the future." + ) + else: + raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) + + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(input_size, output_size), + torch.nn.LayerNorm(output_size), + torch.nn.Dropout(dropout_rate), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif isinstance(input_layer, torch.nn.Module): + self.embed = torch.nn.Sequential( + input_layer, + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer is None: + self.embed = torch.nn.Sequential( + pos_enc_class(output_size, positional_dropout_rate) + ) + else: + raise ValueError("unknown input_layer: " + input_layer) + self.normalize_before = normalize_before + if positionwise_layer_type == "linear": + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + elif positionwise_layer_type == "conv1d": + positionwise_layer = MultiLayeredConv1d + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d-linear": + positionwise_layer = Conv1dLinear + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + else: + raise NotImplementedError("Support only linear or conv1d.") + + if selfattention_layer_type == "selfattn": + encoder_selfattn_layer = MultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + elif selfattention_layer_type == "legacy_rel_selfattn": + assert pos_enc_layer_type == "legacy_rel_pos" + encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + logging.warning( + "Using legacy_rel_selfattn and it will be deprecated in the future." + ) + elif selfattention_layer_type == "rel_selfattn": + assert pos_enc_layer_type == "rel_pos" + encoder_selfattn_layer = RelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + zero_triu, + ) + else: + raise ValueError("unknown encoder_attn_layer: " + selfattention_layer_type) + + convolution_layer = ConvolutionModule + convolution_layer_args = (output_size, cnn_module_kernel, activation) + encoder_selfattn_layer_raw = MultiHeadedAttention + encoder_selfattn_layer_args_raw = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + self.encoders = repeat( + num_blocks, + lambda lnum: EncoderLayer( + output_size, + encoder_selfattn_layer_raw(*encoder_selfattn_layer_args_raw), + encoder_selfattn_layer(*encoder_selfattn_layer_args), + positionwise_layer(*positionwise_layer_args), + positionwise_layer(*positionwise_layer_args) if macaron_style else None, + convolution_layer(*convolution_layer_args) if use_cnn_module else None, + dropout_rate, + normalize_before, + concat_after, + ), + ) + if self.normalize_before: + self.after_norm = LayerNorm(output_size) + self.conv1 = torch.nn.Conv2d(8, 16, [5, 7], stride=[1, 1], padding=(2, 3)) + + self.conv2 = torch.nn.Conv2d(16, 32, [5, 7], stride=[1, 1], padding=(2, 3)) + + self.conv3 = torch.nn.Conv2d(32, 16, [5, 7], stride=[1, 1], padding=(2, 3)) + + self.conv4 = torch.nn.Conv2d(16, 1, [5, 7], stride=[1, 1], padding=(2, 3)) + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + channel_size: torch.Tensor, + prev_states: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Calculate forward propagation. + Args: + xs_pad (torch.Tensor): Input tensor (#batch, L, input_size). + ilens (torch.Tensor): Input length (#batch). + prev_states (torch.Tensor): Not to be used now. + Returns: + torch.Tensor: Output tensor (#batch, L, output_size). + torch.Tensor: Output length (#batch). + torch.Tensor: Not to be used now. + """ + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + if ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + else: + xs_pad = self.embed(xs_pad) + xs_pad, masks, channel_size = self.encoders(xs_pad, masks, channel_size) + if isinstance(xs_pad, tuple): + xs_pad = xs_pad[0] + + t_leng = xs_pad.size(1) + d_dim = xs_pad.size(2) + xs_pad = xs_pad.reshape(-1, channel_size, t_leng, d_dim) + # pdb.set_trace() + if channel_size < 8: + repeat_num = math.ceil(8 / channel_size) + xs_pad = xs_pad.repeat(1, repeat_num, 1, 1)[:, 0:8, :, :] + xs_pad = self.conv1(xs_pad) + xs_pad = self.conv2(xs_pad) + xs_pad = self.conv3(xs_pad) + xs_pad = self.conv4(xs_pad) + xs_pad = xs_pad.squeeze().reshape(-1, t_leng, d_dim) + mask_tmp = masks.size(1) + masks = masks.reshape(-1, channel_size, mask_tmp, t_leng)[:, 0, :, :] + + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + + olens = masks.squeeze(1).sum(1) + return xs_pad, olens, None + + def forward_hidden( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Calculate forward propagation. + Args: + xs_pad (torch.Tensor): Input tensor (#batch, L, input_size). + ilens (torch.Tensor): Input length (#batch). + prev_states (torch.Tensor): Not to be used now. + Returns: + torch.Tensor: Output tensor (#batch, L, output_size). + torch.Tensor: Output length (#batch). + torch.Tensor: Not to be used now. + """ + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + if ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + else: + xs_pad = self.embed(xs_pad) + num_layer = len(self.encoders) + for idx, encoder in enumerate(self.encoders): + xs_pad, masks = encoder(xs_pad, masks) + if idx == num_layer // 2 - 1: + hidden_feature = xs_pad + if isinstance(xs_pad, tuple): + xs_pad = xs_pad[0] + hidden_feature = hidden_feature[0] + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + self.hidden_feature = self.after_norm(hidden_feature) + + olens = masks.squeeze(1).sum(1) + return xs_pad, olens, None diff --git a/demo/Step-Audio-EditX/funasr_detach/models/model_hf/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/model_hf/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/monotonic_aligner/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/monotonic_aligner/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/monotonic_aligner/model.py b/demo/Step-Audio-EditX/funasr_detach/models/monotonic_aligner/model.py new file mode 100644 index 0000000000000000000000000000000000000000..1e0dcae7f41f36ef6ba305f9b0d7ed0455a70910 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/monotonic_aligner/model.py @@ -0,0 +1,238 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import time +import copy +import torch +from torch.cuda.amp import autocast +from typing import Union, Dict, List, Tuple, Optional + +from funasr_detach.register import tables +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.utils import postprocess_utils +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.models.paraformer.cif_predictor import mae_loss +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.utils.timestamp_tools import ts_prediction_lfr6_standard +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank + + +@tables.register("model_classes", "MonotonicAligner") +class MonotonicAligner(torch.nn.Module): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Achieving timestamp prediction while recognizing with non-autoregressive end-to-end ASR model + https://arxiv.org/abs/2301.12343 + """ + + def __init__( + self, + input_size: int = 80, + specaug: Optional[str] = None, + specaug_conf: Optional[Dict] = None, + normalize: str = None, + normalize_conf: Optional[Dict] = None, + encoder: str = None, + encoder_conf: Optional[Dict] = None, + predictor: str = None, + predictor_conf: Optional[Dict] = None, + predictor_bias: int = 0, + length_normalized_loss: bool = False, + **kwargs, + ): + super().__init__() + + if specaug is not None: + specaug_class = tables.specaug_classes.get(specaug) + specaug = specaug_class(**specaug_conf) + if normalize is not None: + normalize_class = tables.normalize_classes.get(normalize) + normalize = normalize_class(**normalize_conf) + encoder_class = tables.encoder_classes.get(encoder) + encoder = encoder_class(input_size=input_size, **encoder_conf) + encoder_output_size = encoder.output_size() + predictor_class = tables.predictor_classes.get(predictor) + predictor = predictor_class(**predictor_conf) + self.specaug = specaug + self.normalize = normalize + self.encoder = encoder + self.predictor = predictor + self.criterion_pre = mae_loss(normalize_length=length_normalized_loss) + self.predictor_bias = predictor_bias + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + assert text_lengths.dim() == 1, text_lengths.shape + # Check that batch_size is unified + assert ( + speech.shape[0] + == speech_lengths.shape[0] + == text.shape[0] + == text_lengths.shape[0] + ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) + batch_size = speech.shape[0] + # for data-parallel + text = text[:, : text_lengths.max()] + speech = speech[:, : speech_lengths.max()] + + # 1. Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + if self.predictor_bias == 1: + _, text = add_sos_eos(text, 1, 2, -1) + text_lengths = text_lengths + self.predictor_bias + _, _, _, _, pre_token_length2 = self.predictor( + encoder_out, text, encoder_out_mask, ignore_id=-1 + ) + + # loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length) + loss_pre = self.criterion_pre( + text_lengths.type_as(pre_token_length2), pre_token_length2 + ) + + loss = loss_pre + stats = dict() + + # Collect Attn branch stats + stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None + stats["loss"] = torch.clone(loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num): + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + ds_alphas, ds_cif_peak, us_alphas, us_peaks = ( + self.predictor.get_upsample_timestamp( + encoder_out, encoder_out_mask, token_num + ) + ) + return ds_alphas, ds_cif_peak, us_alphas, us_peaks + + def encode( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + ind: int + """ + with autocast(False): + + # Data augmentation + if self.specaug is not None and self.training: + speech, speech_lengths = self.specaug(speech, speech_lengths) + + # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + speech, speech_lengths = self.normalize(speech, speech_lengths) + + # Forward encoder + encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + return encoder_out, encoder_out_lens + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + meta_data = {} + # extract fbank feats + time1 = time.perf_counter() + audio_list, text_token_int_list = load_audio_text_image_video( + data_in, + fs=frontend.fs, + audio_fs=kwargs.get("fs", 16000), + data_type=kwargs.get("data_type", "sound"), + tokenizer=tokenizer, + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths = extract_fbank( + audio_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000 + ) + + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + + # Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + + # predictor + text_lengths = torch.tensor([len(i) + 1 for i in text_token_int_list]).to( + encoder_out.device + ) + _, _, us_alphas, us_peaks = self.calc_predictor_timestamp( + encoder_out, encoder_out_lens, token_num=text_lengths + ) + + results = [] + ibest_writer = None + if kwargs.get("output_dir") is not None: + if not hasattr(self, "writer"): + self.writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = self.writer["tp_res"] + + for i, (us_alpha, us_peak, token_int) in enumerate( + zip(us_alphas, us_peaks, text_token_int_list) + ): + token = tokenizer.ids2tokens(token_int) + timestamp_str, timestamp = ts_prediction_lfr6_standard( + us_alpha[: encoder_out_lens[i] * 3], + us_peak[: encoder_out_lens[i] * 3], + copy.copy(token), + ) + text_postprocessed, time_stamp_postprocessed, _ = ( + postprocess_utils.sentence_postprocess(token, timestamp) + ) + result_i = { + "key": key[i], + "text": text_postprocessed, + "timestamp": time_stamp_postprocessed, + } + results.append(result_i) + + if ibest_writer: + # ibest_writer["token"][key[i]] = " ".join(token) + ibest_writer["timestamp_list"][key[i]] = time_stamp_postprocessed + ibest_writer["timestamp_str"][key[i]] = timestamp_str + + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/monotonic_aligner/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/monotonic_aligner/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..86bcb230b701a242fd12c01faa854aac23ef8ed1 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/monotonic_aligner/template.yaml @@ -0,0 +1,115 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: MonotonicAligner +model_conf: + length_normalized_loss: False + predictor_bias: 1 + +# encoder +encoder: SANMEncoder +encoder_conf: + output_size: 320 + attention_heads: 4 + linear_units: 1280 + num_blocks: 30 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + +predictor: CifPredictorV3 +predictor_conf: + idim: 320 + threshold: 1.0 + l_order: 1 + r_order: 1 + tail_threshold: 0.45 + smooth_factor2: 0.25 + noise_threshold2: 0.01 + upsample_times: 3 + use_cif1_cnn: false + upsample_type: cnn_blstm + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 7 + lfr_n: 6 + +specaug: SpecAugLFR +specaug_conf: + apply_time_warp: false + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + lfr_rate: 6 + num_freq_mask: 1 + apply_time_mask: true + time_mask_width_range: + - 0 + - 12 + num_time_mask: 1 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + val_scheduler_criterion: + - valid + - acc + best_model_criterion: + - - valid + - acc + - max + keep_nbest_models: 10 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0005 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true + +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/mossformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/mossformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/mossformer/e2e_ss.py b/demo/Step-Audio-EditX/funasr_detach/models/mossformer/e2e_ss.py new file mode 100644 index 0000000000000000000000000000000000000000..593174032120c46d9e4b4c66af1076a213295209 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/mossformer/e2e_ss.py @@ -0,0 +1,98 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +import copy +from funasr_detach.models.base_model import FunASRModel +from funasr_detach.models.encoder.mossformer_encoder import ( + MossFormerEncoder, + MossFormer_MaskNet, +) +from funasr_detach.models.decoder.mossformer_decoder import MossFormerDecoder + + +class MossFormer(FunASRModel): + """The MossFormer model for separating input mixed speech into different speaker's speech. + + Arguments + --------- + in_channels : int + Number of channels at the output of the encoder. + out_channels : int + Number of channels that would be inputted to the intra and inter blocks. + num_blocks : int + Number of layers of Dual Computation Block. + norm : str + Normalization type. + num_spks : int + Number of sources (speakers). + skip_around_intra : bool + Skip connection around intra. + use_global_pos_enc : bool + Global positional encodings. + max_length : int + Maximum sequence length. + kernel_size: int + Encoder and decoder kernel size + """ + + def __init__( + self, + in_channels=512, + out_channels=512, + num_blocks=24, + kernel_size=16, + norm="ln", + num_spks=2, + skip_around_intra=True, + use_global_pos_enc=True, + max_length=20000, + ): + super(MossFormer, self).__init__() + self.num_spks = num_spks + # Encoding + self.enc = MossFormerEncoder( + kernel_size=kernel_size, out_channels=in_channels, in_channels=1 + ) + + ##Compute Mask + self.mask_net = MossFormer_MaskNet( + in_channels=in_channels, + out_channels=out_channels, + num_blocks=num_blocks, + norm=norm, + num_spks=num_spks, + skip_around_intra=skip_around_intra, + use_global_pos_enc=use_global_pos_enc, + max_length=max_length, + ) + self.dec = MossFormerDecoder( + in_channels=out_channels, + out_channels=1, + kernel_size=kernel_size, + stride=kernel_size // 2, + bias=False, + ) + + def forward(self, input): + x = self.enc(input) + mask = self.mask_net(x) + x = torch.stack([x] * self.num_spks) + sep_x = x * mask + + # Decoding + est_source = torch.cat( + [self.dec(sep_x[i]).unsqueeze(-1) for i in range(self.num_spks)], + dim=-1, + ) + T_origin = input.size(1) + T_est = est_source.size(1) + if T_origin > T_est: + est_source = F.pad(est_source, (0, 0, 0, T_origin - T_est)) + else: + est_source = est_source[:, :T_origin, :] + + out = [] + for spk in range(self.num_spks): + out.append(est_source[:, :, spk]) + return out diff --git a/demo/Step-Audio-EditX/funasr_detach/models/mossformer/mossformer.py b/demo/Step-Audio-EditX/funasr_detach/models/mossformer/mossformer.py new file mode 100644 index 0000000000000000000000000000000000000000..764350b769b4b2341db038ffcc25d5cf8844cb6a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/mossformer/mossformer.py @@ -0,0 +1,324 @@ +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange + + +def identity(t, *args, **kwargs): + return t + + +def append_dims(x, num_dims): + if num_dims <= 0: + return x + return x.view(*x.shape, *((1,) * num_dims)) + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +def padding_to_multiple_of(n, mult): + remainder = n % mult + if remainder == 0: + return 0 + return mult - remainder + + +class Transpose(nn.Module): + """Wrapper class of torch.transpose() for Sequential module.""" + + def __init__(self, shape: tuple): + super(Transpose, self).__init__() + self.shape = shape + + def forward(self, x): + return x.transpose(*self.shape) + + +class DepthwiseConv1d(nn.Module): + """ + When groups == in_channels and out_channels == K * in_channels, where K is a positive integer, + this operation is termed in literature as depthwise convolution. + Args: + in_channels (int): Number of channels in the input + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the convolving kernel + stride (int, optional): Stride of the convolution. Default: 1 + padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 + bias (bool, optional): If True, adds a learnable bias to the output. Default: True + Inputs: inputs + - **inputs** (batch, in_channels, time): Tensor containing input vector + Returns: outputs + - **outputs** (batch, out_channels, time): Tensor produces by depthwise 1-D convolution. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: int = 0, + bias: bool = False, + ) -> None: + super(DepthwiseConv1d, self).__init__() + assert ( + out_channels % in_channels == 0 + ), "out_channels should be constant multiple of in_channels" + self.conv = nn.Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + groups=in_channels, + stride=stride, + padding=padding, + bias=bias, + ) + + def forward(self, inputs): + return self.conv(inputs) + + +class ConvModule(nn.Module): + """ + Conformer convolution module starts with a pointwise convolution and a gated linear unit (GLU). + This is followed by a single 1-D depthwise convolution layer. Batchnorm is deployed just after the convolution + to aid training deep models. + Args: + in_channels (int): Number of channels in the input + kernel_size (int or tuple, optional): Size of the convolving kernel Default: 31 + dropout_p (float, optional): probability of dropout + Inputs: inputs + inputs (batch, time, dim): Tensor contains input sequences + Outputs: outputs + outputs (batch, time, dim): Tensor produces by conformer convolution module. + """ + + def __init__( + self, + in_channels: int, + kernel_size: int = 17, + expansion_factor: int = 2, + dropout_p: float = 0.1, + ) -> None: + super(ConvModule, self).__init__() + assert ( + kernel_size - 1 + ) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding" + assert expansion_factor == 2, "Currently, Only Supports expansion_factor 2" + + self.sequential = nn.Sequential( + Transpose(shape=(1, 2)), + DepthwiseConv1d( + in_channels, + in_channels, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + ), + ) + + def forward(self, inputs): + return inputs + self.sequential(inputs).transpose(1, 2) + + +class OffsetScale(nn.Module): + def __init__(self, dim, heads=1): + super().__init__() + self.gamma = nn.Parameter(torch.ones(heads, dim)) + self.beta = nn.Parameter(torch.zeros(heads, dim)) + nn.init.normal_(self.gamma, std=0.02) + + def forward(self, x): + out = einsum("... d, h d -> ... h d", x, self.gamma) + self.beta + return out.unbind(dim=-2) + + +class FFConvM(nn.Module): + def __init__(self, dim_in, dim_out, norm_klass=nn.LayerNorm, dropout=0.1): + super().__init__() + self.mdl = nn.Sequential( + norm_klass(dim_in), + nn.Linear(dim_in, dim_out), + nn.SiLU(), + ConvModule(dim_out), + nn.Dropout(dropout), + ) + + def forward( + self, + x, + ): + output = self.mdl(x) + return output + + +class FLASH_ShareA_FFConvM(nn.Module): + def __init__( + self, + *, + dim, + group_size=256, + query_key_dim=128, + expansion_factor=1.0, + causal=False, + dropout=0.1, + rotary_pos_emb=None, + norm_klass=nn.LayerNorm, + shift_tokens=True + ): + super().__init__() + hidden_dim = int(dim * expansion_factor) + self.group_size = group_size + self.causal = causal + self.shift_tokens = shift_tokens + + # positional embeddings + self.rotary_pos_emb = rotary_pos_emb + # norm + self.dropout = nn.Dropout(dropout) + # projections + + self.to_hidden = FFConvM( + dim_in=dim, + dim_out=hidden_dim, + norm_klass=norm_klass, + dropout=dropout, + ) + self.to_qk = FFConvM( + dim_in=dim, + dim_out=query_key_dim, + norm_klass=norm_klass, + dropout=dropout, + ) + + self.qk_offset_scale = OffsetScale(query_key_dim, heads=4) + + self.to_out = FFConvM( + dim_in=dim * 2, + dim_out=dim, + norm_klass=norm_klass, + dropout=dropout, + ) + + self.gateActivate = nn.Sigmoid() + + def forward(self, x, *, mask=None): + """ + b - batch + n - sequence length (within groups) + g - group dimension + d - feature dimension (keys) + e - feature dimension (values) + i - sequence dimension (source) + j - sequence dimension (target) + """ + + normed_x = x + + # do token shift - a great, costless trick from an independent AI researcher in Shenzhen + residual = x + + if self.shift_tokens: + x_shift, x_pass = normed_x.chunk(2, dim=-1) + x_shift = F.pad(x_shift, (0, 0, 1, -1), value=0.0) + normed_x = torch.cat((x_shift, x_pass), dim=-1) + + # initial projections + + v, u = self.to_hidden(normed_x).chunk(2, dim=-1) + qk = self.to_qk(normed_x) + + # offset and scale + quad_q, lin_q, quad_k, lin_k = self.qk_offset_scale(qk) + att_v, att_u = self.cal_attention(x, quad_q, lin_q, quad_k, lin_k, v, u) + out = (att_u * v) * self.gateActivate(att_v * u) + x = x + self.to_out(out) + return x + + def cal_attention(self, x, quad_q, lin_q, quad_k, lin_k, v, u, mask=None): + b, n, device, g = x.shape[0], x.shape[-2], x.device, self.group_size + + if exists(mask): + lin_mask = rearrange(mask, "... -> ... 1") + lin_k = lin_k.masked_fill(~lin_mask, 0.0) + + # rotate queries and keys + + if exists(self.rotary_pos_emb): + quad_q, lin_q, quad_k, lin_k = map( + self.rotary_pos_emb.rotate_queries_or_keys, + (quad_q, lin_q, quad_k, lin_k), + ) + + # padding for groups + + padding = padding_to_multiple_of(n, g) + + if padding > 0: + quad_q, quad_k, lin_q, lin_k, v, u = map( + lambda t: F.pad(t, (0, 0, 0, padding), value=0.0), + (quad_q, quad_k, lin_q, lin_k, v, u), + ) + + mask = default(mask, torch.ones((b, n), device=device, dtype=torch.bool)) + mask = F.pad(mask, (0, padding), value=False) + + # group along sequence + + quad_q, quad_k, lin_q, lin_k, v, u = map( + lambda t: rearrange(t, "b (g n) d -> b g n d", n=self.group_size), + (quad_q, quad_k, lin_q, lin_k, v, u), + ) + + if exists(mask): + mask = rearrange(mask, "b (g j) -> b g 1 j", j=g) + + # calculate quadratic attention output + + sim = einsum("... i d, ... j d -> ... i j", quad_q, quad_k) / g + + attn = F.relu(sim) ** 2 + attn = self.dropout(attn) + + if exists(mask): + attn = attn.masked_fill(~mask, 0.0) + + if self.causal: + causal_mask = torch.ones((g, g), dtype=torch.bool, device=device).triu(1) + attn = attn.masked_fill(causal_mask, 0.0) + + quad_out_v = einsum("... i j, ... j d -> ... i d", attn, v) + quad_out_u = einsum("... i j, ... j d -> ... i d", attn, u) + + # calculate linear attention output + + if self.causal: + lin_kv = einsum("b g n d, b g n e -> b g d e", lin_k, v) / g + # exclusive cumulative sum along group dimension + lin_kv = lin_kv.cumsum(dim=1) + lin_kv = F.pad(lin_kv, (0, 0, 0, 0, 1, -1), value=0.0) + lin_out_v = einsum("b g d e, b g n d -> b g n e", lin_kv, lin_q) + + lin_ku = einsum("b g n d, b g n e -> b g d e", lin_k, u) / g + # exclusive cumulative sum along group dimension + lin_ku = lin_ku.cumsum(dim=1) + lin_ku = F.pad(lin_ku, (0, 0, 0, 0, 1, -1), value=0.0) + lin_out_u = einsum("b g d e, b g n d -> b g n e", lin_ku, lin_q) + else: + lin_kv = einsum("b g n d, b g n e -> b d e", lin_k, v) / n + lin_out_v = einsum("b g n d, b d e -> b g n e", lin_q, lin_kv) + + lin_ku = einsum("b g n d, b g n e -> b d e", lin_k, u) / n + lin_out_u = einsum("b g n d, b d e -> b g n e", lin_q, lin_ku) + + # fold back groups into full sequence, and excise out padding + return map( + lambda t: rearrange(t, "b g n d -> b (g n) d")[:, :n], + (quad_out_v + lin_out_v, quad_out_u + lin_out_u), + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/mossformer/mossformer_decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/mossformer/mossformer_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..ff04ca6396eec3b34276a3f981c494c0328b0424 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/mossformer/mossformer_decoder.py @@ -0,0 +1,50 @@ +import torch +import torch.nn as nn + + +class MossFormerDecoder(nn.ConvTranspose1d): + """A decoder layer that consists of ConvTranspose1d. + + Arguments + --------- + kernel_size : int + Length of filters. + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + + + Example + --------- + >>> x = torch.randn(2, 100, 1000) + >>> decoder = Decoder(kernel_size=4, in_channels=100, out_channels=1) + >>> h = decoder(x) + >>> h.shape + torch.Size([2, 1003]) + """ + + def __init__(self, *args, **kwargs): + super(MossFormerDecoder, self).__init__(*args, **kwargs) + + def forward(self, x): + """Return the decoded output. + + Arguments + --------- + x : torch.Tensor + Input tensor with dimensionality [B, N, L]. + where, B = Batchsize, + N = number of filters + L = time points + """ + + if x.dim() not in [2, 3]: + raise RuntimeError("{} accept 3/4D tensor as input".format(self.__name__)) + x = super().forward(x if x.dim() == 3 else torch.unsqueeze(x, 1)) + + if torch.squeeze(x).dim() == 1: + x = torch.squeeze(x, dim=1) + else: + x = torch.squeeze(x) + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/mossformer/mossformer_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/mossformer/mossformer_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..5201eae0cd2bc70f3bbacb74fbe1c99cf1bfd9cd --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/mossformer/mossformer_encoder.py @@ -0,0 +1,436 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +try: + from rotary_embedding_torch import RotaryEmbedding +except: + print( + "If you want use mossformer, please install rotary_embedding_torch by: \n pip install -U rotary_embedding_torch" + ) +from funasr_detach.models.transformer.layer_norm import ( + GlobalLayerNorm, + CumulativeLayerNorm, + ScaleNorm, +) +from funasr_detach.models.transformer.embedding import ScaledSinuEmbedding +from funasr_detach.models.transformer.mossformer import FLASH_ShareA_FFConvM + + +def select_norm(norm, dim, shape): + """Just a wrapper to select the normalization type.""" + + if norm == "gln": + return GlobalLayerNorm(dim, shape, elementwise_affine=True) + if norm == "cln": + return CumulativeLayerNorm(dim, elementwise_affine=True) + if norm == "ln": + return nn.GroupNorm(1, dim, eps=1e-8) + else: + return nn.BatchNorm1d(dim) + + +class MossformerBlock(nn.Module): + def __init__( + self, + *, + dim, + depth, + group_size=256, + query_key_dim=128, + expansion_factor=4.0, + causal=False, + attn_dropout=0.1, + norm_type="scalenorm", + shift_tokens=True + ): + super().__init__() + assert norm_type in ( + "scalenorm", + "layernorm", + ), "norm_type must be one of scalenorm or layernorm" + + if norm_type == "scalenorm": + norm_klass = ScaleNorm + elif norm_type == "layernorm": + norm_klass = nn.LayerNorm + + self.group_size = group_size + + rotary_pos_emb = RotaryEmbedding(dim=min(32, query_key_dim)) + # max rotary embedding dimensions of 32, partial Rotary embeddings, from Wang et al - GPT-J + self.layers = nn.ModuleList( + [ + FLASH_ShareA_FFConvM( + dim=dim, + group_size=group_size, + query_key_dim=query_key_dim, + expansion_factor=expansion_factor, + causal=causal, + dropout=attn_dropout, + rotary_pos_emb=rotary_pos_emb, + norm_klass=norm_klass, + shift_tokens=shift_tokens, + ) + for _ in range(depth) + ] + ) + + def forward(self, x, *, mask=None): + ii = 0 + for flash in self.layers: + x = flash(x, mask=mask) + ii = ii + 1 + return x + + +class MossFormer_MaskNet(nn.Module): + """The MossFormer module for computing output masks. + + Arguments + --------- + in_channels : int + Number of channels at the output of the encoder. + out_channels : int + Number of channels that would be inputted to the intra and inter blocks. + num_blocks : int + Number of layers of Dual Computation Block. + norm : str + Normalization type. + num_spks : int + Number of sources (speakers). + skip_around_intra : bool + Skip connection around intra. + use_global_pos_enc : bool + Global positional encodings. + max_length : int + Maximum sequence length. + + Example + --------- + >>> mossformer_block = MossFormerM(1, 64, 8) + >>> mossformer_masknet = MossFormer_MaskNet(64, 64, intra_block, num_spks=2) + >>> x = torch.randn(10, 64, 2000) + >>> x = mossformer_masknet(x) + >>> x.shape + torch.Size([2, 10, 64, 2000]) + """ + + def __init__( + self, + in_channels, + out_channels, + num_blocks=24, + norm="ln", + num_spks=2, + skip_around_intra=True, + use_global_pos_enc=True, + max_length=20000, + ): + super(MossFormer_MaskNet, self).__init__() + self.num_spks = num_spks + self.num_blocks = num_blocks + self.norm = select_norm(norm, in_channels, 3) + self.conv1d_encoder = nn.Conv1d(in_channels, out_channels, 1, bias=False) + self.use_global_pos_enc = use_global_pos_enc + + if self.use_global_pos_enc: + self.pos_enc = ScaledSinuEmbedding(out_channels) + + self.mdl = Computation_Block( + num_blocks, + out_channels, + norm, + skip_around_intra=skip_around_intra, + ) + + self.conv1d_out = nn.Conv1d( + out_channels, out_channels * num_spks, kernel_size=1 + ) + self.conv1_decoder = nn.Conv1d(out_channels, in_channels, 1, bias=False) + self.prelu = nn.PReLU() + self.activation = nn.ReLU() + # gated output layer + self.output = nn.Sequential(nn.Conv1d(out_channels, out_channels, 1), nn.Tanh()) + self.output_gate = nn.Sequential( + nn.Conv1d(out_channels, out_channels, 1), nn.Sigmoid() + ) + + def forward(self, x): + """Returns the output tensor. + + Arguments + --------- + x : torch.Tensor + Input tensor of dimension [B, N, S]. + + Returns + ------- + out : torch.Tensor + Output tensor of dimension [spks, B, N, S] + where, spks = Number of speakers + B = Batchsize, + N = number of filters + S = the number of time frames + """ + + # before each line we indicate the shape after executing the line + + # [B, N, L] + x = self.norm(x) + + # [B, N, L] + x = self.conv1d_encoder(x) + if self.use_global_pos_enc: + # x = self.pos_enc(x.transpose(1, -1)).transpose(1, -1) + x * ( + # x.size(1) ** 0.5) + base = x + x = x.transpose(1, -1) + emb = self.pos_enc(x) + emb = emb.transpose(0, -1) + # print('base: {}, emb: {}'.format(base.shape, emb.shape)) + x = base + emb + + # [B, N, S] + # for i in range(self.num_modules): + # x = self.dual_mdl[i](x) + x = self.mdl(x) + x = self.prelu(x) + + # [B, N*spks, S] + x = self.conv1d_out(x) + B, _, S = x.shape + + # [B*spks, N, S] + x = x.view(B * self.num_spks, -1, S) + + # [B*spks, N, S] + x = self.output(x) * self.output_gate(x) + + # [B*spks, N, S] + x = self.conv1_decoder(x) + + # [B, spks, N, S] + _, N, L = x.shape + x = x.view(B, self.num_spks, N, L) + x = self.activation(x) + + # [spks, B, N, S] + x = x.transpose(0, 1) + + return x + + +class MossFormerEncoder(nn.Module): + """Convolutional Encoder Layer. + + Arguments + --------- + kernel_size : int + Length of filters. + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + + Example + ------- + >>> x = torch.randn(2, 1000) + >>> encoder = Encoder(kernel_size=4, out_channels=64) + >>> h = encoder(x) + >>> h.shape + torch.Size([2, 64, 499]) + """ + + def __init__(self, kernel_size=2, out_channels=64, in_channels=1): + super(MossFormerEncoder, self).__init__() + self.conv1d = nn.Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=kernel_size // 2, + groups=1, + bias=False, + ) + self.in_channels = in_channels + + def forward(self, x): + """Return the encoded output. + + Arguments + --------- + x : torch.Tensor + Input tensor with dimensionality [B, L]. + Return + ------ + x : torch.Tensor + Encoded tensor with dimensionality [B, N, T_out]. + + where B = Batchsize + L = Number of timepoints + N = Number of filters + T_out = Number of timepoints at the output of the encoder + """ + # B x L -> B x 1 x L + if self.in_channels == 1: + x = torch.unsqueeze(x, dim=1) + # B x 1 x L -> B x N x T_out + x = self.conv1d(x) + x = F.relu(x) + + return x + + +class MossFormerM(nn.Module): + """This class implements the transformer encoder. + + Arguments + --------- + num_blocks : int + Number of mossformer blocks to include. + d_model : int + The dimension of the input embedding. + attn_dropout : float + Dropout for the self-attention (Optional). + group_size: int + the chunk size + query_key_dim: int + the attention vector dimension + expansion_factor: int + the expansion factor for the linear projection in conv module + causal: bool + true for causal / false for non causal + + Example + ------- + >>> import torch + >>> x = torch.rand((8, 60, 512)) + >>> net = TransformerEncoder_MossFormerM(num_blocks=8, d_model=512) + >>> output, _ = net(x) + >>> output.shape + torch.Size([8, 60, 512]) + """ + + def __init__( + self, + num_blocks, + d_model=None, + causal=False, + group_size=256, + query_key_dim=128, + expansion_factor=4.0, + attn_dropout=0.1, + ): + super().__init__() + + self.mossformerM = MossformerBlock( + dim=d_model, + depth=num_blocks, + group_size=group_size, + query_key_dim=query_key_dim, + expansion_factor=expansion_factor, + causal=causal, + attn_dropout=attn_dropout, + ) + self.norm = nn.LayerNorm(d_model, eps=1e-6) + + def forward( + self, + src, + ): + """ + Arguments + ---------- + src : torch.Tensor + Tensor shape [B, L, N], + where, B = Batchsize, + L = time points + N = number of filters + The sequence to the encoder layer (required). + src_mask : tensor + The mask for the src sequence (optional). + src_key_padding_mask : tensor + The mask for the src keys per batch (optional). + """ + output = self.mossformerM(src) + output = self.norm(output) + + return output + + +class Computation_Block(nn.Module): + """Computation block for dual-path processing. + + Arguments + --------- + out_channels : int + Dimensionality of inter/intra model. + norm : str + Normalization type. + skip_around_intra : bool + Skip connection around the intra layer. + + Example + --------- + >>> comp_block = Computation_Block(64) + >>> x = torch.randn(10, 64, 100) + >>> x = comp_block(x) + >>> x.shape + torch.Size([10, 64, 100]) + """ + + def __init__( + self, + num_blocks, + out_channels, + norm="ln", + skip_around_intra=True, + ): + super(Computation_Block, self).__init__() + + ##MossFormer2M: MossFormer with recurrence + # self.intra_mdl = MossFormer2M(num_blocks=num_blocks, d_model=out_channels) + ##MossFormerM: the orignal MossFormer + self.intra_mdl = MossFormerM(num_blocks=num_blocks, d_model=out_channels) + self.skip_around_intra = skip_around_intra + + # Norm + self.norm = norm + if norm is not None: + self.intra_norm = select_norm(norm, out_channels, 3) + + def forward(self, x): + """Returns the output tensor. + + Arguments + --------- + x : torch.Tensor + Input tensor of dimension [B, N, S]. + + + Return + --------- + out: torch.Tensor + Output tensor of dimension [B, N, S]. + where, B = Batchsize, + N = number of filters + S = sequence time index + """ + B, N, S = x.shape + # intra RNN + # [B, S, N] + intra = x.permute(0, 2, 1).contiguous() # .view(B, S, N) + + intra = self.intra_mdl(intra) + + # [B, N, S] + intra = intra.permute(0, 2, 1).contiguous() + if self.norm is not None: + intra = self.intra_norm(intra) + + # [B, N, S] + if self.skip_around_intra: + intra = intra + x + + out = intra + return out diff --git a/demo/Step-Audio-EditX/funasr_detach/models/normalize/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/normalize/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/normalize/global_mvn.py b/demo/Step-Audio-EditX/funasr_detach/models/normalize/global_mvn.py new file mode 100644 index 0000000000000000000000000000000000000000..db6772aaafc0c3fce9ea052112d688baea69312e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/normalize/global_mvn.py @@ -0,0 +1,116 @@ +from pathlib import Path +from typing import Tuple +from typing import Union + +import numpy as np +import torch + +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.register import tables + + +@tables.register("normalize_classes", "GlobalMVN") +class GlobalMVN(torch.nn.Module): + """Apply global mean and variance normalization + TODO(kamo): Make this class portable somehow + Args: + stats_file: npy file + norm_means: Apply mean normalization + norm_vars: Apply var normalization + eps: + """ + + def __init__( + self, + stats_file: Union[Path, str], + norm_means: bool = True, + norm_vars: bool = True, + eps: float = 1.0e-20, + ): + super().__init__() + self.norm_means = norm_means + self.norm_vars = norm_vars + self.eps = eps + stats_file = Path(stats_file) + + self.stats_file = stats_file + stats = np.load(stats_file) + if isinstance(stats, np.ndarray): + # Kaldi like stats + count = stats[0].flatten()[-1] + mean = stats[0, :-1] / count + var = stats[1, :-1] / count - mean * mean + else: + # New style: Npz file + count = stats["count"] + sum_v = stats["sum"] + sum_square_v = stats["sum_square"] + mean = sum_v / count + var = sum_square_v / count - mean * mean + std = np.sqrt(np.maximum(var, eps)) + + self.register_buffer("mean", torch.from_numpy(mean)) + self.register_buffer("std", torch.from_numpy(std)) + + def extra_repr(self): + return ( + f"stats_file={self.stats_file}, " + f"norm_means={self.norm_means}, norm_vars={self.norm_vars}" + ) + + def forward( + self, x: torch.Tensor, ilens: torch.Tensor = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward function + Args: + x: (B, L, ...) + ilens: (B,) + """ + if ilens is None: + ilens = x.new_full([x.size(0)], x.size(1)) + norm_means = self.norm_means + norm_vars = self.norm_vars + self.mean = self.mean.to(x.device, x.dtype) + self.std = self.std.to(x.device, x.dtype) + mask = make_pad_mask(ilens, x, 1) + + # feat: (B, T, D) + if norm_means: + if x.requires_grad: + x = x - self.mean + else: + x -= self.mean + if x.requires_grad: + x = x.masked_fill(mask, 0.0) + else: + x.masked_fill_(mask, 0.0) + + if norm_vars: + x /= self.std + + return x, ilens + + def inverse( + self, x: torch.Tensor, ilens: torch.Tensor = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + if ilens is None: + ilens = x.new_full([x.size(0)], x.size(1)) + norm_means = self.norm_means + norm_vars = self.norm_vars + self.mean = self.mean.to(x.device, x.dtype) + self.std = self.std.to(x.device, x.dtype) + mask = make_pad_mask(ilens, x, 1) + + if x.requires_grad: + x = x.masked_fill(mask, 0.0) + else: + x.masked_fill_(mask, 0.0) + + if norm_vars: + x *= self.std + + # feat: (B, T, D) + if norm_means: + x += self.mean + x.masked_fill_(make_pad_mask(ilens, x, 1), 0.0) + return x, ilens diff --git a/demo/Step-Audio-EditX/funasr_detach/models/normalize/utterance_mvn.py b/demo/Step-Audio-EditX/funasr_detach/models/normalize/utterance_mvn.py new file mode 100644 index 0000000000000000000000000000000000000000..3895cdafb93547c8b0e04bf7f8a28fee94fd8d53 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/normalize/utterance_mvn.py @@ -0,0 +1,87 @@ +from typing import Tuple + +import torch + +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.register import tables + + +@tables.register("normalize_classes", "UtteranceMVN") +class UtteranceMVN(torch.nn.Module): + def __init__( + self, + norm_means: bool = True, + norm_vars: bool = False, + eps: float = 1.0e-20, + ): + super().__init__() + self.norm_means = norm_means + self.norm_vars = norm_vars + self.eps = eps + + def extra_repr(self): + return f"norm_means={self.norm_means}, norm_vars={self.norm_vars}" + + def forward( + self, x: torch.Tensor, ilens: torch.Tensor = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward function + + Args: + x: (B, L, ...) + ilens: (B,) + + """ + return utterance_mvn( + x, + ilens, + norm_means=self.norm_means, + norm_vars=self.norm_vars, + eps=self.eps, + ) + + +def utterance_mvn( + x: torch.Tensor, + ilens: torch.Tensor = None, + norm_means: bool = True, + norm_vars: bool = False, + eps: float = 1.0e-20, +) -> Tuple[torch.Tensor, torch.Tensor]: + """Apply utterance mean and variance normalization + + Args: + x: (B, T, D), assumed zero padded + ilens: (B,) + norm_means: + norm_vars: + eps: + + """ + if ilens is None: + ilens = x.new_full([x.size(0)], x.size(1)) + ilens_ = ilens.to(x.device, x.dtype).view(-1, *[1 for _ in range(x.dim() - 1)]) + # Zero padding + if x.requires_grad: + x = x.masked_fill(make_pad_mask(ilens, x, 1), 0.0) + else: + x.masked_fill_(make_pad_mask(ilens, x, 1), 0.0) + # mean: (B, 1, D) + mean = x.sum(dim=1, keepdim=True) / ilens_ + + if norm_means: + x -= mean + + if norm_vars: + var = x.pow(2).sum(dim=1, keepdim=True) / ilens_ + std = torch.clamp(var.sqrt(), min=eps) + x = x / std.sqrt() + return x, ilens + else: + if norm_vars: + y = x - mean + y.masked_fill_(make_pad_mask(ilens, y, 1), 0.0) + var = y.pow(2).sum(dim=1, keepdim=True) / ilens_ + std = torch.clamp(var.sqrt(), min=eps) + x /= std + return x, ilens diff --git a/demo/Step-Audio-EditX/funasr_detach/models/paraformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/paraformer/cif_predictor.py b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/cif_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..d9d556d278b2e8cd895d631721c7b35d088835c7 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/cif_predictor.py @@ -0,0 +1,622 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +import logging +import numpy as np + +from funasr_detach.register import tables +from funasr_detach.train_utils.device_funcs import to_device +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +@tables.register("predictor_classes", "CifPredictor") +class CifPredictor(torch.nn.Module): + def __init__( + self, + idim, + l_order, + r_order, + threshold=1.0, + dropout=0.1, + smooth_factor=1.0, + noise_threshold=0, + tail_threshold=0.45, + ): + super().__init__() + + self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0) + self.cif_conv1d = torch.nn.Conv1d( + idim, idim, l_order + r_order + 1, groups=idim + ) + self.cif_output = torch.nn.Linear(idim, 1) + self.dropout = torch.nn.Dropout(p=dropout) + self.threshold = threshold + self.smooth_factor = smooth_factor + self.noise_threshold = noise_threshold + self.tail_threshold = tail_threshold + + def forward( + self, + hidden, + target_label=None, + mask=None, + ignore_id=-1, + mask_chunk_predictor=None, + target_label_length=None, + ): + h = hidden + context = h.transpose(1, 2) + queries = self.pad(context) + memory = self.cif_conv1d(queries) + output = memory + context + output = self.dropout(output) + output = output.transpose(1, 2) + output = torch.relu(output) + output = self.cif_output(output) + alphas = torch.sigmoid(output) + alphas = torch.nn.functional.relu( + alphas * self.smooth_factor - self.noise_threshold + ) + if mask is not None: + mask = mask.transpose(-1, -2).float() + alphas = alphas * mask + if mask_chunk_predictor is not None: + alphas = alphas * mask_chunk_predictor + alphas = alphas.squeeze(-1) + mask = mask.squeeze(-1) + if target_label_length is not None: + target_length = target_label_length + elif target_label is not None: + target_length = (target_label != ignore_id).float().sum(-1) + else: + target_length = None + token_num = alphas.sum(-1) + if target_length is not None: + alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) + elif self.tail_threshold > 0.0: + hidden, alphas, token_num = self.tail_process_fn( + hidden, alphas, token_num, mask=mask + ) + + acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) + + if target_length is None and self.tail_threshold > 0.0: + token_num_int = torch.max(token_num).type(torch.int32).item() + acoustic_embeds = acoustic_embeds[:, :token_num_int, :] + + return acoustic_embeds, token_num, alphas, cif_peak + + def tail_process_fn(self, hidden, alphas, token_num=None, mask=None): + b, t, d = hidden.size() + tail_threshold = self.tail_threshold + if mask is not None: + zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device) + ones_t = torch.ones_like(zeros_t) + mask_1 = torch.cat([mask, zeros_t], dim=1) + mask_2 = torch.cat([ones_t, mask], dim=1) + mask = mask_2 - mask_1 + tail_threshold = mask * tail_threshold + alphas = torch.cat([alphas, zeros_t], dim=1) + alphas = torch.add(alphas, tail_threshold) + else: + tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to( + alphas.device + ) + tail_threshold = torch.reshape(tail_threshold, (1, 1)) + alphas = torch.cat([alphas, tail_threshold], dim=1) + zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device) + hidden = torch.cat([hidden, zeros], dim=1) + token_num = alphas.sum(dim=-1) + token_num_floor = torch.floor(token_num) + + return hidden, alphas, token_num_floor + + def gen_frame_alignments( + self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None + ): + batch_size, maximum_length = alphas.size() + int_type = torch.int32 + + is_training = self.training + if is_training: + token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) + else: + token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) + + max_token_num = torch.max(token_num).item() + + alphas_cumsum = torch.cumsum(alphas, dim=1) + alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) + alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) + + index = torch.ones([batch_size, max_token_num], dtype=int_type) + index = torch.cumsum(index, dim=1) + index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) + + index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) + index_div_bool_zeros = index_div.eq(0) + index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 + index_div_bool_zeros_count = torch.clamp( + index_div_bool_zeros_count, 0, encoder_sequence_length.max() + ) + token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to( + token_num.device + ) + index_div_bool_zeros_count *= token_num_mask + + index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat( + 1, 1, maximum_length + ) + ones = torch.ones_like(index_div_bool_zeros_count_tile) + zeros = torch.zeros_like(index_div_bool_zeros_count_tile) + ones = torch.cumsum(ones, dim=2) + cond = index_div_bool_zeros_count_tile == ones + index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) + + index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type( + torch.bool + ) + index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type( + int_type + ) + index_div_bool_zeros_count_tile_out = torch.sum( + index_div_bool_zeros_count_tile, dim=1 + ) + index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type( + int_type + ) + predictor_mask = ( + ( + ~make_pad_mask( + encoder_sequence_length, maxlen=encoder_sequence_length.max() + ) + ) + .type(int_type) + .to(encoder_sequence_length.device) + ) + index_div_bool_zeros_count_tile_out = ( + index_div_bool_zeros_count_tile_out * predictor_mask + ) + + predictor_alignments = index_div_bool_zeros_count_tile_out + predictor_alignments_length = predictor_alignments.sum(-1).type( + encoder_sequence_length.dtype + ) + return predictor_alignments.detach(), predictor_alignments_length.detach() + + +@tables.register("predictor_classes", "CifPredictorV2") +class CifPredictorV2(torch.nn.Module): + def __init__( + self, + idim, + l_order, + r_order, + threshold=1.0, + dropout=0.1, + smooth_factor=1.0, + noise_threshold=0, + tail_threshold=0.0, + tf2torch_tensor_name_prefix_torch="predictor", + tf2torch_tensor_name_prefix_tf="seq2seq/cif", + tail_mask=True, + ): + super(CifPredictorV2, self).__init__() + + self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0) + self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1) + self.cif_output = torch.nn.Linear(idim, 1) + self.dropout = torch.nn.Dropout(p=dropout) + self.threshold = threshold + self.smooth_factor = smooth_factor + self.noise_threshold = noise_threshold + self.tail_threshold = tail_threshold + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + self.tail_mask = tail_mask + + def forward( + self, + hidden, + target_label=None, + mask=None, + ignore_id=-1, + mask_chunk_predictor=None, + target_label_length=None, + ): + h = hidden + context = h.transpose(1, 2) + queries = self.pad(context) + output = torch.relu(self.cif_conv1d(queries)) + output = output.transpose(1, 2) + + output = self.cif_output(output) + alphas = torch.sigmoid(output) + alphas = torch.nn.functional.relu( + alphas * self.smooth_factor - self.noise_threshold + ) + if mask is not None: + mask = mask.transpose(-1, -2).float() + alphas = alphas * mask + if mask_chunk_predictor is not None: + alphas = alphas * mask_chunk_predictor + alphas = alphas.squeeze(-1) + mask = mask.squeeze(-1) + if target_label_length is not None: + target_length = target_label_length.squeeze(-1) + elif target_label is not None: + target_length = (target_label != ignore_id).float().sum(-1) + else: + target_length = None + token_num = alphas.sum(-1) + if target_length is not None: + alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) + elif self.tail_threshold > 0.0: + if self.tail_mask: + hidden, alphas, token_num = self.tail_process_fn( + hidden, alphas, token_num, mask=mask + ) + else: + hidden, alphas, token_num = self.tail_process_fn( + hidden, alphas, token_num, mask=None + ) + + acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) + if target_length is None and self.tail_threshold > 0.0: + token_num_int = torch.max(token_num).type(torch.int32).item() + acoustic_embeds = acoustic_embeds[:, :token_num_int, :] + + return acoustic_embeds, token_num, alphas, cif_peak + + def forward_chunk(self, hidden, cache=None, **kwargs): + is_final = kwargs.get("is_final", False) + batch_size, len_time, hidden_size = hidden.shape + h = hidden + context = h.transpose(1, 2) + queries = self.pad(context) + output = torch.relu(self.cif_conv1d(queries)) + output = output.transpose(1, 2) + output = self.cif_output(output) + alphas = torch.sigmoid(output) + alphas = torch.nn.functional.relu( + alphas * self.smooth_factor - self.noise_threshold + ) + + alphas = alphas.squeeze(-1) + + token_length = [] + list_fires = [] + list_frames = [] + cache_alphas = [] + cache_hiddens = [] + + if cache is not None and "chunk_size" in cache: + alphas[:, : cache["chunk_size"][0]] = 0.0 + if not is_final: + alphas[:, sum(cache["chunk_size"][:2]) :] = 0.0 + if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache: + cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device) + cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device) + hidden = torch.cat((cache["cif_hidden"], hidden), dim=1) + alphas = torch.cat((cache["cif_alphas"], alphas), dim=1) + if cache is not None and is_final: + tail_hidden = torch.zeros( + (batch_size, 1, hidden_size), device=hidden.device + ) + tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device) + tail_alphas = torch.tile(tail_alphas, (batch_size, 1)) + hidden = torch.cat((hidden, tail_hidden), dim=1) + alphas = torch.cat((alphas, tail_alphas), dim=1) + + len_time = alphas.shape[1] + for b in range(batch_size): + integrate = 0.0 + frames = torch.zeros((hidden_size), device=hidden.device) + list_frame = [] + list_fire = [] + for t in range(len_time): + alpha = alphas[b][t] + if alpha + integrate < self.threshold: + integrate += alpha + list_fire.append(integrate) + frames += alpha * hidden[b][t] + else: + frames += (self.threshold - integrate) * hidden[b][t] + list_frame.append(frames) + integrate += alpha + list_fire.append(integrate) + integrate -= self.threshold + frames = integrate * hidden[b][t] + + cache_alphas.append(integrate) + if integrate > 0.0: + cache_hiddens.append(frames / integrate) + else: + cache_hiddens.append(frames) + + token_length.append(torch.tensor(len(list_frame), device=alphas.device)) + list_fires.append(list_fire) + list_frames.append(list_frame) + + cache["cif_alphas"] = torch.stack(cache_alphas, axis=0) + cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0) + cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0) + cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0) + + max_token_len = max(token_length) + if max_token_len == 0: + return hidden, torch.stack(token_length, 0), None, None + list_ls = [] + for b in range(batch_size): + pad_frames = torch.zeros( + (max_token_len - token_length[b], hidden_size), device=alphas.device + ) + if token_length[b] == 0: + list_ls.append(pad_frames) + else: + list_frames[b] = torch.stack(list_frames[b]) + list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0)) + + cache["cif_alphas"] = torch.stack(cache_alphas, axis=0) + cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0) + cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0) + cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0) + return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None + + def tail_process_fn(self, hidden, alphas, token_num=None, mask=None): + b, t, d = hidden.size() + tail_threshold = self.tail_threshold + if mask is not None: + zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device) + ones_t = torch.ones_like(zeros_t) + mask_1 = torch.cat([mask, zeros_t], dim=1) + mask_2 = torch.cat([ones_t, mask], dim=1) + mask = mask_2 - mask_1 + tail_threshold = mask * tail_threshold + alphas = torch.cat([alphas, zeros_t], dim=1) + alphas = torch.add(alphas, tail_threshold) + else: + tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to( + alphas.device + ) + tail_threshold = torch.reshape(tail_threshold, (1, 1)) + if b > 1: + alphas = torch.cat([alphas, tail_threshold.repeat(b, 1)], dim=1) + else: + alphas = torch.cat([alphas, tail_threshold], dim=1) + zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device) + hidden = torch.cat([hidden, zeros], dim=1) + token_num = alphas.sum(dim=-1) + token_num_floor = torch.floor(token_num) + + return hidden, alphas, token_num_floor + + def gen_frame_alignments( + self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None + ): + batch_size, maximum_length = alphas.size() + int_type = torch.int32 + + is_training = self.training + if is_training: + token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) + else: + token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) + + max_token_num = torch.max(token_num).item() + + alphas_cumsum = torch.cumsum(alphas, dim=1) + alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) + alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) + + index = torch.ones([batch_size, max_token_num], dtype=int_type) + index = torch.cumsum(index, dim=1) + index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) + + index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) + index_div_bool_zeros = index_div.eq(0) + index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 + index_div_bool_zeros_count = torch.clamp( + index_div_bool_zeros_count, 0, encoder_sequence_length.max() + ) + token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to( + token_num.device + ) + index_div_bool_zeros_count *= token_num_mask + + index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat( + 1, 1, maximum_length + ) + ones = torch.ones_like(index_div_bool_zeros_count_tile) + zeros = torch.zeros_like(index_div_bool_zeros_count_tile) + ones = torch.cumsum(ones, dim=2) + cond = index_div_bool_zeros_count_tile == ones + index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) + + index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type( + torch.bool + ) + index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type( + int_type + ) + index_div_bool_zeros_count_tile_out = torch.sum( + index_div_bool_zeros_count_tile, dim=1 + ) + index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type( + int_type + ) + predictor_mask = ( + ( + ~make_pad_mask( + encoder_sequence_length, maxlen=encoder_sequence_length.max() + ) + ) + .type(int_type) + .to(encoder_sequence_length.device) + ) + index_div_bool_zeros_count_tile_out = ( + index_div_bool_zeros_count_tile_out * predictor_mask + ) + + predictor_alignments = index_div_bool_zeros_count_tile_out + predictor_alignments_length = predictor_alignments.sum(-1).type( + encoder_sequence_length.dtype + ) + return predictor_alignments.detach(), predictor_alignments_length.detach() + + def gen_tf2torch_map_dict(self): + + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + map_dict_local = { + ## predictor + "{}.cif_conv1d.weight".format(tensor_name_prefix_torch): { + "name": "{}/conv1d/kernel".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": (2, 1, 0), + }, # (256,256,3),(3,256,256) + "{}.cif_conv1d.bias".format(tensor_name_prefix_torch): { + "name": "{}/conv1d/bias".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.cif_output.weight".format(tensor_name_prefix_torch): { + "name": "{}/conv1d_1/kernel".format(tensor_name_prefix_tf), + "squeeze": 0, + "transpose": (1, 0), + }, # (1,256),(1,256,1) + "{}.cif_output.bias".format(tensor_name_prefix_torch): { + "name": "{}/conv1d_1/bias".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (1,),(1,) + } + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + map_dict = self.gen_tf2torch_map_dict() + var_dict_torch_update = dict() + for name in sorted(var_dict_torch.keys(), reverse=False): + names = name.split(".") + if names[0] == self.tf2torch_tensor_name_prefix_torch: + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"]) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape + ) + ) + + return var_dict_torch_update + + +class mae_loss(torch.nn.Module): + + def __init__(self, normalize_length=False): + super(mae_loss, self).__init__() + self.normalize_length = normalize_length + self.criterion = torch.nn.L1Loss(reduction="sum") + + def forward(self, token_length, pre_token_length): + loss_token_normalizer = token_length.size(0) + if self.normalize_length: + loss_token_normalizer = token_length.sum().type(torch.float32) + loss = self.criterion(token_length, pre_token_length) + loss = loss / loss_token_normalizer + return loss + + +def cif(hidden, alphas, threshold): + batch_size, len_time, hidden_size = hidden.size() + + # loop varss + integrate = torch.zeros([batch_size], device=hidden.device) + frame = torch.zeros([batch_size, hidden_size], device=hidden.device) + # intermediate vars along time + list_fires = [] + list_frames = [] + + for t in range(len_time): + alpha = alphas[:, t] + distribution_completion = ( + torch.ones([batch_size], device=hidden.device) - integrate + ) + + integrate += alpha + list_fires.append(integrate) + + fire_place = integrate >= threshold + integrate = torch.where( + fire_place, + integrate - torch.ones([batch_size], device=hidden.device), + integrate, + ) + cur = torch.where(fire_place, distribution_completion, alpha) + remainds = alpha - cur + + frame += cur[:, None] * hidden[:, t, :] + list_frames.append(frame) + frame = torch.where( + fire_place[:, None].repeat(1, hidden_size), + remainds[:, None] * hidden[:, t, :], + frame, + ) + + fires = torch.stack(list_fires, 1) + frames = torch.stack(list_frames, 1) + list_ls = [] + len_labels = torch.round(alphas.sum(-1)).int() + max_label_len = len_labels.max() + for b in range(batch_size): + fire = fires[b, :] + l = torch.index_select( + frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze() + ) + pad_l = torch.zeros( + [max_label_len - l.size(0), hidden_size], device=hidden.device + ) + list_ls.append(torch.cat([l, pad_l], 0)) + return torch.stack(list_ls, 0), fires + + +def cif_wo_hidden(alphas, threshold): + batch_size, len_time = alphas.size() + + # loop varss + integrate = torch.zeros([batch_size], device=alphas.device) + # intermediate vars along time + list_fires = [] + + for t in range(len_time): + alpha = alphas[:, t] + + integrate += alpha + list_fires.append(integrate) + + fire_place = integrate >= threshold + integrate = torch.where( + fire_place, + integrate - torch.ones([batch_size], device=alphas.device) * threshold, + integrate, + ) + + fires = torch.stack(list_fires, 1) + return fires diff --git a/demo/Step-Audio-EditX/funasr_detach/models/paraformer/decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..110433d635e36413d805c8114cfb099f05efc369 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/decoder.py @@ -0,0 +1,680 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +from typing import List, Tuple + +from funasr_detach.register import tables +from funasr_detach.models.scama import utils as myutils +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.decoder import DecoderLayer +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.embedding import PositionalEncoding +from funasr_detach.models.transformer.attention import MultiHeadedAttention +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.decoder import BaseTransformerDecoder +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, +) +from funasr_detach.models.sanm.positionwise_feed_forward import ( + PositionwiseFeedForwardDecoderSANM, +) +from funasr_detach.models.sanm.attention import ( + MultiHeadedAttentionSANMDecoder, + MultiHeadedAttentionCrossAtt, +) + + +class DecoderLayerSANM(torch.nn.Module): + """Single decoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + src_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + + + """ + + def __init__( + self, + size, + self_attn, + src_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + ): + """Construct an DecoderLayer object.""" + super(DecoderLayerSANM, self).__init__() + self.size = size + self.self_attn = self_attn + self.src_attn = src_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(size) + if self_attn is not None: + self.norm2 = LayerNorm(size) + if src_attn is not None: + self.norm3 = LayerNorm(size) + self.dropout = torch.nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear1 = torch.nn.Linear(size + size, size) + self.concat_linear2 = torch.nn.Linear(size + size, size) + self.reserve_attn = False + self.attn_mat = [] + + def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + # tgt = self.dropout(tgt) + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn: + if self.normalize_before: + tgt = self.norm2(tgt) + x, _ = self.self_attn(tgt, tgt_mask) + x = residual + self.dropout(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm3(x) + if self.reserve_attn: + x_src_attn, attn_mat = self.src_attn( + x, memory, memory_mask, ret_attn=True + ) + self.attn_mat.append(attn_mat) + else: + x_src_attn = self.src_attn(x, memory, memory_mask, ret_attn=False) + x = residual + self.dropout(x_src_attn) + # x = residual + self.dropout(self.src_attn(x, memory, memory_mask)) + + return x, tgt_mask, memory, memory_mask, cache + + def forward_one_step(self, tgt, tgt_mask, memory, memory_mask=None, cache=None): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + # tgt = self.dropout(tgt) + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn: + if self.normalize_before: + tgt = self.norm2(tgt) + if self.training: + cache = None + x, cache = self.self_attn(tgt, tgt_mask, cache=cache) + x = residual + self.dropout(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm3(x) + + x = residual + self.dropout(self.src_attn(x, memory, memory_mask)) + + return x, tgt_mask, memory, memory_mask, cache + + def forward_chunk( + self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0 + ): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn: + if self.normalize_before: + tgt = self.norm2(tgt) + x, fsmn_cache = self.self_attn(tgt, None, fsmn_cache) + x = residual + self.dropout(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm3(x) + + x, opt_cache = self.src_attn.forward_chunk( + x, memory, opt_cache, chunk_size, look_back + ) + x = residual + x + + return x, memory, fsmn_cache, opt_cache + + +@tables.register("decoder_classes", "ParaformerSANMDecoder") +class ParaformerSANMDecoder(BaseTransformerDecoder): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition + https://arxiv.org/abs/2006.01713 + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + wo_input_layer: bool = False, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + att_layer_num: int = 6, + kernel_size: int = 21, + sanm_shfit: int = 0, + lora_list: List[str] = None, + lora_rank: int = 8, + lora_alpha: int = 16, + lora_dropout: float = 0.1, + chunk_multiply_factor: tuple = (1,), + tf2torch_tensor_name_prefix_torch: str = "decoder", + tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder", + ): + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + if wo_input_layer: + self.embed = None + else: + if input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(vocab_size, attention_dim), + # pos_enc_class(attention_dim, positional_dropout_rate), + ) + elif input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(vocab_size, attention_dim), + torch.nn.LayerNorm(attention_dim), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + else: + raise ValueError( + f"only 'embed' or 'linear' is supported: {input_layer}" + ) + + self.normalize_before = normalize_before + if self.normalize_before: + self.after_norm = LayerNorm(attention_dim) + if use_output_layer: + self.output_layer = torch.nn.Linear(attention_dim, vocab_size) + else: + self.output_layer = None + + self.att_layer_num = att_layer_num + self.num_blocks = num_blocks + if sanm_shfit is None: + sanm_shfit = (kernel_size - 1) // 2 + self.decoders = repeat( + att_layer_num, + lambda lnum: DecoderLayerSANM( + attention_dim, + MultiHeadedAttentionSANMDecoder( + attention_dim, + self_attention_dropout_rate, + kernel_size, + sanm_shfit=sanm_shfit, + ), + MultiHeadedAttentionCrossAtt( + attention_heads, + attention_dim, + src_attention_dropout_rate, + lora_list, + lora_rank, + lora_alpha, + lora_dropout, + ), + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + if num_blocks - att_layer_num <= 0: + self.decoders2 = None + else: + self.decoders2 = repeat( + num_blocks - att_layer_num, + lambda lnum: DecoderLayerSANM( + attention_dim, + MultiHeadedAttentionSANMDecoder( + attention_dim, + self_attention_dropout_rate, + kernel_size, + sanm_shfit=0, + ), + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + self.decoders3 = repeat( + 1, + lambda lnum: DecoderLayerSANM( + attention_dim, + None, + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + self.chunk_multiply_factor = chunk_multiply_factor + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + return_hidden: bool = False, + return_both: bool = False, + chunk_mask: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward decoder. + + Args: + hs_pad: encoded memory, float32 (batch, maxlen_in, feat) + hlens: (batch) + ys_in_pad: + input token ids, int64 (batch, maxlen_out) + if input_layer == "embed" + input tensor (batch, maxlen_out, #mels) in the other cases + ys_in_lens: (batch) + Returns: + (tuple): tuple containing: + + x: decoded token score before softmax (batch, maxlen_out, token) + if use_output_layer is True, + olens: (batch, ) + """ + tgt = ys_in_pad + tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None] + + memory = hs_pad + memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :] + if chunk_mask is not None: + memory_mask = memory_mask * chunk_mask + if tgt_mask.size(1) != memory_mask.size(1): + memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1) + + x = tgt + x, tgt_mask, memory, memory_mask, _ = self.decoders( + x, tgt_mask, memory, memory_mask + ) + if self.decoders2 is not None: + x, tgt_mask, memory, memory_mask, _ = self.decoders2( + x, tgt_mask, memory, memory_mask + ) + x, tgt_mask, memory, memory_mask, _ = self.decoders3( + x, tgt_mask, memory, memory_mask + ) + if self.normalize_before: + hidden = self.after_norm(x) + + olens = tgt_mask.sum(1) + if self.output_layer is not None and return_hidden is False: + x = self.output_layer(hidden) + return x, olens + if return_both: + x = self.output_layer(hidden) + return x, hidden, olens + return hidden, olens + + def score(self, ys, state, x): + """Score.""" + ys_mask = myutils.sequence_mask( + torch.tensor([len(ys)], dtype=torch.int32), device=x.device + )[:, :, None] + logp, state = self.forward_one_step( + ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state + ) + return logp.squeeze(0), state + + def forward_chunk( + self, + memory: torch.Tensor, + tgt: torch.Tensor, + cache: dict = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward decoder. + + Args: + hs_pad: encoded memory, float32 (batch, maxlen_in, feat) + hlens: (batch) + ys_in_pad: + input token ids, int64 (batch, maxlen_out) + if input_layer == "embed" + input tensor (batch, maxlen_out, #mels) in the other cases + ys_in_lens: (batch) + Returns: + (tuple): tuple containing: + + x: decoded token score before softmax (batch, maxlen_out, token) + if use_output_layer is True, + olens: (batch, ) + """ + x = tgt + if cache["decode_fsmn"] is None: + cache_layer_num = len(self.decoders) + if self.decoders2 is not None: + cache_layer_num += len(self.decoders2) + fsmn_cache = [None] * cache_layer_num + else: + fsmn_cache = cache["decode_fsmn"] + + if cache["opt"] is None: + cache_layer_num = len(self.decoders) + opt_cache = [None] * cache_layer_num + else: + opt_cache = cache["opt"] + + for i in range(self.att_layer_num): + decoder = self.decoders[i] + x, memory, fsmn_cache[i], opt_cache[i] = decoder.forward_chunk( + x, + memory, + fsmn_cache=fsmn_cache[i], + opt_cache=opt_cache[i], + chunk_size=cache["chunk_size"], + look_back=cache["decoder_chunk_look_back"], + ) + + if self.num_blocks - self.att_layer_num > 1: + for i in range(self.num_blocks - self.att_layer_num): + j = i + self.att_layer_num + decoder = self.decoders2[i] + x, memory, fsmn_cache[j], _ = decoder.forward_chunk( + x, memory, fsmn_cache=fsmn_cache[j] + ) + + for decoder in self.decoders3: + x, memory, _, _ = decoder.forward_chunk(x, memory) + if self.normalize_before: + x = self.after_norm(x) + if self.output_layer is not None: + x = self.output_layer(x) + + cache["decode_fsmn"] = fsmn_cache + if ( + cache["decoder_chunk_look_back"] > 0 + or cache["decoder_chunk_look_back"] == -1 + ): + cache["opt"] = opt_cache + return x + + def forward_one_step( + self, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + cache: List[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + + Args: + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + memory: encoded memory, float32 (batch, maxlen_in, feat) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + x = self.embed(tgt) + if cache is None: + cache_layer_num = len(self.decoders) + if self.decoders2 is not None: + cache_layer_num += len(self.decoders2) + cache = [None] * cache_layer_num + new_cache = [] + # for c, decoder in zip(cache, self.decoders): + for i in range(self.att_layer_num): + decoder = self.decoders[i] + c = cache[i] + x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step( + x, tgt_mask, memory, None, cache=c + ) + new_cache.append(c_ret) + + if self.num_blocks - self.att_layer_num > 1: + for i in range(self.num_blocks - self.att_layer_num): + j = i + self.att_layer_num + decoder = self.decoders2[i] + c = cache[j] + x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step( + x, tgt_mask, memory, None, cache=c + ) + new_cache.append(c_ret) + + for decoder in self.decoders3: + + x, tgt_mask, memory, memory_mask, _ = decoder.forward_one_step( + x, tgt_mask, memory, None, cache=None + ) + + if self.normalize_before: + y = self.after_norm(x[:, -1]) + else: + y = x[:, -1] + if self.output_layer is not None: + y = torch.log_softmax(self.output_layer(y), dim=-1) + + return y, new_cache + + +@tables.register("decoder_classes", "ParaformerSANDecoder") +class ParaformerSANDecoder(BaseTransformerDecoder): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition + https://arxiv.org/abs/2006.01713 + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + embeds_id: int = -1, + ): + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + MultiHeadedAttention( + attention_heads, attention_dim, self_attention_dropout_rate + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + self.embeds_id = embeds_id + self.attention_dim = attention_dim + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward decoder. + + Args: + hs_pad: encoded memory, float32 (batch, maxlen_in, feat) + hlens: (batch) + ys_in_pad: + input token ids, int64 (batch, maxlen_out) + if input_layer == "embed" + input tensor (batch, maxlen_out, #mels) in the other cases + ys_in_lens: (batch) + Returns: + (tuple): tuple containing: + + x: decoded token score before softmax (batch, maxlen_out, token) + if use_output_layer is True, + olens: (batch, ) + """ + tgt = ys_in_pad + tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device) + + memory = hs_pad + memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to( + memory.device + ) + # Padding for Longformer + if memory_mask.shape[-1] != memory.shape[1]: + padlen = memory.shape[1] - memory_mask.shape[-1] + memory_mask = torch.nn.functional.pad( + memory_mask, (0, padlen), "constant", False + ) + + # x = self.embed(tgt) + x = tgt + embeds_outputs = None + for layer_id, decoder in enumerate(self.decoders): + x, tgt_mask, memory, memory_mask = decoder(x, tgt_mask, memory, memory_mask) + if layer_id == self.embeds_id: + embeds_outputs = x + if self.normalize_before: + x = self.after_norm(x) + if self.output_layer is not None: + x = self.output_layer(x) + + olens = tgt_mask.sum(1) + if embeds_outputs is not None: + return x, olens, embeds_outputs + else: + return x, olens diff --git a/demo/Step-Audio-EditX/funasr_detach/models/paraformer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..7389121c550c7d3f8a811ce478d57cb104030a7d --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/model.py @@ -0,0 +1,611 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import time +import torch +import logging +from torch.cuda.amp import autocast +from typing import Union, Dict, List, Tuple, Optional + +from funasr_detach.register import tables +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.utils import postprocess_utils +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.models.paraformer.search import Hypothesis +from funasr_detach.models.paraformer.cif_predictor import mae_loss +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank + + +@tables.register("model_classes", "Paraformer") +class Paraformer(torch.nn.Module): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition + https://arxiv.org/abs/2206.08317 + """ + + def __init__( + self, + specaug: Optional[str] = None, + specaug_conf: Optional[Dict] = None, + normalize: str = None, + normalize_conf: Optional[Dict] = None, + encoder: str = None, + encoder_conf: Optional[Dict] = None, + decoder: str = None, + decoder_conf: Optional[Dict] = None, + ctc: str = None, + ctc_conf: Optional[Dict] = None, + predictor: str = None, + predictor_conf: Optional[Dict] = None, + ctc_weight: float = 0.5, + input_size: int = 80, + vocab_size: int = -1, + ignore_id: int = -1, + blank_id: int = 0, + sos: int = 1, + eos: int = 2, + lsm_weight: float = 0.0, + length_normalized_loss: bool = False, + # report_cer: bool = True, + # report_wer: bool = True, + # sym_space: str = "", + # sym_blank: str = "", + # extract_feats_in_collect_stats: bool = True, + # predictor=None, + predictor_weight: float = 0.0, + predictor_bias: int = 0, + sampling_ratio: float = 0.2, + share_embedding: bool = False, + # preencoder: Optional[AbsPreEncoder] = None, + # postencoder: Optional[AbsPostEncoder] = None, + use_1st_decoder_loss: bool = False, + **kwargs, + ): + + super().__init__() + + if specaug is not None: + specaug_class = tables.specaug_classes.get(specaug) + specaug = specaug_class(**specaug_conf) + if normalize is not None: + normalize_class = tables.normalize_classes.get(normalize) + normalize = normalize_class(**normalize_conf) + encoder_class = tables.encoder_classes.get(encoder) + encoder = encoder_class(input_size=input_size, **encoder_conf) + encoder_output_size = encoder.output_size() + + if decoder is not None: + decoder_class = tables.decoder_classes.get(decoder) + decoder = decoder_class( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + **decoder_conf, + ) + if ctc_weight > 0.0: + + if ctc_conf is None: + ctc_conf = {} + + ctc = CTC( + odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf + ) + if predictor is not None: + predictor_class = tables.predictor_classes.get(predictor) + predictor = predictor_class(**predictor_conf) + + # note that eos is the same as sos (equivalent ID) + self.blank_id = blank_id + self.sos = sos if sos is not None else vocab_size - 1 + self.eos = eos if eos is not None else vocab_size - 1 + self.vocab_size = vocab_size + self.ignore_id = ignore_id + self.ctc_weight = ctc_weight + # self.token_list = token_list.copy() + # + # self.frontend = frontend + self.specaug = specaug + self.normalize = normalize + # self.preencoder = preencoder + # self.postencoder = postencoder + self.encoder = encoder + # + # if not hasattr(self.encoder, "interctc_use_conditioning"): + # self.encoder.interctc_use_conditioning = False + # if self.encoder.interctc_use_conditioning: + # self.encoder.conditioning_layer = torch.nn.Linear( + # vocab_size, self.encoder.output_size() + # ) + # + # self.error_calculator = None + # + if ctc_weight == 1.0: + self.decoder = None + else: + self.decoder = decoder + + self.criterion_att = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + # + # if report_cer or report_wer: + # self.error_calculator = ErrorCalculator( + # token_list, sym_space, sym_blank, report_cer, report_wer + # ) + # + if ctc_weight == 0.0: + self.ctc = None + else: + self.ctc = ctc + # + # self.extract_feats_in_collect_stats = extract_feats_in_collect_stats + self.predictor = predictor + self.predictor_weight = predictor_weight + self.predictor_bias = predictor_bias + self.sampling_ratio = sampling_ratio + self.criterion_pre = mae_loss(normalize_length=length_normalized_loss) + # self.step_cur = 0 + # + self.share_embedding = share_embedding + if self.share_embedding: + self.decoder.embed = None + + self.use_1st_decoder_loss = use_1st_decoder_loss + self.length_normalized_loss = length_normalized_loss + self.beam_search = None + self.error_calculator = None + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + # import pdb; + # pdb.set_trace() + if len(text_lengths.size()) > 1: + text_lengths = text_lengths[:, 0] + if len(speech_lengths.size()) > 1: + speech_lengths = speech_lengths[:, 0] + + batch_size = speech.shape[0] + + # Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + + loss_ctc, cer_ctc = None, None + loss_pre = None + stats = dict() + + # decoder: CTC branch + if self.ctc_weight != 0.0: + loss_ctc, cer_ctc = self._calc_ctc_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # Collect CTC branch stats + stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None + stats["cer_ctc"] = cer_ctc + + # decoder: Attention decoder branch + loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = ( + self._calc_att_loss(encoder_out, encoder_out_lens, text, text_lengths) + ) + + # 3. CTC-Att loss definition + if self.ctc_weight == 0.0: + loss = loss_att + loss_pre * self.predictor_weight + else: + loss = ( + self.ctc_weight * loss_ctc + + (1 - self.ctc_weight) * loss_att + + loss_pre * self.predictor_weight + ) + + # Collect Attn branch stats + stats["loss_att"] = loss_att.detach() if loss_att is not None else None + stats["pre_loss_att"] = ( + pre_loss_att.detach() if pre_loss_att is not None else None + ) + stats["acc"] = acc_att + stats["cer"] = cer_att + stats["wer"] = wer_att + stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None + + stats["loss"] = torch.clone(loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + if self.length_normalized_loss: + batch_size = (text_lengths + self.predictor_bias).sum() + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def encode( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + ind: int + """ + with autocast(False): + + # Data augmentation + if self.specaug is not None and self.training: + speech, speech_lengths = self.specaug(speech, speech_lengths) + + # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + speech, speech_lengths = self.normalize(speech, speech_lengths) + + # Forward encoder + encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + return encoder_out, encoder_out_lens + + def calc_predictor(self, encoder_out, encoder_out_lens): + + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor( + encoder_out, None, encoder_out_mask, ignore_id=self.ignore_id + ) + return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index + + def cal_decoder_with_predictor( + self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens + ): + + decoder_outs = self.decoder( + encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens + ) + decoder_out = decoder_outs[0] + decoder_out = torch.log_softmax(decoder_out, dim=-1) + return decoder_out, ys_pad_lens + + def _calc_att_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + if self.predictor_bias == 1: + _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_pad_lens = ys_pad_lens + self.predictor_bias + pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor( + encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id + ) + + # 0. sampler + decoder_out_1st = None + pre_loss_att = None + if self.sampling_ratio > 0.0: + + sematic_embeds, decoder_out_1st = self.sampler( + encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds + ) + else: + sematic_embeds = pre_acoustic_embeds + + # 1. Forward decoder + decoder_outs = self.decoder( + encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens + ) + decoder_out, _ = decoder_outs[0], decoder_outs[1] + + if decoder_out_1st is None: + decoder_out_1st = decoder_out + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_pad) + acc_att = th_accuracy( + decoder_out_1st.view(-1, self.vocab_size), + ys_pad, + ignore_label=self.ignore_id, + ) + loss_pre = self.criterion_pre( + ys_pad_lens.type_as(pre_token_length), pre_token_length + ) + + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out_1st.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att + + def sampler( + self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds + ): + + tgt_mask = ( + ~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None] + ).to(ys_pad.device) + ys_pad_masked = ys_pad * tgt_mask[:, :, 0] + if self.share_embedding: + ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked] + else: + ys_pad_embed = self.decoder.embed(ys_pad_masked) + with torch.no_grad(): + decoder_outs = self.decoder( + encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens + ) + decoder_out, _ = decoder_outs[0], decoder_outs[1] + pred_tokens = decoder_out.argmax(-1) + nonpad_positions = ys_pad.ne(self.ignore_id) + seq_lens = (nonpad_positions).sum(1) + same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1) + input_mask = torch.ones_like(nonpad_positions) + bsz, seq_len = ys_pad.size() + for li in range(bsz): + target_num = ( + ((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio + ).long() + if target_num > 0: + input_mask[li].scatter_( + dim=0, + index=torch.randperm(seq_lens[li])[:target_num].to( + input_mask.device + ), + value=0, + ) + input_mask = input_mask.eq(1) + input_mask = input_mask.masked_fill(~nonpad_positions, False) + input_mask_expand_dim = input_mask.unsqueeze(2).to( + pre_acoustic_embeds.device + ) + + sematic_embeds = pre_acoustic_embeds.masked_fill( + ~input_mask_expand_dim, 0 + ) + ys_pad_embed.masked_fill(input_mask_expand_dim, 0) + return sematic_embeds * tgt_mask, decoder_out * tgt_mask + + def _calc_ctc_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + # Calc CTC loss + loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens) + + # Calc CER using CTC + cer_ctc = None + if not self.training and self.error_calculator is not None: + ys_hat = self.ctc.argmax(encoder_out).data + cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True) + return loss_ctc, cer_ctc + + def init_beam_search( + self, + **kwargs, + ): + from funasr_detach.models.paraformer.search import BeamSearchPara + from funasr_detach.models.transformer.scorers.ctc import CTCPrefixScorer + from funasr_detach.models.transformer.scorers.length_bonus import LengthBonus + + # 1. Build ASR model + scorers = {} + + if self.ctc != None: + ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos) + scorers.update(ctc=ctc) + token_list = kwargs.get("token_list") + scorers.update( + length_bonus=LengthBonus(len(token_list)), + ) + + # 3. Build ngram model + # ngram is not supported now + ngram = None + scorers["ngram"] = ngram + + weights = dict( + decoder=1.0 - kwargs.get("decoding_ctc_weight"), + ctc=kwargs.get("decoding_ctc_weight", 0.0), + lm=kwargs.get("lm_weight", 0.0), + ngram=kwargs.get("ngram_weight", 0.0), + length_bonus=kwargs.get("penalty", 0.0), + ) + beam_search = BeamSearchPara( + beam_size=kwargs.get("beam_size", 2), + weights=weights, + scorers=scorers, + sos=self.sos, + eos=self.eos, + vocab_size=len(token_list), + token_list=token_list, + pre_beam_score_key=None if self.ctc_weight == 1.0 else "full", + ) + # beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval() + # for scorer in scorers.values(): + # if isinstance(scorer, torch.nn.Module): + # scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval() + self.beam_search = beam_search + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + # init beamsearch + is_use_ctc = ( + kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None + ) + is_use_lm = ( + kwargs.get("lm_weight", 0.0) > 0.00001 + and kwargs.get("lm_file", None) is not None + ) + if self.beam_search is None and (is_use_lm or is_use_ctc): + logging.info("enable beam_search") + self.init_beam_search(**kwargs) + self.nbest = kwargs.get("nbest", 1) + + meta_data = {} + if ( + isinstance(data_in, torch.Tensor) + and kwargs.get("data_type", "sound") == "fbank" + ): # fbank + speech, speech_lengths = data_in, data_lengths + if len(speech.shape) < 3: + speech = speech[None, :, :] + if speech_lengths is not None: + speech_lengths = speech_lengths.squeeze(-1) + else: + speech_lengths = speech.shape[1] + else: + # extract fbank feats + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_in, + fs=frontend.fs, + audio_fs=kwargs.get("fs", 16000), + data_type=kwargs.get("data_type", "sound"), + tokenizer=tokenizer, + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths = extract_fbank( + audio_sample_list, + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() + * frontend.frame_shift + * frontend.lfr_n + / 1000 + ) + + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + # Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + # predictor + predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = ( + predictor_outs[0], + predictor_outs[1], + predictor_outs[2], + predictor_outs[3], + ) + pre_token_length = pre_token_length.round().long() + if torch.max(pre_token_length) < 1: + return [] + decoder_outs = self.cal_decoder_with_predictor( + encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length + ) + decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] + + results = [] + b, n, d = decoder_out.size() + if isinstance(key[0], (list, tuple)): + key = key[0] + if len(key) < b: + key = key * b + for i in range(b): + x = encoder_out[i, : encoder_out_lens[i], :] + am_scores = decoder_out[i, : pre_token_length[i], :] + if self.beam_search is not None: + nbest_hyps = self.beam_search( + x=x, + am_scores=am_scores, + maxlenratio=kwargs.get("maxlenratio", 0.0), + minlenratio=kwargs.get("minlenratio", 0.0), + ) + + nbest_hyps = nbest_hyps[: self.nbest] + else: + + yseq = am_scores.argmax(dim=-1) + score = am_scores.max(dim=-1)[0] + score = torch.sum(score, dim=-1) + # pad with mask tokens to ensure compatibility with sos/eos tokens + yseq = torch.tensor( + [self.sos] + yseq.tolist() + [self.eos], device=yseq.device + ) + nbest_hyps = [Hypothesis(yseq=yseq, score=score)] + for nbest_idx, hyp in enumerate(nbest_hyps): + ibest_writer = None + if kwargs.get("output_dir") is not None: + if not hasattr(self, "writer"): + self.writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = self.writer[f"{nbest_idx+1}best_recog"] + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list( + filter( + lambda x: x != self.eos + and x != self.sos + and x != self.blank_id, + token_int, + ) + ) + + if tokenizer is not None: + # Change integer-ids to tokens + token = tokenizer.ids2tokens(token_int) + text_postprocessed = tokenizer.tokens2text(token) + if not hasattr(tokenizer, "bpemodel"): + text_postprocessed, _ = postprocess_utils.sentence_postprocess( + token + ) + + result_i = {"key": key[i], "text": text_postprocessed} + + if ibest_writer is not None: + ibest_writer["token"][key[i]] = " ".join(token) + # ibest_writer["text"][key[i]] = text + ibest_writer["text"][key[i]] = text_postprocessed + else: + result_i = {"key": key[i], "token_int": token_int} + results.append(result_i) + + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/paraformer/search.py b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/search.py new file mode 100644 index 0000000000000000000000000000000000000000..44c867f515de2af7209e7a730edb616b365527d1 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/search.py @@ -0,0 +1,459 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +import logging +from itertools import chain +from typing import Any, Dict, List, NamedTuple, Tuple, Union + +from funasr_detach.metrics.common import end_detect +from funasr_detach.models.transformer.scorers.scorer_interface import ( + PartialScorerInterface, + ScorerInterface, +) + + +class Hypothesis(NamedTuple): + """Hypothesis data type.""" + + yseq: torch.Tensor + score: Union[float, torch.Tensor] = 0 + scores: Dict[str, Union[float, torch.Tensor]] = dict() + states: Dict[str, Any] = dict() + + def asdict(self) -> dict: + """Convert data to JSON-friendly dict.""" + return self._replace( + yseq=self.yseq.tolist(), + score=float(self.score), + scores={k: float(v) for k, v in self.scores.items()}, + )._asdict() + + +class BeamSearchPara(torch.nn.Module): + """Beam search implementation.""" + + def __init__( + self, + scorers: Dict[str, ScorerInterface], + weights: Dict[str, float], + beam_size: int, + vocab_size: int, + sos: int, + eos: int, + token_list: List[str] = None, + pre_beam_ratio: float = 1.5, + pre_beam_score_key: str = None, + ): + """Initialize beam search. + + Args: + scorers (dict[str, ScorerInterface]): Dict of decoder modules + e.g., Decoder, CTCPrefixScorer, LM + The scorer will be ignored if it is `None` + weights (dict[str, float]): Dict of weights for each scorers + The scorer will be ignored if its weight is 0 + beam_size (int): The number of hypotheses kept during search + vocab_size (int): The number of vocabulary + sos (int): Start of sequence id + eos (int): End of sequence id + token_list (list[str]): List of tokens for debug log + pre_beam_score_key (str): key of scores to perform pre-beam search + pre_beam_ratio (float): beam size in the pre-beam search + will be `int(pre_beam_ratio * beam_size)` + + """ + super().__init__() + # set scorers + self.weights = weights + self.scorers = dict() + self.full_scorers = dict() + self.part_scorers = dict() + # this module dict is required for recursive cast + # `self.to(device, dtype)` in `recog.py` + self.nn_dict = torch.nn.ModuleDict() + for k, v in scorers.items(): + w = weights.get(k, 0) + if w == 0 or v is None: + continue + assert isinstance( + v, ScorerInterface + ), f"{k} ({type(v)}) does not implement ScorerInterface" + self.scorers[k] = v + if isinstance(v, PartialScorerInterface): + self.part_scorers[k] = v + else: + self.full_scorers[k] = v + if isinstance(v, torch.nn.Module): + self.nn_dict[k] = v + + # set configurations + self.sos = sos + self.eos = eos + self.token_list = token_list + self.pre_beam_size = int(pre_beam_ratio * beam_size) + self.beam_size = beam_size + self.n_vocab = vocab_size + if ( + pre_beam_score_key is not None + and pre_beam_score_key != "full" + and pre_beam_score_key not in self.full_scorers + ): + raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}") + self.pre_beam_score_key = pre_beam_score_key + self.do_pre_beam = ( + self.pre_beam_score_key is not None + and self.pre_beam_size < self.n_vocab + and len(self.part_scorers) > 0 + ) + + def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]: + """Get an initial hypothesis data. + + Args: + x (torch.Tensor): The encoder output feature + + Returns: + Hypothesis: The initial hypothesis. + + """ + init_states = dict() + init_scores = dict() + for k, d in self.scorers.items(): + init_states[k] = d.init_state(x) + init_scores[k] = 0.0 + return [ + Hypothesis( + score=0.0, + scores=init_scores, + states=init_states, + yseq=torch.tensor([self.sos], device=x.device), + ) + ] + + @staticmethod + def append_token(xs: torch.Tensor, x: int) -> torch.Tensor: + """Append new token to prefix tokens. + + Args: + xs (torch.Tensor): The prefix token + x (int): The new token to append + + Returns: + torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device + + """ + x = torch.tensor([x], dtype=xs.dtype, device=xs.device) + return torch.cat((xs, x)) + + def score_full( + self, hyp: Hypothesis, x: torch.Tensor + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.full_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.full_scorers` + and tensor score values of shape: `(self.n_vocab,)`, + and state dict that has string keys + and state values of `self.full_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.full_scorers.items(): + scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x) + return scores, states + + def score_partial( + self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.part_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + ids (torch.Tensor): 1D tensor of new partial tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.part_scorers` + and tensor score values of shape: `(len(ids),)`, + and state dict that has string keys + and state values of `self.part_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.part_scorers.items(): + scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x) + return scores, states + + def beam( + self, weighted_scores: torch.Tensor, ids: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute topk full token ids and partial token ids. + + Args: + weighted_scores (torch.Tensor): The weighted sum scores for each tokens. + Its shape is `(self.n_vocab,)`. + ids (torch.Tensor): The partial token ids to compute topk + + Returns: + Tuple[torch.Tensor, torch.Tensor]: + The topk full token ids and partial token ids. + Their shapes are `(self.beam_size,)` + + """ + # no pre beam performed + if weighted_scores.size(0) == ids.size(0): + top_ids = weighted_scores.topk(self.beam_size)[1] + return top_ids, top_ids + + # mask pruned in pre-beam not to select in topk + tmp = weighted_scores[ids] + weighted_scores[:] = -float("inf") + weighted_scores[ids] = tmp + top_ids = weighted_scores.topk(self.beam_size)[1] + local_ids = weighted_scores[ids].topk(self.beam_size)[1] + return top_ids, local_ids + + @staticmethod + def merge_scores( + prev_scores: Dict[str, float], + next_full_scores: Dict[str, torch.Tensor], + full_idx: int, + next_part_scores: Dict[str, torch.Tensor], + part_idx: int, + ) -> Dict[str, torch.Tensor]: + """Merge scores for new hypothesis. + + Args: + prev_scores (Dict[str, float]): + The previous hypothesis scores by `self.scorers` + next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers` + full_idx (int): The next token id for `next_full_scores` + next_part_scores (Dict[str, torch.Tensor]): + scores of partial tokens by `self.part_scorers` + part_idx (int): The new token id for `next_part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are scalar tensors by the scorers. + + """ + new_scores = dict() + for k, v in next_full_scores.items(): + new_scores[k] = prev_scores[k] + v[full_idx] + for k, v in next_part_scores.items(): + new_scores[k] = prev_scores[k] + v[part_idx] + return new_scores + + def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any: + """Merge states for new hypothesis. + + Args: + states: states of `self.full_scorers` + part_states: states of `self.part_scorers` + part_idx (int): The new token id for `part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are states of the scorers. + + """ + new_states = dict() + for k, v in states.items(): + new_states[k] = v + for k, d in self.part_scorers.items(): + new_states[k] = d.select_state(part_states[k], part_idx) + return new_states + + def search( + self, running_hyps: List[Hypothesis], x: torch.Tensor, am_score: torch.Tensor + ) -> List[Hypothesis]: + """Search new tokens for running hypotheses and encoded speech x. + + Args: + running_hyps (List[Hypothesis]): Running hypotheses on beam + x (torch.Tensor): Encoded speech feature (T, D) + + Returns: + List[Hypotheses]: Best sorted hypotheses + + """ + best_hyps = [] + part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam + for hyp in running_hyps: + # scoring + weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device) + weighted_scores += am_score + scores, states = self.score_full(hyp, x) + for k in self.full_scorers: + weighted_scores += self.weights[k] * scores[k] + # partial scoring + if self.do_pre_beam: + pre_beam_scores = ( + weighted_scores + if self.pre_beam_score_key == "full" + else scores[self.pre_beam_score_key] + ) + part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1] + part_scores, part_states = self.score_partial(hyp, part_ids, x) + for k in self.part_scorers: + weighted_scores[part_ids] += self.weights[k] * part_scores[k] + # add previous hyp score + weighted_scores += hyp.score + + # update hyps + for j, part_j in zip(*self.beam(weighted_scores, part_ids)): + # will be (2 x beam at most) + best_hyps.append( + Hypothesis( + score=weighted_scores[j], + yseq=self.append_token(hyp.yseq, j), + scores=self.merge_scores( + hyp.scores, scores, j, part_scores, part_j + ), + states=self.merge_states(states, part_states, part_j), + ) + ) + + # sort and prune 2 x beam -> beam + best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[ + : min(len(best_hyps), self.beam_size) + ] + return best_hyps + + def forward( + self, + x: torch.Tensor, + am_scores: torch.Tensor, + maxlenratio: float = 0.0, + minlenratio: float = 0.0, + ) -> List[Hypothesis]: + """Perform beam search. + + Args: + x (torch.Tensor): Encoded speech feature (T, D) + maxlenratio (float): Input length ratio to obtain max output length. + If maxlenratio=0.0 (default), it uses a end-detect function + to automatically find maximum hypothesis lengths + If maxlenratio<0.0, its absolute value is interpreted + as a constant max output length. + minlenratio (float): Input length ratio to obtain min output length. + + Returns: + list[Hypothesis]: N-best decoding results + + """ + # set length bounds + maxlen = am_scores.shape[0] + logging.info("decoder input length: " + str(x.shape[0])) + logging.info("max output length: " + str(maxlen)) + + # main loop of prefix search + running_hyps = self.init_hyp(x) + ended_hyps = [] + for i in range(maxlen): + logging.debug("position " + str(i)) + best = self.search(running_hyps, x, am_scores[i]) + # post process of one iteration + running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps) + # end detection + if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i): + logging.info(f"end detected at {i}") + break + if len(running_hyps) == 0: + logging.info("no hypothesis. Finish decoding.") + break + else: + logging.debug(f"remained hypotheses: {len(running_hyps)}") + + nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True) + # check the number of hypotheses reaching to eos + if len(nbest_hyps) == 0: + logging.warning( + "there is no N-best results, perform recognition " + "again with smaller minlenratio." + ) + return ( + [] + if minlenratio < 0.1 + else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1)) + ) + + # report the best result + best = nbest_hyps[0] + for k, v in best.scores.items(): + logging.info( + f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}" + ) + logging.info(f"total log probability: {best.score:.2f}") + logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}") + logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}") + if self.token_list is not None: + logging.info( + "best hypo: " + + "".join([self.token_list[x.item()] for x in best.yseq[1:-1]]) + + "\n" + ) + return nbest_hyps + + def post_process( + self, + i: int, + maxlen: int, + maxlenratio: float, + running_hyps: List[Hypothesis], + ended_hyps: List[Hypothesis], + ) -> List[Hypothesis]: + """Perform post-processing of beam search iterations. + + Args: + i (int): The length of hypothesis tokens. + maxlen (int): The maximum length of tokens in beam search. + maxlenratio (int): The maximum length ratio in beam search. + running_hyps (List[Hypothesis]): The running hypotheses in beam search. + ended_hyps (List[Hypothesis]): The ended hypotheses in beam search. + + Returns: + List[Hypothesis]: The new running hypotheses. + + """ + logging.debug(f"the number of running hypotheses: {len(running_hyps)}") + if self.token_list is not None: + logging.debug( + "best hypo: " + + "".join([self.token_list[x.item()] for x in running_hyps[0].yseq[1:]]) + ) + # add eos in the final loop to avoid that there are no ended hyps + if i == maxlen - 1: + logging.info("adding in the last position in the loop") + running_hyps = [ + h._replace(yseq=self.append_token(h.yseq, self.eos)) + for h in running_hyps + ] + + # add ended hypotheses to a final list, and removed them from current hypotheses + # (this will be a problem, number of hyps < beam) + remained_hyps = [] + for hyp in running_hyps: + if hyp.yseq[-1] == self.eos: + # e.g., Word LM needs to add final score + for k, d in chain(self.full_scorers.items(), self.part_scorers.items()): + s = d.final_score(hyp.states[k]) + hyp.scores[k] += s + hyp = hyp._replace(score=hyp.score + self.weights[k] * s) + ended_hyps.append(hyp) + else: + remained_hyps.append(hyp) + return remained_hyps diff --git a/demo/Step-Audio-EditX/funasr_detach/models/paraformer/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cb216fbf2cbd89b8e719476e88d3ebd529736c0b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/paraformer/template.yaml @@ -0,0 +1,122 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: Paraformer +model_conf: + ctc_weight: 0.0 + lsm_weight: 0.1 + length_normalized_loss: true + predictor_weight: 1.0 + predictor_bias: 1 + sampling_ratio: 0.75 + +# encoder +encoder: SANMEncoder +encoder_conf: + output_size: 512 + attention_heads: 4 + linear_units: 2048 + num_blocks: 50 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + +# decoder +decoder: ParaformerSANMDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 16 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + att_layer_num: 16 + kernel_size: 11 + sanm_shfit: 0 + +predictor: CifPredictorV2 +predictor_conf: + idim: 512 + threshold: 1.0 + l_order: 1 + r_order: 1 + tail_threshold: 0.45 + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 7 + lfr_n: 6 + +specaug: SpecAugLFR +specaug_conf: + apply_time_warp: false + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + lfr_rate: 6 + num_freq_mask: 1 + apply_time_mask: true + time_mask_width_range: + - 0 + - 12 + num_time_mask: 1 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + keep_nbest_models: 10 + avg_nbest_model: 5 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0005 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/paraformer_streaming/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/paraformer_streaming/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/paraformer_streaming/model.py b/demo/Step-Audio-EditX/funasr_detach/models/paraformer_streaming/model.py new file mode 100644 index 0000000000000000000000000000000000000000..877ec5609507e3bcbd00248bbb3632f1048d2376 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/paraformer_streaming/model.py @@ -0,0 +1,834 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import time +import torch +import logging +from typing import Dict, Tuple +from contextlib import contextmanager +from distutils.version import LooseVersion + +from funasr_detach.register import tables +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.utils import postprocess_utils +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.models.paraformer.model import Paraformer +from funasr_detach.models.paraformer.search import Hypothesis +from funasr_detach.models.paraformer.cif_predictor import mae_loss +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank + + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "ParaformerStreaming") +class ParaformerStreaming(Paraformer): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition + https://arxiv.org/abs/2206.08317 + """ + + def __init__( + self, + *args, + **kwargs, + ): + + super().__init__(*args, **kwargs) + + # import pdb; + # pdb.set_trace() + self.sampling_ratio = kwargs.get("sampling_ratio", 0.2) + + self.scama_mask = None + if ( + hasattr(self.encoder, "overlap_chunk_cls") + and self.encoder.overlap_chunk_cls is not None + ): + from funasr_detach.models.scama.chunk_utilis import ( + build_scama_mask_for_cross_attention_decoder, + ) + + self.build_scama_mask_for_cross_attention_decoder_fn = ( + build_scama_mask_for_cross_attention_decoder + ) + self.decoder_attention_chunk_type = kwargs.get( + "decoder_attention_chunk_type", "chunk" + ) + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + # import pdb; + # pdb.set_trace() + decoding_ind = kwargs.get("decoding_ind") + if len(text_lengths.size()) > 1: + text_lengths = text_lengths[:, 0] + if len(speech_lengths.size()) > 1: + speech_lengths = speech_lengths[:, 0] + + batch_size = speech.shape[0] + + # Encoder + if hasattr(self.encoder, "overlap_chunk_cls"): + ind = self.encoder.overlap_chunk_cls.random_choice( + self.training, decoding_ind + ) + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind) + else: + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + + loss_ctc, cer_ctc = None, None + loss_pre = None + stats = dict() + + # decoder: CTC branch + + if self.ctc_weight > 0.0: + if hasattr(self.encoder, "overlap_chunk_cls"): + encoder_out_ctc, encoder_out_lens_ctc = ( + self.encoder.overlap_chunk_cls.remove_chunk( + encoder_out, encoder_out_lens, chunk_outs=None + ) + ) + else: + encoder_out_ctc, encoder_out_lens_ctc = encoder_out, encoder_out_lens + + loss_ctc, cer_ctc = self._calc_ctc_loss( + encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths + ) + # Collect CTC branch stats + stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None + stats["cer_ctc"] = cer_ctc + + # decoder: Attention decoder branch + loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = ( + self._calc_att_predictor_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + ) + + # 3. CTC-Att loss definition + if self.ctc_weight == 0.0: + loss = loss_att + loss_pre * self.predictor_weight + else: + loss = ( + self.ctc_weight * loss_ctc + + (1 - self.ctc_weight) * loss_att + + loss_pre * self.predictor_weight + ) + + # Collect Attn branch stats + stats["loss_att"] = loss_att.detach() if loss_att is not None else None + stats["pre_loss_att"] = ( + pre_loss_att.detach() if pre_loss_att is not None else None + ) + stats["acc"] = acc_att + stats["cer"] = cer_att + stats["wer"] = wer_att + stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None + + stats["loss"] = torch.clone(loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + if self.length_normalized_loss: + batch_size = (text_lengths + self.predictor_bias).sum() + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def encode_chunk( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + cache: dict = None, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Frontend + Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + ind: int + """ + with autocast(False): + + # Data augmentation + if self.specaug is not None and self.training: + speech, speech_lengths = self.specaug(speech, speech_lengths) + + # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + speech, speech_lengths = self.normalize(speech, speech_lengths) + + # Forward encoder + encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk( + speech, speech_lengths, cache=cache["encoder"] + ) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + return encoder_out, torch.tensor([encoder_out.size(1)]) + + def _calc_att_predictor_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + if self.predictor_bias == 1: + _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_pad_lens = ys_pad_lens + self.predictor_bias + mask_chunk_predictor = None + if self.encoder.overlap_chunk_cls is not None: + mask_chunk_predictor = ( + self.encoder.overlap_chunk_cls.get_mask_chunk_predictor( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + encoder_out = encoder_out * mask_shfit_chunk + pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor( + encoder_out, + ys_pad, + encoder_out_mask, + ignore_id=self.ignore_id, + mask_chunk_predictor=mask_chunk_predictor, + target_label_length=ys_pad_lens, + ) + predictor_alignments, predictor_alignments_len = ( + self.predictor.gen_frame_alignments(pre_alphas, encoder_out_lens) + ) + + scama_mask = None + if ( + self.encoder.overlap_chunk_cls is not None + and self.decoder_attention_chunk_type == "chunk" + ): + encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur + attention_chunk_center_bias = 0 + attention_chunk_size = encoder_chunk_size + decoder_att_look_back_factor = ( + self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur + ) + mask_shift_att_chunk_decoder = ( + self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn( + predictor_alignments=predictor_alignments, + encoder_sequence_length=encoder_out_lens, + chunk_size=1, + encoder_chunk_size=encoder_chunk_size, + attention_chunk_center_bias=attention_chunk_center_bias, + attention_chunk_size=attention_chunk_size, + attention_chunk_type=self.decoder_attention_chunk_type, + step=None, + predictor_mask_chunk_hopping=mask_chunk_predictor, + decoder_att_look_back_factor=decoder_att_look_back_factor, + mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder, + target_length=ys_pad_lens, + is_training=self.training, + ) + elif self.encoder.overlap_chunk_cls is not None: + encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk( + encoder_out, encoder_out_lens, chunk_outs=None + ) + # 0. sampler + decoder_out_1st = None + pre_loss_att = None + if self.sampling_ratio > 0.0: + if self.step_cur < 2: + logging.info( + "enable sampler in paraformer, sampling_ratio: {}".format( + self.sampling_ratio + ) + ) + if self.use_1st_decoder_loss: + sematic_embeds, decoder_out_1st, pre_loss_att = self.sampler_with_grad( + encoder_out, + encoder_out_lens, + ys_pad, + ys_pad_lens, + pre_acoustic_embeds, + scama_mask, + ) + else: + sematic_embeds, decoder_out_1st = self.sampler( + encoder_out, + encoder_out_lens, + ys_pad, + ys_pad_lens, + pre_acoustic_embeds, + scama_mask, + ) + else: + if self.step_cur < 2: + logging.info( + "disable sampler in paraformer, sampling_ratio: {}".format( + self.sampling_ratio + ) + ) + sematic_embeds = pre_acoustic_embeds + + # 1. Forward decoder + decoder_outs = self.decoder( + encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, scama_mask + ) + decoder_out, _ = decoder_outs[0], decoder_outs[1] + + if decoder_out_1st is None: + decoder_out_1st = decoder_out + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_pad) + acc_att = th_accuracy( + decoder_out_1st.view(-1, self.vocab_size), + ys_pad, + ignore_label=self.ignore_id, + ) + loss_pre = self.criterion_pre( + ys_pad_lens.type_as(pre_token_length), pre_token_length + ) + + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out_1st.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att + + def sampler( + self, + encoder_out, + encoder_out_lens, + ys_pad, + ys_pad_lens, + pre_acoustic_embeds, + chunk_mask=None, + ): + + tgt_mask = ( + ~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None] + ).to(ys_pad.device) + ys_pad_masked = ys_pad * tgt_mask[:, :, 0] + if self.share_embedding: + ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked] + else: + ys_pad_embed = self.decoder.embed(ys_pad_masked) + with torch.no_grad(): + decoder_outs = self.decoder( + encoder_out, + encoder_out_lens, + pre_acoustic_embeds, + ys_pad_lens, + chunk_mask, + ) + decoder_out, _ = decoder_outs[0], decoder_outs[1] + pred_tokens = decoder_out.argmax(-1) + nonpad_positions = ys_pad.ne(self.ignore_id) + seq_lens = (nonpad_positions).sum(1) + same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1) + input_mask = torch.ones_like(nonpad_positions) + bsz, seq_len = ys_pad.size() + for li in range(bsz): + target_num = ( + ((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio + ).long() + if target_num > 0: + input_mask[li].scatter_( + dim=0, + index=torch.randperm(seq_lens[li])[:target_num].cuda(), + value=0, + ) + input_mask = input_mask.eq(1) + input_mask = input_mask.masked_fill(~nonpad_positions, False) + input_mask_expand_dim = input_mask.unsqueeze(2).to( + pre_acoustic_embeds.device + ) + + sematic_embeds = pre_acoustic_embeds.masked_fill( + ~input_mask_expand_dim, 0 + ) + ys_pad_embed.masked_fill(input_mask_expand_dim, 0) + return sematic_embeds * tgt_mask, decoder_out * tgt_mask + + def calc_predictor(self, encoder_out, encoder_out_lens): + + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + mask_chunk_predictor = None + if self.encoder.overlap_chunk_cls is not None: + mask_chunk_predictor = ( + self.encoder.overlap_chunk_cls.get_mask_chunk_predictor( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + encoder_out = encoder_out * mask_shfit_chunk + pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index = ( + self.predictor( + encoder_out, + None, + encoder_out_mask, + ignore_id=self.ignore_id, + mask_chunk_predictor=mask_chunk_predictor, + target_label_length=None, + ) + ) + predictor_alignments, predictor_alignments_len = ( + self.predictor.gen_frame_alignments( + pre_alphas, + ( + encoder_out_lens + 1 + if self.predictor.tail_threshold > 0.0 + else encoder_out_lens + ), + ) + ) + + scama_mask = None + if ( + self.encoder.overlap_chunk_cls is not None + and self.decoder_attention_chunk_type == "chunk" + ): + encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur + attention_chunk_center_bias = 0 + attention_chunk_size = encoder_chunk_size + decoder_att_look_back_factor = ( + self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur + ) + mask_shift_att_chunk_decoder = ( + self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn( + predictor_alignments=predictor_alignments, + encoder_sequence_length=encoder_out_lens, + chunk_size=1, + encoder_chunk_size=encoder_chunk_size, + attention_chunk_center_bias=attention_chunk_center_bias, + attention_chunk_size=attention_chunk_size, + attention_chunk_type=self.decoder_attention_chunk_type, + step=None, + predictor_mask_chunk_hopping=mask_chunk_predictor, + decoder_att_look_back_factor=decoder_att_look_back_factor, + mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder, + target_length=None, + is_training=self.training, + ) + self.scama_mask = scama_mask + + return pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index + + def calc_predictor_chunk(self, encoder_out, encoder_out_lens, cache=None, **kwargs): + is_final = kwargs.get("is_final", False) + + return self.predictor.forward_chunk( + encoder_out, cache["encoder"], is_final=is_final + ) + + def cal_decoder_with_predictor( + self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens + ): + decoder_outs = self.decoder( + encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, self.scama_mask + ) + decoder_out = decoder_outs[0] + decoder_out = torch.log_softmax(decoder_out, dim=-1) + return decoder_out, ys_pad_lens + + def cal_decoder_with_predictor_chunk( + self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, cache=None + ): + decoder_outs = self.decoder.forward_chunk( + encoder_out, sematic_embeds, cache["decoder"] + ) + decoder_out = decoder_outs + decoder_out = torch.log_softmax(decoder_out, dim=-1) + return decoder_out, ys_pad_lens + + def init_cache(self, cache: dict = {}, **kwargs): + chunk_size = kwargs.get("chunk_size", [0, 10, 5]) + encoder_chunk_look_back = kwargs.get("encoder_chunk_look_back", 0) + decoder_chunk_look_back = kwargs.get("decoder_chunk_look_back", 0) + batch_size = 1 + + enc_output_size = kwargs["encoder_conf"]["output_size"] + feats_dims = ( + kwargs["frontend_conf"]["n_mels"] * kwargs["frontend_conf"]["lfr_m"] + ) + cache_encoder = { + "start_idx": 0, + "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)), + "cif_alphas": torch.zeros((batch_size, 1)), + "chunk_size": chunk_size, + "encoder_chunk_look_back": encoder_chunk_look_back, + "last_chunk": False, + "opt": None, + "feats": torch.zeros( + (batch_size, chunk_size[0] + chunk_size[2], feats_dims) + ), + "tail_chunk": False, + } + cache["encoder"] = cache_encoder + + cache_decoder = { + "decode_fsmn": None, + "decoder_chunk_look_back": decoder_chunk_look_back, + "opt": None, + "chunk_size": chunk_size, + } + cache["decoder"] = cache_decoder + cache["frontend"] = {} + cache["prev_samples"] = torch.empty(0) + + return cache + + def generate_chunk( + self, + speech, + speech_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + cache = kwargs.get("cache", {}) + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + + # Encoder + # + encoder_out, encoder_out_lens = self.encode_chunk( + speech, speech_lengths, cache=cache, is_final=kwargs.get("is_final", False) + ) + print(speech.shape, encoder_out.shape, encoder_out_lens) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + # predictor + predictor_outs = self.calc_predictor_chunk( + encoder_out, + encoder_out_lens, + cache=cache, + is_final=kwargs.get("is_final", False), + ) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = ( + predictor_outs[0], + predictor_outs[1], + predictor_outs[2], + predictor_outs[3], + ) + pre_token_length = pre_token_length.round().long() + if torch.max(pre_token_length) < 1: + return [] + decoder_outs = self.cal_decoder_with_predictor_chunk( + encoder_out, + encoder_out_lens, + pre_acoustic_embeds, + pre_token_length, + cache=cache, + ) + decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] + + results = [] + b, n, d = decoder_out.size() + if isinstance(key[0], (list, tuple)): + key = key[0] + for i in range(b): + x = encoder_out[i, : encoder_out_lens[i], :] + am_scores = decoder_out[i, : pre_token_length[i], :] + if self.beam_search is not None: + nbest_hyps = self.beam_search( + x=x, + am_scores=am_scores, + maxlenratio=kwargs.get("maxlenratio", 0.0), + minlenratio=kwargs.get("minlenratio", 0.0), + ) + + nbest_hyps = nbest_hyps[: self.nbest] + else: + + yseq = am_scores.argmax(dim=-1) + score = am_scores.max(dim=-1)[0] + score = torch.sum(score, dim=-1) + # pad with mask tokens to ensure compatibility with sos/eos tokens + yseq = torch.tensor( + [self.sos] + yseq.tolist() + [self.eos], device=yseq.device + ) + nbest_hyps = [Hypothesis(yseq=yseq, score=score)] + for nbest_idx, hyp in enumerate(nbest_hyps): + + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list( + filter( + lambda x: x != self.eos + and x != self.sos + and x != self.blank_id, + token_int, + ) + ) + + # Change integer-ids to tokens + token = tokenizer.ids2tokens(token_int) + # text = tokenizer.tokens2text(token) + + result_i = token + + results.extend(result_i) + + return results + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + cache: dict = {}, + **kwargs, + ): + + # init beamsearch + is_use_ctc = ( + kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None + ) + is_use_lm = ( + kwargs.get("lm_weight", 0.0) > 0.00001 + and kwargs.get("lm_file", None) is not None + ) + if self.beam_search is None and (is_use_lm or is_use_ctc): + logging.info("enable beam_search") + self.init_beam_search(**kwargs) + self.nbest = kwargs.get("nbest", 1) + + if len(cache) == 0: + self.init_cache(cache, **kwargs) + + meta_data = {} + chunk_size = kwargs.get("chunk_size", [0, 10, 5]) + chunk_stride_samples = int(chunk_size[1] * 960) # 600ms + + time1 = time.perf_counter() + cfg = {"is_final": kwargs.get("is_final", False)} + audio_sample_list = load_audio_text_image_video( + data_in, + fs=frontend.fs, + audio_fs=kwargs.get("fs", 16000), + data_type=kwargs.get("data_type", "sound"), + tokenizer=tokenizer, + cache=cfg, + ) + # import pdb; pdb.set_trace() + _is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True + + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + assert len(audio_sample_list) == 1, "batch_size must be set 1" + + audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0])) + + n = int(len(audio_sample) // chunk_stride_samples + int(_is_final)) + m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final))) + tokens = [] + for i in range(n): + kwargs["is_final"] = _is_final and i == n - 1 + audio_sample_i = audio_sample[ + i * chunk_stride_samples : (i + 1) * chunk_stride_samples + ] + + # extract fbank feats + speech, speech_lengths = extract_fbank( + [audio_sample_i], + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + cache=cache["frontend"], + is_final=kwargs["is_final"], + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() + * frontend.frame_shift + * frontend.lfr_n + / 1000 + ) + if len(speech) == 0: + break + tokens_i = self.generate_chunk( + speech, + speech_lengths, + key=key, + tokenizer=tokenizer, + cache=cache, + frontend=frontend, + **kwargs, + ) + tokens.extend(tokens_i) + + text_postprocessed, _ = postprocess_utils.sentence_postprocess(tokens) + + result_i = {"key": key[0], "text": text_postprocessed} + result = [result_i] + + cache["prev_samples"] = audio_sample[:-m] + if _is_final: + self.init_cache(cache, **kwargs) + + if kwargs.get("output_dir"): + if not hasattr(self, "writer"): + self.writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = self.writer[f"{1}best_recog"] + ibest_writer["token"][key[0]] = " ".join(tokens) + ibest_writer["text"][key[0]] = text_postprocessed + + return result, meta_data + + def infer_encoder( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + cache: dict = {}, + **kwargs, + ): + if len(cache) == 0: + self.init_cache(cache, **kwargs) + + meta_data = {} + chunk_size = kwargs.get("chunk_size", [0, 10, 5]) + chunk_stride_samples = int(chunk_size[1] * 960) # 600ms + + time1 = time.perf_counter() + cfg = {"is_final": kwargs.get("is_final", False)} + if isinstance(data_in[0], torch.Tensor): + audio_sample_list = data_in + else: + audio_sample_list = load_audio_text_image_video( + data_in, + fs=frontend.fs, + audio_fs=kwargs.get("fs", 16000), + data_type=kwargs.get("data_type", "sound"), + tokenizer=tokenizer, + cache=cfg, + ) + + _is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + assert len(audio_sample_list) == 1, "batch_size must be set 1" + + audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0])) + + n = int(len(audio_sample) // chunk_stride_samples + int(_is_final)) + m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final))) + encoder_outs = [] + meta_data["batch_data_time"] = 0.0 + meta_data["extract_feat"] = 0.0 + for i in range(n): + kwargs["is_final"] = _is_final and i == n - 1 + audio_sample_i = audio_sample[ + i * chunk_stride_samples : (i + 1) * chunk_stride_samples + ] + time2 = time.perf_counter() + # extract fbank feats + if kwargs["is_final"] and len(audio_sample_i) == 0: + break + try: + speech, speech_lengths = extract_fbank( + [audio_sample_i], + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + cache=cache["frontend"], + is_final=kwargs["is_final"], + ) + except: + if i == n - 1 and audio_sample_i.shape[0] < 480: + print(f"Warning!!!, skip {audio_sample_i.shape[0]} samples") + break + else: + raise RuntimeError("infer failed") + time3 = time.perf_counter() + if len(speech) == 0 and kwargs["is_final"]: + break + meta_data["extract_feat"] = meta_data["extract_feat"] + time3 - time2 + meta_data["batch_data_time"] = ( + meta_data["batch_data_time"] + + speech_lengths.sum().item() + * frontend.frame_shift + * frontend.lfr_n + / 1000 + ) + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + encoder_out, encoder_out_lens = self.encode_chunk( + speech, + speech_lengths, + cache=cache, + is_final=kwargs.get("is_final", False), + ) + encoder_outs.append(encoder_out[:, (-speech_lengths[0]) :]) + + if i == n - 1: + break + speech_out = [] + if len(encoder_outs) > 0: + speech_out = torch.cat(encoder_outs, dim=1) + result_i = {"key": key[0], "enc_out": speech_out} + result = [result_i] + + if m > 0: # tail exists + cache["prev_samples"] = audio_sample[-m:] + else: + cache["prev_samples"] = torch.empty(0) + + if _is_final: + self.init_cache(cache, **kwargs) + + return result, meta_data, cache diff --git a/demo/Step-Audio-EditX/funasr_detach/models/paraformer_streaming/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/paraformer_streaming/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0bff13911b1dc4a76a8f71f7d9e8d3ecf4db556c --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/paraformer_streaming/template.yaml @@ -0,0 +1,143 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: ParaformerStreaming +model_conf: + ctc_weight: 0.0 + lsm_weight: 0.1 + length_normalized_loss: true + predictor_weight: 1.0 + predictor_bias: 1 + sampling_ratio: 0.75 + +# encoder +encoder: SANMEncoderChunkOpt +encoder_conf: + output_size: 512 + attention_heads: 4 + linear_units: 2048 + num_blocks: 50 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe_online + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + chunk_size: + - 12 + - 15 + stride: + - 8 + - 10 + pad_left: + - 0 + - 0 + encoder_att_look_back_factor: + - 4 + - 4 + decoder_att_look_back_factor: + - 1 + - 1 + +# decoder +decoder: ParaformerSANMDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 16 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + att_layer_num: 16 + kernel_size: 11 + sanm_shfit: 5 + +predictor: CifPredictorV2 +predictor_conf: + idim: 512 + threshold: 1.0 + l_order: 1 + r_order: 1 + tail_threshold: 0.45 + +# frontend related +frontend: WavFrontendOnline +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 7 + lfr_n: 6 + +specaug: SpecAugLFR +specaug_conf: + apply_time_warp: false + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + lfr_rate: 6 + num_freq_mask: 1 + apply_time_mask: true + time_mask_width_range: + - 0 + - 12 + num_time_mask: 1 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + val_scheduler_criterion: + - valid + - acc + best_model_criterion: + - - valid + - acc + - max + keep_nbest_models: 10 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0005 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_decoder/wkv_cuda.cu b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_decoder/wkv_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..c2457959645481972f58cc75cfa0e2c49fa1baa8 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_decoder/wkv_cuda.cu @@ -0,0 +1,135 @@ +// Copied from https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4/cuda/wkv_cuda.cu + +#include +#include + +#define MIN_VALUE (-1e38) + +template +__global__ void kernel_forward(const int B, const int T, const int C, + const F *__restrict__ const _w, const F *__restrict__ const _u, const F *__restrict__ const _k, const F *__restrict__ const _v, + F *__restrict__ const _y) { + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + const int _b = idx / C; + const int _c = idx % C; + const int _offset = _b * T * C + _c; + + F u = _u[_c]; + F w = _w[_c]; + const F *__restrict__ const k = _k + _offset; + const F *__restrict__ const v = _v + _offset; + F *__restrict__ const y = _y + _offset; + + // aa and bb are running sums divided by exp(pp) (to avoid overflow) + F aa = 0, bb = 0, pp = MIN_VALUE; + for (int i = 0; i < T; i++) { + const int ii = i * C; + const F kk = k[ii]; + const F vv = v[ii]; + + F ww = u + kk; + F p = max(pp, ww); + F e1 = exp(pp - p); + F e2 = exp(ww - p); + y[ii] = (e1 * aa + e2 * vv) / (e1 * bb + e2); + + ww = w + pp; + p = max(ww, kk); + e1 = exp(ww - p); + e2 = exp(kk - p); + aa = e1 * aa + e2 * vv; + bb = e1 * bb + e2; + pp = p; + } +} + +template +__global__ void kernel_backward(const int B, const int T, const int C, + const F *__restrict__ const _w, const F *__restrict__ const _u, const F *__restrict__ const _k, const F *__restrict__ const _v, + const F *__restrict__ const _y, const F *__restrict__ const _gy, + F *__restrict__ const _gw, F *__restrict__ const _gu, F *__restrict__ const _gk, F *__restrict__ const _gv) { + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + const int _b = idx / C; + const int _c = idx % C; + const int _offset = _b * T * C + _c; + + F u = _u[_c]; + F w = _w[_c]; + const F *__restrict__ const k = _k + _offset; + const F *__restrict__ const v = _v + _offset; + const F *__restrict__ const y = _y + _offset; + const F *__restrict__ const gy = _gy + _offset; + F *__restrict__ const gk = _gk + _offset; + F *__restrict__ const gv = _gv + _offset; + + F q[Tmax], r[Tmax]; + + F gw = 0, gu = 0, aa = 0, bb = 0, ga = 0, gb = 0, pp = MIN_VALUE; + for (int i = 0; i < T; i++) { + const int ii = i * C; + const F kk = k[ii]; + const F vv = v[ii]; + const F yy = y[ii]; + + F ww = u + kk; + F p = max(pp, ww); + F e1 = exp(pp - p); + F e2 = exp(ww - p); + const F qq = gy[ii] / (e1 * bb + e2); + gw += (ga - gb * yy) * e1 * qq; + gu += (vv - yy) * e2 * qq; + q[i] = qq; + r[i] = ww - p; + + ww = w + pp; + p = max(ww, kk); + e1 = exp(ww - p); + e2 = exp(kk - p); + ga = e1 * (aa + ga); + gb = e1 * (bb + gb); + aa = e1 * aa + e2 * vv; + bb = e1 * bb + e2; + pp = p; + } + const int _offsetBC = _b * C + _c; + _gw[_offsetBC] = gw * _w[_c]; // multiply by w because of w -> -exp(w) in python forward() + _gu[_offsetBC] = gu; + + aa = 0, bb = 0, pp = MIN_VALUE; + for (int i = T - 1; i >= 0; i--) { + const int ii = i * C; + const F kk = k[ii]; + const F vv = v[ii]; + const F yy = y[ii]; + const F qq = q[i]; + const F rr = r[i]; + + F e1 = qq * exp(rr); + F e2 = exp(kk + pp); + gk[ii] = e1 * (vv - yy) + e2 * (aa * vv + bb); + gv[ii] = e1 + e2 * aa; + + const F ww = w + pp; + const F www = rr - u - kk; + const F p = max(ww, www); + e1 = exp(ww - p); + e2 = qq * exp(www - p); + aa = e1 * aa + e2; + bb = e1 * bb - e2 * yy; + pp = p; + } +} + +void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y) { + dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance + assert(B * C % threadsPerBlock.x == 0); + dim3 numBlocks(B * C / threadsPerBlock.x); + kernel_forward<<>>(B, T, C, w, u, k, v, y); +} + +void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv) { + dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance + assert(B * C % threadsPerBlock.x == 0); + dim3 numBlocks(B * C / threadsPerBlock.x); + kernel_backward<<>>(B, T, C, w, u, k, v, y, gy, gw, gu, gk, gv); +} diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_decoder/wkv_op.cpp b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_decoder/wkv_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c45e8013ec8eafe8261fff447bee2db6f7c65327 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_decoder/wkv_op.cpp @@ -0,0 +1,37 @@ +/* + * Bsed on https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4/cuda/wkv_op.cpp + Function signatures were modified based on https://github.com/huggingface/transformers/blob/main/src/transformers/kernels/rwkv/wkv_op.cpp + + */ + +#include + +void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y); + +void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv); + +void forward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) { + const int B = k.size(0); + const int T = k.size(1); + const int C = k.size(2); + + cuda_forward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), y.data_ptr()); +} + +void backward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y, torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) { + const int B = k.size(0); + const int T = k.size(1); + const int C = k.size(2); + + cuda_backward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), y.data_ptr(), gy.data_ptr(), gw.data_ptr(), gu.data_ptr(), gk.data_ptr(), gv.data_ptr()); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &forward, "wkv forward"); + m.def("backward", &backward, "wkv backward"); +} + +TORCH_LIBRARY(wkv_decoder, m) { + m.def("forward", forward); + m.def("backward", backward); +} diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_encoder/wkv_cuda.cu b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_encoder/wkv_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..c2457959645481972f58cc75cfa0e2c49fa1baa8 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_encoder/wkv_cuda.cu @@ -0,0 +1,135 @@ +// Copied from https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4/cuda/wkv_cuda.cu + +#include +#include + +#define MIN_VALUE (-1e38) + +template +__global__ void kernel_forward(const int B, const int T, const int C, + const F *__restrict__ const _w, const F *__restrict__ const _u, const F *__restrict__ const _k, const F *__restrict__ const _v, + F *__restrict__ const _y) { + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + const int _b = idx / C; + const int _c = idx % C; + const int _offset = _b * T * C + _c; + + F u = _u[_c]; + F w = _w[_c]; + const F *__restrict__ const k = _k + _offset; + const F *__restrict__ const v = _v + _offset; + F *__restrict__ const y = _y + _offset; + + // aa and bb are running sums divided by exp(pp) (to avoid overflow) + F aa = 0, bb = 0, pp = MIN_VALUE; + for (int i = 0; i < T; i++) { + const int ii = i * C; + const F kk = k[ii]; + const F vv = v[ii]; + + F ww = u + kk; + F p = max(pp, ww); + F e1 = exp(pp - p); + F e2 = exp(ww - p); + y[ii] = (e1 * aa + e2 * vv) / (e1 * bb + e2); + + ww = w + pp; + p = max(ww, kk); + e1 = exp(ww - p); + e2 = exp(kk - p); + aa = e1 * aa + e2 * vv; + bb = e1 * bb + e2; + pp = p; + } +} + +template +__global__ void kernel_backward(const int B, const int T, const int C, + const F *__restrict__ const _w, const F *__restrict__ const _u, const F *__restrict__ const _k, const F *__restrict__ const _v, + const F *__restrict__ const _y, const F *__restrict__ const _gy, + F *__restrict__ const _gw, F *__restrict__ const _gu, F *__restrict__ const _gk, F *__restrict__ const _gv) { + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + const int _b = idx / C; + const int _c = idx % C; + const int _offset = _b * T * C + _c; + + F u = _u[_c]; + F w = _w[_c]; + const F *__restrict__ const k = _k + _offset; + const F *__restrict__ const v = _v + _offset; + const F *__restrict__ const y = _y + _offset; + const F *__restrict__ const gy = _gy + _offset; + F *__restrict__ const gk = _gk + _offset; + F *__restrict__ const gv = _gv + _offset; + + F q[Tmax], r[Tmax]; + + F gw = 0, gu = 0, aa = 0, bb = 0, ga = 0, gb = 0, pp = MIN_VALUE; + for (int i = 0; i < T; i++) { + const int ii = i * C; + const F kk = k[ii]; + const F vv = v[ii]; + const F yy = y[ii]; + + F ww = u + kk; + F p = max(pp, ww); + F e1 = exp(pp - p); + F e2 = exp(ww - p); + const F qq = gy[ii] / (e1 * bb + e2); + gw += (ga - gb * yy) * e1 * qq; + gu += (vv - yy) * e2 * qq; + q[i] = qq; + r[i] = ww - p; + + ww = w + pp; + p = max(ww, kk); + e1 = exp(ww - p); + e2 = exp(kk - p); + ga = e1 * (aa + ga); + gb = e1 * (bb + gb); + aa = e1 * aa + e2 * vv; + bb = e1 * bb + e2; + pp = p; + } + const int _offsetBC = _b * C + _c; + _gw[_offsetBC] = gw * _w[_c]; // multiply by w because of w -> -exp(w) in python forward() + _gu[_offsetBC] = gu; + + aa = 0, bb = 0, pp = MIN_VALUE; + for (int i = T - 1; i >= 0; i--) { + const int ii = i * C; + const F kk = k[ii]; + const F vv = v[ii]; + const F yy = y[ii]; + const F qq = q[i]; + const F rr = r[i]; + + F e1 = qq * exp(rr); + F e2 = exp(kk + pp); + gk[ii] = e1 * (vv - yy) + e2 * (aa * vv + bb); + gv[ii] = e1 + e2 * aa; + + const F ww = w + pp; + const F www = rr - u - kk; + const F p = max(ww, www); + e1 = exp(ww - p); + e2 = qq * exp(www - p); + aa = e1 * aa + e2; + bb = e1 * bb - e2 * yy; + pp = p; + } +} + +void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y) { + dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance + assert(B * C % threadsPerBlock.x == 0); + dim3 numBlocks(B * C / threadsPerBlock.x); + kernel_forward<<>>(B, T, C, w, u, k, v, y); +} + +void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv) { + dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance + assert(B * C % threadsPerBlock.x == 0); + dim3 numBlocks(B * C / threadsPerBlock.x); + kernel_backward<<>>(B, T, C, w, u, k, v, y, gy, gw, gu, gk, gv); +} diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_encoder/wkv_op.cpp b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_encoder/wkv_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..eae2fe0284ed106aa9938b30878544c9f66c2511 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/cuda_encoder/wkv_op.cpp @@ -0,0 +1,37 @@ +/* + * Bsed on https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4/cuda/wkv_op.cpp + Function signatures were modified based on https://github.com/huggingface/transformers/blob/main/src/transformers/kernels/rwkv/wkv_op.cpp + + */ + +#include + +void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y); + +void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv); + +void forward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) { + const int B = k.size(0); + const int T = k.size(1); + const int C = k.size(2); + + cuda_forward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), y.data_ptr()); +} + +void backward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y, torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) { + const int B = k.size(0); + const int T = k.size(1); + const int C = k.size(2); + + cuda_backward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), y.data_ptr(), gy.data_ptr(), gw.data_ptr(), gu.data_ptr(), gk.data_ptr(), gv.data_ptr()); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &forward, "wkv forward"); + m.def("backward", &backward, "wkv backward"); +} + +TORCH_LIBRARY(wkv_encoder, m) { + m.def("forward", forward); + m.def("backward", backward); +} diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv.py b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv.py new file mode 100644 index 0000000000000000000000000000000000000000..0a39a5e485e4f761d1ef6136afac221d29f651ea --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +from typing import Dict, Optional, Tuple + +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.rwkv_bat.rwkv_feed_forward import FeedForward +from funasr_detach.models.rwkv_bat.rwkv_attention import ( + EncoderSelfAttention, + DecoderSelfAttention, +) + + +class RWKV(torch.nn.Module): + """RWKV module. + + Args: + size: Input/Output size. + linear_size: Feed-forward hidden size. + attention_size: SelfAttention hidden size. + context_size: Context size for WKV computation. + block_id: Block index. + num_blocks: Number of blocks in the architecture. + normalization_class: Normalization layer class. + normalization_args: Normalization layer arguments. + att_dropout_rate: Dropout rate for the attention module. + ffn_dropout_rate: Dropout rate for the feed-forward module. + + """ + + def __init__( + self, + size: int, + linear_size: int, + attention_size: int, + context_size: int, + block_id: int, + num_blocks: int, + att_dropout_rate: float = 0.0, + ffn_dropout_rate: float = 0.0, + dropout_rate: float = 0.0, + ) -> None: + """Construct a RWKV object.""" + super().__init__() + + self.layer_norm_att = LayerNorm(size) + self.layer_norm_ffn = LayerNorm(size) + + self.att = EncoderSelfAttention( + size, attention_size, context_size, block_id, att_dropout_rate, num_blocks + ) + self.dropout_att = torch.nn.Dropout(p=dropout_rate) + + self.ffn = FeedForward( + size, linear_size, block_id, ffn_dropout_rate, num_blocks + ) + self.dropout_ffn = torch.nn.Dropout(p=dropout_rate) + + def forward( + self, + x: torch.Tensor, + state: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Compute receptance weighted key value. + + Args: + x: RWKV input sequences. (B, L, size) + state: Decoder hidden states. [5 x (B, D_att/size, N)] + + Returns: + x: RWKV output sequences. (B, L, size) + x: Decoder hidden states. [5 x (B, D_att/size, N)] + + """ + att, state = self.att(self.layer_norm_att(x), state=state) + x = x + self.dropout_att(att) + ffn, state = self.ffn(self.layer_norm_ffn(x), state=state) + x = x + self.dropout_ffn(ffn) + return x, state + + +class RWKVDecoderLayer(torch.nn.Module): + """RWKV module. + + Args: + size: Input/Output size. + linear_size: Feed-forward hidden size. + attention_size: SelfAttention hidden size. + context_size: Context size for WKV computation. + block_id: Block index. + num_blocks: Number of blocks in the architecture. + normalization_class: Normalization layer class. + normalization_args: Normalization layer arguments. + att_dropout_rate: Dropout rate for the attention module. + ffn_dropout_rate: Dropout rate for the feed-forward module. + + """ + + def __init__( + self, + size: int, + linear_size: int, + attention_size: int, + context_size: int, + block_id: int, + num_blocks: int, + att_dropout_rate: float = 0.0, + ffn_dropout_rate: float = 0.0, + dropout_rate: float = 0.0, + ) -> None: + """Construct a RWKV object.""" + super().__init__() + + self.layer_norm_att = LayerNorm(size) + self.layer_norm_ffn = LayerNorm(size) + + self.att = DecoderSelfAttention( + size, attention_size, context_size, block_id, att_dropout_rate, num_blocks + ) + self.dropout_att = torch.nn.Dropout(p=dropout_rate) + + self.ffn = FeedForward( + size, linear_size, block_id, ffn_dropout_rate, num_blocks + ) + self.dropout_ffn = torch.nn.Dropout(p=dropout_rate) + + def forward( + self, + x: torch.Tensor, + state: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Compute receptance weighted key value. + + Args: + x: RWKV input sequences. (B, L, size) + state: Decoder hidden states. [5 x (B, D_att/size, N)] + + Returns: + x: RWKV output sequences. (B, L, size) + x: Decoder hidden states. [5 x (B, D_att/size, N)] + + """ + att, state = self.att(self.layer_norm_att(x), state=state) + x = x + self.dropout_att(att) + + ffn, state = self.ffn(self.layer_norm_ffn(x), state=state) + x = x + self.dropout_ffn(ffn) + + return x, state diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_attention.py b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..7bed6e3c51a65bdadb8bfcf64d6b882bf70177b3 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_attention.py @@ -0,0 +1,632 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import math +import torch +from pathlib import Path +from importlib.util import find_spec +from typing import List, Optional, Tuple, Union + + +wkv_kernel_encoder = None +wkv_kernel_decoder = None + + +class WKVLinearAttentionEncoder(torch.autograd.Function): + """WKVLinearAttention function definition.""" + + @staticmethod + def forward( + ctx, + time_decay: torch.Tensor, + time_first: torch.Tensor, + key: torch.Tensor, + value: torch.tensor, + ) -> torch.Tensor: + """WKVLinearAttention function forward pass. + + Args: + time_decay: Channel-wise time decay vector. (D_att) + time_first: Channel-wise time first vector. (D_att) + key: Key tensor. (B, U, D_att) + value: Value tensor. (B, U, D_att) + + Returns: + out: Weighted Key-Value tensor. (B, U, D_att) + + """ + batch, length, dim = key.size() + + assert length <= wkv_kernel_encoder.context_size, ( + f"Cannot process key of length {length} while context_size " + f"is ({wkv_kernel_encoder.context_size}). Limit should be increased." + ) + + assert batch * dim % min(dim, 32) == 0, ( + f"batch size ({batch}) by dimension ({dim}) should be a multiple of " + f"{min(dim, 32)}" + ) + + ctx.input_dtype = key.dtype + + time_decay = -torch.exp(time_decay.float().contiguous()) + time_first = time_first.float().contiguous() + + key = key.float().contiguous() + value = value.float().contiguous() + + out = torch.empty_like(key, memory_format=torch.contiguous_format) + + wkv_kernel_encoder.forward(time_decay, time_first, key, value, out) + ctx.save_for_backward(time_decay, time_first, key, value, out) + + return out + + @staticmethod + def backward( + ctx, grad_output: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """WKVLinearAttention function backward pass. + + Args: + grad_output: Output gradient. (B, U, D_att) + + Returns: + grad_time_decay: Gradient for channel-wise time decay vector. (D_att) + grad_time_first: Gradient for channel-wise time first vector. (D_att) + grad_key: Gradient for key tensor. (B, U, D_att) + grad_value: Gradient for value tensor. (B, U, D_att) + + """ + time_decay, time_first, key, value, output = ctx.saved_tensors + grad_dtype = ctx.input_dtype + + batch, _, dim = key.size() + + grad_time_decay = torch.empty( + (batch, dim), + memory_format=torch.contiguous_format, + dtype=time_decay.dtype, + device=time_decay.device, + ) + + grad_time_first = torch.empty( + (batch, dim), + memory_format=torch.contiguous_format, + dtype=time_decay.dtype, + device=time_decay.device, + ) + + grad_key = torch.empty_like(key, memory_format=torch.contiguous_format) + grad_value = torch.empty_like(value, memory_format=torch.contiguous_format) + + wkv_kernel_encoder.backward( + time_decay, + time_first, + key, + value, + output, + grad_output.contiguous(), + grad_time_decay, + grad_time_first, + grad_key, + grad_value, + ) + + grad_time_decay = torch.sum(grad_time_decay, dim=0) + grad_time_first = torch.sum(grad_time_first, dim=0) + + return ( + grad_time_decay, + grad_time_first, + grad_key, + grad_value, + ) + + +class WKVLinearAttentionDecoder(torch.autograd.Function): + """WKVLinearAttention function definition.""" + + @staticmethod + def forward( + ctx, + time_decay: torch.Tensor, + time_first: torch.Tensor, + key: torch.Tensor, + value: torch.tensor, + ) -> torch.Tensor: + """WKVLinearAttention function forward pass. + + Args: + time_decay: Channel-wise time decay vector. (D_att) + time_first: Channel-wise time first vector. (D_att) + key: Key tensor. (B, U, D_att) + value: Value tensor. (B, U, D_att) + + Returns: + out: Weighted Key-Value tensor. (B, U, D_att) + + """ + batch, length, dim = key.size() + + assert length <= wkv_kernel_decoder.context_size, ( + f"Cannot process key of length {length} while context_size " + f"is ({wkv_kernel.context_size}). Limit should be increased." + ) + + assert batch * dim % min(dim, 32) == 0, ( + f"batch size ({batch}) by dimension ({dim}) should be a multiple of " + f"{min(dim, 32)}" + ) + + ctx.input_dtype = key.dtype + + time_decay = -torch.exp(time_decay.float().contiguous()) + time_first = time_first.float().contiguous() + + key = key.float().contiguous() + value = value.float().contiguous() + + out = torch.empty_like(key, memory_format=torch.contiguous_format) + + wkv_kernel_decoder.forward(time_decay, time_first, key, value, out) + ctx.save_for_backward(time_decay, time_first, key, value, out) + + return out + + @staticmethod + def backward( + ctx, grad_output: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """WKVLinearAttention function backward pass. + + Args: + grad_output: Output gradient. (B, U, D_att) + + Returns: + grad_time_decay: Gradient for channel-wise time decay vector. (D_att) + grad_time_first: Gradient for channel-wise time first vector. (D_att) + grad_key: Gradient for key tensor. (B, U, D_att) + grad_value: Gradient for value tensor. (B, U, D_att) + + """ + time_decay, time_first, key, value, output = ctx.saved_tensors + grad_dtype = ctx.input_dtype + + batch, _, dim = key.size() + + grad_time_decay = torch.empty( + (batch, dim), + memory_format=torch.contiguous_format, + dtype=time_decay.dtype, + device=time_decay.device, + ) + + grad_time_first = torch.empty( + (batch, dim), + memory_format=torch.contiguous_format, + dtype=time_decay.dtype, + device=time_decay.device, + ) + + grad_key = torch.empty_like(key, memory_format=torch.contiguous_format) + grad_value = torch.empty_like(value, memory_format=torch.contiguous_format) + + wkv_kernel_decoder.backward( + time_decay, + time_first, + key, + value, + output, + grad_output.contiguous(), + grad_time_decay, + grad_time_first, + grad_key, + grad_value, + ) + + grad_time_decay = torch.sum(grad_time_decay, dim=0) + grad_time_first = torch.sum(grad_time_first, dim=0) + + return ( + grad_time_decay, + grad_time_first, + grad_key, + grad_value, + ) + + +def load_encoder_wkv_kernel(context_size: int) -> None: + """Load WKV CUDA kernel. + + Args: + context_size: Context size. + + """ + from torch.utils.cpp_extension import load + + global wkv_kernel_encoder + + if ( + wkv_kernel_encoder is not None + and wkv_kernel_encoder.context_size == context_size + ): + return + + if find_spec("ninja") is None: + raise ImportError( + "Ninja package was not found. WKV kernel module can't be loaded " + "for training. Please, 'pip install ninja' in your environment." + ) + + if not torch.cuda.is_available(): + raise ImportError( + "CUDA is currently a requirement for WKV kernel loading. " + "Please set your devices properly and launch again." + ) + + kernel_folder = Path(__file__).resolve().parent / "cuda_encoder" + kernel_files = [kernel_folder / f for f in ["wkv_op.cpp", "wkv_cuda.cu"]] + + kernel_cflags = [ + "-res-usage", + "--maxrregcount 60", + "--use_fast_math", + "-O3", + "-Xptxas -O3", + f"-DTmax={context_size}", + ] + wkv_kernel_encoder = load( + name=f"encoder_wkv_{context_size}", + sources=kernel_files, + verbose=True, + extra_cuda_cflags=kernel_cflags, + ) + wkv_kernel_encoder.context_size = context_size + + +def load_decoder_wkv_kernel(context_size: int) -> None: + """Load WKV CUDA kernel. + + Args: + context_size: Context size. + + """ + from torch.utils.cpp_extension import load + + global wkv_kernel_decoder + + if ( + wkv_kernel_decoder is not None + and wkv_kernel_decoder.context_size == context_size + ): + return + + if find_spec("ninja") is None: + raise ImportError( + "Ninja package was not found. WKV kernel module can't be loaded " + "for training. Please, 'pip install ninja' in your environment." + ) + + if not torch.cuda.is_available(): + raise ImportError( + "CUDA is currently a requirement for WKV kernel loading. " + "Please set your devices properly and launch again." + ) + + kernel_folder = Path(__file__).resolve().parent / "cuda_decoder" + kernel_files = [kernel_folder / f for f in ["wkv_op.cpp", "wkv_cuda.cu"]] + + kernel_cflags = [ + "-res-usage", + "--maxrregcount 60", + "--use_fast_math", + "-O3", + "-Xptxas -O3", + f"-DTmax={context_size}", + ] + wkv_kernel_decoder = load( + name=f"decoder_wkv_{context_size}", + sources=kernel_files, + verbose=True, + extra_cuda_cflags=kernel_cflags, + ) + wkv_kernel_decoder.context_size = context_size + + +class SelfAttention(torch.nn.Module): + """SelfAttention module definition. + + Args: + size: Input/Output size. + attention_size: Attention hidden size. + context_size: Context size for WKV kernel. + block_id: Block index. + num_blocks: Number of blocks in the architecture. + + """ + + def __init__( + self, + size: int, + attention_size: int, + block_id: int, + dropout_rate: float, + num_blocks: int, + ) -> None: + """Construct a SelfAttention object.""" + super().__init__() + self.time_shift = torch.nn.ZeroPad2d((0, 0, 1, -1)) + + self.time_decay = torch.nn.Parameter(torch.empty(attention_size)) + self.time_first = torch.nn.Parameter(torch.empty(attention_size)) + + self.time_mix_key = torch.nn.Parameter(torch.empty(1, 1, size)) + self.time_mix_value = torch.nn.Parameter(torch.empty(1, 1, size)) + self.time_mix_receptance = torch.nn.Parameter(torch.empty(1, 1, size)) + + self.proj_key = torch.nn.Linear(size, attention_size, bias=True) + self.proj_value = torch.nn.Linear(size, attention_size, bias=True) + self.proj_receptance = torch.nn.Linear(size, attention_size, bias=True) + + self.proj_output = torch.nn.Linear(attention_size, size, bias=True) + + self.block_id = block_id + + self.reset_parameters(size, attention_size, block_id, num_blocks) + self.dropout = torch.nn.Dropout(p=dropout_rate) + + def reset_parameters( + self, size: int, attention_size: int, block_id: int, num_blocks: int + ) -> None: + """Reset module parameters. + + Args: + size: Block size. + attention_size: Attention hidden size. + block_id: Block index. + num_blocks: Number of blocks in the architecture. + + """ + ratio_0_to_1 = block_id / (num_blocks - 1) + ratio_1_to_almost0 = 1.0 - (block_id / num_blocks) + + time_weight = torch.ones(1, 1, size) + + for i in range(size): + time_weight[0, 0, i] = i / size + + decay_speed = [ + -5 + 8 * (h / (attention_size - 1)) ** (0.7 + 1.3 * ratio_0_to_1) + for h in range(attention_size) + ] + decay_speed = torch.tensor( + decay_speed, dtype=self.time_decay.dtype, device=self.time_decay.device + ) + + zigzag = ( + torch.tensor( + [(i + 1) % 3 - 1 for i in range(attention_size)], + dtype=self.time_first.dtype, + device=self.time_first.device, + ) + * 0.5 + ) + + with torch.no_grad(): + self.time_decay.data = decay_speed + self.time_first.data = torch.ones_like( + self.time_first * math.log(0.3) + zigzag + ) + + self.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0) + self.time_mix_value.data = ( + torch.pow(time_weight, ratio_1_to_almost0) + 0.3 * ratio_0_to_1 + ) + self.time_mix_receptance.data = torch.pow( + time_weight, 0.5 * ratio_1_to_almost0 + ) + + @torch.no_grad() + def wkv_linear_attention( + self, + time_decay: torch.Tensor, + time_first: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + state: Tuple[torch.Tensor, torch.Tensor, torch.Tensor], + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]: + """Compute WKV with state (i.e.: for inference). + + Args: + time_decay: Channel-wise time decay vector. (D_att) + time_first: Channel-wise time first vector. (D_att) + key: Key tensor. (B, 1, D_att) + value: Value tensor. (B, 1, D_att) + state: Decoder hidden states. [3 x (B, D_att)] + + Returns: + output: Weighted Key-Value. (B, 1, D_att) + state: Decoder hidden states. [3 x (B, 1, D_att)] + + """ + num_state, den_state, max_state = state + time_decay = -torch.exp(time_decay) + max_for_output = torch.maximum(max_state, (time_first + key)) + + e1 = torch.exp(max_state - max_for_output) + e2 = torch.exp((time_first + key) - max_for_output) + + numerator = e1 * num_state + e2 * value + denominator = e1 * den_state + e2 + + max_for_state = torch.maximum(key, (max_state + time_decay)) + + e1 = torch.exp((max_state + time_decay) - max_for_state) + e2 = torch.exp(key - max_for_state) + + wkv = numerator / denominator + + state = [e1 * num_state + e2 * value, e1 * den_state + e2, max_for_state] + + return wkv, state + + +class DecoderSelfAttention(SelfAttention): + """SelfAttention module definition. + + Args: + size: Input/Output size. + attention_size: Attention hidden size. + context_size: Context size for WKV kernel. + block_id: Block index. + num_blocks: Number of blocks in the architecture. + + """ + + def __init__( + self, + size: int, + attention_size: int, + context_size: int, + block_id: int, + dropout_rate: float, + num_blocks: int, + ) -> None: + """Construct a SelfAttention object.""" + super().__init__(size, attention_size, block_id, dropout_rate, num_blocks) + # load_decoder_wkv_kernel(context_size) + + def forward( + self, + x: torch.Tensor, + state: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, Optional[List[torch.Tensor]]]: + """Compute time mixing. + + Args: + x: SelfAttention input sequences. (B, U, size) + state: Decoder hidden states. [5 x (B, 1, D_att, N)] + + Returns: + x: SelfAttention output sequences. (B, U, size) + + """ + shifted_x = ( + self.time_shift(x) if state is None else state[1][..., self.block_id] + ) + + key = x * self.time_mix_key + shifted_x * (1 - self.time_mix_key) + value = x * self.time_mix_value + shifted_x * (1 - self.time_mix_value) + receptance = x * self.time_mix_receptance + shifted_x * ( + 1 - self.time_mix_receptance + ) + + key = self.proj_key(key) + value = self.proj_value(value) + receptance = torch.sigmoid(self.proj_receptance(receptance)) + + if state is not None: + state[1][..., self.block_id] = x + + wkv, att_state = self.wkv_linear_attention( + self.time_decay, + self.time_first, + key, + value, + tuple(s[..., self.block_id] for s in state[2:]), + ) + + state[2][..., self.block_id] = att_state[0] + state[3][..., self.block_id] = att_state[1] + state[4][..., self.block_id] = att_state[2] + else: + wkv = WKVLinearAttentionDecoder.apply( + self.time_decay, self.time_first, key, value + ) + + wkv = self.dropout(wkv) + x = self.proj_output(receptance * wkv) + + return x, state + + +class EncoderSelfAttention(SelfAttention): + """SelfAttention module definition. + + Args: + size: Input/Output size. + attention_size: Attention hidden size. + context_size: Context size for WKV kernel. + block_id: Block index. + num_blocks: Number of blocks in the architecture. + + """ + + def __init__( + self, + size: int, + attention_size: int, + context_size: int, + block_id: int, + dropout_rate: float, + num_blocks: int, + ) -> None: + """Construct a SelfAttention object.""" + super().__init__(size, attention_size, block_id, dropout_rate, num_blocks) + # load_encoder_wkv_kernel(context_size) + + def forward( + self, + x: torch.Tensor, + state: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, Optional[List[torch.Tensor]]]: + """Compute time mixing. + + Args: + x: SelfAttention input sequences. (B, U, size) + state: Decoder hidden states. [5 x (B, 1, D_att, N)] + + Returns: + x: SelfAttention output sequences. (B, U, size) + + """ + shifted_x = ( + self.time_shift(x) if state is None else state[1][..., self.block_id] + ) + + key = x * self.time_mix_key + shifted_x * (1 - self.time_mix_key) + value = x * self.time_mix_value + shifted_x * (1 - self.time_mix_value) + receptance = x * self.time_mix_receptance + shifted_x * ( + 1 - self.time_mix_receptance + ) + + key = self.proj_key(key) + value = self.proj_value(value) + receptance = torch.sigmoid(self.proj_receptance(receptance)) + + if state is not None: + state[1][..., self.block_id] = x + + wkv, att_state = self.wkv_linear_attention( + self.time_decay, + self.time_first, + key, + value, + tuple(s[..., self.block_id] for s in state[2:]), + ) + + state[2][..., self.block_id] = att_state[0] + state[3][..., self.block_id] = att_state[1] + state[4][..., self.block_id] = att_state[2] + else: + wkv = WKVLinearAttentionEncoder.apply( + self.time_decay, self.time_first, key, value + ) + + wkv = self.dropout(wkv) + x = self.proj_output(receptance * wkv) + + return x, state diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..20ac74d08bb64f7500668151d04229336d5b9841 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_encoder.py @@ -0,0 +1,158 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +from typing import Dict, List, Optional, Tuple + +from funasr_detach.register import tables +from funasr_detach.models.rwkv_bat.rwkv import RWKV +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.nets_utils import make_source_mask +from funasr_detach.models.rwkv_bat.rwkv_subsampling import RWKVConvInput + + +@tables.register("encoder_classes", "RWKVEncoder") +class RWKVEncoder(torch.nn.Module): + """RWKV encoder module. + + Based on https://arxiv.org/pdf/2305.13048.pdf. + + Args: + vocab_size: Vocabulary size. + output_size: Input/Output size. + context_size: Context size for WKV computation. + linear_size: FeedForward hidden size. + attention_size: SelfAttention hidden size. + normalization_type: Normalization layer type. + normalization_args: Normalization layer arguments. + num_blocks: Number of RWKV blocks. + embed_dropout_rate: Dropout rate for embedding layer. + att_dropout_rate: Dropout rate for the attention module. + ffn_dropout_rate: Dropout rate for the feed-forward module. + """ + + def __init__( + self, + input_size: int, + output_size: int = 512, + context_size: int = 1024, + linear_size: Optional[int] = None, + attention_size: Optional[int] = None, + num_blocks: int = 4, + att_dropout_rate: float = 0.0, + ffn_dropout_rate: float = 0.0, + dropout_rate: float = 0.0, + subsampling_factor: int = 4, + time_reduction_factor: int = 1, + kernel: int = 3, + **kwargs, + ) -> None: + """Construct a RWKVEncoder object.""" + super().__init__() + + self.embed = RWKVConvInput( + input_size, + [output_size // 4, output_size // 2, output_size], + subsampling_factor, + conv_kernel_size=kernel, + output_size=output_size, + ) + + self.subsampling_factor = subsampling_factor + + linear_size = output_size * 4 if linear_size is None else linear_size + attention_size = output_size if attention_size is None else attention_size + + self.rwkv_blocks = torch.nn.ModuleList( + [ + RWKV( + output_size, + linear_size, + attention_size, + context_size, + block_id, + num_blocks, + att_dropout_rate=att_dropout_rate, + ffn_dropout_rate=ffn_dropout_rate, + dropout_rate=dropout_rate, + ) + for block_id in range(num_blocks) + ] + ) + + self.embed_norm = LayerNorm(output_size) + self.final_norm = LayerNorm(output_size) + + self._output_size = output_size + self.context_size = context_size + + self.num_blocks = num_blocks + self.time_reduction_factor = time_reduction_factor + + def output_size(self) -> int: + return self._output_size + + def forward(self, x: torch.Tensor, x_len) -> torch.Tensor: + """Encode source label sequences. + + Args: + x: Encoder input sequences. (B, L) + + Returns: + out: Encoder output sequences. (B, U, D) + + """ + _, length, _ = x.size() + + assert ( + length <= self.context_size * self.subsampling_factor + ), "Context size is too short for current length: %d versus %d" % ( + length, + self.context_size * self.subsampling_factor, + ) + mask = make_source_mask(x_len).to(x.device) + x, mask = self.embed(x, mask, None) + x = self.embed_norm(x) + olens = mask.eq(0).sum(1) + + if self.training: + for block in self.rwkv_blocks: + x, _ = block(x) + else: + x = self.rwkv_infer(x) + + x = self.final_norm(x) + + if self.time_reduction_factor > 1: + x = x[:, :: self.time_reduction_factor, :] + olens = torch.floor_divide(olens - 1, self.time_reduction_factor) + 1 + + return x, olens, None + + def rwkv_infer(self, xs_pad): + + batch_size = xs_pad.shape[0] + + hidden_sizes = [self._output_size for i in range(5)] + + state = [ + torch.zeros( + (batch_size, 1, hidden_sizes[i], self.num_blocks), + dtype=torch.float32, + device=xs_pad.device, + ) + for i in range(5) + ] + + state[4] -= 1e-30 + + xs_out = [] + for t in range(xs_pad.shape[1]): + x_t = xs_pad[:, t, :] + for idx, block in enumerate(self.rwkv_blocks): + x_t, state = block(x_t, state=state) + xs_out.append(x_t) + xs_out = torch.cat(xs_out, dim=1) + return xs_out diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_feed_forward.py b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_feed_forward.py new file mode 100644 index 0000000000000000000000000000000000000000..da737160aa617e0fa462e7277bb053ba2c9aab12 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_feed_forward.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +from typing import List, Optional, Tuple + + +class FeedForward(torch.nn.Module): + """FeedForward module definition. + + Args: + size: Input/Output size. + hidden_size: Hidden size. + block_id: Block index. + num_blocks: Number of blocks in the architecture. + + """ + + def __init__( + self, + size: int, + hidden_size: int, + block_id: int, + dropout_rate: float, + num_blocks: int, + ) -> None: + """Construct a FeedForward object.""" + super().__init__() + + self.time_shift = torch.nn.ZeroPad2d((0, 0, 1, -1)) + + self.time_mix_key = torch.nn.Parameter(torch.empty(1, 1, size)) + self.time_mix_receptance = torch.nn.Parameter(torch.empty(1, 1, size)) + + self.proj_key = torch.nn.Linear(size, hidden_size, bias=True) + self.proj_value = torch.nn.Linear(hidden_size, size, bias=True) + self.proj_receptance = torch.nn.Linear(size, size, bias=True) + + self.block_id = block_id + + self.reset_parameters(size, block_id, num_blocks) + self.dropout = torch.nn.Dropout(p=dropout_rate) + + def reset_parameters(self, size: int, block_id: int, num_blocks: int) -> None: + """Reset module parameters. + + Args: + size: Block size. + block_id: Block index. + num_blocks: Number of blocks in the architecture. + + """ + ratio_1_to_almost0 = 1.0 - (block_id / num_blocks) + + time_weight = torch.ones(1, 1, size) + + for i in range(size): + time_weight[0, 0, i] = i / size + + with torch.no_grad(): + self.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0) + self.time_mix_receptance.data = torch.pow(time_weight, ratio_1_to_almost0) + + def forward( + self, x: torch.Tensor, state: Optional[List[torch.Tensor]] = None + ) -> Tuple[torch.Tensor, Optional[List[torch.Tensor]]]: + """Compute channel mixing. + + Args: + x: FeedForward input sequences. (B, U, size) + state: Decoder hidden state. [5 x (B, 1, size, N)] + + Returns: + x: FeedForward output sequences. (B, U, size) + state: Decoder hidden state. [5 x (B, 1, size, N)] + + """ + shifted_x = ( + self.time_shift(x) if state is None else state[0][..., self.block_id] + ) + + key = x * self.time_mix_key + shifted_x * (1 - self.time_mix_key) + receptance = x * self.time_mix_receptance + shifted_x * ( + 1 - self.time_mix_receptance + ) + + key = torch.square(torch.relu(self.proj_key(key))) + value = self.proj_value(self.dropout(key)) + receptance = torch.sigmoid(self.proj_receptance(receptance)) + + if state is not None: + state[0][..., self.block_id] = x + + x = receptance * value + + return x, state diff --git a/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_subsampling.py b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_subsampling.py new file mode 100644 index 0000000000000000000000000000000000000000..9b10718cf72e99865226c02bf87761d9acba2cc7 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/rwkv_bat/rwkv_subsampling.py @@ -0,0 +1,261 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import math +import torch +from typing import Optional, Tuple, Union +from funasr_detach.models.transformer.utils.nets_utils import pad_to_len + + +class TooShortUttError(Exception): + """Raised when the utt is too short for subsampling. + + Args: + message (str): Message for error catch + actual_size (int): the short size that cannot pass the subsampling + limit (int): the limit size for subsampling + + """ + + def __init__(self, message, actual_size, limit): + """Construct a TooShortUttError for error handler.""" + super().__init__(message) + self.actual_size = actual_size + self.limit = limit + + +def check_short_utt(ins, size): + """Check if the utterance is too short for subsampling.""" + if isinstance(ins, Conv2dSubsampling2) and size < 3: + return True, 3 + if isinstance(ins, Conv2dSubsampling) and size < 7: + return True, 7 + if isinstance(ins, Conv2dSubsampling6) and size < 11: + return True, 11 + if isinstance(ins, Conv2dSubsampling8) and size < 15: + return True, 15 + return False, -1 + + +class RWKVConvInput(torch.nn.Module): + """Streaming ConvInput module definition. + Args: + input_size: Input size. + conv_size: Convolution size. + subsampling_factor: Subsampling factor. + output_size: Block output dimension. + """ + + def __init__( + self, + input_size: int, + conv_size: Union[int, Tuple], + subsampling_factor: int = 4, + conv_kernel_size: int = 3, + output_size: Optional[int] = None, + ) -> None: + """Construct a ConvInput object.""" + super().__init__() + if subsampling_factor == 1: + conv_size1, conv_size2, conv_size3 = conv_size + + self.conv = torch.nn.Sequential( + torch.nn.Conv2d( + 1, + conv_size1, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size1, + conv_size1, + conv_kernel_size, + stride=[1, 2], + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size1, + conv_size2, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size2, + conv_size2, + conv_kernel_size, + stride=[1, 2], + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size2, + conv_size3, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size3, + conv_size3, + conv_kernel_size, + stride=[1, 2], + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + ) + + output_proj = conv_size3 * ((input_size // 2) // 2) + + self.subsampling_factor = 1 + + self.stride_1 = 1 + + self.create_new_mask = self.create_new_vgg_mask + + else: + conv_size1, conv_size2, conv_size3 = conv_size + + kernel_1 = int(subsampling_factor / 2) + + self.conv = torch.nn.Sequential( + torch.nn.Conv2d( + 1, + conv_size1, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size1, + conv_size1, + conv_kernel_size, + stride=[kernel_1, 2], + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size1, + conv_size2, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size2, + conv_size2, + conv_kernel_size, + stride=[2, 2], + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size2, + conv_size3, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size3, + conv_size3, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + ) + + output_proj = conv_size3 * ((input_size // 2) // 2) + + self.subsampling_factor = subsampling_factor + + self.create_new_mask = self.create_new_vgg_mask + + self.stride_1 = kernel_1 + + self.min_frame_length = 7 + + if output_size is not None: + self.output = torch.nn.Linear(output_proj, output_size) + self.output_size = output_size + else: + self.output = None + self.output_size = output_proj + + def forward( + self, + x: torch.Tensor, + mask: Optional[torch.Tensor], + chunk_size: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Encode input sequences. + Args: + x: ConvInput input sequences. (B, T, D_feats) + mask: Mask of input sequences. (B, 1, T) + Returns: + x: ConvInput output sequences. (B, sub(T), D_out) + mask: Mask of output sequences. (B, 1, sub(T)) + """ + if mask is not None: + mask = self.create_new_mask(mask) + olens = max(mask.eq(0).sum(1)) + + b, t, f = x.size() + x = x.unsqueeze(1) # (b. 1. t. f) + + if chunk_size is not None: + max_input_length = int( + chunk_size + * self.subsampling_factor + * (math.ceil(float(t) / (chunk_size * self.subsampling_factor))) + ) + x = map(lambda inputs: pad_to_len(inputs, max_input_length, 1), x) + x = list(x) + x = torch.stack(x, dim=0) + N_chunks = max_input_length // (chunk_size * self.subsampling_factor) + x = x.view(b * N_chunks, 1, chunk_size * self.subsampling_factor, f) + + x = self.conv(x) + + _, c, _, f = x.size() + if chunk_size is not None: + x = x.transpose(1, 2).contiguous().view(b, -1, c * f)[:, :olens, :] + else: + x = x.transpose(1, 2).contiguous().view(b, -1, c * f) + + if self.output is not None: + x = self.output(x) + + return x, mask[:, :olens][:, : x.size(1)] + + def create_new_vgg_mask(self, mask: torch.Tensor) -> torch.Tensor: + """Create a new mask for VGG output sequences. + Args: + mask: Mask of input sequences. (B, T) + Returns: + mask: Mask of output sequences. (B, sub(T)) + """ + if self.subsampling_factor > 1: + return mask[:, ::2][:, :: self.stride_1] + else: + return mask + + def get_size_before_subsampling(self, size: int) -> int: + """Return the original size before subsampling for a given size. + Args: + size: Number of frames after subsampling. + Returns: + : Number of frames before subsampling. + """ + return size * self.subsampling_factor diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/attention.py b/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..51b9baadb5873a4cb3da2b53ebc468675dcada4f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/attention.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + + +"""Multi-Head Attention layer definition.""" + +import math + +import numpy +import torch +from torch import nn +from typing import Optional, Tuple + +import torch.nn.functional as F +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +import funasr_detach.models.lora.layers as lora + + +class CosineDistanceAttention(nn.Module): + """Compute Cosine Distance between spk decoder output and speaker profile + Args: + profile_path: speaker profile file path (.npy file) + """ + + def __init__(self): + super().__init__() + self.softmax = nn.Softmax(dim=-1) + + def forward(self, spk_decoder_out, profile, profile_lens=None): + """ + Args: + spk_decoder_out(torch.Tensor):(B, L, D) + spk_profiles(torch.Tensor):(B, N, D) + """ + x = spk_decoder_out.unsqueeze(2) # (B, L, 1, D) + if profile_lens is not None: + + mask = (make_pad_mask(profile_lens)[:, None, :]).to(profile.device) + min_value = float( + numpy.finfo(torch.tensor(0, dtype=x.dtype).numpy().dtype).min + ) + weights_not_softmax = F.cosine_similarity( + x, profile.unsqueeze(1), dim=-1 + ).masked_fill(mask, min_value) + weights = self.softmax(weights_not_softmax).masked_fill( + mask, 0.0 + ) # (B, L, N) + else: + x = x[:, -1:, :, :] + weights_not_softmax = F.cosine_similarity( + x, profile.unsqueeze(1).to(x.device), dim=-1 + ) + weights = self.softmax(weights_not_softmax) # (B, 1, N) + spk_embedding = torch.matmul(weights, profile.to(weights.device)) # (B, L, D) + + return spk_embedding, weights diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/beam_search_sa_asr.py b/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/beam_search_sa_asr.py new file mode 100644 index 0000000000000000000000000000000000000000..4625d7f98cd1be39f93bd8c4bdf32b2252d493c8 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/beam_search_sa_asr.py @@ -0,0 +1,559 @@ +"""Beam search module.""" + +from itertools import chain +import logging +from typing import Any +from typing import Dict +from typing import List +from typing import NamedTuple +from typing import Tuple +from typing import Union + +import torch + +from funasr_detach.metrics import end_detect +from funasr_detach.models.transformer.scorers.scorer_interface import ( + PartialScorerInterface, +) +from funasr_detach.models.transformer.scorers.scorer_interface import ScorerInterface +from funasr_detach.models.decoder.abs_decoder import AbsDecoder + + +class Hypothesis(NamedTuple): + """Hypothesis data type.""" + + yseq: torch.Tensor + spk_weigths: List + score: Union[float, torch.Tensor] = 0 + scores: Dict[str, Union[float, torch.Tensor]] = dict() + states: Dict[str, Any] = dict() + + def asdict(self) -> dict: + """Convert data to JSON-friendly dict.""" + return self._replace( + yseq=self.yseq.tolist(), + score=float(self.score), + scores={k: float(v) for k, v in self.scores.items()}, + )._asdict() + + +class BeamSearch(torch.nn.Module): + """Beam search implementation.""" + + def __init__( + self, + scorers: Dict[str, ScorerInterface], + weights: Dict[str, float], + beam_size: int, + vocab_size: int, + sos: int, + eos: int, + token_list: List[str] = None, + pre_beam_ratio: float = 1.5, + pre_beam_score_key: str = None, + ): + """Initialize beam search. + + Args: + scorers (dict[str, ScorerInterface]): Dict of decoder modules + e.g., Decoder, CTCPrefixScorer, LM + The scorer will be ignored if it is `None` + weights (dict[str, float]): Dict of weights for each scorers + The scorer will be ignored if its weight is 0 + beam_size (int): The number of hypotheses kept during search + vocab_size (int): The number of vocabulary + sos (int): Start of sequence id + eos (int): End of sequence id + token_list (list[str]): List of tokens for debug log + pre_beam_score_key (str): key of scores to perform pre-beam search + pre_beam_ratio (float): beam size in the pre-beam search + will be `int(pre_beam_ratio * beam_size)` + + """ + super().__init__() + # set scorers + self.weights = weights + self.scorers = dict() + self.full_scorers = dict() + self.part_scorers = dict() + # this module dict is required for recursive cast + # `self.to(device, dtype)` in `recog.py` + self.nn_dict = torch.nn.ModuleDict() + for k, v in scorers.items(): + w = weights.get(k, 0) + if w == 0 or v is None: + continue + assert isinstance( + v, ScorerInterface + ), f"{k} ({type(v)}) does not implement ScorerInterface" + self.scorers[k] = v + if isinstance(v, PartialScorerInterface): + self.part_scorers[k] = v + else: + self.full_scorers[k] = v + if isinstance(v, torch.nn.Module): + self.nn_dict[k] = v + + # set configurations + self.sos = sos + self.eos = eos + self.token_list = token_list + self.pre_beam_size = int(pre_beam_ratio * beam_size) + self.beam_size = beam_size + self.n_vocab = vocab_size + if ( + pre_beam_score_key is not None + and pre_beam_score_key != "full" + and pre_beam_score_key not in self.full_scorers + ): + raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}") + self.pre_beam_score_key = pre_beam_score_key + self.do_pre_beam = ( + self.pre_beam_score_key is not None + and self.pre_beam_size < self.n_vocab + and len(self.part_scorers) > 0 + ) + + def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]: + """Get an initial hypothesis data. + + Args: + x (torch.Tensor): The encoder output feature + + Returns: + Hypothesis: The initial hypothesis. + + """ + init_states = dict() + init_scores = dict() + for k, d in self.scorers.items(): + init_states[k] = d.init_state(x) + init_scores[k] = 0.0 + return [ + Hypothesis( + score=0.0, + scores=init_scores, + states=init_states, + yseq=torch.tensor([self.sos], device=x.device), + spk_weigths=[], + ) + ] + + @staticmethod + def append_token(xs: torch.Tensor, x: int) -> torch.Tensor: + """Append new token to prefix tokens. + + Args: + xs (torch.Tensor): The prefix token + x (int): The new token to append + + Returns: + torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device + + """ + x = torch.tensor([x], dtype=xs.dtype, device=xs.device) + return torch.cat((xs, x)) + + def score_full( + self, + hyp: Hypothesis, + asr_enc: torch.Tensor, + spk_enc: torch.Tensor, + profile: torch.Tensor, + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.full_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.full_scorers` + and tensor score values of shape: `(self.n_vocab,)`, + and state dict that has string keys + and state values of `self.full_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.full_scorers.items(): + if isinstance(d, AbsDecoder): + scores[k], spk_weigths, states[k] = d.score( + hyp.yseq, hyp.states[k], asr_enc, spk_enc, profile + ) + else: + scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], asr_enc) + return scores, spk_weigths, states + + def score_partial( + self, + hyp: Hypothesis, + ids: torch.Tensor, + asr_enc: torch.Tensor, + spk_enc: torch.Tensor, + profile: torch.Tensor, + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.part_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + ids (torch.Tensor): 1D tensor of new partial tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.part_scorers` + and tensor score values of shape: `(len(ids),)`, + and state dict that has string keys + and state values of `self.part_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.part_scorers.items(): + if isinstance(d, AbsDecoder): + scores[k], states[k] = d.score_partial( + hyp.yseq, ids, hyp.states[k], asr_enc, spk_enc, profile + ) + else: + scores[k], states[k] = d.score_partial( + hyp.yseq, ids, hyp.states[k], asr_enc + ) + return scores, states + + def beam( + self, weighted_scores: torch.Tensor, ids: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute topk full token ids and partial token ids. + + Args: + weighted_scores (torch.Tensor): The weighted sum scores for each tokens. + Its shape is `(self.n_vocab,)`. + ids (torch.Tensor): The partial token ids to compute topk + + Returns: + Tuple[torch.Tensor, torch.Tensor]: + The topk full token ids and partial token ids. + Their shapes are `(self.beam_size,)` + + """ + # no pre beam performed + if weighted_scores.size(0) == ids.size(0): + top_ids = weighted_scores.topk(self.beam_size)[1] + return top_ids, top_ids + + # mask pruned in pre-beam not to select in topk + tmp = weighted_scores[ids] + weighted_scores[:] = -float("inf") + weighted_scores[ids] = tmp + top_ids = weighted_scores.topk(self.beam_size)[1] + local_ids = weighted_scores[ids].topk(self.beam_size)[1] + return top_ids, local_ids + + @staticmethod + def merge_scores( + prev_scores: Dict[str, float], + next_full_scores: Dict[str, torch.Tensor], + full_idx: int, + next_part_scores: Dict[str, torch.Tensor], + part_idx: int, + ) -> Dict[str, torch.Tensor]: + """Merge scores for new hypothesis. + + Args: + prev_scores (Dict[str, float]): + The previous hypothesis scores by `self.scorers` + next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers` + full_idx (int): The next token id for `next_full_scores` + next_part_scores (Dict[str, torch.Tensor]): + scores of partial tokens by `self.part_scorers` + part_idx (int): The new token id for `next_part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are scalar tensors by the scorers. + + """ + new_scores = dict() + for k, v in next_full_scores.items(): + new_scores[k] = prev_scores[k] + v[full_idx] + for k, v in next_part_scores.items(): + new_scores[k] = prev_scores[k] + v[part_idx] + return new_scores + + def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any: + """Merge states for new hypothesis. + + Args: + states: states of `self.full_scorers` + part_states: states of `self.part_scorers` + part_idx (int): The new token id for `part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are states of the scorers. + + """ + new_states = dict() + for k, v in states.items(): + new_states[k] = v + for k, d in self.part_scorers.items(): + new_states[k] = d.select_state(part_states[k], part_idx) + return new_states + + def search( + self, + running_hyps: List[Hypothesis], + asr_enc: torch.Tensor, + spk_enc: torch.Tensor, + profile: torch.Tensor, + ) -> List[Hypothesis]: + """Search new tokens for running hypotheses and encoded speech x. + + Args: + running_hyps (List[Hypothesis]): Running hypotheses on beam + x (torch.Tensor): Encoded speech feature (T, D) + + Returns: + List[Hypotheses]: Best sorted hypotheses + + """ + # import ipdb;ipdb.set_trace() + best_hyps = [] + part_ids = torch.arange(self.n_vocab, device=asr_enc.device) # no pre-beam + for hyp in running_hyps: + # scoring + weighted_scores = torch.zeros( + self.n_vocab, dtype=asr_enc.dtype, device=asr_enc.device + ) + scores, spk_weigths, states = self.score_full( + hyp, asr_enc, spk_enc, profile + ) + for k in self.full_scorers: + weighted_scores += self.weights[k] * scores[k] + # partial scoring + if self.do_pre_beam: + pre_beam_scores = ( + weighted_scores + if self.pre_beam_score_key == "full" + else scores[self.pre_beam_score_key] + ) + part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1] + part_scores, part_states = self.score_partial( + hyp, part_ids, asr_enc, spk_enc, profile + ) + for k in self.part_scorers: + weighted_scores[part_ids] += self.weights[k] * part_scores[k] + # add previous hyp score + weighted_scores += hyp.score + + # update hyps + for j, part_j in zip(*self.beam(weighted_scores, part_ids)): + # will be (2 x beam at most) + best_hyps.append( + Hypothesis( + score=weighted_scores[j], + yseq=self.append_token(hyp.yseq, j), + scores=self.merge_scores( + hyp.scores, scores, j, part_scores, part_j + ), + states=self.merge_states(states, part_states, part_j), + spk_weigths=hyp.spk_weigths + [spk_weigths], + ) + ) + + # sort and prune 2 x beam -> beam + best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[ + : min(len(best_hyps), self.beam_size) + ] + return best_hyps + + def forward( + self, + asr_enc: torch.Tensor, + spk_enc: torch.Tensor, + profile: torch.Tensor, + maxlenratio: float = 0.0, + minlenratio: float = 0.0, + ) -> List[Hypothesis]: + """Perform beam search. + + Args: + x (torch.Tensor): Encoded speech feature (T, D) + maxlenratio (float): Input length ratio to obtain max output length. + If maxlenratio=0.0 (default), it uses a end-detect function + to automatically find maximum hypothesis lengths + minlenratio (float): Input length ratio to obtain min output length. + + Returns: + list[Hypothesis]: N-best decoding results + + """ + # import ipdb;ipdb.set_trace() + # set length bounds + if maxlenratio == 0: + maxlen = asr_enc.shape[0] + else: + maxlen = max(1, int(maxlenratio * asr_enc.size(0))) + minlen = int(minlenratio * asr_enc.size(0)) + logging.info("decoder input length: " + str(asr_enc.shape[0])) + logging.info("max output length: " + str(maxlen)) + logging.info("min output length: " + str(minlen)) + + # main loop of prefix search + running_hyps = self.init_hyp(asr_enc) + ended_hyps = [] + for i in range(maxlen): + logging.debug("position " + str(i)) + best = self.search(running_hyps, asr_enc, spk_enc, profile) + # import pdb;pdb.set_trace() + # post process of one iteration + running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps) + # end detection + if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i): + logging.info(f"end detected at {i}") + break + if len(running_hyps) == 0: + logging.info("no hypothesis. Finish decoding.") + break + else: + logging.debug(f"remained hypotheses: {len(running_hyps)}") + + nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True) + # check the number of hypotheses reaching to eos + if len(nbest_hyps) == 0: + logging.warning( + "there is no N-best results, perform recognition " + "again with smaller minlenratio." + ) + return ( + [] + if minlenratio < 0.1 + else self.forward( + asr_enc, spk_enc, profile, maxlenratio, max(0.0, minlenratio - 0.1) + ) + ) + + # report the best result + best = nbest_hyps[0] + for k, v in best.scores.items(): + logging.info( + f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}" + ) + logging.info(f"total log probability: {best.score:.2f}") + logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}") + logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}") + if self.token_list is not None: + logging.info( + "best hypo: " + + "".join([self.token_list[x] for x in best.yseq[1:-1]]) + + "\n" + ) + return nbest_hyps + + def post_process( + self, + i: int, + maxlen: int, + maxlenratio: float, + running_hyps: List[Hypothesis], + ended_hyps: List[Hypothesis], + ) -> List[Hypothesis]: + """Perform post-processing of beam search iterations. + + Args: + i (int): The length of hypothesis tokens. + maxlen (int): The maximum length of tokens in beam search. + maxlenratio (int): The maximum length ratio in beam search. + running_hyps (List[Hypothesis]): The running hypotheses in beam search. + ended_hyps (List[Hypothesis]): The ended hypotheses in beam search. + + Returns: + List[Hypothesis]: The new running hypotheses. + + """ + logging.debug(f"the number of running hypotheses: {len(running_hyps)}") + if self.token_list is not None: + logging.debug( + "best hypo: " + + "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]]) + ) + # add eos in the final loop to avoid that there are no ended hyps + if i == maxlen - 1: + logging.info("adding in the last position in the loop") + running_hyps = [ + h._replace(yseq=self.append_token(h.yseq, self.eos)) + for h in running_hyps + ] + + # add ended hypotheses to a final list, and removed them from current hypotheses + # (this will be a problem, number of hyps < beam) + remained_hyps = [] + for hyp in running_hyps: + if hyp.yseq[-1] == self.eos: + # e.g., Word LM needs to add final score + for k, d in chain(self.full_scorers.items(), self.part_scorers.items()): + s = d.final_score(hyp.states[k]) + hyp.scores[k] += s + hyp = hyp._replace(score=hyp.score + self.weights[k] * s) + ended_hyps.append(hyp) + else: + remained_hyps.append(hyp) + return remained_hyps + + +def beam_search( + x: torch.Tensor, + sos: int, + eos: int, + beam_size: int, + vocab_size: int, + scorers: Dict[str, ScorerInterface], + weights: Dict[str, float], + token_list: List[str] = None, + maxlenratio: float = 0.0, + minlenratio: float = 0.0, + pre_beam_ratio: float = 1.5, + pre_beam_score_key: str = "full", +) -> list: + """Perform beam search with scorers. + + Args: + x (torch.Tensor): Encoded speech feature (T, D) + sos (int): Start of sequence id + eos (int): End of sequence id + beam_size (int): The number of hypotheses kept during search + vocab_size (int): The number of vocabulary + scorers (dict[str, ScorerInterface]): Dict of decoder modules + e.g., Decoder, CTCPrefixScorer, LM + The scorer will be ignored if it is `None` + weights (dict[str, float]): Dict of weights for each scorers + The scorer will be ignored if its weight is 0 + token_list (list[str]): List of tokens for debug log + maxlenratio (float): Input length ratio to obtain max output length. + If maxlenratio=0.0 (default), it uses a end-detect function + to automatically find maximum hypothesis lengths + minlenratio (float): Input length ratio to obtain min output length. + pre_beam_score_key (str): key of scores to perform pre-beam search + pre_beam_ratio (float): beam size in the pre-beam search + will be `int(pre_beam_ratio * beam_size)` + + Returns: + list: N-best decoding results + + """ + ret = BeamSearch( + scorers, + weights, + beam_size=beam_size, + vocab_size=vocab_size, + pre_beam_ratio=pre_beam_ratio, + pre_beam_score_key=pre_beam_score_key, + sos=sos, + eos=eos, + token_list=token_list, + ).forward(x=x, maxlenratio=maxlenratio, minlenratio=minlenratio) + return [h.asdict() for h in ret] diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/e2e_sa_asr.py b/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/e2e_sa_asr.py new file mode 100644 index 0000000000000000000000000000000000000000..1dea10d4996dfddf155b549a669d8e267a00fa03 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/e2e_sa_asr.py @@ -0,0 +1,531 @@ +# Copyright ESPnet (https://github.com/espnet/espnet). All Rights Reserved. +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +import logging +from contextlib import contextmanager +from distutils.version import LooseVersion +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple +from typing import Union + +import torch +import torch.nn.functional as F + +from funasr_detach.layers.abs_normalize import AbsNormalize +from funasr_detach.losses.label_smoothing_loss import ( + LabelSmoothingLoss, + NllLoss, +) # noqa: H301 +from funasr_detach.models.ctc import CTC +from funasr_detach.models.decoder.abs_decoder import AbsDecoder +from funasr_detach.models.encoder.abs_encoder import AbsEncoder +from funasr_detach.frontends.abs_frontend import AbsFrontend +from funasr_detach.models.postencoder.abs_postencoder import AbsPostEncoder +from funasr_detach.models.preencoder.abs_preencoder import AbsPreEncoder +from funasr_detach.models.specaug.abs_specaug import AbsSpecAug +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.metrics import ErrorCalculator +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.base_model import FunASRModel + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +class SAASRModel(FunASRModel): + """CTC-attention hybrid Encoder-Decoder model""" + + def __init__( + self, + vocab_size: int, + max_spk_num: int, + token_list: Union[Tuple[str, ...], List[str]], + frontend: Optional[AbsFrontend], + specaug: Optional[AbsSpecAug], + normalize: Optional[AbsNormalize], + asr_encoder: AbsEncoder, + spk_encoder: torch.nn.Module, + decoder: AbsDecoder, + ctc: CTC, + spk_weight: float = 0.5, + ctc_weight: float = 0.5, + interctc_weight: float = 0.0, + ignore_id: int = -1, + lsm_weight: float = 0.0, + length_normalized_loss: bool = False, + report_cer: bool = True, + report_wer: bool = True, + sym_space: str = "", + sym_blank: str = "", + extract_feats_in_collect_stats: bool = True, + ): + assert 0.0 <= ctc_weight <= 1.0, ctc_weight + assert 0.0 <= interctc_weight < 1.0, interctc_weight + + super().__init__() + # note that eos is the same as sos (equivalent ID) + self.blank_id = 0 + self.sos = 1 + self.eos = 2 + self.vocab_size = vocab_size + self.max_spk_num = max_spk_num + self.ignore_id = ignore_id + self.spk_weight = spk_weight + self.ctc_weight = ctc_weight + self.interctc_weight = interctc_weight + self.token_list = token_list.copy() + + self.frontend = frontend + self.specaug = specaug + self.normalize = normalize + self.asr_encoder = asr_encoder + self.spk_encoder = spk_encoder + + if not hasattr(self.asr_encoder, "interctc_use_conditioning"): + self.asr_encoder.interctc_use_conditioning = False + if self.asr_encoder.interctc_use_conditioning: + self.asr_encoder.conditioning_layer = torch.nn.Linear( + vocab_size, self.asr_encoder.output_size() + ) + + self.error_calculator = None + + # we set self.decoder = None in the CTC mode since + # self.decoder parameters were never used and PyTorch complained + # and threw an Exception in the multi-GPU experiment. + # thanks Jeff Farris for pointing out the issue. + if ctc_weight == 1.0: + self.decoder = None + else: + self.decoder = decoder + + self.criterion_att = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + self.criterion_spk = NllLoss( + size=max_spk_num, + padding_idx=ignore_id, + normalize_length=length_normalized_loss, + ) + + if report_cer or report_wer: + self.error_calculator = ErrorCalculator( + token_list, sym_space, sym_blank, report_cer, report_wer + ) + + if ctc_weight == 0.0: + self.ctc = None + else: + self.ctc = ctc + + self.extract_feats_in_collect_stats = extract_feats_in_collect_stats + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + profile: torch.Tensor, + profile_lengths: torch.Tensor, + text_id: torch.Tensor, + text_id_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Decoder + Calc loss + + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + profile: (Batch, Length, Dim) + profile_lengths: (Batch,) + """ + assert text_lengths.dim() == 1, text_lengths.shape + # Check that batch_size is unified + assert ( + speech.shape[0] + == speech_lengths.shape[0] + == text.shape[0] + == text_lengths.shape[0] + ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) + batch_size = speech.shape[0] + + # for data-parallel + text = text[:, : text_lengths.max()] + + # 1. Encoder + asr_encoder_out, encoder_out_lens, spk_encoder_out = self.encode( + speech, speech_lengths + ) + intermediate_outs = None + if isinstance(asr_encoder_out, tuple): + intermediate_outs = asr_encoder_out[1] + asr_encoder_out = asr_encoder_out[0] + + loss_att, loss_spk, acc_att, acc_spk, cer_att, wer_att = ( + None, + None, + None, + None, + None, + None, + ) + loss_ctc, cer_ctc = None, None + stats = dict() + + # 1. CTC branch + if self.ctc_weight != 0.0: + loss_ctc, cer_ctc = self._calc_ctc_loss( + asr_encoder_out, encoder_out_lens, text, text_lengths + ) + + # Intermediate CTC (optional) + loss_interctc = 0.0 + if self.interctc_weight != 0.0 and intermediate_outs is not None: + for layer_idx, intermediate_out in intermediate_outs: + # we assume intermediate_out has the same length & padding + # as those of encoder_out + loss_ic, cer_ic = self._calc_ctc_loss( + intermediate_out, encoder_out_lens, text, text_lengths + ) + loss_interctc = loss_interctc + loss_ic + + # Collect Intermedaite CTC stats + stats["loss_interctc_layer{}".format(layer_idx)] = ( + loss_ic.detach() if loss_ic is not None else None + ) + stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic + + loss_interctc = loss_interctc / len(intermediate_outs) + + # calculate whole encoder loss + loss_ctc = ( + 1 - self.interctc_weight + ) * loss_ctc + self.interctc_weight * loss_interctc + + # 2b. Attention decoder branch + if self.ctc_weight != 1.0: + loss_att, loss_spk, acc_att, acc_spk, cer_att, wer_att = ( + self._calc_att_loss( + asr_encoder_out, + spk_encoder_out, + encoder_out_lens, + text, + text_lengths, + profile, + profile_lengths, + text_id, + text_id_lengths, + ) + ) + + # 3. CTC-Att loss definition + if self.ctc_weight == 0.0: + loss_asr = loss_att + elif self.ctc_weight == 1.0: + loss_asr = loss_ctc + else: + loss_asr = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + + if self.spk_weight == 0.0: + loss = loss_asr + else: + loss = self.spk_weight * loss_spk + (1 - self.spk_weight) * loss_asr + + stats = dict( + loss=loss.detach(), + loss_asr=loss_asr.detach(), + loss_att=loss_att.detach() if loss_att is not None else None, + loss_ctc=loss_ctc.detach() if loss_ctc is not None else None, + loss_spk=loss_spk.detach() if loss_spk is not None else None, + acc=acc_att, + acc_spk=acc_spk, + cer=cer_att, + wer=wer_att, + cer_ctc=cer_ctc, + ) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def collect_feats( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Dict[str, torch.Tensor]: + if self.extract_feats_in_collect_stats: + feats, feats_lengths = self._extract_feats(speech, speech_lengths) + else: + # Generate dummy stats if extract_feats_in_collect_stats is False + logging.warning( + "Generating dummy stats for feats and feats_lengths, " + "because encoder_conf.extract_feats_in_collect_stats is " + f"{self.extract_feats_in_collect_stats}" + ) + feats, feats_lengths = speech, speech_lengths + return {"feats": feats, "feats_lengths": feats_lengths} + + def encode( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Frontend + Encoder. Note that this method is used by asr_inference.py + + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + """ + with autocast(False): + # 1. Extract feats + feats, feats_lengths = self._extract_feats(speech, speech_lengths) + + # 2. Data augmentation + feats_raw = feats.clone() + if self.specaug is not None and self.training: + feats, feats_lengths = self.specaug(feats, feats_lengths) + + # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + feats, feats_lengths = self.normalize(feats, feats_lengths) + + # 4. Forward encoder + # feats: (Batch, Length, Dim) + # -> encoder_out: (Batch, Length2, Dim2) + if self.asr_encoder.interctc_use_conditioning: + encoder_out, encoder_out_lens, _ = self.asr_encoder( + feats, feats_lengths, ctc=self.ctc + ) + else: + encoder_out, encoder_out_lens, _ = self.asr_encoder(feats, feats_lengths) + intermediate_outs = None + if isinstance(encoder_out, tuple): + intermediate_outs = encoder_out[1] + encoder_out = encoder_out[0] + + encoder_out_spk_ori = self.spk_encoder(feats_raw, feats_lengths)[0] + # import ipdb;ipdb.set_trace() + if encoder_out_spk_ori.size(1) != encoder_out.size(1): + encoder_out_spk = F.interpolate( + encoder_out_spk_ori.transpose(-2, -1), + size=(encoder_out.size(1)), + mode="nearest", + ).transpose(-2, -1) + else: + encoder_out_spk = encoder_out_spk_ori + + assert encoder_out.size(0) == speech.size(0), ( + encoder_out.size(), + speech.size(0), + ) + assert encoder_out.size(1) <= encoder_out_lens.max(), ( + encoder_out.size(), + encoder_out_lens.max(), + ) + assert encoder_out_spk.size(0) == speech.size(0), ( + encoder_out_spk.size(), + speech.size(0), + ) + + if intermediate_outs is not None: + return (encoder_out, intermediate_outs), encoder_out_lens, encoder_out_spk + + return encoder_out, encoder_out_lens, encoder_out_spk + + def _extract_feats( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + assert speech_lengths.dim() == 1, speech_lengths.shape + + # for data-parallel + speech = speech[:, : speech_lengths.max()] + + if self.frontend is not None: + # Frontend + # e.g. STFT and Feature extract + # data_loader may send time-domain signal in this case + # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim) + feats, feats_lengths = self.frontend(speech, speech_lengths) + else: + # No frontend and no feature extract + feats, feats_lengths = speech, speech_lengths + return feats, feats_lengths + + def nll( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ) -> torch.Tensor: + """Compute negative log likelihood(nll) from transformer-decoder + + Normally, this function is called in batchify_nll. + + Args: + encoder_out: (Batch, Length, Dim) + encoder_out_lens: (Batch,) + ys_pad: (Batch, Length) + ys_pad_lens: (Batch,) + """ + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + # 1. Forward decoder + decoder_out, _ = self.decoder( + encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens + ) # [batch, seqlen, dim] + batch_size = decoder_out.size(0) + decoder_num_class = decoder_out.size(2) + # nll: negative log-likelihood + nll = torch.nn.functional.cross_entropy( + decoder_out.view(-1, decoder_num_class), + ys_out_pad.view(-1), + ignore_index=self.ignore_id, + reduction="none", + ) + nll = nll.view(batch_size, -1) + nll = nll.sum(dim=1) + assert nll.size(0) == batch_size + return nll + + def batchify_nll( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + batch_size: int = 100, + ): + """Compute negative log likelihood(nll) from transformer-decoder + + To avoid OOM, this fuction seperate the input into batches. + Then call nll for each batch and combine and return results. + Args: + encoder_out: (Batch, Length, Dim) + encoder_out_lens: (Batch,) + ys_pad: (Batch, Length) + ys_pad_lens: (Batch,) + batch_size: int, samples each batch contain when computing nll, + you may change this to avoid OOM or increase + GPU memory usage + """ + total_num = encoder_out.size(0) + if total_num <= batch_size: + nll = self.nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens) + else: + nll = [] + start_idx = 0 + while True: + end_idx = min(start_idx + batch_size, total_num) + batch_encoder_out = encoder_out[start_idx:end_idx, :, :] + batch_encoder_out_lens = encoder_out_lens[start_idx:end_idx] + batch_ys_pad = ys_pad[start_idx:end_idx, :] + batch_ys_pad_lens = ys_pad_lens[start_idx:end_idx] + batch_nll = self.nll( + batch_encoder_out, + batch_encoder_out_lens, + batch_ys_pad, + batch_ys_pad_lens, + ) + nll.append(batch_nll) + start_idx = end_idx + if start_idx == total_num: + break + nll = torch.cat(nll) + assert nll.size(0) == total_num + return nll + + def _calc_att_loss( + self, + asr_encoder_out: torch.Tensor, + spk_encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + profile: torch.Tensor, + profile_lens: torch.Tensor, + text_id: torch.Tensor, + text_id_lengths: torch.Tensor, + ): + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + # 1. Forward decoder + decoder_out, weights_no_pad, _ = self.decoder( + asr_encoder_out, + spk_encoder_out, + encoder_out_lens, + ys_in_pad, + ys_in_lens, + profile, + profile_lens, + ) + + spk_num_no_pad = weights_no_pad.size(-1) + pad = (0, self.max_spk_num - spk_num_no_pad) + weights = F.pad(weights_no_pad, pad, mode="constant", value=0) + + # pre_id=weights.argmax(-1) + # pre_text=decoder_out.argmax(-1) + # id_mask=(pre_id==text_id).to(dtype=text_id.dtype) + # pre_text_mask=pre_text*id_mask+1-id_mask #相同的地方不变，不同的地方设为1() + # padding_mask= ys_out_pad != self.ignore_id + # numerator = torch.sum(pre_text_mask.masked_select(padding_mask) == ys_out_pad.masked_select(padding_mask)) + # denominator = torch.sum(padding_mask) + # sd_acc = float(numerator) / float(denominator) + + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_out_pad) + loss_spk = self.criterion_spk(torch.log(weights), text_id) + + acc_spk = th_accuracy( + weights.view(-1, self.max_spk_num), + text_id, + ignore_label=self.ignore_id, + ) + acc_att = th_accuracy( + decoder_out.view(-1, self.vocab_size), + ys_out_pad, + ignore_label=self.ignore_id, + ) + + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, loss_spk, acc_att, acc_spk, cer_att, wer_att + + def _calc_ctc_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + # Calc CTC loss + loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens) + + # Calc CER using CTC + cer_ctc = None + if not self.training and self.error_calculator is not None: + ys_hat = self.ctc.argmax(encoder_out).data + cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True) + return loss_ctc, cer_ctc diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/transformer_decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/transformer_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..8e9abb2283856745bafb1494821d5f9e0c60ab67 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sa_asr/transformer_decoder.py @@ -0,0 +1,768 @@ +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Decoder definition.""" +from typing import Any +from typing import List +from typing import Sequence +from typing import Tuple + +import torch +from torch import nn + + +from funasr_detach.models.transformer.attention import MultiHeadedAttention +from funasr_detach.models.sa_asr.attention import CosineDistanceAttention +from funasr_detach.models.transformer.utils.dynamic_conv import DynamicConvolution +from funasr_detach.models.transformer.utils.dynamic_conv2d import DynamicConvolution2D +from funasr_detach.models.transformer.embedding import PositionalEncoding +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.lightconv import LightweightConvolution +from funasr_detach.models.transformer.utils.lightconv2d import LightweightConvolution2D +from funasr_detach.models.transformer.utils.mask import subsequent_mask +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, # noqa: H301 +) +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.scorers.scorer_interface import ( + BatchScorerInterface, +) + +from funasr_detach.register import tables + + +class DecoderLayer(nn.Module): + """Single decoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + src_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + + + """ + + def __init__( + self, + size, + self_attn, + src_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + ): + """Construct an DecoderLayer object.""" + super(DecoderLayer, self).__init__() + self.size = size + self.self_attn = self_attn + self.src_attn = src_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(size) + self.norm2 = LayerNorm(size) + self.norm3 = LayerNorm(size) + self.dropout = nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear1 = nn.Linear(size + size, size) + self.concat_linear2 = nn.Linear(size + size, size) + + def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + + if cache is None: + tgt_q = tgt + tgt_q_mask = tgt_mask + else: + # compute only the last frame query keeping dim: max_time_out -> 1 + assert cache.shape == ( + tgt.shape[0], + tgt.shape[1] - 1, + self.size, + ), f"{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}" + tgt_q = tgt[:, -1:, :] + residual = residual[:, -1:, :] + tgt_q_mask = None + if tgt_mask is not None: + tgt_q_mask = tgt_mask[:, -1:, :] + + if self.concat_after: + tgt_concat = torch.cat( + (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)), dim=-1 + ) + x = residual + self.concat_linear1(tgt_concat) + else: + x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + if self.concat_after: + x_concat = torch.cat( + (x, self.src_attn(x, memory, memory, memory_mask)), dim=-1 + ) + x = residual + self.concat_linear2(x_concat) + else: + x = residual + self.dropout(self.src_attn(x, memory, memory, memory_mask)) + if not self.normalize_before: + x = self.norm2(x) + + residual = x + if self.normalize_before: + x = self.norm3(x) + x = residual + self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm3(x) + + if cache is not None: + x = torch.cat([cache, x], dim=1) + + return x, tgt_mask, memory, memory_mask + + +class BaseTransformerDecoder(nn.Module, BatchScorerInterface): + """Base class of Transfomer decoder module. + + Args: + vocab_size: output dim + encoder_output_size: dimension of attention + attention_heads: the number of heads of multi head attention + linear_units: the number of units of position-wise feed forward + num_blocks: the number of decoder blocks + dropout_rate: dropout rate + self_attention_dropout_rate: dropout rate for attention + input_layer: input layer type + use_output_layer: whether to use output layer + pos_enc_class: PositionalEncoding or ScaledPositionalEncoding + normalize_before: whether to use layer_norm before the first block + concat_after: whether to concat attention layer's input and output + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. + i.e. x -> x + att(x) + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + ): + super().__init__() + attention_dim = encoder_output_size + + if input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(vocab_size, attention_dim), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + elif input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(vocab_size, attention_dim), + torch.nn.LayerNorm(attention_dim), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + else: + raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}") + + self.normalize_before = normalize_before + if self.normalize_before: + self.after_norm = LayerNorm(attention_dim) + if use_output_layer: + self.output_layer = torch.nn.Linear(attention_dim, vocab_size) + else: + self.output_layer = None + + # Must set by the inheritance + self.decoders = None + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward decoder. + + Args: + hs_pad: encoded memory, float32 (batch, maxlen_in, feat) + hlens: (batch) + ys_in_pad: + input token ids, int64 (batch, maxlen_out) + if input_layer == "embed" + input tensor (batch, maxlen_out, #mels) in the other cases + ys_in_lens: (batch) + Returns: + (tuple): tuple containing: + + x: decoded token score before softmax (batch, maxlen_out, token) + if use_output_layer is True, + olens: (batch, ) + """ + tgt = ys_in_pad + # tgt_mask: (B, 1, L) + tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device) + # m: (1, L, L) + m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0) + # tgt_mask: (B, L, L) + tgt_mask = tgt_mask & m + + memory = hs_pad + memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to( + memory.device + ) + # Padding for Longformer + if memory_mask.shape[-1] != memory.shape[1]: + padlen = memory.shape[1] - memory_mask.shape[-1] + memory_mask = torch.nn.functional.pad( + memory_mask, (0, padlen), "constant", False + ) + + x = self.embed(tgt) + x, tgt_mask, memory, memory_mask = self.decoders( + x, tgt_mask, memory, memory_mask + ) + if self.normalize_before: + x = self.after_norm(x) + if self.output_layer is not None: + x = self.output_layer(x) + + olens = tgt_mask.sum(1) + return x, olens + + def forward_one_step( + self, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + cache: List[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + + Args: + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + memory: encoded memory, float32 (batch, maxlen_in, feat) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + x = self.embed(tgt) + if cache is None: + cache = [None] * len(self.decoders) + new_cache = [] + for c, decoder in zip(cache, self.decoders): + x, tgt_mask, memory, memory_mask = decoder( + x, tgt_mask, memory, None, cache=c + ) + new_cache.append(x) + + if self.normalize_before: + y = self.after_norm(x[:, -1]) + else: + y = x[:, -1] + if self.output_layer is not None: + y = torch.log_softmax(self.output_layer(y), dim=-1) + + return y, new_cache + + def score(self, ys, state, x): + """Score.""" + ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0) + logp, state = self.forward_one_step( + ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state + ) + return logp.squeeze(0), state + + def batch_score( + self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor + ) -> Tuple[torch.Tensor, List[Any]]: + """Score new token batch. + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, List[Any]]: Tuple of + batchfied scores for next token with shape of `(n_batch, n_vocab)` + and next state list for ys. + + """ + # merge states + n_batch = len(ys) + n_layers = len(self.decoders) + if states[0] is None: + batch_state = None + else: + # transpose state of [batch, layer] into [layer, batch] + batch_state = [ + torch.stack([states[b][i] for b in range(n_batch)]) + for i in range(n_layers) + ] + + # batch decoding + ys_mask = subsequent_mask(ys.size(-1), device=xs.device).unsqueeze(0) + logp, states = self.forward_one_step(ys, ys_mask, xs, cache=batch_state) + + # transpose state of [layer, batch] into [batch, layer] + state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)] + return logp, state_list + + +@tables.register("decoder_classes", "TransformerDecoder") +class TransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + ): + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + MultiHeadedAttention( + attention_heads, attention_dim, self_attention_dropout_rate + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + +@tables.register("decoder_classes", "ParaformerDecoderSAN") +class ParaformerDecoderSAN(BaseTransformerDecoder): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition + https://arxiv.org/abs/2006.01713 + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + embeds_id: int = -1, + ): + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + MultiHeadedAttention( + attention_heads, attention_dim, self_attention_dropout_rate + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + self.embeds_id = embeds_id + self.attention_dim = attention_dim + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward decoder. + + Args: + hs_pad: encoded memory, float32 (batch, maxlen_in, feat) + hlens: (batch) + ys_in_pad: + input token ids, int64 (batch, maxlen_out) + if input_layer == "embed" + input tensor (batch, maxlen_out, #mels) in the other cases + ys_in_lens: (batch) + Returns: + (tuple): tuple containing: + + x: decoded token score before softmax (batch, maxlen_out, token) + if use_output_layer is True, + olens: (batch, ) + """ + tgt = ys_in_pad + tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device) + + memory = hs_pad + memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to( + memory.device + ) + # Padding for Longformer + if memory_mask.shape[-1] != memory.shape[1]: + padlen = memory.shape[1] - memory_mask.shape[-1] + memory_mask = torch.nn.functional.pad( + memory_mask, (0, padlen), "constant", False + ) + + # x = self.embed(tgt) + x = tgt + embeds_outputs = None + for layer_id, decoder in enumerate(self.decoders): + x, tgt_mask, memory, memory_mask = decoder(x, tgt_mask, memory, memory_mask) + if layer_id == self.embeds_id: + embeds_outputs = x + if self.normalize_before: + x = self.after_norm(x) + if self.output_layer is not None: + x = self.output_layer(x) + + olens = tgt_mask.sum(1) + if embeds_outputs is not None: + return x, olens, embeds_outputs + else: + return x, olens + + +@tables.register("decoder_classes", "LightweightConvolutionTransformerDecoder") +class LightweightConvolutionTransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + conv_wshare: int = 4, + conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), + conv_usebias: int = False, + ): + if len(conv_kernel_length) != num_blocks: + raise ValueError( + "conv_kernel_length must have equal number of values to num_blocks: " + f"{len(conv_kernel_length)} != {num_blocks}" + ) + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + LightweightConvolution( + wshare=conv_wshare, + n_feat=attention_dim, + dropout_rate=self_attention_dropout_rate, + kernel_size=conv_kernel_length[lnum], + use_kernel_mask=True, + use_bias=conv_usebias, + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + +@tables.register("decoder_classes", "LightweightConvolution2DTransformerDecoder") +class LightweightConvolution2DTransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + conv_wshare: int = 4, + conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), + conv_usebias: int = False, + ): + if len(conv_kernel_length) != num_blocks: + raise ValueError( + "conv_kernel_length must have equal number of values to num_blocks: " + f"{len(conv_kernel_length)} != {num_blocks}" + ) + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + LightweightConvolution2D( + wshare=conv_wshare, + n_feat=attention_dim, + dropout_rate=self_attention_dropout_rate, + kernel_size=conv_kernel_length[lnum], + use_kernel_mask=True, + use_bias=conv_usebias, + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + +@tables.register("decoder_classes", "DynamicConvolutionTransformerDecoder") +class DynamicConvolutionTransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + conv_wshare: int = 4, + conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), + conv_usebias: int = False, + ): + if len(conv_kernel_length) != num_blocks: + raise ValueError( + "conv_kernel_length must have equal number of values to num_blocks: " + f"{len(conv_kernel_length)} != {num_blocks}" + ) + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + attention_dim = encoder_output_size + + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + DynamicConvolution( + wshare=conv_wshare, + n_feat=attention_dim, + dropout_rate=self_attention_dropout_rate, + kernel_size=conv_kernel_length[lnum], + use_kernel_mask=True, + use_bias=conv_usebias, + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + +@tables.register("decoder_classes", "DynamicConvolution2DTransformerDecoder") +class DynamicConvolution2DTransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + conv_wshare: int = 4, + conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), + conv_usebias: int = False, + ): + if len(conv_kernel_length) != num_blocks: + raise ValueError( + "conv_kernel_length must have equal number of values to num_blocks: " + f"{len(conv_kernel_length)} != {num_blocks}" + ) + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + attention_dim = encoder_output_size + + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + DynamicConvolution2D( + wshare=conv_wshare, + n_feat=attention_dim, + dropout_rate=self_attention_dropout_rate, + kernel_size=conv_kernel_length[lnum], + use_kernel_mask=True, + use_bias=conv_usebias, + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sanm/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/sanm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sanm/attention.py b/demo/Step-Audio-EditX/funasr_detach/models/sanm/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..64b2076365c4747f552914109e0693b298738baa --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sanm/attention.py @@ -0,0 +1,718 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Multi-Head Attention layer definition.""" + +import math + +import numpy +import torch +from torch import nn +from typing import Optional, Tuple + +import torch.nn.functional as F +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +import funasr_detach.models.lora.layers as lora + + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, n_head, n_feat, dropout_rate): + """Construct an MultiHeadedAttention object.""" + super(MultiHeadedAttention, self).__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.attn = None + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv(self, query, key, value): + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + + return q, k, v + + def forward_attention(self, value, scores, mask): + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2). + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + if mask is not None: + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + min_value = float( + numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min + ) + scores = scores.masked_fill(mask, min_value) + self.attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0 + ) # (batch, head, time1, time2) + else: + self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(self.attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward(self, query, key, value, mask): + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q, k, v = self.forward_qkv(query, key, value) + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask) + + +class MultiHeadedAttentionSANM(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__( + self, + n_head, + in_feat, + n_feat, + dropout_rate, + kernel_size, + sanm_shfit=0, + lora_list=None, + lora_rank=8, + lora_alpha=16, + lora_dropout=0.1, + ): + """Construct an MultiHeadedAttention object.""" + super().__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + # self.linear_q = nn.Linear(n_feat, n_feat) + # self.linear_k = nn.Linear(n_feat, n_feat) + # self.linear_v = nn.Linear(n_feat, n_feat) + if lora_list is not None: + if "o" in lora_list: + self.linear_out = lora.Linear( + n_feat, + n_feat, + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + ) + else: + self.linear_out = nn.Linear(n_feat, n_feat) + lora_qkv_list = ["q" in lora_list, "k" in lora_list, "v" in lora_list] + if lora_qkv_list == [False, False, False]: + self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3) + else: + self.linear_q_k_v = lora.MergedLinear( + in_feat, + n_feat * 3, + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + enable_lora=lora_qkv_list, + ) + else: + self.linear_out = nn.Linear(n_feat, n_feat) + self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3) + self.attn = None + self.dropout = nn.Dropout(p=dropout_rate) + + self.fsmn_block = nn.Conv1d( + n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False + ) + # padding + left_padding = (kernel_size - 1) // 2 + if sanm_shfit > 0: + left_padding = left_padding + sanm_shfit + right_padding = kernel_size - 1 - left_padding + self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0) + + def forward_fsmn(self, inputs, mask, mask_shfit_chunk=None): + b, t, d = inputs.size() + if mask is not None: + mask = torch.reshape(mask, (b, -1, 1)) + if mask_shfit_chunk is not None: + mask = mask * mask_shfit_chunk + inputs = inputs * mask + + x = inputs.transpose(1, 2) + x = self.pad_fn(x) + x = self.fsmn_block(x) + x = x.transpose(1, 2) + x += inputs + x = self.dropout(x) + if mask is not None: + x = x * mask + return x + + def forward_qkv(self, x): + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k). + + """ + b, t, d = x.size() + q_k_v = self.linear_q_k_v(x) + q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1) + q_h = torch.reshape(q, (b, t, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + k_h = torch.reshape(k, (b, t, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time2, d_k) + v_h = torch.reshape(v, (b, t, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time2, d_k) + + return q_h, k_h, v_h, v + + def forward_attention(self, value, scores, mask, mask_att_chunk_encoder=None): + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2). + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + if mask is not None: + if mask_att_chunk_encoder is not None: + mask = mask * mask_att_chunk_encoder + + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + + min_value = float( + numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min + ) + scores = scores.masked_fill(mask, min_value) + self.attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0 + ) # (batch, head, time1, time2) + else: + self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(self.attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None): + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q_h, k_h, v_h, v = self.forward_qkv(x) + fsmn_memory = self.forward_fsmn(v, mask, mask_shfit_chunk) + q_h = q_h * self.d_k ** (-0.5) + scores = torch.matmul(q_h, k_h.transpose(-2, -1)) + att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder) + return att_outs + fsmn_memory + + def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0): + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q_h, k_h, v_h, v = self.forward_qkv(x) + if chunk_size is not None and look_back > 0 or look_back == -1: + if cache is not None: + k_h_stride = k_h[:, :, : -(chunk_size[2]), :] + v_h_stride = v_h[:, :, : -(chunk_size[2]), :] + k_h = torch.cat((cache["k"], k_h), dim=2) + v_h = torch.cat((cache["v"], v_h), dim=2) + + cache["k"] = torch.cat((cache["k"], k_h_stride), dim=2) + cache["v"] = torch.cat((cache["v"], v_h_stride), dim=2) + if look_back != -1: + cache["k"] = cache["k"][:, :, -(look_back * chunk_size[1]) :, :] + cache["v"] = cache["v"][:, :, -(look_back * chunk_size[1]) :, :] + else: + cache_tmp = { + "k": k_h[:, :, : -(chunk_size[2]), :], + "v": v_h[:, :, : -(chunk_size[2]), :], + } + cache = cache_tmp + fsmn_memory = self.forward_fsmn(v, None) + q_h = q_h * self.d_k ** (-0.5) + scores = torch.matmul(q_h, k_h.transpose(-2, -1)) + att_outs = self.forward_attention(v_h, scores, None) + return att_outs + fsmn_memory, cache + + +class MultiHeadedAttentionSANMDecoder(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, n_feat, dropout_rate, kernel_size, sanm_shfit=0): + """Construct an MultiHeadedAttention object.""" + super(MultiHeadedAttentionSANMDecoder, self).__init__() + + self.dropout = nn.Dropout(p=dropout_rate) + + self.fsmn_block = nn.Conv1d( + n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False + ) + # padding + # padding + left_padding = (kernel_size - 1) // 2 + if sanm_shfit > 0: + left_padding = left_padding + sanm_shfit + right_padding = kernel_size - 1 - left_padding + self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0) + self.kernel_size = kernel_size + + def forward(self, inputs, mask, cache=None, mask_shfit_chunk=None): + """ + :param x: (#batch, time1, size). + :param mask: Mask tensor (#batch, 1, time) + :return: + """ + # print("in fsmn, inputs", inputs.size()) + b, t, d = inputs.size() + # logging.info( + # "mask: {}".format(mask.size())) + if mask is not None: + mask = torch.reshape(mask, (b, -1, 1)) + # logging.info("in fsmn, mask: {}, {}".format(mask.size(), mask[0:100:50, :, :])) + if mask_shfit_chunk is not None: + # logging.info("in fsmn, mask_fsmn: {}, {}".format(mask_shfit_chunk.size(), mask_shfit_chunk[0:100:50, :, :])) + mask = mask * mask_shfit_chunk + # logging.info("in fsmn, mask_after_fsmn: {}, {}".format(mask.size(), mask[0:100:50, :, :])) + # print("in fsmn, mask", mask.size()) + # print("in fsmn, inputs", inputs.size()) + inputs = inputs * mask + + x = inputs.transpose(1, 2) + b, d, t = x.size() + if cache is None: + # print("in fsmn, cache is None, x", x.size()) + + x = self.pad_fn(x) + if not self.training: + cache = x + else: + # print("in fsmn, cache is not None, x", x.size()) + # x = torch.cat((x, cache), dim=2)[:, :, :-1] + # if t < self.kernel_size: + # x = self.pad_fn(x) + x = torch.cat((cache[:, :, 1:], x), dim=2) + x = x[:, :, -(self.kernel_size + t - 1) :] + # print("in fsmn, cache is not None, x_cat", x.size()) + cache = x + x = self.fsmn_block(x) + x = x.transpose(1, 2) + # print("in fsmn, fsmn_out", x.size()) + if x.size(1) != inputs.size(1): + inputs = inputs[:, -1, :] + + x = x + inputs + x = self.dropout(x) + if mask is not None: + x = x * mask + return x, cache + + +class MultiHeadedAttentionCrossAtt(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__( + self, + n_head, + n_feat, + dropout_rate, + lora_list=None, + lora_rank=8, + lora_alpha=16, + lora_dropout=0.1, + encoder_output_size=None, + ): + """Construct an MultiHeadedAttention object.""" + super(MultiHeadedAttentionCrossAtt, self).__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + if lora_list is not None: + if "q" in lora_list: + self.linear_q = lora.Linear( + n_feat, + n_feat, + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + ) + else: + self.linear_q = nn.Linear(n_feat, n_feat) + lora_kv_list = ["k" in lora_list, "v" in lora_list] + if lora_kv_list == [False, False]: + self.linear_k_v = nn.Linear( + n_feat if encoder_output_size is None else encoder_output_size, + n_feat * 2, + ) + else: + self.linear_k_v = lora.MergedLinear( + n_feat if encoder_output_size is None else encoder_output_size, + n_feat * 2, + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + enable_lora=lora_kv_list, + ) + if "o" in lora_list: + self.linear_out = lora.Linear( + n_feat, + n_feat, + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + ) + else: + self.linear_out = nn.Linear(n_feat, n_feat) + else: + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k_v = nn.Linear( + n_feat if encoder_output_size is None else encoder_output_size, + n_feat * 2, + ) + self.linear_out = nn.Linear(n_feat, n_feat) + self.attn = None + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv(self, x, memory): + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k). + + """ + + # print("in forward_qkv, x", x.size()) + b = x.size(0) + q = self.linear_q(x) + q_h = torch.reshape(q, (b, -1, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + + k_v = self.linear_k_v(memory) + k, v = torch.split(k_v, int(self.h * self.d_k), dim=-1) + k_h = torch.reshape(k, (b, -1, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time2, d_k) + v_h = torch.reshape(v, (b, -1, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time2, d_k) + + return q_h, k_h, v_h + + def forward_attention(self, value, scores, mask, ret_attn=False): + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2). + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + if mask is not None: + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + min_value = float( + numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min + ) + # logging.info( + # "scores: {}, mask_size: {}".format(scores.size(), mask.size())) + scores = scores.masked_fill(mask, min_value) + self.attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0 + ) # (batch, head, time1, time2) + else: + self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + p_attn = self.dropout(self.attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + if ret_attn: + return self.linear_out(x), self.attn # (batch, time1, d_model) + return self.linear_out(x) # (batch, time1, d_model) + + def forward(self, x, memory, memory_mask, ret_attn=False): + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q_h, k_h, v_h = self.forward_qkv(x, memory) + q_h = q_h * self.d_k ** (-0.5) + scores = torch.matmul(q_h, k_h.transpose(-2, -1)) + return self.forward_attention(v_h, scores, memory_mask, ret_attn=ret_attn) + + def forward_chunk(self, x, memory, cache=None, chunk_size=None, look_back=0): + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q_h, k_h, v_h = self.forward_qkv(x, memory) + if chunk_size is not None and look_back > 0: + if cache is not None: + k_h = torch.cat((cache["k"], k_h), dim=2) + v_h = torch.cat((cache["v"], v_h), dim=2) + cache["k"] = k_h[:, :, -(look_back * chunk_size[1]) :, :] + cache["v"] = v_h[:, :, -(look_back * chunk_size[1]) :, :] + else: + cache_tmp = { + "k": k_h[:, :, -(look_back * chunk_size[1]) :, :], + "v": v_h[:, :, -(look_back * chunk_size[1]) :, :], + } + cache = cache_tmp + q_h = q_h * self.d_k ** (-0.5) + scores = torch.matmul(q_h, k_h.transpose(-2, -1)) + return self.forward_attention(v_h, scores, None), cache + + +class MultiHeadSelfAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, n_head, in_feat, n_feat, dropout_rate): + """Construct an MultiHeadedAttention object.""" + super(MultiHeadSelfAttention, self).__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_out = nn.Linear(n_feat, n_feat) + self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3) + self.attn = None + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv(self, x): + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k). + + """ + b, t, d = x.size() + q_k_v = self.linear_q_k_v(x) + q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1) + q_h = torch.reshape(q, (b, t, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + k_h = torch.reshape(k, (b, t, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time2, d_k) + v_h = torch.reshape(v, (b, t, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time2, d_k) + + return q_h, k_h, v_h, v + + def forward_attention(self, value, scores, mask, mask_att_chunk_encoder=None): + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2). + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + if mask is not None: + if mask_att_chunk_encoder is not None: + mask = mask * mask_att_chunk_encoder + + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + + min_value = float( + numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min + ) + scores = scores.masked_fill(mask, min_value) + self.attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0 + ) # (batch, head, time1, time2) + else: + self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(self.attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward(self, x, mask, mask_att_chunk_encoder=None): + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q_h, k_h, v_h, v = self.forward_qkv(x) + q_h = q_h * self.d_k ** (-0.5) + scores = torch.matmul(q_h, k_h.transpose(-2, -1)) + att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder) + return att_outs diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sanm/decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/sanm/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..6b4adcdbe4ba4a6cd9352903ccfe8a78e97692a6 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sanm/decoder.py @@ -0,0 +1,517 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +from typing import List +from typing import Tuple +import logging +import torch +import torch.nn as nn +import numpy as np + +from funasr_detach.models.scama import utils as myutils +from funasr_detach.models.transformer.decoder import BaseTransformerDecoder + +from funasr_detach.models.sanm.attention import ( + MultiHeadedAttentionSANMDecoder, + MultiHeadedAttentionCrossAtt, +) +from funasr_detach.models.transformer.embedding import PositionalEncoding +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.sanm.positionwise_feed_forward import ( + PositionwiseFeedForwardDecoderSANM, +) +from funasr_detach.models.transformer.utils.repeat import repeat + +from funasr_detach.register import tables + + +class DecoderLayerSANM(nn.Module): + """Single decoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + src_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + + + """ + + def __init__( + self, + size, + self_attn, + src_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + ): + """Construct an DecoderLayer object.""" + super(DecoderLayerSANM, self).__init__() + self.size = size + self.self_attn = self_attn + self.src_attn = src_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(size) + if self_attn is not None: + self.norm2 = LayerNorm(size) + if src_attn is not None: + self.norm3 = LayerNorm(size) + self.dropout = nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear1 = nn.Linear(size + size, size) + self.concat_linear2 = nn.Linear(size + size, size) + + def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + # tgt = self.dropout(tgt) + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn: + if self.normalize_before: + tgt = self.norm2(tgt) + x, _ = self.self_attn(tgt, tgt_mask) + x = residual + self.dropout(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm3(x) + + x = residual + self.dropout(self.src_attn(x, memory, memory_mask)) + + return x, tgt_mask, memory, memory_mask, cache + + def forward_one_step(self, tgt, tgt_mask, memory, memory_mask=None, cache=None): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + # tgt = self.dropout(tgt) + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn: + if self.normalize_before: + tgt = self.norm2(tgt) + if self.training: + cache = None + x, cache = self.self_attn(tgt, tgt_mask, cache=cache) + x = residual + self.dropout(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm3(x) + + x = residual + self.dropout(self.src_attn(x, memory, memory_mask)) + + return x, tgt_mask, memory, memory_mask, cache + + def forward_chunk( + self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0 + ): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn: + if self.normalize_before: + tgt = self.norm2(tgt) + x, fsmn_cache = self.self_attn(tgt, None, fsmn_cache) + x = residual + self.dropout(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm3(x) + + x, opt_cache = self.src_attn.forward_chunk( + x, memory, opt_cache, chunk_size, look_back + ) + x = residual + x + + return x, memory, fsmn_cache, opt_cache + + +@tables.register("decoder_classes", "FsmnDecoder") +class FsmnDecoder(BaseTransformerDecoder): + """ + Author: Zhifu Gao, Shiliang Zhang, Ming Lei, Ian McLoughlin + San-m: Memory equipped self-attention for end-to-end speech recognition + https://arxiv.org/abs/2006.01713 + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + att_layer_num: int = 6, + kernel_size: int = 21, + sanm_shfit: int = None, + concat_embeds: bool = False, + attention_dim: int = None, + tf2torch_tensor_name_prefix_torch: str = "decoder", + tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder", + embed_tensor_name_prefix_tf: str = None, + ): + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + if attention_dim is None: + attention_dim = encoder_output_size + + if input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(vocab_size, attention_dim), + ) + elif input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(vocab_size, attention_dim), + torch.nn.LayerNorm(attention_dim), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + else: + raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}") + + self.normalize_before = normalize_before + if self.normalize_before: + self.after_norm = LayerNorm(attention_dim) + if use_output_layer: + self.output_layer = torch.nn.Linear(attention_dim, vocab_size) + else: + self.output_layer = None + + self.att_layer_num = att_layer_num + self.num_blocks = num_blocks + if sanm_shfit is None: + sanm_shfit = (kernel_size - 1) // 2 + self.decoders = repeat( + att_layer_num, + lambda lnum: DecoderLayerSANM( + attention_dim, + MultiHeadedAttentionSANMDecoder( + attention_dim, + self_attention_dropout_rate, + kernel_size, + sanm_shfit=sanm_shfit, + ), + MultiHeadedAttentionCrossAtt( + attention_heads, + attention_dim, + src_attention_dropout_rate, + encoder_output_size=encoder_output_size, + ), + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + if num_blocks - att_layer_num <= 0: + self.decoders2 = None + else: + self.decoders2 = repeat( + num_blocks - att_layer_num, + lambda lnum: DecoderLayerSANM( + attention_dim, + MultiHeadedAttentionSANMDecoder( + attention_dim, + self_attention_dropout_rate, + kernel_size, + sanm_shfit=sanm_shfit, + ), + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + self.decoders3 = repeat( + 1, + lambda lnum: DecoderLayerSANM( + attention_dim, + None, + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + if concat_embeds: + self.embed_concat_ffn = repeat( + 1, + lambda lnum: DecoderLayerSANM( + attention_dim + encoder_output_size, + None, + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim + encoder_output_size, + linear_units, + dropout_rate, + adim=attention_dim, + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + else: + self.embed_concat_ffn = None + self.concat_embeds = concat_embeds + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + self.embed_tensor_name_prefix_tf = embed_tensor_name_prefix_tf + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + chunk_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward decoder. + + Args: + hs_pad: encoded memory, float32 (batch, maxlen_in, feat) + hlens: (batch) + ys_in_pad: + input token ids, int64 (batch, maxlen_out) + if input_layer == "embed" + input tensor (batch, maxlen_out, #mels) in the other cases + ys_in_lens: (batch) + Returns: + (tuple): tuple containing: + + x: decoded token score before softmax (batch, maxlen_out, token) + if use_output_layer is True, + olens: (batch, ) + """ + tgt = ys_in_pad + tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None] + + memory = hs_pad + memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :] + if chunk_mask is not None: + memory_mask = memory_mask * chunk_mask + if tgt_mask.size(1) != memory_mask.size(1): + memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1) + + x = self.embed(tgt) + + if pre_acoustic_embeds is not None and self.concat_embeds: + x = torch.cat((x, pre_acoustic_embeds), dim=-1) + x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None) + + x, tgt_mask, memory, memory_mask, _ = self.decoders( + x, tgt_mask, memory, memory_mask + ) + if self.decoders2 is not None: + x, tgt_mask, memory, memory_mask, _ = self.decoders2( + x, tgt_mask, memory, memory_mask + ) + x, tgt_mask, memory, memory_mask, _ = self.decoders3( + x, tgt_mask, memory, memory_mask + ) + if self.normalize_before: + x = self.after_norm(x) + if self.output_layer is not None: + x = self.output_layer(x) + + olens = tgt_mask.sum(1) + return x, olens + + def score( + self, + ys, + state, + x, + x_mask=None, + pre_acoustic_embeds: torch.Tensor = None, + ): + """Score.""" + ys_mask = myutils.sequence_mask( + torch.tensor([len(ys)], dtype=torch.int32), device=x.device + )[:, :, None] + logp, state = self.forward_one_step( + ys.unsqueeze(0), + ys_mask, + x.unsqueeze(0), + memory_mask=x_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + cache=state, + ) + return logp.squeeze(0), state + + def forward_one_step( + self, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + memory_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + cache: List[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + + Args: + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + memory: encoded memory, float32 (batch, maxlen_in, feat) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + + x = tgt[:, -1:] + tgt_mask = None + x = self.embed(x) + + if pre_acoustic_embeds is not None and self.concat_embeds: + x = torch.cat((x, pre_acoustic_embeds), dim=-1) + x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None) + + if cache is None: + cache_layer_num = len(self.decoders) + if self.decoders2 is not None: + cache_layer_num += len(self.decoders2) + cache = [None] * cache_layer_num + new_cache = [] + # for c, decoder in zip(cache, self.decoders): + for i in range(self.att_layer_num): + decoder = self.decoders[i] + c = cache[i] + x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step( + x, tgt_mask, memory, memory_mask, cache=c + ) + new_cache.append(c_ret) + + if self.num_blocks - self.att_layer_num >= 1: + for i in range(self.num_blocks - self.att_layer_num): + j = i + self.att_layer_num + decoder = self.decoders2[i] + c = cache[j] + x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step( + x, tgt_mask, memory, memory_mask, cache=c + ) + new_cache.append(c_ret) + + for decoder in self.decoders3: + x, tgt_mask, memory, memory_mask, _ = decoder.forward_one_step( + x, tgt_mask, memory, None, cache=None + ) + + if self.normalize_before: + y = self.after_norm(x[:, -1]) + else: + y = x[:, -1] + if self.output_layer is not None: + y = self.output_layer(y) + y = torch.log_softmax(y, dim=-1) + + return y, new_cache diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sanm/encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/sanm/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..8a0ff685ced640e96db158f5a5c5bde45e5c0d4b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sanm/encoder.py @@ -0,0 +1,491 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +from typing import List +from typing import Optional +from typing import Sequence +from typing import Tuple +from typing import Union +import logging +import torch +import torch.nn as nn +import torch.nn.functional as F + +import numpy as np +from funasr_detach.train_utils.device_funcs import to_device +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.sanm.attention import ( + MultiHeadedAttention, + MultiHeadedAttentionSANM, +) +from funasr_detach.models.transformer.embedding import ( + SinusoidalPositionEncoder, + StreamSinusoidalPositionEncoder, +) +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear +from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, # noqa: H301 +) +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling2 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling6 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling8 +from funasr_detach.models.transformer.utils.subsampling import TooShortUttError +from funasr_detach.models.transformer.utils.subsampling import check_short_utt + + +from funasr_detach.models.ctc.ctc import CTC + +from funasr_detach.register import tables + + +class EncoderLayerSANM(nn.Module): + def __init__( + self, + in_size, + size, + self_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + stochastic_depth_rate=0.0, + ): + """Construct an EncoderLayer object.""" + super(EncoderLayerSANM, self).__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(in_size) + self.norm2 = LayerNorm(size) + self.dropout = nn.Dropout(dropout_rate) + self.in_size = in_size + self.size = size + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear = nn.Linear(size + size, size) + self.stochastic_depth_rate = stochastic_depth_rate + self.dropout_rate = dropout_rate + + def forward( + self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None + ): + """Compute encoded features. + + Args: + x_input (torch.Tensor): Input tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + skip_layer = False + # with stochastic depth, residual connection `x + f(x)` becomes + # `x <- x + 1 / (1 - p) * f(x)` at training time. + stoch_layer_coeff = 1.0 + if self.training and self.stochastic_depth_rate > 0: + skip_layer = torch.rand(1).item() < self.stochastic_depth_rate + stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate) + + if skip_layer: + if cache is not None: + x = torch.cat([cache, x], dim=1) + return x, mask + + residual = x + if self.normalize_before: + x = self.norm1(x) + + if self.concat_after: + x_concat = torch.cat( + ( + x, + self.self_attn( + x, + mask, + mask_shfit_chunk=mask_shfit_chunk, + mask_att_chunk_encoder=mask_att_chunk_encoder, + ), + ), + dim=-1, + ) + if self.in_size == self.size: + x = residual + stoch_layer_coeff * self.concat_linear(x_concat) + else: + x = stoch_layer_coeff * self.concat_linear(x_concat) + else: + if self.in_size == self.size: + x = residual + stoch_layer_coeff * self.dropout( + self.self_attn( + x, + mask, + mask_shfit_chunk=mask_shfit_chunk, + mask_att_chunk_encoder=mask_att_chunk_encoder, + ) + ) + else: + x = stoch_layer_coeff * self.dropout( + self.self_attn( + x, + mask, + mask_shfit_chunk=mask_shfit_chunk, + mask_att_chunk_encoder=mask_att_chunk_encoder, + ) + ) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm2(x) + + return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder + + def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0): + """Compute encoded features. + + Args: + x_input (torch.Tensor): Input tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + + residual = x + if self.normalize_before: + x = self.norm1(x) + + if self.in_size == self.size: + attn, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back) + x = residual + attn + else: + x, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back) + + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + self.feed_forward(x) + if not self.normalize_before: + x = self.norm2(x) + + return x, cache + + +@tables.register("encoder_classes", "SANMEncoder") +class SANMEncoder(nn.Module): + """ + Author: Zhifu Gao, Shiliang Zhang, Ming Lei, Ian McLoughlin + San-m: Memory equipped self-attention for end-to-end speech recognition + https://arxiv.org/abs/2006.01713 + """ + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: Optional[str] = "conv2d", + pos_enc_class=SinusoidalPositionEncoder, + normalize_before: bool = True, + concat_after: bool = False, + positionwise_layer_type: str = "linear", + positionwise_conv_kernel_size: int = 1, + padding_idx: int = -1, + interctc_layer_idx: List[int] = [], + interctc_use_conditioning: bool = False, + kernel_size: int = 11, + sanm_shfit: int = 0, + lora_list: List[str] = None, + lora_rank: int = 8, + lora_alpha: int = 16, + lora_dropout: float = 0.1, + selfattention_layer_type: str = "sanm", + tf2torch_tensor_name_prefix_torch: str = "encoder", + tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder", + ): + super().__init__() + self._output_size = output_size + + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(input_size, output_size), + torch.nn.LayerNorm(output_size), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate) + elif input_layer == "conv2d2": + self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate) + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate) + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate) + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), + SinusoidalPositionEncoder(), + ) + elif input_layer is None: + if input_size == output_size: + self.embed = None + else: + self.embed = torch.nn.Linear(input_size, output_size) + elif input_layer == "pe": + self.embed = SinusoidalPositionEncoder() + elif input_layer == "pe_online": + self.embed = StreamSinusoidalPositionEncoder() + else: + raise ValueError("unknown input_layer: " + input_layer) + self.normalize_before = normalize_before + if positionwise_layer_type == "linear": + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d": + positionwise_layer = MultiLayeredConv1d + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d-linear": + positionwise_layer = Conv1dLinear + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + else: + raise NotImplementedError("Support only linear or conv1d.") + + if selfattention_layer_type == "selfattn": + encoder_selfattn_layer = MultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + + elif selfattention_layer_type == "sanm": + encoder_selfattn_layer = MultiHeadedAttentionSANM + encoder_selfattn_layer_args0 = ( + attention_heads, + input_size, + output_size, + attention_dropout_rate, + kernel_size, + sanm_shfit, + lora_list, + lora_rank, + lora_alpha, + lora_dropout, + ) + + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + output_size, + attention_dropout_rate, + kernel_size, + sanm_shfit, + lora_list, + lora_rank, + lora_alpha, + lora_dropout, + ) + self.encoders0 = repeat( + 1, + lambda lnum: EncoderLayerSANM( + input_size, + output_size, + encoder_selfattn_layer(*encoder_selfattn_layer_args0), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + self.encoders = repeat( + num_blocks - 1, + lambda lnum: EncoderLayerSANM( + output_size, + output_size, + encoder_selfattn_layer(*encoder_selfattn_layer_args), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + ), + ) + if self.normalize_before: + self.after_norm = LayerNorm(output_size) + + self.interctc_layer_idx = interctc_layer_idx + if len(interctc_layer_idx) > 0: + assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks + self.interctc_use_conditioning = interctc_use_conditioning + self.conditioning_layer = None + self.dropout = nn.Dropout(dropout_rate) + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ctc: CTC = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Embed positions in tensor. + + Args: + xs_pad: input tensor (B, L, D) + ilens: input length (B) + prev_states: Not to be used now. + Returns: + position embedded tensor and mask + """ + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + xs_pad = xs_pad * self.output_size() ** 0.5 + if self.embed is None: + xs_pad = xs_pad + elif ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling2) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + else: + xs_pad = self.embed(xs_pad) + + # xs_pad = self.dropout(xs_pad) + encoder_outs = self.encoders0(xs_pad, masks) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + intermediate_outs = [] + if len(self.interctc_layer_idx) == 0: + encoder_outs = self.encoders(xs_pad, masks) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + else: + for layer_idx, encoder_layer in enumerate(self.encoders): + encoder_outs = encoder_layer(xs_pad, masks) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + + if layer_idx + 1 in self.interctc_layer_idx: + encoder_out = xs_pad + + # intermediate outputs are also normalized + if self.normalize_before: + encoder_out = self.after_norm(encoder_out) + + intermediate_outs.append((layer_idx + 1, encoder_out)) + + if self.interctc_use_conditioning: + ctc_out = ctc.softmax(encoder_out) + xs_pad = xs_pad + self.conditioning_layer(ctc_out) + + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + + olens = masks.squeeze(1).sum(1) + if len(intermediate_outs) > 0: + return (xs_pad, intermediate_outs), olens, None + return xs_pad, olens, None + + def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}): + if len(cache) == 0: + return feats + cache["feats"] = to_device(cache["feats"], device=feats.device) + overlap_feats = torch.cat((cache["feats"], feats), dim=1) + cache["feats"] = overlap_feats[ + :, -(cache["chunk_size"][0] + cache["chunk_size"][2]) :, : + ] + return overlap_feats + + def forward_chunk( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + cache: dict = None, + ctc: CTC = None, + ): + xs_pad *= self.output_size() ** 0.5 + if self.embed is None: + xs_pad = xs_pad + else: + xs_pad = self.embed(xs_pad, cache) + if cache["tail_chunk"]: + xs_pad = to_device(cache["feats"], device=xs_pad.device) + else: + xs_pad = self._add_overlap_chunk(xs_pad, cache) + encoder_outs = self.encoders0(xs_pad, None, None, None, None) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + intermediate_outs = [] + if len(self.interctc_layer_idx) == 0: + encoder_outs = self.encoders(xs_pad, None, None, None, None) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + else: + for layer_idx, encoder_layer in enumerate(self.encoders): + encoder_outs = encoder_layer(xs_pad, None, None, None, None) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + if layer_idx + 1 in self.interctc_layer_idx: + encoder_out = xs_pad + + # intermediate outputs are also normalized + if self.normalize_before: + encoder_out = self.after_norm(encoder_out) + + intermediate_outs.append((layer_idx + 1, encoder_out)) + + if self.interctc_use_conditioning: + ctc_out = ctc.softmax(encoder_out) + xs_pad = xs_pad + self.conditioning_layer(ctc_out) + + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + + if len(intermediate_outs) > 0: + return (xs_pad, intermediate_outs), None, None + return xs_pad, ilens, None diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sanm/model.py b/demo/Step-Audio-EditX/funasr_detach/models/sanm/model.py new file mode 100644 index 0000000000000000000000000000000000000000..0c8c103b3bf79f71f0b521604e930faec12178ac --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sanm/model.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import logging + +import torch + +from funasr_detach.models.transformer.model import Transformer +from funasr_detach.register import tables + + +@tables.register("model_classes", "SANM") +class SANM(Transformer): + """ + Author: Zhifu Gao, Shiliang Zhang, Ming Lei, Ian McLoughlin + San-m: Memory equipped self-attention for end-to-end speech recognition + https://arxiv.org/abs/2006.01713 + """ + + def __init__( + self, + *args, + **kwargs, + ): + + super().__init__(*args, **kwargs) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sanm/positionwise_feed_forward.py b/demo/Step-Audio-EditX/funasr_detach/models/sanm/positionwise_feed_forward.py new file mode 100644 index 0000000000000000000000000000000000000000..63a77c5fd0952e5ce07379881dc7937fd6f37254 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sanm/positionwise_feed_forward.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + + +"""Positionwise feed forward layer definition.""" + +import torch + +from funasr_detach.models.transformer.layer_norm import LayerNorm + + +class PositionwiseFeedForwardDecoderSANM(torch.nn.Module): + """Positionwise feed forward layer. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + + """ + + def __init__( + self, idim, hidden_units, dropout_rate, adim=None, activation=torch.nn.ReLU() + ): + """Construct an PositionwiseFeedForward object.""" + super(PositionwiseFeedForwardDecoderSANM, self).__init__() + self.w_1 = torch.nn.Linear(idim, hidden_units) + self.w_2 = torch.nn.Linear( + hidden_units, idim if adim is None else adim, bias=False + ) + self.dropout = torch.nn.Dropout(dropout_rate) + self.activation = activation + self.norm = LayerNorm(hidden_units) + + def forward(self, x): + """Forward function.""" + return self.w_2(self.norm(self.dropout(self.activation(self.w_1(x))))) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sanm/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/sanm/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..328eb1dd7fd55c63ec10fa422ec3d31a44079f9d --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sanm/template.yaml @@ -0,0 +1,121 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: SANM +model_conf: + ctc_weight: 0.0 + lsm_weight: 0.1 + length_normalized_loss: true + +# encoder +encoder: SANMEncoder +encoder_conf: + output_size: 512 + attention_heads: 4 + linear_units: 2048 + num_blocks: 50 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + +# decoder +decoder: FsmnDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 16 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + att_layer_num: 16 + kernel_size: 11 + sanm_shfit: 0 + + + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 7 + lfr_n: 6 + +specaug: SpecAugLFR +specaug_conf: + apply_time_warp: false + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + lfr_rate: 6 + num_freq_mask: 1 + apply_time_mask: true + time_mask_width_range: + - 0 + - 12 + num_time_mask: 1 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + val_scheduler_criterion: + - valid + - acc + best_model_criterion: + - - valid + - acc + - max + keep_nbest_models: 10 + avg_nbest_model: 5 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0005 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true + +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/scama/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/scama/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/scama/beam_search.py b/demo/Step-Audio-EditX/funasr_detach/models/scama/beam_search.py new file mode 100644 index 0000000000000000000000000000000000000000..4b7623acf9733636c37ab7e4a8f7ee64a12fa774 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/scama/beam_search.py @@ -0,0 +1,1013 @@ +"""Beam search module.""" + +from itertools import chain +import logging +from typing import Any +from typing import Dict +from typing import List +from typing import NamedTuple +from typing import Tuple +from typing import Union + +import torch + +from funasr_detach.metrics.common import end_detect +from funasr_detach.models.transformer.scorers.scorer_interface import ( + PartialScorerInterface, +) +from funasr_detach.models.transformer.scorers.scorer_interface import ScorerInterface + + +class Hypothesis(NamedTuple): + """Hypothesis data type.""" + + yseq: torch.Tensor + score: Union[float, torch.Tensor] = 0 + scores: Dict[str, Union[float, torch.Tensor]] = dict() + states: Dict[str, Any] = dict() + + def asdict(self) -> dict: + """Convert data to JSON-friendly dict.""" + return self._replace( + yseq=self.yseq.tolist(), + score=float(self.score), + scores={k: float(v) for k, v in self.scores.items()}, + )._asdict() + + +class BeamSearchScama(torch.nn.Module): + """Beam search implementation.""" + + def __init__( + self, + scorers: Dict[str, ScorerInterface], + weights: Dict[str, float], + beam_size: int, + vocab_size: int, + sos: int, + eos: int, + token_list: List[str] = None, + pre_beam_ratio: float = 1.5, + pre_beam_score_key: str = None, + ): + """Initialize beam search. + + Args: + scorers (dict[str, ScorerInterface]): Dict of decoder modules + e.g., Decoder, CTCPrefixScorer, LM + The scorer will be ignored if it is `None` + weights (dict[str, float]): Dict of weights for each scorers + The scorer will be ignored if its weight is 0 + beam_size (int): The number of hypotheses kept during search + vocab_size (int): The number of vocabulary + sos (int): Start of sequence id + eos (int): End of sequence id + token_list (list[str]): List of tokens for debug log + pre_beam_score_key (str): key of scores to perform pre-beam search + pre_beam_ratio (float): beam size in the pre-beam search + will be `int(pre_beam_ratio * beam_size)` + + """ + super().__init__() + # set scorers + self.weights = weights + self.scorers = dict() + self.full_scorers = dict() + self.part_scorers = dict() + # this module dict is required for recursive cast + # `self.to(device, dtype)` in `recog.py` + self.nn_dict = torch.nn.ModuleDict() + for k, v in scorers.items(): + w = weights.get(k, 0) + if w == 0 or v is None: + continue + assert isinstance( + v, ScorerInterface + ), f"{k} ({type(v)}) does not implement ScorerInterface" + self.scorers[k] = v + if isinstance(v, PartialScorerInterface): + self.part_scorers[k] = v + else: + self.full_scorers[k] = v + if isinstance(v, torch.nn.Module): + self.nn_dict[k] = v + + # set configurations + self.sos = sos + self.eos = eos + self.token_list = token_list + self.pre_beam_size = int(pre_beam_ratio * beam_size) + self.beam_size = beam_size + self.n_vocab = vocab_size + if ( + pre_beam_score_key is not None + and pre_beam_score_key != "full" + and pre_beam_score_key not in self.full_scorers + ): + raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}") + self.pre_beam_score_key = pre_beam_score_key + self.do_pre_beam = ( + self.pre_beam_score_key is not None + and self.pre_beam_size < self.n_vocab + and len(self.part_scorers) > 0 + ) + + def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]: + """Get an initial hypothesis data. + + Args: + x (torch.Tensor): The encoder output feature + + Returns: + Hypothesis: The initial hypothesis. + + """ + init_states = dict() + init_scores = dict() + for k, d in self.scorers.items(): + init_states[k] = d.init_state(x) + init_scores[k] = 0.0 + return [ + Hypothesis( + score=0.0, + scores=init_scores, + states=init_states, + yseq=torch.tensor([self.sos], device=x.device), + ) + ] + + @staticmethod + def append_token(xs: torch.Tensor, x: int) -> torch.Tensor: + """Append new token to prefix tokens. + + Args: + xs (torch.Tensor): The prefix token + x (int): The new token to append + + Returns: + torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device + + """ + x = torch.tensor([x], dtype=xs.dtype, device=xs.device) + return torch.cat((xs, x)) + + def score_full( + self, + hyp: Hypothesis, + x: torch.Tensor, + x_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.full_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.full_scorers` + and tensor score values of shape: `(self.n_vocab,)`, + and state dict that has string keys + and state values of `self.full_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.full_scorers.items(): + scores[k], states[k] = d.score( + hyp.yseq, + hyp.states[k], + x, + x_mask=x_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + ) + return scores, states + + def score_partial( + self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.part_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + ids (torch.Tensor): 1D tensor of new partial tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.part_scorers` + and tensor score values of shape: `(len(ids),)`, + and state dict that has string keys + and state values of `self.part_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.part_scorers.items(): + scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x) + return scores, states + + def beam( + self, weighted_scores: torch.Tensor, ids: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute topk full token ids and partial token ids. + + Args: + weighted_scores (torch.Tensor): The weighted sum scores for each tokens. + Its shape is `(self.n_vocab,)`. + ids (torch.Tensor): The partial token ids to compute topk + + Returns: + Tuple[torch.Tensor, torch.Tensor]: + The topk full token ids and partial token ids. + Their shapes are `(self.beam_size,)` + + """ + # no pre beam performed + if weighted_scores.size(0) == ids.size(0): + top_ids = weighted_scores.topk(self.beam_size)[1] + return top_ids, top_ids + + # mask pruned in pre-beam not to select in topk + tmp = weighted_scores[ids] + weighted_scores[:] = -float("inf") + weighted_scores[ids] = tmp + top_ids = weighted_scores.topk(self.beam_size)[1] + local_ids = weighted_scores[ids].topk(self.beam_size)[1] + return top_ids, local_ids + + @staticmethod + def merge_scores( + prev_scores: Dict[str, float], + next_full_scores: Dict[str, torch.Tensor], + full_idx: int, + next_part_scores: Dict[str, torch.Tensor], + part_idx: int, + ) -> Dict[str, torch.Tensor]: + """Merge scores for new hypothesis. + + Args: + prev_scores (Dict[str, float]): + The previous hypothesis scores by `self.scorers` + next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers` + full_idx (int): The next token id for `next_full_scores` + next_part_scores (Dict[str, torch.Tensor]): + scores of partial tokens by `self.part_scorers` + part_idx (int): The new token id for `next_part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are scalar tensors by the scorers. + + """ + new_scores = dict() + for k, v in next_full_scores.items(): + new_scores[k] = prev_scores[k] + v[full_idx] + for k, v in next_part_scores.items(): + new_scores[k] = prev_scores[k] + v[part_idx] + return new_scores + + def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any: + """Merge states for new hypothesis. + + Args: + states: states of `self.full_scorers` + part_states: states of `self.part_scorers` + part_idx (int): The new token id for `part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are states of the scorers. + + """ + new_states = dict() + for k, v in states.items(): + new_states[k] = v + for k, d in self.part_scorers.items(): + new_states[k] = d.select_state(part_states[k], part_idx) + return new_states + + def search( + self, + running_hyps: List[Hypothesis], + x: torch.Tensor, + x_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + ) -> List[Hypothesis]: + """Search new tokens for running hypotheses and encoded speech x. + + Args: + running_hyps (List[Hypothesis]): Running hypotheses on beam + x (torch.Tensor): Encoded speech feature (T, D) + + Returns: + List[Hypotheses]: Best sorted hypotheses + + """ + best_hyps = [] + part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam + for hyp in running_hyps: + # scoring + weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device) + scores, states = self.score_full( + hyp, x, x_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds + ) + for k in self.full_scorers: + weighted_scores += self.weights[k] * scores[k] + # partial scoring + if self.do_pre_beam: + pre_beam_scores = ( + weighted_scores + if self.pre_beam_score_key == "full" + else scores[self.pre_beam_score_key] + ) + part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1] + part_scores, part_states = self.score_partial(hyp, part_ids, x) + for k in self.part_scorers: + weighted_scores[part_ids] += self.weights[k] * part_scores[k] + # add previous hyp score + weighted_scores += hyp.score + + # update hyps + for j, part_j in zip(*self.beam(weighted_scores, part_ids)): + # will be (2 x beam at most) + best_hyps.append( + Hypothesis( + score=weighted_scores[j], + yseq=self.append_token(hyp.yseq, j), + scores=self.merge_scores( + hyp.scores, scores, j, part_scores, part_j + ), + states=self.merge_states(states, part_states, part_j), + ) + ) + + # sort and prune 2 x beam -> beam + best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[ + : min(len(best_hyps), self.beam_size) + ] + return best_hyps + + def forward( + self, + x: torch.Tensor, + scama_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + maxlenratio: float = 0.0, + minlenratio: float = 0.0, + maxlen: int = None, + minlen: int = 0, + ) -> List[Hypothesis]: + """Perform beam search. + + Args: + x (torch.Tensor): Encoded speech feature (T, D) + maxlenratio (float): Input length ratio to obtain max output length. + If maxlenratio=0.0 (default), it uses a end-detect function + to automatically find maximum hypothesis lengths + If maxlenratio<0.0, its absolute value is interpreted + as a constant max output length. + minlenratio (float): Input length ratio to obtain min output length. + + Returns: + list[Hypothesis]: N-best decoding results + + """ + if maxlen is None: + # set length bounds + if maxlenratio == 0: + maxlen = x.shape[0] + elif maxlenratio < 0: + maxlen = -1 * int(maxlenratio) + else: + maxlen = max(1, int(maxlenratio * x.size(0))) + minlen = int(minlenratio * x.size(0)) + + logging.info("decoder input length: " + str(x.shape[0])) + logging.info("max output length: " + str(maxlen)) + logging.info("min output length: " + str(minlen)) + + # main loop of prefix search + running_hyps = self.init_hyp(x) + ended_hyps = [] + for i in range(maxlen): + logging.debug("position " + str(i)) + mask_enc = None + if scama_mask is not None: + token_num_predictor = scama_mask.size(1) + token_id_slice = min(i, token_num_predictor - 1) + mask_enc = scama_mask[:, token_id_slice : token_id_slice + 1, :] + # if mask_enc.size(1) == 0: + # mask_enc = scama_mask[:, -2:-1, :] + # # mask_enc = torch.zeros_like(mask_enc) + pre_acoustic_embeds_cur = None + if pre_acoustic_embeds is not None: + b, t, d = pre_acoustic_embeds.size() + pad = torch.zeros((b, 1, d), dtype=pre_acoustic_embeds.dtype).to( + device=pre_acoustic_embeds.device + ) + pre_acoustic_embeds = torch.cat((pre_acoustic_embeds, pad), dim=1) + token_id_slice = min(i, t) + pre_acoustic_embeds_cur = pre_acoustic_embeds[ + :, token_id_slice : token_id_slice + 1, : + ] + + best = self.search( + running_hyps, + x, + x_mask=mask_enc, + pre_acoustic_embeds=pre_acoustic_embeds_cur, + ) + # post process of one iteration + running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps) + # end detection + if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i): + logging.info(f"end detected at {i}") + break + if len(running_hyps) == 0: + logging.info("no hypothesis. Finish decoding.") + break + else: + logging.debug(f"remained hypotheses: {len(running_hyps)}") + + nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True) + # check the number of hypotheses reaching to eos + if len(nbest_hyps) == 0: + logging.warning( + "there is no N-best results, perform recognition " + "again with smaller minlenratio." + ) + return ( + [] + if minlenratio < 0.1 + else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1)) + ) + + # report the best result + for x in nbest_hyps: + yseq = "".join([self.token_list[x] for x in x.yseq]) + logging.debug( + "nbest: y: {}, yseq: {}, score: {}".format(x.yseq, yseq, x.score) + ) + best = nbest_hyps[0] + for k, v in best.scores.items(): + logging.info( + f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}" + ) + logging.info(f"total log probability: {best.score:.2f}") + logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}") + logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}") + if self.token_list is not None: + logging.info( + "best hypo: " + + "".join([self.token_list[x] for x in best.yseq[1:-1]]) + + "\n" + ) + return nbest_hyps + + def post_process( + self, + i: int, + maxlen: int, + maxlenratio: float, + running_hyps: List[Hypothesis], + ended_hyps: List[Hypothesis], + ) -> List[Hypothesis]: + """Perform post-processing of beam search iterations. + + Args: + i (int): The length of hypothesis tokens. + maxlen (int): The maximum length of tokens in beam search. + maxlenratio (int): The maximum length ratio in beam search. + running_hyps (List[Hypothesis]): The running hypotheses in beam search. + ended_hyps (List[Hypothesis]): The ended hypotheses in beam search. + + Returns: + List[Hypothesis]: The new running hypotheses. + + """ + logging.debug(f"the number of running hypotheses: {len(running_hyps)}") + if self.token_list is not None: + logging.debug( + "best hypo: " + + "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]]) + ) + # add eos in the final loop to avoid that there are no ended hyps + if i == maxlen - 1: + logging.info("adding in the last position in the loop") + running_hyps = [ + h._replace(yseq=self.append_token(h.yseq, self.eos)) + for h in running_hyps + ] + + # add ended hypotheses to a final list, and removed them from current hypotheses + # (this will be a problem, number of hyps < beam) + remained_hyps = [] + for hyp in running_hyps: + if hyp.yseq[-1] == self.eos: + # e.g., Word LM needs to add final score + for k, d in chain(self.full_scorers.items(), self.part_scorers.items()): + s = d.final_score(hyp.states[k]) + hyp.scores[k] += s + hyp = hyp._replace(score=hyp.score + self.weights[k] * s) + ended_hyps.append(hyp) + else: + remained_hyps.append(hyp) + return remained_hyps + + +class BeamSearchScamaStreaming(torch.nn.Module): + """Beam search implementation.""" + + def __init__( + self, + scorers: Dict[str, ScorerInterface], + weights: Dict[str, float], + beam_size: int, + vocab_size: int, + sos: int, + eos: int, + token_list: List[str] = None, + pre_beam_ratio: float = 1.5, + pre_beam_score_key: str = None, + ): + """Initialize beam search. + + Args: + scorers (dict[str, ScorerInterface]): Dict of decoder modules + e.g., Decoder, CTCPrefixScorer, LM + The scorer will be ignored if it is `None` + weights (dict[str, float]): Dict of weights for each scorers + The scorer will be ignored if its weight is 0 + beam_size (int): The number of hypotheses kept during search + vocab_size (int): The number of vocabulary + sos (int): Start of sequence id + eos (int): End of sequence id + token_list (list[str]): List of tokens for debug log + pre_beam_score_key (str): key of scores to perform pre-beam search + pre_beam_ratio (float): beam size in the pre-beam search + will be `int(pre_beam_ratio * beam_size)` + + """ + super().__init__() + # set scorers + self.weights = weights + self.scorers = dict() + self.full_scorers = dict() + self.part_scorers = dict() + # this module dict is required for recursive cast + # `self.to(device, dtype)` in `recog.py` + self.nn_dict = torch.nn.ModuleDict() + for k, v in scorers.items(): + w = weights.get(k, 0) + if w == 0 or v is None: + continue + assert isinstance( + v, ScorerInterface + ), f"{k} ({type(v)}) does not implement ScorerInterface" + self.scorers[k] = v + if isinstance(v, PartialScorerInterface): + self.part_scorers[k] = v + else: + self.full_scorers[k] = v + if isinstance(v, torch.nn.Module): + self.nn_dict[k] = v + + # set configurations + self.sos = sos + self.eos = eos + self.token_list = token_list + self.pre_beam_size = int(pre_beam_ratio * beam_size) + self.beam_size = beam_size + self.n_vocab = vocab_size + if ( + pre_beam_score_key is not None + and pre_beam_score_key != "full" + and pre_beam_score_key not in self.full_scorers + ): + raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}") + self.pre_beam_score_key = pre_beam_score_key + self.do_pre_beam = ( + self.pre_beam_score_key is not None + and self.pre_beam_size < self.n_vocab + and len(self.part_scorers) > 0 + ) + + def init_hyp(self, x) -> List[Hypothesis]: + """Get an initial hypothesis data. + + Args: + x (torch.Tensor): The encoder output feature + + Returns: + Hypothesis: The initial hypothesis. + + """ + init_states = dict() + init_scores = dict() + for k, d in self.scorers.items(): + init_states[k] = d.init_state(x) + init_scores[k] = 0.0 + return [ + Hypothesis( + score=0.0, + scores=init_scores, + states=init_states, + yseq=torch.tensor([self.sos], device=x.device), + ) + ] + + @staticmethod + def append_token(xs: torch.Tensor, x: int) -> torch.Tensor: + """Append new token to prefix tokens. + + Args: + xs (torch.Tensor): The prefix token + x (int): The new token to append + + Returns: + torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device + + """ + x = torch.tensor([x], dtype=xs.dtype, device=xs.device) + return torch.cat((xs, x)) + + def score_full( + self, + hyp: Hypothesis, + x: torch.Tensor, + x_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + cache: dict = {}, + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.full_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.full_scorers` + and tensor score values of shape: `(self.n_vocab,)`, + and state dict that has string keys + and state values of `self.full_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.full_scorers.items(): + scores[k], states[k] = d.score( + hyp.yseq, + hyp.states[k], + x, + x_mask=x_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + cache=cache, + ) + return scores, states + + def score_partial( + self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.part_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + ids (torch.Tensor): 1D tensor of new partial tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.part_scorers` + and tensor score values of shape: `(len(ids),)`, + and state dict that has string keys + and state values of `self.part_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.part_scorers.items(): + scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x) + return scores, states + + def beam( + self, weighted_scores: torch.Tensor, ids: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute topk full token ids and partial token ids. + + Args: + weighted_scores (torch.Tensor): The weighted sum scores for each tokens. + Its shape is `(self.n_vocab,)`. + ids (torch.Tensor): The partial token ids to compute topk + + Returns: + Tuple[torch.Tensor, torch.Tensor]: + The topk full token ids and partial token ids. + Their shapes are `(self.beam_size,)` + + """ + # no pre beam performed + if weighted_scores.size(0) == ids.size(0): + top_ids = weighted_scores.topk(self.beam_size)[1] + return top_ids, top_ids + + # mask pruned in pre-beam not to select in topk + tmp = weighted_scores[ids] + weighted_scores[:] = -float("inf") + weighted_scores[ids] = tmp + top_ids = weighted_scores.topk(self.beam_size)[1] + local_ids = weighted_scores[ids].topk(self.beam_size)[1] + return top_ids, local_ids + + @staticmethod + def merge_scores( + prev_scores: Dict[str, float], + next_full_scores: Dict[str, torch.Tensor], + full_idx: int, + next_part_scores: Dict[str, torch.Tensor], + part_idx: int, + ) -> Dict[str, torch.Tensor]: + """Merge scores for new hypothesis. + + Args: + prev_scores (Dict[str, float]): + The previous hypothesis scores by `self.scorers` + next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers` + full_idx (int): The next token id for `next_full_scores` + next_part_scores (Dict[str, torch.Tensor]): + scores of partial tokens by `self.part_scorers` + part_idx (int): The new token id for `next_part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are scalar tensors by the scorers. + + """ + new_scores = dict() + for k, v in next_full_scores.items(): + new_scores[k] = prev_scores[k] + v[full_idx] + for k, v in next_part_scores.items(): + new_scores[k] = prev_scores[k] + v[part_idx] + return new_scores + + def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any: + """Merge states for new hypothesis. + + Args: + states: states of `self.full_scorers` + part_states: states of `self.part_scorers` + part_idx (int): The new token id for `part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are states of the scorers. + + """ + new_states = dict() + for k, v in states.items(): + new_states[k] = v + for k, d in self.part_scorers.items(): + new_states[k] = d.select_state(part_states[k], part_idx) + return new_states + + def search( + self, + running_hyps: List[Hypothesis], + x: torch.Tensor, + x_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + cache: dict = {}, + ) -> List[Hypothesis]: + """Search new tokens for running hypotheses and encoded speech x. + + Args: + running_hyps (List[Hypothesis]): Running hypotheses on beam + x (torch.Tensor): Encoded speech feature (T, D) + + Returns: + List[Hypotheses]: Best sorted hypotheses + + """ + best_hyps = [] + part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam + for hyp in running_hyps: + # scoring + weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device) + scores, states = self.score_full( + hyp, + x, + x_mask=x_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + cache=cache, + ) + for k in self.full_scorers: + weighted_scores += self.weights[k] * scores[k] + # partial scoring + if self.do_pre_beam: + pre_beam_scores = ( + weighted_scores + if self.pre_beam_score_key == "full" + else scores[self.pre_beam_score_key] + ) + part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1] + part_scores, part_states = self.score_partial(hyp, part_ids, x) + for k in self.part_scorers: + weighted_scores[part_ids] += self.weights[k] * part_scores[k] + # add previous hyp score + weighted_scores += hyp.score + + # update hyps + for j, part_j in zip(*self.beam(weighted_scores, part_ids)): + # will be (2 x beam at most) + best_hyps.append( + Hypothesis( + score=weighted_scores[j], + yseq=self.append_token(hyp.yseq, j), + scores=self.merge_scores( + hyp.scores, scores, j, part_scores, part_j + ), + states=self.merge_states(states, part_states, part_j), + ) + ) + + # sort and prune 2 x beam -> beam + best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[ + : min(len(best_hyps), self.beam_size) + ] + return best_hyps + + def forward( + self, + x: torch.Tensor, + scama_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + maxlenratio: float = 0.0, + minlenratio: float = 0.0, + maxlen: int = None, + minlen: int = 0, + cache: dict = {}, + ) -> List[Hypothesis]: + """Perform beam search. + + Args: + x (torch.Tensor): Encoded speech feature (T, D) + maxlenratio (float): Input length ratio to obtain max output length. + If maxlenratio=0.0 (default), it uses a end-detect function + to automatically find maximum hypothesis lengths + If maxlenratio<0.0, its absolute value is interpreted + as a constant max output length. + minlenratio (float): Input length ratio to obtain min output length. + + Returns: + list[Hypothesis]: N-best decoding results + + """ + if maxlen is None: + # set length bounds + if maxlenratio == 0: + maxlen = x.shape[0] + elif maxlenratio < 0: + maxlen = -1 * int(maxlenratio) + else: + maxlen = max(1, int(maxlenratio * x.size(0))) + minlen = int(minlenratio * x.size(0)) + + logging.info("decoder input length: " + str(x.shape[0])) + logging.info("max output length: " + str(maxlen)) + logging.info("min output length: " + str(minlen)) + + # main loop of prefix search + # running_hyps = self.init_hyp(x) + running_hyps = cache["running_hyps"] + ended_hyps = [] + for i in range(maxlen): + logging.debug("position " + str(i)) + mask_enc = None + # if scama_mask is not None: + # token_num_predictor = scama_mask.size(1) + # token_id_slice = min(i, token_num_predictor-1) + # mask_enc = scama_mask[:, token_id_slice:token_id_slice+1, :] + # # if mask_enc.size(1) == 0: + # # mask_enc = scama_mask[:, -2:-1, :] + # # # mask_enc = torch.zeros_like(mask_enc) + pre_acoustic_embeds_cur = None + if pre_acoustic_embeds is not None: + b, t, d = pre_acoustic_embeds.size() + pad = torch.zeros((b, 1, d), dtype=pre_acoustic_embeds.dtype).to( + device=pre_acoustic_embeds.device + ) + pre_acoustic_embeds = torch.cat((pre_acoustic_embeds, pad), dim=1) + token_id_slice = min(i, t) + pre_acoustic_embeds_cur = pre_acoustic_embeds[ + :, token_id_slice : token_id_slice + 1, : + ] + + best = self.search( + running_hyps, + x, + x_mask=mask_enc, + pre_acoustic_embeds=pre_acoustic_embeds_cur, + cache=cache["decoder"], + ) + # post process of one iteration + running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps) + # end detection + if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i): + logging.info(f"end detected at {i}") + break + if len(running_hyps) == 0: + logging.info("no hypothesis. Finish decoding.") + break + else: + logging.debug(f"remained hypotheses: {len(running_hyps)}") + + nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True) + # check the number of hypotheses reaching to eos + if len(nbest_hyps) == 0: + logging.warning( + "there is no N-best results, perform recognition " + "again with smaller minlenratio." + ) + return ( + [] + if minlenratio < 0.1 + else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1)) + ) + + # report the best result + for x in nbest_hyps: + yseq = "".join([self.token_list[x] for x in x.yseq]) + logging.debug( + "nbest: y: {}, yseq: {}, score: {}".format(x.yseq, yseq, x.score) + ) + best = nbest_hyps[0] + for k, v in best.scores.items(): + logging.info( + f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}" + ) + logging.info(f"total log probability: {best.score:.2f}") + logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}") + logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}") + if self.token_list is not None: + logging.info( + "best hypo: " + + "".join([self.token_list[x] for x in best.yseq[1:-1]]) + + "\n" + ) + return nbest_hyps + + def post_process( + self, + i: int, + maxlen: int, + maxlenratio: float, + running_hyps: List[Hypothesis], + ended_hyps: List[Hypothesis], + ) -> List[Hypothesis]: + """Perform post-processing of beam search iterations. + + Args: + i (int): The length of hypothesis tokens. + maxlen (int): The maximum length of tokens in beam search. + maxlenratio (int): The maximum length ratio in beam search. + running_hyps (List[Hypothesis]): The running hypotheses in beam search. + ended_hyps (List[Hypothesis]): The ended hypotheses in beam search. + + Returns: + List[Hypothesis]: The new running hypotheses. + + """ + logging.debug(f"the number of running hypotheses: {len(running_hyps)}") + if self.token_list is not None: + logging.debug( + "best hypo: " + + "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]]) + ) + # add eos in the final loop to avoid that there are no ended hyps + if i == maxlen - 1: + logging.info("adding in the last position in the loop") + running_hyps = [ + h._replace(yseq=self.append_token(h.yseq, self.eos)) + for h in running_hyps + ] + + # add ended hypotheses to a final list, and removed them from current hypotheses + # (this will be a problem, number of hyps < beam) + remained_hyps = [] + for hyp in running_hyps: + if hyp.yseq[-1] == self.eos: + # e.g., Word LM needs to add final score + for k, d in chain(self.full_scorers.items(), self.part_scorers.items()): + s = d.final_score(hyp.states[k]) + hyp.scores[k] += s + hyp = hyp._replace(score=hyp.score + self.weights[k] * s) + ended_hyps.append(hyp) + else: + remained_hyps.append(hyp) + return remained_hyps diff --git a/demo/Step-Audio-EditX/funasr_detach/models/scama/chunk_utilis.py b/demo/Step-Audio-EditX/funasr_detach/models/scama/chunk_utilis.py new file mode 100644 index 0000000000000000000000000000000000000000..5a19ca5ea70e060db9afed10a9978ee39ae5c87e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/scama/chunk_utilis.py @@ -0,0 +1,591 @@ +import math +import torch +import numpy as np +import torch.nn.functional as F + +from funasr_detach.models.scama.utils import sequence_mask +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +class overlap_chunk: + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + San-m: Memory equipped self-attention for end-to-end speech recognition + https://arxiv.org/abs/2006.01713 + + """ + + def __init__( + self, + chunk_size: tuple = (16,), + stride: tuple = (10,), + pad_left: tuple = (0,), + encoder_att_look_back_factor: tuple = (1,), + shfit_fsmn: int = 0, + decoder_att_look_back_factor: tuple = (1,), + ): + + pad_left = self.check_chunk_size_args(chunk_size, pad_left) + encoder_att_look_back_factor = self.check_chunk_size_args( + chunk_size, encoder_att_look_back_factor + ) + decoder_att_look_back_factor = self.check_chunk_size_args( + chunk_size, decoder_att_look_back_factor + ) + ( + self.chunk_size, + self.stride, + self.pad_left, + self.encoder_att_look_back_factor, + self.decoder_att_look_back_factor, + ) = ( + chunk_size, + stride, + pad_left, + encoder_att_look_back_factor, + decoder_att_look_back_factor, + ) + self.shfit_fsmn = shfit_fsmn + self.x_add_mask = None + self.x_rm_mask = None + self.x_len = None + self.mask_shfit_chunk = None + self.mask_chunk_predictor = None + self.mask_att_chunk_encoder = None + self.mask_shift_att_chunk_decoder = None + self.chunk_outs = None + ( + self.chunk_size_cur, + self.stride_cur, + self.pad_left_cur, + self.encoder_att_look_back_factor_cur, + self.chunk_size_pad_shift_cur, + ) = (None, None, None, None, None) + + def check_chunk_size_args(self, chunk_size, x): + if len(x) < len(chunk_size): + x = [x[0] for i in chunk_size] + return x + + def get_chunk_size(self, ind: int = 0): + # with torch.no_grad: + ( + chunk_size, + stride, + pad_left, + encoder_att_look_back_factor, + decoder_att_look_back_factor, + ) = ( + self.chunk_size[ind], + self.stride[ind], + self.pad_left[ind], + self.encoder_att_look_back_factor[ind], + self.decoder_att_look_back_factor[ind], + ) + ( + self.chunk_size_cur, + self.stride_cur, + self.pad_left_cur, + self.encoder_att_look_back_factor_cur, + self.chunk_size_pad_shift_cur, + self.decoder_att_look_back_factor_cur, + ) = ( + chunk_size, + stride, + pad_left, + encoder_att_look_back_factor, + chunk_size + self.shfit_fsmn, + decoder_att_look_back_factor, + ) + return ( + self.chunk_size_cur, + self.stride_cur, + self.pad_left_cur, + self.encoder_att_look_back_factor_cur, + self.chunk_size_pad_shift_cur, + ) + + def random_choice(self, training=True, decoding_ind=None): + chunk_num = len(self.chunk_size) + ind = 0 + if training and chunk_num > 1: + ind = torch.randint(0, chunk_num, ()).cpu().item() + if not training and decoding_ind is not None: + ind = int(decoding_ind) + + return ind + + def gen_chunk_mask(self, x_len, ind=0, num_units=1, num_units_predictor=1): + + with torch.no_grad(): + x_len = x_len.cpu().numpy() + x_len_max = x_len.max() + + ( + chunk_size, + stride, + pad_left, + encoder_att_look_back_factor, + chunk_size_pad_shift, + ) = self.get_chunk_size(ind) + shfit_fsmn = self.shfit_fsmn + pad_right = chunk_size - stride - pad_left + + chunk_num_batch = np.ceil(x_len / stride).astype(np.int32) + x_len_chunk = ( + (chunk_num_batch - 1) * chunk_size_pad_shift + + shfit_fsmn + + pad_left + + 0 + + x_len + - (chunk_num_batch - 1) * stride + ) + x_len_chunk = x_len_chunk.astype(x_len.dtype) + x_len_chunk_max = x_len_chunk.max() + + chunk_num = int(math.ceil(x_len_max / stride)) + dtype = np.int32 + max_len_for_x_mask_tmp = max(chunk_size, x_len_max + pad_left) + x_add_mask = np.zeros([0, max_len_for_x_mask_tmp], dtype=dtype) + x_rm_mask = np.zeros([max_len_for_x_mask_tmp, 0], dtype=dtype) + mask_shfit_chunk = np.zeros([0, num_units], dtype=dtype) + mask_chunk_predictor = np.zeros([0, num_units_predictor], dtype=dtype) + mask_shift_att_chunk_decoder = np.zeros([0, 1], dtype=dtype) + mask_att_chunk_encoder = np.zeros( + [0, chunk_num * chunk_size_pad_shift], dtype=dtype + ) + for chunk_ids in range(chunk_num): + # x_mask add + fsmn_padding = np.zeros( + (shfit_fsmn, max_len_for_x_mask_tmp), dtype=dtype + ) + x_mask_cur = np.diag(np.ones(chunk_size, dtype=np.float32)) + x_mask_pad_left = np.zeros( + (chunk_size, chunk_ids * stride), dtype=dtype + ) + x_mask_pad_right = np.zeros( + (chunk_size, max_len_for_x_mask_tmp), dtype=dtype + ) + x_cur_pad = np.concatenate( + [x_mask_pad_left, x_mask_cur, x_mask_pad_right], axis=1 + ) + x_cur_pad = x_cur_pad[:chunk_size, :max_len_for_x_mask_tmp] + x_add_mask_fsmn = np.concatenate([fsmn_padding, x_cur_pad], axis=0) + x_add_mask = np.concatenate([x_add_mask, x_add_mask_fsmn], axis=0) + + # x_mask rm + fsmn_padding = np.zeros( + (max_len_for_x_mask_tmp, shfit_fsmn), dtype=dtype + ) + padding_mask_left = np.zeros( + (max_len_for_x_mask_tmp, pad_left), dtype=dtype + ) + padding_mask_right = np.zeros( + (max_len_for_x_mask_tmp, pad_right), dtype=dtype + ) + x_mask_cur = np.diag(np.ones(stride, dtype=dtype)) + x_mask_cur_pad_top = np.zeros((chunk_ids * stride, stride), dtype=dtype) + x_mask_cur_pad_bottom = np.zeros( + (max_len_for_x_mask_tmp, stride), dtype=dtype + ) + x_rm_mask_cur = np.concatenate( + [x_mask_cur_pad_top, x_mask_cur, x_mask_cur_pad_bottom], axis=0 + ) + x_rm_mask_cur = x_rm_mask_cur[:max_len_for_x_mask_tmp, :stride] + x_rm_mask_cur_fsmn = np.concatenate( + [ + fsmn_padding, + padding_mask_left, + x_rm_mask_cur, + padding_mask_right, + ], + axis=1, + ) + x_rm_mask = np.concatenate([x_rm_mask, x_rm_mask_cur_fsmn], axis=1) + + # fsmn_padding_mask + pad_shfit_mask = np.zeros([shfit_fsmn, num_units], dtype=dtype) + ones_1 = np.ones([chunk_size, num_units], dtype=dtype) + mask_shfit_chunk_cur = np.concatenate([pad_shfit_mask, ones_1], axis=0) + mask_shfit_chunk = np.concatenate( + [mask_shfit_chunk, mask_shfit_chunk_cur], axis=0 + ) + + # predictor mask + zeros_1 = np.zeros( + [shfit_fsmn + pad_left, num_units_predictor], dtype=dtype + ) + ones_2 = np.ones([stride, num_units_predictor], dtype=dtype) + zeros_3 = np.zeros( + [chunk_size - stride - pad_left, num_units_predictor], dtype=dtype + ) + ones_zeros = np.concatenate([ones_2, zeros_3], axis=0) + mask_chunk_predictor_cur = np.concatenate([zeros_1, ones_zeros], axis=0) + mask_chunk_predictor = np.concatenate( + [mask_chunk_predictor, mask_chunk_predictor_cur], axis=0 + ) + + # encoder att mask + zeros_1_top = np.zeros( + [shfit_fsmn, chunk_num * chunk_size_pad_shift], dtype=dtype + ) + + zeros_2_num = max(chunk_ids - encoder_att_look_back_factor, 0) + zeros_2 = np.zeros( + [chunk_size, zeros_2_num * chunk_size_pad_shift], dtype=dtype + ) + + encoder_att_look_back_num = max(chunk_ids - zeros_2_num, 0) + zeros_2_left = np.zeros([chunk_size, shfit_fsmn], dtype=dtype) + ones_2_mid = np.ones([stride, stride], dtype=dtype) + zeros_2_bottom = np.zeros([chunk_size - stride, stride], dtype=dtype) + zeros_2_right = np.zeros([chunk_size, chunk_size - stride], dtype=dtype) + ones_2 = np.concatenate([ones_2_mid, zeros_2_bottom], axis=0) + ones_2 = np.concatenate([zeros_2_left, ones_2, zeros_2_right], axis=1) + ones_2 = np.tile(ones_2, [1, encoder_att_look_back_num]) + + zeros_3_left = np.zeros([chunk_size, shfit_fsmn], dtype=dtype) + ones_3_right = np.ones([chunk_size, chunk_size], dtype=dtype) + ones_3 = np.concatenate([zeros_3_left, ones_3_right], axis=1) + + zeros_remain_num = max(chunk_num - 1 - chunk_ids, 0) + zeros_remain = np.zeros( + [chunk_size, zeros_remain_num * chunk_size_pad_shift], dtype=dtype + ) + + ones2_bottom = np.concatenate( + [zeros_2, ones_2, ones_3, zeros_remain], axis=1 + ) + mask_att_chunk_encoder_cur = np.concatenate( + [zeros_1_top, ones2_bottom], axis=0 + ) + mask_att_chunk_encoder = np.concatenate( + [mask_att_chunk_encoder, mask_att_chunk_encoder_cur], axis=0 + ) + + # decoder fsmn_shift_att_mask + zeros_1 = np.zeros([shfit_fsmn, 1]) + ones_1 = np.ones([chunk_size, 1]) + mask_shift_att_chunk_decoder_cur = np.concatenate( + [zeros_1, ones_1], axis=0 + ) + mask_shift_att_chunk_decoder = np.concatenate( + [mask_shift_att_chunk_decoder, mask_shift_att_chunk_decoder_cur], + axis=0, + ) + + self.x_add_mask = x_add_mask[:x_len_chunk_max, : x_len_max + pad_left] + self.x_len_chunk = x_len_chunk + self.x_rm_mask = x_rm_mask[:x_len_max, :x_len_chunk_max] + self.x_len = x_len + self.mask_shfit_chunk = mask_shfit_chunk[:x_len_chunk_max, :] + self.mask_chunk_predictor = mask_chunk_predictor[:x_len_chunk_max, :] + self.mask_att_chunk_encoder = mask_att_chunk_encoder[ + :x_len_chunk_max, :x_len_chunk_max + ] + self.mask_shift_att_chunk_decoder = mask_shift_att_chunk_decoder[ + :x_len_chunk_max, : + ] + self.chunk_outs = ( + self.x_add_mask, + self.x_len_chunk, + self.x_rm_mask, + self.x_len, + self.mask_shfit_chunk, + self.mask_chunk_predictor, + self.mask_att_chunk_encoder, + self.mask_shift_att_chunk_decoder, + ) + + return self.chunk_outs + + def split_chunk(self, x, x_len, chunk_outs): + """ + :param x: (b, t, d) + :param x_length: (b) + :param ind: int + :return: + """ + x = x[:, : x_len.max(), :] + b, t, d = x.size() + x_len_mask = (~make_pad_mask(x_len, maxlen=t)).to(x.device) + x *= x_len_mask[:, :, None] + + x_add_mask = self.get_x_add_mask(chunk_outs, x.device, dtype=x.dtype) + x_len_chunk = self.get_x_len_chunk(chunk_outs, x_len.device, dtype=x_len.dtype) + pad = (0, 0, self.pad_left_cur, 0) + x = F.pad(x, pad, "constant", 0.0) + b, t, d = x.size() + x = torch.transpose(x, 1, 0) + x = torch.reshape(x, [t, -1]) + x_chunk = torch.mm(x_add_mask, x) + x_chunk = torch.reshape(x_chunk, [-1, b, d]).transpose(1, 0) + + return x_chunk, x_len_chunk + + def remove_chunk(self, x_chunk, x_len_chunk, chunk_outs): + x_chunk = x_chunk[:, : x_len_chunk.max(), :] + b, t, d = x_chunk.size() + x_len_chunk_mask = (~make_pad_mask(x_len_chunk, maxlen=t)).to(x_chunk.device) + x_chunk *= x_len_chunk_mask[:, :, None] + + x_rm_mask = self.get_x_rm_mask(chunk_outs, x_chunk.device, dtype=x_chunk.dtype) + x_len = self.get_x_len(chunk_outs, x_len_chunk.device, dtype=x_len_chunk.dtype) + x_chunk = torch.transpose(x_chunk, 1, 0) + x_chunk = torch.reshape(x_chunk, [t, -1]) + x = torch.mm(x_rm_mask, x_chunk) + x = torch.reshape(x, [-1, b, d]).transpose(1, 0) + + return x, x_len + + def get_x_add_mask(self, chunk_outs=None, device="cpu", idx=0, dtype=torch.float32): + with torch.no_grad(): + x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx] + x = torch.from_numpy(x).type(dtype).to(device) + return x + + def get_x_len_chunk( + self, chunk_outs=None, device="cpu", idx=1, dtype=torch.float32 + ): + with torch.no_grad(): + x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx] + x = torch.from_numpy(x).type(dtype).to(device) + return x + + def get_x_rm_mask(self, chunk_outs=None, device="cpu", idx=2, dtype=torch.float32): + with torch.no_grad(): + x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx] + x = torch.from_numpy(x).type(dtype).to(device) + return x + + def get_x_len(self, chunk_outs=None, device="cpu", idx=3, dtype=torch.float32): + with torch.no_grad(): + x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx] + x = torch.from_numpy(x).type(dtype).to(device) + return x + + def get_mask_shfit_chunk( + self, + chunk_outs=None, + device="cpu", + batch_size=1, + num_units=1, + idx=4, + dtype=torch.float32, + ): + with torch.no_grad(): + x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx] + x = np.tile( + x[ + None, + :, + :, + ], + [batch_size, 1, num_units], + ) + x = torch.from_numpy(x).type(dtype).to(device) + return x + + def get_mask_chunk_predictor( + self, + chunk_outs=None, + device="cpu", + batch_size=1, + num_units=1, + idx=5, + dtype=torch.float32, + ): + with torch.no_grad(): + x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx] + x = np.tile( + x[ + None, + :, + :, + ], + [batch_size, 1, num_units], + ) + x = torch.from_numpy(x).type(dtype).to(device) + return x + + def get_mask_att_chunk_encoder( + self, chunk_outs=None, device="cpu", batch_size=1, idx=6, dtype=torch.float32 + ): + with torch.no_grad(): + x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx] + x = np.tile( + x[ + None, + :, + :, + ], + [batch_size, 1, 1], + ) + x = torch.from_numpy(x).type(dtype).to(device) + return x + + def get_mask_shift_att_chunk_decoder( + self, chunk_outs=None, device="cpu", batch_size=1, idx=7, dtype=torch.float32 + ): + with torch.no_grad(): + x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx] + x = np.tile(x[None, None, :, 0], [batch_size, 1, 1]) + x = torch.from_numpy(x).type(dtype).to(device) + return x + + +def build_scama_mask_for_cross_attention_decoder( + predictor_alignments: torch.Tensor, + encoder_sequence_length: torch.Tensor, + chunk_size: int = 5, + encoder_chunk_size: int = 5, + attention_chunk_center_bias: int = 0, + attention_chunk_size: int = 1, + attention_chunk_type: str = "chunk", + step=None, + predictor_mask_chunk_hopping: torch.Tensor = None, + decoder_att_look_back_factor: int = 1, + mask_shift_att_chunk_decoder: torch.Tensor = None, + target_length: torch.Tensor = None, + is_training=True, + dtype: torch.dtype = torch.float32, +): + with torch.no_grad(): + device = predictor_alignments.device + batch_size, chunk_num = predictor_alignments.size() + maximum_encoder_length = encoder_sequence_length.max().item() + int_type = predictor_alignments.dtype + if not is_training: + target_length = predictor_alignments.sum(dim=-1).type( + encoder_sequence_length.dtype + ) + maximum_target_length = target_length.max() + predictor_alignments_cumsum = torch.cumsum(predictor_alignments, dim=1) + predictor_alignments_cumsum = predictor_alignments_cumsum[:, None, :].repeat( + 1, maximum_target_length, 1 + ) + + index = torch.ones([batch_size, maximum_target_length], dtype=int_type).to( + device + ) + index = torch.cumsum(index, dim=1) + index = index[:, :, None].repeat(1, 1, chunk_num) + + index_div = torch.floor(torch.divide(predictor_alignments_cumsum, index)).type( + int_type + ) + index_div_bool_zeros = index_div == 0 + index_div_bool_zeros_count = ( + torch.sum(index_div_bool_zeros.type(int_type), dim=-1) + 1 + ) + + index_div_bool_zeros_count = torch.clip( + index_div_bool_zeros_count, min=1, max=chunk_num + ) + + index_div_bool_zeros_count *= chunk_size + index_div_bool_zeros_count += attention_chunk_center_bias + index_div_bool_zeros_count = torch.clip( + index_div_bool_zeros_count - 1, min=0, max=maximum_encoder_length + ) + index_div_bool_zeros_count_ori = index_div_bool_zeros_count + + index_div_bool_zeros_count = ( + torch.floor(index_div_bool_zeros_count / encoder_chunk_size) + 1 + ) * encoder_chunk_size + max_len_chunk = ( + math.ceil(maximum_encoder_length / encoder_chunk_size) * encoder_chunk_size + ) + + mask_flip, mask_flip2 = None, None + if attention_chunk_size is not None: + index_div_bool_zeros_count_beg = ( + index_div_bool_zeros_count - attention_chunk_size + ) + index_div_bool_zeros_count_beg = torch.clip( + index_div_bool_zeros_count_beg, 0, max_len_chunk + ) + index_div_bool_zeros_count_beg_mask = sequence_mask( + index_div_bool_zeros_count_beg, + maxlen=max_len_chunk, + dtype=int_type, + device=device, + ) + mask_flip = 1 - index_div_bool_zeros_count_beg_mask + attention_chunk_size2 = attention_chunk_size * ( + decoder_att_look_back_factor + 1 + ) + index_div_bool_zeros_count_beg = ( + index_div_bool_zeros_count - attention_chunk_size2 + ) + + index_div_bool_zeros_count_beg = torch.clip( + index_div_bool_zeros_count_beg, 0, max_len_chunk + ) + index_div_bool_zeros_count_beg_mask = sequence_mask( + index_div_bool_zeros_count_beg, + maxlen=max_len_chunk, + dtype=int_type, + device=device, + ) + mask_flip2 = 1 - index_div_bool_zeros_count_beg_mask + + mask = sequence_mask( + index_div_bool_zeros_count, maxlen=max_len_chunk, dtype=dtype, device=device + ) + + if predictor_mask_chunk_hopping is not None: + b, k, t = mask.size() + predictor_mask_chunk_hopping = predictor_mask_chunk_hopping[ + :, None, :, 0 + ].repeat(1, k, 1) + + mask_mask_flip = mask + if mask_flip is not None: + mask_mask_flip = mask_flip * mask + + def _fn(): + mask_sliced = mask[:b, :k, encoder_chunk_size:t] + zero_pad_right = torch.zeros( + [b, k, encoder_chunk_size], dtype=mask_sliced.dtype + ).to(device) + mask_sliced = torch.cat([mask_sliced, zero_pad_right], dim=2) + _, _, tt = predictor_mask_chunk_hopping.size() + pad_right_p = max_len_chunk - tt + predictor_mask_chunk_hopping_pad = torch.nn.functional.pad( + predictor_mask_chunk_hopping, [0, pad_right_p], "constant", 0 + ) + masked = mask_sliced * predictor_mask_chunk_hopping_pad + + mask_true = mask_mask_flip + masked + return mask_true + + mask = _fn() if t > chunk_size else mask_mask_flip + + if mask_flip2 is not None: + mask *= mask_flip2 + + mask_target = sequence_mask( + target_length, maxlen=maximum_target_length, dtype=mask.dtype, device=device + ) + mask = mask[:, :maximum_target_length, :] * mask_target[:, :, None] + + mask_len = sequence_mask( + encoder_sequence_length, + maxlen=maximum_encoder_length, + dtype=mask.dtype, + device=device, + ) + mask = mask[:, :, :maximum_encoder_length] * mask_len[:, None, :] + + if attention_chunk_type == "full": + mask = torch.ones_like(mask).to(device) + if mask_shift_att_chunk_decoder is not None: + mask = mask * mask_shift_att_chunk_decoder + mask = ( + mask[:, :maximum_target_length, :maximum_encoder_length] + .type(dtype) + .to(device) + ) + + return mask diff --git a/demo/Step-Audio-EditX/funasr_detach/models/scama/decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/scama/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..702ae298db67a53d9e9f635a4399e3ffc590fc1e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/scama/decoder.py @@ -0,0 +1,1057 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +from typing import List +from typing import Tuple +import logging +import torch +import torch.nn as nn +import numpy as np + +from funasr_detach.models.scama import utils as myutils +from funasr_detach.models.transformer.decoder import BaseTransformerDecoder + +from funasr_detach.models.sanm.attention import ( + MultiHeadedAttentionSANMDecoder, + MultiHeadedAttentionCrossAtt, +) +from funasr_detach.models.transformer.embedding import PositionalEncoding +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.sanm.positionwise_feed_forward import ( + PositionwiseFeedForwardDecoderSANM, +) +from funasr_detach.models.transformer.utils.repeat import repeat + +from funasr_detach.register import tables + + +class DecoderLayerSANM(nn.Module): + """Single decoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + src_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + + + """ + + def __init__( + self, + size, + self_attn, + src_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + ): + """Construct an DecoderLayer object.""" + super(DecoderLayerSANM, self).__init__() + self.size = size + self.self_attn = self_attn + self.src_attn = src_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(size) + if self_attn is not None: + self.norm2 = LayerNorm(size) + if src_attn is not None: + self.norm3 = LayerNorm(size) + self.dropout = nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear1 = nn.Linear(size + size, size) + self.concat_linear2 = nn.Linear(size + size, size) + + def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + # tgt = self.dropout(tgt) + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn: + if self.normalize_before: + tgt = self.norm2(tgt) + x, _ = self.self_attn(tgt, tgt_mask) + x = residual + self.dropout(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm3(x) + + x = residual + self.dropout(self.src_attn(x, memory, memory_mask)) + + return x, tgt_mask, memory, memory_mask, cache + + def forward_one_step(self, tgt, tgt_mask, memory, memory_mask=None, cache=None): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + # tgt = self.dropout(tgt) + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn: + if self.normalize_before: + tgt = self.norm2(tgt) + if self.training: + cache = None + x, cache = self.self_attn(tgt, tgt_mask, cache=cache) + x = residual + self.dropout(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm3(x) + + x = residual + self.dropout(self.src_attn(x, memory, memory_mask)) + + return x, tgt_mask, memory, memory_mask, cache + + def forward_chunk( + self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0 + ): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn: + if self.normalize_before: + tgt = self.norm2(tgt) + x, fsmn_cache = self.self_attn(tgt, None, fsmn_cache) + x = residual + self.dropout(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm3(x) + + x, opt_cache = self.src_attn.forward_chunk( + x, memory, opt_cache, chunk_size, look_back + ) + x = residual + x + + return x, memory, fsmn_cache, opt_cache + + +@tables.register("decoder_classes", "FsmnDecoderSCAMAOpt") +class FsmnDecoderSCAMAOpt(BaseTransformerDecoder): + """ + Author: Shiliang Zhang, Zhifu Gao, Haoneng Luo, Ming Lei, Jie Gao, Zhijie Yan, Lei Xie + SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition + https://arxiv.org/abs/2006.01712 + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + att_layer_num: int = 6, + kernel_size: int = 21, + sanm_shfit: int = None, + concat_embeds: bool = False, + attention_dim: int = None, + tf2torch_tensor_name_prefix_torch: str = "decoder", + tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder", + embed_tensor_name_prefix_tf: str = None, + ): + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + if attention_dim is None: + attention_dim = encoder_output_size + + if input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(vocab_size, attention_dim), + ) + elif input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(vocab_size, attention_dim), + torch.nn.LayerNorm(attention_dim), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + else: + raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}") + + self.normalize_before = normalize_before + if self.normalize_before: + self.after_norm = LayerNorm(attention_dim) + if use_output_layer: + self.output_layer = torch.nn.Linear(attention_dim, vocab_size) + else: + self.output_layer = None + + self.att_layer_num = att_layer_num + self.num_blocks = num_blocks + if sanm_shfit is None: + sanm_shfit = (kernel_size - 1) // 2 + self.decoders = repeat( + att_layer_num, + lambda lnum: DecoderLayerSANM( + attention_dim, + MultiHeadedAttentionSANMDecoder( + attention_dim, + self_attention_dropout_rate, + kernel_size, + sanm_shfit=sanm_shfit, + ), + MultiHeadedAttentionCrossAtt( + attention_heads, + attention_dim, + src_attention_dropout_rate, + encoder_output_size=encoder_output_size, + ), + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + if num_blocks - att_layer_num <= 0: + self.decoders2 = None + else: + self.decoders2 = repeat( + num_blocks - att_layer_num, + lambda lnum: DecoderLayerSANM( + attention_dim, + MultiHeadedAttentionSANMDecoder( + attention_dim, + self_attention_dropout_rate, + kernel_size, + sanm_shfit=sanm_shfit, + ), + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + self.decoders3 = repeat( + 1, + lambda lnum: DecoderLayerSANM( + attention_dim, + None, + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim, linear_units, dropout_rate + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + if concat_embeds: + self.embed_concat_ffn = repeat( + 1, + lambda lnum: DecoderLayerSANM( + attention_dim + encoder_output_size, + None, + None, + PositionwiseFeedForwardDecoderSANM( + attention_dim + encoder_output_size, + linear_units, + dropout_rate, + adim=attention_dim, + ), + dropout_rate, + normalize_before, + concat_after, + ), + ) + else: + self.embed_concat_ffn = None + self.concat_embeds = concat_embeds + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + self.embed_tensor_name_prefix_tf = embed_tensor_name_prefix_tf + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + chunk_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward decoder. + + Args: + hs_pad: encoded memory, float32 (batch, maxlen_in, feat) + hlens: (batch) + ys_in_pad: + input token ids, int64 (batch, maxlen_out) + if input_layer == "embed" + input tensor (batch, maxlen_out, #mels) in the other cases + ys_in_lens: (batch) + Returns: + (tuple): tuple containing: + + x: decoded token score before softmax (batch, maxlen_out, token) + if use_output_layer is True, + olens: (batch, ) + """ + tgt = ys_in_pad + tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None] + + memory = hs_pad + memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :] + if chunk_mask is not None: + memory_mask = memory_mask * chunk_mask + if tgt_mask.size(1) != memory_mask.size(1): + memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1) + + x = self.embed(tgt) + + if pre_acoustic_embeds is not None and self.concat_embeds: + x = torch.cat((x, pre_acoustic_embeds), dim=-1) + x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None) + + x, tgt_mask, memory, memory_mask, _ = self.decoders( + x, tgt_mask, memory, memory_mask + ) + if self.decoders2 is not None: + x, tgt_mask, memory, memory_mask, _ = self.decoders2( + x, tgt_mask, memory, memory_mask + ) + x, tgt_mask, memory, memory_mask, _ = self.decoders3( + x, tgt_mask, memory, memory_mask + ) + if self.normalize_before: + x = self.after_norm(x) + if self.output_layer is not None: + x = self.output_layer(x) + + olens = tgt_mask.sum(1) + return x, olens + + def score( + self, + ys, + state, + x, + x_mask=None, + pre_acoustic_embeds: torch.Tensor = None, + ): + """Score.""" + ys_mask = myutils.sequence_mask( + torch.tensor([len(ys)], dtype=torch.int32), device=x.device + )[:, :, None] + logp, state = self.forward_one_step( + ys.unsqueeze(0), + ys_mask, + x.unsqueeze(0), + memory_mask=x_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + cache=state, + ) + return logp.squeeze(0), state + + def forward_one_step( + self, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + memory_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + cache: List[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + + Args: + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + memory: encoded memory, float32 (batch, maxlen_in, feat) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + + x = tgt[:, -1:] + tgt_mask = None + x = self.embed(x) + + if pre_acoustic_embeds is not None and self.concat_embeds: + x = torch.cat((x, pre_acoustic_embeds), dim=-1) + x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None) + + if cache is None: + cache_layer_num = len(self.decoders) + if self.decoders2 is not None: + cache_layer_num += len(self.decoders2) + cache = [None] * cache_layer_num + new_cache = [] + # for c, decoder in zip(cache, self.decoders): + for i in range(self.att_layer_num): + decoder = self.decoders[i] + c = cache[i] + x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step( + x, tgt_mask, memory, memory_mask, cache=c + ) + new_cache.append(c_ret) + + if self.num_blocks - self.att_layer_num >= 1: + for i in range(self.num_blocks - self.att_layer_num): + j = i + self.att_layer_num + decoder = self.decoders2[i] + c = cache[j] + x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step( + x, tgt_mask, memory, memory_mask, cache=c + ) + new_cache.append(c_ret) + + for decoder in self.decoders3: + x, tgt_mask, memory, memory_mask, _ = decoder.forward_one_step( + x, tgt_mask, memory, None, cache=None + ) + + if self.normalize_before: + y = self.after_norm(x[:, -1]) + else: + y = x[:, -1] + if self.output_layer is not None: + y = self.output_layer(y) + y = torch.log_softmax(y, dim=-1) + + return y, new_cache + + def gen_tf2torch_map_dict(self): + + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + embed_tensor_name_prefix_tf = ( + self.embed_tensor_name_prefix_tf + if self.embed_tensor_name_prefix_tf is not None + else tensor_name_prefix_tf + ) + map_dict_local = { + ## decoder + # ffn + "{}.decoders.layeridx.norm1.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.norm1.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.feed_forward.w_1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.decoders.layeridx.feed_forward.w_1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders.layeridx.feed_forward.norm.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders.layeridx.feed_forward.norm.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders.layeridx.feed_forward.w_2.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,1024),(1,1024,256) + # fsmn + "{}.decoders.layeridx.norm2.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.norm2.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.self_attn.fsmn_block.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/depth_conv_w".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 2, 0), + }, # (256,1,31),(1,31,256,1) + # src att + "{}.decoders.layeridx.norm3.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.norm3.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.src_attn.linear_q.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,256),(1,256,256) + "{}.decoders.layeridx.src_attn.linear_q.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders.layeridx.src_attn.linear_k_v.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.decoders.layeridx.src_attn.linear_k_v.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders.layeridx.src_attn.linear_out.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,256),(1,256,256) + "{}.decoders.layeridx.src_attn.linear_out.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + # dnn + "{}.decoders3.layeridx.norm1.weight".format(tensor_name_prefix_torch): { + "name": "{}/decoder_dnn_layer_layeridx/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders3.layeridx.norm1.bias".format(tensor_name_prefix_torch): { + "name": "{}/decoder_dnn_layer_layeridx/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.decoders3.layeridx.feed_forward.w_1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.decoders3.layeridx.feed_forward.w_1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders3.layeridx.feed_forward.norm.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders3.layeridx.feed_forward.norm.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.decoders3.layeridx.feed_forward.w_2.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/decoder_dnn_layer_layeridx/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,1024),(1,1024,256) + # embed_concat_ffn + "{}.embed_concat_ffn.layeridx.norm1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/LayerNorm/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.embed_concat_ffn.layeridx.norm1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/LayerNorm/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.embed_concat_ffn.layeridx.feed_forward.w_1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/conv1d/kernel".format(tensor_name_prefix_tf), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.embed_concat_ffn.layeridx.feed_forward.w_1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/conv1d/bias".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.embed_concat_ffn.layeridx.feed_forward.norm.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/LayerNorm_1/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.embed_concat_ffn.layeridx.feed_forward.norm.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/LayerNorm_1/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.embed_concat_ffn.layeridx.feed_forward.w_2.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/cif_concat/conv1d_1/kernel".format(tensor_name_prefix_tf), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,1024),(1,1024,256) + # out norm + "{}.after_norm.weight".format(tensor_name_prefix_torch): { + "name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.after_norm.bias".format(tensor_name_prefix_torch): { + "name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + # in embed + "{}.embed.0.weight".format(tensor_name_prefix_torch): { + "name": "{}/w_embs".format(embed_tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (4235,256),(4235,256) + # out layer + "{}.output_layer.weight".format(tensor_name_prefix_torch): { + "name": [ + "{}/dense/kernel".format(tensor_name_prefix_tf), + "{}/w_embs".format(embed_tensor_name_prefix_tf), + ], + "squeeze": [None, None], + "transpose": [(1, 0), None], + }, # (4235,256),(256,4235) + "{}.output_layer.bias".format(tensor_name_prefix_torch): { + "name": [ + "{}/dense/bias".format(tensor_name_prefix_tf), + ( + "seq2seq/2bias" + if tensor_name_prefix_tf == "seq2seq/decoder/inputter_1" + else "seq2seq/bias" + ), + ], + "squeeze": [None, None], + "transpose": [None, None], + }, # (4235,),(4235,) + } + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + + map_dict = self.gen_tf2torch_map_dict() + var_dict_torch_update = dict() + decoder_layeridx_sets = set() + for name in sorted(var_dict_torch.keys(), reverse=False): + names = name.split(".") + if names[0] == self.tf2torch_tensor_name_prefix_torch: + if names[1] == "decoders": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + layeridx_bias = 0 + layeridx += layeridx_bias + decoder_layeridx_sets.add(layeridx) + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + + elif names[1] == "decoders2": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + name_q = name_q.replace("decoders2", "decoders") + layeridx_bias = len(decoder_layeridx_sets) + + layeridx += layeridx_bias + if "decoders." in name: + decoder_layeridx_sets.add(layeridx) + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + + elif names[1] == "decoders3": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + + layeridx_bias = 0 + layeridx += layeridx_bias + if "decoders." in name: + decoder_layeridx_sets.add(layeridx) + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + + elif names[1] == "embed" or names[1] == "output_layer": + name_tf = map_dict[name]["name"] + if isinstance(name_tf, list): + idx_list = 0 + if name_tf[idx_list] in var_dict_tf.keys(): + pass + else: + idx_list = 1 + data_tf = var_dict_tf[name_tf[idx_list]] + if map_dict[name]["squeeze"][idx_list] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name]["squeeze"][idx_list] + ) + if map_dict[name]["transpose"][idx_list] is not None: + data_tf = np.transpose( + data_tf, map_dict[name]["transpose"][idx_list] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf[idx_list], + var_dict_tf[name_tf[idx_list]].shape, + ) + ) + + else: + data_tf = var_dict_tf[name_tf] + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name]["squeeze"] + ) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf, + var_dict_tf[name_tf].shape, + ) + ) + + elif names[1] == "after_norm": + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape + ) + ) + + elif names[1] == "embed_concat_ffn": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + + layeridx_bias = 0 + layeridx += layeridx_bias + if "decoders." in name: + decoder_layeridx_sets.add(layeridx) + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + + return var_dict_torch_update diff --git a/demo/Step-Audio-EditX/funasr_detach/models/scama/encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/scama/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..c155d5a092117cde1757d4c930405fe0f8f3048a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/scama/encoder.py @@ -0,0 +1,740 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +from typing import List +from typing import Optional +from typing import Sequence +from typing import Tuple +from typing import Union +import logging +import torch +import torch.nn as nn +import torch.nn.functional as F +from funasr_detach.models.scama.chunk_utilis import overlap_chunk +import numpy as np +from funasr_detach.train_utils.device_funcs import to_device +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.sanm.attention import ( + MultiHeadedAttention, + MultiHeadedAttentionSANM, +) +from funasr_detach.models.transformer.embedding import ( + SinusoidalPositionEncoder, + StreamSinusoidalPositionEncoder, +) +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear +from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, # noqa: H301 +) +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling2 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling6 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling8 +from funasr_detach.models.transformer.utils.subsampling import TooShortUttError +from funasr_detach.models.transformer.utils.subsampling import check_short_utt +from funasr_detach.models.transformer.utils.mask import subsequent_mask, vad_mask + +from funasr_detach.models.ctc.ctc import CTC + +from funasr_detach.register import tables + + +class EncoderLayerSANM(nn.Module): + def __init__( + self, + in_size, + size, + self_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + stochastic_depth_rate=0.0, + ): + """Construct an EncoderLayer object.""" + super(EncoderLayerSANM, self).__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(in_size) + self.norm2 = LayerNorm(size) + self.dropout = nn.Dropout(dropout_rate) + self.in_size = in_size + self.size = size + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear = nn.Linear(size + size, size) + self.stochastic_depth_rate = stochastic_depth_rate + self.dropout_rate = dropout_rate + + def forward( + self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None + ): + """Compute encoded features. + + Args: + x_input (torch.Tensor): Input tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + skip_layer = False + # with stochastic depth, residual connection `x + f(x)` becomes + # `x <- x + 1 / (1 - p) * f(x)` at training time. + stoch_layer_coeff = 1.0 + if self.training and self.stochastic_depth_rate > 0: + skip_layer = torch.rand(1).item() < self.stochastic_depth_rate + stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate) + + if skip_layer: + if cache is not None: + x = torch.cat([cache, x], dim=1) + return x, mask + + residual = x + if self.normalize_before: + x = self.norm1(x) + + if self.concat_after: + x_concat = torch.cat( + ( + x, + self.self_attn( + x, + mask, + mask_shfit_chunk=mask_shfit_chunk, + mask_att_chunk_encoder=mask_att_chunk_encoder, + ), + ), + dim=-1, + ) + if self.in_size == self.size: + x = residual + stoch_layer_coeff * self.concat_linear(x_concat) + else: + x = stoch_layer_coeff * self.concat_linear(x_concat) + else: + if self.in_size == self.size: + x = residual + stoch_layer_coeff * self.dropout( + self.self_attn( + x, + mask, + mask_shfit_chunk=mask_shfit_chunk, + mask_att_chunk_encoder=mask_att_chunk_encoder, + ) + ) + else: + x = stoch_layer_coeff * self.dropout( + self.self_attn( + x, + mask, + mask_shfit_chunk=mask_shfit_chunk, + mask_att_chunk_encoder=mask_att_chunk_encoder, + ) + ) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm2(x) + + return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder + + def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0): + """Compute encoded features. + + Args: + x_input (torch.Tensor): Input tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + + residual = x + if self.normalize_before: + x = self.norm1(x) + + if self.in_size == self.size: + attn, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back) + x = residual + attn + else: + x, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back) + + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + self.feed_forward(x) + if not self.normalize_before: + x = self.norm2(x) + + return x, cache + + +@tables.register("encoder_classes", "SANMEncoderChunkOpt") +class SANMEncoderChunkOpt(nn.Module): + """ + Author: Shiliang Zhang, Zhifu Gao, Haoneng Luo, Ming Lei, Jie Gao, Zhijie Yan, Lei Xie + SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition + https://arxiv.org/abs/2006.01712 + """ + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: Optional[str] = "conv2d", + pos_enc_class=SinusoidalPositionEncoder, + normalize_before: bool = True, + concat_after: bool = False, + positionwise_layer_type: str = "linear", + positionwise_conv_kernel_size: int = 1, + padding_idx: int = -1, + interctc_layer_idx: List[int] = [], + interctc_use_conditioning: bool = False, + kernel_size: int = 11, + sanm_shfit: int = 0, + selfattention_layer_type: str = "sanm", + chunk_size: Union[int, Sequence[int]] = (16,), + stride: Union[int, Sequence[int]] = (10,), + pad_left: Union[int, Sequence[int]] = (0,), + encoder_att_look_back_factor: Union[int, Sequence[int]] = (1,), + decoder_att_look_back_factor: Union[int, Sequence[int]] = (1,), + tf2torch_tensor_name_prefix_torch: str = "encoder", + tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder", + ): + super().__init__() + self._output_size = output_size + + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(input_size, output_size), + torch.nn.LayerNorm(output_size), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate) + elif input_layer == "conv2d2": + self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate) + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate) + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate) + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer is None: + if input_size == output_size: + self.embed = None + else: + self.embed = torch.nn.Linear(input_size, output_size) + elif input_layer == "pe": + self.embed = SinusoidalPositionEncoder() + elif input_layer == "pe_online": + self.embed = StreamSinusoidalPositionEncoder() + else: + raise ValueError("unknown input_layer: " + input_layer) + self.normalize_before = normalize_before + if positionwise_layer_type == "linear": + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d": + positionwise_layer = MultiLayeredConv1d + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d-linear": + positionwise_layer = Conv1dLinear + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + else: + raise NotImplementedError("Support only linear or conv1d.") + + if selfattention_layer_type == "selfattn": + encoder_selfattn_layer = MultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + elif selfattention_layer_type == "sanm": + encoder_selfattn_layer = MultiHeadedAttentionSANM + encoder_selfattn_layer_args0 = ( + attention_heads, + input_size, + output_size, + attention_dropout_rate, + kernel_size, + sanm_shfit, + ) + + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + output_size, + attention_dropout_rate, + kernel_size, + sanm_shfit, + ) + self.encoders0 = repeat( + 1, + lambda lnum: EncoderLayerSANM( + input_size, + output_size, + encoder_selfattn_layer(*encoder_selfattn_layer_args0), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + self.encoders = repeat( + num_blocks - 1, + lambda lnum: EncoderLayerSANM( + output_size, + output_size, + encoder_selfattn_layer(*encoder_selfattn_layer_args), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + ), + ) + if self.normalize_before: + self.after_norm = LayerNorm(output_size) + + self.interctc_layer_idx = interctc_layer_idx + if len(interctc_layer_idx) > 0: + assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks + self.interctc_use_conditioning = interctc_use_conditioning + self.conditioning_layer = None + shfit_fsmn = (kernel_size - 1) // 2 + self.overlap_chunk_cls = overlap_chunk( + chunk_size=chunk_size, + stride=stride, + pad_left=pad_left, + shfit_fsmn=shfit_fsmn, + encoder_att_look_back_factor=encoder_att_look_back_factor, + decoder_att_look_back_factor=decoder_att_look_back_factor, + ) + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ctc: CTC = None, + ind: int = 0, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Embed positions in tensor. + + Args: + xs_pad: input tensor (B, L, D) + ilens: input length (B) + prev_states: Not to be used now. + Returns: + position embedded tensor and mask + """ + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + xs_pad *= self.output_size() ** 0.5 + if self.embed is None: + xs_pad = xs_pad + elif ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling2) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + else: + xs_pad = self.embed(xs_pad) + + mask_shfit_chunk, mask_att_chunk_encoder = None, None + if self.overlap_chunk_cls is not None: + ilens = masks.squeeze(1).sum(1) + chunk_outs = self.overlap_chunk_cls.gen_chunk_mask(ilens, ind) + xs_pad, ilens = self.overlap_chunk_cls.split_chunk( + xs_pad, ilens, chunk_outs=chunk_outs + ) + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + mask_shfit_chunk = self.overlap_chunk_cls.get_mask_shfit_chunk( + chunk_outs, xs_pad.device, xs_pad.size(0), dtype=xs_pad.dtype + ) + mask_att_chunk_encoder = self.overlap_chunk_cls.get_mask_att_chunk_encoder( + chunk_outs, xs_pad.device, xs_pad.size(0), dtype=xs_pad.dtype + ) + + encoder_outs = self.encoders0( + xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder + ) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + intermediate_outs = [] + if len(self.interctc_layer_idx) == 0: + encoder_outs = self.encoders( + xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder + ) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + else: + for layer_idx, encoder_layer in enumerate(self.encoders): + encoder_outs = encoder_layer( + xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder + ) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + if layer_idx + 1 in self.interctc_layer_idx: + encoder_out = xs_pad + + # intermediate outputs are also normalized + if self.normalize_before: + encoder_out = self.after_norm(encoder_out) + + intermediate_outs.append((layer_idx + 1, encoder_out)) + + if self.interctc_use_conditioning: + ctc_out = ctc.softmax(encoder_out) + xs_pad = xs_pad + self.conditioning_layer(ctc_out) + + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + + olens = masks.squeeze(1).sum(1) + if len(intermediate_outs) > 0: + return (xs_pad, intermediate_outs), olens, None + return xs_pad, olens, None + + def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}): + if len(cache) == 0: + return feats + cache["feats"] = to_device(cache["feats"], device=feats.device) + overlap_feats = torch.cat((cache["feats"], feats), dim=1) + cache["feats"] = overlap_feats[ + :, -(cache["chunk_size"][0] + cache["chunk_size"][2]) :, : + ] + return overlap_feats + + def forward_chunk( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + cache: dict = None, + **kwargs, + ): + is_final = kwargs.get("is_final", False) + xs_pad *= self.output_size() ** 0.5 + if self.embed is None: + xs_pad = xs_pad + else: + xs_pad = self.embed(xs_pad, cache) + if cache["tail_chunk"]: + xs_pad = to_device(cache["feats"], device=xs_pad.device) + else: + xs_pad = self._add_overlap_chunk(xs_pad, cache) + if cache["opt"] is None: + cache_layer_num = len(self.encoders0) + len(self.encoders) + new_cache = [None] * cache_layer_num + else: + new_cache = cache["opt"] + + for layer_idx, encoder_layer in enumerate(self.encoders0): + encoder_outs = encoder_layer.forward_chunk( + xs_pad, + new_cache[layer_idx], + cache["chunk_size"], + cache["encoder_chunk_look_back"], + ) + xs_pad, new_cache[0] = encoder_outs[0], encoder_outs[1] + + for layer_idx, encoder_layer in enumerate(self.encoders): + encoder_outs = encoder_layer.forward_chunk( + xs_pad, + new_cache[layer_idx + len(self.encoders0)], + cache["chunk_size"], + cache["encoder_chunk_look_back"], + ) + xs_pad, new_cache[layer_idx + len(self.encoders0)] = ( + encoder_outs[0], + encoder_outs[1], + ) + + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + if ( + cache["encoder_chunk_look_back"] > 0 + or cache["encoder_chunk_look_back"] == -1 + ): + cache["opt"] = new_cache + + return xs_pad, ilens, None + + def gen_tf2torch_map_dict(self): + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + map_dict_local = { + ## encoder + # cicd + "{}.encoders.layeridx.norm1.weight".format(tensor_name_prefix_torch): { + "name": "{}/layer_layeridx/multi_head/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.encoders.layeridx.norm1.bias".format(tensor_name_prefix_torch): { + "name": "{}/layer_layeridx/multi_head/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.encoders.layeridx.self_attn.linear_q_k_v.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/multi_head/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (768,256),(1,256,768) + "{}.encoders.layeridx.self_attn.linear_q_k_v.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/multi_head/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (768,),(768,) + "{}.encoders.layeridx.self_attn.fsmn_block.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/multi_head/depth_conv_w".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 2, 0), + }, # (256,1,31),(1,31,256,1) + "{}.encoders.layeridx.self_attn.linear_out.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/multi_head/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,256),(1,256,256) + "{}.encoders.layeridx.self_attn.linear_out.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/multi_head/conv1d_1/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + # ffn + "{}.encoders.layeridx.norm2.weight".format(tensor_name_prefix_torch): { + "name": "{}/layer_layeridx/ffn/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.encoders.layeridx.norm2.bias".format(tensor_name_prefix_torch): { + "name": "{}/layer_layeridx/ffn/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.encoders.layeridx.feed_forward.w_1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/ffn/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.encoders.layeridx.feed_forward.w_1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/ffn/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.encoders.layeridx.feed_forward.w_2.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/ffn/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,1024),(1,1024,256) + "{}.encoders.layeridx.feed_forward.w_2.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/ffn/conv1d_1/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + # out norm + "{}.after_norm.weight".format(tensor_name_prefix_torch): { + "name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.after_norm.bias".format(tensor_name_prefix_torch): { + "name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + } + + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + + map_dict = self.gen_tf2torch_map_dict() + + var_dict_torch_update = dict() + for name in sorted(var_dict_torch.keys(), reverse=False): + names = name.split(".") + if names[0] == self.tf2torch_tensor_name_prefix_torch: + if names[1] == "encoders0": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + + name_q = name_q.replace("encoders0", "encoders") + layeridx_bias = 0 + layeridx += layeridx_bias + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + elif names[1] == "encoders": + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + layeridx_bias = 1 + layeridx += layeridx_bias + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_v, var_dict_tf[name_tf].shape + ) + ) + + elif names[1] == "after_norm": + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape + ) + ) + + return var_dict_torch_update diff --git a/demo/Step-Audio-EditX/funasr_detach/models/scama/model.py b/demo/Step-Audio-EditX/funasr_detach/models/scama/model.py new file mode 100644 index 0000000000000000000000000000000000000000..1511c308876fb1a0a6c3a9b079813d1bf10490f0 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/scama/model.py @@ -0,0 +1,766 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import time +import torch +import torch.nn as nn +import torch.functional as F +import logging +from typing import Dict, Tuple +from contextlib import contextmanager +from distutils.version import LooseVersion + +from funasr_detach.register import tables +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.utils import postprocess_utils +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.models.paraformer.model import Paraformer +from funasr_detach.models.paraformer.search import Hypothesis +from funasr_detach.models.paraformer.cif_predictor import mae_loss +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank +from funasr_detach.models.scama.utils import sequence_mask + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "SCAMA") +class SCAMA(nn.Module): + """ + Author: Shiliang Zhang, Zhifu Gao, Haoneng Luo, Ming Lei, Jie Gao, Zhijie Yan, Lei Xie + SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition + https://arxiv.org/abs/2006.01712 + """ + + def __init__( + self, + specaug: str = None, + specaug_conf: dict = None, + normalize: str = None, + normalize_conf: dict = None, + encoder: str = None, + encoder_conf: dict = None, + decoder: str = None, + decoder_conf: dict = None, + ctc: str = None, + ctc_conf: dict = None, + ctc_weight: float = 0.5, + predictor: str = None, + predictor_conf: dict = None, + predictor_bias: int = 0, + predictor_weight: float = 0.0, + input_size: int = 80, + vocab_size: int = -1, + ignore_id: int = -1, + blank_id: int = 0, + sos: int = 1, + eos: int = 2, + lsm_weight: float = 0.0, + length_normalized_loss: bool = False, + share_embedding: bool = False, + **kwargs, + ): + + super().__init__() + + if specaug is not None: + specaug_class = tables.specaug_classes.get(specaug) + specaug = specaug_class(**specaug_conf) + + if normalize is not None: + normalize_class = tables.normalize_classes.get(normalize) + normalize = normalize_class(**normalize_conf) + + encoder_class = tables.encoder_classes.get(encoder) + encoder = encoder_class(input_size=input_size, **encoder_conf) + encoder_output_size = encoder.output_size() + + decoder_class = tables.decoder_classes.get(decoder) + decoder = decoder_class( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + **decoder_conf, + ) + if ctc_weight > 0.0: + + if ctc_conf is None: + ctc_conf = {} + + ctc = CTC( + odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf + ) + + predictor_class = tables.predictor_classes.get(predictor) + predictor = predictor_class(**predictor_conf) + + # note that eos is the same as sos (equivalent ID) + self.blank_id = blank_id + self.sos = sos if sos is not None else vocab_size - 1 + self.eos = eos if eos is not None else vocab_size - 1 + self.vocab_size = vocab_size + self.ignore_id = ignore_id + self.ctc_weight = ctc_weight + + self.specaug = specaug + self.normalize = normalize + + self.encoder = encoder + + if ctc_weight == 1.0: + self.decoder = None + else: + self.decoder = decoder + + self.criterion_att = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + if ctc_weight == 0.0: + self.ctc = None + else: + self.ctc = ctc + + self.predictor = predictor + self.predictor_weight = predictor_weight + self.predictor_bias = predictor_bias + + self.criterion_pre = mae_loss(normalize_length=length_normalized_loss) + + self.share_embedding = share_embedding + if self.share_embedding: + self.decoder.embed = None + + self.length_normalized_loss = length_normalized_loss + self.beam_search = None + self.error_calculator = None + + if self.encoder.overlap_chunk_cls is not None: + from funasr_detach.models.scama.chunk_utilis import ( + build_scama_mask_for_cross_attention_decoder, + ) + + self.build_scama_mask_for_cross_attention_decoder_fn = ( + build_scama_mask_for_cross_attention_decoder + ) + self.decoder_attention_chunk_type = kwargs.get( + "decoder_attention_chunk_type", "chunk" + ) + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + + decoding_ind = kwargs.get("decoding_ind") + if len(text_lengths.size()) > 1: + text_lengths = text_lengths[:, 0] + if len(speech_lengths.size()) > 1: + speech_lengths = speech_lengths[:, 0] + + batch_size = speech.shape[0] + + # Encoder + ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind) + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind) + + loss_ctc, cer_ctc = None, None + loss_pre = None + stats = dict() + + # decoder: CTC branch + + if self.ctc_weight > 0.0: + + encoder_out_ctc, encoder_out_lens_ctc = ( + self.encoder.overlap_chunk_cls.remove_chunk( + encoder_out, encoder_out_lens, chunk_outs=None + ) + ) + + loss_ctc, cer_ctc = self._calc_ctc_loss( + encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths + ) + # Collect CTC branch stats + stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None + stats["cer_ctc"] = cer_ctc + + # decoder: Attention decoder branch + loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_predictor_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # 3. CTC-Att loss definition + if self.ctc_weight == 0.0: + loss = loss_att + loss_pre * self.predictor_weight + else: + loss = ( + self.ctc_weight * loss_ctc + + (1 - self.ctc_weight) * loss_att + + loss_pre * self.predictor_weight + ) + + # Collect Attn branch stats + stats["loss_att"] = loss_att.detach() if loss_att is not None else None + stats["acc"] = acc_att + stats["cer"] = cer_att + stats["wer"] = wer_att + stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None + + stats["loss"] = torch.clone(loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + if self.length_normalized_loss: + batch_size = (text_lengths + self.predictor_bias).sum() + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def encode( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + ind: int + """ + with autocast(False): + + # Data augmentation + if self.specaug is not None and self.training: + speech, speech_lengths = self.specaug(speech, speech_lengths) + + # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + speech, speech_lengths = self.normalize(speech, speech_lengths) + + # Forward encoder + encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + return encoder_out, encoder_out_lens + + def encode_chunk( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + cache: dict = None, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Frontend + Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + ind: int + """ + with autocast(False): + + # Data augmentation + if self.specaug is not None and self.training: + speech, speech_lengths = self.specaug(speech, speech_lengths) + + # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + speech, speech_lengths = self.normalize(speech, speech_lengths) + + # Forward encoder + encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk( + speech, speech_lengths, cache=cache["encoder"] + ) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + return encoder_out, torch.tensor([encoder_out.size(1)]) + + def calc_predictor_chunk(self, encoder_out, encoder_out_lens, cache=None, **kwargs): + is_final = kwargs.get("is_final", False) + + return self.predictor.forward_chunk( + encoder_out, cache["encoder"], is_final=is_final + ) + + def _calc_att_predictor_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + encoder_out_mask = sequence_mask( + encoder_out_lens, + maxlen=encoder_out.size(1), + dtype=encoder_out.dtype, + device=encoder_out.device, + )[:, None, :] + mask_chunk_predictor = None + if self.encoder.overlap_chunk_cls is not None: + mask_chunk_predictor = ( + self.encoder.overlap_chunk_cls.get_mask_chunk_predictor( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + encoder_out = encoder_out * mask_shfit_chunk + pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor( + encoder_out, + ys_out_pad, + encoder_out_mask, + ignore_id=self.ignore_id, + mask_chunk_predictor=mask_chunk_predictor, + target_label_length=ys_in_lens, + ) + predictor_alignments, predictor_alignments_len = ( + self.predictor.gen_frame_alignments(pre_alphas, encoder_out_lens) + ) + + encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur + attention_chunk_center_bias = 0 + attention_chunk_size = encoder_chunk_size + decoder_att_look_back_factor = ( + self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur + ) + mask_shift_att_chunk_decoder = ( + self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn( + predictor_alignments=predictor_alignments, + encoder_sequence_length=encoder_out_lens, + chunk_size=1, + encoder_chunk_size=encoder_chunk_size, + attention_chunk_center_bias=attention_chunk_center_bias, + attention_chunk_size=attention_chunk_size, + attention_chunk_type=self.decoder_attention_chunk_type, + step=None, + predictor_mask_chunk_hopping=mask_chunk_predictor, + decoder_att_look_back_factor=decoder_att_look_back_factor, + mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder, + target_length=ys_in_lens, + is_training=self.training, + ) + + # try: + # 1. Forward decoder + decoder_out, _ = self.decoder( + encoder_out, + encoder_out_lens, + ys_in_pad, + ys_in_lens, + chunk_mask=scama_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + ) + + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_out_pad) + acc_att = th_accuracy( + decoder_out.view(-1, self.vocab_size), + ys_out_pad, + ignore_label=self.ignore_id, + ) + # predictor loss + loss_pre = self.criterion_pre( + ys_in_lens.type_as(pre_token_length), pre_token_length + ) + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att, loss_pre + + def calc_predictor_mask( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor = None, + ys_pad_lens: torch.Tensor = None, + ): + # ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + # ys_in_lens = ys_pad_lens + 1 + ys_out_pad, ys_in_lens = None, None + + encoder_out_mask = sequence_mask( + encoder_out_lens, + maxlen=encoder_out.size(1), + dtype=encoder_out.dtype, + device=encoder_out.device, + )[:, None, :] + mask_chunk_predictor = None + + mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + encoder_out = encoder_out * mask_shfit_chunk + pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor( + encoder_out, + ys_out_pad, + encoder_out_mask, + ignore_id=self.ignore_id, + mask_chunk_predictor=mask_chunk_predictor, + target_label_length=ys_in_lens, + ) + predictor_alignments, predictor_alignments_len = ( + self.predictor.gen_frame_alignments(pre_alphas, encoder_out_lens) + ) + + encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur + attention_chunk_center_bias = 0 + attention_chunk_size = encoder_chunk_size + decoder_att_look_back_factor = ( + self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur + ) + mask_shift_att_chunk_decoder = ( + self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn( + predictor_alignments=predictor_alignments, + encoder_sequence_length=encoder_out_lens, + chunk_size=1, + encoder_chunk_size=encoder_chunk_size, + attention_chunk_center_bias=attention_chunk_center_bias, + attention_chunk_size=attention_chunk_size, + attention_chunk_type=self.decoder_attention_chunk_type, + step=None, + predictor_mask_chunk_hopping=mask_chunk_predictor, + decoder_att_look_back_factor=decoder_att_look_back_factor, + mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder, + target_length=ys_in_lens, + is_training=self.training, + ) + + return ( + pre_acoustic_embeds, + pre_token_length, + predictor_alignments, + predictor_alignments_len, + scama_mask, + ) + + def init_beam_search( + self, + **kwargs, + ): + + from funasr_detach.models.scama.beam_search import BeamSearchScamaStreaming + + from funasr_detach.models.transformer.scorers.ctc import CTCPrefixScorer + from funasr_detach.models.transformer.scorers.length_bonus import LengthBonus + + # 1. Build ASR model + scorers = {} + + if self.ctc != None: + ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos) + scorers.update(ctc=ctc) + token_list = kwargs.get("token_list") + scorers.update( + decoder=self.decoder, + length_bonus=LengthBonus(len(token_list)), + ) + + # 3. Build ngram model + # ngram is not supported now + ngram = None + scorers["ngram"] = ngram + + weights = dict( + decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.0), + ctc=kwargs.get("decoding_ctc_weight", 0.0), + lm=kwargs.get("lm_weight", 0.0), + ngram=kwargs.get("ngram_weight", 0.0), + length_bonus=kwargs.get("penalty", 0.0), + ) + + beam_search = BeamSearchScamaStreaming( + beam_size=kwargs.get("beam_size", 2), + weights=weights, + scorers=scorers, + sos=self.sos, + eos=self.eos, + vocab_size=len(token_list), + token_list=token_list, + pre_beam_score_key=None if self.ctc_weight == 1.0 else "full", + ) + # beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval() + # for scorer in scorers.values(): + # if isinstance(scorer, torch.nn.Module): + # scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval() + self.beam_search = beam_search + + def generate_chunk( + self, + speech, + speech_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + cache = kwargs.get("cache", {}) + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + + # Encoder + encoder_out, encoder_out_lens = self.encode_chunk( + speech, speech_lengths, cache=cache, is_final=kwargs.get("is_final", False) + ) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + if "running_hyps" not in cache: + running_hyps = self.beam_search.init_hyp(encoder_out) + cache["running_hyps"] = running_hyps + + # predictor + predictor_outs = self.calc_predictor_chunk( + encoder_out, + encoder_out_lens, + cache=cache, + is_final=kwargs.get("is_final", False), + ) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = ( + predictor_outs[0], + predictor_outs[1], + predictor_outs[2], + predictor_outs[3], + ) + pre_token_length = pre_token_length.round().long() + + if torch.max(pre_token_length) < 1: + return [] + maxlen = minlen = pre_token_length + if kwargs.get("is_final", False): + maxlen += kwargs.get("token_num_relax", 5) + minlen = max(0, minlen - kwargs.get("token_num_relax", 5)) + # c. Passed the encoder result and the beam search + nbest_hyps = self.beam_search( + x=encoder_out[0], + scama_mask=None, + pre_acoustic_embeds=pre_acoustic_embeds, + maxlen=int(maxlen), + minlen=int(minlen), + cache=cache, + ) + + cache["running_hyps"] = nbest_hyps + nbest_hyps = nbest_hyps[: self.nbest] + + results = [] + for hyp in nbest_hyps: + # assert isinstance(hyp, (Hypothesis)), type(hyp) + + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list( + filter( + lambda x: x != self.eos + and x != self.sos + and x != self.blank_id, + token_int, + ) + ) + + # Change integer-ids to tokens + token = tokenizer.ids2tokens(token_int) + # text = tokenizer.tokens2text(token) + + result_i = token + + results.extend(result_i) + + return results + + def init_cache(self, cache: dict = {}, **kwargs): + device = kwargs.get("device", "cuda") + + chunk_size = kwargs.get("chunk_size", [0, 10, 5]) + encoder_chunk_look_back = kwargs.get("encoder_chunk_look_back", 0) + decoder_chunk_look_back = kwargs.get("decoder_chunk_look_back", 0) + batch_size = 1 + + enc_output_size = kwargs["encoder_conf"]["output_size"] + feats_dims = ( + kwargs["frontend_conf"]["n_mels"] * kwargs["frontend_conf"]["lfr_m"] + ) + + cache_encoder = { + "start_idx": 0, + "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)).to( + device=device + ), + "cif_alphas": torch.zeros((batch_size, 1)).to(device=device), + "chunk_size": chunk_size, + "encoder_chunk_look_back": encoder_chunk_look_back, + "last_chunk": False, + "opt": None, + "feats": torch.zeros( + (batch_size, chunk_size[0] + chunk_size[2], feats_dims) + ).to(device=device), + "tail_chunk": False, + } + cache["encoder"] = cache_encoder + + cache_decoder = { + "decode_fsmn": None, + "decoder_chunk_look_back": decoder_chunk_look_back, + "opt": None, + "chunk_size": chunk_size, + } + cache["decoder"] = cache_decoder + cache["frontend"] = {} + + cache["prev_samples"] = torch.empty(0).to(device=device) + + return cache + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + cache: dict = {}, + **kwargs, + ): + + # init beamsearch + is_use_ctc = ( + kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None + ) + is_use_lm = ( + kwargs.get("lm_weight", 0.0) > 0.00001 + and kwargs.get("lm_file", None) is not None + ) + + if self.beam_search is None: + + logging.info("enable beam_search") + self.init_beam_search(**kwargs) + self.nbest = kwargs.get("nbest", 1) + + if len(cache) == 0: + self.init_cache(cache, **kwargs) + + meta_data = {} + chunk_size = kwargs.get("chunk_size", [0, 10, 5]) + chunk_stride_samples = int(chunk_size[1] * 960) # 600ms + + time1 = time.perf_counter() + cfg = {"is_final": kwargs.get("is_final", False)} + audio_sample_list = load_audio_text_image_video( + data_in, + fs=frontend.fs, + audio_fs=kwargs.get("fs", 16000), + data_type=kwargs.get("data_type", "sound"), + tokenizer=tokenizer, + cache=cfg, + ) + _is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True + + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + assert len(audio_sample_list) == 1, "batch_size must be set 1" + + audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0])) + + n = int(len(audio_sample) // chunk_stride_samples + int(_is_final)) + m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final))) + tokens = [] + for i in range(n): + kwargs["is_final"] = _is_final and i == n - 1 + audio_sample_i = audio_sample[ + i * chunk_stride_samples : (i + 1) * chunk_stride_samples + ] + + # extract fbank feats + speech, speech_lengths = extract_fbank( + [audio_sample_i], + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + cache=cache["frontend"], + is_final=kwargs["is_final"], + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() + * frontend.frame_shift + * frontend.lfr_n + / 1000 + ) + + tokens_i = self.generate_chunk( + speech, + speech_lengths, + key=key, + tokenizer=tokenizer, + cache=cache, + frontend=frontend, + **kwargs, + ) + tokens.extend(tokens_i) + + text_postprocessed, _ = postprocess_utils.sentence_postprocess(tokens) + + result_i = {"key": key[0], "text": text_postprocessed} + result = [result_i] + + cache["prev_samples"] = audio_sample[:-m] + if _is_final: + self.init_cache(cache, **kwargs) + + if kwargs.get("output_dir"): + writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = writer[f"{1}best_recog"] + ibest_writer["token"][key[0]] = " ".join(tokens) + ibest_writer["text"][key[0]] = text_postprocessed + + return result, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/scama/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/scama/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..76e235be2dfc758309b87e05cf9419b4a15e440f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/scama/template.yaml @@ -0,0 +1,127 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: SCAMA +model_conf: + ctc_weight: 0.0 + lsm_weight: 0.1 + length_normalized_loss: true + +# encoder +encoder: SANMEncoderChunkOpt +encoder_conf: + output_size: 512 + attention_heads: 4 + linear_units: 2048 + num_blocks: 50 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + +# decoder +decoder: FsmnDecoderSCAMAOpt +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 16 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + att_layer_num: 16 + kernel_size: 11 + sanm_shfit: 0 + +predictor: CifPredictorV2 +predictor_conf: + idim: 512 + threshold: 1.0 + l_order: 1 + r_order: 1 + tail_threshold: 0.45 + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 7 + lfr_n: 6 + +specaug: SpecAugLFR +specaug_conf: + apply_time_warp: false + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + lfr_rate: 6 + num_freq_mask: 1 + apply_time_mask: true + time_mask_width_range: + - 0 + - 12 + num_time_mask: 1 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + val_scheduler_criterion: + - valid + - acc + best_model_criterion: + - - valid + - acc + - max + keep_nbest_models: 10 + avg_nbest_model: 5 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0005 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true + +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/scama/utils.py b/demo/Step-Audio-EditX/funasr_detach/models/scama/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..38c22b762149347038be859513c3cbd8f0ad54c9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/scama/utils.py @@ -0,0 +1,91 @@ +import os +import yaml +import torch +import numpy as np +from torch.nn import functional as F + + +def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None): + if maxlen is None: + maxlen = lengths.max() + row_vector = torch.arange(0, maxlen, 1).to(lengths.device) + matrix = torch.unsqueeze(lengths, dim=-1) + mask = row_vector < matrix + mask = mask.detach() + + return mask.type(dtype).to(device) if device is not None else mask.type(dtype) + + +def apply_cmvn(inputs, mvn): + device = inputs.device + dtype = inputs.dtype + frame, dim = inputs.shape + meams = np.tile(mvn[0:1, :dim], (frame, 1)) + vars = np.tile(mvn[1:2, :dim], (frame, 1)) + inputs -= torch.from_numpy(meams).type(dtype).to(device) + inputs *= torch.from_numpy(vars).type(dtype).to(device) + + return inputs.type(torch.float32) + + +def drop_and_add( + inputs: torch.Tensor, + outputs: torch.Tensor, + training: bool, + dropout_rate: float = 0.1, + stoch_layer_coeff: float = 1.0, +): + + outputs = F.dropout(outputs, p=dropout_rate, training=training, inplace=True) + outputs *= stoch_layer_coeff + + input_dim = inputs.size(-1) + output_dim = outputs.size(-1) + + if input_dim == output_dim: + outputs += inputs + return outputs + + +def proc_tf_vocab(vocab_path): + with open(vocab_path, encoding="utf-8") as f: + token_list = [line.rstrip() for line in f] + if "" not in token_list: + token_list.append("") + return token_list + + +def gen_config_for_tfmodel(config_path, vocab_path, output_dir): + token_list = proc_tf_vocab(vocab_path) + with open(config_path, encoding="utf-8") as f: + config = yaml.safe_load(f) + + config["token_list"] = token_list + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + with open(os.path.join(output_dir, "config.yaml"), "w", encoding="utf-8") as f: + yaml_no_alias_safe_dump(config, f, indent=4, sort_keys=False) + + +class NoAliasSafeDumper(yaml.SafeDumper): + # Disable anchor/alias in yaml because looks ugly + def ignore_aliases(self, data): + return True + + +def yaml_no_alias_safe_dump(data, stream=None, **kwargs): + """Safe-dump in yaml with no anchor/alias""" + return yaml.dump( + data, stream, allow_unicode=True, Dumper=NoAliasSafeDumper, **kwargs + ) + + +if __name__ == "__main__": + import sys + + config_path = sys.argv[1] + vocab_path = sys.argv[2] + output_dir = sys.argv[3] + gen_config_for_tfmodel(config_path, vocab_path, output_dir) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/seaco_paraformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/seaco_paraformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/seaco_paraformer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/seaco_paraformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..add7dc61787b244167fd9cca07813c7c38fded9e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/seaco_paraformer/model.py @@ -0,0 +1,646 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import os +import re +import time +import copy +import torch +import codecs +import logging +import tempfile +import requests +import numpy as np +from typing import Dict, Tuple +from contextlib import contextmanager +from distutils.version import LooseVersion + +from funasr_detach.register import tables +from funasr_detach.utils import postprocess_utils +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.models.paraformer.model import Paraformer +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.models.paraformer.search import Hypothesis +from funasr_detach.models.paraformer.cif_predictor import mae_loss +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.bicif_paraformer.model import BiCifParaformer +from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss +from funasr_detach.utils.timestamp_tools import ts_prediction_lfr6_standard +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank + + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "SeacoParaformer") +class SeacoParaformer(BiCifParaformer, Paraformer): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability + https://arxiv.org/abs/2308.03266 + """ + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.inner_dim = kwargs.get("inner_dim", 256) + self.bias_encoder_type = kwargs.get("bias_encoder_type", "lstm") + bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0) + bias_encoder_bid = kwargs.get("bias_encoder_bid", False) + seaco_lsm_weight = kwargs.get("seaco_lsm_weight", 0.0) + seaco_length_normalized_loss = kwargs.get("seaco_length_normalized_loss", True) + + # bias encoder + if self.bias_encoder_type == "lstm": + self.bias_encoder = torch.nn.LSTM( + self.inner_dim, + self.inner_dim, + 2, + batch_first=True, + dropout=bias_encoder_dropout_rate, + bidirectional=bias_encoder_bid, + ) + if bias_encoder_bid: + self.lstm_proj = torch.nn.Linear(self.inner_dim * 2, self.inner_dim) + else: + self.lstm_proj = None + self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim) + elif self.bias_encoder_type == "mean": + self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim) + else: + logging.error( + "Unsupport bias encoder type: {}".format(self.bias_encoder_type) + ) + + # seaco decoder + seaco_decoder = kwargs.get("seaco_decoder", None) + if seaco_decoder is not None: + seaco_decoder_conf = kwargs.get("seaco_decoder_conf") + seaco_decoder_class = tables.decoder_classes.get(seaco_decoder) + self.seaco_decoder = seaco_decoder_class( + vocab_size=self.vocab_size, + encoder_output_size=self.inner_dim, + **seaco_decoder_conf, + ) + self.hotword_output_layer = torch.nn.Linear(self.inner_dim, self.vocab_size) + self.criterion_seaco = LabelSmoothingLoss( + size=self.vocab_size, + padding_idx=self.ignore_id, + smoothing=seaco_lsm_weight, + normalize_length=seaco_length_normalized_loss, + ) + self.train_decoder = kwargs.get("train_decoder", False) + self.NO_BIAS = kwargs.get("NO_BIAS", 8377) + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Decoder + Calc loss + + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + assert text_lengths.dim() == 1, text_lengths.shape + # Check that batch_size is unified + assert ( + speech.shape[0] + == speech_lengths.shape[0] + == text.shape[0] + == text_lengths.shape[0] + ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) + + hotword_pad = kwargs.get("hotword_pad") + hotword_lengths = kwargs.get("hotword_lengths") + dha_pad = kwargs.get("dha_pad") + + batch_size = speech.shape[0] + self.step_cur += 1 + # for data-parallel + text = text[:, : text_lengths.max()] + speech = speech[:, : speech_lengths.max()] + + # 1. Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + if self.predictor_bias == 1: + _, ys_pad = add_sos_eos(text, self.sos, self.eos, self.ignore_id) + ys_lengths = text_lengths + self.predictor_bias + + stats = dict() + loss_seaco = self._calc_seaco_loss( + encoder_out, + encoder_out_lens, + ys_pad, + ys_lengths, + hotword_pad, + hotword_lengths, + dha_pad, + ) + if self.train_decoder: + loss_att, acc_att = self._calc_att_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + loss = loss_seaco + loss_att + stats["loss_att"] = torch.clone(loss_att.detach()) + stats["acc_att"] = acc_att + else: + loss = loss_seaco + stats["loss_seaco"] = torch.clone(loss_seaco.detach()) + stats["loss"] = torch.clone(loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + if self.length_normalized_loss: + batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size) + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def _merge(self, cif_attended, dec_attended): + return cif_attended + dec_attended + + def _calc_seaco_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_lengths: torch.Tensor, + hotword_pad: torch.Tensor, + hotword_lengths: torch.Tensor, + dha_pad: torch.Tensor, + ): + # predictor forward + encoder_out_mask = ( + ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + pre_acoustic_embeds, _, _, _ = self.predictor( + encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id + ) + # decoder forward + decoder_out, _ = self.decoder( + encoder_out, + encoder_out_lens, + pre_acoustic_embeds, + ys_lengths, + return_hidden=True, + ) + selected = self._hotword_representation(hotword_pad, hotword_lengths) + contextual_info = ( + selected.squeeze(0) + .repeat(encoder_out.shape[0], 1, 1) + .to(encoder_out.device) + ) + num_hot_word = contextual_info.shape[1] + _contextual_length = ( + torch.Tensor([num_hot_word]) + .int() + .repeat(encoder_out.shape[0]) + .to(encoder_out.device) + ) + # dha core + cif_attended, _ = self.seaco_decoder( + contextual_info, _contextual_length, pre_acoustic_embeds, ys_lengths + ) + dec_attended, _ = self.seaco_decoder( + contextual_info, _contextual_length, decoder_out, ys_lengths + ) + merged = self._merge(cif_attended, dec_attended) + dha_output = self.hotword_output_layer( + merged[:, :-1] + ) # remove the last token in loss calculation + loss_att = self.criterion_seaco(dha_output, dha_pad) + return loss_att + + def _seaco_decode_with_ASF( + self, + encoder_out, + encoder_out_lens, + sematic_embeds, + ys_pad_lens, + hw_list, + nfilter=50, + seaco_weight=1.0, + ): + # decoder forward + decoder_out, decoder_hidden, _ = self.decoder( + encoder_out, + encoder_out_lens, + sematic_embeds, + ys_pad_lens, + return_hidden=True, + return_both=True, + ) + decoder_pred = torch.log_softmax(decoder_out, dim=-1) + if hw_list is not None: + hw_lengths = [len(i) for i in hw_list] + hw_list_ = [torch.Tensor(i).long() for i in hw_list] + hw_list_pad = pad_list(hw_list_, 0).to(encoder_out.device) + selected = self._hotword_representation( + hw_list_pad, torch.Tensor(hw_lengths).int().to(encoder_out.device) + ) + contextual_info = ( + selected.squeeze(0) + .repeat(encoder_out.shape[0], 1, 1) + .to(encoder_out.device) + ) + num_hot_word = contextual_info.shape[1] + _contextual_length = ( + torch.Tensor([num_hot_word]) + .int() + .repeat(encoder_out.shape[0]) + .to(encoder_out.device) + ) + + # ASF Core + if nfilter > 0 and nfilter < num_hot_word: + for dec in self.seaco_decoder.decoders: + dec.reserve_attn = True + # cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens) + dec_attended, _ = self.seaco_decoder( + contextual_info, _contextual_length, decoder_hidden, ys_pad_lens + ) + # cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist() + hotword_scores = ( + self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1] + ) + # hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device) + dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word - 1))[ + 1 + ].tolist() + add_filter = dec_filter + add_filter.append(len(hw_list_pad) - 1) + # filter hotword embedding + selected = selected[add_filter] + # again + contextual_info = ( + selected.squeeze(0) + .repeat(encoder_out.shape[0], 1, 1) + .to(encoder_out.device) + ) + num_hot_word = contextual_info.shape[1] + _contextual_length = ( + torch.Tensor([num_hot_word]) + .int() + .repeat(encoder_out.shape[0]) + .to(encoder_out.device) + ) + for dec in self.seaco_decoder.decoders: + dec.attn_mat = [] + dec.reserve_attn = False + + # SeACo Core + cif_attended, _ = self.seaco_decoder( + contextual_info, _contextual_length, sematic_embeds, ys_pad_lens + ) + dec_attended, _ = self.seaco_decoder( + contextual_info, _contextual_length, decoder_hidden, ys_pad_lens + ) + merged = self._merge(cif_attended, dec_attended) + + dha_output = self.hotword_output_layer( + merged + ) # remove the last token in loss calculation + dha_pred = torch.log_softmax(dha_output, dim=-1) + + def _merge_res(dec_output, dha_output): + lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0]) + dha_ids = dha_output.max(-1)[-1] # [0] + dha_mask = (dha_ids == 8377).int().unsqueeze(-1) + a = (1 - lmbd) / lmbd + b = 1 / lmbd + a, b = a.to(dec_output.device), b.to(dec_output.device) + dha_mask = (dha_mask + a.reshape(-1, 1, 1)) / b.reshape(-1, 1, 1) + # logits = dec_output * dha_mask + dha_output[:,:,:-1] * (1-dha_mask) + logits = dec_output * dha_mask + dha_output[:, :, :] * (1 - dha_mask) + return logits + + merged_pred = _merge_res(decoder_pred, dha_pred) + # import pdb; pdb.set_trace() + return merged_pred + else: + return decoder_pred + + def _hotword_representation(self, hotword_pad, hotword_lengths): + if self.bias_encoder_type != "lstm": + logging.error("Unsupported bias encoder type") + hw_embed = self.decoder.embed(hotword_pad) + hw_embed, (_, _) = self.bias_encoder(hw_embed) + if self.lstm_proj is not None: + hw_embed = self.lstm_proj(hw_embed) + _ind = np.arange(0, hw_embed.shape[0]).tolist() + selected = hw_embed[ + _ind, [i - 1 for i in hotword_lengths.detach().cpu().tolist()] + ] + return selected + + """ + def calc_predictor(self, encoder_out, encoder_out_lens): + encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to( + encoder_out.device) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = self.predictor(encoder_out, + None, + encoder_out_mask, + ignore_id=self.ignore_id) + return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index + + + def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num): + encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to( + encoder_out.device) + ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out, + encoder_out_mask, + token_num) + return ds_alphas, ds_cif_peak, us_alphas, us_peaks + """ + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + + # init beamsearch + is_use_ctc = ( + kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None + ) + is_use_lm = ( + kwargs.get("lm_weight", 0.0) > 0.00001 + and kwargs.get("lm_file", None) is not None + ) + if self.beam_search is None and (is_use_lm or is_use_ctc): + logging.info("enable beam_search") + self.init_beam_search(**kwargs) + self.nbest = kwargs.get("nbest", 1) + + meta_data = {} + + # extract fbank feats + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000) + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths = extract_fbank( + audio_sample_list, + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000 + ) + + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + + # hotword + self.hotword_list = self.generate_hotwords_list( + kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend + ) + + # Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + # predictor + predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens) + pre_acoustic_embeds, pre_token_length, _, _ = ( + predictor_outs[0], + predictor_outs[1], + predictor_outs[2], + predictor_outs[3], + ) + pre_token_length = pre_token_length.round().long() + if torch.max(pre_token_length) < 1: + return [] + + decoder_out = self._seaco_decode_with_ASF( + encoder_out, + encoder_out_lens, + pre_acoustic_embeds, + pre_token_length, + hw_list=self.hotword_list, + ) + # decoder_out, _ = decoder_outs[0], decoder_outs[1] + _, _, us_alphas, us_peaks = self.calc_predictor_timestamp( + encoder_out, encoder_out_lens, pre_token_length + ) + + results = [] + b, n, d = decoder_out.size() + for i in range(b): + x = encoder_out[i, : encoder_out_lens[i], :] + am_scores = decoder_out[i, : pre_token_length[i], :] + if self.beam_search is not None: + nbest_hyps = self.beam_search( + x=x, + am_scores=am_scores, + maxlenratio=kwargs.get("maxlenratio", 0.0), + minlenratio=kwargs.get("minlenratio", 0.0), + ) + + nbest_hyps = nbest_hyps[: self.nbest] + else: + + yseq = am_scores.argmax(dim=-1) + score = am_scores.max(dim=-1)[0] + score = torch.sum(score, dim=-1) + # pad with mask tokens to ensure compatibility with sos/eos tokens + yseq = torch.tensor( + [self.sos] + yseq.tolist() + [self.eos], device=yseq.device + ) + nbest_hyps = [Hypothesis(yseq=yseq, score=score)] + for nbest_idx, hyp in enumerate(nbest_hyps): + ibest_writer = None + if kwargs.get("output_dir") is not None: + if not hasattr(self, "writer"): + self.writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"] + + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list( + filter( + lambda x: x != self.eos + and x != self.sos + and x != self.blank_id, + token_int, + ) + ) + + if tokenizer is not None: + # Change integer-ids to tokens + token = tokenizer.ids2tokens(token_int) + text = tokenizer.tokens2text(token) + + _, timestamp = ts_prediction_lfr6_standard( + us_alphas[i][: encoder_out_lens[i] * 3], + us_peaks[i][: encoder_out_lens[i] * 3], + copy.copy(token), + vad_offset=kwargs.get("begin_time", 0), + ) + + text_postprocessed, time_stamp_postprocessed, word_lists = ( + postprocess_utils.sentence_postprocess(token, timestamp) + ) + + result_i = { + "key": key[i], + "text": text_postprocessed, + "timestamp": time_stamp_postprocessed, + } + + if ibest_writer is not None: + ibest_writer["token"][key[i]] = " ".join(token) + ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed + ibest_writer["text"][key[i]] = text_postprocessed + else: + result_i = {"key": key[i], "token_int": token_int} + results.append(result_i) + + return results, meta_data + + def generate_hotwords_list( + self, hotword_list_or_file, tokenizer=None, frontend=None + ): + def load_seg_dict(seg_dict_file): + seg_dict = {} + assert isinstance(seg_dict_file, str) + with open(seg_dict_file, "r", encoding="utf8") as f: + lines = f.readlines() + for line in lines: + s = line.strip().split() + key = s[0] + value = s[1:] + seg_dict[key] = " ".join(value) + return seg_dict + + def seg_tokenize(txt, seg_dict): + pattern = re.compile(r"^[\u4E00-\u9FA50-9]+$") + out_txt = "" + for word in txt: + word = word.lower() + if word in seg_dict: + out_txt += seg_dict[word] + " " + else: + if pattern.match(word): + for char in word: + if char in seg_dict: + out_txt += seg_dict[char] + " " + else: + out_txt += "" + " " + else: + out_txt += "" + " " + return out_txt.strip().split() + + seg_dict = None + if frontend.cmvn_file is not None: + model_dir = os.path.dirname(frontend.cmvn_file) + seg_dict_file = os.path.join(model_dir, "seg_dict") + if os.path.exists(seg_dict_file): + seg_dict = load_seg_dict(seg_dict_file) + else: + seg_dict = None + # for None + if hotword_list_or_file is None: + hotword_list = None + # for local txt inputs + elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith( + ".txt" + ): + logging.info("Attempting to parse hotwords from local txt...") + hotword_list = [] + hotword_str_list = [] + with codecs.open(hotword_list_or_file, "r") as fin: + for line in fin.readlines(): + hw = line.strip() + hw_list = hw.split() + if seg_dict is not None: + hw_list = seg_tokenize(hw_list, seg_dict) + hotword_str_list.append(hw) + hotword_list.append(tokenizer.tokens2ids(hw_list)) + hotword_list.append([self.sos]) + hotword_str_list.append("") + logging.info( + "Initialized hotword list from file: {}, hotword list: {}.".format( + hotword_list_or_file, hotword_str_list + ) + ) + # for url, download and generate txt + elif hotword_list_or_file.startswith("http"): + logging.info("Attempting to parse hotwords from url...") + work_dir = tempfile.TemporaryDirectory().name + if not os.path.exists(work_dir): + os.makedirs(work_dir) + text_file_path = os.path.join( + work_dir, os.path.basename(hotword_list_or_file) + ) + local_file = requests.get(hotword_list_or_file) + open(text_file_path, "wb").write(local_file.content) + hotword_list_or_file = text_file_path + hotword_list = [] + hotword_str_list = [] + with codecs.open(hotword_list_or_file, "r") as fin: + for line in fin.readlines(): + hw = line.strip() + hw_list = hw.split() + if seg_dict is not None: + hw_list = seg_tokenize(hw_list, seg_dict) + hotword_str_list.append(hw) + hotword_list.append(tokenizer.tokens2ids(hw_list)) + hotword_list.append([self.sos]) + hotword_str_list.append("") + logging.info( + "Initialized hotword list from file: {}, hotword list: {}.".format( + hotword_list_or_file, hotword_str_list + ) + ) + # for text str input + elif not hotword_list_or_file.endswith(".txt"): + logging.info("Attempting to parse hotwords as str...") + hotword_list = [] + hotword_str_list = [] + for hw in hotword_list_or_file.strip().split(): + hotword_str_list.append(hw) + hw_list = hw.strip().split() + if seg_dict is not None: + hw_list = seg_tokenize(hw_list, seg_dict) + hotword_list.append(tokenizer.tokens2ids(hw_list)) + hotword_list.append([self.sos]) + hotword_str_list.append("") + logging.info("Hotword list: {}.".format(hotword_str_list)) + else: + hotword_list = None + return hotword_list diff --git a/demo/Step-Audio-EditX/funasr_detach/models/seaco_paraformer/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/seaco_paraformer/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6e40aef3d945a7009bd00bd3b58c29e53c40bfb2 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/seaco_paraformer/template.yaml @@ -0,0 +1,156 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: SeacoParaformer +model_conf: + ctc_weight: 0.0 + lsm_weight: 0.1 + length_normalized_loss: true + predictor_weight: 1.0 + predictor_bias: 1 + sampling_ratio: 0.75 + inner_dim: 512 + bias_encoder_type: lstm + bias_encoder_bid: false + seaco_lsm_weight: 0.1 + seaco_length_normal: true + train_decoder: false + NO_BIAS: 8377 + +# encoder +encoder: SANMEncoder +encoder_conf: + output_size: 512 + attention_heads: 4 + linear_units: 2048 + num_blocks: 50 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + +# decoder +decoder: ParaformerSANMDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 16 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + att_layer_num: 16 + kernel_size: 11 + sanm_shfit: 0 + +# seaco decoder +seaco_decoder: ParaformerSANMDecoder +seaco_decoder_conf: + attention_heads: 4 + linear_units: 1024 + num_blocks: 4 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + kernel_size: 21 + sanm_shfit: 0 + use_output_layer: false + wo_input_layer: true + +predictor: CifPredictorV3 +predictor_conf: + idim: 512 + threshold: 1.0 + l_order: 1 + r_order: 1 + tail_threshold: 0.45 + smooth_factor2: 0.25 + noise_threshold2: 0.01 + upsample_times: 3 + use_cif1_cnn: false + upsample_type: cnn_blstm + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 7 + lfr_n: 6 + dither: 0.0 + +specaug: SpecAugLFR +specaug_conf: + apply_time_warp: false + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + lfr_rate: 6 + num_freq_mask: 1 + apply_time_mask: true + time_mask_width_range: + - 0 + - 12 + num_time_mask: 1 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + val_scheduler_criterion: + - valid + - acc + best_model_criterion: + - - valid + - acc + - max + keep_nbest_models: 10 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0005 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/attention.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..1af010d33080c478f4f81e771f44e55e0f07f811 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/attention.py @@ -0,0 +1,328 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Multi-Head Attention layer definition.""" + +import math + +import numpy +import torch +from torch import nn +from typing import Optional, Tuple + +import torch.nn.functional as F +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +import funasr_detach.models.lora.layers as lora + + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, n_head, n_feat, dropout_rate): + """Construct an MultiHeadedAttention object.""" + super(MultiHeadedAttention, self).__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.attn = None + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv(self, query, key, value): + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + + return q, k, v + + def forward_attention(self, value, scores, mask): + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2). + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + if mask is not None: + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + min_value = float( + numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min + ) + scores = scores.masked_fill(mask, min_value) + self.attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0 + ) # (batch, head, time1, time2) + else: + self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(self.attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward(self, query, key, value, mask): + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q, k, v = self.forward_qkv(query, key, value) + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask) + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding (new implementation). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + Paper: https://arxiv.org/abs/1901.02860 + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + zero_triu (bool): Whether to zero the upper triangular part of attention matrix. + + """ + + def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate) + self.zero_triu = zero_triu + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x): + """Compute relative positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + torch.Tensor: Output tensor. + + """ + zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x)[ + :, :, :, : x.size(-1) // 2 + 1 + ] # only keep the positions from 0 to time2 + + if self.zero_triu: + ones = torch.ones((x.size(2), x.size(3)), device=x.device) + x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] + + return x + + def forward(self, query, key, value, pos_emb, mask): + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, 2*time1-1, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, 2*time1-1) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k + ) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask) + + +class MultiHeadSelfAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, n_head, in_feat, n_feat, dropout_rate): + """Construct an MultiHeadedAttention object.""" + super(MultiHeadSelfAttention, self).__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_out = nn.Linear(n_feat, n_feat) + self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3) + self.attn = None + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv(self, x): + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k). + + """ + b, t, d = x.size() + q_k_v = self.linear_q_k_v(x) + q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1) + q_h = torch.reshape(q, (b, t, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + k_h = torch.reshape(k, (b, t, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time2, d_k) + v_h = torch.reshape(v, (b, t, self.h, self.d_k)).transpose( + 1, 2 + ) # (batch, head, time2, d_k) + + return q_h, k_h, v_h, v + + def forward_attention(self, value, scores, mask, mask_att_chunk_encoder=None): + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2). + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + if mask is not None: + if mask_att_chunk_encoder is not None: + mask = mask * mask_att_chunk_encoder + + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + + min_value = float( + numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min + ) + scores = scores.masked_fill(mask, min_value) + self.attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0 + ) # (batch, head, time1, time2) + else: + self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(self.attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward(self, x, mask, mask_att_chunk_encoder=None): + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q_h, k_h, v_h, v = self.forward_qkv(x) + q_h = q_h * self.d_k ** (-0.5) + scores = torch.matmul(q_h, k_h.transpose(-2, -1)) + att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder) + return att_outs diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/e2e_diar_sond.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/e2e_diar_sond.py new file mode 100644 index 0000000000000000000000000000000000000000..876433b8b2bfe08647ac4ce21fafcdffb1849285 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/e2e_diar_sond.py @@ -0,0 +1,626 @@ +#!/usr/bin/env python3 +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) +import logging +import random +from contextlib import contextmanager +from distutils.version import LooseVersion +from itertools import permutations +from typing import Dict +from typing import Optional +from typing import Tuple, List + +import numpy as np +import torch +from torch.nn import functional as F + +from funasr_detach.models.transformer.utils.nets_utils import to_device +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.decoder.abs_decoder import AbsDecoder +from funasr_detach.models.encoder.abs_encoder import AbsEncoder +from funasr_detach.frontends.abs_frontend import AbsFrontend +from funasr_detach.models.specaug.abs_specaug import AbsSpecAug +from funasr_detach.models.specaug.abs_profileaug import AbsProfileAug +from funasr_detach.layers.abs_normalize import AbsNormalize +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.base_model import FunASRModel +from funasr_detach.losses.label_smoothing_loss import ( + LabelSmoothingLoss, + SequenceBinaryCrossEntropy, +) +from funasr_detach.utils.misc import int2vec +from funasr_detach.utils.hinter import hint_once + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +class DiarSondModel(FunASRModel): + """Speaker overlap-aware neural diarization model + reference: https://arxiv.org/abs/2211.10243 + """ + + def __init__( + self, + vocab_size: int, + frontend: Optional[AbsFrontend], + specaug: Optional[AbsSpecAug], + profileaug: Optional[AbsProfileAug], + normalize: Optional[AbsNormalize], + encoder: torch.nn.Module, + speaker_encoder: Optional[torch.nn.Module], + ci_scorer: torch.nn.Module, + cd_scorer: Optional[torch.nn.Module], + decoder: torch.nn.Module, + token_list: list, + lsm_weight: float = 0.1, + length_normalized_loss: bool = False, + max_spk_num: int = 16, + label_aggregator: Optional[torch.nn.Module] = None, + normalize_speech_speaker: bool = False, + ignore_id: int = -1, + speaker_discrimination_loss_weight: float = 1.0, + inter_score_loss_weight: float = 0.0, + inputs_type: str = "raw", + model_regularizer_weight: float = 0.0, + freeze_encoder: bool = False, + onfly_shuffle_speaker: bool = True, + ): + + super().__init__() + + self.encoder = encoder + self.speaker_encoder = speaker_encoder + self.ci_scorer = ci_scorer + self.cd_scorer = cd_scorer + self.normalize = normalize + self.frontend = frontend + self.specaug = specaug + self.profileaug = profileaug + self.label_aggregator = label_aggregator + self.decoder = decoder + self.token_list = token_list + self.max_spk_num = max_spk_num + self.normalize_speech_speaker = normalize_speech_speaker + self.ignore_id = ignore_id + self.model_regularizer_weight = model_regularizer_weight + self.freeze_encoder = freeze_encoder + self.onfly_shuffle_speaker = onfly_shuffle_speaker + self.criterion_diar = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + self.criterion_bce = SequenceBinaryCrossEntropy( + normalize_length=length_normalized_loss + ) + self.pse_embedding = self.generate_pse_embedding() + self.power_weight = torch.from_numpy( + 2 ** np.arange(max_spk_num)[np.newaxis, np.newaxis, :] + ).float() + self.int_token_arr = torch.from_numpy( + np.array(self.token_list).astype(int)[np.newaxis, np.newaxis, :] + ).int() + self.speaker_discrimination_loss_weight = speaker_discrimination_loss_weight + self.inter_score_loss_weight = inter_score_loss_weight + self.forward_steps = 0 + self.inputs_type = inputs_type + self.to_regularize_parameters = None + + def get_regularize_parameters(self): + to_regularize_parameters, normal_parameters = [], [] + for name, param in self.named_parameters(): + if ( + "encoder" in name + and "weight" in name + and "bn" not in name + and ( + "conv2" in name + or "conv1" in name + or "conv_sc" in name + or "dense" in name + ) + ): + to_regularize_parameters.append((name, param)) + else: + normal_parameters.append((name, param)) + self.to_regularize_parameters = to_regularize_parameters + return to_regularize_parameters, normal_parameters + + def generate_pse_embedding(self): + embedding = np.zeros((len(self.token_list), self.max_spk_num), dtype=np.float32) + for idx, pse_label in enumerate(self.token_list): + emb = int2vec(int(pse_label), vec_dim=self.max_spk_num, dtype=np.float32) + embedding[idx] = emb + return torch.from_numpy(embedding) + + def rand_permute_speaker(self, raw_profile, raw_binary_labels): + """ + raw_profile: B, N, D + raw_binary_labels: B, T, N + """ + assert ( + raw_profile.shape[1] == raw_binary_labels.shape[2] + ), "Num profile: {}, Num label: {}".format( + raw_profile.shape[1], raw_binary_labels.shape[-1] + ) + profile = torch.clone(raw_profile) + binary_labels = torch.clone(raw_binary_labels) + bsz, num_spk = profile.shape[0], profile.shape[1] + for i in range(bsz): + idx = list(range(num_spk)) + random.shuffle(idx) + profile[i] = profile[i][idx, :] + binary_labels[i] = binary_labels[i][:, idx] + + return profile, binary_labels + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor = None, + profile: torch.Tensor = None, + profile_lengths: torch.Tensor = None, + binary_labels: torch.Tensor = None, + binary_labels_lengths: torch.Tensor = None, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Speaker Encoder + CI Scorer + CD Scorer + Decoder + Calc loss + + Args: + speech: (Batch, samples) or (Batch, frames, input_size) + speech_lengths: (Batch,) default None for chunk interator, + because the chunk-iterator does not + have the speech_lengths returned. + see in + espnet2/iterators/chunk_iter_factory.py + profile: (Batch, N_spk, dim) + profile_lengths: (Batch,) + binary_labels: (Batch, frames, max_spk_num) + binary_labels_lengths: (Batch,) + """ + assert speech.shape[0] <= binary_labels.shape[0], ( + speech.shape, + binary_labels.shape, + ) + batch_size = speech.shape[0] + if self.freeze_encoder: + hint_once("Freeze encoder", "freeze_encoder", rank=0) + self.encoder.eval() + self.forward_steps = self.forward_steps + 1 + if self.pse_embedding.device != speech.device: + self.pse_embedding = self.pse_embedding.to(speech.device) + self.power_weight = self.power_weight.to(speech.device) + self.int_token_arr = self.int_token_arr.to(speech.device) + + if self.onfly_shuffle_speaker: + hint_once( + "On-the-fly shuffle speaker permutation.", + "onfly_shuffle_speaker", + rank=0, + ) + profile, binary_labels = self.rand_permute_speaker(profile, binary_labels) + + # 0a. Aggregate time-domain labels to match forward outputs + if self.label_aggregator is not None: + binary_labels, binary_labels_lengths = self.label_aggregator( + binary_labels, binary_labels_lengths + ) + # 0b. augment profiles + if self.profileaug is not None and self.training: + speech, profile, binary_labels = self.profileaug( + speech, + speech_lengths, + profile, + profile_lengths, + binary_labels, + binary_labels_lengths, + ) + + # 1. Calculate power-set encoding (PSE) labels + pad_bin_labels = F.pad( + binary_labels, + (0, self.max_spk_num - binary_labels.shape[2]), + "constant", + 0.0, + ) + raw_pse_labels = torch.sum( + pad_bin_labels * self.power_weight, dim=2, keepdim=True + ) + pse_labels = torch.argmax( + (raw_pse_labels.int() == self.int_token_arr).float(), dim=2 + ) + + # 2. Network forward + pred, inter_outputs = self.prediction_forward( + speech, speech_lengths, profile, profile_lengths, return_inter_outputs=True + ) + (speech, speech_lengths), (profile, profile_lengths), (ci_score, cd_score) = ( + inter_outputs + ) + + # If encoder uses conv* as input_layer (i.e., subsampling), + # the sequence length of 'pred' might be slightly less than the + # length of 'spk_labels'. Here we force them to be equal. + length_diff_tolerance = 2 + length_diff = abs(pse_labels.shape[1] - pred.shape[1]) + if length_diff <= length_diff_tolerance: + min_len = min(pred.shape[1], pse_labels.shape[1]) + pse_labels = pse_labels[:, :min_len] + pred = pred[:, :min_len] + cd_score = cd_score[:, :min_len] + ci_score = ci_score[:, :min_len] + + loss_diar = self.classification_loss(pred, pse_labels, binary_labels_lengths) + loss_spk_dis = self.speaker_discrimination_loss(profile, profile_lengths) + loss_inter_ci, loss_inter_cd = self.internal_score_loss( + cd_score, ci_score, pse_labels, binary_labels_lengths + ) + regularizer_loss = None + if ( + self.model_regularizer_weight > 0 + and self.to_regularize_parameters is not None + ): + regularizer_loss = self.calculate_regularizer_loss() + label_mask = make_pad_mask( + binary_labels_lengths, maxlen=pse_labels.shape[1] + ).to(pse_labels.device) + loss = ( + loss_diar + + self.speaker_discrimination_loss_weight * loss_spk_dis + + self.inter_score_loss_weight * (loss_inter_ci + loss_inter_cd) + ) + # if regularizer_loss is not None: + # loss = loss + regularizer_loss * self.model_regularizer_weight + + ( + correct, + num_frames, + speech_scored, + speech_miss, + speech_falarm, + speaker_scored, + speaker_miss, + speaker_falarm, + speaker_error, + ) = self.calc_diarization_error( + pred=F.embedding(pred.argmax(dim=2) * (~label_mask), self.pse_embedding), + label=F.embedding(pse_labels * (~label_mask), self.pse_embedding), + length=binary_labels_lengths, + ) + + if speech_scored > 0 and num_frames > 0: + sad_mr, sad_fr, mi, fa, cf, acc, der = ( + speech_miss / speech_scored, + speech_falarm / speech_scored, + speaker_miss / speaker_scored, + speaker_falarm / speaker_scored, + speaker_error / speaker_scored, + correct / num_frames, + (speaker_miss + speaker_falarm + speaker_error) / speaker_scored, + ) + else: + sad_mr, sad_fr, mi, fa, cf, acc, der = 0, 0, 0, 0, 0, 0, 0 + + stats = dict( + loss=loss.detach(), + loss_diar=loss_diar.detach() if loss_diar is not None else None, + loss_spk_dis=loss_spk_dis.detach() if loss_spk_dis is not None else None, + loss_inter_ci=loss_inter_ci.detach() if loss_inter_ci is not None else None, + loss_inter_cd=loss_inter_cd.detach() if loss_inter_cd is not None else None, + regularizer_loss=( + regularizer_loss.detach() if regularizer_loss is not None else None + ), + sad_mr=sad_mr, + sad_fr=sad_fr, + mi=mi, + fa=fa, + cf=cf, + acc=acc, + der=der, + forward_steps=self.forward_steps, + ) + + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def calculate_regularizer_loss(self): + regularizer_loss = 0.0 + for name, param in self.to_regularize_parameters: + regularizer_loss = regularizer_loss + torch.norm(param, p=2) + return regularizer_loss + + def classification_loss( + self, + predictions: torch.Tensor, + labels: torch.Tensor, + prediction_lengths: torch.Tensor, + ) -> torch.Tensor: + mask = make_pad_mask(prediction_lengths, maxlen=labels.shape[1]) + pad_labels = labels.masked_fill( + mask.to(predictions.device), value=self.ignore_id + ) + loss = self.criterion_diar(predictions.contiguous(), pad_labels) + + return loss + + def speaker_discrimination_loss( + self, profile: torch.Tensor, profile_lengths: torch.Tensor + ) -> torch.Tensor: + profile_mask = ( + torch.linalg.norm(profile, ord=2, dim=2, keepdim=True) > 0 + ).float() # (B, N, 1) + mask = torch.matmul(profile_mask, profile_mask.transpose(1, 2)) # (B, N, N) + mask = mask * (1.0 - torch.eye(self.max_spk_num).unsqueeze(0).to(mask)) + + eps = 1e-12 + coding_norm = ( + torch.linalg.norm( + profile * profile_mask + (1 - profile_mask) * eps, dim=2, keepdim=True + ) + * profile_mask + ) + # profile: Batch, N, dim + cos_theta = ( + F.cosine_similarity( + profile.unsqueeze(2), profile.unsqueeze(1), dim=-1, eps=eps + ) + * mask + ) + cos_theta = torch.clip(cos_theta, -1 + eps, 1 - eps) + loss = (F.relu(mask * coding_norm * (cos_theta - 0.0))).sum() / mask.sum() + + return loss + + def calculate_multi_labels(self, pse_labels, pse_labels_lengths): + mask = make_pad_mask(pse_labels_lengths, maxlen=pse_labels.shape[1]) + padding_labels = pse_labels.masked_fill(mask.to(pse_labels.device), value=0).to( + pse_labels + ) + multi_labels = F.embedding(padding_labels, self.pse_embedding) + + return multi_labels + + def internal_score_loss( + self, + cd_score: torch.Tensor, + ci_score: torch.Tensor, + pse_labels: torch.Tensor, + pse_labels_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + multi_labels = self.calculate_multi_labels(pse_labels, pse_labels_lengths) + ci_loss = self.criterion_bce(ci_score, multi_labels, pse_labels_lengths) + cd_loss = self.criterion_bce(cd_score, multi_labels, pse_labels_lengths) + return ci_loss, cd_loss + + def collect_feats( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + profile: torch.Tensor = None, + profile_lengths: torch.Tensor = None, + binary_labels: torch.Tensor = None, + binary_labels_lengths: torch.Tensor = None, + ) -> Dict[str, torch.Tensor]: + feats, feats_lengths = self._extract_feats(speech, speech_lengths) + return {"feats": feats, "feats_lengths": feats_lengths} + + def encode_speaker( + self, + profile: torch.Tensor, + profile_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + with autocast(False): + if profile.shape[1] < self.max_spk_num: + profile = F.pad( + profile, + [0, 0, 0, self.max_spk_num - profile.shape[1], 0, 0], + "constant", + 0.0, + ) + profile_mask = ( + torch.linalg.norm(profile, ord=2, dim=2, keepdim=True) > 0 + ).float() + profile = F.normalize(profile, dim=2) + if self.speaker_encoder is not None: + profile = self.speaker_encoder(profile, profile_lengths)[0] + return profile * profile_mask, profile_lengths + else: + return profile, profile_lengths + + def encode_speech( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + if self.encoder is not None and self.inputs_type == "raw": + speech, speech_lengths = self.encode(speech, speech_lengths) + speech_mask = ~make_pad_mask(speech_lengths, maxlen=speech.shape[1]) + speech_mask = speech_mask.to(speech.device).unsqueeze(-1).float() + return speech * speech_mask, speech_lengths + else: + return speech, speech_lengths + + @staticmethod + def concate_speech_ivc(speech: torch.Tensor, ivc: torch.Tensor) -> torch.Tensor: + nn, tt = ivc.shape[1], speech.shape[1] + speech = speech.unsqueeze(dim=1) # B x 1 x T x D + speech = speech.expand(-1, nn, -1, -1) # B x N x T x D + ivc = ivc.unsqueeze(dim=2) # B x N x 1 x D + ivc = ivc.expand(-1, -1, tt, -1) # B x N x T x D + sd_in = torch.cat([speech, ivc], dim=3) # B x N x T x 2D + return sd_in + + def calc_similarity( + self, + speech_encoder_outputs: torch.Tensor, + speaker_encoder_outputs: torch.Tensor, + seq_len: torch.Tensor = None, + spk_len: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + bb, tt = speech_encoder_outputs.shape[0], speech_encoder_outputs.shape[1] + d_sph, d_spk = speech_encoder_outputs.shape[2], speaker_encoder_outputs.shape[2] + if self.normalize_speech_speaker: + speech_encoder_outputs = F.normalize(speech_encoder_outputs, dim=2) + speaker_encoder_outputs = F.normalize(speaker_encoder_outputs, dim=2) + ge_in = self.concate_speech_ivc(speech_encoder_outputs, speaker_encoder_outputs) + ge_in = torch.reshape(ge_in, [bb * self.max_spk_num, tt, d_sph + d_spk]) + ge_len = seq_len.unsqueeze(1).expand(-1, self.max_spk_num) + ge_len = torch.reshape(ge_len, [bb * self.max_spk_num]) + cd_simi = self.cd_scorer(ge_in, ge_len)[0] + cd_simi = torch.reshape(cd_simi, [bb, self.max_spk_num, tt, 1]) + cd_simi = cd_simi.squeeze(dim=3).permute([0, 2, 1]) + + if isinstance(self.ci_scorer, AbsEncoder): + ci_simi = self.ci_scorer(ge_in, ge_len)[0] + ci_simi = torch.reshape(ci_simi, [bb, self.max_spk_num, tt]).permute( + [0, 2, 1] + ) + else: + ci_simi = self.ci_scorer(speech_encoder_outputs, speaker_encoder_outputs) + + return ci_simi, cd_simi + + def post_net_forward(self, simi, seq_len): + logits = self.decoder(simi, seq_len)[0] + + return logits + + def prediction_forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + profile: torch.Tensor, + profile_lengths: torch.Tensor, + return_inter_outputs: bool = False, + ) -> [torch.Tensor, Optional[list]]: + # speech encoding + speech, speech_lengths = self.encode_speech(speech, speech_lengths) + # speaker encoding + profile, profile_lengths = self.encode_speaker(profile, profile_lengths) + # calculating similarity + ci_simi, cd_simi = self.calc_similarity( + speech, profile, speech_lengths, profile_lengths + ) + similarity = torch.cat([cd_simi, ci_simi], dim=2) + # post net forward + logits = self.post_net_forward(similarity, speech_lengths) + + if return_inter_outputs: + return logits, [ + (speech, speech_lengths), + (profile, profile_lengths), + (ci_simi, cd_simi), + ] + return logits + + def encode( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Frontend + Encoder + + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch,) + """ + with autocast(False): + # 1. Extract feats + feats, feats_lengths = self._extract_feats(speech, speech_lengths) + + # 2. Data augmentation + if self.specaug is not None and self.training: + feats, feats_lengths = self.specaug(feats, feats_lengths) + + # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + feats, feats_lengths = self.normalize(feats, feats_lengths) + + # 4. Forward encoder + # feats: (Batch, Length, Dim) + # -> encoder_out: (Batch, Length2, Dim) + encoder_outputs = self.encoder(feats, feats_lengths) + encoder_out, encoder_out_lens = encoder_outputs[:2] + + assert encoder_out.size(0) == speech.size(0), ( + encoder_out.size(), + speech.size(0), + ) + assert encoder_out.size(1) <= encoder_out_lens.max(), ( + encoder_out.size(), + encoder_out_lens.max(), + ) + + return encoder_out, encoder_out_lens + + def _extract_feats( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + batch_size = speech.shape[0] + speech_lengths = ( + speech_lengths + if speech_lengths is not None + else torch.ones(batch_size).int() * speech.shape[1] + ) + + assert speech_lengths.dim() == 1, speech_lengths.shape + + # for data-parallel + speech = speech[:, : speech_lengths.max()] + + if self.frontend is not None: + # Frontend + # e.g. STFT and Feature extract + # data_loader may send time-domain signal in this case + # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim) + feats, feats_lengths = self.frontend(speech, speech_lengths) + else: + # No frontend and no feature extract + feats, feats_lengths = speech, speech_lengths + return feats, feats_lengths + + @staticmethod + def calc_diarization_error(pred, label, length): + # Note (jiatong): Credit to https://github.com/hitachi-speech/EEND + + (batch_size, max_len, num_output) = label.size() + # mask the padding part + mask = ~make_pad_mask(length, maxlen=label.shape[1]).unsqueeze(-1).numpy() + + # pred and label have the shape (batch_size, max_len, num_output) + label_np = label.data.cpu().numpy().astype(int) + pred_np = (pred.data.cpu().numpy() > 0).astype(int) + label_np = label_np * mask + pred_np = pred_np * mask + length = length.data.cpu().numpy() + + # compute speech activity detection error + n_ref = np.sum(label_np, axis=2) + n_sys = np.sum(pred_np, axis=2) + speech_scored = float(np.sum(n_ref > 0)) + speech_miss = float(np.sum(np.logical_and(n_ref > 0, n_sys == 0))) + speech_falarm = float(np.sum(np.logical_and(n_ref == 0, n_sys > 0))) + + # compute speaker diarization error + speaker_scored = float(np.sum(n_ref)) + speaker_miss = float(np.sum(np.maximum(n_ref - n_sys, 0))) + speaker_falarm = float(np.sum(np.maximum(n_sys - n_ref, 0))) + n_map = np.sum(np.logical_and(label_np == 1, pred_np == 1), axis=2) + speaker_error = float(np.sum(np.minimum(n_ref, n_sys) - n_map)) + correct = float(1.0 * np.sum((label_np == pred_np) * mask) / num_output) + num_frames = np.sum(length) + return ( + correct, + num_frames, + speech_scored, + speech_miss, + speech_falarm, + speaker_scored, + speaker_miss, + speaker_falarm, + speaker_error, + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/ci_scorers.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/ci_scorers.py new file mode 100644 index 0000000000000000000000000000000000000000..d40c8dfebcedffaf09068ea4bddffce95155823b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/ci_scorers.py @@ -0,0 +1,38 @@ +import torch +from torch.nn import functional as F + + +class DotScorer(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward( + self, + xs_pad: torch.Tensor, + spk_emb: torch.Tensor, + ): + # xs_pad: B, T, D + # spk_emb: B, N, D + scores = torch.matmul(xs_pad, spk_emb.transpose(1, 2)) + return scores + + def convert_tf2torch(self, var_dict_tf, var_dict_torch): + return {} + + +class CosScorer(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward( + self, + xs_pad: torch.Tensor, + spk_emb: torch.Tensor, + ): + # xs_pad: B, T, D + # spk_emb: B, N, D + scores = F.cosine_similarity(xs_pad.unsqueeze(2), spk_emb.unsqueeze(1), dim=-1) + return scores + + def convert_tf2torch(self, var_dict_tf, var_dict_torch): + return {} diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/conv_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/conv_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..0f82a9379cf281e6c74b0475f6db621f9697e56b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/conv_encoder.py @@ -0,0 +1,299 @@ +from typing import List +from typing import Optional +from typing import Sequence +from typing import Tuple +from typing import Union +import logging +import torch +import torch.nn as nn +from torch.nn import functional as F +import numpy as np +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.encoder.abs_encoder import AbsEncoder +import math +from funasr_detach.models.transformer.utils.repeat import repeat + + +class EncoderLayer(nn.Module): + def __init__( + self, + input_units, + num_units, + kernel_size=3, + activation="tanh", + stride=1, + include_batch_norm=False, + residual=False, + ): + super().__init__() + left_padding = math.ceil((kernel_size - stride) / 2) + right_padding = kernel_size - stride - left_padding + self.conv_padding = nn.ConstantPad1d((left_padding, right_padding), 0.0) + self.conv1d = nn.Conv1d( + input_units, + num_units, + kernel_size, + stride, + ) + self.activation = self.get_activation(activation) + if include_batch_norm: + self.bn = nn.BatchNorm1d(num_units, momentum=0.99, eps=1e-3) + self.residual = residual + self.include_batch_norm = include_batch_norm + self.input_units = input_units + self.num_units = num_units + self.stride = stride + + @staticmethod + def get_activation(activation): + if activation == "tanh": + return nn.Tanh() + else: + return nn.ReLU() + + def forward(self, xs_pad, ilens=None): + outputs = self.conv1d(self.conv_padding(xs_pad)) + if self.residual and self.stride == 1 and self.input_units == self.num_units: + outputs = outputs + xs_pad + + if self.include_batch_norm: + outputs = self.bn(outputs) + + # add parenthesis for repeat module + return self.activation(outputs), ilens + + +class ConvEncoder(AbsEncoder): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Convolution encoder in OpenNMT framework + """ + + def __init__( + self, + num_layers, + input_units, + num_units, + kernel_size=3, + dropout_rate=0.3, + position_encoder=None, + activation="tanh", + auxiliary_states=True, + out_units=None, + out_norm=False, + out_residual=False, + include_batchnorm=False, + regularization_weight=0.0, + stride=1, + tf2torch_tensor_name_prefix_torch: str = "speaker_encoder", + tf2torch_tensor_name_prefix_tf: str = "EAND/speaker_encoder", + ): + super().__init__() + self._output_size = num_units + + self.num_layers = num_layers + self.input_units = input_units + self.num_units = num_units + self.kernel_size = kernel_size + self.dropout_rate = dropout_rate + self.position_encoder = position_encoder + self.out_units = out_units + self.auxiliary_states = auxiliary_states + self.out_norm = out_norm + self.activation = activation + self.out_residual = out_residual + self.include_batch_norm = include_batchnorm + self.regularization_weight = regularization_weight + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + if isinstance(stride, int): + self.stride = [stride] * self.num_layers + else: + self.stride = stride + self.downsample_rate = 1 + for s in self.stride: + self.downsample_rate *= s + + self.dropout = nn.Dropout(dropout_rate) + self.cnn_a = repeat( + self.num_layers, + lambda lnum: EncoderLayer( + input_units if lnum == 0 else num_units, + num_units, + kernel_size, + activation, + self.stride[lnum], + include_batchnorm, + residual=True if lnum > 0 else False, + ), + ) + + if self.out_units is not None: + left_padding = math.ceil((kernel_size - stride) / 2) + right_padding = kernel_size - stride - left_padding + self.out_padding = nn.ConstantPad1d((left_padding, right_padding), 0.0) + self.conv_out = nn.Conv1d( + num_units, + out_units, + kernel_size, + ) + + if self.out_norm: + self.after_norm = LayerNorm(out_units) + + def output_size(self) -> int: + return self.num_units + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + + inputs = xs_pad + if self.position_encoder is not None: + inputs = self.position_encoder(inputs) + + if self.dropout_rate > 0: + inputs = self.dropout(inputs) + + outputs, _ = self.cnn_a(inputs.transpose(1, 2), ilens) + + if self.out_units is not None: + outputs = self.conv_out(self.out_padding(outputs)) + + outputs = outputs.transpose(1, 2) + if self.out_norm: + outputs = self.after_norm(outputs) + + if self.out_residual: + outputs = outputs + inputs + + return outputs, ilens, None + + def gen_tf2torch_map_dict(self): + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + map_dict_local = { + # torch: conv1d.weight in "out_channel in_channel kernel_size" + # tf : conv1d.weight in "kernel_size in_channel out_channel" + # torch: linear.weight in "out_channel in_channel" + # tf : dense.weight in "in_channel out_channel" + "{}.cnn_a.0.conv1d.weight".format(tensor_name_prefix_torch): { + "name": "{}/cnn_a/conv1d/kernel".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": (2, 1, 0), + }, + "{}.cnn_a.0.conv1d.bias".format(tensor_name_prefix_torch): { + "name": "{}/cnn_a/conv1d/bias".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.cnn_a.layeridx.conv1d.weight".format(tensor_name_prefix_torch): { + "name": "{}/cnn_a/conv1d_layeridx/kernel".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": (2, 1, 0), + }, + "{}.cnn_a.layeridx.conv1d.bias".format(tensor_name_prefix_torch): { + "name": "{}/cnn_a/conv1d_layeridx/bias".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + } + if self.out_units is not None: + # add output layer + map_dict_local.update( + { + "{}.conv_out.weight".format(tensor_name_prefix_torch): { + "name": "{}/cnn_a/conv1d_{}/kernel".format( + tensor_name_prefix_tf, self.num_layers + ), + "squeeze": None, + "transpose": (2, 1, 0), + }, # tf: (1, 256, 256) -> torch: (256, 256, 1) + "{}.conv_out.bias".format(tensor_name_prefix_torch): { + "name": "{}/cnn_a/conv1d_{}/bias".format( + tensor_name_prefix_tf, self.num_layers + ), + "squeeze": None, + "transpose": None, + }, # tf: (256,) -> torch: (256,) + } + ) + + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + + map_dict = self.gen_tf2torch_map_dict() + + var_dict_torch_update = dict() + for name in sorted(var_dict_torch.keys(), reverse=False): + if name.startswith(self.tf2torch_tensor_name_prefix_torch): + # process special (first and last) layers + if name in map_dict: + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"]) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape + ) + ) + # process general layers + else: + # self.tf2torch_tensor_name_prefix_torch may include ".", solve this case + names = name.replace( + self.tf2torch_tensor_name_prefix_torch, "todo" + ).split(".") + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf, + var_dict_tf[name_tf].shape, + ) + ) + else: + logging.warning("{} is missed from tf checkpoint".format(name)) + + return var_dict_torch_update diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/ecapa_tdnn_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/ecapa_tdnn_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..d4ebc358979441ba292aa3e5f69d890cb187af16 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/ecapa_tdnn_encoder.py @@ -0,0 +1,683 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class _BatchNorm1d(nn.Module): + def __init__( + self, + input_shape=None, + input_size=None, + eps=1e-05, + momentum=0.1, + affine=True, + track_running_stats=True, + combine_batch_time=False, + skip_transpose=False, + ): + super().__init__() + self.combine_batch_time = combine_batch_time + self.skip_transpose = skip_transpose + + if input_size is None and skip_transpose: + input_size = input_shape[1] + elif input_size is None: + input_size = input_shape[-1] + + self.norm = nn.BatchNorm1d( + input_size, + eps=eps, + momentum=momentum, + affine=affine, + track_running_stats=track_running_stats, + ) + + def forward(self, x): + shape_or = x.shape + if self.combine_batch_time: + if x.ndim == 3: + x = x.reshape(shape_or[0] * shape_or[1], shape_or[2]) + else: + x = x.reshape(shape_or[0] * shape_or[1], shape_or[3], shape_or[2]) + + elif not self.skip_transpose: + x = x.transpose(-1, 1) + + x_n = self.norm(x) + + if self.combine_batch_time: + x_n = x_n.reshape(shape_or) + elif not self.skip_transpose: + x_n = x_n.transpose(1, -1) + + return x_n + + +class _Conv1d(nn.Module): + def __init__( + self, + out_channels, + kernel_size, + input_shape=None, + in_channels=None, + stride=1, + dilation=1, + padding="same", + groups=1, + bias=True, + padding_mode="reflect", + skip_transpose=False, + ): + super().__init__() + self.kernel_size = kernel_size + self.stride = stride + self.dilation = dilation + self.padding = padding + self.padding_mode = padding_mode + self.unsqueeze = False + self.skip_transpose = skip_transpose + + if input_shape is None and in_channels is None: + raise ValueError("Must provide one of input_shape or in_channels") + + if in_channels is None: + in_channels = self._check_input_shape(input_shape) + + self.conv = nn.Conv1d( + in_channels, + out_channels, + self.kernel_size, + stride=self.stride, + dilation=self.dilation, + padding=0, + groups=groups, + bias=bias, + ) + + def forward(self, x): + if not self.skip_transpose: + x = x.transpose(1, -1) + + if self.unsqueeze: + x = x.unsqueeze(1) + + if self.padding == "same": + x = self._manage_padding(x, self.kernel_size, self.dilation, self.stride) + + elif self.padding == "causal": + num_pad = (self.kernel_size - 1) * self.dilation + x = F.pad(x, (num_pad, 0)) + + elif self.padding == "valid": + pass + + else: + raise ValueError( + "Padding must be 'same', 'valid' or 'causal'. Got " + self.padding + ) + + wx = self.conv(x) + + if self.unsqueeze: + wx = wx.squeeze(1) + + if not self.skip_transpose: + wx = wx.transpose(1, -1) + + return wx + + def _manage_padding( + self, + x, + kernel_size: int, + dilation: int, + stride: int, + ): + # Detecting input shape + L_in = x.shape[-1] + + # Time padding + padding = get_padding_elem(L_in, stride, kernel_size, dilation) + + # Applying padding + x = F.pad(x, padding, mode=self.padding_mode) + + return x + + def _check_input_shape(self, shape): + """Checks the input shape and returns the number of input channels.""" + + if len(shape) == 2: + self.unsqueeze = True + in_channels = 1 + elif self.skip_transpose: + in_channels = shape[1] + elif len(shape) == 3: + in_channels = shape[2] + else: + raise ValueError("conv1d expects 2d, 3d inputs. Got " + str(len(shape))) + + # Kernel size must be odd + if self.kernel_size % 2 == 0: + raise ValueError( + "The field kernel size must be an odd number. Got %s." + % (self.kernel_size) + ) + return in_channels + + +def get_padding_elem(L_in: int, stride: int, kernel_size: int, dilation: int): + if stride > 1: + n_steps = math.ceil(((L_in - kernel_size * dilation) / stride) + 1) + L_out = stride * (n_steps - 1) + kernel_size * dilation + padding = [kernel_size // 2, kernel_size // 2] + + else: + L_out = (L_in - dilation * (kernel_size - 1) - 1) // stride + 1 + + padding = [(L_in - L_out) // 2, (L_in - L_out) // 2] + return padding + + +# Skip transpose as much as possible for efficiency +class Conv1d(_Conv1d): + def __init__(self, *args, **kwargs): + super().__init__(skip_transpose=True, *args, **kwargs) + + +class BatchNorm1d(_BatchNorm1d): + def __init__(self, *args, **kwargs): + super().__init__(skip_transpose=True, *args, **kwargs) + + +def length_to_mask(length, max_len=None, dtype=None, device=None): + assert len(length.shape) == 1 + + if max_len is None: + max_len = length.max().long().item() # using arange to generate mask + mask = torch.arange(max_len, device=length.device, dtype=length.dtype).expand( + len(length), max_len + ) < length.unsqueeze(1) + + if dtype is None: + dtype = length.dtype + + if device is None: + device = length.device + + mask = torch.as_tensor(mask, dtype=dtype, device=device) + return mask + + +class TDNNBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + dilation, + activation=nn.ReLU, + groups=1, + ): + super(TDNNBlock, self).__init__() + self.conv = Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + dilation=dilation, + groups=groups, + ) + self.activation = activation() + self.norm = BatchNorm1d(input_size=out_channels) + + def forward(self, x): + return self.norm(self.activation(self.conv(x))) + + +class Res2NetBlock(torch.nn.Module): + """An implementation of Res2NetBlock w/ dilation. + + Arguments + --------- + in_channels : int + The number of channels expected in the input. + out_channels : int + The number of output channels. + scale : int + The scale of the Res2Net block. + kernel_size: int + The kernel size of the Res2Net block. + dilation : int + The dilation of the Res2Net block. + + Example + ------- + >>> inp_tensor = torch.rand([8, 120, 64]).transpose(1, 2) + >>> layer = Res2NetBlock(64, 64, scale=4, dilation=3) + >>> out_tensor = layer(inp_tensor).transpose(1, 2) + >>> out_tensor.shape + torch.Size([8, 120, 64]) + """ + + def __init__(self, in_channels, out_channels, scale=8, kernel_size=3, dilation=1): + super(Res2NetBlock, self).__init__() + assert in_channels % scale == 0 + assert out_channels % scale == 0 + + in_channel = in_channels // scale + hidden_channel = out_channels // scale + + self.blocks = nn.ModuleList( + [ + TDNNBlock( + in_channel, + hidden_channel, + kernel_size=kernel_size, + dilation=dilation, + ) + for i in range(scale - 1) + ] + ) + self.scale = scale + + def forward(self, x): + y = [] + for i, x_i in enumerate(torch.chunk(x, self.scale, dim=1)): + if i == 0: + y_i = x_i + elif i == 1: + y_i = self.blocks[i - 1](x_i) + else: + y_i = self.blocks[i - 1](x_i + y_i) + y.append(y_i) + y = torch.cat(y, dim=1) + return y + + +class SEBlock(nn.Module): + """An implementation of squeeze-and-excitation block. + + Arguments + --------- + in_channels : int + The number of input channels. + se_channels : int + The number of output channels after squeeze. + out_channels : int + The number of output channels. + + Example + ------- + >>> inp_tensor = torch.rand([8, 120, 64]).transpose(1, 2) + >>> se_layer = SEBlock(64, 16, 64) + >>> lengths = torch.rand((8,)) + >>> out_tensor = se_layer(inp_tensor, lengths).transpose(1, 2) + >>> out_tensor.shape + torch.Size([8, 120, 64]) + """ + + def __init__(self, in_channels, se_channels, out_channels): + super(SEBlock, self).__init__() + + self.conv1 = Conv1d( + in_channels=in_channels, out_channels=se_channels, kernel_size=1 + ) + self.relu = torch.nn.ReLU(inplace=True) + self.conv2 = Conv1d( + in_channels=se_channels, out_channels=out_channels, kernel_size=1 + ) + self.sigmoid = torch.nn.Sigmoid() + + def forward(self, x, lengths=None): + L = x.shape[-1] + if lengths is not None: + mask = length_to_mask(lengths * L, max_len=L, device=x.device) + mask = mask.unsqueeze(1) + total = mask.sum(dim=2, keepdim=True) + s = (x * mask).sum(dim=2, keepdim=True) / total + else: + s = x.mean(dim=2, keepdim=True) + + s = self.relu(self.conv1(s)) + s = self.sigmoid(self.conv2(s)) + + return s * x + + +class AttentiveStatisticsPooling(nn.Module): + """This class implements an attentive statistic pooling layer for each channel. + It returns the concatenated mean and std of the input tensor. + + Arguments + --------- + channels: int + The number of input channels. + attention_channels: int + The number of attention channels. + + Example + ------- + >>> inp_tensor = torch.rand([8, 120, 64]).transpose(1, 2) + >>> asp_layer = AttentiveStatisticsPooling(64) + >>> lengths = torch.rand((8,)) + >>> out_tensor = asp_layer(inp_tensor, lengths).transpose(1, 2) + >>> out_tensor.shape + torch.Size([8, 1, 128]) + """ + + def __init__(self, channels, attention_channels=128, global_context=True): + super().__init__() + + self.eps = 1e-12 + self.global_context = global_context + if global_context: + self.tdnn = TDNNBlock(channels * 3, attention_channels, 1, 1) + else: + self.tdnn = TDNNBlock(channels, attention_channels, 1, 1) + self.tanh = nn.Tanh() + self.conv = Conv1d( + in_channels=attention_channels, out_channels=channels, kernel_size=1 + ) + + def forward(self, x, lengths=None): + """Calculates mean and std for a batch (input tensor). + + Arguments + --------- + x : torch.Tensor + Tensor of shape [N, C, L]. + """ + L = x.shape[-1] + + def _compute_statistics(x, m, dim=2, eps=self.eps): + mean = (m * x).sum(dim) + std = torch.sqrt((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(eps)) + return mean, std + + if lengths is None: + lengths = torch.ones(x.shape[0], device=x.device) + + # Make binary mask of shape [N, 1, L] + mask = length_to_mask(lengths * L, max_len=L, device=x.device) + mask = mask.unsqueeze(1) + + # Expand the temporal context of the pooling layer by allowing the + # self-attention to look at global properties of the utterance. + if self.global_context: + # torch.std is unstable for backward computation + # https://github.com/pytorch/pytorch/issues/4320 + total = mask.sum(dim=2, keepdim=True).float() + mean, std = _compute_statistics(x, mask / total) + mean = mean.unsqueeze(2).repeat(1, 1, L) + std = std.unsqueeze(2).repeat(1, 1, L) + attn = torch.cat([x, mean, std], dim=1) + else: + attn = x + + # Apply layers + attn = self.conv(self.tanh(self.tdnn(attn))) + + # Filter out zero-paddings + attn = attn.masked_fill(mask == 0, float("-inf")) + + attn = F.softmax(attn, dim=2) + mean, std = _compute_statistics(x, attn) + # Append mean and std of the batch + pooled_stats = torch.cat((mean, std), dim=1) + pooled_stats = pooled_stats.unsqueeze(2) + + return pooled_stats + + +class SERes2NetBlock(nn.Module): + """An implementation of building block in ECAPA-TDNN, i.e., + TDNN-Res2Net-TDNN-SEBlock. + + Arguments + ---------- + out_channels: int + The number of output channels. + res2net_scale: int + The scale of the Res2Net block. + kernel_size: int + The kernel size of the TDNN blocks. + dilation: int + The dilation of the Res2Net block. + activation : torch class + A class for constructing the activation layers. + groups: int + Number of blocked connections from input channels to output channels. + + Example + ------- + >>> x = torch.rand(8, 120, 64).transpose(1, 2) + >>> conv = SERes2NetBlock(64, 64, res2net_scale=4) + >>> out = conv(x).transpose(1, 2) + >>> out.shape + torch.Size([8, 120, 64]) + """ + + def __init__( + self, + in_channels, + out_channels, + res2net_scale=8, + se_channels=128, + kernel_size=1, + dilation=1, + activation=torch.nn.ReLU, + groups=1, + ): + super().__init__() + self.out_channels = out_channels + self.tdnn1 = TDNNBlock( + in_channels, + out_channels, + kernel_size=1, + dilation=1, + activation=activation, + groups=groups, + ) + self.res2net_block = Res2NetBlock( + out_channels, out_channels, res2net_scale, kernel_size, dilation + ) + self.tdnn2 = TDNNBlock( + out_channels, + out_channels, + kernel_size=1, + dilation=1, + activation=activation, + groups=groups, + ) + self.se_block = SEBlock(out_channels, se_channels, out_channels) + + self.shortcut = None + if in_channels != out_channels: + self.shortcut = Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + ) + + def forward(self, x, lengths=None): + residual = x + if self.shortcut: + residual = self.shortcut(x) + + x = self.tdnn1(x) + x = self.res2net_block(x) + x = self.tdnn2(x) + x = self.se_block(x, lengths) + + return x + residual + + +class ECAPA_TDNN(torch.nn.Module): + """An implementation of the speaker embedding model in a paper. + "ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in + TDNN Based Speaker Verification" (https://arxiv.org/abs/2005.07143). + + Arguments + --------- + activation : torch class + A class for constructing the activation layers. + channels : list of ints + Output channels for TDNN/SERes2Net layer. + kernel_sizes : list of ints + List of kernel sizes for each layer. + dilations : list of ints + List of dilations for kernels in each layer. + lin_neurons : int + Number of neurons in linear layers. + groups : list of ints + List of groups for kernels in each layer. + + Example + ------- + >>> input_feats = torch.rand([5, 120, 80]) + >>> compute_embedding = ECAPA_TDNN(80, lin_neurons=192) + >>> outputs = compute_embedding(input_feats) + >>> outputs.shape + torch.Size([5, 1, 192]) + """ + + def __init__( + self, + input_size, + lin_neurons=192, + activation=torch.nn.ReLU, + channels=[512, 512, 512, 512, 1536], + kernel_sizes=[5, 3, 3, 3, 1], + dilations=[1, 2, 3, 4, 1], + attention_channels=128, + res2net_scale=8, + se_channels=128, + global_context=True, + groups=[1, 1, 1, 1, 1], + window_size=20, + window_shift=1, + ): + + super().__init__() + assert len(channels) == len(kernel_sizes) + assert len(channels) == len(dilations) + self.channels = channels + self.blocks = nn.ModuleList() + self.window_size = window_size + self.window_shift = window_shift + + # The initial TDNN layer + self.blocks.append( + TDNNBlock( + input_size, + channels[0], + kernel_sizes[0], + dilations[0], + activation, + groups[0], + ) + ) + + # SE-Res2Net layers + for i in range(1, len(channels) - 1): + self.blocks.append( + SERes2NetBlock( + channels[i - 1], + channels[i], + res2net_scale=res2net_scale, + se_channels=se_channels, + kernel_size=kernel_sizes[i], + dilation=dilations[i], + activation=activation, + groups=groups[i], + ) + ) + + # Multi-layer feature aggregation + self.mfa = TDNNBlock( + channels[-1], + channels[-1], + kernel_sizes[-1], + dilations[-1], + activation, + groups=groups[-1], + ) + + # Attentive Statistical Pooling + self.asp = AttentiveStatisticsPooling( + channels[-1], + attention_channels=attention_channels, + global_context=global_context, + ) + self.asp_bn = BatchNorm1d(input_size=channels[-1] * 2) + + # Final linear transformation + self.fc = Conv1d( + in_channels=channels[-1] * 2, + out_channels=lin_neurons, + kernel_size=1, + ) + + def windowed_pooling(self, x, lengths=None): + # x: Batch, Channel, Time + tt = x.shape[2] + num_chunk = int(math.ceil(tt / self.window_shift)) + pad = self.window_size // 2 + x = F.pad(x, (pad, pad, 0, 0), "reflect") + stat_list = [] + + for i in range(num_chunk): + # B x C + st, ed = i * self.window_shift, i * self.window_shift + self.window_size + x = self.asp( + x[:, :, st:ed], + lengths=( + torch.clamp(lengths - i, 0, self.window_size) + if lengths is not None + else None + ), + ) + x = self.asp_bn(x) + x = self.fc(x) + stat_list.append(x) + + return torch.cat(stat_list, dim=2) + + def forward(self, x, lengths=None): + """Returns the embedding vector. + + Arguments + --------- + x : torch.Tensor + Tensor of shape (batch, time, channel). + lengths: torch.Tensor + Tensor of shape (batch, ) + """ + # Minimize transpose for efficiency + x = x.transpose(1, 2) + + xl = [] + for layer in self.blocks: + try: + x = layer(x, lengths=lengths) + except TypeError: + x = layer(x) + xl.append(x) + + # Multi-layer feature aggregation + x = torch.cat(xl[1:], dim=1) + x = self.mfa(x) + + if self.window_size is None: + # Attentive Statistical Pooling + x = self.asp(x, lengths=lengths) + x = self.asp_bn(x) + # Final linear transformation + x = self.fc(x) + # x = x.transpose(1, 2) + x = x.squeeze(2) # -> B, C + else: + x = self.windowed_pooling(x, lengths) + x = x.transpose(1, 2) # -> B, T, C + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/fsmn_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/fsmn_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..5950c3e79502c66be471fc0b413d25dffcae0b14 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/fsmn_encoder.py @@ -0,0 +1,369 @@ +from typing import List +from typing import Optional +from typing import Sequence +from typing import Tuple +from typing import Union +import logging +import torch +import torch.nn as nn +from torch.nn import functional as F +import numpy as np +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.encoder.abs_encoder import AbsEncoder +import math +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.utils.multi_layer_conv import FsmnFeedForward + + +class FsmnBlock(torch.nn.Module): + def __init__( + self, + n_feat, + dropout_rate, + kernel_size, + fsmn_shift=0, + ): + super().__init__() + self.dropout = nn.Dropout(p=dropout_rate) + self.fsmn_block = nn.Conv1d( + n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False + ) + # padding + left_padding = (kernel_size - 1) // 2 + if fsmn_shift > 0: + left_padding = left_padding + fsmn_shift + right_padding = kernel_size - 1 - left_padding + self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0) + + def forward(self, inputs, mask, mask_shfit_chunk=None): + b, t, d = inputs.size() + if mask is not None: + mask = torch.reshape(mask, (b, -1, 1)) + if mask_shfit_chunk is not None: + mask = mask * mask_shfit_chunk + + inputs = inputs * mask + x = inputs.transpose(1, 2) + x = self.pad_fn(x) + x = self.fsmn_block(x) + x = x.transpose(1, 2) + x = x + inputs + x = self.dropout(x) + return x * mask + + +class EncoderLayer(torch.nn.Module): + def __init__(self, in_size, size, feed_forward, fsmn_block, dropout_rate=0.0): + super().__init__() + self.in_size = in_size + self.size = size + self.ffn = feed_forward + self.memory = fsmn_block + self.dropout = nn.Dropout(dropout_rate) + + def forward( + self, xs_pad: torch.Tensor, mask: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + # xs_pad in Batch, Time, Dim + + context = self.ffn(xs_pad)[0] + memory = self.memory(context, mask) + + memory = self.dropout(memory) + if self.in_size == self.size: + return memory + xs_pad, mask + + return memory, mask + + +class FsmnEncoder(AbsEncoder): + """Encoder using Fsmn""" + + def __init__( + self, + in_units, + filter_size, + fsmn_num_layers, + dnn_num_layers, + num_memory_units=512, + ffn_inner_dim=2048, + dropout_rate=0.0, + shift=0, + position_encoder=None, + sample_rate=1, + out_units=None, + tf2torch_tensor_name_prefix_torch="post_net", + tf2torch_tensor_name_prefix_tf="EAND/post_net", + ): + """Initializes the parameters of the encoder. + + Args: + filter_size: the total order of memory block + fsmn_num_layers: The number of fsmn layers. + dnn_num_layers: The number of dnn layers + num_units: The number of memory units. + ffn_inner_dim: The number of units of the inner linear transformation + in the feed forward layer. + dropout_rate: The probability to drop units from the outputs. + shift: left padding, to control delay + position_encoder: The :class:`opennmt.layers.position.PositionEncoder` to + apply on inputs or ``None``. + """ + super(FsmnEncoder, self).__init__() + self.in_units = in_units + self.filter_size = filter_size + self.fsmn_num_layers = fsmn_num_layers + self.dnn_num_layers = dnn_num_layers + self.num_memory_units = num_memory_units + self.ffn_inner_dim = ffn_inner_dim + self.dropout_rate = dropout_rate + self.shift = shift + if not isinstance(shift, list): + self.shift = [shift for _ in range(self.fsmn_num_layers)] + self.sample_rate = sample_rate + if not isinstance(sample_rate, list): + self.sample_rate = [sample_rate for _ in range(self.fsmn_num_layers)] + self.position_encoder = position_encoder + self.dropout = nn.Dropout(dropout_rate) + self.out_units = out_units + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + + self.fsmn_layers = repeat( + self.fsmn_num_layers, + lambda lnum: EncoderLayer( + in_units if lnum == 0 else num_memory_units, + num_memory_units, + FsmnFeedForward( + in_units if lnum == 0 else num_memory_units, + ffn_inner_dim, + num_memory_units, + 1, + dropout_rate, + ), + FsmnBlock( + num_memory_units, dropout_rate, filter_size, self.shift[lnum] + ), + ), + ) + + self.dnn_layers = repeat( + dnn_num_layers, + lambda lnum: FsmnFeedForward( + num_memory_units, + ffn_inner_dim, + num_memory_units, + 1, + dropout_rate, + ), + ) + if out_units is not None: + self.conv1d = nn.Conv1d(num_memory_units, out_units, 1, 1) + + def output_size(self) -> int: + return self.num_memory_units + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + inputs = xs_pad + if self.position_encoder is not None: + inputs = self.position_encoder(inputs) + + inputs = self.dropout(inputs) + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + inputs = self.fsmn_layers(inputs, masks)[0] + inputs = self.dnn_layers(inputs)[0] + + if self.out_units is not None: + inputs = self.conv1d(inputs.transpose(1, 2)).transpose(1, 2) + + return inputs, ilens, None + + def gen_tf2torch_map_dict(self): + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + map_dict_local = { + # torch: conv1d.weight in "out_channel in_channel kernel_size" + # tf : conv1d.weight in "kernel_size in_channel out_channel" + # torch: linear.weight in "out_channel in_channel" + # tf : dense.weight in "in_channel out_channel" + # for fsmn_layers + "{}.fsmn_layers.layeridx.ffn.norm.bias".format(tensor_name_prefix_torch): { + "name": "{}/fsmn_layer_layeridx/ffn/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, + "{}.fsmn_layers.layeridx.ffn.norm.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/fsmn_layer_layeridx/ffn/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, + "{}.fsmn_layers.layeridx.ffn.w_1.bias".format(tensor_name_prefix_torch): { + "name": "{}/fsmn_layer_layeridx/ffn/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, + "{}.fsmn_layers.layeridx.ffn.w_1.weight".format(tensor_name_prefix_torch): { + "name": "{}/fsmn_layer_layeridx/ffn/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": (2, 1, 0), + }, + "{}.fsmn_layers.layeridx.ffn.w_2.weight".format(tensor_name_prefix_torch): { + "name": "{}/fsmn_layer_layeridx/ffn/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": (2, 1, 0), + }, + "{}.fsmn_layers.layeridx.memory.fsmn_block.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/fsmn_layer_layeridx/memory/depth_conv_w".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 2, 0), + }, # (1, 31, 512, 1) -> (31, 512, 1) -> (512, 1, 31) + # for dnn_layers + "{}.dnn_layers.layeridx.norm.bias".format(tensor_name_prefix_torch): { + "name": "{}/dnn_layer_layeridx/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, + "{}.dnn_layers.layeridx.norm.weight".format(tensor_name_prefix_torch): { + "name": "{}/dnn_layer_layeridx/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, + "{}.dnn_layers.layeridx.w_1.bias".format(tensor_name_prefix_torch): { + "name": "{}/dnn_layer_layeridx/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, + "{}.dnn_layers.layeridx.w_1.weight".format(tensor_name_prefix_torch): { + "name": "{}/dnn_layer_layeridx/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": (2, 1, 0), + }, + "{}.dnn_layers.layeridx.w_2.weight".format(tensor_name_prefix_torch): { + "name": "{}/dnn_layer_layeridx/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": (2, 1, 0), + }, + } + if self.out_units is not None: + # add output layer + map_dict_local.update( + { + "{}.conv1d.weight".format(tensor_name_prefix_torch): { + "name": "{}/conv1d/kernel".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": (2, 1, 0), + }, + "{}.conv1d.bias".format(tensor_name_prefix_torch): { + "name": "{}/conv1d/bias".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + } + ) + + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + + map_dict = self.gen_tf2torch_map_dict() + + var_dict_torch_update = dict() + for name in sorted(var_dict_torch.keys(), reverse=False): + if name.startswith(self.tf2torch_tensor_name_prefix_torch): + # process special (first and last) layers + if name in map_dict: + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"]) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape + ) + ) + # process general layers + else: + # self.tf2torch_tensor_name_prefix_torch may include ".", solve this case + names = name.replace( + self.tf2torch_tensor_name_prefix_torch, "todo" + ).split(".") + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf, + var_dict_tf[name_tf].shape, + ) + ) + else: + logging.warning("{} is missed from tf checkpoint".format(name)) + + return var_dict_torch_update diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/resnet34_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/resnet34_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..d9b72ab2077ebc9bcc1d45d336398d3a41fd024d --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/resnet34_encoder.py @@ -0,0 +1,1117 @@ +import torch +from torch.nn import functional as F +from funasr_detach.models.encoder.abs_encoder import AbsEncoder +from typing import Tuple, Optional +from funasr_detach.models.pooling.statistic_pooling import ( + statistic_pooling, + windowed_statistic_pooling, +) +from collections import OrderedDict +import logging +import numpy as np + + +class BasicLayer(torch.nn.Module): + + def __init__( + self, in_filters: int, filters: int, stride: int, bn_momentum: float = 0.5 + ): + + super().__init__() + self.stride = stride + self.in_filters = in_filters + self.filters = filters + + self.bn1 = torch.nn.BatchNorm2d( + in_filters, eps=1e-3, momentum=bn_momentum, affine=True + ) + self.relu1 = torch.nn.ReLU() + self.conv1 = torch.nn.Conv2d(in_filters, filters, 3, stride, bias=False) + + self.bn2 = torch.nn.BatchNorm2d( + filters, eps=1e-3, momentum=bn_momentum, affine=True + ) + self.relu2 = torch.nn.ReLU() + self.conv2 = torch.nn.Conv2d(filters, filters, 3, 1, bias=False) + + if in_filters != filters or stride > 1: + self.conv_sc = torch.nn.Conv2d(in_filters, filters, 1, stride, bias=False) + self.bn_sc = torch.nn.BatchNorm2d( + filters, eps=1e-3, momentum=bn_momentum, affine=True + ) + + def proper_padding(self, x, stride): + # align padding mode to tf.layers.conv2d with padding_mod="same" + if stride == 1: + return F.pad(x, (1, 1, 1, 1), "constant", 0) + elif stride == 2: + h, w = x.size(2), x.size(3) + # (left, right, top, bottom) + return F.pad(x, (w % 2, 1, h % 2, 1), "constant", 0) + + def forward(self, xs_pad, ilens): + identity = xs_pad + if self.in_filters != self.filters or self.stride > 1: + identity = self.conv_sc(identity) + identity = self.bn_sc(identity) + + xs_pad = self.relu1(self.bn1(xs_pad)) + xs_pad = self.proper_padding(xs_pad, self.stride) + xs_pad = self.conv1(xs_pad) + + xs_pad = self.relu2(self.bn2(xs_pad)) + xs_pad = self.proper_padding(xs_pad, 1) + xs_pad = self.conv2(xs_pad) + + if self.stride == 2: + ilens = (ilens + 1) // self.stride + + return xs_pad + identity, ilens + + +class BasicBlock(torch.nn.Module): + def __init__(self, in_filters, filters, num_layer, stride, bn_momentum=0.5): + super().__init__() + self.num_layer = num_layer + + for i in range(num_layer): + layer = BasicLayer( + in_filters if i == 0 else filters, + filters, + stride if i == 0 else 1, + bn_momentum, + ) + self.add_module("layer_{}".format(i), layer) + + def forward(self, xs_pad, ilens): + + for i in range(self.num_layer): + xs_pad, ilens = self._modules["layer_{}".format(i)](xs_pad, ilens) + + return xs_pad, ilens + + +class ResNet34(AbsEncoder): + def __init__( + self, + input_size, + use_head_conv=True, + batchnorm_momentum=0.5, + use_head_maxpool=False, + num_nodes_pooling_layer=256, + layers_in_block=(3, 4, 6, 3), + filters_in_block=(32, 64, 128, 256), + ): + super(ResNet34, self).__init__() + + self.use_head_conv = use_head_conv + self.use_head_maxpool = use_head_maxpool + self.num_nodes_pooling_layer = num_nodes_pooling_layer + self.layers_in_block = layers_in_block + self.filters_in_block = filters_in_block + self.input_size = input_size + + pre_filters = filters_in_block[0] + if use_head_conv: + self.pre_conv = torch.nn.Conv2d( + 1, pre_filters, 3, 1, 1, bias=False, padding_mode="zeros" + ) + self.pre_conv_bn = torch.nn.BatchNorm2d( + pre_filters, eps=1e-3, momentum=batchnorm_momentum + ) + + if use_head_maxpool: + self.head_maxpool = torch.nn.MaxPool2d(3, 1, padding=1) + + for i in range(len(layers_in_block)): + if i == 0: + in_filters = pre_filters if self.use_head_conv else 1 + else: + in_filters = filters_in_block[i - 1] + + block = BasicBlock( + in_filters, + filters=filters_in_block[i], + num_layer=layers_in_block[i], + stride=1 if i == 0 else 2, + bn_momentum=batchnorm_momentum, + ) + self.add_module("block_{}".format(i), block) + + self.resnet0_dense = torch.nn.Conv2d( + filters_in_block[-1], num_nodes_pooling_layer, 1 + ) + self.resnet0_bn = torch.nn.BatchNorm2d( + num_nodes_pooling_layer, eps=1e-3, momentum=batchnorm_momentum + ) + + self.time_ds_ratio = 8 + + def output_size(self) -> int: + return self.num_nodes_pooling_layer + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + + features = xs_pad + assert ( + features.size(-1) == self.input_size + ), "Dimension of features {} doesn't match the input_size {}.".format( + features.size(-1), self.input_size + ) + features = torch.unsqueeze(features, dim=1) + if self.use_head_conv: + features = self.pre_conv(features) + features = self.pre_conv_bn(features) + features = F.relu(features) + + if self.use_head_maxpool: + features = self.head_maxpool(features) + + resnet_outs, resnet_out_lens = features, ilens + for i in range(len(self.layers_in_block)): + block = self._modules["block_{}".format(i)] + resnet_outs, resnet_out_lens = block(resnet_outs, resnet_out_lens) + + features = self.resnet0_dense(resnet_outs) + features = F.relu(features) + features = self.resnet0_bn(features) + + return features, resnet_out_lens + + +# Note: For training, this implement is not equivalent to tf because of the kernel_regularizer in tf.layers. +# TODO: implement kernel_regularizer in torch with munal loss addition or weigth_decay in the optimizer +class ResNet34_SP_L2Reg(AbsEncoder): + def __init__( + self, + input_size, + use_head_conv=True, + batchnorm_momentum=0.5, + use_head_maxpool=False, + num_nodes_pooling_layer=256, + layers_in_block=(3, 4, 6, 3), + filters_in_block=(32, 64, 128, 256), + tf2torch_tensor_name_prefix_torch="encoder", + tf2torch_tensor_name_prefix_tf="EAND/speech_encoder", + tf_train_steps=720000, + ): + super(ResNet34_SP_L2Reg, self).__init__() + + self.use_head_conv = use_head_conv + self.use_head_maxpool = use_head_maxpool + self.num_nodes_pooling_layer = num_nodes_pooling_layer + self.layers_in_block = layers_in_block + self.filters_in_block = filters_in_block + self.input_size = input_size + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + self.tf_train_steps = tf_train_steps + + pre_filters = filters_in_block[0] + if use_head_conv: + self.pre_conv = torch.nn.Conv2d( + 1, pre_filters, 3, 1, 1, bias=False, padding_mode="zeros" + ) + self.pre_conv_bn = torch.nn.BatchNorm2d( + pre_filters, eps=1e-3, momentum=batchnorm_momentum + ) + + if use_head_maxpool: + self.head_maxpool = torch.nn.MaxPool2d(3, 1, padding=1) + + for i in range(len(layers_in_block)): + if i == 0: + in_filters = pre_filters if self.use_head_conv else 1 + else: + in_filters = filters_in_block[i - 1] + + block = BasicBlock( + in_filters, + filters=filters_in_block[i], + num_layer=layers_in_block[i], + stride=1 if i == 0 else 2, + bn_momentum=batchnorm_momentum, + ) + self.add_module("block_{}".format(i), block) + + self.resnet0_dense = torch.nn.Conv1d( + filters_in_block[-1] * input_size // 8, num_nodes_pooling_layer, 1 + ) + self.resnet0_bn = torch.nn.BatchNorm1d( + num_nodes_pooling_layer, eps=1e-3, momentum=batchnorm_momentum + ) + + self.time_ds_ratio = 8 + + def output_size(self) -> int: + return self.num_nodes_pooling_layer + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + + features = xs_pad + assert ( + features.size(-1) == self.input_size + ), "Dimension of features {} doesn't match the input_size {}.".format( + features.size(-1), self.input_size + ) + features = torch.unsqueeze(features, dim=1) + if self.use_head_conv: + features = self.pre_conv(features) + features = self.pre_conv_bn(features) + features = F.relu(features) + + if self.use_head_maxpool: + features = self.head_maxpool(features) + + resnet_outs, resnet_out_lens = features, ilens + for i in range(len(self.layers_in_block)): + block = self._modules["block_{}".format(i)] + resnet_outs, resnet_out_lens = block(resnet_outs, resnet_out_lens) + + # B, C, T, F + bb, cc, tt, ff = resnet_outs.shape + resnet_outs = torch.reshape(resnet_outs.permute(0, 3, 1, 2), [bb, ff * cc, tt]) + features = self.resnet0_dense(resnet_outs) + features = F.relu(features) + features = self.resnet0_bn(features) + + return features, resnet_out_lens + + def gen_tf2torch_map_dict(self): + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + train_steps = self.tf_train_steps + map_dict_local = { + # torch: conv1d.weight in "out_channel in_channel kernel_size" + # tf : conv1d.weight in "kernel_size in_channel out_channel" + # torch: linear.weight in "out_channel in_channel" + # tf : dense.weight in "in_channel out_channel" + "{}.pre_conv.weight".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv/kernel".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": (3, 2, 0, 1), + }, + "{}.pre_conv_bn.bias".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.weight".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.running_mean".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/moving_mean".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.running_var".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/moving_variance".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.num_batches_tracked".format( + tensor_name_prefix_torch + ): train_steps, + } + for layer_idx in range(3): + map_dict_local.update( + { + "{}.resnet{}_dense.weight".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_dense/kernel".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": (2, 1, 0) if layer_idx == 0 else (1, 0), + }, + "{}.resnet{}_dense.bias".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_dense/bias".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.weight".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_bn/gamma".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.bias".format(tensor_name_prefix_torch, layer_idx): { + "name": "{}/resnet{}_bn/beta".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.running_mean".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_bn/moving_mean".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.running_var".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_bn/moving_variance".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.num_batches_tracked".format( + tensor_name_prefix_torch, layer_idx + ): train_steps, + } + ) + + for block_idx in range(len(self.layers_in_block)): + for layer_idx in range(self.layers_in_block[block_idx]): + for i in ["1", "2", "_sc"]: + map_dict_local.update( + { + "{}.block_{}.layer_{}.conv{}.weight".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/conv{}/kernel".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": (3, 2, 0, 1), + }, + "{}.block_{}.layer_{}.bn{}.weight".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/gamma".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.bias".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/beta".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.running_mean".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/moving_mean".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.running_var".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/moving_variance".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.num_batches_tracked".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): train_steps, + } + ) + + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + + map_dict = self.gen_tf2torch_map_dict() + + var_dict_torch_update = dict() + for name in sorted(var_dict_torch.keys(), reverse=False): + if name.startswith(self.tf2torch_tensor_name_prefix_torch): + if name in map_dict: + if "num_batches_tracked" not in name: + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name]["squeeze"] + ) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf, + var_dict_tf[name_tf].shape, + ) + ) + else: + var_dict_torch_update[name] = ( + torch.Tensor(map_dict[name]).type(torch.int64).to("cpu") + ) + logging.info( + "torch tensor: {}, manually assigning to: {}".format( + name, map_dict[name] + ) + ) + else: + logging.warning("{} is missed from tf checkpoint".format(name)) + + return var_dict_torch_update + + +class ResNet34Diar(ResNet34): + def __init__( + self, + input_size, + embedding_node="resnet1_dense", + use_head_conv=True, + batchnorm_momentum=0.5, + use_head_maxpool=False, + num_nodes_pooling_layer=256, + layers_in_block=(3, 4, 6, 3), + filters_in_block=(32, 64, 128, 256), + num_nodes_resnet1=256, + num_nodes_last_layer=256, + pooling_type="window_shift", + pool_size=20, + stride=1, + tf2torch_tensor_name_prefix_torch="encoder", + tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder", + ): + """ + Author: Speech Lab, Alibaba Group, China + SOND: Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis + https://arxiv.org/abs/2211.10243 + """ + + super(ResNet34Diar, self).__init__( + input_size, + use_head_conv=use_head_conv, + batchnorm_momentum=batchnorm_momentum, + use_head_maxpool=use_head_maxpool, + num_nodes_pooling_layer=num_nodes_pooling_layer, + layers_in_block=layers_in_block, + filters_in_block=filters_in_block, + ) + + self.embedding_node = embedding_node + self.num_nodes_resnet1 = num_nodes_resnet1 + self.num_nodes_last_layer = num_nodes_last_layer + self.pooling_type = pooling_type + self.pool_size = pool_size + self.stride = stride + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + + self.resnet1_dense = torch.nn.Linear( + num_nodes_pooling_layer * 2, num_nodes_resnet1 + ) + self.resnet1_bn = torch.nn.BatchNorm1d( + num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum + ) + + self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer) + self.resnet2_bn = torch.nn.BatchNorm1d( + num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum + ) + + def output_size(self) -> int: + if self.embedding_node.startswith("resnet1"): + return self.num_nodes_resnet1 + elif self.embedding_node.startswith("resnet2"): + return self.num_nodes_last_layer + + return self.num_nodes_pooling_layer + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + + endpoints = OrderedDict() + res_out, ilens = super().forward(xs_pad, ilens) + endpoints["resnet0_bn"] = res_out + if self.pooling_type == "frame_gsp": + features = statistic_pooling(res_out, ilens, (3,)) + else: + features, ilens = windowed_statistic_pooling( + res_out, ilens, (2, 3), self.pool_size, self.stride + ) + features = features.transpose(1, 2) + endpoints["pooling"] = features + + features = self.resnet1_dense(features) + endpoints["resnet1_dense"] = features + features = F.relu(features) + endpoints["resnet1_relu"] = features + features = self.resnet1_bn(features.transpose(1, 2)).transpose(1, 2) + endpoints["resnet1_bn"] = features + + features = self.resnet2_dense(features) + endpoints["resnet2_dense"] = features + features = F.relu(features) + endpoints["resnet2_relu"] = features + features = self.resnet2_bn(features.transpose(1, 2)).transpose(1, 2) + endpoints["resnet2_bn"] = features + + return endpoints[self.embedding_node], ilens, None + + def gen_tf2torch_map_dict(self): + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + train_steps = 300000 + map_dict_local = { + # torch: conv1d.weight in "out_channel in_channel kernel_size" + # tf : conv1d.weight in "kernel_size in_channel out_channel" + # torch: linear.weight in "out_channel in_channel" + # tf : dense.weight in "in_channel out_channel" + "{}.pre_conv.weight".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv/kernel".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": (3, 2, 0, 1), + }, + "{}.pre_conv_bn.bias".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.weight".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.running_mean".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/moving_mean".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.running_var".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/moving_variance".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.num_batches_tracked".format( + tensor_name_prefix_torch + ): train_steps, + } + for layer_idx in range(3): + map_dict_local.update( + { + "{}.resnet{}_dense.weight".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_dense/kernel".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": (3, 2, 0, 1) if layer_idx == 0 else (1, 0), + }, + "{}.resnet{}_dense.bias".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_dense/bias".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.weight".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_bn/gamma".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.bias".format(tensor_name_prefix_torch, layer_idx): { + "name": "{}/resnet{}_bn/beta".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.running_mean".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_bn/moving_mean".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.running_var".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_bn/moving_variance".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.num_batches_tracked".format( + tensor_name_prefix_torch, layer_idx + ): train_steps, + } + ) + + for block_idx in range(len(self.layers_in_block)): + for layer_idx in range(self.layers_in_block[block_idx]): + for i in ["1", "2", "_sc"]: + map_dict_local.update( + { + "{}.block_{}.layer_{}.conv{}.weight".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/conv{}/kernel".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": (3, 2, 0, 1), + }, + "{}.block_{}.layer_{}.bn{}.weight".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/gamma".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.bias".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/beta".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.running_mean".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/moving_mean".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.running_var".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/moving_variance".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.num_batches_tracked".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): train_steps, + } + ) + + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + + map_dict = self.gen_tf2torch_map_dict() + + var_dict_torch_update = dict() + for name in sorted(var_dict_torch.keys(), reverse=False): + if name.startswith(self.tf2torch_tensor_name_prefix_torch): + if name in map_dict: + if "num_batches_tracked" not in name: + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name]["squeeze"] + ) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf, + var_dict_tf[name_tf].shape, + ) + ) + else: + var_dict_torch_update[name] = ( + torch.Tensor(map_dict[name]).type(torch.int64).to("cpu") + ) + logging.info( + "torch tensor: {}, manually assigning to: {}".format( + name, map_dict[name] + ) + ) + else: + logging.warning("{} is missed from tf checkpoint".format(name)) + + return var_dict_torch_update + + +class ResNet34SpL2RegDiar(ResNet34_SP_L2Reg): + def __init__( + self, + input_size, + embedding_node="resnet1_dense", + use_head_conv=True, + batchnorm_momentum=0.5, + use_head_maxpool=False, + num_nodes_pooling_layer=256, + layers_in_block=(3, 4, 6, 3), + filters_in_block=(32, 64, 128, 256), + num_nodes_resnet1=256, + num_nodes_last_layer=256, + pooling_type="window_shift", + pool_size=20, + stride=1, + tf2torch_tensor_name_prefix_torch="encoder", + tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder", + ): + """ + Author: Speech Lab, Alibaba Group, China + TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization + https://arxiv.org/abs/2303.05397 + """ + + super(ResNet34SpL2RegDiar, self).__init__( + input_size, + use_head_conv=use_head_conv, + batchnorm_momentum=batchnorm_momentum, + use_head_maxpool=use_head_maxpool, + num_nodes_pooling_layer=num_nodes_pooling_layer, + layers_in_block=layers_in_block, + filters_in_block=filters_in_block, + ) + + self.embedding_node = embedding_node + self.num_nodes_resnet1 = num_nodes_resnet1 + self.num_nodes_last_layer = num_nodes_last_layer + self.pooling_type = pooling_type + self.pool_size = pool_size + self.stride = stride + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + + self.resnet1_dense = torch.nn.Linear( + num_nodes_pooling_layer * 2, num_nodes_resnet1 + ) + self.resnet1_bn = torch.nn.BatchNorm1d( + num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum + ) + + self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer) + self.resnet2_bn = torch.nn.BatchNorm1d( + num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum + ) + + def output_size(self) -> int: + if self.embedding_node.startswith("resnet1"): + return self.num_nodes_resnet1 + elif self.embedding_node.startswith("resnet2"): + return self.num_nodes_last_layer + + return self.num_nodes_pooling_layer + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + + endpoints = OrderedDict() + res_out, ilens = super().forward(xs_pad, ilens) + endpoints["resnet0_bn"] = res_out + if self.pooling_type == "frame_gsp": + features = statistic_pooling(res_out, ilens, (2,)) + else: + features, ilens = windowed_statistic_pooling( + res_out, ilens, (2,), self.pool_size, self.stride + ) + features = features.transpose(1, 2) + endpoints["pooling"] = features + + features = self.resnet1_dense(features) + endpoints["resnet1_dense"] = features + features = F.relu(features) + endpoints["resnet1_relu"] = features + features = self.resnet1_bn(features.transpose(1, 2)).transpose(1, 2) + endpoints["resnet1_bn"] = features + + features = self.resnet2_dense(features) + endpoints["resnet2_dense"] = features + features = F.relu(features) + endpoints["resnet2_relu"] = features + features = self.resnet2_bn(features.transpose(1, 2)).transpose(1, 2) + endpoints["resnet2_bn"] = features + + return endpoints[self.embedding_node], ilens, None + + def gen_tf2torch_map_dict(self): + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + train_steps = 720000 + map_dict_local = { + # torch: conv1d.weight in "out_channel in_channel kernel_size" + # tf : conv1d.weight in "kernel_size in_channel out_channel" + # torch: linear.weight in "out_channel in_channel" + # tf : dense.weight in "in_channel out_channel" + "{}.pre_conv.weight".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv/kernel".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": (3, 2, 0, 1), + }, + "{}.pre_conv_bn.bias".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.weight".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.running_mean".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/moving_mean".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.running_var".format(tensor_name_prefix_torch): { + "name": "{}/pre_conv_bn/moving_variance".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, + "{}.pre_conv_bn.num_batches_tracked".format( + tensor_name_prefix_torch + ): train_steps, + } + for layer_idx in range(3): + map_dict_local.update( + { + "{}.resnet{}_dense.weight".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_dense/kernel".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": (2, 1, 0) if layer_idx == 0 else (1, 0), + }, + "{}.resnet{}_dense.bias".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_dense/bias".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.weight".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_bn/gamma".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.bias".format(tensor_name_prefix_torch, layer_idx): { + "name": "{}/resnet{}_bn/beta".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.running_mean".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_bn/moving_mean".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.running_var".format( + tensor_name_prefix_torch, layer_idx + ): { + "name": "{}/resnet{}_bn/moving_variance".format( + tensor_name_prefix_tf, layer_idx + ), + "squeeze": None, + "transpose": None, + }, + "{}.resnet{}_bn.num_batches_tracked".format( + tensor_name_prefix_torch, layer_idx + ): train_steps, + } + ) + + for block_idx in range(len(self.layers_in_block)): + for layer_idx in range(self.layers_in_block[block_idx]): + for i in ["1", "2", "_sc"]: + map_dict_local.update( + { + "{}.block_{}.layer_{}.conv{}.weight".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/conv{}/kernel".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": (3, 2, 0, 1), + }, + "{}.block_{}.layer_{}.bn{}.weight".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/gamma".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.bias".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/beta".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.running_mean".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/moving_mean".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.running_var".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): { + "name": "{}/block_{}/layer_{}/bn{}/moving_variance".format( + tensor_name_prefix_tf, block_idx, layer_idx, i + ), + "squeeze": None, + "transpose": None, + }, + "{}.block_{}.layer_{}.bn{}.num_batches_tracked".format( + tensor_name_prefix_torch, block_idx, layer_idx, i + ): train_steps, + } + ) + + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + + map_dict = self.gen_tf2torch_map_dict() + + var_dict_torch_update = dict() + for name in sorted(var_dict_torch.keys(), reverse=False): + if name.startswith(self.tf2torch_tensor_name_prefix_torch): + if name in map_dict: + if "num_batches_tracked" not in name: + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name]["squeeze"] + ) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf, + var_dict_tf[name_tf].shape, + ) + ) + else: + var_dict_torch_update[name] = ( + torch.from_numpy(np.array(map_dict[name])) + .type(torch.int64) + .to("cpu") + ) + logging.info( + "torch tensor: {}, manually assigning to: {}".format( + name, map_dict[name] + ) + ) + else: + logging.warning("{} is missed from tf checkpoint".format(name)) + + return var_dict_torch_update diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/self_attention_encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/self_attention_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..260e3f45f2e7f5fe120e3b915e178f694fb98370 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/encoder/self_attention_encoder.py @@ -0,0 +1,560 @@ +from typing import List +from typing import Optional +from typing import Sequence +from typing import Tuple +from typing import Union +import logging +import torch +import torch.nn as nn +from funasr_detach.models.scama.chunk_utilis import overlap_chunk +import numpy as np +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.sond.attention import MultiHeadSelfAttention +from funasr_detach.models.transformer.embedding import SinusoidalPositionEncoder +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear +from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, # noqa: H301 +) +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling2 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling6 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling8 +from funasr_detach.models.transformer.utils.subsampling import TooShortUttError +from funasr_detach.models.transformer.utils.subsampling import check_short_utt +from funasr_detach.models.ctc import CTC +from funasr_detach.models.encoder.abs_encoder import AbsEncoder + + +class EncoderLayer(nn.Module): + def __init__( + self, + in_size, + size, + self_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + stochastic_depth_rate=0.0, + ): + """Construct an EncoderLayer object.""" + super(EncoderLayer, self).__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(in_size) + self.norm2 = LayerNorm(size) + self.dropout = nn.Dropout(dropout_rate) + self.in_size = in_size + self.size = size + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear = nn.Linear(size + size, size) + self.stochastic_depth_rate = stochastic_depth_rate + self.dropout_rate = dropout_rate + + def forward(self, x, mask, cache=None, mask_att_chunk_encoder=None): + """Compute encoded features. + + Args: + x_input (torch.Tensor): Input tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + skip_layer = False + # with stochastic depth, residual connection `x + f(x)` becomes + # `x <- x + 1 / (1 - p) * f(x)` at training time. + stoch_layer_coeff = 1.0 + if self.training and self.stochastic_depth_rate > 0: + skip_layer = torch.rand(1).item() < self.stochastic_depth_rate + stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate) + + if skip_layer: + if cache is not None: + x = torch.cat([cache, x], dim=1) + return x, mask + + residual = x + if self.normalize_before: + x = self.norm1(x) + + if self.concat_after: + x_concat = torch.cat( + ( + x, + self.self_attn( + x, mask, mask_att_chunk_encoder=mask_att_chunk_encoder + ), + ), + dim=-1, + ) + if self.in_size == self.size: + x = residual + stoch_layer_coeff * self.concat_linear(x_concat) + else: + x = stoch_layer_coeff * self.concat_linear(x_concat) + else: + if self.in_size == self.size: + x = residual + stoch_layer_coeff * self.dropout( + self.self_attn( + x, mask, mask_att_chunk_encoder=mask_att_chunk_encoder + ) + ) + else: + x = stoch_layer_coeff * self.dropout( + self.self_attn( + x, mask, mask_att_chunk_encoder=mask_att_chunk_encoder + ) + ) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm2(x) + + return x, mask, cache, mask_att_chunk_encoder + + +class SelfAttentionEncoder(AbsEncoder): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + Self attention encoder in OpenNMT framework + """ + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: Optional[str] = "conv2d", + pos_enc_class=SinusoidalPositionEncoder, + normalize_before: bool = True, + concat_after: bool = False, + positionwise_layer_type: str = "linear", + positionwise_conv_kernel_size: int = 1, + padding_idx: int = -1, + interctc_layer_idx: List[int] = [], + interctc_use_conditioning: bool = False, + tf2torch_tensor_name_prefix_torch: str = "encoder", + tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder", + out_units=None, + ): + super().__init__() + self._output_size = output_size + + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(input_size, output_size), + torch.nn.LayerNorm(output_size), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate) + elif input_layer == "conv2d2": + self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate) + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate) + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate) + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), + SinusoidalPositionEncoder(), + ) + elif input_layer is None: + if input_size == output_size: + self.embed = None + else: + self.embed = torch.nn.Linear(input_size, output_size) + elif input_layer == "pe": + self.embed = SinusoidalPositionEncoder() + elif input_layer == "null": + self.embed = None + else: + raise ValueError("unknown input_layer: " + input_layer) + self.normalize_before = normalize_before + if positionwise_layer_type == "linear": + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d": + positionwise_layer = MultiLayeredConv1d + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d-linear": + positionwise_layer = Conv1dLinear + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + else: + raise NotImplementedError("Support only linear or conv1d.") + + self.encoders = repeat( + num_blocks, + lambda lnum: ( + EncoderLayer( + output_size, + output_size, + MultiHeadSelfAttention( + attention_heads, + output_size, + output_size, + attention_dropout_rate, + ), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + ) + if lnum > 0 + else EncoderLayer( + input_size, + output_size, + MultiHeadSelfAttention( + attention_heads, + ( + input_size + if input_layer == "pe" or input_layer == "null" + else output_size + ), + output_size, + attention_dropout_rate, + ), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + ) + ), + ) + if self.normalize_before: + self.after_norm = LayerNorm(output_size) + + self.interctc_layer_idx = interctc_layer_idx + if len(interctc_layer_idx) > 0: + assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks + self.interctc_use_conditioning = interctc_use_conditioning + self.conditioning_layer = None + self.dropout = nn.Dropout(dropout_rate) + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + self.out_units = out_units + if out_units is not None: + self.output_linear = nn.Linear(output_size, out_units) + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ctc: CTC = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Embed positions in tensor. + + Args: + xs_pad: input tensor (B, L, D) + ilens: input length (B) + prev_states: Not to be used now. + Returns: + position embedded tensor and mask + """ + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + xs_pad = xs_pad * self.output_size() ** 0.5 + if self.embed is None: + xs_pad = xs_pad + elif ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling2) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + else: + xs_pad = self.embed(xs_pad) + + xs_pad = self.dropout(xs_pad) + # encoder_outs = self.encoders0(xs_pad, masks) + # xs_pad, masks = encoder_outs[0], encoder_outs[1] + intermediate_outs = [] + if len(self.interctc_layer_idx) == 0: + encoder_outs = self.encoders(xs_pad, masks) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + else: + for layer_idx, encoder_layer in enumerate(self.encoders): + encoder_outs = encoder_layer(xs_pad, masks) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + + if layer_idx + 1 in self.interctc_layer_idx: + encoder_out = xs_pad + + # intermediate outputs are also normalized + if self.normalize_before: + encoder_out = self.after_norm(encoder_out) + + intermediate_outs.append((layer_idx + 1, encoder_out)) + + if self.interctc_use_conditioning: + ctc_out = ctc.softmax(encoder_out) + xs_pad = xs_pad + self.conditioning_layer(ctc_out) + + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + + if self.out_units is not None: + xs_pad = self.output_linear(xs_pad) + olens = masks.squeeze(1).sum(1) + if len(intermediate_outs) > 0: + return (xs_pad, intermediate_outs), olens, None + return xs_pad, olens, None + + def gen_tf2torch_map_dict(self): + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + map_dict_local = { + # cicd + # torch: conv1d.weight in "out_channel in_channel kernel_size" + # tf : conv1d.weight in "kernel_size in_channel out_channel" + # torch: linear.weight in "out_channel in_channel" + # tf : dense.weight in "in_channel out_channel" + "{}.encoders.layeridx.norm1.weight".format(tensor_name_prefix_torch): { + "name": "{}/layer_layeridx/multi_head/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.encoders.layeridx.norm1.bias".format(tensor_name_prefix_torch): { + "name": "{}/layer_layeridx/multi_head/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.encoders.layeridx.self_attn.linear_q_k_v.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/multi_head/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (768,256),(1,256,768) + "{}.encoders.layeridx.self_attn.linear_q_k_v.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/multi_head/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (768,),(768,) + "{}.encoders.layeridx.self_attn.linear_out.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/multi_head/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,256),(1,256,256) + "{}.encoders.layeridx.self_attn.linear_out.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/multi_head/conv1d_1/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + # ffn + "{}.encoders.layeridx.norm2.weight".format(tensor_name_prefix_torch): { + "name": "{}/layer_layeridx/ffn/LayerNorm/gamma".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.encoders.layeridx.norm2.bias".format(tensor_name_prefix_torch): { + "name": "{}/layer_layeridx/ffn/LayerNorm/beta".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.encoders.layeridx.feed_forward.w_1.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/ffn/conv1d/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (1024,256),(1,256,1024) + "{}.encoders.layeridx.feed_forward.w_1.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/ffn/conv1d/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (1024,),(1024,) + "{}.encoders.layeridx.feed_forward.w_2.weight".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/ffn/conv1d_1/kernel".format( + tensor_name_prefix_tf + ), + "squeeze": 0, + "transpose": (1, 0), + }, # (256,1024),(1,1024,256) + "{}.encoders.layeridx.feed_forward.w_2.bias".format( + tensor_name_prefix_torch + ): { + "name": "{}/layer_layeridx/ffn/conv1d_1/bias".format( + tensor_name_prefix_tf + ), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + # out norm + "{}.after_norm.weight".format(tensor_name_prefix_torch): { + "name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + "{}.after_norm.bias".format(tensor_name_prefix_torch): { + "name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + } + if self.out_units is not None: + map_dict_local.update( + { + "{}.output_linear.weight".format(tensor_name_prefix_torch): { + "name": "{}/conv1d/kernel".format(tensor_name_prefix_tf), + "squeeze": 0, + "transpose": (1, 0), + }, + "{}.output_linear.bias".format(tensor_name_prefix_torch): { + "name": "{}/conv1d/bias".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + } + ) + + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + + map_dict = self.gen_tf2torch_map_dict() + + var_dict_torch_update = dict() + for name in sorted(var_dict_torch.keys(), reverse=False): + if name.startswith(self.tf2torch_tensor_name_prefix_torch): + # process special (first and last) layers + if name in map_dict: + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"]) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape + ) + ) + # process general layers + else: + # self.tf2torch_tensor_name_prefix_torch may include ".", solve this case + names = name.replace( + self.tf2torch_tensor_name_prefix_torch, "todo" + ).split(".") + layeridx = int(names[2]) + name_q = name.replace(".{}.".format(layeridx), ".layeridx.") + if name_q in map_dict.keys(): + name_v = map_dict[name_q]["name"] + name_tf = name_v.replace("layeridx", "{}".format(layeridx)) + data_tf = var_dict_tf[name_tf] + if map_dict[name_q]["squeeze"] is not None: + data_tf = np.squeeze( + data_tf, axis=map_dict[name_q]["squeeze"] + ) + if map_dict[name_q]["transpose"] is not None: + data_tf = np.transpose( + data_tf, map_dict[name_q]["transpose"] + ) + data_tf = ( + torch.from_numpy(data_tf).type(torch.float32).to("cpu") + ) + assert ( + var_dict_torch[name].size() == data_tf.size() + ), "{}, {}, {} != {}".format( + name, name_tf, var_dict_torch[name].size(), data_tf.size() + ) + var_dict_torch_update[name] = data_tf + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, + data_tf.size(), + name_tf, + var_dict_tf[name_tf].shape, + ) + ) + else: + logging.warning("{} is missed from tf checkpoint".format(name)) + + return var_dict_torch_update diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/label_aggregation.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/label_aggregation.py new file mode 100644 index 0000000000000000000000000000000000000000..69513760877ec619ba55c79956829087a4204b70 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/label_aggregation.py @@ -0,0 +1,113 @@ +import torch +from typing import Optional +from typing import Tuple +from torch.nn import functional as F +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask + + +class LabelAggregate(torch.nn.Module): + def __init__( + self, + win_length: int = 512, + hop_length: int = 128, + center: bool = True, + ): + super().__init__() + + self.win_length = win_length + self.hop_length = hop_length + self.center = center + + def extra_repr(self): + return ( + f"win_length={self.win_length}, " + f"hop_length={self.hop_length}, " + f"center={self.center}, " + ) + + def forward( + self, input: torch.Tensor, ilens: torch.Tensor = None + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """LabelAggregate forward function. + + Args: + input: (Batch, Nsamples, Label_dim) + ilens: (Batch) + Returns: + output: (Batch, Frames, Label_dim) + + """ + bs = input.size(0) + max_length = input.size(1) + label_dim = input.size(2) + + # NOTE(jiatong): + # The default behaviour of label aggregation is compatible with + # torch.stft about framing and padding. + + # Step1: center padding + if self.center: + pad = self.win_length // 2 + max_length = max_length + 2 * pad + input = torch.nn.functional.pad(input, (0, 0, pad, pad), "constant", 0) + input[:, :pad, :] = input[:, pad : (2 * pad), :] + input[:, (max_length - pad) : max_length, :] = input[ + :, (max_length - 2 * pad) : (max_length - pad), : + ] + nframe = (max_length - self.win_length) // self.hop_length + 1 + + # Step2: framing + output = input.as_strided( + (bs, nframe, self.win_length, label_dim), + (max_length * label_dim, self.hop_length * label_dim, label_dim, 1), + ) + + # Step3: aggregate label + output = torch.gt(output.sum(dim=2, keepdim=False), self.win_length // 2) + output = output.float() + + # Step4: process lengths + if ilens is not None: + if self.center: + pad = self.win_length // 2 + ilens = ilens + 2 * pad + + olens = (ilens - self.win_length) // self.hop_length + 1 + output.masked_fill_(make_pad_mask(olens, output, 1), 0.0) + else: + olens = None + + return output.to(input.dtype), olens + + +class LabelAggregateMaxPooling(torch.nn.Module): + def __init__( + self, + hop_length: int = 8, + ): + super().__init__() + + self.hop_length = hop_length + + def extra_repr(self): + return f"hop_length={self.hop_length}, " + + def forward( + self, input: torch.Tensor, ilens: torch.Tensor = None + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """LabelAggregate forward function. + + Args: + input: (Batch, Nsamples, Label_dim) + ilens: (Batch) + Returns: + output: (Batch, Frames, Label_dim) + + """ + + output = F.max_pool1d( + input.transpose(1, 2), self.hop_length, self.hop_length + ).transpose(1, 2) + olens = ilens // self.hop_length + + return output.to(input.dtype), olens diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/pooling/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/pooling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/pooling/pooling_layers.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/pooling/pooling_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..929c3a9457258ea850765285d2f6c64abdd99772 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/pooling/pooling_layers.py @@ -0,0 +1,109 @@ +# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved. +# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""This implementation is adapted from https://github.com/wenet-e2e/wespeaker.""" + +import torch +import torch.nn as nn + + +class TAP(nn.Module): + """ + Temporal average pooling, only first-order mean is considered + """ + + def __init__(self, **kwargs): + super(TAP, self).__init__() + + def forward(self, x): + pooling_mean = x.mean(dim=-1) + # To be compatable with 2D input + pooling_mean = pooling_mean.flatten(start_dim=1) + return pooling_mean + + +class TSDP(nn.Module): + """ + Temporal standard deviation pooling, only second-order std is considered + """ + + def __init__(self, **kwargs): + super(TSDP, self).__init__() + + def forward(self, x): + # The last dimension is the temporal axis + pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-8) + pooling_std = pooling_std.flatten(start_dim=1) + return pooling_std + + +class TSTP(nn.Module): + """ + Temporal statistics pooling, concatenate mean and std, which is used in + x-vector + Comment: simple concatenation can not make full use of both statistics + """ + + def __init__(self, **kwargs): + super(TSTP, self).__init__() + + def forward(self, x): + # The last dimension is the temporal axis + pooling_mean = x.mean(dim=-1) + pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-8) + pooling_mean = pooling_mean.flatten(start_dim=1) + pooling_std = pooling_std.flatten(start_dim=1) + + stats = torch.cat((pooling_mean, pooling_std), 1) + return stats + + +class ASTP(nn.Module): + """Attentive statistics pooling: Channel- and context-dependent + statistics pooling, first used in ECAPA_TDNN. + """ + + def __init__(self, in_dim, bottleneck_dim=128, global_context_att=False): + super(ASTP, self).__init__() + self.global_context_att = global_context_att + + # Use Conv1d with stride == 1 rather than Linear, then we don't + # need to transpose inputs. + if global_context_att: + self.linear1 = nn.Conv1d( + in_dim * 3, bottleneck_dim, kernel_size=1 + ) # equals W and b in the paper + else: + self.linear1 = nn.Conv1d( + in_dim, bottleneck_dim, kernel_size=1 + ) # equals W and b in the paper + self.linear2 = nn.Conv1d( + bottleneck_dim, in_dim, kernel_size=1 + ) # equals V and k in the paper + + def forward(self, x): + """ + x: a 3-dimensional tensor in tdnn-based architecture (B,F,T) + or a 4-dimensional tensor in resnet architecture (B,C,F,T) + 0-dim: batch-dimension, last-dim: time-dimension (frame-dimension) + """ + if len(x.shape) == 4: + x = x.reshape(x.shape[0], x.shape[1] * x.shape[2], x.shape[3]) + assert len(x.shape) == 3 + + if self.global_context_att: + context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x) + context_std = torch.sqrt( + torch.var(x, dim=-1, keepdim=True) + 1e-10 + ).expand_as(x) + x_in = torch.cat((x, context_mean, context_std), dim=1) + else: + x_in = x + + # DON'T use ReLU here! ReLU may be hard to converge. + alpha = torch.tanh(self.linear1(x_in)) # alpha = F.relu(self.linear1(x_in)) + alpha = torch.softmax(self.linear2(alpha), dim=2) + mean = torch.sum(alpha * x, dim=2) + var = torch.sum(alpha * (x**2), dim=2) - mean**2 + std = torch.sqrt(var.clamp(min=1e-10)) + return torch.cat([mean, std], dim=1) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/pooling/statistic_pooling.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/pooling/statistic_pooling.py new file mode 100644 index 0000000000000000000000000000000000000000..fcb5a01bb6486cd37ddfda304bc0626cd164fde8 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/pooling/statistic_pooling.py @@ -0,0 +1,101 @@ +import torch +from typing import Tuple +from typing import Union +from funasr_detach.models.transformer.utils.nets_utils import make_non_pad_mask +from torch.nn import functional as F +import math + +VAR2STD_EPSILON = 1e-12 + + +class StatisticPooling(torch.nn.Module): + def __init__(self, pooling_dim: Union[int, Tuple] = 2, eps=1e-12): + super(StatisticPooling, self).__init__() + if isinstance(pooling_dim, int): + pooling_dim = (pooling_dim,) + self.pooling_dim = pooling_dim + self.eps = eps + + def forward(self, xs_pad, ilens=None): + # xs_pad in (Batch, Channel, Time, Frequency) + + if ilens is None: + masks = torch.ones_like(xs_pad).to(xs_pad) + else: + masks = make_non_pad_mask(ilens, xs_pad, length_dim=2).to(xs_pad) + mean = torch.sum(xs_pad, dim=self.pooling_dim, keepdim=True) / torch.sum( + masks, dim=self.pooling_dim, keepdim=True + ) + squared_difference = torch.pow(xs_pad - mean, 2.0) + variance = torch.sum( + squared_difference, dim=self.pooling_dim, keepdim=True + ) / torch.sum(masks, dim=self.pooling_dim, keepdim=True) + for i in reversed(self.pooling_dim): + mean, variance = torch.squeeze(mean, dim=i), torch.squeeze(variance, dim=i) + + mask = torch.less_equal(variance, self.eps).float() + variance = (1.0 - mask) * variance + mask * self.eps + stddev = torch.sqrt(variance) + + stat_pooling = torch.cat([mean, stddev], dim=1) + + return stat_pooling + + def convert_tf2torch(self, var_dict_tf, var_dict_torch): + return {} + + +def statistic_pooling( + xs_pad: torch.Tensor, ilens: torch.Tensor = None, pooling_dim: Tuple = (2, 3) +) -> torch.Tensor: + # xs_pad in (Batch, Channel, Time, Frequency) + + if ilens is None: + seq_mask = torch.ones_like(xs_pad).to(xs_pad) + else: + seq_mask = make_non_pad_mask(ilens, xs_pad, length_dim=2).to(xs_pad) + mean = torch.sum(xs_pad, dim=pooling_dim, keepdim=True) / torch.sum( + seq_mask, dim=pooling_dim, keepdim=True + ) + squared_difference = torch.pow(xs_pad - mean, 2.0) + variance = torch.sum(squared_difference, dim=pooling_dim, keepdim=True) / torch.sum( + seq_mask, dim=pooling_dim, keepdim=True + ) + for i in reversed(pooling_dim): + mean, variance = torch.squeeze(mean, dim=i), torch.squeeze(variance, dim=i) + + value_mask = torch.less_equal(variance, VAR2STD_EPSILON).float() + variance = (1.0 - value_mask) * variance + value_mask * VAR2STD_EPSILON + stddev = torch.sqrt(variance) + + stat_pooling = torch.cat([mean, stddev], dim=1) + + return stat_pooling + + +def windowed_statistic_pooling( + xs_pad: torch.Tensor, + ilens: torch.Tensor = None, + pooling_dim: Tuple = (2, 3), + pooling_size: int = 20, + pooling_stride: int = 1, +) -> Tuple[torch.Tensor, int]: + # xs_pad in (Batch, Channel, Time, Frequency) + + tt = xs_pad.shape[2] + num_chunk = int(math.ceil(tt / pooling_stride)) + pad = pooling_size // 2 + if len(xs_pad.shape) == 4: + features = F.pad(xs_pad, (0, 0, pad, pad), "replicate") + else: + features = F.pad(xs_pad, (pad, pad), "replicate") + stat_list = [] + + for i in range(num_chunk): + # B x C + st, ed = i * pooling_stride, i * pooling_stride + pooling_size + stat = statistic_pooling(features[:, :, st:ed], pooling_dim=pooling_dim) + stat_list.append(stat.unsqueeze(2)) + + # B x C x T + return torch.cat(stat_list, dim=2), ilens / pooling_stride diff --git a/demo/Step-Audio-EditX/funasr_detach/models/sond/sv_decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/sond/sv_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..acef9d88bd62349df776af52f62ba1031c11bcbd --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/sond/sv_decoder.py @@ -0,0 +1,43 @@ +import torch +from torch.nn import functional as F +from funasr_detach.models.decoder.abs_decoder import AbsDecoder + + +class DenseDecoder(AbsDecoder): + def __init__( + self, + vocab_size, + encoder_output_size, + num_nodes_resnet1: int = 256, + num_nodes_last_layer: int = 256, + batchnorm_momentum: float = 0.5, + ): + super(DenseDecoder, self).__init__() + self.resnet1_dense = torch.nn.Linear(encoder_output_size, num_nodes_resnet1) + self.resnet1_bn = torch.nn.BatchNorm1d( + num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum + ) + + self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer) + self.resnet2_bn = torch.nn.BatchNorm1d( + num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum + ) + + self.output_dense = torch.nn.Linear( + num_nodes_last_layer, vocab_size, bias=False + ) + + def forward(self, features): + embeddings = {} + features = self.resnet1_dense(features) + embeddings["resnet1_dense"] = features + features = F.relu(features) + features = self.resnet1_bn(features) + + features = self.resnet2_dense(features) + embeddings["resnet2_dense"] = features + features = F.relu(features) + features = self.resnet2_bn(features) + + features = self.output_dense(features) + return features, embeddings diff --git a/demo/Step-Audio-EditX/funasr_detach/models/specaug/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/specaug/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/specaug/mask_along_axis.py b/demo/Step-Audio-EditX/funasr_detach/models/specaug/mask_along_axis.py new file mode 100644 index 0000000000000000000000000000000000000000..560c1438847223054cc15698cf68c4f730ddcf06 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/specaug/mask_along_axis.py @@ -0,0 +1,338 @@ +import math +import torch +from typing import Sequence +from typing import Union + + +def mask_along_axis( + spec: torch.Tensor, + spec_lengths: torch.Tensor, + mask_width_range: Sequence[int] = (0, 30), + dim: int = 1, + num_mask: int = 2, + replace_with_zero: bool = True, +): + """Apply mask along the specified direction. + + Args: + spec: (Batch, Length, Freq) + spec_lengths: (Length): Not using lengths in this implementation + mask_width_range: Select the width randomly between this range + """ + + org_size = spec.size() + if spec.dim() == 4: + # spec: (Batch, Channel, Length, Freq) -> (Batch * Channel, Length, Freq) + spec = spec.view(-1, spec.size(2), spec.size(3)) + + B = spec.shape[0] + # D = Length or Freq + D = spec.shape[dim] + # mask_length: (B, num_mask, 1) + mask_length = torch.randint( + mask_width_range[0], + mask_width_range[1], + (B, num_mask), + device=spec.device, + ).unsqueeze(2) + + # mask_pos: (B, num_mask, 1) + mask_pos = torch.randint( + 0, max(1, D - mask_length.max()), (B, num_mask), device=spec.device + ).unsqueeze(2) + + # aran: (1, 1, D) + aran = torch.arange(D, device=spec.device)[None, None, :] + # mask: (Batch, num_mask, D) + mask = (mask_pos <= aran) * (aran < (mask_pos + mask_length)) + # Multiply masks: (Batch, num_mask, D) -> (Batch, D) + mask = mask.any(dim=1) + if dim == 1: + # mask: (Batch, Length, 1) + mask = mask.unsqueeze(2) + elif dim == 2: + # mask: (Batch, 1, Freq) + mask = mask.unsqueeze(1) + + if replace_with_zero: + value = 0.0 + else: + value = spec.mean() + + if spec.requires_grad: + spec = spec.masked_fill(mask, value) + else: + spec = spec.masked_fill_(mask, value) + spec = spec.view(*org_size) + return spec, spec_lengths + + +def mask_along_axis_lfr( + spec: torch.Tensor, + spec_lengths: torch.Tensor, + mask_width_range: Sequence[int] = (0, 30), + dim: int = 1, + num_mask: int = 2, + replace_with_zero: bool = True, + lfr_rate: int = 1, +): + """Apply mask along the specified direction. + + Args: + spec: (Batch, Length, Freq) + spec_lengths: (Length): Not using lengths in this implementation + mask_width_range: Select the width randomly between this range + lfr_rate：low frame rate + """ + + org_size = spec.size() + if spec.dim() == 4: + # spec: (Batch, Channel, Length, Freq) -> (Batch * Channel, Length, Freq) + spec = spec.view(-1, spec.size(2), spec.size(3)) + + B = spec.shape[0] + # D = Length or Freq + D = spec.shape[dim] // lfr_rate + # mask_length: (B, num_mask, 1) + mask_length = torch.randint( + mask_width_range[0], + mask_width_range[1], + (B, num_mask), + device=spec.device, + ).unsqueeze(2) + if lfr_rate > 1: + mask_length = mask_length.repeat(1, lfr_rate, 1) + # mask_pos: (B, num_mask, 1) + mask_pos = torch.randint( + 0, max(1, D - mask_length.max()), (B, num_mask), device=spec.device + ).unsqueeze(2) + if lfr_rate > 1: + mask_pos_raw = mask_pos.clone() + mask_pos = torch.zeros((B, 0, 1), device=spec.device, dtype=torch.int32) + for i in range(lfr_rate): + mask_pos_i = mask_pos_raw + D * i + mask_pos = torch.cat((mask_pos, mask_pos_i), dim=1) + # aran: (1, 1, D) + D = spec.shape[dim] + aran = torch.arange(D, device=spec.device)[None, None, :] + # mask: (Batch, num_mask, D) + mask = (mask_pos <= aran) * (aran < (mask_pos + mask_length)) + # Multiply masks: (Batch, num_mask, D) -> (Batch, D) + mask = mask.any(dim=1) + if dim == 1: + # mask: (Batch, Length, 1) + mask = mask.unsqueeze(2) + elif dim == 2: + # mask: (Batch, 1, Freq) + mask = mask.unsqueeze(1) + + if replace_with_zero: + value = 0.0 + else: + value = spec.mean() + + if spec.requires_grad: + spec = spec.masked_fill(mask, value) + else: + spec = spec.masked_fill_(mask, value) + spec = spec.view(*org_size) + return spec, spec_lengths + + +class MaskAlongAxis(torch.nn.Module): + def __init__( + self, + mask_width_range: Union[int, Sequence[int]] = (0, 30), + num_mask: int = 2, + dim: Union[int, str] = "time", + replace_with_zero: bool = True, + ): + if isinstance(mask_width_range, int): + mask_width_range = (0, mask_width_range) + if len(mask_width_range) != 2: + raise TypeError( + f"mask_width_range must be a tuple of int and int values: " + f"{mask_width_range}", + ) + + assert mask_width_range[1] > mask_width_range[0] + if isinstance(dim, str): + if dim == "time": + dim = 1 + elif dim == "freq": + dim = 2 + else: + raise ValueError("dim must be int, 'time' or 'freq'") + if dim == 1: + self.mask_axis = "time" + elif dim == 2: + self.mask_axis = "freq" + else: + self.mask_axis = "unknown" + + super().__init__() + self.mask_width_range = mask_width_range + self.num_mask = num_mask + self.dim = dim + self.replace_with_zero = replace_with_zero + + def extra_repr(self): + return ( + f"mask_width_range={self.mask_width_range}, " + f"num_mask={self.num_mask}, axis={self.mask_axis}" + ) + + def forward(self, spec: torch.Tensor, spec_lengths: torch.Tensor = None): + """Forward function. + + Args: + spec: (Batch, Length, Freq) + """ + + return mask_along_axis( + spec, + spec_lengths, + mask_width_range=self.mask_width_range, + dim=self.dim, + num_mask=self.num_mask, + replace_with_zero=self.replace_with_zero, + ) + + +class MaskAlongAxisVariableMaxWidth(torch.nn.Module): + """Mask input spec along a specified axis with variable maximum width. + + Formula: + max_width = max_width_ratio * seq_len + """ + + def __init__( + self, + mask_width_ratio_range: Union[float, Sequence[float]] = (0.0, 0.05), + num_mask: int = 2, + dim: Union[int, str] = "time", + replace_with_zero: bool = True, + ): + if isinstance(mask_width_ratio_range, float): + mask_width_ratio_range = (0.0, mask_width_ratio_range) + if len(mask_width_ratio_range) != 2: + raise TypeError( + f"mask_width_ratio_range must be a tuple of float and float values: " + f"{mask_width_ratio_range}", + ) + + assert mask_width_ratio_range[1] > mask_width_ratio_range[0] + if isinstance(dim, str): + if dim == "time": + dim = 1 + elif dim == "freq": + dim = 2 + else: + raise ValueError("dim must be int, 'time' or 'freq'") + if dim == 1: + self.mask_axis = "time" + elif dim == 2: + self.mask_axis = "freq" + else: + self.mask_axis = "unknown" + + super().__init__() + self.mask_width_ratio_range = mask_width_ratio_range + self.num_mask = num_mask + self.dim = dim + self.replace_with_zero = replace_with_zero + + def extra_repr(self): + return ( + f"mask_width_ratio_range={self.mask_width_ratio_range}, " + f"num_mask={self.num_mask}, axis={self.mask_axis}" + ) + + def forward(self, spec: torch.Tensor, spec_lengths: torch.Tensor = None): + """Forward function. + + Args: + spec: (Batch, Length, Freq) + """ + + max_seq_len = spec.shape[self.dim] + min_mask_width = math.floor(max_seq_len * self.mask_width_ratio_range[0]) + min_mask_width = max([0, min_mask_width]) + max_mask_width = math.floor(max_seq_len * self.mask_width_ratio_range[1]) + max_mask_width = min([max_seq_len, max_mask_width]) + + if max_mask_width > min_mask_width: + return mask_along_axis( + spec, + spec_lengths, + mask_width_range=(min_mask_width, max_mask_width), + dim=self.dim, + num_mask=self.num_mask, + replace_with_zero=self.replace_with_zero, + ) + return spec, spec_lengths + + +class MaskAlongAxisLFR(torch.nn.Module): + def __init__( + self, + mask_width_range: Union[int, Sequence[int]] = (0, 30), + num_mask: int = 2, + dim: Union[int, str] = "time", + replace_with_zero: bool = True, + lfr_rate: int = 1, + ): + if isinstance(mask_width_range, int): + mask_width_range = (0, mask_width_range) + if len(mask_width_range) != 2: + raise TypeError( + f"mask_width_range must be a tuple of int and int values: " + f"{mask_width_range}", + ) + + assert mask_width_range[1] > mask_width_range[0] + if isinstance(dim, str): + if dim == "time": + dim = 1 + lfr_rate = 1 + elif dim == "freq": + dim = 2 + else: + raise ValueError("dim must be int, 'time' or 'freq'") + if dim == 1: + self.mask_axis = "time" + lfr_rate = 1 + elif dim == 2: + self.mask_axis = "freq" + else: + self.mask_axis = "unknown" + + super().__init__() + self.mask_width_range = mask_width_range + self.num_mask = num_mask + self.dim = dim + self.replace_with_zero = replace_with_zero + self.lfr_rate = lfr_rate + + def extra_repr(self): + return ( + f"mask_width_range={self.mask_width_range}, " + f"num_mask={self.num_mask}, axis={self.mask_axis}" + ) + + def forward(self, spec: torch.Tensor, spec_lengths: torch.Tensor = None): + """Forward function. + + Args: + spec: (Batch, Length, Freq) + """ + + return mask_along_axis_lfr( + spec, + spec_lengths, + mask_width_range=self.mask_width_range, + dim=self.dim, + num_mask=self.num_mask, + replace_with_zero=self.replace_with_zero, + lfr_rate=self.lfr_rate, + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/specaug/profileaug.py b/demo/Step-Audio-EditX/funasr_detach/models/specaug/profileaug.py new file mode 100644 index 0000000000000000000000000000000000000000..c72781ac306b62ceae5d844d7d65f9224eb809b5 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/specaug/profileaug.py @@ -0,0 +1,153 @@ +from typing import Tuple, Optional +import numpy as np +import torch +from torch.nn import functional as F +import torch.nn as nn + + +class ProfileAug(nn.Module): + """ + Implement the augmentation for profiles including: + - Split aug: split one profile into two profiles, i.e., main and inaccurate, labels assigned to main + - Merge aug: merge two profiles into one, labels are also merged into one, the other set to zero + - Disturb aug: disturb some profile with others to simulate the inaccurate clustering centroids. + """ + + def __init__( + self, + apply_split_aug: bool = True, + split_aug_prob: float = 0.05, + apply_merge_aug: bool = True, + merge_aug_prob: float = 0.2, + apply_disturb_aug: bool = True, + disturb_aug_prob: float = 0.4, + disturb_alpha: float = 0.2, + ) -> None: + super().__init__() + self.apply_split_aug = apply_split_aug + self.split_aug_prob = split_aug_prob + self.apply_merge_aug = apply_merge_aug + self.merge_aug_prob = merge_aug_prob + self.apply_disturb_aug = apply_disturb_aug + self.disturb_aug_prob = disturb_aug_prob + self.disturb_alpha = disturb_alpha + + def split_aug( + self, profile: torch.Tensor, binary_labels: torch.Tensor, mask: torch.Tensor + ): + # B, N + bsz, dim = profile.shape[0], profile.shape[-1] + profile_norm = torch.linalg.norm(profile, dim=-1, keepdim=False) + spk_count = binary_labels.sum(dim=1) + prob = np.random.rand(bsz) + batch_indices = np.nonzero(prob < self.split_aug_prob)[0] + for idx in batch_indices: + valid_spk_idx = torch.nonzero(spk_count[idx] * mask[idx]) + pad_spk_idx = torch.nonzero((spk_count[idx] == 0) * mask[idx]) + if len(valid_spk_idx) == 0 or len(pad_spk_idx) == 0: + continue + split_spk_idx = valid_spk_idx[torch.randint(len(valid_spk_idx), ())] + to_cover_idx = pad_spk_idx[torch.randint(len(pad_spk_idx), ())] + disturb_vec = torch.randn((dim,)).to(profile) + disturb_vec = F.normalize(disturb_vec, dim=-1) + profile[idx, to_cover_idx] = F.normalize( + profile[idx, split_spk_idx] + self.disturb_alpha * disturb_vec + ) + mask[idx, split_spk_idx] = 0 + mask[idx, to_cover_idx] = 0 + return profile, binary_labels, mask + + def merge_aug( + self, profile: torch.Tensor, binary_labels: torch.Tensor, mask: torch.Tensor + ): + bsz, dim = profile.shape[0], profile.shape[-1] + profile_norm = torch.linalg.norm(profile, dim=-1, keepdim=False) + spk_count = binary_labels.sum(dim=1) + prob = np.random.rand(bsz) + batch_indices = np.nonzero(prob < self.merge_aug_prob)[0] + for idx in batch_indices: + valid_spk_idx = torch.nonzero(profile_norm[idx] * mask[idx]) + if len(valid_spk_idx) == 0: + continue + to_merge = torch.randint(len(valid_spk_idx), (2,)) + spk_idx_1, spk_idx_2 = ( + valid_spk_idx[to_merge[0]], + valid_spk_idx[to_merge[1]], + ) + # merge profile + profile[idx, spk_idx_1] = profile[idx, spk_idx_1] + profile[idx, spk_idx_2] + profile[idx, spk_idx_1] = F.normalize(profile[idx, spk_idx_1], dim=-1) + profile[idx, spk_idx_2] = 0 + # merge binary labels + binary_labels[idx, :, spk_idx_1] = ( + binary_labels[idx, :, spk_idx_1] + binary_labels[idx, :, spk_idx_2] + ) + binary_labels[idx, :, spk_idx_1] = ( + binary_labels[idx, :, spk_idx_1] > 0 + ).to(binary_labels) + binary_labels[idx, :, spk_idx_2] = 0 + + mask[idx, spk_idx_1] = 0 + mask[idx, spk_idx_2] = 0 + + return profile, binary_labels, mask + + def disturb_aug( + self, profile: torch.Tensor, binary_labels: torch.Tensor, mask: torch.Tensor + ): + bsz, dim = profile.shape[0], profile.shape[-1] + profile_norm = torch.linalg.norm(profile, dim=-1, keepdim=False) + spk_count = binary_labels.sum(dim=1) + prob = np.random.rand(bsz) + batch_indices = np.nonzero(prob < self.disturb_aug_prob)[0] + for idx in batch_indices: + pos_spk_idx = torch.nonzero(spk_count[idx] * mask[idx]) + valid_spk_idx = torch.nonzero(profile_norm[idx] * mask[idx]) + if len(pos_spk_idx) == 0 or len(valid_spk_idx) == 0: + continue + to_disturb_idx = pos_spk_idx[torch.randint(len(pos_spk_idx), ())] + disturb_idx = valid_spk_idx[torch.randint(len(valid_spk_idx), ())] + alpha = self.disturb_alpha * torch.rand(()).item() + profile[idx, to_disturb_idx] = (1 - alpha) * profile[ + idx, to_disturb_idx + ] + alpha * profile[idx, disturb_idx] + profile[idx, to_disturb_idx] = F.normalize( + profile[idx, to_disturb_idx], dim=-1 + ) + mask[idx, to_disturb_idx] = 0 + + return profile, binary_labels, mask + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor = None, + profile: torch.Tensor = None, + profile_lengths: torch.Tensor = None, + binary_labels: torch.Tensor = None, + labels_length: torch.Tensor = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + + # copy inputs to avoid inplace-operation + speech, profile, binary_labels = ( + torch.clone(speech), + torch.clone(profile), + torch.clone(binary_labels), + ) + profile = F.normalize(profile, dim=-1) + + profile_mask = torch.ones(profile.shape[:2]).to(profile) + if self.apply_disturb_aug: + profile, binary_labels, profile_mask = self.disturb_aug( + profile, binary_labels, profile_mask + ) + if self.apply_split_aug: + profile, binary_labels, profile_mask = self.split_aug( + profile, binary_labels, profile_mask + ) + if self.apply_merge_aug: + profile, binary_labels, profile_mask = self.merge_aug( + profile, binary_labels, profile_mask + ) + + return speech, profile, binary_labels diff --git a/demo/Step-Audio-EditX/funasr_detach/models/specaug/specaug.py b/demo/Step-Audio-EditX/funasr_detach/models/specaug/specaug.py new file mode 100644 index 0000000000000000000000000000000000000000..e9fbd389c5802edb88c4868ff249fc9df00c7688 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/specaug/specaug.py @@ -0,0 +1,190 @@ +"""SpecAugment module.""" + +from typing import Optional +from typing import Sequence +from typing import Union + +from funasr_detach.models.specaug.mask_along_axis import MaskAlongAxis +from funasr_detach.models.specaug.mask_along_axis import MaskAlongAxisVariableMaxWidth +from funasr_detach.models.specaug.mask_along_axis import MaskAlongAxisLFR +from funasr_detach.models.specaug.time_warp import TimeWarp +from funasr_detach.register import tables + +import torch.nn as nn + + +@tables.register("specaug_classes", "SpecAug") +class SpecAug(nn.Module): + """Implementation of SpecAug. + + Reference: + Daniel S. Park et al. + "SpecAugment: A Simple Data + Augmentation Method for Automatic Speech Recognition" + + .. warning:: + When using cuda mode, time_warp doesn't have reproducibility + due to `torch.nn.functional.interpolate`. + + """ + + def __init__( + self, + apply_time_warp: bool = True, + time_warp_window: int = 5, + time_warp_mode: str = "bicubic", + apply_freq_mask: bool = True, + freq_mask_width_range: Union[int, Sequence[int]] = (0, 20), + num_freq_mask: int = 2, + apply_time_mask: bool = True, + time_mask_width_range: Optional[Union[int, Sequence[int]]] = None, + time_mask_width_ratio_range: Optional[Union[float, Sequence[float]]] = None, + num_time_mask: int = 2, + ): + if not apply_time_warp and not apply_time_mask and not apply_freq_mask: + raise ValueError( + "Either one of time_warp, time_mask, or freq_mask should be applied" + ) + if ( + apply_time_mask + and (time_mask_width_range is not None) + and (time_mask_width_ratio_range is not None) + ): + raise ValueError( + 'Either one of "time_mask_width_range" or ' + '"time_mask_width_ratio_range" can be used' + ) + super().__init__() + self.apply_time_warp = apply_time_warp + self.apply_freq_mask = apply_freq_mask + self.apply_time_mask = apply_time_mask + + if apply_time_warp: + self.time_warp = TimeWarp(window=time_warp_window, mode=time_warp_mode) + else: + self.time_warp = None + + if apply_freq_mask: + self.freq_mask = MaskAlongAxis( + dim="freq", + mask_width_range=freq_mask_width_range, + num_mask=num_freq_mask, + ) + else: + self.freq_mask = None + + if apply_time_mask: + if time_mask_width_range is not None: + self.time_mask = MaskAlongAxis( + dim="time", + mask_width_range=time_mask_width_range, + num_mask=num_time_mask, + ) + elif time_mask_width_ratio_range is not None: + self.time_mask = MaskAlongAxisVariableMaxWidth( + dim="time", + mask_width_ratio_range=time_mask_width_ratio_range, + num_mask=num_time_mask, + ) + else: + raise ValueError( + 'Either one of "time_mask_width_range" or ' + '"time_mask_width_ratio_range" should be used.' + ) + else: + self.time_mask = None + + def forward(self, x, x_lengths=None): + if self.time_warp is not None: + x, x_lengths = self.time_warp(x, x_lengths) + if self.freq_mask is not None: + x, x_lengths = self.freq_mask(x, x_lengths) + if self.time_mask is not None: + x, x_lengths = self.time_mask(x, x_lengths) + return x, x_lengths + + +@tables.register("specaug_classes", "SpecAugLFR") +class SpecAugLFR(nn.Module): + """Implementation of SpecAug. + lfr_rate：low frame rate + """ + + def __init__( + self, + apply_time_warp: bool = True, + time_warp_window: int = 5, + time_warp_mode: str = "bicubic", + apply_freq_mask: bool = True, + freq_mask_width_range: Union[int, Sequence[int]] = (0, 20), + num_freq_mask: int = 2, + lfr_rate: int = 0, + apply_time_mask: bool = True, + time_mask_width_range: Optional[Union[int, Sequence[int]]] = None, + time_mask_width_ratio_range: Optional[Union[float, Sequence[float]]] = None, + num_time_mask: int = 2, + ): + if not apply_time_warp and not apply_time_mask and not apply_freq_mask: + raise ValueError( + "Either one of time_warp, time_mask, or freq_mask should be applied" + ) + if ( + apply_time_mask + and (time_mask_width_range is not None) + and (time_mask_width_ratio_range is not None) + ): + raise ValueError( + 'Either one of "time_mask_width_range" or ' + '"time_mask_width_ratio_range" can be used' + ) + super().__init__() + self.apply_time_warp = apply_time_warp + self.apply_freq_mask = apply_freq_mask + self.apply_time_mask = apply_time_mask + + if apply_time_warp: + self.time_warp = TimeWarp(window=time_warp_window, mode=time_warp_mode) + else: + self.time_warp = None + + if apply_freq_mask: + self.freq_mask = MaskAlongAxisLFR( + dim="freq", + mask_width_range=freq_mask_width_range, + num_mask=num_freq_mask, + lfr_rate=lfr_rate + 1, + ) + + else: + self.freq_mask = None + + if apply_time_mask: + if time_mask_width_range is not None: + self.time_mask = MaskAlongAxisLFR( + dim="time", + mask_width_range=time_mask_width_range, + num_mask=num_time_mask, + lfr_rate=lfr_rate + 1, + ) + elif time_mask_width_ratio_range is not None: + self.time_mask = MaskAlongAxisVariableMaxWidth( + dim="time", + mask_width_ratio_range=time_mask_width_ratio_range, + num_mask=num_time_mask, + ) + else: + raise ValueError( + 'Either one of "time_mask_width_range" or ' + '"time_mask_width_ratio_range" should be used.' + ) + else: + self.time_mask = None + + def forward(self, x, x_lengths=None): + if self.time_warp is not None: + x, x_lengths = self.time_warp(x, x_lengths) + if self.freq_mask is not None: + x, x_lengths = self.freq_mask(x, x_lengths) + if self.time_mask is not None: + x, x_lengths = self.time_mask(x, x_lengths) + return x, x_lengths diff --git a/demo/Step-Audio-EditX/funasr_detach/models/specaug/time_warp.py b/demo/Step-Audio-EditX/funasr_detach/models/specaug/time_warp.py new file mode 100644 index 0000000000000000000000000000000000000000..13f64080bd9ceea537c3ceda39ce7a8d1af8de14 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/specaug/time_warp.py @@ -0,0 +1,89 @@ +"""Time warp module.""" + +import torch + +from funasr_detach.models.transformer.utils.nets_utils import pad_list + +DEFAULT_TIME_WARP_MODE = "bicubic" + + +def time_warp(x: torch.Tensor, window: int = 80, mode: str = DEFAULT_TIME_WARP_MODE): + """Time warping using torch.interpolate. + + Args: + x: (Batch, Time, Freq) + window: time warp parameter + mode: Interpolate mode + """ + + # bicubic supports 4D or more dimension tensor + org_size = x.size() + if x.dim() == 3: + # x: (Batch, Time, Freq) -> (Batch, 1, Time, Freq) + x = x[:, None] + + t = x.shape[2] + if t - window <= window: + return x.view(*org_size) + + center = torch.randint(window, t - window, (1,))[0] + warped = torch.randint(center - window, center + window, (1,))[0] + 1 + + # left: (Batch, Channel, warped, Freq) + # right: (Batch, Channel, time - warped, Freq) + left = torch.nn.functional.interpolate( + x[:, :, :center], (warped, x.shape[3]), mode=mode, align_corners=False + ) + right = torch.nn.functional.interpolate( + x[:, :, center:], (t - warped, x.shape[3]), mode=mode, align_corners=False + ) + + if x.requires_grad: + x = torch.cat([left, right], dim=-2) + else: + x[:, :, :warped] = left + x[:, :, warped:] = right + + return x.view(*org_size) + + +class TimeWarp(torch.nn.Module): + """Time warping using torch.interpolate. + + Args: + window: time warp parameter + mode: Interpolate mode + """ + + def __init__(self, window: int = 80, mode: str = DEFAULT_TIME_WARP_MODE): + super().__init__() + self.window = window + self.mode = mode + + def extra_repr(self): + return f"window={self.window}, mode={self.mode}" + + def forward(self, x: torch.Tensor, x_lengths: torch.Tensor = None): + """Forward function. + + Args: + x: (Batch, Time, Freq) + x_lengths: (Batch,) + """ + + if x_lengths is None or all(le == x_lengths[0] for le in x_lengths): + # Note that applying same warping for each sample + y = time_warp(x, window=self.window, mode=self.mode) + else: + # FIXME(kamo): I have no idea to batchify Timewarp + ys = [] + for i in range(x.size(0)): + _y = time_warp( + x[i][None, : x_lengths[i]], + window=self.window, + mode=self.mode, + )[0] + ys.append(_y) + y = pad_list(ys, 0.0) + + return y, x_lengths diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transducer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/transducer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transducer/beam_search_transducer.py b/demo/Step-Audio-EditX/funasr_detach/models/transducer/beam_search_transducer.py new file mode 100644 index 0000000000000000000000000000000000000000..8b70ea7cea34e0e8c16647543d8227e0d4a25eb0 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transducer/beam_search_transducer.py @@ -0,0 +1,706 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +import numpy as np +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union + +from funasr_detach.models.transducer.joint_network import JointNetwork + + +@dataclass +class Hypothesis: + """Default hypothesis definition for Transducer search algorithms. + + Args: + score: Total log-probability. + yseq: Label sequence as integer ID sequence. + dec_state: RNNDecoder or StatelessDecoder state. + ((N, 1, D_dec), (N, 1, D_dec) or None) or None + lm_state: RNNLM state. ((N, D_lm), (N, D_lm)) or None + + """ + + score: float + yseq: List[int] + dec_state: Optional[Tuple[torch.Tensor, Optional[torch.Tensor]]] = None + lm_state: Optional[Union[Dict[str, Any], List[Any]]] = None + + +@dataclass +class ExtendedHypothesis(Hypothesis): + """Extended hypothesis definition for NSC beam search and mAES. + + Args: + : Hypothesis dataclass arguments. + dec_out: Decoder output sequence. (B, D_dec) + lm_score: Log-probabilities of the LM for given label. (vocab_size) + + """ + + dec_out: torch.Tensor = None + lm_score: torch.Tensor = None + + +class BeamSearchTransducer: + """Beam search implementation for Transducer. + + Args: + decoder: Decoder module. + joint_network: Joint network module. + beam_size: Size of the beam. + lm: LM class. + lm_weight: LM weight for soft fusion. + search_type: Search algorithm to use during inference. + max_sym_exp: Number of maximum symbol expansions at each time step. (TSD) + u_max: Maximum expected target sequence length. (ALSD) + nstep: Number of maximum expansion steps at each time step. (mAES) + expansion_gamma: Allowed logp difference for prune-by-value method. (mAES) + expansion_beta: + Number of additional candidates for expanded hypotheses selection. (mAES) + score_norm: Normalize final scores by length. + nbest: Number of final hypothesis. + streaming: Whether to perform chunk-by-chunk beam search. + + """ + + def __init__( + self, + decoder, + joint_network: JointNetwork, + beam_size: int, + lm: Optional[torch.nn.Module] = None, + lm_weight: float = 0.1, + search_type: str = "default", + max_sym_exp: int = 3, + u_max: int = 50, + nstep: int = 2, + expansion_gamma: float = 2.3, + expansion_beta: int = 2, + score_norm: bool = False, + nbest: int = 1, + streaming: bool = False, + ) -> None: + """Construct a BeamSearchTransducer object.""" + super().__init__() + + self.decoder = decoder + self.joint_network = joint_network + + self.vocab_size = decoder.vocab_size + + assert beam_size <= self.vocab_size, ( + "beam_size (%d) should be smaller than or equal to vocabulary size (%d)." + % ( + beam_size, + self.vocab_size, + ) + ) + self.beam_size = beam_size + + if search_type == "default": + self.search_algorithm = self.default_beam_search + elif search_type == "tsd": + assert max_sym_exp > 1, "max_sym_exp (%d) should be greater than one." % ( + max_sym_exp + ) + self.max_sym_exp = max_sym_exp + + self.search_algorithm = self.time_sync_decoding + elif search_type == "alsd": + assert not streaming, "ALSD is not available in streaming mode." + + assert u_max >= 0, "u_max should be a positive integer, a portion of max_T." + self.u_max = u_max + + self.search_algorithm = self.align_length_sync_decoding + elif search_type == "maes": + assert self.vocab_size >= beam_size + expansion_beta, ( + "beam_size (%d) + expansion_beta (%d) " + " should be smaller than or equal to vocab size (%d)." + % (beam_size, expansion_beta, self.vocab_size) + ) + self.max_candidates = beam_size + expansion_beta + + self.nstep = nstep + self.expansion_gamma = expansion_gamma + + self.search_algorithm = self.modified_adaptive_expansion_search + else: + raise NotImplementedError( + "Specified search type (%s) is not supported." % search_type + ) + + self.use_lm = lm is not None + + if self.use_lm: + assert hasattr(lm, "rnn_type"), "Transformer LM is currently not supported." + + self.sos = self.vocab_size - 1 + + self.lm = lm + self.lm_weight = lm_weight + + self.score_norm = score_norm + self.nbest = nbest + + self.reset_inference_cache() + + def __call__( + self, + enc_out: torch.Tensor, + is_final: bool = True, + ) -> List[Hypothesis]: + """Perform beam search. + + Args: + enc_out: Encoder output sequence. (T, D_enc) + is_final: Whether enc_out is the final chunk of data. + + Returns: + nbest_hyps: N-best decoding results + + """ + self.decoder.set_device(enc_out.device) + + hyps = self.search_algorithm(enc_out) + + if is_final: + self.reset_inference_cache() + + return self.sort_nbest(hyps) + + self.search_cache = hyps + + return hyps + + def reset_inference_cache(self) -> None: + """Reset cache for decoder scoring and streaming.""" + self.decoder.score_cache = {} + self.search_cache = None + + def sort_nbest(self, hyps: List[Hypothesis]) -> List[Hypothesis]: + """Sort in-place hypotheses by score or score given sequence length. + + Args: + hyps: Hypothesis. + + Return: + hyps: Sorted hypothesis. + + """ + if self.score_norm: + hyps.sort(key=lambda x: x.score / len(x.yseq), reverse=True) + else: + hyps.sort(key=lambda x: x.score, reverse=True) + + return hyps[: self.nbest] + + def recombine_hyps(self, hyps: List[Hypothesis]) -> List[Hypothesis]: + """Recombine hypotheses with same label ID sequence. + + Args: + hyps: Hypotheses. + + Returns: + final: Recombined hypotheses. + + """ + final = {} + + for hyp in hyps: + str_yseq = "_".join(map(str, hyp.yseq)) + + if str_yseq in final: + final[str_yseq].score = np.logaddexp(final[str_yseq].score, hyp.score) + else: + final[str_yseq] = hyp + + return [*final.values()] + + def select_k_expansions( + self, + hyps: List[ExtendedHypothesis], + topk_idx: torch.Tensor, + topk_logp: torch.Tensor, + ) -> List[ExtendedHypothesis]: + """Return K hypotheses candidates for expansion from a list of hypothesis. + + K candidates are selected according to the extended hypotheses probabilities + and a prune-by-value method. Where K is equal to beam_size + beta. + + Args: + hyps: Hypotheses. + topk_idx: Indices of candidates hypothesis. + topk_logp: Log-probabilities of candidates hypothesis. + + Returns: + k_expansions: Best K expansion hypotheses candidates. + + """ + k_expansions = [] + + for i, hyp in enumerate(hyps): + hyp_i = [ + (int(k), hyp.score + float(v)) + for k, v in zip(topk_idx[i], topk_logp[i]) + ] + k_best_exp = max(hyp_i, key=lambda x: x[1])[1] + + k_expansions.append( + sorted( + filter( + lambda x: (k_best_exp - self.expansion_gamma) <= x[1], hyp_i + ), + key=lambda x: x[1], + reverse=True, + ) + ) + + return k_expansions + + def create_lm_batch_inputs(self, hyps_seq: List[List[int]]) -> torch.Tensor: + """Make batch of inputs with left padding for LM scoring. + + Args: + hyps_seq: Hypothesis sequences. + + Returns: + : Padded batch of sequences. + + """ + max_len = max([len(h) for h in hyps_seq]) + + return torch.LongTensor( + [[self.sos] + ([0] * (max_len - len(h))) + h[1:] for h in hyps_seq], + device=self.decoder.device, + ) + + def default_beam_search(self, enc_out: torch.Tensor) -> List[Hypothesis]: + """Beam search implementation without prefix search. + + Modified from https://arxiv.org/pdf/1211.3711.pdf + + Args: + enc_out: Encoder output sequence. (T, D) + + Returns: + nbest_hyps: N-best hypothesis. + + """ + beam_k = min(self.beam_size, (self.vocab_size - 1)) + max_t = len(enc_out) + + if self.search_cache is not None: + kept_hyps = self.search_cache + else: + kept_hyps = [ + Hypothesis( + score=0.0, + yseq=[0], + dec_state=self.decoder.init_state(1), + ) + ] + + for t in range(max_t): + hyps = kept_hyps + kept_hyps = [] + + while True: + max_hyp = max(hyps, key=lambda x: x.score) + hyps.remove(max_hyp) + + label = torch.full( + (1, 1), + max_hyp.yseq[-1], + dtype=torch.long, + device=self.decoder.device, + ) + dec_out, state = self.decoder.score( + label, + max_hyp.yseq, + max_hyp.dec_state, + ) + + logp = torch.log_softmax( + self.joint_network(enc_out[t : t + 1, :], dec_out), + dim=-1, + ).squeeze(0) + top_k = logp[1:].topk(beam_k, dim=-1) + + kept_hyps.append( + Hypothesis( + score=(max_hyp.score + float(logp[0:1])), + yseq=max_hyp.yseq, + dec_state=max_hyp.dec_state, + lm_state=max_hyp.lm_state, + ) + ) + + if self.use_lm: + lm_scores, lm_state = self.lm.score( + torch.LongTensor( + [self.sos] + max_hyp.yseq[1:], device=self.decoder.device + ), + max_hyp.lm_state, + None, + ) + else: + lm_state = max_hyp.lm_state + + for logp, k in zip(*top_k): + score = max_hyp.score + float(logp) + + if self.use_lm: + score += self.lm_weight * lm_scores[k + 1] + + hyps.append( + Hypothesis( + score=score, + yseq=max_hyp.yseq + [int(k + 1)], + dec_state=state, + lm_state=lm_state, + ) + ) + + hyps_max = float(max(hyps, key=lambda x: x.score).score) + kept_most_prob = sorted( + [hyp for hyp in kept_hyps if hyp.score > hyps_max], + key=lambda x: x.score, + ) + if len(kept_most_prob) >= self.beam_size: + kept_hyps = kept_most_prob + break + + return kept_hyps + + def align_length_sync_decoding( + self, + enc_out: torch.Tensor, + ) -> List[Hypothesis]: + """Alignment-length synchronous beam search implementation. + + Based on https://ieeexplore.ieee.org/document/9053040 + + Args: + h: Encoder output sequences. (T, D) + + Returns: + nbest_hyps: N-best hypothesis. + + """ + t_max = int(enc_out.size(0)) + u_max = min(self.u_max, (t_max - 1)) + + B = [Hypothesis(yseq=[0], score=0.0, dec_state=self.decoder.init_state(1))] + final = [] + + if self.use_lm: + B[0].lm_state = self.lm.zero_state() + + for i in range(t_max + u_max): + A = [] + + B_ = [] + B_enc_out = [] + for hyp in B: + u = len(hyp.yseq) - 1 + t = i - u + + if t > (t_max - 1): + continue + + B_.append(hyp) + B_enc_out.append((t, enc_out[t])) + + if B_: + beam_enc_out = torch.stack([b[1] for b in B_enc_out]) + beam_dec_out, beam_state = self.decoder.batch_score(B_) + + beam_logp = torch.log_softmax( + self.joint_network(beam_enc_out, beam_dec_out), + dim=-1, + ) + beam_topk = beam_logp[:, 1:].topk(self.beam_size, dim=-1) + + if self.use_lm: + beam_lm_scores, beam_lm_states = self.lm.batch_score( + self.create_lm_batch_inputs([b.yseq for b in B_]), + [b.lm_state for b in B_], + None, + ) + + for i, hyp in enumerate(B_): + new_hyp = Hypothesis( + score=(hyp.score + float(beam_logp[i, 0])), + yseq=hyp.yseq[:], + dec_state=hyp.dec_state, + lm_state=hyp.lm_state, + ) + + A.append(new_hyp) + + if B_enc_out[i][0] == (t_max - 1): + final.append(new_hyp) + + for logp, k in zip(beam_topk[0][i], beam_topk[1][i] + 1): + new_hyp = Hypothesis( + score=(hyp.score + float(logp)), + yseq=(hyp.yseq[:] + [int(k)]), + dec_state=self.decoder.select_state(beam_state, i), + lm_state=hyp.lm_state, + ) + + if self.use_lm: + new_hyp.score += self.lm_weight * beam_lm_scores[i, k] + new_hyp.lm_state = beam_lm_states[i] + + A.append(new_hyp) + + B = sorted(A, key=lambda x: x.score, reverse=True)[: self.beam_size] + B = self.recombine_hyps(B) + + if final: + return final + + return B + + def time_sync_decoding(self, enc_out: torch.Tensor) -> List[Hypothesis]: + """Time synchronous beam search implementation. + + Based on https://ieeexplore.ieee.org/document/9053040 + + Args: + enc_out: Encoder output sequence. (T, D) + + Returns: + nbest_hyps: N-best hypothesis. + + """ + if self.search_cache is not None: + B = self.search_cache + else: + B = [ + Hypothesis( + yseq=[0], + score=0.0, + dec_state=self.decoder.init_state(1), + ) + ] + + if self.use_lm: + B[0].lm_state = self.lm.zero_state() + + for enc_out_t in enc_out: + A = [] + C = B + + enc_out_t = enc_out_t.unsqueeze(0) + + for v in range(self.max_sym_exp): + D = [] + + beam_dec_out, beam_state = self.decoder.batch_score(C) + + beam_logp = torch.log_softmax( + self.joint_network(enc_out_t, beam_dec_out), + dim=-1, + ) + beam_topk = beam_logp[:, 1:].topk(self.beam_size, dim=-1) + + seq_A = [h.yseq for h in A] + + for i, hyp in enumerate(C): + if hyp.yseq not in seq_A: + A.append( + Hypothesis( + score=(hyp.score + float(beam_logp[i, 0])), + yseq=hyp.yseq[:], + dec_state=hyp.dec_state, + lm_state=hyp.lm_state, + ) + ) + else: + dict_pos = seq_A.index(hyp.yseq) + + A[dict_pos].score = np.logaddexp( + A[dict_pos].score, (hyp.score + float(beam_logp[i, 0])) + ) + + if v < (self.max_sym_exp - 1): + if self.use_lm: + beam_lm_scores, beam_lm_states = self.lm.batch_score( + self.create_lm_batch_inputs([c.yseq for c in C]), + [c.lm_state for c in C], + None, + ) + + for i, hyp in enumerate(C): + for logp, k in zip(beam_topk[0][i], beam_topk[1][i] + 1): + new_hyp = Hypothesis( + score=(hyp.score + float(logp)), + yseq=(hyp.yseq + [int(k)]), + dec_state=self.decoder.select_state(beam_state, i), + lm_state=hyp.lm_state, + ) + + if self.use_lm: + new_hyp.score += self.lm_weight * beam_lm_scores[i, k] + new_hyp.lm_state = beam_lm_states[i] + + D.append(new_hyp) + + C = sorted(D, key=lambda x: x.score, reverse=True)[: self.beam_size] + + B = sorted(A, key=lambda x: x.score, reverse=True)[: self.beam_size] + + return B + + def modified_adaptive_expansion_search( + self, + enc_out: torch.Tensor, + ) -> List[ExtendedHypothesis]: + """Modified version of Adaptive Expansion Search (mAES). + + Based on AES (https://ieeexplore.ieee.org/document/9250505) and + NSC (https://arxiv.org/abs/2201.05420). + + Args: + enc_out: Encoder output sequence. (T, D_enc) + + Returns: + nbest_hyps: N-best hypothesis. + + """ + if self.search_cache is not None: + kept_hyps = self.search_cache + else: + init_tokens = [ + ExtendedHypothesis( + yseq=[0], + score=0.0, + dec_state=self.decoder.init_state(1), + ) + ] + + beam_dec_out, beam_state = self.decoder.batch_score( + init_tokens, + ) + + if self.use_lm: + beam_lm_scores, beam_lm_states = self.lm.batch_score( + self.create_lm_batch_inputs([h.yseq for h in init_tokens]), + [h.lm_state for h in init_tokens], + None, + ) + + lm_state = beam_lm_states[0] + lm_score = beam_lm_scores[0] + else: + lm_state = None + lm_score = None + + kept_hyps = [ + ExtendedHypothesis( + yseq=[0], + score=0.0, + dec_state=self.decoder.select_state(beam_state, 0), + dec_out=beam_dec_out[0], + lm_state=lm_state, + lm_score=lm_score, + ) + ] + + for enc_out_t in enc_out: + hyps = kept_hyps + kept_hyps = [] + + beam_enc_out = enc_out_t.unsqueeze(0) + + list_b = [] + for n in range(self.nstep): + beam_dec_out = torch.stack([h.dec_out for h in hyps]) + + beam_logp, beam_idx = torch.log_softmax( + self.joint_network(beam_enc_out, beam_dec_out), + dim=-1, + ).topk(self.max_candidates, dim=-1) + + k_expansions = self.select_k_expansions(hyps, beam_idx, beam_logp) + + list_exp = [] + for i, hyp in enumerate(hyps): + for k, new_score in k_expansions[i]: + new_hyp = ExtendedHypothesis( + yseq=hyp.yseq[:], + score=new_score, + dec_out=hyp.dec_out, + dec_state=hyp.dec_state, + lm_state=hyp.lm_state, + lm_score=hyp.lm_score, + ) + + if k == 0: + list_b.append(new_hyp) + else: + new_hyp.yseq.append(int(k)) + + if self.use_lm: + new_hyp.score += self.lm_weight * float(hyp.lm_score[k]) + + list_exp.append(new_hyp) + + if not list_exp: + kept_hyps = sorted( + self.recombine_hyps(list_b), key=lambda x: x.score, reverse=True + )[: self.beam_size] + + break + else: + beam_dec_out, beam_state = self.decoder.batch_score( + list_exp, + ) + + if self.use_lm: + beam_lm_scores, beam_lm_states = self.lm.batch_score( + self.create_lm_batch_inputs([h.yseq for h in list_exp]), + [h.lm_state for h in list_exp], + None, + ) + + if n < (self.nstep - 1): + for i, hyp in enumerate(list_exp): + hyp.dec_out = beam_dec_out[i] + hyp.dec_state = self.decoder.select_state(beam_state, i) + + if self.use_lm: + hyp.lm_state = beam_lm_states[i] + hyp.lm_score = beam_lm_scores[i] + + hyps = list_exp[:] + else: + beam_logp = torch.log_softmax( + self.joint_network(beam_enc_out, beam_dec_out), + dim=-1, + ) + + for i, hyp in enumerate(list_exp): + hyp.score += float(beam_logp[i, 0]) + + hyp.dec_out = beam_dec_out[i] + hyp.dec_state = self.decoder.select_state(beam_state, i) + + if self.use_lm: + hyp.lm_state = beam_lm_states[i] + hyp.lm_score = beam_lm_scores[i] + + kept_hyps = sorted( + self.recombine_hyps(list_b + list_exp), + key=lambda x: x.score, + reverse=True, + )[: self.beam_size] + + return kept_hyps diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transducer/joint_network.py b/demo/Step-Audio-EditX/funasr_detach/models/transducer/joint_network.py new file mode 100644 index 0000000000000000000000000000000000000000..e98b2d01f53744620d18d85ab8038a19cb9248a0 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transducer/joint_network.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch + +from funasr_detach.register import tables +from funasr_detach.models.transformer.utils.nets_utils import get_activation + + +@tables.register("joint_network_classes", "joint_network") +class JointNetwork(torch.nn.Module): + """Transducer joint network module. + + Args: + output_size: Output size. + encoder_size: Encoder output size. + decoder_size: Decoder output size.. + joint_space_size: Joint space size. + joint_act_type: Type of activation for joint network. + **activation_parameters: Parameters for the activation function. + + """ + + def __init__( + self, + output_size: int, + encoder_size: int, + decoder_size: int, + joint_space_size: int = 256, + joint_activation_type: str = "tanh", + ) -> None: + """Construct a JointNetwork object.""" + super().__init__() + + self.lin_enc = torch.nn.Linear(encoder_size, joint_space_size) + self.lin_dec = torch.nn.Linear(decoder_size, joint_space_size, bias=False) + + self.lin_out = torch.nn.Linear(joint_space_size, output_size) + + self.joint_activation = get_activation(joint_activation_type) + + def forward( + self, + enc_out: torch.Tensor, + dec_out: torch.Tensor, + project_input: bool = True, + ) -> torch.Tensor: + """Joint computation of encoder and decoder hidden state sequences. + + Args: + enc_out: Expanded encoder output state sequences (B, T, 1, D_enc) + dec_out: Expanded decoder output state sequences (B, 1, U, D_dec) + + Returns: + joint_out: Joint output state sequences. (B, T, U, D_out) + + """ + if project_input: + joint_out = self.joint_activation( + self.lin_enc(enc_out) + self.lin_dec(dec_out) + ) + else: + joint_out = self.joint_activation(enc_out + dec_out) + return self.lin_out(joint_out) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transducer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/transducer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..cd843129fd45ec0ed823282f3500f0408633ab08 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transducer/model.py @@ -0,0 +1,558 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import time +import torch +import logging +from contextlib import contextmanager +from typing import Dict, Optional, Tuple +from distutils.version import LooseVersion + +from funasr_detach.register import tables +from funasr_detach.utils import postprocess_utils +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.transformer.scorers.ctc import CTCPrefixScorer +from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss +from funasr_detach.models.transformer.scorers.length_bonus import LengthBonus +from funasr_detach.models.transformer.utils.nets_utils import get_transducer_task_io +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank +from funasr_detach.models.transducer.beam_search_transducer import BeamSearchTransducer + + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +@tables.register("model_classes", "Transducer") +class Transducer(torch.nn.Module): + def __init__( + self, + frontend: Optional[str] = None, + frontend_conf: Optional[Dict] = None, + specaug: Optional[str] = None, + specaug_conf: Optional[Dict] = None, + normalize: str = None, + normalize_conf: Optional[Dict] = None, + encoder: str = None, + encoder_conf: Optional[Dict] = None, + decoder: str = None, + decoder_conf: Optional[Dict] = None, + joint_network: str = None, + joint_network_conf: Optional[Dict] = None, + transducer_weight: float = 1.0, + fastemit_lambda: float = 0.0, + auxiliary_ctc_weight: float = 0.0, + auxiliary_ctc_dropout_rate: float = 0.0, + auxiliary_lm_loss_weight: float = 0.0, + auxiliary_lm_loss_smoothing: float = 0.0, + input_size: int = 80, + vocab_size: int = -1, + ignore_id: int = -1, + blank_id: int = 0, + sos: int = 1, + eos: int = 2, + lsm_weight: float = 0.0, + length_normalized_loss: bool = False, + # report_cer: bool = True, + # report_wer: bool = True, + # sym_space: str = "", + # sym_blank: str = "", + # extract_feats_in_collect_stats: bool = True, + share_embedding: bool = False, + # preencoder: Optional[AbsPreEncoder] = None, + # postencoder: Optional[AbsPostEncoder] = None, + **kwargs, + ): + + super().__init__() + + if specaug is not None: + specaug_class = tables.specaug_classes.get(specaug) + specaug = specaug_class(**specaug_conf) + if normalize is not None: + normalize_class = tables.normalize_classes.get(normalize) + normalize = normalize_class(**normalize_conf) + encoder_class = tables.encoder_classes.get(encoder) + encoder = encoder_class(input_size=input_size, **encoder_conf) + encoder_output_size = encoder.output_size() + + decoder_class = tables.decoder_classes.get(decoder) + decoder = decoder_class( + vocab_size=vocab_size, + **decoder_conf, + ) + decoder_output_size = decoder.output_size + + joint_network_class = tables.joint_network_classes.get(joint_network) + joint_network = joint_network_class( + vocab_size, + encoder_output_size, + decoder_output_size, + **joint_network_conf, + ) + + self.criterion_transducer = None + self.error_calculator = None + + self.use_auxiliary_ctc = auxiliary_ctc_weight > 0 + self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0 + + if self.use_auxiliary_ctc: + self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size) + self.ctc_dropout_rate = auxiliary_ctc_dropout_rate + + if self.use_auxiliary_lm_loss: + self.lm_lin = torch.nn.Linear(decoder.output_size, vocab_size) + self.lm_loss_smoothing = auxiliary_lm_loss_smoothing + + self.transducer_weight = transducer_weight + self.fastemit_lambda = fastemit_lambda + + self.auxiliary_ctc_weight = auxiliary_ctc_weight + self.auxiliary_lm_loss_weight = auxiliary_lm_loss_weight + self.blank_id = blank_id + self.sos = sos if sos is not None else vocab_size - 1 + self.eos = eos if eos is not None else vocab_size - 1 + self.vocab_size = vocab_size + self.ignore_id = ignore_id + self.frontend = frontend + self.specaug = specaug + self.normalize = normalize + self.encoder = encoder + self.decoder = decoder + self.joint_network = joint_network + + self.criterion_att = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + self.length_normalized_loss = length_normalized_loss + self.beam_search = None + self.ctc = None + self.ctc_weight = 0.0 + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + if len(text_lengths.size()) > 1: + text_lengths = text_lengths[:, 0] + if len(speech_lengths.size()) > 1: + speech_lengths = speech_lengths[:, 0] + + batch_size = speech.shape[0] + # 1. Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + if ( + hasattr(self.encoder, "overlap_chunk_cls") + and self.encoder.overlap_chunk_cls is not None + ): + encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk( + encoder_out, encoder_out_lens, chunk_outs=None + ) + # 2. Transducer-related I/O preparation + decoder_in, target, t_len, u_len = get_transducer_task_io( + text, + encoder_out_lens, + ignore_id=self.ignore_id, + ) + + # 3. Decoder + self.decoder.set_device(encoder_out.device) + decoder_out = self.decoder(decoder_in, u_len) + + # 4. Joint Network + joint_out = self.joint_network( + encoder_out.unsqueeze(2), decoder_out.unsqueeze(1) + ) + + # 5. Losses + loss_trans, cer_trans, wer_trans = self._calc_transducer_loss( + encoder_out, + joint_out, + target, + t_len, + u_len, + ) + + loss_ctc, loss_lm = 0.0, 0.0 + + if self.use_auxiliary_ctc: + loss_ctc = self._calc_ctc_loss( + encoder_out, + target, + t_len, + u_len, + ) + + if self.use_auxiliary_lm_loss: + loss_lm = self._calc_lm_loss(decoder_out, target) + + loss = ( + self.transducer_weight * loss_trans + + self.auxiliary_ctc_weight * loss_ctc + + self.auxiliary_lm_loss_weight * loss_lm + ) + + stats = dict( + loss=loss.detach(), + loss_transducer=loss_trans.detach(), + aux_ctc_loss=loss_ctc.detach() if loss_ctc > 0.0 else None, + aux_lm_loss=loss_lm.detach() if loss_lm > 0.0 else None, + cer_transducer=cer_trans, + wer_transducer=wer_trans, + ) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + + return loss, stats, weight + + def encode( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Frontend + Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + ind: int + """ + with autocast(False): + + # Data augmentation + if self.specaug is not None and self.training: + speech, speech_lengths = self.specaug(speech, speech_lengths) + + # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + speech, speech_lengths = self.normalize(speech, speech_lengths) + + # Forward encoder + # feats: (Batch, Length, Dim) + # -> encoder_out: (Batch, Length2, Dim2) + encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths) + intermediate_outs = None + if isinstance(encoder_out, tuple): + intermediate_outs = encoder_out[1] + encoder_out = encoder_out[0] + + if intermediate_outs is not None: + return (encoder_out, intermediate_outs), encoder_out_lens + + return encoder_out, encoder_out_lens + + def _calc_transducer_loss( + self, + encoder_out: torch.Tensor, + joint_out: torch.Tensor, + target: torch.Tensor, + t_len: torch.Tensor, + u_len: torch.Tensor, + ) -> Tuple[torch.Tensor, Optional[float], Optional[float]]: + """Compute Transducer loss. + + Args: + encoder_out: Encoder output sequences. (B, T, D_enc) + joint_out: Joint Network output sequences (B, T, U, D_joint) + target: Target label ID sequences. (B, L) + t_len: Encoder output sequences lengths. (B,) + u_len: Target label ID sequences lengths. (B,) + + Return: + loss_transducer: Transducer loss value. + cer_transducer: Character error rate for Transducer. + wer_transducer: Word Error Rate for Transducer. + + """ + if self.criterion_transducer is None: + try: + from warp_rnnt import rnnt_loss as RNNTLoss + + self.criterion_transducer = RNNTLoss + + except ImportError: + logging.error( + "warp-rnnt was not installed." + "Please consult the installation documentation." + ) + exit(1) + + log_probs = torch.log_softmax(joint_out, dim=-1) + + loss_transducer = self.criterion_transducer( + log_probs, + target, + t_len, + u_len, + reduction="mean", + blank=self.blank_id, + fastemit_lambda=self.fastemit_lambda, + gather=True, + ) + + if not self.training and (self.report_cer or self.report_wer): + if self.error_calculator is None: + from funasr_detach.metrics import ( + ErrorCalculatorTransducer as ErrorCalculator, + ) + + self.error_calculator = ErrorCalculator( + self.decoder, + self.joint_network, + self.token_list, + self.sym_space, + self.sym_blank, + report_cer=self.report_cer, + report_wer=self.report_wer, + ) + + cer_transducer, wer_transducer = self.error_calculator( + encoder_out, target, t_len + ) + + return loss_transducer, cer_transducer, wer_transducer + + return loss_transducer, None, None + + def _calc_ctc_loss( + self, + encoder_out: torch.Tensor, + target: torch.Tensor, + t_len: torch.Tensor, + u_len: torch.Tensor, + ) -> torch.Tensor: + """Compute CTC loss. + + Args: + encoder_out: Encoder output sequences. (B, T, D_enc) + target: Target label ID sequences. (B, L) + t_len: Encoder output sequences lengths. (B,) + u_len: Target label ID sequences lengths. (B,) + + Return: + loss_ctc: CTC loss value. + + """ + ctc_in = self.ctc_lin( + torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate) + ) + ctc_in = torch.log_softmax(ctc_in.transpose(0, 1), dim=-1) + + target_mask = target != 0 + ctc_target = target[target_mask].cpu() + + with torch.backends.cudnn.flags(deterministic=True): + loss_ctc = torch.nn.functional.ctc_loss( + ctc_in, + ctc_target, + t_len, + u_len, + zero_infinity=True, + reduction="sum", + ) + loss_ctc /= target.size(0) + + return loss_ctc + + def _calc_lm_loss( + self, + decoder_out: torch.Tensor, + target: torch.Tensor, + ) -> torch.Tensor: + """Compute LM loss. + + Args: + decoder_out: Decoder output sequences. (B, U, D_dec) + target: Target label ID sequences. (B, L) + + Return: + loss_lm: LM loss value. + + """ + lm_loss_in = self.lm_lin(decoder_out[:, :-1, :]).view(-1, self.vocab_size) + lm_target = target.view(-1).type(torch.int64) + + with torch.no_grad(): + true_dist = lm_loss_in.clone() + true_dist.fill_(self.lm_loss_smoothing / (self.vocab_size - 1)) + + # Ignore blank ID (0) + ignore = lm_target == 0 + lm_target = lm_target.masked_fill(ignore, 0) + + true_dist.scatter_(1, lm_target.unsqueeze(1), (1 - self.lm_loss_smoothing)) + + loss_lm = torch.nn.functional.kl_div( + torch.log_softmax(lm_loss_in, dim=1), + true_dist, + reduction="none", + ) + loss_lm = loss_lm.masked_fill(ignore.unsqueeze(1), 0).sum() / decoder_out.size( + 0 + ) + + return loss_lm + + def init_beam_search( + self, + **kwargs, + ): + + # 1. Build ASR model + scorers = {} + + if self.ctc != None: + ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos) + scorers.update(ctc=ctc) + token_list = kwargs.get("token_list") + scorers.update( + length_bonus=LengthBonus(len(token_list)), + ) + + # 3. Build ngram model + # ngram is not supported now + ngram = None + scorers["ngram"] = ngram + + beam_search = BeamSearchTransducer( + self.decoder, + self.joint_network, + kwargs.get("beam_size", 2), + nbest=1, + ) + # beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval() + # for scorer in scorers.values(): + # if isinstance(scorer, torch.nn.Module): + # scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval() + self.beam_search = beam_search + + def inference( + self, + data_in: list, + data_lengths: list = None, + key: list = None, + tokenizer=None, + **kwargs, + ): + + if kwargs.get("batch_size", 1) > 1: + raise NotImplementedError("batch decoding is not implemented") + + # init beamsearch + is_use_ctc = ( + kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None + ) + is_use_lm = ( + kwargs.get("lm_weight", 0.0) > 0.00001 + and kwargs.get("lm_file", None) is not None + ) + # if self.beam_search is None and (is_use_lm or is_use_ctc): + logging.info("enable beam_search") + self.init_beam_search(**kwargs) + self.nbest = kwargs.get("nbest", 1) + + meta_data = {} + # extract fbank feats + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000) + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths = extract_fbank( + audio_sample_list, + data_type=kwargs.get("data_type", "sound"), + frontend=self.frontend, + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() + * self.frontend.frame_shift + * self.frontend.lfr_n + / 1000 + ) + + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + + # Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + # c. Passed the encoder result and the beam search + nbest_hyps = self.beam_search(encoder_out[0], is_final=True) + nbest_hyps = nbest_hyps[: self.nbest] + + results = [] + b, n, d = encoder_out.size() + for i in range(b): + + for nbest_idx, hyp in enumerate(nbest_hyps): + ibest_writer = None + if kwargs.get("output_dir") is not None: + if not hasattr(self, "writer"): + self.writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"] + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq # [1:last_pos] + else: + token_int = hyp.yseq # [1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list( + filter( + lambda x: x != self.eos + and x != self.sos + and x != self.blank_id, + token_int, + ) + ) + + # Change integer-ids to tokens + token = tokenizer.ids2tokens(token_int) + text = tokenizer.tokens2text(token) + + text_postprocessed, _ = postprocess_utils.sentence_postprocess(token) + result_i = { + "key": key[i], + "token": token, + "text": text, + "text_postprocessed": text_postprocessed, + } + results.append(result_i) + + if ibest_writer is not None: + ibest_writer["token"][key[i]] = " ".join(token) + ibest_writer["text"][key[i]] = text + ibest_writer["text_postprocessed"][key[i]] = text_postprocessed + + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transducer/rnn_decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/transducer/rnn_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..711fea543ce13827531576db8a7c2c3a500663b3 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transducer/rnn_decoder.py @@ -0,0 +1,341 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +import random +import numpy as np +import torch.nn as nn +import torch.nn.functional as F + +from funasr_detach.register import tables +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.utils.nets_utils import to_device +from funasr_detach.models.language_model.rnn.attentions import initial_att + + +def build_attention_list( + eprojs: int, + dunits: int, + atype: str = "location", + num_att: int = 1, + num_encs: int = 1, + aheads: int = 4, + adim: int = 320, + awin: int = 5, + aconv_chans: int = 10, + aconv_filts: int = 100, + han_mode: bool = False, + han_type=None, + han_heads: int = 4, + han_dim: int = 320, + han_conv_chans: int = -1, + han_conv_filts: int = 100, + han_win: int = 5, +): + + att_list = torch.nn.ModuleList() + if num_encs == 1: + for i in range(num_att): + att = initial_att( + atype, + eprojs, + dunits, + aheads, + adim, + awin, + aconv_chans, + aconv_filts, + ) + att_list.append(att) + elif num_encs > 1: # no multi-speaker mode + if han_mode: + att = initial_att( + han_type, + eprojs, + dunits, + han_heads, + han_dim, + han_win, + han_conv_chans, + han_conv_filts, + han_mode=True, + ) + return att + else: + att_list = torch.nn.ModuleList() + for idx in range(num_encs): + att = initial_att( + atype[idx], + eprojs, + dunits, + aheads[idx], + adim[idx], + awin[idx], + aconv_chans[idx], + aconv_filts[idx], + ) + att_list.append(att) + else: + raise ValueError( + "Number of encoders needs to be more than one. {}".format(num_encs) + ) + return att_list + + +@tables.register("decoder_classes", "rnn_decoder") +class RNNDecoder(nn.Module): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + rnn_type: str = "lstm", + num_layers: int = 1, + hidden_size: int = 320, + sampling_probability: float = 0.0, + dropout: float = 0.0, + context_residual: bool = False, + replace_sos: bool = False, + num_encs: int = 1, + att_conf: dict = None, + ): + # FIXME(kamo): The parts of num_spk should be refactored more more more + if rnn_type not in {"lstm", "gru"}: + raise ValueError(f"Not supported: rnn_type={rnn_type}") + + super().__init__() + eprojs = encoder_output_size + self.dtype = rnn_type + self.dunits = hidden_size + self.dlayers = num_layers + self.context_residual = context_residual + self.sos = vocab_size - 1 + self.eos = vocab_size - 1 + self.odim = vocab_size + self.sampling_probability = sampling_probability + self.dropout = dropout + self.num_encs = num_encs + + # for multilingual translation + self.replace_sos = replace_sos + + self.embed = torch.nn.Embedding(vocab_size, hidden_size) + self.dropout_emb = torch.nn.Dropout(p=dropout) + + self.decoder = torch.nn.ModuleList() + self.dropout_dec = torch.nn.ModuleList() + self.decoder += [ + ( + torch.nn.LSTMCell(hidden_size + eprojs, hidden_size) + if self.dtype == "lstm" + else torch.nn.GRUCell(hidden_size + eprojs, hidden_size) + ) + ] + self.dropout_dec += [torch.nn.Dropout(p=dropout)] + for _ in range(1, self.dlayers): + self.decoder += [ + ( + torch.nn.LSTMCell(hidden_size, hidden_size) + if self.dtype == "lstm" + else torch.nn.GRUCell(hidden_size, hidden_size) + ) + ] + self.dropout_dec += [torch.nn.Dropout(p=dropout)] + # NOTE: dropout is applied only for the vertical connections + # see https://arxiv.org/pdf/1409.2329.pdf + + if context_residual: + self.output = torch.nn.Linear(hidden_size + eprojs, vocab_size) + else: + self.output = torch.nn.Linear(hidden_size, vocab_size) + + self.att_list = build_attention_list( + eprojs=eprojs, dunits=hidden_size, **att_conf + ) + + def zero_state(self, hs_pad): + return hs_pad.new_zeros(hs_pad.size(0), self.dunits) + + def rnn_forward(self, ey, z_list, c_list, z_prev, c_prev): + if self.dtype == "lstm": + z_list[0], c_list[0] = self.decoder[0](ey, (z_prev[0], c_prev[0])) + for i in range(1, self.dlayers): + z_list[i], c_list[i] = self.decoder[i]( + self.dropout_dec[i - 1](z_list[i - 1]), + (z_prev[i], c_prev[i]), + ) + else: + z_list[0] = self.decoder[0](ey, z_prev[0]) + for i in range(1, self.dlayers): + z_list[i] = self.decoder[i]( + self.dropout_dec[i - 1](z_list[i - 1]), z_prev[i] + ) + return z_list, c_list + + def forward(self, hs_pad, hlens, ys_in_pad, ys_in_lens, strm_idx=0): + # to support mutiple encoder asr mode, in single encoder mode, + # convert torch.Tensor to List of torch.Tensor + if self.num_encs == 1: + hs_pad = [hs_pad] + hlens = [hlens] + + # attention index for the attention module + # in SPA (speaker parallel attention), + # att_idx is used to select attention module. In other cases, it is 0. + att_idx = min(strm_idx, len(self.att_list) - 1) + + # hlens should be list of list of integer + hlens = [list(map(int, hlens[idx])) for idx in range(self.num_encs)] + + # get dim, length info + olength = ys_in_pad.size(1) + + # initialization + c_list = [self.zero_state(hs_pad[0])] + z_list = [self.zero_state(hs_pad[0])] + for _ in range(1, self.dlayers): + c_list.append(self.zero_state(hs_pad[0])) + z_list.append(self.zero_state(hs_pad[0])) + z_all = [] + if self.num_encs == 1: + att_w = None + self.att_list[att_idx].reset() # reset pre-computation of h + else: + att_w_list = [None] * (self.num_encs + 1) # atts + han + att_c_list = [None] * self.num_encs # atts + for idx in range(self.num_encs + 1): + # reset pre-computation of h in atts and han + self.att_list[idx].reset() + + # pre-computation of embedding + eys = self.dropout_emb(self.embed(ys_in_pad)) # utt x olen x zdim + + # loop for an output sequence + for i in range(olength): + if self.num_encs == 1: + att_c, att_w = self.att_list[att_idx]( + hs_pad[0], hlens[0], self.dropout_dec[0](z_list[0]), att_w + ) + else: + for idx in range(self.num_encs): + att_c_list[idx], att_w_list[idx] = self.att_list[idx]( + hs_pad[idx], + hlens[idx], + self.dropout_dec[0](z_list[0]), + att_w_list[idx], + ) + hs_pad_han = torch.stack(att_c_list, dim=1) + hlens_han = [self.num_encs] * len(ys_in_pad) + att_c, att_w_list[self.num_encs] = self.att_list[self.num_encs]( + hs_pad_han, + hlens_han, + self.dropout_dec[0](z_list[0]), + att_w_list[self.num_encs], + ) + if i > 0 and random.random() < self.sampling_probability: + z_out = self.output(z_all[-1]) + z_out = np.argmax(z_out.detach().cpu(), axis=1) + z_out = self.dropout_emb(self.embed(to_device(self, z_out))) + ey = torch.cat((z_out, att_c), dim=1) # utt x (zdim + hdim) + else: + # utt x (zdim + hdim) + ey = torch.cat((eys[:, i, :], att_c), dim=1) + z_list, c_list = self.rnn_forward(ey, z_list, c_list, z_list, c_list) + if self.context_residual: + z_all.append( + torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1) + ) # utt x (zdim + hdim) + else: + z_all.append(self.dropout_dec[-1](z_list[-1])) # utt x (zdim) + + z_all = torch.stack(z_all, dim=1) + z_all = self.output(z_all) + z_all.masked_fill_( + make_pad_mask(ys_in_lens, z_all, 1), + 0, + ) + return z_all, ys_in_lens + + def init_state(self, x): + # to support mutiple encoder asr mode, in single encoder mode, + # convert torch.Tensor to List of torch.Tensor + if self.num_encs == 1: + x = [x] + + c_list = [self.zero_state(x[0].unsqueeze(0))] + z_list = [self.zero_state(x[0].unsqueeze(0))] + for _ in range(1, self.dlayers): + c_list.append(self.zero_state(x[0].unsqueeze(0))) + z_list.append(self.zero_state(x[0].unsqueeze(0))) + # TODO(karita): support strm_index for `asr_mix` + strm_index = 0 + att_idx = min(strm_index, len(self.att_list) - 1) + if self.num_encs == 1: + a = None + self.att_list[att_idx].reset() # reset pre-computation of h + else: + a = [None] * (self.num_encs + 1) # atts + han + for idx in range(self.num_encs + 1): + # reset pre-computation of h in atts and han + self.att_list[idx].reset() + return dict( + c_prev=c_list[:], + z_prev=z_list[:], + a_prev=a, + workspace=(att_idx, z_list, c_list), + ) + + def score(self, yseq, state, x): + # to support mutiple encoder asr mode, in single encoder mode, + # convert torch.Tensor to List of torch.Tensor + if self.num_encs == 1: + x = [x] + + att_idx, z_list, c_list = state["workspace"] + vy = yseq[-1].unsqueeze(0) + ey = self.dropout_emb(self.embed(vy)) # utt list (1) x zdim + if self.num_encs == 1: + att_c, att_w = self.att_list[att_idx]( + x[0].unsqueeze(0), + [x[0].size(0)], + self.dropout_dec[0](state["z_prev"][0]), + state["a_prev"], + ) + else: + att_w = [None] * (self.num_encs + 1) # atts + han + att_c_list = [None] * self.num_encs # atts + for idx in range(self.num_encs): + att_c_list[idx], att_w[idx] = self.att_list[idx]( + x[idx].unsqueeze(0), + [x[idx].size(0)], + self.dropout_dec[0](state["z_prev"][0]), + state["a_prev"][idx], + ) + h_han = torch.stack(att_c_list, dim=1) + att_c, att_w[self.num_encs] = self.att_list[self.num_encs]( + h_han, + [self.num_encs], + self.dropout_dec[0](state["z_prev"][0]), + state["a_prev"][self.num_encs], + ) + ey = torch.cat((ey, att_c), dim=1) # utt(1) x (zdim + hdim) + z_list, c_list = self.rnn_forward( + ey, z_list, c_list, state["z_prev"], state["c_prev"] + ) + if self.context_residual: + logits = self.output( + torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1) + ) + else: + logits = self.output(self.dropout_dec[-1](z_list[-1])) + logp = F.log_softmax(logits, dim=1).squeeze(0) + return ( + logp, + dict( + c_prev=c_list[:], + z_prev=z_list[:], + a_prev=att_w, + workspace=(att_idx, z_list, c_list), + ), + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transducer/rnnt_decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/transducer/rnnt_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..9437b9151f5403593a8f3d0b470c50837e875009 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transducer/rnnt_decoder.py @@ -0,0 +1,274 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import torch +from typing import List, Optional, Tuple + +from funasr_detach.register import tables +from funasr_detach.models.specaug.specaug import SpecAug +from funasr_detach.models.transducer.beam_search_transducer import Hypothesis + + +@tables.register("decoder_classes", "rnnt_decoder") +class RNNTDecoder(torch.nn.Module): + """RNN decoder module. + + Args: + vocab_size: Vocabulary size. + embed_size: Embedding size. + hidden_size: Hidden size.. + rnn_type: Decoder layers type. + num_layers: Number of decoder layers. + dropout_rate: Dropout rate for decoder layers. + embed_dropout_rate: Dropout rate for embedding layer. + embed_pad: Embedding padding symbol ID. + + """ + + def __init__( + self, + vocab_size: int, + embed_size: int = 256, + hidden_size: int = 256, + rnn_type: str = "lstm", + num_layers: int = 1, + dropout_rate: float = 0.0, + embed_dropout_rate: float = 0.0, + embed_pad: int = 0, + use_embed_mask: bool = False, + ) -> None: + """Construct a RNNDecoder object.""" + super().__init__() + + if rnn_type not in ("lstm", "gru"): + raise ValueError(f"Not supported: rnn_type={rnn_type}") + + self.embed = torch.nn.Embedding(vocab_size, embed_size, padding_idx=embed_pad) + self.dropout_embed = torch.nn.Dropout(p=embed_dropout_rate) + + rnn_class = torch.nn.LSTM if rnn_type == "lstm" else torch.nn.GRU + + self.rnn = torch.nn.ModuleList( + [rnn_class(embed_size, hidden_size, 1, batch_first=True)] + ) + + for _ in range(1, num_layers): + self.rnn += [rnn_class(hidden_size, hidden_size, 1, batch_first=True)] + + self.dropout_rnn = torch.nn.ModuleList( + [torch.nn.Dropout(p=dropout_rate) for _ in range(num_layers)] + ) + + self.dlayers = num_layers + self.dtype = rnn_type + + self.output_size = hidden_size + self.vocab_size = vocab_size + + self.device = next(self.parameters()).device + self.score_cache = {} + + self.use_embed_mask = use_embed_mask + if self.use_embed_mask: + self._embed_mask = SpecAug( + time_mask_width_range=3, + num_time_mask=4, + apply_freq_mask=False, + apply_time_warp=False, + ) + + def forward( + self, + labels: torch.Tensor, + label_lens: torch.Tensor, + states: Optional[Tuple[torch.Tensor, Optional[torch.Tensor]]] = None, + ) -> torch.Tensor: + """Encode source label sequences. + + Args: + labels: Label ID sequences. (B, L) + states: Decoder hidden states. + ((N, B, D_dec), (N, B, D_dec) or None) or None + + Returns: + dec_out: Decoder output sequences. (B, U, D_dec) + + """ + if states is None: + states = self.init_state(labels.size(0)) + + dec_embed = self.dropout_embed(self.embed(labels)) + if self.use_embed_mask and self.training: + dec_embed = self._embed_mask(dec_embed, label_lens)[0] + dec_out, states = self.rnn_forward(dec_embed, states) + return dec_out + + def rnn_forward( + self, + x: torch.Tensor, + state: Tuple[torch.Tensor, Optional[torch.Tensor]], + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]: + """Encode source label sequences. + + Args: + x: RNN input sequences. (B, D_emb) + state: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None) + + Returns: + x: RNN output sequences. (B, D_dec) + (h_next, c_next): Decoder hidden states. + (N, B, D_dec), (N, B, D_dec) or None) + + """ + h_prev, c_prev = state + h_next, c_next = self.init_state(x.size(0)) + + for layer in range(self.dlayers): + if self.dtype == "lstm": + x, (h_next[layer : layer + 1], c_next[layer : layer + 1]) = self.rnn[ + layer + ](x, hx=(h_prev[layer : layer + 1], c_prev[layer : layer + 1])) + else: + x, h_next[layer : layer + 1] = self.rnn[layer]( + x, hx=h_prev[layer : layer + 1] + ) + + x = self.dropout_rnn[layer](x) + + return x, (h_next, c_next) + + def score( + self, + label: torch.Tensor, + label_sequence: List[int], + dec_state: Tuple[torch.Tensor, Optional[torch.Tensor]], + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]: + """One-step forward hypothesis. + + Args: + label: Previous label. (1, 1) + label_sequence: Current label sequence. + dec_state: Previous decoder hidden states. + ((N, 1, D_dec), (N, 1, D_dec) or None) + + Returns: + dec_out: Decoder output sequence. (1, D_dec) + dec_state: Decoder hidden states. + ((N, 1, D_dec), (N, 1, D_dec) or None) + + """ + str_labels = "_".join(map(str, label_sequence)) + + if str_labels in self.score_cache: + dec_out, dec_state = self.score_cache[str_labels] + else: + dec_embed = self.embed(label) + dec_out, dec_state = self.rnn_forward(dec_embed, dec_state) + + self.score_cache[str_labels] = (dec_out, dec_state) + + return dec_out[0], dec_state + + def batch_score( + self, + hyps: List[Hypothesis], + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]: + """One-step forward hypotheses. + + Args: + hyps: Hypotheses. + + Returns: + dec_out: Decoder output sequences. (B, D_dec) + states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None) + + """ + labels = torch.LongTensor([[h.yseq[-1]] for h in hyps], device=self.device) + dec_embed = self.embed(labels) + + states = self.create_batch_states([h.dec_state for h in hyps]) + dec_out, states = self.rnn_forward(dec_embed, states) + + return dec_out.squeeze(1), states + + def set_device(self, device: torch.device) -> None: + """Set GPU device to use. + + Args: + device: Device ID. + + """ + self.device = device + + def init_state( + self, batch_size: int + ) -> Tuple[torch.Tensor, Optional[torch.tensor]]: + """Initialize decoder states. + + Args: + batch_size: Batch size. + + Returns: + : Initial decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None) + + """ + h_n = torch.zeros( + self.dlayers, + batch_size, + self.output_size, + device=self.device, + ) + + if self.dtype == "lstm": + c_n = torch.zeros( + self.dlayers, + batch_size, + self.output_size, + device=self.device, + ) + + return (h_n, c_n) + + return (h_n, None) + + def select_state( + self, states: Tuple[torch.Tensor, Optional[torch.Tensor]], idx: int + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Get specified ID state from decoder hidden states. + + Args: + states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None) + idx: State ID to extract. + + Returns: + : Decoder hidden state for given ID. ((N, 1, D_dec), (N, 1, D_dec) or None) + + """ + return ( + states[0][:, idx : idx + 1, :], + states[1][:, idx : idx + 1, :] if self.dtype == "lstm" else None, + ) + + def create_batch_states( + self, + new_states: List[Tuple[torch.Tensor, Optional[torch.Tensor]]], + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Create decoder hidden states. + + Args: + new_states: Decoder hidden states. [N x ((1, D_dec), (1, D_dec) or None)] + + Returns: + states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None) + + """ + return ( + torch.cat([s[0] for s in new_states], dim=1), + ( + torch.cat([s[1] for s in new_states], dim=1) + if self.dtype == "lstm" + else None + ), + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/attention.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..5b5c49a436c78614bcce93efda7bb3d6e81c5ff7 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/attention.py @@ -0,0 +1,532 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Multi-Head Attention layer definition.""" + +import math + +import numpy +import torch +from torch import nn +from typing import Optional, Tuple + +import torch.nn.functional as F +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +import funasr_detach.models.lora.layers as lora + + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, n_head, n_feat, dropout_rate): + """Construct an MultiHeadedAttention object.""" + super(MultiHeadedAttention, self).__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.attn = None + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv(self, query, key, value): + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + + return q, k, v + + def forward_attention(self, value, scores, mask): + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2). + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + if mask is not None: + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + min_value = float( + numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min + ) + scores = scores.masked_fill(mask, min_value) + self.attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0 + ) # (batch, head, time1, time2) + else: + self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(self.attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward(self, query, key, value, mask): + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q, k, v = self.forward_qkv(query, key, value) + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask) + + +class LegacyRelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding (old version). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + Paper: https://arxiv.org/abs/1901.02860 + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + zero_triu (bool): Whether to zero the upper triangular part of attention matrix. + + """ + + def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate) + self.zero_triu = zero_triu + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x): + """Compute relative positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, head, time1, time2). + + Returns: + torch.Tensor: Output tensor. + + """ + zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x) + + if self.zero_triu: + ones = torch.ones((x.size(2), x.size(3))) + x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] + + return x + + def forward(self, query, key, value, pos_emb, mask): + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + pos_emb (torch.Tensor): Positional embedding tensor (#batch, time1, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time1) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k + ) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask) + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding (new implementation). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + Paper: https://arxiv.org/abs/1901.02860 + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + zero_triu (bool): Whether to zero the upper triangular part of attention matrix. + + """ + + def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate) + self.zero_triu = zero_triu + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x): + """Compute relative positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + torch.Tensor: Output tensor. + + """ + zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x)[ + :, :, :, : x.size(-1) // 2 + 1 + ] # only keep the positions from 0 to time2 + + if self.zero_triu: + ones = torch.ones((x.size(2), x.size(3)), device=x.device) + x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] + + return x + + def forward(self, query, key, value, pos_emb, mask): + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, 2*time1-1, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, 2*time1-1) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k + ) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask) + + +class RelPositionMultiHeadedAttentionChunk(torch.nn.Module): + """RelPositionMultiHeadedAttention definition. + Args: + num_heads: Number of attention heads. + embed_size: Embedding size. + dropout_rate: Dropout rate. + """ + + def __init__( + self, + num_heads: int, + embed_size: int, + dropout_rate: float = 0.0, + simplified_attention_score: bool = False, + ) -> None: + """Construct an MultiHeadedAttention object.""" + super().__init__() + + self.d_k = embed_size // num_heads + self.num_heads = num_heads + + assert self.d_k * num_heads == embed_size, ( + "embed_size (%d) must be divisible by num_heads (%d)", + (embed_size, num_heads), + ) + + self.linear_q = torch.nn.Linear(embed_size, embed_size) + self.linear_k = torch.nn.Linear(embed_size, embed_size) + self.linear_v = torch.nn.Linear(embed_size, embed_size) + + self.linear_out = torch.nn.Linear(embed_size, embed_size) + + if simplified_attention_score: + self.linear_pos = torch.nn.Linear(embed_size, num_heads) + + self.compute_att_score = self.compute_simplified_attention_score + else: + self.linear_pos = torch.nn.Linear(embed_size, embed_size, bias=False) + + self.pos_bias_u = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k)) + self.pos_bias_v = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + self.compute_att_score = self.compute_attention_score + + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.attn = None + + def rel_shift(self, x: torch.Tensor, left_context: int = 0) -> torch.Tensor: + """Compute relative positional encoding. + Args: + x: Input sequence. (B, H, T_1, 2 * T_1 - 1) + left_context: Number of frames in left context. + Returns: + x: Output sequence. (B, H, T_1, T_2) + """ + batch_size, n_heads, time1, n = x.shape + time2 = time1 + left_context + + batch_stride, n_heads_stride, time1_stride, n_stride = x.stride() + + return x.as_strided( + (batch_size, n_heads, time1, time2), + (batch_stride, n_heads_stride, time1_stride - n_stride, n_stride), + storage_offset=(n_stride * (time1 - 1)), + ) + + def compute_simplified_attention_score( + self, + query: torch.Tensor, + key: torch.Tensor, + pos_enc: torch.Tensor, + left_context: int = 0, + ) -> torch.Tensor: + """Simplified attention score computation. + Reference: https://github.com/k2-fsa/icefall/pull/458 + Args: + query: Transformed query tensor. (B, H, T_1, d_k) + key: Transformed key tensor. (B, H, T_2, d_k) + pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size) + left_context: Number of frames in left context. + Returns: + : Attention score. (B, H, T_1, T_2) + """ + pos_enc = self.linear_pos(pos_enc) + + matrix_ac = torch.matmul(query, key.transpose(2, 3)) + + matrix_bd = self.rel_shift( + pos_enc.transpose(1, 2).unsqueeze(2).repeat(1, 1, query.size(2), 1), + left_context=left_context, + ) + + return (matrix_ac + matrix_bd) / math.sqrt(self.d_k) + + def compute_attention_score( + self, + query: torch.Tensor, + key: torch.Tensor, + pos_enc: torch.Tensor, + left_context: int = 0, + ) -> torch.Tensor: + """Attention score computation. + Args: + query: Transformed query tensor. (B, H, T_1, d_k) + key: Transformed key tensor. (B, H, T_2, d_k) + pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size) + left_context: Number of frames in left context. + Returns: + : Attention score. (B, H, T_1, T_2) + """ + p = self.linear_pos(pos_enc).view(pos_enc.size(0), -1, self.num_heads, self.d_k) + + query = query.transpose(1, 2) + q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2) + q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2) + + matrix_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1)) + + matrix_bd = torch.matmul(q_with_bias_v, p.permute(0, 2, 3, 1)) + matrix_bd = self.rel_shift(matrix_bd, left_context=left_context) + + return (matrix_ac + matrix_bd) / math.sqrt(self.d_k) + + def forward_qkv( + self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Transform query, key and value. + Args: + query: Query tensor. (B, T_1, size) + key: Key tensor. (B, T_2, size) + v: Value tensor. (B, T_2, size) + Returns: + q: Transformed query tensor. (B, H, T_1, d_k) + k: Transformed key tensor. (B, H, T_2, d_k) + v: Transformed value tensor. (B, H, T_2, d_k) + """ + n_batch = query.size(0) + + q = ( + self.linear_q(query) + .view(n_batch, -1, self.num_heads, self.d_k) + .transpose(1, 2) + ) + k = ( + self.linear_k(key) + .view(n_batch, -1, self.num_heads, self.d_k) + .transpose(1, 2) + ) + v = ( + self.linear_v(value) + .view(n_batch, -1, self.num_heads, self.d_k) + .transpose(1, 2) + ) + + return q, k, v + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor, + chunk_mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """Compute attention context vector. + Args: + value: Transformed value. (B, H, T_2, d_k) + scores: Attention score. (B, H, T_1, T_2) + mask: Source mask. (B, T_2) + chunk_mask: Chunk mask. (T_1, T_1) + Returns: + attn_output: Transformed value weighted by attention score. (B, T_1, H * d_k) + """ + batch_size = scores.size(0) + mask = mask.unsqueeze(1).unsqueeze(2) + if chunk_mask is not None: + mask = chunk_mask.unsqueeze(0).unsqueeze(1) | mask + scores = scores.masked_fill(mask, float("-inf")) + self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0) + + attn_output = self.dropout(self.attn) + attn_output = torch.matmul(attn_output, value) + + attn_output = self.linear_out( + attn_output.transpose(1, 2) + .contiguous() + .view(batch_size, -1, self.num_heads * self.d_k) + ) + + return attn_output + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + pos_enc: torch.Tensor, + mask: torch.Tensor, + chunk_mask: Optional[torch.Tensor] = None, + left_context: int = 0, + ) -> torch.Tensor: + """Compute scaled dot product attention with rel. positional encoding. + Args: + query: Query tensor. (B, T_1, size) + key: Key tensor. (B, T_2, size) + value: Value tensor. (B, T_2, size) + pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size) + mask: Source mask. (B, T_2) + chunk_mask: Chunk mask. (T_1, T_1) + left_context: Number of frames in left context. + Returns: + : Output tensor. (B, T_1, H * d_k) + """ + q, k, v = self.forward_qkv(query, key, value) + scores = self.compute_att_score(q, k, pos_enc, left_context=left_context) + return self.forward_attention(v, scores, mask, chunk_mask=chunk_mask) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/decoder.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..1cf7355c6ea6421ff32febd49909ce2a1c71a05e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/decoder.py @@ -0,0 +1,653 @@ +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Decoder definition.""" +from typing import Any +from typing import List +from typing import Sequence +from typing import Tuple + +import torch +from torch import nn + + +from funasr_detach.models.transformer.attention import MultiHeadedAttention +from funasr_detach.models.transformer.utils.dynamic_conv import DynamicConvolution +from funasr_detach.models.transformer.utils.dynamic_conv2d import DynamicConvolution2D +from funasr_detach.models.transformer.embedding import PositionalEncoding +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.lightconv import LightweightConvolution +from funasr_detach.models.transformer.utils.lightconv2d import LightweightConvolution2D +from funasr_detach.models.transformer.utils.mask import subsequent_mask +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, # noqa: H301 +) +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.transformer.scorers.scorer_interface import ( + BatchScorerInterface, +) + +from funasr_detach.register import tables + + +class DecoderLayer(nn.Module): + """Single decoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + src_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + + + """ + + def __init__( + self, + size, + self_attn, + src_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + ): + """Construct an DecoderLayer object.""" + super(DecoderLayer, self).__init__() + self.size = size + self.self_attn = self_attn + self.src_attn = src_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(size) + self.norm2 = LayerNorm(size) + self.norm3 = LayerNorm(size) + self.dropout = nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear1 = nn.Linear(size + size, size) + self.concat_linear2 = nn.Linear(size + size, size) + + def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None): + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in). + cache (List[torch.Tensor]): List of cached tensors. + Each tensor shape should be (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor(#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + + if cache is None: + tgt_q = tgt + tgt_q_mask = tgt_mask + else: + # compute only the last frame query keeping dim: max_time_out -> 1 + assert cache.shape == ( + tgt.shape[0], + tgt.shape[1] - 1, + self.size, + ), f"{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}" + tgt_q = tgt[:, -1:, :] + residual = residual[:, -1:, :] + tgt_q_mask = None + if tgt_mask is not None: + tgt_q_mask = tgt_mask[:, -1:, :] + + if self.concat_after: + tgt_concat = torch.cat( + (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)), dim=-1 + ) + x = residual + self.concat_linear1(tgt_concat) + else: + x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + if self.concat_after: + x_concat = torch.cat( + (x, self.src_attn(x, memory, memory, memory_mask)), dim=-1 + ) + x = residual + self.concat_linear2(x_concat) + else: + x = residual + self.dropout(self.src_attn(x, memory, memory, memory_mask)) + if not self.normalize_before: + x = self.norm2(x) + + residual = x + if self.normalize_before: + x = self.norm3(x) + x = residual + self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm3(x) + + if cache is not None: + x = torch.cat([cache, x], dim=1) + + return x, tgt_mask, memory, memory_mask + + +class BaseTransformerDecoder(nn.Module, BatchScorerInterface): + """Base class of Transfomer decoder module. + + Args: + vocab_size: output dim + encoder_output_size: dimension of attention + attention_heads: the number of heads of multi head attention + linear_units: the number of units of position-wise feed forward + num_blocks: the number of decoder blocks + dropout_rate: dropout rate + self_attention_dropout_rate: dropout rate for attention + input_layer: input layer type + use_output_layer: whether to use output layer + pos_enc_class: PositionalEncoding or ScaledPositionalEncoding + normalize_before: whether to use layer_norm before the first block + concat_after: whether to concat attention layer's input and output + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. + i.e. x -> x + att(x) + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + ): + super().__init__() + attention_dim = encoder_output_size + + if input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(vocab_size, attention_dim), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + elif input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(vocab_size, attention_dim), + torch.nn.LayerNorm(attention_dim), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(attention_dim, positional_dropout_rate), + ) + else: + raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}") + + self.normalize_before = normalize_before + if self.normalize_before: + self.after_norm = LayerNorm(attention_dim) + if use_output_layer: + self.output_layer = torch.nn.Linear(attention_dim, vocab_size) + else: + self.output_layer = None + + # Must set by the inheritance + self.decoders = None + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward decoder. + + Args: + hs_pad: encoded memory, float32 (batch, maxlen_in, feat) + hlens: (batch) + ys_in_pad: + input token ids, int64 (batch, maxlen_out) + if input_layer == "embed" + input tensor (batch, maxlen_out, #mels) in the other cases + ys_in_lens: (batch) + Returns: + (tuple): tuple containing: + + x: decoded token score before softmax (batch, maxlen_out, token) + if use_output_layer is True, + olens: (batch, ) + """ + tgt = ys_in_pad + # tgt_mask: (B, 1, L) + tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device) + # m: (1, L, L) + m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0) + # tgt_mask: (B, L, L) + tgt_mask = tgt_mask & m + + memory = hs_pad + memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to( + memory.device + ) + # Padding for Longformer + if memory_mask.shape[-1] != memory.shape[1]: + padlen = memory.shape[1] - memory_mask.shape[-1] + memory_mask = torch.nn.functional.pad( + memory_mask, (0, padlen), "constant", False + ) + + x = self.embed(tgt) + x, tgt_mask, memory, memory_mask = self.decoders( + x, tgt_mask, memory, memory_mask + ) + if self.normalize_before: + x = self.after_norm(x) + if self.output_layer is not None: + x = self.output_layer(x) + + olens = tgt_mask.sum(1) + return x, olens + + def forward_one_step( + self, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + cache: List[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + + Args: + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + memory: encoded memory, float32 (batch, maxlen_in, feat) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + x = self.embed(tgt) + if cache is None: + cache = [None] * len(self.decoders) + new_cache = [] + for c, decoder in zip(cache, self.decoders): + x, tgt_mask, memory, memory_mask = decoder( + x, tgt_mask, memory, None, cache=c + ) + new_cache.append(x) + + if self.normalize_before: + y = self.after_norm(x[:, -1]) + else: + y = x[:, -1] + if self.output_layer is not None: + y = torch.log_softmax(self.output_layer(y), dim=-1) + + return y, new_cache + + def score(self, ys, state, x): + """Score.""" + ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0) + logp, state = self.forward_one_step( + ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state + ) + return logp.squeeze(0), state + + def batch_score( + self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor + ) -> Tuple[torch.Tensor, List[Any]]: + """Score new token batch. + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, List[Any]]: Tuple of + batchfied scores for next token with shape of `(n_batch, n_vocab)` + and next state list for ys. + + """ + # merge states + n_batch = len(ys) + n_layers = len(self.decoders) + if states[0] is None: + batch_state = None + else: + # transpose state of [batch, layer] into [layer, batch] + batch_state = [ + torch.stack([states[b][i] for b in range(n_batch)]) + for i in range(n_layers) + ] + + # batch decoding + ys_mask = subsequent_mask(ys.size(-1), device=xs.device).unsqueeze(0) + logp, states = self.forward_one_step(ys, ys_mask, xs, cache=batch_state) + + # transpose state of [layer, batch] into [batch, layer] + state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)] + return logp, state_list + + +@tables.register("decoder_classes", "TransformerDecoder") +class TransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + ): + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + MultiHeadedAttention( + attention_heads, attention_dim, self_attention_dropout_rate + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + +@tables.register("decoder_classes", "LightweightConvolutionTransformerDecoder") +class LightweightConvolutionTransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + conv_wshare: int = 4, + conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), + conv_usebias: int = False, + ): + if len(conv_kernel_length) != num_blocks: + raise ValueError( + "conv_kernel_length must have equal number of values to num_blocks: " + f"{len(conv_kernel_length)} != {num_blocks}" + ) + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + LightweightConvolution( + wshare=conv_wshare, + n_feat=attention_dim, + dropout_rate=self_attention_dropout_rate, + kernel_size=conv_kernel_length[lnum], + use_kernel_mask=True, + use_bias=conv_usebias, + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + +@tables.register("decoder_classes", "LightweightConvolution2DTransformerDecoder") +class LightweightConvolution2DTransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + conv_wshare: int = 4, + conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), + conv_usebias: int = False, + ): + if len(conv_kernel_length) != num_blocks: + raise ValueError( + "conv_kernel_length must have equal number of values to num_blocks: " + f"{len(conv_kernel_length)} != {num_blocks}" + ) + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + + attention_dim = encoder_output_size + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + LightweightConvolution2D( + wshare=conv_wshare, + n_feat=attention_dim, + dropout_rate=self_attention_dropout_rate, + kernel_size=conv_kernel_length[lnum], + use_kernel_mask=True, + use_bias=conv_usebias, + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + +@tables.register("decoder_classes", "DynamicConvolutionTransformerDecoder") +class DynamicConvolutionTransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + conv_wshare: int = 4, + conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), + conv_usebias: int = False, + ): + if len(conv_kernel_length) != num_blocks: + raise ValueError( + "conv_kernel_length must have equal number of values to num_blocks: " + f"{len(conv_kernel_length)} != {num_blocks}" + ) + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + attention_dim = encoder_output_size + + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + DynamicConvolution( + wshare=conv_wshare, + n_feat=attention_dim, + dropout_rate=self_attention_dropout_rate, + kernel_size=conv_kernel_length[lnum], + use_kernel_mask=True, + use_bias=conv_usebias, + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) + + +@tables.register("decoder_classes", "DynamicConvolution2DTransformerDecoder") +class DynamicConvolution2DTransformerDecoder(BaseTransformerDecoder): + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + conv_wshare: int = 4, + conv_kernel_length: Sequence[int] = (11, 11, 11, 11, 11, 11), + conv_usebias: int = False, + ): + if len(conv_kernel_length) != num_blocks: + raise ValueError( + "conv_kernel_length must have equal number of values to num_blocks: " + f"{len(conv_kernel_length)} != {num_blocks}" + ) + super().__init__( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + dropout_rate=dropout_rate, + positional_dropout_rate=positional_dropout_rate, + input_layer=input_layer, + use_output_layer=use_output_layer, + pos_enc_class=pos_enc_class, + normalize_before=normalize_before, + ) + attention_dim = encoder_output_size + + self.decoders = repeat( + num_blocks, + lambda lnum: DecoderLayer( + attention_dim, + DynamicConvolution2D( + wshare=conv_wshare, + n_feat=attention_dim, + dropout_rate=self_attention_dropout_rate, + kernel_size=conv_kernel_length[lnum], + use_kernel_mask=True, + use_bias=conv_usebias, + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ), + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + concat_after, + ), + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/embedding.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..c3bb83e63bed8d56b200770c340000d9947802c4 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/embedding.py @@ -0,0 +1,557 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Positional Encoding Module.""" + +import math +import torch +import torch.nn.functional as F +from torch import einsum + + +def _pre_hook( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, +): + """Perform pre-hook in load_state_dict for backward compatibility. + + Note: + We saved self.pe until v.0.5.2 but we have omitted it later. + Therefore, we remove the item "pe" from `state_dict` for backward compatibility. + + """ + k = prefix + "pe" + if k in state_dict: + state_dict.pop(k) + + +class PositionalEncoding(torch.nn.Module): + """Positional encoding. + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + reverse (bool): Whether to reverse the input position. Only for + the class LegacyRelPositionalEncoding. We remove it in the current + class RelPositionalEncoding. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): + """Construct an PositionalEncoding object.""" + super(PositionalEncoding, self).__init__() + self.d_model = d_model + self.reverse = reverse + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + self._register_load_state_dict_pre_hook(_pre_hook) + + def extend_pe(self, x): + """Reset the positional encodings.""" + if self.pe is not None: + if self.pe.size(1) >= x.size(1): + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + pe = torch.zeros(x.size(1), self.d_model) + if self.reverse: + position = torch.arange( + x.size(1) - 1, -1, -1.0, dtype=torch.float32 + ).unsqueeze(1) + else: + position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) + self.pe = pe.to(device=x.device, dtype=x.dtype) + + def forward(self, x: torch.Tensor): + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + self.extend_pe(x) + x = x * self.xscale + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class ScaledPositionalEncoding(PositionalEncoding): + """Scaled positional encoding module. + + See Sec. 3.2 https://arxiv.org/abs/1809.08895 + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len) + self.alpha = torch.nn.Parameter(torch.tensor(1.0)) + + def reset_parameters(self): + """Reset parameters.""" + self.alpha.data = torch.tensor(1.0) + + def forward(self, x): + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + + """ + self.extend_pe(x) + x = x + self.alpha * self.pe[:, : x.size(1)] + return self.dropout(x) + + +class LearnableFourierPosEnc(torch.nn.Module): + """Learnable Fourier Features for Positional Encoding. + + See https://arxiv.org/pdf/2106.02795.pdf + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + gamma (float): init parameter for the positional kernel variance + see https://arxiv.org/pdf/2106.02795.pdf. + apply_scaling (bool): Whether to scale the input before adding the pos encoding. + hidden_dim (int): if not None, we modulate the pos encodings with + an MLP whose hidden layer has hidden_dim neurons. + """ + + def __init__( + self, + d_model, + dropout_rate=0.0, + max_len=5000, + gamma=1.0, + apply_scaling=False, + hidden_dim=None, + ): + """Initialize class.""" + super(LearnableFourierPosEnc, self).__init__() + + self.d_model = d_model + + if apply_scaling: + self.xscale = math.sqrt(self.d_model) + else: + self.xscale = 1.0 + + self.dropout = torch.nn.Dropout(dropout_rate) + self.max_len = max_len + + self.gamma = gamma + if self.gamma is None: + self.gamma = self.d_model // 2 + + assert ( + d_model % 2 == 0 + ), "d_model should be divisible by two in order to use this layer." + self.w_r = torch.nn.Parameter(torch.empty(1, d_model // 2)) + self._reset() # init the weights + + self.hidden_dim = hidden_dim + if self.hidden_dim is not None: + self.mlp = torch.nn.Sequential( + torch.nn.Linear(d_model, hidden_dim), + torch.nn.GELU(), + torch.nn.Linear(hidden_dim, d_model), + ) + + def _reset(self): + self.w_r.data = torch.normal( + 0, (1 / math.sqrt(self.gamma)), (1, self.d_model // 2) + ) + + def extend_pe(self, x): + """Reset the positional encodings.""" + position_v = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1).to(x) + + cosine = torch.cos(torch.matmul(position_v, self.w_r)) + sine = torch.sin(torch.matmul(position_v, self.w_r)) + pos_enc = torch.cat((cosine, sine), -1) + pos_enc /= math.sqrt(self.d_model) + + if self.hidden_dim is None: + return pos_enc.unsqueeze(0) + else: + return self.mlp(pos_enc.unsqueeze(0)) + + def forward(self, x: torch.Tensor): + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + pe = self.extend_pe(x) + x = x * self.xscale + pe + return self.dropout(x) + + +class LegacyRelPositionalEncoding(PositionalEncoding): + """Relative positional encoding module (old version). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + See : Appendix B in https://arxiv.org/abs/1901.02860 + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__( + d_model=d_model, + dropout_rate=dropout_rate, + max_len=max_len, + reverse=True, + ) + + def forward(self, x): + """Compute positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Positional embedding tensor (1, time, `*`). + + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.pe[:, : x.size(1)] + return self.dropout(x), self.dropout(pos_emb) + + +class RelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module (new implementation). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + See : Appendix B in https://arxiv.org/abs/1901.02860 + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Construct an PositionalEncoding object.""" + super(RelPositionalEncoding, self).__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x): + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i= length: + if self.pe.dtype != dtype or self.pe.device != device: + self.pe = self.pe.to(dtype=dtype, device=device) + return + pe = torch.zeros(length, self.d_model) + position = torch.arange(0, length, dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) + self.pe = pe.to(device=device, dtype=dtype) + + def forward(self, x: torch.Tensor, start_idx: int = 0): + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + + """ + self.extend_pe(x.size(1) + start_idx, x.device, x.dtype) + x = x * self.xscale + self.pe[:, start_idx : start_idx + x.size(1)] + return self.dropout(x) + + +class SinusoidalPositionEncoder(torch.nn.Module): + """ """ + + def __int__(self, d_model=80, dropout_rate=0.1): + pass + + def encode( + self, + positions: torch.Tensor = None, + depth: int = None, + dtype: torch.dtype = torch.float32, + ): + batch_size = positions.size(0) + positions = positions.type(dtype) + device = positions.device + log_timescale_increment = torch.log( + torch.tensor([10000], dtype=dtype, device=device) + ) / (depth / 2 - 1) + inv_timescales = torch.exp( + torch.arange(depth / 2, device=device).type(dtype) + * (-log_timescale_increment) + ) + inv_timescales = torch.reshape(inv_timescales, [batch_size, -1]) + scaled_time = torch.reshape(positions, [1, -1, 1]) * torch.reshape( + inv_timescales, [1, 1, -1] + ) + encoding = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=2) + return encoding.type(dtype) + + def forward(self, x): + batch_size, timesteps, input_dim = x.size() + positions = torch.arange(1, timesteps + 1, device=x.device)[None, :] + position_encoding = self.encode(positions, input_dim, x.dtype).to(x.device) + + return x + position_encoding + + +class StreamSinusoidalPositionEncoder(torch.nn.Module): + """ """ + + def __int__(self, d_model=80, dropout_rate=0.1): + pass + + def encode( + self, + positions: torch.Tensor = None, + depth: int = None, + dtype: torch.dtype = torch.float32, + ): + batch_size = positions.size(0) + positions = positions.type(dtype) + log_timescale_increment = torch.log(torch.tensor([10000], dtype=dtype)) / ( + depth / 2 - 1 + ) + inv_timescales = torch.exp( + torch.arange(depth / 2).type(dtype) * (-log_timescale_increment) + ) + inv_timescales = torch.reshape(inv_timescales, [batch_size, -1]) + scaled_time = torch.reshape(positions, [1, -1, 1]) * torch.reshape( + inv_timescales, [1, 1, -1] + ) + encoding = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=2) + return encoding.type(dtype) + + def forward(self, x, cache=None): + batch_size, timesteps, input_dim = x.size() + start_idx = 0 + if cache is not None: + start_idx = cache["start_idx"] + cache["start_idx"] += timesteps + positions = torch.arange(1, timesteps + start_idx + 1)[None, :] + position_encoding = self.encode(positions, input_dim, x.dtype).to(x.device) + return x + position_encoding[:, start_idx : start_idx + timesteps] + + +class StreamingRelPositionalEncoding(torch.nn.Module): + """Relative positional encoding. + Args: + size: Module size. + max_len: Maximum input length. + dropout_rate: Dropout rate. + """ + + def __init__( + self, size: int, dropout_rate: float = 0.0, max_len: int = 5000 + ) -> None: + """Construct a RelativePositionalEncoding object.""" + super().__init__() + + self.size = size + + self.pe = None + self.dropout = torch.nn.Dropout(p=dropout_rate) + + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + self._register_load_state_dict_pre_hook(_pre_hook) + + def extend_pe(self, x: torch.Tensor, left_context: int = 0) -> None: + """Reset positional encoding. + Args: + x: Input sequences. (B, T, ?) + left_context: Number of frames in left context. + """ + time1 = x.size(1) + left_context + + if self.pe is not None: + if self.pe.size(1) >= time1 * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(device=x.device, dtype=x.dtype) + return + + pe_positive = torch.zeros(time1, self.size) + pe_negative = torch.zeros(time1, self.size) + + position = torch.arange(0, time1, dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.size, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.size) + ) + + pe_positive[:, 0::2] = torch.sin(position * div_term) + pe_positive[:, 1::2] = torch.cos(position * div_term) + pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0) + + pe_negative[:, 0::2] = torch.sin(-1 * position * div_term) + pe_negative[:, 1::2] = torch.cos(-1 * position * div_term) + pe_negative = pe_negative[1:].unsqueeze(0) + + self.pe = torch.cat([pe_positive, pe_negative], dim=1).to( + dtype=x.dtype, device=x.device + ) + + def forward(self, x: torch.Tensor, left_context: int = 0) -> torch.Tensor: + """Compute positional encoding. + Args: + x: Input sequences. (B, T, ?) + left_context: Number of frames in left context. + Returns: + pos_enc: Positional embedding sequences. (B, 2 * (T - 1), ?) + """ + self.extend_pe(x, left_context=left_context) + + time1 = x.size(1) + left_context + + pos_enc = self.pe[ + :, self.pe.size(1) // 2 - time1 + 1 : self.pe.size(1) // 2 + x.size(1) + ] + pos_enc = self.dropout(pos_enc) + + return pos_enc + + +class ScaledSinuEmbedding(torch.nn.Module): + def __init__(self, dim): + super().__init__() + self.scale = torch.nn.Parameter( + torch.ones( + 1, + ) + ) + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer("inv_freq", inv_freq) + + def forward(self, x): + n, device = x.shape[1], x.device + t = torch.arange(n, device=device).type_as(self.inv_freq) + sinu = einsum("i , j -> i j", t, self.inv_freq) + emb = torch.cat((sinu.sin(), sinu.cos()), dim=-1) + return emb * self.scale diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/encoder.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..3c72d588c457985898efbcbb6b938c1e2c1ca9fb --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/encoder.py @@ -0,0 +1,335 @@ +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Transformer encoder definition.""" + +from typing import List +from typing import Optional +from typing import Tuple + +import torch +from torch import nn +import logging + +from funasr_detach.models.transformer.attention import MultiHeadedAttention +from funasr_detach.models.transformer.embedding import PositionalEncoding +from funasr_detach.models.transformer.layer_norm import LayerNorm +from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear +from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask +from funasr_detach.models.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, +) +from funasr_detach.models.transformer.utils.repeat import repeat +from funasr_detach.models.ctc.ctc import CTC + +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling2 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling6 +from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling8 +from funasr_detach.models.transformer.utils.subsampling import TooShortUttError +from funasr_detach.models.transformer.utils.subsampling import check_short_utt + +from funasr_detach.register import tables + + +class EncoderLayer(nn.Module): + """Encoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance + can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance + can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): Whether to use layer_norm before the first block. + concat_after (bool): Whether to concat attention layer's input and output. + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. i.e. x -> x + att(x) + stochastic_depth_rate (float): Proability to skip this layer. + During training, the layer may skip residual computation and return input + as-is with given probability. + """ + + def __init__( + self, + size, + self_attn, + feed_forward, + dropout_rate, + normalize_before=True, + concat_after=False, + stochastic_depth_rate=0.0, + ): + """Construct an EncoderLayer object.""" + super(EncoderLayer, self).__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.norm1 = LayerNorm(size) + self.norm2 = LayerNorm(size) + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + self.concat_after = concat_after + if self.concat_after: + self.concat_linear = nn.Linear(size + size, size) + self.stochastic_depth_rate = stochastic_depth_rate + + def forward(self, x, mask, cache=None): + """Compute encoded features. + + Args: + x_input (torch.Tensor): Input tensor (#batch, time, size). + mask (torch.Tensor): Mask tensor for the input (#batch, time). + cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time). + + """ + skip_layer = False + # with stochastic depth, residual connection `x + f(x)` becomes + # `x <- x + 1 / (1 - p) * f(x)` at training time. + stoch_layer_coeff = 1.0 + if self.training and self.stochastic_depth_rate > 0: + skip_layer = torch.rand(1).item() < self.stochastic_depth_rate + stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate) + + if skip_layer: + if cache is not None: + x = torch.cat([cache, x], dim=1) + return x, mask + + residual = x + if self.normalize_before: + x = self.norm1(x) + + if cache is None: + x_q = x + else: + assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size) + x_q = x[:, -1:, :] + residual = residual[:, -1:, :] + mask = None if mask is None else mask[:, -1:, :] + + if self.concat_after: + x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1) + x = residual + stoch_layer_coeff * self.concat_linear(x_concat) + else: + x = residual + stoch_layer_coeff * self.dropout( + self.self_attn(x_q, x, x, mask) + ) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm2(x) + + if cache is not None: + x = torch.cat([cache, x], dim=1) + + return x, mask + + +@tables.register("encoder_classes", "TransformerEncoder") +class TransformerEncoder(nn.Module): + """Transformer encoder module. + + Args: + input_size: input dim + output_size: dimension of attention + attention_heads: the number of heads of multi head attention + linear_units: the number of units of position-wise feed forward + num_blocks: the number of decoder blocks + dropout_rate: dropout rate + attention_dropout_rate: dropout rate in attention + positional_dropout_rate: dropout rate after adding positional encoding + input_layer: input layer type + pos_enc_class: PositionalEncoding or ScaledPositionalEncoding + normalize_before: whether to use layer_norm before the first block + concat_after: whether to concat attention layer's input and output + if True, additional linear will be applied. + i.e. x -> x + linear(concat(x, att(x))) + if False, no additional linear will be applied. + i.e. x -> x + att(x) + positionwise_layer_type: linear of conv1d + positionwise_conv_kernel_size: kernel size of positionwise conv1d layer + padding_idx: padding_idx for input_layer=embed + """ + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: Optional[str] = "conv2d", + pos_enc_class=PositionalEncoding, + normalize_before: bool = True, + concat_after: bool = False, + positionwise_layer_type: str = "linear", + positionwise_conv_kernel_size: int = 1, + padding_idx: int = -1, + interctc_layer_idx: List[int] = [], + interctc_use_conditioning: bool = False, + ): + super().__init__() + self._output_size = output_size + + if input_layer == "linear": + self.embed = torch.nn.Sequential( + torch.nn.Linear(input_size, output_size), + torch.nn.LayerNorm(output_size), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer == "conv2d": + self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate) + elif input_layer == "conv2d2": + self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate) + elif input_layer == "conv2d6": + self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate) + elif input_layer == "conv2d8": + self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate) + elif input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), + pos_enc_class(output_size, positional_dropout_rate), + ) + elif input_layer is None: + if input_size == output_size: + self.embed = None + else: + self.embed = torch.nn.Linear(input_size, output_size) + else: + raise ValueError("unknown input_layer: " + input_layer) + self.normalize_before = normalize_before + if positionwise_layer_type == "linear": + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d": + positionwise_layer = MultiLayeredConv1d + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + elif positionwise_layer_type == "conv1d-linear": + positionwise_layer = Conv1dLinear + positionwise_layer_args = ( + output_size, + linear_units, + positionwise_conv_kernel_size, + dropout_rate, + ) + else: + raise NotImplementedError("Support only linear or conv1d.") + self.encoders = repeat( + num_blocks, + lambda lnum: EncoderLayer( + output_size, + MultiHeadedAttention( + attention_heads, output_size, attention_dropout_rate + ), + positionwise_layer(*positionwise_layer_args), + dropout_rate, + normalize_before, + concat_after, + ), + ) + if self.normalize_before: + self.after_norm = LayerNorm(output_size) + + self.interctc_layer_idx = interctc_layer_idx + if len(interctc_layer_idx) > 0: + assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks + self.interctc_use_conditioning = interctc_use_conditioning + self.conditioning_layer = None + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs_pad: torch.Tensor, + ilens: torch.Tensor, + prev_states: torch.Tensor = None, + ctc: CTC = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """Embed positions in tensor. + + Args: + xs_pad: input tensor (B, L, D) + ilens: input length (B) + prev_states: Not to be used now. + Returns: + position embedded tensor and mask + """ + masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) + + if self.embed is None: + xs_pad = xs_pad + elif ( + isinstance(self.embed, Conv2dSubsampling) + or isinstance(self.embed, Conv2dSubsampling2) + or isinstance(self.embed, Conv2dSubsampling6) + or isinstance(self.embed, Conv2dSubsampling8) + ): + short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) + if short_status: + raise TooShortUttError( + f"has {xs_pad.size(1)} frames and is too short for subsampling " + + f"(it needs more than {limit_size} frames), return empty results", + xs_pad.size(1), + limit_size, + ) + xs_pad, masks = self.embed(xs_pad, masks) + else: + xs_pad = self.embed(xs_pad) + + intermediate_outs = [] + if len(self.interctc_layer_idx) == 0: + xs_pad, masks = self.encoders(xs_pad, masks) + else: + for layer_idx, encoder_layer in enumerate(self.encoders): + xs_pad, masks = encoder_layer(xs_pad, masks) + + if layer_idx + 1 in self.interctc_layer_idx: + encoder_out = xs_pad + + # intermediate outputs are also normalized + if self.normalize_before: + encoder_out = self.after_norm(encoder_out) + + intermediate_outs.append((layer_idx + 1, encoder_out)) + + if self.interctc_use_conditioning: + ctc_out = ctc.softmax(encoder_out) + xs_pad = xs_pad + self.conditioning_layer(ctc_out) + + if self.normalize_before: + xs_pad = self.after_norm(xs_pad) + + olens = masks.squeeze(1).sum(1) + if len(intermediate_outs) > 0: + return (xs_pad, intermediate_outs), olens, None + return xs_pad, olens, None diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/layer_norm.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/layer_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..6aec37500abd2a17ad7da57e5bf37e46fcf328df --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/layer_norm.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Layer normalization module.""" + +import torch +import torch.nn as nn + + +class LayerNorm(torch.nn.LayerNorm): + """Layer normalization module. + + Args: + nout (int): Output dim size. + dim (int): Dimension to be normalized. + + """ + + def __init__(self, nout, dim=-1): + """Construct an LayerNorm object.""" + super(LayerNorm, self).__init__(nout, eps=1e-12) + self.dim = dim + + def forward(self, x): + """Apply layer normalization. + + Args: + x (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Normalized tensor. + + """ + if self.dim == -1: + return super(LayerNorm, self).forward(x) + return ( + super(LayerNorm, self) + .forward(x.transpose(self.dim, -1)) + .transpose(self.dim, -1) + ) + + +class GlobalLayerNorm(nn.Module): + """Calculate Global Layer Normalization. + + Arguments + --------- + dim : (int or list or torch.Size) + Input shape from an expected input of size. + eps : float + A value added to the denominator for numerical stability. + elementwise_affine : bool + A boolean value that when set to True, + this module has learnable per-element affine parameters + initialized to ones (for weights) and zeros (for biases). + + Example + ------- + >>> x = torch.randn(5, 10, 20) + >>> GLN = GlobalLayerNorm(10, 3) + >>> x_norm = GLN(x) + """ + + def __init__(self, dim, shape, eps=1e-8, elementwise_affine=True): + super(GlobalLayerNorm, self).__init__() + self.dim = dim + self.eps = eps + self.elementwise_affine = elementwise_affine + + if self.elementwise_affine: + if shape == 3: + self.weight = nn.Parameter(torch.ones(self.dim, 1)) + self.bias = nn.Parameter(torch.zeros(self.dim, 1)) + if shape == 4: + self.weight = nn.Parameter(torch.ones(self.dim, 1, 1)) + self.bias = nn.Parameter(torch.zeros(self.dim, 1, 1)) + else: + self.register_parameter("weight", None) + self.register_parameter("bias", None) + + def forward(self, x): + """Returns the normalized tensor. + + Arguments + --------- + x : torch.Tensor + Tensor of size [N, C, K, S] or [N, C, L]. + """ + # x = N x C x K x S or N x C x L + # N x 1 x 1 + # cln: mean,var N x 1 x K x S + # gln: mean,var N x 1 x 1 + if x.dim() == 3: + mean = torch.mean(x, (1, 2), keepdim=True) + var = torch.mean((x - mean) ** 2, (1, 2), keepdim=True) + if self.elementwise_affine: + x = self.weight * (x - mean) / torch.sqrt(var + self.eps) + self.bias + else: + x = (x - mean) / torch.sqrt(var + self.eps) + + if x.dim() == 4: + mean = torch.mean(x, (1, 2, 3), keepdim=True) + var = torch.mean((x - mean) ** 2, (1, 2, 3), keepdim=True) + if self.elementwise_affine: + x = self.weight * (x - mean) / torch.sqrt(var + self.eps) + self.bias + else: + x = (x - mean) / torch.sqrt(var + self.eps) + return x + + +class CumulativeLayerNorm(nn.LayerNorm): + """Calculate Cumulative Layer Normalization. + + Arguments + --------- + dim : int + Dimension that you want to normalize. + elementwise_affine : True + Learnable per-element affine parameters. + + Example + ------- + >>> x = torch.randn(5, 10, 20) + >>> CLN = CumulativeLayerNorm(10) + >>> x_norm = CLN(x) + """ + + def __init__(self, dim, elementwise_affine=True): + super(CumulativeLayerNorm, self).__init__( + dim, elementwise_affine=elementwise_affine, eps=1e-8 + ) + + def forward(self, x): + """Returns the normalized tensor. + + Arguments + --------- + x : torch.Tensor + Tensor size [N, C, K, S] or [N, C, L] + """ + # x: N x C x K x S or N x C x L + # N x K x S x C + if x.dim() == 4: + x = x.permute(0, 2, 3, 1).contiguous() + # N x K x S x C == only channel norm + x = super().forward(x) + # N x C x K x S + x = x.permute(0, 3, 1, 2).contiguous() + if x.dim() == 3: + x = torch.transpose(x, 1, 2) + # N x L x C == only channel norm + x = super().forward(x) + # N x C x L + x = torch.transpose(x, 1, 2) + return x + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.scale = dim**-0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/model.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..5cca392848385f5fe3f4e1808a969db855209f24 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/model.py @@ -0,0 +1,478 @@ +import logging +from typing import Union, Dict, List, Tuple, Optional + +import time +import torch +import torch.nn as nn +from torch.cuda.amp import autocast + +from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.metrics.compute_acc import th_accuracy + +# from funasr_detach.models.e2e_asr_common import ErrorCalculator +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank +from funasr_detach.utils import postprocess_utils +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.register import tables + + +@tables.register("model_classes", "Transformer") +class Transformer(nn.Module): + """CTC-attention hybrid Encoder-Decoder model""" + + def __init__( + self, + specaug: str = None, + specaug_conf: dict = None, + normalize: str = None, + normalize_conf: dict = None, + encoder: str = None, + encoder_conf: dict = None, + decoder: str = None, + decoder_conf: dict = None, + ctc: str = None, + ctc_conf: dict = None, + ctc_weight: float = 0.5, + interctc_weight: float = 0.0, + input_size: int = 80, + vocab_size: int = -1, + ignore_id: int = -1, + blank_id: int = 0, + sos: int = 1, + eos: int = 2, + lsm_weight: float = 0.0, + length_normalized_loss: bool = False, + report_cer: bool = True, + report_wer: bool = True, + sym_space: str = "", + sym_blank: str = "", + # extract_feats_in_collect_stats: bool = True, + share_embedding: bool = False, + # preencoder: Optional[AbsPreEncoder] = None, + # postencoder: Optional[AbsPostEncoder] = None, + **kwargs, + ): + + super().__init__() + + if specaug is not None: + specaug_class = tables.specaug_classes.get(specaug) + specaug = specaug_class(**specaug_conf) + if normalize is not None: + normalize_class = tables.normalize_classes.get(normalize) + normalize = normalize_class(**normalize_conf) + encoder_class = tables.encoder_classes.get(encoder) + encoder = encoder_class(input_size=input_size, **encoder_conf) + encoder_output_size = encoder.output_size() + if decoder is not None: + decoder_class = tables.decoder_classes.get(decoder) + decoder = decoder_class( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + **decoder_conf, + ) + if ctc_weight > 0.0: + + if ctc_conf is None: + ctc_conf = {} + + ctc = CTC( + odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf + ) + + self.blank_id = blank_id + self.sos = sos if sos is not None else vocab_size - 1 + self.eos = eos if eos is not None else vocab_size - 1 + self.vocab_size = vocab_size + self.ignore_id = ignore_id + self.ctc_weight = ctc_weight + self.specaug = specaug + self.normalize = normalize + self.encoder = encoder + + if not hasattr(self.encoder, "interctc_use_conditioning"): + self.encoder.interctc_use_conditioning = False + if self.encoder.interctc_use_conditioning: + self.encoder.conditioning_layer = torch.nn.Linear( + vocab_size, self.encoder.output_size() + ) + self.interctc_weight = interctc_weight + + # self.error_calculator = None + if ctc_weight == 1.0: + self.decoder = None + else: + self.decoder = decoder + + self.criterion_att = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + # + # if report_cer or report_wer: + # self.error_calculator = ErrorCalculator( + # token_list, sym_space, sym_blank, report_cer, report_wer + # ) + # + self.error_calculator = None + if ctc_weight == 0.0: + self.ctc = None + else: + self.ctc = ctc + + self.share_embedding = share_embedding + if self.share_embedding: + self.decoder.embed = None + + self.length_normalized_loss = length_normalized_loss + self.beam_search = None + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + # import pdb; + # pdb.set_trace() + if len(text_lengths.size()) > 1: + text_lengths = text_lengths[:, 0] + if len(speech_lengths.size()) > 1: + speech_lengths = speech_lengths[:, 0] + + batch_size = speech.shape[0] + + # 1. Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + intermediate_outs = None + if isinstance(encoder_out, tuple): + intermediate_outs = encoder_out[1] + encoder_out = encoder_out[0] + + loss_att, acc_att, cer_att, wer_att = None, None, None, None + loss_ctc, cer_ctc = None, None + stats = dict() + + # decoder: CTC branch + if self.ctc_weight != 0.0: + loss_ctc, cer_ctc = self._calc_ctc_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # Collect CTC branch stats + stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None + stats["cer_ctc"] = cer_ctc + + # Intermediate CTC (optional) + loss_interctc = 0.0 + if self.interctc_weight != 0.0 and intermediate_outs is not None: + for layer_idx, intermediate_out in intermediate_outs: + # we assume intermediate_out has the same length & padding + # as those of encoder_out + loss_ic, cer_ic = self._calc_ctc_loss( + intermediate_out, encoder_out_lens, text, text_lengths + ) + loss_interctc = loss_interctc + loss_ic + + # Collect Intermedaite CTC stats + stats["loss_interctc_layer{}".format(layer_idx)] = ( + loss_ic.detach() if loss_ic is not None else None + ) + stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic + + loss_interctc = loss_interctc / len(intermediate_outs) + + # calculate whole encoder loss + loss_ctc = ( + 1 - self.interctc_weight + ) * loss_ctc + self.interctc_weight * loss_interctc + + # decoder: Attention decoder branch + loss_att, acc_att, cer_att, wer_att = self._calc_att_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # 3. CTC-Att loss definition + if self.ctc_weight == 0.0: + loss = loss_att + elif self.ctc_weight == 1.0: + loss = loss_ctc + else: + loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + + # Collect Attn branch stats + stats["loss_att"] = loss_att.detach() if loss_att is not None else None + stats["acc"] = acc_att + stats["cer"] = cer_att + stats["wer"] = wer_att + + # Collect total loss stats + stats["loss"] = torch.clone(loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + if self.length_normalized_loss: + batch_size = int((text_lengths + 1).sum()) + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def encode( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Frontend + Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + ind: int + """ + with autocast(False): + + # Data augmentation + if self.specaug is not None and self.training: + speech, speech_lengths = self.specaug(speech, speech_lengths) + + # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + speech, speech_lengths = self.normalize(speech, speech_lengths) + + # Forward encoder + # feats: (Batch, Length, Dim) + # -> encoder_out: (Batch, Length2, Dim2) + if self.encoder.interctc_use_conditioning: + encoder_out, encoder_out_lens, _ = self.encoder( + speech, speech_lengths, ctc=self.ctc + ) + else: + encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths) + intermediate_outs = None + if isinstance(encoder_out, tuple): + intermediate_outs = encoder_out[1] + encoder_out = encoder_out[0] + + if intermediate_outs is not None: + return (encoder_out, intermediate_outs), encoder_out_lens + + return encoder_out, encoder_out_lens + + def _calc_att_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + # 1. Forward decoder + decoder_out, _ = self.decoder( + encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens + ) + + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_out_pad) + acc_att = th_accuracy( + decoder_out.view(-1, self.vocab_size), + ys_out_pad, + ignore_label=self.ignore_id, + ) + + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att + + def _calc_ctc_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + # Calc CTC loss + loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens) + + # Calc CER using CTC + cer_ctc = None + if not self.training and self.error_calculator is not None: + ys_hat = self.ctc.argmax(encoder_out).data + cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True) + return loss_ctc, cer_ctc + + def init_beam_search( + self, + **kwargs, + ): + from funasr_detach.models.transformer.search import BeamSearch + from funasr_detach.models.transformer.scorers.ctc import CTCPrefixScorer + from funasr_detach.models.transformer.scorers.length_bonus import LengthBonus + + # 1. Build ASR model + scorers = {} + + if self.ctc != None: + ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos) + scorers.update(ctc=ctc) + token_list = kwargs.get("token_list") + scorers.update( + decoder=self.decoder, + length_bonus=LengthBonus(len(token_list)), + ) + + # 3. Build ngram model + # ngram is not supported now + ngram = None + scorers["ngram"] = ngram + + weights = dict( + decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.5), + ctc=kwargs.get("decoding_ctc_weight", 0.5), + lm=kwargs.get("lm_weight", 0.0), + ngram=kwargs.get("ngram_weight", 0.0), + length_bonus=kwargs.get("penalty", 0.0), + ) + beam_search = BeamSearch( + beam_size=kwargs.get("beam_size", 10), + weights=weights, + scorers=scorers, + sos=self.sos, + eos=self.eos, + vocab_size=len(token_list), + token_list=token_list, + pre_beam_score_key=None if self.ctc_weight == 1.0 else "full", + ) + + self.beam_search = beam_search + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + + if kwargs.get("batch_size", 1) > 1: + raise NotImplementedError("batch decoding is not implemented") + + # init beamsearch + if self.beam_search is None: + logging.info("enable beam_search") + self.init_beam_search(**kwargs) + self.nbest = kwargs.get("nbest", 1) + + meta_data = {} + if ( + isinstance(data_in, torch.Tensor) + and kwargs.get("data_type", "sound") == "fbank" + ): # fbank + speech, speech_lengths = data_in, data_lengths + if len(speech.shape) < 3: + speech = speech[None, :, :] + if speech_lengths is None: + speech_lengths = speech.shape[1] + else: + # extract fbank feats + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_in, + fs=frontend.fs, + audio_fs=kwargs.get("fs", 16000), + data_type=kwargs.get("data_type", "sound"), + tokenizer=tokenizer, + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths = extract_fbank( + audio_sample_list, + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() + * frontend.frame_shift + * frontend.lfr_n + / 1000 + ) + + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + # Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + # c. Passed the encoder result and the beam search + nbest_hyps = self.beam_search( + x=encoder_out[0], + maxlenratio=kwargs.get("maxlenratio", 0.0), + minlenratio=kwargs.get("minlenratio", 0.0), + ) + + nbest_hyps = nbest_hyps[: self.nbest] + + results = [] + b, n, d = encoder_out.size() + for i in range(b): + + for nbest_idx, hyp in enumerate(nbest_hyps): + ibest_writer = None + if kwargs.get("output_dir") is not None: + if not hasattr(self, "writer"): + self.writer = DatadirWriter(kwargs.get("output_dir")) + ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"] + + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list( + filter( + lambda x: x != self.eos + and x != self.sos + and x != self.blank_id, + token_int, + ) + ) + + # Change integer-ids to tokens + token = tokenizer.ids2tokens(token_int) + text = tokenizer.tokens2text(token) + + text_postprocessed, _ = postprocess_utils.sentence_postprocess(token) + result_i = {"key": key[i], "token": token, "text": text_postprocessed} + results.append(result_i) + + if ibest_writer is not None: + ibest_writer["token"][key[i]] = " ".join(token) + ibest_writer["text"][key[i]] = text_postprocessed + + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/positionwise_feed_forward.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/positionwise_feed_forward.py new file mode 100644 index 0000000000000000000000000000000000000000..4fab20ca4b8951d5d943c21be7553af368c2395a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/positionwise_feed_forward.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Positionwise feed forward layer definition.""" + +import torch + +from funasr_detach.models.transformer.layer_norm import LayerNorm + + +class PositionwiseFeedForward(torch.nn.Module): + """Positionwise feed forward layer. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()): + """Construct an PositionwiseFeedForward object.""" + super(PositionwiseFeedForward, self).__init__() + self.w_1 = torch.nn.Linear(idim, hidden_units) + self.w_2 = torch.nn.Linear(hidden_units, idim) + self.dropout = torch.nn.Dropout(dropout_rate) + self.activation = activation + + def forward(self, x): + """Forward function.""" + return self.w_2(self.dropout(self.activation(self.w_1(x)))) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..24134d8b02377f5fd6223a12b470d09f4124cfe7 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/__init__.py @@ -0,0 +1 @@ +"""Initialize sub package.""" diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/ctc.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/ctc.py new file mode 100644 index 0000000000000000000000000000000000000000..b3ff45c7c37f85241a2bcbc1625b78d4f73cde9a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/ctc.py @@ -0,0 +1,160 @@ +"""ScorerInterface implementation for CTC.""" + +import numpy as np +import torch + +from funasr_detach.models.transformer.scorers.ctc_prefix_score import CTCPrefixScore +from funasr_detach.models.transformer.scorers.ctc_prefix_score import CTCPrefixScoreTH +from funasr_detach.models.transformer.scorers.scorer_interface import ( + BatchPartialScorerInterface, +) + + +class CTCPrefixScorer(BatchPartialScorerInterface): + """Decoder interface wrapper for CTCPrefixScore.""" + + def __init__(self, ctc: torch.nn.Module, eos: int): + """Initialize class. + + Args: + ctc (torch.nn.Module): The CTC implementation. + For example, :class:`espnet.nets.pytorch_backend.ctc.CTC` + eos (int): The end-of-sequence id. + + """ + self.ctc = ctc + self.eos = eos + self.impl = None + + def init_state(self, x: torch.Tensor): + """Get an initial state for decoding. + + Args: + x (torch.Tensor): The encoded feature tensor + + Returns: initial state + + """ + logp = self.ctc.log_softmax(x.unsqueeze(0)).detach().squeeze(0).cpu().numpy() + # TODO(karita): use CTCPrefixScoreTH + self.impl = CTCPrefixScore(logp, 0, self.eos, np) + return 0, self.impl.initial_state() + + def select_state(self, state, i, new_id=None): + """Select state with relative ids in the main beam search. + + Args: + state: Decoder state for prefix tokens + i (int): Index to select a state in the main beam search + new_id (int): New label id to select a state if necessary + + Returns: + state: pruned state + + """ + if type(state) == tuple: + if len(state) == 2: # for CTCPrefixScore + sc, st = state + return sc[i], st[i] + else: # for CTCPrefixScoreTH (need new_id > 0) + r, log_psi, f_min, f_max, scoring_idmap = state + s = log_psi[i, new_id].expand(log_psi.size(1)) + if scoring_idmap is not None: + return r[:, :, i, scoring_idmap[i, new_id]], s, f_min, f_max + else: + return r[:, :, i, new_id], s, f_min, f_max + return None if state is None else state[i] + + def score_partial(self, y, ids, state, x): + """Score new token. + + Args: + y (torch.Tensor): 1D prefix token + next_tokens (torch.Tensor): torch.int64 next token to score + state: decoder state for prefix tokens + x (torch.Tensor): 2D encoder feature that generates ys + + Returns: + tuple[torch.Tensor, Any]: + Tuple of a score tensor for y that has a shape `(len(next_tokens),)` + and next state for ys + + """ + prev_score, state = state + presub_score, new_st = self.impl(y.cpu(), ids.cpu(), state) + tscore = torch.as_tensor( + presub_score - prev_score, device=x.device, dtype=x.dtype + ) + return tscore, (presub_score, new_st) + + def batch_init_state(self, x: torch.Tensor): + """Get an initial state for decoding. + + Args: + x (torch.Tensor): The encoded feature tensor + + Returns: initial state + + """ + logp = self.ctc.log_softmax(x.unsqueeze(0)) # assuming batch_size = 1 + xlen = torch.tensor([logp.size(1)]) + self.impl = CTCPrefixScoreTH(logp, xlen, 0, self.eos) + return None + + def batch_score_partial(self, y, ids, state, x): + """Score new token. + + Args: + y (torch.Tensor): 1D prefix token + ids (torch.Tensor): torch.int64 next token to score + state: decoder state for prefix tokens + x (torch.Tensor): 2D encoder feature that generates ys + + Returns: + tuple[torch.Tensor, Any]: + Tuple of a score tensor for y that has a shape `(len(next_tokens),)` + and next state for ys + + """ + batch_state = ( + ( + torch.stack([s[0] for s in state], dim=2), + torch.stack([s[1] for s in state]), + state[0][2], + state[0][3], + ) + if state[0] is not None + else None + ) + return self.impl(y, batch_state, ids) + + def extend_prob(self, x: torch.Tensor): + """Extend probs for decoding. + + This extension is for streaming decoding + as in Eq (14) in https://arxiv.org/abs/2006.14941 + + Args: + x (torch.Tensor): The encoded feature tensor + + """ + logp = self.ctc.log_softmax(x.unsqueeze(0)) + self.impl.extend_prob(logp) + + def extend_state(self, state): + """Extend state for decoding. + + This extension is for streaming decoding + as in Eq (14) in https://arxiv.org/abs/2006.14941 + + Args: + state: The states of hyps + + Returns: exteded state + + """ + new_state = [] + for s in state: + new_state.append(self.impl.extend_state(s)) + + return new_state diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/ctc_prefix_score.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/ctc_prefix_score.py new file mode 100644 index 0000000000000000000000000000000000000000..22a130dfa1f5270cc6926b138e2381a96b8c3d31 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/ctc_prefix_score.py @@ -0,0 +1,359 @@ +#!/usr/bin/env python3 + +# Copyright 2018 Mitsubishi Electric Research Labs (Takaaki Hori) +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +import torch + +import numpy as np +import six + + +class CTCPrefixScoreTH(object): + """Batch processing of CTCPrefixScore + + which is based on Algorithm 2 in WATANABE et al. + "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION," + but extended to efficiently compute the label probablities for multiple + hypotheses simultaneously + See also Seki et al. "Vectorized Beam Search for CTC-Attention-Based + Speech Recognition," In INTERSPEECH (pp. 3825-3829), 2019. + """ + + def __init__(self, x, xlens, blank, eos, margin=0): + """Construct CTC prefix scorer + + :param torch.Tensor x: input label posterior sequences (B, T, O) + :param torch.Tensor xlens: input lengths (B,) + :param int blank: blank label id + :param int eos: end-of-sequence id + :param int margin: margin parameter for windowing (0 means no windowing) + """ + # In the comment lines, + # we assume T: input_length, B: batch size, W: beam width, O: output dim. + self.logzero = -10000000000.0 + self.blank = blank + self.eos = eos + self.batch = x.size(0) + self.input_length = x.size(1) + self.odim = x.size(2) + self.dtype = x.dtype + self.device = ( + torch.device("cuda:%d" % x.get_device()) + if x.is_cuda + else torch.device("cpu") + ) + # Pad the rest of posteriors in the batch + # TODO(takaaki-hori): need a better way without for-loops + for i, l in enumerate(xlens): + if l < self.input_length: + x[i, l:, :] = self.logzero + x[i, l:, blank] = 0 + # Reshape input x + xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O) + xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim) + self.x = torch.stack([xn, xb]) # (2, T, B, O) + self.end_frames = torch.as_tensor(xlens) - 1 + + # Setup CTC windowing + self.margin = margin + if margin > 0: + self.frame_ids = torch.arange( + self.input_length, dtype=self.dtype, device=self.device + ) + # Base indices for index conversion + self.idx_bh = None + self.idx_b = torch.arange(self.batch, device=self.device) + self.idx_bo = (self.idx_b * self.odim).unsqueeze(1) + + def __call__(self, y, state, scoring_ids=None, att_w=None): + """Compute CTC prefix scores for next labels + + :param list y: prefix label sequences + :param tuple state: previous CTC state + :param torch.Tensor pre_scores: scores for pre-selection of hypotheses (BW, O) + :param torch.Tensor att_w: attention weights to decide CTC window + :return new_state, ctc_local_scores (BW, O) + """ + output_length = len(y[0]) - 1 # ignore sos + last_ids = [yi[-1] for yi in y] # last output label ids + n_bh = len(last_ids) # batch * hyps + n_hyps = n_bh // self.batch # assuming each utterance has the same # of hyps + self.scoring_num = scoring_ids.size(-1) if scoring_ids is not None else 0 + # prepare state info + if state is None: + r_prev = torch.full( + (self.input_length, 2, self.batch, n_hyps), + self.logzero, + dtype=self.dtype, + device=self.device, + ) + r_prev[:, 1] = torch.cumsum(self.x[0, :, :, self.blank], 0).unsqueeze(2) + r_prev = r_prev.view(-1, 2, n_bh) + s_prev = 0.0 + f_min_prev = 0 + f_max_prev = 1 + else: + r_prev, s_prev, f_min_prev, f_max_prev = state + + # select input dimensions for scoring + if self.scoring_num > 0: + scoring_idmap = torch.full( + (n_bh, self.odim), -1, dtype=torch.long, device=self.device + ) + snum = self.scoring_num + if self.idx_bh is None or n_bh > len(self.idx_bh): + self.idx_bh = torch.arange(n_bh, device=self.device).view(-1, 1) + scoring_idmap[self.idx_bh[:n_bh], scoring_ids] = torch.arange( + snum, device=self.device + ) + scoring_idx = ( + scoring_ids + self.idx_bo.repeat(1, n_hyps).view(-1, 1) + ).view(-1) + x_ = torch.index_select( + self.x.view(2, -1, self.batch * self.odim), 2, scoring_idx + ).view(2, -1, n_bh, snum) + else: + scoring_ids = None + scoring_idmap = None + snum = self.odim + x_ = self.x.unsqueeze(3).repeat(1, 1, 1, n_hyps, 1).view(2, -1, n_bh, snum) + + # new CTC forward probs are prepared as a (T x 2 x BW x S) tensor + # that corresponds to r_t^n(h) and r_t^b(h) in a batch. + r = torch.full( + (self.input_length, 2, n_bh, snum), + self.logzero, + dtype=self.dtype, + device=self.device, + ) + if output_length == 0: + r[0, 0] = x_[0, 0] + + r_sum = torch.logsumexp(r_prev, 1) + log_phi = r_sum.unsqueeze(2).repeat(1, 1, snum) + if scoring_ids is not None: + for idx in range(n_bh): + pos = scoring_idmap[idx, last_ids[idx]] + if pos >= 0: + log_phi[:, idx, pos] = r_prev[:, 1, idx] + else: + for idx in range(n_bh): + log_phi[:, idx, last_ids[idx]] = r_prev[:, 1, idx] + + # decide start and end frames based on attention weights + if att_w is not None and self.margin > 0: + f_arg = torch.matmul(att_w, self.frame_ids) + f_min = max(int(f_arg.min().cpu()), f_min_prev) + f_max = max(int(f_arg.max().cpu()), f_max_prev) + start = min(f_max_prev, max(f_min - self.margin, output_length, 1)) + end = min(f_max + self.margin, self.input_length) + else: + f_min = f_max = 0 + start = max(output_length, 1) + end = self.input_length + + # compute forward probabilities log(r_t^n(h)) and log(r_t^b(h)) + for t in range(start, end): + rp = r[t - 1] + rr = torch.stack([rp[0], log_phi[t - 1], rp[0], rp[1]]).view( + 2, 2, n_bh, snum + ) + r[t] = torch.logsumexp(rr, 1) + x_[:, t] + + # compute log prefix probabilities log(psi) + log_phi_x = torch.cat((log_phi[0].unsqueeze(0), log_phi[:-1]), dim=0) + x_[0] + if scoring_ids is not None: + log_psi = torch.full( + (n_bh, self.odim), self.logzero, dtype=self.dtype, device=self.device + ) + log_psi_ = torch.logsumexp( + torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0), + dim=0, + ) + for si in range(n_bh): + log_psi[si, scoring_ids[si]] = log_psi_[si] + else: + log_psi = torch.logsumexp( + torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0), + dim=0, + ) + + for si in range(n_bh): + log_psi[si, self.eos] = r_sum[self.end_frames[si // n_hyps], si] + + # exclude blank probs + log_psi[:, self.blank] = self.logzero + + return (log_psi - s_prev), (r, log_psi, f_min, f_max, scoring_idmap) + + def index_select_state(self, state, best_ids): + """Select CTC states according to best ids + + :param state : CTC state + :param best_ids : index numbers selected by beam pruning (B, W) + :return selected_state + """ + r, s, f_min, f_max, scoring_idmap = state + # convert ids to BHO space + n_bh = len(s) + n_hyps = n_bh // self.batch + vidx = (best_ids + (self.idx_b * (n_hyps * self.odim)).view(-1, 1)).view(-1) + # select hypothesis scores + s_new = torch.index_select(s.view(-1), 0, vidx) + s_new = s_new.view(-1, 1).repeat(1, self.odim).view(n_bh, self.odim) + # convert ids to BHS space (S: scoring_num) + if scoring_idmap is not None: + snum = self.scoring_num + hyp_idx = (best_ids // self.odim + (self.idx_b * n_hyps).view(-1, 1)).view( + -1 + ) + label_ids = torch.fmod(best_ids, self.odim).view(-1) + score_idx = scoring_idmap[hyp_idx, label_ids] + score_idx[score_idx == -1] = 0 + vidx = score_idx + hyp_idx * snum + else: + snum = self.odim + # select forward probabilities + r_new = torch.index_select(r.view(-1, 2, n_bh * snum), 2, vidx).view( + -1, 2, n_bh + ) + return r_new, s_new, f_min, f_max + + def extend_prob(self, x): + """Extend CTC prob. + + :param torch.Tensor x: input label posterior sequences (B, T, O) + """ + + if self.x.shape[1] < x.shape[1]: # self.x (2,T,B,O); x (B,T,O) + # Pad the rest of posteriors in the batch + # TODO(takaaki-hori): need a better way without for-loops + xlens = [x.size(1)] + for i, l in enumerate(xlens): + if l < self.input_length: + x[i, l:, :] = self.logzero + x[i, l:, self.blank] = 0 + tmp_x = self.x + xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O) + xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim) + self.x = torch.stack([xn, xb]) # (2, T, B, O) + self.x[:, : tmp_x.shape[1], :, :] = tmp_x + self.input_length = x.size(1) + self.end_frames = torch.as_tensor(xlens) - 1 + + def extend_state(self, state): + """Compute CTC prefix state. + + + :param state : CTC state + :return ctc_state + """ + + if state is None: + # nothing to do + return state + else: + r_prev, s_prev, f_min_prev, f_max_prev = state + + r_prev_new = torch.full( + (self.input_length, 2), + self.logzero, + dtype=self.dtype, + device=self.device, + ) + start = max(r_prev.shape[0], 1) + r_prev_new[0:start] = r_prev + for t in six.moves.range(start, self.input_length): + r_prev_new[t, 1] = r_prev_new[t - 1, 1] + self.x[0, t, :, self.blank] + + return (r_prev_new, s_prev, f_min_prev, f_max_prev) + + +class CTCPrefixScore(object): + """Compute CTC label sequence scores + + which is based on Algorithm 2 in WATANABE et al. + "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION," + but extended to efficiently compute the probablities of multiple labels + simultaneously + """ + + def __init__(self, x, blank, eos, xp): + self.xp = xp + self.logzero = -10000000000.0 + self.blank = blank + self.eos = eos + self.input_length = len(x) + self.x = x + + def initial_state(self): + """Obtain an initial CTC state + + :return: CTC state + """ + # initial CTC state is made of a frame x 2 tensor that corresponds to + # r_t^n() and r_t^b(), where 0 and 1 of axis=1 represent + # superscripts n and b (non-blank and blank), respectively. + r = self.xp.full((self.input_length, 2), self.logzero, dtype=np.float32) + r[0, 1] = self.x[0, self.blank] + for i in six.moves.range(1, self.input_length): + r[i, 1] = r[i - 1, 1] + self.x[i, self.blank] + return r + + def __call__(self, y, cs, r_prev): + """Compute CTC prefix scores for next labels + + :param y : prefix label sequence + :param cs : array of next labels + :param r_prev: previous CTC state + :return ctc_scores, ctc_states + """ + # initialize CTC states + output_length = len(y) - 1 # ignore sos + # new CTC states are prepared as a frame x (n or b) x n_labels tensor + # that corresponds to r_t^n(h) and r_t^b(h). + r = self.xp.ndarray((self.input_length, 2, len(cs)), dtype=np.float32) + xs = self.x[:, cs] + if output_length == 0: + r[0, 0] = xs[0] + r[0, 1] = self.logzero + else: + r[output_length - 1] = self.logzero + + # prepare forward probabilities for the last label + r_sum = self.xp.logaddexp( + r_prev[:, 0], r_prev[:, 1] + ) # log(r_t^n(g) + r_t^b(g)) + last = y[-1] + if output_length > 0 and last in cs: + log_phi = self.xp.ndarray((self.input_length, len(cs)), dtype=np.float32) + for i in six.moves.range(len(cs)): + log_phi[:, i] = r_sum if cs[i] != last else r_prev[:, 1] + else: + log_phi = r_sum + + # compute forward probabilities log(r_t^n(h)), log(r_t^b(h)), + # and log prefix probabilities log(psi) + start = max(output_length, 1) + log_psi = r[start - 1, 0] + for t in six.moves.range(start, self.input_length): + r[t, 0] = self.xp.logaddexp(r[t - 1, 0], log_phi[t - 1]) + xs[t] + r[t, 1] = ( + self.xp.logaddexp(r[t - 1, 0], r[t - 1, 1]) + self.x[t, self.blank] + ) + log_psi = self.xp.logaddexp(log_psi, log_phi[t - 1] + xs[t]) + + # get P(...eos|X) that ends with the prefix itself + eos_pos = self.xp.where(cs == self.eos)[0] + if len(eos_pos) > 0: + log_psi[eos_pos] = r_sum[-1] # log(r_T^n(g) + r_T^b(g)) + + # exclude blank probs + blank_pos = self.xp.where(cs == self.blank)[0] + if len(blank_pos) > 0: + log_psi[blank_pos] = self.logzero + + # return the log prefix probability and CTC states, where the label axis + # of the CTC states is moved to the first axis to slice it easily + return log_psi, self.xp.rollaxis(r, 2) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/length_bonus.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/length_bonus.py new file mode 100644 index 0000000000000000000000000000000000000000..45475d32692adffa9a439a177b91ff9725bd0caf --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/length_bonus.py @@ -0,0 +1,64 @@ +"""Length bonus module.""" + +from typing import Any +from typing import List +from typing import Tuple + +import torch + +from funasr_detach.models.transformer.scorers.scorer_interface import ( + BatchScorerInterface, +) + + +class LengthBonus(BatchScorerInterface): + """Length bonus in beam search.""" + + def __init__(self, n_vocab: int): + """Initialize class. + + Args: + n_vocab (int): The number of tokens in vocabulary for beam search + + """ + self.n = n_vocab + + def score(self, y, state, x): + """Score new token. + + Args: + y (torch.Tensor): 1D torch.int64 prefix tokens. + state: Scorer state for prefix tokens + x (torch.Tensor): 2D encoder feature that generates ys. + + Returns: + tuple[torch.Tensor, Any]: Tuple of + torch.float32 scores for next token (n_vocab) + and None + + """ + return torch.tensor([1.0], device=x.device, dtype=x.dtype).expand(self.n), None + + def batch_score( + self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor + ) -> Tuple[torch.Tensor, List[Any]]: + """Score new token batch. + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, List[Any]]: Tuple of + batchfied scores for next token with shape of `(n_batch, n_vocab)` + and next state list for ys. + + """ + return ( + torch.tensor([1.0], device=xs.device, dtype=xs.dtype).expand( + ys.shape[0], self.n + ), + None, + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/scorer_interface.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/scorer_interface.py new file mode 100644 index 0000000000000000000000000000000000000000..1a680c742bfa9fc88c9bc17005ae88cd1f851237 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/scorers/scorer_interface.py @@ -0,0 +1,188 @@ +"""Scorer interface module.""" + +from typing import Any +from typing import List +from typing import Tuple + +import torch +import warnings + + +class ScorerInterface: + """Scorer interface for beam search. + + The scorer performs scoring of the all tokens in vocabulary. + + Examples: + * Search heuristics + * :class:`espnet.nets.scorers.length_bonus.LengthBonus` + * Decoder networks of the sequence-to-sequence models + * :class:`espnet.nets.pytorch_backend.nets.transformer.decoder.Decoder` + * :class:`espnet.nets.pytorch_backend.nets.rnn.decoders.Decoder` + * Neural language models + * :class:`espnet.nets.pytorch_backend.lm.transformer.TransformerLM` + * :class:`espnet.nets.pytorch_backend.lm.default.DefaultRNNLM` + * :class:`espnet.nets.pytorch_backend.lm.seq_rnn.SequentialRNNLM` + + """ + + def init_state(self, x: torch.Tensor) -> Any: + """Get an initial state for decoding (optional). + + Args: + x (torch.Tensor): The encoded feature tensor + + Returns: initial state + + """ + return None + + def select_state(self, state: Any, i: int, new_id: int = None) -> Any: + """Select state with relative ids in the main beam search. + + Args: + state: Decoder state for prefix tokens + i (int): Index to select a state in the main beam search + new_id (int): New label index to select a state if necessary + + Returns: + state: pruned state + + """ + return None if state is None else state[i] + + def score( + self, y: torch.Tensor, state: Any, x: torch.Tensor + ) -> Tuple[torch.Tensor, Any]: + """Score new token (required). + + Args: + y (torch.Tensor): 1D torch.int64 prefix tokens. + state: Scorer state for prefix tokens + x (torch.Tensor): The encoder feature that generates ys. + + Returns: + tuple[torch.Tensor, Any]: Tuple of + scores for next token that has a shape of `(n_vocab)` + and next state for ys + + """ + raise NotImplementedError + + def final_score(self, state: Any) -> float: + """Score eos (optional). + + Args: + state: Scorer state for prefix tokens + + Returns: + float: final score + + """ + return 0.0 + + +class BatchScorerInterface(ScorerInterface): + """Batch scorer interface.""" + + def batch_init_state(self, x: torch.Tensor) -> Any: + """Get an initial state for decoding (optional). + + Args: + x (torch.Tensor): The encoded feature tensor + + Returns: initial state + + """ + return self.init_state(x) + + def batch_score( + self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor + ) -> Tuple[torch.Tensor, List[Any]]: + """Score new token batch (required). + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, List[Any]]: Tuple of + batchfied scores for next token with shape of `(n_batch, n_vocab)` + and next state list for ys. + + """ + warnings.warn( + "{} batch score is implemented through for loop not parallelized".format( + self.__class__.__name__ + ) + ) + scores = list() + outstates = list() + for i, (y, state, x) in enumerate(zip(ys, states, xs)): + score, outstate = self.score(y, state, x) + outstates.append(outstate) + scores.append(score) + scores = torch.cat(scores, 0).view(ys.shape[0], -1) + return scores, outstates + + +class PartialScorerInterface(ScorerInterface): + """Partial scorer interface for beam search. + + The partial scorer performs scoring when non-partial scorer finished scoring, + and receives pre-pruned next tokens to score because it is too heavy to score + all the tokens. + + Examples: + * Prefix search for connectionist-temporal-classification models + * :class:`espnet.nets.scorers.ctc.CTCPrefixScorer` + + """ + + def score_partial( + self, y: torch.Tensor, next_tokens: torch.Tensor, state: Any, x: torch.Tensor + ) -> Tuple[torch.Tensor, Any]: + """Score new token (required). + + Args: + y (torch.Tensor): 1D prefix token + next_tokens (torch.Tensor): torch.int64 next token to score + state: decoder state for prefix tokens + x (torch.Tensor): The encoder feature that generates ys + + Returns: + tuple[torch.Tensor, Any]: + Tuple of a score tensor for y that has a shape `(len(next_tokens),)` + and next state for ys + + """ + raise NotImplementedError + + +class BatchPartialScorerInterface(BatchScorerInterface, PartialScorerInterface): + """Batch partial scorer interface for beam search.""" + + def batch_score_partial( + self, + ys: torch.Tensor, + next_tokens: torch.Tensor, + states: List[Any], + xs: torch.Tensor, + ) -> Tuple[torch.Tensor, Any]: + """Score new token (required). + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + next_tokens (torch.Tensor): torch.int64 tokens to score (n_batch, n_token). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, Any]: + Tuple of a score tensor for ys that has a shape `(n_batch, n_vocab)` + and next states for ys + """ + raise NotImplementedError diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/search.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/search.py new file mode 100644 index 0000000000000000000000000000000000000000..9a61f013a06a201faeaeb9bf9aea9671413d9a4c --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/search.py @@ -0,0 +1,462 @@ +from itertools import chain +import logging +from typing import Any +from typing import Dict +from typing import List +from typing import NamedTuple +from typing import Tuple +from typing import Union + +import torch + +from funasr_detach.metrics.common import end_detect +from funasr_detach.models.transformer.scorers.scorer_interface import ( + PartialScorerInterface, +) +from funasr_detach.models.transformer.scorers.scorer_interface import ScorerInterface + + +class Hypothesis(NamedTuple): + """Hypothesis data type.""" + + yseq: torch.Tensor + score: Union[float, torch.Tensor] = 0 + scores: Dict[str, Union[float, torch.Tensor]] = dict() + states: Dict[str, Any] = dict() + + def asdict(self) -> dict: + """Convert data to JSON-friendly dict.""" + return self._replace( + yseq=self.yseq.tolist(), + score=float(self.score), + scores={k: float(v) for k, v in self.scores.items()}, + )._asdict() + + +class BeamSearch(torch.nn.Module): + """Beam search implementation.""" + + def __init__( + self, + scorers: Dict[str, ScorerInterface], + weights: Dict[str, float], + beam_size: int, + vocab_size: int, + sos: int, + eos: int, + token_list: List[str] = None, + pre_beam_ratio: float = 1.5, + pre_beam_score_key: str = None, + ): + """Initialize beam search. + + Args: + scorers (dict[str, ScorerInterface]): Dict of decoder modules + e.g., Decoder, CTCPrefixScorer, LM + The scorer will be ignored if it is `None` + weights (dict[str, float]): Dict of weights for each scorers + The scorer will be ignored if its weight is 0 + beam_size (int): The number of hypotheses kept during search + vocab_size (int): The number of vocabulary + sos (int): Start of sequence id + eos (int): End of sequence id + token_list (list[str]): List of tokens for debug log + pre_beam_score_key (str): key of scores to perform pre-beam search + pre_beam_ratio (float): beam size in the pre-beam search + will be `int(pre_beam_ratio * beam_size)` + + """ + super().__init__() + # set scorers + self.weights = weights + self.scorers = dict() + self.full_scorers = dict() + self.part_scorers = dict() + # this module dict is required for recursive cast + # `self.to(device, dtype)` in `recog.py` + self.nn_dict = torch.nn.ModuleDict() + for k, v in scorers.items(): + w = weights.get(k, 0) + if w == 0 or v is None: + continue + assert isinstance( + v, ScorerInterface + ), f"{k} ({type(v)}) does not implement ScorerInterface" + self.scorers[k] = v + if isinstance(v, PartialScorerInterface): + self.part_scorers[k] = v + else: + self.full_scorers[k] = v + if isinstance(v, torch.nn.Module): + self.nn_dict[k] = v + + # set configurations + self.sos = sos + self.eos = eos + self.token_list = token_list + self.pre_beam_size = int(pre_beam_ratio * beam_size) + self.beam_size = beam_size + self.n_vocab = vocab_size + if ( + pre_beam_score_key is not None + and pre_beam_score_key != "full" + and pre_beam_score_key not in self.full_scorers + ): + raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}") + self.pre_beam_score_key = pre_beam_score_key + self.do_pre_beam = ( + self.pre_beam_score_key is not None + and self.pre_beam_size < self.n_vocab + and len(self.part_scorers) > 0 + ) + + def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]: + """Get an initial hypothesis data. + + Args: + x (torch.Tensor): The encoder output feature + + Returns: + Hypothesis: The initial hypothesis. + + """ + init_states = dict() + init_scores = dict() + for k, d in self.scorers.items(): + init_states[k] = d.init_state(x) + init_scores[k] = 0.0 + return [ + Hypothesis( + score=0.0, + scores=init_scores, + states=init_states, + yseq=torch.tensor([self.sos], device=x.device), + ) + ] + + @staticmethod + def append_token(xs: torch.Tensor, x: int) -> torch.Tensor: + """Append new token to prefix tokens. + + Args: + xs (torch.Tensor): The prefix token + x (int): The new token to append + + Returns: + torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device + + """ + x = torch.tensor([x], dtype=xs.dtype, device=xs.device) + return torch.cat((xs, x)) + + def score_full( + self, hyp: Hypothesis, x: torch.Tensor + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.full_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.full_scorers` + and tensor score values of shape: `(self.n_vocab,)`, + and state dict that has string keys + and state values of `self.full_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.full_scorers.items(): + scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x) + return scores, states + + def score_partial( + self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.part_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + ids (torch.Tensor): 1D tensor of new partial tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.part_scorers` + and tensor score values of shape: `(len(ids),)`, + and state dict that has string keys + and state values of `self.part_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.part_scorers.items(): + scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x) + return scores, states + + def beam( + self, weighted_scores: torch.Tensor, ids: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute topk full token ids and partial token ids. + + Args: + weighted_scores (torch.Tensor): The weighted sum scores for each tokens. + Its shape is `(self.n_vocab,)`. + ids (torch.Tensor): The partial token ids to compute topk + + Returns: + Tuple[torch.Tensor, torch.Tensor]: + The topk full token ids and partial token ids. + Their shapes are `(self.beam_size,)` + + """ + # no pre beam performed + if weighted_scores.size(0) == ids.size(0): + top_ids = weighted_scores.topk(self.beam_size)[1] + return top_ids, top_ids + + # mask pruned in pre-beam not to select in topk + tmp = weighted_scores[ids] + weighted_scores[:] = -float("inf") + weighted_scores[ids] = tmp + top_ids = weighted_scores.topk(self.beam_size)[1] + local_ids = weighted_scores[ids].topk(self.beam_size)[1] + return top_ids, local_ids + + @staticmethod + def merge_scores( + prev_scores: Dict[str, float], + next_full_scores: Dict[str, torch.Tensor], + full_idx: int, + next_part_scores: Dict[str, torch.Tensor], + part_idx: int, + ) -> Dict[str, torch.Tensor]: + """Merge scores for new hypothesis. + + Args: + prev_scores (Dict[str, float]): + The previous hypothesis scores by `self.scorers` + next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers` + full_idx (int): The next token id for `next_full_scores` + next_part_scores (Dict[str, torch.Tensor]): + scores of partial tokens by `self.part_scorers` + part_idx (int): The new token id for `next_part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are scalar tensors by the scorers. + + """ + new_scores = dict() + for k, v in next_full_scores.items(): + new_scores[k] = prev_scores[k] + v[full_idx] + for k, v in next_part_scores.items(): + new_scores[k] = prev_scores[k] + v[part_idx] + return new_scores + + def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any: + """Merge states for new hypothesis. + + Args: + states: states of `self.full_scorers` + part_states: states of `self.part_scorers` + part_idx (int): The new token id for `part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are states of the scorers. + + """ + new_states = dict() + for k, v in states.items(): + new_states[k] = v + for k, d in self.part_scorers.items(): + new_states[k] = d.select_state(part_states[k], part_idx) + return new_states + + def search( + self, running_hyps: List[Hypothesis], x: torch.Tensor + ) -> List[Hypothesis]: + """Search new tokens for running hypotheses and encoded speech x. + + Args: + running_hyps (List[Hypothesis]): Running hypotheses on beam + x (torch.Tensor): Encoded speech feature (T, D) + + Returns: + List[Hypotheses]: Best sorted hypotheses + + """ + best_hyps = [] + part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam + for hyp in running_hyps: + # scoring + weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device) + scores, states = self.score_full(hyp, x) + for k in self.full_scorers: + weighted_scores += self.weights[k] * scores[k] + # partial scoring + if self.do_pre_beam: + pre_beam_scores = ( + weighted_scores + if self.pre_beam_score_key == "full" + else scores[self.pre_beam_score_key] + ) + part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1] + part_scores, part_states = self.score_partial(hyp, part_ids, x) + for k in self.part_scorers: + weighted_scores[part_ids] += self.weights[k] * part_scores[k] + # add previous hyp score + weighted_scores += hyp.score + + # update hyps + for j, part_j in zip(*self.beam(weighted_scores, part_ids)): + # will be (2 x beam at most) + best_hyps.append( + Hypothesis( + score=weighted_scores[j], + yseq=self.append_token(hyp.yseq, j), + scores=self.merge_scores( + hyp.scores, scores, j, part_scores, part_j + ), + states=self.merge_states(states, part_states, part_j), + ) + ) + + # sort and prune 2 x beam -> beam + best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[ + : min(len(best_hyps), self.beam_size) + ] + return best_hyps + + def forward( + self, x: torch.Tensor, maxlenratio: float = 0.0, minlenratio: float = 0.0 + ) -> List[Hypothesis]: + """Perform beam search. + + Args: + x (torch.Tensor): Encoded speech feature (T, D) + maxlenratio (float): Input length ratio to obtain max output length. + If maxlenratio=0.0 (default), it uses a end-detect function + to automatically find maximum hypothesis lengths + If maxlenratio<0.0, its absolute value is interpreted + as a constant max output length. + minlenratio (float): Input length ratio to obtain min output length. + + Returns: + list[Hypothesis]: N-best decoding results + + """ + # set length bounds + if maxlenratio == 0: + maxlen = x.shape[0] + elif maxlenratio < 0: + maxlen = -1 * int(maxlenratio) + else: + maxlen = max(1, int(maxlenratio * x.size(0))) + minlen = int(minlenratio * x.size(0)) + logging.info("decoder input length: " + str(x.shape[0])) + logging.info("max output length: " + str(maxlen)) + logging.info("min output length: " + str(minlen)) + + # main loop of prefix search + running_hyps = self.init_hyp(x) + ended_hyps = [] + for i in range(maxlen): + logging.debug("position " + str(i)) + best = self.search(running_hyps, x) + # post process of one iteration + running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps) + # end detection + if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i): + logging.info(f"end detected at {i}") + break + if len(running_hyps) == 0: + logging.info("no hypothesis. Finish decoding.") + break + else: + logging.debug(f"remained hypotheses: {len(running_hyps)}") + + nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True) + # check the number of hypotheses reaching to eos + if len(nbest_hyps) == 0: + logging.warning( + "there is no N-best results, perform recognition " + "again with smaller minlenratio." + ) + return ( + [] + if minlenratio < 0.1 + else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1)) + ) + + # report the best result + best = nbest_hyps[0] + for k, v in best.scores.items(): + logging.info( + f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}" + ) + logging.info(f"total log probability: {best.score:.2f}") + logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}") + logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}") + if self.token_list is not None: + logging.info( + "best hypo: " + + "".join([self.token_list[x] for x in best.yseq[1:-1]]) + + "\n" + ) + return nbest_hyps + + def post_process( + self, + i: int, + maxlen: int, + maxlenratio: float, + running_hyps: List[Hypothesis], + ended_hyps: List[Hypothesis], + ) -> List[Hypothesis]: + """Perform post-processing of beam search iterations. + + Args: + i (int): The length of hypothesis tokens. + maxlen (int): The maximum length of tokens in beam search. + maxlenratio (int): The maximum length ratio in beam search. + running_hyps (List[Hypothesis]): The running hypotheses in beam search. + ended_hyps (List[Hypothesis]): The ended hypotheses in beam search. + + Returns: + List[Hypothesis]: The new running hypotheses. + + """ + logging.debug(f"the number of running hypotheses: {len(running_hyps)}") + if self.token_list is not None: + logging.debug( + "best hypo: " + + "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]]) + ) + # add eos in the final loop to avoid that there are no ended hyps + if i == maxlen - 1: + logging.info("adding in the last position in the loop") + running_hyps = [ + h._replace(yseq=self.append_token(h.yseq, self.eos)) + for h in running_hyps + ] + + # add ended hypotheses to a final list, and removed them from current hypotheses + # (this will be a problem, number of hyps < beam) + remained_hyps = [] + for hyp in running_hyps: + if hyp.yseq[-1] == self.eos: + # e.g., Word LM needs to add final score + for k, d in chain(self.full_scorers.items(), self.part_scorers.items()): + s = d.final_score(hyp.states[k]) + hyp.scores[k] += s + hyp = hyp._replace(score=hyp.score + self.weights[k] * s) + ended_hyps.append(hyp) + else: + remained_hyps.append(hyp) + return remained_hyps diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/transformer/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..646ac31656f43466df41ee16f3acbb04490b756b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/template.yaml @@ -0,0 +1,110 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: Transformer +model_conf: + ctc_weight: 0.3 + lsm_weight: 0.1 # label smoothing option + length_normalized_loss: false + +# encoder +encoder: TransformerEncoder +encoder_conf: + output_size: 256 # dimension of attention + attention_heads: 4 + linear_units: 2048 # the number of units of position-wise feed forward + num_blocks: 12 # the number of encoder blocks + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.0 + input_layer: conv2d # encoder architecture type + normalize_before: true + +# decoder +decoder: TransformerDecoder +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 6 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.0 + src_attention_dropout_rate: 0.0 + + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 1 + lfr_n: 1 + +specaug: SpecAug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_range: + - 0 + - 40 + num_time_mask: 2 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + val_scheduler_criterion: + - valid + - acc + best_model_criterion: + - - valid + - acc + - max + keep_nbest_models: 10 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.002 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/add_sos_eos.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/add_sos_eos.py new file mode 100644 index 0000000000000000000000000000000000000000..08b8ee859c9d2a4b5482871049c2cdf2ab459c6d --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/add_sos_eos.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Unility functions for Transformer.""" + +import torch +from funasr_detach.models.transformer.utils.nets_utils import pad_list + + +def add_sos_eos(ys_pad, sos, eos, ignore_id): + """Add and labels. + + :param torch.Tensor ys_pad: batch of padded target sequences (B, Lmax) + :param int sos: index of + :param int eos: index of + :param int ignore_id: index of padding + :return: padded tensor (B, Lmax) + :rtype: torch.Tensor + :return: padded tensor (B, Lmax) + :rtype: torch.Tensor + """ + + _sos = ys_pad.new([sos]) + _eos = ys_pad.new([eos]) + ys = [y[y != ignore_id] for y in ys_pad] # parse padded ys + ys_in = [torch.cat([_sos, y], dim=0) for y in ys] + ys_out = [torch.cat([y, _eos], dim=0) for y in ys] + return pad_list(ys_in, eos), pad_list(ys_out, ignore_id) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/dynamic_conv.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/dynamic_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..ce7129f28767a635a802c391e4a5432f7585ec04 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/dynamic_conv.py @@ -0,0 +1,125 @@ +"""Dynamic Convolution module.""" + +import numpy +import torch +from torch import nn +import torch.nn.functional as F + + +MIN_VALUE = float(numpy.finfo(numpy.float32).min) + + +class DynamicConvolution(nn.Module): + """Dynamic Convolution layer. + + This implementation is based on + https://github.com/pytorch/fairseq/tree/master/fairseq + + Args: + wshare (int): the number of kernel of convolution + n_feat (int): the number of features + dropout_rate (float): dropout_rate + kernel_size (int): kernel size (length) + use_kernel_mask (bool): Use causal mask or not for convolution kernel + use_bias (bool): Use bias term or not. + + """ + + def __init__( + self, + wshare, + n_feat, + dropout_rate, + kernel_size, + use_kernel_mask=False, + use_bias=False, + ): + """Construct Dynamic Convolution layer.""" + super(DynamicConvolution, self).__init__() + + assert n_feat % wshare == 0 + self.wshare = wshare + self.use_kernel_mask = use_kernel_mask + self.dropout_rate = dropout_rate + self.kernel_size = kernel_size + self.attn = None + + # linear -> GLU -- -> lightconv -> linear + # \ / + # Linear + self.linear1 = nn.Linear(n_feat, n_feat * 2) + self.linear2 = nn.Linear(n_feat, n_feat) + self.linear_weight = nn.Linear(n_feat, self.wshare * 1 * kernel_size) + nn.init.xavier_uniform(self.linear_weight.weight) + self.act = nn.GLU() + + # dynamic conv related + self.use_bias = use_bias + if self.use_bias: + self.bias = nn.Parameter(torch.Tensor(n_feat)) + + def forward(self, query, key, value, mask): + """Forward of 'Dynamic Convolution'. + + This function takes query, key and value but uses only quert. + This is just for compatibility with self-attention layer (attention.py) + + Args: + query (torch.Tensor): (batch, time1, d_model) input tensor + key (torch.Tensor): (batch, time2, d_model) NOT USED + value (torch.Tensor): (batch, time2, d_model) NOT USED + mask (torch.Tensor): (batch, time1, time2) mask + + Return: + x (torch.Tensor): (batch, time1, d_model) output + + """ + # linear -> GLU -- -> lightconv -> linear + # \ / + # Linear + x = query + B, T, C = x.size() + H = self.wshare + k = self.kernel_size + + # first liner layer + x = self.linear1(x) + + # GLU activation + x = self.act(x) + + # get kernel of convolution + weight = self.linear_weight(x) # B x T x kH + weight = F.dropout(weight, self.dropout_rate, training=self.training) + weight = weight.view(B, T, H, k).transpose(1, 2).contiguous() # B x H x T x k + weight_new = torch.zeros(B * H * T * (T + k - 1), dtype=weight.dtype) + weight_new = weight_new.view(B, H, T, T + k - 1).fill_(float("-inf")) + weight_new = weight_new.to(x.device) # B x H x T x T+k-1 + weight_new.as_strided( + (B, H, T, k), ((T + k - 1) * T * H, (T + k - 1) * T, T + k, 1) + ).copy_(weight) + weight_new = weight_new.narrow(-1, int((k - 1) / 2), T) # B x H x T x T(k) + if self.use_kernel_mask: + kernel_mask = torch.tril(torch.ones(T, T, device=x.device)).unsqueeze(0) + weight_new = weight_new.masked_fill(kernel_mask == 0.0, float("-inf")) + weight_new = F.softmax(weight_new, dim=-1) + self.attn = weight_new + weight_new = weight_new.view(B * H, T, T) + + # convolution + x = x.transpose(1, 2).contiguous() # B x C x T + x = x.view(B * H, int(C / H), T).transpose(1, 2) + x = torch.bmm(weight_new, x) # BH x T x C/H + x = x.transpose(1, 2).contiguous().view(B, C, T) + + if self.use_bias: + x = x + self.bias.view(1, -1, 1) + x = x.transpose(1, 2) # B x T x C + + if mask is not None and not self.use_kernel_mask: + mask = mask.transpose(-1, -2) + x = x.masked_fill(mask == 0, 0.0) + + # second linear layer + x = self.linear2(x) + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/dynamic_conv2d.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/dynamic_conv2d.py new file mode 100644 index 0000000000000000000000000000000000000000..1a703b8744bde7161dd4492c527a64ae5e860292 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/dynamic_conv2d.py @@ -0,0 +1,138 @@ +"""Dynamic 2-Dimensional Convolution module.""" + +import numpy +import torch +from torch import nn +import torch.nn.functional as F + + +MIN_VALUE = float(numpy.finfo(numpy.float32).min) + + +class DynamicConvolution2D(nn.Module): + """Dynamic 2-Dimensional Convolution layer. + + This implementation is based on + https://github.com/pytorch/fairseq/tree/master/fairseq + + Args: + wshare (int): the number of kernel of convolution + n_feat (int): the number of features + dropout_rate (float): dropout_rate + kernel_size (int): kernel size (length) + use_kernel_mask (bool): Use causal mask or not for convolution kernel + use_bias (bool): Use bias term or not. + + """ + + def __init__( + self, + wshare, + n_feat, + dropout_rate, + kernel_size, + use_kernel_mask=False, + use_bias=False, + ): + """Construct Dynamic 2-Dimensional Convolution layer.""" + super(DynamicConvolution2D, self).__init__() + + assert n_feat % wshare == 0 + self.wshare = wshare + self.use_kernel_mask = use_kernel_mask + self.dropout_rate = dropout_rate + self.kernel_size = kernel_size + self.padding_size = int(kernel_size / 2) + self.attn_t = None + self.attn_f = None + + # linear -> GLU -- -> lightconv -> linear + # \ / + # Linear + self.linear1 = nn.Linear(n_feat, n_feat * 2) + self.linear2 = nn.Linear(n_feat * 2, n_feat) + self.linear_weight = nn.Linear(n_feat, self.wshare * 1 * kernel_size) + nn.init.xavier_uniform(self.linear_weight.weight) + self.linear_weight_f = nn.Linear(n_feat, kernel_size) + nn.init.xavier_uniform(self.linear_weight_f.weight) + self.act = nn.GLU() + + # dynamic conv related + self.use_bias = use_bias + if self.use_bias: + self.bias = nn.Parameter(torch.Tensor(n_feat)) + + def forward(self, query, key, value, mask): + """Forward of 'Dynamic 2-Dimensional Convolution'. + + This function takes query, key and value but uses only query. + This is just for compatibility with self-attention layer (attention.py) + + Args: + query (torch.Tensor): (batch, time1, d_model) input tensor + key (torch.Tensor): (batch, time2, d_model) NOT USED + value (torch.Tensor): (batch, time2, d_model) NOT USED + mask (torch.Tensor): (batch, time1, time2) mask + + Return: + x (torch.Tensor): (batch, time1, d_model) output + + """ + # linear -> GLU -- -> lightconv -> linear + # \ / + # Linear + x = query + B, T, C = x.size() + H = self.wshare + k = self.kernel_size + + # first liner layer + x = self.linear1(x) + + # GLU activation + x = self.act(x) + + # convolution of frequency axis + weight_f = self.linear_weight_f(x).view(B * T, 1, k) # B x T x k + self.attn_f = weight_f.view(B, T, k).unsqueeze(1) + xf = F.conv1d( + x.view(1, B * T, C), weight_f, padding=self.padding_size, groups=B * T + ) + xf = xf.view(B, T, C) + + # get kernel of convolution + weight = self.linear_weight(x) # B x T x kH + weight = F.dropout(weight, self.dropout_rate, training=self.training) + weight = weight.view(B, T, H, k).transpose(1, 2).contiguous() # B x H x T x k + weight_new = torch.zeros(B * H * T * (T + k - 1), dtype=weight.dtype) + weight_new = weight_new.view(B, H, T, T + k - 1).fill_(float("-inf")) + weight_new = weight_new.to(x.device) # B x H x T x T+k-1 + weight_new.as_strided( + (B, H, T, k), ((T + k - 1) * T * H, (T + k - 1) * T, T + k, 1) + ).copy_(weight) + weight_new = weight_new.narrow(-1, int((k - 1) / 2), T) # B x H x T x T(k) + if self.use_kernel_mask: + kernel_mask = torch.tril(torch.ones(T, T, device=x.device)).unsqueeze(0) + weight_new = weight_new.masked_fill(kernel_mask == 0.0, float("-inf")) + weight_new = F.softmax(weight_new, dim=-1) + self.attn_t = weight_new + weight_new = weight_new.view(B * H, T, T) + + # convolution + x = x.transpose(1, 2).contiguous() # B x C x T + x = x.view(B * H, int(C / H), T).transpose(1, 2) + x = torch.bmm(weight_new, x) + x = x.transpose(1, 2).contiguous().view(B, C, T) + + if self.use_bias: + x = x + self.bias.view(1, -1, 1) + x = x.transpose(1, 2) # B x T x C + x = torch.cat((x, xf), -1) # B x T x Cx2 + + if mask is not None and not self.use_kernel_mask: + mask = mask.transpose(-1, -2) + x = x.masked_fill(mask == 0, 0.0) + + # second linear layer + x = self.linear2(x) + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/lightconv.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/lightconv.py new file mode 100644 index 0000000000000000000000000000000000000000..4417ee0ec3c50ea08ecc7faca144eeb0e114e343 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/lightconv.py @@ -0,0 +1,112 @@ +"""Lightweight Convolution Module.""" + +import numpy +import torch +from torch import nn +import torch.nn.functional as F + + +MIN_VALUE = float(numpy.finfo(numpy.float32).min) + + +class LightweightConvolution(nn.Module): + """Lightweight Convolution layer. + + This implementation is based on + https://github.com/pytorch/fairseq/tree/master/fairseq + + Args: + wshare (int): the number of kernel of convolution + n_feat (int): the number of features + dropout_rate (float): dropout_rate + kernel_size (int): kernel size (length) + use_kernel_mask (bool): Use causal mask or not for convolution kernel + use_bias (bool): Use bias term or not. + + """ + + def __init__( + self, + wshare, + n_feat, + dropout_rate, + kernel_size, + use_kernel_mask=False, + use_bias=False, + ): + """Construct Lightweight Convolution layer.""" + super(LightweightConvolution, self).__init__() + + assert n_feat % wshare == 0 + self.wshare = wshare + self.use_kernel_mask = use_kernel_mask + self.dropout_rate = dropout_rate + self.kernel_size = kernel_size + self.padding_size = int(kernel_size / 2) + + # linear -> GLU -> lightconv -> linear + self.linear1 = nn.Linear(n_feat, n_feat * 2) + self.linear2 = nn.Linear(n_feat, n_feat) + self.act = nn.GLU() + + # lightconv related + self.weight = nn.Parameter( + torch.Tensor(self.wshare, 1, kernel_size).uniform_(0, 1) + ) + self.use_bias = use_bias + if self.use_bias: + self.bias = nn.Parameter(torch.Tensor(n_feat)) + + # mask of kernel + kernel_mask0 = torch.zeros(self.wshare, int(kernel_size / 2)) + kernel_mask1 = torch.ones(self.wshare, int(kernel_size / 2 + 1)) + self.kernel_mask = torch.cat((kernel_mask1, kernel_mask0), dim=-1).unsqueeze(1) + + def forward(self, query, key, value, mask): + """Forward of 'Lightweight Convolution'. + + This function takes query, key and value but uses only query. + This is just for compatibility with self-attention layer (attention.py) + + Args: + query (torch.Tensor): (batch, time1, d_model) input tensor + key (torch.Tensor): (batch, time2, d_model) NOT USED + value (torch.Tensor): (batch, time2, d_model) NOT USED + mask (torch.Tensor): (batch, time1, time2) mask + + Return: + x (torch.Tensor): (batch, time1, d_model) output + + """ + # linear -> GLU -> lightconv -> linear + x = query + B, T, C = x.size() + H = self.wshare + + # first liner layer + x = self.linear1(x) + + # GLU activation + x = self.act(x) + + # lightconv + x = x.transpose(1, 2).contiguous().view(-1, H, T) # B x C x T + weight = F.dropout(self.weight, self.dropout_rate, training=self.training) + if self.use_kernel_mask: + self.kernel_mask = self.kernel_mask.to(x.device) + weight = weight.masked_fill(self.kernel_mask == 0.0, float("-inf")) + weight = F.softmax(weight, dim=-1) + x = F.conv1d(x, weight, padding=self.padding_size, groups=self.wshare).view( + B, C, T + ) + if self.use_bias: + x = x + self.bias.view(1, -1, 1) + x = x.transpose(1, 2) # B x T x C + + if mask is not None and not self.use_kernel_mask: + mask = mask.transpose(-1, -2) + x = x.masked_fill(mask == 0, 0.0) + + # second linear layer + x = self.linear2(x) + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/lightconv2d.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/lightconv2d.py new file mode 100644 index 0000000000000000000000000000000000000000..56f4f8a3788942b566fde29681fc1ec6b47c7ad0 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/lightconv2d.py @@ -0,0 +1,124 @@ +"""Lightweight 2-Dimensional Convolution module.""" + +import numpy +import torch +from torch import nn +import torch.nn.functional as F + + +MIN_VALUE = float(numpy.finfo(numpy.float32).min) + + +class LightweightConvolution2D(nn.Module): + """Lightweight 2-Dimensional Convolution layer. + + This implementation is based on + https://github.com/pytorch/fairseq/tree/master/fairseq + + Args: + wshare (int): the number of kernel of convolution + n_feat (int): the number of features + dropout_rate (float): dropout_rate + kernel_size (int): kernel size (length) + use_kernel_mask (bool): Use causal mask or not for convolution kernel + use_bias (bool): Use bias term or not. + + """ + + def __init__( + self, + wshare, + n_feat, + dropout_rate, + kernel_size, + use_kernel_mask=False, + use_bias=False, + ): + """Construct Lightweight 2-Dimensional Convolution layer.""" + super(LightweightConvolution2D, self).__init__() + + assert n_feat % wshare == 0 + self.wshare = wshare + self.use_kernel_mask = use_kernel_mask + self.dropout_rate = dropout_rate + self.kernel_size = kernel_size + self.padding_size = int(kernel_size / 2) + + # linear -> GLU -> lightconv -> linear + self.linear1 = nn.Linear(n_feat, n_feat * 2) + self.linear2 = nn.Linear(n_feat * 2, n_feat) + self.act = nn.GLU() + + # lightconv related + self.weight = nn.Parameter( + torch.Tensor(self.wshare, 1, kernel_size).uniform_(0, 1) + ) + self.weight_f = nn.Parameter(torch.Tensor(1, 1, kernel_size).uniform_(0, 1)) + self.use_bias = use_bias + if self.use_bias: + self.bias = nn.Parameter(torch.Tensor(n_feat)) + + # mask of kernel + kernel_mask0 = torch.zeros(self.wshare, int(kernel_size / 2)) + kernel_mask1 = torch.ones(self.wshare, int(kernel_size / 2 + 1)) + self.kernel_mask = torch.cat((kernel_mask1, kernel_mask0), dim=-1).unsqueeze(1) + + def forward(self, query, key, value, mask): + """Forward of 'Lightweight 2-Dimensional Convolution'. + + This function takes query, key and value but uses only query. + This is just for compatibility with self-attention layer (attention.py) + + Args: + query (torch.Tensor): (batch, time1, d_model) input tensor + key (torch.Tensor): (batch, time2, d_model) NOT USED + value (torch.Tensor): (batch, time2, d_model) NOT USED + mask (torch.Tensor): (batch, time1, time2) mask + + Return: + x (torch.Tensor): (batch, time1, d_model) output + + """ + # linear -> GLU -> lightconv -> linear + x = query + B, T, C = x.size() + H = self.wshare + + # first liner layer + x = self.linear1(x) + + # GLU activation + x = self.act(x) + + # convolution along frequency axis + weight_f = F.softmax(self.weight_f, dim=-1) + weight_f = F.dropout(weight_f, self.dropout_rate, training=self.training) + weight_new = torch.zeros( + B * T, 1, self.kernel_size, device=x.device, dtype=x.dtype + ).copy_(weight_f) + xf = F.conv1d( + x.view(1, B * T, C), weight_new, padding=self.padding_size, groups=B * T + ).view(B, T, C) + + # lightconv + x = x.transpose(1, 2).contiguous().view(-1, H, T) # B x C x T + weight = F.dropout(self.weight, self.dropout_rate, training=self.training) + if self.use_kernel_mask: + self.kernel_mask = self.kernel_mask.to(x.device) + weight = weight.masked_fill(self.kernel_mask == 0.0, float("-inf")) + weight = F.softmax(weight, dim=-1) + x = F.conv1d(x, weight, padding=self.padding_size, groups=self.wshare).view( + B, C, T + ) + if self.use_bias: + x = x + self.bias.view(1, -1, 1) + x = x.transpose(1, 2) # B x T x C + x = torch.cat((x, xf), -1) # B x T x Cx2 + + if mask is not None and not self.use_kernel_mask: + mask = mask.transpose(-1, -2) + x = x.masked_fill(mask == 0, 0.0) + + # second linear layer + x = self.linear2(x) + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/mask.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/mask.py new file mode 100644 index 0000000000000000000000000000000000000000..0bbe02db47ecafdb2e97897383631a1189942491 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/mask.py @@ -0,0 +1,52 @@ +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Mask module.""" + +import torch + + +def subsequent_mask(size, device="cpu", dtype=torch.bool): + """Create mask for subsequent steps (size, size). + + :param int size: size of mask + :param str device: "cpu" or "cuda" or torch.Tensor.device + :param torch.dtype dtype: result dtype + :rtype: torch.Tensor + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + ret = torch.ones(size, size, device=device, dtype=dtype) + return torch.tril(ret, out=ret) + + +def target_mask(ys_in_pad, ignore_id): + """Create mask for decoder self-attention. + + :param torch.Tensor ys_pad: batch of padded target sequences (B, Lmax) + :param int ignore_id: index of padding + :param torch.dtype dtype: result dtype + :rtype: torch.Tensor (B, Lmax, Lmax) + """ + ys_mask = ys_in_pad != ignore_id + m = subsequent_mask(ys_mask.size(-1), device=ys_mask.device).unsqueeze(0) + return ys_mask.unsqueeze(-2) & m + + +def vad_mask(size, vad_pos, device="cpu", dtype=torch.bool): + """Create mask for decoder self-attention. + + :param int size: size of mask + :param int vad_pos: index of vad index + :param str device: "cpu" or "cuda" or torch.Tensor.device + :param torch.dtype dtype: result dtype + :rtype: torch.Tensor (B, Lmax, Lmax) + """ + ret = torch.ones(size, size, device=device, dtype=dtype) + if vad_pos <= 0 or vad_pos >= size: + return ret + sub_corner = torch.zeros(vad_pos - 1, size - vad_pos, device=device, dtype=dtype) + ret[0 : vad_pos - 1, vad_pos:] = sub_corner + return ret diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/multi_layer_conv.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/multi_layer_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..44d37bbc2d26a8044d98bcf0259abbf20d8e10ae --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/multi_layer_conv.py @@ -0,0 +1,160 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Tomoki Hayashi +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Layer modules for FFT block in FastSpeech (Feed-forward Transformer).""" + +import torch + + +class MultiLayeredConv1d(torch.nn.Module): + """Multi-layered conv1d for Transformer block. + + This is a module of multi-leyered conv1d designed + to replace positionwise feed-forward network + in Transforner block, which is introduced in + `FastSpeech: Fast, Robust and Controllable Text to Speech`_. + + .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`: + https://arxiv.org/pdf/1905.09263.pdf + + """ + + def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate): + """Initialize MultiLayeredConv1d module. + + Args: + in_chans (int): Number of input channels. + hidden_chans (int): Number of hidden channels. + kernel_size (int): Kernel size of conv1d. + dropout_rate (float): Dropout rate. + + """ + super(MultiLayeredConv1d, self).__init__() + self.w_1 = torch.nn.Conv1d( + in_chans, + hidden_chans, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + ) + self.w_2 = torch.nn.Conv1d( + hidden_chans, + in_chans, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + ) + self.dropout = torch.nn.Dropout(dropout_rate) + + def forward(self, x): + """Calculate forward propagation. + + Args: + x (torch.Tensor): Batch of input tensors (B, T, in_chans). + + Returns: + torch.Tensor: Batch of output tensors (B, T, hidden_chans). + + """ + x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1) + return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1) + + +class FsmnFeedForward(torch.nn.Module): + """Position-wise feed forward for FSMN blocks. + + This is a module of multi-leyered conv1d designed + to replace position-wise feed-forward network + in FSMN block. + """ + + def __init__(self, in_chans, hidden_chans, out_chans, kernel_size, dropout_rate): + """Initialize FsmnFeedForward module. + + Args: + in_chans (int): Number of input channels. + hidden_chans (int): Number of hidden channels. + out_chans (int): Number of output channels. + kernel_size (int): Kernel size of conv1d. + dropout_rate (float): Dropout rate. + + """ + super(FsmnFeedForward, self).__init__() + self.w_1 = torch.nn.Conv1d( + in_chans, + hidden_chans, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + ) + self.w_2 = torch.nn.Conv1d( + hidden_chans, + out_chans, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + bias=False, + ) + self.norm = torch.nn.LayerNorm(hidden_chans) + self.dropout = torch.nn.Dropout(dropout_rate) + + def forward(self, x, ilens=None): + """Calculate forward propagation. + + Args: + x (torch.Tensor): Batch of input tensors (B, T, in_chans). + + Returns: + torch.Tensor: Batch of output tensors (B, T, out_chans). + + """ + x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1) + return ( + self.w_2(self.norm(self.dropout(x)).transpose(-1, 1)).transpose(-1, 1), + ilens, + ) + + +class Conv1dLinear(torch.nn.Module): + """Conv1D + Linear for Transformer block. + + A variant of MultiLayeredConv1d, which replaces second conv-layer to linear. + + """ + + def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate): + """Initialize Conv1dLinear module. + + Args: + in_chans (int): Number of input channels. + hidden_chans (int): Number of hidden channels. + kernel_size (int): Kernel size of conv1d. + dropout_rate (float): Dropout rate. + + """ + super(Conv1dLinear, self).__init__() + self.w_1 = torch.nn.Conv1d( + in_chans, + hidden_chans, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + ) + self.w_2 = torch.nn.Linear(hidden_chans, in_chans) + self.dropout = torch.nn.Dropout(dropout_rate) + + def forward(self, x): + """Calculate forward propagation. + + Args: + x (torch.Tensor): Batch of input tensors (B, T, in_chans). + + Returns: + torch.Tensor: Batch of output tensors (B, T, hidden_chans). + + """ + x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1) + return self.w_2(self.dropout(x)) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/nets_utils.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/nets_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e1dfc073a546cd4e8d9482f609f728233fc69340 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/nets_utils.py @@ -0,0 +1,752 @@ +# -*- coding: utf-8 -*- + +"""Network related utility tools.""" + +import logging +from typing import Dict, List, Tuple + +import numpy as np +import torch + + +def to_device(m, x): + """Send tensor into the device of the module. + + Args: + m (torch.nn.Module): Torch module. + x (Tensor): Torch tensor. + + Returns: + Tensor: Torch tensor located in the same place as torch module. + + """ + if isinstance(m, torch.nn.Module): + device = next(m.parameters()).device + elif isinstance(m, torch.Tensor): + device = m.device + else: + raise TypeError( + "Expected torch.nn.Module or torch.tensor, " f"bot got: {type(m)}" + ) + return x.to(device) + + +def pad_list(xs, pad_value): + """Perform padding for the list of tensors. + + Args: + xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)]. + pad_value (float): Value for padding. + + Returns: + Tensor: Padded tensor (B, Tmax, `*`). + + Examples: + >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)] + >>> x + [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])] + >>> pad_list(x, 0) + tensor([[1., 1., 1., 1.], + [1., 1., 0., 0.], + [1., 0., 0., 0.]]) + + """ + n_batch = len(xs) + max_len = max(x.size(0) for x in xs) + pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value) + + for i in range(n_batch): + pad[i, : xs[i].size(0)] = xs[i] + + return pad + + +def pad_list_all_dim(xs, pad_value): + """Perform padding for the list of tensors. + + Args: + xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)]. + pad_value (float): Value for padding. + + Returns: + Tensor: Padded tensor (B, Tmax, `*`). + + Examples: + >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)] + >>> x + [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])] + >>> pad_list(x, 0) + tensor([[1., 1., 1., 1.], + [1., 1., 0., 0.], + [1., 0., 0., 0.]]) + + """ + n_batch = len(xs) + num_dim = len(xs[0].shape) + max_len_all_dim = [] + for i in range(num_dim): + max_len_all_dim.append(max(x.size(i) for x in xs)) + pad = xs[0].new(n_batch, *max_len_all_dim).fill_(pad_value) + + for i in range(n_batch): + if num_dim == 1: + pad[i, : xs[i].size(0)] = xs[i] + elif num_dim == 2: + pad[i, : xs[i].size(0), : xs[i].size(1)] = xs[i] + elif num_dim == 3: + pad[i, : xs[i].size(0), : xs[i].size(1), : xs[i].size(2)] = xs[i] + else: + raise ValueError( + "pad_list_all_dim only support 1-D, 2-D and 3-D tensors, not {}-D.".format( + num_dim + ) + ) + + return pad + + +def make_pad_mask(lengths, xs=None, length_dim=-1, maxlen=None): + """Make mask tensor containing indices of padded part. + + Args: + lengths (LongTensor or List): Batch of lengths (B,). + xs (Tensor, optional): The reference tensor. + If set, masks will be the same shape as this tensor. + length_dim (int, optional): Dimension indicator of the above tensor. + See the example. + + Returns: + Tensor: Mask tensor containing indices of padded part. + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (including 1.2) + + Examples: + With only lengths. + + >>> lengths = [5, 3, 2] + >>> make_pad_mask(lengths) + masks = [[0, 0, 0, 0 ,0], + [0, 0, 0, 1, 1], + [0, 0, 1, 1, 1]] + + With the reference tensor. + + >>> xs = torch.zeros((3, 2, 4)) + >>> make_pad_mask(lengths, xs) + tensor([[[0, 0, 0, 0], + [0, 0, 0, 0]], + [[0, 0, 0, 1], + [0, 0, 0, 1]], + [[0, 0, 1, 1], + [0, 0, 1, 1]]], dtype=torch.uint8) + >>> xs = torch.zeros((3, 2, 6)) + >>> make_pad_mask(lengths, xs) + tensor([[[0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1]], + [[0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1]], + [[0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8) + + With the reference tensor and dimension indicator. + + >>> xs = torch.zeros((3, 6, 6)) + >>> make_pad_mask(lengths, xs, 1) + tensor([[[0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 1]], + [[0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1]], + [[0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1]]], dtype=torch.uint8) + >>> make_pad_mask(lengths, xs, 2) + tensor([[[0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1]], + [[0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1]], + [[0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8) + + """ + if length_dim == 0: + raise ValueError("length_dim cannot be 0: {}".format(length_dim)) + + if not isinstance(lengths, list): + lengths = lengths.tolist() + bs = int(len(lengths)) + if maxlen is None: + if xs is None: + maxlen = int(max(lengths)) + else: + maxlen = xs.size(length_dim) + else: + assert xs is None + assert maxlen >= int(max(lengths)) + + seq_range = torch.arange(0, maxlen, dtype=torch.int64) + seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen) + seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + + if xs is not None: + assert xs.size(0) == bs, (xs.size(0), bs) + + if length_dim < 0: + length_dim = xs.dim() + length_dim + # ind = (:, None, ..., None, :, , None, ..., None) + ind = tuple( + slice(None) if i in (0, length_dim) else None for i in range(xs.dim()) + ) + mask = mask[ind].expand_as(xs).to(xs.device) + return mask + + +def make_non_pad_mask(lengths, xs=None, length_dim=-1): + """Make mask tensor containing indices of non-padded part. + + Args: + lengths (LongTensor or List): Batch of lengths (B,). + xs (Tensor, optional): The reference tensor. + If set, masks will be the same shape as this tensor. + length_dim (int, optional): Dimension indicator of the above tensor. + See the example. + + Returns: + ByteTensor: mask tensor containing indices of padded part. + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (including 1.2) + + Examples: + With only lengths. + + >>> lengths = [5, 3, 2] + >>> make_non_pad_mask(lengths) + masks = [[1, 1, 1, 1 ,1], + [1, 1, 1, 0, 0], + [1, 1, 0, 0, 0]] + + With the reference tensor. + + >>> xs = torch.zeros((3, 2, 4)) + >>> make_non_pad_mask(lengths, xs) + tensor([[[1, 1, 1, 1], + [1, 1, 1, 1]], + [[1, 1, 1, 0], + [1, 1, 1, 0]], + [[1, 1, 0, 0], + [1, 1, 0, 0]]], dtype=torch.uint8) + >>> xs = torch.zeros((3, 2, 6)) + >>> make_non_pad_mask(lengths, xs) + tensor([[[1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0]], + [[1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0]], + [[1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8) + + With the reference tensor and dimension indicator. + + >>> xs = torch.zeros((3, 6, 6)) + >>> make_non_pad_mask(lengths, xs, 1) + tensor([[[1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0]], + [[1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0]], + [[1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0]]], dtype=torch.uint8) + >>> make_non_pad_mask(lengths, xs, 2) + tensor([[[1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0]], + [[1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0]], + [[1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8) + + """ + return ~make_pad_mask(lengths, xs, length_dim) + + +def mask_by_length(xs, lengths, fill=0): + """Mask tensor according to length. + + Args: + xs (Tensor): Batch of input tensor (B, `*`). + lengths (LongTensor or List): Batch of lengths (B,). + fill (int or float): Value to fill masked part. + + Returns: + Tensor: Batch of masked input tensor (B, `*`). + + Examples: + >>> x = torch.arange(5).repeat(3, 1) + 1 + >>> x + tensor([[1, 2, 3, 4, 5], + [1, 2, 3, 4, 5], + [1, 2, 3, 4, 5]]) + >>> lengths = [5, 3, 2] + >>> mask_by_length(x, lengths) + tensor([[1, 2, 3, 4, 5], + [1, 2, 3, 0, 0], + [1, 2, 0, 0, 0]]) + + """ + assert xs.size(0) == len(lengths) + ret = xs.data.new(*xs.size()).fill_(fill) + for i, l in enumerate(lengths): + ret[i, :l] = xs[i, :l] + return ret + + +def to_torch_tensor(x): + """Change to torch.Tensor or ComplexTensor from numpy.ndarray. + + Args: + x: Inputs. It should be one of numpy.ndarray, Tensor, ComplexTensor, and dict. + + Returns: + Tensor or ComplexTensor: Type converted inputs. + + Examples: + >>> xs = np.ones(3, dtype=np.float32) + >>> xs = to_torch_tensor(xs) + tensor([1., 1., 1.]) + >>> xs = torch.ones(3, 4, 5) + >>> assert to_torch_tensor(xs) is xs + >>> xs = {'real': xs, 'imag': xs} + >>> to_torch_tensor(xs) + ComplexTensor( + Real: + tensor([1., 1., 1.]) + Imag; + tensor([1., 1., 1.]) + ) + + """ + # If numpy, change to torch tensor + if isinstance(x, np.ndarray): + if x.dtype.kind == "c": + # Dynamically importing because torch_complex requires python3 + from torch_complex.tensor import ComplexTensor + + return ComplexTensor(x) + else: + return torch.from_numpy(x) + + # If {'real': ..., 'imag': ...}, convert to ComplexTensor + elif isinstance(x, dict): + # Dynamically importing because torch_complex requires python3 + from torch_complex.tensor import ComplexTensor + + if "real" not in x or "imag" not in x: + raise ValueError("has 'real' and 'imag' keys: {}".format(list(x))) + # Relative importing because of using python3 syntax + return ComplexTensor(x["real"], x["imag"]) + + # If torch.Tensor, as it is + elif isinstance(x, torch.Tensor): + return x + + else: + error = ( + "x must be numpy.ndarray, torch.Tensor or a dict like " + "{{'real': torch.Tensor, 'imag': torch.Tensor}}, " + "but got {}".format(type(x)) + ) + try: + from torch_complex.tensor import ComplexTensor + except Exception: + # If PY2 + raise ValueError(error) + else: + # If PY3 + if isinstance(x, ComplexTensor): + return x + else: + raise ValueError(error) + + +def get_subsample(train_args, mode, arch): + """Parse the subsampling factors from the args for the specified `mode` and `arch`. + + Args: + train_args: argument Namespace containing options. + mode: one of ('asr', 'mt', 'st') + arch: one of ('rnn', 'rnn-t', 'rnn_mix', 'rnn_mulenc', 'transformer') + + Returns: + np.ndarray / List[np.ndarray]: subsampling factors. + """ + if arch == "transformer": + return np.array([1]) + + elif mode == "mt" and arch == "rnn": + # +1 means input (+1) and layers outputs (train_args.elayer) + subsample = np.ones(train_args.elayers + 1, dtype=np.int32) + logging.warning("Subsampling is not performed for machine translation.") + logging.info("subsample: " + " ".join([str(x) for x in subsample])) + return subsample + + elif ( + (mode == "asr" and arch in ("rnn", "rnn-t")) + or (mode == "mt" and arch == "rnn") + or (mode == "st" and arch == "rnn") + ): + subsample = np.ones(train_args.elayers + 1, dtype=np.int32) + if train_args.etype.endswith("p") and not train_args.etype.startswith("vgg"): + ss = train_args.subsample.split("_") + for j in range(min(train_args.elayers + 1, len(ss))): + subsample[j] = int(ss[j]) + else: + logging.warning( + "Subsampling is not performed for vgg*. " + "It is performed in max pooling layers at CNN." + ) + logging.info("subsample: " + " ".join([str(x) for x in subsample])) + return subsample + + elif mode == "asr" and arch == "rnn_mix": + subsample = np.ones( + train_args.elayers_sd + train_args.elayers + 1, dtype=np.int32 + ) + if train_args.etype.endswith("p") and not train_args.etype.startswith("vgg"): + ss = train_args.subsample.split("_") + for j in range( + min(train_args.elayers_sd + train_args.elayers + 1, len(ss)) + ): + subsample[j] = int(ss[j]) + else: + logging.warning( + "Subsampling is not performed for vgg*. " + "It is performed in max pooling layers at CNN." + ) + logging.info("subsample: " + " ".join([str(x) for x in subsample])) + return subsample + + elif mode == "asr" and arch == "rnn_mulenc": + subsample_list = [] + for idx in range(train_args.num_encs): + subsample = np.ones(train_args.elayers[idx] + 1, dtype=np.int32) + if train_args.etype[idx].endswith("p") and not train_args.etype[ + idx + ].startswith("vgg"): + ss = train_args.subsample[idx].split("_") + for j in range(min(train_args.elayers[idx] + 1, len(ss))): + subsample[j] = int(ss[j]) + else: + logging.warning( + "Encoder %d: Subsampling is not performed for vgg*. " + "It is performed in max pooling layers at CNN.", + idx + 1, + ) + logging.info("subsample: " + " ".join([str(x) for x in subsample])) + subsample_list.append(subsample) + return subsample_list + + else: + raise ValueError("Invalid options: mode={}, arch={}".format(mode, arch)) + + +def rename_state_dict( + old_prefix: str, new_prefix: str, state_dict: Dict[str, torch.Tensor] +): + """Replace keys of old prefix with new prefix in state dict.""" + # need this list not to break the dict iterator + old_keys = [k for k in state_dict if k.startswith(old_prefix)] + if len(old_keys) > 0: + logging.warning(f"Rename: {old_prefix} -> {new_prefix}") + for k in old_keys: + v = state_dict.pop(k) + new_k = k.replace(old_prefix, new_prefix) + state_dict[new_k] = v + + +class Swish(torch.nn.Module): + """Swish activation definition. + + Swish(x) = (beta * x) * sigmoid(x) + where beta = 1 defines standard Swish activation. + + References: + https://arxiv.org/abs/2108.12943 / https://arxiv.org/abs/1710.05941v1. + E-swish variant: https://arxiv.org/abs/1801.07145. + + Args: + beta: Beta parameter for E-Swish. + (beta >= 1. If beta < 1, use standard Swish). + use_builtin: Whether to use PyTorch function if available. + + """ + + def __init__(self, beta: float = 1.0, use_builtin: bool = False) -> None: + super().__init__() + + self.beta = beta + + if beta > 1: + self.swish = lambda x: (self.beta * x) * torch.sigmoid(x) + else: + if use_builtin: + self.swish = torch.nn.SiLU() + else: + self.swish = lambda x: x * torch.sigmoid(x) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward computation.""" + return self.swish(x) + + +def get_activation(act): + """Return activation function.""" + + activation_funcs = { + "hardtanh": torch.nn.Hardtanh, + "tanh": torch.nn.Tanh, + "relu": torch.nn.ReLU, + "selu": torch.nn.SELU, + "swish": Swish, + } + + return activation_funcs[act]() + + +class TooShortUttError(Exception): + """Raised when the utt is too short for subsampling. + + Args: + message: Error message to display. + actual_size: The size that cannot pass the subsampling. + limit: The size limit for subsampling. + + """ + + def __init__(self, message: str, actual_size: int, limit: int) -> None: + """Construct a TooShortUttError module.""" + super().__init__(message) + + self.actual_size = actual_size + self.limit = limit + + +def check_short_utt(sub_factor: int, size: int) -> Tuple[bool, int]: + """Check if the input is too short for subsampling. + + Args: + sub_factor: Subsampling factor for Conv2DSubsampling. + size: Input size. + + Returns: + : Whether an error should be sent. + : Size limit for specified subsampling factor. + + """ + if sub_factor == 2 and size < 3: + return True, 7 + elif sub_factor == 4 and size < 7: + return True, 7 + elif sub_factor == 6 and size < 11: + return True, 11 + + return False, -1 + + +def sub_factor_to_params(sub_factor: int, input_size: int) -> Tuple[int, int, int]: + """Get conv2D second layer parameters for given subsampling factor. + + Args: + sub_factor: Subsampling factor (1/X). + input_size: Input size. + + Returns: + : Kernel size for second convolution. + : Stride for second convolution. + : Conv2DSubsampling output size. + + """ + if sub_factor == 2: + return 3, 1, (((input_size - 1) // 2 - 2)) + elif sub_factor == 4: + return 3, 2, (((input_size - 1) // 2 - 1) // 2) + elif sub_factor == 6: + return 5, 3, (((input_size - 1) // 2 - 2) // 3) + else: + raise ValueError( + "subsampling_factor parameter should be set to either 2, 4 or 6." + ) + + +def make_chunk_mask( + size: int, + chunk_size: int, + left_chunk_size: int = 0, + device: torch.device = None, +) -> torch.Tensor: + """Create chunk mask for the subsequent steps (size, size). + + Reference: https://github.com/k2-fsa/icefall/blob/master/icefall/utils.py + + Args: + size: Size of the source mask. + chunk_size: Number of frames in chunk. + left_chunk_size: Size of the left context in chunks (0 means full context). + device: Device for the mask tensor. + + Returns: + mask: Chunk mask. (size, size) + + """ + mask = torch.zeros(size, size, device=device, dtype=torch.bool) + + for i in range(size): + if left_chunk_size < 0: + start = 0 + else: + start = max((i // chunk_size - left_chunk_size) * chunk_size, 0) + + end = min((i // chunk_size + 1) * chunk_size, size) + mask[i, start:end] = True + + return ~mask + + +def make_source_mask(lengths: torch.Tensor) -> torch.Tensor: + """Create source mask for given lengths. + + Reference: https://github.com/k2-fsa/icefall/blob/master/icefall/utils.py + + Args: + lengths: Sequence lengths. (B,) + + Returns: + : Mask for the sequence lengths. (B, max_len) + + """ + max_len = lengths.max() + batch_size = lengths.size(0) + + expanded_lengths = torch.arange(max_len).expand(batch_size, max_len).to(lengths) + + return expanded_lengths >= lengths.unsqueeze(1) + + +def get_transducer_task_io( + labels: torch.Tensor, + encoder_out_lens: torch.Tensor, + ignore_id: int = -1, + blank_id: int = 0, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get Transducer loss I/O. + + Args: + labels: Label ID sequences. (B, L) + encoder_out_lens: Encoder output lengths. (B,) + ignore_id: Padding symbol ID. + blank_id: Blank symbol ID. + + Returns: + decoder_in: Decoder inputs. (B, U) + target: Target label ID sequences. (B, U) + t_len: Time lengths. (B,) + u_len: Label lengths. (B,) + + """ + + def pad_list(labels: List[torch.Tensor], padding_value: int = 0): + """Create padded batch of labels from a list of labels sequences. + + Args: + labels: Labels sequences. [B x (?)] + padding_value: Padding value. + + Returns: + labels: Batch of padded labels sequences. (B,) + + """ + batch_size = len(labels) + + padded = ( + labels[0] + .new(batch_size, max(x.size(0) for x in labels), *labels[0].size()[1:]) + .fill_(padding_value) + ) + + for i in range(batch_size): + padded[i, : labels[i].size(0)] = labels[i] + + return padded + + device = labels.device + + labels_unpad = [y[y != ignore_id] for y in labels] + blank = labels[0].new([blank_id]) + + decoder_in = pad_list( + [torch.cat([blank, label], dim=0) for label in labels_unpad], blank_id + ).to(device) + + target = pad_list(labels_unpad, blank_id).type(torch.int32).to(device) + + encoder_out_lens = list(map(int, encoder_out_lens)) + t_len = torch.IntTensor(encoder_out_lens).to(device) + + u_len = torch.IntTensor([y.size(0) for y in labels_unpad]).to(device) + + return decoder_in, target, t_len, u_len + + +def pad_to_len(t: torch.Tensor, pad_len: int, dim: int): + """Pad the tensor `t` at `dim` to the length `pad_len` with right padding zeros.""" + if t.size(dim) == pad_len: + return t + else: + pad_size = list(t.shape) + pad_size[dim] = pad_len - t.size(dim) + return torch.cat( + [t, torch.zeros(*pad_size, dtype=t.dtype, device=t.device)], dim=dim + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/repeat.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/repeat.py new file mode 100644 index 0000000000000000000000000000000000000000..9c1c1259e3038be92914d3a983ccba22adc729e9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/repeat.py @@ -0,0 +1,137 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Repeat the same layer definition.""" + +from typing import Dict, List, Optional +from funasr_detach.models.transformer.layer_norm import LayerNorm +import torch + + +class MultiSequential(torch.nn.Sequential): + """Multi-input multi-output torch.nn.Sequential.""" + + def __init__(self, *args, layer_drop_rate=0.0): + """Initialize MultiSequential with layer_drop. + + Args: + layer_drop_rate (float): Probability of dropping out each fn (layer). + + """ + super(MultiSequential, self).__init__(*args) + self.layer_drop_rate = layer_drop_rate + + def forward(self, *args): + """Repeat.""" + _probs = torch.empty(len(self)).uniform_() + for idx, m in enumerate(self): + if not self.training or (_probs[idx] >= self.layer_drop_rate): + args = m(*args) + return args + + +def repeat(N, fn, layer_drop_rate=0.0): + """Repeat module N times. + + Args: + N (int): Number of repeat time. + fn (Callable): Function to generate module. + layer_drop_rate (float): Probability of dropping out each fn (layer). + + Returns: + MultiSequential: Repeated model instance. + + """ + return MultiSequential(*[fn(n) for n in range(N)], layer_drop_rate=layer_drop_rate) + + +class MultiBlocks(torch.nn.Module): + """MultiBlocks definition. + Args: + block_list: Individual blocks of the encoder architecture. + output_size: Architecture output size. + norm_class: Normalization module class. + norm_args: Normalization module arguments. + """ + + def __init__( + self, + block_list: List[torch.nn.Module], + output_size: int, + norm_class: torch.nn.Module = LayerNorm, + ) -> None: + """Construct a MultiBlocks object.""" + super().__init__() + + self.blocks = torch.nn.ModuleList(block_list) + self.norm_blocks = norm_class(output_size) + + self.num_blocks = len(block_list) + + def reset_streaming_cache(self, left_context: int, device: torch.device) -> None: + """Initialize/Reset encoder streaming cache. + Args: + left_context: Number of left frames during chunk-by-chunk inference. + device: Device to use for cache tensor. + """ + for idx in range(self.num_blocks): + self.blocks[idx].reset_streaming_cache(left_context, device) + + def forward( + self, + x: torch.Tensor, + pos_enc: torch.Tensor, + mask: torch.Tensor, + chunk_mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """Forward each block of the encoder architecture. + Args: + x: MultiBlocks input sequences. (B, T, D_block_1) + pos_enc: Positional embedding sequences. + mask: Source mask. (B, T) + chunk_mask: Chunk mask. (T_2, T_2) + Returns: + x: Output sequences. (B, T, D_block_N) + """ + for block_index, block in enumerate(self.blocks): + x, mask, pos_enc = block(x, pos_enc, mask, chunk_mask=chunk_mask) + + x = self.norm_blocks(x) + + return x + + def chunk_forward( + self, + x: torch.Tensor, + pos_enc: torch.Tensor, + mask: torch.Tensor, + chunk_size: int = 0, + left_context: int = 0, + right_context: int = 0, + ) -> torch.Tensor: + """Forward each block of the encoder architecture. + Args: + x: MultiBlocks input sequences. (B, T, D_block_1) + pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_att) + mask: Source mask. (B, T_2) + left_context: Number of frames in left context. + right_context: Number of frames in right context. + Returns: + x: MultiBlocks output sequences. (B, T, D_block_N) + """ + for block_idx, block in enumerate(self.blocks): + x, pos_enc = block.chunk_forward( + x, + pos_enc, + mask, + chunk_size=chunk_size, + left_context=left_context, + right_context=right_context, + ) + + x = self.norm_blocks(x) + + return x diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/subsampling.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/subsampling.py new file mode 100644 index 0000000000000000000000000000000000000000..3b76d93182c9a19b8e456dd74618a9f8f7f34a5e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/subsampling.py @@ -0,0 +1,688 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2019 Shigeki Karita +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Subsampling layer definition.""" +import numpy as np +import torch +import torch.nn.functional as F +from funasr_detach.models.transformer.embedding import PositionalEncoding +import logging +from funasr_detach.models.scama.utils import sequence_mask +from funasr_detach.models.transformer.utils.nets_utils import ( + sub_factor_to_params, + pad_to_len, +) +from typing import Optional, Tuple, Union +import math + + +class TooShortUttError(Exception): + """Raised when the utt is too short for subsampling. + + Args: + message (str): Message for error catch + actual_size (int): the short size that cannot pass the subsampling + limit (int): the limit size for subsampling + + """ + + def __init__(self, message, actual_size, limit): + """Construct a TooShortUttError for error handler.""" + super().__init__(message) + self.actual_size = actual_size + self.limit = limit + + +def check_short_utt(ins, size): + """Check if the utterance is too short for subsampling.""" + if isinstance(ins, Conv2dSubsampling2) and size < 3: + return True, 3 + if isinstance(ins, Conv2dSubsampling) and size < 7: + return True, 7 + if isinstance(ins, Conv2dSubsampling6) and size < 11: + return True, 11 + if isinstance(ins, Conv2dSubsampling8) and size < 15: + return True, 15 + return False, -1 + + +class Conv2dSubsampling(torch.nn.Module): + """Convolutional 2D subsampling (to 1/4 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + pos_enc (torch.nn.Module): Custom position encoding layer. + + """ + + def __init__(self, idim, odim, dropout_rate, pos_enc=None): + """Construct an Conv2dSubsampling object.""" + super(Conv2dSubsampling, self).__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + ) + self.out = torch.nn.Sequential( + torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim), + pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), + ) + + def forward(self, x, x_mask): + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 4. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 4. + + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + if x_mask is None: + return x, None + return x, x_mask[:, :, :-2:2][:, :, :-2:2] + + def __getitem__(self, key): + """Get item. + + When reset_parameters() is called, if use_scaled_pos_enc is used, + return the positioning encoding. + + """ + if key != -1: + raise NotImplementedError("Support only `-1` (for `reset_parameters`).") + return self.out[key] + + +class Conv2dSubsamplingPad(torch.nn.Module): + """Convolutional 2D subsampling (to 1/4 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + pos_enc (torch.nn.Module): Custom position encoding layer. + + """ + + def __init__(self, idim, odim, dropout_rate, pos_enc=None): + """Construct an Conv2dSubsampling object.""" + super(Conv2dSubsamplingPad, self).__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2, padding=(0, 0)), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2, padding=(0, 0)), + torch.nn.ReLU(), + ) + self.out = torch.nn.Sequential( + torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim), + pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), + ) + self.pad_fn = torch.nn.ConstantPad1d((0, 4), 0.0) + + def forward(self, x, x_mask): + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 4. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 4. + + """ + x = x.transpose(1, 2) + x = self.pad_fn(x) + x = x.transpose(1, 2) + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + if x_mask is None: + return x, None + x_len = torch.sum(x_mask[:, 0, :], dim=-1) + x_len = (x_len - 1) // 2 + 1 + x_len = (x_len - 1) // 2 + 1 + mask = sequence_mask(x_len, None, x_len.dtype, x[0].device) + return x, mask[:, None, :] + + def __getitem__(self, key): + """Get item. + + When reset_parameters() is called, if use_scaled_pos_enc is used, + return the positioning encoding. + + """ + if key != -1: + raise NotImplementedError("Support only `-1` (for `reset_parameters`).") + return self.out[key] + + +class Conv2dSubsampling2(torch.nn.Module): + """Convolutional 2D subsampling (to 1/2 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + pos_enc (torch.nn.Module): Custom position encoding layer. + + """ + + def __init__(self, idim, odim, dropout_rate, pos_enc=None): + """Construct an Conv2dSubsampling2 object.""" + super(Conv2dSubsampling2, self).__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 1), + torch.nn.ReLU(), + ) + self.out = torch.nn.Sequential( + torch.nn.Linear(odim * (((idim - 1) // 2 - 2)), odim), + pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), + ) + + def forward(self, x, x_mask): + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 2. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 2. + + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + if x_mask is None: + return x, None + return x, x_mask[:, :, :-2:2][:, :, :-2:1] + + def __getitem__(self, key): + """Get item. + + When reset_parameters() is called, if use_scaled_pos_enc is used, + return the positioning encoding. + + """ + if key != -1: + raise NotImplementedError("Support only `-1` (for `reset_parameters`).") + return self.out[key] + + +class Conv2dSubsampling6(torch.nn.Module): + """Convolutional 2D subsampling (to 1/6 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + pos_enc (torch.nn.Module): Custom position encoding layer. + + """ + + def __init__(self, idim, odim, dropout_rate, pos_enc=None): + """Construct an Conv2dSubsampling6 object.""" + super(Conv2dSubsampling6, self).__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 5, 3), + torch.nn.ReLU(), + ) + self.out = torch.nn.Sequential( + torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim), + pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), + ) + + def forward(self, x, x_mask): + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 6. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 6. + + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + if x_mask is None: + return x, None + return x, x_mask[:, :, :-2:2][:, :, :-4:3] + + +class Conv2dSubsampling8(torch.nn.Module): + """Convolutional 2D subsampling (to 1/8 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + pos_enc (torch.nn.Module): Custom position encoding layer. + + """ + + def __init__(self, idim, odim, dropout_rate, pos_enc=None): + """Construct an Conv2dSubsampling8 object.""" + super(Conv2dSubsampling8, self).__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + ) + self.out = torch.nn.Sequential( + torch.nn.Linear(odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim), + pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), + ) + + def forward(self, x, x_mask): + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 8. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 8. + + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + if x_mask is None: + return x, None + return x, x_mask[:, :, :-2:2][:, :, :-2:2][:, :, :-2:2] + + +class Conv1dSubsampling(torch.nn.Module): + """Convolutional 1D subsampling (to 1/2 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + pos_enc (torch.nn.Module): Custom position encoding layer. + + """ + + def __init__( + self, + idim, + odim, + kernel_size, + stride, + pad, + tf2torch_tensor_name_prefix_torch: str = "stride_conv", + tf2torch_tensor_name_prefix_tf: str = "seq2seq/proj_encoder/downsampling", + ): + super(Conv1dSubsampling, self).__init__() + self.conv = torch.nn.Conv1d(idim, odim, kernel_size, stride) + self.pad_fn = torch.nn.ConstantPad1d(pad, 0.0) + self.stride = stride + self.odim = odim + self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch + self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf + + def output_size(self) -> int: + return self.odim + + def forward(self, x, x_len): + """Subsample x.""" + x = x.transpose(1, 2) # (b, d ,t) + x = self.pad_fn(x) + # x = F.relu(self.conv(x)) + x = F.leaky_relu(self.conv(x), negative_slope=0.0) + x = x.transpose(1, 2) # (b, t ,d) + + if x_len is None: + + return x, None + x_len = (x_len - 1) // self.stride + 1 + return x, x_len + + def gen_tf2torch_map_dict(self): + tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch + tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf + map_dict_local = { + ## predictor + "{}.conv.weight".format(tensor_name_prefix_torch): { + "name": "{}/conv1d/kernel".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": (2, 1, 0), + }, # (256,256,3),(3,256,256) + "{}.conv.bias".format(tensor_name_prefix_torch): { + "name": "{}/conv1d/bias".format(tensor_name_prefix_tf), + "squeeze": None, + "transpose": None, + }, # (256,),(256,) + } + return map_dict_local + + def convert_tf2torch( + self, + var_dict_tf, + var_dict_torch, + ): + + map_dict = self.gen_tf2torch_map_dict() + + var_dict_torch_update = dict() + for name in sorted(var_dict_torch.keys(), reverse=False): + names = name.split(".") + if names[0] == self.tf2torch_tensor_name_prefix_torch: + name_tf = map_dict[name]["name"] + data_tf = var_dict_tf[name_tf] + if map_dict[name]["squeeze"] is not None: + data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"]) + if map_dict[name]["transpose"] is not None: + data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) + data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") + + var_dict_torch_update[name] = data_tf + + logging.info( + "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( + name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape + ) + ) + return var_dict_torch_update + + +class StreamingConvInput(torch.nn.Module): + """Streaming ConvInput module definition. + Args: + input_size: Input size. + conv_size: Convolution size. + subsampling_factor: Subsampling factor. + vgg_like: Whether to use a VGG-like network. + output_size: Block output dimension. + """ + + def __init__( + self, + input_size: int, + conv_size: Union[int, Tuple], + subsampling_factor: int = 4, + vgg_like: bool = True, + conv_kernel_size: int = 3, + output_size: Optional[int] = None, + ) -> None: + """Construct a ConvInput object.""" + super().__init__() + if vgg_like: + if subsampling_factor == 1: + conv_size1, conv_size2 = conv_size + + self.conv = torch.nn.Sequential( + torch.nn.Conv2d( + 1, + conv_size1, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size1, + conv_size1, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.MaxPool2d((1, 2)), + torch.nn.Conv2d( + conv_size1, + conv_size2, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size2, + conv_size2, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.MaxPool2d((1, 2)), + ) + + output_proj = conv_size2 * ((input_size // 2) // 2) + + self.subsampling_factor = 1 + + self.stride_1 = 1 + + self.create_new_mask = self.create_new_vgg_mask + + else: + conv_size1, conv_size2 = conv_size + + kernel_1 = int(subsampling_factor / 2) + + self.conv = torch.nn.Sequential( + torch.nn.Conv2d( + 1, + conv_size1, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size1, + conv_size1, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.MaxPool2d((kernel_1, 2)), + torch.nn.Conv2d( + conv_size1, + conv_size2, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size2, + conv_size2, + conv_kernel_size, + stride=1, + padding=(conv_kernel_size - 1) // 2, + ), + torch.nn.ReLU(), + torch.nn.MaxPool2d((2, 2)), + ) + + output_proj = conv_size2 * ((input_size // 2) // 2) + + self.subsampling_factor = subsampling_factor + + self.create_new_mask = self.create_new_vgg_mask + + self.stride_1 = kernel_1 + + else: + if subsampling_factor == 1: + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, conv_size, 3, [1, 2], [1, 0]), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size, conv_size, conv_kernel_size, [1, 2], [1, 0] + ), + torch.nn.ReLU(), + ) + + output_proj = conv_size * (((input_size - 1) // 2 - 1) // 2) + + self.subsampling_factor = subsampling_factor + self.kernel_2 = conv_kernel_size + self.stride_2 = 1 + + self.create_new_mask = self.create_new_conv2d_mask + + else: + kernel_2, stride_2, conv_2_output_size = sub_factor_to_params( + subsampling_factor, + input_size, + ) + + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, conv_size, 3, 2, [1, 0]), + torch.nn.ReLU(), + torch.nn.Conv2d( + conv_size, + conv_size, + kernel_2, + stride_2, + [(kernel_2 - 1) // 2, 0], + ), + torch.nn.ReLU(), + ) + + output_proj = conv_size * conv_2_output_size + + self.subsampling_factor = subsampling_factor + self.kernel_2 = kernel_2 + self.stride_2 = stride_2 + + self.create_new_mask = self.create_new_conv2d_mask + + self.vgg_like = vgg_like + self.min_frame_length = 7 + + if output_size is not None: + self.output = torch.nn.Linear(output_proj, output_size) + self.output_size = output_size + else: + self.output = None + self.output_size = output_proj + + def forward( + self, + x: torch.Tensor, + mask: Optional[torch.Tensor], + chunk_size: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Encode input sequences. + Args: + x: ConvInput input sequences. (B, T, D_feats) + mask: Mask of input sequences. (B, 1, T) + Returns: + x: ConvInput output sequences. (B, sub(T), D_out) + mask: Mask of output sequences. (B, 1, sub(T)) + """ + if mask is not None: + mask = self.create_new_mask(mask) + olens = max(mask.eq(0).sum(1)) + + b, t, f = x.size() + x = x.unsqueeze(1) # (b. 1. t. f) + + if chunk_size is not None: + max_input_length = int( + chunk_size + * self.subsampling_factor + * (math.ceil(float(t) / (chunk_size * self.subsampling_factor))) + ) + x = map(lambda inputs: pad_to_len(inputs, max_input_length, 1), x) + x = list(x) + x = torch.stack(x, dim=0) + N_chunks = max_input_length // (chunk_size * self.subsampling_factor) + x = x.view(b * N_chunks, 1, chunk_size * self.subsampling_factor, f) + + x = self.conv(x) + + _, c, _, f = x.size() + if chunk_size is not None: + x = x.transpose(1, 2).contiguous().view(b, -1, c * f)[:, :olens, :] + else: + x = x.transpose(1, 2).contiguous().view(b, -1, c * f) + + if self.output is not None: + x = self.output(x) + + return x, mask[:, :olens][:, : x.size(1)] + + def create_new_vgg_mask(self, mask: torch.Tensor) -> torch.Tensor: + """Create a new mask for VGG output sequences. + Args: + mask: Mask of input sequences. (B, T) + Returns: + mask: Mask of output sequences. (B, sub(T)) + """ + if self.subsampling_factor > 1: + vgg1_t_len = mask.size(1) - (mask.size(1) % (self.subsampling_factor // 2)) + mask = mask[:, :vgg1_t_len][:, :: self.subsampling_factor // 2] + + vgg2_t_len = mask.size(1) - (mask.size(1) % 2) + mask = mask[:, :vgg2_t_len][:, ::2] + else: + mask = mask + + return mask + + def create_new_conv2d_mask(self, mask: torch.Tensor) -> torch.Tensor: + """Create new conformer mask for Conv2d output sequences. + Args: + mask: Mask of input sequences. (B, T) + Returns: + mask: Mask of output sequences. (B, sub(T)) + """ + if self.subsampling_factor > 1: + return mask[:, ::2][:, :: self.stride_2] + else: + return mask + + def get_size_before_subsampling(self, size: int) -> int: + """Return the original size before subsampling for a given size. + Args: + size: Number of frames after subsampling. + Returns: + : Number of frames before subsampling. + """ + return size * self.subsampling_factor diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/subsampling_without_posenc.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/subsampling_without_posenc.py new file mode 100644 index 0000000000000000000000000000000000000000..1fc63d077d89f5b9699b9a5188dd1f8afc9c4b21 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/subsampling_without_posenc.py @@ -0,0 +1,61 @@ +# Copyright 2020 Emiru Tsunoo +# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) + +"""Subsampling layer definition.""" + +import math +import torch + + +class Conv2dSubsamplingWOPosEnc(torch.nn.Module): + """Convolutional 2D subsampling. + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + kernels (list): kernel sizes + strides (list): stride sizes + + """ + + def __init__(self, idim, odim, dropout_rate, kernels, strides): + """Construct an Conv2dSubsamplingWOPosEnc object.""" + assert len(kernels) == len(strides) + super().__init__() + conv = [] + olen = idim + for i, (k, s) in enumerate(zip(kernels, strides)): + conv += [ + torch.nn.Conv2d(1 if i == 0 else odim, odim, k, s), + torch.nn.ReLU(), + ] + olen = math.floor((olen - k) / s + 1) + self.conv = torch.nn.Sequential(*conv) + self.out = torch.nn.Linear(odim * olen, odim) + self.strides = strides + self.kernels = kernels + + def forward(self, x, x_mask): + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 4. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 4. + + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + if x_mask is None: + return x, None + for k, s in zip(self.kernels, self.strides): + x_mask = x_mask[:, :, : -k + 1 : s] + return x, x_mask diff --git a/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/vgg2l.py b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/vgg2l.py new file mode 100644 index 0000000000000000000000000000000000000000..1fc8d9961b41a19f1b4d64ccfd28d2986adcf7bc --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/transformer/utils/vgg2l.py @@ -0,0 +1,90 @@ +"""VGG2L module definition for custom encoder.""" + +from typing import Tuple, Union + +import torch + + +class VGG2L(torch.nn.Module): + """VGG2L module for custom encoder. + + Args: + idim: Input dimension. + odim: Output dimension. + pos_enc: Positional encoding class. + + """ + + def __init__(self, idim: int, odim: int, pos_enc: torch.nn.Module = None): + """Construct a VGG2L object.""" + super().__init__() + + self.vgg2l = torch.nn.Sequential( + torch.nn.Conv2d(1, 64, 3, stride=1, padding=1), + torch.nn.ReLU(), + torch.nn.Conv2d(64, 64, 3, stride=1, padding=1), + torch.nn.ReLU(), + torch.nn.MaxPool2d((3, 2)), + torch.nn.Conv2d(64, 128, 3, stride=1, padding=1), + torch.nn.ReLU(), + torch.nn.Conv2d(128, 128, 3, stride=1, padding=1), + torch.nn.ReLU(), + torch.nn.MaxPool2d((2, 2)), + ) + + if pos_enc is not None: + self.output = torch.nn.Sequential( + torch.nn.Linear(128 * ((idim // 2) // 2), odim), pos_enc + ) + else: + self.output = torch.nn.Linear(128 * ((idim // 2) // 2), odim) + + def forward(self, feats: torch.Tensor, feats_mask: torch.Tensor) -> Union[ + Tuple[torch.Tensor, torch.Tensor], + Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor], + ]: + """Forward VGG2L bottleneck. + + Args: + feats: Feature sequences. (B, F, D_feats) + feats_mask: Mask of feature sequences. (B, 1, F) + + Returns: + vgg_output: VGG output sequences. + (B, sub(F), D_out) or ((B, sub(F), D_out), (B, sub(F), D_att)) + vgg_mask: Mask of VGG output sequences. (B, 1, sub(F)) + + """ + feats = feats.unsqueeze(1) + vgg_output = self.vgg2l(feats) + + b, c, t, f = vgg_output.size() + + vgg_output = self.output( + vgg_output.transpose(1, 2).contiguous().view(b, t, c * f) + ) + + if feats_mask is not None: + vgg_mask = self.create_new_mask(feats_mask) + else: + vgg_mask = feats_mask + + return vgg_output, vgg_mask + + def create_new_mask(self, feats_mask: torch.Tensor) -> torch.Tensor: + """Create a subsampled mask of feature sequences. + + Args: + feats_mask: Mask of feature sequences. (B, 1, F) + + Returns: + vgg_mask: Mask of VGG2L output sequences. (B, 1, sub(F)) + + """ + vgg1_t_len = feats_mask.size(2) - (feats_mask.size(2) % 3) + vgg_mask = feats_mask[:, :, :vgg1_t_len][:, :, ::3] + + vgg2_t_len = vgg_mask.size(2) - (vgg_mask.size(2) % 2) + vgg_mask = vgg_mask[:, :, :vgg2_t_len][:, :, ::2] + + return vgg_mask diff --git a/demo/Step-Audio-EditX/funasr_detach/models/uniasr/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/uniasr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/uniasr/beam_search.py b/demo/Step-Audio-EditX/funasr_detach/models/uniasr/beam_search.py new file mode 100644 index 0000000000000000000000000000000000000000..1d2edff79f70e56e011954945ed1d9db1158b731 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/uniasr/beam_search.py @@ -0,0 +1,519 @@ +"""Beam search module.""" + +from itertools import chain +import logging +from typing import Any +from typing import Dict +from typing import List +from typing import NamedTuple +from typing import Tuple +from typing import Union + +import torch + +from funasr_detach.metrics.common import end_detect +from funasr_detach.models.transformer.scorers.scorer_interface import ( + PartialScorerInterface, +) +from funasr_detach.models.transformer.scorers.scorer_interface import ScorerInterface + + +class Hypothesis(NamedTuple): + """Hypothesis data type.""" + + yseq: torch.Tensor + score: Union[float, torch.Tensor] = 0 + scores: Dict[str, Union[float, torch.Tensor]] = dict() + states: Dict[str, Any] = dict() + + def asdict(self) -> dict: + """Convert data to JSON-friendly dict.""" + return self._replace( + yseq=self.yseq.tolist(), + score=float(self.score), + scores={k: float(v) for k, v in self.scores.items()}, + )._asdict() + + +class BeamSearchScama(torch.nn.Module): + """Beam search implementation.""" + + def __init__( + self, + scorers: Dict[str, ScorerInterface], + weights: Dict[str, float], + beam_size: int, + vocab_size: int, + sos: int, + eos: int, + token_list: List[str] = None, + pre_beam_ratio: float = 1.5, + pre_beam_score_key: str = None, + ): + """Initialize beam search. + + Args: + scorers (dict[str, ScorerInterface]): Dict of decoder modules + e.g., Decoder, CTCPrefixScorer, LM + The scorer will be ignored if it is `None` + weights (dict[str, float]): Dict of weights for each scorers + The scorer will be ignored if its weight is 0 + beam_size (int): The number of hypotheses kept during search + vocab_size (int): The number of vocabulary + sos (int): Start of sequence id + eos (int): End of sequence id + token_list (list[str]): List of tokens for debug log + pre_beam_score_key (str): key of scores to perform pre-beam search + pre_beam_ratio (float): beam size in the pre-beam search + will be `int(pre_beam_ratio * beam_size)` + + """ + super().__init__() + # set scorers + self.weights = weights + self.scorers = dict() + self.full_scorers = dict() + self.part_scorers = dict() + # this module dict is required for recursive cast + # `self.to(device, dtype)` in `recog.py` + self.nn_dict = torch.nn.ModuleDict() + for k, v in scorers.items(): + w = weights.get(k, 0) + if w == 0 or v is None: + continue + assert isinstance( + v, ScorerInterface + ), f"{k} ({type(v)}) does not implement ScorerInterface" + self.scorers[k] = v + if isinstance(v, PartialScorerInterface): + self.part_scorers[k] = v + else: + self.full_scorers[k] = v + if isinstance(v, torch.nn.Module): + self.nn_dict[k] = v + + # set configurations + self.sos = sos + self.eos = eos + self.token_list = token_list + self.pre_beam_size = int(pre_beam_ratio * beam_size) + self.beam_size = beam_size + self.n_vocab = vocab_size + if ( + pre_beam_score_key is not None + and pre_beam_score_key != "full" + and pre_beam_score_key not in self.full_scorers + ): + raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}") + self.pre_beam_score_key = pre_beam_score_key + self.do_pre_beam = ( + self.pre_beam_score_key is not None + and self.pre_beam_size < self.n_vocab + and len(self.part_scorers) > 0 + ) + + def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]: + """Get an initial hypothesis data. + + Args: + x (torch.Tensor): The encoder output feature + + Returns: + Hypothesis: The initial hypothesis. + + """ + init_states = dict() + init_scores = dict() + for k, d in self.scorers.items(): + init_states[k] = d.init_state(x) + init_scores[k] = 0.0 + return [ + Hypothesis( + score=0.0, + scores=init_scores, + states=init_states, + yseq=torch.tensor([self.sos], device=x.device), + ) + ] + + @staticmethod + def append_token(xs: torch.Tensor, x: int) -> torch.Tensor: + """Append new token to prefix tokens. + + Args: + xs (torch.Tensor): The prefix token + x (int): The new token to append + + Returns: + torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device + + """ + x = torch.tensor([x], dtype=xs.dtype, device=xs.device) + return torch.cat((xs, x)) + + def score_full( + self, + hyp: Hypothesis, + x: torch.Tensor, + x_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.full_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.full_scorers` + and tensor score values of shape: `(self.n_vocab,)`, + and state dict that has string keys + and state values of `self.full_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.full_scorers.items(): + scores[k], states[k] = d.score( + hyp.yseq, + hyp.states[k], + x, + x_mask=x_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + ) + return scores, states + + def score_partial( + self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.part_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + ids (torch.Tensor): 1D tensor of new partial tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.part_scorers` + and tensor score values of shape: `(len(ids),)`, + and state dict that has string keys + and state values of `self.part_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.part_scorers.items(): + scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x) + return scores, states + + def beam( + self, weighted_scores: torch.Tensor, ids: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute topk full token ids and partial token ids. + + Args: + weighted_scores (torch.Tensor): The weighted sum scores for each tokens. + Its shape is `(self.n_vocab,)`. + ids (torch.Tensor): The partial token ids to compute topk + + Returns: + Tuple[torch.Tensor, torch.Tensor]: + The topk full token ids and partial token ids. + Their shapes are `(self.beam_size,)` + + """ + # no pre beam performed + if weighted_scores.size(0) == ids.size(0): + top_ids = weighted_scores.topk(self.beam_size)[1] + return top_ids, top_ids + + # mask pruned in pre-beam not to select in topk + tmp = weighted_scores[ids] + weighted_scores[:] = -float("inf") + weighted_scores[ids] = tmp + top_ids = weighted_scores.topk(self.beam_size)[1] + local_ids = weighted_scores[ids].topk(self.beam_size)[1] + return top_ids, local_ids + + @staticmethod + def merge_scores( + prev_scores: Dict[str, float], + next_full_scores: Dict[str, torch.Tensor], + full_idx: int, + next_part_scores: Dict[str, torch.Tensor], + part_idx: int, + ) -> Dict[str, torch.Tensor]: + """Merge scores for new hypothesis. + + Args: + prev_scores (Dict[str, float]): + The previous hypothesis scores by `self.scorers` + next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers` + full_idx (int): The next token id for `next_full_scores` + next_part_scores (Dict[str, torch.Tensor]): + scores of partial tokens by `self.part_scorers` + part_idx (int): The new token id for `next_part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are scalar tensors by the scorers. + + """ + new_scores = dict() + for k, v in next_full_scores.items(): + new_scores[k] = prev_scores[k] + v[full_idx] + for k, v in next_part_scores.items(): + new_scores[k] = prev_scores[k] + v[part_idx] + return new_scores + + def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any: + """Merge states for new hypothesis. + + Args: + states: states of `self.full_scorers` + part_states: states of `self.part_scorers` + part_idx (int): The new token id for `part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and `self.part_scorers`. + Its values are states of the scorers. + + """ + new_states = dict() + for k, v in states.items(): + new_states[k] = v + for k, d in self.part_scorers.items(): + new_states[k] = d.select_state(part_states[k], part_idx) + return new_states + + def search( + self, + running_hyps: List[Hypothesis], + x: torch.Tensor, + x_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + ) -> List[Hypothesis]: + """Search new tokens for running hypotheses and encoded speech x. + + Args: + running_hyps (List[Hypothesis]): Running hypotheses on beam + x (torch.Tensor): Encoded speech feature (T, D) + + Returns: + List[Hypotheses]: Best sorted hypotheses + + """ + best_hyps = [] + part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam + for hyp in running_hyps: + # scoring + weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device) + scores, states = self.score_full( + hyp, x, x_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds + ) + for k in self.full_scorers: + weighted_scores += self.weights[k] * scores[k] + # partial scoring + if self.do_pre_beam: + pre_beam_scores = ( + weighted_scores + if self.pre_beam_score_key == "full" + else scores[self.pre_beam_score_key] + ) + part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1] + part_scores, part_states = self.score_partial(hyp, part_ids, x) + for k in self.part_scorers: + weighted_scores[part_ids] += self.weights[k] * part_scores[k] + # add previous hyp score + weighted_scores += hyp.score + + # update hyps + for j, part_j in zip(*self.beam(weighted_scores, part_ids)): + # will be (2 x beam at most) + best_hyps.append( + Hypothesis( + score=weighted_scores[j], + yseq=self.append_token(hyp.yseq, j), + scores=self.merge_scores( + hyp.scores, scores, j, part_scores, part_j + ), + states=self.merge_states(states, part_states, part_j), + ) + ) + + # sort and prune 2 x beam -> beam + best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[ + : min(len(best_hyps), self.beam_size) + ] + return best_hyps + + def forward( + self, + x: torch.Tensor, + scama_mask: torch.Tensor = None, + pre_acoustic_embeds: torch.Tensor = None, + maxlenratio: float = 0.0, + minlenratio: float = 0.0, + maxlen: int = None, + minlen: int = 0, + ) -> List[Hypothesis]: + """Perform beam search. + + Args: + x (torch.Tensor): Encoded speech feature (T, D) + maxlenratio (float): Input length ratio to obtain max output length. + If maxlenratio=0.0 (default), it uses a end-detect function + to automatically find maximum hypothesis lengths + If maxlenratio<0.0, its absolute value is interpreted + as a constant max output length. + minlenratio (float): Input length ratio to obtain min output length. + + Returns: + list[Hypothesis]: N-best decoding results + + """ + if maxlen is None: + # set length bounds + if maxlenratio == 0: + maxlen = x.shape[0] + elif maxlenratio < 0: + maxlen = -1 * int(maxlenratio) + else: + maxlen = max(1, int(maxlenratio * x.size(0))) + minlen = int(minlenratio * x.size(0)) + + logging.info("decoder input length: " + str(x.shape[0])) + logging.info("max output length: " + str(maxlen)) + logging.info("min output length: " + str(minlen)) + + # main loop of prefix search + running_hyps = self.init_hyp(x) + ended_hyps = [] + for i in range(maxlen): + logging.debug("position " + str(i)) + mask_enc = None + if scama_mask is not None: + token_num_predictor = scama_mask.size(1) + token_id_slice = min(i, token_num_predictor - 1) + mask_enc = scama_mask[:, token_id_slice : token_id_slice + 1, :] + # if mask_enc.size(1) == 0: + # mask_enc = scama_mask[:, -2:-1, :] + # # mask_enc = torch.zeros_like(mask_enc) + pre_acoustic_embeds_cur = None + if pre_acoustic_embeds is not None: + b, t, d = pre_acoustic_embeds.size() + pad = torch.zeros((b, 1, d), dtype=pre_acoustic_embeds.dtype).to( + device=pre_acoustic_embeds.device + ) + pre_acoustic_embeds = torch.cat((pre_acoustic_embeds, pad), dim=1) + token_id_slice = min(i, t) + pre_acoustic_embeds_cur = pre_acoustic_embeds[ + :, token_id_slice : token_id_slice + 1, : + ] + + best = self.search( + running_hyps, + x, + x_mask=mask_enc, + pre_acoustic_embeds=pre_acoustic_embeds_cur, + ) + # post process of one iteration + running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps) + # end detection + if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i): + logging.info(f"end detected at {i}") + break + if len(running_hyps) == 0: + logging.info("no hypothesis. Finish decoding.") + break + else: + logging.debug(f"remained hypotheses: {len(running_hyps)}") + + nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True) + # check the number of hypotheses reaching to eos + if len(nbest_hyps) == 0: + logging.warning( + "there is no N-best results, perform recognition " + "again with smaller minlenratio." + ) + return ( + [] + if minlenratio < 0.1 + else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1)) + ) + + # report the best result + for x in nbest_hyps: + yseq = "".join([self.token_list[x] for x in x.yseq]) + logging.debug( + "nbest: y: {}, yseq: {}, score: {}".format(x.yseq, yseq, x.score) + ) + best = nbest_hyps[0] + for k, v in best.scores.items(): + logging.info( + f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}" + ) + logging.info(f"total log probability: {best.score:.2f}") + logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}") + logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}") + if self.token_list is not None: + logging.info( + "best hypo: " + + "".join([self.token_list[x] for x in best.yseq[1:-1]]) + + "\n" + ) + return nbest_hyps + + def post_process( + self, + i: int, + maxlen: int, + maxlenratio: float, + running_hyps: List[Hypothesis], + ended_hyps: List[Hypothesis], + ) -> List[Hypothesis]: + """Perform post-processing of beam search iterations. + + Args: + i (int): The length of hypothesis tokens. + maxlen (int): The maximum length of tokens in beam search. + maxlenratio (int): The maximum length ratio in beam search. + running_hyps (List[Hypothesis]): The running hypotheses in beam search. + ended_hyps (List[Hypothesis]): The ended hypotheses in beam search. + + Returns: + List[Hypothesis]: The new running hypotheses. + + """ + logging.debug(f"the number of running hypotheses: {len(running_hyps)}") + if self.token_list is not None: + logging.debug( + "best hypo: " + + "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]]) + ) + # add eos in the final loop to avoid that there are no ended hyps + if i == maxlen - 1: + logging.info("adding in the last position in the loop") + running_hyps = [ + h._replace(yseq=self.append_token(h.yseq, self.eos)) + for h in running_hyps + ] + + # add ended hypotheses to a final list, and removed them from current hypotheses + # (this will be a problem, number of hyps < beam) + remained_hyps = [] + for hyp in running_hyps: + if hyp.yseq[-1] == self.eos: + # e.g., Word LM needs to add final score + for k, d in chain(self.full_scorers.items(), self.part_scorers.items()): + s = d.final_score(hyp.states[k]) + hyp.scores[k] += s + hyp = hyp._replace(score=hyp.score + self.weights[k] * s) + ended_hyps.append(hyp) + else: + remained_hyps.append(hyp) + return remained_hyps diff --git a/demo/Step-Audio-EditX/funasr_detach/models/uniasr/model.py b/demo/Step-Audio-EditX/funasr_detach/models/uniasr/model.py new file mode 100644 index 0000000000000000000000000000000000000000..c987c0cdd43e374b1a28edc571fea288e738f64b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/uniasr/model.py @@ -0,0 +1,1090 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import time +import torch +import logging +from torch.cuda.amp import autocast +from typing import Union, Dict, List, Tuple, Optional + +from funasr_detach.register import tables +from funasr_detach.models.ctc.ctc import CTC +from funasr_detach.utils import postprocess_utils +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.utils.datadir_writer import DatadirWriter +from funasr_detach.models.paraformer.cif_predictor import mae_loss +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list +from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank +from funasr_detach.models.scama.utils import sequence_mask + + +@tables.register("model_classes", "UniASR") +class UniASR(torch.nn.Module): + """ + Author: Speech Lab of DAMO Academy, Alibaba Group + """ + + def __init__( + self, + specaug: str = None, + specaug_conf: dict = None, + normalize: str = None, + normalize_conf: dict = None, + encoder: str = None, + encoder_conf: dict = None, + encoder2: str = None, + encoder2_conf: dict = None, + decoder: str = None, + decoder_conf: dict = None, + decoder2: str = None, + decoder2_conf: dict = None, + predictor: str = None, + predictor_conf: dict = None, + predictor_bias: int = 0, + predictor_weight: float = 0.0, + predictor2: str = None, + predictor2_conf: dict = None, + predictor2_bias: int = 0, + predictor2_weight: float = 0.0, + ctc: str = None, + ctc_conf: dict = None, + ctc_weight: float = 0.5, + ctc2: str = None, + ctc2_conf: dict = None, + ctc2_weight: float = 0.5, + decoder_attention_chunk_type: str = "chunk", + decoder_attention_chunk_type2: str = "chunk", + stride_conv=None, + stride_conv_conf: dict = None, + loss_weight_model1: float = 0.5, + input_size: int = 80, + vocab_size: int = -1, + ignore_id: int = -1, + blank_id: int = 0, + sos: int = 1, + eos: int = 2, + lsm_weight: float = 0.0, + length_normalized_loss: bool = False, + share_embedding: bool = False, + **kwargs, + ): + super().__init__() + + if specaug is not None: + specaug_class = tables.specaug_classes.get(specaug) + specaug = specaug_class(**specaug_conf) + if normalize is not None: + normalize_class = tables.normalize_classes.get(normalize) + normalize = normalize_class(**normalize_conf) + + encoder_class = tables.encoder_classes.get(encoder) + encoder = encoder_class(input_size=input_size, **encoder_conf) + encoder_output_size = encoder.output_size() + + decoder_class = tables.decoder_classes.get(decoder) + decoder = decoder_class( + vocab_size=vocab_size, + encoder_output_size=encoder_output_size, + **decoder_conf, + ) + predictor_class = tables.predictor_classes.get(predictor) + predictor = predictor_class(**predictor_conf) + + from funasr_detach.models.transformer.utils.subsampling import Conv1dSubsampling + + stride_conv = Conv1dSubsampling( + **stride_conv_conf, + idim=input_size + encoder_output_size, + odim=input_size + encoder_output_size, + ) + stride_conv_output_size = stride_conv.output_size() + + encoder_class = tables.encoder_classes.get(encoder2) + encoder2 = encoder_class(input_size=stride_conv_output_size, **encoder2_conf) + encoder2_output_size = encoder2.output_size() + + decoder_class = tables.decoder_classes.get(decoder2) + decoder2 = decoder_class( + vocab_size=vocab_size, + encoder_output_size=encoder2_output_size, + **decoder2_conf, + ) + predictor_class = tables.predictor_classes.get(predictor2) + predictor2 = predictor_class(**predictor2_conf) + + self.blank_id = blank_id + self.sos = sos + self.eos = eos + self.vocab_size = vocab_size + self.ignore_id = ignore_id + self.ctc_weight = ctc_weight + self.ctc2_weight = ctc2_weight + + self.specaug = specaug + self.normalize = normalize + + self.encoder = encoder + + self.error_calculator = None + + self.decoder = decoder + self.ctc = None + self.ctc2 = None + + self.criterion_att = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + self.predictor = predictor + self.predictor_weight = predictor_weight + self.criterion_pre = mae_loss(normalize_length=length_normalized_loss) + self.encoder1_encoder2_joint_training = kwargs.get( + "encoder1_encoder2_joint_training", True + ) + + if self.encoder.overlap_chunk_cls is not None: + from funasr_detach.models.scama.chunk_utilis import ( + build_scama_mask_for_cross_attention_decoder, + ) + + self.build_scama_mask_for_cross_attention_decoder_fn = ( + build_scama_mask_for_cross_attention_decoder + ) + self.decoder_attention_chunk_type = decoder_attention_chunk_type + + self.encoder2 = encoder2 + self.decoder2 = decoder2 + self.ctc2_weight = ctc2_weight + + self.predictor2 = predictor2 + self.predictor2_weight = predictor2_weight + self.decoder_attention_chunk_type2 = decoder_attention_chunk_type2 + self.stride_conv = stride_conv + self.loss_weight_model1 = loss_weight_model1 + if self.encoder2.overlap_chunk_cls is not None: + from funasr_detach.models.scama.chunk_utilis import ( + build_scama_mask_for_cross_attention_decoder, + ) + + self.build_scama_mask_for_cross_attention_decoder_fn2 = ( + build_scama_mask_for_cross_attention_decoder + ) + self.decoder_attention_chunk_type2 = decoder_attention_chunk_type2 + + self.length_normalized_loss = length_normalized_loss + self.enable_maas_finetune = kwargs.get("enable_maas_finetune", False) + self.freeze_encoder2 = kwargs.get("freeze_encoder2", False) + self.beam_search = None + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + **kwargs, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + decoding_ind = kwargs.get("decoding_ind", None) + if len(text_lengths.size()) > 1: + text_lengths = text_lengths[:, 0] + if len(speech_lengths.size()) > 1: + speech_lengths = speech_lengths[:, 0] + + batch_size = speech.shape[0] + + ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind) + # 1. Encoder + if self.enable_maas_finetune: + with torch.no_grad(): + speech_raw, encoder_out, encoder_out_lens = self.encode( + speech, speech_lengths, ind=ind + ) + else: + speech_raw, encoder_out, encoder_out_lens = self.encode( + speech, speech_lengths, ind=ind + ) + + loss_att, acc_att, cer_att, wer_att = None, None, None, None + loss_ctc, cer_ctc = None, None + stats = dict() + loss_pre = None + loss, loss1, loss2 = 0.0, 0.0, 0.0 + + if self.loss_weight_model1 > 0.0: + ## model1 + # 1. CTC branch + if self.enable_maas_finetune: + with torch.no_grad(): + + loss_att, acc_att, cer_att, wer_att, loss_pre = ( + self._calc_att_predictor_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + ) + + loss = loss_att + loss_pre * self.predictor_weight + + # Collect Attn branch stats + stats["loss_att"] = ( + loss_att.detach() if loss_att is not None else None + ) + stats["acc"] = acc_att + stats["cer"] = cer_att + stats["wer"] = wer_att + stats["loss_pre"] = ( + loss_pre.detach().cpu() if loss_pre is not None else None + ) + else: + + loss_att, acc_att, cer_att, wer_att, loss_pre = ( + self._calc_att_predictor_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + ) + + loss = loss_att + loss_pre * self.predictor_weight + + # Collect Attn branch stats + stats["loss_att"] = loss_att.detach() if loss_att is not None else None + stats["acc"] = acc_att + stats["cer"] = cer_att + stats["wer"] = wer_att + stats["loss_pre"] = ( + loss_pre.detach().cpu() if loss_pre is not None else None + ) + + loss1 = loss + + if self.loss_weight_model1 < 1.0: + ## model2 + + # encoder2 + if self.freeze_encoder2: + with torch.no_grad(): + encoder_out, encoder_out_lens = self.encode2( + encoder_out, + encoder_out_lens, + speech_raw, + speech_lengths, + ind=ind, + ) + else: + encoder_out, encoder_out_lens = self.encode2( + encoder_out, encoder_out_lens, speech_raw, speech_lengths, ind=ind + ) + + intermediate_outs = None + if isinstance(encoder_out, tuple): + intermediate_outs = encoder_out[1] + encoder_out = encoder_out[0] + + loss_att, acc_att, cer_att, wer_att, loss_pre = ( + self._calc_att_predictor_loss2( + encoder_out, encoder_out_lens, text, text_lengths + ) + ) + + loss = loss_att + loss_pre * self.predictor2_weight + + # Collect Attn branch stats + stats["loss_att2"] = loss_att.detach() if loss_att is not None else None + stats["acc2"] = acc_att + stats["cer2"] = cer_att + stats["wer2"] = wer_att + stats["loss_pre2"] = ( + loss_pre.detach().cpu() if loss_pre is not None else None + ) + + loss2 = loss + + loss = loss1 * self.loss_weight_model1 + loss2 * (1 - self.loss_weight_model1) + stats["loss1"] = torch.clone(loss1.detach()) + stats["loss2"] = torch.clone(loss2.detach()) + stats["loss"] = torch.clone(loss.detach()) + # force_gatherable: to-device and to-tensor if scalar for DataParallel + if self.length_normalized_loss: + batch_size = int((text_lengths + 1).sum()) + + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def collect_feats( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Dict[str, torch.Tensor]: + if self.extract_feats_in_collect_stats: + feats, feats_lengths = self._extract_feats(speech, speech_lengths) + else: + # Generate dummy stats if extract_feats_in_collect_stats is False + logging.warning( + "Generating dummy stats for feats and feats_lengths, " + "because encoder_conf.extract_feats_in_collect_stats is " + f"{self.extract_feats_in_collect_stats}" + ) + feats, feats_lengths = speech, speech_lengths + return {"feats": feats, "feats_lengths": feats_lengths} + + def encode( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + **kwargs, + ): + """Frontend + Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + """ + ind = kwargs.get("ind", 0) + with autocast(False): + # Data augmentation + if self.specaug is not None and self.training: + speech, speech_lengths = self.specaug(speech, speech_lengths) + + # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + speech, speech_lengths = self.normalize(speech, speech_lengths) + + speech_raw = speech.clone().to(speech.device) + + # 4. Forward encoder + encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths, ind=ind) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + return speech_raw, encoder_out, encoder_out_lens + + def encode2( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + **kwargs, + ): + """Frontend + Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + """ + + ind = kwargs.get("ind", 0) + encoder_out_rm, encoder_out_lens_rm = ( + self.encoder.overlap_chunk_cls.remove_chunk( + encoder_out, + encoder_out_lens, + chunk_outs=None, + ) + ) + # residual_input + encoder_out = torch.cat((speech, encoder_out_rm), dim=-1) + encoder_out_lens = encoder_out_lens_rm + if self.stride_conv is not None: + speech, speech_lengths = self.stride_conv(encoder_out, encoder_out_lens) + if not self.encoder1_encoder2_joint_training: + speech = speech.detach() + speech_lengths = speech_lengths.detach() + # 4. Forward encoder + # feats: (Batch, Length, Dim) + # -> encoder_out: (Batch, Length2, Dim2) + + encoder_out, encoder_out_lens, _ = self.encoder2( + speech, speech_lengths, ind=ind + ) + if isinstance(encoder_out, tuple): + encoder_out = encoder_out[0] + + return encoder_out, encoder_out_lens + + def nll( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ) -> torch.Tensor: + """Compute negative log likelihood(nll) from transformer-decoder + Normally, this function is called in batchify_nll. + Args: + encoder_out: (Batch, Length, Dim) + encoder_out_lens: (Batch,) + ys_pad: (Batch, Length) + ys_pad_lens: (Batch,) + """ + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + # 1. Forward decoder + decoder_out, _ = self.decoder( + encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens + ) # [batch, seqlen, dim] + batch_size = decoder_out.size(0) + decoder_num_class = decoder_out.size(2) + # nll: negative log-likelihood + nll = torch.nn.functional.cross_entropy( + decoder_out.view(-1, decoder_num_class), + ys_out_pad.view(-1), + ignore_index=self.ignore_id, + reduction="none", + ) + nll = nll.view(batch_size, -1) + nll = nll.sum(dim=1) + assert nll.size(0) == batch_size + return nll + + def batchify_nll( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + batch_size: int = 100, + ): + """Compute negative log likelihood(nll) from transformer-decoder + To avoid OOM, this fuction seperate the input into batches. + Then call nll for each batch and combine and return results. + Args: + encoder_out: (Batch, Length, Dim) + encoder_out_lens: (Batch,) + ys_pad: (Batch, Length) + ys_pad_lens: (Batch,) + batch_size: int, samples each batch contain when computing nll, + you may change this to avoid OOM or increase + GPU memory usage + """ + total_num = encoder_out.size(0) + if total_num <= batch_size: + nll = self.nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens) + else: + nll = [] + start_idx = 0 + while True: + end_idx = min(start_idx + batch_size, total_num) + batch_encoder_out = encoder_out[start_idx:end_idx, :, :] + batch_encoder_out_lens = encoder_out_lens[start_idx:end_idx] + batch_ys_pad = ys_pad[start_idx:end_idx, :] + batch_ys_pad_lens = ys_pad_lens[start_idx:end_idx] + batch_nll = self.nll( + batch_encoder_out, + batch_encoder_out_lens, + batch_ys_pad, + batch_ys_pad_lens, + ) + nll.append(batch_nll) + start_idx = end_idx + if start_idx == total_num: + break + nll = torch.cat(nll) + assert nll.size(0) == total_num + return nll + + def _calc_att_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + # 1. Forward decoder + decoder_out, _ = self.decoder( + encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens + ) + + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_out_pad) + acc_att = th_accuracy( + decoder_out.view(-1, self.vocab_size), + ys_out_pad, + ignore_label=self.ignore_id, + ) + + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att + + def _calc_att_predictor_loss( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + encoder_out_mask = sequence_mask( + encoder_out_lens, + maxlen=encoder_out.size(1), + dtype=encoder_out.dtype, + device=encoder_out.device, + )[:, None, :] + mask_chunk_predictor = None + if self.encoder.overlap_chunk_cls is not None: + mask_chunk_predictor = ( + self.encoder.overlap_chunk_cls.get_mask_chunk_predictor( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + encoder_out = encoder_out * mask_shfit_chunk + pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor( + encoder_out, + ys_out_pad, + encoder_out_mask, + ignore_id=self.ignore_id, + mask_chunk_predictor=mask_chunk_predictor, + target_label_length=ys_in_lens, + ) + predictor_alignments, predictor_alignments_len = ( + self.predictor.gen_frame_alignments(pre_alphas, encoder_out_lens) + ) + + scama_mask = None + if ( + self.encoder.overlap_chunk_cls is not None + and self.decoder_attention_chunk_type == "chunk" + ): + encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur + attention_chunk_center_bias = 0 + attention_chunk_size = encoder_chunk_size + decoder_att_look_back_factor = ( + self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur + ) + mask_shift_att_chunk_decoder = ( + self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn( + predictor_alignments=predictor_alignments, + encoder_sequence_length=encoder_out_lens, + chunk_size=1, + encoder_chunk_size=encoder_chunk_size, + attention_chunk_center_bias=attention_chunk_center_bias, + attention_chunk_size=attention_chunk_size, + attention_chunk_type=self.decoder_attention_chunk_type, + step=None, + predictor_mask_chunk_hopping=mask_chunk_predictor, + decoder_att_look_back_factor=decoder_att_look_back_factor, + mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder, + target_length=ys_in_lens, + is_training=self.training, + ) + elif self.encoder.overlap_chunk_cls is not None: + encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk( + encoder_out, encoder_out_lens, chunk_outs=None + ) + # try: + # 1. Forward decoder + decoder_out, _ = self.decoder( + encoder_out, + encoder_out_lens, + ys_in_pad, + ys_in_lens, + chunk_mask=scama_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + ) + + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_out_pad) + acc_att = th_accuracy( + decoder_out.view(-1, self.vocab_size), + ys_out_pad, + ignore_label=self.ignore_id, + ) + # predictor loss + loss_pre = self.criterion_pre( + ys_in_lens.type_as(pre_token_length), pre_token_length + ) + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att, loss_pre + + def _calc_att_predictor_loss2( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ): + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + encoder_out_mask = sequence_mask( + encoder_out_lens, + maxlen=encoder_out.size(1), + dtype=encoder_out.dtype, + device=encoder_out.device, + )[:, None, :] + mask_chunk_predictor = None + if self.encoder2.overlap_chunk_cls is not None: + mask_chunk_predictor = ( + self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + encoder_out = encoder_out * mask_shfit_chunk + pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2( + encoder_out, + ys_out_pad, + encoder_out_mask, + ignore_id=self.ignore_id, + mask_chunk_predictor=mask_chunk_predictor, + target_label_length=ys_in_lens, + ) + predictor_alignments, predictor_alignments_len = ( + self.predictor2.gen_frame_alignments(pre_alphas, encoder_out_lens) + ) + + scama_mask = None + if ( + self.encoder2.overlap_chunk_cls is not None + and self.decoder_attention_chunk_type2 == "chunk" + ): + encoder_chunk_size = ( + self.encoder2.overlap_chunk_cls.chunk_size_pad_shift_cur + ) + attention_chunk_center_bias = 0 + attention_chunk_size = encoder_chunk_size + decoder_att_look_back_factor = ( + self.encoder2.overlap_chunk_cls.decoder_att_look_back_factor_cur + ) + mask_shift_att_chunk_decoder = ( + self.encoder2.overlap_chunk_cls.get_mask_shift_att_chunk_decoder( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn2( + predictor_alignments=predictor_alignments, + encoder_sequence_length=encoder_out_lens, + chunk_size=1, + encoder_chunk_size=encoder_chunk_size, + attention_chunk_center_bias=attention_chunk_center_bias, + attention_chunk_size=attention_chunk_size, + attention_chunk_type=self.decoder_attention_chunk_type2, + step=None, + predictor_mask_chunk_hopping=mask_chunk_predictor, + decoder_att_look_back_factor=decoder_att_look_back_factor, + mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder, + target_length=ys_in_lens, + is_training=self.training, + ) + elif self.encoder2.overlap_chunk_cls is not None: + encoder_out, encoder_out_lens = ( + self.encoder2.overlap_chunk_cls.remove_chunk( + encoder_out, encoder_out_lens, chunk_outs=None + ) + ) + # try: + # 1. Forward decoder + decoder_out, _ = self.decoder2( + encoder_out, + encoder_out_lens, + ys_in_pad, + ys_in_lens, + chunk_mask=scama_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + ) + + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_out_pad) + acc_att = th_accuracy( + decoder_out.view(-1, self.vocab_size), + ys_out_pad, + ignore_label=self.ignore_id, + ) + # predictor loss + loss_pre = self.criterion_pre( + ys_in_lens.type_as(pre_token_length), pre_token_length + ) + # Compute cer/wer using attention-decoder + if self.training or self.error_calculator is None: + cer_att, wer_att = None, None + else: + ys_hat = decoder_out.argmax(dim=-1) + cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu()) + + return loss_att, acc_att, cer_att, wer_att, loss_pre + + def calc_predictor_mask( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor = None, + ys_pad_lens: torch.Tensor = None, + ): + # ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + # ys_in_lens = ys_pad_lens + 1 + ys_out_pad, ys_in_lens = None, None + + encoder_out_mask = sequence_mask( + encoder_out_lens, + maxlen=encoder_out.size(1), + dtype=encoder_out.dtype, + device=encoder_out.device, + )[:, None, :] + mask_chunk_predictor = None + if self.encoder.overlap_chunk_cls is not None: + mask_chunk_predictor = ( + self.encoder.overlap_chunk_cls.get_mask_chunk_predictor( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + encoder_out = encoder_out * mask_shfit_chunk + pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor( + encoder_out, + ys_out_pad, + encoder_out_mask, + ignore_id=self.ignore_id, + mask_chunk_predictor=mask_chunk_predictor, + target_label_length=ys_in_lens, + ) + predictor_alignments, predictor_alignments_len = ( + self.predictor.gen_frame_alignments(pre_alphas, encoder_out_lens) + ) + + scama_mask = None + if ( + self.encoder.overlap_chunk_cls is not None + and self.decoder_attention_chunk_type == "chunk" + ): + encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur + attention_chunk_center_bias = 0 + attention_chunk_size = encoder_chunk_size + decoder_att_look_back_factor = ( + self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur + ) + mask_shift_att_chunk_decoder = ( + self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn( + predictor_alignments=predictor_alignments, + encoder_sequence_length=encoder_out_lens, + chunk_size=1, + encoder_chunk_size=encoder_chunk_size, + attention_chunk_center_bias=attention_chunk_center_bias, + attention_chunk_size=attention_chunk_size, + attention_chunk_type=self.decoder_attention_chunk_type, + step=None, + predictor_mask_chunk_hopping=mask_chunk_predictor, + decoder_att_look_back_factor=decoder_att_look_back_factor, + mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder, + target_length=ys_in_lens, + is_training=self.training, + ) + elif self.encoder.overlap_chunk_cls is not None: + encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk( + encoder_out, encoder_out_lens, chunk_outs=None + ) + + return ( + pre_acoustic_embeds, + pre_token_length, + predictor_alignments, + predictor_alignments_len, + scama_mask, + ) + + def calc_predictor_mask2( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + ys_pad: torch.Tensor = None, + ys_pad_lens: torch.Tensor = None, + ): + # ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + # ys_in_lens = ys_pad_lens + 1 + ys_out_pad, ys_in_lens = None, None + + encoder_out_mask = sequence_mask( + encoder_out_lens, + maxlen=encoder_out.size(1), + dtype=encoder_out.dtype, + device=encoder_out.device, + )[:, None, :] + mask_chunk_predictor = None + if self.encoder2.overlap_chunk_cls is not None: + mask_chunk_predictor = ( + self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + encoder_out = encoder_out * mask_shfit_chunk + pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2( + encoder_out, + ys_out_pad, + encoder_out_mask, + ignore_id=self.ignore_id, + mask_chunk_predictor=mask_chunk_predictor, + target_label_length=ys_in_lens, + ) + predictor_alignments, predictor_alignments_len = ( + self.predictor2.gen_frame_alignments(pre_alphas, encoder_out_lens) + ) + + scama_mask = None + if ( + self.encoder2.overlap_chunk_cls is not None + and self.decoder_attention_chunk_type2 == "chunk" + ): + encoder_chunk_size = ( + self.encoder2.overlap_chunk_cls.chunk_size_pad_shift_cur + ) + attention_chunk_center_bias = 0 + attention_chunk_size = encoder_chunk_size + decoder_att_look_back_factor = ( + self.encoder2.overlap_chunk_cls.decoder_att_look_back_factor_cur + ) + mask_shift_att_chunk_decoder = ( + self.encoder2.overlap_chunk_cls.get_mask_shift_att_chunk_decoder( + None, device=encoder_out.device, batch_size=encoder_out.size(0) + ) + ) + scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn2( + predictor_alignments=predictor_alignments, + encoder_sequence_length=encoder_out_lens, + chunk_size=1, + encoder_chunk_size=encoder_chunk_size, + attention_chunk_center_bias=attention_chunk_center_bias, + attention_chunk_size=attention_chunk_size, + attention_chunk_type=self.decoder_attention_chunk_type2, + step=None, + predictor_mask_chunk_hopping=mask_chunk_predictor, + decoder_att_look_back_factor=decoder_att_look_back_factor, + mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder, + target_length=ys_in_lens, + is_training=self.training, + ) + elif self.encoder2.overlap_chunk_cls is not None: + encoder_out, encoder_out_lens = ( + self.encoder2.overlap_chunk_cls.remove_chunk( + encoder_out, encoder_out_lens, chunk_outs=None + ) + ) + + return ( + pre_acoustic_embeds, + pre_token_length, + predictor_alignments, + predictor_alignments_len, + scama_mask, + ) + + def init_beam_search( + self, + **kwargs, + ): + from funasr_detach.models.uniasr.beam_search import BeamSearchScama + from funasr_detach.models.transformer.scorers.ctc import CTCPrefixScorer + from funasr_detach.models.transformer.scorers.length_bonus import LengthBonus + + decoding_mode = kwargs.get("decoding_mode", "model1") + if decoding_mode == "model1": + decoder = self.decoder + else: + decoder = self.decoder2 + # 1. Build ASR model + scorers = {} + + if self.ctc != None: + ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos) + scorers.update(ctc=ctc) + token_list = kwargs.get("token_list") + scorers.update( + decoder=decoder, + length_bonus=LengthBonus(len(token_list)), + ) + + # 3. Build ngram model + # ngram is not supported now + ngram = None + scorers["ngram"] = ngram + + weights = dict( + decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.0), + ctc=kwargs.get("decoding_ctc_weight", 0.0), + lm=kwargs.get("lm_weight", 0.0), + ngram=kwargs.get("ngram_weight", 0.0), + length_bonus=kwargs.get("penalty", 0.0), + ) + beam_search = BeamSearchScama( + beam_size=kwargs.get("beam_size", 5), + weights=weights, + scorers=scorers, + sos=self.sos, + eos=self.eos, + vocab_size=len(token_list), + token_list=token_list, + pre_beam_score_key=None if self.ctc_weight == 1.0 else "full", + ) + + self.beam_search = beam_search + + def inference( + self, + data_in, + data_lengths=None, + key: list = None, + tokenizer=None, + frontend=None, + **kwargs, + ): + + decoding_model = kwargs.get("decoding_model", "normal") + token_num_relax = kwargs.get("token_num_relax", 5) + if decoding_model == "fast": + decoding_ind = 0 + decoding_mode = "model1" + elif decoding_model == "offline": + decoding_ind = 1 + decoding_mode = "model2" + else: + decoding_ind = 0 + decoding_mode = "model2" + # init beamsearch + + if self.beam_search is None: + logging.info("enable beam_search") + self.init_beam_search(decoding_mode=decoding_mode, **kwargs) + self.nbest = kwargs.get("nbest", 1) + + meta_data = {} + if ( + isinstance(data_in, torch.Tensor) + and kwargs.get("data_type", "sound") == "fbank" + ): # fbank + speech, speech_lengths = data_in, data_lengths + if len(speech.shape) < 3: + speech = speech[None, :, :] + if speech_lengths is None: + speech_lengths = speech.shape[1] + else: + # extract fbank feats + time1 = time.perf_counter() + audio_sample_list = load_audio_text_image_video( + data_in, + fs=frontend.fs, + audio_fs=kwargs.get("fs", 16000), + data_type=kwargs.get("data_type", "sound"), + tokenizer=tokenizer, + ) + time2 = time.perf_counter() + meta_data["load_data"] = f"{time2 - time1:0.3f}" + speech, speech_lengths = extract_fbank( + audio_sample_list, + data_type=kwargs.get("data_type", "sound"), + frontend=frontend, + ) + time3 = time.perf_counter() + meta_data["extract_feat"] = f"{time3 - time2:0.3f}" + meta_data["batch_data_time"] = ( + speech_lengths.sum().item() + * frontend.frame_shift + * frontend.lfr_n + / 1000 + ) + + speech = speech.to(device=kwargs["device"]) + speech_lengths = speech_lengths.to(device=kwargs["device"]) + speech_raw = speech.clone().to(device=kwargs["device"]) + # Encoder + _, encoder_out, encoder_out_lens = self.encode( + speech, speech_lengths, ind=decoding_ind + ) + if decoding_mode == "model1": + predictor_outs = self.calc_predictor_mask(encoder_out, encoder_out_lens) + else: + encoder_out, encoder_out_lens = self.encode2( + encoder_out, + encoder_out_lens, + speech_raw, + speech_lengths, + ind=decoding_ind, + ) + predictor_outs = self.calc_predictor_mask2(encoder_out, encoder_out_lens) + + scama_mask = predictor_outs[4] + pre_token_length = predictor_outs[1] + pre_acoustic_embeds = predictor_outs[0] + maxlen = pre_token_length.sum().item() + token_num_relax + minlen = max(0, pre_token_length.sum().item() - token_num_relax) + # c. Passed the encoder result and the beam search + nbest_hyps = self.beam_search( + x=encoder_out[0], + scama_mask=scama_mask, + pre_acoustic_embeds=pre_acoustic_embeds, + maxlenratio=0.0, + minlenratio=0.0, + maxlen=int(maxlen), + minlen=int(minlen), + ) + + nbest_hyps = nbest_hyps[: self.nbest] + + results = [] + for hyp in nbest_hyps: + + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list(filter(lambda x: x != 0, token_int)) + + # Change integer-ids to tokens + token = tokenizer.ids2tokens(token_int) + text_postprocessed = tokenizer.tokens2text(token) + if not hasattr(tokenizer, "bpemodel"): + text_postprocessed, _ = postprocess_utils.sentence_postprocess(token) + + result_i = {"key": key[0], "text": text_postprocessed} + results.append(result_i) + + return results, meta_data diff --git a/demo/Step-Audio-EditX/funasr_detach/models/uniasr/template.yaml b/demo/Step-Audio-EditX/funasr_detach/models/uniasr/template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..26bb4f16feed5e21d937744bd5477a3175ff9a01 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/uniasr/template.yaml @@ -0,0 +1,206 @@ +# This is an example that demonstrates how to configure a model file. +# You can modify the configuration according to your own requirements. + +# to print the register_table: +# from funasr.register import tables +# tables.print() + +# network architecture +model: UniASR +model_conf: + ctc_weight: 0.0 + lsm_weight: 0.1 + length_normalized_loss: true + predictor_weight: 1.0 + decoder_attention_chunk_type: chunk + ctc_weight2: 0.0 + predictor_weight2: 1.0 + decoder_attention_chunk_type2: chunk + loss_weight_model1: 0.5 + + +# encoder +encoder: SANMEncoderChunkOpt +encoder_conf: + output_size: 320 + attention_heads: 4 + linear_units: 1280 + num_blocks: 35 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 11 + sanm_shfit: 0 + selfattention_layer_type: sanm + chunk_size: + - 20 + - 60 + stride: + - 10 + - 40 + pad_left: + - 5 + - 10 + encoder_att_look_back_factor: + - 0 + - 0 + decoder_att_look_back_factor: + - 0 + - 0 + +# decoder +decoder: FsmnDecoderSCAMAOpt +decoder_conf: + attention_dim: 256 + attention_heads: 4 + linear_units: 1024 + num_blocks: 12 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + att_layer_num: 6 + kernel_size: 11 + concat_embeds: true + +# predictor +predictor: CifPredictorV2 +predictor_conf: + idim: 320 + threshold: 1.0 + l_order: 1 + r_order: 1 + + +# encoder2 +encoder2: SANMEncoderChunkOpt +encoder2_conf: + output_size: 320 + attention_heads: 4 + linear_units: 1280 + num_blocks: 20 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: pe + pos_enc_class: SinusoidalPositionEncoder + normalize_before: true + kernel_size: 21 + sanm_shfit: 0 + selfattention_layer_type: sanm + chunk_size: + - 45 + - 70 + stride: + - 35 + - 50 + pad_left: + - 5 + - 10 + encoder_att_look_back_factor: + - 0 + - 0 + decoder_att_look_back_factor: + - 0 + - 0 + +# decoder +decoder2: FsmnDecoderSCAMAOpt +decoder2_conf: + attention_dim: 320 + attention_heads: 4 + linear_units: 1280 + num_blocks: 12 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.1 + src_attention_dropout_rate: 0.1 + att_layer_num: 6 + kernel_size: 11 + concat_embeds: true + +predictor2: CifPredictorV2 +predictor2_conf: + idim: 320 + threshold: 1.0 + l_order: 1 + r_order: 1 + +stride_conv: stride_conv1d +stride_conv_conf: + kernel_size: 2 + stride: 2 + pad: + - 0 + - 1 + +# frontend related +frontend: WavFrontend +frontend_conf: + fs: 16000 + window: hamming + n_mels: 80 + frame_length: 25 + frame_shift: 10 + lfr_m: 7 + lfr_n: 6 + dither: 0.0 + +specaug: SpecAugLFR +specaug_conf: + apply_time_warp: false + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 30 + lfr_rate: 6 + num_freq_mask: 1 + apply_time_mask: true + time_mask_width_range: + - 0 + - 12 + num_time_mask: 1 + +train_conf: + accum_grad: 1 + grad_clip: 5 + max_epoch: 150 + keep_nbest_models: 10 + avg_nbest_model: 5 + log_interval: 50 + +optim: adam +optim_conf: + lr: 0.0001 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 30000 + +dataset: AudioDataset +dataset_conf: + index_ds: IndexDSJsonl + batch_sampler: DynamicBatchLocalShuffleSampler + batch_type: example # example or length + batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len; + max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, + buffer_size: 500 + shuffle: True + num_workers: 0 + +tokenizer: CharTokenizer +tokenizer_conf: + unk_symbol: + split_with_space: true + + +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: true +normalize: null diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/whisper/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/model.py b/demo/Step-Audio-EditX/funasr_detach/models/whisper/model.py new file mode 100644 index 0000000000000000000000000000000000000000..818b90124bd5546c5bb833eb38a116a33d84ff9a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/model.py @@ -0,0 +1,306 @@ +from dataclasses import dataclass +from typing import Dict +from typing import Iterable, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch import nn + + +from funasr_detach.models.whisper.utils.decoding import ( + detect_language as detect_language_function, + decode as decode_function, +) +from funasr_detach.register import tables + + +@dataclass +class ModelDimensions: + n_mels: int + n_audio_ctx: int + n_audio_state: int + n_audio_head: int + n_audio_layer: int + n_vocab: int + n_text_ctx: int + n_text_state: int + n_text_head: int + n_text_layer: int + + +class LayerNorm(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + return super().forward(x.float()).type(x.dtype) + + +class Linear(nn.Linear): + def forward(self, x: Tensor) -> Tensor: + return F.linear( + x, + self.weight.to(x.dtype), + None if self.bias is None else self.bias.to(x.dtype), + ) + + +class Conv1d(nn.Conv1d): + def _conv_forward( + self, x: Tensor, weight: Tensor, bias: Optional[Tensor] + ) -> Tensor: + return super()._conv_forward( + x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) + ) + + +def sinusoids(length, channels, max_timescale=10000): + """Returns sinusoids for positional embedding""" + assert channels % 2 == 0 + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, n_state: int, n_head: int): + super().__init__() + self.n_head = n_head + self.query = Linear(n_state, n_state) + self.key = Linear(n_state, n_state, bias=False) + self.value = Linear(n_state, n_state) + self.out = Linear(n_state, n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + q = self.query(x) + + if kv_cache is None or xa is None or self.key not in kv_cache: + # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; + # otherwise, perform key/value projections for self- or cross-attention as usual. + k = self.key(x if xa is None else xa) + v = self.value(x if xa is None else xa) + else: + # for cross-attention, calculate keys and values once and reuse in subsequent calls. + k = kv_cache[self.key] + v = kv_cache[self.value] + + wv, qk = self.qkv_attention(q, k, v, mask) + return self.out(wv), qk + + def qkv_attention( + self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None + ): + n_batch, n_ctx, n_state = q.shape + scale = (n_state // self.n_head) ** -0.25 + q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale + k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale + v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) + + qk = q @ k + if mask is not None: + qk = qk + mask[:n_ctx, :n_ctx] + qk = qk.float() + + w = F.softmax(qk, dim=-1).to(q.dtype) + return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach() + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): + super().__init__() + + self.attn = MultiHeadAttention(n_state, n_head) + self.attn_ln = LayerNorm(n_state) + + self.cross_attn = ( + MultiHeadAttention(n_state, n_head) if cross_attention else None + ) + self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None + + n_mlp = n_state * 4 + self.mlp = nn.Sequential( + Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state) + ) + self.mlp_ln = LayerNorm(n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0] + if self.cross_attn: + x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0] + x = x + self.mlp(self.mlp_ln(x)) + return x + + +@tables.register("encoder_classes", "WhisperEncoder") +class AudioEncoder(nn.Module): + def __init__( + self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): + super().__init__() + self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) + self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) + self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] + ) + self.ln_post = LayerNorm(n_state) + + def forward(self, x: Tensor): + """ + x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) + the mel spectrogram of the audio + """ + x = F.gelu(self.conv1(x)) + x = F.gelu(self.conv2(x)) + x = x.permute(0, 2, 1) + + assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" + x = (x + self.positional_embedding).to(x.dtype) + + for block in self.blocks: + x = block(x) + + x = self.ln_post(x) + return x + + +@tables.register("decoder_classes", "WhisperDecoder") +class TextDecoder(nn.Module): + def __init__( + self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): + super().__init__() + + self.token_embedding = nn.Embedding(n_vocab, n_state) + self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ + ResidualAttentionBlock(n_state, n_head, cross_attention=True) + for _ in range(n_layer) + ] + ) + self.ln = LayerNorm(n_state) + + mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): + """ + x : torch.LongTensor, shape = (batch_size, <= n_ctx) + the text tokens + xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) + the encoded audio features to be attended on + """ + offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 + x = ( + self.token_embedding(x) + + self.positional_embedding[offset : offset + x.shape[-1]] + ) + x = x.to(xa.dtype) + + for block in self.blocks: + x = block(x, xa, mask=self.mask, kv_cache=kv_cache) + + x = self.ln(x) + logits = ( + x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1) + ).float() + + return logits + + +@tables.register("model_classes", "Whisper") +class Whisper(nn.Module): + def __init__(self, dims: dict): + super().__init__() + dims = ModelDimensions(**dims) + self.dims = dims + self.sos = 1 + self.eos = 1 + self.encoder = AudioEncoder( + self.dims.n_mels, + self.dims.n_audio_ctx, + self.dims.n_audio_state, + self.dims.n_audio_head, + self.dims.n_audio_layer, + ) + self.decoder = TextDecoder( + self.dims.n_vocab, + self.dims.n_text_ctx, + self.dims.n_text_state, + self.dims.n_text_head, + self.dims.n_text_layer, + ) + + def embed_audio(self, mel: torch.Tensor): + return self.encoder(mel) + + def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): + return self.decoder(tokens, audio_features) + + def forward( + self, mel: torch.Tensor, tokens: torch.Tensor + ) -> Dict[str, torch.Tensor]: + return self.decoder(tokens, self.encoder(mel)) + + @property + def device(self): + return next(self.parameters()).device + + @property + def is_multilingual(self): + return self.dims.n_vocab == 51865 + + def install_kv_cache_hooks(self, cache: Optional[dict] = None): + """ + The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value + tensors calculated for the previous positions. This method returns a dictionary that stores + all caches, and the necessary hooks for the key and value projection modules that save the + intermediate tensors to be reused during later calculations. + + Returns + ------- + cache : Dict[nn.Module, torch.Tensor] + A dictionary object mapping the key/value projection modules to its cache + hooks : List[RemovableHandle] + List of PyTorch RemovableHandle objects to stop the hooks to be called + """ + cache = {**cache} if cache is not None else {} + hooks = [] + + def save_to_cache(module, _, output): + if ( + module not in cache + or output.shape[1] > self.decoder.positional_embedding.shape[0] + ): + cache[module] = ( + output # save as-is, for the first token or cross attention + ) + else: + cache[module] = torch.cat([cache[module], output], dim=1).detach() + return cache[module] + + def install_hooks(layer: nn.Module): + if isinstance(layer, MultiHeadAttention): + hooks.append(layer.key.register_forward_hook(save_to_cache)) + hooks.append(layer.value.register_forward_hook(save_to_cache)) + + self.decoder.apply(install_hooks) + return cache, hooks + + detect_language = detect_language_function + decode = decode_function diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/merges.txt b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..a508d868b201f0a442f5b5d027688aadfa6df447 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T +c t +Ġ S +i d +o t +Ġ I +u t +e t +Ġ A +Ġ is +Ġ on +i m +a m +o w +a y +a d +s e +Ġth at +Ġ C +i g +Ġf or +a c +Ġ y +v er +u r +Ġ u +l d +Ġs t +Ġ M +' s +Ġ he +Ġ it +at ion +it h +i r +c e +Ġy ou +i l +Ġ B +Ġw h +o l +Ġ P +Ġw ith +Ġ 1 +t er +c h +Ġa s +Ġw e +Ġ ( +n d +i ll +Ġ D +i f +Ġ 2 +a g +er s +k e +Ġ " +Ġ H +e m +Ġc on +Ġ W +Ġ R +he r +Ġw as +Ġ r +o d +Ġ F +u l +at e +Ġa t +r i +p p +o re +ĠT he +Ġs e +u s +Ġp ro +Ġh a +u m +Ġa re +Ġd e +a in +an d +Ġo r +ig h +es t +is t +a b +r om +Ġ N +t h +Ġc om +Ġ G +u n +o p +0 0 +Ġ L +Ġn ot +es s +Ġe x +Ġ v +re s +Ġ E +e w +it y +an t +Ġb y +e l +o s +or t +o c +q u +Ġf rom +Ġha ve +Ġs u +i ve +ou ld +Ġs h +Ġth is +n t +r a +p e +igh t +ar t +m ent +Ġa l +u st +en d +- - +al l +Ġ O +ac k +Ġc h +Ġ le +i es +re d +ar d +â Ģ +ou t +Ġ J +Ġa b +e ar +i v +al ly +ou r +o st +g h +p t +Ġp l +as t +Ġc an +a k +om e +u d +T he +Ġh is +Ġd o +Ġg o +Ġh as +g e +' t +Ġ U +r ou +Ġs a +Ġ j +Ġb ut +Ġw or +Ġa ll +e ct +Ġ k +am e +Ġw ill +o k +Ġw he +Ġthe y +id e +0 1 +f f +ic h +p l +t her +Ġt r +. . +Ġin t +i e +u re +ag e +Ġn e +i al +a p +in e +ic e +Ġm e +Ġo ut +an s +on e +on g +ion s +Ġwh o +Ġ K +Ġu p +Ġthe ir +Ġa d +Ġ 3 +Ġu s +at ed +ou s +Ġm ore +u e +o g +ĠS t +in d +i ke +Ġs o +im e +p er +. " +b er +i z +a ct +Ġon e +Ġsa id +Ġ - +a re +Ġyou r +c c +ĠT h +Ġc l +e p +a ke +ab le +i p +Ġcon t +Ġwh ich +i a +Ġ im +Ġab out +Ġwe re +ver y +u b +Ġh ad +Ġ en +Ġcom p +, " +ĠI n +Ġu n +Ġa g +i re +ac e +a u +ar y +Ġw ould +as s +r y +Ġ âĢ +c l +o ok +e re +s o +Ġ V +ig n +i b +Ġof f +Ġt e +v en +Ġ Y +i le +o se +it e +or m +Ġ2 01 +Ġre s +Ġm an +Ġp er +Ġo ther +or d +ul t +Ġbe en +Ġl ike +as e +an ce +k s +ay s +ow n +en ce +Ġd is +ct ion +Ġan y +Ġa pp +Ġs p +in t +res s +ation s +a il +Ġ 4 +ic al +Ġthe m +Ġhe r +ou nt +ĠC h +Ġa r +Ġ if +Ġthe re +Ġp e +Ġy ear +a v +Ġm y +Ġs ome +Ġwhe n +ou gh +ac h +Ġth an +r u +on d +ic k +Ġo ver +ve l +Ġ qu +Ċ Ċ +Ġs c +re at +re e +ĠI t +ou nd +p ort +Ġal so +Ġp art +f ter +Ġk n +Ġbe c +Ġt ime +en s +Ġ 5 +op le +Ġwh at +Ġn o +d u +m er +an g +Ġn ew +-- -- +Ġg et +or y +it ion +ing s +Ġj ust +Ġint o +Ġ 0 +ent s +o ve +t e +Ġpe ople +Ġp re +Ġit s +Ġre c +Ġt w +i an +ir st +ar k +or s +Ġwor k +ad e +o b +Ġs he +Ġo ur +w n +in k +l ic +Ġ1 9 +ĠH e +is h +nd er +au se +Ġh im +on s +Ġ [ +Ġ ro +f orm +i ld +at es +ver s +Ġon ly +o ll +Ġs pe +c k +e ll +am p +Ġa cc +Ġb l +i ous +ur n +f t +o od +Ġh ow +he d +Ġ ' +Ġa fter +a w +Ġat t +o v +n e +Ġpl ay +er v +ic t +Ġc ould +it t +Ġa m +Ġf irst +Ġ 6 +Ġa ct +Ġ $ +e c +h ing +u al +u ll +Ġcom m +o y +o ld +c es +at er +Ġf e +Ġbe t +w e +if f +Ġtw o +oc k +Ġb ack +) . +id ent +Ġu nder +rou gh +se l +x t +Ġm ay +rou nd +Ġp o +p h +is s +Ġd es +Ġm ost +Ġd id +Ġad d +j ect +Ġin c +f ore +Ġp ol +on t +Ġag ain +cl ud +ter n +Ġkn ow +Ġne ed +Ġcon s +Ġc o +Ġ . +Ġw ant +Ġse e +Ġ 7 +n ing +i ew +ĠTh is +c ed +Ġe ven +Ġin d +t y +ĠW e +at h +Ġthe se +Ġp r +Ġu se +Ġbec ause +Ġf l +n g +Ġn ow +ĠâĢ ĵ +c om +is e +Ġm ake +Ġthe n +ow er +Ġe very +ĠU n +Ġse c +os s +u ch +Ġe m +Ġ = +ĠR e +i ed +r it +Ġin v +le ct +Ġsu pp +at ing +Ġl ook +m an +pe ct +Ġ 8 +ro w +Ġb u +Ġwhe re +if ic +Ġyear s +i ly +Ġd iff +Ġsh ould +Ġre m +T h +I n +Ġe v +d ay +' re +ri b +Ġre l +s s +Ġde f +Ġr ight +Ġs y +) , +l es +00 0 +he n +Ġth rough +ĠT r +_ _ +Ġw ay +Ġd on +Ġ , +Ġ1 0 +as ed +Ġas s +ub lic +Ġre g +ĠA nd +i x +Ġ very +Ġin clud +ot her +Ġim p +ot h +Ġsu b +ĠâĢ Ķ +Ġbe ing +ar g +ĠW h += = +ib le +Ġdo es +an ge +r am +Ġ 9 +er t +p s +it ed +ation al +Ġb r +Ġd own +Ġman y +ak ing +Ġc all +ur ing +it ies +Ġp h +ic s +al s +Ġde c +at ive +en er +Ġbe fore +il ity +Ġwe ll +Ġm uch +ers on +Ġth ose +Ġsu ch +Ġ ke +Ġ end +ĠB ut +as on +t ing +Ġl ong +e f +Ġth ink +y s +Ġbe l +Ġs m +it s +a x +Ġo wn +Ġpro v +Ġs et +if e +ment s +b le +w ard +Ġsh ow +Ġp res +m s +om et +Ġo b +Ġs ay +ĠS h +t s +f ul +Ġe ff +Ġg u +Ġin st +u nd +re n +c ess +Ġ ent +ĠY ou +Ġgo od +Ġst art +in ce +Ġm ade +t t +st em +ol og +u p +Ġ | +um p +Ġhe l +ver n +ul ar +u ally +Ġa c +Ġm on +Ġl ast +Ġ2 00 +1 0 +Ġst ud +u res +ĠA r +sel f +ar s +mer ic +u es +c y +Ġm in +oll ow +Ġc ol +i o +Ġm od +Ġc ount +ĠC om +he s +Ġf in +a ir +i er +âĢ Ķ +re ad +an k +at ch +e ver +Ġst r +Ġpo int +or k +ĠN ew +Ġs ur +o ol +al k +em ent +Ġus ed +ra ct +we en +Ġs ame +ou n +ĠA l +c i +Ġdiff ere +Ġwh ile +---- ---- +Ġg ame +ce pt +Ġs im +.. . +Ġin ter +e k +Ġre port +Ġpro du +Ġst ill +l ed +a h +Ġhe re +Ġwor ld +Ġth ough +Ġn um +ar ch +im es +al e +ĠS e +ĠI f +/ / +ĠL e +Ġre t +Ġre f +Ġtr ans +n er +ut ion +ter s +Ġt ake +ĠC l +Ġcon f +w ay +a ve +Ġgo ing +Ġs l +u g +ĠA meric +Ġspe c +Ġh and +Ġbet ween +ist s +ĠD e +o ot +I t +Ġe ar +Ġagain st +Ġh igh +g an +a z +at her +Ġex p +Ġo p +Ġin s +Ġg r +Ġhel p +Ġre qu +et s +in s +ĠP ro +is m +Ġf ound +l and +at a +us s +am es +Ġp erson +Ġg reat +p r +Ġs ign +ĠA n +' ve +Ġs omet +Ġs er +h ip +Ġr un +Ġ : +Ġt er +ire ct +Ġf ollow +Ġd et +ic es +Ġf ind +1 2 +Ġm em +Ġc r +e red +e x +Ġex t +ut h +en se +c o +Ġte am +v ing +ou se +as h +at t +v ed +Ġsy stem +ĠA s +d er +iv es +m in +Ġle ad +ĠB l +c ent +Ġa round +Ġgo vern +Ġc ur +vel op +an y +Ġc our +al th +ag es +iz e +Ġc ar +od e +Ġl aw +Ġre ad +' m +c on +Ġre al +Ġsupp ort +Ġ1 2 +.. .. +Ġre ally +n ess +Ġf act +Ġd ay +Ġb oth +y ing +Ġs erv +ĠF or +Ġth ree +Ġw om +Ġm ed +od y +ĠThe y +5 0 +Ġex per +t on +Ġe ach +ak es +Ġc he +Ġc re +in es +Ġre p +1 9 +g g +ill ion +Ġg rou +ut e +i k +W e +g et +E R +Ġm et +Ġs ays +o x +Ġd uring +er n +iz ed +a red +Ġf am +ic ally +Ġha pp +ĠI s +Ġch ar +m ed +v ent +Ġg ener +i ent +p le +i et +re nt +1 1 +v es +pt ion +Ġ2 0 +form ation +Ġc or +Ġoff ic +ie ld +Ġto o +is ion +Ġin f +Ġ Z +t he +o ad +Ġp ublic +Ġpro g +r ic +* * +Ġw ar +Ġp ower +v iew +Ġf ew +Ġl oc +Ġdiffere nt +Ġst ate +Ġhe ad +' ll +Ġp oss +Ġst at +re t +ant s +Ġv al +Ġis s +Ġc le +i vers +an c +Ġex pl +Ġan other +Ġ Q +Ġa v +th ing +n ce +W h +Ġch ild +Ġs ince +i red +l ess +Ġl ife +Ġde velop +itt le +Ġde p +Ġp ass +ã ĥ +Ġt urn +or n +Th is +b ers +ro ss +ĠA d +Ġf r +Ġres p +Ġsec ond +o h +Ġ / +Ġdis c +Ġ & +Ġsomet hing +Ġcomp le +Ġ ed +Ġf il +Ġmon th +a j +u c +Ġgovern ment +Ġwith out +Ġle g +Ġd ist +Ġp ut +Ġqu est +an n +Ġpro t +2 0 +Ġne ver +i ence +Ġle vel +Ġar t +Ġth ings +Ġm ight +Ġeff ect +Ġcont ro +Ġc ent +Ġ1 8 +Ġall ow +Ġbel ie +ch ool +ot t +Ġinc re +Ġfe el +Ġres ult +Ġl ot +Ġf un +ot e +Ġt y +ere st +Ġcont in +Ġus ing +Ġb ig +2 01 +Ġas k +Ġb est +Ġ ) +I N +Ġo pp +3 0 +Ġnum ber +in ess +S t +le ase +Ġc a +Ġm ust +Ġd irect +Ġg l +Ġ < +Ġop en +Ġp ost +Ġcom e +Ġse em +ord ing +Ġwe ek +ate ly +it al +Ġe l +ri end +Ġf ar +Ġt ra +in al +Ġp ri +ĠU S +Ġpl ace +Ġfor m +Ġto ld +" : +ain s +at ure +ĠTr ump +Ġst and +Ġ # +id er +ĠF r +Ġne xt +Ġs oc +Ġp ur +Ġle t +Ġl ittle +Ġh um +Ġ i +r on +1 5 +Ġ1 5 +Ġcomm un +Ġm ark +ĠThe re +Ġw r +ĠTh at +Ġin formation +w ays +Ġb us +a pp +Ġinv est +m e +Ġh ard +ain ed +e ad +Ġim port +Ġapp ro +Ġt est +Ġt ri +Ġre st +os ed +Ġf ull +Ġc are +ĠS p +Ġc ase +O N +Ġs k +Ġl ess +Ġ + +Ġpart ic +ĠP l +ab ly +u ck +is hed +ch n +b e +Ġl ist +at or +Ġto p +Ġad v +ĠB e +ru ct +Ġd em +r ation +l ing +g y +re en +g er +Ġh ome +Ġle ft +Ġbet ter +Ġd ata +Ġ1 1 +Ġatt ack +Ġpro ble +l ine +ard s +Ġbe h +r al +ĠH ow +ĠS he +ar ge +Ġ -- +: // +Ġb ro +ĠP h +at s +Ġbu ild +w w +id ed +a im +as es +en cy +Ġm ain +in ed +Ġinclud ing +Ġ { +Ġg ot +Ġint erest +Ġke ep +Ġ X +Ġe as +ain ing +Ġcl ass +âĢ ¦ +ĠN o +Ġv ar +Ġsm all +amp le +A T +Ġ ide +ĠS o +Ġre ce +Ġpol it +Ġm ov +Ġpl an +Ġper cent +iv ing +Ġc amp +Ġp ay +1 4 +s c +is ed +Ġu nt +one y +pl oy +== == +Ġdid n +ĠI nd +el s +ert ain +Ġp os +__ __ +i ver +Ġpro cess +Ġprog ram +if ied +ĠR ep +1 6 +u ro +olog y +at ter +in a +Ġn ame +ĠA ll +Ġf our +Ġret urn +v ious +b s +Ġcall ed +Ġm ove +ĠS c +ir d +Ġgrou p +Ġb re +Ġm en +Ġc ap +t en +e e +Ġd ri +le g +he re +uth or +Ġp at +Ġcur rent +id es +Ġp op +t o +ent ion +Ġal ways +Ġm il +Ġwom en +Ġ1 6 +Ġo ld +iv en +ra ph +ĠO r +r or +ent ly +Ġn ear +ĠE x +re am +s h +Ġ1 4 +Ġf ree +iss ion +st and +ĠC on +al ity +us ed +1 3 +Ġdes ign +Ġch ange +Ġch ang +Ġb o +Ġv is +em ber +Ġb ook +read y +Ġk ill +2 5 +pp ed +Ġa way +Ġab le +Ġcount ry +Ġcon st +ar n +Ġor der +A R +i or +i um +or th +1 8 +ail able +Ġs w +Ġm illion +Ġ1 3 +at ic +t ed +ĠG o +Ġo per +en g +Ġth ing +aj or +con om +ĠCom m +Ġwh y +u red +ur al +Ġs chool +b y +ĠM ar +Ġa ff +Ġd ays +Ġan n +us h +an e +I f +e g +Ġpro f +Ġhe alth +ou th +B ut +ion al +. , +Ġs ol +Ġal ready +Ġ3 0 +Ġchar act +H e +Ġf riend +E S +i ans +ic le +' d +ĠO n +Ġle ast +Ġp rom +Ġd r +Ġh ist +it her +Ġ est +i qu +1 7 +s on +Ġte ll +Ġt alk +oh n +o int +le ction +A N +Ġunt il +au gh +Ġl ater +Ġ ve +Ġv iew +end ing +iv ed +Ġwor d +w are +Ġc ost +Ġen ough +Ġg ive +ĠUn ited +Ġte chn +are nt +O R +Ġp ar +ĠD r +Ġ201 6 +r ist +er ing +Ġ  +Ġl arge +s ide +ac y +cc ess +Ġw in +Ġimport ant +Ġ19 9 +Ġdoes n +Ġ1 7 +Ġbus iness +Ġcle ar +Ġre se +" , +ur y +Ġe qu +as ter +al f +ĠAmeric an +n ect +Ġex pect +ivers ity +Ġo cc +ĠF l +Ġk ind +Ġme an +Ġp ast +Ġde v +Ġb as +le t +ra ft +Ġor gan +Ġde l +Ġper form +Ġst ory +Ġse ason +ĠC ol +Ġcl aim +Ġc ame +Ġwith in +Ġl ine +Ġpro ject +ĠA t +Ġcontro l +end ed +ĠS y +Ġa ir +iz ation +Ġ * +le y +Ġm oney +id d +Y ou +f or +Ġfam ily +Ġm aking +Ġb it +Ġpol ice +Ġhapp en +Ġ vers +on y +u ff +ĠW hen +Ġs it +ide o +l f +is on +Ġsu re +g in +Ġapp ear +Ġl ight +Ġ es +o f +Ġw ater +Ġt imes +n ot +Ġg row +Ġcomp any +ĠT e +ow s +Ġm ar +our ce +i ol +ar m +b r +Ġex ample +Ġcon c +Ġf ore +ĠT o +p ro +E N +ri es +Ġ2 5 +ĠC an +ne y +Ġact ually +Ġe ver +ur ity +ak en +ap s +Ġt ax +Ġm ajor +am a +Ġof ten +er al +Ġhum an +Ġj ob +is ter +Ġav ailable +oc r +en n +a id +iv id +Ġrec ord +? " +Ġs ing +ĠA m +id ence +Ġnew s +st er +Ġe conom +Ġfollow ing +ĠB r +is ing +Ġh our +m ost +um ent +Ġse x +Ġdes c +Ġbec ome +ĠE d +Ġto ok +Ġha ving +Ġprodu ct +a ult +A s +ar ing +Ġme ans +Ġh op +un e +Ġch o +Ġc ertain +Ġn on +Ġde al +2 4 +le ment +oc i +en e +Ġs ide +ĠP r +ĠM ay +Ġre ason +u ed +c hed +ul ation +Ġe lect +Ġoffic ial +Ġposs ible +Ġh old +and s +ot s +Ġc ity +or ies +Ġse ver +Ġchild ren +Ġon ce +Ġact iv +l er +Ġn ight +it ions +ĠJ ohn +a pe +pl ay +Ġd one +Ġl im +Ġwork ing +ĠP res +or ld +e b +ĠC o +Ġb ody +ail s +ut es +ĠM r +Ġwhe ther +Ġa uthor +ro p +Ġpro per +Ġse en +) ; +Ġf ac +ĠS u +Ġcon d +it ing +Ġcour se +Ġ } +-------- -------- +a ign +Ġev ent +Ġen g +Ġp ot +Ġin tern +i am +Ġsh ort +em pt +ã Ĥ +ĠG od +il ar +8 0 +Ġor ig +I S +our n +ab ility +it ive +Ġd am +Ġ1 00 +Ġp ress +Ġdo ing +Ġprot ect +r ing +Ġthough t +Ġquest ion +re w +ĠW ar +Ġsever al +ĠSt ate +Ġg iven +Ġf und +ĠT w +Ġw ent +an ces +w ork +p or +m y +4 0 +Ġar g +art ment +ust om +Ġpol ic +Ġme et +Ġc reat +2 2 +ĠSt ates +Ġg ames +ra w +ut ure +Ġunder stand +ur s +ĠO b +l ish +s y +Ġm akes +Ġw on +ag on +Ġh tt +Ġl ove +ent ial +Ġcomple te +p ar +ĠI m +A L +Ġacc ount + ł +ore d +ver t +Ġ ident +Ġ201 5 +Ġother s +ĠM in +i ber +ver age +The re +ition al +d d +Ġpro b +Ġyou ng +Ġal ong +Ġacc ording +Ġy et +Ġmem bers +ĠWh at +o id +ĠM an +A nd +Ġam ong +a i +Ġem ploy +ĠR es +Ġ > +Ġinv ol +Ġl ow +a f +ĠC ar +Ġh ig +ĠO ne +ĠS ec +in ation +Ġlike ly +Ġan t +ag ed +ĠR uss +Ġb en +Ġre le +F or +b ack +ĠN ot +Ġpres ident +b all +Ġacc ess +ivid ual +ĠD em +ĠE uro +6 0 +Ġkn own +ir l +ĠG r +Ġear ly +u se +iet y +âĢ ĵ +Ġf ight +Ġs ent +Ġto day +Ġmark et +" . +Ġb ased +Ġstr ong +ur ther +Ġde b +m ber +Ġproble m +Ġde ath +Ġsoc ial +im ate +A S +ort un +Ġcamp aign +er y +C h +Ġe y +i ally +Ġm us +w h +p os +Ġ er +Ġsa f +Ġmonth s +ir on +Ġv iol +Ġf ive +Ġst re +Ġplay ers +in c +al d +y ear +a un +Ġsu ccess +Ġpres ent +ere nce +Ġ201 4 +Ġsu gg +Ġpartic ular +Ġtr y +Ġsugg est +ĠCh rist +on es +Ġpri v +2 3 +Ġc rit +Ġl and +Ġloc al +if y +2 9 +Ġa ut +E D +ĠG u +Ġm ult +Ġpolit ical +Ġask ed +Ġfor mer +it ter +ri pt +Ġcl ose +Ġp ract +ĠY ork +Ġget ting +Ġac ross +Ġcom b +Ġbelie ve +Ġ z +Ġto get +Ġtoget her +ĠC ent +ir c +Ġind ividual +ĠM c +2 7 +is k +ĠE ng +Ġf ace +Ġ2 4 +Ġval ue +Ġare a +e v +Ġw rit +ĠPres ident +Ġv ot +Ġke y +Ġm om +p ut +Ġany thing +Ġexper ience +att le +Ġm ind +a ff +om m +Ġf uture +g ed +Ġc ut +Ġto t +it ch +Ġv ideo +Ġinvest ig +Ġn et +ĠM y +r ict +i en +. ) +Ġimp ro +th ough +ward s +Ġcon nect +ĠM ed +sel ves +ens ive +m b +o ber +at ors +A n +Ġ5 0 +Ġre du +res ent +Ġab ove +Ġf re +ĠEuro pe +s w +Ġam ount +ĠA pp +Ġe ither +Ġmil it +Ġan al +Ġf ail +ĠE n +al es +Ġspec ial +Ġbl ack +I T +c her +Ġlook ing +Ġf ire +y n +Ġal most +o on +Ġstud y +Ġm iss +c hes +ro wn +Ġt re +Ġcommun ity +Ġmed ia +Ġf ood +Ġcom es +ĠUn iversity +Ġsing le +Wh at +u ly +Ġh alf +ag ue +h od +ĠRep ublic +Ġstart ed +Ġqu ick +ot o +b ook +Ġiss ue +it or +Ġel se +Ġcons ider +2 6 +ro du +Ġt aken +2 8 +9 9 +ĠW ith +Ġtr ue +Ġw a +Ġtr ad +Ġag o +Ġm ess +ie f +Ġadd ed +o ke +Ġb ad +Ġf av +3 3 +Ġsim ilar +as k +ĠD on +Ġcharact er +ort s +ĠH ouse +Ġreport ed +Ġty pe +v al +i od +ĠHow ever +Ġt arg +Ġent ire +pp ing +Ġhist ory +Ġl ive +ff ic +.... .... +ed eral +Ġtr ying +Ġdisc uss +ĠH ar +ac es +l ished +Ġse lf +os p +re st +Ġro om +el t +Ġf all +ol ution +Ġe t +Ġ x +Ġis n +Ġide a +b o +Ġs ound +ĠD ep +Ġsome one +ci ally +ull y +Ġf oc +Ġob ject +if t +ap er +Ġplay er +Ġr ather +Ġserv ice +as hing +ĠD o +ĠP art +ru g +m on +p ly +Ġm or +Ġnot hing +Ġprov ide +I C +un g +Ġpart y +Ġex ist +Ġm ag +7 0 +Ġr ul +Ġh ouse +Ġbeh ind +Ġhow ever +ĠW orld +Ġs um +Ġapp lic +Ġ ; +Ġfun ction +g r +ĠP ol +Ġfr ont +2 00 +Ġser ies +Ġt em +Ġty p +ill s +Ġo pt +Ġpoint s +Ġbel ow +itt ed +Ġspec ific +Ġ201 7 +um b +Ġr a +Ġpre vious +Ġpre t +re me +Ġc ustom +Ġcour t +ĠM e +Ġre pl +Ġwho le +g o +c er +Ġt reat +ĠA ct +Ġprob ably +Ġle arn +end er +ĠA ss +Ġvers ion +n ow +Ġche ck +ĠC al +R E +min ist +O n +our ces +Ġben ef +Ġd oc +Ġdet er +Ġen c +Ġsu per +Ġadd ress +Ġv ict +Ġ201 3 +Ġme as +t r +Ġf ield +W hen +Ġsign ific +u ge +Ġfe at +Ġcomm on +l oad +Ġbe gin +Ġbr ing +Ġa ction +er man +Ġdesc rib +Ġind ust +Ġwant ed +ri ed +m ing +Ġatt empt +4 5 +f er +Ġd ue +ress ion +# # +Ġsh all +Ġs ix +o o +Ġst ep +Ġp ub +Ġhim self +Ġ2 3 +Ġc op +Ġd est +Ġst op +A C +ib ility +Ġl ab +ic ult +Ġhour s +Ġcre ate +Ġf urther +ĠAmeric a +ĠC ity +Ġd ou +he ad +S T +ĠN orth +c ing +Ġn ational +u le +ĠIn st +Ġt aking +ĠQ u +ir t +Ġre d +Ġrese arch +v iron +ĠG e +Ġbre ak +an a +Ġsp ace +ater ial +Ġrec ent +ĠA b +Ġgener al +Ġh it +Ġper iod +Ġevery thing +ive ly +Ġph ys +Ġsay ing +an ks +Ġc ou +Ġc ult +ac ed +e al +u ation +Ġc oun +l u +Ġinclud e +Ġpos ition +ĠA fter +ĠCan ad +ĠE m +Ġim m +ĠR ed +Ġp ick +Ġcom pl +Ġm atter +re g +e xt +ang u +is c +o le +a ut +Ġcomp et +e ed +f ect +Ġ2 1 +ĠS en +ĠThe se +as ing +Ġcan not +Ġin it +Ġrel ations +ac hed +Ġb ar +Ġ4 0 +ĠT H +Ġ201 2 +Ġv ol +Ġg round +Ġsec urity +Ġup d +il t +3 5 +Ġconc ern +ĠJ ust +Ġwh ite +Ġseem s +ĠH er +pe cially +i ents +Ġann oun +Ġf ig +ight s +Ġst ri +l ike +id s +Ġs us +Ġw atch +Ġ â +Ġw ind +ĠC ont +Ġit self +Ġm ass +A l +y le +iqu e +ĠN ational +Ġab s +Ġp ack +Ġout side +Ġan im +Ġp ain +et er +Ġman ag +du ct +og n +Ġ ] +ĠSe pt +se c +o ff +ĠJ an +Ġf oot +ad es +Ġth ird +Ġm ot +Ġev idence +int on +Ġth reat +a pt +pl es +c le +Ġl o +Ġde cl +Ġit em +med i +Ġrep resent +om b +am er +Ġsignific ant +og raph +s u +Ġc al +i res +00 00 +I D +A M +Ġsim ply +Ġlong er +Ġf ile +O T +c he +S o +ate g +or g +ĠH is +Ġen er +Ġd om +Ġup on +il i +": " +Ġthem selves +Ġcom ing +Ġqu ite +Ġdiff icult +ĠB ar +il ities +re l +end s +c ial +6 4 +Ġwom an +ra p +y r +Ġne cess +ip s +Ġte xt +Ġrequ ire +Ġmilit ary +Ġre view +Ġresp ons +7 5 +Ġsub ject +Ġinst ead +Ġiss ues +Ġg en +" ," +Ġmin utes +Ġwe ap +r ay +am ed +t ime +b l +H ow +Ġc ode +ĠS m +Ġhig her +ĠSt e +r is +Ġp age +Ġstud ents +ĠIn tern +Ġmet hod +ĠA ug +ĠP er +ĠA g +Ġpolic y +ĠS w +Ġex ec +Ġac cept +um e +rib ut +Ġword s +Ġfin al +Ġchang es +ĠDem ocr +Ġfriend s +Ġres pect +Ġe p +Ġcomp an +iv il +Ġdam age +** ** +og le +viron ment +Ġne g +ent al +Ġa p +Ġtot al +iv al +! " +l im +Ġneed s +Ġag re +Ġdevelop ment +Ġa ge +ip le +2 1 +Ġresult s +ĠA f +S h +Ġg un +ĠOb ama +ro ll +Ġ @ +Ġright s +ĠB rit +Ġrun ning +Ġwas n +Ġp ort +Ġr ate +Ġpret ty +Ġtarg et +Ġsa w +Ġc irc +Ġwor ks +ic ro +al t +o ver +ww w +Th at +l ier +Ġevery one +ud e +Ġp ie +idd le +ra el +Ġr ad +Ġbl ock +Ġw alk +T o +ã ģ +n es +ĠA ust +a ul +ro te +ĠS outh +ess ion +op h +Ġshow s +Ġs ite +Ġj o +Ġr isk +cl us +l t +Ġin j +id ing +ĠS pe +Ġch all +ir m +Ġ2 2 +itt ing +st r +Ġh y +L E +ke y +Ġbe gan +at ur +ashing ton +l am +ĠD av +b it +Ġs ize +ĠP ar +3 8 +ourn al +f ace +Ġdec ision +Ġl arg +Ġj ud +re ct +Ġcontin ue +ĠO ct +ove red +ĠI nt +==== ==== +Ġp arent +ĠW ill +Ġeas y +Ġd rug +ang er +Ġs ense +Ġd i +id ay +Ġener gy +ist ic +Ġass oci +ar ter +ob al +e ks +ĠE l +ur ch +Ġg irl +o e +it le +Ġ2 8 +ĠC he +Ġrequ est +Ġso on +Ġh ost +k y +Ġst ates +om es +Ġm aterial +le x +Ġmom ent +Ġan sw +on se +Ġes pecially +Ġn orm +Ġserv ices +p ite +r an +Ġro le +4 4 +) : +Ġc red +C l +____ ____ +Ġm at +Ġl og +ĠCl inton +O U +Ġoff ice +Ġ2 6 +Ġch arg +Ġtr ack +m a +Ġhe art +Ġb all +Ġperson al +Ġbuild ing +n a +s et +b ody +ĠBl ack +Ġincre ase +itt en +Ġneed ed +3 6 +3 2 += " +Ġl ost +Ġbec ame +Ġgrou ps +ĠM us +Ġw rote +ĠP e +Ġpro p +j oy +à © +ĠWh ite +Ġde ad +. ' +Ġhtt p +Ġwe bs +O S +Ġins ide +Ġwr ong +Ġstat ement +Ġ ... +y l +Ġfil m +Ġmus ic +Ġsh are +ific ation +Ġre lease +Ġfor ward +Ġst ay +Ġcomp ut +it te +s er +Ġorig inal +Ġc ard +Ġc and +Ġd iv +at ural +Ġfav or +O M +Ġc ases +us es +Ġse ction +Ġle ave +g ing +ov ed +ĠW ashington +3 9 +ĠG l +Ġrequ ired +act ion +ap an +o or +it er +ĠK ing +Ġcount ries +ĠG erman +ll ing +Ġ2 7 +3 4 +Ġquest ions +Ġpr im +Ġc ell +Ġsh oot +Ġany one +ĠW est +Ġaff ect +ep end +Ġon line +ĠIs rael +ĠSept ember +Ġab ility +Ġcont ent +is es +Ġre ve +Ġl aun +Ġind ic +Ġfor ce +c ast +Ġso ld +av ing +f l +Ġso ft +Ġcompan ies +ce ed +Ġart icle +Ġa ud +Ġre v +Ġed uc +Ġplay ing +0 5 +Ġhe ld +ct or +Ġrele ased +Ġf ederal +3 7 +Ġad minist +Ġinter view +Ġinst all +Ġrece ived +Ġs ource +u k +P h +Ġser ious +Ġcre ated +Ġc ause +Ġim medi +Ġdef in +u el +ĠDep artment +ct ions +ĠC our +ĠN ow +z e +it es +it ution +Ġl ate +Ġspe ak +n ers +Ġleg al +ar i +ĠC or +Ġwe eks +Ġmod el +Ġp red +Ġex act +B C +ĠB y +IN G +os ing +Ġt akes +Ġreg ard +Ġopp ortun +Ġpr ice +Ġ19 8 +ĠA pr +f ully +Ġor d +Ġproble ms +ru ction +h am +ĠC ount +le ge +Ġlead ers +E T +le v +Ġde ep +olog ical +es e +h aps +ĠS ome +Ġp ers +Ġcont ract +Ġrelations hip +s p +ou d +Ġb ase +4 8 +m it +A d +anc ial +Ġcons um +Ġpot ential +Ġl angu +re m +et h +Ġrel ig +ress ed +6 6 +Ġl ink +Ġl ower +ay er +ĠJ une +Ġf em +un t +er c +ur d +Ġcont act +Ġ ill +Ġm other +Ġest ab +h tt +ĠM arch +ĠB ro +ĠCh ina +Ġ2 9 +Ġs qu +Ġprov ided +Ġa verage +as ons +Ġ201 1 +Ġex am +l in +5 5 +n ed +Ġper fect +Ġt ou +al se +u x +Ġbu y +Ġsh ot +Ġcol lect +Ġph ot +Ġplay ed +Ġsur pr +Ġofficial s +Ġsim ple +av y +Ġindust ry +Ġhand s +g round +Ġp ull +Ġr ound +Ġus er +Ġr ange +u ary +Ġpriv ate +op s +e es +Ġw ays +ĠM ich +Ġve h +Ġex cept +Ġter ms +im um +pp er +I ON +ore s +ĠDr agon +ou l +Ġd en +Ġperform ance +Ġb ill +c il +4 7 +Ġen vironment +Ġex c +ad d +Ġwor th +Ġp ict +Ġch ance +Ġ201 8 +b or +Ġspe ed +ict ion +Ġal leg +ĠJ apan +at ory +re et +Ġm atch +ĠI I +Ġst ru +ord er +Ġst e +Ġl iving +Ġst ruct +in o +Ġse par +her n +Ġresp onse +Ġen joy +Ġv ia +A D +um ents +ace book +Ġmem ber +ib r +iz ing +Ġto ol +ĠM on +ĠWh ile +h ood +ĠA ng +ĠD ef +Ġoff er +T r +a ur +Ġturn ed +ĠJ uly +d own +an ced +Ġrec ently +ĠE ar +Ġc e +ĠSt ar +ĠC ong +rough t +Ġbl ood +Ġhop e +Ġcom ment +ain t +Ġar ri +il es +Ġpartic ip +ough t +ri ption +0 8 +4 9 +Ġg ave +Ġse lect +Ġkill ed +sy ch +Ġgo es +i j +Ġc oll +Ġimp act +at ives +ĠS er +0 9 +ĠAug ust +Ġb oy +d e +ĠD es +Ġf elt +U S +Ġexpect ed +Ġim age +ĠM ark +cc ording +o ice +E C +ĠM ag +en ed +h old +ĠP ost +Ġpre vent +N o +Ġinvol ved +Ġey es +Ġquick ly +A t +un k +Ġbeh av +Ġ ur +Ġl ed +c ome +e y +Ġcand id +Ġear lier +Ġfoc us +et y +P ro +led ge +ix ed +ill ed +Ġpop ular +A P +Ġset t +l ight +Ġvar ious +in ks +Ġlevel s +Ġro ad +ell ig +ab les +he l +itte e +ĠG ener +y pe +Ġhe ard +ic les +Ġm is +Ġus ers +ĠS an +Ġimpro ve +Ġf ather +Ġse arch +The y +v il +Ġprof ess +Ġkn ew +Ġl oss +Ġev ents +6 5 +Ġb illion +0 7 +0 2 +ĠNew s +ĠA M +Ġco ver +w here +ens ion +Ġb ott +Ġare as +en ces +op e +ĠTw itter +a el +Ġget s +ĠGo ogle +Ġs n +i ant +Ġv ote +Ġnear ly +Ġinclud ed +Ġrec ogn +z z +m m +al ed +Ġhappen ed +0 4 +Ġh ot +Ġwho se +Ġc ivil +Ġsu ff +o es +it iz +ĠSy ri +Ġresp ond +Ġh on +Ġfeat ures +Ġeconom ic +ĠApr il +r im +Ġtechn ology +Ġo ption +ag ing +Ġpur ch +R e +Ġl at +ch ie +is l +Ġrec omm +u f +Ġtr aining +Ġeffect s +Ġf ast +Ġ201 0 +Ġocc ur +Ġwebs ite +Ġem ail +Ġs ens +e ch +Ġo il +Ġinf lu +Ġcurrent ly +ĠS ch +ĠAd d +Ġgo al +Ġsc ient +Ġcon v +1 00 +em y +Ġdec ided +Ġtra vel +Ġm ention +L L +0 3 +Ġe lection +Ġph one +Ġlook s +Ġsit uation +Ġc y +Ġh or +b ed +ĠCour t +a ily +av es +Ġqu ality +ĠCom p +w ise +Ġt able +Ġst aff +ĠW ind +et t +Ġtri ed +ide red +Ġadd ition +Ġb ox +Ġl ack +ar ily +Ġw ide +Ġm id +Ġbo ard +ys is +Ġant i +h a +Ġd ig +en ing +Ġd ro +C on +6 8 +Ġsl ow +b ased +se qu +Ġp ath +E x +ak er +Ġwork ed +Ġp en +Ġeng ine +Ġlook ed +ĠSu per +ĠS erv +Ġvict im +U n +Ġproper ty +Ġint rodu +Ġexec ut +ĠP M +L e +Ġcol or +ĠM ore +Ġ6 0 +Ġnet work +Ġd ate +c ul +id ge +Ġext ra +3 1 +Ġs le +6 7 +Ġw ond +Ġreport s +j ust +ĠAust ral +Ġcap ital +Ġen s +Ġcomm and +Ġallow ed +Ġpre p +Ġca pt +h ib +Ġnum bers +ch an +Ġf air +m p +om s +Ġre ach +W ith +t ain +Ġbro ad +Ġcou ple +ec ause +ly ing +ĠF eb +Ġsc reen +Ġl ives +Ġpri or +ĠCong ress +A r +Ġappro ach +Ġe mer +ar ies +ĠD is +s erv +ĠN e +Ġbu ilt +c ies +Ġre pe +Ġrul es +for ce +ĠP al +Ġfin ancial +Ġcons idered +ĠCh ar +n ces +ĠI S +Ġb rought +Ġb i +i ers +ĠS im +O P +Ġproduct s +Ġvis it +Ġdoc ument +Ġcon duct +Ġcomplete ly +in ing +ĠCal if +ib ly +Ġwr itten +ĠT V +em ents +Ġd raw +O ne +Ġpub lished +Ġsec ret +r ain +he t +ĠF acebook +ond ay +ĠU p +Ġsex ual +Ġth ous +ĠP at +Ġ ess +Ġstand ard +Ġar m +g es +ect ion +Ġf ell +Ġfore ign +an i +ĠFr iday +Ġreg ular +in ary +Ġincre ased +Ġus ually +Ġdem on +Ġd ark +Ġadd itional +ro l +ĠO f +Ġprodu ction +! ! +und red +Ġintern ational +id ents +ĠF ree +rou p +Ġr ace +Ġm ach +Ġh uge +A ll +le ar +ove mber +Ġto wn +Ġatt ention +ĠO ff +y ond +ĠThe n +f ield +Ġter ror +ra z +ĠB o +Ġmeet ing +ĠP ark +Ġar rest +Ġf ear +Ġa w +ĠV al +or ing +' , +Ġext reme +ar r +Ġwork ers +A fter +Ġ3 1 +n et +am ent +Ġdirect ly +Ġpop ulation +ub e +ĠOct ober +ĠI N +ĠJan uary +5 9 +ĠDav id +Ġc ross +ce mber +ĠF irst +Ġmess age +ir it +Ġn ation +Ġp oll +is ions +Ġansw er +n y +is ode +Ġcar ry +ĠRuss ia +Ġhe ar +eng th +ro y +Ġn atural +in ally +Ġdo g +m itted +Ġtr ade +Ġsub st +Ġmult iple +ĠAf ric +Ġf ans +Ġs ort +Ġgl obal +ic ation +ĠW ed +ar a +Ġa chie +Ġlangu age +ve y +Ġt al +Ġnecess ary +Ġdet ails +Ġs en +ĠS und +ĠRe g +ĠR ec +0 6 +Ġs il +ress ive +Ġmed ical +un ch +orn ia +Ġu nd +f ort +oc ks +ĠM onday +ues day +c raft +7 7 +ur t +Ġ ver +ĠH ill +Ġrece ive +Ġmor ning +es tern +Ġb ank +Ġs at +ir th +ĠH igh +Ġdev ice +ĠTH E +ĠCent er +Ġsaf e +Ġp le +ĠCanad a +Ġsystem s +Ġass ist +Ġsur v +Ġb attle +ĠS oc +vert is +S he +Ġp aper +Ġgrow th +Ġc ast +S c +Ġpl ans +ll ed +Ġpart s +Ġw all +Ġmove ment +Ġpract ice +im ately +Ġdis play +Ġsomet imes +om p +ĠP aul +ĠY es +k ing +5 8 +o ly +Ġs on +Ġav oid +ok es +ĠJ ew +Ġto wards +as c +Ġ // +ĠK ore +Ġtalk ing +Ġcor rect +Ġsp ent +ic ks +i able +e ared +Ġter m +Ġwant s +om ing +Ġ ut +Ġdou b +Ġfor ces +Ġp lease +6 9 +ĠN ovember +at form +ond on +Ġon es +Ġimmedi ately +ĠRuss ian +ĠM et +Ġde g +Ġparent s +C H +ĠAmeric ans +al y +ĠM od +Ġsh own +Ġcond itions +Ġst uff +Ġre b +ĠY our +Ġinclud es +n own +ĠS am +Ġexper ien +m ission +ĠE ven +augh t +Ġannoun ced +ĠRepublic an +Ġdeter min +Ġdescrib ed +ĠCount y +( ) +Ġdo or +Ġchang ed +Ġne igh +ĠH ere +Ġcle an +Ġp an +ĠDe cember +ĠEurope an +ir ing +ap ter +Ġcl ub +ĠT uesday +Ġp aid +ĠN et +Ġattack s +Ġcharact ers +Ġal one +Ġdirect or +d om +Ġ3 5 +Ġl oad +Ġr out +ĠCalif ornia +Ġfin ally +Ġr ac +Ġcont r +Ġexact ly +res h +p ri +ĠIs lam +Ġn ature +Ġcare er +Ġlat est +Ġcon vers +ĠS l +p ose +ci ent +ĠIn c +iv ity +8 8 +ĠA tt +ĠM or +nes day +Ġwe ight +k en +Ġnot e +Ġteam s +Ġ \ +air s +ĠG reen +Ġh undred +on ent +Ġstre ng +Ġcons ist +ic ated +Ġreg ul +Ġl ic +ast ic +Ġt en +urs day +ellig ence +ous ly +ĠU K +B I +Ġcost s +Ġind epend +ĠA P +Ġnorm al +Ġh om +Ġob vious +Ġs we +Ġst ar +Ġread y +ac her +Ġimp lement +g est +Ġs ong +ĠG et +ĠL ab +Ġinterest ing +us ing +Ġg iving +ĠSund ay +Ġet c +Ġm iddle +Ġrem ember +r ight +os ition +ut ions +Ġm ax +4 6 +Ġyour self +Ġdem and +Ġtreat ment +Ġd anger +ĠC ons +Ġgu y +ĠBrit ish +Ġphys ical +Ġrel ated +Ġrem ain +Ġcould n +Ġref er +Ġc itiz +b ox +EN T +bo ard +Ġin n +I G +er o +ĠSt reet +osp ital +ren ch +cher s +Ġst ra +O L +ag er +ĠA N +Ġeas ily +I A +en ge +in y +Ġcl os +ock ed +Ġus es +ĠC oun +I m +u ild +? ? +m ore +Ġan g +Ġwr ite +ol ute +5 7 +Ġlead er +Ġread ing +< / +Ġaut om +est s +4 3 +Ġleg isl +ĠG old +Ġdesign ed +ĠS T +ĠLe g +a res +Ġbe aut +ĠT ex +Ġappear s +Ġstru gg +ĠR om +Ġ 00 +Ġcho ice +Ġparticular ly +ĠF rom +op er +ĠL ondon +ann ed +Ġallow s +ob ile +Ġdiffere nce +âĢ ¢ +ĠV iew +ĠWed nesday +Ġal though +Ġrel ative +Ġapplic ation +ate ver +Ġare n +Ġmy self +Ġim ag +Ġdis e +Ġsoc iety +Ġfre qu +ĠEng lish +Ġpo or +ĠD ay +Ġwrit ing +Ġse ven +Ġstart ing +Ġb ud +Ġpr int +ĠTr ans +uf act +ĠSt ud +n ew +Ġcr im +Ġg ives +Ġco ol +a e +i ance +ĠGener al +Ġthink ing +Ġsa ve +Ġlim ited +ĠPart y +Ġmean ing +p en +ow ers +ĠJ ack +E M +Ġn ice +ru pt +Ġg as +Ġe ight +Ġfe et +Ġeff ort +Ġ ign +ic it +B l +co in +Ġop in +Ġbr ain +Wh ile +he st +ĠTh ursday +Ġwould n +augh ter +Ġtou ch +le ments +Ġstud ies +Ġcent er +c ont +or ge +Ġcomput er +Ġinvestig ation +P l +or ks +Ġ200 8 +Ġincre asing +Ġst ore +Ġcom ments +Ġb al +m en +Ġdo ll +Ġl iber +Ġw ife +Ġlaw s +atur day +it ness +Ġmod ern +ĠS k +Ġadminist ration +Ġopportun ity +Ġs al +Ġpower ful +M y +Ġclaim s +ĠEar th +ord s +Ġt itle +Ġes c +n ame +N ot +om en +Ġbe yond +Ġc amer +Ġse ll +it ute +ear ch +Ġapp l +im ent +4 2 +ĠAr t +Ġun f +Ġviol ence +ur g +ĠE ast +Ġcomp ared +Ġopt ions +Ġthrough out +Ġv s +ig r +. [ +ac hes +7 8 +Ġfil es +F L +E L +ar ian +ĠJ ames +ĠA ir +an ch +Ġdet ail +Ġpie ce +P S +Ġn amed +Ġeduc ation +Ġdri ve +Ġitem s +Ġstud ent +ic ed +: : +ic o +Ġth row +Ġsc ene +Ġcomple x +Ġ200 9 +Ġpre c +ĠB re +7 9 +Ġcon cept +Ġstat us +am ing +Ġd ied +Ġknow ledge +Ġbegin ning +O D +ru ary +Ġcertain ly +Ġgu ys +Ġsl ight +in n +ound s +Ġf ine +Ġf at +ic ations +Ġper haps +ĠA nt +Ġinc ome +Ġhtt ps +Ġmajor ity +port s +st on +Ġgreat er +Ġfe ed +ent ially +Ġsaf ety +Ġun ique +and om +Ġg one +Ġshow ed +Ġhist or +Ġcoun ter +i us +id a +Ġlead ing +i pe +Ġs end +ĠDon ald +er ve +Ġdef ense +ines e +Ġy es +ĠF ire +ĠMus lim +ra q +Ġcontin ued +os h +Ġprov ides +Ġpr ison +ĠP re +Ġhapp y +Ġeconom y +Ġtr ust +ag s +ĠG ame +Ġweap ons +um an +ĠC le +it ation +Ġanal ysis +ĠT imes +Ġsc ience +- > +Ġfig ure +Ġdis app +ent y +Ġsoft ware +Ġu lt +Ġoffic ers +N ew +I s +Ġrem ains +ĠInd ia +Ġp sych +ri ef +Ġc at +es c +Ġob serv +Ġst age +ĠD ark +Ġent er +ch ange +Ġpass ed +Ġdes pite +ĠO ut +Ġmov ie +r s +Ġv oice +m ine +ĠPl ay +Ġto ward +ĠT er +Ġreg ion +Ġval ues +or ters +Ġm ount +Ġoffic er +ĠO ther +b an +Ġh ous +w ood +ro om +I V +ĠS un +se e +ĠO ver +ro g +9 0 +Ġl ay +ĠT ur +a wn +Ġpress ure +ĠS ub +Ġbook s +ed om +ĠS and +A A +ag o +Ġre asons +f ord +Ġactiv ity +U T +N ow +ĠSen ate +ce ll +n ight +Ġcall s +in ter +Ġlet ter +ĠR ob +ĠJ e +Ġcho ose +ĠL aw +G et +B e +Ġro b +Ġtyp es +Ġpl atform +Ġqu arter +R A +ĠT ime +Ġmay be +ĠC r +9 5 +p re +Ġmov ing +Ġl if +Ġgo ld +Ġs om +Ġpat ients +Ġtr uth +ĠK e +ur ance +ant ly +m ar +Ġchar ge +ĠG reat +Ġce le +---------------- ---------------- +Ġro ck +ro id +an cy +Ġcred it +a ud +B y +ĠE very +Ġmov ed +ing er +rib ution +Ġn ames +Ġstra ight +ĠHe alth +ĠW ell +Ġfe ature +Ġr ule +Ġsc he +in ated +ĠMich ael +ber g +4 1 +il ed +b and +Ġcl ick +ĠAng el +on ents +Â Ń +ĠI raq +ĠS aturday +Ġa ware +p art +Ġpat tern +O W +ĠL et +Ġgr ad +ign ed +Ġassoci ated +Ġst yle +n o +i ation +a ith +il ies +Ġst ories +ur ation +Ġindividual s +ĠâĢ ¦ +m iss +ĠAss oci +ish ing +ab y +Ġsum mer +ĠB en +Ġ3 2 +Ġar ch +ut y +ĠTex as +h ol +Ġfull y +Ġm ill +Ġfollow ed +ĠB ill +ĠInd ian +ĠSec ret +ĠB el +ĠFeb ruary +Ġjob s +Ġseem ed +ĠGo vern +i pped +Ġreal ity +Ġl ines +Ġp ark +Ġmeas ure +ĠO ur +I M +Ġbro ther +Ġgrow ing +Ġb an +Ġest im +Ġc ry +ĠS chool +Ġme chan +ĠO F +ĠWind ows +Ġr ates +ĠO h +Ġpos itive +Ġcult ure +ist ics +ic a +Ġh ar +y a +ite ly +i pp +Ġm ap +en cies +ĠWill iam +I I +ak ers +5 6 +ĠM art +ĠR em +Ġal tern +it ude +Ġco ach +row d +D on +Ġk ids +Ġj ournal +Ġcor por +Ġf alse +Ġwe b +Ġsle ep +Ġcont ain +Ġst o +Ġb ed +iver se +ĠR ich +ĠCh inese +Ġp un +Ġme ant +k nown +Ġnot ice +Ġfavor ite +a ven +Ġcond ition +Ġpur pose +) ) +Ġorgan ization +Ġchall eng +Ġman ufact +Ġsus p +ĠA c +Ġcrit ic +un es +uc lear +Ġm er +vent ion +Ġ8 0 +Ġm ist +ĠU s +ĠT or +htt p +ol f +Ġlarg er +Ġadv ant +Ġrese ar +Ġact ions +m l +Ġke pt +Ġa im +, ' +c ol +Ġbenef its +if ying +Ġact ual +ĠIntern ational +Ġveh icle +Ġch ief +Ġeff orts +ĠLe ague +ĠM ost +Ġwa it +Ġad ult +Ġover all +Ġspe ech +Ġhigh ly +Ġfem ale +Ġer ror +Ġeffect ive +5 4 +Ġenc our +w ell +Ġfail ed +Ġcons erv +Ġprogram s +Ġt rou +Ġa head +5 00 +vertis ement +I P +ĠF ound +p ir +Ġ % +Ġcr ime +and er +Ġloc ation +ĠI ran +Ġbehav ior +az ing +Ġr are +Ġem b +Ġca used +Ġsh ip +Ġact ive +Ġcont ribut +Ġg reen +Ġac qu +Ġref lect +ven ue +Ġf irm +Ġb irth +] . +Ġclear ly +Ġem ot +Ġag ency +ri age +Ġmem ory +9 8 +S A +ĠSe e +ac ing +C C +Ġbig gest +Ġr ap +Ġbas ic +Ġb and +e at +Ġsus pect +ĠM ac +Ġ9 0 +m ark +ist an +Ġsp read +am s +k i +as y +ra v +ĠR ober +Ġdemon str +r ated +Ġabs olute +Ġpl aces +Ġim pl +ibr ary +Ġc ards +Ġdest roy +Ġv irt +ve re +Ġapp eared +y an +p oint +Ġbe g +Ġtem per +s pe +ant ed +ear s +ĠD irect +Ġl ength +Ġbl og +am b +Ġint eg +Ġres ources +ac c +if ul +Ġsp ot +Ġfor ced +Ġthous ands +ĠMin ister +Ġqu al +ĠF rench +at ically +Ġgener ally +Ġdr ink +Ġth us +I L +od es +Ġappro pri +ĠRe ad +Ġwh om +Ġey e +Ġcol lege +Ġ4 5 +ire ction +Ġens ure +Ġapp arent +id ers +Ġrelig ious +Ġmin or +ol ic +Ġt ro +ĠWh y +rib ute +m et +Ġprim ary +Ġdevelop ed +Ġpe ace +Ġsk in +st e +av a +Ġbl ue +Ġfam ilies +Ġ ir +Ġapp ly +Ġin form +ĠSm ith +C T +i i +Ġlim it +Ġres ist +........ ........ +um n +Ġconf lic +Ġtw e +ud d +ĠT om +Ġl iter +qu e +b on +Ġha ir +Ġevent ually +Ġp us +Ġhelp ed +Ġag g +or ney +ĠApp le +Ġf it +ĠS ur +Ġpre m +Ġs ales +Ġsecond s +Ġstreng th +Ġfeel ing +¿ ½ +Ġt our +Ġknow s +o om +Ġex erc +Ġsom ew +ï ¿½ +> > +Ġsp okes +Ġide as +Ġreg ist +so ft +ĠD el +ĠP C +Ġpro pos +Ġlaun ch +Ġbott om +T H +ĠP lease +v est +it z +ĠIn ter +Ġsc ript +Ġr at +ar ning +Ġ il +ĠJ er +ĠA re +Ġwh atever +ok en +ci ence +Ġmod e +Ġag ree +Ġs ources +Ġinit ial +Ġrest rict +Ġwond er +us ion +## ## +ĠS il +vil le +Ġb urn +t w +as ion +Ġ £ +Ġn or +u ing +Ġre ached +Ġs un +Ġc ateg +ig ration +Ġc ook +Ġprom ot +Ġm ale +Ġcl imate +Ġf ix +Ġalleg ed +U R +all ed +Ġim ages +C ont +ot a +Ġschool s +i os +Ġd rop +Ġst ream +ĠM o +Ġprevious ly +al ing +Ġp et +Ġdou ble +Ġ( @ +ann el +Ġdef ault +t ies +Ġr ank +ĠD ec +ĠCoun cil +Ġweap on +Ġst ock +Ġanal y +ĠSt r +Ġpict ure +ĠPol ice +f erence +Ġcent ury +Ġcitiz ens +Ġon to +Ġexp and +Ġhe ro +ĠS ol +Ġw ild +Ġupd ate +Ġcustom ers +r ont +d ef +Ġl ik +Ġcrim inal +ĠChrist ian +S P +7 6 +Ġle aving +Ġother wise +ĠD ist +Ġbas is +5 2 +5 3 +ic ip +ĠB er +Ġrecomm end +Ġfl oor +Ġc rowd +ol es +Ġ7 0 +Ġcent ral +ĠE v +Ġd ream +Ġdown load +Ġconf ir +ĠTh om +Ġwind ow +Ġhapp ens +Ġun it +Ġt end +Ġs pl +Ġbec omes +Ġfight ing +Ġpred ict +ĠP ress +ĠP ower +Ġhe avy +ak ed +Ġf an +or ter +ate gy +B A +iz es +Ġsp end +H ere +Ġ200 7 +Ġad op +ĠH am +Ġfoot ball +ĠP ort +od ay +5 1 +amp ions +Ġtrans fer +h t +Ġ3 8 +ter m +ac ity +Ġb ur +] , +tern al +r ig +b ut +Ġthere fore +ĠB ecause +res p +re y +Ġm ission +S ome +Ġnot ed +Ġass um +Ġdise ase +Ġed it +Ġprog ress +r d +ĠB rown +oc al +Ġadd ing +Ġra ised +ĠAn y +Ġt ick +Ġsee ing +ĠPe ople +Ġagre ement +Ġser ver +Ġw at +Ġdeb ate +Ġsupp osed +il ing +Ġlarg est +Ġsuccess ful +ĠP ri +ĠDemocr atic +Ġj ump +ĠSyri a +Ġown ers +Ġoff ers +Ġshoot ing +Ġeff ic +se y +Ġha ven +ver se +te red +ĠL ight +im al +ĠB ig +Ġdef end +Ġbe at +Ġrecord s +% ) +Ġsc en +Ġemploy ees +Ġdev ices +he m +Ġcom mer +ĠM ex +Ġbenef it +ĠPro f +Ġil leg +Ġsur face +ĠAl so +Ġh arm +ing ly +w ide +ĠA lex +Ġsh ut +ĠC ur +Ġl ose +p m +Ġchall enge +se mb +Ġst ation +Ġint elligence +Ġacc ur +ĠFl or +Ġrequ ires +ĠM al +b um +Ġh ospital +Ġsp irit +Ġoff ered +Ġprodu ce +ĠComm un +Ġcreat ing +Ġcr is +s pect +Ġend ed +Ġd aily +Ġvot ers +land s +i as +i h +on a +Ġsm art +ĠOff ice +ĠL ord +ri al +ĠIntern et +Ġcirc um +Ġextreme ly +' . +Ġopin ion +ĠM il +Ġg ain +B S +ĠF in +y p +Ġuse ful +Ġbud get +Ġcom fort +is f +Ġback ground +el ine +Ġep isode +Ġen emy +Ġtri al +Ġestab lish +d ate +ĠC ap +Ġcontin ues +Ġshow ing +ĠUn ion +w ith +Ġpost ed +ĠSy stem +Ġe at +ri an +Ġr ise +ĠGerman y +il s +Ġsign ed +Ġv ill +Ġgr and +m or +ĠEng land +Ġproject s +um ber +Ġconf erence +z a +Ġrespons ible +ĠAr ab +Ġlearn ed +âĢĶ âĢĶ +i pping +ĠGe orge +O C +Ġreturn ed +ĠAustral ia +Ġb rief +Q u +Ġbr and +ill ing +ab led +Ġhig hest +Ġtr ain +ĠComm ission +wh ile +Ġn om +cept ion +Ġm ut +ĠBl ue +Ġinc ident +v ant +8 6 +ĠI D +Ġn uclear +7 4 +ĠL ike +ĠR E +ĠM icro +l i +m ail +Ġcharg es +8 9 +Ġad just +ad o +Ġear th +N A +Ġpr ices +P A +Ġd raft +Ġrun s +Ġcandid ate +ens es +Ġmanag ement +ĠPh il +ĠM iss +Ġte ach +g ram +Ġunderstand ing +a it +ic ago +A dd +ĠE p +sec ut +Ġsepar ate +Ġinst ance +Ġe th +Ġun less +**** **** +ĠF ore +in ate +Ġoper ations +S p +Ġf aith +g ar +ĠCh urch +ron ic +Ġconf ig +os ure +Ġactiv ities +Ġtrad itional +Ġ3 6 +Ġd irection +Ġmach ine +Ġsur round +Ġp ush +un ction +ĠE U +Ġeas ier +Ġarg ument +G B +Ġm icro +Ġsp ending +iz ations +Ġthe ory +ad ow +Ġcall ing +ĠL ast +Ġd er +Ġinflu ence +Ġcomm it +Ġph oto +Ġun c +ist ry +g n +ast e +ack s +Ġdis p +ad y +d o +ĠG ood +Ġ ` +Ġw ish +Ġreve aled +Âł Âł +l ig +Ġen force +ĠComm ittee +Ġche m +Ġmil es +Ġinterest ed +Ġsol ution +ic y +in ct +Ġ- > +ĠD et +Ġrem oved +Ġcomp ar +e ah +Ġpl ant +ĠS ince +Ġachie ve +Ġadvant age +Ġslight ly +b ing +Ġpl aced +u nder +201 5 +ĠM ad +Ġt im +os es +Ġc ru +ĠR ock +Ġmost ly +Ġneg ative +Ġset ting +Ġprodu ced +Ġm ur +Ġconnect ion +ĠM er +Ġdri ver +Ġexecut ive +Ġass ault +Ġb orn +ĠV er +t ained +Ġstruct ure +Ġredu ce +Ġdec ades +Ġd ed +u ke +ĠM any +idd en +Ġle ague +S e +Ġjo in +Ġdis co +Ġd ie +c ks +act ions +Ġass ess +ag n +Ġgo als +our s +I R +Ġsen ior +ill er +m od +ip ment +oc ol +u y +ĠQ ue +Ġpart ies +ir gin +Ġle arning +it able +Ġstre et +Ġcamer a +A pp +Ġsk ills +b re +c ious +Ġcele br +ĠFr anc +Ġexist ing +Ġwill ing +l or +Ġ id +ĠSp ace +Ġcrit ical +ĠL a +ortun ately +Ġser ve +Ġc old +Ġspec ies +T S +Ġanim als +ĠB ay +Ġold er +ĠU nder +est ic +ĠT re +Ġte acher +Ġpre fer +v is +Ġth read +ĠM att +Ġmanag er +ãĥ » +Ġprofess ional +ĠV ol +Ġnot es +The se +ul a +Ġf resh +ent ed +u zz +ed y +clus ion +ĠR el +Ġdoub t +E O +Ġopen ed +ĠB it +Ad vertisement +Ġgu ess +ĠU N +Ġse qu +Ġexpl ain +ott en +Ġatt ract +ak s +Ġstr ing +Ġcont ext +oss ible +ĠRepublic ans +Ġsol id +Ġc ities +Ġask ing +Ġr andom +u ps +ur ies +ar ant +dd en +g l +ĠFlor ida +Ġdep end +ĠSc ott +Ġ3 3 +Ġi T +ic on +Ġmention ed +Ġ2 000 +Ġclaim ed +Ġdefin itely +ul f +Ġc ore +Ġopen ing +ĠCon st +wh ich +ĠT ra +A G +7 2 +Ġbelie ved +ad a +Ġ4 8 +ĠSec urity +yr ight +ĠP et +ĠL ou +Ġhold ing +======== ======== +Ġ ice +Ġb row +Ġauthor ities +h ost +w ord +Ġsc ore +ĠD iv +Ġcell s +Ġtrans l +Ġneigh bor +Ġrem ove +u ct +Ġdist rict +ĠA ccording +Ġwor se +Ġconcern s +Ġpresident ial +Ġpolic ies +ĠH all +7 3 +Ġh us +A Y +Ġ200 6 +ĠJ ud +Ġindepend ent +ĠJust ice +ili ar +pr int +igh ter +Ġprotect ion +z en +Ġsu dden +h ouse +ĠJ es +P R +ĠIn f +Ġb ul +Ġ _ +ĠServ ice +ĠP R +Ġstr ategy +ff ect +Ġgirl s +Ġmiss ing +oy al +ĠTe am +ul ated +Ġd at +Ġpolit ics +ab or +A ccording +Ġspe ll +Ġg raph +ort hern +T C +A b +Ġlab or +is her +Ġk ick +ĠiT unes +Ġstep s +pos es +Ġsmall er +E n +ber t +Ġro ll +Ġresear chers +Ġcl osed +Ġtrans port +Ġlaw y +________ ________ +ĠCh icago +Ġas pect +Ġn one +Ġmar riage +9 6 +Ġe lements +ĠF re +ĠS al +Ġd ram +F C +t op +e qu +Ġhe aring +Ġsupport ed +Ġtest ing +co hol +Ġmass ive +Ġst ick +Ġgu ard +is co +ph one +F rom +How ever +Ġb order +Ġcop y +ograph y +l ist +7 1 +Ġown er +cl ass +ru it +r ate +ĠO nce +Ġdig ital +Ġt ask +ER S +Ġinc red +t es ++ + +ĠFr ance +Ġb reat +ow l +Ġiss ued +ĠW estern +Ġdet ect +Ġpart ners +Ġsh ared +ĠC all +Ġcan cer +ac he +rib e +Ġexpl ained +Ġhe at +{ " +Ġinvest ment +ĠB ook +Ġw ood +Ġtool s +ĠAl though +Ġbelie f +Ġcris is +Ġg e +ĠM P +Ġoper ation +ty pe +~ ~ +g a +Ġcont ains +ant a +Ġexp ress +ĠG roup +ĠJ ournal +k a +Ġam b +ĠUS A +Ġfind ing +Ġfund ing +h ow +Ġestab lished +ide os +Ġdeg ree +Ġdanger ous +ang ing +Ġfre edom +pp ort +out hern +Ġch urch +Ġc atch +ĠTw o +Ġpres ence +ĠGu ard +U p +Ġauthor ity +ĠPro ject +Ġbut ton +Ġcon sequ +Ġval id +Ġwe ak +Ġstart s +Ġref erence +ĠM em +" ) +U N +or age +ĠO pen +Ġcol lection +y m +g ency +Ġbeaut iful +ro s +Ġtell s +Ġwa iting +n el +Ġprov iding +ĠDemocr ats +Ġd aughter +Ġm aster +Ġpur poses +ĠJapan ese +Ġequ al +Ġturn s +Ġdoc uments +Ġwatch ing +R es +Ġr an +201 4 +Ġre ject +ĠKore a +Ġvictim s +Le vel +ere nces +Ġw itness +Ġ3 4 +Ġre form +com ing +Ġocc up +Ġc aught +Ġtra ffic +ad ing +Ġmod els +ar io +Ġserv ed +Ġb atter +u ate +ĠSecret ary +Ġagre ed +Ġtr uly +yn am +ĠR et +Ġun its +ĠRes earch +h and +az ine +ĠM ike +Ġvar iety +ot al +Ġam azing +Ġconfir med +Ġentire ly +Ġpurch ase +Ġe lement +Ġc ash +Ġdeter mine +D e +Ġc ars +ĠW all +â ĸ +Ġview s +Ġdrug s +Ġdep artment +ĠSt ep +u it +Ġ3 9 +as ure +ĠCl ass +Ġc overed +ĠB ank +Ġme re +u ana +Ġmult i +Ġm ix +Ġun like +lev ision +Ġsto pped +Ġs em +ĠG al +ul es +Ġwe l +ĠJohn son +l a +Ġsk ill +Ġbec oming +ri e +Ġappropri ate +f e +ell ow +ĠPro t +ul ate +oc ation +Ġweek end +od ies +Ġsit es +Ġanim al +ĠT im +Ġsc ale +Ġcharg ed +Ġinst ruct +ill a +Ġmethod s +Ġc ert +Ġjud ge +ĠH el +Ġdoll ars +Ġstand ing +ĠS qu +Ġdeb t +l iam +Ġdri ving +ĠS um +ĠEd ition +Ġal bum +and on +I F +ĠU k +6 3 +ad er +Ġcommer cial +es h +ĠGovern ment +Ġdisc overed +Ġout put +ĠHill ary +ĠCar ol +Ġ200 5 +Ġab use +anc ing +Ġsw itch +Ġann ual +T w +Ġst ated +ag ement +in ner +Ġdem ocr +Ġres idents +Ġallow ing +Ġfact ors +od d +Ġf uck +em ies +Ġoccur red +ot i +Ġn orth +ĠP ublic +Ġinj ury +Ġins urance +C L +oll y +ã Ģ +Ġrepe ated +Ġar ms +ang ed +Ġconst ruction +Ġf le +P U +ic ians +Ġfor ms +ĠMc C +ant ic +Ġm ental +p ire +Ġequ ipment +Ġf ant +Ġdiscuss ion +Ġregard ing +k in +ar p +Ġch air +og ue +Ġpro ceed +ĠI d +O ur +Ġmur der +M an +Ġ4 9 +as p +Ġsupp ly +Ġin put +Ġwe alth +liam ent +Ġpro ced +or ial +ĠSt at +ĠN FL +hen s +ĠInst itute +Ġput ting +ourn ament +et ic +Ġloc ated +Ġk id +er ia +r un +Ġpr inc +Ġ ! +go ing +ĠB et +Ġcl ot +Ġtell ing +Ġprop osed +i ot +or ry +Ġfund s +g ment +ĠL ife +Ġb aby +ĠB ack +Ġsp oke +Im age +Ġear n +ĠA T +g u +Ġex change +ĠL in +ov ing +Ġp air +M ore +az on +Ġarrest ed +Ġkill ing +c an +ĠC ard +y d +Ġident ified +Ġm obile +Ġthan ks +ony m +ĠF orm +Ġhundred s +ĠCh ris +ĠC at +Ġtre nd +h at +ĠA v +om an +Ġelect ric +ĠW il +S E +O f +Ġrest aur +ot ed +Ġtr ig +Ġn ine +Ġb omb +Wh y + ¯ +Ġco verage +Ġapp eal +ĠRober t +ĠS up +Ġfin ished +Ġfl ow +Ġdel iver +Ġcal cul +Ġphot os +Ġph il +Ġpie ces +Ġapp re +k es +Ġr ough +D o +Ġpart ner +Ġconcern ed +Ġ3 7 +ĠG en +C ol +ct ors +Ġ= > +st ate +Ġsuggest ed +ĠFor ce +C E +Ġher self +ĠPl an +w orks +o oth +ren cy +Ġcor ner +Ġhus band +Ġintern et +ĠA ut +em s +os en +ĠAt l +g en +Ġbal ance +6 2 +Ġsound s +te xt +Ġar r +ov es +Ġmill ions +Ġrad io +Ġsat isf +ĠD am +M r +G o +S pe +Ġcomb at +r ant +ĠG ree +Ġf uel +Ġdist ance +Ġtest s +Ġdec re +ĠE r +Ġman aged +D S +Ġt it +Ġmeas ures +ĠL iber +Ġatt end +as hed +ĠJ ose +ĠN ight +d it +ĠN ov +ĠE nd +out s +Ġgener ation +Ġadv oc +y th +Ġconvers ation +ĠS ky +act ive +ce l +ri er +ĠFr ank +Ġg ender +Ġcon cent +Ġcar ried +and a +ĠV irgin +Ġarri ved +ic ide +ad ed +Ġfail ure +Ġmin imum +le ts +Ġwor st +Ġkeep ing +Ġint ended +Ġilleg al +Ġsub sc +Ġdetermin ed +Ġtri p +Y es +Ġra ise +Ġ ~ +Ġfeel s +Ġpack age +ĠJ o +h i +201 6 +re al +Ġf ra +Ġsy mb +M e +uck y +p ret +ĠK h +ĠEd it +ĠWe b +em ic +ĠCol or +Ġjust ice +I nt +Ġfar m +ck now +" > +el ess +Ġredu ced +Ġ5 00 +x x +ĠR ad +ĠW ood +Ġcl in +Ġhy p +il er +ur a +k ins +8 5 +6 1 +ĠThe ir +ĠM ary +Ġs an +Ġno vel +ĠWh o +Ġcap acity +Ġimp ossible +Ġpl ays +Ġmin ister +ij uana +ic ate +ĠS et +Ġf ram +Ġ ing +Ġcommun ities +ĠF BI +it a +Ġb on +Ġstr ateg +Ġinterest s +l ock +g ers +m as +ĠAN D +Ġconflic t +Ġrequire ments +Ġs ac +Ġoper ating +in i +rel ated +Ġcomm itted +Ġrelative ly +Ġs outh +¯ ¯ +Ġaff ord +Ġident ity +Ġdec isions +Ġacc used +pl ace +Ġvict ory +o ch +i at +N ame +C om +t ion +ed s +Ġsee k +Ġt ight +ĠIm ages +Ġinit i +Ġhum ans +Ġfam iliar +Ġaud ience +Ġintern al +vent ure +Ġs ides +ĠT O +Ġd im +Ġcon clud +Ġapp oint +Ġenforce ment +ĠJ im +ĠAssoci ation +Ġcircum st +ĠCanad ian +Ġjo ined +Ġdiffere nces +ĠL os +Ġprot est +Ġtw ice +w in +Ġgl ass +ars h +ĠAr my +Ġexp ression +Ġdec ide +Ġplan ning +an ia +Ġhand le +ĠMicro soft +ĠN or +Ġmax imum +ĠRe v +Ġse a +Ġev al +Ġhel ps +re f +Ġb ound +Ġm outh +Ġstand ards +Ġcl im +ĠC amp +ĠF ox +cl es +Ġar my +ĠTe chn +ack ing +x y +S S +Ġ4 2 +Ġbu g +ĠUk rain +ĠM ax +ĠJ ones +ĠSh ow +l o +Ġplan et +Ġ7 5 +Ġwin ning +Ġf aster +Ġspe ct +Ġbro ken +T R +Ġdef ined +Ġhealth y +Ġcompet ition +htt ps +ĠIs land +ĠF e +Ġannoun ce +ĠC up +ĠInst ead +Ġcl ient +Ġposs ibly +se ction +ock et +l ook +Ġfin ish +Ġcre w +Ġres erv +Ġed itor +Ġh ate +Ġs ale +Ġcontro vers +Ġp ages +w ing +Ġnum er +Ġopp osition +Ġ200 4 +Ġref uge +Ġfl ight +Ġap art +ĠL at +A meric +ĠAfric a +Ġapplic ations +ĠPal est +ĠB ur +Ġg ar +ĠSoc ial +Ġup gr +Ġsh ape +Ġspe aking +ans ion +a o +ĠS n +Ġwor ry +ĠBrit ain +P lease +rou d +Ġh un +Ġintrodu ced +Ġd iet +I nd +ĠSec ond +Ġfun ctions +ut s +ĠE ach +ĠJe ff +Ġst ress +Ġaccount s +Ġgu arant +ĠAn n +ed ia +Ġhon est +Ġt ree +ĠAfric an +ĠB ush +} , +Ġs ch +ĠOn ly +Ġf if +ig an +Ġexerc ise +ĠEx p +Ġscient ists +Ġlegisl ation +ĠW ork +ĠS pr +à Ĥ +ĠH uman +Ġ è +Ġsur vey +Ġr ich +ri p +Ġmain tain +Ġfl o +Ġleaders hip +st ream +ĠIslam ic +Ġ 01 +ĠCol lege +Ġmag ic +ĠPr ime +Ġfig ures +201 7 +ind er +x ual +ĠDe ad +Ġabsolute ly +Ġfour th +Ġpresent ed +resp ond +rib le +Ġal cohol +at o +ĠD E +por ary +Ġgr ab +Ġvar i +Ġqu ant +ĠPh oto +Ġpl us +r ick +ar ks +Ġaltern ative +Ġp il +Ġappro x +th at +Ġobject s +ĠR o +ĠAnd roid +Ġsignificant ly +ĠR oad +k ay +R ead +av or +Ġa cknow +ĠH D +ĠS ing +O r +ĠM ont +Ġun s +pro f +Ġneg oti +ĠAr ch +ik i +Ġte levision +ĠJew ish +Ġcomm ittee +Ġmot or +Ġappear ance +Ġs itting +Ġstri ke +ĠD own +com p +ĠH ist +Ġf old +ac ement +ĠLou is +Ġbel ong +ĠâĢ ¢ +Ġm ort +Ġprep ared +Ġ6 4 +ĠM aster +Ġind eed +ĠD en +Ġre nt +T A +our ney +ar c +S u +9 7 +Ġadv ice +Ġchang ing +Ġlist ed +Ġlaun ched +is ation +ĠP eter +is hes +Ġl ived +ĠM el +ĠSup reme +ĠF ederal +Ġ) ; +ruct ure +Ġset s +Ġphil os +u ous +Ġ ł +Ġappl ied +ĠN OT +Ġhous ing +ĠM ount +Ġo dd +Ġsu st +D A +ffic ient +Ġ ? +ol ved +Ġp owers +Ġth r +Ġrem aining +ĠW ater +L C +Ġca uses +ãģ ® +Ġman ner +ad s +Ġsuggest s +Ġend s +stand ing +f ig +ĠD un +id th +Ġg ay +Ġter min +ĠAngel es +M S +Ġscient ific +Ġco al +ap ers +b ar +ĠThom as +Ġsy m +ĠR un +th is +P C +igr ants +Ġmin ute +ĠDist rict +cell ent +Ġle aves +Ġcomple ted +am in +Ġfoc used +Ġmon itor +Ġveh icles +M A +ĠM ass +ĠGr and +Ġaffect ed +itution al +Ġconst ruct +Ġfollow s +Ġt on +re ens +Ġh omes +ĠE xt +ĠLe vel +r ast +ĠI r +Ġel im +Ġlarge ly +ĠJ oe +Ġvot es +all s +Ġbusiness es +ĠFound ation +ĠCent ral +Ġy ards +Ġmaterial s +ul ner +Ġgu ide +Ġclos er +um s +Ġsp orts +ed er +J ust +Ġtax es +8 4 +ĠO ld +Ġdec ade +ol a +Ġv ir +Ġdro pped +Ġdel ay +it ect +Ġsec ure +ste in +le vel +Ġtre ated +Ġfil ed +ain e +Ġv an +Ġm ir +Ġcol umn +ict ed +e per +Ġro t +Ġcons ult +Ġent ry +Ġmar ijuana +ĠD ou +Ġapparent ly +ok ing +clus ive +Ġincre ases +an o +Ġspecific ally +Ġte le +ens ions +Ġrelig ion +ab ilities +Ġfr ame +ĠN ote +ĠLe e +Ġhelp ing +Ġed ge +ost on +Ġorgan izations +à ĥ +ĠB oth +hip s +Ġbig ger +Ġbo ost +ĠSt and +Ġro w +ul s +ab ase +Ġr id +L et +are n +ra ve +Ġst ret +P D +Ġv ision +Ġwe aring +Ġappre ci +Ġa ward +ĠU se +Ġfact or +w ar +ul ations +) ( +Ġg od +Ġter rit +Ġpar am +ast s +8 7 +Ġen emies +ĠG ames +F F +Ġacc ident +W ell +ĠMart in +T ER +Ġat h +ĠHe ll +Ġfor g +Ġve ter +ĠMed ic +f ree +Ġst ars +Ġexp ensive +Ġac ad +ra wn +ĠW he +Ġl ock +Ġform at +Ġsold iers +s m +Ġag ent +Ġrespons ibility +or a +ĠS cience +Ġrap id +Ġt ough +ĠJes us +Ġbelie ves +M L +Ġwe ar +le te +Ãĥ ÃĤ +ĠD ri +Ġcomm ission +ĠB ob +O h +ap ed +Ġwar m +ÃĥÃĤ ÃĥÃĤ +Ġ200 3 +ort ion +Ġhas n +ust er +Ġun ivers +ĠI ll +Ġk ing +olog ies +9 4 +ĠT em +ĠM os +Ġpat ient +ĠMex ico +ce an +ĠDe ath +ĠSand ers +y ou +ĠC ast +ĠComp any +pt y +Ġhappen ing +F P +ĠB attle +Ġb ought +A m +M od +U s +ut ers +ĠC re +ĠTh ose +Ġ4 4 +is er +Ġs oul +ĠT op +ĠHar ry +ĠA w +Ġse at +ff ee +Ġrev olution +Ġ( " +ĠD uring +et te +Ġr ing +Ġoff ensive +Ġreturn s +Ġv ideos +Ġdis cl +Ġfam ous +en ced +ĠS ign +ĠR iver +Ġ3 00 +P M +ĠB us +ĠC H +Ġcandid ates +ard en +Ġpercent age +Ġvis ual +Ġthan k +Ġtrou ble +ner gy +Ġ200 1 +Ġpro ve +ash ion +Ġen h +ĠL ong +U M +Ġconnect ed +Ġposs ibility +O ver +Ġexper t +Ġl ibrary +art s +ĠDirect or +Ġfell ow +9 2 +ir ty +Ġd ry +Ġsign s +ĠL ove +Ġqu iet +f oot +Ġp ure +ĠH un +Ġf illed +ph as +ĠE lect +end ment +ĠEx pl +Ġun able +n s +m o +Ġv ast +ob e +Ġident ify +app ing +ĠCarol ina +g ress +Ġpro te +Ġf ish +Ġcircumst ances +raz y +ĠPh ot +Ġb odies +ĠM ur +Ġdevelop ing +ĠA R +Ġexperien ced +Ġsubst ant +ĠBo ard +es ome +Ġdom estic +Ġcomb ined +ĠP ut +Ġchem ical +ĠCh ild +Ġpo ol +ĠC y +Ġe gg +c ons +st ers +Ġh urt +Ġmark ets +Ġconserv ative +Ġsupp orters +Ġag encies +id el +O b +ur b +Ġ4 3 +ĠDef ense +y e +ĠA p +du le +Ġtemper ature +Ġconduct ed +ĠCh ief +Ġpull ed +Ġf ol +L ast +ont o +os is +V ER +D es +ĠP an +F irst +Ġadv ance +Ġlic ense +r ors +ĠJ on +Ġimag ine +Ġhe ll +Ġf ixed +Ġinc or +os ite +ĠL og +ick en +] : +Ġsurpr ise +h ab +Ġc raft +ol t +ĠJ ul +Ġd ial +Ġrele vant +Ġent ered +Ġlead s +ĠA D +ĠCle an +Ġpict ures +ess or +Ġal t +Ġpay ing +P er +ĠMark et +Ġupd ates +am ily +ĠT ype +ĠH ome +Ġ5 5 +semb ly +rom e +8 3 +Ġgreat est +Ġhe ight +Ġhe av +ain ts +Ġlist en +as er +ĠS H +Ġcap able +ac le +Ġpers pect +in ating +Ġoff ering +ry pt +ĠDe velop +ab in +r c +Ġbr ight +al ty +ar row +Ġsupp l +ind ing +ack ed +gy pt +ĠAn other +p g +ĠVirgin ia +ĠL u +Ġpl anned +Ġp it +Ġswe et +T ype +ĠD i +Ġtyp ically +ĠFranc isco +Ġpro spect +ĠD an +Ġte en +re es +Ġsc hed +Ġh ol +Ġsc r +Ġlot s +l ife +Ġnews p +Ġfor get +ĠN one +ĠM iddle +ĠR yan +ed d +Ġse vere +Ġsu it +ll er +9 3 +Ġcor respond +Ġexpl os +u ations +Ġfl ag +g ame +r id +Ġpr in +ĠD ata +Ġde ploy +ĠEn ter +su it +gh an +ĠM en +Ġthough ts +Ġmat ters +Ġad apt +ĠA ri +Ġf ill +Ġfor th +Ġs am +Ġ4 1 +Ġpay ment +ĠH or +Ġsp ring +du c +Ġl osing +Ġbring ing +F O +al a +Ġdist ribution +he red +b our +ĠIsrael i +om a +Ġcomb ination +Ġpl enty +V E +C an +ĠH aw +Ġper man +ĠSpe cial +Ġto w +Ġsee king +Ġexam ples +Ġclass es +c r +Ġbe er +Ġmov es +ĠI P +ĠK n +Ġpan el +E ven +Ġproper ly +Ġr is +Ġpl ug +Ġestim ated +E very +Ġdef ensive +ag raph +Ġpre gn +Ġinst it +ĠV ict +Ġvol ume +Ġpos itions +Ġl inks +ĠPro gram +ĠWe ek +ag ues +Ġtrans form +k er +ĠC EO +Ġc as +Ġopp onent +Ġtwe et +ĠC ode +Ġsh op +Ġf ly +Ġtal ks +Ġb ag +Ph one +Ġa id +Ġpl ants +Ġ6 5 +Ġatt orney +ar ters +qu est +ĠMag ic +Ġbeg ins +Ġmy ster +Ġenvironment al +Ġst orage +N N +Ġm arg +Ġs ke +Ġmet al +ell y +Ġord ered +Ġrem ained +Ġl oved +Ġprom pt +Ġupd ated +Ġexper ts +Ġwalk ing +Ġan cient +Ġperform ed +AT E +Ġne ither +i ency +Ġmanufact ure +ĠP ak +Ġselect ed +Ġm ine +Ġult imately +Ġexpl an +Ġlab el +ĠServ ices +ribut ed +Tr ump +Ġsy n +ĠU lt +S C +Ġme at +Ġg iant +ĠW ars +ĠO N +Ġad m +Ġinter pret +Ġeven ing +Ġev il +ĠB oston +ĠW ild +Ġ à +ĠBit coin +ĠAm azon +D r +ĠIn formation +Ġobvious ly +Ġadv anced +Ph oto +ol ar +Ġwe ather +Ġsymb ol +Ġso le +Ġpot entially +ost er +Ġorig inally +m un +3 00 +az e +ess ions +Ġde ck +Ġst ood +Ġyou th +ĠB ern +R ep +ĠT est +Ġbas ically +ot ic +Ġinvol ve +ol it +ly n +S ee +Ġair craft +Ġconf irm +E W +Ġmess ages +ĠRich ard +Ġk it +Ġpro hib +Ġv ulner +is ters +Ġexist ence +Ġturn ing +ĠS P +Ġdes ire +Ġfl at +Ġm ent +se ason +ang es +Ġneighbor hood +ĠL ake +AT ION +Ġpoint ed +b ur +Ġinn ov +uc ks +U L +Ġprofess or +Ġexp ressed +A B +ic ious +Ġ200 2 +ĠDe v +Ġs ession +Ġb are +s en +Ġdis s +ĠC ath +ĠP ass +ĠP oint +Ġdo ctor +or row +ail ed +ĠR ub +ĠD C +ĠChar l +p erson +Ġwrit er +igh ters +ure au +Ġob lig +Ġrecord ed +Ġbro ke +Ġord ers +il ty +Ġmot ion +in ity +l aw +ad ium +Ġimm igration +Ġcontr ast +Ġb att +Ġex cellent +Ġtechn ical +am i +Ġt un +Ġcl oud +ĠY ear +ge on +Ġcre ation +Ġstr ange +Ġa uth +Ġfor t +b orn +Ġext ent +ĠT oday +ĠCl ub +Ġr ain +Ġs ample +Ġaccept ed +Ġt act +Ġf ired +ĠS on +Ġstand s +Ġb oot +Ġ4 7 +Ġstat ements +Ġvers ions +Ġse lling +ound ed +Ġ199 0 +Ġwere n +ĠW atch +Ġexper iment +P ost +Ġret ail +ul ed +In st +un te +ãĥ ¼ +Ġdep art +Ġb ond +i very +om pl +Ġre action +ĠSyri an +ĠP ac +app ed +ani el +D P +Ġres olution +Ġre act +Ġappro ved +on om +m ond +ĠO ffic +-- - +Ġrepl ace +Ġt ack +Ġsp ort +Ġch ain +Ġemer gency +r ad +ĠPalest in +Ġ4 6 +Ġautom atically +Ġrout e +Ġp al +Ġb anks +ĠPar is +ĠMed ia +ro ad +ic ing +i xt +ist ed +Ġg rew +Ġco ord +ĠW here +om in +Ġsub s +� � +Ġ ± +Ġcorpor ate +Ġse lection +n oon +ĠRep ort +c s +clud ing +ord ers +anc he +ĠIt s +Ġslow ly +ĠE gypt +ĠA cc +Ġcol le +iqu es +E X +Ġattempt s +ur l +ĠC ross +Ġfind ings +ĠS C +ĠO R +Ġind ex +ens ity +ĠW ay +ĠL and +Ġsh ock +d is +Ġd ynam +Ġc art +m osp +S ince +i est +ĠB oy +Ġst orm +ĠCont in +201 3 +he w +il it +Ġess ential +iqu id +O ther +ive red +Ġreason able +A ct +Ġsub sequ +ĠP ack +ĠF ort +Ġconsider ing +Ġun iversity +l og +Ġmar ried +Ġill ust +ĠTr ue +£ ı +Ġnumer ous +rast ructure +Ġserious ly +Ġrefer red +u a +Ġconsist ent +on na +ĠRe al +ru ption +ci ples +Ġfact s +9 1 +ot es +er g +The n +Ġacc ompl +N ote +Ġre venue +Ġpass ing +Ġm al +e en +ĠY et +Ġg ather +ter day +ew ork +ĠA uthor +P e +Ġopt im +Ġr ub +Ġè £ı +Ġun known +st one +Ġun ion +ol ve +Ġopportun ities +Ġbrow ser +ĠW al +ĠC ost +Ġreport ing +st s +p et +Ġs and +Ġsudden ly +Ġsurpr ising +ĠV R +Ġsomew hat +ĠB as +ult ure +iz z +ĠC D +Ġchalleng es +Ġsett ings +Ġexperien ces +ĠF ull +Ġcan n +Ġrece iving +ES T +Ġj oint +Ġcult ural +Ġa st +8 2 +as tern +ce ived +ĠC ru +Ġb ull +p ired +am m +Ġfac ing +p ower +Ġb oss +ĠH ol +Ġinst r +Ġincreasing ly +Ġsh ift +Ġstre ets +ĠWilliam s +ab b +Ġl ie +Ġl augh +ĠC a +P L +Ġadult s +Ġcustom er +Ġob tained +Ġsupport ing +ht ml +f ire +Ġdetail ed +Ġpick ed +ĠR ight +ld er +E E +st ood +ĠK im +Ġw ire +Ġs ight +Ġdevelop ers +Ġpers ons +Ġs ad +Ġc up +Ġwar ning +Ġboy s +l ong +Ġb ird +f o +Ġw al +Ġobserv ed +Ġz one +iven ess +Ġch annel +c ript +Ġref used +ĠAg ain +Ġsu c +Ġspokes man +ĠRe f +r ite +ou ston +ãĥ ³ +ĠS her +Ġact s +ĠN ame +Ġstrugg le +ar ry +omet imes +Ġdisc rim +H T +Ġcateg ory +Ġreal ize +Ġemploy ee +ĠAf ghan +en ger +Ġgun s +ĠSte ve +ĠM ot +ĠO l +ok ed +Ġth ick +Ġfair ly +ill y +Ġsur ve +ĠM at +we ight +â Ķ +Ġtro ops +Ġag ents +Ġbatter y +Ġmot iv +à ¡ +S ec +d en +o very +L S +Ġfl u +Ġconf ident +ĠO per +Ġem pty +Ġp hen +Ġse ctor +Ġexc ited +Ġrem ote +ap h +o en +Ġdestroy ed +Ġmor al +ĠH P +ĠR on +Ġd ress +ĠB at +Ġl it +ĠM S +Ġa f +H L +r um +is ms +Ġshould n +Ġsym pt +ĠTor onto +het ic +Ġcar bon +Ġinstall ed +Ġviol ent +Ġsol ar +j a +Ġpract ices +Ġr ide +ĠP enn +Ġimpro ved +Ġaud io +Ġbehav i +ĠP S +Ġe ating +D ata +ĠRe view +p ass +cl aim +u ated +ang ers +c hen +Ġproper ties +Ġany where +An other +Ġbl ow +ĠJack son +Ġp roud +Ġplan e +l ines +Ġsqu are +Ġpro of +ans as +Ġtalk ed +m akers +Ġs ister +Ġhold s +Ġres ident +Ġ= = +Ġresist ance +Ġspl it +Ġpro secut +Ġconf idence +res ents +Ġcut s +Ġexcept ion +Ġz ero +Get ty +Ġcop yright +Ġtot ally +orm al +ific ations +ĠAustral ian +Ġs ick +Ġ1 50 +Ġhouse hold +Ġfe es +Ġdri vers +og en +ĠN Y +Ġnecess arily +Ġregul ations +ear ing +s l +Ġperspect ive +c are +ic ial +H is +Ġesc ape +Ġsurpr ised +ĠV an +ur rent +Ġv ac +8 1 +ĠTh us +Ġem phas +ĠCh ampions +ĠI ce +Ġn arr +Ġhead s +Ġca using +b el +f ortunately +ĠM a +Ġtarg ets +ci pl +Ġafter noon +Ġadd s +ĠMay be +ĠF our +ess ed +ple te +Ġus ual +ch o +ing u +Ġwith d +ĠE nergy +ĠE conom +O O +Ġart icles +Ġinj ured +Ġman age +Ġexpl ains +Ġdi agn +R ec +at ures +Ġlink ed +Ġdiscuss ed +Ġexpl o +Ġocc asion +ath an +Ġopp osite +Ġfac es +Ġden ied +ĠK night +Ġn ut +Ġapprox imately +Ġdisapp oint +onym ous +ĠB est +ĠL o +ĠH y +ĠA ff +Ġvot ing +an while +ĠII I +Ġinstit utions +ag ram +ĠD aily +Ġdr ag +Ġnear by +Ġgu ilty +Ġcon ver +P re +s hip +Ġre ward +Ġphilos oph +ĠS S +u gh +Ġapp s +f riend +Ġu pper +Ġad vert +Ġs now +Ġfr ust +Ġour selves +F r +ĠD ie +amp ion +Ġdis miss +Ġc ere +Ġsign al +f rom +Ġ ). +Ġ5 2 +Ġcr imes +it ors +est ival +use um +Ġcoun cil +ĠS aud +M ay +ĠG un +ic ian +et her +Ġsu fficient +ĠH en +so le +Ġhistor ical +ĠF ar +ĠT urn +Ġp in +Ġsuc ceed +m at +ly mp +Ġtrad ition +ĠO k +Ġc ro +Ġdesc ription +al le +Ġsk y +T e +Ġwide ly +Ġw ave +Ġdefin ition +ĠJew s +Ġcy cle +Ġref ere +Ġbr ings +us al +Ġal ive +Ġfrequ ently +Ġint ention +ĠCont rol +l v +y stem +Ġpriv acy +g ent +ren ce +ĠQu est +ĠChrist mas +Ġr ail +Ġco oper +Ġtest ed +ĠC apt +as ks +Ġcomfort able +Ġdel ivered +sc ape +Ġdep th +ĠG OP +Ġwrit es +Ġass ets +Ġsa v +im ents +Ġtrans ition +Ġart ist +ĠL ook +Ġl ob +Ġcomp onents +ar ity +Ġwalk ed +Ġro ot +Ġparticip ants +Ġnot iced +Ġres c +Ġn av +ĠAd minist +d a +ut ral +pl ate +Ġimport ance +Ġass ert +ious ly +c ription +Ġinj uries +ĠChe ck +Ġregist ered +Ġint ent +Ġmiss ed +ograph ic +Ġsent ence +oun ter +Ġassist ance +ev in +Ġdat abase +Ġbuild ings +Ġclass ic +Ġth inks +ĠOh io +P r +ug g +Ġfe e +p an +Ġeffect ively +Ġfac ility +Ġbe ar +Ġch apter +Ġdog s +ĠCol umb +Ġl atter +it ial +Ġad mitted +T V +ĠGe org +Ġpost s +\ \ +Ġlawy er +Ġequ ival +Ġm and +Ġcontro lled +ĠW alk +ĠAnd rew +Ġmen u +am ental +Ġprotect ed +v a +Ġadminist r +or al +Ġre in +ĠS ar +Ġamount s +Ġn ative +ĠM oon +Ġrep resents +Ġab andon +Ġcarry ing +Ġt ank +m ary +Ġdecl ared +T ube +Ġh at +Ġpun ish +el lect +m es +Ġun iverse +ĠR od +ph y +Ġinf rastructure +Ġ5 1 +Ġopp osed +ow nt +c a +ĠM ake +Ġhard ware +Ġco ffee +R el +b al +w orld +ĠS af +ĠSe a +in als +Ġown ed +Ġh all +ers ion +Ġdescrib e +ĠP ot +Ġport ion +Ġat mosp +Ġgovern ments +Ġdep ending +Ġoff ense +Ġtr ick +aw a +ĠL ine +ĠV is +ĠH ard +ĠOr ig +ĠCl ick +Ġdes k +ĠVal ley +ĠS ov +Ġmov ies +Ġrem ark +Ġm ail +Ġcons cious +Ġrul ing +ĠR ights +Ġmed ic +he nt +ĠW omen +> < +Ġrepl aced +ĠP rem +ĠTh anks +Ġre new +ĠB all +if orm +Ġsh ots +C omm +Ġar med +Ġconst ant +Ġt aste +Ġreal ized +Ġbu ff +Ġm o +Ġeffic ient +M ost +or ation +if ies +Ġcommun ication +Ġfl ood +Ġconsequ ences +Ġany way +ig g +ĠG M +ĠTh ank +Ġ iron +Ġev olution +ĠC op +tw itter +Ġ9 5 +Ġrelationship s +ad el +ĠYou ng +Ġpropos al +ay ers +uild ing +ĠH ot +OR E +c os +Ġcoll abor +P G +ax y +Ġknow ing +Ġsupport s +ow ed +Ġcontrol s +Ġmere ly +um er +Ġath let +Ġf ashion +p ath +Ġg ift +Ġer a +AN D +Ġkind s +ĠKore an +Ġleg it +ul ous +Ġess entially +Ġthe rap +n ic +Ġsuff ered +Ġh ur +Ġprom ise +Ġex cess +Ġover w +Ġpr ime +ĠH ouston +er ry +ĠM s +R S +201 2 +Ġst ores +ĠO lymp +Ġj ourney +Al though +S ub +ĠE duc +ĠCh apter +Ġrequest s +Ġconsum ers +Ġt iny +Ġis ol +ĠF air +b a +ĠY OU +Ġcr ash +ce ler +Ġemot ional +Ġgood s +Ġelect ed +Ġmod er +ĠLin ux +Ġbl ocks +Ġis land +ĠSoc iety +Ġelect ions +Ġbroad cast +Ġche ap +Ġn ations +Ġse asons +4 00 +Ġwas te +ĠS at +Ġfield s +em ploy +Ġprof ile +Ġauth ors +AL L +ĠG ra +w est +ĠT y +Ġdeath s +Ġv acc +Ġfor med +Ġd u +Ġon going +ĠMuslim s +el f +ig ure +Ġass ume +ĠUkrain e +w ater +Ġco ast +Ġvot ed +g or +ĠA S +ĠMich igan +az a +ĠAr m +i ro +Ġf lex +as ters +' ' +Ġwel come +ar l +Ġloc ations +ig ation +ĠF il +Ġbu ying +Ġarch itect +Ġhard er +ĠC ub +Ġinter face +Ġrestaur ant +Ġdisco ver +Ġex ceed +Ġfav our +ger y +Ġd uty +Ġp itch +ad or +ĠM ach +b oy +Ġrespond ed +Ġext ended +her s +M any +ra id +if er +ĠIn s +S er +Ġmed ium +s he +ĠS ports +Ġmag azine +ut ation +Ġlim its +ĠG all +Ġex ternal +raz il +Ġyoung er +t le +Ġrem ind +ĠC ON +Ġimmedi ate +Ġh idden +Ġvol unte +Ġsim pl +od cast +Ġph ase +d r +Ġpl ot +Ġexp osure +R I +og rap +v in +an ish +ĠAc ad +ĠEng ine +Ġexp ansion +ĠP ay +Y our +Ġpus hed +ĠE ll +ĠHe ad +Ġmarket ing +ĠA C +k et +Ġh its +Ġg ro +ĠA ge +ĠSc ot +] [ +Ġst im +Ġi Phone +Ī Ĵ +Ġn arrow +ĠGet ty +ĠTur key +Ġperfect ly +Ġen able +ut ch +Ġprec ise +Ġreg ime +Ġsh if +Ġcomp ens +g un +d iv +Ġch osen +ĠK en +An y +Ġtre es +Ġrecomm ended +ĠR en +u able +ĠH T +F ollow +E G +ĠH and +ĠK enn +Ġarg uments +Ġex ists +Ġb ike +ĠCons erv +Ġbre aking +ĠG ar +Ġc razy +Ġvirt ual +ay lor +ix el +Ġ19 80 +Ġper mission +ĠSer ies +Ġconsum er +Ġclose ly +c alled +Ġ5 4 +Ġhop es +Ġar ray +ĠW in +ĠLab our +Ġsp ons +ĠI re +Ġp ow +Ġread ers +Ġemploy ment +Ġcreat ure +Ġresult ing +Ġaccur ate +Ġmom ents +Ġarg ued +Ġp ed +D uring +Ġ5 3 +ĠT al +Ġs ought +Ġsuff ering +Ġ icon +le e +Ġ( $ +al ian + ° +Ġp ra +Ġbon us +( " +k o +Ġact ing +D E +f all +Ġcompar ison +Ġsm ooth +ĠN AS +u pp +ĠJose ph +ep ing +ĠT ake +ĠM id +Ġs ending +f ast +ĠF all +Ġdeal ing +us er +ĠOr gan +C o +Ġatt ached +Ġse es +% . +Ġtyp ical +AR T +Ġfind s +ĠAs ia +um in +ĠC ore +ĠE nt +in ent +u ce +ĠBl ood +ĠN ever +Ġem ails +Ġhigh light +Ġconf ront +at us +ut ed +Ġun us +Ġtop ic +ĠAd am +Ġb le +at i +Ġunder stood +S et +st ruct +T P +Ġm ob +a a +ĠSt art +pect ed +se ll +Ġded icated +ĠC A +u an +Ġsong s +esc ription +Ġte ch +Ġr ape +Ġas ide +Ġgr ant +Ġ5 6 +s ub +Ġarg ue +Ġcont aining +Ġsche dule +Ġliber al +Ġpublic ly +Ġheav ily +ĠU t +in er +ĠS ection +ĠC are +we et +l s +D is +âĶ Ģ +ĠF ollow +B ack +ĠI T +Ġb es +j i +ĠH it +est ed +Ġevery body +ĠSw ed +Ġfem in +Ġfac ilities +Ġcon ven +C omp +ĠO S +c ore +Ġan x +Ġdiv ision +ĠC am +ĠSt an +m ates +Ġexpl ore +pl om +Ġsh ares +pl oad +an es +Ġide al +et ers +ĠB ase +Ġpl astic +Ġdist inct +ĠNet work +ĠSe attle +Ġtrad ing +ens us +int end +Ġex hib +Ġinit ially +ĠF ood +Ġthous and +ĠBus iness +act er +Ġpar agraph +Ġrough ly +Ġw ww +Ġcreat ive +ĠCon f +Ġconsum ption +Ġfil ms +ag an +Ġob tain +Ġt all +Ġt or +Ġacknow led +Ġg rown +al o +K E +Ġ4 00 +end ers +t aining +U G +Ġsu icide +Ġwat ched +ĠL ist +al i +re hens +Ġsurround ing +Ġp ip +Ġf lying +ĠJ ava +ord an +Ġserv ing +in ations +p ost +Ġsh o +A v +Ġj ail +z y +Ġ199 9 +Ġ< / +Ġliter ally +ĠS ir +Ġexp osed +Ġl ies +st ar +Ġb at +Ġear ned +ĠD ig +Ġspec ified +ĠSe ason +Ġdeg rees +Don ald +Ġcent re +Ġsh aring +Ġwin ter +ĠC O +C he +Ġ Î +M P +Ġun w +Ġfew er +ĠM ir +Ġsomew here +ĠK ey +Ġattack ed +ĠK ir +Ġdom ain +Ġstrong er +Ġ9 9 +Ġpen alty +I d +Sc ript +Ġdecl ined +Ġne ck +Ġfra ud +Ġcur rency +Ġr ising +R C +âĢ¦ âĢ¦ +H z +Ġt ab +Ġtal ent +n am +ĠN BA +Ġvill age +Ġleg s +ĠN ext +E d +Ġac id +Ġhy d +8 00 +Ġinvol ving +ĠIm age +ĠBe fore +F l +Ġyes terday +S ource +Ġterror ist +Ġsu p +Ġsy nt +ĠSaud i +Ġw est +Ġr u +b urg +Ġvis ible +Ġstru ck +r ison +Ġaw esome +Ġd rawn +Ġansw ers +ĠG irl +ĠR am +Ġthreat s +Ġdef eat +os it +Ġv ent +atur ally +Americ an +end a +ĠH oly +Ġr um +% , +c ase +ĠHist ory +ĠYou Tube +Ġsit uations +ĠD NA +S te +Ġsa ved +It em +Ġrec ip +olog ist +Ġfac ed +Ġel ig +O nce +ĠL i +u h +Ġmist ake +ĠDiv ision +ĠB ell +Ġsympt oms + ® +Ġdom in +Ġfall ing +Ġend ing +as hes +Ġmat ches +ĠOn line +Ġexplan ation +D ef +red it +Ġany more +ĠT otal +ĠF OR +us hed +Ġlet ters +Ġris ks +ĠO K +Ġreported ly +: \ +Ġpl ate +Ġsubject s +Ġattempt ed +if ier +ian a +Ġunlike ly +ĠTh ough +um a +ĠIn vest +ĠPr in +ic an +ĠD ar +ĠColor ado +au g +Ġve get +a os +ri a +Ġshe l +Ġmark ed +Ġ( ) +Ġsp r +p o +ĠL ink +Ġdef e +ĠJ r +Ġthem e +Ġpass ion +ĠP en +Ġinf o +iz er +Ġsh it +ĠC ivil +ap se +c re +Ġpo ly +Ġcomp onent +ĠChar les +ĠIre land +ĠPro v +Ġdo ctors +Ġgr anted +Ġpain t +Ġhon or +Ġsm oke +Ġpay ments +Ġprim arily +ĠKing dom +r ich +ate ll +Ġde als +Ġsched uled +Ġfund amental +Ġprote in +Ġnewsp aper +Ġcl ients +yth on +ĠD ate +h us +Ġfeed back +Ġstret ch +Ġc ock +Ġhot el +ĠQue en +Ġsu gar +Ġj u +Ġmil k +Ġappro val +ĠL ive +Ġequival ent +ef ully +Ġins ert +z ona +Ġext ension +d ri +J ohn +Ġacc omp +S m +ĠF und +Ġconst antly +Ġ` ` +Ġgener ated +ĠA ction +ĠP sych +ĠT ri +Ġrecogn ize +Ġv ary +ph a +ĠR a +d f +et ch +ĠSov iet +Tw o +Ġpattern s +Ġprof ession +an ing +T ime +ĠL im +Ġcol ors +ĠA z +ĠT R +Ġinf ect +Ġphen omen +Ġshe ll +Al so +Ġput s +Ġdel ivery +Ġbro wn +Ġprocess ing +Ġlight s +ess age +ĠBro ok +ĠA ud +l ation +Ġindust rial +L ike +ĠB razil +rou s +ES S +ĠL uc +Ġsome how +Ġ8 5 +Ġpro port +Ġpolit icians +Ġindic ate +Ġh ole +Ġtechn iques +Ġcompet itive +Ġph r +Ġv o +ist ent +ĠD ream +Ġcamp us +Ġaspect s +Ġhelp ful +Ġsh ield +or se +Ġtrig ger +m al +Ġ5 8 +Ġt ort +Ġperson ally +Ġt ag +Ġkeep s +ĠV ideo +Ġben ch +Ġg ap +a ire +Ġe ast +Ġrec overy +per ial +Ġprof it +ĠM ic +Ġ5 7 +Ġcol on +Ġstrong ly +st yle +Ġalleg ations +h an +Ġrep orters +j o +r ine +arg et +and al +Ġ0 3 +Ġfl ash +tr ans +Ġstr ict +Ġpark ing +ĠPak istan +Ġl i +Ġwe ird +ĠE ric +Ġreg ions +ĠJ un +Ġint ellect +ĠW H +od ing +rib utes +up id +ĠT it +Ġf inger +or ia +Ġe lev +ĠF ield +Ġcon clusion +; ; +Ġfeel ings +Ġext ensive +Ġm ixed +Ġne uro +v y +Ġhar ass +ĠC irc +ou ch +Ġterrit ory +Ġsuccess fully +M ar +Ġing red +Ġoverw hel +Ġl ayer +V iew +Ġall ies +ill ance +ĠTh ree +Ġb unch +Ġnorm ally +Ġnet works +Ġsac r +ĠC IA +b les +Ġch ose +Ġopp onents +Ġregard less +Ġfr anch +Ġpre f +ĠP o +Ġbr idge +ann a +ĠSil ver +Ġw age +p age +ri or +Ġrad ical +ĠL ittle +Ġman ip +Ġsecret ary +Ġg ang +D R +F A +Ġdec ent +ĠSp irit +Ġun cle +ĠDevelop ment +Ġinvest ors +Ġwall s +Ġpub lish +Ġgener ate +iss ions +c ar +Ġprom ote +Ġcut ting +Ġche st +Ġdrink ing +Ġcollect ed +Ġ7 2 +Ġhop ing +Ġem br +gor ith +Ġwar ned +Ġinstruct ions +O G +ĠD id +ĠAg ency +Ġg ear +Ġcritic ism +ĠF urther +Ġut il +ann y +R ed +Ġcoun sel +ĠAs ian +Ġredu ction +p ool +Ġteach ing +Ġdeep ly +i y +Ġestim ates +Ġcho ices +Ġperman ent +in em +ke l +Ġf asc +p se +f ile +ĠL ow +ĠP erson +Ġt ournament +st al +Ġm el +U ST +ĠR ay +az i +V al +Ġcont ained +ĠH olly +Ġw ake +Ġreve al +Ġprocess es +ĠIS IS +Ġ0 9 +Ġbl ind +Ġste el +ĠB ad +Ġcare fully +app y +ro it +Ġg aming +Ġhous es +ĠC oll +Ġtr uck +er m +Ġsc ored +Ġocc as +ret urn +b ound +v ar +Ġsh arp +Ġaf raid +ĠE X +am ber +c ific +Ġsche me +N C +ĠPol it +Ġdecl ine +Ġ199 8 +Ġpus hing +Ġposs ession +Ġpriv ile +Ġteacher s +Ġy ield +H A +ĠDav is +it led +#### #### +Ġr ig +ĠD aniel +ac on +Ġh ide +ut en +Ġcolle agues +Ġprin ciples +Ġl oud +Ġs in +ĠDem on +Ġst one +Ġ0 2 +Ġt aught +Ġter rible +Ġst uck +ĠPol icy +te en +Ġimplement ation +ĠB BC +ĠAP I +Ġwhe el +all as +Ġch ampions +ol ars +play er +Ġrepeated ly +ĠSt ill +Ġlik es +ast y +es ter +ĠCath olic +R L +Ġb ath +Ġno ise +t itle +Ġn orthern +P art +Ġmag n +Ġf ab +ĠAs h +Ġdis pl +Ġtick et +Ġm urd +Ġalong side +ĠMus ic +Ġr iver +ĠSte el +ĠC L +ĠPl ayer +ĠM ult +ow ing +re p +s ize +Ġt ur +ĠGeorg ia +isc al +ra ction +Ġc able +Ġ5 9 +Ġw ins +Ġup coming +Ġsurv ive +Ġins pired +ĠEduc ation +Ġstat istics +ĠF oot +iam i +Ġy ellow +ĠP age +. - +ĠH as +Ġur ban +Ġa x +es sel +\ " +Ġquarter back +Ġreg ister +ĠLab or +Ġab ilities +ĠF amily +Ġvar iable +ĠPr ice +Ġcont em +Ġth in +ĠE qu +d ata +Ġg otten +Ġconst it +Ġas ks +Ġt ail +Ġexc iting +ĠE ffect +ĠSp anish +Ġencour age +ins on +ĠA h +Ġcommit ment +C S +Ġr ally +Ġ: : +Ġsubs id +Ġsp in +Ġcapt ured +201 8 +Ġinn oc +Ġalleged ly +ĠC ome +Ġart ists +ĠN umber +Ġelect ronic +Ġreg ional +ap es +Ġw ra +Ġmy th +pr ise +ĠM iller +ĠC reat +ĠEp isode +b ell +Ġdirect ed +Ġext ract +Ġs orry +Ġv ice +ag ger +ĠSu pport +Ġ6 6 +ĠI ron +Ġwonder ful +Ġg ra +N et +ion e +E ng +Ġsh ips +ik es +ĠK evin +it ar +Ġactiv ists +tr ue +ĠAri zona +ent h +ĠDes pite +ĠS E +Ġha bit +ern el +Ġin qu +Ġab ortion +Ġv oid +Ġexpl icit +Ġeng aged +Ġang ry +Ġr ating +Ġfr ag +b ro +ick ing +d ev +Ġwor ried +Ġob ser +Ġap artment +ĠG T +Ġest ate +ĠConst itution +em on +ĠS now +Ġcount y +Ġdis ag +ĠStep hen +Ġimm igrants +w ind +ĠN ations +Ġfol ks +O ut +Ġg all +Ġtarget ed +Ġst ead +ĠB on +ĠL ib +Ġinform ed +Ġ12 0 +ch ain +idel ines +or ough +Ġdri ven +Ġregular ly +Ġbas ket +Ġprinc iple +oc ument +Ġst un +ib ilities +ĠRom an +ĠAb out +Ġal ert +Ġdemocr acy +Ġrepresent ed +H S +c ers +p arent +Ar t +p ack +Ġdi plom +re ts +ĠN O +Ġcapt ure +ĠAd v +Ħ ¢ +Ġannounce ment +ĠL ear +Ġh ook +Ġpur s +ĠS uch +ĠC amer +Ġrefuge es +ĠV e +P ol +Ġrecogn ized +l ib +Ġhad n +A ss +Ġpil ot +us hing +Ġreturn ing +Ġtra il +ĠSt one +Ġrout ine +Ġcour ts +Ġdes per +Ġfriend ly +ĠIt aly +Ġpl ed +Ġbreat h +Ġstud io +N S +Ġimp ressive +ĠAfghan istan +Ġf ing +Ġd ownt +ink ing +ĠR og +i ary +col or +se x +ar on +Ġf ault +ĠN ick +D own +ĠR ose +ĠS outhern +X X +is odes +L ist +6 00 +Ġout come +er r +Ġelse where +Ġret ire +Ġp ounds +ĠGl obal +Pe ople +Ġcommun ications +Ġlo an +Ġrat io +ĠEm pire +Ġg onna +Ġinv ent +D F +Ġ19 70 +ĠComm on +p at +Ġprom ised +Ġd inner +ĠH om +Ġcreat es +Ġoper ate +ver ty +ĠJ ordan +et ime +Ġsust ain +R eg +Ġincred ible +im a +Ġwar rant +Ġm m +A tt +Ġlaw suit +Ġreview s +it ure +ĠS ource +l ights +ĠF ord +Ġ6 3 +g roup +st ore +Ġfeat ured +Ġfore ver +Ġpo verty +ĠP op +ĠC NN +az z +ab is +ach ing +Ġl aid +ĠSu pp +Ġfil ter +en a +ĠCommun ity +Ġcreat ures +u ction +ĠR oyal +Ġassoci ation +ĠCon nect +ĠBr ad +âĸ Ī +l ers +the re +ĠG i +Ġval uable +AC K +ĠT aylor +Ġl iquid +ĠAtt orney +ĠCar l +ĠF inal +ag a +ĠWil son +B ecause +ĠProf essor +ak a +Ġincred ibly +r ance +! ) +R ef +s k +Ġsol utions +Ġatmosp here +Ġbl ame +um es +ĠN ob +C A +um ps +r ical +ĠPut in +ĠD est +or ic +ĠP A +Ġrespect ively +w an +Ġfif th +â Ħ¢ +ĠC ry +Ġgovern or +res ident +Ġpurch ased +Ġh ack +Ġint ense +ob s +Ġorig in +Ġdef ine +Ġcare ful +** * +Ġshould er +Cl ick +Ġt ied +Ġdest ruction +ou red +Ġno body +Ġh o +ĠEx per +Ġt ip +" ; +Ġtechn ique +Ġj ur +ĠP ok +b ow +Ġleg end +Ġacc ord +Ġbus y +ĠInt el +Ġh ang +ak i +. ] +âĢĶâĢĶ âĢĶâĢĶ +Ġsur gery +Ġrep rodu +Ġun iform +Ġscen es +c ode +Ġ6 2 +l isher +ĠH ave +ph ia +Ġcry pt +Ġrec on +Ġsc ream +Ġadop ted +Ġsc ores +N e +ĠIt alian +in cluding +B O +Ġindic ated +Ġent ertain +G u +T ext +i el +Ġtw enty +Ġeng age +off s +ĠPac ific +Ġsm ile +Ġperson nel +Ġto ler +Ġdo ors +Ġt one +Ġmach ines +Ġent ering +ten ance +C O +ĠJer sey +Ġfore st +Ġhor se +Ġcompl aint +ĠSpr ing +y o +ĠPl us +ed ing +ĠRet urn +qu arters +ial s +c ow +Ġacad emic +Ġf ruit +Ġ199 6 +og ether +Ġw ine +Ġpur su +ĠSte ven +Ġlic ens +Wh o +Ġclot hes +re ction +Ġsqu ad +Ġst able +Ġr aw +z ens +St ar +ut ies +anc er +Ġke ys +ĠM u +Ġcompl icated +ig er +ĠTe xt +Ġabs or +Ġ6 8 +Ġfun ny +Ġrel ief +ĠL ew +ĠC ook +Ġch art +Ġdraw ing +G E +Ġmod ule +ĠB ull +I LL +Ġs alt +0000 0000 +il le +Ġres ource +aw ay +adel phia +ĠB ru +Ġ6 7 +Ġsome body +Ġparticip ate +Ġro se +we red +Ġmus cle +Ġcons ent +Ġcontin uing +ĠGuard ian +ĠOr der +reg on +Ġre ar +Ġprov ision +Ġlik ed +ri ent +Ġb ra +Tr ans +Ġmeet ings +Ġto x +Ġcon vent +Ġaut o +Ġrec ording +ĠSo ft +00 1 +ĠR oll +Ġprogram ming +Ġp ic +Ġprov ed +Ġst ab +ĠA st +Ġca ption +ul ating +ĠAtt ack +Ġnew ly +Ġ199 7 +f r +Ġdis cipl +ĠGree k +Ġed ition +ĠDo es +ĠB ox +if le +ack et +Ġpass es +Ġgu est +Ġac celer +it als +U D +Ġaut hent +ĠR est +ov al +t a +u ine +Ġarm or +ĠT own +Ġcomp at +Ġinc hes +Des pite +Ġass ign +he rent +Ġprep are +ĠM eg +oc key +Ġdep ends +Ġtrack s +w atch +Ġl ists +ĠN orthern +Ġal ter +re c +ĠE astern +Ġcond em +Ġevery where +? ' +Ġaff ili +Ġf ought +": {" +Ġm ac +it arian +Ġsc ope +ĠA L +aw s +ar ms +Ġqu e +Ġenjoy ed +nes ota +Ġagg ressive +ĠSt ory +ĠI V +Ġrec ipe +Ġrare ly +ĠMed ical +val ue +ang el +ay ing +omet hing +Ġsub section +Ġs outhern +Ġfrequ ency +re te +roll ed +ult s +ĠN ic +Ġbeh alf +Ġsequ ence +ab et +Ġcontrovers ial +Ġcomp rom +Ġwork er +Ġmain ly +Ġal gorith +ĠM ajor +or ce +g ender +Ġorgan ized +Ġf ake +Ġconclud ed +ĠE D +ĠEx ec +r age +Ġch ances +ber ry +ĠTr ad +Ġconfig uration +Ġwithd raw +Ġf ro +ud es +ĠBro ther +ĠB rian +Ġtri es +Ġsam ples +Ġb id +ĠGold en +Ġphot ograph +if est +ĠD O +ĠPar liament +******** ******** +R em +Ġcont est +Ġsign ing +p x +ĠZ eal +âĶĢ âĶĢ +E ar +Ġex it +Be fore +ĠCor por +n ull +mon th +Ġrac ial +ott ed +ĠV eg +ĠRe uters +Ġsw ord +ps on +ĠRom ney +a ed +Ġt rib +Ġin ner +Ġprot ocol +ĠB i +ĠM iami +ever al +p ress +Ġsh ipping +ĠAm endment +ĠHow ard +con nect +ĠD isc +ĠJ ac +iam ond +ĠThere fore +s es +ĠPrin cess +ĠUS B +ĠAn th +Ġsurve illance +Ġap olog +Ġ6 1 +ow a +Ġf ulf +j s +Ġl uck +ust ed +Ġ § +n i +Ġant icip +em an +Ġwin ner +Ġsil ver +ll a +ic ity +Ġunus ual +Ġcr ack +Ġt ies +e z +Ġpract ical +Ġprov ince +ĠPl ace +Ġprior ity +IC E +Ġdescrib es +Ġbr anch +F orm +ask a +miss ions +b i +Ġp orn +ĠTur k +Ġent hus +Ġf ighters +Ġ0 8 +ĠDet roit +Ġfound ation +av id +A re +Ġjud gment +cl ing +Ġsol ve +ĠDes ign +W here +hes is +ĠT ro +a fter +Ġne utral +ĠPalestin ian +ĠHolly wood +Ġadv is +ĠN on +y es +ol is +Ġrep utation +Ġsm ell +Ġb read +ĠB ul +ĠBe ach +Ġclaim ing +Ġgen etic +Ġtechn ologies +Ġupgr ade +row s +Ġdevelop er +ĠJ osh +ĠDis ney +erv ed +ip al +Ġun ex +Ġbare ly +t hen +ĠP ub +Ġill ness +et ary +ĠB al +Ġp atch +Ġbut t +Ġst upid +ĠD og +ĠD allas +f ront +ie ce +Ġprot ests +Ġch at +oen ix +Ġw ing +Ġpar liament +Ġ7 7 +ose xual +Ġre nder +pt ions +ĠCo ast +os a +ĠG reg +h op +ĠMan agement +Ġbit coin +Ġrec over +Ġincor por +or ne +ĠUs ing +Ġpre ced +Ġthreat ened +Ġspirit ual +ĠE vent +ĠF red +Ġadvert ising +Ġimprove ments +ĠC ustom +Ġer rors +Ġsens itive +ĠN avy +Ġcre am +L ook +Ġex clusive +Ġcomp rehens +Ġde leg +Ġcon ce +Ġrem em +Ġstruct ures +Ġst ored +N D +Ġ1 000 +U P +ĠB udd +A F +w oman +ĠAcad emy +ð Ł +se a +Ġtem porary +Ab out +es ters +Ġtick ets +Ġposs ess +in ch +o z +Ġl a +Ġcontract s +Ġun p +Ġc ig +ĠK at +ult ural +as m +Ġmount ain +ĠCapt ain +St ep +m aking +ĠSp ain +Ġequ ally +Ġl ands +at ers +Ġreject ed +er a +im m +ri x +C D +Ġtrans action +g ener +less ly +Ġ| | +Ġc os +ĠHen ry +Ġprov isions +Ġg ained +Ġdirect ory +Ġra ising +ĠS ep +ol en +ond er +Ġcon sole +in st +Ġb om +Ġunc ertain +1 50 +ock ing +Ġmeas ured +Ġpl ain +Ġse ats +Ġd ict +S L +af e +Ġest imate +iz on +at hered +Ġcontribut ed +Ġep isodes +omm od +G r +AN T +Ġ6 9 +G ener +Ġ2 50 +vious ly +rog en +Ġterror ism +Ġmove ments +ent le +oun ce +ĠS oul +Ġpre v +ĠT able +act s +ri ors +t ab +Ġsuff er +Ġn erv +Ġmain stream +ĠW olf +Ġfranch ise +b at +Ġdem ands +Ġag enda +Ġdo zen +Ġclin ical +iz ard +ĠO p +t d +Ġvis ited +ĠPer haps +Ġact or +Ġde lic +Ġcont ribute +Ġin ject +ĠE s +ac co +Ġlist ening +Ġcon gress +epend ent +Ġprem ium +Ġ7 6 +ĠIr ish +Ġass igned +ĠPh ys +Ġworld wide +Ġnarr ative +ot ype +m ont +b ase +ĠB owl +ĠAdminist ration +Ġrel ation +ĠE V +C P +Ġco vers +Ġ7 8 +Ġcert ific +Ġgr ass +Ġ0 4 +pir acy +ir a +Ġengine ering +ĠM ars +Ġun employ +ĠFore ign +st ract +Ġv en +Ġst eal +Ġrepl ied +Ġult imate +Ġtit les +d ated +Ġj oy +a us +Ġhy per +ak u +Ġoffic ially +ĠPro duct +Ġdifficult y +per or +Ġresult ed +rib ed +l ink +wh o +~~ ~~ +ĠSpe ed +ĠV iet +W ind +ĠBar ack +Ġrestrict ions +ĠSh are +Ġ199 5 +ition ally +Ġbeaut y +op t +Ġm aps +ĠC R +ĠN ation +ĠCru z +W ill +Ġelectric ity +Ġor g +Ġb urd +Ġviol ation +Ġus age +Ġper mit +ĠCh ron +ĠF ant +Ġn aturally +Ġ0 7 +Ġth rown +ĠAw oken +Ġal ien +ĠHer o +ĠK ent +ĠR ick +ri ke +Ġp ace +}, {" +G L +Ġpo ison +ĠT ower +Ġform al +al ysis +Ġgen uine +Ġk il +a ver +Ġproced ure +ĠPro p +intend o +ĠM ain +as ant +Ġtr ained +G ame +ĠL oad +ĠM A +Ġcru cial +Ġle ts +ĠF R +Ġch ampion +1 01 +ĠCon ference +Ġwrit ers +Ġconnect ions +Ġo kay +ir ms +ĠR and +Ġenc ounter +ĠB uff +Ġachie ved +Ġche cks +isc ons +Ġassist ant +Ġwhen ever +ĠA ccess +ĠU r +b in +Ġcl ock +is p +op her +Ġb orrow +Ġm ad +Ġperson ality +on ly +IS T +ab ama +Ġg ains +Ġcommon ly +Ġter r +Ġhyp ot +Ġre ly +Ġt iss +iscons in +Ġrid ic +f unction +ĠO regon +Ġun com +r ating +el and +ĠN C +Ġm oon +ann on +Ġvulner able +ut ive +³³ ³³ +ĠRad io +Ġw estern +se ct +ĠT ony +Ġocc urs +ĠO s +ĠH on +Ã Ń +Ġv essel +ĠScot land +Ġdiscrim ination +Ġsubsequ ent +st ring +Ġfant asy +ĠSh adow +Ġtest im +W E +it i +r as +Ġbo at +Ġmar ks +Ġord inary +Ġre n +Ġrepresent ative +Ġpet ition +Ġ7 3 +Ġad venture +Ġign ore +ĠPhil adelphia +ĠS av +V P +Ġfact ory +Ġt asks +Ġdep ression +z ed +................ ................ +ĠSt orm +Ġc ogn +Ġelig ible +Ġredu cing +v ia +Ġ0 5 +Ġstri king +Ġdoll ar +h o +O V +Ġinstr ument +Ġphilosoph y +ĠMo ore +ĠA venue +Ġrul ed +ĠFr ont +IN E +ĠM ah +Ġscen ario +ĠNAS A +Ġen orm +Ġdeb ut +Ġte a +T oday +Ġabs ence +S im +Ġh am +le ep +Ġt ables +ĠHe art +M I +K e +re qu +V D +m ap +Ġchair man +Ġp ump +Ġrapid ly +v i +Ġsubstant ial +E P +d es +ch ant +ili pp +ĠS anta +ri ers +anche ster +L oad +ĠC ase +Ġsa ving +Ġ7 4 +ĠA FP +er ning +oun ced +ĠMin nesota +ĠW as +Ġrec ru +Ġassess ment +ĠB ron +U E +Ġdynam ic +Ġf urn +ul ator +Ġprop ag +h igh +Ġacc ommod +Ġst ack +ĠS us +w rit +Ġre ven +ĠGod d +ĠZeal and +ab s +Ġbr ut +Ġper pet +h ot +Ġhard ly +ĠB urn +ãĤ ¹ +Ġst y +Ġtrans actions +Ġg ate +Ġsc reens +Ġsub mitted +Ġ1 01 +Ġlangu ages +ugh t +em en +Ġfall s +Ġc oc +Ĥ ¬ +Ġstri kes +p a +Ġdel iber +ĠI M +Ġrel ax +ann els +ĠSen ator +Ġext rem +Ġ} , +ĠDe b +Ġbe ll +Ġdis order +c ut +Ġi OS +Ġl ocked +Ġem issions +Ġshort ly +" ] +ĠJud ge +ĠS ometimes +Ġr ival +Ġd ust +Ġreach ing +F ile +¯¯ ¯¯ +ino is +ĠJ ason +Ġs atell +are t +Ġst ations +Ġag ric +ĠTechn ology +com es +ĠUn fortunately +ĠChild ren +Ġappl ies +ast ed +Ġan ger +ail ability +ĠDam age +Ġcomp are +ĠStand ard +Ġaim ed +ĠB a +angu age +Ġreg ulation +Ġj ury +Ġair port +Ġse ctions +ĠPr ince +em ed +Ġmedic ine +Ġh itting +Ġsp ark +ol ves +Ġad s +St ate +Ġfood s +Ġrepl acement +Ġch icken +Ġlow est +Ġmind s +Ġinvol ves +u i +Ġarr ang +Ġproced ures +ĠWh ich +ivers ary +Ġb ills +Ġimprove ment +Ġin ev +Ġexpect ations +Ġintellect ual +Ġsp aces +Ġmechan ism +2 50 +bre ak +ĠZ e +ĠT enn +ĠB alt +Ġbar rel +Ġstat ic +man n +Pol ice +Ġt ips +Ġhand ling +c us +od ed +il ton +ir y +Ġjournal ists +our se +Ġcom ic +Ġnom ine +IT Y +Ġvers us +Ġlo op +Ġsur f +ĠInd ust +ĠHun ter +Ġbelief s +is an +Ġset up +Ġbre w +im age +Ġcomput ers +f ol +} ," +ĠMed al +Ġtax p +Ġdisplay ed +Ġg rav +Ġf iscal +M on +ĠMos cow +ĠK ong +ĠCent re +Ġcamer as +ĠMr s +ĠH ay +Ġa ver +ĠK elly +p y +Ġrequire ment +Ġent itled +omb ie +Ġsh adow +ag ic +ĠA k +Ġel ite +Ġdiv ided +Ġhead ing +Ġcop ies +Ġloss es +Ġv it +k ed +ĠB ry +Ġan s +ĠSte am +Ġrep orter +he im +ĠIt em +Ġsuper ior +d on +ere nt +à ¶ +Ġtherap y +Ġpe ak +ĠMod el +Ġl ying +Ġg am +z er +r itten +Ġrespons es +Ġconsider ation +ĠB ible +Ġl oyal +Ġinst ant +Ġp m +ĠFore st +à ¼ +Ġext end +Ġconv icted +Ġfound er +Ġconv in +ĠO ak +che ck +Ġsch olars +p ed +Ġover se +T op +c ount +ĠAr k + · +Ġ0 6 +ĠL A +m d +ĠLat in +im ental +ĠC PU +Ġsubst ance +Ġminor ity +Ġmanufact uring +E r +ocol ate +Ġatt ended +ĠMan ager +r ations +Ġappreci ate +om y +GB T +id ency +B L +Ġguarant ee +pos ition +Ġo cean +clud e +Ġhead ed +Ġt ape +Ġlo ose +Ġlog ic +Ġpro ven +Ġsp ir +Ġad mit +is a +Ġinvestig ate +Ġ199 4 +sy lv +ĠL ost +c est +Ġ7 1 +Ġrequest ed +Ġwind ows +ĠPok é +ĠWith out +M et +Ġbehavi our +Ġread er +Ġh ung +ĠKe ep +Ġro les +Ġimplement ed +Ġbl ank +Ġserv es +ĠJ ay +Ġc ited +ĠF riend +prof it +ap on +Ġrep air +it em +arr ass +Ġcrit ics +ad i +ĠF ather +Ġsh out +Ġf ool +Ġ8 8 +Ġprodu cing +Ġl ib +Ġround s +Ġcirc le +Ġpre par +Ġsub mit +Ġn ic +mor row +ãĥ « +U nder +Ġv ital +ater n +Ġpass word +Ġpublic ation +Ġprom inent +Ġspeak s +Ġb ars +Ġde eper +ĠM ill +port ed +Ġw id +Ġbut ter +Ġsm oking +Ġindic ates +K ey +rop ri +ĠF ile +all ing +ast ing +ĠR us +Ġad j +Ġ7 9 +av al +Ġpres um +bur gh +on ic +Ġf ur +Ġpoll s +ik a +Ġsecond ary +Ġmon ster +ig s +ĠCur rent +E vent +Ġowners hip +end ar +Ġarri ve +ĠT ax +Ġn ull +ĠPri v +Ġth ro +Ġk iss +c at +Ġup set +ang le +it ches +ect or +olog ists +ĠGal axy +Ġcor ruption +Ġh int +ent er +ĠH ospital +Ġgreat ly +Ġbeg un +es y +Ġso il +ĠAnt on +Ġmain tenance +ãĥ © +Ġdo zens +Ġhuman ity +ĠAl abama +Ġr om +w orth +ap ing +sylv ania +l ah +Ġg athered +G A +Ġattack ing +f ound +ĠSqu are +Ġar bit +ict ions +ĠW isconsin +Ġd ance +ĠS aint +arch y +Ġbase ball +Ġcontribut ions +Ġliter ature +Ġex ha +per ty +t est +Ġb ab +Ġcontain er +let ter +Ġfall en +Ġwebs ites +Ġbott le +ĠS ac +Ġbre ast +ĠP L +Ġveter an +Ġinterview s +ĠA le +Ġb anned +eng ers +ĠRev olution +in th +Ġconc erning +IV E +Ġexp enses +ĠMatt hew +ĠColumb ia +d s +ist ance +Ġent ity +.. ." +Ġrel iable +Ġpar alle +ĠChrist ians +Ġopin ions +Ġin du +l ow +Ġcompet e +Ġth orough +Ġemploy ed +Ġestablish ment +ig en +ĠC ro +Ġlawy ers +ĠSt ation +T E +ĠL ind +ĠP ur +it ary +Ġeffic iency +âĢ IJ +ĠL y +Ġm ask +Ġdis aster +Ġag es +ER E +es is +ĠH old +Ġcas ual +b led +Ġen abled +ĠEn vironment +ĠInt elligence +i per +ĠM ap +ĠB E +Ġemer ged +is dom +Ġc abin +Ġregist ration +Ġfing ers +Ġro ster +Ġfram ework +ĠDo ctor +et ts +Ġtransport ation +Ġaware ness +H er +Ġattempt ing +O ff +ĠSt ore +ÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤ +ĠK now +Ġdef ence +Ġsc an +ĠT en +ĠCh air +ĠP H +ĠAtl anta +Ġfuck ing +Ġans wered +b n +ĠK ar +Ġcateg ories +Ġr ational +Ġc ust +Ġrob ot +Ġcorrect ly +Ġg if +Ġgraph ics +m ic +Ġground s +ĠO pp +i ate +Ġdist ributed +Ġsan ctions +Ġchalleng ing +ut o +Ġingred ients +Ġinv ited +Ġfound ed +ĠRe qu +d ed +Ġb owl +Ġbrother s +ĠH a +I O +Ġw ages +im ore +oc ial +Ġse ed +ative ly +Ġaddress es +ĠI owa +ab eth +Ġatt itude +is d +ch ild +Ġm ole +Ġdisco very +y ard +B r +Ġ8 2 +Ġsuppl ies +ell ing +Ġdist ingu +C R +Ġre cept +Ġ vert +Ġsw im +b ec +d oor +ĠY eah +Ġg al +Ġinter act +ĠE SP +ĠC S +amp s +Ġconvin ced +Ġobject ive +Ġdis h +ĠPhot os +l ad +Ġdownt own +o il +in ction +Ġto morrow +ĠC OM +Ġsurv ival +sh ot +Ġsett lement +C ons +ĠX box +int erest +ĠS M +arg o +en ess +Ġeth nic +b ered +M in +ĠT ok +Ġinc ent +ĠComm and +Ġmain tained +Ġbreak s +br idge +at ar +ag g +ĠF inally +un icip +ĠO nt +le ft +Ġrecogn ition +Ġ* / +ĠP ers +Ġwe lf +Ġaddress ed +ĠK ansas +Ġvir us +Ġwhere as +Ġp apers +ram s +ĠMin istry +Ġple asure +Ġacqu ired +Ġd uration +j pg +Ġcal m +ĠN HL +Ġburn ing +Ġfold er +ick ed +ĠP y +ĠIll inois +Cl ass +ĠGodd ess +Ġperform ing +Ġwelf are +j ar +In ter +Ġl in +Ġenh ance +Ġnot ion +f are +yp es +ĠAre a +Ġcann abis +ĠDie go +f s +ĠM anchester +com m +in ite +Ġcover ing +ĠS ound +Ġ19 60 +Ġ8 4 +e lect +z ing +Ġcitiz en +Ġph ones +Ġr aid +Ġign ored +ĠOb ject +Ġu pload +c ard +Ġmod ified +Ġroom s +ia h +r ange +he ast +ach us +Ġsuggest ing +âĢ ĭ +gr ade +E l +Ġclot hing +Ġr h +ĠH an +un ity +en cing +ĠAust in +sec ution +t ra +d em +ĠQ ual +Ġhe aven +Ġst ages +Ġw edd +pl us +ific ial +ĠIm m +ĠH o +iet ies +Ġphr ase +Ġbr ill +act ory +Ġprov iders +Ġsil ence +Ġa er +ĠA I +ĠAd venture +Ġplatform s +Ġdemonstr ated +Ġinter f +ing ton +Ġr aces +Ġgr ade +ult ane +ĠTh rough +f alse +Ġb ow +ĠA B +Ġfl avor +Ġhistor ic +g ov +Ġcol our +Ġview ed +ĠEm ail +el come +Ġinter vention +Ġd iversity +Ġperiod s +Ġre verse +ĠV ery +Ġqu ote +ĠLe ft +th rough +Ġsc rew +Ġland ing +Ġp ill +Ġw et +Ġprot esters +Ġrepe at +av ed +er k +Ġsal ary +ĠPenn sylvania +St ill +Ġmay or +Ġkit chen +Ġfeat uring +ĠM useum +ĠT ournament +ĠF al +Ġser vers +U C +Ġany body +im g +ĠTr ade +ixt ure +the less +Ġfin ance +Ġcl osing +ĠPat ri +i ac +ab el +Ġ> > +or ous +Ġf irms +sc reen +un a +Ġemb arrass +ul se +Ġlet ting +Ġth rew +ile y +Ġch annels +l an +ĠVeg as +Ġse ar +Ġfant astic +ar re +uzz le +ĠD er +Th ose +Ġsw ing +Ġshe et +ind ex +co ver +og an +Ġvari ables +ĠTe ch +Ġsp oken +ac hel +ĠD a +ĠMount ain +Ġload ed +Ġfoot age +vers ion +Ġun l +ĠPh oenix +Ġthrow ing +Ġf iring +Ġtrack ing +Ġw idth +Ġstrugg ling +ro oms +ot ion +Ġmonth ly +ĠSer ver +Ġegg s +op en +M C +Ġ199 3 +Ġh ired +Ġstay ed +ĠAll en +Ġst ro +Ġ9 8 +st ep +ĠTurk ish +Ġfab ric +ist ing +ĠD om +Ġd ates +Ġpr on +Ġbasket ball +Ġl ucky +ĠArab ia +Ġassum ed +est y +Ġaff airs +Ġgl ad +ĠInd eed +ĠF A +ĠW ord +Ġjo ining +if ice +p read +ir ts +ĠSe lect +Ġpop ulations +aw are +Ġn ose +Ġcompl aints +st art +Ġsc oring +Th anks +Ġmin ing +Ġvisit ors +S H +Ġdam aged +Ġcharacter istics +ĠP ent +D C +Ġ8 3 +ĠS ix +r ates +Ġfl ags +ĠB rew +d og +M ark +// // +Ġexec ution +Ġj oke +ph ones +Ġtestim ony +Ġob st +Q L +ĠC ut +Ġstud ied +ĠN intendo +ick et +ĠN BC +Ġl ad +ĠB ra +ĠM oh +Ġk ernel +Ġoverwhel ming +Ġag ed +Ġapplic able +ĠC ond +Ġroad s +ĠBl ock +m ade +od ge +Ġcomm ands +Ġoff ices +vel and +Ġt ut +Ġrece iver +ĠF ro +Ġsho pping +Ġi P +ĠSt re +ĠA BC +Ġentertain ment +ĠB ow +ort ed +M c +Ġread s +gr ad +ĠCol lect +Ġâ ĪĴ +ĠCap ital +eder ation +Ġemploy er +Ġinvolve ment +Ġanx iety +al ia +Ġro of +ĠAm ong +ĠDemocr at +Ġstat s +ĠV ill +Ġconst itutional +Ġrefer ring +itt y +Ġtack le +out ube +Ġback ed +ĠH ong +ĠBro ad +Ġe le +ĠO tt +Ġ199 2 +h our +achus etts +C al +Ġdefe ated +Ġ8 1 +es p +Ġseem ingly +w as +ĠJ enn +ĠK urd +Ġg ene +Ġdisc ount +R et +EC T +( ); +Ġclub s +Ġs id +ĠM arsh +Che ck +Ġp p +ĠE ag +ides pread +Ġbe ings +F T +Ġintrodu ction +ĠCh ange +AR D +Ġ1 10 +ad ows +ier ce +Ġme al +a uthor +ĠB ang +lah oma +Ġr anks +201 1 +?? ?? +m ax +Ġcoll apse +Ġop ens +Ġe cho +Ġs oph +Ġrac ist +Ġenorm ous +Ġw aves +Ġt ap +Ġcomprehens ive +. -- +ĠR oy +Ġfarm ers +Rel ated +a ired +ron es +ĠC rim +Ġproport ion +Ġdesign s +Ġnegoti ations +Ġvirt ually +ĠBat man +Ġwar n +Ġlegit imate +m ate +Ġcon vention +, , +net ic +ĠS D +Ġconsist ently +Ġcompens ation +Ġpunish ment +Ġy e +Ġt ie +ĠB ureau +ir lf +ĠB u +ĠA ren +ĠPh ilipp +Ġkn ife +Ġmem ories +ĠR oss +Ġang le +Ġ8 6 +ĠTh under +Ġre nd +ĠT our +Ġcount s +s ung +ĠIm p +Ġeduc ational +Ġaccess ible +C OM +Ġd rew +y er +G l +am ine +OR T +O B +I B +m aster +Ġtri als +og y +h ar +ĠTr ust +Ġprefer red +irlf riend +ĠN ev +Ġb in +Ġc ow +P age +Ġsign ature +ĠB L +7 00 +Ġret ired +Ġby tes +Ġneigh b +ĠLeg end +Ġdev ast +Ġsuspect ed +is ons +ĠPoké mon +sc ale +Ġcap abilities +Ġre vel +Ġche ese +d y +igr ant +Ġfail ing +b its +ĠHer oes +ĠG host +ĠS cient +Ġappoint ed +ur i +Ġinst itution +Ġexpand ed +g reg +Ġmonitor ing +Ġp odcast +Ġcoal ition +Ġ9 6 +J o +Ġst olen +ĠS ab +Ġstop s +Ġhol iday +Ġint r +C ar +Bl ack +ĠL GBT +Ġwar ming +ĠAnd erson +Ġ8 9 +Ġprodu cer +M ed +Ġaccur acy +ĠMar vel +iz abeth +ĠPat rick +m ony +Ġmin i +ac les +Ġover t +the y +Ġmembers hip +ĠV en +Ġex ch +Ġrem oval +ĠD ave +T Y +m ad +ĠF ind +Ġad equ +Ġe c +Ġte eth +Ġemot ion +Ġper m +Ġsole ly +d b +Ġextra ord +IG HT +c al +Ġgu idelines +Ġd ying +Ġsusp ended +ĠPrem ier +ĠAnth ony +el ve +Ġd ad +ĠE th +ĠFoot ball +Ġabandon ed +Ġ< < +Ġm arch +Ġhor ror +âĢ¦ " +Ġchild hood +Ġcampaign s +Ġl unch +ĠAl bert +bl ock +âĸĪ âĸĪ +ound ing +Ġb one +or gan +ad ers +ĠFl ash +ĠDri ve +Ġton ight +Ġw ars +ĠF L +Ġform ation +con st +New s +Ġcom pe +or ious +ĠSt aff +Ġdiscuss ions +ĠProt ection +ĠJ am +Ġcrit eria +Ġinstall ation +Ġaccompl ish +iz za +Ġpub lisher +Ġresc ue +ĠT ry +U LL +ĠS om +ĠH op +ore t +th s +ord on +Ġp ocket +ĠIn v +Down load +ĠCr ime +Ġb ene +ĠGu ide +ĠAs sembly +Ġparam eters +I E +ĠAlex ander +Ġconc ert +ĠSc he +Ġsh oes +Ġvis iting +Ġrec all +Ġb ub +Ġr ural +Ġconc rete +ĠR os +N ext +R uss +Ġlo ans +ĠSh ield +Ġtre m +hem at +k g +ĠHar ris +is ition +ĠM ove +ĠF C +Ġf ate +ĠCh o +Ġt ired +Ġprinc ipal +h ist +ien ces +ath y +Ġse vent +Ġm ood +Ġstrateg ic +Ġdise ases +Ġfor um +Ġtem por +Ġhead quarters +P ar +ig e +fl ix +Ġgu itar +Ġ9 4 +On ly +Ġrele ases +ro ph +================ ================ +Ġ6 00 +ĠContin ue +ig ate +ĠC rit +sy stem +Ġdis abled +Ġunex pected +ith ub +Ġuncle ar +ĠE st +Ġcontr ad +Ġstrateg ies +vent ures +Ġpass age +AM E +Ġimpro ving +Ġreve als +Ġdecre ase +ov a +Ġann oy +ĠSh ort +ĠL ibrary +Ġcy ber +n ell +ĠH ur +ĠC B +Ġphot ograp +U I +Ġs ed +G e +Ġ8 7 +Ġd iverse +Ġencour aged +Ġcons piracy +Ġbird s +Ġoper ator +Ġhand ful +Ġclass ified +? ) +Ġdram atic +Ġinvestig ators +it o +Ġw idespread +ĠR oom +-------------------------------- -------------------------------- +Ġcollect ive +Ġjournal ist +St ring +Ġtemper atures +il a +Ġgu id +Ġins pect +Ġmiss ile +ĠMay or +Ġman ual +Ġsim ultane +Ġrat ings +Ġsu ck +Ġ9 7 +Ġunivers al +Ġph arm +Ġdis rupt +ian o +A V +Ġf t +Ġstat ist +old s +ĠWalk er +ph p +Ġunder t +ĠL as +ish op +nt il +res hold +ĠWhe ther +M s +Ġden y +ĠCl oud +Ġprov ider +Ġsurv iv +ĠUp date +h as +Ġmist akes +ch arge +pl ed +r ity +Ġn ode +ĠMass achusetts +ool s +lic ation +Ġf ails +em ale +or i +back s +Ġsh irt +Ġ' ' +ĠN AT +Ġwat ers +els on +Ġe ase +Ġsc ar +Ġcont ents +m ind +Ġcont ribution +Ġsh r +Ġhand ed +Ġst ability +Ġtra ve +E m +Ġmir ror +12 3 +Ġwe igh +Ġf iction +ou ver +ist ant +r ition +ĠF ed +Ġphys ically +Ġst ake +ĠArt icle +ĠAr c +ĠLew is +ĠM ind +Ġdemonstr ate +Ġprof its +v ision +om ic +ol id +Ġbatt les +Ġdri ves +Ġeas tern +ĠS ony +!! ! +ar ation +v ard +ĠG L +port ation +Ġ9 2 +Ġlaw makers +Ġprotect ing +ĠE PA +Ġy eah +Ġsh ame +ol ph +e ven +x it +Ġatt ach +Ġrepresent ing +Ġob s +ĠUt ah +iff s +ĠFre edom +à ³ +A K +Ġinc idents +it age +Ġview ers +c d +Ġm ouse +Ġcl ar +Ġaccord ance +Ġb ot +c or +ĠSum mer +he ld +Ġinnoc ent +Ġiniti ative +ol s +________________ ________________ +Ġsp ots +p ace +Ġconvent ional +Ġcorpor ations +Ġblock ed +H D +at tered +Ġref ers +Ġbu ck +ĠDig ital +12 0 +Ġtop ics +T F +Ä ģ +br id +re ement +Ġunder lying +ĠM ember +Ġinvestig ating +Ġpregn ancy +Ġtouch down +ĠB and +ĠCall er +Ġinst ances +P P +w a +G ood +Ġ199 1 +ĠC old +Ġfear s +Ġrem arks +Ĩ Ĵ +at al +Ġm it +Ġexper iments +i pt +Col or +ind u +Up date +Ġ9 3 +A g +Ġ å +anc ouver +B oth +Ġjud ges +Ob ject +Ġst ere +umb n +Ġparticip ation +ĠSt ars +ĠJ ere +Ġweek ly +ĠB an +Ġconvers ations +ĠP itt +u z +ĠIndian a +ĠK ick +Ġinf ection +Ġhero es +Ġsett led +Ġstri p +Ġh al +Ġd ump +ĠS ci +Ġl es +Ġref erences +ĠU RL +ĠBr idge +Ġwant ing +For ce +Ġex clus +Me anwhile +m n +Ġg entle +m aker +sen al +ĠG ro +ou ri +ĠR ain +ĠAll iance +Ġl ift +el a +S D +ĠCle veland +Ġrank ed +Ġst adium +Ġdead ly +ä ¸ +Ġr iding +ar ia +ĠAr mor +Ġdocument ation +ĠGree ce +ree k +Ġl ens +ĠS a +Ġg ross +ĠE mer +ag ers +ĠD ub +ĠR h +ĠAM D +Ġarri val +Ġdes ert +Ġsupp lement +ĠRes p +Ġkn ee +Ġmarg in +f ont +og g +201 0 +ĠP ir +ĠP rom +iv als +Ġint ake +Ġdifferent ly +ug s +Ġb its +clud ed +Ġsearch ing +ĠD u +um ble +Ġfunction al +ĠBalt imore +ĠC ould +Ġdes ired +Ġcirc uit +ĠL yn +ĠG O +ĠF alse +re pre +' : +alt ies +Ġmin im +Ġdro ve +ĠSh ould +Ġh ip +Ġpro s +Ġut ility +ĠN ature +ĠM ode +P resident +o pp +r at +form ance +Ġconcent ration +Ġf ont +ĠB ud +Ġam id +Ġre vers +ĠM L +B ar +Ġinter action +Ġjur isd +Ġspell s +d ep +f il +Ġcivil ians +ut ter +ĠCo oper +ĠBel ow +Ġent rance +Ġcon vert +Ġcontrovers y +ow ered +Ġcontr ary +Ġar c +ĠExec utive +ĠOffic er +Ġpack ages +Ġprog ressive +w idth +Ġreserv ed +v ol +ĠSam sung +Ġprint ed +Ġcent ers +Ġintrodu ce +ĠKenn edy +Ġodd s +Ġsure ly +Ġindepend ence +Ġpass engers +repre ne +ĠBe h +Ġl oves +ĠESP N +Ġfac ilit +Ġident ical +Ġdo ct +Ġpartners hip +con f +ĠH ide +Ġconf used +ĠC ow +M en +Ġw rest +ĠIraq i +Ġh oles +ĠStud ies +Ġpregn ant +h ard +Ġsign als +I X +Ġpull ing +Ġgrad uate +Ġnomine e +D ate +Ġper mitted +Ġâ Ĥ¬ +ĠOk lahoma +St art +Ġauthor ized +Ġal arm +ĠC os +v an +Ġgener ations +c ular +Ġdr agon +ĠSoft ware +ĠEd ward +Ġcontro ller +S en +ge red +ĠV ik +Ġappro ached +Th ank +Ġcan ce +Ġform ula +ĠSm all +Ġweak ness +Ġr amp +it udes +j ud +Ġbrill iant +Ġacc us +s ource +Ġ8 00 +ĠE vil +S w +Ġhom eless +we ek +i ens +r ics +ĠTh ird +T O +Ġorgan ic +Ġpresent ation +ag h +ĠDown load +v ation +Ġas sembly +or able +hold ers +ĠBern ie +ĠHel p +Ġt ong +ĠF ight +Ġbe ach +B ook +ĠL ic +Ġr ush +ĠR ound +ou p +ĠMar x +Ġcalcul ated +ĠDe vil +ĠSar ah +Ġoccasion ally +Ġbul let +Av ailable +g ate +Ġ9 1 +Ġh osp +Ġprom ises +ĠH IV +ĠSt adium +ĠSt ock +ĠCorpor ation +g age +N G +ĠC redit +Ġs ne +ib l +Ġacc um +s uch +Ġterror ists +Ġconscious ness +ĠZ h +Ġdram a +ool a +pir ation +Ġlab our +ĠN in +Ġut ter +Ġdemocr atic +Ġass ass +il ation +Ġg est +Ġab road +Ġmet ab +Ġs orts +Ġfl av +U B +Ġm g +ĠNot hing +ĠO d +Ġmus ical +200 9 +Ġdro ps +oc ated +ater al +0000 00 +Ġg re +Ġequ ality +Ġburd en +Ġv ig +ĠLe ader +-------- ---- +Ġcere mony +Ġf ighter +Ġact ors +Ġ æ +am an +F i +Ġal ign +put er +Ġe lder +ĠN SA +Ġrepresent ation +ĠOnt ario +IT H +usal em +Ġharass ment +itz er +Ġsy mp +Ġbox es +ĠD R +Ġman ifest +at re +Ġ ^ +Ġd ies +le ton +Ġmiss ions +et he +Ġres olve +Ġfollow ers +Ġas c +Ġk m +l ord +am med +Ġsil ent +ĠAssoci ated +Ġtim ing +Ġprison ers +ĠK ings +ĠF ive +Ġtow er +Ġappro aches +Ġprecise ly +Ġb ureau +ĠM other +ĠI ss +Ġkey board +it ual +Ġfund ed +Ġstay ing +Ġpsych ological +Ġm ile +ĠLe on +ĠBar b +w ill +Ġw ider +ĠAtl antic +Ġt ill +ĠR ome +ro t +Ġaccomp an +Ġfl our +ac o +W orld +ĠExp ress +ĠY u +C or +Ġple ased +part y +Ġpoint ing +Ġinf lation +Ġro y +Ġ ), +ain er +Ġwedd ing +orm on +Ġrequ iring +Ġqual ified +Ġse gment +EN D +Ġs izes +e als +Ġcor rupt +ass ador +Ġcele b +Ġdream s +ĠM ess +Ġcheck ing +ĠV ersion +Ġprep aring +Ġact ively +ĠD iff +Ġl ux +ĠW inter +act eria +ĠN E +Ġdep uty +Ġtrans gender +Ġsum mary +Ġin her +er ies +ch ar +ĠY an +Ġkn ock +ĠP ath +Ġl ip +roll er +Ġimp ression +Ġcelebr ate +Ġsl ide +Ġgu ests +Ġcl ip +F S +Ġsav ings +Ġcapt ain +Ġleg acy +ĠDen ver +Ġw ounded +tab oola +AC T +Ġpurs ue +Ġo xy +Ġ q +Ġsem i +ĠN eed +ĠAff airs +Ġob sc +Ġcheck ed +Ġd ual +C ode +ĠM D +le m +ult y +Ġ © +ĠEl izabeth +Ġcent uries +ard ed +s rc +Ġev ident +enn is +at in +Ġunemploy ment +ĠMar io +Ġint im +Ch rist +Ġbi ological +Ġsold ier +ĠAdd ed +Ġm ath +ĠG il +Ġbi as +Ġd ating +ĠO cean +Ġm ice +M us +h ire +ĠT es +Ser ver +lim ited +S ize +Ġmet ers +Ġrock et +es see +Ġcertific ate +ĠIran ian +AS S +Ġgr id +D ec +Ġro lling +com mun +ĠSwed en +b ury +Ġtiss ue +Ġrac ism +ĠL ocal +Ġmyster y +Ġexam ine +Ġst em +Ġs its +Ġhop ed +ot ing +Ġdial ogue +Ġpers u +W atch +l ay +M AN +Ġch ronic +ĠPort land +mark et +ĠS EC +Ġparalle l +Ġsc andal +Ġcar ries +Ġphenomen on +h uman +ack er +ĠO x +Ġretire ment +tain ment +ov ie +ĠG ear +Ġd uties +Ġdo se +Ġsc roll +M B +in f +Ġsa uce +Ġland scape +red dit +ĠChampions hip +ĠRed dit +al id +Ġco in +Ġover s +Ġpost ing +ab out +Ġf el +and y +Ġb old +Ġfocus ing +e ffect +G R +Ġde emed +Ġrecommend ations +Ġste pped +Ġvot er +ĠDe ep +ĠInst agram +Ġmoder ate +ĠMary land +Ġrestrict ed +ĠM B +ĠCh all +Ġto b +Ġc ir +ĠO cc +ĠE ver +Ġcoll aps +IN FO += - +ĠP ict +ĠAcc ount +n c +Ġo ught +Ġex port +Ġdr unk +( ' +Ġw ise +ĠM ort +ne cess +Ġan cest +ĠInc re +Ġfrequ ent +m ir +Ġinterpret ation +Ġdepend ent +Ġco ins +ĠB ol +V ideo +ĠJust in +Ġfat al +Ġcook ing +Ġconf usion +ip her +Ġcust ody +ĠMor gan +om ach +ĠGovern or +Ġrestaur ants +el ing +Ġacknowled ged +Ġthe r +Ġgen es +ch ing +He y +Ġtact ics +ĠMex ican +Ġv end +Ġhe s +qu er +Ġnot ing +ĠCamer on +Ġtarget ing +ro ck +Ġcred its +Ġemot ions +Ġrepresent atives +new s +Ġlegisl ative +Ġrem oving +Ġtweet ed +ĠCar ter +ĠF ixed +Ġfor cing +Ġspeak er +Ġm ales +ĠViet nam +l ined +Ġconcept s +Ġvo ices +o ir +ĠT rib +W he +ĠJer usalem +ĠS ant +Ġc ul +Ġl ady +ĠHaw ai +Ġar ts +ĠIn n +ĠMach ine +ĠEm peror +Ġsl ot +g ly +ĠPro cess +II I +Ġathlet es +ĠTem ple +ĠRep resent +Ġpres c +Ġt ons +Ġgold en +Ġp unch +ĠG R +iver pool +Ġen act +Ġlob by +Ġm os +Ġpick ing +Ġlif etime +Ġcogn itive +E ach +z o +Ġd ub +Ġcons ists +ol n +Ġf estival +am ous +Ġint ellig +w ords +ĠSm art +Ġde le +Ġl apt +Ġmag ical +ĠS in +b us +ur ities +igh th +ĠRub y +ĠS ure +ol ving +Ġj un +O ST +Ġimp osed +Ġast ron +Ġcor rel +ĠN S +ĠK it +ĠF uture +b urn +Ġimm une +oc us +Ġcour ses +ĠSt ring +Ġle an +Ġg host +Ġout comes +Ġexp ense +Ġevery day +Ġaccept able +A h +Ġequ ipped +Ġor ange +F R +ĠD utch +Th ough +ĠR ank +Q U +ĠRober ts +wh at +re nd +Ġdisapp ear +Ġsp awn +ĠL am +o is +Ġdes erve +Ġmin imal +Ġnerv ous +ĠW ould +Ġro ok +ĠV ancouver +Ġres ign +sh ire +ĠW orks +ĠB uild +Ġafford able +ĠG ary +ĠAren a +Ġh anging +Ġimpl ications +ĠS ong +Ġmain taining +Ġgu ards +C ON +Ġder ived +Ġexecut ed +Ġthe ories +Ġqu oted +ĠAnd re +og a +sel ess +in fo +ĠBel g +Ġt ears +ĠSur v +Ġbirth day +ig ious +im mer +Ġspect rum +Ġarchitect ure +Ġrec ruit +arm a +T able +Ġmon sters +ĠG ov +Ġdest ination +Ġattract ive +Ġf oss +ĠMore over +Ġpres ents +TH E +Ġrep ly +pt on +Ġc um +Ġdel ight +Ġaffect s +Ġdon ations +ĠT oy +ĠH im +M ENT +Ġover come +it ched +ĠFant asy +ĠH at +ĠBe ast +b ott +Ġinvestig ations +R un +Ġhun ting +d i +f und +Ġs essions +est yle +Ġport ray +oid s +Y eah +Ġcommun icate +Ġcom edy +ĠY ang +Ġbel t +ĠMar ine +Ġpredict ed +Pl ay +Ġimportant ly +Ġremark able +Ġelim inate +D avid +Ġb ind +V ID +Ġadvoc ates +ĠG aza +im p +D B +ĠN a +ĠSim ilar +I ES +Ġchar ity +v as +m ath +Ġâ ĸ +ok er +nd um +Ġcap s +ĠH al +2 000 +e an +Ġfle et +Ġrec re +R ight +Ġsleep ing +ij ing +k ind +Ġdesign ated +à ¤ +Ġanim ation +ke e +ĠInt rodu +Ġ/ > +Ġdelay ed +Ġtrem end +Ġcur ious +U se +Ġle ct +d am +Ġinnov ation +ĠPoint s +Ġload ing +Ġdisp ute +ct ic +ird s +ĠB Y +Ġn urs +ĠVal ue +ION S +ĠH um +Ġtem plate +m ers +Ġappear ances +ĠEnter tainment +Ġtransl ation +Ġsa ke +Ġbene ath +Ġin hib +Ġe uro +abet es +Ġstud ying +ĠM as +Ġper ceived +Ġexam ined +Ġe ager +Ġco aches +Ġim per +ch i +Ġprodu ces +" ). +ĠEvery one +Ġm unicip +Ġg irlfriend +Ġh ire +ĠV ice +Ġsu itable +op y +Ġin equ +ĠD uke +f ish +f irst +ĠO bs +Ġinter ior +ĠBru ce +ĠR y +Ġanal ys +Ġconsider able +Ġfore cast +Ġf ert +ors hip +ĠD rug +ĠA LL +: " +th ur +ĠM ail +Ġball ot +Ġinst antly +ĠCh annel +Ġp icks +Ġ198 9 +Ġt ent +ol i +Ġcivil ian +b ling +ell o +b u +Ġin ch +Ġlog o +Ġcooper ation +Ġwal ks +Ġinvest ments +Ġimp rison +ĠF estival +ĠK y +Ġleg ally +Ġg ri +ch arg +S l +Ġthreat ening +du ction +fl ow +Ġdismiss ed +ibr aries +c ap +e le +ĠMc G +ĠHar vard +ĠConserv ative +ĠC BS +p ng +Ġro ots +ĠH aving +umb led +ĠF un +\ / +ĠS earch +ple x +Ġdiscuss ing +Ġcontin u +ĠT ai +ĠW ik +F ree +f it +Ġref use +Ġmanag ing +Ġsy nd +ip edia +w alk +Ġprofession als +Ġguid ance +Ġunivers ities +Ġas semb +unt u +F inally +AS E +ĠAut o +ĠH ad +Ġann iversary +L D +ĠD ur +ĠUlt imate +ih ad +pro duct +Ġtrans it +Ġrest ore +Ġexpl aining +Ġass et +Ġtransfer red +Ġbur st +ap olis +ĠMag azine +ĠC ra +ĠB R +gg ed +ĠH E +M ich +b et +ĠL ady +yl um +erv es +Ġme ets +wh ite +L og +Ġcorrespond ing +Ġins isted +G G +Ġsurround ed +Ġt ens +Ġl ane +Ġco inc +h ome +Ġexist ed +ect ed +ĠDou ble +lam m +Ġske pt +ex p +Ġper ception +ie v +ĠBe ing +o ft +Ġadop t +. : +] ; +Wind ows +Ġsatell ite +AS H +Ġinf ant +d escription +ĠMe anwhile +c m +oc a +ĠT reat +act or +Ġtob acco +ĠN orm +em ption +Ġfl esh +Ġj e +o op +ĠHe aven +Ġbe ating +an im +Ġgather ing +Ġcult iv +G O +ab e +ĠJon athan +ĠSaf ety +Ġbad ly +pro t +Ġcho osing +Ġcontact ed +Ġqu it +Ġdist ur +Ġst ir +Ġto ken +D et +ĠP a +Ġfunction ality +00 3 +s ome +Ġlimit ations +Ġmet h +b uild +con fig +N T +re ll +ble m +ĠM om +Ġveter ans +ĠH u +Ġtrend s +are r +ĠG iven +ĠCa ption +m ay +AS T +Ġwond ering +ĠCl ark +n ormal +Ġsepar ated +Ġdes p +st ic +b rew +Ġrel ating +ĠN ik +ĠF arm +Ġenthus i +g ood +d eb +Ġactiv ist +Ġm art +Ġexplos ion +ĠEconom ic +L ink +Ġins ight +Ġconven ient +Ġcounter part +su pport +ĠV irt +ag en +ĠTenn essee +ĠSim on +ĠA ward +OC K +ĠF igure +Ġoverse as +Ġpr ide +ĠC as +n ote +m g +C urrent +Ġdispl ays +cont ent +Ġtravel ing +Ġhosp itals +ĠFin ancial +ĠP ast +Ġdefend ant +Ġstream ing +m ble +ĠBer lin +uk i +Ġdist ribut +Ġant ib +Ġch ocolate +ĠCast le +Ġinter rupt +ĠR ow +Ġconvers ion +Ġbug s +ĠR ather +li est +L Y +ĠJe an +com mon +ak h +Ġ1 30 +ot ton +ĠDe an +Ġam endment +Ġgame play +ĠWar ren +od a +Ġhigh lights +Ġir re +ĠNAT O +Ġball s +Ġdemand ing +U RE +ĠL uke +F igure +st op +on ia +z one +iz ers +ĠW R +Ġaward ed +Ġregul atory +ĠH art +ĠS N +pl ing +Ġs our +ĠP ixel +us ive +Ġf et +ĠS ent +Ġautom atic +Ġf er +vern ment +ĠKh an +T ON +f ather +Ġextraord inary +th rop +ĠP ython +ĠG PU +Ġsex ually +Ġdesk top +it ivity +ĠAnton io +Ġo rient +Ġe ars +ob by +ous es +vertis ements +Ġmanufacture rs +ic ient +min ute +Ġconv iction +Ġg arden +p ublic +Ġsatisf ied +f old +O K +Ġin hab +ĠTh ink +Ġprogram me +Ġst omach +Ġcoord in +Ġh oly +Ġth reshold +Ġr het +Ġser ial +Ġemploy ers +ĠEvery thing +ra h +Ġb other +Ġbr ands +Val ue +ĠT ed +ĠPlan et +Ġp ink +ĠFurther more +s a +P E +re ck +ĠUS D +ot te +Ġ& & +Ġland ed +g ets +Ġprodu cers +Ġhealth care +Ġdomin ant +Ġdest ro +Ġam ended +ch ron +Ġf its +ĠSy d +ĠAuthor ity +AT CH +Ġfight s +ĠL LC +Ġ-- - +ĠCor p +Ġtox ic +spe cific +ĠC orn +ĠChe l +Ġtele phone +ĠP ant +Ġmyster ious +aun ch +od ox +med ia +Ġwitness es +ag u +Ġquestion ed +ĠBre xit +ĠRem ember +ene z +Ġend orse +iat ric +ĠId ent +Ġridic ulous +1 10 +Ġpr ayer +Ġscient ist +Ġ19 50 +ĠA qu +Ġunder ground +ĠU FC +m are +ĠL ater +w ich +Ġsubsc rib +Ġhost s +Ġer r +Ġgr ants +ant om +Ġsum mon +ear ly +ĠC lear +ĠPr im +Ġsusp ension +Ġguarant eed +app er +Ġr ice +ĠSe an +ĠSh in +Ġrefere ndum +Ġfl ed +r ust +Ġ3 60 +ter y +Ġsh ocked +B R +ĠO il +ĠAll ah +Ġpart ly +Ġign or +Ġtrans mission +Ġhom osexual +ivers al +Ġhop efully +ãĤ ¤ +Ġless on +L eg +Ġ .. +Y et +t able +app ropri +re tt +Ġbo ards +Ġincor rect +Ġb acteria +ar u +am ac +Ġsn ap +.' " +Ġpar ad +t em +he art +Ġav ailability +Ġw isdom +Ġ( + +Ġpri est +ĠÂł ĠÂł +O pen +Ġsp an +Ġparam eter +Ġconv ince +Ġ( %) +r ac +Ġf o +Ġsafe ly +Ġconver ted +ĠOlymp ic +Ġres erve +Ġhe aling +ĠM ine +M ax +Ġin herent +ĠGra ham +Ġinteg rated +D em +Ġpip eline +Ġapp lying +Ġem bed +ĠCharl ie +Ġc ave +200 8 +Ġcons ensus +Ġre wards +P al +ĠHT ML +Ġpopular ity +look ing +ĠSw ord +ĠAr ts +' ) +Ġelect ron +clus ions +Ġinteg rity +Ġexclus ively +Ġgr ace +Ġtort ure +Ġburn ed +tw o +Ġ18 0 +P rodu +Ġent reprene +raph ics +Ġg ym +ric ane +ĠT am +Ġadministr ative +Ġmanufacture r +Ġ vel +ĠN i +Ġisol ated +ĠMedic ine +Ġback up +Ġpromot ing +Ġcommand er +Ġfle e +ĠRus sell +Ġforg otten +ĠMiss ouri +Ġres idence +m ons +Ġrese mb +Ġw and +Ġmeaning ful +P T +Ġb ol +Ġhe lic +Ġwealth y +Ġr ifle +str ong +row ing +pl an +as ury +âĢ¦ . +Ġexpand ing +ĠHam ilton +Ġrece ives +S I +eat ures +ĠAn im +RE E +P ut +Ġbrief ly +ri ve +Ġstim ul +Ġ`` ( +Ġ __ +Ġch ip +Ġha z +Ġpri ze +ĠTh ings +AC E +ul in +d ict +ok u +Ġassoci ate +ock ets +y outube +St ory +ateg ory +Ġm ild +ail ing +ĠY e +O rig +ĠK a +or ig +Ġpropag anda +Ġan onymous +Ġstrugg led +Ġout rage +AT ED +ĠBe ijing +r ary +Ġle ather +Ġworld s +Ġbroad er +12 5 +id al +ĠBet ter +Ġt ear +E xt +Ġpropos als +Ġit er +ĠSqu ad +Ġvol unt +m i +D id +ĠP u +p in +Ġspeak ers +Ġb orders +Ġfig ured += ' +Ġsimultane ously +aed a +Ġcharg ing +Ġur ged +Ġcon j +25 6 +ĠG ordon +mer ce +Ġdocument ary +Sh are +it ol +ON E +ĠG arden +h att +ĠThom pson +ane ous +ap ore +Ġt anks +Ġless ons +tr ack +Ġout standing +Ġvolunte ers +Ġsp ray +Ġmanag ers +l arge +Ġcamp s +Ġart ificial +ĠR u +Ġb ags +th al +Ġcompat ible +ĠBl ade +Ġf ed +Ġarg ues +F I +Ġunf air +Ġcor n +Ġoff set +Ġdirect ions +Ġdisappoint ed +ĠCon vention +Ġview ing +M E +oc ity +Ġtown s +Ġlay ers +Ġro lled +Ġjump ed +Ġatt ribute +Ġun necess +inc oln +Ġsupp ose +ĠNet her +ch a +Ġbur ied +Ġsix th +B en +ress ing +OU R +Ġw ound +Ġcy cl +Ġmechan isms +Ġcongress ional +ĠE lement +Ġagre ements +Ġdec or +Ġclos est +ĠM it +Go ogle +} } +Ġm ixture +Ġflu id +S ign +ĠSch olar +Ġp ist +ask et +ab ling +Ġrac ing +he ro +ri el +ass y +Ġche aper +b en +Ġvert ical +amac are +ĠRead ing +g ments +Ġhelic op +Ġsacr ifice +ay a +p aren +V A +ĠL es +ĠStud io +Ġviol ations +ĠAn na +ac er +é ¾ +ĠR at +ĠBe ck +ĠD ick +ĠA CT +Ġcomp osition +Ġtext ure +ĠO wn +Ġsmart phone +ĠN A +Ġfor b +im port +Ġdef ending +il st +re r +Ġo h +ĠJere my +Ġbank ing +cept ions +Ġrespect ive +/ . +Ġdr inks +ĠW i +Ġb ands +ĠL iverpool +Ġg rip +ĠB uy +Ġopen ly +Ġreview ed +per t +Ġver ify +ĠCo le +ĠW ales +M O +Ġun pre +Ġshel ter +ĠIm perial +Ġgu i +ĠD ak +Ġsuggest ions +Ġexplicit ly +Ġsl ave +Ġblock chain +Ġcompet ing +Ġprom ising +S ON +Ġsoc cer +Ġconst itution +4 29 +Ġdist ract +ĠU ser +es ides +ĠMet hod +ĠTok yo +Ġaccompan ied +Cl ient +s ur +al og +Ġident ification +Ġinv asion +as ma +Ġindust ries +pp ers +Ġsub tle +ĠUn it +n atural +Ġsurv ived +Ġfl aw +ĺ ħ +ĠH oll +Ġdef icit +Ġtut orial +ĠCh ance +Ġarg uing +Ġcontem porary +Ġinteg ration +for ward +Ġt um +it is +Ġh iding +ĠD omin +ĠT an +ĠB uilding +ĠV in +Ġspokes person +ĠNot es +Ġemer ging +Ġprepar ation +Ġpro st +Ġsuspect s +Ġaut onom +D escription +Ġdeal t +ĠP ear +Ġstead y +Ġdecre ased +Ġso vere +ĠCl in +Ġgrad ually +ors es +ĠW AR +S erv +ãĤ ¢ +h r +Ġd irty +ĠB arn +ĠB C +Ġd il +Ġcal endar +Ġcompl iance +Ġch amber +b b +Ġpass enger +ate ful +ĠT itle +ĠSyd ney +ĠG ot +Ġdark ness +Ġdef ect +Ġpack ed +ass ion +Ġgod s +Ġh arsh +IC K +le ans +Ġalgorith m +Ġoxy gen +Ġvis its +Ġbl ade +Ġkil omet +ĠKent ucky +Ġkill er +P ack +enn y +Ġdiv ine +Ġnom ination +be ing +Ġeng ines +Ġc ats +Ġbuff er +ĠPh ill +Ġtra ff +AG E +Ġtong ue +Ġrad iation +ere r +m em +ĠExpl icit +é¾ į +Ġcou ples +Ġphys ics +ĠMc K +Ġpolit ically +aw ks +ĠBl oom +Ġwor ship +e ger +ut er +ĠF O +Ġmat hemat +Ġsent enced +Ġdis k +ĠM arg +Ġ/ * +P I +Ġoption al +Ġbab ies +Ġse eds +ĠScott ish +Ġth y +] ] +ĠHit ler +P H +ng th +Ġrec overed +ing e +Ġpow der +Ġl ips +Ġdesign er +Ġdis orders +Ġcour age +Ġch aos +" },{" +Ġcar rier +b ably +H igh +ĠR T +es ity +l en +Ġrout es +u ating +F il +N OT +w all +s burgh +Ġeng aging +ĠJava Script +ore r +li hood +Ġun ions +ĠF ederation +ĠTes la +Ġcomple tion +ĠT a +Ġprivile ge +ĠOr ange +Ġne ur +paren cy +Ġb ones +Ġtit led +Ġprosecut ors +ĠM E +Ġengine er +ĠUn iverse +ĠH ig +n ie +o ard +Ġheart s +ĠG re +uss ion +Ġmin istry +Ġpen et +ĠN ut +ĠO w +ĠX P +in stein +Ġbul k +S ystem +ic ism +ĠMarket able +Ġpre val +Ġpost er +Ġatt ending +ur able +Ġlicens ed +ĠG h +et ry +ĠTrad able +Ġbl ast +à ¤ +ĠTit an +ell ed +d ie +H ave +ĠFl ame +Ġprof ound +Ġparticip ating +Ġan ime +ĠE ss +Ġspec ify +Ġregard ed +ĠSpe ll +Ġs ons +own ed +Ġm erc +Ġexper imental +land o +h s +ĠDun geon +in os +Ġcomp ly +ĠSystem s +ar th +Ġse ized +l ocal +ĠGirl s +ud o +on ed +ĠF le +Ġconstruct ed +Ġhost ed +Ġsc ared +act ic +ĠIs lands +ĠM ORE +Ġbl ess +Ġblock ing +Ġch ips +Ġev ac +P s +Ġcorpor ation +Ġo x +Ġlight ing +Ġneighb ors +ĠU b +ar o +Ġbe ef +ĠU ber +F acebook +ar med +it ate +ĠR ating +ĠQu ick +Ġoccup ied +Ġaim s +ĠAdd itionally +ĠInt erest +Ġdram atically +Ġhe al +Ġpain ting +Ġengine ers +M M +ĠM ust +Ġquant ity +P aul +Ġearn ings +ĠPost s +st ra +ãĥ¼ ãĥ +Ġst ance +Ġdro pping +sc ript +Ġd ressed +M ake +Ġjust ify +ĠL td +Ġprompt ed +Ġscr ut +Ġspeed s +ĠGi ants +om er +ĠEd itor +Ġdescrib ing +ĠL ie +ment ed +Ġnow here +oc aly +Ġinst ruction +fort able +Ġent ities +Ġc m +ĠN atural +Ġinqu iry +Ġpress ed +iz ont +for ced +Ġra ises +ĠNet flix +ĠS ide +Ġout er +Ġamong st +im s +ows ki +Ġclim b +ne ver +Ġcomb ine +d ing +Ġcomp r +Ġsignific ance +Ġremem bered +ĠNev ada +ĠT el +ĠSc ar +ĠWar riors +ĠJ ane +Ġcou p +b as +Ġtermin al +, - +O H +Ġt ension +Ġw ings +ĠMy ster +�� �� +ĠUn like +val id +viron ments +ĠAl i +Ġn aked +book s +ĠM un +ĠG ulf +Ġd ensity +Ġdim in +Ġdesper ate +Ġpres idency +Ġ198 6 +h y +IN D +Ġun lock +im ens +Ġhand led +ĠE b +Ġdisapp eared +Ġgen re +Ġ198 8 +Ġdetermin ation +St ream +ik o +ap ters +Ġacknow ledge +J an +Ġcapital ism +P at +Ġ20 20 +Ġpain ful +Ġcur ve +Ġbom bs +st orm +ĠMet al +en cer +ĠF ig +ĠA aron +anc hes +Ġins piration +Ġexha ust +t ains +ash i +Ġdesc ript +Ġr itual +ĠChel sea +Ġpromot ion +ĠH ung +ĠW ard +iv a +ĠE T +Ġto ss +all ow +ĠFranc is +D ep +Ġhapp iness +ĠGl ass +Ġbet a +Ġstreng then +N E +o a +Ġbutt ons +ĠMur ray +Ġkick ed +Qu est +ĠT alk +ĠS everal +ĠZ ero +Ġdr one +ul k +Ġc am +ĠM obile +Ġprevent ing +Ġret ro +ĠA x +Ġcru el +Ġflo at +. ), +Ġfil ing +ĠGr ant +ĠB or +Ġr ib +Ġchampions hip +ĠM erc +Ġsty les +Ġc ake +Ġbuild s +ĠS elf +io x +Ġep ic +oy d +B el +ĠSt ew +. ( +ah u +ĠBe yond +Ġout s +Ġsol o +ĠT ree +Ġpres erve +Ġt ub +AR E +ro c +ĠIm pro +ĠW right +Ġbu nd +Ġtr aged +Ġoccas ional +b ian +Sec ond +r ons +Ġinter actions +form ed +s ing +Ġown s +Ġh ockey +Gener al +Ġlog ical +Ġexp end +Ġesc al +ĠGr iff +ĠC rown +ĠRes erve +Ġsto pping +Ġexc use +sec ond +Ġoper ated +Ġre aches +ĠMal ays +Ġpoll ution +ĠBrook lyn +Ġde lete +Ġhas h +Bl ock +ah a +âĢ ³ +Ġsh orter +p iece +> >> +ĠM ormon +t or +Ġpartic les +ĠB art +ry ption +Ġad min +Ġsqu ee +VID IA +Ġcreat or +iam eter +ic ular +N BC +Ġgrab bed +Ġn odd +Ġr ated +Ġrot ation +Ġgr asp +Ġexcess ive +ĠE C +ĠWh it +Ġinvent ory +ault s +ĠF B +Ġe cosystem +Ġbill ions +Ġvent ure +n amed +Ġdef ender +out e +Inst ead +ir able +W ar +Ġassum ption +Ġb ite +Ġearth qu +t ail +sp ace +Ġgif ts +boy s +Ġinev itable +Ġstruct ural +Ġbenef icial +Ġcompe lling +h ole +erv ation +Ġco at +o j +inc arn +ĠY ears +Ġdetermin ing +Ġrhet oric +Ġbound aries +Ġwh ites +A nt +add y +) - +ra ham +eter min +Ġhar vest +ĠCon c +Ġlapt op +ĠM atch +Ġenjoy ing +cc a +oll ar +Ġtri ps +Ġadd iction +ĠS ak +Ġpow ered +Ġc ous +ĠRuss ians +ie re +Ġret rie +qu ality +Ġdiff er +Ġking dom +ĠL aur +ĠCap itol +Ġcon clusions +ĠAl tern +ĠN av +Ġtrans parent +B ER +G roup +ĠCom plete +Ġinf er +Ġint rig +Ġins ane +R O +oph ob +is en +qu al +Mich ael +Ġm useum +ĠP ope +Ġres et +r ative +f ive +Ġagg reg +itte es +osit ory +Ġcar b +ĠRec ord +Ġdec ides +ĠF ix +Ġexcept ions +ĠCommission er +un s +ĠEnvironment al +Ġlegend ary +ist ence +Ġtun nel +k m +Ġins ult +Ġt roll +Ġsh ake +Ġdet ention +qu es +ĠCh rome +ĠF iles +Ġsub t +Ġprospect s +Ġpro l +re nder +pro of +Ġperform ances +St r +Ġh ref +ern ame +Ġachieve ment +Ġf ut +F ull +ĠLe ban +go ogle +ãĥ Ī +amp a +May be +Ġproject ed +ĠE mb +Ġcol leg +Ġa wards +Ġâ Ķ +G old +ĠBl ake +ĠR aj +if ting +Ġp ending +Ġinst inct +Ġdevelop ments +Con nect +ĠM and +ĠW ITH +ĠPhilipp ines +prof ile +Ġalt ogether +ĠB und +ĠT D +oo oo +amp ed +ip h +Ġste am +Ġold est +Ġdet ection +ul pt +Ġ ç +ĠWay ne +200 6 +f a +Ġcir cles +ĠF u +Ġdon ors +appropri ate +ĠDak ota +j amin +Ġmotiv ated +Ġpurch ases +ĠLouis iana +ĠS pl +Ġgl obe +Ġ10 5 +z ip +c all +Ġdepart ments +Ġsustain able +10 5 +ĠO P +if iers +Ġprevent ed +Ġinc omp +ĠComm ander +Ġdom inated +Ġ » +Ġinvest ed +Ġcomplex ity +Ġin cl +Ġens uring +Ġreal m +yn c +ĠInd ependent +r ained +ĠJ en +ĠFl ight +Ġat he +Ġspec ulation +ĠT E +oc ate +t ic +Ġpl aint +her ry +Ġto y +Ġ1 11 +Ġpl ates +st atus +ĠIs a +Ġdev oted +C op +ĠE S +25 5 +ur rency +M ain +Ġsl aves +Ġpe pper +Ġqu otes +Ġce iling +ĠF ish +Ġtrans formation +Ġfra ction +Ġadvant ages +Ġto ile +Ġstun ning +Ġmo ist +bre aking +s i +ĠL ocation +ĠMed ium +Ġtext s +Ġu gly +Ġb io +. âĢĶ +ĠB ased +Ġtr ains +ĠW ing +ĠAn cient +ĠRec ords +ĠH ope +Spe cial +ades h +ob i +[ / +Ġtempor arily +V er +h u +os er +Ġover night +Ġm amm +ĠTre asury +ĠV enezuel +ĠMeg a +Ġt ar +Ġexpect s +bl ack +or ph +\\ \\ +Ġaccept ance +Ġrad ar +s is +Ġjun ior +Ġfram es +Ġobserv ation +ac ies +P ower +ĠAdv anced +M ag +olog ically +ĠMe chan +Ġsent ences +Ġanaly sts +augh ters +force ment +Ġv ague +Ġcl ause +Ġdirect ors +Ġeval uate +Ġcabin et +M att +ĠClass ic +A ng +Ġcl er +ĠB uck +Ġresear cher +Ġ16 0 +Ġpoor ly +Ġexperien cing +ĠP ed +ĠMan hattan +Ġfre ed +Ġthem es +ad vant +Ġn in +Ġpra ise +10 4 +ĠLib ya +b est +Ġtrust ed +Ġce ase +Ġd ign +D irect +Ġbomb ing +Ġm igration +ĠSci ences +Ġmunicip al +ĠA verage +Ġgl ory +Ġreve aling +Ġare na +Ġuncertain ty +Ġbattle field +ia o +G od +Ġc inem +ra pe +el le +ap ons +Ġlist ing +Ġwa ited +Ġsp otted +ke ley +ĠAud io +e or +ard ing +idd ing +ig ma +ĠN eg +Ġl one +Ġ ---- +ex e +d eg +Ġtrans f +Ġwas h +Ġsl avery +Ġexpl oring +ĠW W +ats on +Ġen cl +l ies +ĠC reek +Ġwood en +Man ager +ĠBr and +um my +ĠAr thur +Ġbureau cr +Ġbl end +ar ians +F urther +Ġsupposed ly +Ġwind s +Ġ19 79 +Ġgrav ity +Ġanalys es +ĠTra vel +ĠV eter +Ġd umb +Ġaltern ate +g al +Ġconsum ed +Ġeffect iveness +.' ' +Ġpath s +ond a +L A +ĠStr ong +Ġen ables +Ġesc aped +Ġ" " +Ġ1 12 +Ġ198 3 +Ġsm iled +Ġtend ency +F ire +Ġp ars +ĠR oc +Ġl ake +Ġf itness +ĠA th +ĠH orn +Ġh ier +Ġimp ose +m other +Ġp ension +ic ut +bor ne +ic iary +. _ +ĠS U +Ġpol ar +is y +eng u +itial ized +AT A +w rite +Ġexerc ises +ĠD iamond +ot ypes +Ġharm ful +on z +Ġprint ing +st ory +Ġexpert ise +ĠG er +Ġtraged y +ĠF ly +Ġd ivid +amp ire +st ock +M em +Ġre ign +Ġun ve +Ġam end +ĠProp het +Ġmut ual +ĠF ac +Ġrepl acing +H ar +ĠCirc uit +Ġthro at +ĠSh ot +Ġbatter ies +Ġto ll +Ġaddress ing +ĠMedic aid +Ġp upp +ĠN ar +ol k +Ġequ ity +M R +ĠHis pan +ĠL arge +m id +D ev +Ġexp ed +Ġdem o +ĠMarsh all +erg us +Ġf iber +Ġdiv orce +ĠCre ate +Ġsl ower +ĠPark er +ĠStud ent +ĠTr aining +Ret urn +ĠT ru +Ġc ub +ĠRe ached +Ġpan ic +Ġqu arters +Ġre ct +Ġtreat ing +Ġr ats +ĠChristian ity +ol er +Ġsac red +Ġdecl are +ul ative +et ing +Ġdeliver ing +est one +Ġt el +ĠL arry +Ġmet a +ac cept +art z +ĠRog er +hand ed +Ġhead er +Ġtra pped +ĠCent ury +Ġkn ocked +ĠOx ford +Ġsurviv ors +b ot +Ġdemon stration +Ġd irt +Ġass ists +OM E +ĠD raft +ortun ate +fol io +pe red +ust ers +g t +ĠL ock +Ġjud icial +ver ted +Ġsec ured +out ing +ĠBook s +Ġhost ing +Ġlif ted +l ength +Ġj er +Ġwhe els +ĠR ange +umbn ails +Ġdiagn osis +te ch +ĠStew art +ĠP ract +Ġnation wide +Ġde ar +Ġoblig ations +Ġgrow s +Ġmand atory +Ġsusp icious +! ' +A pr +G reat +Ġmort gage +Ġprosecut or +Ġeditor ial +ĠK r +Ġprocess ed +ung le +Ġflex ibility +Ear lier +ĠC art +ĠS ug +Ġfoc uses +Ġstart up +Ġbre ach +ĠT ob +cy cle +ãĢ Į +ro se +Ġb izarre +ãĢ į +Ġveget ables +$ $ +Ġret reat +osh i +ĠSh op +ĠG round +ĠSt op +ĠHawai i +ĠA y +Per haps +ĠBe aut +uff er +enn a +Ġproduct ivity +F ixed +cont rol +Ġabs ent +ĠCamp aign +G reen +Ġident ifying +Ġreg ret +Ġpromot ed +ĠSe ven +Ġer u +ne ath +aug hed +ĠP in +ĠL iving +C ost +om atic +me ga +ĠN ig +oc y +Ġin box +Ġem pire +Ġhor izont +Ġbr anches +Ġmet aph +Act ive +ed i +ĠFil m +ĠS omething +Ġmod s +inc ial +ĠOrig inal +G en +Ġspir its +Ġear ning +H ist +Ġr iders +Ġsacr ific +M T +ĠV A +ĠS alt +Ġoccup ation +ĠM i +Ġdis g +lic t +Ġn it +Ġn odes +e em +ĠP ier +Ġhat red +ps y +ãĥ ī +Ġthe ater +Ġsophistic ated +Ġdef ended +Ġbes ides +Ġthorough ly +ĠMedic are +Ġbl amed +arent ly +Ġcry ing +F OR +pri v +Ġsing ing +ĠI l +Ġc ute +o ided +olit ical +ĠNe uro +å ¤ +Ġdon ation +ĠEag les +ĠG ive +T om +Ġsubstant ially +ĠLic ense +ĠJ a +Ġg rey +ĠAn imal +ĠE R +ĠU nd +Ġke en +Ġconclud e +ĠMississ ippi +Eng ine +ĠStud ios +P ress +o vers +ll ers +Ġ3 50 +ĠR angers +Ġr ou +ert o +E p +iss a +iv an +Ġse al +ĠReg ist +dis play +Ġwe aken +u um +ĠComm ons +ĠS ay +Ġcult ures +Ġl aughed +Ġsl ip +Ġtreat ments +iz able +m art +ĠR ice +Ġbe ast +Ġob esity +ĠLa ure +ig a +Wh ich +hold er +Ġelder ly +Ġp ays +Ġcompl ained +Ġc rop +Ġpro c +Ġexplos ive +ĠF an +ĠAr senal +A uthor +ef ul +Ġme als +Ġ( - +id ays +Ġimag ination +Ġann ually +Ġm s +as ures +H ead +ik h +m atic +Ġboy friend +ĠCom puter +Ġb ump +Ġsur ge +ĠCra ig +ĠKir k +D el +medi ate +Ġscen arios +ĠM ut +ĠSt ream +Ġcompet itors +Ù Ħ +ĠStan ford +ĠRes ources +az ed +b age +Ġorgan is +ĠRe lease +Ġsepar ately +Ġha bits +Ġmeasure ments +ĠCl ose +Ġaccomp any +Ġg ly +Ġt ang +ĠR ou +Ġplug in +Ġcon vey +ĠChall enge +oot s +j an +Ġcur s +ĠRel ations +ke eper +Ġapproach ing +p ing +Spe aking +Ġarrang ement +ĠV I +are ttes +Ġaffect ing +Ġperm its +b ecause +Ġu seless +ĠH us +!! !! +Ġdestro ying +Un fortunately +Ġfasc inating +S em +Ġelect oral +Ġtrans parency +ĠCh aos +Ġvolunte er +Ġstatist ical +Ġactiv ated +ro x +We b +H E +ĠHamp shire +is ive +M ap +Ġtr ash +ĠLaw rence +st ick +C r +Ġr ings +EX T +Ġoper ational +op es +D oes +ĠEv ans +Ġwitness ed +P ort +Ġlaunch ing +ec onom +w ear +ĠPart icip +um m +cul es +ĠR AM +ĠT un +Ġass ured +Ġb inary +Ġbet ray +Ġexpl oration +ĠF el +Ġad mission +it ated +S y +Ġav oided +ĠSim ulator +Ġcelebr ated +ĠElect ric +¥ ŀ +Ġcl uster +itzer land +he alth +L ine +ĠN ash +at on +Ġsp are +Ġenter prise +ĠD IS +clud es +Ġfl ights +Ġreg ards +ĠÃ Ĺ +h alf +Ġtr ucks +Ġcontact s +Ġunc ons +ĠCl imate +Ġimm ense +N EW +oc c +ect ive +Ġemb od +Ġpat rol +Ġbes ide +Ġv iable +Ġcre ep +Ġtrig gered +ver ning +Ġcompar able +q l +Ġg aining +ass es +Ġ( ); +ĠG rey +ĠM LS +s ized +Ġpros per +" ? +Ġpoll ing +Ġsh ar +ĠR C +Ġfire arm +or ient +Ġf ence +Ġvari ations +g iving +ĠP i +osp el +Ġpled ge +Ġc ure +Ġsp y +Ġviol ated +Ġr ushed +Ġstro ke +ĠBl og +sel s +ĠE c +,' ' +Ġp ale +ĠColl ins +ter ror +ĠCanad ians +Ġt une +Ġlabor atory +Ġn ons +t arian +Ġdis ability +ĠG am +Ġsing er +al g +ĠSen ior +Ġtrad ed +ĠWar rior +Ġinf ring +ĠFrank lin +Ġstr ain +ĠSwed ish +Ġsevent h +ĠB enn +ĠT ell +Ġsynd rome +Ġwond ered +id en +++ ++ +ig o +Ġpur ple +Ġjournal ism +Ġreb el +Ġf u +bl og +Ġinv ite +ren cies +ĠCont act +Is rael +ĠCont ent +Ġche er +Ġbed room +ĠEngine ering +ĠQue ens +Ġd well +ĠPlay Station +ĠD im +ĠCol on +l r +Ġoper ates +Ġmotiv ation +US A +ast ered +C ore +ĠTr uth +ol o +OS E +ĠMem ory +Ġpred ec +Ġan arch +Ġ19 20 +ĠY am +à ¨ +b id +Ġgr ateful +Ġexc itement +Ġtre asure +Ġlong est +ct ive +Ġdes erves +Ġreserv es +Ġcop s +ĠOtt awa +ĠEgypt ian +ank ed +Ġart if +Ġhypot hesis +: / +Ġpurch asing +Ġlove ly +H P +Ġdiv ide +Ġstrict ly +Ġquestion ing +Ġtaxp ayers +ĠJ oy +Ġroll s +ĠHe avy +Ġp orts +Ġmag netic +Ġinf lamm +Ġbr ush +t ics +â ĪĴ +Ġbott les +pp y +Ġp add +ãĤ ¯ +m illion +Ġdevast ating +Ġcomp iled +Ġmed ication +Ġtw elve +ĠPer ry +Sp ace +im b +y our +Ġle aked +ĠT ar +Ġun ity +Ġinfect ed +Ġtravel ed +ID E +ĠMc Donald +t xt +ĠPr inc +Ġinter ven +ĠTai wan +ĠP ow +Ġbe aring +ĠTh read +Ġz ones +iz ards +un ks +Ch apter +ll or +Ġ · +Ġw ounds +Ġdisc retion +Ġsucceed ed +ik ing +Ġicon ic +C all +Ġscreen ing +ĠM is +ict s +Ġmin isters +Ġsepar ation +Pl ayer +Ġb ip +Ġbel oved +Ġcount ing +ĠE ye +ar ound +ing ing +Ġtable t +Ġoff ence +in ance +h ave +ĠInf o +ĠNin ja +Ġprotect ive +ĠC ass +M ac +ĠQual ity +N orth +Ġ ic +ĠCub a +ĠChron icle +ĠPro perty +Ġfast est +ot os +ĠG erm +OW N +Ġbo om +ĠStan ley +ergus on +Ġcle ver +Ġent ers +m ode +ter ior +ĠS ens +Ġlin ear +AR K +Ġcomp aring +Ġpure ly +Ġsaf er +ĠPot ter +Ġc ups +R T +Ġgl uc +Ġatt ributed +Ġdu pl +ĠP ap +Ġprec ious +Ġp a +iction ary +ĠT ig +ĠTo o +ol utions +st an +Ġrob ots +Ġlob b +Ġstat ute +Ġprevent ion +w estern +16 0 +ĠAct ive +ĠMar ia +h al +N one +ell ar +ĠK B +ĠPart ners +ĠSing le +ĠFollow ing +ang o +ac ious +Ġth ou +Ġk g +Ġinflu ential +ĠFriend s +S ur +ain ted +Ġfor ums +Ġst arter +Ġcitizens hip +ĠE lection +on ge +ot ation +os ph +;; ;; +ut ical +p ur +ere n +Ġaccus ations +bit ious +ab bit +ĠOr d +Post ed +ir k +Ġsens itivity +ic he +ĠAm y +ĠF ab +Ġsum mit +Ġped est +Ġrub ber +Ġagric ultural +Ġcan cel +A E +Ġin aug +Ġcont am +Ġfirm ly +i w +st age +ĠK an +Ġt ier +Ġinv ention +Ġtransl ated +ĠR ules +B ox +Tw itter +ID S +Ġp izza +Ġdeb ug +ĠD rop +v s +Ġh orses +b ig +Ġb oring +Ġh ood +ĠMcC ain +at ched +ĠBro s +Ġsk ip +Ġess ay +st at +ĠLeg ends +Ġam munition +au c +Ġshoot er +Ġun h +Ġsuppl ied +Ġgener ic +ĠS K +ib an +yr ics +Ġ25 5 +Ġclim bing +Form er +Ġfl ip +Ġjump ing +Ġfrust ration +ĠTer ry +Ġneighborhood s +Ġmed ian +be an +Ġbr ains +Follow ing +Ġsh aped +Ġdraw s +Ġal tered +J ack +Ġrecip es +Ġsk illed +we alth +ach i +e lection +Ġbehavi ors +de als +ĠU ntil +F e +Ġdecl aration +mar ks +ĠBet ween +cel ona +Ġres on +Ġbub ble +Am ong +Ġim perial +G S +Ġfemin ist +200 5 +ĠK yle +Ġaccount ing +ĠTe le +ĠT yr +Ġconnect ing +Ġre hab +ĠP red +s im +Ġmeant ime +Ġphys ician +M W +ĠCamp bell +ĠBr andon +Ġcontribut ing +ĠR ule +ĠWe ight +ĠN ap +Ġinter active +Ġv ag +Ġhel met +ĠCom b +f our +Ġsh ipped +Ġcomple ting +ĠP D +PD ATE +Ġspread ing +Ġsc ary +erv ing +ĠG as +Ġfr ank +s chool +Ġrom antic +Ġstab il +R ob +Ġaccur ately +Ġac ute +ĠH ann +Ġsymbol s +Ġcivil ization +ĠA W +Ġlight ning +Ġcons iders +Ġven ue +Ġ × +Ġo ven +ĠS F +h is +Ġn u +ĠLear n +Ġpe oples +Ġst d +Ġsle e +Ġs lic +ĠStat istics +Ġcor ners +ĠB aker +Ġ: ) +ment ation +ol ver +Ġlaugh ing +ĠT odd +ond e +ĠH ills +Ġn uts +ĠW oman +pl ane +Ġl iver +ĠIn side +S orry +Ġagre es +Ġfund ament +ĠF isher +Ġa uction +Ġthread s +gl as +ĠBas ic +ĠN at +Ġlack ing +Ġceleb ration +j u +Ġs illy +E uro +Ġt att +ight y +cont rolled +T est +ĠSing h +Ġr age +Ġrh yth +o ffic +ĠPh antom +Ġhead lines +Ġrespond ing +ĠMor ning +Ġvit amin +Ġboot s +ĠS ite +al in +p i +Ġvir al +ĠU C +D ER +ĠSe x +Ġst ocks +c urrent +Ġch urches +ĠR are +ĠMur phy +Ġden ial +ĠG aming +Ġtou g +Ġn ick +Ġm akers +ĠRon ald +Ġgener ous +ĠD oc +ĠMor ris +Ġtransform ed +ĠN ormal +Ġ10 4 +ĠKick starter +ĠUp on +On line +ĠI RS +Ġw rap +Ġl oving +Ġarri ves +ĠD ue +Ġhe ter +ĠM ade +Ġrent al +Ġbelong s +Ġatt orneys +Ġcro ps +Ġmat ched +ul um +ol ine +10 9 +Ġdis par +Ġbuy ers +ĠCam bridge +Ġeth ics +rou ps +Ġjust ified +Ġmarg inal +Ġrespect ed +win ning +Ġnodd ed +ĠSer ge +ĠForm er +C raft +######## ######## +ĠWar ner +Ġd ash +et e +Ġent ert +ĠE scape +out heast +Ġkn ees +ĠB omb +Ġr ug +P ass +Ġatt itudes +go vernment +ĠPri or +Ġqual ities +Ġnot ification +ĠPh one +l ie +Ġanticip ated +ĠCom bat +ĠBar ry +Ġ198 2 +Us ers +on er +Ġcomput ing +ĠConnect icut +Ġless er +Ġpe ers +ĠC u +Ġtechn ically +Ġsub mission +ĠUn iversal +Ġman ually +our ge +Ġrespond ents +ĠB TC +ĠH ost +Ġf are +ĠB ird +Ġrece ipt +al so +Ġj ack +Ġagric ulture +Ġsk ull +Ġ! = +Ġpass ive +ĠC I +Ġsoc ieties +Ġremind ed +Ġinter ference +B uy +Ġâ ľ +g on +Ġscrut iny +ĠW itch +Ġconduct ing +Ġ ãĥ +Ġexch anges +ĠMit chell +Ġinhab it +Ġtw ist +B D +Ġwhere ver +group on +Ġj okes +ĠBen jamin +ĠR andom +fr ame +ĠL ions +Ġhighlight ed +ĠArk ansas +E nt +Ġp ile +Ġpre lim +g s +mind ed +Ġfel ony +ĠG A +ĠL uck +Ġpract ically +ĠB os +Ġact ress +D am +ĠB ou +Ġvis a +Ġembed ded +Ġhy brid +Ġear liest +Ġsoon er +s ocial +ĠH A +Ġste ep +Ġdis advant +Ġexplo it +ĠE gg +ĠUlt ra +Ġnecess ity +L ocal +ie ge +Ġd ated +Ġmass es +Ġsubsc ription +pl ess +Ġan onym +Ġpresum ably +Bl ue +The ir +asket ball +ĠPhil ip +Ġcom ed +load ed +r ane +Ġref lection +Ch ina +Ġext ends +Ġform ing +Ġund ers +200 1 +Ġgr at +Ġconcent rations +Ġins ulin +Ġsec ular +Ġwh ilst +Ġwin ners +Ad vertisements +Ġdeliber ately +ĠWork ing +Ġs ink +et ics +d ale +Ġmand ate +Ġg ram +Ġvac ation +Ġwarn ings +ri pp +ĠTH AT +Ġcomment ary +Ġint u +Ġa est +Ġreason ing +Ġbreak down +ĠZ ombie +Ġ-- > +ĠPolit ical +c ott +Ġthr ust +Ġtechn ological +Ġdec iding +Ġtraff icking +L ong +W elcome +pr ising +ĠCommun ications +Ġend ors +Ġsw ift +Ġmetab ol +co ins +res a +ĠHT TP +Ġen roll +ĠH appy +us r +int age +Ġ[ " +u ably +ĠM aterial +Ġrepe al +Se pt +k h +ĠMod i +Ġunder neath +ĠI L +sh ore +Ġdiagn osed +ace utical +Ġsh ower +au x +ĠSw itch +ĠStre ngth +Ġj ihad +n ational +Ġtra uma +uss y +on i +Ġcons olid +Ġcal ories +ĠF lynn +ag ged +16 8 +ĠP ink +Ġfulf ill +Ġch ains +Ġnot ably +ĠA V +L ife +ĠCh uck +m us +ĠUr ban +ĠH end +Ġdep osit +ĠS ad +Ġaff air +OR K +ie val +ĠF DA +Ġt rop +ĠOver all +Ġvirt ue +Ġsatisf action +au nd +Ġl un +ĠSw itzerland +ĠOper ation +pro cess +Ġsh ook +Ġcount ies +le ased +ĠCharl otte +1 12 +Ġtrans cript +Ġre dd +p ush +ĠHe y +ĠAn alysis +[ " +Ġaltern atives +ard less +Ġele ph +Ġpre jud +ĠLe af +H aving +ĠH ub +Ġexpress ions +ĠVol ume +Ġshock ing +ĠRed s +Ġread ily +Ġplan ets +ad ata +Ġcollaps ed +ĠMad rid +Ġir rit +i pper +ĠEn c +ĠW ire +Ġbu zz +ĠG P +ash a +Ġaccident ally +ur u +Ġfrust rated +ĠS A +Ġhung ry +ĠH uff +Ġlab els +ant o +ĠE P +Ġbar riers +) | +ĠBer keley +ĠJ ets +Ġp airs +ĠL an +J ames +ĠB ear +Ġhum or +ĠLiber ty +Ġmagn itude +Ġag ing +ĠM ason +Ġfriends hip +umb ling +Ġemer ge +Ġnewsp apers +Ġam bitious +ĠRich ards +atern al +Ġ198 1 +Ġcook ies +Ġsc ulpt +Ġpur suit +L ocation +Ġscript s +p c +Ġarrang ements +Ġd iameter +Ġl oses +am ation +Ġl iqu +ĠJ ake +aret te +Ġunderstand s +ĠZ en +v m +Ġappro ve +Ġw ip +Ġult ra +Ġint end +ĠD I +asc ular +Ġst ays +ĠK or +ĠK l +Ġinvest ing +L a +Ġbelie ving +b ad +m outh +Ġtaxp ayer +ãĥ ĥ +ĠQue bec +Ġl ap +ĠSw iss +d rop +Ġdr ain +ir i +et c +ft en +ĠN ex +Ġst raw +Ġscream ing +Ġcount ed +Ġdam aging +Ġamb assador +cent ury +Ġpro x +Ġarrest s +u v +il ateral +ĠCh arg +Ġpresc ribed +Ġindepend ently +Ġf ierce +ĠB aby +Ġb rave +Ġsu its += > +Ġbas eline +ĠR ate +Ġis lands +Ġ( ( +g reen +ix els +Ġname ly +ĠVill age +th an +am y +V ersion +g mail +ential s +ĠS ud +ĠMel bourne +Ġarri ving +Ġquant um +e ff +rop olitan +T ri +Ġfun eral +ĠI R +ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ +ĠC ob +it ably +Ġt urb +Ġcomb o +Re view +Ġdeploy ment +u ity +ĠB ott +Ġinv isible +Ġrender ing +Ġunl ocked +Ġa qu +ĠVlad imir +Ġp ad +ĠBr ain +ĠLeg acy +dr agon +ĠKurd ish +Ġsound ed +Ġdet ained +ĠD M +g ary +Ġd aughters +Ġdistur bing +uk a +ĠPar ad +Ġt ast +Ġunf ortunate +Ġu l +em in +Ġattend ance +tr l +Ġpar ks +ĠMem orial +ĠAl ice +oth y +gu ard +ĠD ise +ĠSh an +ĠFor um +R ich +Ġshif ted +ue z +Ġl ighter +ĠMag n +Ġc od +S ch +ham mad +P ub +3 50 +ĠP okemon +Ġprot otype +Ġun re +B ase +ĠStud ents +ĠRep ly +ĠCommun ist +Ġg au +ĠTy ler +I Z +Ġparticip ated +Ġsup rem +ĠDet ails +Ġvessel s +ro d +Ġt ribe +ke ep +Ġassum ptions +Ġp ound +Ġcr ude +ĠAv ailable +Ġswim ming +Ġin clusion +Ġadv ances +c ulation +Ġconserv ation +Ġover d +ĠBuff alo +Art icle +ed ge +Ġaw a +ĠMad ison +Ġsid ew +Ġcat ast +ĠK rist +uc le +ĠHigh way +ĠTer ror +Ġactiv ation +Ġuncons cious +ĠSat an +ĠSus an +ill ery +Ġarr anged +i op +Ġrum ors +ur ring +th ink +ĠKe ith +ĠK ind +Ġavoid ing +by n +n ut +ĠSpe aker +r us +n ames +Ġgu ilt +ĠOlymp ics +Ġsa il +ĠM es +lev ant +ĠColumb us +a ft +C ity +S outh +ĠHar vey +ĠP un +S everal +Ġment ally +Ġimp ress +m ount +ĠUb untu +âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ +ĠSuper man +ĠMP s +Ġintent ions +ĠR acing +Ġlike lihood +Ġ2 40 +T otal +Ġto ys +ĠW atson +Ġur ge +L ear +ĠP aper +Ġoccur ring +ĠB eng +ĠC ert +Ġst ones +T im +ĠTw in +z b +ĠD ynam +Ġpolit ician +k ens +ĠEnter prise +UT ERS +Ġab ol +Ġref resh +Ġarbit rary +pe ction +Ġtrou bles +Ġ} ); +t v +Ġpil ots +Ġdist ribute +Ġaud it +Ġp ause +orig inal +Ġr ivals + £ +F ig +T L +ab il +ry ing +L in +ion ed +l on +Ġf ancy +Ġcr ashed +Ġt ract +Ġshe d +Ġcons ume +B ased +down load +in it +Ġvolt age +Int rodu +Ġcondem ned +ĠFin ance +res pect +Ġex cluded +Ġestablish ing +her ic +Ġher itage +Ġspect acular +Ġun st +ĠSnow den +ĠL ane +S an +Ġprotect ions +st ruction +inc inn +Ġmac ro +C ustom +ios ity +Ġes p +Ġfunction ing +Ġm ush +Ġp uzzle +Ġeth ical +M al +Ġgo verning +ĠF erguson +Ġrest ored +Ġst ressed +ĠCoun ter +ĠK as +cl ip +AN S +Ġse iz +U K +by ss +old own +ap i +Ġperman ently +oun ters +W est +Th rough +L ight +at oes +Ġne at +Ġc ord +ure r +Ġsevere ly +ĠA ven +Ġinter rog +Ġtri ple +G iven +N umber +Ġar ise +Ġs her +pl ant +Ġfl ower +ĠC ou +Ġat e +Ġnew er +b ul +Ġmean while +ĠL air +Ġadjust ment +ĠCop yright +Ġd ivers +i ological +Ġgam ers +o at +Ġhistor ically +Ġanal og +Ġlong time +Ġpres cription +ĠM ist +ĠHy per +ĠM aine +ĠDe ity +Ġmulti pl +ĠRe incarn +ĠH yd +ĠP ic +S il +r ants +ĠC ris +. ; +( { +epend ence +Ġrec y +ate ur +Ġqu ad +Ġgl ob +Ġcon ced +te am +Ġcapital ist +ĠL ot +Ġroy al +ĠCy ber +Ġblack s +met ic +ri v +ĠD anny +Ġsp o +ĠR O +Ġanim ated +rypt ed +ĠDep uty +Ġrend ered +F E +Ġstre ak +Ġcloud s +ĠDou g +~~~~ ~~~~ +Ġdisc our +ĠVe h +Ġpsych ology +ĠJ ourney +Ġcry stal +ĠFro st +Ġsuspic ion +Ġrel ate +or us +ĠC rypt +ĠN VIDIA +com ed +ut ing +incinn ati +Ġvulner ability +ost ic +Ġisol ation +Ġcool ing +ĠCoal ition +Ġ1 19 +F our +ĠDe al +Ġâ ī +se mble +ram ent +ĠBar celona +Ġ10 2 +Ġcoc aine +ocaly pse +F eb +ogen ic +Ġmut ation +Ġcrypt oc +ĠK el +ĠG it +a is +Ġs isters +AN K +Ġactiv ate +T er +Ġd read +yl on +Ġprop ri +A ust +ĠDef ault +Ġout door +Ġshe er +ce ive +Ġg ently +Ð ¾ +Pro gram +Ġâ ĨĴ +Ġve gan +ĠCr us +Ġrespons ibilities +ĠH R +OL D +Ġprev ents +Ġst iff +ĠW ere +Ġathlet ic +ĠSc ore +Ġ) : +Ġcolumn s +ĠL oc +av ailable +ĠF ram +ĠS essions +Ġcompan ion +Ġpack s +14 0 +ĠKn ights +Ġf art +Ġstream s +Ġsh ore +Ġapp eals +ĠPer formance +h aul +ĠSt ra +ĠN ag +10 3 +ĠTrans portation +B B +E v +z an +P ublic +Ġtw in +uls ion +M ult +Ġelect ro +Ġstat ue +ation ally +ĠN ort +Ġins pection +/ * +ig ue +Ġcomp assion +ĠT ales +ĠSte in +ĠSc reen +ĠB ug +ĠL ion +g irl +Ġwithdraw al +Ġobject ives +Ġblood y +Ġprelim inary +Ġj acket +Ġdim ensions +ĠC ool +ĠOcc up +Ġw reck +Ġdoub led +ank ing +Ġ19 75 +Ġglass es +ĠW ang +pro v +P ath +connect ed +ĠMult i +ĠNor way +agon ist +Ġfe ared +Ġtouch ing +Ġarg uably +¯¯¯¯ ¯¯¯¯ +ĠNC AA +che m +Ġsp at +ĠW WE +ĠC el +ig ger +Ġattack er +ĠJo in +ob ject +ett a +Ġelim inated +d et +Ġdest ruct +ĠLuc as +ct uary +18 0 +ĠBr ady +ĠBl ues +B ay +au kee +Ġtim eline +Ġdeleg ates +w ritten +uff icient +Ġsh apes +Cop yright +ou ble +serv ice +Ġp ione +Ġcolleg es +Ġrow s +Ġsp ite +Ġassess ed +3 60 +Ġle ase +Ġconfident ial +ck er +ĠMan ning +ĠV oice +Ġse aled +Ġcalcul ate +N O +ĠAss istant +Ġteen ager +ul ent +ather ine +Ġm ock +Ġd iamond +Ġf est +Ġsw itched +Ġres ume +ĠPu erto +Ġl anes +ir ation +ĠSimilar ly +Ġro d +ĠS el +ĠPal ace +ĠLim ited +e ous +Ġvar iant +Ġw ard +Ġ) ) +Sh ow +OO K +A lex +ĠN ep +br is +ĠWik ipedia +Ġexcept ional +Ġman ages +ĠD raw +Ag ain +Ġco pper +ut t +Ġex ports +Ġport folio +Ġelev ated +R ated +ĠOther wise +ĠT act +ĠShe l +ĠT X +" âĢĶ +Ġres ur +ĠW a +ven ant +Ġmon etary +pe ople +E mail +Ġfif ty +ĠS weet +ĠMalays ia +Ġconf using +ĠR io +ud a +uten ant +" ); +Ġpra ised +Ġvol umes +t urn +Ġm ature +Ġnon profit +Ġpassion ate +ĠPriv ate +Ġ10 3 +Ġdesc end +ç ¥ŀ +uff y +head ed +Whe ther +ri en +ze ch +be it +Ġch rom +ĠMc M +Ġd ancing +Ġe leg +ĠNot iced +11 5 +Ġadvoc acy +ENT S +amb ling +ĠMin or +ĠF inn +Ġprior ities +Ġthere of +ĠSt age +ĠRog ers +Ġsubst itute +ĠJ ar +ĠJeff erson +Ġlight ly +10 2 +ĠL isa +u its +ys ical +Ġshif ts +Ġd rones +Ġwork place +Ġres id +ens ed +ah n +Ġpref erences +ser ver +Ġdeb ates +d oc +ĠGod s +Ġhelicop ter +Ġhon our +Ġconsider ably +ed ed +ĠF emale +ĠAn ne +Ġre un +ĠF ace +ĠHall ow +ĠBud get +Ġcondem n +Ġt ender +Pro f +ocr atic +ĠTurn er +ĠAg ric +Ġ19 76 +Ġa pt +d isc +ĠF ighter +ĠA ur +Ġgar bage +in put +ĠK arl +ĠOl iver +ĠL anguage +k n +N on +ĠCl ar +Ġtrad itions +Ġad vertisement +ĠS or +Ġarch ive +Ġvill ages +7 50 +Ġimplement ing +w aukee +Ġdiet ary +Ġswitch ing +Rep ublic +Ġvel ocity +Ġc it +ĠA wards +Ġfin ancing +Ġlast ed +) ] +Ġrem inder +P erson +Ġprec ision +Ġdesign ers +ĠF ried +ĠB order +Ġtr agic +Ġw ield +Ġiniti atives +ĠT ank +w er +Ġjo ins +R o +in ery +Ġar row +Ġgener ating +found er +Ġsear ches +Ġrandom ly +A ccess +Ġb atch +Ġp osed +l at +Ġpursu ing +as a +Ġtest ified +form ing +ĠSh ar +w iki +ĠE ither +S ometimes +Ġsen ators +ĠJohn ny +ĠTal iban +ĠG PS +":" / +ãģ® å +Ġanaly zed +ĠRub io +ĠMove ment +op ard +ii i +St and +f ight +Ġign oring +i ang +ĠG N +so ever +ĠST AT +Ġref using +Ġswe at +Ġb ay +P ORT +ir med +ak y +Ġdis pro +Ġlabel ed +Ġ10 8 +H ello +Ġple asant +ab a +Ġtri umph +Ġab oard +Ġinc om +ĠC row +le tt +Ġfol k +Ġch ase +` ` +ĠBr us +Ġte ens +c ue +Ġter rain +h yd +il ight +OR Y +Su pport +ew s +ll i +rain ts +ĠC and +Ġab used +ach ment +l arg +B as +ĠC ancer +Ġ19 78 +Ġsupp orter +ac cess +ĠTer min +ĠT ampa +ĠAN Y +Ġnew est +ĠCrim inal +ed u +Ġ19 30 +Ġadm its +Ġend e +Ġfail ures +ur ate +ful ness +cy cl +ĠSub ject +Ġinf inite +th ree +W A +p it +ĠInst all +R ad +ili ation +G M +Ġcontin ent +Ġaccommod ate +ĠCl ay +Ġp up +ĠF unction +Ġham mer +ĠAlbert a +Ġrev ised +Ġminor ities +Ġmeasure ment +Con nell +Ġdis able +ĠM ix +In cre +Ġfor k +ĠR osen +Ġimpl ies +umb lr +AN G +Ġprote ins +Ġagg ression +Ġfacilit ate +S N +Ġilleg ally +u er +Ġacad em +Ġp uzz +ĠSh ift +p ay +oll o +Ġaud iences +B uild +Ġno ble +Ġsynt ax +â ĺħ +Ġbe am +ĠB ed +ĠA ld +Ġorig ins +v ideo +Ġ19 77 +ĠAss ault +Ġgar age +Te am +Ġver dict +Ġd war +ĠVirt ual +e vent +Ke ep +Ġsent iment +Ġwild life +sh irt +Ġb urg +Ġrecommend ation +rep resent +Ġgall ery +own ers +Ġsch olar +Ġconven ience +ĠSw ift +Ġconv inc +C ap +Ġwar fare +ĠVis ual +Ġconst itute +Ġab ort +ĠWe ather +ĠLook ing +ĠH em +Ġmart ial +Ġinc oming +et ition +Ġtoler ance +ĠCre ated +Ġfl ows +ĠE lder +Ġsoul s +Ġf oul +ĠP ain +ĠC AN +Ġ2 20 +b c +he nd +Ġgen ius +R eal +ĠW r +omet er +p ad +Ġlim iting +ĠS i +ĠL ore +ĠAd ventures +Ġvar ied +D isc +f in +ĠPerson al +Ch ris +Ġinv ented +Ġd ive +ĠR ise +Ġo z +ĠCom ics +Ġexp ose +ĠRe b +let ters +s ite +im ated +Ġh acking +Ġeduc ated +ĠNob ody +Ġdep ri +Ġincent ive +ãĤ · +Ġovers ight +Ġtrib es +ĠBelg ium +Ġlicens ing +our t +Produ ct +ah l +ĠG em +Ġspecial ist +Ġc ra +ann ers +ĠCor byn +Ġ19 73 +RE AD +Ġsum mar +Ġover look +ĠApp lication +Ġin appropriate +Ġdownload ed +Q ue +ĠB ears +Ġth umb +ĠChar acter +ĠReincarn ated +ĠS id +Ġdemonstr ates +s ky +ĠBloom berg +ĠAr ray +ĠRes ults +ĠFour th +ĠED T +ĠO scar +c end +Ġ10 6 +ĠN ULL +ĠH ERE +m atch +ĠBr un +Ġgluc ose +ie g +eg u +Ġcert ified +Ġrel ie +Ġhuman itarian +Ġpr ayers +K ing +Ġn an +h ou +10 8 +ul u +Ġrenew able +Ġdistingu ish +Ġd ense +ĠV ent +ĠPack age +ĠB oss +Ġedit ors +Ġm igr +T ra +ĠPet ers +ĠAr ctic +200 4 +ĠC ape +Ġloc ally +Ġlast ing +Ġhand y +. ). +P an +ĠR ES +Ind ex +Ġt ensions +Ġformer ly +Ġide ological +Ġsens ors +Ġdeal ers +Ġdef ines +S k +Ġproceed s +Ġpro xy +az ines +ĠB ash +ĠP ad +ĠC raft +eal ous +Ġshe ets +omet ry +J une +cl ock +T T +ĠThe atre +ĠB uzz +Ġch apters +Ġmill enn +Ġd ough +ĠCongress ional +Ġimag ined +av ior +Ġclin ic +Ġ19 45 +Ġhold er +ro ot +oles ter +Ġrest art +B N +ĠHam as +ĠJ ob +Ġor b +Ġr am +Ġdiscl ose +Ġtransl ate +Ġimm igrant +Ġannoy ing +Ġtreat y +an ium +ĠTe a +ĠLeg ion +Ġcrowd s +ĠB ec +ĠA er +oh yd +B ro +Look ing +Ġl bs +Ġagg ress +Ġse am +Ġinter cept +ĠM I +mer cial +act iv +ĠC it +Ġdim ension +Ġconsist ency +Ġr ushing +ĠDou glas +Ġtr im +Inst all +ick er +Ġsh y +10 6 +Ġment ions +pe lled +ĠT ak +c ost +Ġclass room +Ġfort une +dri ven +Ġun le +ĠWhe el +Ġinvest or +ĠM asters +k it +Ġassoci ations +ĠEv olution +op ing +us cript +Ġprov incial +ĠWal ter +av i +S O +Ġun limited +Eng lish +ĠC ards +ĠEb ola +ne red +Ġreven ge +Ġout right +um per +Ġf itting +ĠSol id +Ġform ally +Ġproblem atic +Ġhaz ard +Ġenc ryption +Ġstraight forward +ĠA K +Ġp se +ĠOr b +ĠCh amber +ĠM ak +Cont ents +Ġloyal ty +Ġl yrics +ĠSy m +Ġwel comed +Ġcook ed +Ġmon op +Ġn urse +Ġmis leading +Ġe ternal +Ġshif ting +Ġ+ = +V is +Ġinst itutional +ill ary +Ġp ant +VER T +ĠA CC +ĠEn h +Ġinc on +ĠRE UTERS +Ġdon ated +âĢ¦âĢ¦ âĢ¦âĢ¦ +In tern +Ġexhib it +Ġt ire +ĠR ic +ĠCh ampion +ĠMu hammad +N ING +ĠSoc cer +Ġmob ility +Ġvary ing +ĠM ovie +Ġl ord +o ak +F ield +Ġve ctor +us ions +Ġsc rap +Ġen abling +m ake +T or +. * +| | +ĠWe bsite +ĠN PC +Ġsocial ist +ĠBill y +ĠAdd itional +Ġc argo +Ġfar ms +ĠSo on +ĠPri ze +Ġmid night +Ġ9 00 +se en +ĠSp ot +Ġshe ep +Ġspons ored +ĠH i +ĠJ ump +Ġ19 67 +Micro soft +ĠAg ent +Ġch arts +d ir +Ġadj acent +Ġtr icks +Ġman ga +Ġex agger +/ > +foot ball +ĠF CC +G C +ĠT ier +and ra +OU ND +% ), +Ġfru its +V C +ĠA A +R ober +Ġmid st +â Ĺ +ank a +Ġlegisl ature +ĠNe il +Ġtour ists +" " +ĠWar ning +ĠNever theless +ĠOffic ial +ĠWh atever +Ġm old +Ġdraft ed +Ġsubst ances +Ġbre ed +Ġt ags +ĠT ask +Ġver b +Ġmanufact ured +com ments +ĠPol ish +Pro v +Ġdetermin es +Ob ama +k ers +Ġutter ly +Ġse ct +sc he +ĠG ates +ĠCh ap +Ġal uminum +Ġz ombie +ĠT ouch +ĠU P +Ġsatisf y +Ġpred omin +asc ript +Ġelabor ate +Ġ19 68 +Ġmeas uring +ĠV ari +any ahu +Ġs ir +ul ates +id ges +ick ets +ĠSp encer +T M +oub ted +Ġpre y +Ġinstall ing +ĠC ab +re ed +re ated +Su pp +Ġwr ist +ĠK erry +10 7 +ĠK le +ĠR achel +Ġc otton +ĠA RE +ĠE le +Cont rol +Ġload s +ĠD od +an as +b one +Ġclass ical +ĠReg ional +ĠInt eg +V M +Ġdes ires +Ġaut ism +support ed +ĠM essage +Ġcomp act +writ er +Ġ10 9 +ĠHur ricane +c ision +Ġcy cles +Ġdr ill +Ġcolle ague +Ġm aker +G erman +Ġmist aken +S un +ĠG ay +Ġwhat soever +Ġsell s +ĠA irl +l iv +ĠO ption +Ġsol ved +Ġse ctors +Ġhorizont al +Ġequ ation +ĠSk ill +ĠB io +g ement +ĠSn ap +ĠLeg al +Ġtradem ark +Ġmake up +Ġassemb led +Ġsa ves +ĠHallow een +ĠVer mont +ĠFR OM +Ġfar ming +ĠP odcast +accept able +ĠHig her +Ġas leep +ull ivan +Ġrefere n +ĠLe v +Ġbul lets +ok o +H C +Ġst airs +Ġmain tains +ĠL ower +ĠV i +Ġmar ine +Ġac res +Ġcoordin ator +ĠJ oh +Ġcounterpart s +ĠBrother s +Ġind ict +b ra +Ġch unk +Ġc ents +H ome +ĠMon th +Ġaccording ly +if les +ĠGerm ans +ĠSy n +H ub +Ġey eb +âĶĢâĶĢ âĶĢâĶĢ +Ġr anges +ĠHoll and +ĠRob ot +f c +M ike +Ġpl asma +Ġsw ap +Ġath lete +ĠR ams +,' " +Ġinfect ions +Ġcor rid +Ġv ib +Ġpat ches +Ġtradition ally +Ġrevel ation +Ġswe ep +Ġgl ance +Ġin ex +200 3 +ĠR aw +work ing +os ures +ĠD at +ĠLyn ch +Ġle verage +ĠRe id +Ġcorrel ation +ian ces +av ascript +Ġrep ository +ret ty +Ġ19 72 +24 0 +Ġo un +p ol +ĠRe ed +Ġtact ical +is ite +App le +ĠQu inn +Ġrap ed +ill o +Euro pe +Ġalgorith ms +ĠRod rig +i u +Ġill um +Ġf ame +Ġintrodu cing +Ġdel ays +ĠRaid ers +Ġwh istle +Ġnovel s +ĠRe ally +Ġder iv +Ġpublic ations +ĠNe ither +ĠCom merce +Ġa ston +l anguage +Not es +ĠR oth +ĠF ear +Ġm ate +Ġpar ade +ĠQ B +Ġman eu +ĠC incinnati +m itting +Ġwa ist +ĠR ew +Ġdisc ont +Ð ° +Ġst aring +Ġal ias +Ġsec urities +Ġtoile t +ĠJ edi +Ġun law +v ised +//// //// +] ( +ĠWe iss +Ġpre st +ĠComp an +Ġmem o +ĠGr ace +J uly +ĠEl ite +cent er +ĠSt ay +Ġgal axy +Ġto oth +ĠS ettings +Ġsubject ed +ãĤ ¦ +Ġline back +Ġretail ers +ĠW ant +Ġd angers +A ir +Ġvolunt ary +ew ay +Ġinterpret ed +ot ine +à § +Ġp el +Serv ice +ĠEvent ually +Ġcare ers +Ġthreat en +Ġmem or +ĠBrad ley +anc ies +s n +ĠUn known +N ational +Ġsh adows +ail and +ĠD ash +Every one +izz ard +M arch += ( +Ġpull s +Ġstr anger +Ġback wards +ĠBern ard +imens ional +Ġch ron +Ġtheoret ical +k top +Ġw are +ĠInvest ig +ĠIn iti +ĠOper ations +o ven +oc ide +* / +Ġfl ames +ĠC ash +sh it +Ġc ab +ĠAn aly +ĠSe ah +Ġdefin ing +Ġorder ing +Ġimm un +Ġpers istent +AC H +Russ ian +m ans +Ġh ind +Ġphot ography + © +Ġh ug +Ġ10 7 +ĠH ence +i ots +ude au +Ġsubsid ies +Ġroutine ly +ĠDev ice +it ic +Ġdisg ust +land er +Ġ19 40 +Ġassign ment +ĠB esides +w ick +ĠD ust +us c +struct ed +11 1 +de velop +Ġf ond +Ġinter section +Ġdign ity +Ġcommission er +With out +re ach +Ġcart oon +Ġsc ales +ãĥ Ń +F IG +Ġsurve ys +ĠIndones ia +Ġart work +Ġun ch +Ġcy cling +un ct +au er +or ate +ĠOb viously +Ġcharacter ized +fe ld +Ġaff irm +Ġinn ings +Ġ é +Ġal iens +Ġcl oth +et ooth +ĠC ertain + § +Ġdig est +k now +ĠX L +Ġpredict ions +Ġd in +W AR +Ġafter math +Ex ample +ĠSu ccess +ĠTh r +IG N +Ġmin er +B us +Ġcl arity +heim er +ĠO UT +ĠS end +ĠCirc le +ĠD iet +Ġpron ounced +Ġcreat ors +Ġearthqu ake +atter y +ge ons +Ġo d +Ġlay ing +or p +U lt +pro ject +Ġunder min +Ġsequ el +S am +ĠDark ness +Ġre ception +b ull +Y S +ĠV ir +Ġsequ ences +ĠCo in +Ġout fit +ĠW ait +1 19 +Ġdel ivers +.... .. +Ġbl own +ĠE sc +ĠM ath +per m +ĠU l +Ġgl im +Ġfac ial +Ġgreen house +Ġto kens +/ - +ĠAnn ual +ĠON E +Ġteen age +ĠPhys ical +ĠL ang +ĠC elt +Ġsu ed +ivid ually +Ġpat ience +ch air +reg ular +Ġa ug +in v +ex cept +ĠL il +Ġn est +f d +s um +ĠCh ase +Russ ia +ĠJenn ifer +Ġoff season +Over all +F ore +Ġr iot +A ud +form er +Ġdefend ers +ĠC T +iot ic +rib ly +Ġautom ated +Ġpen is +Ġins ist +Ġdi agram +ĠS QL +ĠG arc +Ġw itch +cl ient +ier ra +am bers +Ġrec ount +f ar +V ery +oster one +Ġappreci ated +ĠPer fect +S ection +Ġd oses +oca ust +Ġcost ly +Ġg rams +ĠSh i +Ġwrest ling +Ġ19 71 +Ġtro phy +Ġn erve +ĠK az +ĠExper ience +Ġpled ged +Ġplay back +Ġcreat ivity +by e +Ġattack ers +Ġhold ers +ĠCo ach +ĠPh D +Ġtransf ers +Ġcol ored +ĠH indu +Ġd rown +Ġlist ened +ĠW A +ias m +P O +Ġappeal ing +Ġdiscl osed +ĠCh icken +ag ging +Ġple aded +Ġnav igation +ĠReturn s +Ġ[ [ +R OR +E A +Ġphotograp her +ĠR ider +ipp ers +Ġsl ice +Ġe rect +Ġhe d +iss ance +ĠVik ings +ur ious +Ġapp et +oubted ly +Ch ild +Ġauthent ic +o os +ĠM aking +Ġannoun cing +Ġb od +Ġmet er +ĠN ine +ĠR ogue +Ġwork force +Ġrenew ed +Ġorganis ations +ac s +P LE +Sh ort +Ġcomp ounds +ĠVis it +Ġen velop +ear th +Ġsupport ive +gg le +ĠBrus sels +ĠGu ild +Cre ate +RE L +Ġaver aged +Ġ19 69 +ri ages +Ġlength y +Ġforg ot +O kay +ĠE rd +Ġdeal er +Ġrec ession +D D +Ġdesper ately +Ġhun ger +Ġst icks +Ġm ph +ĠF aith +Ġintention ally +Ġdem ol +ue ller +ĠS ale +Ġde bris +s pring +Ġle ap +>> >> +Ġcontain ers +se lling +rane an +atter ing +Ġcomment ed +ĠC M +on ut +Ġwood s +es pecially +Ġorgan ize +iv ic +ĠWood s +ang a +s qu +Ġm aj +am on +Ġax is +Ġ19 74 +ĠDen mark +Ġwar rior +ĠP and +Ġout lined +ĠB O +ins ula +z illa +eb ook +Ġd are +Ġsear ched +Ġnav igate +S n +writ ing +Ġun ited +J apan +ĠHe brew +Ġfl ame +Ġrel ies +Ġcatch ing +ĠSh o +Ġimprison ment +Ġp ockets +Ġclos ure +ĠF am +t im +ade qu +Act ivity +Ġrecru iting +ĠW ATCH +ĠArgent ina +d est +Ġapolog ize +or o +Ġlack s +Ġtun ed +ĠGriff in +Ġinf amous +Ġcelebr ity +ss on +Ġ ---------------------------------------------------------------- +ĠIs is +ĠDis play +Ġcred ibility +Ġeconom ies +Ġhead line +ĠCow boys +Ġind ef +Ġl ately +Ġincent ives +but ton +ĠM ob +A ut +Ġres igned +ĠO m +c amp +Ġprof iles +Ġsche mes +olph ins +ay ed +Cl inton +en h +ĠY ahoo +Ġab st +Ġan k +su its +Ġw ished +ĠMar co +udd en +Ġsp here +ĠB ishop +Ġincorpor ated +ĠPl ant +11 4 +Ġh ated +p ic +Ġdon ate +Ġl ined +Ġbe ans +Ġsteal ing +Ġcost ume +Ġsher iff +Ġfor ty +Ġint act +Ġadapt ed +Ġtrave lling +b art +Ġnice ly +Ġdri ed +Ġsc al +os ity +NOT E +ĠB h +ĠBron cos +ĠI gn +Ġint imate +Ġchem istry +Ġopt imal +D eb +ĠGener ation +Ġ] , +ich i +ĠW ii +ĠYOU R +vent ions +W rite +Ġpop ul +un ning +ĠW or +V ol +Ġqu een +head s +K K +Ġanaly ze +op ic +ear chers +Ġd ot +leg raph +ast ically +Ġupgr ades +Ġca res +Ġext ending +Ġfree ze +Ġin ability +Ġorg ans +Ġpret end +Ġout let +11 3 +ol an +ĠM all +ul ing +t alk +Ġexpress ing +ĠAl ways +ĠBe gin +f iles +Ġlic enses +% % +ĠM itt +Ġfil ters +ĠMil waukee +G N +Ġunf old +M o +Ġnut rition +pp o +B o +Ġfound ing +Ġunder mine +Ġeas iest +ĠC zech +ĠM ack +Ġsexual ity +ĠN ixon +W in +ĠAr n +ĠK in +ãĤ £ +ic er +Ġfort un +Ġsurf aces +agh d +Ġcar riers +ĠP ART +ĠT ib +Ġinter val +Ġfrust rating +ĠSh ip +ĠAr med +ff e +Ġbo ats +ĠAb raham +in is +Ġsu ited +th read +i ov +ab ul +ĠVenezuel a +Ġto m +su per +Ġcast le +alth ough +iox ide +ec hes +Ġevolution ary +Ġnegoti ate +Ġconfront ed +Rem ember +Ġ17 0 +S uch +Ġ9 11 +m ult +ĠA byss +ur ry +ke es +spe c +ĠBarb ara +Ġbelong ing +Ġvill ain +ist ani +Ġaccount able +Ġport ions +ĠDe cl +U r +ĠK ate +g re +Ġmag azines +UC K +Ġregul ate +om on +ĠAl most +Ġover view +Ġsc ram +Ġl oot +ĠF itz +Ġcharacter istic +ĠSn ake +s ay +ĠR ico +Ġtra it +ĠJo ined +au cus +Ġadapt ation +ĠAirl ines +Ġarch ae +ĠI de +Ġb ikes +Ġliter ary +Ġinflu ences +ĠUs ed +C reat +Ġple a +ĠDef ence +ĠAss ass +Ġp ond +UL T +) " +Ġeval uated +Ġob taining +Ġdem ographic +Ġvig il +ale y +Ġsp ouse +ĠSeah awks +resp ons +ĠB elt +um atic +Ġr ises +run ner +ĠMichel le +Ġpot ent +r ace +ĠP AC +F ind +olester ol +IS S +ĠIntrodu ced +ress es +ign ment +O s +ĠT u +ĠDe x +ic ides +Ġspark ed +ĠLaur a +ĠBry ant +Ġsm iling +ĠNex us +Ġdefend ants +ĠCat al +Ġdis hes +sh aped +Ġpro long +m t +( $ +ãĢ Ĥ +Ġcalcul ations +ĠS ame +Ġp iv +H H +Ġcance lled +Ġgr in +Ġterrit ories +ist ically +C ome +ĠP arent +Pro ject +Ġneg lig +ĠPriv acy +Ġam mo +LE CT +olute ly +ĠEp ic +Ġmis under +w al +Apr il +m os +path y +ĠC arson +Ġalbum s +ĠE asy +Ġpist ol +< < +Ġ\ ( +t arget +hel p +Ġinter pre +cons cious +ĠH ousing +ĠJ oint +12 7 +Ġbe ers +s cience +ĠFire fox +effect ive +ĠC abin +ĠO kay +ĠApp lic +Ġspace craft +ĠS R +ve t +ĠStr ange +S B +Ġcor ps +iber al +e fficient +Ġpreval ence +Ġeconom ists +11 8 +Th read +ord able +OD E +ĠC ant +=- =- +if iable +ĠA round +Ġpo le +Ġwilling ness +CL A +ĠK id +Ġcomple ment +Ġsc attered +Ġin mates +Ġble eding +e very +Ġque ue +ĠTr ain +Ġh ij +Ġme lee +ple ted +Ġdig it +Ġg em +offic ial +Ġlif ting +Ð µ +Re qu +it utes +Ġpack aging +ĠWork ers +h ran +ĠLeban on +ol esc +Ġpun ished +ĠJ uan +Ġj am +ĠD ocument +Ġm apping +ic ates +Ġinev itably +Ġvan illa +ĠT on +Ġwat ches +Ġle agues +Ġiniti ated +deg ree +port ion +Ġrec alls +Ġru in +Ġm elt +I AN +Ġhe m +Ex p +Ġb aking +ĠCol omb +at ible +Ġrad ius +pl ug +ĠI F +et ically +Ġf ict +H ER +ĠT ap +atin um +Ġin k +Ġco h +ĠW izard +b oth +te x +Ġsp ends +ĠCurrent ly +ĠP it +Ġneur ons +ig nt +Ġr all +Ġbus es +b uilding +Ġadjust ments +Ġc ried +ibl ical +att ed +ĠZ ion +ĠM atter +Ġmed itation +ĠD ennis +Ġour s +ĠT ab +Ġrank ings +ort al +Ġad vers +Ġsur render +ĠG ob +ci um +om as +im eter +Ġmulti player +Ġhero in +Ġoptim istic +Ġindic ator +ĠBr ig +Ġgro cery +Ġapplic ant +ĠRock et +v id +Ex ception +p ent +Ġorgan izing +Ġenc ounters +ĠT OD +Ġjew el +S ave +ĠChrist ie +Ġhe ating +Ġl azy +ĠC P +Ġcous in +Con fig +Ġreg ener +Ġne arest +Ġachie ving +EN S +th row +ĠRich mond +ant le +200 2 +Ġan ten +b ird +13 3 +Ġn arc +r aint +un ny +ĠHispan ic +ourn aments +Ġprop he +ĠTh ailand +ĠT i +Ġinject ion +Ġinher it +rav is +Ġmed i +Ġwho ever +ĠDE BUG +G P +ĠH ud +C ard +p rom +Ġp or +Ġover head +L aw +Ġviol ate +Ġhe ated +Ġdescript ions +Ġachieve ments +ĠBe er +ĠQu ant +W as +Ġe ighth +ĠI v +Ġspecial ized +U PDATE +ĠD elta +P op +J ul +ĠAs k +oph y +Ġnews letters +ĠT ool +Ġg ard +ĠConf eder +ĠGM T +ĠAb bott +Ġimm unity +ĠV M +Is lam +Ġimpl icit +w d +Ġ19 44 +rav ity +omet ric +Ġsurv iving +ur ai +ĠPr ison +Ġr ust +ĠSk etch +Ġbe es +ĠThe ory +Ġmer it +T ex +ch at +Ġm im +Ġpast e +ĠK och +Ġignor ance +ĠSh oot +Ġbas ement +Un ited +ĠAd vis +he ight +Ġf oster +Ġdet ain +in formation +Ġne ural +' ; +Ġprov es +all ery +Ġinv itation +um bers +Ġc attle +Ġbicy cle +z i +Ġconsult ant +Ġap ology +ĠT iger +Ġ12 3 +99 9 +Ġind ividually +r t +ig ion +ĠBrazil ian +Ġdist urb +Ġentreprene urs +Ġfore sts +cer pt +pl ates +p her +clip se +Ġtw itter +Ġac ids +ograph ical +h um +ĠB ald +if ully +Ġcomp iler +ĠD A +Ġdon or +as i +Ġtrib al +l ash +ĠCon fig +Ġapplic ants +Ġsal aries +13 5 +Put in +ĠF ocus +ir s +Ġmisc onduct +ĠH az +Ġeat en +M obile +Mus lim +ĠMar cus +v iol +Ġfavor able +Ġst ub +ad in +ĠH ob +Ġfaith ful +Ġelectron ics +Ġvac uum +w ait +back ed +econom ic +d ist +Ġten ure +Ġsince re +ĠT ogether +ĠW ave +Ġprog ression +Ġden ying +Ġdist ress +br aska +th ird +Ġmix ing +Ġcolon ial +Ġpriv ately +Ġun rest +atern ity +Ġprem ises +ant i +greg ation +Ġlic ence +ĠH ind +ĠSam uel +Ġconvinc ing +ĠA ce +ĠR ust +ĠNet anyahu +Ġhand les +ĠP atch +orient ed +ah o +ĠG onz +Ġhack ers +claim er +Ġcustom s +ĠGr an +f ighters +Ġl uc +Ġman uscript +aren thood +Ġdev il +Ġwar riors +Ġoff enders +Will iam +Ġhol idays +Ġnight mare +Ġle ver +iff erent +St at +Ġexhib ition +put ed +ĠP ure +Ġal pha +Ġenthus iasm +ĠRepresent atives +E AR +ĠT yp +Ġwhe at +ĠAl f +Ġcor rection +Ġev angel +AT T +M iss +Ġs oup +Ġimpl ied +par am +Ġsex y +ĠL ux +Ġrep ublic +p atch +ab lish +Ġic ons +Ġfather s +ĠG ET +ĠCar ib +Ġregul ated +ĠCo hen +ĠBob by +Ġn er +Ġb ent +vent ory +ĠAl ong +ĠE ST +ĠWall ace +Ġmurd ers +r ise +ke ll +ĠCommon wealth +Ġn asty +et a +ĠM IT +Ġadminist ered +Ġgenuine ly +Ed itor +n ick +Ġhyd ro +**************** **************** +ĠB le +Ġfin es +Ġg orge +aus ible +r h +Ġapp le +ment ioned +Ġro pe +ot yp +H R +Ġdisappoint ing +Ġc age +n ik +Ġdoub ts +ĠF REE +print s +ĠM UST +Ġvend ors +ĠIn qu +Ġliber als +Ġcontract or +Ġup side +child ren +Ġtrick y +Ġregul ators +charg ed +l iter +Ġ *** +Ġreb ell +l ang +Ġloc als +Ġphys icians +Ġhe y +ar se +t m +ĠLe x +Ġbehavior al +success ful +F X +Ġbr ick +ov ic +Ġcon form +Ġreview ing +Ġins ights +Ġbi ology +ĠRem ove +ĠExt ra +Ġcomm itting +indu ced +ignt y +ig m +Ġat omic +Comm on +ĠE M +ĠP ere +ĠIt ems +e h +Ġpres erved +ĠH ood +Ġprison er +Ġbankrupt cy +Ġg ren +us hes +Ġexplo itation +Ġsign atures +Ġfin an +] ," +ĠM R +Ġme g +rem lin +Ġmusic ians +Ġselect ing +Ġexam ining +IN K +l ated +H i +Ġart ic +Ġp ets +Ġimp air +ĠM AN +Ġtable ts +in clude +R ange +Ġca ut +Ġlog s +Ġmount ing +Ġun aware +Ġdynam ics +ĠPalest ine +ĠQu arter +ĠPur ple +Ġm a +ĠIm port +Ġcollect ions +ci ation +Ġsuccess or +Ġcl one +Ġaim ing +Ġposs essed +Ġstick ing +Ġsh aking +Ġloc ate +ĠH ockey +T urn +17 0 +Ġfif teen +ĠHar rison +Ġcontinu ously +ĠT C +ĠVal ent +ĠRes cue +Ġby pass +am ount +Ġm ast +Ġprotect s +Ġart istic +Ġsomet ime +Ġsh oe +Ġshout ed +ific ant +et itive +ĠReg ister +ĠJ in +Ġconcent rated +ling ton +on ies +Ġgener ator +yr im +ĠAr men +Ġclear ing +id o +ĠT W +al ph +Ġlad ies +H ard +Ġdial og +Ġinput s +æ ľ +Ġpos es +Ġsl ots +ĠPrem ium +Ġle aks +Ġboss es +Ġ11 3 +c ourse +A cc +ĠNew ton +ĠAust ria +ĠM age +Ġte aches +ab ad +Ġwe ars +Ġc yl +Ġcur se +ĠS ales +ĠW ings +Ġp sy +Ġg aps +ĠIce land +ĠP interest +Ġland lord +Ġdefin itions +ĠK er +Ġsufficient ly +ĠP ence +ĠArch itect +Ġsur pass +Ġ11 4 +Ġsuper hero +ĠDise ase +Ġpri ests +ĠC ulture +Ġdefin itive +Ġsecret ly +ĠD ance +inst all +ch ief +ĠJess ica +W ould +Up dated +Ġlock er +ĠK ay +Ġmem orial +è ¦ +f at +Ġdis gu +Ġflav ors +ĠBase ball +ĠRes istance +Ġk icks +Ġen v +Ġteen agers +D ark +ĠC AR +Ġh alt +ĠL G +ĠGab riel +Ġfe ver +Ġs atur +Ġm all +Ġaffili ate +ĠS leep +ĠSpe cific +ĠV el +Ġj ar +ĠSac red +ĠEd wards +ĠA CL +Ġret ained +ĠG iant +Ġlim itation +in ces +Ġref usal +ĠT ale +ĠBut ler +Ġacc idents +ĠC SS +Ġimport ed +ĠCop y +Î ± +ER T +z el +Ġdiv isions +h ots +ĠAl b +ĠD S +Load er +W ashington +at isf +ĠCreat ive +\ . +ĠAut om +red ict +Ġrecept or +ĠCarl os +Met hod +ok a +Ġmal icious +Ġste pping +, [ +ĠD ad +Ġatt raction +ĠEffect s +ĠPir ate +ĠC er +ĠIndust ry +ĠR ud +Ġchar ter +Ġd ining +Ġins ists +Ġconfig ure +Ġ( # +ĠSim ple +ĠSc roll +UT C +17 5 +ĠK on +Ġmarket place +Ġ ãĤ +Ġref res +Ġg ates +er red +ĠP od +Ġbeh ave +Fr ank +n ode +Ġendors ed +he tt +as ive +ĠHom eland +Ġr ides +ĠLe ave +er ness +Ġflood ing +A FP +Ġris en +Ġcontin ually +Ġun anim +ĠCont ract +ĠP as +Ġgu ided +ĠCh ile +b d +Ġsu cc +pt ic +Ġcomm ittees +ĠL uther +ĠAny one +Ġs ab +12 4 +Ġp ixel +ĠB ak +ĠT ag +ĠBenn ett +En ter +sm all +ĠPresident ial +Ġp ul +Ġcontr ace +arch ive +Ġcoast al +ĠK ids +19 2 +âĢ ² +ick y +ING TON +Ġw olf +ĠSt alin +T ur +id get +am as +ĠUn less +Ġspons or +Ġmor ph +ĠCho ose +Ġrun ner +Ġun bel +Ġm ud +ĠMan a +Ġdub bed +Ġg odd +ure rs +wind ow +Ġrel ied +Ġcelebr ating +os c +Ġ13 5 +Ġlobb ying +Ġincom plete +Ġrestrict ion +Ġinc ap +it us +Ġexpect ation +ĠAp ollo +Ġint ens +Ġsyn c +G H +Ġmanip ulation +B Y +Ġspe ar +Ġbre asts +Ġvol can +il ia +M aterial +Ġform ats +ĠB ast +Ġparliament ary +Ġsn ake +Ġserv ants +ĠTr udeau +ĠGr im +ĠArab ic +ĠSC P +ĠBoy s +st ation +Ġprospect ive +ord e +in itialized +Ġb ored +AB LE +Ġaccess ed +Ġtax i +ĠShe ll +aid en +urs ed +in ates +ĠIns urance +ĠPet e +Sept ember +6 50 +Ġad ventures +ĠCo ver +Ġt ribute +Ġsk etch +Ġem power +Ġ Ø +ĠGl enn +ĠD aw += \" +ĠPolit ics +Ġgu ides +Ġd ioxide +ĠG ore +ĠBr ight +ĠS ierra +Ġval ued +c ond +Ġpo inter +Se lect +Ġrisk y +Ġabsor b +im ages +Ġref uses +Ġbon uses +__ _ +Ġh ilar +ĠF eatures +2 20 +ĠCollect or +F oot +Ġ19 64 +cul us +Ġd awn +Ġwork out +ĠL O +Ġphilosoph ical +ĠSand y +ĠYou th +Ġl iable +A f +bl ue +Ġovert urn +less ness +ĠTrib une +ĠIn g +Ġfact ories +Ġcat ches +Ġpr one +Ġmat rix +Ġlog in +Ġin acc +Ġex ert +s ys +Ġneed le +ĠQ ur +Ġnot ified +ould er +t x +Ġremind s +Ġpublisher s +Ġn ort +Ġg it +Ġfl ies +ĠEm ily +Ġflow ing +ĠAl ien +ĠStr ateg +Ġhard est +Ġmod ification +AP I +ĠM Y +Ġcr ashes +st airs +n umber +Ġur ging +ch annel +ĠFal con +Ġinhabit ants +Ġterr ifying +Ġutil ize +Ġban ner +Ġcig arettes +Ġsens es +ĠHol mes +Ġpract ition +ĠPhill ips +ott o +Ġcomp ile +Mod el +ĠK o +Ġ[ ] +Americ ans +ĠTer ms +Ġmed ications +ĠAn a +Ġfundament ally +ĠNot ice +Ġwe aker +Ġ 0000 +Ġgar lic +Ġout break +Ġeconom ist +ĠB irth +Ġobst acles +ar cer +ĠOr thodox +Ġplace bo +ĠC rew +asp berry +ĠAng els +Ġdis charge +Ġdestruct ive +11 7 +ĠR ising +Ġd airy +l ate +Ġcoll ision +ĠTig ers +ean or +ocument ed +ĠIn valid +Ġd ont +ĠL iter +ĠV a +Ġhyd rogen +Ġvari ants +ĠBrown s +Ġ19 65 +Ġind igenous +Ġtrad es +Ġremain der +Ġswe pt +ĠImp act +Ġred ist +Ġun int +grad uate +ãĥ ķ +ĠW ILL +ãģ® ç +ĠCrit ical +Ġf isher +Ġv icious +Ġrevers ed +Y ear +ĠS ox +Ġshoot ings +Ġfil ming +Ġtouchdown s +ai res +m el +Ġgrand father +Ġaffect ion +ing le +Ġover ly +Add itional +Ġsup reme +ĠGr ad +Ġsport ing +Ġmer cy +ĠBrook s +ount y +Ġperform s +Ġtight ly +Ġdem ons +Ġkill ings +Ġfact ion +ĠNov a +aut s +Ġund oubtedly +ar in +Ġunder way +ra k +Ġl iv +ĠReg ion +Ġbrief ing +s ers +cl oud +ĠM ik +us p +Ġpred iction +az or +Ġport able +ĠG and +Ġpresent ing +Ġ10 80 + » +ush i +ĠSp ark +there um +Ġjust ification +ĠN y +Ġcontract ors +ming ham +ĠSt yle +å ħ +ĠChron icles +ĠPict ure +Ġprov ing +Ġw ives +set t +Ġmole cules +ĠFair y +Ġconsist ing +Ġp ier +al one +in ition +Ġn ucle +j son +Ġg otta +Ġmob il +Ġver bal +ar ium +Ġmon ument +uck ed +Ġ25 6 +T ech +mine craft +ĠTr ack +Ġt ile +Ġcompat ibility +as is +Ġs add +Ġinstruct ed +ĠM ueller +Ġle thal +Ġhorm one +Ġor che +el se +Ġske let +Ġentert aining +Ġminim ize +ag ain +Ġunder go +Ġconst raints +Ġcig arette +ĠIslam ist +Ġtravel s +ĠPant hers +l ings +C are +Ġlaw suits +ur as +Ġcry st +Ġlow ered +Ġaer ial +Ġcomb inations +Ġha un +Ġch a +Ġv ine +Ġquant ities +Ġlink ing +b ank +Ġso y +B ill +ĠAngel a +Ġrecip ient +ĠProt est +Ġs ocket +Ġsolid arity +Ġâ Ĩ +m ill +Ġvar ies +ĠPak istani +Dr agon +Ġun e +Ġhor izon +³³³³ ³³³³ +Ġprov inces +Ġfrank ly +Ġenact ed +not es +[ ' +Ġ19 2 +ocr acy +Ġendorse ment +Ġover time +Tr ue +L ab +lic ted +ĠD NC +Ġbe ats +ĠJam ie +15 2 +ĠIN T +Cont act +Ġaccount ed +h ash +ĠPack ers +p ires +Ġles bian +Ġamend ments +Ġhop eful +ĠFin land +Ġspot light +Ġconfig ured +Ġtrou bled +Ġg aze +ĠCal gary +Ġrel iability +Ġins urg +sw er +b uy +ĠSk in +Ġp ixels +Ġhand gun +Ġpar as +Ġcateg or +ĠE L +ĠRe x +Ind eed +Ġkind a +Ġconj unction +ĠBry an +ĠMan ufact +y ang +Pl us +S QL +ish ment +Ġdom inate +Ġn ail +Ġo ath +Ġeru pt +ĠF ine +it bart +ĠCh ip +ĠAb d +ĠN am +Ġbuy er +Ġdiss ent +Le aks +Cont in +Ġr ider +ĠSome one +Ġill usion +c in +ĠBoe ing +Ġin adequ +ov ation +i ants +Ġreb uild +4 50 +ĠDest iny +S W +ĠT ill +H it +ia z +ĠBang l +acher s +ĠRe form +Ġse gments +Ġsystem atic +d c +ĠConserv atives +Ġport al +h or +ĠDragon bound +Ġdrag ged +om o +Ġthe e +ad vert +ĠRep orts +ĠE t +Ġbarrel s +Aug ust +Ġcompar isons +Ġhe x +Ġan throp +" [ +bor ough +ab i +Ġpict ured +play ing +ĠAdd ress +ĠMir ror +Sm ith +Ġt ires +ĠN PR +AA AA +Ġclass ification +ĠTh an +ĠH arm +ĠR A +Ġreject ion +min ation +Ġr anged +ĠF alls +D I +H ost +ãĤ ´ +ĠEx ample +list ed +th irds +Ġsaf egu +br and +Ġprob able +Can ada +IT ION +ĠQ aeda +Ġch ick +Ġimport s +h it +l oc +W W +Ġble w +Ġany time +Ġwh oles +ik ed +Ġcal culation +cre ate +ĠO ri +Ġupgr aded +Ġapp ar +ut ory +ĠM ol +B rit +ĠJ ong +IN AL +ĠStart ing +Ġd ice +urt le +Ġre lying +cl osure +Ġprof itable +Ġsl aughter +ĠMan ual +c aster +Ġ" $ +Ġfe ather +ĠSim ply +ie ves +Ġdeter ior +ĠPC I +Ġst amp +Ġfl aws +Ġsh ade +ham mer +Ġpass port +Ġcont ing +am el +Ġobser vers +Ġneg lect +ĠR B +ĠBrother hood +Ġskept ical +f amily +us k +Ġemotion ally +â Ļ +ĠBet a +ason able +id ity +ĠM ul +Ġkick ing +ĠC arm +oll ah +VERT IS +ĠAt hen +Ġlad der +ĠBul let +å £ +00 01 +ĠWild life +ĠM ask +ĠN an +R ev +Ġun acceptable +leg al +Ġcrowd ed +ag i +ĠC ox +j e +Ġmor ality +Ġfu els +Ġc ables +Ġman kind +ĠCarib bean +Ġanch or +Ġby te +ĠO ften +ĠO z +Ġcraft ed +Ġhistor ian +ĠW u +Ġtow ers +ĠCitiz ens +Ġhel m +Ġcred entials +Ġsing ular +ĠJes se +Ġtack les +Ġcont empt +Ġa fore +ĠSh adows +Ġn il +Ġur gent +app le +bl ood +Ġv on +Ġoff line +Ġbreat he +Ġj umps +Ġirre levant +ox ic +om al +import ant +J im +Ġgl oves +arm ing +dep th +Ġtal ents +ook ie +ĠS B +Ġpal m +uff s +est a +IG H +Ġcan on +ĠVer izon +ĠP le +Ġcou pled +vel t +Ġfundra ising +ĠGet ting +ĠD LC +Ġmathemat ical +ĠH S +ĠCard inals +te lling +Ġspons ors +Ġ Ï +ĠBull s +op tion +Ġprop ose +Ġmem orable +Ġembr aced +Ġdecl ining +He alth +ed a +Ġ} ; +Ġsp am +m ile +Ġpit cher +ĠE ight +Ġcar ing +ut ic +ro le +Ġair line +ernand ez +ĠAth let +Ġcert ification +ux e +rig er +Ġem pir +Ġsens ation +Ġdis m +Ġb olt +Ġev olve +H ouse +Ġconsult ation +ĠD uty +Ġtou ches +ĠN athan +Ġf aint +h ad +" ( +ĠCons umer +ĠExt reme +Ġ12 7 +ĠHer m +ĠSac rament +iz oph +Ġanx ious +ul ously +Ġsoc ially +ĠU TC +Ġsol ving +ĠLet ter +Hist ory +ed uc +Pr ice +) ); +Ġrel oad +am ic +Ġp ork +Ġdisc ourse +Ġt ournaments +ai ro +ĠK ur +ĠCost a +Ġviol ating +Ġinterf ere +Ġrecre ational +uff le +Ġspe eches +Ġneed ing +Ġremem bers +Ġcred ited +n ia +f ocused +amer a +Ġb ru +um bs +ĠCub an +Ġpreced ing +Ġnons ense +ac ial +Ġsmart phones +ĠSt ories +S ports +ĠEmer gency +oun cing +ef ined +Ġb er +Ġconsult ing +Ġm asters +he astern +." [ +ĠRun ning +Ġsus cept +ĠF eng +Americ a +pr ises +st itial +ĠWeek ly +ĠGreat er +mod ules +if ter +G raphics +ul er +Ġwho lly +Ġsupp ress +Ġconce aled +Ġhapp ily +Ġaccept s +ĠEn joy +Ġr ivers +ĠEx cept +2 25 +ĠN HS +ĠMc Connell +Ġp ussy +fer red +ut able +Ġatt ain +Ġ> = +Ġdepos its +roph ic +Ġnot orious +ĠSh aw +il itation +Ġepid emic +all ic +Ġsmall est +ov ich +Ġaccess ories +per ties +Ġsur plus +ĠMe ch +Ġamb ig +ĠImm igration +Ġch im +ev al +Ġpract icing +ĠMyster y +Ġdom ains +ĠSil icon +app s +Ġkilomet ers +e a +ĠSm ash +Ġwarrant y +Ġn ost +s il +re v +J on +ĠDub lin +Ġtast es +Ġb out +g reat +er ror +Ġsw itches +ĠB apt +D O +ok i +Ġsour ced +pro du +Ġattach ment +ĠIss ue +ĠQuest ion +Jo in +Ġf itted +Ġunlaw ful +^ ^ +ere k +Ġauthent ication +Ġst ole +Ġaccount ability +l abel +S earch +Ġal beit +atic an +fund ed +ĠAdd ing +ĠI Q +Ġsub mar +l it +a que +ĠLear ning +Ġint eger +M aster +ĠCh rom +Ġprem ier +O p +ĠLi u +Ġbl essed +ĠGl obe +ĠResp onse +Ġlegit im +ĠMer kel +Ġdispos al + ´ +Ġgau ge +pe at +Ġindu ced +Ġquestion able +arth y +ĠV it +ĠF eed +U ntil +U t +worth y +R Y +ĠH erald +ĠHam mer +Ġmed al +ĠR ivers +ĠH ack +Ġclar ify +Ġtrack ed +Ġautonom ous +Ġten ant +ĠQ atar +er ie +Ġgr im +ĠMon itor +Ġresist ant +ĠSpe c +ĠWell s +N AS +14 8 +Ġmin ers +iot ics +Ġmiss es +11 6 +g ian +g it +ĠE yes +p res +Ġgrad uated +Ġang el +Ġsyn chron +Ġefficient ly +Ġtrans mitted +H arry +Ġglob ally +EN CE +ĠMont ana +r aged +ĠPre vention +Ġp iss +ĠL l +Ġshe lf +ĠB JP +ĠTest ament +ĠL ate +ik er +ĠH app +ĠJul ian +h all +Ġsp ont +Ġshut down +Ġincons istent +Ġsubscrib ers +Ġske leton +ĠNe braska +Ġins pire +ĠV oid +F eed +Ġang les +ĠSpr ings +Ġbench mark +Ġvacc ines +izoph ren +se xual +uff ed +Ġsh ine +ĠK ath +Ġgest ure +ine a +Ġr ip +Ġopp ression +Ġcons cience +b t +ĠL um +Ġinc idence +ĠF a +w r +Ġmin eral +ĠSp urs +alk y +Ġth under +Ġop io +Be ing +ĠPal m +Ġwas ted +Ġl b +i aries +ĠIniti ative +Ġcur ric +Ġmark er +ĠMc L +Ġext ensions +ĠP v +ĠAr ms +Ġoffer ings +Ġdef enses +Ġvend or +Ġcontrad ict +ĠCol in +Ġredd it +Ġper ipher +12 2 +Ġs ins +E dit +IC T +So ft +ĠSh ah +Ġadministr ator +ĠT rip +Ġporn ography +Ġtu ition +in ence +ĠPro gress +Ġcat alog +Ġsu ite +Ġh ike +Ġreprodu ctive +eng ine +Ġd rought +ĠNo ah +Ġ2 30 +Ġd ude +Ġrelax ed +Ġpart ition +Ġparticip ant +Ġtel esc +Ġfe as +ĠF F +own er +Ġswe eping +Ġl enses +Ġmatch up +ĠRe pl +ourn als +Ġcred ible +Ġgrand mother +Ġther mal +Ġsubscrib ing +Ġident ities +col m +U CT +Ġreluct ant +us ers +ĠC ort +Ġassist ed +OS S +ATION S +IS H +Ġpharm aceutical +ic able +ad ian +ĠSon ic +ĠF ury +ĠM ong +A H +ĠPsych ology +Ġph osph +Ġtreat s +Ń Ķ +Ġstead ily +ĠHell o +Ġrel ates +Ġcl ue +Ex pl +a uth +Ġrev ision +Ġe ld +os ion +Ġbr on +14 4 +ri kes +Ġmin es +Ġblank et +ĠF ail +el ed +ĠIm agine +ĠPl anned +a ic +Re quest +M ad +ĠHor se +ĠEag le +Ġcap ac +15 7 +Ġl ing +ĠN ice +ĠP arenthood +min ster +og s +ens itive +Not hing +Ġcar n +F in +ĠP E +Ġr ifles +ĠL P +S and +Ġgui Active +Ġtour ist +C NN +Ġunve iled +Ġpredec essor +} { +u ber +Ġoff shore +Ġopt ical +ĠR ot +ĠPear l +et on +Ġst ared +Ġfart her +at ility +cont in +ĠG y +ĠF oster +ĠC oc +ri ents +Ġdesign ing +ĠEconom y +ON G +W omen +ĠN ancy +er ver +Ġmas cul +Ġcasual ties +Ġ2 25 +ĠS ullivan +ĠCh oice +Ġa ster +w s +Ġhot els +Ġconsider ations +Ġcou ch +ĠSt rip +ĠG n +Ġmanip ulate +l ied +Ġsynt hetic +Ġassault ed +Ġoff enses +ĠDra ke +Ġim pe +Oct ober +ĠHer itage +h l +ĠBl air +Un like +Ġg rief +Ġ4 50 +Ġopt ed +Ġresign ation +il o +Ġver se +ĠT omb +Ġu pt +Ġa ired +ĠH ook +ĠML B +Ġassum es +out ed +ĠV ers +Ġinfer ior +Ġbund le +ĠD NS +ograp her +Ġmult ip +ĠSoul s +Ġillust rated +Ġtact ic +Ġdress ing +Ġdu o +Con f +Ġrel ent +Ġc ant +Ġscar ce +Ġcand y +ĠC F +Ġaffili ated +Ġspr int +yl an +ĠGarc ia +Ġj unk +Pr int +ex ec +C rit +Ġport rait +ir ies +ĠOF F +Ġdisp utes +W R +L ove +ãģ Ħ +ĠRe yn +Ġh ipp +op ath +Ġflo ors +ĠFe el +Ġwor ries +Ġsett lements +ĠP os +Ġmos que +Ġfin als +Ġcr ushed +ĠPro bably +ĠB ot +ĠM ans +ĠPer iod +Ġsovere ignty +Ġsell er +Ġap ost +Ġam ateur +Ġd orm +Ġconsum ing +Ġarm our +ĠRo ose +Ġint ensive +Ġelim inating +ĠSun ni +ĠAle ppo +j in +Ġadv ise +p al +ĠH alo +Ġdes cent +Ġsimpl er +Ġbo oth +ST R +L ater +ĠC ave +== = +Ġm ol +Ġf ist +Ġshot gun +su pp +Ġrob bery +E ffect +Ġobsc ure +ĠProf essional +Ġemb assy +Ġmilit ant +Ġinc arcer +Ġgener ates +Ġlaun ches +Ġadministr ators +Ġsh aft +Ġcirc ular +Ġfresh man +ĠW es +ĠJo el +ĠD rew +ĠDun can +ĠApp arently +s ight +ĠIntern al +ĠInd ividual +ĠF E +Ġb ore +ĠM t +Ġbroad ly +ĠO ptions +ount ain +ip es +ĠV ideos +20 4 +Ġh ills +Ġsim ulation +Ġdisappoint ment +it an +ĠLabor atory +Ġup ward +Ġbound ary +Ġdark er +h art +Ġdomin ance +C ong +ĠOr acle +ĠL ords +Ġscholars hip +ĠVin cent +ed e +ĠR ah +Ġencour ages +ro v +Ġqu o +Ġprem ise +ĠCris is +ĠHol ocaust +Ġrhyth m +Ġmet ric +cl ub +Ġtransport ed +Ġn od +ĠP ist +Ġancest ors +ĠFred er +th umbnails +ĠC E +ON D +Ph il +ven ge +ĠProduct s +cast le +Ġqual ifying +ĠK aren +VERTIS EMENT +Ġmight y +Ġexplan ations +Ġfix ing +D i +Ġdecl aring +Ġanonym ity +Ġju ven +ĠN ord +ĠDo om +ĠAct ually +O k +ph is +ĠDes ert +Ġ11 6 +I K +ĠF M +Ġinc omes +V EL +ok ers +Ġpe cul +Ġlight weight +g ue +Ġacc ent +Ġincre ment +ĠCh an +Ġcompl aining +ĠB aghd +Ġmidfield er +Ġover haul +Pro cess +ĠH ollow +ĠTit ans +Sm all +man uel +ĠUn ity +ĠEv ents +S ty +Ġdispro portion +n esty +en es +ĠC od +Ġdemonstr ations +ĠCrim son +ĠO H +Ġen rolled +Ġc el +ĠBre tt +Ġa ide +Ġhe els +Ġbroad band +Ġmark ing +Ġw izard +ĠN J +ĠChief s +Ġingred ient +Ġd ug +ĠSh ut +urch ase +end or +Ġfar mer +ĠGold man +12 9 +15 5 +Or der +Ġl ion +i ably +Ġst ain +ar ray +ilit ary +ĠFA Q +Ġexpl oded +ĠMcC arthy +ĠT weet +ĠG reens +ek ing +l n +ens en +Ġmotor cycle +Ġpartic le +Ġch olesterol +B ron +Ġst air +Ġox id +Ġdes irable +ib les +Ġthe or +for cing +Ġpromot ional +ov o +b oot +ĠBon us +raw ling +Ġshort age +ĠP sy +Ġrecru ited +Ġinf ants +Ġtest osterone +Ġded uct +Ġdistinct ive +Ġfirm ware +bu ilt +14 5 +Ġexpl ored +Ġfact ions +Ġv ide +Ġtatt oo +Ġfinan cially +Ġfat igue +Ġproceed ing +const itutional +Ġmis er +Ġch airs +gg ing +ipp le +Ġd ent +Ġdis reg +ç Ķ +st ant +ll o +b ps +aken ing +Ġab normal +ĠE RA +å£ « +ĠH BO +ĠM AR +Ġcon cess +Ġserv ant +Ġas pir +l av +ĠPan el +am o +Ġprec ip +Ġrecord ings +Ġproceed ed +Ġcol ony +ĠT ang +ab lo +Ġstri pped +Le ft +to o +Ġpot atoes +Ġfin est +% ). +Ġc rap +ĠZ ach +ab ases +ĠG oth +Ġbillion aire +w olf +Ġsan ction +S K +Ġlog ged +P o +ey ed +un al +Ġcr icket +Ġarm ies +Ġunc overed +Cl oud +ó n +Ġreb ounds +Ġm es +O per +P ac +Ġnation ally +Ġinsert ed +p ict +Ġgovern ance +Ð ¸ +Ġprivile ges +G ET +Ġfavor ites +im ity +Ġlo ver +the m +em pl +Ġgorge ous +An n +Ġsl ipped +Ġve to +B ob +Ġsl im +u cc +ĠF ame +udden ly +Ġden ies +ĠM aur +Ġdist ances +Ġw anna +t ar +ĠS ER +Ġâ Ī +Ġle mon +at hetic +Ġlit eral +Ġdistingu ished +Ġansw ering +G I +Ġrelig ions +ĠPhil os +ĠL ay +Ġcomp os +ire ments +ĠK os +ine z +roll ing +Ġyoung est +and ise +ĠB orn +Ġalt ar +am ina +ĠB oot +v oc +Ġdig ging +Ġpress ures +Ġl en +26 4 +Ġassass ination +ĠBir mingham +ĠMy th +Ġsovere ign +ĠArt ist +ĠPhot ograph +Ġdep icted +Ġdisp ens +orth y +Ġamb ul +int eg +ĠC ele +ĠTib et +Ġhier archy +Ġc u +Ġpre season +ĠPet erson +Ġcol ours +Ġworry ing +Ġback ers +ĠPal mer +ĠÎ ¼ +Ġcontribut or +Ġhear ings +Ġur ine +Ġ Ù +ourge ois +Sim ilar +ĠZ immer +s omething +ĠUS C +Ġstrength s +ĠF I +Ġlog ging +As ked +ĠTh ai +in qu +ĠW alt +Ġcrew s +it ism +3 01 +Ġshar ply +um ed +Ġred irect +r ators +In f +ĠWe apons +Ġte asp +19 99 +L ive +ĠEs pecially +ĠS ter +ĠVeter ans +Ġint ro +other apy +Ġmal ware +Ġbre eding +Ġmole cular +ĠR oute +ĠCom ment +oc hem +Ġa in +Se ason +Ġlineback er +Ä « +ĠEconom ics +es ar +ĠL ives +ĠEm ma +Ġk in +ĠTer rit +Ġpl anted +ot on +ĠBut ter +ĠSp ons +P ER +Ġdun geon +Ġsymb olic +Ġfil med +Ġdi ets +Ġconclud es +Ġcertain ty +ĠForm at +Ġstr angers +form at +ĠPh ase +Ġcop ied +Ġmet res +ld a +ĠUs ers +Ġdeliber ate +Ġwas hed +ĠL ance +im ation +Ġimpro per +ĠGen esis +ick r +ĠK ush +Ġreal ise +Ġembarrass ing +alk ing +b ucks +Ġver ified +Ġout line +year s +ĠIn come +20 2 +Ġz ombies +F inal +ĠMill enn +Ġmod ifications +ĠV ision +ĠM oses +ver b +iter ranean +ĠJ et +Ġnav al +ĠA gg +Ġur l +Ġvict ories +Ġnon etheless +Ġinj ust +ĠF act +ç ļ +Ġins ufficient +re view +face book +Ġnegoti ating +Ġguarant ees +im en +uten berg +Ġg ambling +Ġcon gr +Load ing +Ġnever theless +Ġpres idents +ĠIndust rial +Ġ11 8 +Ġp oured +ĠT ory +Ġ17 5 +Ġ: = +Sc ott +ange red +T ok +Ġorgan izers +M at +ĠG rowth +Ġad ul +Ġens ures +Ġ11 7 +é¾į å +Ġmass acre +Ġgr ades +be fore +AD VERTISEMENT +ĠSl ow +ĠM MA +âĢĶ " +ĠV atican +Q aeda +Ġo we +66 66 +ĠS orry +ĠGr ass +Ġbackground s +Ġexha usted +Ġcl an +Ġcomprom ised +ĠE lf +ĠIsa ac +ens on +In vest +IF A +Ġinterrupt ed +ãĥī ãĥ© +Ġtw isted +ĠDrag ons +M ode +ĠK remlin +Ġfert il +he res +ph an +ĠN ode +f ed +ĠOr c +Ġunw illing +C ent +Ġprior it +Ġgrad uates +Ġsubject ive +Ġiss uing +ĠL t +Ġview er +Ġw oke +Th us +bro ok +Ġdep ressed +Ġbr acket +ĠG or +ĠFight ing +Ġstri ker +Rep ort +ĠPortug al +Ġne o +w ed +19 9 +Ġflee ing +sh adow +ident ified +US E +Ste am +Ġstret ched +Ġrevel ations +art ed +ĠD w +Ġalign ment +est on +ĠJ ared +S ep +Ġblog s +up date +g om +r isk +Ġcl ash +ĠH our +Ġrun time +Ġunw anted +Ġsc am +Ġr ack +Ġen light +on est +ĠF err +Ġconv ictions +Ġp iano +Ġcirc ulation +ĠW elcome +Ġback lash +ĠW ade +Ġrece ivers +ot ive +J eff +Ġnetwork ing +ĠPre p +ĠExpl orer +Ġlect ure +Ġupload ed +ĠMe at +B LE +ĠNaz is +ĠSy nd +st ud +ro ots +ri ans +Ġportray ed +Ġ ?? +ĠBudd ha +s un +Rober t +ĠCom plex +Ġover see +Ġste alth +T itle +ĠJ obs +ĠK um +Ġappreci ation +ĠM OD +Ġbas ics +Ġcl ips +Ġnurs ing +Ġpropos ition +Ġreal ised +ĠNY C +Ġall ocated +ri um +ar an +ĠPro duction +ĠV ote +Ġsm ugg +Ġhun ter +az er +ĠCh anges +Ġfl uct +y on +Ar ray +Ġk its +W ater +Ġuncom mon +Ġrest ing +ell s +w ould +Ġpurs ued +Ġassert ion +omet own +ĠMos ul +ĠPl atform +io let +Ġshare holders +Ġtra ils +P ay +ĠEn forcement +ty pes +ĠAn onymous +Ġsatisf ying +il ogy +Ġ( ' +w ave +c ity +Ste ve +Ġconfront ation +ĠE ld +C apt +ah an +ht m +ĠC trl +ON S +2 30 +if a +hold ing +Ġdelic ate +Ġj aw +ĠGo ing +or um +S al +Ġd ull +ĠB eth +Ġpr isons +Ġe go +ĠEl sa +avor ite +ĠG ang +ĠN uclear +Ġsp ider +ats u +Ġsam pling +Ġabsor bed +ĠPh arm +iet h +Ġbuck et +ĠRec omm +O F +ĠF actory +AN CE +Ġb acter +H as +ĠObs erv +12 1 +Ġprem iere +De velop +Ġcur rencies +C ast +Ġaccompany ing +ĠNash ville +Ġfat ty +ĠBre nd +Ġloc ks +Ġcent ered +ĠU T +augh s +or ie +ĠAff ordable +v ance +D L +em et +Ġthr one +ĠBlu etooth +Ġn aming +if ts +AD E +Ġcorrect ed +Ġprompt ly +ĠST R +Ġgen ome +Ġcop e +Ġval ley +Ġround ed +ĠK end +al ion +p ers +Ġtour ism +Ġst ark +v l +Ġblow ing +ĠSche dule +st d +Ġunh appy +Ġlit igation +ced es +Ġand roid +Ġinteg ral +ere rs +ud ed +t ax +Ġre iter +ĠMot ors +oci ated +Ġwond ers +ĠAp ost +uck ing +ĠRoose velt +f ram +Ġyield s +Ġconstit utes +aw k +Int erest +Ġinter im +Ġbreak through +ĠC her +Ġpro sec +ĠD j +ĠM T +Res p +ĠP T +Ġs perm +ed it +B T +Lin ux +count ry +le ague +Ġd ick +Ġo ct +Ġinsert ing +Ġsc ra +ĠBrew ing +Ġ19 66 +Ġrun ners +Ġpl un +id y +ĠD ian +Ġdys function +Ġex clusion +Ġdis gr +Ġincorpor ate +Ġrecon c +Ġnom inated +ĠAr cher +d raw +achel or +Ġwrit ings +Ġshall ow +Ġh ast +ĠB MW +ĠR S +Ġth igh +Ġ19 63 +Ġl amb +Ġfav ored +ag le +Ġcool er +ĠH ours +ĠG U +ĠOrig in +Ġglim pse +---------------- ---- +L im +Ġche ek +Ġj ealous +- ' +Ġhar ness +ĠPo ison +Ġdis abilities +ne apolis +Ġout look +Ġnot ify +ĠIndian apolis +Ġab rupt +ns ic +Ġenc rypted +Ġfor fe +reat h +Ġr abb +Ġfound ations +Ġcompl iment +ĠInter view +ĠS we +Ġad olesc +Ġmon itors +ĠSacrament o +Ġtime ly +Ġcontem pl +Ġposition ed +Ġpost ers +ph ies +iov ascular +v oid +ĠFif th +Ġinvestig ative +OU N +Ġinteg rate +ĠIN C +ish a +ibl ings +ĠRe quest +ĠRodrig uez +Ġsl ides +ĠD X +Ġfemin ism +Ġdat as +Ġb end +ir us +ĠNig eria +F ox +Ch ange +Ġair plane +ĠLad en +Ġpublic ity +ixt y +Ġcommit ments +Ġaggreg ate +Ġdisplay ing +ĠAr row +Ġ12 2 +Ġrespect s +and roid +s ix +ĠSh a +Ġrest oration +) \ +W S +oy s +Ġillust rate +with out +12 6 +ĠâĶ Ĥ +Ġpick up +n els +Ġ .... +f ood +ĠF en +) ? +Ġphenomen a +Ġcompan ions +ĠW rite +Ġsp ill +Ġbr idges +ĠUp dated +ĠF o +Ġinsect s +ASH INGTON +Ġsc are +il tr +ĠZh ang +Ġsever ity +Ġind ul +14 9 +ĠCo ffee +Ġnorm s +Ġp ulse +ĠF T +Ġhorr ific +ĠDest roy +ĠJ SON +Ġo live +Ġdiscuss es +R est +E lect +ĠW inn +ĠSurv iv +ĠH ait +S ure +op ed +Ġro oted +ĠS ke +ĠBron ze +Ġl ol +Def ault +Ġcommod ity +red ited +Ġliber tarian +Ġforb idden +Ġgr an +à ¨ +Ġl ag +en z +dri ve +Ġmathemat ics +Ġw ires +Ġcrit ically +Ġcarb ohyd +ĠChance llor +ĠEd die +Ġban ning +ĠF ri +Ġcompl ications +et ric +ĠBangl adesh +Ġband width +St op +ĠOrig inally +Ġhalf way +yn asty +sh ine +Ġt ales +rit ies +av ier +Ġspin ning +ĠWH O +Ġneighbour hood +b ach +Ġcommer ce +ĠS le +B U +Ġentreprene ur +Ġpecul iar +ĠCom ments +f re +3 20 +IC S +Ġimag ery +ĠCan on +ĠElect ronic +sh ort +( ( +D ig +Ġcomm em +u ced +Ġincl ined +ĠSum mon +Ġcl iff +ĠMed iterranean +Ġpo etry +Ġprosper ity +ĠRe ce +Ġp ills +m ember +Ġfin ale +un c +ĠG ig +ä ½ +Ġl od +Ġback ward +- + +ĠFor ward +Ġth ri +s ure +Ġso ap +ĠF X +R ES +ĠSe xual +oul os +Ġfool ish +Ġright eous +Ġco ff +terror ism +ust ain +ot er +Ġab uses +ne xt +Ġab usive +Ġthere after +Ġprohib ition +ĠS UP +Ġd ip +Ġr ipped +Ġinher ited +Ġb ats +st ru +G T +Ġflaw ed +ph abet +Ġf og +do ors +Ġim aging +Ġdig its +ĠHung ary +Ġar rog +Ġteach ings +Ġprotocol s +ĠB anks +à ¸ +p ound +ĠC urt +." ) +. / +Ġex emption +end ix +ĠM ull +Ġimpro ves +ĠG amer +d imensional +I con +ĠMarg aret +St atus +d ates +Ġint ends +Ġdep ict +Ġpark ed +J oe +ĠMar ines +chn ology +! ). +Ġjud ged +Ġwe ights +R ay +Ġapart ments +he ster +Ġrein force +Ġoff ender +occ up +Ġs ore +e pt +ĠPH P +ĠB row +Ġauthor ization +ĠR isk +ĠDel aware +ĠQ U +Ġnot ifications +Ġsun light +Ġex clude +d at +Ġm esh +ĠSud an +Ġbelong ed +Ġsub way +Ġno on +ĠInter ior +ol ics +ĠL akers +Ġc oding +Dis claimer +Cal if +O ld +Ġdis l +???? ? +Ġconfir ms +Ġrecruit ment +Ġhom icide +Cons ider +ĠJeff rey +ft y +} ; +Ġobject ion +do ing +ĠLe o +W ant +Ġgl ow +ĠClar ke +ĠNorm an +Ġver ification +Ġpack et +ĠForm ula +Ġpl ag +es ville +Ġshout ing +Ġo v +ĠR EC +ĠB ub +Ġn inth +Ġener g +Ġvalid ity +Ġup s +j ack +Ġneighbor ing +ĠN ec +ew orks +ĠH ab +are z +Ġsp ine +Ġevent ual +ĠLe aders +ĠC arn +Ġprob ation +Ġrom ance +ms g +ĠMechan ical +ER Y +R ock +Ġpart isan +N ode +ass ets +min ent +Ġforeign ers +Ġtest ify +ĠUs ually +l ords +ĠG ren +ĠPow ell +BI L +Ġs r +Ġadd ict +Ġshell s +Ġs igh +ĠY ale +tern ity +Ġ7 50 +E U +ĠR ifle +Ġpat ron +em a +ĠB annon +an ity +Ġtrop ical +ĠV II +c ross +Every thing +ĠIS O +Ġhum ble +ass ing +ĠF IG +Ġupd ating +ys on +Ġcal cium +Ġcompet ent +Ġste ering +Pro t +ĠS Y +ĠFin als +ĠR ug +15 9 +13 7 +ĠG olf +Ġ12 6 +Ġaccommod ation +ĠHug hes +Ġaest hetic +art isan +ĠTw ilight +Ġpr ince +ĠAgric ulture +ĠDis co +Ġpreced ent +Ġtyp ing +author ized +O ption +ĠA ub +l ishes +ach t +m ag +P eter +ĠU FO +mont on +ĠL ith +Ġa rom +Ġsec uring +Ġconf ined +priv ate +Ġsw ords +Ġmark ers +Ġmetab olic +se lect +ĠCur se +ĠO t +g ressive +Ġinc umb +ĠS aga +Ġpr iced +Ġclear ance +Cont ent +Ġdr illing +Ġnot ices +Ġb ourgeois +Ġv est +Ġcook ie +ĠGuard ians +ry s +in yl +Ġ12 4 +Ġpl ausible +on gh +ĠOd in +Ġconcept ion +ĠY uk +ĠBaghd ad +ĠFl ag +Aust ral +ĠI BM +Ġintern ationally +ĠWiki Leaks +I ED +Ġc yn +Ġcho oses +ĠP ill +Ġcomb ining +Ġrad i +ĠMoh ammed +def ense +atch ing +Sub ject +ic iency +Fr ame +Ġ{ " +Ġche ss +Ġtim er +19 0 +Ġt in +Ġord inance +emet ery +Ġacc using +Ġnotice able +Ġcent res +Ġl id +ĠM ills +img ur +Ġz oom +erg ic +Ġcomp ression +pr im +f ind +Ġsur g +Ġp and +ĠK ee +ĠCh ad +cell ence +oy le +Ġsocial ism +ĠT ravis +ĠM Hz +Ġgu ild +ALL Y +ĠSub scribe +ĠRel ated +Ġoccur rence +itch ing +Ġfict ional +Ġcr ush +ĠE A +c od +m ix +ĠTri ple +Ġretrie ve +Ġstimul us +Ġpsych iat +ĠDo or +Ġhomosexual ity +Ġelement ary +Ġcell ular +id ian +ĠL aun +Ġintrig uing +Ġfo am +ĠB ass +id i +its u +Ġass ure +Ġcongr at +Ġbusiness man +ĠBo ost +cl ose +Ġl ied +Ġsc iences +ĠO mega +ĠG raphics +Ġ< = +sp oken +Ġconnect ivity +S aturday +ĠAven gers +Ġto ggle +Ġank le +Ġnational ist +mod el +ĠP ool +ophob ia +V ar +ĠM ons +ator ies +Ġaggress ively +C lear +For ge +act ers +Ġhed ge +Ġpip es +Ġbl unt +Ġs q +Ġremote ly +W ed +as ers +Ġref riger +Ġt iles +Ġresc ued +Ġcompr ised +ins ky +Ġman if +avan augh +Ġprol ifer +Ġal igned +x ml +Ġtri v +Ġcoord ination +ĠP ER +ĠQu ote +13 4 +b f +ĠS aw +Ġtermin ation +Ġ19 0 +Ġadd itions +Ġtri o +Ġproject ions +Ġpositive ly +Ġin clusive +Ġmem br +19 90 +old er +Ġpract iced +ink le +Ar ch +Ġstar ters +ari us +Ġinter mediate +ĠBen ef +ĠK iller +Ġinter ventions +ĠK il +ĠF lying +In v +Ġprem ature +Ġpsych iatric +Ġind ie +Ġcoll ar +ĠRain bow +af i +Ġdis ruption +ĠFO X +cast ing +Ġmis dem +c ro +Ġw ipe +ard on +Ġb ast +ĠTom my +ĠRepresent ative +Ġbell y +ĠP O +ĠBre itbart +13 2 +Ġmess aging +Sh ould +Ref erences +ĠG RE +ist ical +L P +ĠC av +ĠC razy +Ġintu itive +ke eping +ĠM oss +Ġdiscont in +ĠMod ule +Ġun related +ĠPract ice +ĠTrans port +Ġstatist ically +orn s +Ġs ized +p u +Ġca f +ĠWorld s +ĠRod gers +ĠL un +ĠCom ic +l iving +Ġc ared +Ġclim bed +) { +Ġconsist ed +Ġmed ieval +fol k +Ġh acked +Ġd ire +ĠHerm ione +Ġt ended +ce ans +D aniel +w ent +Ġlegisl ators +Ġred es +g ames +Ġg n +am iliar +Ġ+ + +gg y +th reat +Ġmag net +Ġper ceive +Ġz ip +Ġindict ment +Ġcrit ique +g ard +ĠSaf e +ĠC ream +Ġad vent +ob a +Ġv owed +ous ands +Ġsk i +Ġabort ions +u art +Ġstun ned +Ġadv ancing +Ġlack ed +Ġ\ " +Ġsch izophren +Ġeleg ant +Ġconf erences +Ġcance led +ĠHud son +ĠHop efully +Ġtr ump +Ġfrequ encies +Ġmet eor +ĠJun ior +ĠFle et +ĠMal colm +ĠT ools +Ġ ........ +Ġh obby +ĠEurope ans +Ġ15 00 +ĠInt o +Ġs way +ĠApp ro +ĠCom pl +Comm unity +Ġt ide +ĠSum mit +ä » +Ġinter vals +ĠE ther +Ġhabit at +ĠSteven s +lish ing +ĠDom ain +Ġtrig gers +Ġch asing +Ġchar m +ĠFl ower +it ored +Ġbless ing +Ġtext ures +F ive +Ġliqu or +R P +F IN +Ġ19 62 +C AR +Un known +Ġres il +ĠL ily +Ġabund ance +Ġpredict able +r ar +Ġbull shit +le en +che t +M or +M uch +ä ¹ +Ġemphas ized +Ġcr ust +Ġprim itive +Ġenjoy able +ĠPict ures +Ġteam mate +pl er +ĠT ol +ĠK ane +Ġsummon ed +th y +ram a +ĠH onda +Ġreal izing +Ġquick er +Ġconcent rate +cle ar +Ġ2 10 +ĠErd ogan +ar is +Ġrespond s +ĠB I +Ġelig ibility +Ġpus hes +ĠId aho +Ġagg rav +Ġru ins +ur ations +Ġb ans +Ġan at +sh are +Ġgr ind +h in +um en +Ġut ilities +ĠYan kees +Ġdat abases +ĠD D +Ġdispl aced +Ġdepend encies +Ġstim ulation +h un +h ouses +ĠP retty +ĠRaven s +ĠTOD AY +Ġassoci ates +Ġthe rape +cl ed +Ġde er +Ġrep airs +rent ice +Ġrecept ors +Ġrem ed +ĠC e +Ġmar riages +Ġball ots +ĠSold ier +Ġhilar ious +op l +13 8 +Ġinherent ly +Ġignor ant +Ġb ounce +ĠE aster +REL ATED +ĠCur rency +E V +ãĥ ŀ +ĠLe ad +Ġdece ased +B rien +ĠMus k +J S +Ġmer ge +heart ed +c reat +m itt +m und +ĠâĢ ĭ +ĠB ag +Ġproject ion +Ġj ava +ĠStand ards +ĠLeon ard +Ġcoc onut +ĠPop ulation +Ġtra ject +Ġimp ly +Ġcur iosity +ĠD B +ĠF resh +ĠP or +Ġheav ier +ne ys +gom ery +Ġdes erved +Ġphr ases +ĠG C +Ġye ast +d esc +De ath +Ġreb oot +Ġmet adata +IC AL +Ġrep ay +ĠInd ependence +Ġsubur ban +ical s +Ġat op +Ġall ocation +gener ation +ĠG ram +Ġmoist ure +Ġp ine +ĠLiber als +Ġa ides +Ġund erest +ĠBer ry +Ġcere mon +3 70 +ast rous +ĠPir ates +Ġt ense +ĠIndust ries +ĠApp eals +ĠN ear +Ġè£ı ç +Ġlo vers +ĠC AP +ĠC raw +Ġg iants +Ġeffic acy +E lement +ĠBeh avior +ĠToy ota +Ġint est +P riv +A I +Ġmaneu ver +Ġperfect ion +Ġb ang +p aper +r ill +Ge orge +b order +in ters +ĠS eth +Ġcl ues +ĠLe vi +ĠRe venue +14 7 +Ġv apor +Ġfortun ate +Ġthreat ens +Ġve t +Ġdepend ency +ers ed +art icle +ĠBl izzard +Ġch lor +Ġmin us +ĠB ills +Ġcryptoc urrency +Ġmetabol ism +ter ing +Ġp estic +step s +ĠTre asure +ract ed +ĠConst ant +Ġtem p +13 9 +ĠDet ective +ur ally +Ġrecover ing +Ġcort ex +Ġ14 4 +cl osed +Ġprejud ice +aun ted +Ġstorm s +ĠN OW +Ġmach inery +Add ress +Ġcompe lled +27 0 +Ġdesp air +b ane +Ġveget able +Ġbed s +Lear n +Ġcolor ful +Ġsp ike +Ġmarg ins +Ġsymp athy +Ġworks hop +ĠC BC +S at +Ġburn s +ĠG ender +Ġ12 9 +ĠC able +Ġdeb ts +ĠThe resa +Ġreflect ing +Ġa irst +Ġr im +ram id +Ġweakness es +W rit +ogg le +t i +ĠCh arge +Ġwe ighed +Ġ( . +Ġl aughter +Ġrou ter +ĠDemocr acy +D ear +Ġhas ht +Ġd y +Ġhint s +run ning +Ġfin ishes +ar us +M ass +res ult +asc us +Ġv intage +Ġcon qu +Ġwild ly +ac ist +Ġl ingu +Ġprot agonist +st rom +te enth +ĠSol o +m ac +f illed +Ġre nown +it ives +Ġmot ive +ĠAnt ar +ĠM ann +ĠAd just +Ġrock ets +Ġtrou bling +e i +Ġorgan isms +ass is +Christ ian +Ġ14 5 +ĠH ass +Ġsw all +Ġw ax +ĠSurv ival +V S +ĠM urd +v d +stand ard +Ġdrag ons +Ġacceler ation +r ational +f inal +Ġp aired +ĠE thereum +Ġinterf aces +Ġres ent +Ġartif acts +Å « +are l +Ġcompet itor +ĠNich olas +ĠSur face +c pp +ĠT ot +Ġeconom ically +Ġorgan ised +Ġen forced +in ho +Ġvar ieties +Ġab dom +ĠBa iley +id av +ĠSal v +p aid +Ġalt itude +ess ert +ĠG utenberg +are a +op oulos +Ġprofess ors +igg s +ĠF ate +he y +Ġ3 000 +D ist +Ġtw ins +c ill +ĠM aps +Ġtra ps +Ġwe ed +ĠK iss +Ġy oga +Ġrecip ients +ĠWest minster +Ġpool s +ĠWal mart +18 8 +ĠSchool s +att ack +ĠAR M +par agraph +W arning +j l +Ġself ish +anche z +ĠHe ights +F re +ĠS oph +Ġ -------------------------------- +t ml +33 3 +Ġraid s +Ġsatell ites +KE Y +Ġlast s +Ñ Ĥ +In s +ĠD ame +Ġunp redict +// / +gh ai +Ġart illery +Ġcru ise +Ġg el +ĠCabin et +Ġbl ows +ĠE sp +Ġprox imity +ot he +ĠSk ills +ĠU pper +ob o +ĠN DP +Ġenjoy s +Ġrepe ating +ĠConst ruction +ĠQuest ions +H illary +Ġu int +Ġprocess ors +ĠGib son +ĠMult iple +q a +ĠB om +ĠM iles +vent ional +Ġhur ts +s kin +ĠA IDS +Ġadvis ers +ĠR oot +Ġmethod ology +ĠD ale +Ġdet on +ĠKnow ledge +sequ ently +Ġ12 1 +Ġconnect s +C y +ĠD anger +Ġcontribut ors +ĠB ent +Ġbr ass +ĠGun s +int o +ĠFort une +Ġbro ker +bal ance +Ġlength s +Ġv ic +Ġaver aging +Ġappropri ately +ĠCamer a +Ġsand wich +ĠCD C +Ġcoord inate +Ġnav ig +Ġgood ness +l aim +Ġbra ke +Ġextrem ist +ĠW ake +ĠM end +ĠT iny +ĠC OL +ĠR F +ĠD ual +ĠW ine +C ase +Ġref ined +Ġl amp +L ead +Ġb apt +ĠCar b +ĠS add +ĠMin neapolis +PD F +Ear ly +ĠH idden +I ts +ĠT IME +Ġp ap +Ġcommission ed +ĠF ew +ĠCol ts +ĠB ren +Ġbot hered +Ġlike wise +Ex per +ĠSch w +c ry +n n +ĠM itch +im on +M G +b m +UM P +r ays +Ġregist ry +Ġ2 70 +ach ine +re lla +ant ing +00 000 +Ġru ined +sp ot +Ġt a +Ġmaxim ize +Ġincon ven +D ead +H uman +En abled +ĠMar ie +Ġch ill +ĠParad ise +Ġstar ring +ĠLat ino +ĠProt ocol +ĠE VER +Ġsuppl iers +m essage +ĠBro ck +Ġser um +âĸĪâĸĪ âĸĪâĸĪ +Ġen comp +Ġamb ition +ues e +Ġar rows +And rew +Ġanten na +Ġ19 61 +ĠB ark +Ġb ool +ãĤ ª +ĠSt orage +Ġrail way +Ġtoug her +ĠC ad +Ġwas hing +P y +' ] +em bed +ĠMem phis +ack le +Ġfam ously +ĠF ortunately +ov ies +Ġmind set +Ġsne ak +ĠD h +RA W +ĠSim pson +Ġliv est +Ġland mark +Ġc ement +L ow +Ġthr illed +ĠCour se +in el +Ġch uck +id ate +gl obal +Ġwh it +Ġ � +ad ays +s ki +ĠS V +Ġvir uses +30 6 +ĠResp ons +Ġthe aters +ĠBr anch +ĠGene va +ĠM K +Ġunbel iev +Ġcommun ist +Orig inal +ĠRe ceived +ĠTrans fer +ĠAr g +In put +ĠStr ategy +Ġpal ace +the ning +D ri +Ġsent encing +umbn ail +Ġp ins +re cy +Ġs iblings +Get ting +ĠB U +ĠNorth west +Ġprolong ed +ĠSak ura +C omb +ĠB our +Ġinadequ ate +ĠK ash +Ġus ername +ĠImpro ve +Ġbatt ling +ĠM AC +Ġcurric ulum +Ġs oda +ĠC annon +Ġsens ible +sp ons +De cember +Ġw icked +ĠP engu +Ġdict ators +ĠHe arts +og yn +Ġsimilar ities +ĠSt ats +Ġh ollow +it ations +": [ +Ġh over +ĠList en +s ch +S und +Ġc ad +ĠPar ks +Ġl ur +Ġhy pe +ĠL em +N AME +is ure +Fr iday +Ġshoot s +Ġclos es +Ġd b +ĠR idge +ĠDiff erent +Ġrepl ies +ĠBroad way +op ers +Ġint oler +ĠZe us +akes pe +Ġpropri etary +Ġrequest ing +Ġcontro llers +ĠM IN +im edia +be cca +Ġexp ans +Ġoil s +B ot +ĠCh and +Ġpr inter +Ġto pped +ĠP OL +ĠEar lier +S ocial +av in +Ġdecre ases +ĠSe b +Ġspecific ations +ĠBl ast +ĠK urt +Ġfre el +B rown +Ġdil ig +ro e +ĠPro blem +ĠQu ad +Ġdecent ral +ĠV ector +an ut +Ġplug ins +ĠGreg ory +Ġfuck ed +el ines +ĠAmb assador +t ake +Ġcle ans +ong yang +An onymous +st ro +" } +al ine +ĠO dd +ĠE ug +2 16 +Ġbo il +ĠP owers +Ġnurs es +Ob viously +ĠTechn ical +Ġexceed ed +OR S +Ġextrem ists +Ġtr aces +ex pl +Ġcom r +ĠS ach +) / +Ġm asks +Ġsc i +B on +Ġreg ression +we gian +Ġadvis or +it ures +ĠV o +ex ample +ĠInst ruct +Ġs iege +Ġredu ctions +pt r +Ġstat utory +Ġrem oves +Ġp uck +red its +Ġbe e +Ġsal ad +Ġpromot ions +ĠJosh ua +with standing +ET H +ĠCh a +im us +Ġexpend iture +aun ting +Ġdelight ed +Ġ15 5 +be h +Ġcar pet +ĠSp art +Ġj ungle +l ists +Ġbull ying +ĠNob el +ĠGl en +Ġreferen ced +Ġintrodu ces +se in +Ġcho pped +gl ass +ĠW rest +Ġneutral ity +Ġâ Ļ +Ġinvestig ator +Ġshel ves +Ġun constitutional +Ġreprodu ction +Ġmer chant +m ia +Ġmet rics +Ġexplos ives +ĠSon ia +Ġbod ily +Ġthick ness +Ġpredomin antly +ĠAb ility +Ġmon itored +IC H +Ġ] . +ĠMart inez +Ġvis ibility +Ġqu eries +Ġgen ocide +ĠWar fare +Qu ery +Ġstud ios +Ġemb ry +Ġcorrid or +Ġclean ed +com plete +ĠM H +Ġenroll ment +ING S +Ġimpact ed +Ġdis astrous +ĠY un +ĠCl aire +ĠBas ically +y t +uster ity +Ġindirect ly +w ik +Ġd od +ĠCar r +Ġam p +Ġprohib it +ĠIn itial +ĠR d +ij i +Ġeduc ate +c orn +i ott +ĠBeaut y +Ġdetect ive +ĠCon n +s ince +Ġst agger +Ġob ese +Ġb ree +olog ic +is se +walk er +Ġbl ades +Ġlaw ful +fun c +ĠBeh ind +Ġappet ite +Ġ( * +Ġt ennis +Ġoff spring +Ġj ets +Ġstruct ured +Ġafore mentioned +N ov +Ġsc aling +f ill +Ġst ew +Ġcur b +ĠStep han +ed In +S F +ob ic +é ŃĶ +ou g +ĠM M +Ġgen etically +ope z +13 6 +Ġu mb +anc ers +Ġcoh ort +Ġmerch andise +Ġimp osing +ĠLegisl ature +ĠArch ive +iv ia +ĠN aval +Ġoff ences +Ġmir acle +Ġsn apped +Ġf oes +Ġextensive ly +ĠR af +Ġc ater +ed ience +K it +ĠB in +Ġrecomm ends +ĠC ities +Ġrig id +ĠRE AD +ĠNob le +ĠT ian +Ġcertific ates +ant is +o iler +ĠBudd hist +d id +Ġsurvey ed +Ġdown ward +Ġprint s +ĠMot ion +ron ics +ĠS ans +oss ibly +u ctions +Ġcolon ies +ĠDan ish +un it +Ġsp oil +Ġadvis ory +ber ries +Pl an +Ġspecific ation +op hers +ĠRes ource +Ġsh irts +prising ly +commun ications +Ġtriv ial +Ġmention ing +ise xual +Ġsupp lements +Ġsuper vision +B P +v or +Ġw it +Ġco oldown +Ġplaint iff +ĠReview s +ĠS ri +ĠM int +ĠSug ar +Ġafter ward +ĠPri est +ĠInvest ment +og ene +ĠT aking +Ġstretch ing +Ġinflamm ation +ĠTe hran +Ġl ining +Ġfree zing +ĠEnt ity +Ġins piring +spe cial +pr ice +Ġsu e +ĠP orter +oun ge +ET A +ĠD erek +ĠLu is +u o +ym ph +Ġex terior +ih il +ĠAsh ley +in ator +Ġnut rients +ĠTh rones +Ġfin ances +ĠIn spect +Ġspe cially +ĠRequ ired +ĠP TS +ĠViol ence +oint ed +sh ots +Ġex cerpt +co on +IN S +ĠG ri +Ġrecogn ised +We ek +You ng +Ġv om +is le +ĠCur ry +ĠBudd h +Ġnot ebook +Ġd urable +/ ? +ĠG ad +ĠP upp +Ġforg ive +p ark +Ġpersonal ities +an alysis +cl amation +Ġelev ator +Ġware house +ĠR ole +un n +Ġillust ration +ĠSc an +Ġatmosp heric +Im port +AN C +rict ed +f u +01 0 +Ġar che +Ġreward ed +akespe are +Ġintern ally +ĠR BI +alk er +Ġeleph ant +ow itz +ĠP izza +Ġbip artisan +é s +Ġslow ed +ĠSt ark +Ġover ride +OU S +Ġ3 20 +undred s +ĠDe ck +ĠC ensus +be e +14 6 +ot or +Ġ ip +Ġu b +oc ations +ĠBut ton +r ice +Ġc ripp +ff f +Ġorig inated +Ġoverwhel med +app a +Ġfore most +âĢ ij +ĠL EG +re lease +eat ured +at ches +Ġre ps +Ġl ending +ĠRe ference +ĠCl ient +16 5 +vent h +Com plete +ĠPat rol +Ġsw orn +c am +Ġshut tle +ĠR alph +Ġh ometown +- , +on al +ĠB P +å ı +Ġpersu ade +ĠAlex and +Ġcomb ines +Ġv ivid +ĠL ag +Ġenc oding +Ġsal vation +w en +ĠRec overy +i ya +Un iversity +ĠB iden +Ġbud gets +ĠTex ans +f its +Ġhon ored +Ġp ython +T D +## # +cl one +Ġbl ink +ĠL iquid +Ġunemploy ed +Ġcl ashes +ĠCoun sel +Ġdirect ing +Ġpun ct +ĠFal cons +Ġsh ark +ĠDam ascus +Ġje ans +Ġemb ark +Ġse ize +Ġup wards +2 80 +ĠE z +ĠAny thing +Ġex otic +l ower +ĠCreat or +ĠU m +Ġsubur bs +ber ger +ĠW end +Ġm int +ĠX X +ĠD ro +Ġsuff ers +Ġher b +t ree +Ġfrag ile +Ġflood ed +ĠAl cohol +ole an +ny der +ĠK O +F ram +Ġ13 6 +Ġow ed +ĠMe lee +ĠH ash +Ġwh isk +Ġsu do +r r +Qu ick +app ro +Ġi i +ĠEx amples +he e +Ġpromot es +per ature +k ar +ĠHon or +Ġs odium +ĠL if +ros so +intend ent +Ġcorrespond ent +F ound +sec ret +Ġident ifies +ag ne +Ġl ou +ĠP P +Ġcoinc idence +m ove +Ġmilit ia +Ġinf iltr +ĠPrim ary +Ġpitch ing +ĠI b +ĠGO OD +ãĤ ¸ +ĠW izards +ir al +ĠVen us +R R +ĠâĢ ķ +ĠCase y +Ġsad ly +Ġadm ire +Ġembarrass ed +c b +M el +Ġtub es +Ġbeaut ifully +ĠQueens land +Bel ow +re z +qu et +ple asant +Ġ « +C amp +Ġdec isive +19 98 +ĠL amb +ut ton +h n +ĠJ agu +au nder +ĠC ord +Ġcl erk +Ġca ffe +Ġwip ed +Ġre im +ĠMount ains +Ġimprison ed +Ġdevelop s +ĠP ra +Ġmodel ing +Any one +ance l +ĠS it +Ġshield s +Ġl awn +Ġcard iovascular +Ġdemonstr ating +Ġpar se +ĠIsrael is +Ġeuro s +14 3 +Ġgl orious +ins ki +ec d +Ġcondition ing +Ġhel pless +Ġmicro sc +ĠHar bor +Ġst akes +Ġ2 60 +Ġun equ +ĠFl oyd +Ġd amp +Ġappar atus +ĠLaw s +Ġcoun ters +Ġindu ce +at able +ĠAh med +Ġsl am +N ovember +Ġpers ist +Ġim minent +á n +Ġsh red +Ġph ases +ĠEd monton +ĠArm strong +ĠMe et +ĠK itty +Ñ Ģ +c irc +ĠAd ult +Ġa rose +ĠX en +D an +g ow +Ġsuper f +ĠAd mir +Ġend ure +Ġkey word +yr us +Ġy arn +Ġpath way +ĠHop kins +mid t +Ġcens orship +d ependent +Ġinstruct or +S ources +Ġto e +Ġball oon +N ob +Ġsw ear +ĠCast ro +Ġgl oss +ĠK avanaugh +Ġremark ably +Ph otos +ĠN om +ĠS outheast +y ers +Ġvalid ation +Ġcann on +ĠVict ory +ĠPier re +Ġcaut ious +Aud io +Ġf etch +ĠG ift +ĠH yp +Ġrem edy +Z E +Ġsc ent +Ġbe ard +ĠR ut +- " +Ġpat ents +H y +Ġun just +Ġpot ato +Ġforth coming +Ġche f +ĠR ift +aff e +ĠR OM +ĠL aunch +Ġp ads +ĠNe o +Ġon set +Ġsquee ze +s afe +Ġpref ix +ĠT M +ĠN early +ĠClin ical +ĠM ental +ot iation +ĠUn ic +ant ry +ĠC ir +Ġep it +à ¦ +Ġextract ed +verse ly +ri ad +Ġstr ains +Ġto ps +Ġpo em +ĠRand y +ĠMap le +TH ER +up iter +ĠSS D +ļ é +Ġun con +per ing +Ġsle pt +in ers +Ġunder water +ĠEv idence +g one +20 5 +Ġhistor ians +Ġsynt hesis +Ġf rog +b asketball +Ġvibr ant +Ġsub ord +Ġ3 65 +ĠD ial +Ġcooper ate +HA HA +Ġgreet ed +15 8 +Ġj azz +Ġinto x +ĠWalk ing +Ġsuper visor +ĠF usion +ĠMer cedes +s end +H am +s d +n l +Ġtour s +ĠF IFA +Ġcul p +g d +30 4 +Ġple as +Ġillust rates +ĠColomb ia +Ġhighlight ing +ĠSum mary +Ġexp osing +ĠD ru +Ġir ony +r itional +ĠCar roll +ĠEll is +P ict +ĠR apt +Ġad apter +Ġun m +Ġcor pse +Ġceleb rities +D en +at um +ĠAp ocalypse +ĠW ag +lin ing +Ġhorm ones +R ub +ĠX i +ĠV aults +20 8 +alky rie +inos aur +Ġfeed s +v ity +Ġdefe ating +W ait +Ġemphas ize +ĠSteel ers +yr inth +le ys +ĠWhe never +Current ly +ĠCl ock +Ġcollect ively +any on +ĠJ P +Ġment ality +Ġdownload s +Ġsurround ings +ĠBarn es +Ġflags hip +Ġindic ators +Ġgra pp +Jan uary +ĠElement al +ĠAthen a +ib al +Ġs ights +Ġcap ita +ĠTreat y +Ġvo iced +ĠG az +let te +Ġy a +Ġexp ired +Leg end +H ot +n ature +Ġunst able +Ġ2 80 +à º +Com ment +AL E +Ġquest s +Ġhand ler +n is +Ġvers atile +Ġconce al +enge ance +ĠInter active +Ġobs essed +ĠDog s +Ġcr acked +S ound +s v +ĠD ylan +ro ads +f x +ĠCath olics +ĠH ag +Ġsl ammed +Ġgl owing +s ale +Ġtiss ues +ĠCh i +ne e +Ġc her +s ic +ur rection +Ġb acon +ul atory +) ." +Ġir regular +FOR M +ass ed +Ġintention al +Ġcompens ate +ĠSpe aking +ĠS ets +15 3 +Ġconvent ions +b ands +em ade +Ġe cc +ĠWin ston +ĠAssass in +ĠBelg ian +Ġdepend ence +Ġnic he +Ġb ark +ĠJ azz +Ġdisadvant age +Ġgas oline +Ġ16 5 +çļ Ħ +ess a +mod ule +ang ular +O Y +ĠTreat ment +it as +ol ation +ĠArn old +Ġfe ud +ĠN est +Ġthe atre +ew ater +Ġmin ors +olic y +ĠH aven +div ision +Ġtr unk +F ar +ĠP ull +Ġcapt uring +Ġ18 00 +ĠTe en +Ġex empl +Ġclin ics +ĠB urg +Ġsubst it +Ġpay load +ĠL av +ĠT roy +ĠW itness +Ġfrag ments +Ġpass words +Ġg ospel +ĠG in +Ġten ants +ol ith +S ix +Pre vious +ĠAg es +ĠDar win +Ġbl at +Ġem pathy +sm ith +b ag +ĠE cho +ĠC amb +ĠM add +ĠB oo +Ġred e +ĠBurn ing +Ġsmooth ly +ĠAd rian +ĠV ampire +ĠMon sters +ste am +Sty le +M a +re a +ĠD war +aly st +urs or +Ġelim ination +Ġcrypt o +ch t +ĠE ternal +âĢ¦ ] +ĠS orce +I ll +N ER +Ġu h +Con clusion +w age +Ġresp ir +Ġrem inis +het ical +Ġg y +Ġutil ized +ic idal +Ġ19 00 +Ġhun ters +ĠSw an +ĠRe act +Ġvis itor +ĠThanks giving +30 8 +Post s +Ġh ips +19 97 +om ers +Ġkn ocking +ĠVeh icle +Ġt il +Ġ13 8 +Ġm i +ĠInvest igation +ĠKen ya +Ġcas ino +Ġmot ives +Ġreg ain +re x +Ġweek ends +Ġstab bed +bor o +Ġexplo ited +ĠHA VE +ĠTe levision +c ock +Ġprepar ations +Ġende av +ĠRem ote +ĠM aker +ĠPro du +ĠEv an +Ġinform ational +ĠLouis ville +15 4 +ĠDream s +Ġpl ots +ĠRun ner +Ġhur ting +Ġacad emy +ĠMont gomery +n m +ĠL anc +ĠAl z +2 10 +el ong +Ġretail er +Ġar ising +Ġrebell ion +Ġbl onde +play ed +Ġinstrument al +C ross +Ġret ention +Ġtherape utic +Ġse as +Ġinfant ry +ĠCl int +Ġprompt ing +Ġbit ch +Ġst ems +ĠK ra +Ġthe sis +ĠB og +ru ed +Ġk ings +Ġcl ay +ific ent +ĠY ES +ĠTh ing +ĠCub s +vey ard +els h +in arily +ĠE y +ĠRoll ing +Ġev olving +Ind ia +Ġrecogn izes +Ġgrad uation +is ers +Ġfert ility +ĠMil an +Comm and +Ġbox ing +Ġ19 43 +Ġgl uten +ĠEm ir +Ġid ol +Ġcon ceived +ĠCre ation +Mer it +udd y +uss ions +ĠLie utenant +iet al +Ġunch anged +ĠSc ale +ĠCrime a +ball s +ator ial +Ġdepth s +Ġempir ical +Ġtrans m +Ġuns afe +miss ible +com fort +15 6 +Ġmechan ic +00 2 +l ins +Ġsm oked +P os +Ġslow ing +Ġl av +Tex as +Ġche ating +ĠMet ropolitan +eth yl +Ġdiscover ing +as se +Ġpen cil +ĠPy ongyang +Ġclos et +ĠShe et +ĠEnt ry +ou stic +Ġmy st +er ate +ari at +Ġminer als +Ġmusic ian +ĠP ul +ĠM az +24 9 +Ġper missions +Ġ iv +en ary +ick ers +ĠB ing +he a +en able +Ġgri ev +Ġassert ed +ĠColon el +Ġaff idav +w o +Ġse ated +ĠR ide +Ġpaint ings +ĠP ix +Ġ13 7 +ish i +umb ai +g otten +ĠEar l +Ġin ning +Ġc ensus +Ġtrave lled +ĠCons ult +18 5 +b ind +Ġsimpl icity +Ġoverlook ed +ĠHelp ful +Ġmon key +Ġoverwhelming ly +Bl ood +ĠFl int +ĠJ ama +ĠPres ent +ĠR age +ĠT A +pt ive +Ġturn out +w ald +ĠD olphins +ĠV PN +Ġon ion +Ġcraft ing +m ma +ĠMerc ury +Ġarr ange +Ġalert s +ĠO T +zb ollah +Ġg ases +ĠRichards on +s al +l ar +Ġfro st +Ġlower ing +Ġacc laim +Ġstart ups +ĠG ain +ess ment +Ġguard ian +äº º +ĠP ie +ĠL inks +Ġmer its +Ġaw ake +Ġparent al +Ġexceed s +Ġid le +ĠPil ot +Ġe Bay +ĠAc cept +ipe g +C am +ĠK ot +Ġtrad ers +olit ics +unk er +ĠP ale +os i +an mar +Ġ19 47 +ĠF ell +est ial +it ating +G F +ĠS r +if ted +Ġconnect or +ĠB one +ill es +2 60 +h ma +Ġoverl ap +ĠGit Hub +Ġclean er +ĠBapt ist +ĠW AS +Ġlung s +Ñ ģ +ĠB UT +Ġc ite +Ġpit ched +reat ment +Ġtro phies +ĠN u +38 6 +ĠPr ide +Ġattend ees +[ ] +17 9 +Ġspat ial +Ġpri zes +ĠRel igion +Ġshow case +ĠC ategory +vid ia +T arget +Pro perty +? , +Ġf usion +p ie +ĠU CLA +Ġsound track +Ġprin cess +ĠC aval +sh ould +Ġlim bs +Back ground +Ġlone ly +Ġc ores +ĠT ail +she et +Ġ13 2 +R a +ãĤ « +ĠB olt +Ġbook ed +Ġadmin ister +Ġequ als +w y +Ġobserv ing +ĠBar on +ĠAd obe +Ġv irgin +ĠSocial ist +M ove +gh azi +ĠLind a +2 12 +Ġbre wing +Ġmerch ants +bur se +Ġdiv or +Ġmet als +ĠN er +Ġsum s +ĠEn emy +Ġen vision +Ġgrant ing +ĠH oney +ĠSk yrim +Ġsoc io +gr aded +Ġselect ive +W ASHINGTON +Ġ19 48 +ĠSir ius +ĠG ross +act ivity +ĠI van +Ġfur ious +BS D +ĠPre vious +Ġrespons ive +Ġchar itable +Ġle aning +ĠP ew +Ġviol ates +\\\\ \\\\ +ĠCom ing +w ire +Ġpo et +Ġres olutions +comm and +ĠPortug uese +Ġnick name +Ġde af +Feb ruary +Ġrecogn ise +Ġentire ty +Ġseason al +pl aced +ĠTe legraph +Ġmicro phone +our ing +Ġgr ains +Ġgovern ed +Ġpost p +ĠW aters +in ement +Ġund ocumented +ĠCom cast +Ġf ox +Ġassault s +re on +man y +ĠJen kins +ĠAny way +Ġassess ments +Ġdown s +ĠM ouse +Ġsuper b +k t +ĠD ow +Ġtax ation +4 01 +Ġsm iles +Ġundert aken +Ġex h +Ġenthusi astic +Ġtw ent +Ġgovernment al +Ġautonom y +ĠTechn ologies +ĠCh ain +Ġpreval ent +f b +Ġnic otine +og ram +j ob +Ġawa iting +ĠMen u +Ġdep uties +k ov +ish ops +But ton +ĠShan ghai +Ġdies el +ĠD uck +R yan +ĠPC s +N F +j ury +ent e +Ġinacc urate +edd y +Wh atever +Ġshow c +ĠN ad +od us +et r +Ġplaint iffs +ĠW OR +ĠAss ange +Ġpriv at +Ġpremium s +Ġt am +UR L +Ġel ites +ĠR anger +otten ham +ĠH off +ĠAt hens +Ġdefin ite +Ġs ighed +Ġeven ly +2 11 +ĠAm ber +ak ia +Ġmail ing +Ġcr ashing +ĠConfeder ate +ru gged +W al +ĠDep ths +Ġjuven ile +Ġreact or +Introdu ction +ĠDel uxe +19 95 +ĠS anchez +ĠM ead +iv able +: - +ĠPlan ning +ĠT rap +qu in +ĠProt ect +ve red +In formation +Ġkid ney +inn amon +l as +Ġpolic ing +Ġtoler ate +ĠQ i +Ġbi ased +F ort +ĠK i +s ave +Ġprivile ged +Ġbe asts +ĠGl as +ĠC inem +Ġcome back +Sund ay +Ġext inction +h ops +Ġtrans mit +Ġdoub les +ĠFl at +16 7 +Ġdis puted +Ġinjust ice +f oo +V ict +role um +ĠJul ie +Con text +ĠR arity +iss ue +Comp onent +Ġcounsel ing +an ne +d ark +Ġobject ions +u ilt +Ġg ast +Ġpl ac +Ġun used +ãĥ ĩ +ĠT rial +ĠJ as +hed ral +ob b +Ġtempor al +ĠPR O +ĠN W +ĠAnn iversary +L arge +Ġther m +Ġd avid +Ġsystem ic +ĠSh ir +m ut +ĠNe pt +add ress +Ġscan ning +Ġunderstand able +Ġcan vas +C at +ĠZ oo +Ġang els +L O +ĠStat ement +ĠS ig +ov able +ĠA way +sh aring +ocr ats +st ated +Ġweigh ing +N or +w ild +B ey +Ġaston ishing +ĠReyn olds +Ġop ener +Ġtrain er +Ġsurg ical +p n +Ġadjust ing +whe el +Ġf rown +erv ative +Ġsusp end +With in +te in +Ġobst acle +Ġliber ties +ym es +Ġur anium +ans om +an ol +ub a +ĠL oss +Ġa rous +ĠHend erson +W ow +s pl +c ur +ĠÂ Ń +Ġtheir s +Dam age +Ġdownload ing +Ġdisc ern +ĠSt o +ĠFl a +Ġh ath +ĠA j +Ġun pleasant +Europe an +exp ensive +Ġscreens hot +ĠU V +Ġall ied +ĠPers ian +Ġmonop oly +Ġat om +ĠReds kins +"> < +Ġcan cell +Ġcinem a +13 1 +f air +ĠAlf red +Ġd uck +arg s +22 3 +ĠIS I +Ġsign aling +in ar +Ġlaugh s +Ġfor wards +Ġreck less +Ġlisten ers +at ivity +Ġvast ly +n ant +L ess +ĠHun ting +ĠScient ific +IT ED +Ġkn ight +ĠH TC +us a +t mp +Ġr ude +ĠLegend ary +Ġar ises +B ad +ĠCl aim +pe g +Ġreal ities +Th ink +Ġ ° +Ġro de +Ġstri ve +Ġan ecd +Ġshort s +Ġhypot hes +Ġcoord inated +ĠGand hi +ĠF PS +R ED +Ġsuscept ible +Ġshr ink +ĠCh art +Hel p +Ġ ion +de ep +rib es +ĠK ai +ĠCustom er +Sum mary +Ġc ough +w ife +Ġl end +Ġposition ing +Ġlot tery +ĠC anyon +Ġf ade +Ġbron ze +ĠKenn y +Ġbo asts +ĠEnh anced +rec ord +Ġemer gence +Ġa kin +ĠB ert +it ous +âĸ ij +Ġst ip +Ġexch anged +om ore +als h +Ġreserv oir +Ġstand point +W M +Ġiniti ate +Ġdec ay +Ġbrew ery +Ġter ribly +Ġmort al +lev ard +Ġrev is +N I +el o +Ġconf ess +ĠMS NBC +Ġsub missions +Cont roller +Ġ20 2 +ĠR uth +} ); +ĠAz ure +Ġ ." +20 6 +ĠMarket ing +Ġl aund +ien cies +Ġrenown ed +ĠT rou +ĠN GO +ble ms +Ġterr ified +Ġwar ns +Ġper t +Ġuns ure +4 80 +ale z +ult z +ĠOut side +Ġst yl +ĠUnder ground +Ġp anc +Ġd ictionary +Ġf oe +rim inal +ĠNor wegian +Ġj ailed +Ġm aternal +é e +ĠLu cy +c op +Ch o +Ġuns igned +ĠZe lda +ĠIns ider +ĠContin ued +Ġ13 3 +ĠNar uto +ĠMajor ity +16 9 +ĠW o +ãĤ ĵ +Ġpast or +Ġinform al +Ð ½ +an throp +jo in +ãģ Ĺ +it ational +N P +ĠWrit ing +f n +ĠB ever +19 5 +Ġy elling +Ġdr astically +Ġe ject +Ġne ut +Ġth rive +ĠFre qu +ou x +Ġpossess es +ĠSen ators +ĠD ES +ĠSh akespeare +ĠFran co +ĠL B +uch i +Ġinc arn +Ġfound ers +F unction +Ġbright ness +ĠB T +Ġwh ale +ĠThe ater +m ass +ĠD oll +S omething +Ġecho ed +ĠHe x +c rit +af ia +Ġgodd ess +Ġele ven +ĠPre view +ĠAur ora +Ġ4 01 +uls ive +ĠLog an +in burgh +ĠCent ers +ĠON LY +ĠA id +Ġparad ox +Ġh urd +ĠL C +D ue +c ourt +Ġoff ended +Ġeval uating +ĠMatthew s +Ġto mb +Ġpay roll +Ġextra ction +ĠH ands +if i +Ġsuper natural +ĠCOM M +] = +dog s +Ġ5 12 +ĠMe eting +Rich ard +ĠMax imum +Ġide als +Th ings +m and +ĠReg ardless +Ġhum ili +b uffer +L ittle +ĠD ani +ĠN ak +Ġliber ation +ĠA be +ĠO L +Ġstuff ed +ac a +ind a +raph ic +Ġmos qu +Ġcampaign ing +Ġoccup y +S qu +r ina +ĠW el +ĠV S +Ġphys ic +Ġp uls +r int +oad ed +ET F +ĠArch ives +Ġven ues +h ner +ĠTur bo +Ġl ust +Ġappeal ed +que z +il ib +ĠTim othy +Ġo mn +d ro +Ġobs ession +ĠSav age +19 96 +Gl obal +J es +2 14 +Ġsl iding +Ġdisapp ro +ĠMag ical +Ġvolunt arily +g b +ane y +Ġprop het +ĠRe in +ĠJul ia +ĠW orth +aur us +Ġb ounds +ie u +)) ) +Ġcro re +ĠCitiz en +S ky +Ġcolumn ist +Ġseek ers +ond o +IS A +ĠL ength +Ġnost alg +Ġnew com +Ġdet rim +ent ric +3 75 +ĠG E +Ġaut op +Ġacadem ics +App Data +ĠS hen +Ġid iot +ĠTrans it +Ġteasp oon +W il +K O +ĠCom edy +> , +Ġpop ulated +W D +Ġp igs +ĠO culus +Ġsymp athetic +Ġmar athon +19 8 +Ġseiz ure +s ided +Ġd op +irt ual +L and +ĠFl oor +osa urs +... ] +Ġl os +Ġsubsid iary +E Y +ĠPart s +ĠSt ef +ĠJud iciary +Ġ13 4 +Ġmir rors +Ġk et +t imes +Ġneuro log +Ġc av +ĠGu est +Ġtum or +sc ill +ĠLl oyd +E st +Ġcle arer +Ġstere otypes +Ġd ur +not hing +Red dit +Ġnegoti ated +---------------- -------- +23 5 +Ġfl own +ĠSe oul +ĠRes ident +ĠS CH +Ġdisappear ance +ĠV ince +g rown +Ġgrab s +r il +ĠInf inite +ĠTw enty +Ġpedest rian +Ġjer sey +ĠF ur +ĠInf inity +ĠEll iott +Ġment or +Ġmor ally +Ġob ey +sec ure +iff e +Ġantib iotics +ang led +ĠFre eman +ĠIntrodu ction +J un +Ġm arsh +ic ans +ĠEV ENTS +och ond +W all +icult y +Ġmisdem eanor +Ġl y +Th omas +ĠRes olution +Ġanim ations +ĠD ry +Ġinter course +ĠNew castle +ĠH og +ĠEqu ipment +17 7 +Ġterrit orial +Ġarch ives +20 3 +Fil ter +ĠMun ich +Ġcommand ed +ĠW and +Ġpit ches +ĠCro at +Ġrat ios +ĠM its +Ġaccum ulated +ĠSpecific ally +Ġgentle man +acer b +Ġp enn +Ġa ka +ĠF uk +Ġinterven e +ĠRef uge +ĠAlz heimer +Ġsuccess ion +oh an +d oes +L ord +Ġsepar at +Ġcorrespond ence +Ġsh iny +P rior +Ġs ulf +Ġmiser able +Ġded ication +( ). +Ġspecial ists +Ġdefect s +ĠC ult +ĠX ia +Ġje opard +ĠO re +Ab ility +Ġle ar +Ġamb itions +ĠB MI +ĠArab s +Ġ19 42 +Ġpres ervation +ific ate +Ġash amed +l oss +ĠRest aur +Ġrese mble +Ġen rich +ĠK N +ĠCl an +fl oat +Ġplay able +IT T +Ġharm ony +arr ison +ĠWe instein +w ere +Ġpoison ing +ĠCom put +ĠWord Press +m ajor +ĠVal ve +F an +ĠTh row +ĠRom ans +ĠDep ression +ad os +Ġtort ured +Ġbal ancing +bott om +Ġacqu iring +ĠMon te +ard i +Ġa ura +Ġ# # +ĠStand ing +ĠAtl as +C F +Ġintr ins +ĠBen ghazi +Ġcamp ing +Ġt apped +bl ade +st rous +ĠR abb +ĠW ritten +t ip +ĠNe igh +ster dam +ĠAll ow +ĠHe aling +ĠR hod +n um +Ġcaffe ine +ĠPer cent +Ġbo o +Ġapp les +30 5 +Ġwel coming +Ġappl aud +Ġa usterity + ± +ĠRe ality +ef e +å ® +Ġsu cks +Ġtab s +ĠPay Pal +Ġback pack +Ġgif ted +abul ary +ĠSc out +ir teen +Ġch in +Ġo mitted +Ġnegative ly +Ġaccess ing +ĠE arn +Ġambul ance +Ġhead phones +Ġ20 5 +ĠRef resh +p resident +ĠKit chen +ĠEnt ered +ĠS nyder +00 5 +om ical +Ġborrow ed +ĠN em +Ġav iation +Ġst all +rim ination +Ġuniform s +it ime +ĠSim mons +ener gy +ab lished +y y +qual ified +Ġrall ies +ĠSt uart +fl ight +Ġgang s +r ag +Ġv ault +lu x +ĠCom par +Ġdesign ation +20 9 +ĠJ os +d ollar +z ero +Ġwell s +30 3 +Ġconstitu ents +Ġhe ck +Ġc ows +Ġcommand ers +Ġdifferent ial +ĠC atherine +29 9 +Ġval ve +Ġbr ace +Ġperspect ives +c ert +f act +icular ly +ĠMc N +pl anes +Ġint ric +Ġpe as +ov an +Ġtoss ed +ret ch +ĠL opez +Ġunf amiliar +de ath +ĠA part +ĠCh ang +Ġrelie ved +rop he +Ġair ports +Ġfre ak +ut il +M ill +ĠCh in +ĠOw en +m ale +ĠBro ken +ĠWind s +ro b +r ising +Ġfire fighters +Ġauthor itarian +Ġ14 8 +Bit coin +ex ternal +Ġbrow sers +iche ver +or ian +Ġun b +Ġpo ke +ĠZ ot +M id +ĠPop ular +Ġco vert +Ġcont ributes +Ġ6 50 +Ġcont ention +G ate +Ġcons oles +Ġchrom os +ĠI X +Ġvis ually +ĠE isen +Ġjewel ry +Ġdeleg ation +Ġacceler ate +ĠR iley +Ġsl ope +Ġind oor +it ially +Ġhuge ly +Ġtun nels +Ġfin ed +Ġdirect ive +Ġfore head +ustom ed +Ġsk ate +Mus ic +g as +Ġrecogn izing +am bo +Ġover weight +ĠGr ade +Ù Ĭ +Ġsound ing +Ġlock ing +ĠR EM +St ore +Ġexc av +ĠLike wise +ĠL ights +Ġel bow +ĠSupp ly +w ic +Ġhands ome +19 94 +C oll +Ġadequ ately +ĠAssoci ate +Ġstri ps +Ġcrack down +Ġmar vel +ĠK un +Ġpass ages +@@ @@ +ĠT all +Ġthought ful +names e +Ġprost itution +bus iness +Ġball istic +person al +c ig +iz ational +R ound +ĠÂłĠÂł ĠÂłĠÂł +ĠCole man +Ġadm itting +ĠPl ug +Ġbit coins +ĠSu z +Ġfair ness +Ġsupp lier +Ġcatast rophic +ĠHel en +o qu +M arc +ĠArt icles +g ie +Ġend angered +Ġdest iny +ĠVol t +ol ia +ax is +Ġche at +Ġun ified +IC O +qu ote +30 2 +ĠS ed +Ġsupp ression +Ġanaly zing +Ġsqu at +Ġfig uring +Ġcoordin ates +Ġch unks +Ġ19 46 +Ġsub p +Ġw iki +ĠFor bes +ĠJ upiter +ĠE rik +im er +ĠCom mercial +\ ) +Ġlegitim acy +Ġd ental +ĠMe an +Ġdefic its +5 50 +Orig inally +ĠHor ror +Ġcontam ination +ll ah +Ġconf isc +ĠCl are +T B +ĠF ailed +an ed +Ġrul er +ĠCont roller +Ġfemin ists +F ix +g ay +20 7 +Ġr abbit +Th ird +ownt own +Ġgl ue +Ġvol atile +Ġsh ining +Ġf oll +Ġimp aired +Ġsup ers +æ Ī +Ġcl utch +ļé ĨĴ +Ġpro let +Ġ( ! +Ġy elled +ĠK iev +ĠEr n +ĠSh ock +K B +Ġsit uated +qu ery +ĠN as +Ġan nex +char acter +ĠHol iday +Ġautom ation +ĠJ ill +ĠRem astered +Ġl inem +Ġwild erness +ĠHor izon +ĠGu inea +A Z +Ġmain land +Ġsec recy +LE ASE +Ġp unk +ĠProv ince +( ), +Spe ed +Ġhand ing +ĠSeb ast +S ir +r ase +Ġj ournals +Ġcon gest +ĠT ut +ir rel +Ġschizophren ia +Ġmis ogyn +health y +I ron +Ġreact ed +- $ +25 2 +Ġpl ural +Ġpl um +Ġbarg ain +Ġground ed +f inder +Ġdis se +ĠL az +O OD +Ġat roc +F actory +Ġmin ions +Ġo ri +ĠB rave +ĠP RE +ĠMy anmar +ĠH od +Ġexped ition +Ġexpl ode +ĠCo ord +Ġext r +ĠB rief +ĠAD HD +Ġhard core +feed ing +Ġd ile +ĠF ruit +Ġvacc ination +ĠM ao +osp here +Ġcont ests +- | +Ġf ren +isp here +R om +ĠSh arp +ĠTre nd +Ġdis connect +âĢ¢ âĢ¢ +Ġper secution +Ear th +Ġhealth ier +38 4 +Ġc ob +ĠTr inity +OW S +AN N +Ġspecial ty +Ġg ru +Ġcooper ative +wh y +Start ing +ĠIss ues +st re +ens or +Ġ18 5 +Ad v +! ? +ĠRe vel +em ia +ĠH ulk +Ġcelebr ations +ĠS ou +ra ud +ĠKle in +Ġun real +con text +Ġpartners hips +Ġadop ting +t ical +Ġspl ash +ĠHe zbollah +c ategory +cycl op +xt on +ĠD ot +urd y +t z +Ġenvelop e +ĠN L +â ķ +Ġwhere in +Spe c +18 4 +Ġte lev +al iation +Ġmyth s +å ° +Ġrig orous +Ġcommun icating +Ġobser ver +Ġre he +ĠW ash +Ġapolog ized +ĠT in +Ġexpend itures +work ers +d ocument +Ġhes itate +ĠLen in +Ġunpredict able +Ġrenew al +cl er +ok ia +ĠCON T +Ġpost season +Tok ens +Ġex acerb +Ġbet ting +Ġ14 7 +Ġelev ation +W ood +ĠSol omon +19 4 +00 4 +out put +Ġredu nd +ĠM umbai +Ġp H +Ġreprodu ce +ĠD uration +MA X +Ġb og +C BS +ĠBal ance +ĠS gt +ĠRec ent +Ġc d +Ġpo pped +Ġincomp et +pro p +ay an +g uy +Pac ific +Ġty r +Ġ{ { +ĠMy stic +ĠD ana +Ġmast urb +Ġge ometry +à ¢ +ĠCor rect +Ġtraject ory +Ġdistract ed +Ġf oo +ĠW elsh +L uc +m ith +Ġrug by +Ġrespir atory +Ġtri angle +Ġ2 15 +Ġunder graduate +ĠSuper ior +ch anging +_ - +Ġright ly +Ġrefere e +Ġluc rative +Ġun authorized +Ġresemb les +ĠGN U +ĠDer by +Ġpath ways +ĠL ed +Ġend urance +Ġst int +Ġcollect or +F ast +Ġd ots +Ġnational s +ĠSec urities +Ġwh ip +Par am +Ġlearn s +M agic +Ġdetail ing +m oon +Ġbroadcast ing +Ġb aked +26 5 +hol m +ĠS ah +ĠHus sein +ĠCourt esy +17 4 +Ġ14 6 +Ġge ographic +pe ace +Ġjud ging +ĠS tern +B ur +Ġstory line +G un +ĠSt ick +24 5 +30 7 +ãĤ´ ãĥ³ +ĠAdminist rator +Ġbur nt +Ġp ave +ch oes +Ex ec +Ġcamp uses +Res ult +Ġmut ations +ĠCh arter +Ġcapt ures +Ġcomp ares +Ġbad ge +S cient +Ġer ad +ier y +o i +ett es +ĠE state +Ġst rap +Ġproud ly +Ġf ried +Ġwithd rawn +ĠV oy +ph ony +It ems +ĠP ierce +b ard +Ġann otation +ant on +ill on +Im pro +... ) +Ġhapp ier +---- -- +ad just +Ġstaff ers +Ġactiv ism +Ġper f +Ġal right +N eed +Ġcomm ence +Ġopio id +ĠAm anda +E s +ĠP ars +ĠK aw +W orks +24 8 +Ġind o +t c +end ant +ĠM oto +Ġlegal ization +OT E +Ġtask ed +Ġt sp +ĠACT IONS +16 6 +Ġrefres hing +ĠN R +ĠPere z +Ġinfring ement +S Y +List en +in ning +k u +Ġrot ate +pro gram +ar ah +Des ign +Ġ( £ +Ġst oring +Ġwar rants +Ġjud gement +ĠB rist +us ually +ph oto +ĠR an +ĠP ine +Ġoutrage ous +ĠValent ine +lu ence +ĠEvery body +Al tern +Ġrele vance +Ġtermin ated +Ġd essert +Ġfulf illed +Ġprosecut ed +ĠW ords +Ġm igrant +Ġcultiv ation +ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ +idel ity +ĠV ern +ĠLog in +Ġmetaph or +ĠT ip +Ġrecru its +ĠP ig +rib ing +Ġenthusi asts +ex per +Ġfright ening +ĠH air +ans on +str ate +Ġh i +He ight +Ġown ing +n one +Ġdis like +Ġkn ives +pher d +Ġloud ly +ĠAP Is +Dis play +ĠL ac +ĠUS S +ab l +ver ages +J ew +Ġ17 2 +ĠHist orical +at oon +ĠPhys ics +in tern +Ġwarm th +Ġto pp +D M +Ġgun man +Ġem peror +od i +ãĥ £ +in atory +ĠR ib +Ġ13 1 +ĠSat urn +ĠSh ining +Ġw aking +Qu otes +Ġcomed ian +en berg + ½ +Ġbelie vers +Ġpaper work +c ustom +Ġle v +Ġl ament +Ġpour ing +22 2 +p olitical +ĠSupp lement +m aid +Ġcruel ty +Ġt read +ys ics +A w +rit es +Ġmod ifier +ĠP osition +Ad am +l b +ub s +Ġimper fect +Ġcl usters +ĠEngine er +ĠC herry +Ġinaug uration +ĠS au +Ġembod iment +ĠUn cle +Ġover r +Ġexplos ions +c ule +ĠPrinc eton +ĠAndre a +Ġincorrect ly +Ġearn est +Ġpil gr +ĠS print +Ġslee ve +Ġhe ars +ĠAm azing +Ġbrow sing +ag in +Ġhom eland +Ġha w +Ġd iving +ist ered +17 8 +Ġbarg aining +ĠArc ade +Ġdeleg ate +ters on +................................ ................................ +ĠJackson ville +27 5 +Ġst agn +Ġad am +ĠSher man +C B +Ġsub urb +ĠFood s +Ġconver ting +ĠAr ist +Ġch ambers +l ove +Ġam ino +ĠG an +Ġmad ness +m c +ĠUS E +def ined +Ġul tr +ind ust +Ġw olves +l ance +Add itionally +Ġcr acks +as ia +ĠRe ason +ĠP ump +Ġaccident al +ĠL aser +ĠR id +Ġinitial ized +ell i +Ġun named +Ġn oun +ĠPass ed +Ġhost age +ĠEth iop +sh irts +Ġun rel +ĠEmb assy +Ġ19 41 +Ġat oms +Ġpur ported +16 4 +ĠF i +Ġgall ons +ĠMon ica +Ġp g +en ment +Ġsort ed +ĠG ospel +Ġhe ights +Ġtr aced +Ġunder going +She ll +Ġs acks +Ġproport ions +Ġhall uc +F ont +ac et +Ġwar mer +ĠIN TER +Ġgrab bing +Pl ug +Ġreal ization +ĠBur ke +Ġen chant +AT ER +ĠSe ed +Ġabund ant +F M +Ġc ivic +V s +is i +Ġv ow +Ġre per +ĠPartners hip +Ġpenet ration +Ġax e +Ġsh attered +ĠZ ombies +Ġv inyl +ĠAl ert +e on +Ġoblig ed +ĠIll ust +ĠPl aza +ĠFront ier +Ġdavid jl +ĠSer ial +ĠH av +ĠNut rition +B i +Ġâĸ Ī +ĠJ ays +lin ux +Ġhur ry +Ġv oy +Ġhop eless +ĠSte alth +Ġ ãģ +ess ors +tt le +b org +ĠSaf ari +f ell +Ġw ary +d ue +ĠAb ove +H a +E LL +Ġnot or +ĠW on +T oo +Ġoccup ations +Ġposs essions +Ġinv iting +Ġpred ators +Ġacceler ated +Ġ15 7 +uter te +ĠC ube +e ast +acc ount +G ive +Ġtrans plant +red ients +id able +Ġscreens hots +ĠG und +ĠF S +Ġtravel ers +Ġsens ory +ĠF iat +ĠRock ets +İ ĭ +_ { +F riend +Ġchar ming +AL S +Ġenjoy ment +m ph +Ġ5 000 +ĠRE G +Ù Ĩ +b ia +Ġcomp ilation +ro st +ĠV P +ĠSch ne +201 9 +Ġcop ying +M ORE +ĠFl ore +f alls +2 15 +t otal +Ġdis ciples +d ouble +Ġexceed ing +Ġsm ashed +Ġconcept ual +ĠRom ania +ĠB rent +ĠI CE +ĠT ou +Ġg rap +Ġn ails +18 9 +ãĥ ĺ +Ġproc ure +e ur +Ġconfir ming +ĠC ec +aw i +ĠEd en +Ġn g +Ġengine ered +at ics +Ġhook ed +Ġdisgust ing +ĠMur der +ãĤ ¿ +L ibrary +Ġ16 8 +Al most +hem atic +Men u +ĠNot re +ĠJ ur +Ġkidn apped +Ġhack er +ĠJ ade +Ġcreep y +Ġdraw ings +ĠSpons or +Ġcycl ists +ĠGob lin +Ġoptim ized +Ġst aged +ĠMc D +bet ween +A ge +en o +S ex +ĠW ide +n ings +av is +Ġincap able +ĠK ob +Ġreward ing +ĠL one +oles cent +Ġcontract ed +Ġstick y +J ose +B all +f est +ĠIn put +ĠRec ently +Ġto mat +squ are +App lication +Ġnit rogen +Ġdupl icate +ĠRec on +ĠD ear +L ondon +Ġint ra +Ġd ock +Ġout reach +ĠM illion +Ġmamm als +am pton +V AL +Ġsn aps +Ġd os +ĠWh ole +ĠRead y +T ry +ĠWinn ipeg +ear ance +Ġinc urred +ren ched +ĠNS W +il ot +rain e +Ġc ube +g ot +Ġrun way +etermin ed +ĠHaw ks +Ġsurviv or +ĠW ish +ĠD in +ĠDE F +ĠV ault +18 7 +Ġmush rooms +Ġcris p +be y +ĠDisco very +Ġdevelopment al +Ġparad igm +Ġcha otic +ĠT su +Ġ3 33 +b ons +Ġbacter ial +Ġcomm its +Ġcos mic +Ġme ga +oc ative +ĠP aint +ophob ic +Ġv ain +Ġcar ved +ĠTh ief +ĠG ul +ows hip +Ġc ites +ĠEd inburgh +Ġdimin ished +Ġacknowled ges +ĠK ills +Ġmic row +ĠHer a +Ġsen iors +Ġwhere by +H op +at ron +Ġun available +ĠN ate +Ġ4 80 +Ġsl ated +ĠRe becca +ĠB attery +Ġgram mar +Ġhead set +Ġcurs or +Ġex cluding +any e +aunder ing +eb in +Ġfeas ible +ĠPub lishing +ĠLab s +ĠCl iff +ĠFerr ari +Ġp ac +vis ible +mark ed +pe ll +Ġpol ite +Ġstagger ing +ĠGal actic +Ġsuper st +Ġpar an +ĠOffic ers +ãĢ ģ +Ġspecific s +ul us +23 9 +ĠP aste +AM P +ĠPan ama +ĠDe lete +angu ard +rest rial +Ġhero ic +ĠD y +ا ÙĦ +Ġincumb ent +Ġcr unch +t ro +Ġsc oop +Ġblog ger +Ġsell ers +ure n +Ġmedic ines +ĠC aps +ĠAnim ation +ox y +Ġout ward +Ġinqu iries +22 9 +Ġpsych ologist +ĠS ask +ev il +Ġcontam inated +ãĤ ¨ +he rence +Ġbrand ed +ĠAbd ul +z h +Ġparagraph s +Ġmin s +Ġcor related +er b +Ġimp art +Ġmil estone +ĠSol utions +ot le +Ġunder cover +Ġmar ched +ĠCharg ers +f ax +ĠSec rets +Ġr uth +we ather +Ġfemin ine +Ġsh am +Ġprest igious +igg ins +Ġs ung +hist ory +ett le +gg ie +Ġout dated +ol and +Ġper ceptions +ĠS ession +ĠDod gers +u j +ĠE ND +D oc +Ġdefic iency +Gr and +ĠJ oker +Ġretro spect +Ġdiagn ostic +Ġharm less +Ġro gue +ĠA val +E qu +Ġtrans c +ĠRoberts on +ĠDep ending +ĠBurn s +iv o +Ġhost ility +F eatures +ĵ ĺ +Ġdis comfort +ĠL CD +spec ified +ĠEx pect +3 40 +Ġimper ative +ĠReg ular +Ch inese +Ġstate wide +Ġsy mm +Ġlo ops +Ġaut umn +N ick +Ġsh aping +Ġqu ot +Ġc herry +ĠCross ref +è¦ ļéĨĴ +Stand ard +he ed +ĠD ell +ĠViet namese +Ġo st +ĠV alkyrie +O A +Ass ad +Ġreb ound +ĠTra ffic +pl aces +æ ĺ +ĠB uc +17 2 +Ġshel ters +Ġins isting +ĠCertain ly +ĠKenn eth +ĠT CP +Ġpen al +ĠRe play +he ard +Ġdial ect +iz a +ĠF Y +it cher +ĠD L +Ġspir al +Ġquarterback s +Ġh ull +Ġgo ogle +Ġto dd +ĠSter ling +ĠPl ate +Ġsp ying +mb ol +ĠReal m +ĠPro ced +ĠCr ash +Ġtermin ate +Ġprotest ing +C enter +gu ided +Ġun cover +Ġboy cott +Ġreal izes +s ound +Ġpret ending +ĠV as +19 80 +Ġfram ed +Ġ13 9 +Ġdesc ended +Ġrehab ilitation +Ġborrow ing +ĠB uch +Ġbl ur +R on +ĠFro zen +en za +Ch ief +ĠP oor +Ġtransl ates +M IN +Ġ2 12 +J ECT +Ġerupt ed +Ġsuccess es +S EC +Ġpl ague +Ġg ems +d oms +Ġstret ches +ĠSp y +Ġstory telling +C redit +ĠP ush +Ġtra ction +Ġin effective +ĠL una +Ġt apes +Ġanaly tics +erc ise +Ġprogram mes +ĠCar bon +Ġbeh old +he avy +ĠConserv ation +ĠF IR +Ġs ack +ter min +ric ks +Ġhous ed +Ġunus ually +I ce +Ġexecut ing +ĠMor oc +ed ay +Ġed itions +Ġsm arter +ĠB A +Ġout law +Ġvan ished +ib a +AL SE +ĠSil va +23 8 +C ould +Ġphilos opher +Ġevac uated +Sec ret +14 2 +Ġvis as +ãĤ ¬ +ĠM alt +ĠClear ly +ĠN iger +ĠC airo +ĠF ist +3 80 +ĠX ML +aut o +it ant +Ġrein forced +Rec ord +ĠSurviv or +G Hz +Ġscrew s +parent s +Ġo ceans +ma res +Ġbra kes +vas ive +Ġhell o +ĠS IM +rim p +Ġo re +ĠArm our +24 7 +Ġterr ific +Ġt ones +14 1 +ĠMin utes +Ep isode +Ġcur ves +Ġinflamm atory +Ġbat ting +ĠBeaut iful +L ay +Ġunp op +v able +Ġr iots +ĠTact ics +b augh +ĠC ock +Ġorg asm +ĠS as +Ġconstruct or +et z +G ov +Ġant agon +Ġthe at +Ġde eds +ha o +c uts +ĠMc Cl +Ġu m +ĠScient ists +Ġgrass roots +ys sey +"] => +Ġsurf aced +Ġsh ades +Ġneighb ours +Ġad vertis +oy a +Ġmer ged +Up on +Ġg ad +Ġanticip ate +Any way +Ġsl ogan +Ġdis respect +I ran +ĠT B +act ed +Ġsubp oen +medi ately +OO OO +Ġwa iver +Ġvulner abilities +ott esville +ĠHuff ington +J osh +ĠD H +M onday +ĠEll en +K now +x on +it ems +22 8 +Ġf ills +ĠN ike +Ġcum ulative +and als +I r +Ġ ì +Ġfr iction +ig ator +Ġsc ans +ĠVi enna +ld om +Ġperform ers +P rim +Ġb idding +M ur +Ġlean ed +ĠPri x +al ks +Ġ[ âĢ¦] +ĠTw itch +ĠDevelop er +ĠG ir +Ġcall back +Ab stract +Ġacc ustomed +Ġfreed oms +ĠP G +ur acy +Ġl ump +is man +,, ,, +19 92 +ĠR ED +Ġwor m +M atch +ĠPl atinum +I J +ĠOwn er +Tri via +com pl +Ġnew born +Ġfant as +O wn +Ġ19 59 +Ġsymp ath +Ġub iqu +Ġoutput s +Ġal lev +Ġpr ag +K evin +Ġfav ors +Ġbur ial +Ġn urt +so lete +c ache +Ġ15 6 +Ġunl ocks +te chn +M aking +Ġcon quer +ad ic +æ ĸ +Ġel f +Ġelect orate +ĠKurd s +ĠSt ack +ĠSam urai +Ġâ ĺħ +Ġ{ } +ĠS aid +ĠFall out +Ġkind ness +ĠCustom s +ĠBou levard +Ġhelicop ters +ot ics +ĠVe get +com ment +Ġcritic ised +Ġpol ished +ĠRem ix +ĠC ultural +Ġrec ons +Ġdo i +at em +Sc reen +Ġbar red +Com ments +ĠGener ally +Ġsl ap +7 20 +V ari +p ine +Ġem pt +Ġh ats +ĠPlay ing +l ab +a verage +form s +ĠC otton +Ġcan s +ĠD ON +ĠSom alia +C rypt +ĠIncre ases +E ver +mod ern +Ġsur geon +3 000 +Ġrandom ized +================================ ================================ +B ern +im pl +ĠC OR +Ġpro claim +th ouse +Ġto es +Ġam ple +Ġpres erving +Ġdis bel +gr and +B esides +Ġsil k +ĠPat tern +h m +Ġenter prises +Ġaffidav it +ĠAdvis ory +Ġadvert ised +ĠRel igious +se ctions +psy ch +ĠField s +aw ays +Ġhasht ag +ĠNight mare +Ġv ampire +Ġfore nsic +rosso ver +n ar +Ġn avy +Ġvac ant +ĠD uel +Ġhall way +Ġface book +ident ally +ĠN RA +Ġm att +Ġhur ricane +ĠKir by +ĠP uzzle +Ġsk irt +ou st +du llah +Ġanal ogy +in ion +Ġtomat oes +ĠN V +ĠPe ak +ĠMe yer +Ġappoint ments +Ġm asc +Ġal ley +re hend +Ġchar ities +Ġund o +Ġdest inations +ĠTest ing +"> " +c ats +* . +Ġgest ures +gener al +Le ague +Ġpack ets +ĠInspect or +ĠBer g +Ġfraud ulent +Ġcritic ize +F un +Ġbl aming +nd ra +Ġsl ash +ĠE ston +Ġpropos ing +Ġwh ales +Ġtherap ist +Ġsub set +Ġle isure +EL D +ĠC VE +ĠAct ivity +Ġcul min +sh op +ĠD AY +is cher +ĠAdmir al +ĠAtt acks +Ġ19 58 +Ġmem oir +Ġfold ed +Ġsex ist +Ġ15 3 +ĠL I +Ġread ings +Ġembarrass ment +ĠEmploy ment +w art +ch in +Ġcontin uation +l ia +Rec ently +Ġd uel +Ġevac uation +ĠKash mir +Ġdis position +ĠR ig +Ġbol ts +Ġins urers +4 67 +M ex +Ġret aliation +Ġmis ery +Ġunre asonable +r aining +I mm +ĠP U +em er +Ġgen ital +ãĤ ³ +ĠC andy +Ġon ions +ĠP att +lin er +Ġconced ed +Ġf a +Ġfor c +ĠH ernandez +ĠGe off +deb ian +ĠTe ams +Ġc ries +Ġhome owners +23 7 +A BC +Ġst itch +Ġstat istic +Ġhead ers +ĠBi ology +Ġmot ors +ĠG EN +ĠL ip +Ġh ates +Ġhe el +S elf +i pl +ED IT +ort ing +Ġann ot +ĠSpe ech +old emort +ĠJ avascript +ĠLe Bron +Ġfoot print +Ġf n +Ġseiz ures +n as +h ide +Ġ19 54 +ĠBe e +ĠDecl aration +ĠKat ie +Ġreserv ations +N R +f emale +Ġsatur ated +Ġb iblical +Ġtroll s +Dev ice +ph otos +Ġdr ums +ãĥīãĥ© ãĤ´ãĥ³ +N ight +f ighter +ĠH ak +ri ber +Ġc ush +Ġdiscipl inary +ba um +ĠG H +ĠSch midt +ilib rium +Ġs ixty +ĠKush ner +ro ts +Ġp und +ĠR ac +Ġspr ings +Ġcon ve +Bus iness +F all +Ġqual ifications +Ġvers es +Ġnarc iss +ĠK oh +ĠW ow +ĠCharl ottesville +ed o +Ġinterrog ation +ĠW ool +36 5 +B rian +Ġâľ ĵ +Ġalleg es +ond s +id ation +ĠJack ie +y u +Ġl akes +Ġworth while +Ġcryst als +ĠJud a +Ġcomp rehend +Ġfl ush +Ġabsor ption +ĠO C +Ġfright ened +ĠCh ocolate +Mart in +Ġbu ys +Ġbu cks +Ġapp ell +ĠChampions hips +Ġlist ener +ĠDef ensive +Ġc z +ud s +ĠM ate +Ġre play +Ġdecor ated +Ġs unk +ĠV IP +ĠAn k +Ġ19 5 +aa aa +Nob ody +ĠMil k +ĠG ur +ĠM k +ĠS ara +Ġse ating +ĠW id +Tr ack +Ġemploy s +Ġgig antic +AP P +ãĤ § +in ventory +Ġtow el +at che +l asting +ĠT L +Ġlat ency +Ġkn e +B er +me aning +Ġup held +Ġplay ground +Ġm ant +S ide +Ġstere o +Ġnorth west +Ġexception ally +Ġr ays +Ġrec urring +D rive +Ġup right +Ġab duct +ĠMar athon +Ġgood bye +Ġal phabet +h p +Ġcourt room +ring ton +ot hing +T ag +Ġdiplom ats +Ġbar bar +ĠAqu a +18 3 +33 33 +Ġmat urity +Ġinst ability +ĠAp ache +Ġ= == +Ġfast ing +ĠGr id +Mod Loader +Ġ15 2 +A bs +ĠOper ating +ett i +Ġacqu aint +Don nell +ĠK em +ĠFor ge +Ġarm ored +M il +Ġphilos ophers +in vest +Pl ayers +â Ī +Ġmy riad +Ġcomr ades +R ot +Ġremember ing +Ġcorrespond s +Ġprogram mers +ĠLyn n +Ġo lig +Ġco herent +yn chron +ĠChem ical +Ġj ugg +p air +post s +E ye +ĠIn ner +Ġsem ester +ott est +ĠEmir ates +ric anes +or ously +m its +ĠW is +Ġd odge +l ocation +Ġf aded +Am azon +ĠPro ceed +ĠIN FO +j ournal +ĠTru ck +T en +Ġ2 17 +Ġstat utes +m obile +ĠT ypes +Rec omm +b uster +pe x +Ġleg ends +Ġhead ache +f aced +ĠWi Fi +if ty +ĠH ER +Ġcirc uits +ER ROR +22 6 +ol in +Ġcyl inder +osp ace +ik ers +P rem +Qu ant +Ġconflic ting +Ġslight est +Ġfor ged +ion age +Step hen +ĠK ub +ĠOpp ortun +ĠHe al +Ġbl o +Ġrul ers +Ġh uh +Ġsubmar ine +f y +ass er +Ġallow ance +ĠKas ich +ĠT as +ĠAustral ians +Forge ModLoader +ĠâĨ ij +ĠMat rix +am ins +Ġ12 00 +ĠAc qu +23 6 +D ocument +ĠBre aking +19 3 +ĠSub st +ĠRoll er +ĠPro perties +ĠN I +t ier +Ġcr ushing +Ġadvoc ating +Further more +keep ers +Ġsex ism +x d +Ġcall er +ĠS ense +chie ve +ĠT F +Ġfuel ed +Ġreminis cent +Ġobs ess +ur st +Ġup hold +ĠF ans +het ics +Ġâ Ĺ +ĠB ath +Ġbe verage +Ġo scill +25 4 +Ġpol es +Ġgrad ual +Ġex ting +ĠS uff +ĠS uddenly +Ġlik ing +Ġ19 49 +un ciation +am ination +ĠO mar +ĠL V +ĠCon sequently +Ġsynt hes +ĠG IF +Ġp ains +Ġinteract ing +u ously +inc re +Ġrum or +ĠScient ology +19 7 +ĠZ ig +Ġspe lling +ĠA SS +Ġexting u +ms on +Ġg h +Ġremark ed +ĠStrateg ic +ĠM ON +å ¥ +g ae +ĠWH AT +E ric +ĠCamp us +Ġmeth ane +Ġimag in +J UST +ĠAl m +X T +i q +ĠR SS +Ġwrong doing +att a +Ġbig ot +Ġdemonstr ators +ĠCal vin +ĠV illa +Ġmembr ane +ĠAw esome +Ġbenef ic +26 8 +Ġmagn ificent +ĠL ots +G reg +ĠBor is +Ġdetain ees +ĠH erman +Ġwhis pered +Ġa we +Prof essor +fund ing +Ġphys iological +ĠDest ruction +Ġlim b +Ġmanip ulated +Ġbub bles +Ġpse ud +Ġhyd ra +ĠBrist ol +Ġst ellar +ĠExp ansion +ĠK ell +ĠInterest ingly +Ġm ans +Ġdrag ging +Ġec ological +ĠF it +Ġg ent +Ġbenef ited +ĠHait i +Ġpoly g +ãĥ İ +Ġ20 30 +Ġpro w +Ġrecon struction +Ġwas t +Ġpsych ic +ĠGree ks +Hand ler +16 2 +ĠP ulse +Ġsol icit +Ġsy s +Ġinflu x +ĠG entle +per cent +Ġprolifer ation +Ġtax able +Ġdisreg ard +Ġesc aping +Ġg inger +Ġwith stand +Ġdevast ated +ĠD ew +ser ies +Ġinject ed +ela ide +Ġturn over +he at +Ļ Ĥ +H appy +ĠSil ent +ãĤ Ń +iv ism +Ġir rational +AM A +Ġre ef +r ub +Ġ16 2 +Ġbank ers +ĠEth ics +v v +Ġcritic isms +K n +18 6 +M ovie +ĠT ories +Ġno od +Ġdist ortion +F alse +od ore +Ġt asty +Res earch +ĠU ID +- ) +Ġdivor ced +ĠM U +ĠHay es +ĠIs n +ian i +ĠH Q +Ġ" # +ign ant +Ġtra umatic +ĠL ing +H un +Ġsab ot +on line +r andom +Ġren amed +ra red +K A +d ead +é t +ĠAss istance +Ġse af +++++ ++++ +Ġse ldom +ĠWeb b +Ġbo olean +u let +Ġref rain +ĠDI Y +ru le +Ġshut ting +Ġutil izing +load ing +ĠPar am +co al +oot er +Ġattract ing +ĠD ol +Ġher s +ag netic +ĠRe ach +im o +Ġdisc arded +ĠP ip +01 5 +ü r +Ġm ug +Im agine +C OL +Ġcurs ed +ĠSh ows +ĠCurt is +ĠSach s +spe aking +ĠV ista +ĠFram ework +ong o +Ġsub reddit +Ġcr us +ĠO val +R ow +g rowing +Ġinstall ment +Ġgl ac +ĠAdv ance +EC K +ĠLGBT Q +LE Y +Ġac et +Ġsuccess ive +ĠNic ole +Ġ19 57 +Qu ote +Ġcircumst ance +ack ets +Ġ14 2 +ort ium +Ġguess ed +ĠFr ame +Ġperpet rators +ĠAv iation +ĠBen ch +Ġhand c +A p +Ġ19 56 +25 9 +r and +Net Message +d in +urt les +h ig +ĠV III +ff iti +ĠSw ords +b ial +Ġkidn apping +dev ice +Ġb arn +ĠEl i +auc as +S end +Con structed +Ġ ½ +Ġneed les +Ġad vertisements +Ġv ou +Ġexhib ited +ĠFort ress +As k +B erry +TY PE +Ġcan cers +ump ing +ĠTerrit ory +Ġpr ud +Ġn as +Ġathe ist +Ġbal ances +ãģ Ł +ĠSh awn +& & +Ġland sc +ĠR GB +Ġpet ty +Ġex cellence +Ġtransl ations +Ġpar cel +ĠChe v +E ast +ĠOut put +im i +Ġamb ient +ĠTh reat +Ġvill ains +Ġ5 50 +IC A +Ġtall er +Ġle aking +c up +Ġpol ish +Ġinfect ious +ĠK C +Ġ@ @ +back ground +Ġbureaucr acy +ĠS ai +un less +it ious +ĠSky pe +At l +ID ENT +00 8 +Ġhyp ocr +Ġpit chers +Ġguess ing +ĠF INAL +Bet ween +Ġvill agers +Ġ25 2 +f ashion +ĠTun is +Be h +ĠEx c +ĠM ID +28 8 +ĠHas kell +19 6 +ĠN OR +Ġspec s +Ġinv ari +Ġgl ut +ĠC ars +Ġimp ulse +Ġhon ors +g el +Ġjurisd ictions +ĠBund le +ul as +Calif ornia +ĠIncre ase +Ġp ear +Ġsing les +Ġc ues +Ġunder went +ĠW S +Ġexagger ated +Ġdub ious +Ġfl ashing +L OG +) ]. +J ournal +t g +V an +ĠI stanbul +ĠIn sp +ĠFrank en +D raw +Ġsad ness +Ġiron ic +ĠF ry +x c +Ġ16 4 +is ch +W ay +ĠProtest ant +h orn +Ġun aff +ĠV iv +ill as +ĠProduct ions +ĠH ogan +Ġper imeter +ĠS isters +Ġspont aneous +Ġdown side +Ġdescend ants +Ġor n +w orm +Japan ese +Ġ19 55 +Ġ15 1 +ĠDo ing +els en +umb les +Ġrad ically +ĠDr um +ĠB ach +Ġli abilities +ĠO B +ĠElement ary +Ġmem e +yn es +Ġfinger print +ĠGr ab +Ġundert ake +Mem bers +ĠRead er +ĠSim s +g od +Ġhypot hetical +s cient +ĠA J +Ġchar ism +Ġad missions +ĠMiss ile +tr ade +Ġexerc ising +ĠBack ground +W ritten +Ġvoc als +whe ther +Ġv i +ĠW inner +Ġl itter +ĠSh ooting +ST EM +ãĤ ¡ +ĠA FL +Ġvari ability +Ġe ats +ĠD PS +b row +Ġeleph ants +Ġstr at +Ġ Å +Ġsett lers +Matt hew +Ġin advert +H I +ĠIM F +ĠGo al +Ġnerv es +John son +ey e +ablish ment +Th ursday +BIL ITY +H ad +am oto +het amine +ep s +Ġmit ochond +Ġcomp ressed +ĠTre vor +ĠAnim als +T ool +L ock +Ġtwe ak +Ġpin ch +Ġcancell ation +P ot +Ġfoc al +ĠAst ron +17 3 +ĠA SC +ĠO THER +umn i +Ġdem ise +d l +Ù ħ +Sem itism +Ġcr acking +Ġcollabor ative +Ġexpl ores +s ql +Ġher bs +Ġconfig urations +m is +ĠRes ult +ace y +ĠSm oke +Ġsan ct +el ia +Ġdeg ener +Ġdeep est +Ġscream ed +Ġn ap +Soft ware +ĠST AR +E F +ĠX in +spons ored +mans hip +23 3 +Ġprim aries +Ġfilter ing +Ġas semble +m il +ĠMy ers +b ows +Ġpun ched +M ic +Ġinnov ations +Ġfun c +and o +Ġfr acking +ĠV ul +о Ð +osh op +ĠIm mun +Ġsett ling +Ġadolesc ents +Ġreb uilding +Ġtransform ing +Ġpar ole +Ġhar bor +Ġbook ing +ot ional +onge vity +ĠY o +b ug +Ġemer ges +ĠMethod s +ĠCh u +P res +ĠDun geons +Ġtra iling +ĠR um +ĠH ugh +å¤ © +ĠE ra +ĠBatt les +Res ults +ĠTr ading +Ġvers a +c ss +ax ies +he et +Ġgre ed +19 89 +Ġgard ens +Ġconting ent +P ark +ĠLeaf s +h ook +ro be +Ġdiplom acy +ĠF uel +ĠInv asion +Ġupgr ading +M ale +Ġe lic +Ġrelent less +ĠCo venant +ap esh +ĠT rop +T y +pro duction +art y +Ġpun ches +ak o +cyclop edia +ĠR abbit +ĠHD MI +Ġ14 1 +Ġf oil +Item Image +ĠF G +Ġimplement ations +ĠP om +ixt ures +Ġaw ait +Ġ3 30 +am us +Ġumb rella +Ġfore see +se par +Ġcircum cision +Ġperipher al +S ay +ĠExper t +In c +Ġwithd rew +ĠAnd ers +f ried +Ġradio active +ĠOp ening +Ġboard ing +ĠN D +Ġover throw +Act iv +W P +ĠAct s +× Ļ +Ġmot ions +v ic +ĠM ighty +ĠDef ender +a er +Ġthank ful +ĠK illing +ĠBr is +mo il +Ġpredict ing +26 6 +ch oice +Ġkill ers +Ġinc ub +ĠChe st +ather ing +Ġpro claimed +fl ower +oss om +umbled ore +ĠCy cling +ĠOccup y +AG ES +P en +ĠY ug +Ġpack aged +Ġheight ened +c ot +st ack +C ond +Ġst amps +m age +Ġpersu aded +Ġens l +ĠCard inal +Ġsol itary +Ġpossess ing +ĠC ork +Ġev id +ĠT ay +Ġbl ues +Ġextrem ism +Ġlun ar +Ġcl own +Te chn +Ġfest ivals +ĠPv P +ĠL ar +Ġconsequ ently +p resent +Ġsom eday +ç İĭ +ĠMet eor +Ġtour ing +c ulture +Ġbe aches +S hip +c ause +ĠFl ood +ãĥ ¯ +Ġpur ity +th ose +Ġem ission +b olt +Ġch ord +ĠScript ure +L u +Ġ$ { +cre ated +Other s +25 8 +Ġelement al +Ġannoy ed +ĠA E +d an +ĠS ag +Res earchers +Ġfair y +âĢĵ âĢĵ +======== ==== +Sm art +GG GG +Ġskelet ons +Ġpup ils +link ed +Ġur gency +en abled +ĠF uck +Ġcoun cill +r ab +U AL +T I +Ġlif es +Ġconf essed +B ug +Ġharm on +ĠCON FIG +ĠNe utral +D ouble +Ġst aple +ĠSH A +Brit ish +ĠSN P +AT OR +oc o +Ġswing ing +ge x +ole on +pl ain +ĠMiss ing +ĠTro phy +v ari +ran ch +Ġ3 01 +4 40 +00000000 00000000 +Ġrest oring +Ġha ul +uc ing +ner g +Ġfut ures +Ġstrateg ist +quest ion +Ġlater al +ĠB ard +Ġs or +ĠRhod es +ĠD owntown +????? - +ĠL it +ĠB ened +Ġco il +st reet +ĠPort al +FI LE +ĠG ru +* , +23 1 +ne um +Ġsuck ed +Ġr apper +Ġtend encies +ĠLaure n +cell aneous +26 7 +Ġbrow se +Ġover c +head er +o ise +Ġbe et +ĠG le +St ay +Ġm um +Ġtyp ed +Ġdiscount s +T alk +ĠO g +ex isting +ĠS ell +u ph +C I +ĠAust rian +ĠW arm +Ġdismiss al +Ġaver ages +c amera +Ġalleg iance +L AN +=" # +Ġcomment ators +ĠSet ting +ĠMid west +Ġpharm ac +ĠEX P +Ġstain less +Ch icago +Ġt an +24 4 +Ġcountry side +ĠV ac +29 5 +Ġpin ned +Ġcr ises +Ġstandard ized +T ask +ĠJ ail +ĠD ocker +col ored +f orth +" }, +Ġpat rons +Ġsp ice +Ġm ourn +ĠM ood +Ġlaund ry +Ġequ ip +ĠM ole +y ll +ĠTH C +n ation +ĠSher lock +Ġiss u +ĠK re +ĠAmeric as +ĠA AA +Ġsystem atically +Ġcont ra +ĠS ally +Ġrational e +Ġcar riage +Ġpe aks +Ġcontrad iction +ens ation +ĠFail ure +Ġpro ps +Ġnames pace +Ġc ove +field s +ãĤ ĭ +Ġw ool +ĠC atch +Ġpresum ed +ĠD iana +r agon +ig i +Ġh amm +Ġst unt +ĠG UI +ĠObserv atory +ĠSh ore +Ġsmell s +ann ah +Ġcock pit +ĠD uterte +8 50 +Ġopp ressed +bre aker +ĠCont ribut +ĠPer u +ĠMons anto +ĠAtt empt +Ġcommand ing +Ġfr idge +ĠR in +ĠChe ss +ual ity +Ġo l +Republic an +ĠGl ory +ĠW IN +.... ... +ag ent +read ing +Ġin h +J ones +Ġcl icks +al an +Ġ[ ]; +ĠMaj esty +ĠC ed +op us +ate l +à ª +AR C +ĠEc uador +ãĥ ł +ĠK uro +Ġritual s +Ġcapt ive +Ġoun ce +Ġdisag reement +Ġsl og +f uel +P et +M ail +Ġexerc ised +Ġsol ic +Ġrain fall +Ġdev otion +ĠAss essment +Ġrob otic +opt ions +ĠR P +ĠFam ilies +ĠFl ames +Ġassign ments +00 7 +aked own +Ġvoc abulary +Re illy +Ġc aval +g ars +Ġsupp ressed +ĠS ET +ĠJohn s +Ġwar p +bro ken +Ġstat ues +Ġadvoc ated +Ġ2 75 +Ġper il +om orph +ĠF emin +per fect +Ġh atch +L ib +5 12 +Ġlif elong +3 13 +Ġche eks +Ġnum bered +ĠM ug +B ody +ra vel +We ight +ĠJ ak +ĠHe ath +Ġkiss ing +ĠJ UST +Ġw aving +u pload +Ġins ider +ĠPro gressive +ĠFil ter +tt a +ĠBe am +Ġviol ently +ip ation +Ġskept icism +Ġ19 18 +ĠAnn ie +ĠS I +Ġgen etics +Ġon board +at l +ĠFried man +ĠB ri +cept ive +Ġpir ate +ĠRep orter +27 8 +Ġmyth ology +Ġe clipse +Ġsk ins +Ġgly ph +ing ham +F iles +C our +w omen +Ġreg imes +Ġphotograp hed +K at +ĠMA X +Offic ials +Ġunexpected ly +Ġimpress ions +F ront +;;;; ;;;; +Ġsuprem acy +Ġs ang +Ġaggrav ated +Ġabrupt ly +ĠS ector +Ġexc uses +Ġcost ing +ide press +St ack +ĠR NA +ob il +Ġghost s +ld on +at ibility +Top ics +Ġreim burse +ĠH M +ĠDe g +Ġth ief +y et +ogen esis +le aning +ĠK ol +ĠB asketball +Ġf i +ĠSee ing +Ġrecy cling +Ġ[ - +Cong ress +Ġlect ures +P sy +Ġne p +Ġm aid +Ġori ented +A X +Ġrespect ful +re ne +fl ush +ĠUn loaded +re quest +gr id +ĠAltern atively +ĠHug o +Ġdec ree +ĠBuddh ism +and um +And roid +ĠCong o +ĠJoy ce +Ġacknowled ging +hes ive +ĠTom orrow +ĠH iro +th ren +ĠM aced +Ġho ax +ĠIncre ased +ĠPr adesh +W ild +____ __ +16 1 +Ġa unt +Ġdistribut ing +ĠT ucker +ĠSS L +ĠW olves +B uilding +ou lt +ĠLu o +ĠY as +ĠSp ir +ĠSh ape +ĠCamb od +ĠIP v +Ġm l +Ġext rad +39 0 +ĠPenn y +d ream +Ġstation ed +opt ional +ew orthy +. +ĠWorks hop +ĠRet ail +ĠAv atar +6 25 +N a +ĠV C +ĠSec ure +M Y +19 88 +oss ip +Ġpro state +Ġund en +Ġg amer +ĠCont ents +ĠWar hammer +ĠSent inel +3 10 +Ġse gregation +ĠF lex +ĠM AY +Ġdr ills +ĠDrug s +Islam ic +Ġsp ur +Ġca fe +Ġimag inary +Ġgu iding +Ġsw ings +ĠThe me +ob y +Ġn ud +Ġbe gging +Ġstr ongh +Ġreject ing +Ġpedest rians +ĠPro spect +R are +s le +Ġconcess ions +ĠConst itutional +Ġbe ams +Ġfib ers +p oon +Ġinstinct s +pro perty +ĠB IG +Sand ers +im ates +Ġco ating +Ġcorps es +ĠTR UE +check ed +Ġ16 6 +A sh +ĠJ S +ĠF iction +Ġcommun al +Ġener getic +oooo oooo +Ġnow adays +IL D +ib o +ĠSU V +R en +Ġdwell ing +Sil ver +Ġt ally +ĠM oving +Ġcow ard +Ġgener als +Ġhorn s +Ġcirc ulated +Ġrob bed +ĠUn limited +Ġharass ed +Ġinhib it +Ġcomp oser +ĠSpot ify +Ġspread s +3 64 +Ġsu icidal +Ġno ises +ĠSt ur +Ġs aga +ĠK ag +is o +Ġtheoret ically +M oney +Ġsimilar ity +Ġslic ed +ut ils +ing es +" - +Ġan th +Ġimp ed +Mod ule +Through out +Ġmen us +comm ittee +and i +ob j +in av +f ired +ĠAb dullah +Ġund ead +Ġfont s +H old +EN G +Ġsustain ability +Ġfl ick +Ġr azor +ĠF est +ĠChar acters +Ġword ing +Ġpopul ist +Ġcritic izing +Ġm use +v ine +Ġcard board +Ġkind ly +Ġfr inge +ĠThe ft +icult ural +Ġgovern ors +Ġ ���� +Ġ16 3 +Ġtime out +ĠA uth +Child ren +A U +Ġred emption +ĠAl ger +Ġ19 14 +Ġw aved +Ġastron auts +og rams +Ġsw amp +ĠFinn ish +Ġcand le +Ġton nes +ut m +Ġr ay +Ġsp un +Ġfear ful +art icles +Ġca us +or ically +ĠRequ ires +ĠG ol +Ġpop e +Ġinaug ural +Ġg le +AD A +ĠIS IL +ĠOff ensive +Ġwatch dog +Ġbal con +ent ity +ĠH oo +Ġgall on +AC C +Ġdoub ling +Ġimpl ication +ĠS ight +Ġdoct r +---- --- +Ġ\ \ +Ġm alt +R oll +Ġâī ¥ +Ġrec ap +add ing +u ces +ĠB end +fig ure +Ġtur key +Ġsoc ietal +ĠT ickets +Ġcommer cially +Ġsp icy +Ġ2 16 +ĠR amp +Ġsuperior ity +à ¯ +ĠTr acker +C arl +ĠC oy +ĠPatri ot +Ġconsult ed +Ġlist ings +Ġsle w +reens hot +ĠG one +Ġ[ ...] +30 9 +Ġh ottest +Ø ± +Ġrock y +ĠD iaz +Ġmass age +Ġpar aly +Ġp ony +A z +Ġcart ridge +ĠN Z +Ġsn ack +ĠLam ar +ple ment +ĠLes lie +Ġm ater +Ġsn ipp +24 6 +Ġjoint ly +ĠBris bane +ĠiP od +Ġpump ing +Ġgo at +ĠSh aron +eal ing +Ġcor on +Ġan omal +rah im +ĠConnect ion +Ġsculpt ure +Ġsched uling +ĠD addy +at hing +Ġeyeb rows +Ġcur ved +Ġsent iments +Ġdraft ing +D rop +( [ +Ġnom inal +ĠLeaders hip +ĠG row +Ġ17 6 +Ġconstruct ive +iv ation +Ġcorrupt ed +ger ald +ĠC ros +ĠChe ster +ĠL ap +ãģ ª +OT H +D ATA +Ġal mond +pro bably +I mp +Ġfe ast +ĠWar craft +F lor +Ġcheck point +Ġtrans cription +Ġ20 4 +Ġtwe aks +Ġrel ieve +S cience +Ġperform er +Z one +Ġtur moil +ig ated +hib it +ĠC afe +the med +Ġflu or +ben ch +Ġde com +ĠU nt +ĠBar rett +ĠF acts +Ġt asting +ĠPTS D +ĠSe al +ĠJuda ism +ĠDynam ic +ĠC ors +V e +ĠM ing +ĠTrans form +v on +ĠDef enders +ĠTact ical +ĠV on +ĠUn ivers +Ġdist orted +ĠB reath +?' " +Ġag on +ĠDead ly +Ġl an +ĠCy cle +orn ed +Ġrel iably +Ġgl or +ĠMon key +ãĥ ¡ +Ġad ren +Ġmicrow ave +ĠAl ban +irc raft +dig it +sm art +ĠD read +¯¯¯¯¯¯¯¯ ¯¯¯¯¯¯¯¯ +{ { +ĠRoc hester +Ġsimpl ified +Ġinf licted +Ġtake over +Ġyour selves +ad itional +Ġmus cular +K S +Ġing en +T ax +ĠFe ature +27 7 +Ġcru c +Ġcr ate +Ġun identified +Ġacclaim ed +ĠM anga +ĠFr ances +ĠNep al +ĠG erald +ĠKu wait +Ġsl ain +ĠHe b +ĠG oku +ãģ® æ +28 6 +M rs +ĠC ody +ĠSan ctuary +01 6 +Ġdism ant +Ġdatas et +ĠH ond +b uck +ĠPat terson +Ġpal ette +ĠG D +ic ol +ĠL odge +Ġplanet ary +ak in +ĠRegist ered +ab we +ĠPeters burg +Ġha iled +ĠP iece +S che +ĠDO J +Ġen umer +18 1 +ĠObs erver +ĠB old +f ounded +com merce +Ġexplo its +ĠF inding +UR N +ĠS ne +ĠAc id +ay ette +ĠVal ues +Ġdr astic +Ġarchitect ural +Ġ" . +× ķ +ump ed +Ġwra pping +Ġwid ow +ĠSl ayer +l ace +on ce +German y +av oid +Ġtem ples +P AR +à ´ +ĠLuc ifer +ĠFl ickr +l ov +for ces +Ġsc outing +Ġlou der +tes y +Ġbefore hand +Ä ĵ +ĠNe on +ĠW ol +ĠTyp ically +ĠPolit ico +-+ -+ +Ġbuild er +Ġder ive +K ill +Ġp oker +Ġambig uous +Ġlif ts +Ġcy t +Ġrib s +ood le +ĠS ounds +h air +ĠSynd rome +t f +Ġproport ional +u id +Ġper taining +ĠKind le +ĠNeg ro +Ġreiter ated +ĠTon ight +oth s +ĠCorn ell +Ġo wing +Ġ20 8 +elf are +oc ating +ĠB irds +Sub scribe +Ġess ays +Ġburd ens +Ġillust rations +ar ious +ER AL +ĠCal cul +Ġx en +ĠLink edIn +ĠJ ung +Ġredes ign +Con nor +29 6 +Ġrevers al +ĠAd elaide +ĠL L +Ġs inking +Ġg um +US H +c apt +ĠGr imm +Ġfoot steps +ĠCB D +isp ers +Ġpro se +Wed nesday +ĠM ovies +ed in +Ġoverturn ed +Ġcontent ious +US B +~~~~~~~~ ~~~~~~~~ +ĠCo pper +Ġpoint less +N V +val ues +olph in +d ain +Ġdepos ited +ĠG W +Ġpreced ed +ĠCl a +ĠGo lem +ĠN im +ĠÎ ² +ĠEngine ers +m iddle +Ġfl att +oper ative +Ġcouncil s +imb abwe +el in +Ġstress ful +ĠL D +Ġres h +l ake +Ġwheel chair +ĠAltern ative +Ġoptim ize +oper ation +Ġpe ek +Ġones elf +ig il +Ġtrans itions +op athy +bl ank +Ġ16 9 +17 1 +________________________________ ________________________________ +Ġl aundering +En c +ĠD EC +Ġwork outs +Ġsp ikes +Ġdin osaurs +Ġdiscrim inatory +P ool +R ather +38 5 +R NA +tes ters +et o +ĠIdent ity +Ġve in +ĠBur ton +Ġarc ade +4 20 +Ult imately +ĠSad ly +à ° +p ill +Ġcub ic +ĠSpect rum +the se +st ates +Ġun official +h awks +ĠEVER Y +Ġrain bow +Ġincarcer ation +and ing +Ġsy ll +ĠEver ton +Ġ17 9 +ĠSer bia +Ġ18 9 +m eter +ĠMic key +Ġant iqu +Ġfact ual +ne ck +ĠN are +n orm +m ust +Ġhigh ways +Ġgl am +Ġdivid ing +ĠSquad ron +ĠMar tha +Ġbirth s +C over +//////// //////// +ĠW ong +Ph ot +ĠA LS +ri o +ĠNon etheless +ĠL emon +Ġ20 6 +ĠE E +Ġderiv ative +ĠWW II +v ote +Ġthere in +Ġsepar ating +44 6 +sy nc +ĠStre ets +Ġr att +Ġmunicip ality +ĠShort ly +Ġmon k +) ," +Ġscr ub +Ġoper atives +Ne ither +Pl ace +ĠLim it +F emale +ĠAct or +Char acter +Ġconstit uted +35 7 +Ġprotest ed +ĠSt raw +ĠHe ight +ild a +ĠTy ph +Ġflood s +Ġcos metic +W AY +pert ure +up on +t ons +ess ing +ĠP ocket +Ġro oft +ĠC aucas +Ġant idepress +Ġincomp atible +EC D +Ġoper a +ĠCont est +Ġgener ators +l ime +Def ense +19 87 +for um +Ġsav age +ĠHung arian +n z +Ġmet allic +Ġex pelled +Ġres idency +Ġdress es +66 6 +ĠC lement +f ires +C ategory +Ġge ek +al is +Ġc emetery +educ ated +Ġc rawl +ĠUn able +ĠT yson +ak is +Ġp ardon +ĠW ra +Ġstrengthen ed +ĠF ors +33 5 +ĠH C +ĠM ond +Ġvisual s +ĠBeat les +ett lement +Ġ ï +g ro +Ġb ash +Ġpo orest +Ġex cel +Ġaspir ations +ĠM unicip +ens ible +Ġceremon ies +Ġintimid ation +ĠCON TR +be ck +ĠK ap +as u +Ġtradem arks +ĠS ew +ĠComp etition +net work +ĠAr ri +ĠT et +Ro aming +W C +D at +Ġso b +Ġpair ing +Ġoverd ose +SA Y +ab er +Ġrev olt +ĠF ah +act ing +e q +est ation +F ight +ĠMar ks +27 3 +Ġ17 8 +R aw +ãģ ĭ +34 9 +bl ocks +Ġver ge +est ine +ĠPod esta +Ġinv asive +Ġprofound ly +ĠA o +e ach +Ġl est +inter pret +Ġshr inking +Ġerr one +Ġche es +ly s +ĠI vy +ĠDirect ory +Ġhint ed +V ICE +Ġcontact ing +ĠG ent +he i +Ġlabel ing +Ġmerc ury +ĠL ite +Ġexp ires +Ġdest abil +rit is +c u +Ġfeather s +Ġste er +Ġprogram med +ĠV ader +Go ing +ĠE lim +Ġy o +ĠMic he +Ġ20 3 +Ġslee ves +Ġb ully +ĠHum ans +36 8 +Ġcomp ress +ĠBan ner +AR S +Ġa while +Ġcal ib +Ġspons orship +ĠDiff iculty +ĠP apers +Ġident ifier +} . +Ġy og +ĠSh ia +Ġclean up +Ġvib e +int rodu +im ming +Austral ia +Ġout lines +ĠY outube +tr ain +ĠM akes +Ġde ported +Ġcent r +ĠD ug +ĠB oulder +ĠBuff y +Ġinj unction +ĠHar ley +ĠG roups +ĠD umbledore +ĠCl ara +Ġ" - +Ġsacrific ed +ep h +Sh adow +ib ling +Ġfreel ance +Ġevident ly +ph al +Ġret ains +M ir +Ġfin ite +d ar +ĠC ous +Ġrep aired +Ġperiod ic +Ġchampions hips +Ġaster oid +bl ind +Ġexpress ly +ĠAst ros +Ġsc aled +Ġge ographical +ĠRap ids +En joy +Ġel astic +ĠMoh amed +Mark et +be gin +Ġdisco vers +Ġtele communications +Ġscan ner +Ġen large +Ġsh arks +Ġpsy chedel +ĠRou ge +Ġsnap shot +is ine +X P +Ġpestic ides +ĠL SD +ĠDist ribution +re ally +Ġde gradation +Ġdisgu ise +Ġbi om +ĠEX T +Ġequ ations +Ġhaz ards +ĠComp ared +) * +Ġvirt ues +Ġeld ers +Ġenh ancing +ĠAc ross +er os +ang ling +Ġcomb ust +ucc i +Ġconc ussion +Ġcontrace ption +ĠK ang +Ġexpress es +Ġa ux +ĠP ione +Ġexhib its +Deb ug +OT AL +ĠAl ready +ĠWheel er +Ġexp ands +? : +Ġreconc iliation +Ġpir ates +Ġpur se +Ġdiscour age +Ġspect acle +R ank +Ġwra ps +ĠTh ought +Ġimp ending +O pp +ĠAng lo +ĠE UR +Ġscrew ed +ret ched +Ġencour agement +mod els +Ġconf use +mm m +ĠVit amin +âĸij âĸij +C ru +Ġkn ights +Ġdisc ard +Ġb ishops +ĠW ear +ĠGar rett +k an +ãĥ Ł +Ġmascul ine +cap ital +ĠA us +Ġfat ally +th anks +ĠA U +ĠG ut +12 00 +Ġ 00000000 +Ġsur rog +ĠBI OS +ra its +ĠWat ts +Ġresur rection +ĠElect oral +ĠT ips +4 000 +Ġnut rient +Ġdepict ing +Ġspr ink +Ġm uff +ĠL IM +ĠS ample +ps c +ib i +gener ated +Ġspec imens +Ġdiss atisf +Ġtail ored +Ġhold ings +ĠMonth ly +ĠE at +po ons +Ġne c +ĠC age +ĠLot us +ĠLan tern +Ġfront ier +Ġp ensions +Ġj oked +ĠHard y +=-=- =-=- +r ade +U ID +Ġr ails +Ġem it +Ġsl ate +Ġsm ug +Ġsp it +ĠCall s +ĠJac obs +f eat +ĠU E +Ġrest ruct +Ġregener ation +Ġenerg ies +ĠCon nor +OH N +ĠChe ese +Ġg er +Ġresur rect +man agement +N W +Ġpres ently +ĠBru ins +M ember +ĠM ang +id an +Ġboost ing +w yn ++ . +requ isite +ĠNY PD +ĠMe gan +ĠCond itions +Ġp ics +nes ium +ĠR ash +Ġ17 4 +ĠD ucks +Ġemb ro +z u +on ian +rel igious +Ġc raz +ĠAC A +ĠZ ucker +EM A +ĠPro s +We apon +ĠKn ox +ĠAr duino +Ġst ove +Ġheaven s +ĠP urchase +Ġher d +Ġfundra iser +Dig ital +5 000 +Ġprop onents +/ âĢĭ +Ġj elly +ĠVis a +Ġmon ks +Ġadvance ment +ĠW er +Ġ18 7 +e us +ert ility +Ġfet al +Ġ19 36 +L o +Ġout fits +Ġstair case +b omb +Ġcustom ized +cl air +T ree +Ġm apped +ĠConsider ing +ĠTor res +Ġmeth yl +Ġapprox imate +Ġdo om +ĠHans en +Ġc rossover +Ġstand alone +ä ¼ +Ġinv ites +Ġgra veyard +Ġh p +Donald Trump +Ġesc ort +G ar +Ġpredec essors +Ġh ay +Ġen zyme +ĠStra ight +vis ors +I ng +ane ously +ĠApp lied +Ġf ec +ĠDur ant +Ġout spoken +or b +Ġz eal +Ġdisgr ace +' ). +ĠChe ng +28 9 +ĠRen a +ĠSu icide +29 4 +Ġout raged +ĠNew man +ĠN vidia +ĠA ber +ĠB ers +Ġrecre ation +Wind ow +ĠD P +x e +Ġped oph +Ġfall out +ambo o +Ġpresent ations +ĠApp s +Ġh tml +3 45 +ĠX XX +Ġrub bing +ĠLe ather +Ġhum idity +se ys +est ablished +ĠUn its +64 6 +Ġrespect able +A uto +Ġthri ving +ĠInn ovation +ang s +Ext ra +reg ulation +29 8 +p ick +Ex amples +ĠC J +Att ack +Ġdr acon +L T +Ġstick er +re rs +Ġsun ny +I ss +reg ulated +d im +ĠAb stract +Ġhus bands +Off ice +om ination +it ars +AN GE +asc al +ĠK ris +ĠInf antry +Ġm alf +ĠA the +ĠR ally +bal anced +................ ........ +OU P +Ġmole cule +met ics +ĠSpl it +ĠInstruct ions +ĠN ights +c ards +Ġt ug +Ġcon e +å Ń +Ġt x +ĠDisc ussion +Ġcatast rophe +pp e +g io +Ġcommun ism +Ġhal ted +ĠGu ant +cle an +ĠSc hed +ĠK anye +Ġw ander +ĠSer iously +Ġ18 8 +enn ial +f ollow +product ive +ĠFl ow +ĠS ail +Ġc raw +Ġsim ulations +or u +ang les +ĠN olan +Ġmen stru +4 70 +Ġ20 7 +aj a +Ġcas ually +board ing +Ġ2 22 +ov y +ĠN umbers +um at +O E +28 7 +ĠCle mson +Ġcert s +Ġsl id +ĠT ribe +Ġto ast +Ġfort unes +Ġf als +ĠComm ittees +Ġg p +Ġf iery +ĠN ets +ĠAn ime +Pack age +ĠComp are +l aughter +in fect +Ġatroc ities +Ġjust ices +Ġins ults +ĠVern on +Ġsh aken +Ġperson a +est amp +36 7 +br ain +Ġexperiment ing +K en +ĠElect ronics +Ġ16 1 +dom ain +Ġgraph ical +b ishop +Ġwho pping +ĠEv angel +Ġadvertis ers +ĠSpe ar +Ġb ids +Ġdestro ys +ut z +Ġunders c +ĠAD D +Ġan ts +ĠC um +ipp les +ĠF ill +Ġgl anced +Ġind icted +ĠE ff +Ġmis con +ĠDes ktop +Ġab ide +ãĥ Ģ +ĠI o +ĠC oul +Ġcaps ule +ĠCh rys +M ON +Ġund es +ĠI RA +Ġc itation +Ġdict ate +ĠNet works +ĠConf lict +ĠSt uff +x a +is ec +ĠChem istry +Ġquarter ly +William s +an an +O pt +ĠAlexand ria +out heastern +ĠSpring field +ĠBlack s +Ġge ography +24 2 +Ġut most +ĠEx xon +ab outs +E VA +ĠEn able +ĠBar r +Ġdisag reed +ĠCy prus +Ġdement ia +Ġlab s +Ġubiqu itous +ĠLO VE +Ġconsolid ated +s r +Ġcream y +ĠTim ber +Reg ardless +ĠCert ificate +Ġ" ... +ogen ous +Capt ain +Ġinsult ing +ĠSor os +ĠInst r +ĠBulgar ia +bet ter +Ġsuck ing +ĠDavid son +at z +Ġcoll ateral +g if +Ġplag ued +ĠC ancel +ĠGard ner +R B +Ġsix teen +Rem ove +ur istic +c ook +R od +Ġcompr ising +f le +) âĢĶ +ĠVik ing +g rowth +agon al +Ġsr f +af ety +m ot +N early +st own +ĠF actor +Ġautom obile +Ġproced ural +m ask +amp ires +Ġdisapp ears +j ab +3 15 +Ġ19 51 +ne eded +Ġd aring +le ader +Ġp odium +Ġun healthy +Ġm und +Ġpy ramid +oc re +Ġkiss ed +Ġdream ed +ĠFant astic +ĠG ly +å Ĭ +Ġgreat ness +Ġsp ices +Ġmet ropolitan +Ġcomp uls +i ets +101 6 +ĠSh am +ĠP yr +fl ies +ĠMid night +Ġswall owed +Ġgen res +ĠL ucky +ĠRew ards +Ġdisp atch +ĠI PA +ĠApp ly +Ġa ven +al ities +3 12 +th ings +Ġ( ). +Ġm ates +ĠS z +ĠC OP +ol ate +O FF +Ġre charge +c aps +ĠYork er +ic one +Ġgal axies +ile aks +D ave +ĠP uzz +ĠCelt ic +ĠA FC +27 6 +ĠS ons +Ġaffirm ative +H or +Ġtutorial s +ĠC ITY +ĠR osa +ĠExt ension +Ser ies +Ġf ats +Ġr ab +l is +Ġun ic +Ġe ve +ĠSp in +Ġadul thood +ty p +Ġsect arian +Ġcheck out +ĠCy cl +S ingle +Ġmart yr +Ġch illing +88 8 +ou fl +Ġ] ; +Ġcongest ion +m k +ĠWhere as +Ġ19 38 +ur rencies +er ion +Ġbo ast +ĠPat ients +Ġch ap +ĠB D +real DonaldTrump +Ġexam ines +h ov +Ġstart ling +ĠBab ylon +w id +om ew +br ance +ĠOd yssey +w ig +Ġtor ch +ĠV ox +ĠMo z +ĠT roll +ĠAn s +Similar ly +ĠF ul +00 6 +Un less +ĠAl one +st ead +ĠPub lisher +r ights +t u +ĠDoes n +Ġprofession ally +Ġcl o +ic z +Ġste als +Ġ á +19 86 +Ġst urdy +ĠJoh ann +Ġmed als +Ġfil ings +ĠFr aser +d one +Ġmult inational +Ġf eder +Ġworth less +Ġp est +Yes terday +ank ind +Ġg ays +Ġb orne +ĠP OS +Pict ure +Ġpercent ages +25 1 +r ame +Ġpot ions +AM D +ĠLeban ese +Ġr ang +ĠL SU +ong s +Ġpen insula +ĠCl ause +AL K +oh a +ĠMac Book +Ġunanim ous +Ġl enders +Ġhang s +Ġfranch ises +ore rs +ĠUp dates +Ġisol ate +and ro +S oon +Ġdisrupt ive +ĠSur ve +Ġst itches +ĠSc orp +ĠDomin ion +Ġsupp lying +Ar g +Ġtur ret +ĠL uk +Ġbr ackets +* ) +ĠRevolution ary +ĠHon est +Ġnot icing +ĠSh annon +Ġafford ed +Ġth a +ĠJan et +! -- +ĠNare ndra +ĠPl ot +H ol +se ver +e enth +Ġobst ruction +Ġ10 24 +st aff +j as +or get +sc enes +l aughs +ĠF argo +cr ime +Ġorche str +Ġde let +ili ary +rie ved +Ġmilit ar +ĠGreen e +âĹ ı +ãģ ¦ +ĠGu ards +Ġunle ashed +ĠWe ber +Ġadjust able +Ġcal iber +Ġmotiv ations +Ġà ł +m Ah +ĠL anka +hand le +Ġp ent +ĠR av +ĠAng ular +ĠK au +umb ing +Ġphil anthrop +Ġde hyd +Ġtox icity +e er +ĠY ORK +w itz +å ¼ +ĠI E +commun ity +ĠA H +Ġret ali +Ġmass ively +ĠDani els +ĠD EL +Ġcar cin +Ur l +Ġrout ing +ĠNPC s +ĠR AF +ry ce +Ġwa ived +ĠGu atem +Every body +Ġco venant +Ġ17 3 +Ġrelax ing +Ġqu art +al most +Ġguard ed +ĠSold iers +ĠPL AY +Ġout going +L AND +Ġre write +ĠM OV +ĠIm per +ĠS olution +Ġphenomen al +Ġl ongevity +Ġimp at +ĠN issan +ir ie +Ġod or +ĠZ ar +ok s +Ġmilit ias +ĠSP EC +Ġtoler ated +ars er +ĠBrad ford ++ , +Ġsur real +s f +Can adian +Ġresemb lance +Ġcarbohyd rate +VI EW +Ġaccess ory +me al +larg est +ieg el +Some one +Ġtoug hest +os o +Ġfun nel +Ġcondemn ation +lu ent +Ġw ired +ĠSun set +Jes us +ĠP ST +ĠP ages +ĠTy coon +ĠP F +Ġselect ions +Ġ ठ+part isan +Ġhigh s +ĠR une +Ġcraft s +le ad +ĠParent s +Ġre claim +ek er +ĠAll ied +ae per +Ġlo oming +Ġbenefic iaries +ĠH ull +Stud ents +Jew ish +d j +Ġp act +tem plate +ĠOffic ials +ĠBay lor +Ġhe mp +Ġyouth s +ĠLevel s +ĠX iao +ĠC hes +Ġende avor +ĠRem oved +Ġhipp ocamp +H ell +ãĤ Ĭ +80 5 +Ġd inosaur +ĠWr ath +ĠIndones ian +Ġcalcul ator +ĠD ictionary +Ġ4 20 +ĠM AG +( _ +! , +t arians +Ġrestrict ing +rac use +Ġweek day +OU NT +Ġsh rugged +leg round +Ġb ald +ĠDo ctors +Ġt outed +ĠMax well +Ġ2 14 +Ġdiplom at +Ġrep ression +Ġconstitu ency +v ice +r anked +ĠNap oleon +g ang +ĠFore ver +t un +Ġbul b +ĠPD T +ĠC isco +V EN +Ġres umed +Ste ven +ĠManit oba +Ġfab ulous +ĠAg ents +19 84 +Ġam using +ĠMyster ies +Ġor thodox +fl oor +Ġquestion naire +Ġpenet rate +Ġfilm makers +ĠUn c +Ġst amped +Ġth irteen +Ġout field +Ġforward ed +Ġapp ra +Ġa ided +t ry +Ġunf ocused +ĠL iz +ĠWend y +ĠSc ene +Ch arg +Ġreject s +Ġleft ist +ĠProv idence +ĠBr id +reg n +Ġprophe cy +ĠL IVE +4 99 +Ġfor ge +ĠF ML +Ġintrins ic +ĠF rog +Ġw ont +ĠH olt +Ġfam ed +CL US +aeper nick +ĠH ate +ĠC ay +Ġregister ing +ort ality +rop y +ocaly ptic +a an +n av +Ġfasc ist +IF IED +Ġimpl icated +ĠRes ort +ĠChand ler +ĠBr ick +P in +ys c +Us age +ĠHel m +us ra +âĺħ âĺħ +ĠAb bas +Ġunanim ously +Ġke eper +Ġadd icted +?? ? +Ġhelm ets +Ġant ioxid +aps ed +80 8 +gi ene +Ġwa its +Ġmin ion +ra ved +ĠP orsche +Ġdream ing +Ġ17 1 +ĠC ain +Ġun for +ass o +ĠConfig uration +k un +hard t +Ġn ested +ĠL DS +L ES +Ġt ying +en os +Ġc ue +ĠMar qu +sk irts +Ġclick ed +Ġexp iration +ĠAccording ly +ĠW C +Ġbless ings +Ġaddict ive +ĠN arr +y x +ĠJagu ars +Ġrent s +ĠS iber +Ġt ipped +ous se +ĠFitz gerald +Ġhier arch +out ine +Ġwa velength +> . +ch id +ĠProcess ing +/ + +r anking +E asy +ĠConst ruct +Ġt et +ins ured +H UD +Ġqu oting +Ġcommun icated +in x +Ġin mate +Ġerect ed +ĠAbs olutely +ĠSure ly +Ġun im +ĠThr one +he id +Ġcl aws +Ġsuper star +ĠL enn +ĠWh is +U k +ab ol +Ġsk et +ĠN iet +Ġper ks +Ġaff inity +Ġopen ings +phas is +Ġdiscrim inate +T ip +v c +Ġgr inding +ĠJenn y +Ġast hma +hol es +ĠHom er +Ġreg isters +ĠGl ad +Ġcre ations +Ġlith ium +Ġappl ause +unt il +Just ice +ĠTur ks +Ġsc andals +Ġb ake +t ank +M ech +ĠMe ans +ĠM aid +Republic ans +is al +wind ows +ĠSant os +Ġveget ation +33 8 +t ri +Ġfl ux +ins ert +Ġclar ified +Ġmort g +ĠCh im +ĠT ort +Ġdiscl aim +met al +ĠAs ide +Ġindu ction +Ġinf l +Ġathe ists +amp h +Ġe ther +ĠV ital +ĠBu ilt +M ind +Ġweapon ry +S ET +Ġ18 6 +ad min +g am +cont ract +af a +Ġderiv atives +Ġsn acks +Ġch urn +E conom +Ġca pped +ĠUnder standing +ĠH ers +ĠI z +Ġd uct +I ENT +augh ty +Ġâľ Ķ +ĠN P +Ġsa iling +In itialized +Ġt ed +Ġreact ors +ĠL omb +Ġcho ke +ĠW orm +Ġadm iration +Ġsw ung +ens ibly +Ġr ash +ĠGo als +ĠImport ant +Sh ot +ĠR as +Ġtrain ers +ĠB un +Work ing +Ġhar med +ĠPand ora +ĠL TE +Ġmush room +ĠCH AR +ĠF ee +ĠM oy +B orn +ol iberal +ĠMart ial +Ġgentle men +Ġling ering +Offic ial +Ġgra ffiti +ĠN ames +D er +Ġqu int +ist rate +aze era +ĠNOT ICE +ĠFlore nce +Ġpay able +Ġdep icts +ĠSpe cies +He art +âĶĢâĶĢâĶĢâĶĢ âĶĢâĶĢâĶĢâĶĢ +Ġencl osed +Incre ases +D aily +ĠL is +Ġenact ment +ĠB acon +ĠSt eele +dem and +Ġ18 3 +Ġmouth s +Ġstr anded +Ġenhance ment +01 1 +ĠWh ats +Ġhe aled +en y +ĠR ab +Ġ3 40 +ĠLab yrinth +ro ach +ĠY osh +ĠCl ippers +Ġconcert s +Intern et +35 5 +Ġstick ers +Ġter med +ĠAx e +Ġgrand parents +Fr ance +ĠCl im +ĠU h +ul ic +Ġthr ill +cent ric +ĠOver view +ĠCond uct +Ġsubstant ive +Ġ18 2 +m ur +Ġstr ay +ĠCo ff +Ġrep etitive +ĠFor gotten +Ġqual ification +ew itness +ĠZ imbabwe +Ġsim ulated +ĠJ D +25 3 +ĠW are +Ġun sc +T imes +Ġsum mons +Ġdis connected +Ġ18 4 +ci us +ĠGu jar +od ka +Ġer ase +ĠTob acco +elect ed +Ġun cont +ĠShe pard +ĠL amp +Ġalert ed +Ġoper ative +arn a +u int +Ġneglig ence +ac ements +Ġsup ra +Ġprev ail +ĠSh ark +Ġbel ts +ãģ « +Ġt ighter +Engine ers +Ġin active +Ġexp onent +ĠWill ie +a ples +Ġhe ir +ĠH its +ian n +ĠS ays +Ġcurrent s +ĠBeng al +Ġar ist +B uffer +Ġbree ze +ĠWes ley +Col a +Ġpron oun +Ġde ed +ĠK ling +Ġof t +Ġinf lict +Ġpun ishing +Ġn m +ik u +OD UCT +01 4 +Ġsubsid y +ĠDE A +ĠHer bert +ĠJ al +B ank +Ġdef erred +Ġship ment +B ott +Ġal le +b earing +HT ML +Off line +Ġ2 13 +Ġscroll ing +Ġsc anned +ĠLib yan +ĠT OP +ch rom +d t +col umn +Psy NetMessage +Z ero +Ġtor so +0 50 +âķ IJ +Ġimp erson +ĠSchw artz +ud ic +Ġpiss ed +ĠS app +25 7 +ĠIS Ps +og l +Ġsuper vised +Ġad olescent +Ġatt ained +ĠDel ivery +ĠB unny +Ġ19 37 +Ġmini ature +Ġo s +Ġ3 70 +60 8 +ĠMour inho +Ġinn ate +Ġtem po +ĠN M +ĠFall en +00 9 +Ġprov ocative +Stream er +ĠBened ict +ĠBol she +Ġt urtle +ĠPC B +ĠEqu al +Direct or +ĠR end +Ġflu ids +Author ities +Ġcous ins +requ ency +ĠNeigh bor +s ets +sh ared +Char les +pass word +Ġg ears +Ġ2 11 +ĠHard ware +ri ka +Ġup stream +H om +Ġdisproportion ately +iv ities +Ġund efined +Ġelect rons +Ġcommem or +Event ually +Ġ> < +Ġir responsible +2 18 +ĠRe leased +ĠO VER +ĠI GN +ĠB read +st ellar +ĠS age +tt ed +dam age +ed ition +ĠPre c +Ġl ime +Ġconf inement +Ġcal orie +we apon +Ġdiff ering +ĠS ina +m ys +am d +Ġintric ate +k k +ĠP AT +ã o +st ones +lin ks +Ġr anch +Sem itic +Ġdifferent iate +ĠS inger +occup ied +Ġfort ress +c md +Ġinter ception +ĠAnk ara +Ġre pt +ĠSol itaire +Ġrem ake +p red +Ġd ared +aut ions +ĠB ACK +Run ning +Ġdebug ging +Ġgraph s +3 99 +ĠNig el +Ġb un +Ġpill ow +Ġprog ressed +fashion ed +Ġob edience +ER N +Ġrehe ars +C ell +t l +S her +Ġher ald +ĠPay ment +ĠC ory +ĠDe pt +Ġrep ent +ĠWe ak +uck land +Ġple asing +Ġshort ages +Ġjur ors +ĠK ab +q qa +Ant i +Ġw ow +ĠRC MP +Ġt sun +ĠS ic +Ġcomp rises +Ġsp ies +Ġprec inct +n u +Ġur ges +Ġtim ed +Ġstrip es +ĠB oots +Ġy en +Adv anced +Ġdisc rete +ĠArch angel +employ ment +D iff +Ġmon uments +Ġ20 9 +work er +Ġ19 6 +ĠI g +utter stock +T PS +J ac +Ġhomeless ness +Ġcomment ator +Ġrac ially +f ing +se ed +E le +ell ation +Ġeth anol +Ġpar ish +ĠD ong +ĠAw akening +Ġdev iation +ĠB earing +ĠTsu k +Ġrec ess +Ġl ymph +ĠCann abis +å ľ +ĠNEW S +Ġd ra +ĠStef an +ĠWr ong +ĠS AM +Ġloose ly +Ġinterpre ter +ĠPl ain +Go vernment +Ġbigot ry +Ġgren ades +ave z +pict ured +Ġmand ated +ĠMon k +ĠPed ro +Ġl ava +27 4 +Ġcyn ical +ĠScroll s +l ocks +M p +Ġcon gregation +orn ings +ph il +ĠI bid +Ġf erv +Ġdisapp earing +Ġarrog ant +sy n +ĠMa ver +ĠSu it +24 1 +Ġab bre +ack ers +P a +ĠY el +Whe never +Ġ23 5 +ĠV ine +ĠAn at +Ġext inct +LE T +Ġexecut able +V ERS +ox ide +D NA +ĠP rel +Ġresent ment +Ġcompr ise +ĠAv iv +Ġinter ceptions +Ġprol ific +IN A +ĠEr in +though t +2 19 +ĠPsychiat ry +un ky +chem ist +H o +ĠMcC oy +Ġbr icks +L os +ri ly +ĠUS SR +Ġr ud +Ġl aud +ĠW ise +ĠEmer ald +Ġrev ived +Ġdam ned +ĠRep air +id em +ct ica +Ġpatri arch +ĠN urs +me g +Ġcheap est +re ements +empt y +ĠCele br +Ġdepri vation +ch anted +ĠTh umbnails +E nergy +ĠEth an +ĠQ ing +Ġopp oses +W IND +v ik +ĠM au +ĠS UB +66 7 +G RE +ĠVol unte +nt on +C ook +å IJ +es que +Ġplum met +Ġsu ing +Ġpron ounce +Ġresist ing +ĠF ishing +ĠTri als +Ġy ell +Ġ3 10 +Ġin duct +Ġpersonal ized +oft en +R eb +EM BER +Ġview point +Ġexist ential +() ) +rem ove +MENT S +l asses +Ġev apor +Ġa isle +met a +Ġreflect ive +Ġentit lement +Ġdev ised +mus ic +asc ade +Ġwind ing +off set +Ġaccess ibility +ke red +Bet ter +ĠJohn ston +th inking +S now +ĠCroat ia +ĠAt omic +27 1 +34 8 +Ġtext book +ĠSix th +Ġ اÙĦ +Ġsl ider +ĠBur ger +b ol +S ync +Ġgrand children +Ġc erv ++ ) +Ġe ternity +Ġtweet ing +Ġspec ulative +Ġpiv otal +ĠW P +ĠT ER +ynam ic +Ġu pl +ĠC ats +per haps +Ġclass mates +Ġblat ant +' - +Ġl akh +ant ine +ĠB org +i om +/ ( +ĠAthlet ic +Ġs ar +OT A +ĠHoff man +Never theless +Ġad orable +Ġspawn ed +Ass ociated +ĠDom estic +Ġimpl ant +ĠLux em +ĠK ens +Ġp umps +ĠS AT +Att ributes +50 9 +av our +Ġcentral ized +ĠT N +Ġfresh ly +ĠA chieve +Ġouts iders +her ty +ĠRe e +ĠT owers +ĠD art +ak able +Ġm p +ĠHeaven ly +Ġr ipe +ĠCarol ine +ry an +Ġclass ics +Ġret iring +Ġ2 28 +Ġa h +Ġdeal ings +Ġpunch ing +ĠChap man +O ptions +max well +vol ume +Ġst al +Ġex ported +ĠQu ite +Ġnumer ical +B urn +F act +ĠKey stone +Ġtrend ing +Ġalter ing +ĠAfric ans +47 8 +ĠM N +ĠKn ock +Ġtempt ation +Ġprest ige +Over view +ĠTrad itional +ĠBah rain +Priv ate +ĠH OU +Ġbar r +ĠT at +C ube +US D +ĠGrand e +ĠG at +ĠFl o +Ġres ides +Ġind ec +vol ent +Ġperpet ual +ub es +Ġworld view +ĠQuant um +Ġfil tered +Ġen su +orget own +ERS ON +ĠM ild +37 9 +OT T +à ¥ +Ġvit amins +Ġrib bon +Ġsincere ly +ĠH in +Ġeight een +Ġcontradict ory +Ġgl aring +Ġexpect ancy +Ġcons pir +Ġmon strous +Ġ3 80 +re ci +Ġhand ic +Ġpump ed +Ġindic ative +Ġr app +Ġav ail +ĠLEG O +ĠMar ijuana +19 85 +ert on +Ġtwent ieth +################ ################ +ĠSw amp +Ġval uation +Ġaffili ates +adjust ed +ĠFac ility +26 2 +Ġenz ymes +itud inal +Ġimp rint +S ite +Ġinstall er +ĠT RA +m ology +lin ear +ĠCollect ive +ig ating +ĠT oken +Ġspec ulated +K N +ĠC ly +or ity +Ġdef er +Ġinspect ors +appro ved +R M +ĠSun s +Ġinform ing +ĠSy racuse +ib li +7 65 +Ġgl ove +Ġauthor ize +âĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦ +ĠCru ise +Ġcontract ing +she ll +IF E +ĠJew el +p ract +ĠPhot oshop +ĠKnow ing +h arm +Ġattract ions +ad an +et us +01 8 +w agen +Al t +Ġmultip ly +Ġequ ilibrium +: { +ĠF ighters +ĠEd gar +Ġfour teen +Go vern +Ġmis use +Ġab using +Ġancest ry +ram er +64 4 +Ġwor ms +Ġthick er +ĠComb ine +Ġpeas ants +Ġv ind +Ġcon quest +Ġm ocked +Ġc innamon +ĠC ald +ĠGall up +Ġavoid ance +Ġincarn ation +ĠStr at +Ġt asted +ent a +ĠN eal +p ared +Ġtermin ology +ject ion +Scient ists +ĠIN S +ĠDe e +Ġdirect ories +R oad +ĠSh ap +br ight +ĠDirect ors +ĠCol umn +Ġb ob +Ġprefer ably +Ġgl itch +f urt +Ġe g +id is +C BC +Ġsur rendered +Ġtest ament +33 6 +ug gest +ĠN il +an other +Ġpat hetic +ĠDon na +Ġ2 18 +ĠA very +Ġwhis key +Ġf ixture +ĠCon quest +Ġbet s +O cc +ĠLe icester +] ." +Ġ) ); +Ġfl ashes +45 6 +Ġmask ed +ge bra +Ġcomput ed +che l +aud er +Ġdefe ats +ĠLiber ation +ĠOs ama +ĠV ive +Ch anges +Ch annel +Ġtar iffs +Ġm age +ĠS ax +Ġinadvert ently +ĠC RE +ĠRe aper +ink y +gr ading +Ġstere otyp +Ġcur l +ĠF ANT +Ġfram eworks +M om +ĠAn ch +Ġflav our +car bon +Ġperm itting +let cher +ĠMo zilla +ĠPark ing +ĠCh amp +Sc roll +Ġmurd erer +Ġrest ed +Ġow es +ĠP oss +AD D +IF F +res olution +ĠMin ing +Ġcompar ative +D im +Ġneighbour ing +ĠA ST +ĠT oxic +Ġbi ases +Ġgun fire +ur ous +ĠMom ent +19 83 +Ġper vasive +tt p +ĠNorm ally +r ir +S arah +ĠAlb any +Ġun sett +ĠS MS +ip ers +l ayer +ĠWh ites +up le +Ġtur bo +ĠLe eds +Ġthat s +ĠMin er +M ER +ĠRe ign +Ġper me +ĠBl itz +Ġ19 34 +Ġintimid ating +t ube +Ġecc entric +ab olic +box es +ĠAssoci ates +v otes +Ġsim ulate +um bo +aster y +Ġship ments +FF FF +an th +Ġseason ed +Ġexperiment ation +âĸ ł +law s +Me et +idd les +ant ics +R ating +IS IS +h ift +Ġfront s +b uf +01 7 +Ġun att +ĠD il +le ases +ĠGard ens +77 7 +t ouch +ve ll +45 8 +Ġ= ==== +s aving +Ġer osion +ĠQu in +Ġearn s +Ġaccomplish ment +ĠWe i +Ġ< [ +____ _ +Ġir rig +ĠT eddy +Ġconqu ered +ĠArm ored +Ġassert s +Ġmanip ulating +r é +Ġtranscript s +G allery +Ġplot ting +Ne il +Ġbetray al +load er +ĠS ul +Ġdispl acement +Ġroy alty +ĠW I +he it +ĠDev ices +alle l +Ġmunicipal ities +Ġcan al +St ars +ĠU AE +Ġ" âĢ¦ +ĠC U +ab ove +Ġreson ance +ĠguiActive Un +add ed +ĠBra ves +ĠI bn +Ġhere by +ĠB RE +Ġshare holder +ĠH ir +ĠJ i +Ġstrange ly +Ġadm ired +Ġpl ight +Ġb achelor +ĠP ole +cipl inary +T ony +ĠArmen ian +Ġun man +ĠZion ist +St age +isco ver +Ġautom otive +Ġs idelines +Ġsl ick +ĠRena issance +ĠF UN +Im ages +ĠH aj +Ġp ing +Ġshort cut +ĠBl vd +ĠLook s +Ġbur sts +Ġcl amp +Ġm ish +Ġsort ing +Ġpatri ot +Ġcorrect ness +ĠScand inav +ĠCaval iers +p ython +az ar +Ġ3 75 +ĠJa une +40 9 +Ġdetrim ental +Ġstab bing +Ġpoison ed +Ġf ountain +oc ent +or st +ĠMar i +Ġr ains +ĠO vers +ĠInst itution +ud get +AM Y +t ale +ĠK R +ĠPr ices +Ġhead aches +Ġlands l +ĠA ura +Bon us +ĠZ hao +ĠH ip +Ġhop s +ĠKurd istan +Ġexplo iting +ry n +Ġhypocr isy +op ening +Ġgun shot +Ġw ed +inter stitial +Inter stitial +Ġam en +Bre aking +Ġmarket ed +W ire +ĠC rowd +Contin ue +ĠK nown +ĠEffect ive +ore an +iz ons +Jose ph +Ġescal ation +us ername +Ġcur tain +AT ES +ĠP AR +ĠM iy +Ġcounter fe +l ene +Ġcont enders +d aily +ĠAs c +ĠPhill ip +most ly +Ġfil ename +he ne +Ġresemb ling +Ġst aging +ĠCh loe +Ġw iring +H on +ĠRen ew +ott age +ĠHy brid +m uch +Ġstro kes +Ġpolicy makers +AP TER +ĠArk ham +pl ot +Ġassist ants +Ġde port +ĠSe ga +Ġinflu enza +ĠC ursed +ĠK obe +Ġskin ny +Prov ider +ĠR ip +Ġincrement al +product s +B F +Ġd ome +ĠC redits +Ġlos ers +int s +ĠBet ty +ĠTal ent +ĠD AM +L v +E ss +Ġd ens +tem p +J udge +od ic +Ġ' ( +UR ES +ets k +V O +Ġretrie ved +Ġarchitect s +Ù ĩ +Ġeth ic +ĠSecond ary +st ocks +ad ia +Ġ3 25 +ĠOp inion +Ġsimultane ous +Ġd izz +ul p +Ġsmugg ling +ipp ery +R andom +f acing +ĠD as +Ġstock p +Ġdiscl osures +po inter +Ġcor al +ĠSe lection +ĠP ike +ival ent +Ġruth less +ĠR im +Ġensu ing +ĠExper iment +Ġcongress man +Ġbelie ver +Ġun specified +ĠM ord +Ġknowledge able +ĠV ERY +T X +Ġstra ps +Ġtur f +apesh ifter +Ġmar ital +Ġfl ock +ãģ Ĩ +26 3 +AM ES +ĠOpp osition +Ġtre asures +ĠG OD +Ġmodel ed +ĠWOR LD +Ġ( [ +ĠUs age +H F +Ġ$ ( +uss ed +Ġpione er +E ight +par se +b read +rit z +ĠMir anda +ĠK ant +++ ) +ore n +Ġprov oked +Ġbre eds +ĠIn cludes +ĠPast ebin +ĠFl ip +J ava +Ġbr ink +Ġrum ored +Ġun seen +Ġgar nered +ĠDef in +al ted +Ġtatt oos +Ġhes itation +is itions +ĠWe aver +ĠReport ing +Ġtherap ies +Ġconsult ants +Ġresid ual +ĠMal i +ĠRom a +i ago +ĠRes idents +ub i +Ġremed ies +Ġadapt ive +ĠAl ive +ĠBar cl +Ġwal lets +c rypt +etermin ation +ĠPel osi +Ġsl ipping +oton in +Ġall iances +pat rick +ir is +Ġor th +ĠPer kins +ĠDe V +ĠG ets +Ġdry ing +ge e +fore st +ĠFor get +ore m +33 9 +Ġvague ly +ĠD ion +ĠP orn +ĠH OW +Ġp neum +Ġrub ble +ĠT aste +enc ia +ĠG el +Ġd st +Ġ24 5 +ĠMoroc co +inf lamm +ĠTw ins +Ġb ots +d aughter +ĠB alk +Ġbre thren +Ġlog os +Ġgo bl +f ps +Ġsub division +Ġp awn +Ġsquee zed +Ġmor ale +ĠD W +' " +Ġkn ot +ook y +Ġdiv isive +Ġboost ed +ch y +ãĥ IJ +if act +Ġnewcom ers +ĠWrest ling +Ġsc outs +w olves +R at +Ġnin eteenth +ĠOs borne +St ats +Ġem powered +Ġpsych opath +ĠO EM +ugg age +ĠP K +ĠMoh ammad +P ak +Ġanarch ists +ĠExt ract +est hes +ĠStock holm +l oo +ĠG raph +Ġdeploy ing +ĠStr anger +ĠM old +Ġstaff er +Ġdiscount ed +uck le +ple ase +ĠLand ing +ÃŃ a +Ġ19 3 +Ġan te +Ġrep etition +Ġ+ /- +Ġpar ody +Ġlive ly +AA A +ĠHor us +Ġp its +ind ers +L OC +ĠVen ice +40 6 +ĠDis cover +â Ĩ +ellect ual +Ġp ens +Ġey el +ig uous +Im pl +Ġj oking +Ġinv al +ĠBel fast +Ġcredit ors +ĠSky walker +ov sky +Ġcease fire +Ġse als +is oft +) ). +ĠFel ix +IT S +Ġt resp +ĠBlock chain +ew are +ĠSch war +en ne +mount ed +ĠBe acon +les h +Ġimmense ly +Ġche ering +Em ploy +sc ene +ish ly +atche wan +ĠNic olas +Ġdr ained +ĠEx it +ĠAz erb +j un +Ġflo ated +u ania +De ep +Ġsuper v +Ġmyst ical +ĠD ollar +ĠApost le +ĠR EL +ĠProv ided +ĠB ucks +ãĥ ´ +cut ting +Ġenhance ments +ĠPengu ins +ĠIsa iah +Ġj erk +ĠW yn +Ġst alled +Ġcryptoc urrencies +ĠR oland +sing le +Ġl umin +ĠF ellow +ĠCap acity +ĠKaz akh +W N +Ġfin anced +38 9 +Ġt id +Ġcoll usion +ĠMy r +î Ģ +Sen ator +Ġped iatric +Ġneat ly +Ġsandwic hes +ĠArchitect ure +Ġt ucked +Ġbalcon y +Ġearthqu akes +qu ire +F uture +Ġhe fty +é Ĺ +Ġspecial izes +Ġstress es +Ġs ender +Ġmisunder standing +Ġep ile +Ġprov oke +ĠCol ors +Ġdis may +uk o +[ _ +58 6 +ne utral +Ġdon ating +ĠRand all +Mult i +Ġconvenient ly +ĠS ung +ĠC oca +Ġt ents +ĠAc celer +Ġpart nered +27 2 +ir ming +ĠB AS +s ometimes +Ġobject ed +ub ric +p osed +LC S +gr ass +Ġattribut able +V IS +Israel i +Ġrepe ats +ĠR M +v ag +ut a +in ous +Ġin ert +ĠMig uel +æ Ń +ĠHawai ian +B oard +Ġart ific +ĠAzerb ai +as io +ĠR ent +A IN +Ġappl iances +Ġnational ity +Ġass hole +ĠN eb +Ġnot ch +h ani +ĠBr ide +Av ailability +Ġintercept ed +Ġcontin ental +Ġsw elling +ĠPers pect +b ies +. < +ith metic +ĠL ara +Ġtempt ing +add r +Ġoversee ing +cl ad +ĠD V +ĠGing rich +Ġm un +ĠApp ropri +Ġalter ations +ĠPat reon +Ġha voc +Ġdiscipl ines +Ġnotor iously +aku ya +ier i +? ). +ĠW ent +Ġsil icon +Ġtre mb +Cont ainer +K nown +Ġmort ar +est e +ick a +Ar thur +ĠPre viously +ĠMart y +Ġsp arse +g ins +Ġin ward +ĠParticip ant +C opy +ĠM isc +Ġantib iotic +ĠRet ro +Ġel usive +Ġass ail +ĠBatt alion +ĠB ought +Ġdimin ish +ĠEuro pa +s ession +ĠDanger ous +ies el +Ġdisbel ief +Ġbl asts +ext reme +ĠBoy d +ĠProject s +ĠGu ys +Ġunder gone +Ġgr ill +ĠDw ight +Ġ19 7 +US ER +Ġfiles ystem +Ġcl ocks +T aylor +Ġwra pper +Ġfold ing +ous and +ĠPhilipp ine +ATION AL +ĠPer th +Ġas hes +Ġaccum ulate +ĠGate way +Sh op +orks hire +H an +ĠBar rel +ĠLe h +ĠX V +Ġwh im +Ġrep o +ĠC G +ĠM am +Ġincorpor ating +Ġbail out +Ġlingu istic +Ġdis integ +C LE +Ġcinem atic +ĠF iber +S yn +il ion +ĠCom pos +c hens +Ġne oc +Ġbo iled +F INE +on o +un cle +ik en +ĠB M +Î ¹ +Ġreceipt s +Ġdisp osed +ĠTh irty +ĠR ough +ĠA BS +Ġnot withstanding +oll en +# $ +Ġunrel iable +Ġbl oom +Ġmedi ocre +Ġtr am +ĠTas man +Ġsh akes +Ġmanifest o +ĠM W +Ġsatisf actory +Ġsh ores +Ġcomput ation +Ġassert ions +orm ons +ar ag +ab it +Dem ocrats +ĠL oot +ĠVol ks +ha ired +Ġgrav itational +S ing +ĠM iz +Ġthro ttle +Ġtyr anny +ĠView s +Ġrob ber +ĠMinor ity +Ġsh rine +sc ope +pur pose +Ġnucle us +our cing +ĠUS DA +ĠD HS +w ra +ĠBow ie +Sc ale +ĠB EL +x i +I ter +Ġ( ), +w right +Ġsail ors +ous ed +NAS A +ĠPro of +ĠMin eral +t oken +ĠF D +R ew +Ġe ll +6 30 +Ġchance llor +ĠG os +Ġamount ed +ĠRec re +ome z +ĠOpt im +ĠOl ive +Ġtrack er +ow ler +ĠUn ique +R oot +Ġmar itime +ĠQur an +ĠAd apt +Ġecosystem s +ĠRe peat +ĠS oy +ĠI MP +Ġgrad uating +and em +P ur +ĠRes et +ĠTr ick +ĠPh illy +ĠT ue +ĠMalays ian +Ġclim ax +Ġb ury +Ġcons pic +ĠSouth ampton +ĠFl owers +Ġesc orted +ĠEduc ational +ĠI RC +Ġbrut ally +e ating +Ġpill ar +ĠS ang +ĠJ ude +ar ling +ĠAm nesty +Ġrem inding +ĠAdminist rative +hes da +Ġfl ashed +ĠP BS +per ate +fe ature +Ġsw ipe +Ġgra ves +oult ry +26 1 +bre aks +ĠGu er +Ġsh rimp +ĠV oting +qu ist +Ġanaly tical +Ġtables poons +ĠS OU +Ġresear ched +Ġdisrupt ed +Ġj our +Ġrepl ica +Ġcart oons +b ians +} ) +c opy +G ot +ou ched +P UT +Ġsw arm +not ations +s aid +Ġreb uilt +Ġcollabor ate +Ġr aging +Ġn ar +Ġdem ographics +ĠD DR +Ġdist rust +oss ier +ĠK ro +Ġpump kin +Ġreg rets +Ġfatal ities +ĠL ens +ĠO le +p d +Ġpupp et +ĠOut look +ĠSt am +O l +F air +U U +Ġre written +Ä ± +Ġfasc inated +Ġve ctors +Ġtrib unal +u ay +ĠM ats +ĠCo ins +[ [ +Ġ18 1 +Ġrend ers +ĠK aepernick +Ġesp ionage +Ġsum m +Ġd itch +Acc ount +Ġspread sheet +Ġmut ant +p ast +40 7 +Ġd ye +Ġinit iation +Ġ4 000 +Ġpunish able +Ġth inner +ĠKh al +Ġinter medi +D un +ĠGoth am +Ġeager ly +Ġvag inal +p owers +V W +ĠWATCH ED +Ġpred ator +ams ung +Ġdispar ity +Ġ[ * +Ġam ph +Ġout skirts +ĠSpir its +Ġskelet al +Ð » +ĠR ear +Ġissu ance +ĠLog ic +re leased +Z Z +ĠB ound +Ent ry +Ġex its +is ol +ĠFound er +Ġw re +ĠGreen land +ĠM MO +t aker +IN C +ãģ ¾ +Ġhour ly +hen ko +Ġfantas ies +Ġdis ob +Ġdemol ition +ãĥ ĭ +Ġen listed +rat ulations +Ġmis guided +Ġens ured +Ġdiscour aged +m ort +Ġfl ank +Ġc ess +Ġreact s +ĠS ere +s ensitive +ĠSer pent +ass ad +Ġ24 7 +Ġcalm ly +b usters +Ġble ed +ĠSt ro +Ġamuse ment +ĠAntar ctica +Ġs cept +ĠG aw +a q +ason ic +Ġsp rawling +n ative +atur ated +ĠBattle field +IV ERS +E B +ĠG ems +ĠNorth western +ĠFil ms +ĠAut omatic +Ġappre hend +ãģ ¨ +Ġgui Name +Ġback end +Ġevid enced +ge ant +01 2 +ĠS iege +Ġexternal To +Ġunfocused Range +ĠguiActiveUn focused +Ġgui Icon +ĠexternalTo EVA +ĠexternalToEVA Only +F ri +ch ard +en aries +Ġchief s +Ġc f +ĠH UD +Ġcorro bor +Ġd B +ĠT aken +ĠPat ricia +ra il +ĠCh arm +ĠLiber tarian +rie ve +Person al +ĠO UR +ger ies +Ġdump ing +Ġneurolog ical +it imate +ĠClint ons +raft ed +ĠM olly +Ġtermin als +reg ister +Ġfl are +Ġenc oded +Ġautop sy +p el +m achine +Ġexempt ions +ĠRoy als +d istance +Ġdraft s +Ġl ame +ĠC unning +Ġsp ouses +ĠMark ets +ĠCar rier +Ġimp lying +ĠY ak +s id +Ġl oser +Ġvigil ant +Ġimpe achment +Ġaug mented +ĠEmploy ees +Ġunint ended +tern ally +ĠW att +Ġrecogn izable +ess im +æ Ŀ +Ġco ated +r ha +Ġlie utenant +ĠLegisl ation +pub lished +44 4 +01 3 +Ġide ally +ĠPass word +Ġsimpl ify +ĠMet a +ĠM RI +Ġple ading +organ ized +hand ler +Ġun ravel +cor rect +Ġ icy +Ġparan oid +Ġpass er +Ġinspect ions +of er +ĠHealth care +28 3 +ĠBr ut +iol a +for ge +ĠMed ieval +MS N +ie vers +ĠProgram ming +å ī +Ġ2 23 +m u +ĠC LE +ug a +Ġsho ppers +Ġinform ative +ĠPl ans +Ġsupplement ation +ĠT ests +ty ard +ocy tes +ĠVeg a +ĠGujar at +erman ent +Ex cept +ĠL OT +all a +ĠC umm +ĠO sw +Ġven om +ĠDeb t +ĠD OWN +Ġreun ion +Ġm uc +ĠRel ief +Ġge op +ĠðŁ ĺ +al ogue +An th +ech o +Ġcor ros +Ġrepl ication +ĠBl azing +ĠD aughter +Ġinf lic +ĠLind sey +Ù Ī +28 4 +Ex it +Ġgl oom +TA IN +Ġundermin ing +Ġadv ising +h idden +Ġover flow +Ġg or +urd ue +Ġe choes +enh agen +Ġimp uls +d rug +c ash +Ġas ync +Ġmir ac +at ts +p unk +Ġpiv ot +ĠLegisl ative +Ġblog gers +ĠCl aw +s burg +d yl +ĠRecomm end +Ġver te +Ġprohib iting +ĠPant her +Jon athan +Ġo min +Ġhate ful +28 1 +ĠOr che +ĠMurd och +down s +Ġas ymm +G ER +Al ways +Ġinform s +ĠW M +ĠP ony +ĠApp endix +ĠAr lington +J am +Ġmedic inal +ĠS lam +IT IES +Ġre aff +ĠR i +F G +S pring +b ool +Ġthigh s +Ġmark ings +ĠRa qqa +ĠL ak +p oll +ts ky +ĠMort y +ĠDef inition +Ġdeb unk +end ered +ĠLe one +a vers +Ġmortg ages +App arently +N ic +ha us +ĠTh ousands +au ld +Ġm ash +sh oot +Ġdi arr +Ġconscious ly +H ero +e as +ĠN aturally +ĠDestroy er +Ġdash board +serv ices +R og +Ġmillenn ials +Ġinv ade +- ( +Ġcomm issions +ĠA uckland +Ġbroadcast s +Ġfront al +Ġcr ank +ĠHist oric +Ġrum ours +CT V +Ġster il +Ġboost er +rock et +ãĤ ¼ +ut sche +ĠP I +Ġ2 33 +ĠProdu cer +ĠAnaly tics +Ġinval uable +Ġunint ention +ĠC Y +Ġscrut in +Ġg igg +Ġeng ulf +Ġprolet ariat +Ġh acks +ĠH ew +ar ak +ĠSl ime +ield ing +ag her +ĠEll iot +Ġtele com +Ġ2 19 +ult an +ĠAr bor +ĠSc outs +B an +Ġlifes pan +Ġbl asp +38 8 +Ġjud iciary +ĠContin ental +ask ing +Mc C +L ED +Ġbag gage +ĠSorce rer +Ġrem nants +ĠGriff ith +ets u +ĠSub aru +ĠPerson ality +des igned +ush ima +agn ar +Ġrec oil +Ġpass ions +\ ": +Ġte e +Ġabol ition +ĠCreat ing +j ac +Ġ19 4 +01 9 +Ġpill ars +ric hed +/ " +t k +Ġlive lihood +Ġro asted +ah on +ĠH utch +ass ert +Ġdivid end +Ġkn it +Ġd aunting +Ġdisturb ance +Ġsh ale +Ġcultiv ated +Ġrefriger ator +L B +ĠN ET +Ġcommercial s +Ġthink ers +45 5 +Ġch op +B road +Ġsuspic ions +Ġtag ged +l ifting +Ġsty lish +ĠShield s +Short ly +Ġt ails +A uth +ST E +ĠG AME +Ġse ism +ĠK is +olog ne +Ġcow ork +Ġforc ibly +Ġthy roid +ĠP B +AN E +mar ried +h orse +Ġpoly mer +ĠCh al +od or +DE BUG +ĠCon text +Ġbl iss +Ġpin point +ĠMat hemat +leg ram +ĠWeek end +Ġlab elled +Ġb art +it les +Ġest rogen +âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ +" ' +Ġvis ibly +Ġouts ider +aid a +Are a +Ġdisse min +Ġdish onest +ĠCl osed +ĠBullet in +ĠRam sey +sw ord +ĠX I +our ced +S ame +34 6 +ĠRe pe +ĠK ou +c ake +em is +C ache +ĠMe aning +ĠEn light +onom y +Ġmanifest ation +sw orth +J ay +Ġch ore +ö r +D ream +Ġsanction ed +Ġcult urally +ĠA ra +N av +Ġthe ological +Ġstr ut +ĠV O +ĠHand book +Ġconstruct ing +Ġ ¶ +ĠBenef its +ĠPsych ological +s ac +å ¸ +p olicy +ĠMat ters +ĠReport ed +ĠBy te +Ġvit ro +ĠM aiden +Ġl am +ĠJenn ings +Ġgar ment +ĠRut gers +ĠStaff ord +ĠWell ington +Ġinter mitt +Ġn pm +Ġord eal +Ġplug ged +o oming +in ished +fram ework +Ġtim ber +Ġc ass +Ġ8 50 +il ess +ĠRed ux +7 68 +St re +Ġsurpass ed +w hel +Ġparalle ls +Ġve il +ĠG I +ĠR EST +Ġread iness +s ort +Ġmod ifying +ĠSl ate +ru ff +Ġmar ble +Ġinf rared +Ġaud itor +ĠFANT ASY +ĠP overty +ĠS PD +Ġ" ( +K y +RA Y +Ġexecut ions +ĠBever ly +ĠMarx ism +ĠBur st +ĠK ali +est ones +Clear ly +E ll +ãģ § +ĠProceed ings +T oken +IF IC +ñ a +Cent ral +ĠH aley +ĠD rama +Ġform ations +OR N +Book s +Ġdom inating +ĠFly ers +ĠCompan ion +Ġdiscipl ined +ĠYug oslav +ĠSpell s +Ġv engeance +Ġland lords +L en +ĠO gre +ano ia +Ġpier cing +Ġcon greg +Ġscore r +ob ia +Ġnic kel +ĠLear ns +Ġre jo +Ġmaster piece +Fl ash +Ġinhab ited +ĠOpen GL +ĠD ud +ĠI CO +Ġar ter +Ġpl ur +Ġmaster y +Ġlong standing +st ed +Ġw ines +Ġtelev ised +ĠSh rine +ĠBay ern +Ġâ ĵĺ +Ġencl osure +j ohn +Ġprophe ts +ĠRes urrection +ĠOrd ers +Ġun even +r als +Ġd wind +ĠL ah +ĠSl oven +37 8 +Ġins istence +aff le +ĠCl one +Ġhard ship +ĠCongress man +Ġple ad +Ġreview ers +Ġc ured +Ġ19 35 +as ley +f ake +ĠTh inking +yd ia +P ART +ĠD ota +o it +Ġwh ipped +Ġb ouncing +ĠHispan ics +com ings +Ġcann abin +ĠCh ambers +ĠZ ack +Option al +Ġco ats +Ġprow ess +ĠNort on +Ġplain ly +Ġfre ight +Ġinhib ition +Ġcl am +Ġ30 3 +ke f +ale igh +L uke +Ġpsych o +ator ium +M ED +Ġtreat ies +Ġind isc +Ġd c +OP S +Ġresil ient +ĠInter state +Ġsl ack +Ġmund ane +Ġestab lishes +35 9 +Ġstr ained +Ġn ond +S us +Ġcast e +ar ate +ie ving +Ġunfair ly +Ġpars er +on ial +urs ive +V ia +ĠOtt o +ĠAuthor ities +stro ke +K R +ĠMer cy +Ġfurn ished +Ġout set +Ġmet ic +19 82 +olith ic +ĠT ent +og ical +ĠA ircraft +Ġh ides +ĠBec ame +Ġeduc ators +re aching +Ġvol atility +Ġtodd ler +ĠNAS CAR +ĠTw elve +ĠHigh lights +Ġgra pe +Ġspl its +Ġpe asant +Ġre neg +ĠMS I +Tem p +st ars +Ġtre k +ĠHy de +b inding +Ġreal ism +Ġox ide +ĠH os +Ġmount s +Ġbit ing +Ġcollaps ing +Ġpost al +Ġmuse ums +Ġdet ached +Ġrespect ing +Ġmonop ol +Ġwork flow +ĠC ake +Tem plate +ĠOrgan isation +Ġpers istence +36 9 +C oming +B rad +Ġredund ant +ĠG TA +Ġb ending +Ġrev oked +Ġoff ending +Ġfram ing +Ġprint f +Comm un +mem bers +Out side +Ġconst rued +Ġc oded +F ORE +Ġch ast +Ch at +Ind ian +ĠY ard +? !" +ĠP orts +ĠX avier +ĠR ET +' ." +ĠBo at +iv ated +ich t +umer able +D s +ĠDun n +Ġcoff in +Ġsecure ly +ĠRapt ors +ĠB es +Install ation +Ġin ception +ĠHealth y +end ants +Ġpsych ologists +ĠShe ikh +c ultural +ĠBlack Berry +sh ift +F red +oc he +Ġc akes +ĠS EO +ĠG ian +ĠAs ians +og ging +e lement +Ġpund its +ĠV augh +ĠG avin +Ġh itter +Ġdrown ed +Ġch alk +ĠZ ika +Ġmeas les +80 2 +âĢ¦ .. +ĠAW S +] " +Ġdist ort +ĠM ast +Ġantib odies +ĠM ash +Mem ory +ĠUg anda +ĠPro b +Ġvom iting +ĠTurn s +Ġoccup ying +Ġev asion +ĠTher apy +Ġprom o +Ġelect r +Ġblue print +ĠD re +pr iced +ĠDep ot +Ġallev iate +ĠSom ali +m arg +n ine +Ġnostalg ia +ĠShe pherd +Ġcaval ry +Ġtor ped +ĠBlood y +x b +Ġs ank +Ġgo alt +report print +embed reportprint +clone embedreportprint +ĠIn itially +ĠF ischer +Ġnot eworthy +c ern +Ġin efficient +raw download +rawdownload cloneembedreportprint +c ation +ĠD ynasty +l ag +D ES +Ġdistinct ly +ĠEston ia +Ġopen ness +Ġg ossip +ru ck +W idth +ĠIb rahim +Ġpet roleum +Ġav atar +ĠH ed +ath a +ĠHog warts +Ġc aves +67 8 +Ġsafegu ard +ĠM og +iss on +ĠDur ham +sl aught +ĠGrad uate +Ġsub conscious +ĠEx cellent +ĠD um +---- - +Ġp iles +ĠW ORK +ĠG arn +ĠF ol +ĠAT M +Ġavoid s +ĠT ul +Ġble ak +EL Y +iv ist +light ly +P ers +ĠD ob +ĠL S +Ġins anity +Î µ +atal ie +En large +Ġtw ists +Ġfault y +Ġpir acy +Ġimp over +Ġrug ged +ĠF ashion +Ġs ands +' ? +sw ick +Ġn atives +Ġhe n +ĠNo ise +ãĥ Ĺ +Ġg reens +Ġfree zer +Ġd ynasty +ĠFather s +ĠNew ark +Ġarchae ological +Ġo t +ob ar +Ġblock ade +Ġall erg +L V +Ġdeb it +ĠR FC +ĠMil ton +ĠPress ure +Ġwill ingly +Ġdisproportion ate +Ġopp ressive +Ġdiamond s +Ġbelong ings +19 70 +Ġbell s +Ġimperial ism +Ġ2 27 +Ġexpl oding +ĠE clipse +Ġ19 19 +Ġr ant +Ġnom inations +34 7 +Ġpeace fully +ric a +ĠF UCK +Ġvib ration +mal ink +Ġro pes +ĠIv anka +ĠBrew ery +ĠBook er +ĠOw ens +go ers +Serv ices +ĠSn ape +Ġ19 1 +39 5 +Ġ2 99 +just ice +Ġb ri +Ġdisc s +Ġprom inently +Ġvul gar +Ġsk ipping +l ves +Ġtsun ami +37 4 +ĠU rug +ĠE id +rec ated +p hen +Ġfault s +ĠStart ed +9 50 +Ġp i +Ġdetect or +Ġbast ard +Ġvalid ated +Space Engineers +OUR CE +Ġ( ~ +Ġuns ur +Ġaff irmed +Ġfasc ism +Ġres olving +ĠCh avez +ĠC yn +Ġdet ract +L ost +Ġrig ged +Ġhom age +ĠBrun o +55 5 +ec a +Ġpress es +Ġhum our +Ġsp acing +Ġ' / +olk ien +C oun +OP ER +T re +S on +ĠCambod ia +ier re +m ong +o zy +Ġliquid ity +ĠSov iets +ĠFernand o +Ġ2 29 +Ġsl ug +ĠCatal an +elect ric +Ġsc enery +ĠH earth +Ġconst rained +Ġgoal ie +ĠGu idelines +ĠAm mo +ĠPear son +Ġtax ed +Ġfet us +Resp onse +ĠAlex is +th ia +G uy +Ġrecon struct +Ġextrem es +Ġconclud ing +ĠP eg +ook s +Ġded uctions +R ose +Ġground breaking +ĠT arg +ãĥ ģ +ĠRe ve +res ource +Ġmo ons +Ġelectrom agnetic +Ġamid st +ĠVik tor +N ESS +B ACK +Ġcomm ute +ĠAna heim +Ġfluct uations +6 40 +Ġnood les +ĠCop enhagen +ĠT ide +ĠGri zz +ĠS EE +Ġpip elines +Ġsc ars +end o +ag us +ĠE TF +/ # +ĠBec ome +44 8 +Ġvis c +ĠRecomm ended +Ġj umper +Ġcogn ition +Ġassass in +Ġwitness ing +ĠSet up +Ġl ac +v im +IS M +p ages +SS L +35 8 +Ġad ject +indust rial +l ore +cher y +Ġgl itter +Ġc alf +Flor ida +Ġspoil ers +Ġsucceed s +Ġch anting +Ġslog ans +ĠTr acy +Vis it +rol ogy +Ġm ornings +Ġline age +Ġs ip +Ġintense ly +Ġflour ish +ĠSle eping +ĠF em +or por +ĠK lan +ĠDar th +h ack +ĠNi elsen +Ġtum ors +Ġprocure ment +ĠY orkshire +Ġra ided +K Y +An na +Ġ// [ +ĠDis order +ĠMust ang +ĠW en +ĠTry ing +s q +Ġdeliver ies +Ġshut ter +Ġcere bral +Ġbip olar +ĠC N +l ass +j et +Ġdeb ating +> : +Ġe agle +gr ades +ĠD ixon +UG C +M AS +ĠDr aco +ĠMach ines +aff er +Ġem an + ² +pr on +ĠG ym +Ġcompar atively +ĠTrib unal +PR O +Ġle x +Ġfert ile +Ġdep ressing +Ġsuperf icial +ess ential +ĠHun ters +g p +Ġprom inence +L iber +ĠAn cest +ote chnology +Ġm ocking +ĠTra ff +ĸ ļ +Med ium +I raq +Ġpsychiat rist +Quant ity +ĠL ect +Ġno isy +5 20 +G Y +Ġsl apped +ĠM TV +Ġpar a +p ull +Mult iple +as her +Ġn our +ĠSe g +Spe ll +v ous +ord ial +Sen ior +ĠGold berg +ĠPl asma +ne ed +Ġmess enger +ere t +Ġteam ed +Ġliter acy +ĠLe ah +ĠD oyle +Ġem itted +U X +Ġev ade +Ġm aze +Ġwrong ly +ĠL ars +Ġstere otype +Ġpled ges +Ġarom a +ĠM ET +Ġac re +ĠO D +Ġf f +Ġbrew eries +ĠH ilton +und le +ĠK ak +ĠThank fully +ĠCan ucks +in ctions +ĠApp ears +Ġco er +Ġundermin ed +ro vers +And re +Ġbl aze +um ers +Ġfam ine +amp hetamine +ulk an +Am ount +Ġdesper ation +wik ipedia +develop ment +ĠCor inth +uss ia +Jack son +L I +N ative +R s +Oh io +ĠKath leen +F ortunately +Ġattend ant +ĠPre ferred +ĠDid n +ĠV s +M is +Ġrespond ent +Ġb oun +st able +Ġp aved +Ġunex pl +ĠChe ney +L M +ĠC ull +bl own +Ġconfront ing +oc ese +serv ing +W i +ĠLith uania +ann i +Ġst alk +h d +Ġv ener +AP H +ynchron ous +UR R +um ably +hist oric +H alf +H ay +Ġresil ience +spe ction +Ġabandon ing +O bs +ĠDeb bie +Ġgrad ient +ĠPl aint +ĠCan al +AR CH +Ġexpans ive +Ġfun g +Ġb ounced +U nd +Ġprec autions +Ġclar ification +Ġd agger +Ġgri ps +Ġ µ +ĠRiver a +ĠUnd ead +is ites +ĠFIR ST +ñ o +aud i +Ġhost ages +Ġcompl iant +Ġal umni +Se ven +Ġcyber security +e ither +Col lect +Ġinvari ably +ĠS oci +Ġlaw maker +Ġa le +ĠPerson ally +N azi +Ġcustom ization +ĠPro c +ĠSask atchewan +eat uring +Ġsp ared +Ġdiscontin ued +Ġcomput ational +ĠMotor ola +Ġsuprem acist +government al +Ġparad ise +ĠDown ing +ĠNik on +Ġcat alyst +ber ra +Tor onto +8 75 +bet a +ĠMac ron +Ġunreal istic +ve ctor +ĠVeh icles +it iveness +ĠR V +ĠCol bert +s in +o ji +ent in +ĠKr ish +hell o +ff ield +ok y +ĠT ate +Ġmap le +Ġa ids +chem ical +33 4 +n uts +ĠWar p +Ġx x +ĠRob b +umer ous +_- _ +ft ime +ĠV W +Ġw inger +ĠD ome +t ools +ĠP V +ĠGe orgetown +Ġg eared +Ġjihad ists +Ġc p +Ġster oids +M other +cler osis +ĠDR M +nes ia +Ġl inger +Ġimm ersive +ĠC OUN +Ġoutwe igh +ens ual +B and +Ġtransform s +mat ched +ps ons +ĠJud icial +f actor +Ġrefer ral +Ġodd ly +ĠW enger +B ring +ĠB ows +60 2 +IC LE +Ġl ions +ĠAcad emic +ĠTh orn +ĠRa ider +kef eller +St orage +L ower +ĠOr t +ĠEqu ality +AL T +ĠS OC +T ypes +Ġl yn +ĠAss et +co at +TP P +C VE +ĠPione er +app lication +Mod ern +ĠH K +En vironment +Al right +R ain +IP P +ĠShi ite +Ġm ound +ĠAb ilities +cond ition +St aff +Ġcompet ence +ĠM oor +ĠDi ablo +Ġwith held +Ġost ensibly +ĠB rom +Ġms g +Ġden omin +ĠRef erences +ĠF P +Ġplun ged +Ġp amph +m oving +cent ral +Ġdown right +Ġf ading +T al +T yp +ĠTh y +uk es +it he +Ġo ve +Ġbatt led +Ġseaf ood +Ġfig ur +ĠR D +c rop +Ġsqu ads +{ \ +à ¹ +ĠE h +Ġinterview ing +ĠQ in +Ġas piring +PL IC +Ġcla uses +ĠG ast +ĠN ir +Ġl uggage +Ġh ose +Ġsystem d +Ġdesc ending +ĠRev ised +ĠR ails +al ign +70 9 +33 7 +Ġf ug +charg ing +t ags +Ġut er +k ish +WAR NING +49 0 +prof its +Ġvoy age +Ġa ce +ĠV anguard +ĠT anks +ĠM uk +Ġ2 26 +S afe +Ar mor +Ġvolcan ic +Ġwom b +ĠM IL +Ġbegin ner +ĠRec ogn +ĠA AP +PL AY +) ! +Ġdetect ing +c n +Ġbre aches +Bas ically +ĠP ag +ĠMunicip al +ĠInd ie +ĠL af +ĠDis able +ĠOl son +Ġrest rained +Ġrul ings +Ġhum ane +ev ents +ĠCinem a +display Text +ĠH atch +action Date +onna issance +Ġassault ing +ĠL ug +CH AT +Ġvig orous +ĠPer se +Ġintoler ance +ĠSnap chat +ĠSh arks +Ġd ummy +ĠDi agn +ĠGu itar +im eters +40 3 +RE G +A x +Ġsepar ates +ĠMah m +Ġt v +j ah +O OL +C irc +ĠWinds or +uss ian +Ġintu ition +Ġdis dain +ĠDon ovan +Ġ2 21 +E mb +Ġcondem ning +Ġgener osity +zz y +Ġpant ies +ĠPre vent +Action Code +AN A +34 2 +external ActionCode +Ġspec ifying +Ġcryst all +J ere +Ġru pt +ĠApp rentice +Ġprof iling +Ð º +St rike +Ġsid eline +Ġoblig ated +Ġocc ult +Ġbureaucr atic +ant ically +rupt ed +neg ative +ĠEthiop ia +ĠC ivic +Ġins iders +el igible +ĠTV s +ĠB AR +ĠT I +i ologist +ĠA IR +Ġsubstit uted +Ar ab +ĠS aul +ĠY og +p rem +Ġbuild ers +Ġstation ary +Ġdoubt ful +Ġvig orously +Ġthr illing +Ph ysical +ĠCare y +ĠHyd ra +geon ing +ĠS ly +y ton +Ġborrow ers +ĠPark inson +Ġ ë +ĠJama ica +Ġsat ir +Ġinsurg ents +ĠF irm +Ġis ot +ĠK arn +our ning +ak ens +doc s +l ittle +ĠMon aco +CL ASS +Tur key +L y +ĠCon an +ass ic +Ġstar red +ĠPac ers +et ies +Ġt ipping +M oon +ĠR w +s ame +Ġcav ity +Ġgo of +ĠZ o +Sh ock +um mer +Ġemphas izes +Ġreg rett +Ġnovel ty +Ġen vy +ĠPass ive +r w +50 5 +Ġind ifferent +ĠR ica +ĠHim self +ĠFred die +Ġad ip +ä¸ Ģ +Ġbreak out +Ġhur ried +ĠHu ang +ĠD isk +Ġro aming +?????- ?????- +U V +ĠRick y +ĠS igma +Ġmarginal ized +Ġed its +Ġ30 4 +mem ory +Ġspec imen +29 3 +ãģ ¯ +Ġvert ically +Ġaud ition +ĠHe ck +Ġc aster +ĠHold ings +ad al +ĠC ron +ĠL iam +Ġdef lect +P ick +ĠDeb ug +RE F +Ġvers atility +ot hes +class ified +ĠMah ar +ĠH ort +C ounter +st asy +not iced +33 1 +ĠSh im +f uck +ĠB ie +Ġair ing +ĠPro tein +ĠHold ing +Ġspect ators +ili ated +ĠThat cher +n osis +ãĥ¼ ãĥ³ +Te le +B oston +ĠTem pl +st ay +Ġdecl arations +47 9 +Vol ume +ĠDesign er +ĠOver watch +id ae +Ġon wards +Ġn ets +ĠMan ila +part icularly +Ġpolit ic +o other +Ġport raits +Ġpave ment +c ffff +Ġs aints +Ġbegin ners +ES PN +Ġshort comings +âķIJ âķIJ +Ġcom et +ĠOrgan ic +qu el +Ġhospital ized +Bre ak +Ġpe el +dyl ib +asp x +ur ances +ĠT IM +P g +Ġread able +ĠMal ik +Ġm uzzle +Ġbench marks +d al +ĠV acc +ĠH icks +60 9 +ĠB iblical +he ng +Ġover load +ĠCivil ization +Ġimm oral +Ġf ries +ãĤ Ĵ +Ġreprodu ced +Ġform ulation +j ug +ire z +g ear +Ġco ached +Mp Server +ĠS J +ĠK w +In it +d eal +ĠO ro +ĠL oki +ĠSong s +Ġ23 2 +ĠLou ise +asion ally +Ġunc ond +olly wood +Ġprogress ives +ĠEn ough +ĠDo e +Ġwreck age +Ġbr ushed +ĠBase Type +Ġz oning +ish able +het ically +ĠC aucus +ĠH ue +Ġk arma +ĠSport ing +Ġtrad er +Ġseem ing +ĠCapt ure +4 30 +b ish +Ġt unes +Ġindo ors +ĠSp here +ĠD ancing +TER N +Ġno b +ĠG ST +m aps +Ġpe ppers +F it +Ġoverse es +ĠRabb i +ĠR uler +vert ising +off ice +xx x +Ġra ft +Ch anged +Ġtext books +L inks +ĠO mn +ãĢ ij +Ġinconven ience +ĠDon etsk += ~ +Ġimplicit ly +Ġboost s +ĠB ones +ĠBo om +Cour tesy +Ġsens ational +AN Y +Ġgre edy +ed en +Ġinex per +ĠL er +ĠV ale +Ġtight en +ĠE AR +ĠN um +Ġancest or +S ent +ĠH orde +urg ical +all ah +Ġsa p +amb a +ĠSp read +tw itch +Ġgrand son +Ġfract ure +Ġmoder ator +ĠSe venth +ĠRe verse +Ġestim ation +Cho ose +Ġpar ach +Ġbar ric +ãĢ IJ +Ġcomp ass +Ġall ergic +âĢ ķ +OT HER +err illa +Ġw agon +Ġz inc +Ġrub bed +ĠFull er +ĠLuxem bourg +ĠHoo ver +Ġli ar +ĠEven ing +ĠCob b +est eem +Ġselect or +ĠB rawl +is ance +ĠE k +Ġtro op +Ġg uts +ĠApp eal +ĠTibet an +Ġrout ines +ĠM ent +Ġsummar ized +steam apps +Ġtr anqu +Ġ19 29 +or an +ĠAut hent +Ġg maxwell +Ġappre hens +Ġpo ems +Ġsa usage +ĠWeb ster +ur us +Ġthem ed +Ġl ounge +Ġcharg er +Sp oiler +Ġsp illed +h og +ĠSu nder +ĠA in +ĠAng ry +Ġdis qual +ĠFrequ ency +ĠEther net +Ġhel per +Per cent +Ġhorr ifying +Ġa il +ĠAll an +EE E +ĠCross ing +44 9 +Ġh olog +ĠPuzz les +ĠGo es +eren n +60 4 +ãģ ı +ĠRaf ael +Ġatt en +ĠE manuel +Ġup ro +ĠSus p +P sych +ĠTr ainer +ĠN ES +ĠHun ts +bec ue +Ġcounsel or +R ule +Ġtox ins +Ġb anners +r ifice +Ġgreet ing +Ġfren zy +Ġall ocate +Ġ* ) +ex pr +50 3 +ĠCh ick +ĠT orn +Ġconsolid ation +ĠF letcher +sw itch +fr ac +cl ips +ĠMcK in +ĠLun ar +Mon th +IT CH +Ġscholar ly +rap ed +39 8 +Ġ19 10 +Ġe greg +Ġin secure +Ġvict orious +cffff cc +Ġsing led +Ġel ves +ĠW ond +bur st +Ġcam oufl +ĠBL ACK +Ġcondition ed +ç ī +ans wered +Ġcompuls ory +asc ist +Ġpodcast s +ĠFrank furt +bn b +Ġne oliberal +ĠKey board +ĠBel le +w arm +Ġtrust s +Ġins ured +ĠBu cc +us able +60 7 +ĠPl ains +Ġ18 90 +Ġsabot age +Ġlod ged +f elt +Ġg a +ĠN arc +ĠSal em +Ġsevent y +ĠBl ank +p ocket +Ġwhis per +Ġm ating +om ics +ĠSal man +ĠK ad +Ġan gered +Ġcoll isions +Ġextraord inarily +Ġcoerc ion +G host +b irds +è Ģ +k ok +Ġper missible +avor able +Ġpo inters +Ġdiss ip +ac i +Ġtheat rical +ĠCos mic +Ġforget ting +Ġfinal ized +å¤ § +y out +l ibrary +Ġbo oming +ĠBel ieve +ĠTe acher +ĠL iv +ĠGOOD MAN +ĠDomin ican +OR ED +ĠPart ies +Ġprecip itation +ĠSl ot +R oy +ĠComb ined +Ġinteg rating +Ġch rome +Ġintest inal +ĠRe bell +Ġmatch ups +Ġblock buster +ĠLore n +ĠLe vy +Ġpre aching +ĠS ending +ĠPur pose +ra x +f if +Ġauthor itative +ĠP ET +ast ical +Ġdish on +Ġchat ting +Ġ"$ :/ +Connect ion +Ġrecre ate +Ġdel inqu +Ġbro th +ĠD irty +ĠAd min +z man +Ġscholars hips +Ġ25 3 +cont act +als a +7 67 +c reen +abb age +Ġ19 15 +Ġbl ended +Ġal armed +L anguage +35 6 +Ġbl ends +ĠCh anged +W olf +Ġhe pat +Creat ing +Ġper secut +Ġsweet ness +art e +Ġforfe iture +ĠRober to +im pro +N FL +ĠMag net +Det ailed +Ġinsign ificant +ĠPOL IT +ĠBB Q +ĠC PS +Ġse aw +amin er +m L +end if +f inals +Ġ26 5 +u ish +Ġ} ) +ĠPro blems +Ġem blem +Ġserious ness +Ġpars ing +Ġsubst itution +Ġpress ured +Ġrecy cled +ale b +Rub y +Ġprof iciency +Dri ver +ĠW ester +: ' +AF TA +Ġm antle +ĠClay ton +fl ag +Ġpractition er +c overed +ĠSt ruct +add afi +4 25 +ĠTown ship +ĠHyd ro +Lou is +34 3 +Ġcond o +ĠT ao +Ġutil ization +Ġnause a +ĠDem s +rid ges +p ause +Ġform ulas +Ġchall enger +37 6 +Ġdefect ive +ĠRail way +ĠPub Med +Ġyog urt +l bs +ĠNor folk +OP E +ĠMood y +Ġdistribut or +Ġscroll s +Ġextract s +St an +Ġv iability +Ġexp oses +Ġstar vation +ĠStep s +ĠD odd +f ew +ST D +33 2 +Ġclos ures +Ġcomplement ary +ĠS asha +ump y +Ġmon et +Ġartic ulate +ĠDo ct +k iller +Ġsc rim +Ġ2 64 +Ġprost itutes +Ġse vered +Ġattach ments +Ġcool ed +L ev +ĠF alk +f ail +Ġpolic eman +ĠD ag +Ġpray ed +ĠK ernel +Ġcl ut +Ġc ath +Ġan omaly +St orm +em aker +ĠBreak fast +ul i +o ire +J J +h z +Oper ation +ĠS ick +35 4 +ĠGuatem ala +R ate +Ġexp osures +f aces +ĠArch ae +ra f +ĠM ia +Ġ20 25 +Ġop aque +Ġdisgu ised +ĠHead quarters +S ah +Ġp ots +9 78 +ĠM alf +Ġfrown ed +Ġpoison ous +ĠCon vers +ee ks +Ġcr ab +." " +Ġtre ason +Ġr anc +Ġescal ating +Ġwar r +Ġmob s +Ġl amps +ĠSun shine +ĠBrun swick +Ph ones +Ġspe lled +ĠSk ip +Ġ20 50 +Ġ19 11 +ĠPl uto +ĠAm end +Ġme ats +38 7 +Ġst omp +ĠZh ou +ĠLevi athan +ĠHaz ard +ad v +ĠOr well +Ġal oud +Ġb umper +ĠAn arch +ub untu +ĠSer ious +f itting +ĠOption al +ĠCec il +RE AM +Ġser otonin +Ġcultiv ate +ag ogue +} \ +Ġmos ques +ĠSun ny +Ġre active +rev olution +ĠL up +ĠFed ora +Ġdefense man +ĠV ID +ist ine +Ġdrown ing +ĠBroad casting +Ġthr iller +ĠS cy +Ġacceler ating +Ġdirect s +od ied +b ike +d uration +Ġpain fully +R edd +Ġproduct ions +Ġg ag +Ġwh ist +Ġs ock +Ġinf initely +ĠConc ern +ĠCit adel +Ġlie u +Ġcand les +ogene ous +arg er +Ġheaven ly +inflamm atory +Per formance +C s +ruct ose +az aki +Ġp essim +Ġinf erence +Ġpow d +ĠZ oe +Ġpain ts +Ġd azz +pt a +-------- --- +Ġins pir +ĠExper imental +ĠKn ife +reg or +b ors +Ġshow ers +rom eda +Ġs aint +Ġben ign +ĠJ iang +Ġenvision ed +Ġsh roud +IF T +H O +Ġsh uff +ĠI CC +Ġse greg +Ġrevis it +ighth ouse +L i +Ġsub strate +ĠSe as +ĠRew ard +ĠH ep +ĠBr ass +s bm +Ġelim inates +Ġst amina +ĠV AT +ĠLo an +Ġconst raint +Ġappropri ated +Ġp es +ĠA LE +r anging +Ġ40 4 +39 2 +Ġintellectual s +ach u +Ġrestruct uring +ĠLe vin +Ġrun es +Ġdelight ful +Ġcarbohyd rates +ĠMod els +ĠExp o +Ġtransport ing +all oc +Ġring ing +S amsung +Ġscarce ly +ĠURL s +ĠM AS +Ġprot otypes +Ġnarr ator +ĠCPU s +cd n +ĠBart on +Ġdecided ly +ĠSh u +ix ir +oc ious +ĠMy st +N intendo +Ġre use +Ġforg iven +F ew +in ical +n at +Ġseam less +ĠEv a +ĠE VE +ĠJ O +land ers +Ġso fter +neg ie +Ġtrans ient +Ġorb ital +Ġfulf il +ĠK om +Hop efully +Ġdynam ically +ĠHun ger +å Ľ +ĠArmen ia +el man +ber to +Ġp ige +ĠID s +lim it +Ġve ins +Ġso aring +p acks +Gold en +ĠCr ab +ist or +ĠR PM +Ġ$ $ +g ression +Ġjihad ist +Ġgam ble +Ġcare g +Ġinf lated +F ace +ĠFire arms +ĠEm manuel +â Ŀ +Ġsh ocks +gr ab +Ġspl end +ĠHP V +ab ortion +Ab ove +Ent ity +play ers +Ġcomm enced +ul ence +Ġfulfill ment +Ġembod iments +ĠW elfare +Ġha il +Ġ< @ +tt en +Ġcat cher +ĠJ azeera +Ġvolcan o +Ġstabil ize +ĠHand ler +Ġintens ified +ĠAb rams +Ġhum iliation +p aced +60 5 +ĠCent OS +Spe cific +Ġhe ed +ĠC AM +ĠGal ile +D ie +Ġabol ished +ĠThom son +ĠTe achers +ĠW ass +j ong +ĠIS BN +ĠAll ies +sh ake +å · +v ict +How ard +Ġde em +Ġexceed ingly +ĠSmart stocks +ib e +Ġdoor way +Ġcompet ed +ig mat +Ġnational ists +Ġg room +ĠKe en +Ġdispos able +de cl +ĠT olkien +ĠSche me +Ġb iod +Ġav id +ĠEl on +ag ar +ĠT SA +R oman +Ġartific ially +Ġadvis ors +X L +ĠInf erno +36 6 +Ġted ious +ĠPhot ography +ĠCar rie +Ġtro pe +ĠSand ra +Ġdec imal +Que en +ĠGund am +ĠO M +ote ch +N BA +Ġ19 32 +Ġent renched +ĠMar ion +Ġfr aternity +Lab our +Hen ry +Ġlat itude +E ither +Ġenh ances +ĠPot ential +Ġsh ines +id ad +Ġbread th +Ġcapac ities +ĠðŁ ĻĤ +ĠBron x +Ġsex es +Ġdifferent iation +Ġheavy weight +ĠT aj +d ra +Ġmigr ate +Ġexhaust ion +ĠR UN +els ius +ĠCu omo +Ġgu itars +Ġcl ones +ĠSom ew +ĠP ry +------------ - +Ġwarr anted +cy cles +Ġsalv age +Ġdis ks +R ANT +ĠNGO s +ĠMart ian +":[ {" +Ġadd icts +oj ure +il let +Ġamazing ly +art ments +p ixel +ĠGPU s +Lay out +è £ +ĠTam il +ĠBas il +Ġimpart ial +ĠSt ructure +f ork +b ryce +Ġr idge +ĠHamb urg +ri ous +Ġbl itz +cig arettes +Ġcan ned +40 2 +Ġiron ically +Ġcompassion ate +ĠHaw kins +. # +ĠCat hedral +Ġrall ied +in ternal +Ġqu ota +st akes +T EXT +m om +Ġcomple tes +Ġ23 8 +Ġsh rug +ãĥ ij +ĠN inth +Ġrev ise +ĠProv ider +Ġtre acher +Ġqu asi +ĠPR ES +Ġdep osition +Ġconfidential ity +iss ors +Ġim balance +Ġspan ning +Ġang ular +ĠC ul +commun ication +ĠNor a +ĠGen ius +op ter +Ġs acked +Sp ot +Ġfine ly +ĠCH R +28 2 +w aves +Pal est +ĠRo hing +N L +è ¿ +Ġsh itty +ĠSc alia +4 75 +Pro gress +Ġreferen cing +Ġclass rooms +ab ee +Ġs od +hes ion +70 8 +ĠZucker berg +ĠFin ish +ĠScot ia +ĠSav ior +ĠInstall ation +an tha +( - +Ġ30 2 +ĠP unk +Ġcr ater +yout u +Ġro ast +Ġinflu encing +Ġd up +ĠJ R +ĠG rav +Ġstat ure +Ġbath rooms +A side +W iki +me an +ĠZ ak +ĠOn es +ĠN ath +Ġhyper t +Ġcommence ment +C ivil +Ġmoder ately +Ġdistribut ors +Ġbreast feeding +Ġ9 80 +ĠS ik +ĠC ig +ĠAM ER +R IP +ĠCare er +ust ing +Ġmess ed +Ġe h +ĠJ ensen +/ $ +Ġblack mail +Ġconvers ions +Ġscientific ally +Ġmant ra +p aying +Ġiv ory +ĠCour ts +OU GH +aunt let +Ser ial +B row +ĠH undreds +3 23 +Ġpe e +Ġlin ux +Ġsub mer +ĠPrinc ipal +48 5 +ĠD SL +ĠCous ins +Ġdoctr ines +ĠAthlet ics +Ġ3 15 +ĠK arma +Ġatt ent +ur ger +Ġpresc ribe +Ġenc aps +ĠC ame +Ġsecret ive +ĠCr imes +d n +C lean +ĠEgypt ians +ĠCar penter +Ġ ll +H um +ĠMil o +Ġcapital ists +Ġbrief ed +T we +ĠBas in +elve t +M os +Ġplun ge +ĠKa iser +ĠFu j +ill in +Ġsafegu ards +Ġo ste +ĠOpportun ity +ĠM afia +ĠCall ing +ap a +ur ban +br ush +ill ard +c é +int elligence +ĠL ob +ĠDru id +Ġsm oother +Ġfoot ing +Ġmotor ists +arc ity +Ġmascul inity +Ġm ism +Ġabdom inal +ĠTa vern +ĠR oh +Ġesc apes +s igned +Anth ony +Ġsacrific ing +Ġintim acy +Ġan terior +ĠK od +Ġmot if +Ġg raz +Ġvisual ization +Ġguitar ist +ĠTro tsky +m agic +D ar +ĠMor i +Ġw ards +Ġtoile ts +l est +Ġtele port +ĠSund ays +ĠPl at +ET S +Ġe Sports +Pat rick +ĠK atherine +en ko +Ġhas sle +ĠM ick +gg les +Ġh ob +aint ain +Ġair borne +Ġsp ans +Ġch ili +Ġa perture +Ġvolunte ered +ĠInc ident +ĠF res +ĠVeter an +augh tered +ing o +Ġun insured +CL OSE +Ġf use +Ġer otic +Ġadvert ise +ra ising +Text ure +Ġatt ends +ĠRE AL +udd led +Ġsm oot +Ġ30 5 +ĠWill is +Ġbl ond +An alysis +ĠV T +on ica +Ġstrongh old +R F +N M +. >> +Ġprosper ous +Ġbo asted +29 2 +ĠManufact uring +PR ESS +g ren +Ġpharm acy +ĠRoc kefeller +k ai +Ġth umbs +ĠH ut +Ġmother board +Ġguard ians +ĠAl ter +ll ular +Ġsh ack +Ġwise ly +Ġback bone +erv a +Ġsu icides +ĠMcG regor +ij ah +E mer +ĠB rav +Ġdesign ate +P OST +produ ced +Ġcleans ing +irl wind +ex istent +ĠHum ph +ĠPay ne +Ġv ested +Å ¡ +Ġstring ent +ion a +Ġuns ub +Ġsum med +ĠHer cules +sub ject +ĠR agnar +ĠN os +Ġcharacter ization +Ġsav vy +ĠDaw son +ĠCas ino +Ġf ri +ĠBar rier +Ġmis information +Ġins ulation +Ġcorrid ors +Ġair planes +ĠNo ct +ah i +Ġ19 16 +k b +arm ac +Ġsh un +Ġsche ma +Ġhorr ified +Ġ23 9 +aund ers +N B +i ates +er ity +ĠSh ard +Ġr arity +Ġgroup ed +ĠGh ana +again st +ĠBi ological +ĠA ware +ow ell +Ï Ħ +ĠBe au +sh aw +H ack +ĠJul ius +US S +ol son +aun a +c ru +ĠMaur ice +ĠI k +Ġsequ encing +Ġradical s +Ġ( ?, +v irtual +Ġany ways +Ġreper c +Ġhand lers +Ġhes itant +é ĥ +ĠM F +ple mentation +ass ociated +Ġcampaign ed +ĠY ue +ut ations +ĠY oga +Ġsim mer +Ġro ds +Ġmel ody +Ġconv oy +v ideos +Ġscreen ed +N eg +ochem ical +Ġ( )) +Ġultr as +Ġant ip +ĠIsland ers +70 4 +Ġfet ish +Ġridic ulously +ĠK art +Ġmitochond rial +Ġinterf ering +Build er +Ġover fl +Ġac ne +ĠM ud +ĠK err +f lex +ĠPost al +ĠBalt ic +47 7 +ĠPers ons +our age +H B +ĠM use +ĠImm ortal +ĠDri ving +Ġpet itions +Ġsubsc ript +Ġs orce +ĠProcess or +ut on +S ony +Ġph on +Ġr aced +ĠAnth rop +Ġday time +ĠEx ercise +Add ing +Ġeng ages +ĠQual comm +Ġmir acles +Ġmem es +ĠDr ink +ĠOri oles +Ġhair s +ĠPol ar +ath om +Ġsl ippery +ĠR emy +Ġcar amel +ĠY EAR +Ġal k +I gn +a ution +ĠMer lin +ĠC ran +Ġap ologies +Ġ4 10 +Ġout ing +ĠMem ories +app ointed +Ġcount ered +u ld +pos ing +Ġfire wall +ĠW ast +ĠW et +work ed +se ller +Ġrepe aled +ere o +ass uming +BL IC +m ite +ĠCEO s +ĠChap el +ellig ent +________________ ________ +D og +Ġw art +Ġsubsc riber +s ports +Ġbe gged +ĠM V +Ġsem if +eth ical +Ġpre ach +Ġrev ital +Ġpun itive +Ġshort cuts +Ġinstit uted +ĠWars aw +Ġabdom en +ĠK ING +Ġsuper intendent +Ġf ry +ĠGe o +T OR +Ġcontrad ictions +apt ic +Ġlandsc apes +b ugs +Ġcl ust +Ġvol ley +c ribed +Ġt andem +Ġrob es +WH AT +Ġpromot er +Ġel oqu +review ed +ĠD K +ĠPl ato +Ġf ps +T ank +ĠDer rick +Ġpriorit ize +as per +ĠHond uras +ĠCom pleted +ne c +Ġm og +n ir +ĠMay o +DE F +st all +in ness +ĠVolks wagen +Ġprec aution +ĠM ell +i ak +ist ries +Ġ24 8 +Ġoverl apping +Sen ate +ĠEnh ance +res y +rac ial +OR TS +ĠM ormons +Str ong +ĠCo ch +Mex ico +ĠMad uro +Ġj ars +Ġcan e +W ik +oll a +iff erence +Ġphysic ist +ĠMag gie +Ġ28 5 +Ġdep iction +ĠMcL aren +J u +Ġsl ows +Ġcommission ers +ĠWill ow +ĠExpl os +hov ah +Ġtechn ician +Ġhom icides +ĠFl av +ĠTr uman +Ġ100 00 +u ctor +Ġsh ader +News letter +45 7 +Ġre ver +Ġhard ened +Ġwhere abouts +Ġrede velop +Ġcar bs +Ġtra vers +Ġsqu irrel +Ġfoll ower +Ġs ings +50 8 +Ġrabb its +emon ium +Ġdocument ing +Ġmisunder stood +) ' +R ick +gg ies +Ġprem ie +Ġsk ating +Ġpass ports +Ġf ists +aged don +H aw +AC P +0 80 +ĠThough ts +ĠCarl son +Ġpriest hood +h ua +Ġdun geons +ĠLo ans +Ġant is +Ġfamiliar ity +ĠS abb +op al +ĠIn k +st rike +Ġc ram +Ġlegal ized +Ġcu isine +Ġfib re +Tra vel +ĠMon ument +OD Y +eth y +Ġinter state +ĠP UR +em porary +ĠArab ian +develop ed +Ġsadd le +Ġg ithub +ĠOff er +ĠIS P +ro let +ĠSUP ER +ĠDen is +Ġmultipl ier +Ġstir red +Interest ingly +Ġcustom ary +Ġbill ed +he x +Ġmultipl ied +Ġfl ipping +ĠCros by +Ġfundament als +ia e +ĠPlay ed +ĠAt om +am azon +ĠFl am +ee z +activ ated +Ġtables poon +Ġliberal ism +ĠPal in +ĠP atel +N um +ĠT AM +Ġs urn +ĠRel oaded +Ġco ined +" ], +ĠCl ash +ĠAg u +Ġprag matic +ĠActiv ate +Ġ8 02 +Ġtrail ers +Ġsil hou +Ġprob es +Ġcirc us +ĠB ain +ĠLind say +ĠAb bey +Del ivery +Ġconcess ion +Ġgast ro +ĠSpr ite +Ä Ł +and el +Ġg imm +Ġaut obi +ĠT urtle +Ġwonder fully +ĠHar am +ĠWorld wide +ĠHand le +Ġtheor ists +Ġsle ek +ĠZh u +ograph ically +EG A +ĠOwn ers +ath s +ĠAntar ctic +n atal +=" " +fl ags +`` `` +Ġs ul +K h +Ġpot assium +Ġlinem an +Ġcere al +ĠSe asons +Ġ20 22 +Ġmat hematic +Ġastron omers +prof essional +Ġf ares +cknow led +Ġch i +Ġyoung sters +Ġmistaken ly +Ġhem isphere +ĠDiv inity +r one +Ġ" , +r ings +Ġattract s +v ana +å ¹ +C AP +Ġplay list +Ġpor ch +ãģ £ +Ġincorpor ates +Ġso ak +Ġassert ing +ĠTerror ism +ĠP ablo +J a +ces ter +Ġfear ing +ĠPr ayer +Ġescal ated +G W +Ġro be +ĠBright on +ac ists +ĠSym phony +ĠDwar f +ĠPar ade +ĠLe go +Ġinex pl +Ġl ords +le af +RA G +l iber +Ġcig ars +ĠJe hovah +60 6 +WIND OWS +ĠLiber ia +eb us +He avy +Ġl ubric +ĠR W +angu ages +Ġnarrow ed +com puter +ĠE mber +Ġmurder ing +Ġdown stream +ĠT uls +ĠT ables +Top ic +ĠAcc uracy += / +l ost +ĠRe i +Ġprogress es +b ear +Ġestablish ments +Just in +ĠPe ach +ĠG omez +å ¿ +ĠTri angle +Id ent +ĠH ive +Res ources +Ġmix es +ĠAss uming +M u +Ġhyp oc +Ġs ane +ĠW an +id ious +Su ccess +Ġ io +Ang el +Ġdanger ously +ĠCreat ure +W ORK +: [ +ĠKat rina +List ener +M iller +ĠId lib +h ang +Ġcircum vent +h ref +Ġcel estial +ĠWe eks +ĠP ug +ĠDal ton +Ġsubpoen a +uk u +Ġpers isted +pe i +old ing +ĠDoc uments +ĠH ast +ĠC ENT +Ġprim er +Ġsyn onymous +Ġn ib +om bs +Ġnot ation +ĠD ish +ĠAt mosp +Ġforb id +ĠAN G +pat tern +l os +Ġproject iles +b rown +." , +ĠVen om +Ġfierce ly +ub lished +ĠU ran +ĠNic arag +4 10 +ĠC AL +OT OS +ĠMir acle +ĠEn chant +Ġguard ing +app end +Att ach +Ġlevel ed +Ġcond oms +ih ilation +64 9 +Ġnight mares +ĠTHE Y +ĠST ART +ĠK inn +Ġroomm ate +Ġhy giene +o pping +J ob +Ġl vl +ĠV ER +ĠKe eping +ab etic +Ġformat ting +eral a +Ġrev isions +Ġres urg +T el +ĠGood man +35 3 +p od +Ġind isp +ĠTrans lation +Ġg own +ĠM und +Ġc is +Ġby stand +col lect +ĠPun jab +act ively +ĠG amb +te ll +Ġimport ing +g encies +Ġloc om +ĠBr ill +H oly +ĠBer ger +Ġshow down +Ġrespond ers +IL Y +Ġt akedown +le ted +Ġmat tered +Ġpredict ive +Ġover lay +G PU +ĠV ick +Ġconvey ed +T ab +pe er +Sc an +Ġdefensive ly +v ae +Ġappro ving +Ġt iers +ĠV ia +quer ade +ĠSaud is +Ġdemol ished +ĠProp he +Ġmon o +Ġhospital ity +H AM +ĠAri el +M OD +ĠTor ah +Ġbl ah +ĠBel arus +erent ial +ĠT uc +Ġbank er +39 7 +Ġmosqu it +ĠScient ist +ĠMus ical +Ġh ust +Sh ift +Ġtor ment +Ġstand off +E duc +ĠF og +Ġampl ifier +Sh ape +Inst ance +ĠCrit ics +Ġda emon +H ouston +Ġmatt ress +ĠID F +Ġobsc ene +ĠA mer +hett i +Ġcomp iling +35 2 +vere tt +ĠRed uction +ist ration +ĠBl essed +ĠB achelor +3 16 +Ġpr ank +ĠVul can +dd ing +Ġm ourning +ĠQu int +ĠBl aster +test ing +Ġsed iment +>> > +ĠE ternity +ĠWH ERE +ĠM aze +Ġreact ing +ĠAl v +oms day +ĠC RA +Ġtransl ator +Ġbog us +at u +We bsite +oll s +Ġbapt ism +Ġs ibling +ĠAut umn +ve z +ãģ® é +gu ards +Ge org +assad ors +ĠFre ud +Ġcontin ents +ĠReg istry +Bern ie +ĸļ 士 +Ġtoler ant +ĠU W +Ġhor ribly +99 5 +ĠMID I +Ġimpat ient +oc ado +er i +ĠWor st +ĠNor ris +ĠTalk ing +Ġdef ends +ens able +Ġ20 21 +Ġanat omy +L ew +Ġdraw er +ĠCan berra +Ġpatri otic +é¾įå ĸļ士 +ĠAv g +AR M +Ġundis closed +Ġfare well +45 9 +b able +ĠAll ison +OL OG +Ġcon co +t ight +ĠAC PI +ĠM ines +l ich +ĠâĶ ľ +represent ed +200 000 +Ġenthusi ast +OT S +b il +ĠIng redients +Ġinvent or +ĠMy SQL +³³ Âł +ĠAB OUT +with in +Ġm k +B ul +ĠF ake +Ġdracon ian +W a +hel m +ĠTer ran +erv ille +Ġcommon place +SI ZE +Ġ" < +re place +ograph s +ĠSE LECT +inc ible +ĠMost ly +ĠShe ffield +ĠID E +ugg le +Ġcit ations +h urst +ĠUn ix +Ġunle ash +ĠP iper +ĠN ano +Ġsucc umb +Ġreluct ance +Ġ25 00 +ĠMer chant +Ġwire t +Ġcomb os +ĠBirth day +Ġchar coal +ĠU PS +ĠFair fax +Ġdrive way +ĠT ek +ĠP itch +ove re +Ġtechn icians +ĠAct ual +fl ation +ĠF iscal +ĠEm pty +an amo +Ġmag nesium +Ġsl ut +Ġgrow ers +Invest igators +( ): +ĠS atellite +ĠKe ynes +miss ive +l ane +Ġb orough +3 44 +ĠTE AM +ĠBet hesda +C V +h ower +ĠR AD +Ġch ant +ĠR iy +Ġcompos itions +Ġmild ly +Ġmedd ling +Ġag ility +ane ers +5 01 +Ġsyn th +ling er +29 1 +Ġex claimed +Part y +Ġcont amin +ĠMan or +ĠResp ond +Ġpra ising +Ġman ners +fle et +Sum mer +ĠLy nd +ĠDef initely +gr im +Ġbow ling +st ri +ç Ľ +y nt +Ġmand ates +D IV +Ġreconc ile +view s +ĠDam on +vet te +F lo +ĠGreat est +il on +ic ia +Ġportray al +Ġcush ion +50 4 +19 79 +oss al +App lic +sc ription +Ġmit igation +AT S +p ac +Ġer ased +Ġdefic iencies +ĠHolland e +ĠX u +Ġb red +Ġpregn ancies +f emin +Ġem ph +Ġpl anners +Ġout per +utter ing +Ġperpet rator +Ġm otto +ĠEll ison +ĠNE VER +Ġadmitted ly +AR I +ĠAzerbai jan +Ġmill isec +Ġcombust ion +ĠBott le +ĠL und +ĠP s +ĠD ress +Ġfabric ated +Ġbat tered +Ġs idel +ĠNot ting +Fore ign +ĠJer ome +0 20 +ĠAr bit +Ġkn ots +ĠR IGHT +M oving +ãģ Ļ +Ġsur geries +Ġcour thouse +Ġm astered +Ġhover ing +ĠBr an +ĠAl ison +Ġsaf est +m ilitary +Ġbull ied +Ġbar rage +Read er +ES E +ĠGe ographic +T ools +3 14 +ĠGe ek +ro th +gl ers +ĠF IN +Ï ģ +ĠA ston +al tern +48 8 +Ġveter in +G amer +Ġint el +ren ches +Sh ield +Ġam nesty +ĠB har +Ġp iled +Ġhonor able +ĠInst itutes +Ġso aked +Ġcom a +ĠE FF +34 1 +by tes +ĠG mail +le in +ĠCanad iens +m aterial +I l +Ġinstruct ors +ĠK Y +Ġconce ive +ub b +ĠP ossible +Ġeas ing +ĠChrist ina +Ġcar ic +ĠHD R +R OM +Ġsho vel +de lete +Ġp uff +ĠCh anging +Ġseam lessly +Att ribute +Ġacqu isitions +ak ery +ĠE F +Ġaut istic +ĠT akes +ĠPow der +ĠSt ir +5 10 +ĠBub ble +sett ings +ĠF owler +Ġmust ard +Ġmore over +Ġcopyright ed +ĠLED s +15 00 +æ ī +ĠH IS +en f +Ġcust od +ĠH uck +G i +Ġim g +An swer +C t +j ay +ĠInf rastructure +Ġfeder ally +L oc +Ġmicro bes +Ġover run +dd s +ot ent +adi ator +>>>> >>>> +Ġtorn ado +Ġadj ud +Ġintrig ued +Ġs i +ĠRevel ation +pro gress +Ġburgl ary +ĠSai yan +ĠK athy +Ġser pent +ĠAndre as +Ġcomp el +ess ler +ĠPl astic +ĠAd vent +ĠPos itive +ĠQ t +ĠHind us +reg istered +ular ity +Ġrighteous ness +Ġdemon ic +u itive +ĠB DS +ĠGre gg +c ia +ĠCrus ade +ĠSina i +W ARE ++ ( +Ġme ll +Ġder ail +y ards +A st +Ġnotice ably +ĠO ber +R am +Ġun noticed +Ġse q +av age +T s +Ġ6 40 +Ġconced e +Ġ] ) +F ill +Ġcapt ivity +ĠImprove ment +ĠCrus ader +ara oh +M AP +æ Ĺ +Ġstr ide +al ways +F ly +N it +Ġal gae +ĠCook ing +ĠDo ors +Mal ley +Ġpolic emen +ãģ į +Ġastron aut +access ible +49 5 +ĠR AW +cl iffe +udic rous +Ġdep ended +al ach +Ġvent ures +ra ke +Ġt its +ĠH ou +Ġcond om +ormon al +Ġind ent +Ġupload ing +Foot note +Import ant +Ġ27 1 +Ġmind ful +Ġcont ends +C ra +Ġcal ibr +ĠO ECD +plug in +F at +ĠIS S +ĠDynam ics +ans en +68 6 +' ), +Ġsp rite +Ġhand held +ĠH ipp +=~ =~ +Tr ust +Ġsem antics +ĠBund es +ĠRen o +ĠLiter ature +s ense +G ary +ĠA eg +ĠTr in +EE K +Ġcler ic +ĠSS H +Ġch rist +Ġinv ading +ib u +Ġen um +aur a +Ġal lege +ĠInc redible +B BC +Ġth ru +Ġsa iled +Ġem ulate +Ġin security +Ġc rou +Ġaccommod ations +Ġincompet ent +Ġsl ips +ĠEarth qu +s ama +IL LE +Ġi Phones +as aki +Ġby e +Ġar d +Ġext ras +Ġsl aughtered +Ġcrowd funding +res so +Ġfil ib +ĠER ROR +ĠT LS +e gg +ĠIt al +Ġen list +ĠCatal onia +ĠSc ots +Ġser geant +Ġdiss olve +N H +Ġstand ings +ri que +I Q +Ġbenef iciary +Ġaqu arium +You Tube +ĠPower Shell +Ġbright est +ĠWar rant +S old +Writ ing +Ġbegin nings +ĠRes erved +ĠLatin os +head ing +Ġ4 40 +Ġrooft op +AT ING +Ġ3 90 +VP N +G s +k ernel +turn ed +Ġprefer able +Ġturn overs +ĠH els +S a +ĠShin ji +ve h +ĠMOD ULE +V iol +Ġex iting +Ġj ab +ĠVan illa +Ġac ron +ĠG ap +ber n +A k +ĠMc Gu +Ġend lessly +ĠFar age +ĠNo el +V a +M K +Ġbr ute +ĠK ru +ĠES V +ĠOl ivia +âĢ ł +ĠK af +Ġtrust ing +Ġh ots +3 24 +Ġmal aria +Ġj son +Ġp ounding +ort ment +Count ry +Ġpostp oned +Ġunequ iv +? ), +ĠRo oney +udd ing +ĠLe ap +ur rence +sh apeshifter +ĠH AS +os ate +Ġca vern +Ġconserv atism +ĠB AD +Ġmile age +Ġarrest ing +V aults +Ġmix er +Dem ocratic +ĠB enson +Ġauth ored +8 000 +Ġpro active +ĠSpirit ual +t re +Ġincarcer ated +ĠS ort +Ġpe aked +Ġwield ing +re ciation +×Ļ × +P atch +ĠEm my +Ġex qu +tt o +ĠRat io +ĠP icks +ĠG ry +ph ant +Ġf ret +Ġeth n +Ġarch ived +% - +c ases +ĠBl aze +Ġim b +c v +y ss +im ony +Ġcount down +Ġaw akening +ĠTunis ia +ĠRe fer +ĠM J +Ġun natural +ĠCar negie +iz en +ĠN uggets +he ss +Ġev ils +64 7 +Ġintrodu ctory +l oving +ĠMcM ahon +Ġambig uity +L abel +ĠAlm ighty +Ġcolor ing +ĠCl aus +set ting +N ULL +ĠF avorite +ĠS IG +> ( +ĠSh iva +ĠMay er +Ġstorm ed +ĠCo verage +we apons +igh am +Ġun answered +Ġle ve +Ġc oy +c as +b ags +as ured +Se attle +ĠSant orum +ser ious +Ġcourage ous +ĠS oup +Ġconfisc ated +Ġ// / +Ġuncon ventional +Ġmom s +ĠRohing ya +ĠOrche stra +ĠPot ion +Ġdisc redit +ĠF IL +f ixed +ĠDe er +do i +ĠDim ension +Ġbureaucr ats +et een +Ġaction Group +oh m +Ġb umps +ĠUt ility +Ġsubmar ines +ren heit +re search +ĠShap iro +Ġsket ches +Ġde ceptive +ĠV il +es ame +ĠEss entially +Ġramp age +isk y +Ġmut tered +th ritis +Ġ23 6 +f et +b ars +Ġpup il +ĠTh ou +o S +s ong +Ġfract ured +Ġre vert +pict ure +Ġcrit erion +us her +Ġreperc ussions +ĠV intage +ĠSuper intendent +Offic ers +Ġflag ged +Ġbl ames +Ġin verse +ograp hers +Ġmakes hift +Ġdev oid +Ġfoss ils +ĠArist otle +ĠFund s +Ġde pleted +ĠFl u +ĠY uan +Ġw oes +Ġlip id +Ġsit u +requ isites +Ġfurn ish +ĠSam ar +Ġshame ful +Ġadverse ly +Ġad ept +Ġrem orse +Ġmurder ous +uck les +ĠE SL +Ġ3 14 +s ent +Ġred ef +ĠC ache +ĠP urs +ig ans +Ġ4 60 +Ġpres criptions +Ġf res +F uck +ocr ates +Tw enty +ĠWe ird +ĠT oggle +ĠC alled +itiz ens +Ġp oultry +Ġharvest ing +ãĤ¦ ãĤ¹ +Bott om +Ġcaution ed +t n +39 6 +ĠNik ki +Ġeval uations +Ġharass ing +Ġbind ings +ĠMon etary +Ġhit ters +Ġadvers ary +un ts +Ġset back +Ġenc rypt +ĠC ait +Ġl ows +eng es +ĠN orn +Ġbul bs +Ġbott led +ĠVoy ager +3 17 +Ġsp heres +p olitics +Ġsubt ract +Ġsens ations +Ġapp alling +Ġ3 16 +Ġenvironment ally +ĠST EM +Ġpub lishes +5 60 +Ġdilig ence +48 4 +Ġadv ises +Ġpet rol +Ġimag ining +Ġpatrol s +ĠInt eger +ĠAs hes +act us +ĠRad iant +ĠL T +it ability +ht aking +Set ting +Ġnu anced +ĠRe ef +ĠDevelop ers +N i +pie ces +99 0 +Lic ense +Ġlow ers +ĠOtt oman +3 27 +oo o +Ġqu itting +mark ets +Beh ind +Ġbas in +Ġdoc s +an ie +fl ash +ct l +Ġcivil ized +ĠFuk ushima +"] ," +ĠK S +ĠHonest ly +ar at +Ġconstruct s +ĠL ans +ĠD ire +ĠLI KE +ĠTrou ble +Ġwith holding +ĠOb livion +Ġsan ity +any a +Con st +Ġgro cer +ĠC elsius +Ġrecount ed +ĠW ife +B order +ate red +h appy +Ġspo iler +Ġlog ically +H all +Ġsucceed ing +Ġpoly morph +Ġax es +ĠShot gun +ĠS lim +ĠPrin ciples +ĠL eth +art a +Ġsc or +Sc reenshot +Ġrelax ation +#$ #$ +Ġdeter rent +idd y +Ġpower less +Ġles bians +Ġch ords +ĠEd ited +se lected +Ġseparat ists +000 2 +Ġair space +Ġturn around +Ġc unning +P ATH +P oly +Ġbomb ed +Ġt ion +x s +Ġwith hold +Ġw aged +ĠLiber ties +Fl ag +Ġcomfort ing +45 4 +ĠI ris +are rs +Ġr ag +Ġrel ocated +ĠGu arant +Ġstrateg ically +Ġgam ma +uber ty +ĠLock heed +g res +Ġgr illed +ĠLow e +st ats +ĠR ocks +Ġsens ing +Ġrent ing +ĠGe ological +ا Ø +ot rop +Ġse w +Ġimproper ly +48 6 +Ġâĸ ł +Ġstar ving +ĠB j +Disc ussion +3 28 +ĠCom bo +ĠFix es +N AT +Ġstri ving +th ora +Ġharvest ed +ĠP ing +Ġplay ful +Ġaven ues +Ġoccup ational +Ġw akes +ĠCou rier +Ġdrum mer +ĠBrow ser +ĠH outh +it u +Ġapp arel +p aste +Ġhun ted +ĠSecond ly +l ain +X Y +ĠP IN +ic ons +Ġcock tails +Ġs izable +Ġhurd les +est inal +ĠRecre ation +Ġe co +64 8 +ĠD ied +m int +Ġfinger prints +Ġdis pose +ĠBos nia +ts y +22 00 +Ġins pected +ĠF ou +Ġf uss +Ġamb ush +ĠR ak +Ġmanif ested +Pro secut +Ġsuff ice +ren ces +Ġcompens ated +ĠC yrus +Ġgen us +ĠWolver ine +ĠTrend s +Ġh ikes +ĠSe en +Ġen rol +C old +Ġpol itely +ĠSl av +ĠRu pert +Ġey ewitness +ĠAl to +Ġun comp +Ġposter ior +M ust +ĠHer z +Ġprogress ively +Ġ23 4 +Ġind ifference +ĠCunning ham +Ġacadem ia +Ġse wer +Ġast ounding +ĠA ES +r ather +Ġeld est +Ġclim bs +ĠAdd s +Ġout cry +Ġcont ag +ĠH ouses +Ġpe pt +ĠMel ania +interest ed +ĠU CH +ĠR oots +ĠHub bard +ĠT BD +ĠRoman ian +fil ename +St one +ĠIm pl +Ġchromos ome +C le +d x +Ġscram bled +ĠP t +Ġ24 2 +OP LE +Ġtremend ously +St reet +Ġcra ving +Ġbund led +ĠR G +p ipe +Ġinj uring +Ġarc ane +Part icip +ĠHero ic +st y +Ġto pping +ĠTemp est +rent ices +b h +Ġpar anoia +ĠUnic ode +Ġegreg ious +Ġ\ ' +ĠOsw ald +Ġgra vel +ĠSim psons +Ġbl and +ĠGuant anamo +Writ er +lin ers +ĠD ice +J C +Ġpar ity +Ġs ided +Ġ23 7 +ĠPyr rha +at ters +d k +F ine +comp an +Ġform ulated +ĠId ol +il ers +hem oth +ĠF av +Ġintr usion +Ġcar rots +ĠL ayer +ĠH acker +Ġ ---------------- +Ġmoder ation +é ģ +oc oc +Ġcharacter ize +ĠTe resa +Ġsocio economic +Ġper k +ĠParticip ation +tr aining +ĠPaul o +ph ys +Ġtrust worthy +Ġembod ied +ĠMer ch +c urrency +ĠPrior ity +Ġte asing +Ġabsor bing +Ġunf inished +ĠCompar ison +Ġdis ple +writ ers +Ġprofess ions +ĠPengu in +Ġang rily +ĠL INK +68 8 +ĠCor respond +Ġprev ailed +Ġcart el +l p +as ms +ĠRed emption +ĠIslam ists +effect s +d ose +ĠL atter +ĠHal ifax +Ġv as +ĠTop ics +ĠN amed +advert ising +zz a +IC ES +Ġret arded +ach able +ĠPupp et +ĠItem Level +Ġret ract +Ġident ifiable +A aron +ĠB uster +s ol +hel le +as semb +H ope +r anged +B a +ĠP urch +é Ģ +ĠSir i +Ġarri vals +Ġ19 12 +Ġshort ened +Ġ3 12 +Ġdiscrep ancy +ĠTem perature +ĠWal ton +Ġkind erg +p olit +Ġrem ix +Ġconnect ors +ãĥĺ ãĥ© +ĠKazakh stan +dom inated +Ġsu gars +im ble +ĠPan ic +ĠDem and +ĠCol ony +on en +ĠM ER +7 75 +ur ia +aza ar +ĠDeg ree +P ri +Ġsun shine +Ġ25 1 +Ġpsychedel ic +Ġdigit ally +ĠBra un +Ġsh immer +Ġsh ave +ĠTel esc +ĠAst ral +ĠVenezuel an +ĠO G +Ġc rawling +Int eg +ĠFe ather +Ġunfold ing +Ġappropri ation +Ġè£ı è +ĠMob ility +ĠN ey +- . +b ilt +L IN +ĠT ube +ĠCon versely +Ġkey boards +ĠC ao +Ġover th +Ġla ure +>> \ +ĠV iper +ach a +Off set +ĠR aleigh +ĠJ ae +J ordan +j p +Ġtotal itarian +Connect or +Ġobserv es +ĠSpart an +ĠIm mediately +ĠSc al +C ool +Ġt aps +Ġro ar +P ast +Ġch ars +ĠB ender +ĠShe ldon +Ġpain ter +Ġbe acon +ĠCreat ures +Ġdownt urn +Ġh inder +ĠAnd romeda +à Ľ +cc oli +ĠF itness +et rical +Ġutil izes +Ġsen ate +Ġen semble +Ġche ers +T W +Ġaff luent +k il +ry lic +ord ering +Com puter +Ġgru esome +ost ics +ĠUb isoft +ĠKel ley +Ġw rench +Ġbourgeois ie +IB LE +ĠPrest on +w orn +ar ist +reat ing +Ġst ained +ar ine +Ġsl ime +EN N +Ġche sts +Ġground water +ann ot +ĠTr ay +ĠLoc ke +ĠC TR +Ġd udes +ĠEx ternal +ĠDec oder +Ġpar amed +ĠMed line +80 9 +ĠD inner +rup al +g z +ĠG um +ĠDem o +j ee +Ġd h +ber man +arch s +Ġen qu +ĠEp stein +Ġdevast ation +Ġfriends hips +ĠAr d +Ġ23 1 +ĠRub in +ĠDist ance +Ġsp urred +Ġd ossier +Ġover looking +\\\\\\\\ \\\\\\\\ +Fore st +ĠCom es +\ ", +ĠIran ians +Ġf ixtures +L aughs +Ġcur ry +ĠKing ston +Ġsqu ash +Ġcat alogue +Ġabnormal ities +Ġdigest ive +.... ..... +Ġsubord inate +og ly +Ġ24 9 +M iddle +Ġmass ac +Ġburg ers +Ġdown stairs +Ġ19 31 +39 4 +ĠV G +Ġl asers +ĠS ikh +ĠAlex a +der ived +Ġcycl ist +ãģ® éŃĶ +onel iness +!!!! !!!! +Ġbuff s +leg ate +Ġrap ing +Ġrecomm ending +ro red +Ġmult icultural +un ique +Ġbusiness men +Ġune asy +ĠM AP +Ġdisp ersed +cipl ine +J ess +ĠK erala +å § +Ġabst raction +Sur v +U h +Ġprin ters +ij a +ow der +Ġanalog ous +ĠA SP +af er +Ġunfold ed +Ġlevel ing +Ġbre ached +ĠH earing +Ġn at +Ġtransl ating +crit ical +Ġant agonist +ĠYes terday +Ġfuzz y +w ash +m ere +Ġbe wild +ĠM ae +V irgin +ph rase +Ġsign aled +ĠH IGH +Ġprot ester +Ġgar ner +unk nown +Ġk ay +Ġabduct ed +Ġst alking +am n +Ġdes erving +ĠR iv +ĠJ orge +Ġscratch ing +ĠS aving +ip ing +Ġte ase +Ġmission ary +ĠMor row +T IME +P resent +Ġchem otherapy +tern ess +ĠH omes +ĠP urdue +Ġst aunch +ĠWhit ney +ĠTH ERE +Î ¼ +iat us +ĠErn est +ĠDe ploy +Ġcove ted +F ML +ĠDial ogue +Ġex ited +f ruit +Ġner d +":" "," +Ġv ivo +ru ly +4 60 +ĠAm en +rehens ible +Ġâ ĺ +D IR +Ġad herence +Ġche w +ĠCo ke +ĠSerge i +dig ital +ĠNe ck +g ently +enth al +/ ) +Ġwe ary +Ġgu ise +ĠConc ord +ĠOn ion +at cher +Ġb inge +ĠDirect ive +Ġman ned +ans k +Ġill usions +Ġbillion aires +38 3 +oly n +odynam ic +ĠWhe at +ĠA lic +Ġcol oured +ĠN AFTA +ab o +Ġmac ros +ind ependent +s weet +Ġsp ac +ĠK abul +Ġ Ä +em e +Ġdict ated +Ġsh outs += { +Ġr ipping +ĠSh ay +ĠCr icket +direct ed +Ġanalys ed +ĠWAR RANT +ag ons +ĠBlaz ers +Ġche ered +Ġar ithmetic +ĠTan z +37 3 +ĠFl ags +Ġ29 5 +Ġw itches +ĠIn cluded +ĠG ained +ĠBl ades +G am +ĠSam antha +ĠAtl antis +ĠPr att +Ġspo iled +ĠI B +ĠRam irez +Pro bably +re ro +ĠN g +ĠWar lock +t p +Ġover he +Ġadministr ations +Ġt int +Ġreg iment +Ġpist ols +Ġblank ets +Ġep ist +Ġbowl s +Ġhydra ulic +Ġde an +Ġj ung +Ġasc end +70 5 +ĠSant iago +à ® +Ġun avoid +ĠSh aman +re b +Ġstem ming +99 8 +ĠM G +st icks +esthes ia +ER O +Ġmor bid +ĠGr ill +ĠP oe +any l +Ġdele ting +ĠSurve illance +Ġdirect ives +Ġiter ations +ĠR ox +ĠMil ky +F ather +Ġpat ented +44 7 +Ġprec ursor +Ġm aiden +ĠP hen +ĠVe gan +ĠPat ent +K elly +Redd itor +Ġn ods +Ġvent ilation +ĠSchwar z +Ġw izards +Ġomin ous +ĠHe ads +ĠB G +Ġl umber +ĠSp iel +Ġis Enabled +Ġancest ral +ĠSh ips +Ġwrest ler +ph i +Ġy uan +ĠRebell ion +Ġice berg +Ġmag ically +Ġdivers ion +ar ro +yth m +ĠR iders +ĠRob bie +ĠK ara +ĠMain tenance +ĠHer b +Ġhar ms +p acked +ĠFe instein +Ġmarry ing +Ġbl ending +ĠR ates +Ġ18 80 +Ġwr ink +ĠUn ch +ĠTor ch +desc ribed +Ġhuman oid +ilit ating +ĠCon v +ĠFe ld +IGH TS +Ġwhistlebl ower +ort mund +ets y +arre tt +ĠMon o +ĠI ke +ĠC NBC +ĠW AY +ĠMD MA +ĠIndividual s +Ġsupplement al +Ġpower house +ĠSt ru +F ocus +aph ael +ĠCol leg +att i +Z A +Ġp erenn +ĠSign ature +ĠRod ney +Ġcub es +idd led +ĠD ante +ĠIN V +iling ual +ĠC th +Ġso fa +Ġintimid ate +ĠR oe +ĠDi plom +ĠCount ries +ays on +Ġextrad ition +Ġdis abling +ĠCard iff +Ġmemor andum +ĠTr ace +Ġ?? ? +se ctor +ĠRou hani +ĠY ates +ĠFree ze +Ġbl adder +M otor +ĠProm ise +ant asy +Ġforesee able +ĠC ologne +cont ainer +ĠTre es +ĠG ors +ĠSin clair +Ġbar ring +key e +Ġsl ashed +ĠStat istical +é ĩ +Ġâĸ º +All ows +Ġhum ility +Ġdr illed +ĠF urn +44 3 +Ġse wage +Ġhome page +Ġcour tyard +Ġv ile +Ġsubsid iaries +aj o +direct ory +Ġam mon +V ers +charg es +Ġ} } +ĠCh ains +Ġ24 6 +n ob +Ġper cept +Ġg rit +Ġfisher men +ĠIraq is +ĠDIS TR +ĠF ULL +ĠEval uation +g raph +at ial +Ġcooper ating +Ġmel an +Ġenlight ened +Ġal i +t ailed +Ġsal ute +Ġweak est +ĠBull dogs +U A +ĠAll oy +Ġsem en +oc ene +ĠWilliam son +s pr +, âĢĶ +ĠG F +itt ens +Be at +ĠJ unk +iph ate +ĠFarm ers +ĠBit coins +ig ers +d h +ĠL oyal +p ayer +Ġentert ained +Ġpenn ed +Ġcoup on +Que ue +Ġweaken ing +c arry +Ġunderest imate +Ġshoot out +Ġcharism atic +ĠProced ure +Ġprud ent +in ances +Ġric hes +Ġcort ical +Ġstr ides +Ġd rib +ĠOil ers +5 40 +ĠPer form +ĠBang kok +Ġe uth +S ER +Ġsimpl istic +t ops +camp aign +Q uality +Ġimpover ished +ĠEisen hower +Ġaug ment +ĠH arden +Ġinterven ed +Ġlist ens +ĠK ok +Ġs age +Ġrub bish +ĠD ed +Ġm ull +pe lling +Ġvide ot +Produ ction +D J +m iah +Ġadapt ations +Ġmed ically +Ġboard ed +Ġarrog ance +Ġscra pped +Ġopp ress +FORM ATION +Ġj unction +4 15 +EE EE +S kill +Ġsub du +ĠSug gest +ĠP ett +Ġle tt +ĠMan ip +ĠC af +ĠCooper ation +T her +Ġreg ained +¶ æ +ref lect +Ġth ugs +ĠShel by +Ġdict ates +ĠWe iner +ĠH ale +Ġbatt leground +s child +Ġcond ol +h unt +osit ories +Ġacc uses +Fil ename +Ġsh ri +Ġmotiv ate +Ġreflect ions +N ull +ĠL obby +¥ µ +ĠS ATA +ĠBack up +Ñ ĥ +n in +ĠCor rection +Ġju icy +ut ra +ĠP ric +Ġrest raining +ĠAir bnb +ĠAr rest +Ġappropri ations +Ġsl opes +Ġmans laughter +Ġwork ings +ĠH uss +ĠF rey +Le ave +ĠHarm ony +ĠF eder +Ġ4 30 +Ġt rench +Ġglad ly +Ġbull pen +ĠG au +b ones +Ġgro ove +Ġpre text +ã ħĭ +Ġtransm itter +ĠComp onent +Ġunder age +ĠEm pires +T ile +Ġo y +ĠMar vin +ĠC AS +Ġbl oss +Ġrepl icated +ĠMar iners +Marc us +ĠBl ocks +Ġliber ated +Ġbutter fly +Fe el +Ġfer mentation +Ġyou tube +Ġoff end +ĠTer m +res ist +Ġcess ation +Ġinsurg ency +Ġb ir +ĠRa ise +59 5 +Ġhypothes es +50 2 +Ġpl aque +ocr at +Ġjack ets +ĠHuff Post +am ong +Ġconf er +48 7 +ĠL illy +Ġadapt ing +ĠF ay +Ġsh oved +ve c +Ġref ine +Ġg on +Ġgun men +z ai +ĠShut tle +ĠI zan +Ġ19 13 +Ġple thora +· · +Ġ5 10 +Ġp uberty +Ġ24 1 +ĠWe alth +ĠAl ma +ĠM EM +ĠAd ults +C as +pr ison +R ace +Ġwater proof +Ġathlet icism +Ġcapital ize +ĠJu ice +Ġillum inated +ĠP ascal +Ġirrit ation +ĠWitness es +ad le +ĠAst ro +Ġf ax +ĠEl vis +Prim ary +ĠL ich +ĠEl ves +Ġres iding +Ġst umble +3 19 +ĠP KK +Ġadvers aries +D OS +ĠR itual +Ġsm ear +Ġar son +ident al +Ġsc ant +Ġmon archy +Ġhal ftime +Ġresid ue +Ġind ign +ĠSh aun +ĠEl m +aur i +A ff +W ATCH +ĠLy on +hel ps +36 1 +Ġlobby ist +Ġdimin ishing +Ġout breaks +Ġgo ats +f avorite +ĠN ah +son ian +ĠBo oster +Ġsand box +ĠF are +ĠMalt a +Ġatt Rot +ĠM OR +ld e +Ġnavig ating +T ouch +Ġunt rue +ĠDis aster +Ġl udicrous +Pass word +ĠJ FK +blog spot +4 16 +ĠUN DER +ern al +Ġdelay ing +T OP +Ġimpl ants +ĠAV G +ĠH uge +att r +Ġjournal istic +ĠPe yton +ĠI A +R ap +go al +ĠProgram me +Ġsm ashing +w ives +print ln +ĠPl ague +in us +EE P +Ġcru iser +ĠPar ish +umin ium +Ġoccup ants +ĠJ ihad +m op +Ġp int +Ġhe ct +ĠMe cca +direct or +ĠFund ing +ĠM ixed +Ġst ag +T ier +Ġg ust +Ġbright ly +ors i +Ġup hill +R D +Ġles ions +ĠBund y +liv ious +Ġbi ologist +ĠFac ulty +ĠAuthor ization +Ġ24 4 +All ow +ï ¸ +ĠGi ul +Ġpert inent +ot aur +es se +ĠRo of +Ġunman ned +35 1 +ĠSh ak +ĠO rient +Ġend anger +D ir +Ġrepl en +ed ient +Ġtail or +Ġgad gets +Ġaud ible +âĺ Ĩ +N ice +Ġbomb ard +ĠR ape +Ġdef iance +ĠTW O +ĠFilip ino +Ġunaff ected +erv atives +Ġso ared +ĠBol ton +Ġcomprom ising +ĠBrew ers +R AL +ĠA HL +icy cle +Ġv ampires +Ġdi pped +oy er +ĠX III +Ġsidew ays +ĠW aste +ĠD iss +ĠâĶľ âĶĢâĶĢ +$ . +Ġhabit ats +ĠBe ef +tr uth +tr ained +spl it +R us +And y +ĠB ram +RE P +p id +è£ ħ +ĠMut ant +An im +ĠMar ina +Ġfut ile +hig hest +f requency +Ġepile psy +Ġcop ing +Ġconc ise +Ġtr acing +ĠS UN +pan el +ĠSoph ie +ĠCrow ley +ĠAd olf +ĠShoot er +Ġsh aky +ĠI G +ĠL ies +ĠBar ber +p kg +Ġupt ake +Ġpred atory +UL TS +/ ** +Ġintox icated +ĠWest brook +od der +he ment +Ġbas eman +AP D +st orage +ĠFif ty +ed itor +G EN +UT ION +ir ting +Ġse wing +r ift +Ġag ony +ĠS ands +Ġ25 4 +C ash +Ġl odge +Ġp unt +N atural +ĠIde as +Ġerrone ous +ĠSens or +ĠHann ity +Ġ19 21 +Ġm ould +ĠG on +kay a +Ġanonym ously +ĠK EY +Ġsim ulator +W inter +Ġstream ed +50 7 +? ", +Ġte ased +Ġco efficient +Ġwart ime +ĠTH R +' '. +ĠBank ing +mp ire +Ġf andom +Ġl ia +G a +Ġdown hill +Ġinterpre ting +Ind ividual +N orm +Ġjealous y +bit coin +Ġple asures +ĠToy s +ĠChev rolet +ĠAd visor +IZ E +Ġrecept ions +70 6 +C ro +Ġ26 2 +Ġcit rus +ir u +Review er +ject ed +U ES +an z +19 81 +ĠWork er +Ġcompl ied +ores cent +contin ental +T on +ĠPr ism +ĠShe ep +Ġ28 8 +n ox +ĠV og +O rd +Ġreal ms +te k +Ġirrig ation +Ġbicy cles +Ġelectron ically +p oly +t all +() ); +Ġaest hetics +ĠInteg rated +Expl ore +Ġd unk +47 6 +p ain +ĠJac ques +ĠD mit +Fram es +Ġreun ited +Ġhum id +D ro +P olitical +Ġyouth ful +Ġent ails +Ġmosqu ito +36 3 +spe cies +Ġcoord inating +ĠMay hem +ĠMagn us +M ount +Impro ved +ĠST ATE +ATT LE +Ġflow ed +Ġtack led +Ġfashion ed +Ġre organ +iv ari +f inger +Ġreluct antly +et ting +ĠV and +you ng +ĠGar land +Ġpresum ption +Ġamen ities +ĠPle asant +on ential +ĠO xy +Ġmor als +ĠY ah +Read y +Sim on +En h +D emon +Ġcl ich +Mon itor +ĠD U +Ġwel comes +Ġstand out +Ġdread ful +Ġban anas +Ġball oons +h ooting +bas ic +Ġsuff ix +Ġd uly +can o +Ch ain +at os +Ġgeop olitical +Ġ( & +ĠGem ini +ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ +Ġacqu itted +L uck +prot ect +10 24 +Ġsc arcity +Ġmind fulness +ec ided +D N +pr ime +ĠPres idents +ĠVID EO +Ġ( âĪĴ +add ock +N OR +ĠP ru +p un +ĠL OL +)) )) +ĠL iqu +ĠS AS +Ġsty ling +Ġpunish ments +Ġnum b +Ġasc ertain +ĠRock ies +f lu +Th umbnail +Ġperpet rated +ĠSem i +Ġdis arm +ĠOld er +ĠEx ception +Ġexponent ially +ĠCommun ities +Ġabol ish +ĠPart ner +pt oms +Ġ7 77 +ĠFo ley +ĠC ases +Ġgre ase +ĠReb irth +G round +Ġ; ) +ĠDoct rine +ik ini +Y e +ĠBl ossom +Ġpers ists +b ill +Ġinf usion +Ġbud dies +9 11 +ĠPat ient +Ġdem os +Ġacquaint ance +ĠP aw +at ari +Ġx ml +Ġfasc ination +ĠSer ve +Ï Ĥ +br anded +Ġa z +Return s +Ġover shadow +Ġro am +Ġspeed y +n umbered +hel ial +Ġdisc iple +Ġass urances +g iven +pect ing +ĠN atalie +çĶ ° +Ġmosquit oes +rote in +Ġnumer ic +Ġindepend ents +Ġtrans itional +Ġreaction ary +ĠMech dragon +do ctor +Ġshort est +Ġsequ ential +ĠB ac +ĠAccount s +ãģ Į +ach y +ract ive +ĠReg iment +Ġbreat htaking +ffic iency +ĠB ates +Ġ3 11 +Ġward robe +ft s +ĠBer k +Sim ply +ĠRivers ide +iver ing +ident ial +lu cent +Ġen riched +ĠCon ver +ĠG iving +ãĥ Ļ +Ġlegal ize +ĠF TC +Ġfre aking +M ix +Ġter restrial +es ian +ci ents +W ing +LO AD +Ġled ge +ĠViol ent +ĠMet all +Ġ30 8 +Ġs outheastern +hett o +M eat +Ġslow down +Ġret reated +Jere my +end as +**** * +er ic +Ġre ins +opp able +ĠHuman ity +ear ances +rig an +C amera +Ġwa ivers +s oc +Ġalter ation +trans form +ĠC emetery +50 6 +Ġindef inite +Ġstim ulating +y g +60 3 +ĠS op +Ġdescript ive +Ph ase +ĠEd mund +Ġpneum onia +vent us +A mb +Ġlabor atories +ĠEx clusive +ug ar +W ere +Ġmalf unction +Ġhomosexual s +Ġ---- --- +un i +Ġturb ines +ĠEqu ity +D u +Ġmind ed +ĠR H +ĠBlack hawks +Ġfe ats +Ġ17 00 +re pl +36 2 +lad en +Ġindisp ensable +ly ss +tt i +Ġre el +Ġdiver ted +Ġlik eness +Ġsubscript ions +Ġfing ert +Ġfil thy +dest ruct +d raft +ĠBernard ino +l aunch +Ġper plex +ĠS UM +car b +Ġswe ater +ĠVent ure +ĠJ ag +ĠCele b +ĠV oters +Ġstead fast +Ġathlet ics +ĠHans on +ĠDr ac +Tr acker +Ġcomm end +ĠPres idency +ĠD ID +in formed +Ġweb page +P retty +Ġforce fully +ãĥĥ ãĤ¯ +Ġrel ocation +Ġsat ire +â ī +ĠSunder land +æ Ħ +V oice +???? ???? +Ġinform ant +Ġbow el +ĠUn iform +Ġ ..." +Ġpur ge +Ġpic nic +ĠU mb +ĠU PDATE +ĠSapp hire +ĠSt all +le arn +Ġobject ively +Ġob liter +Ġlooph ole +Ġjour neys +Ġo mission +Pro s +ĠSid ney +pl oma +Ġspray ed +Ġg uru +Ġtra itor +Ġtim et +Ġsn apping +ĠSe vent +urn al +ĠUk ip +Ġb owed +por al +l iberal +R os +Quest ions +i OS +Ġsummar ize +ST AT +Ġ18 50 +ap est +Ġl ender +ĠVari able +br inging +ĠL ORD +, ) +Ġcollaps es +x iety +ĠN ed +Y D +ĠSch a +Ġantib ody +Ġdis band +y re +ill usion +Ġro ver +s hed +ĠHiro sh +cc i +Ġcal am +ĠMort on +P interest +Ġ19 28 +ĠE uras +ord es +Ġf ences +ĠIn ventory +ĠVal encia +ĠU d +ĠT iff +Ġsqu e +Ġqu otation +Ġtroubles ome +er ker +QU EST +ĠKing doms +s outh +Ġle vy +Pr ince +ĠSt ing +Ġnick named +Ġapp e +Ġphot ographic +Ġcorp us +re ference +ĠT rog +U nt +) =( +ĠLat via +Ġactiv ating +Ġlicense e +Ġdispar ities +ĠNews letter +ãĥĥ ãĥĪ +Ġfree ing +ĠJe ep +ĠPer ception +ins k +Ġsil icone +ĠHay den +Le an +ĠSuz uki +ibr arian +66 8 +Ġsp or +Ġcorrel ations +ag hetti +Ġtu ber +ĠIP CC +il us +ĠV u +Ġwealth iest +ĠCarb uncle +an za +Ġfool ed +ĠZ ur +Ġd addy +ran o +il ian +Ġknock out +f man +requ ired +ĠWik ileaks +ĠD uffy +ON T +Ġins ol +ĠObject s +Ġb ou +ĠNord ic +ĠIns ert +sc an +Ġd ancers +Ġid iots +major ity +ĠNev ille +ĠFree BSD +Ġt art +pan ic +69 0 +Ġcoc oa +Ġsam pled +Ġlook up +Ind ust +Ġinject ions +gen re +Ġa u +Ġroad way +Ġgen itals +K ind +ĠEx aminer +ĠY az +F resh +Ġpar alysis +ĠAl uminum +Ġre ap +ok é +Ġsl oppy +ĠTun nel +pos ium +ner y +en ic +Ġher bal +ĠOut er +ĠBuild er +Ġinc ur +Ġide ologies +Ġback ups +cons uming +ĠDet ect +de ck +ĠKN OW +ĠG ret +ĠM IC +Ġtough ness +ĠEx hibit +Ġh ive +L es +ĠSCH OOL +ĠAt ari +ald e +ĠN ull +and estine +m ouse +Ġbrig ade +48 9 +Ġrev ol +ĠLaw son +ĠW ah +op oly +eb ted +ĠS aunders +Ġ3 13 +ĠW inc +Ġtab oo +ĠHel met +Ġw edge +ch ip +ĠT ina +b g +Ġinf uri +r n +Ġanomal ies +ĠSy nc +ĠEx am +ĠComm it +ĠDi ary +ĠALS O +ĠDe bor +omed ical +Ġcomprehens ion +6 55 +Ġempower ing +Ġ ire +Ġju ices +ĠE TH +ĠBox ing +=" / +Ġfacilit ated +p oke +ĠPars ons +ĠMod er +tra vel +Ġcivil izations +Ġliber tarians +Ġrun e +ĠCl arks +at hed +Ġcampaign ers +ĠDis patch +ĠFah renheit +ĠCap com +-------- -- +Ġl ace +Ġdr aining +Ġl iner +ĠArt ificial +é n +t ask +] ). +ĠGM O +ĠOper ator +ord inary +ĠInf luence +ĠU ps +Ġpot ency +uss en +osp ons +ĠSw im +ĠDead line +Un ity +Ġcul inary +Ġenlight enment +Ġwe arer +Ġmin ed +Ġp ly +Ġinc est +ĠDVD s +W alk +B TC +Tr ade +Ġdev al +ib and +ĠOvers ight +Palest inian +Ġd art +Ġm ul +L R +Ġrem ovable +ĠReal ms +ì Ŀ +Ġmisc ar +ĠV ulkan +68 5 +è re +ĠS ap +Ġmer ging +ĠCar ly +che ster +Ġbr isk +Ġlux urious +ĠGener ator +Ġbit terness +Ġed ible +Ġ24 3 +T G +Ġrect angle +With No +bel ow +J enn +Ġdark est +Ġh itch +Ġdos age +Ġsc aven +ĠK eller +ĠIllust rated +Certain ly +ĠMaver icks +Marg inal +Ġdiarr hea +Ġenorm ously +Ġ9 99 +sh r +qu art +Ġadam ant +ĠM ew +Ġren ovation +Ġcerv ical +ĠPercent age +en ers +ĠKim ber +Ġflo ats +Ġde x +ĠW itcher +ĠSwan sea +d m +Ġsal ty +y ellow +Ġca pe +ĠDr ain +ĠPaul a +ĠTol edo +les i +Mag azine +ĠW ick +ĠM n +ĠA ck +ĠR iding +AS ON +Ġhom ophobic +AR P +Ġwand ered +C PU +ood oo +ĠP ipe +Ġtight ening +ĠBut t +3 18 +Ġdesert ed +S ession +Ġfacilit ating +J ump +Ġemer gencies +OW ER +Ġexhaust ive +ĠAF TER +Ġheart beat +ĠLab el +ack y +ĠCert ified +ilt ration +Z e +ĠU tt +Ġ13 00 +Ġpres ume +ĠDis p +Ġsur ged +Ġdoll s +Col umb +Ġchim pan +ĠR azor +Ġt icks +Ġcouncill or +Ġpilgr image +ĠReb els +ĠQ C +ĠA uction +x ia +ik k +b red +Ġinsert ion +Ġco arse +d B +SE E +ĠZ ap +ĠF oo +Ġcontem por +ĠQuarter ly +ot ions +ĠAl chemist +ĠT rey +ĠDu o +S weet +80 4 +ĠGi ov +Ġfun n +N in +h off +Ġram ifications +Ġ19 22 +ĠExper ts +az es +Ġgar ments +ar ial +ĠN ab +Ġ25 7 +ĠV ed +Ġhum orous +ĠPom pe +Ġn ylon +Ġlur king +ĠSerge y +ĠMatt is +Ġmisogyn y +ĠComp onents +ĠWatch ing +ĠF olk +ract ical +B ush +Ġt aped +Ġgroup ing +Ġbe ads +Ġ20 48 +Ġcon du +quer que +Read ing +Ġgriev ances +Ult ra +Ġend point +H ig +ĠSt atic +ĠScar borough +L ua +ĠMess i +a qu +ĠPsy Net +ĠR udd +Ġa venue +v p +J er +Ġsh ady +ĠRes ist +ĠArt emis +Ġcare less +Ġbro kers +Ġtemper ament +Ġ5 20 +T ags +ĠTurn ing +Ġut tered +Ġp edd +Ġimpro vised +Ġ: ( +Ġtab l +Ġpl ains +16 00 +press ure +ĠEss ence +marg in +friend s +ĠRest oration +Ġpoll ut +ĠPok er +ĠAugust ine +ĠC IS +ĠSE AL +or ama +Ġth wart +se ek +Ġp agan + º +cp u +Ġg arn +Ġass ortment +ĠI LCS +t ower +Recomm ended +Ġun born +ĠRandom Redditor +ĠRandomRedditor WithNo +Ġparaly zed +Ġeru ption +Ġinter sect +ĠSt oke +ĠS co +B ind +å ¾ +ĠP NG +ĠNeg ative +ĠNO AA +Le on +Ġall oy +ĠL ama +ĠD iversity +5 75 +Ġunderest imated +ĠSc or +Ġm ural +Ġb usted +so on +l if +Ġnone x +Ġall ergy +ĠUnder world +ĠR ays +ĠBl asio +Ġh rs +ĠD ir +Ġ3 27 +by ter +Ġrepl acements +Ġactiv ates +ri ved +M H +Ġp ans +ĠH I +Ġlong itudinal +Ġnu isance +al er +Ġsw ell +ĠS igned +s ci +ĠIs les +ĠA GA +Ġdef iant +Ġson ic +oc on +K C +ĠA im +t ie +ah ah +Ġm L +D X +Ġb isc +ĠBill board +ĠSY STEM +NE Y +ga ard +Ġdist ressed +former ly +Al an +Ġche fs +Ġopt ics +ĠC omet +ĠAM C +Ġredes igned +irm ation +Ġsight ings +38 2 +3 11 +ĠW B +Ġcont raction +ĠT OTAL +D ual +Ġstart led +Ġunderstand ably +Ġsung lasses +ETH OD +Ġd ocker +Ġsurf ing +ĠH EL +ĠSl ack +ton es +Ġsh alt +Vis ual +49 8 +Dep artment +c ussion +Ġunrest ricted +Ġt ad +Ġre name +employ ed +Ġeduc ating +Ġgrin ned +bed room +ĠActiv ities +ĠV elvet +ĠSW AT +Ġsh uffle +ig or +Ġsatur ation +F inding +c ream +ic ter +Ġv odka +tr acking +te c +Ġfore ground +iest a +Ġve hement +ĠEC B +ĠT ie +E y +Ġt urtles +ĠRail road +ĠKat z +ĠFram es +Ġmen ace +ĠFell owship +ĠEss ential +ugg ish +Ġdri p +ch witz +ĠKy oto +s b +ĠN ina +Param eter +Ġal arms +ĠCl aud +Ġpione ering +Ġchief ly +ĠSc ream +Col lection +Ġthank fully +ĠRonald o +åŃ IJ +st rip +ĠDisney land +com mercial +See ing +S oul +Ġevac uate +Ġc iv +ĠAs he +Ġdiv ides +ĠD agger +rehens ive +Ġber ries +ĠD F +Ġs ushi +Ġplur ality +W I +Ġdisadvant aged +Ġbatt alion +ob iles +45 1 +Ġcl ing +Ġunden iable +ĠL ounge +Ġha unt +p he +Ġquant ify +Ġdiff ered +Ġ[* ] +ĠV iz +c um +sl ave +Ġvide og +Ġqu ar +Ġbund les +ĠAl onso +t ackle +Ġneur onal +Ġlandsl ide +conf irmed +ĠDep th +Ġrenew ables +B ear +ĠMaced onia +Ġjer seys +Ġb unk +ĠSp awn +ĠControl s +ĠBuch anan +Ġrobot ics +Ġemphas izing +ĠTut orial +h yp +ist on +Ġmonument al +æ ° +ĠCar ry +Ġt bsp +en ance +H ill +art hed +Ġro tten +De an +Ġtw isting +Ġgood will +Ġimm ersion +L iving +Ġbr ushes +ĠC GI +ĠAt k +tr aditional +Ġph antom +ĠSt amina +Ġexpans ions +ĠMar in +Ġembark ed +ĠE g +int estinal +ĠPE OPLE +ĠBo oth +ĠApp alach +Ġreleg ated +V T +M IT +Ġmust er +Ġwithdraw ing +Ġmicrosc ope +ĠG athering +ĠC rescent +ĠArgent ine +ĠDec re +ĠDomin ic +Ġbud s +ant age +ĠI on +Ġwid ened +ONS ORED +ĠGl oves +iann opoulos +raz en +fe el +Ġrepay ment +Ġhind sight +ĠRE ALLY +ĠPist ol +ĠBra h +Ġwat ts +Ġsurv ives +Ġfl urry +iss y +Al ert +ĠUrug uay +Ph oenix +S low +ĠG rave +ĠF ir +Ġmanage able +Ġtar iff +ĠU DP +ĠPist ons +ĠNiger ian +Ġstrike outs +Ġcos metics +whel ming +f ab +c ape +pro xy +Ġre think +Ġover coming +sim ple +Ġw oo +Ġdistract ing +ĠSt anton +ĠTuls a +ĠD ock +65 9 +Ġdisc ord +ĠEm acs +ĠV es +ĠR OB +Ġreass uring +Ġcons ortium +Muslim s +3 21 +Ġprompt s +se i +ĠH itch +imp osed +ĠF ool +Ġindisc rim +wr ong +bu querque +D avis +! ] +Ġtim eless +ĠNE ED +Ġpestic ide +Ġrally ing +ĠCal der +Ġå ¤ +Ġx p +ĠUn le +ĠEx port +lu aj +B uff +) [ +Ġsq or +S audi +Ġis tg +Ġindul ge +pro c +Ġdisg usted +Ġcomp ounded +Ġn em +Ġschool ing +ĠC ure +process ing +S ol +Ġpro verb +it ized +ĠAlv arez +Ġscar f +Ġrect angular +re ve +Ġh ormonal +ĠSt ress +itiz en +Ġ4 25 +girl s +ĠNo ir +ĠR app +Ġmar ches +ch urch +ĠUs es +Ġ40 5 +ĠBer m +Ġord inances +ĠJud gment +Charg es +ĠZ in +Ġdust y +Ġstraw berries +Ġper ce +ĠTh ur +ĠDebor ah +net flix +ĠLam bert +Ġam used +ĠGu ang +Y OU +R GB +ĠC CTV +Ġf iat +r ang +Ġf ederation +ĠM ant +ĠB ust +ĠM are +respect ive +ĠM igration +ĠB IT +59 0 +Ġpatriot ism +Ġout lining +reg ion +ĠJos é +Ġbl asting +ĠEz ra +B s +Ġundermin es +ĠSm ooth +Ġcl ashed +rad io +Ġtransition ing +ĠBucc aneers +ĠOw l +Ġplug s +Ġh iatus +ĠPin ball +Ġm ig +ĠNut r +ĠWolf e +Ġinteg ers +Ġor bits +ĠEd win +ĠDirect X +b ite +Ġbl azing +v r +Ed ge +ĠP ID +ex it +ĠCom ed +ĠPath finder +ĠGu id +ĠSign s +ĠZ er +ĠAg enda +Ġreimburse ment +M esh +i Phone +ĠMar cos +ĠS ites +h ate +en burg +Ġs ockets +p end +Bat man +v ir +ĠSH OW +Ġprovision al +con n +ĠDeath s +AT IVE +Pro file +sy m +J A +Ġnin ja +inst alled +id ates +eb ra +ĠOm aha +Ġse izing +ĠBe asts +Ġsal ts +M ission +Gener ally +ĠTr ilogy +he on +leg ates +Ġd ime +Ġf aire +par able +G raph +Ġtotal ing +Ġdiagram s +ĠYan uk +ple t +ĠMe h +Ġmyth ical +ĠStep hens +aut ical +ochem istry +Ġkil ograms +Ġel bows +anc ock +ĠB CE +ĠPr ague +Ġimpro v +ĠDev in +Ġ" \ +par alle +Ġsuprem acists +ĠB illion +Ġreg imen +inn acle +Ġrequ isite +ang an +ĠBur lington +ain ment +ĠObject ive +oms ky +G V +Ġun ilateral +Ġt c +Ġh ires +ment al +Ġinvol untary +Ġtrans pl +ĠASC II + ¨ +Ev ents +Ġdoub ted +ĠKa plan +ĠCour age +ig on +ĠMan aging +ĠT art +Ġfalse hood +ĠV iolet +Ġair s +Ġfertil izer +Brit ain +Ġaqu atic +ou f +W ords +ĠHart ford +Ġeven ings +ĠV engeance +qu ite +G all +ĠP ret +Ġp df +ĠL M +ĠSo chi +ĠInter cept +9 20 +Ġprofit ability +ĠId le +ĠMac Donald +ĠEst ablishment +um sy +Ġgather ings +ĠN aj +Charl ie +Ġas cent +ĠProt ector +Ġal gebra +Ġbi os +for ums +EL S +Introdu ced +Ġ3 35 +Ġastron omy +Cont ribut +ĠPol ic +Pl atform +Ġcontain ment +w rap +Ġcoron ary +ĠJ elly +man ager +Ġheart breaking +c air +ĠChe ro +c gi +Med ical +ĠAccount ability +! !" +oph ile +Ġpsych otic +ĠRest rict +Ġequ itable +iss ues +Ġ19 05 +ĠN ek +c ised +ĠTr acking +Ġo zone +Ġcook er +ros is +Ġre open +Ġinf inity +ĠPharm aceutical +ens ional +Att empt +ĠR ory +Mar co +Ġawa its +H OW +t reated +Ġbol st +Ġreve red +Ġp ods +opp ers +00 10 +Ġampl itude +ric an +SP ONSORED +Ġtrou sers +Ġhal ves +ĠK aine +ĠCut ler +ĠA UTH +Ġsplend id +Ġprevent ive +ĠDud ley +if acts +umin ati +ĠY in +Ġad mon +ĠV ag +Ġin verted +Ġhast ily +ĠH ague +L yn +Ġled ger +Ġastron omical +get ting +Ġcirc a +ĠC ic +ĠTenn is +Lim ited +Ġd ru +ĠBY U +Ġtrave llers +Ġp ane +ĠInt ro +Ġpatient ly +Ġa iding +Ġlo os +ĠT ough +Ġ29 3 +Ġconsum es +Source File +Ġ"" " +Ġbond ing +Ġtil ted +Ġmenstru al +ĠCel estial +UL AR +Plug in +Ġrisk ing +N az +ĠRiy adh +Ġacc redited +Ġsk irm +é Ľ +Ġexam iner +Ġmess ing +Ġnear ing +ĠC hern +ĠBeck ham +Ġsw apped +Ġgo ose +K ay +Ġlo fty +ĠWal let +Ġ[ ' +Ġap ocalypse +Ġb amboo +ĠSP ACE +ĠEl ena +Ġ30 6 +ac ons +Ġtight ened +Ġadolesc ence +Ġrain y +Ġvandal ism +ĠNew town +Ġcon ject +c akes +Ġche ated +Ġmoder ators +par ams +E FF +Ġdece it +ĠST L +ĠTanz ania +ĠR I +Ġ19 23 +ĠEx ile +the l +Ġthe olog +Ġquir ky +ĠIr vine +Ġneed y +or is +U m +K a +Ġmail box +3 22 +Ġb os +ĠPet ra +K ING +Ġenlarg ed +O ften +Ġbad ass +Ġ3 43 +ĠPl aces +ĠC AD +Ġpr istine +Ġinterven ing +d irection +Ġl az +ĠD SM +Ġproject ing +ĠF unk +ag og +pay ment +n ov +Ġch atter +AR B +Ġexam inations +ĠHouse hold +ĠG us +F ord +4 14 +B oss +Ġmy stic +Ġle aps +ĠB av +ul z +b udget +Foot ball +Ġsubsid ized +Ġfirst hand +Ġcoinc ide +oc ular +Con n +ĠColl abor +Ġfool s +am ura +ah ar +r ists +Ġsw ollen +Ġexp ended +ĠP au +s up +Ġsp ar +Ġkey note +s uff +Ġunequ al +Ġprogress ing +str ings +ĠGamer gate +Dis ney +ĠEle ven +om nia +Ġscript ed +Ġear ners +bro ther +ĠEn abled +æ ³ +Ġlar vae +ĠL OC +m ess +Wil son +ĠTem plate +success fully +Ġparam ount +Ġcamoufl age +Ġbind s +ĠQu iet +ĠSh utterstock +r ush +Ġmasc ot +fort une +ĠCol t +ĠBe yon +hab i +Ġha irc +Ġ26 7 +ĠDe us +Ġtw itch +Ġconcent rating +Ġn ipples +c ible +Ġg ir +N Z +M ath +n ih +Requ ired +Ġp onder +ĠS AN +Ġwedd ings +Ġl oneliness +N ES +ĠMah jong +69 5 +add le +ĠGar ner +ĠC OUR +Br idge +Ġsp ree +ĠCald well +Ġbri bery +Ġ���� ���� +plug ins +Ġr acket +Ġchamp agne +vers ible +V ote +Ġmod ifiers +May or +6 80 +Ġassemb lies +ĠS ultan +ĠN ing +ĠLad ies +Ġsulf ur +Ġor bs +Ġ---- - +____ ___ +ĠJournal ism +Ġes ports +Ġl ush +Ġh ue +Ġspect ral +H onest +ãĥ ı +Ġbus hes +Ġrein forcement +Ġre opened +ĠWhe els +ĠM org +rie ving +Ġaux iliary +Ġj Query +ĠB AT +tes que +Ġver tex +p ure +f rey +ãĤ º +d os +Ġty ph +Ġc ull +Ġe q +Ġdec on +Ġtoss ing +Ġdispar ate +ĠBr igham +print f +led ged +Ġsu nd +Ġco zy +Ġhepat itis +per forming +Ġav al +ĠG G +f uture +Ġpet ertodd +ĠKos ovo +Ġmagn ets +Al ready +ĠEd ison +ĠCe res +ĠRA ID +Ġbrill iance +57 6 +Ġder ives +Ġhypert ension +ĠÎ Ķ +Ġlamb da +Ġfl air +Ġmission aries +Ġrap es +ĠSt arter +ĠMon ths +Ġdef y +Ġseism ic +ĠR aphael +Ġeuro zone +65 6 +z sche +Ġscr atched +Ġb ows +ĠLenn on +ĠGa ia +Ġdri pping +f acts +A le +Ġfrog s +ĠBre ast +ogene ity +ĠProsecut or +Ġampl ified +ĠHod g +ĠF n +Th ousands +ĠNI H +ĠMonitor ing +FT WARE +ĠPri ebus +ĠG rowing +hun ter +Ġdiagn ose +ĠM ald +ĠL R +Ġcrown ed +Ġburst ing +Ġdiss olution +j avascript +Ġuseful ness +ĠExec ution +: ( +ĠIv ory +a ah +Ġpersecut ed +viol ence +ist as +ĠCr ate +Ġimpuls es +ĠSp ani +ed es +Hand le +ĠZ erg +think able +Last ly +Ġspont aneously +Ġinconven ient +Ġdismiss ing +Ġpl otted +Ġeight y +Ġ7 37 +r ish +ĠThor nton +ath am +Ġsit com +V en +Rec ipe +t el +l und +Ġcle ars +ĠSas uke +Ġ25 8 +Ġopt ing +Ġen raged +est hetic +ĠA e +uch s +Pre p +Fl ow +Ġrun off +ĠE ating +ĠG iles +ĠAct ing +res ources +ib aba +Ġr pm +Ġske wed +ĠBl anc +ĠS akuya +Ġhot ter +Ġ19 24 +op ian +ck o +Ġcr umbling +Ġcapt ains +ĠAppropri ations +le aders +dro pping +an uts +Ġrevers ing +ĠP ose +ĠS ek +Sc ot +ĠIde a +c ise +ĠSloven ia +Ġ3 17 +Do ctor +Ġcro cod +ald i +Se a +ĠFar rell +Ġmerc enaries +ĠR NC +ĠGu ess +Ġp acing +M achine +Streamer Bot +ĠChar ity +Ġ29 8 +Ġcann ons +ĠTob y +TPP StreamerBot +ĠPass ion +cf g +Th om +Ġbad ges +ĠBern stein +. âĢĵ +ĠP OP +ĠCon j +Ġinitial ization +Ġbiod iversity +D ub +Ġfeud al +Ġdisclaim er +Ġc row +Ġign ition +ar f +S HA +Ġk Hz +h azard +ĠArt ists +oe uv +67 9 +ĠRud y +N ine +ĠRam adan +å ½ +itt o +Ġadren aline +C ert +Ġsmell ed +Ġimp unity +Ġag endas +ĠRe born +ĠCon cent +ĠSe ems +Ġo mega +ĠDust in +Ġback er +ĠSau ce +ĠBoy le +W IN +Ġsp ins +Ġpa uses +u pt +Ġshred ded +Ġstra pped +ĠCor ruption +Ġscr atches +Ġn i +Ġatt ire +ĠS AF +Factory Reloaded +ĠI PS +Ġ( % +Ġsem inar +f ocus +c ivil +Ġ18 60 +int osh +Ġcontin ual +Ġabbre vi +ĠS ok +oc obo +X M +Ġfr antic +Ġunavoid able +Ġar tery +Ġannot ations +b ath +Cl imate +Ġd ors +ĠSl ide +co ord +ĠRel oad +ĠL DL +ĠLove craft +Ġunim agin +Ġresemb led +Ġbarr acks +n p +Ġsurrog ate +Ġcategor ized +ãĤ © +Ġvacc inated +Ġdrain age +Ġind ist +ĠWhats App +Ġ18 70 +oler ance +inv oke +am orph +Ġrecon nect +Ġem anc +Ġblind ness +Ġ12 80 +intern et +c ollar +Ġalt ru +Ġab yss +ĠT RI +65 7 +Ġinf used +HE AD +Ġforest ry +ĠWood y +ĠC i +w i +s am +78 4 +hol iday +Ġmog ul +ĠF ees +ĠD EN +In ternal +ur bed +f usc +at om +ĠIll usion +Ġpoll ed +Ġfl ap +Ġco ax +L GBT +An aly +ĠSect ions +ĠCalif orn +em n +Ġh ither +ĠN IGHT +Ġn ailed +ĠPip eline +39 1 +o of +ĠPr imal +vere nd +Ġsl ashing +Ġret ri +avi our +Ġdepart ing +g il +IS C +Ġmid way +Ġultras ound +Ġbeh aving +ĠT ara +class es +V irtual +ĠColon ial +Ġstri pping +Ġorchestr ated +ĠGra ves +45 2 +ĠIron ically +ĠWrit ers +Ġl ends +ĠMan z +Ġra ven +Ġoxid ative +Ġ26 6 +EL F +act ually +asc ar +D raft +Ġfavour able +Ġhumili ating +Ġf idelity +ĠH of +ĠX uan +49 6 +Ġlay ered +at is +79 0 +Ġpay check +it on +K ar +ĠVM ware +ĠFar mer +Ġserv ic +gl omer +Ġsl ump +ĠFab ric +ĠD OC +est ing +Ġreass ure +Ġph yl +v olt +it ory +R ules +Ġoxid ation +Ġpri zed +Ġmist ress +ĠDj ango +WAR N +å ij +Ġenc ode +ĠFeed back +Ġstupid ity +I an +ĠYugoslav ia +× ¨ +ac l +UT E +19 77 +Ġqual ifies +Ġpuls es +pret ty +Ġfro ze +Ġs s +Iter ator +Ġur gently +Ġm ailed +ĠCh am +Ġsust aining +Ġbas il +Ġpupp ies +il ant +ĠP LEASE +l ap +ace ous +F ear +ĠMaster y +aut omatic +ĠT AG +Ġant im +ag les +47 3 +fram es +Ġwh ispers +ĠWho ever +Ġbra very +ĠUK IP +ract ions +"" " +Ġt ame +Ġpart ed +every thing +CON T +Ġind ebted +Ġadd r +re k +IR ED +Ġem inent +cl inton +Ġo usted +Ġreview er +Ġmelt down +Ġre arr +ĠY ao +the real +aby te +Ġst umbling +Ġbat ches +Ġ25 9 +Ġcontrace ptive +Ġprost itute +ens is +De cl +ĠSt rikes +M ilitary +ĠO ath +v acc +pp ings +05 2 +Ġpart Name +amp ing +Rep orts +K I +CH R +Ġsubt ly +sw ers +Bl ake +us ual +Ġcontest ants +Ġcart ridges +ĠGRE AT +Ġbl ush +ĠâĢ º +47 2 +Ġreason ed +ãĥ ¤ +paralle led +Ġd yn +ag ate +Ġnight ly +å Ĩ +55 6 +Ġsem antic +ĠAdv oc +Ġ !! +Ġdisag rees +ĠB W +V eh +Ġharm ing +Ġembr aces +Ġstri ves +Ġin land +ĠK ard +Ġhe ats +ĠGin ny +ut an +ern aut +yl ene +ĠE lev +J D +Ġh ars +ĠStar r +Ġsk ysc +Ġcollabor ators +Us ually +Ġrev olutions +ĠSTAT S +Ġdism antle +Ġconfident ly +Ġkin etic +Al i +Ġpercent ile +Ġextract ing +ill ian +est ead +Ġphysic ists +ĠMarsh al +Ġfell owship +Ġd ashed +ĠU R +ĠSi oux +ĠComp act +am ide +P ython +ĠLe igh +ĠPharm ac +ist rates +her ical +Ġf ue +ĠE min +Ġ( { +ĠNeighbor hood +Ġdisrupt ing +ĠD up +Ġg land +ĠSe v +ĠMar ian +arg on +ĠD und +Ġ< !-- +Ġstr and +Ġstadium s +z os +Ġpsych osis +ĠR ack +Ġbrilliant ly +ï¸ ı +Ġsubmer ged +ĠInst it +ĠCh ow +Ġc ages +ĠH ats +ĠU rs +Ġdil uted +us at +ien ne +ĠMembers hip +ĠBur k +Ġ ie +Ġarche type +D rug +ult on +ĠSp ock +ĠMcK ay +ĠDep end +F eatured +S oc +19 78 +ĠB ere +Ġrelent lessly +Ġcripp ling +Ġar thritis +çĶ Ł +ĠTrop ical +ĠBul g +ĠCher yl +Ġadm irable +Ġsub title +Over ride +Ġorig inating +ĠC CP +Ġsw ore +ĠSo le +ĠDis orders +3 29 +Ġprocess ion +Ġref urb +Ġimm ersed +requ ently +Ġskept ics +Ġcer amic +m itter +en stein +b elt +ĠT IT +b idden +Ġf ir +m ist +> ] +Ġwe ave +ĠParad ox +Ġentr usted +ĠBarcl ays +Ġnovel ist +og ie +80 6 +Ġnin ety +Ġdisag reements +@@@@ @@@@ +ĠAus chwitz +c ars +ĠL ET +t ub +arant ine +P OS +Ġback story +Ġcheer ful +ĠR ag +ek a +bi ased +Ġinexper ienced +ak ra +ĠW itt +t an +Ġrap ist +Ġplate au +ch al +ĠInqu is +exp ression +Ġc ipher +Ġsh aving +add en +re ly +( \ +ism a +ĠReg ulatory +CH AR +ily n +N VIDIA +G U +Ġmur m +la us +Christ opher +Ġcontract ual +ĠPro xy +ĠJa ime +ĠMethod ist +Ġstew ards +st a +per ia +Ġphys iology +Ġbump ed +Ġf ructose +Austral ian +ĠMet allic +ĠMas querade +ar b +Ġprom ul +Ġdown fall +Ġbut cher +Ġb our +ĠIN FORMATION +ĠB is +pect s +ad ena +Ġcontempl ating +ar oo +cent ered +ĠPe aks +Us ed +Ġmod em +Ġg enders +Ġ8 000 +37 1 +Ġm aternity +ĠR az +Ġrock ing +Ġhandgun s +ĠD ACA +Aut om +ĠN ile +Ġtum ult +ĠBenef it +ĠAppro ach +works hop +ĠLe aving +G er +inst ead +Ġvibr ations +Ġrep ositories +49 7 +ĠA unt +ĠJ ub +ĠExp edition +Al pha +Ġs ans +Ġoverd ue +Ġoverc rowd +Ġlegisl atures +Ġp aternal +ĠLeon ardo +Ġexp ressive +Ġdistract ions +Ġsil enced +tr ust +Ġb iking +Ġ5 60 +Ġpropri et +Ġimp osition +Ġcon glomer +Ġ= ================================================================ +ĠTe aching +ĠY ose +int ensive +T own +Ġtroll ing +ĠGr ac +ĠAS US +Y o +Ġspecial s +ĠNep h +ĠGod zilla +Dat abase +ĠHe gel +Ġ27 2 +19 76 +ĠGl oria +Ġdis emb +ĠInvestig ations +ĠB ane +ag ements +St range +Ġtre asury +ĠPl ays +Ġundes irable +Ġwid ening +Ġverb ally +Ġinf ancy +Ġcut ter +f ml +Ġ21 00 +prot otype +f ine +Ġdec riminal +Ġdysfunction al +Ġbes ie +ĠErn st +z eb +Ġnort heastern +Ġa ust +por ate +ĠMar lins +Ġsegreg ated +ew orld +ĠMa her +Ġtra verse +Ġmon astery +ur gy +G ear +s and +Com pl +ĠE MP +Ġpl ent +ĠMer cer +Ġ27 6 +TA BLE +Config uration +H undreds +Ġpr ic +Ġcollabor ating +ĠPar amount +ĠCumm ings +Ġ( < +Ġrecord er +Ġfl ats +Ġ4 16 +wh ose +Font Size +ĠOr bit +Y R +Ġwr ists +Ġb akery +) } +ĠB ounty +ĠLanc aster +Ġend ings +acc ording +ĠSal am +e asy +75 5 +ĠBur r +ĠBarn ett +onom ous +Un ion +Ġpreced ence +ĠScholars hip +ĠU X +Ġroll out +Ġbo on +al m +ĠCan ter +æ µ +Ġround ing +Ġcl ad +Ġv ap +ĠF eatured +is ations +Ġ5 40 +pol ice +Ġunsett ling +Ġdr ifting +ĠLum ia +ĠObama Care +ĠF avor +Hy per +ĠRoth schild +ĠMil iband +an aly +ĠJul iet +H u +Ġrec alling +a head +69 6 +Ġunf avorable +Ġd ances +O x +Ġleg ality +Ġ40 3 +rom ancer +Ġinqu ire +ĠM oves +\ "> +ĠVari ant +ĠMess iah +ĠL CS +ĠBah á +75 6 +Ġeyeb row +Ġ ¥ +ĠMc F +ĠFort y +M as +Ġpan icked +Ġtransform ations +q q +Ġrev olves +ring e +ĠA i +ax e +Ġon ward +ĠC FR +ĠB are +log in +Ġliqu ids +Ġde comp +second ary +il an +ĠCon vert +ami ya +Ġprosecut ing +Ġâī ¡ +ĠYork ers +ĠByr ne +sl ow +aw ei +J ean +Ġ26 9 +ĠSky dragon +Ġ é +ĠNicarag ua +ĠHuck abee +ĠHigh ly +Ġamph ib +ĠPast or +ĠL ets +Ġbl urred +Ġvisc eral +ĠC BO +Ġcollabor ated +z ig +Leg al +Ġapart heid +Ġbr id +Ġpres et +ĠD ET +ĠAM A +× Ķ +arch ing +auc uses +build er +Ġpo etic +Ġem ulator +ĠMole cular +Ġhon oring +ise um +Ġtract or +ĠCl uster +ĠCal m +ared evil +Ġsidew alks +Ġviol in +Ġgeneral ized +ĠAle c +Ġemb argo +Ġfast ball +ĠHT TPS +ĠL ack +ĠCh ill +ri ver +C hel +ĠSw arm +ĠLev ine +ro ying +L aunch +Ġkick er +Ġadd itive +ĠDe als +W idget +cont aining +Ġescal ate +ĠOP EN +Ġtwe aked +Ġst ash +Ġsp arks +ĠEs sex +ĠE cc +Ġconv ict +Ġblog ging +I ER +ĠH L +Ġmurd erers +75 9 +ĠH ib +Ġde pl +ĠJ ord +S ac +Ġdis sect +ĠHow e +os her +Ġcustom izable +ĠFran z +Ġat ro +Ä ĩ +Ġ000 4 +Ġout post +R oss +Ġglyph osate +ĠHast ings +ĠBE FORE +Ġsh ove +o pped +ĠSc ala +Ġam ulet +an ian +Ġexacerb ated +Ġe ater +47 1 +UM E +Ġpul p +izont al +ĠZ am +ĠAT I +imm une +aby tes +Ġunnecess arily +ĠC AT +ĠAx is +Ġvisual ize +à ī +ĠRad ical +f m +Doc uments +ĠFor rest +Ġcontext ual +ĠSy mbol +Ġtent ative +ĠDO ES +ĠGood s +Ġintermitt ent +} : +medi ated +Ġridic ule +Ġathe ism +Ġpath ogens +ĠM um +Ġre introdu +Ġ30 7 +i HUD +Ġflash light +Ġsw earing +Ġp engu +B u +Ġrot ated +ĠCr ane +Ġ() ); +Ġfashion able +Ġendors ing +46 3 +) [ +Ġingest ion +Ġcook s +Ġ9 50 +ot omy +ĠIm am +Ġk a +Ġte aser +ĠGhost s +ĠãĤ µ +19 69 +Ï ĥ +ub by +Ġconver ter +zan ne +end e +ĠPre par +ĠNic kel +ĠChim era +h im +ĠTyr ann +ĠSabb ath +ĠNich ols +Ġra pt +ih ar +Ġshe lling +Ġillum inate +Ġdent ist +ut or +ĠInteg ration +Ġwh ims +ĠLiter ary +Be aut +Ġp archment +ag ara +Br and +Ġder og +âĢ¦ ) +ĠNor se +Ġunw itting +Ġc uc +Ġborder line +Ġupset ting +Ġrec ourse +Ġd raped +ĠRad ar +Ġcold er +ĠPep si +im inary +], [ +65 8 +V i +ĠF rem +ĠP es +Ġveter inary +ĠT ED +ĠEp idem +n ova +k id +Ġdev out +o ct +j ad +M oh +ĠP AY +Ġge ometric +Ġ3 23 +Ġcircum ference +ich ick +19 75 +ĠY uri +ĠSh all +ĠH over +un in +S pr +Ġg raft +ĠHapp iness +Ġdisadvant ages +att acks +Ġhub s +ĠStar Craft +é ĸ +Ġgall eries +ĠKor ra +Ġgrocer ies +ĠGors uch +Ġrap ists +Ġfun gi +ĠTyph oon +V ector +ĠEm press +b attle +4 68 +Ġparas ite +ĠBom ber +S G +ex ist +ĠP f +Ġun se +Ġsurge ons +B irth +ĠUn sure +ĠPrint ed +ĠBehavior al +ĠA ster +Pak istan +Ġun ethical +Ġs v +ĠIo T +Ġlay outs +P ain +Ġconst ants +ĠL W +ĠB ake +Ġtow els +Ġdeterior ation +ĠBol ivia +Ġblind ed +ĠW arden +ĠMist ress +Ġon stage +Ġcl ans +ĠB EST +19 60 +Ġant ique +Ġrhet orical +ĠPer cy +ĠRw anda +, . +B ruce +Ġtra umat +ĠParliament ary +Ġfoot note +id ia +ĠLear ned +se eking +gen ic +Ġdim ensional +H ide +èĢ ħ +Ġintrig ue +in se +Ġle ases +Ġapp rentices +w ashing +Ġ19 26 +V ILLE +Ġsw oop +s cl +Ġbed rooms +on ics +ĠCr unch +comp atible +Ġincap ac +ĠYemen i +ash tra +z hou +d anger +Ġmanifest ations +ĠDem ons +AA F +Secret ary +ACT ED +L OD +Ġam y +ra per +eth nic +4 17 +Ġpos itives +Ġ27 3 +ĠRefuge es +Ġus b +ĠV ald +odd y +ĠMahm oud +As ia +Ġskull s +ĠEx odus +ĠComp et +ĠL IC +ĠM ansion +ĠA me +Ġconsolid ate +storm s +ont ent +99 6 +Ġcl en +Ġm ummy +fl at +75 8 +ĠV OL +oter ic +n en +ĠMin ute +S ov +Ġfin er +R h +ly cer +Ġreinforce ments +ĠJohann es +ĠGall agher +Ġgym n +S uddenly +Ġext ortion +k r +i ator +T a +Ġhippocamp us +N PR +ĠComput ing +Ġsquare ly +Ġmod elling +ĠFor ums +ĠL isp +ĠKrish na +Ġ3 24 +Ġr ushes +Ġens ued +Ġcre eping +on te +n ai +il ater +ĠHorn ets +Ġob livious +IN ST +55 9 +Ġjeopard y +Ġdistingu ishing +j ured +Ġbeg s +sim ilar +ph ot +5 30 +ĠPark way +Ġs inks +ĠHearth stone +ib ur +ĠBat on +Av oid +Ġd ancer +Ġmag istrate +ary n +Ġdisturb ances +ĠRom ero +Ġpar aph +Ġmis chief +âĸ ĵ +ĠSh aria +Ġur inary +r oute +iv as +f itted +Ġeject ed +ĠAl buquerque +Ġ4 70 +Ġirrit ated +ĠZ ip +ĠB iol +à į +Ġden ounce +Ġbin aries +ĠVer se +Ġopp os +ĠKend rick +ĠG PL +Ġsp ew +ĠEl ijah +ĠE as +Ġdr ifted +so far +Ġannoy ance +ĠB ET +47 4 +ĠSt rongh +it ates +ĠCogn itive +oph one +ĠIdent ification +ocr ine +connect ion +Ġbox er +ĠAS D +ĠAre as +Y ang +t ch +ull ah +Ġdece ive +Comb at +ep isode +cre te +W itness +Ġcondol ences +ht ar +Ġhe als +Ġbuck ets +ĠLA W +B lu +Ġsl ab +ĠOR DER +oc l +att on +ĠSteven son +ĠG inger +ĠFriend ly +ĠVander bilt +sp irit +ig l +ĠReg arding +ĠPR OG +Ġse aling +start ing +Ġcard inal +ĠV ec +ĠBe ir +Ġmillisec onds +we ak +per se +Ġster ile +ĠCont emporary +ĠPh ant +ĠCl o +Ġout p +Ġex iled +Ġ27 7 +Ġself ie +Ġman ic +Ġn ano +ter ms +Alex ander +Ġres olves +Ġmillenn ia +Ġexpl odes +Ġconst ellation +Ġadul tery +m otion +D OC +Ġbroad casters +Ġkinderg arten +ĠMay weather +ĠE co +ich o +Ġ28 7 +l aun +Ġm ute +Ġdisc reet +Ġpres chool +Ġpre empt +De lete +ĠFre ed +P i +H K +Ġblock er +ĠC umber +Ġw rought +d ating +Ġins urer +Ġquot as +Ġpre ached +Ġev iction +ĠReg ina +ĠP ens +Ġsevent een +ĠN ass +D ick +Ġfold s +Ġd otted +ĠA ad +Un iversal +Ġp izz +ĠG uru +Ġso ils +Ġno vice +ĠNe ander +Ġst ool +Ġdeton ated +ĠPik achu +ĠMass ive +IV ER +ĠAb del +Ġsubdu ed +Ġtall est +Ġprec arious +Ġa y +r ification +ĠOb j +c ale +Ġun question +cul osis +ad as +igr ated +D ays +Ġque ens +ĠGaz ette +ĠCol our +ĠBow man +ĠJ J +ï ve +Ġdomin ates +Stud ent +Ġm u +Ġback log +ĠElect ro +Tr uth +48 3 +Ġcond ensed +r ules +ĠCons piracy +Ġacron ym +hand led +ĠMat te +j ri +ĠImp ossible +l ude +cre ation +Ġwar med +ĠSl ave +Ġmis led +Ġfer ment +ĠK ah +ink i +ke leton +cy l +ĠKar in +Hun ter +Reg ister +ĠSur rey +Ġst ares +ĠW idth +ĠN ay +ĠSk i +Ġblack list +uck et +Ġexp ulsion +im et +Ġret weet +vant age +Fe ature +Ġtro opers +Ġhom ers +9 69 +Ġconting ency +ĠW TC +ĠBrew er +fore ign +W are +S olar +Ġund ue +RE C +ulner able +path ic +ĠBo ise +Ġ3 22 +Ġarous ed +ĠY ing +ä¸ į +uel ess +Ġp as +Ġmor p +Ġfl oral +Ex press +ud ging +k B +ĠGr anted +Ø ¯ +ĠMich a +ĠGoth ic +ĠSPEC IAL +ĠRic ardo +F ran +Ġadminister ing +6 20 +por a +Ġ ® +Ġcomprom ises +Ġb itten +Ac cept +Th irty +Ð ² +Ġmater ially +ĠTer r +ig matic +ch ains +Ġdo ve +stad t +Mar vel +FA ULT +Ġwind shield +Ġ3 36 +ad ier +Ġsw apping +Ġflaw less +ĠPred ator +ĠMiche le +Ġprop ulsion +ĠPsych ic +Ġassign ing +Ġfabric ation +Ġbar ley +l ust +Ġtow ering +Ġalter cation +ĠBent ley +Sp here +Ġtun a +ĠClass es +Fre edom +un er +L ady +v oice +Ġcool est +or r +Ġpal p +$ { +Ġhyster ia +ĠMet atron +p ants +Ġspawn ing +Exper ts +ĠInvest ors +ĠAn archy +Ġshr unk +ĠVict im +Ġ28 9 +Ġec stasy +ĠB inding +58 5 +ĠMel ody +57 8 +ot ally +ĠE tsy +lig a +Ġapplaud ed +Ġswe ating +Ġredist ributed +Ġpop corn +Ġsem inal +f ur +ĠNeuro science +R and +ĠO st +ĠMadd en +ĠIncre asing +ĠDaw kins +ĠSub way +Ġar sen +cons erv +B UR +Ġsp iked +ĠLy ft +ĠImper ium +ĠDrop box +Ġfav oured +Ġencomp asses +gh ost +Ġins pires +Ġbur geoning +ĠY oshi +ĠVert ical +ĠAud itor +Ġint ending +Ġfilib uster +Bl oom +f ac +ĠCav s +ign ing +Ġcowork ers +ĠBarb arian +rem ember +FL AG +Ġaudit ory +ason ry +Col lege +Ġmut ed +gem ony +ob in +ĠPsych o +9 68 +Ġlav ish +Ġhierarch ical +ĠDr one +ou k +Ġcripp led +ĠMax im +Sl ot +Ġqu iz +ĠV id +if ling +Ġarchae ologists +Ġabandon ment +d ial +le on +ĠF as +T ed +Ġr aspberry +Ġmaneu vers +Ġbehavi ours +Ġins ure +Ġrem od +Sw itch +h oe +Ġsp aced +Ġafford ability +ĠF ern +not ation +ĠBal anced +Ġoccup ies +en vironment +Ġneck lace +Ġsed an +F U +ĠBrav o +Ġab users +ĠAn ita +met adata +ĠG ithub +ait o +ĠF aster +ĠWass erman +ĠF lesh +Ġth orn +r arily +ĠMer ry +w ine +Ġpopul ace +ĠL ann +Ġrepair ing +Ġpsy che +Ġmod ulation +aw aru +âĢĭ âĢĭ +ari j +Ġdecor ations +Ġapolog ise +ĠG arg +app ly +Ġgive away +ĠFl an +ĠWy att +U ber +Ġauthor ised +ĠMor al +HAHA HAHA +activ ate +Ġtorped o +ĠF AR +Ġam assed +ĠA ram +ark in +ĠVict ims +st ab +Ġo m +ĠE CO +Ġopio ids +Ġpurpose ly +ĠV est +Ġer g +at an +ĠSur gery +Ġcorrect ing +ĠOrt iz +ĠBe et +Ġrev oke +Ġfre eway +ĠH iggins +F ail +ĠFar ms +ĠAT P +h ound +Ġp oking +ĠCommun ists +mon ster +iment ary +Ġunlock ing +Ġunf it +we ed +en ario +at ical +ĠEnlight enment +ĠN G +ĠComp ensation +de en +ĠWid ow +ĠCind y +ĠAfter wards +Ġ6 000 +ikh ail +ag ically +Ġrat ified +Ġcasual ty +H OME +p sey +f ee +Ġspark ling +Ġd é +Ġconcert ed +C atal +Ġcomp lying +ĠA res +ĠD ent +Sh ut +Ġsk im +ad minist +Ġhost ilities +ĠG ins +Ġ6 08 +Ġm uddy +ĠMc Int +ĠDec ay +5 25 +Ġconspic uous +ĠEx posure +Ġresc ind +Ġwear able +Ġ3 28 +our met +ah s +ĠRob ots +Ġe clips +inst ance +ĠRE PORT +ĠApp l +0 30 +ĠSk ies +01 00 +Ġfall acy +S ocket +ĠRece iver +Ġsol ves +ĠButter fly +ĠSho pping +ĠFI RE +65 4 +Med ic +Ġsing ers +ĠNeed less +'' '' +isher s +ĠD ive +58 8 +Ġselect ively +Ġcl umsy +88 9 +Ġpurch aser +ear ned +ard y +Ġbenef iting +eng lish +Ġyield ing +ĠP our +Ġspin ach +Ġdel ve +ĠC rom +6 10 +Ġexport ing +ĠMA KE +Ġ26 3 +Ġg rop +Ġenv oy +ĠInqu iry +ĠLu igi +d ry +ĠT uring +Thumbnail Image +ĠVar iety +Ġfac et +Ġfl uffy +Ġexcerpt s +Ġsh orth +ĠOl sen +CL UD +Ġrel iant +ĠUN C +T our +Ġbat hing +Comp any +Ġglobal ization +P red +ĠMalf oy +Ġh oc +j am +craft ed +ĠBond s +ĠKiss inger +Eng land +Ġorder ly +cat entry +Ġ26 1 +Ġexch anging +ĠInt ent +ĠAmend ments +D OM +Ġst out +³³³³³³³³ ³³³³³³³³ +ĠAir bus +Ġ27 8 +hy de +P oll +Item ThumbnailImage +Ġlooph oles +ĠPill ar +Ġexpl or +St retch +A part +Ġun married +Lim it +ĠTransform ers +Ġintellect ually +unct ure +18 00 +Ġd arn +B razil +Ġleft over +ber us +f red +Mine craft +3 26 +ĠForm s +Ġproof s +ĠDes igned +Ġindex es +ĠSupp ose +EM S +ĠL oving +ĠBon nie +im ating +OT US +Ġconduct or +Ġbehav ed +ĠF ren +Ġsy nerg +Ġmillenn ium +Ġcater ing +ĠL auder +W r +ĠY iannopoulos +ĠAT F +Ġensl aved +Ġawaken ed +D VD +ĠED ITION +ĠConc ert +ĠChall enger +ĠH aku +umer ic +Ġdep recated +ĠSH AR +4 12 +Ġdy stop +Ġtremb ling +Ġdread ed +ĠSp ac +p adding +Re pl +ĠG arrison +M ini +Ġun paralleled +am ar +URR ENT +w reck +c ertain +t al +ĠC LS +app ings +Ġsens ed +Ġf encing +ĠPas o +ĠDes k +Ġsc off +Ġcontem plate +ĠL iga +l iquid +75 7 +Ġapp rentice +ĠUCH IJ +5 70 +ĠTh ousand +ĠIll um +Ġchampion ed +ãĤ Į +Ġelect ors +Ġ3 98 +ĠH ancock +round ed +ĠJ OHN +Ġuns atisf +Ġqual ifier +ĠGad get +EN E +Ġdead liest +ĠPl ants +Ġ ions +Ġacc ents +Ġtwe aking +Ġsh aved +F REE +ĠCh aser +Again st +9 60 +Ġmeth amphetamine +Ġnormal ized +Ġ$ \ +ĠPre cision +ĠGu am +Ġch oked +ĠX II +ĠCast ing +Tor rent +Ġscal p +ĠJagu ar +w it +Ġsem ic +ix ie +ĠG ould +Ġconf ines +N usra +ĠL on +ĠJ ugg +y cle +ĠCod ec +E gypt +Ġrest rain +ĠAl iens +Ġch oking +ĠD unk +ĠBell a +ab c +Ġsl ang +Ġneuro trans +s av +Ġempower ment +â ĨĴ +Ġclim bers +ĠM im +ĠF ra +ros se +Cap ital +ĠCth ulhu +Inter face +Ġprof icient +ĠIN TO +Ġ3 18 +ront al +5 80 +ĠDes pair +K enn +Ġscrim mage +ĠCo at +as ions +Ġwall paper +ĠJ ol +Ġresurg ence +Ġant iv +ĠB alls +² ¾ +Ġbuff ers +Ġsub system +ĠSt ellar +ĠL ung +A IDS +Ġerad icate +Ġblat antly +Ġbehav es +ĠN un +Ġant ics +ex port +DE V +w b +Ġph p +ĠInteg rity +Ġexplore r +Ġrev olving +auth ored +g ans +Ġbas k +Ġas ynchronous +å į +TH ING +69 8 +G ene +ĠR acer +ĠN ico +iss ued +Ġser mon +p ossibly +Ġsize of +Ġentrepreneur ial +ox in +ĠMin erva +Ġpl atoon +n os +ri ks +A UT +ĠAval anche +ĠDes c +ij 士 +ĠP oc +Ġconf erred +Î » +Ġpat ched +F BI +66 2 +Ġfract ures +Ġdetect s +Ġded icate +Ġconstitu ent +Ġcos mos +W T +Ġswe ats +Ġspr ung +b ara +s olid +Ġuns us +Ġbul ky +ĠPhilipp e +ĠFen rir +Ġtherap ists +ore al +^^ ^^ +Ġtotal ed +Ġboo ze +ĠR PC +Prosecut ors +Ġdis eng +ĠSh ared +Ġmotor cycles +Ġinvent ions +Ġlett uce +ĠMer ge +ĠJ C +Ġspiritual ity +ĠWAR NING +Ġunl ucky +ĠT ess +Ġtong ues +ĠD UI +T umblr +Ġle ans +Ġinv aders +Ġcan opy +ĠHur ricanes +ĠB ret +ĠAP PLIC +id ine +ick le +Reg arding +Ġve ggies +Ġe jac +ju ven +F ish +D EM +ĠD ino +Th row +ĠCheck ing +be ard +( & +Ġj ails +Ġh r +trans fer +iv ating +Ġfle ets +ĠIm ag +ĠMc Donnell +Ġsnipp et +Is a +ĠCh att +ĠSt ain +ĠSet FontSize +ĠO y +ĠMathemat ics +49 4 +Ġelectro ly +ĠG ott +ĠBr as +B OOK +ĠF inger +d ump +Ġmut ants +Ġrent als +Ġinter tw +Ġc reek +ail a +Bro ther +ĠDisc ord +pe e +raw ler +Ġcar p +Ġ27 9 +ãĤ· ãĥ£ +rel ations +Ġcontr asts +Col umn +Ġrec onnaissance +Ġun know +Ġl ooting +Ġregul ates +Ġopt imum +ĠChero kee +ĠA ry +Lat est +Ġroad side +Ġd anced +ĠUnic orn +A cknowled +Ġuncont roll +ĠM US +at io +ch ance +ha ven +VAL UE +Ġfavour ites +Ġceremon ial +b inary +pe ed +wood s +EM P +Ġv ascular +Ġcontempl ated +Ġbar ren +ĠL IST +Y ellow +ospons ors +Ġwhisk y +ĠM amm +ĠDeV os +min imum +H ung +44 2 +P ic +ĠSnap dragon +77 6 +Ġcar ving +Ġund ecided +Ġadvantage ous +Ġpal ms +ĠA Q +Ġst arch +L oop +Ġpadd le +Ġfl aming +ĠHor izons +An imation +bo ost +Ġprob abilities +ĠM ish +Ġex odus +ĠEditor ial +Ġfung us +Ġdissent ing +ĠDel icious +rog ram +ĠD yn +d isk +t om +Ġfab rics +ĠC ove +ĠB ans +Ġsoft en +ĠCON S +Ġin eligible +Ġestim ating +ĠLex ington +pract ice +of i +Ġshe dding +ĠN ope +Ġbreat hed +ĠCorinth ians +y ne +ek i +B ull +Ġatt aching +reens hots +Ġanaly se +ĠK appa +Ġuns ustainable +Ġinter pol +ank y +he mer +Ġprot agonists +Ġform atted +ĠBry ce +ĠAch illes +ĠAb edin +sh ock +Ġb um +b os +qu a +ĠW arn +q t +ĠDi abetes +8 64 +ĠIn visible +Ġvan ish +Ġtrans mitting +Ġmur ky +ĠFe i +Ġawa ited +ĠJur assic +umm ies +Ġmen acing +g all +C ath +B uilt +ild o +ĠV otes +Ġon t +Ġmun itions +ĠFre em +ÃŃ n +Ġdec ency +lo pp +ie ved +ĠG ord +Ġun thinkable +ĠNews week +Ġ3 21 +He at +Ġpresent er +ji ang +Ġpl ank +ĠAval on +Ġben z +ĠR out +Ġslam ming +ĠD ai +ou ter +ĠCook ie +ĠAlic ia +ge y +Ġvan ity +Ġow l +á µ +t ested +ĠAw akens +Ġcan v +Ġblind ly +ĠRid ley +ĠEm ails +Requ ires +ĠSer bian +ograp hed +if rame +eter ia +Ġaltern ating +qu iet +Ġsoc iology +ĠUn lock +ĠCommun ism +Ġo ps +Ġatt ribution +Ġab duction +ĠAb ram +Ġsidel ined +ĠB OOK +Ġref ining +ĠFe eling +ĠOs lo +ĠPru itt +r ack +ang ible +Ġcaut iously +ĠM ARK +eed s +M ouse +ĠStep h +ĠP air +S ab +99 7 +ĠBa al +B ec +Ġcomm a +ĠP all +ĠG ael +Ġmisunder stand +ĠP esh +Order able +Ġdis mal +ĠSh iny +% " +Ġreal istically +Ġpat io +ĠG w +ĠVirt ue +Ġexhaust ing +wh atever +oph ys +y ip +4 18 +Ad just +ĠWa iting +ess on +ĠMaz da +ĠDo zens +Ġstream lined +Ġincompet ence +ĠM eth +Ġeth os +ON ES +Ġincent iv +Ġgr itty +ĠBut cher +Head er +Ġexp onential +à Ł +Ġcorrel ate +Ġcons ensual +s ounding +R ing +Orig in +Ġcon clusive +fe et +ac ly +ĠF ernandez +Buy able +Ġd ucks +aunt lets +Ġel ong +Ġ28 6 +Ġsim ul +G as +ĠK irst +Ġprot r +ĠRob o +ĠAo E +op ol +Ġpsych ologically +sp in +ilater ally +ĠCon rad +W ave +44 1 +ĠAd vertisement +ĠHarm on +ĠOri ental +is Special +Ġpresum ptive +Ġw il +ĠK ier +ne a +Ġp pm +Ġhar bour +ĠW ired +comp any +Ġcor oner +atur days +ĠP roud +ĠN EXT +ĠFl ake +val ued +ce iver +Ġfra ught +Ġc asing +Ġrun away +Ġg in +ĠLaure nt +ĠHar lem +ĠCur iosity +qu ished +Ġneuro science +ĠH ulu +Ġborrow er +Ġpetition er +ĠCo oldown +W ARD +Ġinv oking +conf idence +For ward +Ġst s +pop ulation +Delivery Date +Fil m +ĠC ov +quick Ship +quickShip Available +prim ary +isSpecial Orderable +inventory Quantity +channel Availability +BO X +ĠMulti player +ĠJen ner +77 8 +ĠM d +Ġ~ /. +M N +Ġchild ish +Ġantioxid ant +ĠChrom ebook +Ġ27 4 +Ġscreen play +Ġadvent urous +ĠRelations hip +respons ive +ming ton +Ġcorner stone +ĠF ey +F IR +Ġrook ies +ĠF eaturing +Ġorig inate +Ġelectro des +ant es +Ġscript ures +Ġgl ued +Ġdiscont ent +Ġaff licted +lay out +B rave +Ġm osa +ĠQuant ity +ĠH ik +w inner +H ours +Ġent ail +ĠCell s +olog ue +Ġv il +Ġpre acher +Ġdecor ative +d ifferent +Ġprejud ices +ĠSm oking +ĠNotting ham +so Type +Ġrhyth ms +ĠAl ph +bl ast +Ste el +ĠDaniel le +Ġstr ife +Ġrem atch +so DeliveryDate +ĠF ork +t rip +ol ulu +hes es +C G +ĠPOLIT ICO +ost a +ĠDr ift +é¾įå ¥ +é¾įå¥ ij士 +Ġvet ting +ĠJin ping +ĠRec ession +Min or +ĠF raud +enf ranch +Ġconven ed +ĠNA ACP +ĠMill ions +ĠFarm ing +ĠW oo +ĠFl are +rit o +imm igrant +Ġvac ancy +ĠHE AD +ĠV aj +eg al +ĠV igil +Stud y +Ġru ining +Ġr acks +Ġhe ater +ĠRand olph +ĠBr ush +ĠT ir +Ø ¨ +Ġc ov +% ] +Ġrecount s +ĠO PT +ĠM elt +Ġtr uce +Ġcas inos +Ġcrus ade +Ġcarn age +Ġstri pe +ĠK yl +Text ures +Ġ6 98 +Ġpro clamation +Ġgood ies +Ġ........ .. +pro claimed +P olit +Ġtop ical +Ġspecial ize +ĠA min +g m +Ġanch ored +Ġbear ings +s ample +ĠHigh land +ĠAut ism +Ġmerc enary +Ġinterview er +L ER +ĠSom ers +Ġembry o +ĠAss y +Ġ28 1 +ĠEd iting +ĠCh osen +6 60 +Ġp ci +ĠThunder bolt +BI LL +Ġchuck led +jri wal +h of +Ġearth ly +() { +ind ependence +Ġdisp ers +ĠV endor +ĠG areth +Ġp als +P enn +ĠSub mit +ic um +Th u +Ġcl andestine +Ġcann ibal +ĠCl erk +E Stream +gal itarian +âĻ ¥ +g ew +Ġhor rend +ĠL ov +ĠRe action +ocr in +Class ic +Ġecho ing +Ġdiscl osing +ĠIns ight +og un +ĠInc arn +upload s +pp erc +guy en +Ġ19 01 +ĠB ars +68 7 +Ġb ribes +ĠFres no +ur at +ĠRe ese +Ġintr usive +Ġgri pping +ĠBlue print +ĠR asm +un ia +man aged +ĠHeb do +Ġ3 45 +Ġdec oding +Ġpo ets +Ġj aws +ĠF IGHT +am eless +ĠMead ows +ĠHar baugh +Inter view +ĠH osp +ĠB RA +Ġdelet ion +m ob +W alker +ĠMoon light +ĠJ ed +ĠSoph ia +Ġus ur +Ġfortun ately +ĠPut ting +ĠF old +Ġsan itation +Ġpart isans +IS ON +B ow +ĠCON C +ĠRed uced +ĠS utton +Ġtouch screen +Ġembry os +âĢ¢âĢ¢ âĢ¢âĢ¢ +ĠK rug +com bat +ĠPet roleum +Ġam d +ĠCos mos +Ġpresc ribing +Ġconform ity +ours es +Ġplent iful +Ġdis illusion +ĠEc ology +itt al +Ġf anc +Ġassass inated +regn ancy +Ġperenn ial +ĠBul lets +Ġst ale +Ġc ached +ĠJud ith +ĠDise ases +All en +Ġl as +Ġsh ards +ĠSu arez +ĠFriend ship +inter face +ĠSupp orters +add ons +46 2 +ĠIm ran +ĠW im +Ġnew found +ĠM b +An imal +Ġd arling +and e +Ġrh y +ĠTw isted +pos al +yn ski +Var ious +× ľ +ĠK iw +uy omi +Ġwell being +ĠL au +an os +Ġunm ist +Ġmac OS +Ġrest room +ĠOl iv +ĠAir ways +Ġtimet able +9 80 +Ġrad ios +v oy +ias co +Ġcloud y +ĠDraw ing +Any thing +Sy ria +ĠH ert +st aking +Ġun checked +Ġb razen +ĠN RS +69 7 +onom ic +est ablish +Ġl eng +Ġdi agonal +ĠF ior +L air +ĠSt ard +Ġdef icient +jo ining +be am +Ġomn ip +Ġbl ender +Ġsun rise +Mo ore +ĠF ault +ĠCost ume +ĠM ub +Fl ags +an se +Ġpay out +ĠGovern ors +ĠD illon +ĠBan ana +N ar +Ġtra iled +Ġimperial ist +um ann +ats uki +4 35 +ĠRoad s +Ġsl ur +ĠIde ally +Ġt renches +C trl +Ġmir rored +ĠZ el +ĠC rest +Comp at +ĠRoll s +sc rib +ĠTra ils +omet ers +w inter +Ġimm ortality +il ated +Ġcontrad icts +un iversal +ill ions +ĠM ama +opt im +AT URE +Ġge o +et ter +ĠCar lo +4 24 +Ġcanon ical +ĠStrongh old +n ear +Ġperf ume +Ġorche stra +od iac +Ġup he +Ġreign ing +vers ive +Ġc aucuses +ĠD EM +Ġinsult ed +Ġ---- -- +ĠCr ush +Ġroot ing +ĠWra ith +Ġwh ore +Ġto fu +C md +ĠB ree +Ġ$ _ +Ġr ive +ĠAd vertising +Ġw att +ĠH O +Ġpersu asive +ĠParam eters +Ġobserv ational +ĠN CT +ĠMo j +ĠSal on +Ġtr unc +Ġexqu isite +ĠMar a +Ġpo op +ĠAN N +Ex c +ĠWonder ful +ĠT aco +Ġhome owner +ĠSmith sonian +orpor ated +mm mm +Ġlo af +ĠYam ato +ĠInd o +Ġcl inging +á s +Ġimm utable +h ub +Or ange +Ġfingert ips +ĠWood en +ĠK idd +ĠJ PM +ĠDam n +C ow +c odes +48 2 +Ġiniti ating +ĠEl k +ĠCut ting +Ġabsent ee +ĠV ance +ĠLil ith +G UI +Ġobsc ured +Ġdwar ves +ĠCh op +ĠB oko +Val ues +Ġmult imedia +Ġbrew ed +Reg ular +CRIP TION +ĠMort al +Ġa pex +Ġtravel er +Ġbo ils +Ġspray ing +Rep resent +ĠStars hip +4 28 +Ġdisappro val +Ġshadow y +Ġlament ed +ĠRe place +ĠFran ç +67 7 +d or +Ġunst oppable +Ġcoh orts +gy n +ĠClass ics +ĠAm ph +Ġsl uggish +ĠAdd iction +ĠPad res +Ġins cription +Ġin human +min us +ĠJere miah +at ars +Ter ror +ĠT os +ĠSh arma +ast a +c atch +Ġpl umbing +ĠTim bers +Sh ar +H al +ĠO sc +Ġcou pling +hum ans +Ġsp onge +Ġid ols +ĠSp a +ĠAdv ocate +ĠBe ats +lu a +Ġtick ing +Ġload er +ĠG ron +8 10 +Ġstim ulated +Ġside bar +ĠManufact urer +ore And +19 73 +Ġpra ises +ĠFl ores +dis able +ĠElect rical +ra ise +E th +Ġmigr ated +Ġlect urer +K ids +ĠCa vern +Ġk ettle +Ġgly c +ĠMand ela +ĠF ully +å§ « +FIN EST +Ġsquee zing +ĠRy der +amp oo +oreAnd Online +Inst oreAndOnline +Buyable InstoreAndOnline +Ġcommem orate +ĠRamp age +Aust in +ĠSh roud +ĠRu ins +9 15 +ĠK H +Ġwater front +ĠE SC +b aby +ĠC out +ĠEm blem +Ġequival ents +49 2 +Un ique +ĠNiet zsche +brow ser +Ġim itation +ĠWere wolf +ĠKir in +ac as +' ," +Ġà ¾ +Review ed +Ġc unt +Ġvo ic +ĠLen ovo +Ġbond ed +48 1 +Ġinhib itors +Ġendeav ors +ĠHav ana +ĠSt out +ĠJ olly +A ctor +*/ ( +Ġoccur rences +ĠT ens +Incre ased +ĠACT ION +Ġ ãĢĮ +ĠRank ings +ĠB reat +Ġ30 9 +D ou +Ġimpact ing +ĠDuc hess +pre fix +Q B +Ġsummon ing +Ġbest owed +ĠKe pler +ĠPOW ER +c ube +ĠK its +ĠG rip +Ġop ium +Ġrep utable +t oc +ich ael +ĠR ipple +Ġcaf é +ĠZ oom +ĠBur ma +Ġwa ive +Ġst alls +Ġdem eanor +inc erity +Ġfluor ide +ĠSH OULD +Par is +Ġlong ing +Ġpl at +Ġgross ly +Ġbull s +Ġshowc asing +ex pected +ĠG addafi +engine ering +Re peat +ĠK ut +Ġconce ivable +Ġtrim med +osc ope +ĠCand idate +ĠT ears +rol og +Lew is +S UP +Ġroad map +Ġsal iva +Ġtrump et +Jim my +Ġmirac ulous +Ġcolon ization +Ġam put +ĠGN OME +ate ch +D ifferent +ĠE LE +ĠGovern ments +ĠA head +ãħĭ ãħĭ +word press +L IB +ĠIn clude +ĠDor othy +0 45 +ĠColomb ian +Ġle ased +88 4 +Ġde grading +ĠDa isy +i ations +Ġbapt ized +Ġsurn ame +co x +Ġblink ed +ãĥ ¢ +Ġpoll en +Ġder mat +Ġre gex +ĠNich olson +ĠE ater +ç ľ +rad or +Ġnarrow er +Ġhur ricanes +Ġhalluc inations +r idden +ISS ION +ĠFire fly +Ġattain ment +Ġnom inate +Ġav ocado +ĠM eredith +Ġt s +Ġreve rence +Ġe uph +Ġcr ates +ĠT EXT +Ġ4 43 +Ġ3 19 +J SON +iqu ette +Ġshort stop +ic key +Ġpro pelled +Ġap i +ĠTh ieves +77 9 +Ġovers aw +Ġcol i +ĠNic ola +Ġover cl +ik awa +ĠC yr +Ġ38 4 +78 9 +ĠAll ows +10 27 +Det roit +TR Y +set up +ĠSocial ism +Sov iet +s usp +ĠAP R +ĠShut down +Ġal uminium +zb ek +ĠL over +GGGG GGGG +Ġdemocr acies +Ġ19 08 +ĠMer rill +ĠFranco is +gd ala +Ġtraff ickers +ĠT il +ĠGo at +Ġsp ed +ĠRes erv +Ġpro d +55 2 +Ġc ac +ĠUn iv +ĠSch we +Ġsw irling +ĠWild erness +ĠEgg s +Ġsadd ened +Ġarch aic +H yd +Ġexcess ively +B RE +Ġaer ospace +ĠVo ices +Cra ig +Ġign ited +In itially +ĠMc A +Ġhand set +Ġreform ing +Ġfrust rations +ĠDead pool +ĠBel ichick +ract or +ĠRagnar ok +ĠD rupal +ĠApp roximately +19 20 +ĠHub ble +arm or +ĠSar as +ĠJon as +Ġnostalg ic +Ġfeas ibility +Sah aran +Ġorb iting +Ġ9 70 +R u +Ġsh in +ĠInvestig ators +Ġinconsist encies +ĠP AN +B G +Ġgraz ing +Ġdetect ors +ĠStart up +ĠFun ny +ĠNa omi +Consider ing +Ġh og +ut f +ce mic +Ġfort ified +ĠFun ctions +Ġcod ec +nut rition +H at +" ! +micro soft +55 8 +ĠTh in +ĠA CE +Al ias +ĠO PS +p apers +P K +ãĢ İ +Ġimpro bable +N orthern +equ al +Ġlook out +Ġty res +ĠMod ified +ĠK op +Abs olutely +Ġbuild up +sil ver +Ġaud i +Ġgro tesque +ĠSab er +ĠPres byter +ON Y +Ġglac iers +ĠSho als +ĠK ass +ĠH RC +ĠNic ol +ĠL unch +ĠF oss +âĸ Ĵ +AD RA +ĠOne Plus +o ing +ground s +Ġincident al +Ġdatas ets +68 9 +ĠClarks on +Ġassemb ling +ĠCorrect ions +Ġdrink ers +Ġqual ifiers +Ġle ash +Ġunf ounded +ĠH undred +Ġkick off +T i +Ġrecon cil +ĠGr ants +ĠCompl iance +ĠDexter ity +Ġ19 06 +w arn +D allas +Max imum +n ard +av ia +be aut +ens itivity +tr ace +Ġpione ers +ĠF ract +ãĢ ı +Ġpre cept +Ġgloss y +ĠI EEE +Ac ross +Ġ6 80 +S leep +che on +Ġsatir ical +ĠMin otaur +ĠCla ude +Ġr é +ape go +Ġcar rot +ĠSem in +ino a +Ġz o +Ind ependent +Ġdiagn oses +ĠC ue +M AR +Ġrend ition +ĠK ik +Ġpath ology +Ġselect s +Link edIn +Ġass ay +ĠD res +Ġtext ual +post ed +IT AL +ĠM aul +N eal +Ġinter connected +Ġerr atic +ĠVir us +Ġ5 30 +Ġenvironmental ists +ĠP helps +Ġeng agements +ĠIN ST +Ġeconom ical +nox ious +Ġg earing +izz y +Ġfavor ably +ĠMcG ill +T erm +Ġh anged +Ġball park +ĠRe yes +Ġbe ware +ĠP sal +ĠMass acre +q i +Ġin accessible +acly sm +Ġfr ay +ill ac +Ġbitter ly +ĠCert ification +Mich igan +Ġir respective +al ore +Em pty +Ġendorse ments +Ġund et +f g +equ ipped +Ġmerc iless +ĠC ust +Ġimm ature +Ġvou cher +ĠBlack well +Ñ ı +h awk +dis ciplinary +ile e +ĠMak oto +ĠD ude +ãĥĩ ãĤ£ +Y ears +Ġin ver +Ġsh aman +ĠY ong +ip el +ell en +ĠCath y +br ids +Ġs arc +65 1 +N ear +Ġground work +Ġam az +Ġ4 15 +ĠHunting ton +hew s +ĠB ung +Ġarbit rarily +ĠW it +ĠAl berto +Ġdis qualified +best os +46 1 +Ġp c +Ġ28 4 +ro bat +Rob in +Ġh ugs +ĠTrans ition +ĠOcc asionally +Ġ3 26 +ĠWh ilst +ĠLe y +Ġspaces hip +cs v +Ġun successfully +ĠA u +le ck +ĠWing ed +ĠGrizz lies +. � +Ġne arer +ĠSorce ress +ĠInd igo +El se +8 40 +let es +Co ach +Ġup bringing +ĠK es +Ġseparat ist +Ġrac ists +Ġch ained +Ġabst inence +lear ning +Ġrein stated +Ġsymm etry +Ġremind ers +ĠChe vy +Ġm ont +Ġexempl ary +ĠT OR +Z X +Ġqual itative +ĠSt amp +ĠSav annah +ĠRoss i +Ġp aed +Ġdispens aries +ĠWall s +ĠCh ronic +Ġcompliment ary +ĠBeir ut +Ġ+ --- +igs list +Ġcrypt ographic +mas ters +ĠCap itals +Ġmax imal +Ġent ropy +Point s +Ġcombat ants +l ip +ĠGl ob +ĠB MC +ph ase +th ank +HT TP +Ġcomm uter +Ġ\( \ +.. / +ĠReg ener +ĠDO I +ĠActiv ision +Ġsl it +os al +RE M +Ġch ants +Y u +Ke ys +Bre xit +ĠFor ced +Ari zona +Ġsquad ron +IS O +ĠMal one +Ġ3 38 +Ġcontrast ing +Ġt idal +Ġlib el +Ġimpl anted +Ġupro ar +ĠC ater +Ġpropos itions +M anchester +ĠEuro s +it amin +G il +ĠEl ven +ĠSe ek +ĠB ai +Ġredevelop ment +ĠTown s +ĠL ub +! ", +al on +K rist +Ġmeas urable +Ġimagin able +Ġapost les +Y N +7 60 +Ġster oid +Ġspecific ity +ĠL ocated +ĠBeck er +ĠE du +ĠDiet ary +uts ch +ĠMar ilyn +Ġbl ister +ĠM EP +ĠK oz +ĠC MS +y ahoo +ĠCar ney +Ġbo asting +ĠC aleb +By te +read s +ad en +Pro blem +ĠWood ward +S we +S up +ĠK GB +Set up +Ġtac it +Ġret ribution +Ġd ues +ĠM ü +. ? +ä¸ Ń +p ots +Ġcame o +ĠP AL +educ ation +A my +like ly +g ling +Ġconstitution ally +ĠHam m +ĠSpe ak +Ġwid gets +br ate +Ġcra ppy +ĠI ter +Ġanticip ating +ĠB out +P ixel +ĠY ep +ĠLaur ie +Ġh ut +Ġbullet in +ĠSal vation +Ġch ats +ear able +Honest ly +AL TH +onse qu +c ult +isco very +ovy ch +Ġse lves +ĠSat oshi +S ounds +Ġconver gence +ĠRosen berg +19 74 +Ġnas al +Ġfull est +Ġfer ocious +x us +ist e +AM S +Ġlobb ied +Ġso othing +ĠGun n +t oday +0 24 +Ġinspir ational +ĠN BN +p b +g ewater +or ah +all owed +ĠCol iseum +Ġspecial izing +Ġinsane ly +ĠT ape +del ay +Ġt arn +ĠP ound +Ġmel anch +Ġdeploy ments +il and +Ġless en +Ġfur ry +ĠUE FA +Ġblood shed +ĠMe ier +ither ing +Ġhe irs +ĠJ aw +ax ter +ĠPublic ations +Ġal ters +int ention +ĠWinc hester +d etermination +ĠLif etime +th in +Mon ster +7 80 +Ġapprox imation +Ġsuper markets +ĠSecond s +or os +h uge +Ġb ribe +ĠLIM ITED +un ed +Ġmis interpret +ĠIn jury +Ġ3 67 +Ġthreshold s +ĠCarn ival +Ġgastro intestinal +Ġguid eline +Ġde ceived +f eatures +Ġpurported ly +ĠRon nie +ĠNew t +Ġsp acious +as us +Ġsuperhero es +ĠCyn thia +le gged +k amp +ch io +Ġth umbnail +ĠShir ley +ill ation +Ġshe ds +ĠZ y +E PA +Ġdam s +Ġy awn +n ah +ĠPe ggy +ĠE rie +ĠJu ventus +ĠF ountain +r x +don ald +al bum +ĠComp rehensive +Ġc aching +ĠU z +ulner ability +ĠPrinc iple +ĠJ ian +ing ers +cast s +ĠOs iris +ch art +t ile +ĠTiff any +ĠPatt on +ĠWh ip +Ġovers ized +J e +ĠCind erella +ĠB orders +ĠDa esh +M ah +Ġdog ma +Ġcommun ists +v u +Coun cil +Ġfresh water +Ġw ounding +Ġdeb acle +Ġyoung ster +Ġthread ed +ĠB ots +ĠSav ings +ãģ Ĥ +ol ing +oh o +Ġillum ination +M RI +Ġlo osen +tr ump +ag ency +ur ion +Ġmoment arily +ĠCh un +ĠBud apest +ĠAl ley +D isk +Ġaston ished +ĠCon quer +ĠAccount ing +h aving +ĠWe in +ĠAl right +Ġrev olver +Ġdel usion +Ġrelic s +Ġad herent +qu ant +Ġhand made +or io +Ġcomb ating +c oded +Ġquad ru +re th +N ik +ĠTrib al +ĠMyster ious +Ġin hal +ĠWin ning +ĠClass ification +ch anged +Ġun ab +Ġsc orn +icip ated +w l +ond uctor +Ġrein forcing +ĠChild hood +an ova +Ġadventure r +Ġdoctor al +ĠStrateg ies +Ġengulf ed +ĠEnc ounter +Ġl ashes +Crit ical +ric ular +ĠU TF +oci ation +check ing +ĠConsult ing +Run time +per iod +ĠAs gard +Ġdist illed +ĠPas adena +ĠD ying +ĠCOUN TY +Ġgran ite +Ġsm ack +Ġparach ute +ĠS UR +Virgin ia +ĠF urious +78 7 +ĠO kin +Ġcam el +ĠM bps +19 72 +ĠCh ao +ĠC yan +j oice +ef er +ĠW rap +ĠDeb ate +S eg +Ġfore arm +ĠIgn ore +Ġtim estamp +Ġprob ing +ĠNo on +ĠGra il +f en +Ġdorm ant +ĠFirst ly +ĠE ighth +ĠH UN +ĠDes ire +or as +Girl s +ĠDes mond +z ar +am ines +O AD +exec ute +Ġbo obs +ĠAT L +_ ( +Chel sea +Ġmasturb ation +ĠCo C +Ġdestroy er +ĠCh omsky +Ġsc atter +ĠAss ets +79 6 +ĠC argo +Ġrecept ive +ĠSc ope +Ġmarket ers +Ġlaun chers +Ġax le +ĠSE A +se q +ĠM off +f inding +ĠGib bs +Georg ia +extreme ly +N J +Ġlab orers +st als +Ġmed iation +ĠH edge +at own +Ġi od +des pite +v ill +J ane +ex istence +Ġcoinc ided +ĠUt ilities +ĠChe ap +Ġlog istical +Ġcul mination +ĠNic otine +p ak +F older +Ġrod ents +st uff +Ġlaw fully +Ġreper to +io ch +j j +Dial ogue +HH HH +lic tion +Look s +Ġ29 7 +Ġtur rets +ĠAb andon +Ġinc ess +ĠTraff ord +Ġcur led +Ġprefer ring +Ġprivat ization +Ġir resist +ĠP anda +ĠSh ake +ĠMc Gr +ãĥ Ħ +und ers +Ġdiscrim inated +Ġbart ender +I LE +Atl antic +Ġprop ensity +ĠW iz +ĠG im +con ference +Ġrein forces +G h +w agon +Ġe erie +F al +Ġhug ged +rac ist +R IC +F u +Ġf iller +ĠSt ub +Ġeng raved +ĠWrest le +Ġimagin ative +ĠPe er +ĠFact ors +an us +ĠDrac ula +mon itor +Ġrou ters +ib ia +ĠBoo lean +end ale +ĠSl aughter +ĠSh ack +R FC +ĠSpiel berg +S ax +ĠPH OTO +ĠCl over +ĠR ae +Dep ending +ĠMem or +ar am +Ġpier ced +Ġcur tains +v ale +ĠInqu isition +ĠP oke +Ġforecast ing +Ġcompl ains +S ense +ĠHer mes +isc overed +Ġb ible +ĠMor ph +Ġg erm +78 5 +D ON +Ġcon gen +Ġcr ane +ĠD PR +Ġrespect fully +R oom +ĠN aw +ĠDal ai +re ason +ĠAng us +Educ ation +ĠTitan ic +Ë ľ +Ġo val +un ited +Ġthird s +Ġmoist ur +ĠC PC +M iami +Ġtent acles +ĠPol aris +ex c +ex clusive +ĠPra irie +Ġcol ossal +ĠBl end +sur prisingly +ÃŃ s +Ġindo ctr +Ġbas al +ĠMP EG +und o +Spl it +Develop ment +Ġlan tern +19 71 +Ġprov ocation +Ġang uish +ĠB ind +ĠLe ia +duc ers +ipp y +conserv ancy +Ġinitial ize +ĠTw ice +ĠSu k +Ġpred ic +Ġdi ploma +Ġsoc iop +Ing redients +Ġhamm ered +ĠIr ma +Q aida +Ġglim ps +ĠB ian +Ġst acking +Ġf end +gov track +Ġun n +dem ocratic +ig ree +Ġ5 80 +Ġ29 4 +Ġstraw berry +ID ER +Ġcher ished +ĠH ots +Ġinfer red +Ġ8 08 +ĠS ocrates +O regon +ĠR oses +ĠFO IA +Ġins ensitive +Ġ40 8 +Recomm end +ĠSh ine +Ġpain staking +UG E +ĠHell er +ĠEnter prises +I OR +ad j +N RS +L G +Ġalien ated +Ġacknowled gement +ĠA UD +ĠRen eg +Ġvou chers +Ġ9 60 +Ġm oot +ĠDim ensions +Ġc abbage +B right +g at +ĠK lu +Ġlat ent +Ġz e +ĠM eng +Ġdis perse +Ġpand emonium +H Q +Ġvirt uous +ĠLoc ations +ee per +prov ided +Ġse ams +ĠW T +iz o +PR OV +Ġtit anium +Ġrecol lection +Ġcr an +Ġ7 80 +ĠN F +49 1 +64 2 +p acking +59 8 +text ure +Sp ider +fre edom +cipl ed +ĠTAM ADRA +âĻ ¦ +aut hent +ĠW ANT +r ified +Ġr ites +Ġuter us +k iss +Ġâī ¤ +Ġsk illet +Ġdis enfranch +ĠGa al +Comp an +Ġage ing +gu ide +B alt +Ġiter ator +Ġdiscretion ary +t ips +Ġprim ates +ĠTechn ique +ĠPay ments +az el +ĠR OCK +stant ial +0 60 +Ġd mg +ĠJack ets +ĠPlay off +Ġnurs ery +ĠSy mb +art on +Ġannex ation +Color ado +Ġco ils +ĠSh oes +âĦ¢ : +ĠRo z +COM PLE +ĠEve rest +ĠTri umph +J oy +G rid +à ¼ +process or +ĠPros per +ĠSever us +ĠSelect ed +r g +ĠTay yip +St ra +Ġski ing +Ġ? ) +Ġpe g +Tes la +Ġtime frame +Ġmaster mind +ĠN B +scient ific +ĠSh it +gener ic +IN TER +N UM +Ġst roll +ĠEn ix +ĠM MR +ĠE MS +m ovie +Ĥ ª +Ġminim izing +idd ling +Ġilleg itimate +Ġprot otyp +Ġpremature ly +Ġmanual s +obb ies +ĠCass idy +D EC +des ktop +Ġaer os +Ġscreen ings +Ġdeb ilitating +ĠGr ind +nature conservancy +Ġf ades +ter mination +assets adobe +F actor +Ġdefinitive ly +P oké +ap ult +ĠLaf ayette +C orn +ĠCor al +Ġstagn ant +T ue +Ġdissatisf action +G ender +Ġkid neys +ĠG ow +ĠDef eat +ĠAsh ton +Ġcart els +Ġfore closure +ĠExpl ore +stre ngth +ot in +Ġveterin arian +Ġf umble +Ġpar ap +ĠSt rait +r ils +Ġpr ick +ĠBerm uda +ĠAm munition +skin ned +Ġab ound +ĠB raz +Ġshar per +ĠAsc ension +Ġ9 78 +Ġpreview s +Ġcommun ion +ĠX Y +Ġph ony +Ġnewcom er +Ġ3 32 +." ," +Ġredist ribution +Prot ect +ĠSo f +K al +Ġlip stick +w orst +Ġtang led +Ġretrospect ive +int eger +Ġvolunte ering +Ġ19 07 +Ġ -------------------- +ic hen +Ġunve iling +Ġsen seless +Ġfisher ies +\ - +Ġh inges +Ġcalcul us +My th +Ġund efeated +Ġoptim izations +Ġdep ress +Ġbill board +ĠY ad +ĠPy ramid +Is n +I de +Ġleg ion +ĠK ramer +ent anyl +Ġpenet rating +ĠHaw th +ĠPR ODUCT +ĠGer ard +ĠP act +ĠIn cluding +ĠEl ias +ĠEl aine +vis ual +Ġhum ming +Ġcond esc +ĠF asc +ä¸ Ĭ +Ġe galitarian +Ġdev s +ĠD ahl +O ps +D H +ĠB ounce +id ated +ald o +Ġrepublic an +Ġh amb +ĠS ett +ograph ies +CH APTER +Ġtrans sexual +Ġsky rocket +ans wer +Ġmark up +Ø ª +Ġhero ine +Comp are +ĠT av +Be ast +Ġsuccess ors +Ġna ïve +ĠBuck ley +st ress +me at +Ġdownload able +Ġindex ed +Ġsc aff +ĠL ump +ĠHom o +Stud io +In sp +Ġr acked +far ious +ĠPet ty +Ex ternal +Ġ19 09 +W ars +com mit +put ers +Ġun ob +ĠEr r +ĠE G +ĠAl am +ĠSiber ia +ĠAtmosp heric +IS TER +ĠSatan ic +trans lation +ĠL oud +tra umatic +l ique +Ġreson ate +ĠWel ch +Ġspark ing +ĠT OM +t one +Ġout l +Ġhandc uffed +ĠSer ie +8 01 +Ġland marks +ĠRee ves +Ġsoft ened +Ġdazz ling +ĠW anted +month s +Mag ikarp +Ġunt reated +ĠBed ford +M i +ĠDynam o +O re +79 5 +Ġwrong ful +Ġl ured +Ġcort isol +Ġve x +d rawn +ile t +Download ha +ĠF action +Ġlab yrinth +Ġhij acked +w aters +er ick +Ġsuper iors +ĠRow ling +ĠGu inness +Ġt d +99 2 +Ġune arthed +Ġcentr if +Ġsham eless +P od +ĠF ib +Ġ icing +Ġpredict or +Ġ29 2 +fore station +con struct +C and +@ # +Ġag itated +Ġre pr +OV A +Ġkn itting +ĠLim a +Ġf odder +68 4 +ĠPerson a +k l +7 01 +Ġbreak up +á ¸ +Ġapp alled +Ġantidepress ants +ĠSus sex +Har ris +ĠTher mal +ee ee +U pload +Ġg ulf +Ġdoor step +ĠSh ank +L U +ĠM EN +ĠP ond +s orry +Ġmis fortune +n ance +Ġb ona +M ut +Ġde graded +ĠL OG +ĠN ess +an imal +Ġa version +und own +Ġsupplement ed +ĠC ups +Ġ50 4 +Ġdep rive +ĠSpark le +Å Ĥ +ĠMed itation +auth ors +ĠSab an +ĠN aked +air d +ĠMand arin +ĠScript ures +ĠPerson nel +ĠMahar ashtra +Ġ19 03 +ĠP ai +ĠMir age +omb at +Access ory +Ġfrag mented +T ogether +Ġbelie vable +ĠGl adiator +al igned +ĠSl ug +M AT +Ġconvert ible +ĠBour bon +amer on +ĠRe hab +nt ax +Ġpowd ered +pill ar +Ġsm oker +ĠMans on +ĠB F +5 11 +ĠGood ell +ĠD AR +m ud +g art +Ġob edient +ĠTrans mission +ĠDon ation +8 80 +Ġbother ing +Material s +ãĤ ± +dest roy +Ġfore going +Ġanarch ism +ĠK ry +ice ps +Ġl ittered +ĠSch iff +Ġanecd otal +un its +Ġf ian +ĠSt im +ĠS OME +ĠInv aders +Ġbehaviour al +ĠVent ures +Ġsub lime +Ġfru ition +ĠPen alty +Ġcorros ion +¶ ħ +Ġlik ened +Ġbesie ged +ween ey +ĠCre ep +Ġlinem en +mult i +ic ably +ud der +Ġvital ity +Ġshort fall +ĠP ants +ap ist +H idden +ĠDro ps +med ical +Ġpron unciation +ĠN RL +Ġinsight ful +J V +ĠBe ard +ĠCh ou +Ġchar ms +Ġb ins +Ġamb assadors +ĠS aturdays +Ġinhib itor +ĠFr anch +6 01 +', ' +ĠCon or +art ney +ĠX peria +g rave +be es +ĠProtest ants +Ġso aking +ĠM andal +Ġph ased +Ġ6 60 +Ġsc ams +Ġbuzz ing +ĠItal ians +ĠLoren zo +ĠJ A +Ġhes itated +Ġcl iffs +ĠG OT +ingu ishable +Ġk o +Ġinter ruption +Z ip +Lear ning +Ġundersc ores +ĠBl ink +K u +57 9 +ĠAut ob +I RE +Ġwater ing +Ġpast ry +8 20 +Ġvision ary +ĠTempl ar +awa ited +Ġpist on +Ġant id +current ly +Ġp ard +Ġw aging +Ġnob ility +ĠY us +Ġinject ing +f aith +ĠP ASS +å º +Ġret ake +ĠPR OC +Ġcat hedral +b ash +Ġwrest lers +Ġpartner ing +Ġn oses +Ġ3 58 +Trans form +am en +Ġb outs +ĠId eal +ĠConstant in +Ġse p +ĠMon arch +att en +ĠPe oples +mod ified +Ġmor atorium +Ġpen chant +Ġoffensive ly +Ġprox ies +ok ane +ĠTaiwan ese +ĠP oo +ĠH OME +us ional +Ġver bs +ĠO man +vis ory +Ġpersu asion +Ġmult it +Ġsc issors +G ay +ow ay +oph ysical +l us +gn u +Ġap ocalyptic +Ġabsurd ity +Ġplay book +Ġautobi ography +I UM +Ġsne aking +ĠSim ulation +pp s +ell ery +Plan et +Ġright fully +Ġn iece +ĠN EC +ĠIP O +ĠDis closure +lean or +ous y +ST ER +Ġ28 2 +Cru z +Ch all +64 3 +ĠSurv ive +ĠF atal +ĠAm id +ap o +We apons +D EN +7 70 +ĠGreen wald +Ġlin en +al os +Ġpollut ants +ĠPCI e +k at +Ġp aw +ĠK raft +C hem +ĠTermin ator +Ġre incarn +Ġ] [ +ĠSe eds +Ġsilhou ette +ĠSt ores +Ġgro oming +ĠD irection +ĠIs abel +ĠBr idges +ðŁ ij +E ED +ĠM orsi +Ġval ves +ĠRank ed +ĠPh arma +ĠOrgan izations +Ġpenet rated +ĠRod ham +ĠProt oss +Ġove rest +Ġex asper +ĠT J +Ġ 000000 +Ġtrick le +Ġbour bon +WH O +Ġw retched +Ġmicrosc opic +Ġcheck list +Ġad orned +R oyal +Ad minist +ĠRet irement +ĠHig hest +We ather +ile ge +Ġincre ments +ĠC osponsors +Ġmas se +ĠS inn +r f +Ġh ordes +as sembly +75 4 +ĠNat asha +ĠTY PE +ĠGEN ERAL +Ġarr anging +Ġ40 7 +l ator +Ġg lean +Ġdisc redited +Ġclin icians +UN E +Ġachie ves +ĠEm erson +com plex += [ +Ġprincip ally +Ġfra il +p icked +Ġthan king +Ġre cl +ĠL AST +Ġsupp ressing +il ic +Ġantidepress ant +ĠLis bon +Ġth or +Ġsp a +Ġking doms +ĠPear ce +em o +Ġpl ung +Ġdiv est +Ġ ******************************** +b is +osp els +ad r +Sp irit +hall a +P ink +end ez +Ġresurrect ed +esc ape +ĠRosen stein +Ġge ological +Ġnecess ities +Ġcarn iv +ĠE lys +ĠBar ney +Ġ29 6 +dig y +ST ON +D OWN +Ġmil estones +Ġk er +Ġdismant ling +Ġre prim +Ġcross ings +19 45 +Ġpatri archy +Ġblasp hemy +Ġ3 59 +met ry +ĠOb esity +ĠDiff erences +bl ocking +ãĥķ ãĤ¡ +ich ita +ĠSab ha +ph alt +ĠCol o +ual a +effic ients +ĠMed ina +con sole +55 7 +ĠHann ibal +ĠHab it +ĠF ever +Ġthen ce +Ġsyn agogue +Ġessential s +Ġw ink +ĠTr ader +ID A +ĠSp oiler +ĠIceland ic +ĠHay ward +Ġpe ac +Ġmal ice +Ġflash back +Ġth w +Ġlay offs +L iquid +Ġtro oper +Ġh inge +ĠRead ers +Ph ill +ĠB auer +Cre ated +Ġaud its +ac compan +Ġunsus pecting +ier a +6666 6666 +Ġbro ch +Ġapprehend ed +ĠM alk +cer ning +ĠCod ex +O VER +M arsh +ĠD eng +ĠExp ression +Ġdisrespect ful +Ġasc ending +t ests +ĠPlaint iff +ster y +ĠAl ibaba +din and +ĠDem psey +Applic ations +mor al +Ġthrough put +Ġquar rel +Ġm ills +Ġhe mor +ĠC ASE +terror ist +st im +ifest yle +ro zen +CE PT +Ar k +u ci +lect ic +Ġirrit ating +she ets +A y +Ġrede emed +Ġhorn y +ĠTe ach +ĠS ear +dem ocracy +4 65 +ĠRest ore +Ġstand by +ĠP is +iff in +Ġsleep y +Ġextr ater +Ġcompl iments +Fram eworks +Ġinstall s +Ġb anging +sur face +found land +Ġmetaph ysical +Ġ28 3 +oul s +dev ices +Ar gs +ĠSac rifice +ĠMcC orm +es on +Cons ervative +ĠM ikhail +see ing +is ively +ĠRo oms +ĠGener ic +Ġenthusi astically +Ġgri pped +Ġcomed ic +ĠElectric ity +Ġgu errilla +Ġdec oration +ĠPerspect ive +Ġconsult ations +Ġun amb +Ġplag iar +Ġmagic ian +Ġe rection +ĠTour ism +or ied +ro xy +11 00 +T am +Ī è +Î ³ +× ª +ĠPred ators +Nit rome +Ġtelesc opes +project s +Ġun protected +Ġst ocked +ĠEnt reprene +nex pected +Ġwast ewater +V ill +Ġint imately +Ġi Cloud +ĠConst able +Ġspo of +Ġne farious +Ġfin s +Ġcens or +ĠMod es +ĠEs per +ar bon +Ġinter sections +Ġlaud ed +Ġphys i +Ġgener ously +ĠThe Nitrome +ĠTheNitrome Fan +Ġar isen +ĠÙ Ī +Ġg lands +ĠPav ilion +ĠGu pta +Ġuniform ly +Ġr amps +ri et +ĠWH EN +ĠVan essa +Ġrout ed +Ġlim p +ĠC PI +p ter +int uitive +Ġv aping +Ġexperiment ed +ĠOlymp us +ĠAm on +Ġsight ing +Ġinfiltr ate +ĠGentle man +Ġsign ings +ĠMe ow +ĠNav igation +che cks +4 33 +Ġel apsed +ĠBulg arian +esp ie +ĠS OM +d uring +Ġsp ills +anc a +ĠPly mouth +M AL +Ġdomest ically +ĠWater gate +ĠF AM +k illed +ed ited +ĠYour self +Ġsynchron ization +ĠPract ices +ST EP +Ġgen omes +ĠQ R +not ice +Ġloc ating +z in +Ġ3 29 +al cohol +Ġk itten +V o +Ġr inse +Ġgrapp le +ĠSc rew +ĠD ul +A IR +Ġle asing +ĠCaf é +Ġro ses +ĠRes pect +Ġmis lead +Ġperfect ed +Ġnud ity +Ġnon partisan +ĠCons umption +Report ing +Ġnu ances +Ġdeduct ible +ĠSh ots +Ġ3 77 +Ġæ ľ +ano oga +Ben ef +ĠB am +ĠS amp +if ix +Ġgal van +ĠMed als +rad ius +Ġno bles +Ġe aves +igr ate +K T +ĠHar bour +u ers +Ġrisk ed +re q +Ġneuro t +get table +ain a +Rom ney +Ġunder pin +Ġlo ft +ĠSub committee +ĠMong ol +b iz +Ġmanif ests +ass isted +ĠG aga +Ġsy nergy +Ġreligious ly +ĠPre f +ĠG erry +T AG +ĠCho i +4 66 +beh ind +ĠO u +Gold Magikarp +Ġhemor rh +R iver +Ġtend on +Ġinj ure +ĠF iona +Ġp ag +Ġag itation +|| || +ur an +ĠE SA +Ġest eem +Ġdod ging +Ġ4 12 +r ss +Ġce ases +ex cluding +Ġint akes +Ġinsert s +Ġemb old +ĠO ral +up uncture +4 11 +ĠUn ified +ĠDe le +Ġfurn ace +ĠCoy otes +ĠBr ach +L abor +Ġhand shake +Ġbru ises +Gr ade +éĹ ĺ +ĠGram my +ile en +St ates +ĠScandinav ian +ĠKard ash +8 66 +Ġeffort lessly +ĠDI RECT +ĠTH EN +ĠMe i +ert ation +19 68 +Ġgro in +w itch +Requ irements +98 5 +Ġroof s +Ġest ates +ĠH F +Ġha ha +Ġdense ly +ĠO CT +Ġpl astics +Ġincident ally +ĠTr acks +ĠTax es +Ġch anted +Ġforce ful +ĠBie ber +ĠK ahn +K ent +ĠC ot +lic ts +F ed +Ġhide ous +ĠVer d +ĠSynd icate +ĠIl legal +J et +ĠD AV +re asonable +c rew +Ġfundamental ist +Ġtruth ful +ĠJ ing +Ġl il +Ġdown ed +Ġen chanted +ĠPolic ies +ĠMcM aster +ĠH are +ides how +Ġpar ams +en cers +gorith m +Ġallow ances +Ġturb ulent +Ġcomplex ities +ĠK T +Ġ3 37 +ĠGen etic +F UN +D oug +t ick +Ġg igs +ument hal +Ġpatriarch al +Ġcal c +, ... +Ġc out +ĠGu an +Ġpath ological +ĠR ivals +Ġunder rated +Ġflu orescent +ĠJ iu +arna ev +ĠQu an +Ġ4 29 +Ġ ਠ+M ario +Con struct +ĠC itation +ĠR acial +ĠR SA +ĠF idel +Ġ3 95 +Person ally +C ause +à » +rad ical +in en +Ġvehement ly +ĠPap a +Ġintern ship +Ġfl akes +ĠRe ck +Luck ily +B ra +20 20 +rav ings +R N +W onder +Ser iously +Ġre usable +Ġpoll uted +ĠP eng +le igh +ind le +Ġcircuit ry +ĠMad onna +ĠB ART +Res idents +att ribute +Phil adelphia +Cl ub +Ġplan ner +Ġfr antically +Ġfaith fully +ĠTerrit ories +ĠL AT +ĠAnders en +an u +ĠP ARK +ĠS ora +i age +ĠPlay offs +ĠG CC +4 27 +Ġab norm +ĠL ever +Ġdisob edience +As ync +ĠShe a +V ert +Ġsk irts +ĠSaw yer +x p +Ġwors ening +Ġsc apego +ĠAng le +oth al +Ġtro ve +ĠSt y +ĠN guyen +mar ine +ide on +Dep ths +Bl og +ĠIll uminati +Ġtract s +Ġorgan ise +Ġo str +F s +Ġlever aging +ĠD aredevil +as ar +Ġl ang +Ġex termin +urs ions +ĠRom o +ãĤ¤ ãĥĪ +Ġcont ended +Ġencounter ing +ĠTable t +ĠAltern ate +sk ill +Ġswe ets +Ġco hesive +cap acity +Ġrep ud +Ġl izard +ro o +Ġpilgr ims +ĠR uff +ĠInstr ument +ĠLog o +uit ous +E H +Ġsales man +Ġank les +L ed +ĠPat ty +ud os +Own er +Ġdiscrep ancies +k j +M U +Ġuncond itional +Dragon Magazine +i ard +O ak +ĠConvers ation +be er +ĠOs aka +D elta +us ky +Ġsecret ion +Ġpl aza +Ġm ing +Ġde pletion +ĠM ous +ĠI TS +ĠH imal +ĠFle ming +Ġcyt ok +ĠH ick +Ġbat ters +ĠInt ellectual +6 75 +é r +IS ION +ĠQu entin +ĠCh apters +ih adi +Ġco aster +WAY S +ĠL izard +ĠY or +and ering +S kin +ha ust +ab by +Ġportray ing +Ġwield ed +d ash +Ġprop onent +Ġr ipple +Ġgrap hene +Ġfly er +Ġrec urrent +Ġdev ils +Ġwater fall +æĺ ¯ +go o +Text Color +Ġtam pering +IV ES +TR UMP +ĠAb el +ĠS AL +ĠHend ricks +ĠLu cius +b ots +Ġ40 96 +IST ORY +Gu est +ĠN X +in ant +Ben z +ĠLoad ed +ĠCle ver +t reatment +Ġta vern +Ġ3 39 +ĠT NT +ific antly +Tem perature +F el +Ġunder world +ĠJud ges +Ġ< + +Ġst ump +Ġoccup ancy +Ġab er +ĠF inder +) ", +ĠN unes +res et +in et +ect omy +Ġwell ness +ĠP eb +quart ered +and an +Ġneg atives +ĠTh iel +ĠCl ip +ĠL TD +Ġbl ight +Ġreperto ire +K yle +Ġqu er +ĠC es +Ġha pl +98 9 +ĠTh ames +isc opal +Des k +ivari ate +ĠEx cellence +found ation +Ġâ ĩ +X i +Ġmyster iously +esty les +Ġper ish +ĠEng els +ĠDE AD +09 0 +}} } +ĠUn real +Ġrest less +ID ES +orth odox +ĠInter mediate +Ġdin ners +ĠTr out +ĠSe ym +ĠHall s +og ged +Ġtraged ies +Ġdid nt +67 6 +Ġail ments +Ġobserv able +ĠV ide +ad apt +ĠD usk +Ġprofessional ism +ĠPres cott +ĠInd ies +p ox +ĠMe hran +W ide +Ġend emic +ĠPar an +B ird +Ġped als +ĠI U +ĠAdam ant +ĠH urt +Ġcorrel ates +urd en +Ġspons oring +cl imate +ĠUnivers ities +ĠK not +enn es +ĠDam ian +ĠAx el +S port +Ġbar b +ĠS no +sh own +ste en +ud ence +Ġnon violent +Ġhom ophobia +Ġbiom ass +ĠDet ail +Ġsrf N +ĠT une +accompan ied +I ENCE +Al bert +ĠMong o +z x +ĠCer berus +or bit +c ens +Ġsl ay +SH ARE +H Y +Ġb rawl +ĠPro be +Ġnonex istent +ĠClare nce +ĠBlack burn +Ġport als +ĠR ita +ĠRem ain +ĠLe vant +Ġtrick ed +ĠF erry +aver ing +ĠStraw berry +ĠAn swers +Ġhorrend ous +ĠA man +Supp lement +ĠT oad +Ġpe eled +Ġman oeuv +ĠU zbek +mond s +ĠH ector +Ġ40 2 +pe es +fix es +Ġd j +Ġres umes +Ġaccount ant +Ġadvers ity +Ġham pered +ĠL arson +Ġd oping +part s +H ur +Ġbe arded +Ġy r +ĠPlug in +å¥ ³ +Ġ/ ** +rol ley +Ġwaters hed +ĠSub mission +if lower +AS C +Ġcho ir +Ġsculpt ures +m A +incre asing +ai i +Ġsne akers +Ġconfront s +ĠEle phant +ĠEl ixir +Ġrec al +ĠT TL +w idget +ĠW ax +ĠGr ayson +Ġha irst +Ġhumili ated +ĠWAR N +app iness +ĠT TC +F uel +Ġpol io +Ġcomplex es +Ġbab e +ĠX IV +P F +). [ +P arts +Ġ4 35 +M eg +ĠY ards +ĠAL P +Ġy ells +Ġprin ces +Ġbull ies +ĠCapital ism +ex empt +FA Q +ĠSp onge +ĠAl a +Ġpleas antly +Ġbu f +Ġden ote +Ġunp ublished +Ġkne eling +asc a +Ġl apse +al ien +99 4 +Ġrefere es +ĠLaw yers +S anta +Ġpuzz ling +ĠProm etheus +ĠPh araoh +ĠDel ay +Ġfacilit ates +ĠC ES +Ġjew els +Ġbook let +ond ing +Ġpolar ization +ĠMor an +ĠSal ad +ĠS OS +ĠAdv ice +PH OTOS +IC AN +iat ures +ex press +ĠWonder land +ĠC ODE +ĠCL ASS +9 75 +Ġg rep +ĠD iesel +ĠGl ac +! ?" +Ġr m +o ine +disc rimination +ĠN urse +m allow +Ġv ortex +ĠCons ortium +Ġlarge Download +stra ight +augh lin +G rad +Ġpublic ized +ĠW aves +ĠRed d +Ġfest ivities +ĠM ane +ar ov +Ġfleet ing +ĠDr unk +ug en +C ele +Ġchromos omes +ĠD OT +-+-+ -+-+ +Ġbus iest +ĠBe aver +Sy rian +ĠK yr +k as +ĠCross Ref +19 50 +76 01 +Ġrepe aling +ĠWin ners +ĠMac ro +ĠD OD +bl ance +S ort +64 1 +Ġmet re +ĠD irk +Ġgo ggles +Ġdraw backs +Ġcomplain ant +Ġauthor izing +Ġantit rust +oper ated +Ġm ah +Ġexagger ation +Am azing +ĠSer aph +Ġha ze +w ow +Ġextingu ished +Ġcan yon +ĠB osh +Ġv ents +Ġsc rape +Cor rect +4 26 +Ġav g +Dem and +ĠâĪ ¼ +Ġmicrobi ota +"} ]," +ĠSt ev +B io +ĠPlan es +Ġsuggest ive +Ġdec ipher +ĠRefuge e +ĠKe jriwal +ĠGreen peace +Ġdecl ass +ĠSound ers +Ġth o +Ġdec rypt +Ġbr ushing +ĠJane iro +ip op +S i +8 77 +ĠGeoff rey +Ġc pu +ĠHaz el +Ġview points +Ġcris py +ĠNot ification +Ġsold er +ĠMod est +ĠHem isphere +Ġcass ette +in cludes +Ġident ifiers +ĠC ALL +in cent +T odd +ĠSwe ep +Ġ3 34 +b oss +Ġsm ir +gin x +Ġtown ship +Ġg rieving +ĠMos que +Net flix +AS ED +ĠMillenn ials +oc om +19 67 +Ġbold ly +s leep +Ġes che +arij uana +Ġsw irl +ĠPen al +Ġneglig ent +ĠStephen son +K ER +ĠZ oro +ris is +Ġlocal ization +ĠSeym our +ĠAng lic +red itation +prot ection +ĠPa ige +Ġo mit +ĠR ousse +ĠT ub +Ġinv itations +t ty +Ġm oss +ph ysical +C redits +Ġan archy +Ġchild care +Ġl ull +ĠM ek +ĠL anguages +lat est +ĠSan ford +Ġus ability +Ġdiff use +ĠD ATA +Ġsp rites +ĠVeget a +ĠProm otion +ãĥ¼ ãĤ¯ +rict ing +z ee +Tur kish +ĠTD s +pro ven +57 1 +Ġsmug glers +707 10 +Ġreform ed +ĠLo is +Ġun fl +ĠWITH OUT +ĠReturn ing +ann ie +ĠTom as +Fr anc +ĠProf it +ĠSER V +ĠR umble +ik uman +es an +Ġt esters +Ġgad get +Ġbrace let +ĠF SA +comp onent +Ġparamed ics +Ġj an +ĠRem em +ĠSk inner +Ġl ov +ĠQu ake +rom a +Ġfl ask +Pr inc +Ġover power +Ġlod ging +ĠK KK +ret te +Ġabsor bs +w rote +Ġ ," +K ings +ĠH ail +ĠFall ing +xt ap +ĠHel ena +ire ns +L arry +Ġpamph let +ĠC PR +G ro +ĠHirosh ima +Ġhol istic +". [ +Ġdet achment +Ġas pire +Ġcompl icit +ĠGreen wood +Ġresp awn +ĠSt upid +ĠFin ished +f al +b ass +Ġab hor +Ġmock ery +ĠFe ast +VID EO +Ġcon sec +ĠHung ry +P ull +ĠH ust +it ance +? ãĢį +) -- +ĠPar allel +con v +4 69 +ha ar +w ant +P aper +m ins +ĠTor o +ĠTR UMP +ĠR ai +D W +ĠW icked +ĠL ep +Ġfun ky +Ġdetrim ent +ios is +ache v +Ġde grade +im ilation +Ġret ard +Ġfrag mentation +Ġcow boy +ĠY PG +ĠH AL +Parent s +ĠS ieg +ĠStra uss +ĠRub ber +× IJ +Fr ag +Ġp t +Ġoption ally +ĠZ IP +ĠTrans cript +ĠD well +88 2 +M erc +ĠM OT +ãĥ¯ ãĥ³ +Ġhun ts +Ġexec utes +In cludes +Ġacid ic +ĠRespons ibility +ĠD umb +we i +And erson +ĠJas per +ight on +abs olutely +Ad ult +Ġpl under +Mor ning +ĠT ours +ĠD ane +Î º +ĠT EST +ĠG ina +Ġcan ine +aw an +Ġsocial ists +ĠS oda +Ġimp etus +ĠSupplement ary +oli ath +ĠKinn ikuman +mitted ly +second s +Ġorganis ers +Ġdocument aries +Vari able +GRE EN +Ġres orts +Ġbr agging +Ġ3 68 +Art ist +w k +bl ers +Un common +ĠRet rieved +Ġhect ares +Ġtox in +r ank +Ġfaith s +ĠG raphic +Ġve c +ĠL IA +Af rican +Ġard ent +end iary +L ake +ĠD OS +cient ious +ĠOk awaru +ĠAll y +ĠTim eline +D ash +ĠI c +contin ue +Ġt idy +Ġinstinct ively +ĠP ossibly +ĠOut door +ĠWould n +Ġl ich +ĠBr ay +ĠA X +Ġà ī +Ġ+ # +\ ' +Direct ory +ab iding +Ġf eral +ic ative +but t +Ġper verse +S alt +Ġwar ped +Ġnin eteen +Ġcabin ets +Ġsrf Attach +ĠSl oan +Ġpower ing +reg ation +F light +se vere +Ġst ren +Ġc og +ap ache +Ġâ Ŀ +Ġcaf eteria +p aces +ĠGrim oire +uton ium +Ġr aining +Ġcir cling +Ġlineback ers +c redit +Ġrep atri +ĠCam den +lic ense +Ġly ric +Ġdescript or +Ġval leys +Ġre q +Ġback stage +ĠPro hibition +ĠK et +Op ening +S ym +æĸ ¹ +Ġserv ings +Ġoverse en +Ġaster oids +ĠMod s +ĠSpr inger +ĠCont ainer +è » +ĠM ens +Ġmult im +Ġfire fighter +pe c +Ġchlor ine +Ð ¼ +end i +Ġsp aring +Ġpolyg amy +ĠR N +ĠP ell +Ġt igers +Ġflash y +ĠMad ame +S word +Ġpref rontal +Ġpre requisite +uc a +Ġw ifi +Ġmiscon ception +Ġharsh ly +ĠStream ing +ot om +ĠGiul iani +foot ed +Ġtub ing +ind ividual +z ek +n uclear +m ol +Ġright ful +49 3 +Ġspecial ization +Ġpassion ately +ĠVel ocity +ĠAv ailability +T enn +Ġl atch +ĠSome body +Ġhel ium +cl aw +Ġdi pping +XX X +Ġinter personal +7 10 +Ġsub ter +Ġbi ologists +ĠLight ing +Ġopt ic +Ġden im +end on +ĠC orm +Ġ3 41 +ĠC oup +Ġfear less +Ġal ot +ĠCliff ord +ĠRun time +ĠProv ision +up dated +lene ck +Ġneur on +Ġgrad ing +ĠC t +sequ ence +in ia +con cept +Ġro aring +ri val +ĠCaucas ian +Ġmon og +key es +Ġappell ate +Ġlia ison +EStream Frame +ĠPl um +! . +Ġsp herical +Ġper ished +Ġbl ot +Ġben ches +Ġ4 11 +Ġpione ered +Ġhur led +Jenn ifer +ĠYose mite +Ch air +Ġreef s +Ġelect or +ĠAnt hem +65 2 +Ġun install +Ġimp ede +Ġbl inking +Ġgot o +Dec re +A ren +Ġstabil ization +ĠDis abled +ĠYanuk ovych +Ġoutlaw ed +ĠVent ura +ten ess +Ġplant ation +Ġy acht +ĠHu awei +Ġsol vent +Ġgr acious +Ġcur iously +Ġcapac itor +Ġc x +ĠRef lex +Ph ys +ĠC f +pt in +cons ervative +Ġinv ocation +c our +F N +ĠNew ly +H our +As ian +ĠLe ading +ĠAer ospace +An ne +Ġpre natal +Ġdeterior ating +H CR +ĠNorm andy +ol ini +ĠAm bro +9 10 +Ġset backs +ĠT RE +Ġs ig +ĠSc ourge +59 7 +79 8 +Game play +Ġm sec +M X +Ġprice y +ĠL LP +aker u +Ġover arching +ĠB ale +Ġworld ly +Cl ark +Ġscen ic +Ġdisl iked +ĠCont rolled +T ickets +ĠE W +ab ies +ĠPl enty +Non etheless +Ġart isan +Trans fer +ĠF amous +Ġinf ield +ble y +Ġunres olved +ĠML A +ãĤ Ĥ +Cor rection +Ġdemocr at +ĠMore no +ro cal +il ings +Ġsail or +Ġr ife +h ung +Ġtrop es +Ġsn atched +ĠL IN +ĠB ib +ES A +ĠPre v +ĠCam el +run time +Ġob noxious +4 37 +Ġsum mers +Ġunexpl ained +ĠWal ters +cal iber +Ġg ull +ĠEnd urance +ä½ ľ +Ġ3 47 +Ir ish +Ġaer obic +Ġcr amped +ĠHon olulu +à © +us erc +ec ast +AC Y +ĠQu ery +ãĤ¹ ãĥĪ +Bet a +Ġsuscept ibility +ĠSh iv +ĠLim baugh +Ġà ĸ +ĠN XT +ĠM uss +ĠBrit ons +ES CO +EG IN +Ġ% % +Ġsec ession +ĠPat ron +ĠLu a +n aires +ĠJPM organ +us b +ocy te +Ġcouncill ors +ĠLi ang +f arm +Ġnerv ously +Ġattract iveness +ĠK ov +j ump +Pl ot +Ġst ains +ĠStat ue +ĠApost les +he ter +ĠSUP PORT +Ġoverwhel m +Y ES +Ġ29 1 +d ensity +Ġtra pping +M it +Ġf ide +ĠPam ela +atl antic +Dam n +Ġp ts +OP A +Ġserv icing +Ġoverfl owing +ul o +ĠE rit +t icket +light ing +ĠH mm +ãĥ¼ ãĥ« +im oto +Ġchuck le +4 23 +ãģ ķ +sh ape +Ġque ues +Ġanch ors +ãĤ¼ ãĤ¦ãĤ¹ +F er +Ġaw oke +Ġ6 66 +h ands +Ġdiver gence +Ġ50 5 +T ips +Ġdep ot +Ġske w +ĠDel iver +op ot +Ġdiv ul +ĠE B +uns igned +ĠUn i +X box +Ġfor ks +Ġ7 02 +å ¯ +Ġpromot ers +ĠV apor +Ġlev ied +sl ot +Ġpig ment +Ġcyl inders +C RE +Ġsn atch +Ġperpet ually +Ġl icking +ĠFe et +ĠKra ken +ĠHold en +ĠCLS ID +m r +Ġproject or +Ġden otes +Ġchap el +ĠTor rent +b ler +R oute +ĠDef endant +ĠPublisher s +ĠM ales +ĠInn ov +ĠAg ility +rit er +ty mology +st ores +L ind +Ġf olly +ĠZur ich +B le +Ġnurt ure +Ġcoast line +uch in +D omin +Ġfri vol +ĠCons olid +res ults +M J +Ġphyl ogen +Ġha uled +ĠW iley +ĠJess ie +ĠPrep are +ĠE ps +Ġtreasure r +I AS +Ġcolon ists +Ġin und +ĠWW F +ĠCon verted +6 000 +out side +ĠApp earance +ĠRel ic +ĠM ister +s aw +Ġresult ant +Ġadject ive +ĠLaure l +ĠHind i +b da +Pe ace +Ġreb irth +Ġmembr anes +Ġforward ing +Ġcoll ided +ĠCar olyn +K ansas +5 99 +ĠSolid GoldMagikarp +Be ck +Ġstress ing +ĠGo o +ĠCooper ative +Ġf s +ĠAr chie +L iter +ĠK lopp +J erry +Ġfoot wear +War ren +Ġsc ree +h are +Under standing +P ed +Ġanth ology +ĠAnn ounce +M ega +Ġflu ent +Ġbond age +ĠDisc ount +il ial +C art +ĠNight mares +Sh am +ĠB oll +uss ie +H ttp +Atl anta +Ġun recogn +ĠB id +Ġunder grad +Ġforg iving +ĠGl over +AAAA AAAA +4 45 +V G +pa io +kill ers +Ġrespons ibly +Ġmobil ize +Ġeffect ed +ĠL umin +Ġk ale +Ġinfring ing +ann ounced +Ġf itt +b atch +ĠT ackle +ĠL ime +ĠAP P +uke mia +Ġrub y +Ġex oner +ĠCas ual +0 70 +Ġpel vic +Ġautom ate +ĠK ear +ĠCoast al +Ġcre ed +Ġbored om +ĠSt un +ri ott +Ĥ İ +Ġregener ate +Ġcomed ians +ĠOP ER +Sp ons +id ium +on is +L ocated +05 7 +Ġsusp ense +ĠD ating +C ass +Ġneoc ons +ĠShin zo +Ġaw oken +ch rist +ĠMess ages +att led +ĠSpr ay +ĠSp ice +C W +Ġshield ing +ĠG aul +Am id +Ġparam ilitary +Ġmult if +ĠTan ner +il k +Ġgodd amn +g ements +Ġbe friend +m obi +Ġ3 88 +fold er +acc a +Ġins in +g ap +N ev +fif th +Ġpsychiat ry +b anks +TH IS +Ġhar b +ac qu +Ġfac ade +ĠPower Point +80 3 +Ġbl uff +Sh ares +Ġfavor ing +El izabeth +Ãį Ãį +Ġr anger +77 2 +ĠAr che +h ak +ĠGen etics +ĠF EMA +Ġev olves +Ġest e +ĠP ets +ĠM é +ĠInterest ing +ĠCanter bury +ch apter +ĠStar fleet +Sp anish +Ġdraw back +ĠNor wich +9 70 +n orth +ag anda +Ġtransform ative +ram ids +bi ology +ad ay +Ġpropag ation +ĠGam ma +ĠDen ise +ĠCalcul ator +ent imes +ĠB ett +Ġapp endix +ĠHD D +AK ING +Ġst igmat +Ġhol ster +Ġord inarily +Ch ance +ĠCont rary +Ġad hesive +Ġgather s +6 12 +re au +ony ms +ew ays +Ġindu ces +Ġinterchange able +se m +Wh it +Ġtr ance +Ġincorpor ation +ĠExt ras +Fin ancial +Ġawkward ly +ĠStur geon +ĠH Y +Norm ally +ĠEnd ing +ĠAss ist +enc rypted +Ġsub jug +Ġn os +Ġfan atic +C ub +C U +?" . +Ġirre versible +å Ĥ +03 1 +ĠH AR +sp read +ul ia += $ +Sc ope +L ots +Ġlif estyles +ol on +Ġf eds +Ġcongrat ulate +web kit +Ġindist inguishable +ĠSw ing +Ġcommand ments +qu ila +ab ella +m ethyl +ann abin +Ġo vere +Ġlob ster +ĠQU EST +ĠCONT IN +bern atorial +:::: :::: +ĠTra ve +ĠSam oa +AN I +75 2 +Ð ´ +userc ontent +ĠMod erate +y eah +ĠK itt +Ġwe e +Ġstuff ing +ĠInter vention +ĠD ign +Ġware houses +ĠF iji +Ġpel lets +Ġtake away +ĠT ABLE +ĠClass ical +col lection +Ġland fall +ĠMus cle +Ġsett les +ĠAD V +Ġ3 44 +L aura +Ġf ared +ĠPart ial +4 36 +oss ibility +ĠD aly +ĠT arant +ĠFu ji +am l +c ence +55 1 +ĠProced ures +ĠO CD +ĠU D +t in +Q UI +ach o +4 38 +Ġgl itches +Ġenchant ment +Ġcalcul ates +IR O +ĠH ua +alys es +ĠL ift +um o +Ġle apt +Ġhypothes ized +ĠGust av +it ans +VERS ION +æ ł +Rog er +Ġr and +ĠAd apter +Ġ3 31 +ĠPet ition +k ies +M ars +Ġunder cut +ze es +ĠLy ons +ĠDH CP +Miss ing +Ġretire es +Ġins idious +el i +> ) +. ãĢį +Ġfinal ists +ĠA ure +Ġacc user +Ġwas tes +ĠY s +ĠL ori +Ġconstitu encies +Ġsupp er +Ġmay hem +or ange +Ġmis placed +Ġmanager ial +Ġex ce +ĠCL I +Ġprim al +ĠL ent +Cry stal +h over +ĠN TS +end um +Ġd w +ĠAl c +n ostic +Ġpres erves +ĠTs arnaev +Ġtri pled +rel ative +Arc ade +k illing +ĠW EEK +ĠH anna +D ust +Com pleted +ģ « +Ġappro ves +ĠSur f +ĠLuther an +ven ants +Ġrobber ies +we ights +soft ware +at ana +ug al +Ġgrav y +ĠC ance +OLOG Y +ly ak +Ton ight +Ġunve il +Ġ19 04 +ĠMin ion +ent ious +st ice +pack ages +ĠG EAR +Ġg ol +ĠHutch inson +ĠProf ession +ĠG UN +ĠDiff erence +ĠTsuk uyomi +ĠLes bian +6 70 +Ġfug itive +ĠPlan etary +-------------------------------- ------------------------ +Ġacc rued +Ġch icks +Ġsto pp +Ġblock ers +C od +Ġcomment ers +ĠSomew here +ĠPhot ographer +the me +Ġmay oral +w u +Ġanten nas +Ġrev amped +ĠSubject s +it é +im ura +Ġentr ances +liter ally +Ġten ets +ĠO MG +ĠMP H +ĠDon key +ĠOff ense +Ġ" + +Sn ap +ĠAF B +Ġan imate +ĠS od +His panic +Ġinconsist ency +D b +F Y +Ex port +Ġa pe +Ġpear l +ib el +ĠPAC s +Ġ{ \ +Ġact u +ĠHS BC +camp us +Ġpay off +Ġde ities +ĠN ato +ou ple +Ġcens ored +ĠCl ojure +Ġconf ounding +en i +Ġreck on +op he +Ġspot ting +Ġsign ifies +Ġprop el +Ġfest ive +S uggest +Ġpled ging +ĠB erman +Ġrebell ious +Ġovershadow ed +Ġinfiltr ated +j obs +67 2 +Ġscal able +Ġdomin ion +ĠNew foundland +ĠMead ow +Ġpart itions +AM I +Ġsupplement ary +str ument +Ġhair y +Ġperpet uate +Ġnuts hell +ĠPot ato +ĠHob bit +Ġcur ses +Flo at +Ġquiet er +Ġfuel ing +Ġcaps ules +ĠL ust +ĠH aunted +Exec utive +Ġchild birth +G re +Ġrad iant +å İ +Ġm alls +Ġin ept +ĠWarrant y +Ġspect ator +E h +t hens +Ġculmin ating +æ © +ary a +ãĤ ® +ilit arian +ĠOR IG +ĠSp ending +pt ives +ĠS iren +ĠRec ording +ay ne +Ġv im +Ġspr ang +T ang +ĠM FT +mor ning +ĠWe ed +m peg +cess ion +ĠCh ung +7 30 +w arning +56 2 +handed ly +P oor +P olitics +: # +Ġp ian +Ġfec es +ĠDocument ation +Ġban ished +Ġ3 99 +ĠAR C +Ġhe inous +J ake +ĠAm ir +way ne +v re +os henko +Ġnotebook s +Ġfound ational +Ġmarvel ous +ixt ape +Ġwithdraw als +Ġh orde +ĠD habi +is able +ĠK D +Ġcontag ious +ĠD ip +ĠAr rows +Ġpronoun s +Ġmorph ine +ĠB US +68 2 +Ġk osher +fin ished +ĠInstr uments +Ġf used +yd en +ĠSal mon +F ab +aff ected +K EN +C ENT +Dom ain +Ġpoke mon +ĠDr inking +G rowing +ĠInvestig ative +ĠA ether +em i +Ġtabl oid +Ġrep ro +ĠNot withstanding +ĠBers erker +Ġdram as +Ġclich é +Ġb ung +ĠU RI +ĠD os +0 44 +Ġpast ors +Ġl s +Ġac rylic +aun ts +Ed ward +Ġmajor ities +B ang +Ġfield ing +ĠRepl acement +ĠAl chemy +pp ard +ĠRome o +ĠSan ct +ĠLav rov +ib ble +Inst ruct +Ġimp ractical +ĠPlay boy +ce phal +Ġsw aps +Ġk an +ĠThe o +Ġillust rating +Ġdismant led +ĠTrans gender +ĠG uth +UG H +Ġtriumph ant +Ġencomp ass +Ġbook mark +udd in +j er +Ġpred icate +ES H +Ġwhen ce +ĠAB E +Ġnon profits +Se qu +Ġdi abetic +Ġp end +Ġheart felt +sh i +Ġinter acts +ĠTele com +Ġbombard ment +dep ending +ĠLow ry +ĠAd mission +ĠBl ooming +ust ration +ene gger +B rew +Ġmol ten +ĠNer d +P IN +âĸ Ģ +ave ment +Ġtou red +Ġco efficients +ĠTray von +ans son +Ġsand y +t old +fl ows +Ġpop ulous +ĠT inder +ĠBl iss +R achel +Min imum +Ġcontest ant +ĠRed uce +ĠMor se +ĠGrass ley +ĠClick er +Ġexp r +Ġs incerity +Ġmar qu +Ġelic it +ĠPro position +ĠDemon ic +Ġtac os +G reek +Ġpost war +Ġin sofar +ĠP ork +Ġ35 2 +doctor al +walk ing +Ġmid term +ĠSam my +sight ed +ĠTR ANS +ic i +AL D +ĠUS L +ĠF ISA +ĠAm pl +ĠAlex andra +ine lli +Tr ain +Ġsign ify +ĠVers us +Ġob fusc +Ġk h +Ġagg ro +ĠRen ault +Ġ3 48 +5 18 +ox icity +0 22 +ĠTw ist +Ġgoof y +D ynamic +Ġbrief ings +m ight +8 99 +Ġderog atory +T ro +Ġfor ging +ĠKor an +ĠMar ried +ĠBuc s +Ġpal ate +ĠCon version +m able +4 13 +Ġ( _ +Ġs iph +ĠN EO +col lege +Ġmarg inally +Ġfl irt +ĠTra ps +ĠP ace +é »Ĵ +Ġgoalt ender +Ġforb ids +Ġcler ks +ĠT ant +ĠRobb ins +ĠPrint ing +Ġpremie red +Ġmagn ification +ĠT G +ĠR ouse +ĠM ock +odynam ics +Ġpre clude +ism o +ĠPul itzer +Ġaval anche +ĠK odi +rib une +ĠL ena +Elect ric +Ġref inery +Ġend owed +Ġcounsel ors +Ġd olphin +ĠM ith +Ġarm oured +hib ited +Beg in +ĠP W +O il +ĠV or +ĠShar if +ĠFraz ier +est ate +Ġj ams +Pro xy +Ġband its +ĠPresbyter ian +ĠPrem iere +t iny +ĠCru el +Test ing +Ġhom er +ĠV ERS +ĠPro l +ĠDep osit +ĠCoff in +Ġsemin ars +Ġs ql +ĠDef endants +Altern atively +ĠR ats +ç « +ethy st +' > +Ġiss uer +58 9 +Ġch aired +ĠAccess ories +man ent +Ġmar row +ĠPrim ordial +C N +Ġlimit less +ĠCarn age +Ġund rafted +q v +IN ESS +on ew +Ġco hesion +98 7 +Ġne cks +Ġfootball er +ĠG ER +Ġdetect able +ĠSupport ing +ĠCS V +oc ally +k Hz +Ġund e +Ġsh one +Ġbud ding +tra k +Stand ing +ĠStar craft +ĠKem p +Ben ch +Ġthw arted +ĠGround s +ath i +L isa +Dial og +ĠS X +V ision +Ġingen ious +Ù IJ +Ġfost ering +ĠZ a +ĠIn gram +Ġ" @ +N aturally +6 16 +0 35 +ĠF AC +H mm +55 4 +Ġacceler ator +ĠV end +Ġsun screen +Ġtuber culosis +rav iolet +ĠFunction al +ĠEr rors +ed ar +19 66 +ĠSpect re +ĠRec ipes +88 5 +ĠM ankind +L iverpool +Ġ| -- +Ġsubst itutes +ĠX T +w ired +Ġinc o +ĠAf gh +E va +ic c +S ong +K night +Ġdilig ently +ĠBroad cast +A id +Ġaf ar +ĠH MS +aton in +ĠGr ateful +Ġfire place +ĠOm ni +e uro +ĠF RE +ĠSh ib +ĠDig est +t oggle +Ġheads ets +Ġdiff usion +ĠSqu irrel +ĠF N +Ġdark ened +out her +Ġsleep s +ĠX er +gun s +Ġset ups +Ġpars ed +Ġmamm oth +ĠCur ious +g ob +ĠFitz patrick +ĠEm il +im ov +........ ..... +ĠB enny +Second ly +Ġheart y +Ġcons on +st ained +Ġgal actic +cl ave +Ġplummet ed +Ġp ests +Ġsw at +Ġrefer rals +ĠLion el +h oly +Ġunder dog +ĠSl ater +ĠProv ide +ĠAm ar +ress or +å Į +ong a +Ġtim id +Ġp iety +ĠD ek +Ġsur ging +az o +Ġ6 10 +Ġdes ks +ĠSp okane +ĠAn field +Ġwars hips +ĠCob ra +Ġar ming +clus ively +ĠBad ge +ag ascar +ĠPR ESS +ĠMcK enzie +ĠFer dinand +burn ing +Af ee +Ġtyr ann +ĠI w +ĠBo one +100 7 +ĠRe pt +Ċ Âł +Ġcar avan +ĠD ill +ĠBundes liga +Ch uck +Ġheal er +ãĥ¼ãĥ Ĩ +ĠH obby +Ġneg ate +Ġcrit iques +section al +mop olitan +Ġd x +Ġouts ourcing +ĠC ipher +t ap +Sh arp +Ġup beat +Ġhang ar +Ġcru ising +ĠNi agara +Ġ3 42 +ill us +ĠS v +Ġsubt itles +Ġsqu ared +Ġbook store +Ġrevolution aries +ĠCarl ton +ab al +Ut ah +Ġdesp ise +ĠU M +cons ider +aid o +Ġc arts +ĠT urtles +Tr aining +Ġhonor ary + ¢ +Ġtri angles +4 22 +Ġreprint ed +Ġgrace ful +ĠMong olia +Ġdisrupt ions +ĠB oh +Ġ3 49 +Ġdr ains +Ġcons ulate +Ġb ends +Ġm afia +ur on +ĠF ulton +m isc +Ġren al +Ġin action +ck ing +Ġphot ons +Ġbru ised +ĠC odes +og i +Ġn ests +ĠLove ly +ĠLib re +ĠD aryl +Ġ# ## +S ys +. ," +Ġfree zes +est ablishment +and owski +Ġcum bers +ĠSt arg +ĠBom bs +Ġleg ions +Ġhand writing +Ġgr un +ĠC ah +sequ ent +Ġm oth +ĠMS M +Ins ert +F if +Ġmot el +Ġdex ter +ĠB ild +hearted ly +Ġpro pe +ĠText ure +ĠJ unction +ynt hesis +oc ard +ĠVer a +ĠBar th +Ġμ g +Ġl ashed +Ġ35 1 +ĠZ amb +ĠSt aples +ĠCort ex +ĠCork er +Ġcontinu um +ĠWR ITE +unt a +rid or +Ġde ems +0 33 +ĠG OLD +p as +Ġrep ressive +ãĥĨ ãĤ£ +Ġbaff led +Sc ar +Ġc rave +Ġ ______ +Ġentrepreneurs hip +ĠDirector ate +Ġ' [ +Ġv ines +Ġasc ended +ĠGR OUP +ĠGood bye +Ġdo gged +ãĥ´ ãĤ¡ +Man ufact +Ġunimagin able +ri ots +ier rez +Ġrel ativity +ĠCraft ing +ra ught +ud en +c ookie +Ġassass ins +Ġdissatisf ied +ac ci +Ġcondu it +Sp read +ĠR ican +n ice +izz le +Ġsc ares +ĠWH Y +ph ans +5 35 +Ġprot racted +ĠKrist en +5 36 +ĠSc rib +ĠNe h +Ġtwent ies +Ġpredic ament +Ġhandc uffs +Ġfruit ful +ĠU L +ĠLud wig +Ġatt est +ĠBre aker +Ġbi ologically +ĠDeal er +Ġrenov ations +f w +ess en +Al ice +ĠHen ri +Ġun ilaterally +ĠS idd +h ai +ĠSt retch +S ales +Ġcumbers ome +ĠJ avier +Ġtrend y +Ġrot ting +ĠChall enges +Ġscra ps +Ġfac ets +ĠVer onica +ĠVer ge +ĠS ana +Al ien +ĠR ih +Ġrad ial +ect ar +Ġ6 30 +cl i +Mar ie +Ġwild fire +ĠCat o +h ander +Ġwait ress +Ġch ops +ĠS ECTION +Ġblunt ly +ĠCat alog +n ian +stud y +Ġpat rolling +ĠT enth +nex us +ĠN ON +op sy +Ġsc athing +s ie +Ġdeterior ated +V B +Naz is +Ġdep ictions +Ġauthent icated +ĠCon ce +k rit +Ġpromul g +ĠL ONG +U FC +ĠVis itors +ĠRec all +Ġrehab ilit +ĠSL I +Ġglac ier +ĠB ite +Ġ50 3 +Ġvom it +Ġfer mented +ĠKh alid +Ġgrad ed +ĠMag icka +ĠIch igo +power ful +ic ators +75 3 +Ġsh rew +Ġ35 6 +Ġlegal izing +Ġall otted +ĠArch demon +ith ing +igg urat +V OL +Le od +Ġo ily +Ġindu cing +Ġamy gdala +Ġadm ins +ĠAcqu isition +C AN +Ġsche matic +Ġmo an +ĠCamer oon +Ġt ink +Ġmer ry +Ġbutter flies +ĠGo ff +Ġworks pace +ĠCor ona +Ġj avascript +ĠD olphin +ĠCant or +4 64 +to e +AP S +ĠAg ing +Ġpadd ed +ĠZ heng +ĠHe ld +Ġest ranged +Ġ7 70 +. } +ĠDun ham +Ġsm okes +Ġcap itals +und ai +Sh in +ĠFound ing +Ġent itle +Ġcenter piece +D iscover +Ġthere to +al ert +ĠN ou +ĠAnaly st +l c +F H +FI ELD +ĠP OV +gr ay +Ġar cs +ĠH OT +Ġr s +Ġoblig atory +ĠArchitect s +ĠS ven +ĠF EC +0 200 +Christ mas +ĠAlban ia +rat om +58 7 +Ġhard ships +Ġaut os +ĠCharg es +Ġap es +Ġ3 76 +wal let +Ġintox ication +Ġgobl in +Ġ5 70 +++++++++ ++++++++ +ĠYel p +ĠMag netic +ĠBr iggs +R ail +Ġspawn s +ĠW iggins +Ġshowc ased +Ġres orted +ub en +Ġwh ipping +Ġim itate +Ġdigest ion +ĠUS PS +ĠG est +Ġye a +ĠT ight +ind al +ic as +` . +C AST +'' ; +ĠF et +opath ic +In valid +Ġregrett ed +Ġbro ccoli +ĠSc ores +e ve +Ġpost ings +Ġaccum ulating +Ġneed less +elf th +Ġmay ors +Ġsc rib +Ġanecd otes +Ġbot ched +ĠRib bon +ĠConstant ine +i uses +ess es +Ġdev ise +Comp ared +Ġp udding +Ġg arg +Ġev oke +79 7 +Ġdet ox +9 09 +ĠPie ces +ĠMcC artney +Ġmet ast +ĠK rypt +P OR +Ġt ending +ĠMerch ants +Pro of +ĠV arg +ĠPort able +ãĥ¼ãĥĨ ãĤ£ +B rain +25 00 +Ġfol iage +Ø ¹ +Ġment ors +ĠA ires +Ġminimal ist +Ġing ested +ĠTro jan +ĠQ ian +inv olved +0 27 +Ġer oded +RA FT +Ġbl urry +M ob +Ġbuff et +ĠFn atic +ae a +KN OWN +ĠIn it +s afety +en um +ACT ION +ĠCrus her +ĠD ates +Ġ ................ +c alling +ak ov +Ġvent ured +Ġ5 55 +au ga +H art +ĠA ero +M AC +Ġthin ly +Ġar ra +ST ATE +ild e +ĠJac qu +ĠFem ales +Ġthe orem +Ġ3 46 +Ġsmart est +ĠPU BLIC +ĠK ron +ĠB its +ĠV essel +ĠTele phone +Ġdec ap +Ġadj unct +ĠS EN +mer ga +Ġred acted +Ġpre historic +Ġexplan atory +ĠRun s +ĠUtt ar +ĠM anny +ĠAUTH OR +ĠUnle ashed +ĠBow ling +be ans +79 3 +Ġunivers es +Ġsens it +ĠK ung +re peat +ctr l +Ġp aced +Ġfull er +Cl ock +Ġrec omb +ĠF aul +ĠB unker +Ġpool ed +Ġan a +ĠM outh +LL OW +hum ane +Ġbull do +ĠMicha els +f am +Ġwreck ed +Ġport rays +ĠWh ale +ĠH es +Ġguess es +ĠBrow se +ĠL APD +Ġconsequ ential +ĠInn ocent +ĠD RAG +Ġtrans gress +ĠO aks +Ġtri via +ĠRes on +ĠA DS +-- + +ĠT oll +Ġgrasp ing +ĠTHE M +ĠT ags +ĠCon clusion +Ġpract icable +Ġho op +Ġunintention ally +Ġign ite +ĠM ov +ur ized +le hem +Ter min +Ġcolour ful +ĠLin ear +ĠEll ie +G y +Ġman power +Ġj s +Ġem oji +ĠSHAR ES +_ . +0000 7 +Ġsophistic ation +Ġunders core +Ġpract ise +Ġbl ob +op ens +Uk raine +Ke eping +Y C +J R +ult imate +Cl aim +Ġautom obiles +99 3 +ste el +Ġpart ing +ĠL ank +... ? +Ġ38 5 +Ġremem brance +Ġe ased +Ġcov ari +ĠS ind +Effect ive +Ġdisse mination +ĠMo ose +ĠCl apper +br ates +App ly +Ġinv is +Ġwors ened +âĢĶ - +Ġlegisl ator +ĠL ol +ĠRow e +Ġdealers hip +um ar +id ences +Ġinvestig ates +Ġc ascade +Ġbid der +ĠB EN +Iron ically +Ġpres iding +Ġd ing +Ġcontrad icted +Ġshut s +ĠF IX +Ġ3 66 +Dist rict +Ġsin ful +ĠChar isma +o ops +Ġtot ality +Ġrest itution +ĠOpt imus +ĠD ah +Ġcl ueless +urn ed +Ġnut rit +Ġland owners +Ġfl ushed +Ġbroad en +m ie +Ġprint ln +Ġn ig +ĠCorp us +J en +Ġprot o +ĠWik imedia +ĠPal o +C OR +Ġstory lines +Ġevangel icals +ĠDar rell +Ġrot or +ĠH W +sk illed +ery l +Ġbe gg +ĠBl umenthal +Ġwe aving +Ġdown wards +ĠJack et +ĠANG EL +Te chnology +Ġes oteric +alde hyde +Ġfur iously +Ġforeign er +We ak +CH O +ĠH ound +Exper ience +ĠPlay station +ĠM IA +ĠU ng +cl oth +ag all +Ġcal ming +iz ens +St ruct +ĠW itches +ĠCeleb ration +Ġ........ ...... +pt roller +ĠTC U +Ġb unny +ãĥ į +ut orial +Ġup scale +ĠSt a +ĠCol ossus +Ġchlor ide +ĠZ ac +ĠRe asons +ĠBrook ings +ĠWH ITE +][ / +ĠL ose +9 05 +Ġunders ide +ern els +Ġv ape +do zen +upp et +ĠST OP +mat ical +ĠStat ements +hed dar +P AC +Custom er +Ġmem os +ĠP J +end ars +ĠLim its +l augh +Ġstabil ized +ĠALE C +Y A +Up grade +al am +Ġtechn o +Ġan ew +fore seen +Ġcolleg iate +ĠPy ro +ĠD ism +Ġfront line +Ġammon ia +I U +Qu ite +John ny +ass in +G OP +ĠSt yles +ĠSovere ign +acter ial +5 49 +ĠR IP +ĠL ists +Ġ3 64 +ĠRece p +s ocket +ĠByr d +ĠCand le +An cient +Ġappell ant +en forcement +ace a +ans ki +Ġold s +88 6 +Ġsl urs +Ġem pires +Ġbuck le +Ġalien ation +ĠAber deen +Ġunic orn +Ġoverr iding +ĠL X +pp a +Ġdesp ised +ĠB ugs +ĠB ST +S outhern +5 33 +Ġhall mark +ĠPost er +Ġstem med +Ġprincip als +ĠT ECH +ĠSand wich +It aly +Ġche esy +ĠSet TextColor +ĠProt ective +ĠC ohn +J O +apt op +Re ason +Lead er +ĠUnder stand +ĠFr idays +ĠContin uous +Ġcl ipping +ĠR ye +Ġber th +tim er +ann is +re act +Ġbuff alo +ĠPar as +Ġ6 55 +Ġpres ided +ĠSun rise +Ġve ts +Ġcl oves +ĠMcC ull +Stre ngth +G AN +Ġill iter +ĠPric ing +l é +Ġresist or +Ġbr un +ĠSuff olk +Ñ ĭ +ĠL iver +Re leased +Ġwhat s +8 60 +ĠMe asures +Ġden ouncing +ĠRy zen +Ġsou ven +Ġcareg ivers +ch ini +ĠScar lett +Ġt rough +Cong ratulations +Ġtax is +ĠTrad ition +j it +Ġtable top +Ġhither to +Ġdis information +off ensive +h ra +ĠDISTR ICT +Ġcompl icate +chen ko +ĠRecon struction +Ġpalp able +Ġa usp +Ġ4 28 +Ġshowc ases +ĠPublic ation +know ledge +inn on +4 19 +Ġretri eval +and ers +Ġref ute +Ġinqu ired +g ur +Ġneg ativity +Ġcons erve +Ġafter life +Ġpres upp +ĠGill espie +Ġm t +ĠD N +T ap +Ġper pend +ĠS my +does n +Ġsp illing +Ġhyp ers +K ate +® , +ke pt +ĠP owered +Ġj a +ĠK lux +ard e +ab an +Ġ4 44 +Ġflatt ened +ĠImprove ments +urg a +ĠK und +Ġins cribed +Ġfac ult +Ġunpre pared +ĠCons umers +Ġsatisf ies +Ġpul monary +Ġinf iltration +Ġex ternally +Ġcongrat ulations +ag han +Ġair liner +Ġfl ung +Ġfly ers +G D +Ġsnipp ets +Ġrec ursive +Ġmaster ing +L ex +Ġovert ly +v g +Ġluck ily +Ġenc ro +ĠLanc et +ĠAbyss al +function al +Ġs ow +Ġsqu id +Ġnar ration +Ġn aughty +ĠHon our +ĠSpart ans +Ġsh atter +ĠTac oma +ĠCal ories +ĠR aces +Sub mit +Ġpurpose fully +w av +ĠY ok +F est +ĠG err +Met ro +Ġit iner +f amous +Ġ" { +in line +was her +Iss ue +ĠCL IENT +oz o +Vers ions +7 25 +ĠGl ock +Ġshield ed +ĠPC R +ENC Y +ĠWe ld +ĠSim pl +Ġredirect ed +ĠK ham +Ġ( > +Ġlab ou +Ġdi apers +ss l +Ġcell ar +organ isms +ore sc +ĠBer ks +did n +Sh ipping +C hest +Ġund one +Ġmillion aire +Ġc ords +ĠYoung er +appropri ately +Ġsequ els +u ve +ant icipated +Ġle wd +ĠSh irt +ĠDmit ry +V eter +Ġsl aying +ĠY ar +Ġcompl ication +I owa +ĠEric a +ĠBL M +g irlfriend +b odied +6 26 +19 63 +Ġintermedi ary +Ġcons olation +M ask +ĠSi em +ow an +Beg inning +Ġfix me +Ġculmin ated +Ġcon duc +ĠVolunte er +Ġpos itional +Ġgre ets +ĠDefin itions +Ġthink er +Ġingen uity +Ġfresh men +ĠMom ents +Ġ35 7 +ate urs +ĠFed Ex +s g +69 4 +Ġdwind ling +ĠBO X +sel age +Ġt mp +Ġst en +ĠS ut +Ġneighbourhood s +Ġclass mate +f ledged +Ġleft ists +Ġclim ates +ATH ER +ĠScy the +ul iffe +Ġs ag +Ġho pped +ĠF t +ĠE ck +ĠC K +ĠDo omsday +k ids +Ġgas ped +Ġmon iker +ĠL od +ĠC FL +t ions +r ums +fol ios +Ġm d +Ġunc anny +Ġtrans ports +ĠLab rador +Ġrail ways +Ġappl iance +ĠCTR L +æ Ģ +Pop ulation +ĠConfeder acy +Ġunb earable +Ġdors al +ĠIn form +op ted +ĠK ILL +Mar x +Ġhypoc ritical +q us +ĠN umerous +ĠGeorg ian +ĠAmbro se +ĠL och +Ġgu bernatorial +ĠX eon +ĠSupp orts +ens er +ee ly +ĠAven ger +19 65 +Ar my +Ġju xtap +Ġcho pping +ĠSpl ash +ĠS ustainable +ĠFin ch +Ġ18 61 +ict ive +at meal +ĠG ohan +Ġlights aber +ĠG PA +ug u +ĠRE PL +vari able +Ġher pes +Ġdesert s +ac iously +Ġsitu ational +week ly +ob l +Ġtext ile +ĠCorn wall +Ġcontrace ptives +ĠA ke +] - +ä¹ ĭ +: , +ĠW em +ĠB ihar +Ġ' . +Ġbe re +Ġanal ogue +ĠCook ies +Ġtake off +Whe el +Ġmaj estic +Ġcomm uting +0 23 +ĠCor pse +ass ment +min i +Ġgor illa +ĠAl as +ere e +Ġacquaint ances +ĠAd vantage +Ġspirit ually +Ġey ed +pm wiki +ĠE nder +Ġtrans lucent +Ġnight time +ĠIM AGES +5 45 +ĠK amp +ĠFre ak +Ġ ig +Port land +4 32 +ĠM ata +Ġmar ines +Ġh ors +ater asu +ĠAtt ribution +Ġ-------- - +Ġk ins +ĠBEL OW +++ + +Ġre eling +ol ed +Ġcl utter +ĠRel ative +Ġ4 27 +B US +Ġa vert +ĠChe ong +ĠA ble +ĠPry or +Develop er +Ġen cyclopedia +ĠUSA F +ĠG arry +Sp ain +Bl ocks +Ġexp osition +ĠGamer Gate +W OR +Ġstockp ile +Ġclot hed +ĠT one +ĠR ue +t umblr +Ġtreacher ous +Ġf rying +Ñ Į +ĠS ph +Ġrest raints +Ġemb odies +ĠG es +S afety +Ġnegoti ators +min ing +ĠAppalach ian +L OS +ĠJenn a +Ġpass ers +ç ĭ +sn ap +Ġshort en +creat or +Ġinn umerable +uther land +67 4 +ĠW OM +ĠAs cend +ĠArm ory +ĠTrans action +K ick +Ġsuit case +day Name +Ġwaste ful +mar riage +ĠMcC abe +ite ch +ĠO ss +Cl osure +ĠTreasure r +Ġindec ent +ĠD ull +Ġresid ences +19 59 +ĠS ettlement +Ham ilton +Ġself ies +ĠRank ing +ĠBark ley +ĠB ore +ĠW CS +ĠMar itime +ĠH uh +ĠForest ry +Ġcultiv ating +ĠBall ard +Ġg arrison +ĠSD L +9 30 +Ġnas cent +Ġirresist ible +Ġaw fully +\/ \/ +Ġequ ate +Ġanthrop ology +ĠSylv ia +Ġintest ine +Ġinnoc uous +cess ive +ag ra +ĠMet roid +G rant +8 55 +ģ ĸ +Ġ" _ +ãĥĥ ãĥī +Ġappra isal +ĠFred dy +04 6 +Ġ40 6 +Ġ18 30 +Ġd ocking +St atic +Ġp ont +ĠVolt age +ĠSt ead +ĠMort gage +ĠJon ah +Y L +CLASS IFIED +Ġas bestos +nik ov +Ġcoll agen +ĠOrb ital +P ocket +7 99 +Ġhy brids +inc hes +Ġinv oice +und y +Ġinequ alities +T rend +w ashed +B ALL +Ġluc id +ĠComment ary +Ġw itty +Br andon +Ġbru ising +Ġ6 20 +es cent +box ing +P OL +Ġ3 78 +R ect +Ġlic ences +ĠMcG ee +p ressed +D anny +Ġj ammed +ord inate +Ġle th +Ġdistingu ishes +ĠYam aha +IL S +ĠH ume +ĠC ategories +Rober ts +Ch art +Ġbeet le +ĠGra veyard +Ġ($ ) +o ÄŁ +Ġtw ilight +are lla +á ½ +Ġbooth s +ĠH HS +ĠFeld man +Ġexcav ation +Ġphilosoph ies +at ography +ĠGar age +te chnology +Ġunfor gettable +Ġver ifying +Ġsubord inates +E ls +Ġne b +G aming +EN A +ĠAchieve ment +it ters +ĠG abe +Ġd umps +for cer +Ġpo ignant +ĠM BA +ĠHe idi +ime i +Ġm ages +Ġliber ate +Ġcircum cised +ĠMer maid +ĠMat th +t ogether +ĠW ichita +Ġstore front +ĠAd in +V II +Four th +Ġexplore rs +W ER +Not able +Bro ok +m ens +F aith +-------- - +ĠJ ou +¬ ¼ +Ġpine apple +Ġam alg +el n +ark able +ĠãĤµ ãĥ¼ãĥĨãĤ£ +ĠãĤµãĥ¼ãĥĨãĤ£ ãĥ¯ãĥ³ +Ġov arian +ĠE choes +Ġhairc ut +Ġp av +Ġch illed +anas ia +Ġsty led +Ġd ab +ni per +Ġminister ial +ĠD UP +T an +Ġsul ph +ĠD eter +ĠBo hem +od an +Ġeduc ator +â ĵĺ +sp ir +Ch icken +ĠE leanor +Ġqu i +Ġheav iest +Ġgrasp ed +U RA +Ġcro oked +Jess ica +pro blem +Ġpred etermined +Ġman iac +Ġbreath s +ĠLauder dale +Ġh obbies +y z +Cr ime +Ġcharism a +d L +Ġle aping +Ġk ittens +Ang elo +ĠJ ACK +ĠSu zanne +Ġhal ting +ENT ION +Ġswall owing +ĠEarthqu ake +Ġeight eenth +ĠN IC +ĠIN F +ĠCons cious +Ġparticular s +circ le +7 40 +Ġbene volent +Ġ7 47 +Ġ4 90 +Ġr undown +ĠVal erie +ĠB UR +Ġcivil isation +ĠS chn +W B +ot ide +intern ational +Ġj ohn +Ġ19 02 +Ġpe anuts +Ġflav ored +k us +Ġro ared +Ġcut off +é £ +Ġorn ament +Ġarchitect ures +Ġ3 69 +ol or +ĠWild e +ĠC RC +ĠAdjust ed +Ġprov oking +land ish +Ġrational ity +Ġjust ifies +Ġdisp el +Ġa meric +ĠPol es +Ø © +Ġen vis +ĠD oodle +ä½ ¿ +igs aw +auld ron +Techn ical +T een +up hem +ĠX iang +Ġdetract ors +ĠZ i +ĠJournal ists +Ġconduc ive +ĠVolunte ers +Ġs d +Know ing +Ġtrans missions +ĠPL AN +ĠL IB +Ġall uded +Ġob e +Ġd ope +ĠGold stein +Ġwavelength s +ĠDest ination +nd a +ug i +Ġattent ive +ĠLe an +ral tar +Ġman g +mb uds +ak ings +b ender +Ġacc ol +Ġcraw led +N OW +Min nesota +Ġflour ished +ĠZ up +ĠSuper visor +ĠOliv ier +Ex cellent +Ġwid en +D one +Ġw ig +Ġmiscon ceptions +Cor p +W an +Ġvener able +ĠNot ably +ĠKling on +an imate +Bo ost +ĠS AY +miss ing +ibli ography +mel on +Ġpay day +Ø ³ +bo le +Ġve iled +ĠAl phabet +It alian +Ġever lasting +ĠR IS +ĠC ree +rom pt +Ġh ating +Ġgrin ning +Ġge ographically +OS H +Ġwe eping +ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł +Ġimpe cc +Let ter +Ġblo ated +PL A +ĠFe in +Ġper sever +Th under +Ġa ur +ĠR L +Ġpit falls +âĸ º +Ġpredomin ant +Ġ5 25 +7 18 +AP E +7 14 +Ġfarm land +ĠQ iao +Ġv iolet +ĠBah amas +Ġinflic ting +ĠE fficiency +Ġhome brew +Ġundert ook +Ġcur ly +ĠHard ing +man ia +59 6 +Ġtem pered +Ġhar rowing +ĠP ledge +ĠFranken stein +è ª +M otion +Ġpredict ably +ĠExpl osion +oc using +er d +col o +FF ER +Ġback field +ĠV IDE +ue bl +N arr +ĠArg ument +Ġgen omic +Ġbout ique +Ġbatt ed +ĠB inary +Ġg amb +ĠRh ythm +67 3 +Ġa float +ĠOlymp ia +Y ING +Ġend if +is in +Ġwin ters +Ġsc attering +I v +D istance +Ġtr u +ĠCom fort +Ġne xus +Ġair flow +ĠByz antine +p ayers +con i +ĠB etsy +D eal +ĠN ug +ĠContin ent +red ibly +Ġoptim izing +al beit +Ġec static +ĠPro to +ç · +iv ot +âĸ Ħ +em p +rou nder +Ġcl out +ĠI ST +66 3 +ĠDoll ars +ĠD AC +Ġsubsc ribed +Ġrehears al +Ġam ps +ĠSh ang +es m +Ġspr inkle +Ġassail ant +ĠO o +ĠCoin base +T act +Ġret ina +Ġn uns +R ON +att o +Ġj ug +ĠSV G +Ġb ikini +ĠFI LE +ĠFound ers +ep ort +ĠK P +Ġrest ores +ĠTh ick +Ġash ore +Ġappro vals +R ender +M AG +G raham +ĠCort ana +ãĥ³ ãĤ¸ +ss h +or ians +ars ity +ĠInsp ired +u pper +Ġsign alling +Ġreb uke +Ġfl ares +Ġdownt ime +Stud ies +Ġstagn ation +ĠSequ ence +Ġgr unt +Ġass ures +ĠPL A +59 2 +Ġintra ven +d epend +Sus an +ĠManz iel +Man ia +Cont ract +Ġsl ams +Ġcult ured +Ġcred itor +L IST +ĠH UM +ĠChatt anooga +serv ed +Ġclo aked +ĠF TP +p owder +ĠSt ella +uct ive +Ġcheap ly +ĠMU CH +ĠGalile o +Ġsu ites +spe ech +Ġdeliber ations +ĠCh ips +« ĺ +Bal ance +ĠWyn ne +ĠAk ron +Ass et +Ġhon oured +Ġed ged +Like wise +anim ous +ĠW age +ĠEz ek +ad vertisement +ĠRT X +ĠM AD +Ġmigr ating +ĠS QU +Ġ4 75 +Ed ited +Ġshorth and +ĠBas ics +Ġcro tch +ĠEV EN +Ġv m +effic iency +Ġcal ves +ĠF rie +ĠBrill iant +Ġstri kers +Ġrepent ance +Ġarter ies +r l +B ed +h ap +Ġcrypt ography +ĠSab res +Ġ4 14 +vi ks +ih ara +aps es +T alking +Ġintertw ined +Ġdoc ks +Ġalle le +ĠArt ifact +ĠH IM +t orn +ç ķ +Ġop acity +ĠE ly +os uke +Ġn ipple +Ġhand written +ĠV K +ĠChamber lain +ĠLa os +ig raph +g row +Ġtr illions +Ġdescend ant +ĠSail or +as uring +Ġce ilings +ĠWare house +f lying +ĠGl ow +Ġn ont +Ġmiscar riage +Ġrig s +Ġmin istries +Ġelabor ated +Ġdel usional +ĠHum ane +Ġ3 79 +n ets +Ġblack out +add ers +Ġn p +ĠT ire +ro sc +Ġsub div +Ġlink age +Ġchron ological +ĠHER O +Ġres ettlement +ĠVin yl +Ġpast oral +ĠMob il +ĠBar bar +Co oldown +ĠF ritz +c riminal +re pe +Ġbell ig +ĠBre ed +Ġ4 18 +Ġsem blance +ij k +Ġcur tail +Ġclin ch +cont ained +ĠProm pt +ast on +Ġw i +Ġpursu its +5 15 +ĠGl oss +Ġfl ips +Ġcoup ons +Ġcl oning +ĠLike ly +Rem oved +ĠQu artz +r ices +ĠSpe ars +Ġp ious +Ġdep reciation +ĠD are +oun ces +am az +O nt +Ġp innacle +d ocker +0 26 +ĠW yr +ĠPro per +Ë Ī +n il +By tes +Ġseek er +t rial +Ġunf olds +ĠMar se +Ġextravag ant +ĠSurviv ors +RED ACTED +ĠSpeed way +ĠCra igslist +sub mit +ĠGener ations +Ġup holding +Ġblood stream +ĠMiss ions +ĠL awn +Ġlim bo +ene i +H uh +ĠWild cats +pre p +ĠMark us +ĠFor bidden +rit ic +IN O +Ġexhib iting +requ ent +ch uk +Ġhabit ual +ĠComp atibility +Dr ag +RIP T +uj ah +GR OUND +Ġdelinqu ent +Ġburn er +Ġcontempor aries +Ġgimm ick +load s +Ġno zzle +p odcast +ĠW ak +ĠStat en +ĠK uh +ãģ ĵ +inter rupted +Ġinv incible +ĠBurn ett +cig arette +ĠPeb ble +ĠTem porary +ĠMar ino +58 2 +Ġwast eland +ident ly +T x +Ġr ite +ĠPan asonic +ĠM iddles +ĠHort on +ae us +Ġc uring +Ġm ats +Ġadj ourn +Ġfears ome +pe z +bo ats +Ġpro pell +Ġconflic ted +ĠAng er +Ġinsurg ent +K arl +Ġco ales +Ġsouth western +Ġdis su +ĠO vert +******** **** +Ġbox ed +ĠBr une +aa a +Ġgard ening +ĠEng el +tr acks +Ġpur ified +Ġplace holder +ĠL ikes +Ġd an +G ab +Ġe ct +ĠF aw +ĠEl iot +Ġ' , +otrop ic +ĠRu in +hed on +Ġca ul +Ġa ft +ĠCad illac +gh a +ass ian +ud eb +ĠT ick +Ġadjust s +AR GET +5 37 +isc he +ant y +ĠFried rich +ĠBl izz +ĠA OL +Camp aign +Ġmamm al +ĠVe il +ĠK ev +ĠMaur it +ĠDam ien +N ation +E astern +Ġ{ : +Ġ= ================================ +Ġstereotyp ical +Ġatt ic +ĠCy borg +requ ire +Ġaward ing +ĠPap ua +bt n +b ent +B oo +Ġ( = +ĠX ander +ĠSomers et +Ġcatch y +Ġcert ify +STR UCT +Ġit al +Ġt ides +ĠBr ands +G ray +comp etitive +Ġcur ator +ĠD G +omin ium +ĠGM Os +ci ating +ĠCarm en +ow ard +Balt imore +Ġr gb +C u +Ġwip es +spe ll +IT NESS +Ġsummar izes +ĠRe vis +Ġwhistlebl owers +ĠBre ach +Ġcro chet +k os +ews ki +Ġrep et +Ġcrim son +ĠKar achi +read able +dim ension +ĠI gor +ild ed +ĠZ ed +ĠKe ane +ĠCos metic +DE P +Ġretreat ing +ĠU A +ens ical +Ġd usk +ĠDick ens +Ġaren as +ĠPass age +level s +Ġcur v +P ope +Ġch ores +ĠEl ise +ĠComp ass +b ub +Ġmamm alian +ĠSans krit +ĠAN C +ĠCr ack +Q ual +L aun +amp unk +Ġlearn ers +Ġglam orous +Ġfur the +erm ott +c and +Gener ic +Ġnarr ated +Ġdisorder ly +ĠTrans actions +ĠDet ention +ĠR oku +Ä į +Ġunder statement +ĠS aur +ĠRodrig o +ĠAS AP +S in +Ġre joice +Method s +Ġelectro de +Ġworsh ipped +Ġid i +ĠPhys icians +Ġpop up +Ġde ft +ĠRem oval +ĠBu enos +ver bs +Ġfun k +ush a +rict ion +ore a +ĠBang alore +ĠKen obi +zz i +Ġnorm ative +Ġgobl ins +Ġcaf es +ĠUN CLASSIFIED +ĠF ired +S IGN +Ġs clerosis +ĠV oter +ĠSon ny +ĠExt end +ĠEV s +Ar senal +Ġp si +Ġwid est +ĠT us +Ġlo oms +Ġjust ifying +ĠGr anger +è ¯ +Ref er +58 3 +Ġflour ishing +ab re +Ġr ave +ĠCont ra +Ġ18 98 +Add s +Ġf ul +ĠCo oke +some one += # +67 1 +Ġy ak +Ġar te +ĠMis cellaneous +ĠDet ection +ĠCl ancy +â ģ +ass ies +Ġval iant +ĠFemin ist +cor ruption +V el +P ear +Ġsucc inct +Ġquick est +k w +Ġsp itting +ĠL ibraries +åħ ī +ant z +D ad +ĠSpec ifications +rup ulous +and r +RES ULTS +Ġsnow ball +Ġpred is +ĠB axter +ĠNurs ing +ĠCh aff +s we +Ġout age +Ġnest ing +Ġnotor iety +tr igger +on ite +j on +Ġf ou +ook ed +ĠCelebr ity +re ality +Ġfat ig +Ġhug ging +Ġbother s +ĠPan zer +ĠCh andra +fig ured +Ġvol ts +ĠCloud s +Ġfee ble +ĠCur ve +ĠAs us +78 6 +abs or +ĠV ICE +ĠH ess +Ġmanufact ures +Ġgri zz +ĠPower ful +ac id +Ġsub sections +ĠKrug man +ĠAl ps +is u +Ġsequ est +ĠUlt ron +ĠT inker +ĠGo ose +Ġmism atch +Att orney +Ġmorph ology +ĠSix ers +ut tered +ĠE LECT +gr an +Rus sell +ĠG SL +Ġfort night +Ġ. ) +Ġapost le +pr one +el ist +Unt itled +ĠIm plementation +ist ors +Ġtank er +Ġpl ush +Ġattend ants +ĠT ik +ĠGreen wich +ĠY on +ĠSP L +cell s +unt led +S olution +ĠQu é +Ġvac ated +Ġupt ick +ĠMer idian +æ ĥ +ĠDr ill +9 25 +58 4 +Ġrenov ated +ĠKub rick +zy k +Ġl ousy +pp el +ohyd rate +ĠI zzy +lesi astical +CC C +ĠAj ax +Ġad apters +ĠPetra eus +Ġaffirm ation +ĠST OR +le ms +ad oes +ĠConstantin ople +Ġp onies +Ġl ighthouse +Ġadherent s +ĠBre es +omorph ic +Fight ing +Ġpl aster +ĠP VC +ĠOb st +Ġdear ly +ĠTo oth +icks on +Ġsh aming +P lex +A gg +ĠâĢ¦ " +Ġsub reddits +Ġpige on +ĠResident ial +ĠPass ing +Ġl um +ĠP ension +Ġpessim istic +Ġ4 32 +z inski +c ade +0 75 +Ġapolog ised +iy ah +Put ting +Ġgloom y +ĠLy me +=-=-=-=- =-=-=-=- +ĠT ome +ĠPsych iatric +ĠH IT +c ms +ap olog +Ġbreak er +Ġdeep en +Ġtheor ist +ĠHigh lands +Ġb aker +Ġst aples +Ġinterf ered +ĠAb ortion +jo ined +ch u +Ġform ulate +Ġvacc inations +Ġban ter +phe us +Ġoutfield er +ĠM eter +Ġ# #### +Ġ18 95 +Ġnarrow ing +ĠST ORY +f p +ĠC ST +ign ore +Ġproclaim ing +ĠR U +ĠB ALL +yn a +65 3 +Ġpos it +P RE +59 4 +ĠRegist rar +ĠPil grim +ic io +Ġpre tt +Ġlif eless +Ġ__ _ +Ne igh +ĠCh urches +orn o +Ġor cs +Ġkind red +ĠAud it +Ġmillenn ial +ĠPers ia +g ravity +ĠDis ability +ĠD ARK +W s +od on +Ġgrand daughter +ĠBro oke +ĠA DA +ER A +Ġpick ups +ĠWil kinson +ĠSh ards +ĠN K +Ġexp el +ĠKis lyak +Ġj argon +Ġpolar ized +ian e +Pub lisher +Ġreb utt +Ġapprehens ion +ĠK essler +Ġpr ism +F UL +19 64 +ĠL oll +ä ¿ +le thal +Å Ł +Ġg hetto +Ġb oulder +ĠSlow ly +ĠOsc ars +ĠInst ruction +ĠUl tr +ĠM oe +N ich +ĠP ATH +( * +ĠRE LEASE +un ing +rou se +en eg +Ġre imb +ĠDet ected +Do S +Ġster ling +Ġaggreg ation +ĠLone ly +ĠAtt end +hig her +Ġairst rike +ks on +SE LECT +Ġdef lation +ĠHer rera +C ole +rit ch +Ġadvis able +F ax +Ġwork around +Ġp id +mort em +ers en +Ġtyp o +Ġal um +78 2 +ĠJam al +script s +Ġcapt ives +ĠPres ence +ĠLie berman +angel o +Ġalcohol ism +ass i +Ġrec ite +Ġgap ing +Ġbask ets +ĠG ou +Brow ser +ne au +Ġcorrect ive +und a +sc oring +ĠX D +Ġfil ament +Ġdeep ening +ĠStain less +Int eger +Ġbu ggy +Ġten ancy +ĠMub arak +Ġt uple +ĠD roid +ĠS itting +Ġforfe it +ĠRasm ussen +ixt ies +es i +ĠKim mel +Ġmetic ulously +Ġap opt +ĠS eller +08 8 +ec ake +hem atically +T N +Ġmind less +Ġdig s +ĠAcc ord +ons ense +em ing +br ace +Ġe Book +ĠDist ribut +ĠInvest ments +w t +] ), +beh avior +56 3 +Ġbl inding +ĠPro testers +top ia +Ġreb orn +ĠKel vin +ĠDo ver +ĠD airy +ĠOut s +Ġ[ / +Ï Ģ +b p +ĠVan ity +ĠRec ap +ĠHOU SE +ĠF ACE +Ġ4 22 +69 2 +ĠAnt ioch +cook ed +Ġcoll ide +Ġa pr +Ġsle eper +ĠJar vis +Ġalternative ly +ĠLe aves +ĠM aw +Ġantiqu ity +ĠAdin ida +Ġab user +Poké mon +Ġass orted +ĠRev ision +ĠP iano +ĠG ideon +O cean +Ġsal on +Ġbust ling +ogn itive +ĠRah man +Ġwa iter +Ġpres ets +ĠO sh +ĠG HC +oper ator +Ġrept iles +Ġ4 13 +ĠG arr +ĠCh ak +Ġhas hes +Ġfail ings +Ġfolk lore +Ġab l +ĠC ena +ĠMac Arthur +ĠCOUR T +Ġperipher y +app ers +Ġreck oned +ĠInf lu +ĠC ET +Ġ3 72 +ĠDefin itive +ass ault +4 21 +Ġreservoir s +Ġd ives +ĠCo il +DA Q +Ġvivid ly +ĠR J +ĠBel lev +Ġec lectic +ĠShow down +ĠK M +ip ed +reet ings +ĠAs uka +L iberal +ĠÏ Ħ +Ġbystand ers +ĠGood win +uk ong +S it +ĠT rem +Ġcrim inally +ĠCirc us +ch rome +88 7 +Ġnan op +ĠOb i +ĠL OW +o gh +ĠAuth ors +ob yl +Ur ban +Ġt i +ĠWe ir +t rap +ag y +Ġparent heses +Ġout numbered +Ġcounter productive +ĠTob ias +ub is +P arser +ST AR +Ġsyn aptic +ĠG ears +Ġh iber +Ġdebunk ed +Ġex alted +aw atts +H OU +Ch urch +ĠPix ie +ĠU ri +ĠForm ation +ĠPred iction +C EO +Ġthro tt +ĠBrit ann +ĠMad agascar +ë ĭ +Ġbill boards +ĠRPG s +ĠBe es +complete ly +F IL +Ġdoes nt +ĠGreen berg +re ys +Ġsl ing +Ġempt ied +ĠPix ar +ĠDh arma +l uck +ingu ished +Ġend ot +Ġbab ys +05 9 +che st +r ats +Ġr idden +Ġbeet les +Ġillum inating +Ġfict itious +ĠProv incial +Ġ7 68 +Ġshe pherd +ĠR ender +Ġ18 96 +C rew +Ġmold ed +ĠXia omi +ĠSp iral +Ġdel im +Ġorgan ising +Ġho ops +ĠBe i +z hen +Ġfuck in +Ġdec ad +Ġun biased +am my +sw ing +Ġsmugg led +Ġk ios +ĠP ERSON +ĠInquis itor +Ġsnow y +Ġscrap ing +ĠBurg ess +P tr +ag ame +R W +Ġdro id +ĠL ys +ĠCass andra +Jac ob +Ġ35 4 +Ġpast ure +Ġfr anc +ĠScot ch +ĠEnd s +ĠI GF +def inition +Ġhyster ical +ĠBrown e +77 1 +Ġmobil ization +æ ķ +iqu eness +Th or +Ġspear headed +Ġembro iled +Ġconject ure +jud icial +Ch oice +Ġpaper back +P ir +Ġrec overs +ĠSur ge +ĠSh ogun +ĠPed iatrics +ãģ ł +Ġsweep s +ĠLabor atories +ĠP acks +al us +add in +Ġhead lights +g ra +Ev idence +COL OR +Ad min +Ĭ ± +Ġconco ct +s ufficient +Ġun marked +Ġrich ness +Ġdiss ertation +Ġseason ing +Ġg ib +ĠM ages +un ctions +ĠN id +che at +ĠTM Z +c itizens +ĠCatholic ism +n b +Ġdisemb ark +ĠPROG RAM +a ques +Ty ler +Or g +ĠSl ay +ĠN ero +ĠTown send +IN TON +te le +Ġmes mer +9 01 +Ġfire ball +ev idence +aff iliated +ĠFrench man +ĠAugust a +0 21 +Ġs led +Ġre used +ĠImmun ity +Ġwrest le +assemb led +Mar ia +Ġgun shots +ĠBarb ie +Ġcannabin oids +ĠTo ast +ĠK inder +IR D +Ġre juven +Ġg ore +Ġrupt ure +Ġbre aching +ĠCart oon +Ġ4 55 +ĠPale o +6 14 +Ġspe ars +ĠAm es +ab us +Mad ison +GR OUP +Ġab orted +y ah +Ġfel on +Ġcaus ation +Ġprep aid +Ġp itted +op lan +ĠShel ley +ĠRus so +ĠP agan +Ġwill fully +ĠCan aver +und rum +ĠSal ary +ĠAr paio +read er +ĠR ational +ĠOver se +ĠCa uses +Ġ* . +Ġw ob +Ke ith +ĠCons ent +man ac +77 3 +6 23 +Ġfate ful +et imes +Ġspir ited +ĠD ys +Ġhe gemony +Ġboy cot +ĠEn rique +em outh +Ġtim elines +ĠSah ara +ĠRel ax +ĠQuin cy +ĠLess ons +ĠE QU +SE A +N K +ĠCost co +Incre ase +Ġmotiv ating +ĠCh ong +am aru +ĠDiv ide +Ġped igree +ĠTasman ia +ĠPrel ude +L as +9 40 +57 4 +Ġch au +ĠSp iegel +un ic +-- > +ĠPhil ips +ĠKaf ka +Ġuphe aval +Ġsent imental +Ġsa x +ĠAk ira +ser ial +Mat rix +Ġelect ing +Ġcomment er +ĠNeb ula +ple ts +ĠNad u +ĠAd ren +Ġen shr +ĠR AND +fin ancial +ĠCly de +uther ford +Ġsign age +Ġde line +Ġphosph ate +rovers ial +f ascist +ĠV all +ĠBeth lehem +Ġfor s +Ġeng lish +S olid +N ature +Ġv a +ĠGu ests +Ġtant al +Ġauto immune +;;;;;;;; ;;;; +ĠTot ally +ĠO v +Ġdef ences +ĠCoc onut +Ġtranqu il +Ġpl oy +Ġflav ours +ĠFl ask +ãĤ¨ ãĥ« +ĠWest on +ĠVol vo +8 70 +Ġmicro phones +ver bal +R PG +Ġi ii +; } +0 28 +Ġhead lined +Ġprim ed +Ġho ard +ĠSh ad +ĠEN TER +Ġtri angular +Ġcap it +l ik +ĠAn cients +Ġl ash +Ġconv ol +Ġcolon el +en emy +G ra +Ġpub s +ut ters +Ġassign s +ĠPen et +ĠMon strous +ĠBow en +il ver +H aunted +ĠD ing +start ed +pl in +Ġcontamin ants +ĠDO E +ff en +ĠTechn ician +R y +Ġrob bers +Ġhot line +ĠGuard iola +ĠKau fman +row er +ĠDres den +ĠAl pine +E lf +Ġf mt +ĠS ard +urs es +g pu +Un ix +Ġunequiv ocally +ĠCitizens hip +qu ad +m ire +ĠS weeney +B attery +6 15 +Ġpanc akes +Ġo ats +M aps +ĠCont rast +mbuds man +ĠE PS +Ġsub committee +Ġsour cing +Ġs izing +ĠBuff er +ĠMand atory +Ġmoder ates +ĠPattern s +ĠCh ocobo +ĠZ an +ĠSTAT ES +ĠJud ging +ĠIn her +* : +Ġb il +ĠY en +Ġexh ilar +oll ower +z ers +Ġsn ug +max imum +Ġdesp icable +ĠP ACK +ĠAn nex +Ġsarcast ic +Ġlate x +Ġt amp +ĠS ao +b ah +ĠRe verend +ĠChin atown +ĠA UT +d ocumented +ĠGA BA +ĠCan aan +ĠÙ ħ +Ġgovern s +pre v +E sc +ĠEst imates +OS P +Ġendeav our +ĠCl osing +omet ime +every one +Ġwor sen +Ġsc anners +Ġdev iations +ĠRobot ics +ĠCom pton +Ġsorce rer +Ġend ogenous +Ġem ulation +ĠPier cing +ĠA ph +ĠS ocket +Ġb ould +ĠO U +ĠBorder lands +Ġ18 63 +G ordon +ĠW TO +Ġrestrict s +Ġmosa ic +Ġmel odies +ç Ħ +T ar +Ġdis son +ĠProv ides +Ġ ...... +b ek +F IX +Ġbro om +ans hip +Do ctors +Ġner ds +ĠReg ions +na issance +Ġmet e +Ġcre pt +pl ings +Ġgirlfriend s +kn it +ig ent +ow e +Ġus hered +ĠB az +M obil +4 34 +ĠPres ents +orig in +Ġins omnia +ĠA ux +4 39 +ĠCh ili +irs ch +G AME +Ġgest ation +alg ia +rom ising +$ , +c row +ĠIn spection +at omic +Rel ations +J OHN +rom an +ĠClock work +ĠBak r +m one +M ET +Ġthirst y +Ġb c +Ġfacult ies +R um +Ġnu ance +ĠD arius +ple ting +fter s +etch up +Reg istration +ĠK E +R ah +Ġpref erential +ĠL ash +ĠH H +Val id +ĠN AV +Ġstar ve +ĠG ong +z ynski +ĠAct ress +Ġw ik +Ġun accompanied +lv l +Br ide +AD S +ĠCommand o +ĠVaugh n +Wal let +Ġho pping +ĠV ie +Ġcave ats +Ġal as +if led +ab use +66 1 +Ġib n +Ġg ul +Ġrob bing +t il +IL A +Ġmit igating +Ġapt ly +Ġty rant +Ġmid day +ĠGil more +ĠDe cker +Ġ§ § +part ial +Ex actly +Ġphen otype +Ġ[+ ] +ĠP lex +ĠI ps +vers ions +Ġe book +Ġch ic +g ross +":" "},{" +ĠSur prisingly +M organ +Ġresid ues +ĠConf ederation +in feld +Ġl yr +mod erate +Ġperpend icular +V K +Ġsynchron ized +Ġrefres hed +Ġad ore +ĠTor ment +ol ina +Ġ26 00 +Item Tracker +Ġp ies +ĠF AT +ĠR HP +0 48 +ĠRES P +ĠB J +all ows +P and +Ġunw elcome +ĠV oc +ĠBast ard +ĠO W +ĠL AR +ĠHeal er +Environment al +ĠKen yan +ĠTr ance +ĠP ats +Ġali ases +ĠGar field +Ġcampaign er +Ġadvance ments +ĠOkin awa +ĠC oh +ows ky +Ġstar ved +Ġsize able +Ġ: -) +Ġm RNA +Ġsusp ensions +ist ar +Scot land +Pr in +-------------------------------- ---------------- +Ġ50 2 +Ġteasp oons +Ġ10 50 +Ġcoerc ive +ĠMason ic +edd ed +ĠPass enger +Ġl att +Ġbr aces +ĠSt eal +ĠNY T +ĠK ats +ĠCel est +ae z +T u +ĠCoul ter +ðŁ ĺ +Fl ickr +ĠWil mington +ith s +++ ; +Ġv ending +Ġneg ro +ĠPh i +ĠYellow stone +Call back +Ġsh ampoo +ĠSh ades +w at +Ġsuper human +Ġridic uled +Ġhol iest +om bo +Ġintern s +Ġh one +ĠPar agu +UR I +Ġd angling +ãĤ » +so v +ict ional +av ailability +Ġrev ocation +Ġd ow +in ic +ĠTHE IR +Ġis o +Ġout ings +ĠLeth al +Ġ) )) +Ġinacc ur +Ġout landish +Ġan us +let ico +id on +l ol +Ġun regulated +Ġsuccumb ed +Ġc uff +ĠWast eland +let al +Ġsub str +Ġcoff ers +Ġautom akers +ov i +ĠX ue +ĠDayton a +Ġjar ring +Ġf umes +Ġdisband ed +z ik +itt on +Ġstriking ly +Ġsp ores +Ad apter +.) : +ĠLynd on +ival ry +Ġor ally +Ġtumult uous +Ġdisple asure +Ġcon es +or rect +Ġappe ase +Ġder by +ĠTrip oli +ĠAl ess +Ġp oked +ĠGu ilty +v P +En ough +Ġorig inals +6 99 +Ġrabb i +Ġproverb ial +Ġpostp one +el ope +ĠMist y +Ġstaff ed +ĠUn employment +redit ary +Ġdilig ent +re comm +me asures +as in +8 25 +Ġpond s +Ġmm ol +ĠS AR +ĠC ARE +Ġ3 71 +Ġclen ched +ĠCors air +Ġcaric ature +z n +att ach +ĠSch ro +spe ak +p ainted +ĠS uc +ĠE NT +Ġcell ul +ĠP aid +di agn +WH ERE +Ġtext ed +B arn +Ġret racted +ĠRe ferred +S av +Ġup keep +Ġwork places +ĠTok ens +Ġampl ify +cl inical +Ġmult ic +mber g +Ġconvol uted +Reg ion +5 65 +ĠTop ic +Ġsn ail +Ġsal ine +Ġins urrection +ĠPet r +f orts +B AT +ĠNav ajo +Ġrud imentary +ĠLak sh +OND ON +Me asure +Ġtransform er +ĠGodd ard +Ġcoinc ides +ir in +R ex +ĠB ok +qu it +Ġshotgun s +Ġprolet arian +Ġsc orp +ĠAd a +5 14 +Ġsl ander +record ed +Ġemb ell +ris ome +Ġapolog izing +ĠMul cair +ĠGib raltar +Cl a +Ġall ot +ĠAtt ention +Ġ4 33 +le ave +Ġwh ine +ĠIss a +ĠFa ust +ĠBar ron +hen y +Ġvictim ized +J ews +Ġnurt uring +ett el +W inged +ĠSub tle +Ġflavor ful +ĠRep s +eng ed +call back +Ġdirection al +Ġcl asp +ĠDirect ions +plan et +icult ure +Hel per +ic ion +ac ia +Ġç ¥ŀ +Ġsur ges +Ġcan oe +ĠPrem iership +be en +Ġdef ied +ĠTro oper +Ġtrip od +Ġgas p +ĠE uph +ĠAd s +vern ight +high ly +R ole +Ġent angled +ĠZe it +6 18 +ĠRust y +Ġhaven s +ĠVaugh an +HA EL +ĠSER VICE +/ , +Ġstr icken +Ġdel usions +Ġb is +ĠH af +Ġgrat ification +Ġent icing +UN CH +Ad ams +ĠOL ED +ĠBeet le +Ġ18 99 +ĠSO FTWARE +ateg or +V L +ĠTot em +ĠG ators +AT URES +Ġimped ance +Reg istered +ĠC ary +ĠAer ial +on ne +en ium +Ġd red +ĠBe g +Ġconcurrent ly +Ġsuper power +ĠX an +j ew +imes ter +ĠDick inson +âĶ ģ +F la +Ġp ree +ĠRoll ins +© ¶æ +Ġden omination +ĠL ana +5 16 +Ġinc iting +sc ribed +j uries +ĠWond ers +app roximately +Ġsusp ending +Ġmountain ous +ĠL augh +oid al +N s +Det ect +) = +ĠL uthor +ĠSchwarz enegger +ĠMull er +ĠDev i +ec ycle +J ar +6 13 +ĠL ongh +B ah +ĠSP ORTS +n w +Ġref inement +Ġwater ways +Ġd iner +Bl ade +68 3 +F ac +Ġinitial s +Ġro g +Ġparan ormal +B UT +Ġ[ ( +ĠSw anson +ĠM esh +âĸ ¬ +Impro ve +ĠRad iation +ĠEst her +ĠE sk +ĠA ly +ik y +Ġir rad +ĠBuck ingham +Ġref ill +Ġ. _ +Re pe +CON CLUS +Ġdifferent iated +Ġchi rop +ĠAt kins +Pat tern +Ġexc ise +Ġcab al +N SA +ĠST A +ĠS IL +ĠPar aly +Ġr ye +ĠHow ell +ĠCount down +ness es +alys ed +Ġres ize +ãĤ ½ +Ġbudget ary +ĠStr as +w ang +Ġap iece +Ġprecinct s +Ġpe ach +Ġsky line +Ġ35 3 +pop ular +App earances +ĠMechan ics +ĠDev Online +S ullivan +Z en +Ġp u +op olis +5 44 +Ġde form +Ġcounter act +ĠL ange +Ġ4 17 +Con sole +77 4 +Ġnodd ing +Ġpopul ism +Ġhe p +Ġcoun selling +compl iance +U FF +Ġunden iably +Ġrail ing +ĠHor owitz +ĠSim one +ĠBung ie +Ġa k +ĠTal ks +x ff +fl ake +Cr ash +Ġsweat y +Ġban quet +ĠOFF IC +Ġinvent ive +Ġastron omer +ĠStam ford +ĠSc are +ĠGRE EN +olic ited +Ġr usher +Ġcent rist +ight ing +Ġsub class +Ġdis av +Ġdef und +ĠN anto +oci ate +m ast +Ġpac if +Ġm end +e ers +imm igration +ESS ION +Ġnumber ing +Ġlaugh able +ĠEnd ed +v iation +em ark +P itt +Ġmetic ulous +ĠL F +Ġcongrat ulated +ĠBir ch +Ġsway ed +Ġsemif inals +Ġhum ankind +m atter +ĠEqu ip +opa usal +S aid +ĠLay out +Ġvo icing +Ġth ug +Ġporn ographic +I PS +Ġmo aning +Ġgriev ance +Ġconf essions +esc al +TEXT URE +Aut hent +os aurus +P urchase +Ġreleg ation +al ter +ĠÂł Âł +Ġr iddled +Ġo gre +ĠLow ell +Occ up +E at +ĠHy der +ĠAdvis er +Com merce +H unt +ĠOr th +ĠComp etitive +ĠCL A +CD C +Ġsal ads +F le +Ġindustrial ized +` , +ĠO WN +Ġbec k +ĠPart icularly +oub t +Ġm M +ĠHuss ain +ĠChen nai +Ġ9 20 +Ġappoint ing +ĠCull en +,,,, ,,,, +Ġp ores +ver ified +Ġbi ochemical +em ate +Ġcoward ly +ĠHels inki +ĠEthiop ian +S OURCE +ER C +est ro +Ġbi otech +ĠS our +Ġbrew er +Bloom berg +Ġintens ify +Gl ass +an co +ĠF DR +gre SQL +ĠF ires +©¶æ ¥µ +ec o +100 1 +ĠHom eless +Ġinstant aneous +ĠH aste +ig el +D iamond +Ġp aving +Ġland fill +Ġd ads +h oun +: ] +Ġinc endiary +ĠLiving ston +ĠHil bert +ĠChe cks +st yles +in ators +ĠCl ive +ph rine +Ġchimpan zees +Ġp all +ĠJ M +ĠAad haar +ð Ŀ +Ġachie vable +dis abled +P ET +OOOO OOOO +M ot +Ġint angible +Ġbal let +ĠWe bs +ĠEst imated +Effect s +Ġb ailed +Josh ua +Ġturb ulence +Ġoccup ant +ĠDay light +Ġ36 1 +me et +Ġstat ically +Ġon look +Ġk i +il legal +Ġvel vet +Ġdehyd ration +Ġacqu ies +ĠRe z +ak ura +ĠU pton +at ro +Ġincomp rehensible +Ġback door +ĠRh ino +7 27 +Ġmath s +) + +Ġhe resy +Ġd f +ĠRoc he +ĠL ydia +Ġpanc reat +re ply +arre ll +Ġsolicit ation +Ġcirc adian +BI P +Ġfor ay +Ġcrypt ic +iz u +ime o +ĠTom ato +ĠH oms +ex amination +Ġqu arry +ĠVal iant +ĠJer icho +ĠIN CLUD +Ġ18 40 +5 19 +Ġres ists +Ġsnap shots +ĠSp ur +ĠAnt iqu +Log in +Ġbest selling +Ġant ic +ĠS utherland +ãĤ¢ ãĥ« +Ġ~ / +ĠP arm +è ĥ +P ages +int ensity +Ġimm obil +Ġ18 65 +zz o +Ġn ifty +Ġf entanyl +ĠPres ervation +op hen +Ġd arts +ĠD inosaur +po inters +ĠR ite +s uggest +aware ness +ĠSher idan +Ġst ances +Ġsor cery +Ġper jury +ĠNik ola +ie ver +Ġf iance +ĠJordan ian +ĠBall oon +Ġn ab +Ġk b +Ġhuman ities +ĠTan aka +hill ary +Ġconsult ancy +ĠZ ub +Ġrem ission +Ġconf id +CH Q +ĠF ug +Ġimpro vis +Y ep +/ _ +Ġunwilling ness +Ġport folios +05 5 +ĠInstruct or +aim an +Ġclaim ants +M bps +ĠBy e +re ceived +T weet +Ġind emn +ri z +am ara +N at +Ġeval uates +ĠL ur +ep ad +FO X +ĠTh ro +Ġrust y +Ġbed rock +ĠOp rah +J B +Ġmanip ulative +Ġwill ful +Ġrel apse +Ġext ant +The me +S ensor +ĠSt ability +go vern +Ġpo ppy +Ġkn ack +Ġins ulated +ĠT ile +ĠExt rem +Ġunt old +Ġconver ge +Ġref uel +ig roup +Ġdistort ions +Ġrav aged +Ġmechan ically +ĠRe illy +ĠN ose +ĠIncarn ation +ĠBeck y +abb ling +Ġt aco +Ġr ake +Ġmelanch oly +Ġillust rious +ĠDart mouth +Gu ide +ĠR azer +ĠBen z +Ult imate +ĠSur prise +Ġpage ant +off er +Who ever +Ġw iser +Ġchem ist +ĠHE LL +ĠBul k +Ġpl utonium +ĠCO VER +Ö ¼ +f ailed +Ġtire lessly +Ġinf ertility +ĠTr ident +ĠShow time +ĠC iv +V ice +requ ires +itt ance +Ġun controlled +interest ing +56 1 +Ġinnov ate +ateg ic +L ie +ĠS elling +U l +Ġsav ior +ĠT osh +Ġsw ast +P ASS +Ġr ink +Ġcard io +ĠI ro +ud i +Ġv antage +Ġv ans +ĠNi ño ++ = +Ġpropag ate +< ? +Ġmethod ological +204 39 +Ġtrig lycer +Ġing rained +ĠAn notations +arr anted +6 17 +ĠS odium +ĠA AC +techn ical +mult ipl +Ġ3 73 +å ĭ +Ġdec isively +Ġboost ers +Ġdessert s +ĠGren ade +Ġtest ifying +ĠSc ully +ID s +Ġlock down +ĠSc her +ĠR é +ĠWhit man +ĠRams ay +rem ote +Ġh ikers +ĠHy undai +Ġcons cientious +Ġcler ics +ĠSiber ian +ut i +is bury +Ġrel ayed +Ġqu artz +ĠC BI +seek ers +ull a +Ġweld ing +ĠSh al +ble acher +T ai +ĠSam son +Ġt umble +ĠInvest or +Ġsub contract +ĠShin ra +ow icz +j andro +d ad +Ġtermin ating +ĠNe ural +ä» £ +Ġleak age +ĠMid lands +ĠCaucas us +í ķ +c it +ll an +iv ably +ĠAlb ion +Ġ4 57 +Ġregist rations +Ġcomr ade +Ġclip board +0 47 +Ġdiscour aging +ĠO ops +Ad apt +Ġem path +n v +ĠPR OT +ĠDon n +ĠP ax +ĠB ayer +t is +Squ are +Ġfoot prints +part icip +ĠChile an +B rend +ind ucing +M agn +Ġclub house +ĠMagn um +Ġenc amp +ĠEth nic +uch a +ere y +Ġw atered +ĠCal ais +Ġcomplex ion +Ġsect s +Ġren ters +Ġbr as +oÄŁ an +Time out +Man agement +Ġinf ographic +P okemon +Cl ar +Ġloc ality +Ġfl ora +as el +P ont +Ġpop ulate +ĠO ng +Ġsubs istence +Ġa uctions +ĠMcA uliffe +ĠL OOK +br inger +Ġtit an +Ġmanif old +ĠâĹ ı +Ġcalibr ated +Ġcal iphate +ĠSH E +ĠCommission ers +ce ivable +j c +W inner +5 24 +Ġcond one +Other wise +Ġp iling +Ġem body +ĠCrime an +ut ics +ĠEx hibition +Ġ4 26 +e ering +Ġv ying +ĠH UGE +* =- +Ġprin cipled +à ¦ +Ġquir ks +ĠEdit ors +put ing +G ES +ĠF TA +ठ¾ +add on +ĠH AM +ĠFrie za +W oman +. $ +Ġc rib +ĠHer od +Ġtim ers +ĠSp aces +ĠMac intosh +at aka +Ġgl ide +Ġsmell ing +ĠB AL +Ġun su +Ġcond os +Ġbicy cl +ĠRev ival +55 3 +Ġjugg ling +H ug +ĠKardash ian +ĠBalk ans +mult iple +Ġnutrit ious +oc ry +19 00 +Ġinteg rates +Ġad joining +ĠF older +roll ment +ven ient +Ġu ber +y i +Ġwh iff +ĠJu ven +ĠB orough +net te +Ġb ilingual +ĠSp arks +ph thal +man ufact +Ġt outing +ĠPH I +Ke efe +Rew ard +Ġinf all +ĠTem per +typ ically +ĠNik ol +Ġregular s +Ġpseud onym +Ġexhib itions +Ġbl aster +Ġ40 9 +w arming +Ġrever ber +Ġrecip rocal +Ġ6 70 +ip ient +b ett +ĠBe gins +Ġit ching +ĠPh ar +Ass uming +Ġem itting +ĠML G +Ġbirth place +Ġt aunt +ĠL uffy +ĠAm it +Ġcir cled +ĠN ost +enn ett +Ġde forestation +ĠHist orically +ĠEvery day +Ġovert ake +79 2 +Ġn un +ĠLuc ia +Ġaccompan ies +ĠSe eking +ĠTr ash +an ism +R ogue +Ġnorth western +ĠSupplement al +ĠNY U +ĠF RI +ĠSat isf +x es +5 17 +Ġreass ured +Ġspor adic +Ġ7 01 +Ġmed ial +Ġcannabin oid +Ġbarbar ic +Ġep is +ĠExplos ive +ĠD ough +Ġuns olved +Support ed +Ġacknowled gment +sp awn +Ġkit chens +Ġ- = +talk ing +ic ist +ĠPeg asus +ĠPS U +Ġphot on +ĠAuthent ication +R G +@# & +76 2 +ĠCl air +Ġdi aper +Ġbr ist +ĠProsecut ors +ĠJ em +6 28 +ĠEvery where +ĠJean ne +equ ality +ãĥ© ãĥ³ +object s +ĠPel icans +Ġ39 2 +Ġbl u +b ys +ĠA go +Ġinstruction al +Ġdiscrim inating +ĠTR AN +ĠCorn el +ag os +Ġty re +Ġas piration +ĠBrid gewater +": - +! ". +ĠEn s +ĠCoc o +P ie +Ġdet ach +ĠC ouch +Ġphys ique +ĠOccup ations +osc opic +en ough +B uzz +App earance +Y P +Ġrac er +Ġcompl icity +r pm +T oy +Ġinterrupt s +ĠCat alyst +Ġut ilitarian +imp act +Ġsp aghetti +Ġp orous +Ġeste emed +Ġinc iner +ĠI OC +7 48 +Ġesp resso +ĠSm ile +abil ia +6 35 +Ġmathematic ian +Ġ4 24 +ĠK L +ĠH IP +Ġover heard +ĠT ud +ĠT ec +Ġqu izz +Ġfl attering +Ġcon n +âĢ İ +Ġatt aches +ĠR OS +ĠAC S +Ġt cp +ĠSh ame +sk ip +res pected +ĠTrin idad +gr ain +Ġfooth old +ĠUnch arted +ĠJul io +z l +av ored +ĠAn xiety +er rors +ĠCent auri +its ch +D addy +Ġclutch ing +ĠIm plement +ĠGut ierrez +Ġ7 60 +Ġtele portation +end ra +Ġrevers ible +st ros +Ad venture +08 3 +Ġliber ating +Ġas phalt +ĠSp end +AR DS +im sy +PR ES +ĠEmer ging +Ġwild fires +Ġtechn ologically +Ġem its +ĠART ICLE +Ġirregular ities +Ġcher ish +çī Ī +Ġst ink +ĠR ost +Econom ic +Ġcough ing +ĠMcC ann +pro perties +ilant ro +Ġreneg oti +Trans lation +Ġin quest +ĠGra pe +oot ers +gu i +ĠSwords man +ace ae +h itting +Ġr c +Ġexert ed +ĠS AP +it ent +Ġperil ous +Ġobsc urity +Ġassass inate +Ġab original +Ġresc uing +ĠSh attered +lock ing +all ion +Ch anging +ĠHar rington +ĠB ord +ĠAfgh ans +Jam ie +aret z +ĠAugust us +Ġ38 6 +8 30 +Ġj og +ok ingly +Tr igger +ĠH OR +Stat istics +Ġviewers hip +Ġadd itives +h ur +Ġmaxim izing +ĠR ove +ĠLou ie +ĠBuck et +ĠCHR IST +ou sel +Ġstre aks +ir ted +Ġt ert +Ġcolonial ism +Ġbur ying +y k +Cond ition +ĠDPR K +By Id +75 1 +âĹ ¼ +Ġwor risome +Ġvoc ational +sl ice +Ġsa ils +ĠCorrection al +95 4 +Ġt ul +K id +l uster +Ġfam ilial +ĠSp it +ĠEp iscopal +Specific ally +ĠVol cano +run s +q s +Ġve tted +Ġcram med +t rop +here r +Thank fully +Ġper cussion +Ġor anges +Ġround up +Ġ4 99 +x ious +Char acters +ĠZion ism +ĠR ao +ÃĽ ÃĽ +W F +Ġunintention al +ONE Y +Gr ab +Com mercial +Ġglut amate +ĠMcK enna +ru ciating +ning ton +ih u +Ch an +ĠSw ap +Ġleaf lets +Ġfunction ally +er ous +F arm +Ġcal oric +ĠLiter ally +con cert +Ġshe nan +Ġrep aid +ey es +Ġbas hing +ĠG orge +Ġcollabor ations +Ġun account +itch ie +Ġteam work +pp elin +Ġpip ing +Ġmin ced +Ġd iam +ri eg +Ġmasc ara +Ġsuck er +ĠMo ons +App s +ĠPe ck +Ġper v +ĠFl oat +o ley +ĠN ish +im ize +Ġarom atic +u in +end ish +! / +ĠB icycle +ĠAS IC +ile ged +ĠQuad ro +ios yn +Ġlock out +ĠW ink +SP EC +Attempt s +Ġseed ed +red o +ias is +Ġsn ag +ãĥķ ãĤ© +ãĤ ¶ +Ġground ing +Ġrelie ver +Ġfrivol ous +ĠG ifts +ĠF aces +Es pecially +Ġmicrobi ome +im ag +ĠSch l +ĠP les +ĠBle ach +ĠIr win +ĠE aton +ĠDisc iple +Ġmultipl ication +Ġcoer ced +Ġ4 19 +st h +E vil +B omb +Ġex orc +Ġstag gered +L ESS +Ġinert ia +ĠED IT +Ġgo b +Tr aditional +Ġclass y +Lear y +ĠP AGE +yr s +Ġtrans porter +Ġmat ured +Ġhij ab +Ġbi ome +Where as +Ġex termination +ĠT ues +ĠT akeru +ĠAud rey +er ial +ĠAd en +aff les +Ġnarciss istic +ĠB aird +UT F +I re +ĠCon nie +Ch amp +Ġwhis pering +ĠH att +D K +Ġdis infect +Ġdeduct ed +Ġpart ake +Ġdown grade +ĠEs ports +ĠContin uing +Ġdemocr atically +icro bial +itt a +Ġlim estone +Ġexempt ed +ĠFren zy +H erm +7 28 +Ġfled gling +Met a +765 61 +69 3 +% : +w ake +5 26 +ĠDis cipline +Ġvirgin ity +ĠLeg ions +ĠFrank ie +int ent +Ġrest rooms +ĠRou ter +da q +Ġobjection able +âĨ ij +w ark +ĠRah ul +g ain +activ ation +abs olute +ĠAccess ed +Ġ24 00 +ogg les +Ġsecond ly +ĠDEF ENSE +Ġpost age +wra pper +sh arp +7 29 +Ġcommun icates +Ġadd on +ĠMil itia +H ong +Ġsl umped +ĠJP EG +ĠI car +ad ish +68 1 +Ġmaj esty +ĠWolf gang +ĠEl astic +u per +Ġv iz +Ġunconscious ly +ĠST D +ĠS ass +Ġflower ing +ĠHel ic +ĠDra per +ĠAm ateur +Ġman ure +Ġdis ingen +ĠLe i +br ing +9 49 +Ġinhib ited +Ġhead quartered +Ġen igmatic +�� � +Ġred ress +R H +Ġratt led +Ġd iction +l io +ĠT BA +ĠSN AP +C alling +Ġfasc ists +ĠD ove +iew icz +0 36 +Ġco asts +ĠR ect +Ġ) ] +L ot +6 29 +ĠS EM +ĠPeters en +ĠExpl ain +ĠBo ards +ĠBe zos +ĠJ ournals +Ġ20 24 +p arser +Ġmist rust +Ġgr ate +ĠL ocked +bo a +S aint +g aming +Ġvow el +in ately +bl ow +All ah +Ġun matched +Ġb ordering +ĠExp end +n r +Or acle +rou ch +Ġcont iguous +ac us +Ġdist raught +58 1 +Ġanat omical +O X +ap ixel +8 33 +ĠPL US +Ġres usc +Ġab iding +57 3 +Ġvac ancies +Em ily +Ġhyp othal +ĠWer ner +ĠWe e +ĠDJ s +5 13 +Ġwitch craft +Ġac upuncture +ent ary +benef it +Product s +ĠP SP +ĠMP G +ĠJ inn +ĠJ arrett +Ġ4 45 +ĠIm aging +ĠP yth +Fin ish +Ġte x +Ġjuven iles +Ġhero ism +Ġdoubt less +ĠA ki +ĠT end +ĠPatri arch +Ġbit ters +ĠTele communications +it atively +ag na +Ġr g +ĠS OLD +Ġcomp ulsion +ĠN asa +ĠKath ryn +Ġmillion aires +Ġintrins ically +Ġbolst ered +time out +fl o +Ġtut or +p our +Stat ement +Ġ{ * +ĠRud olph +ĠKimber ly +rog ens +adi q +] + +Ġindign ation +Ġfract uring +ĠRe leases +ĠGr ain +pro tein +L ago +Ġvac ations +Ġboot ed +ĠTH REE +ĠH G +oresc ence +Ġt f +Ġso ar +iosyn cr +Ġgl ances +ĠSp oon +ĠJ ury +ĠCow boy +Ġcreat ively +Hig her +Ġsolic itor +Ġhaw k +ac io +89 6 +Ġsuperf lu +Ġbombs hell +ct ure +Ġbroker age +Ġraid ing +Ġf rench +Ġang led +Trans action +ĠGen ocide +u pe +ĠHait ian +57 2 +! : +Ġunwitting ly +iter ator +sc roll +Ġtall ied +Ġbi omedical +ĠC ARD +Ġe uphem +Ġbrain storm +a quin +K o +Mic helle +ĠR unes +ĠBall istic +ud ers +Ġmod esty +ĠiP ads +ĠEzek iel +Y E +Ġstars hip +Ġpower fully +Ġper l +ĠSh ade +ĠQu art +ĠE EG +Ġfisher man +OS ED +ĠTyp ical +df x +Ġmes hes +Ġet ched +worth iness +Ġtopp led +Ġ3 96 +or ius +We iss +Ġmy sql +ĠVal halla +Ù Ĵ +le asing +Ġrec omp +rap nel +S el +04 3 +Ġder ailed +ĠGu ides +IR T +Ġde human +ĠBritt any +" )) +Ġex claim +Ġb alk +Ġ8 40 +CLA IM +int el +L AB +Ġpe gged +Ġast roph +sm oking +Ġrig ging +Ġfix ation +Ġcat apult +ins ide +ĠC ascade +ĠBolshe vik +G aza +Dep th +Ġloud spe +Ġalmond s +me yer +l eness +j en +f resh +Ġunbeat en +ĠSqu id +ĠPres umably +Tim er +B W +Ġro sters +Ġell ipt +ĠHar riet +dat abase +ĠMut ual +ĠComm odore +uk ed +kn ife +ĠCOMM UN +h ya +Ġmel ts +arch ives +Ġrat ification +Ġmultip lying +Ġinter oper +Ġasc ert +w ings +ver ting +ĠScorp ion +ay e +ĠPorts mouth +ĠM TA +n it +iaz ep +Ġqu arantine +Ġslides how +Ġcent imeters +Ġsyn opsis +Ġsp ate +th irst +Ġnom inating +ĠMel vin +Pre view +Ġthro b +Ġgener ational +ĠRad ius +rest ling +put able +aw ar +N ECT +Ġunlaw fully +ĠRevel ations +Wik ipedia +sur v +Ġeye ing +ij n +ĠF W +Ġbr unt +Ġinter stellar +Ġcl itor +ĠCroat ian +ĠCh ic +ev a +ĠDis app +ĠA kin +iner ies +d ust +Interest ed +Ġgen esis +ĠE ucl +ö n +p icking +Ġmut ated +Ġdisappro ve +ĠHD L +Ġ6 25 +Ì ¶ +c ancer +Ġsqu ats +Ġle vers +Disc uss += ] +D ex +ĠVIDE OS +A UD +Ġtrans act +ĠKin ect +ĠK uala +ĠC yp +7 47 +Ġsh attering +Ġarsen ic +ĠInt ake +ĠAngel o +ĠQu it +ĠK he +Ġ18 93 +M aker +0 29 +ĠPain ting +Dis able +9 16 +Ġanal ges +Ġtact ile +Ġprop hes +Ġd iced +ĠTravel s +ĠHe ader +ĠClub s +Ass istant +Ġinc rim +Ġd ips +Ġcruc ifix +ĠShan ahan +ĠInter pret +Ġ40 90 +al ogy +abb a +Ġsimul ac +hus band +S IM +Ġrecy cle +uc er +ed ged +Ġre naissance +ĠBomb ay +Cath olic +ĠL INE +ĠCl othing +re ports +Ġpl aus +Ġd ag +ĠM ace +Z I +Ġintr uder +ĠVeter inary +g ru +Ġsne aky +ĠS ie +ĠC innamon +P OSE +Ġcou rier +ĠC NS +Ġemanc ipation +s it +Ġplay through +ĠFac ilities +v irt +ĠG auntlet +Thom pson +Ġunbeliev ably +Param eters +Ġst itching +ign e +ĠTH ESE +Priv acy +Ġshenan igans +Ġvit ri +ĠVal id +59 1 +Ń · +ĠProt otype +ink a +SC P +ĠT id +è Ī +old ed +Ġindividual ity +Ġbark ing +Ġm ars +ĠW D +Ġ8 20 +Ġt ir +Ġsl apping +Ġdisgr untled +ĠAng ola +ri us +ĠTorn ado +ĠTh urs +Ġcapt cha +Ġang st +ĠP og +ĠAssass ins +ĠAd idas +Ġjoy ful +Ġwh ining +Emer gency +Ġphosph orus +Ġatt rition +oph on +ĠTimber wolves +ĠJ ah +ĠBr inging +ĠW ad +ĠEn sure +oh l +ĠX ie +omm el +c mp +Ġz ipper +Ġrel at +ĠCor ridor +m ilo +T ING +Av g +Ġcro pped +] } +Ġr aged +ĠLump ur +ĠGuer rero +our ke +N ut +Ġoff sets +og lu +dr m +Ġmort als +lat able +Ġdismiss ive +ä¸ ī +Ġthro ats +Ġchips et +ĠSpot light +Catal og +art ist +G b +Ġch illy +Ġst oked +Ġ3 74 +W ard +L atin +Ġf iasco +Ġble ach +Ġb rav +Enh anced +Ġin oc +ĠFior ina +_ > +Ġle ukemia +Ġel uc +Ġannoun cer +ĠLith uan +ĠArm ageddon +å ĩ +Len in +ĠR uk +Ġpe pp +ĠRom antic +ĠP IT +ĠInter stellar +ĠAt kinson +R aid +J s +Go al +C ourse +Ġvan ishing +es ley +ĠR ounds +Els a +59 3 +Ġredund ancy +ĠST AND +Ġprop hetic +Ġhabit able +ry u +Ġfaint ly +M ODE +Ġfl anked +IR C +Aw esome +Ġsp urious +ĠZ ah +ĠMS G +Ġsh ading +Ġmotiv ational +ĠSant ana +ĠS PR +Ġexc ruciating +om ial +ĠM iko +ĠLe opard +A byss +Ġ[ | +d irty +Ġbath s +Ġdem oral +and re +P B +Ġun ification +Ġsac rament +Ġ[ & +Ġpric eless +Ġgel atin +Ġeman ating +ĠAll aah +98 6 +Ġout burst +Ġer as +ĠX VI +ĠSP I +O tt +ĠLaz arus +PL IED +F lying +blog s +W isconsin +R aven +Ġreb ate +Ġcreep s +ĠSp an +ĠPain ter +ĠKir a +ĠAm os +ĠCor vette +Cons umer +ĠRec over +ck i +Ġpes ky +ĠIn vention +Compan ies +Ġchalleng ers +ad emic +ĠUkrain ians +ĠNeuro log +ĠFors aken +Ġent rants +Ġemb attled +Ġdef unct +ĠGlac ier +Ġpo isons +ĠH orses +m akes +ĠD irt +Ġ4 23 +hh h +ĠTrans formation +QUI RE +................ .. +Ġtrave ller +ĠSe xy +ĠK ern +ip olar +Ġransom ware +oooooooo oooooooo +E c +rub y +Prof essional +ĠOut break +arg ument +G rey +ĠFif a +ĠCH O +ĠFOR M +ĠAm trak +- [ +Ġcr adle +Ġantioxid ants +ãģ®å ® +7 36 +ĠNAS L +ĠContribut ions +Ind iana +ĠST EP +C SS +Ġsal ient +Ġall ocations +yr ights +Ġm ashed +ĠCut ter +Sex ual +Ġp ounded +Ġfan base +Ġc asc +ĠTrans parency +Ġanaly tic +ĠSummon er +× ŀ +ĠAD C +det ail +Ġvan quished +Ġcr abs +ar ie +Dest roy +ĠS ack +Ġtrans istor +Al abama +ĠK oen +ĠFisher ies +c one +Ġannex ed +ĠM GM +es a +Ġf aked +ĠCong ratulations +Ġhind ered +Ġcorrection al +ĠI TV +lee ve +Ġin appropriately +lic ks +Ġtresp ass +Ġp aws +Ġnegoti ator +ĠChrist ensen +lim its +ĠDian ne +Ġeleg ance +ĠContract s +an ke +Ob j +Ġvigil ance +Ġcast les +ĠN AD +ĠHol o +Ġemph atically +ĠTit us +ĠServ ing +ĠRich ie +ĠP igs +5 68 +Ġanim osity +ĠAtt ributes +ĠU riel +M Q +my ra +ĠApplic ant +Ġpsychiat rists +ĠV ij +ĠAb by +ag ree +P ush +Ġk Wh +hib a +Ġinc ite +ĠWe asley +ĠTax i +minist ic +hy per +ĠF arn +Ġ6 01 +ĠNation wide +F ake +95 2 +Ġma ize +Ġinteract ed +Ġtransition ed +Ġparas itic +Ġharm onic +Ġdec aying +Ġbas eless +ns ics +Ġtrans pired +Ġabund antly +ĠFore nsic +Ġtread mill +ĠJ av +ab and +Ġssh d +Ġfront man +ĠJak arta +oll er +dro ps +ĠSERV ICES +rompt u +oph ical +h ospital +bled on +6 45 +Ġmid range +ĠEV ENT +cul ated +raw led +Ġper ched +Ġover board +ĠPe el +ĠP wr +ĠCar th +ĠCOM PLE +co e +sh all +Ġdeter rence +M ETHOD +ĠAbs ent +M EN +Ġs ill +ĠLE VEL +Y ork +Ġsin ners +ĠOP EC +ĠN ur +ĠDesign s +se lection +Ġunw orthy +CH A +Ġstreng thens +88 3 +ed ly +Ġslic ing +Ġmal nutrition +Ġfilm making +ĠPol k +ur ated +Ġ4 21 +bre akers +!' " +Ġwet lands +ĠDisc rimination +Ġallow able +Ġste ered +ĠSic ily +S AM +Ġmust ache +Ġm ids +Ġcl ipped +Ġcirc ulate +Ġbr ittle +ĠBuild ings +ra ised +ĠRound up +Ġwealth ier +Ġoverw rite +Ġover powered +ĠGerr ard +s ites +PD ATED +Ġacute ly +ĠGam ble +Ġp im +ĠK us +Typ ically +De ploy +ĠMoroc can +p otion +com be +Ġvigil ante +Ġ36 3 +St ew +ĠB agg +Ġres ided +ĠSp o +Ġrem nant +Ġempt iness +br ainer +Ġout patient +pri ority +Ġle ptin +ĠPay ton +ĠGle aming +ĠS hed +ĠPol o +ĠMormon ism +rest ricted +arl ane +w x +Ġcreat ine +ĠAn on +ĠST UD +ĠJ UL +ĠT ee +5 28 +08 9 +Ġhat ched +Dis patch +ĠCompos ite +Ġ45 1 +p uff +ĠX COM +ĠOr n +ĠTH ANK +END ED +ĠAshe ville +Ġà ľ +Ġman go +ĠS lightly +world ly +ĠW ander +ĠExp and +ĠCh r +M ist +Ġorthodox y +ĠUN ESCO +reg ate +Else where +k ie +ir led +Ġtopp le +Ġadopt ive +ĠLeg s +d ress +ĠS agan +b are +ĠGl ou +Cr unch +Ġhelp ers +Ġchron ically +ĠH uma +1 0000 +Ġaccommod ating +äº Ķ +Ġwrink les +Ġdod ged +four th +Ġpre con +Ġcompress or +ĠK are +Ġev ict +ĠWar wick +im ar +Ġmodern ization +Ġband wagon +Ġref uted +Ġnet ted +ĠNa ples +ĠGen ie +per ors +Ġfield ed +Ġde re +ĠPar ables +le es +Ġtr out +asp ers +Ġn ihil +Ġhapp iest +Ġflo ppy +ĠLo ft +ĠHe ard +Ġun ison +Ġl ug +ĠRed mond +class ic +Supp orters +SH IP +G MT +Ġfue lled +ç IJ +Ġd d +ĠEmin em +Ġ18 97 +NY SE +Ġsecret aries +ĠF IA +ĠCanaver al +F avorite +Ġp omp +Ġdetain ee +ers hip +aim on +i our +ĠA pex +Ġplant ations +am ia +ac ion +R ust +Ġtow ed +ĠTru ly +5 77 +Ġshel tered +r ider +W o +Ġl air +ĠInt elligent +impro ve +m atically +Ġet iquette +ad ra +all o +ĠJun o +any thing +ĠStru ggle +ĠPred ict +ĠGr imes +ĠAMER ICA +ct x +ĠSit uation +W OOD +Ġsol uble +me ier +Ġintoler able +ang ering +Ġun interrupted +Ġtool tip +Ġinterrog ated +Ġgun ned +ĠSne ak +æŃ ¦ +Ġt ether +Ġcr umble +L ens +Ġclust ered +ĠSy l +ĠHas an +Ġdystop ian +w ana +Ġjoy stick +ĠTh ib +amm u +Tom orrow +5 46 +Ġoverc ame +Ġminim ized +cept or +Run ner +ENG TH +ĠBrend a +ĠAchieve ments +Ġtor ches +Ġrapp ort +ĠInvestig ator +ĠHand ling +rel ation +g rey +8 15 +Ġk cal +ĠComm ands +d q +Ġcur ls +Ġbe arer +Ġcyn icism +it ri +ĠUse ful +B ee +D CS +Ġab ras +P ract +BIL ITIES +7 12 +Ġdebug ger +Ġdebt or +ĠL ia +ĠK ers +Ġexacerb ate +ĠSt acy +ĠB land +ĠSc enes +Ġbranch ing +âĸĪâĸĪâĸĪâĸĪ âĸĪâĸĪâĸĪâĸĪ +ape ake +Ġs alsa +Ġmish and +ĠKon ami +ĠN ib +Ġanecd ote +Ġagree able +Ï ī +ĠNath aniel +ĠHe isman +ĠB eware +Ġ18 86 +spect ive +69 1 +5 22 +Ġinhib its +Ġhas hing +Ġ18 89 +å° Ĩ +v ich +P ure +Ġsolid ly +Ġaspir in +im aru +Ġstreet car +ĠU CS +ĠJ udd +Ġflash backs +p ins +Ġ14 40 +ĠUN HCR +ĠSym ptoms +T IT +5 38 +F ra +% ); +Ġo oz +Ġcur few +Ġcal med +Ġparticip ates +Te X +Ġnons ensical +Ġfull back +ĠDe L +mon key +h ari +Ġmetabol ites +Ġloot ed +ĠAL WAYS +ĠB CC +L t +oc het +B one +Ġveto ed +Ġg cc +ĠCL ICK +Ġ18 88 +s af +Ġstiff ness +Ġlow ly +ĠGe h +vers on +ors et +Ġun foreseen +Ġan esthesia +ĠOpt ical +Ġrecon structed +ĠT up +sh ows +NEW S +ĠNewsp aper +ĠA SA +ter a +N umbers +Ġinexpl icable +× ij +Ġhard ness +unt arily +ĠA cer +grad ient +ARD IS +Ġwood land +Ġmetaph ors +ĠWem bley +ĠPa vel +phil is +Ġre writing +Ġpercept ual +Ġ10 70 +worm s +ĠDown s +Ġunsur prisingly +Ġtag ging +fl ame +Ġlit res +Ġboun ces +ĠB abe +sh ut +Ġoverd oses +ĠShe ila +ĠCh au +ĠBl ess +Capt ure +ĠSign ificant +ĠSc ion +Ġ38 9 +ĠMc H +ĠTitan ium +ĠMe al +amed a +ag ents +agg ressive +B illy +76 3 +ĠS aying +DER R +it one +Coll ins +B ound +Ġbol ted +ĠDM CA +95 3 +Ġun iqueness +Ġep igen +un ci +ant am +Ġreck oning +ch airs +OG R +ĠSen egal +Ġ18 62 +re levant +Ġ ¯ +Ġpharm acies +ĠG eral +v ier +Y an +OR PG +Ġrab id +b ending +ĠUN ITED +Ġ4 65 +As sembly +Ġwe ep +Ġbe hest +ĠMother s +ĠJ ace +h id +Ġwh irlwind +ĠUN IVERS +Ġut opian +Ġkidn ap +Ph ilipp +K in +89 3 +Ġlivest ream +ĠM ISS +Ġsub versive +ĠTechn iques +ĠJUST ICE +ĠB ASE +Ġ38 7 +Ġassail ants +ĠHard core +Ġsprink led +ĠP se +é ļ +print ed +ĠH au +OR GE +ĠT OUR +Ġl aced +Ġit ch +G iving +Ġport ed +78 1 +//////////////// //////////////// +bre eding +Ġlog ger +ĠH OL +inn ie +First ly +Ġembry onic +Ġdeleg ated +p ai +O IL +Ġcentr ally +ĠR x +ĠSc outing +D utch +Ġhe reditary +ĠCru iser +s at +5 29 +ĠMar riott +other mal +Ġprohib itions +E arn +ĠSt ab +ĠColleg es +ĠBel ief +st retched +ĠL H +ĠEntity Item +C IA +Ġun rem +Ġlaure ate +Ġdenomin ations +sum mary +h ler +S pect +ĠK laus +ĠBe ans +Ġins ur +ĠPA X +Ġfield er +ĠV et +ĠSp arrow +z ie +ĠS Q +ĠMond ays +ĠOff line +ĠLer ner +ĠExt ensions +Ire land +Ġpatron age +Ġcontrast ed +ĠMan ia +h irt +Mos cow +Ġcondem ns +ĠAn ge +Ġcomp osing +ĠPe pe +ĠP addock +Ġheter ogeneity +Ġide ologically +Ġf ishes +Ġcur sing +ĠR utherford +ĠFlo ating +ĠAm elia +Te a +Syn opsis +Ġstun ts +Ġbe ad +Ġstock ing +ĠM ILL +ob ook +mass ive +\ < +Ġh ump +ĠPref erences +Engine Debug +ge ist +ĠNiet o +ome ver +ish y +eval uate +col onial +Altern ative +ĠGo Pro +ĠV ortex +ĠNET WORK +ans ky +Sec ure +ĠTh rust +Sn ake +Ġparcel s +Ġsam urai +Ġactress es +N ap +M F +ifer ation +Be er +5 23 +ĠI ly +oint ment +P ing +Ġstri ped +ĠMell on +oss ession +Ġneut ron +end ium +Ġa ph +ĠFlav oring +Ġ38 3 +Ġrespons iveness +ĠJ indal +ĠHitch cock +Den ver +ĠDRAG ON +sm anship +ĠDu pl +Ġs ly +Ġweb cam +ĠTw ain +ĠDar ling +ili ate +cons umer +D IT +Ġnames ake +Ġun orthodox +Ġfun er +ĠPL oS +ĠCONTR OL +ozy g +ogl obin +F ACE +ER G +ĠD ia +ĠF iesta +ce le +0 34 +Ġencl ave +âĸ¬ âĸ¬ +on ement +al ist +M and +Ġhome grown +ĠF ancy +Ġconcept ions +ĠCont ains +ure en +Ġreiter ate +Ġme ager +Ġinstall ments +Sp awn +6 27 +Ġphot oc +ĠCab rera +ĠRos enthal +ĠLans ing +is ner +Ġinvest s +ĠUFO s +EX P +Hard ware +Ġtr agically +Ġconced es +ie ft +ch am +bor gh +ĠSch r +ĠMel anie +ĠH oy +Ġvisit ation +Ġid iosyncr +Ġfract ions +Ġfore skin +ob os +Ġpo aching +ĠVI EW +Ġstimul ates +ĠG ork +can on +M IC +ĠNem esis +ĠInd ra +ĠDM V +Ġ5 29 +Ġinspect ing +Ġgrand ma +ĠW hedon +ĠSh ant +ĠP urg +ik an +ĠT eg +ĠCL R +z ac +Vict oria +ĠVer ify +ion ics +Ġpart ying +ĠM ou +col our +Ġtestim onies +l ations +Ġpress uring +hi ro +ac ers +Ġf id +ang ler +ĠCS I +Ġhere after +Ġdiss idents +report ing +iph any +che v +Ġsol itude +Ġl obe +Ġind is +Ġcred ential +re cent +ad ult +ĠNir vana +ĠFranch ise +L ayer +H yp +ĠBerks hire +Ġwill s +t if +Ġtot em +ĠJud ah +rep air +Inst ant +5 48 +Ġemb assies +Ġbott leneck +Ġb ount +Ġtyp ew +ĠAl vin +j ing +im ilar +R ush +Ġbr im +ĠHEL P +A im +] ' +Ġpass ively +Ġbound ed +ĠR ated +Ġcriminal ity +Ġbiom ark +Ġdisp atcher +ĠTow ards +Ġ+ ++ +right eous +f rog +ĠP anc +C arter +0 32 +æ© Ł +Ġult raviolet +ĠLic ensed +ĠT ata +ĠBl essing +ĠG AM +Ġchem ically +ĠSe af +ĠRE LE +ĠMerc enary +capital ist +Ġform ulations +Ġann ihilation +ĠVer b +ĠAr gon +Ġun loaded +Ġmorp hed +Ġconqu ering +back er +I ELD +Ġtheft s +Ġfront runner +ĠRoy ale +ĠFund amental +el ight +C hip +necess ary +ay n +ĠSl ip +Ġ4 48 +cern ed +P ause +Ġshock ingly +ĠAB V +Ġcomp osure +7 33 +ĠMotors port +ah ime +Mur ray +M ach +Ġgr ids +Ġdeb ian +Ġfurther more +Ġdexter ity +ĠCollect ions +os lov +il age +b j +ĠMont eneg +Ġstrut Connector +Ġmassac res +Ġbrief s +fet ched +uv ian +ol ition +Fail ure +emon ic +Ġfl ared +Ġclaim ant +Ġc ures +Ġgive aways +ĠSubst ance +al ions +Ġcr inge +ĠK ul +Ġarist ocracy +ĠUl ster +ol ated +h ousing +ĠM IS +Ġgl ared +ĠWil helm +ne eds +lam bda +build ers +ĠV IS +Ġradi ator +ĠGhost busters +Ġ4 36 +act ual +Ġher ds +ç a +watch ing +Ġcounter ing +Ch arge +Ġchar red +Ġwar heads +Ġiod ine +ĠM acy +04 1 +Ġdepart ures +ĠS ins +Ġdy ed +ĠConcept s +g ado +7 13 +Ġquot ations +Ġg ist +ĠChrist y +Ġant igen +ĠHem p +ĠD rawn +ĠB arg +ez vous +Ġp aternity +Ġar du +ĠAnch orage +ĠR ik +Ġover loaded +ĠUs ername +ĠTam my +ĠN au +ĠCell ular +Ġw aning +Ġrod ent +ĠWor cester +il ts +ĠT ad +Ġdwell ings +Ġbull ish +4 31 +Ġretali ate +Ġmig raine +ĠChev ron +CH ECK +Ġdon key +c rim +SP A +ĠAn alog +Ġmarqu ee +ĠHa as +B ir +ĠGD DR +ĠDownload s +Ġwill power +ĠFor th +ĠRecord ed +Ġimp ossibility +ĠLog ged +ĠFr anks +ĠR att +in itions +Ġclean ers +Ġsore ly +Ġflick ering +ĠEx amination +c atching +allow een +Ms g +Ġdun no +F a +Ġdys ph +c razy +.' '. +Ġmain line +Ġc s +Ġp tr +ĠW ally +ig un +95 1 +ĠBig foot +f ights +Ġretrie ving +J r +Ġdupl ication +ĠExpl an +Ġrel ational +Ġqu aint +Ġbisc uits +Ġad o +Ġsh udder +Ġantid ote +blood ed +ks h +Ġsa uces +Ġrein vest +Ġdispens ary +ĠD iver +Ġ9 000 +stud ent +Ġin separ +esc ap +Ġtodd lers +ĠGP IO +ĠAss ignment +head ers +Ġlack luster +Ġab ack +95 6 +Ġtool bar +7 45 +Ġo ust +Ġcontempl ation +ĠPRES IDENT +Ġ4 58 +==== == +Ġguarantee ing +ĠHe ist +ĠCann es +Ļ ½ +Ġcollabor ator +ĠAm p +Ġg ou +ĠSH ALL +st ories +78 3 +Ġmobil ized +Ġbro od +ĠL U +ĠðŁ ij +Ġref in +ĠAnthrop ology +v ind +ill i +Ġwarrant ies +ĠB abel +Ġsw ath +Ġc aches +Ġantagon ists +art ifacts +Ġhot ly +ĠSt arts +ĠG ö +z ag +!! !!! +Ġsc ourge +Ġcons piring +ru its +re verse +ĠShe en +ĠJes uit +ĠGiov anni +ad ies +Ġbutt ocks +ear cher +ac an +Ġvolley ball +Ġshroud ed +Ġscore board +b ats +ĠI PM +Ġass es +Ġde regulation +ĠTe legram +ĠReb oot +Ġ7 000 +ĠCan ary +Ġk ernels +ĠFranç ois +ĠD uff +ĠP on +ĠLe ica +ĠGar min +Ġor phans +ĠClaud ia +Ġcal endars +ĠLe ilan +ent o +R ocket +Ġbr unch +ĠHaw king +ain ers +Ġsens ibilities +Ġk W +ĠK and +Ġre claimed +Ġinteresting ly +× © +rom y +J M +ĠEnhance ment +b ush +Sk ip +Ġrapp ers +Ġg azing +p edia +ath lon +Rev olution +Ġsn ipers +Ġre verted +Ġconglomer ate +T erry +79 4 +Ġhars her +Ġdes olate +ĠHit man +Comm ission +Ġ( / +âĢ¦ ." +Com par +Ġampl ification +om inated +Ġreg ress +ĠColl ider +Ġinform ants +Ġg azed diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/special_tokens_map.json b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..817762d631ad6f9c799f6b9dc713c46420e65546 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/special_tokens_map.json @@ -0,0 +1 @@ +{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"} \ No newline at end of file diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/tokenizer_config.json b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..c92208ce39324aadabc239d73dd9bb3ec1cd45ca --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/tokenizer_config.json @@ -0,0 +1 @@ +{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "gpt2", "tokenizer_class": "GPT2Tokenizer"} \ No newline at end of file diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/vocab.json b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..84ef7fb594b5c0979e48bdeddb60a0adef33df0b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/gpt2/vocab.json @@ -0,0 +1 @@ +{"!":0,"\"":1,"#":2,"$":3,"%":4,"&":5,"'":6,"(":7,")":8,"*":9,"+":10,",":11,"-":12,".":13,"/":14,"0":15,"1":16,"2":17,"3":18,"4":19,"5":20,"6":21,"7":22,"8":23,"9":24,":":25,";":26,"<":27,"=":28,">":29,"?":30,"@":31,"A":32,"B":33,"C":34,"D":35,"E":36,"F":37,"G":38,"H":39,"I":40,"J":41,"K":42,"L":43,"M":44,"N":45,"O":46,"P":47,"Q":48,"R":49,"S":50,"T":51,"U":52,"V":53,"W":54,"X":55,"Y":56,"Z":57,"[":58,"\\":59,"]":60,"^":61,"_":62,"`":63,"a":64,"b":65,"c":66,"d":67,"e":68,"f":69,"g":70,"h":71,"i":72,"j":73,"k":74,"l":75,"m":76,"n":77,"o":78,"p":79,"q":80,"r":81,"s":82,"t":83,"u":84,"v":85,"w":86,"x":87,"y":88,"z":89,"{":90,"|":91,"}":92,"~":93,"¡":94,"¢":95,"£":96,"¤":97,"¥":98,"¦":99,"§":100,"¨":101,"©":102,"ª":103,"«":104,"¬":105,"®":106,"¯":107,"°":108,"±":109,"²":110,"³":111,"´":112,"µ":113,"¶":114,"·":115,"¸":116,"¹":117,"º":118,"»":119,"¼":120,"½":121,"¾":122,"¿":123,"À":124,"Á":125,"Â":126,"Ã":127,"Ä":128,"Å":129,"Æ":130,"Ç":131,"È":132,"É":133,"Ê":134,"Ë":135,"Ì":136,"Í":137,"Î":138,"Ï":139,"Ð":140,"Ñ":141,"Ò":142,"Ó":143,"Ô":144,"Õ":145,"Ö":146,"×":147,"Ø":148,"Ù":149,"Ú":150,"Û":151,"Ü":152,"Ý":153,"Þ":154,"ß":155,"à":156,"á":157,"â":158,"ã":159,"ä":160,"å":161,"æ":162,"ç":163,"è":164,"é":165,"ê":166,"ë":167,"ì":168,"í":169,"î":170,"ï":171,"ð":172,"ñ":173,"ò":174,"ó":175,"ô":176,"õ":177,"ö":178,"÷":179,"ø":180,"ù":181,"ú":182,"û":183,"ü":184,"ý":185,"þ":186,"ÿ":187,"Ā":188,"ā":189,"Ă":190,"ă":191,"Ą":192,"ą":193,"Ć":194,"ć":195,"Ĉ":196,"ĉ":197,"Ċ":198,"ċ":199,"Č":200,"č":201,"Ď":202,"ď":203,"Đ":204,"đ":205,"Ē":206,"ē":207,"Ĕ":208,"ĕ":209,"Ė":210,"ė":211,"Ę":212,"ę":213,"Ě":214,"ě":215,"Ĝ":216,"ĝ":217,"Ğ":218,"ğ":219,"Ġ":220,"ġ":221,"Ģ":222,"ģ":223,"Ĥ":224,"ĥ":225,"Ħ":226,"ħ":227,"Ĩ":228,"ĩ":229,"Ī":230,"ī":231,"Ĭ":232,"ĭ":233,"Į":234,"į":235,"İ":236,"ı":237,"IJ":238,"ij":239,"Ĵ":240,"ĵ":241,"Ķ":242,"ķ":243,"ĸ":244,"Ĺ":245,"ĺ":246,"Ļ":247,"ļ":248,"Ľ":249,"ľ":250,"Ŀ":251,"ŀ":252,"Ł":253,"ł":254,"Ń":255,"Ġt":256,"Ġa":257,"he":258,"in":259,"re":260,"on":261,"Ġthe":262,"er":263,"Ġs":264,"at":265,"Ġw":266,"Ġo":267,"en":268,"Ġc":269,"it":270,"is":271,"an":272,"or":273,"es":274,"Ġb":275,"ed":276,"Ġf":277,"ing":278,"Ġp":279,"ou":280,"Ġan":281,"al":282,"ar":283,"Ġto":284,"Ġm":285,"Ġof":286,"Ġin":287,"Ġd":288,"Ġh":289,"Ġand":290,"ic":291,"as":292,"le":293,"Ġth":294,"ion":295,"om":296,"ll":297,"ent":298,"Ġn":299,"Ġl":300,"st":301,"Ġre":302,"ve":303,"Ġe":304,"ro":305,"ly":306,"Ġbe":307,"Ġg":308,"ĠT":309,"ct":310,"ĠS":311,"id":312,"ot":313,"ĠI":314,"ut":315,"et":316,"ĠA":317,"Ġis":318,"Ġon":319,"im":320,"am":321,"ow":322,"ay":323,"ad":324,"se":325,"Ġthat":326,"ĠC":327,"ig":328,"Ġfor":329,"ac":330,"Ġy":331,"ver":332,"ur":333,"Ġu":334,"ld":335,"Ġst":336,"ĠM":337,"'s":338,"Ġhe":339,"Ġit":340,"ation":341,"ith":342,"ir":343,"ce":344,"Ġyou":345,"il":346,"ĠB":347,"Ġwh":348,"ol":349,"ĠP":350,"Ġwith":351,"Ġ1":352,"ter":353,"ch":354,"Ġas":355,"Ġwe":356,"Ġ(":357,"nd":358,"ill":359,"ĠD":360,"if":361,"Ġ2":362,"ag":363,"ers":364,"ke":365,"Ġ\"":366,"ĠH":367,"em":368,"Ġcon":369,"ĠW":370,"ĠR":371,"her":372,"Ġwas":373,"Ġr":374,"od":375,"ĠF":376,"ul":377,"ate":378,"Ġat":379,"ri":380,"pp":381,"ore":382,"ĠThe":383,"Ġse":384,"us":385,"Ġpro":386,"Ġha":387,"um":388,"Ġare":389,"Ġde":390,"ain":391,"and":392,"Ġor":393,"igh":394,"est":395,"ist":396,"ab":397,"rom":398,"ĠN":399,"th":400,"Ġcom":401,"ĠG":402,"un":403,"op":404,"00":405,"ĠL":406,"Ġnot":407,"ess":408,"Ġex":409,"Ġv":410,"res":411,"ĠE":412,"ew":413,"ity":414,"ant":415,"Ġby":416,"el":417,"os":418,"ort":419,"oc":420,"qu":421,"Ġfrom":422,"Ġhave":423,"Ġsu":424,"ive":425,"ould":426,"Ġsh":427,"Ġthis":428,"nt":429,"ra":430,"pe":431,"ight":432,"art":433,"ment":434,"Ġal":435,"ust":436,"end":437,"--":438,"all":439,"ĠO":440,"ack":441,"Ġch":442,"Ġle":443,"ies":444,"red":445,"ard":446,"âĢ":447,"out":448,"ĠJ":449,"Ġab":450,"ear":451,"iv":452,"ally":453,"our":454,"ost":455,"gh":456,"pt":457,"Ġpl":458,"ast":459,"Ġcan":460,"ak":461,"ome":462,"ud":463,"The":464,"Ġhis":465,"Ġdo":466,"Ġgo":467,"Ġhas":468,"ge":469,"'t":470,"ĠU":471,"rou":472,"Ġsa":473,"Ġj":474,"Ġbut":475,"Ġwor":476,"Ġall":477,"ect":478,"Ġk":479,"ame":480,"Ġwill":481,"ok":482,"Ġwhe":483,"Ġthey":484,"ide":485,"01":486,"ff":487,"ich":488,"pl":489,"ther":490,"Ġtr":491,"..":492,"Ġint":493,"ie":494,"ure":495,"age":496,"Ġne":497,"ial":498,"ap":499,"ine":500,"ice":501,"Ġme":502,"Ġout":503,"ans":504,"one":505,"ong":506,"ions":507,"Ġwho":508,"ĠK":509,"Ġup":510,"Ġtheir":511,"Ġad":512,"Ġ3":513,"Ġus":514,"ated":515,"ous":516,"Ġmore":517,"ue":518,"og":519,"ĠSt":520,"ind":521,"ike":522,"Ġso":523,"ime":524,"per":525,".\"":526,"ber":527,"iz":528,"act":529,"Ġone":530,"Ġsaid":531,"Ġ-":532,"are":533,"Ġyour":534,"cc":535,"ĠTh":536,"Ġcl":537,"ep":538,"ake":539,"able":540,"ip":541,"Ġcont":542,"Ġwhich":543,"ia":544,"Ġim":545,"Ġabout":546,"Ġwere":547,"very":548,"ub":549,"Ġhad":550,"Ġen":551,"Ġcomp":552,",\"":553,"ĠIn":554,"Ġun":555,"Ġag":556,"ire":557,"ace":558,"au":559,"ary":560,"Ġwould":561,"ass":562,"ry":563,"ĠâĢ":564,"cl":565,"ook":566,"ere":567,"so":568,"ĠV":569,"ign":570,"ib":571,"Ġoff":572,"Ġte":573,"ven":574,"ĠY":575,"ile":576,"ose":577,"ite":578,"orm":579,"Ġ201":580,"Ġres":581,"Ġman":582,"Ġper":583,"Ġother":584,"ord":585,"ult":586,"Ġbeen":587,"Ġlike":588,"ase":589,"ance":590,"ks":591,"ays":592,"own":593,"ence":594,"Ġdis":595,"ction":596,"Ġany":597,"Ġapp":598,"Ġsp":599,"int":600,"ress":601,"ations":602,"ail":603,"Ġ4":604,"ical":605,"Ġthem":606,"Ġher":607,"ount":608,"ĠCh":609,"Ġar":610,"Ġif":611,"Ġthere":612,"Ġpe":613,"Ġyear":614,"av":615,"Ġmy":616,"Ġsome":617,"Ġwhen":618,"ough":619,"ach":620,"Ġthan":621,"ru":622,"ond":623,"ick":624,"Ġover":625,"vel":626,"Ġqu":627,"ĊĊ":628,"Ġsc":629,"reat":630,"ree":631,"ĠIt":632,"ound":633,"port":634,"Ġalso":635,"Ġpart":636,"fter":637,"Ġkn":638,"Ġbec":639,"Ġtime":640,"ens":641,"Ġ5":642,"ople":643,"Ġwhat":644,"Ġno":645,"du":646,"mer":647,"ang":648,"Ġnew":649,"----":650,"Ġget":651,"ory":652,"ition":653,"ings":654,"Ġjust":655,"Ġinto":656,"Ġ0":657,"ents":658,"ove":659,"te":660,"Ġpeople":661,"Ġpre":662,"Ġits":663,"Ġrec":664,"Ġtw":665,"ian":666,"irst":667,"ark":668,"ors":669,"Ġwork":670,"ade":671,"ob":672,"Ġshe":673,"Ġour":674,"wn":675,"ink":676,"lic":677,"Ġ19":678,"ĠHe":679,"ish":680,"nder":681,"ause":682,"Ġhim":683,"ons":684,"Ġ[":685,"Ġro":686,"form":687,"ild":688,"ates":689,"vers":690,"Ġonly":691,"oll":692,"Ġspe":693,"ck":694,"ell":695,"amp":696,"Ġacc":697,"Ġbl":698,"ious":699,"urn":700,"ft":701,"ood":702,"Ġhow":703,"hed":704,"Ġ'":705,"Ġafter":706,"aw":707,"Ġatt":708,"ov":709,"ne":710,"Ġplay":711,"erv":712,"ict":713,"Ġcould":714,"itt":715,"Ġam":716,"Ġfirst":717,"Ġ6":718,"Ġact":719,"Ġ$":720,"ec":721,"hing":722,"ual":723,"ull":724,"Ġcomm":725,"oy":726,"old":727,"ces":728,"ater":729,"Ġfe":730,"Ġbet":731,"we":732,"iff":733,"Ġtwo":734,"ock":735,"Ġback":736,").":737,"ident":738,"Ġunder":739,"rough":740,"sel":741,"xt":742,"Ġmay":743,"round":744,"Ġpo":745,"ph":746,"iss":747,"Ġdes":748,"Ġmost":749,"Ġdid":750,"Ġadd":751,"ject":752,"Ġinc":753,"fore":754,"Ġpol":755,"ont":756,"Ġagain":757,"clud":758,"tern":759,"Ġknow":760,"Ġneed":761,"Ġcons":762,"Ġco":763,"Ġ.":764,"Ġwant":765,"Ġsee":766,"Ġ7":767,"ning":768,"iew":769,"ĠThis":770,"ced":771,"Ġeven":772,"Ġind":773,"ty":774,"ĠWe":775,"ath":776,"Ġthese":777,"Ġpr":778,"Ġuse":779,"Ġbecause":780,"Ġfl":781,"ng":782,"Ġnow":783,"ĠâĢĵ":784,"com":785,"ise":786,"Ġmake":787,"Ġthen":788,"ower":789,"Ġevery":790,"ĠUn":791,"Ġsec":792,"oss":793,"uch":794,"Ġem":795,"Ġ=":796,"ĠRe":797,"ied":798,"rit":799,"Ġinv":800,"lect":801,"Ġsupp":802,"ating":803,"Ġlook":804,"man":805,"pect":806,"Ġ8":807,"row":808,"Ġbu":809,"Ġwhere":810,"ific":811,"Ġyears":812,"ily":813,"Ġdiff":814,"Ġshould":815,"Ġrem":816,"Th":817,"In":818,"Ġev":819,"day":820,"'re":821,"rib":822,"Ġrel":823,"ss":824,"Ġdef":825,"Ġright":826,"Ġsy":827,"),":828,"les":829,"000":830,"hen":831,"Ġthrough":832,"ĠTr":833,"__":834,"Ġway":835,"Ġdon":836,"Ġ,":837,"Ġ10":838,"ased":839,"Ġass":840,"ublic":841,"Ġreg":842,"ĠAnd":843,"ix":844,"Ġvery":845,"Ġinclud":846,"other":847,"Ġimp":848,"oth":849,"Ġsub":850,"ĠâĢĶ":851,"Ġbeing":852,"arg":853,"ĠWh":854,"==":855,"ible":856,"Ġdoes":857,"ange":858,"ram":859,"Ġ9":860,"ert":861,"ps":862,"ited":863,"ational":864,"Ġbr":865,"Ġdown":866,"Ġmany":867,"aking":868,"Ġcall":869,"uring":870,"ities":871,"Ġph":872,"ics":873,"als":874,"Ġdec":875,"ative":876,"ener":877,"Ġbefore":878,"ility":879,"Ġwell":880,"Ġmuch":881,"erson":882,"Ġthose":883,"Ġsuch":884,"Ġke":885,"Ġend":886,"ĠBut":887,"ason":888,"ting":889,"Ġlong":890,"ef":891,"Ġthink":892,"ys":893,"Ġbel":894,"Ġsm":895,"its":896,"ax":897,"Ġown":898,"Ġprov":899,"Ġset":900,"ife":901,"ments":902,"ble":903,"ward":904,"Ġshow":905,"Ġpres":906,"ms":907,"omet":908,"Ġob":909,"Ġsay":910,"ĠSh":911,"ts":912,"ful":913,"Ġeff":914,"Ġgu":915,"Ġinst":916,"und":917,"ren":918,"cess":919,"Ġent":920,"ĠYou":921,"Ġgood":922,"Ġstart":923,"ince":924,"Ġmade":925,"tt":926,"stem":927,"olog":928,"up":929,"Ġ|":930,"ump":931,"Ġhel":932,"vern":933,"ular":934,"ually":935,"Ġac":936,"Ġmon":937,"Ġlast":938,"Ġ200":939,"10":940,"Ġstud":941,"ures":942,"ĠAr":943,"self":944,"ars":945,"meric":946,"ues":947,"cy":948,"Ġmin":949,"ollow":950,"Ġcol":951,"io":952,"Ġmod":953,"Ġcount":954,"ĠCom":955,"hes":956,"Ġfin":957,"air":958,"ier":959,"âĢĶ":960,"read":961,"ank":962,"atch":963,"ever":964,"Ġstr":965,"Ġpoint":966,"ork":967,"ĠNew":968,"Ġsur":969,"ool":970,"alk":971,"ement":972,"Ġused":973,"ract":974,"ween":975,"Ġsame":976,"oun":977,"ĠAl":978,"ci":979,"Ġdiffere":980,"Ġwhile":981,"--------":982,"Ġgame":983,"cept":984,"Ġsim":985,"...":986,"Ġinter":987,"ek":988,"Ġreport":989,"Ġprodu":990,"Ġstill":991,"led":992,"ah":993,"Ġhere":994,"Ġworld":995,"Ġthough":996,"Ġnum":997,"arch":998,"imes":999,"ale":1000,"ĠSe":1001,"ĠIf":1002,"//":1003,"ĠLe":1004,"Ġret":1005,"Ġref":1006,"Ġtrans":1007,"ner":1008,"ution":1009,"ters":1010,"Ġtake":1011,"ĠCl":1012,"Ġconf":1013,"way":1014,"ave":1015,"Ġgoing":1016,"Ġsl":1017,"ug":1018,"ĠAmeric":1019,"Ġspec":1020,"Ġhand":1021,"Ġbetween":1022,"ists":1023,"ĠDe":1024,"oot":1025,"It":1026,"Ġear":1027,"Ġagainst":1028,"Ġhigh":1029,"gan":1030,"az":1031,"ather":1032,"Ġexp":1033,"Ġop":1034,"Ġins":1035,"Ġgr":1036,"Ġhelp":1037,"Ġrequ":1038,"ets":1039,"ins":1040,"ĠPro":1041,"ism":1042,"Ġfound":1043,"land":1044,"ata":1045,"uss":1046,"ames":1047,"Ġperson":1048,"Ġgreat":1049,"pr":1050,"Ġsign":1051,"ĠAn":1052,"'ve":1053,"Ġsomet":1054,"Ġser":1055,"hip":1056,"Ġrun":1057,"Ġ:":1058,"Ġter":1059,"irect":1060,"Ġfollow":1061,"Ġdet":1062,"ices":1063,"Ġfind":1064,"12":1065,"Ġmem":1066,"Ġcr":1067,"ered":1068,"ex":1069,"Ġext":1070,"uth":1071,"ense":1072,"co":1073,"Ġteam":1074,"ving":1075,"ouse":1076,"ash":1077,"att":1078,"ved":1079,"Ġsystem":1080,"ĠAs":1081,"der":1082,"ives":1083,"min":1084,"Ġlead":1085,"ĠBl":1086,"cent":1087,"Ġaround":1088,"Ġgovern":1089,"Ġcur":1090,"velop":1091,"any":1092,"Ġcour":1093,"alth":1094,"ages":1095,"ize":1096,"Ġcar":1097,"ode":1098,"Ġlaw":1099,"Ġread":1100,"'m":1101,"con":1102,"Ġreal":1103,"Ġsupport":1104,"Ġ12":1105,"....":1106,"Ġreally":1107,"ness":1108,"Ġfact":1109,"Ġday":1110,"Ġboth":1111,"ying":1112,"Ġserv":1113,"ĠFor":1114,"Ġthree":1115,"Ġwom":1116,"Ġmed":1117,"ody":1118,"ĠThey":1119,"50":1120,"Ġexper":1121,"ton":1122,"Ġeach":1123,"akes":1124,"Ġche":1125,"Ġcre":1126,"ines":1127,"Ġrep":1128,"19":1129,"gg":1130,"illion":1131,"Ġgrou":1132,"ute":1133,"ik":1134,"We":1135,"get":1136,"ER":1137,"Ġmet":1138,"Ġsays":1139,"ox":1140,"Ġduring":1141,"ern":1142,"ized":1143,"ared":1144,"Ġfam":1145,"ically":1146,"Ġhapp":1147,"ĠIs":1148,"Ġchar":1149,"med":1150,"vent":1151,"Ġgener":1152,"ient":1153,"ple":1154,"iet":1155,"rent":1156,"11":1157,"ves":1158,"ption":1159,"Ġ20":1160,"formation":1161,"Ġcor":1162,"Ġoffic":1163,"ield":1164,"Ġtoo":1165,"ision":1166,"Ġinf":1167,"ĠZ":1168,"the":1169,"oad":1170,"Ġpublic":1171,"Ġprog":1172,"ric":1173,"**":1174,"Ġwar":1175,"Ġpower":1176,"view":1177,"Ġfew":1178,"Ġloc":1179,"Ġdifferent":1180,"Ġstate":1181,"Ġhead":1182,"'ll":1183,"Ġposs":1184,"Ġstat":1185,"ret":1186,"ants":1187,"Ġval":1188,"Ġiss":1189,"Ġcle":1190,"ivers":1191,"anc":1192,"Ġexpl":1193,"Ġanother":1194,"ĠQ":1195,"Ġav":1196,"thing":1197,"nce":1198,"Wh":1199,"Ġchild":1200,"Ġsince":1201,"ired":1202,"less":1203,"Ġlife":1204,"Ġdevelop":1205,"ittle":1206,"Ġdep":1207,"Ġpass":1208,"ãĥ":1209,"Ġturn":1210,"orn":1211,"This":1212,"bers":1213,"ross":1214,"ĠAd":1215,"Ġfr":1216,"Ġresp":1217,"Ġsecond":1218,"oh":1219,"Ġ/":1220,"Ġdisc":1221,"Ġ&":1222,"Ġsomething":1223,"Ġcomple":1224,"Ġed":1225,"Ġfil":1226,"Ġmonth":1227,"aj":1228,"uc":1229,"Ġgovernment":1230,"Ġwithout":1231,"Ġleg":1232,"Ġdist":1233,"Ġput":1234,"Ġquest":1235,"ann":1236,"Ġprot":1237,"20":1238,"Ġnever":1239,"ience":1240,"Ġlevel":1241,"Ġart":1242,"Ġthings":1243,"Ġmight":1244,"Ġeffect":1245,"Ġcontro":1246,"Ġcent":1247,"Ġ18":1248,"Ġallow":1249,"Ġbelie":1250,"chool":1251,"ott":1252,"Ġincre":1253,"Ġfeel":1254,"Ġresult":1255,"Ġlot":1256,"Ġfun":1257,"ote":1258,"Ġty":1259,"erest":1260,"Ġcontin":1261,"Ġusing":1262,"Ġbig":1263,"201":1264,"Ġask":1265,"Ġbest":1266,"Ġ)":1267,"IN":1268,"Ġopp":1269,"30":1270,"Ġnumber":1271,"iness":1272,"St":1273,"lease":1274,"Ġca":1275,"Ġmust":1276,"Ġdirect":1277,"Ġgl":1278,"Ġ<":1279,"Ġopen":1280,"Ġpost":1281,"Ġcome":1282,"Ġseem":1283,"ording":1284,"Ġweek":1285,"ately":1286,"ital":1287,"Ġel":1288,"riend":1289,"Ġfar":1290,"Ġtra":1291,"inal":1292,"Ġpri":1293,"ĠUS":1294,"Ġplace":1295,"Ġform":1296,"Ġtold":1297,"\":":1298,"ains":1299,"ature":1300,"ĠTrump":1301,"Ġstand":1302,"Ġ#":1303,"ider":1304,"ĠFr":1305,"Ġnext":1306,"Ġsoc":1307,"Ġpur":1308,"Ġlet":1309,"Ġlittle":1310,"Ġhum":1311,"Ġi":1312,"ron":1313,"15":1314,"Ġ15":1315,"Ġcommun":1316,"Ġmark":1317,"ĠThere":1318,"Ġwr":1319,"ĠThat":1320,"Ġinformation":1321,"ways":1322,"Ġbus":1323,"app":1324,"Ġinvest":1325,"me":1326,"Ġhard":1327,"ained":1328,"ead":1329,"Ġimport":1330,"Ġappro":1331,"Ġtest":1332,"Ġtri":1333,"Ġrest":1334,"osed":1335,"Ġfull":1336,"Ġcare":1337,"ĠSp":1338,"Ġcase":1339,"ON":1340,"Ġsk":1341,"Ġless":1342,"Ġ+":1343,"Ġpartic":1344,"ĠPl":1345,"ably":1346,"uck":1347,"ished":1348,"chn":1349,"be":1350,"Ġlist":1351,"ator":1352,"Ġtop":1353,"Ġadv":1354,"ĠBe":1355,"ruct":1356,"Ġdem":1357,"ration":1358,"ling":1359,"gy":1360,"reen":1361,"ger":1362,"Ġhome":1363,"Ġleft":1364,"Ġbetter":1365,"Ġdata":1366,"Ġ11":1367,"Ġattack":1368,"Ġproble":1369,"line":1370,"ards":1371,"Ġbeh":1372,"ral":1373,"ĠHow":1374,"ĠShe":1375,"arge":1376,"Ġ--":1377,"://":1378,"Ġbro":1379,"ĠPh":1380,"ats":1381,"Ġbuild":1382,"ww":1383,"ided":1384,"aim":1385,"ases":1386,"ency":1387,"Ġmain":1388,"ined":1389,"Ġincluding":1390,"Ġ{":1391,"Ġgot":1392,"Ġinterest":1393,"Ġkeep":1394,"ĠX":1395,"Ġeas":1396,"aining":1397,"Ġclass":1398,"âĢ¦":1399,"ĠNo":1400,"Ġvar":1401,"Ġsmall":1402,"ample":1403,"AT":1404,"Ġide":1405,"ĠSo":1406,"Ġrece":1407,"Ġpolit":1408,"Ġmov":1409,"Ġplan":1410,"Ġpercent":1411,"iving":1412,"Ġcamp":1413,"Ġpay":1414,"14":1415,"sc":1416,"ised":1417,"Ġunt":1418,"oney":1419,"ploy":1420,"====":1421,"Ġdidn":1422,"ĠInd":1423,"els":1424,"ertain":1425,"Ġpos":1426,"____":1427,"iver":1428,"Ġprocess":1429,"Ġprogram":1430,"ified":1431,"ĠRep":1432,"16":1433,"uro":1434,"ology":1435,"atter":1436,"ina":1437,"Ġname":1438,"ĠAll":1439,"Ġfour":1440,"Ġreturn":1441,"vious":1442,"bs":1443,"Ġcalled":1444,"Ġmove":1445,"ĠSc":1446,"ird":1447,"Ġgroup":1448,"Ġbre":1449,"Ġmen":1450,"Ġcap":1451,"ten":1452,"ee":1453,"Ġdri":1454,"leg":1455,"here":1456,"uthor":1457,"Ġpat":1458,"Ġcurrent":1459,"ides":1460,"Ġpop":1461,"to":1462,"ention":1463,"Ġalways":1464,"Ġmil":1465,"Ġwomen":1466,"Ġ16":1467,"Ġold":1468,"iven":1469,"raph":1470,"ĠOr":1471,"ror":1472,"ently":1473,"Ġnear":1474,"ĠEx":1475,"ream":1476,"sh":1477,"Ġ14":1478,"Ġfree":1479,"ission":1480,"stand":1481,"ĠCon":1482,"ality":1483,"used":1484,"13":1485,"Ġdesign":1486,"Ġchange":1487,"Ġchang":1488,"Ġbo":1489,"Ġvis":1490,"ember":1491,"Ġbook":1492,"ready":1493,"Ġkill":1494,"25":1495,"pped":1496,"Ġaway":1497,"Ġable":1498,"Ġcountry":1499,"Ġconst":1500,"arn":1501,"Ġorder":1502,"AR":1503,"ior":1504,"ium":1505,"orth":1506,"18":1507,"ailable":1508,"Ġsw":1509,"Ġmillion":1510,"Ġ13":1511,"atic":1512,"ted":1513,"ĠGo":1514,"Ġoper":1515,"eng":1516,"Ġthing":1517,"ajor":1518,"conom":1519,"ĠComm":1520,"Ġwhy":1521,"ured":1522,"ural":1523,"Ġschool":1524,"by":1525,"ĠMar":1526,"Ġaff":1527,"Ġdays":1528,"Ġann":1529,"ush":1530,"ane":1531,"If":1532,"eg":1533,"Ġprof":1534,"Ġhealth":1535,"outh":1536,"But":1537,"ional":1538,".,":1539,"Ġsol":1540,"Ġalready":1541,"Ġ30":1542,"Ġcharact":1543,"He":1544,"Ġfriend":1545,"ES":1546,"ians":1547,"icle":1548,"'d":1549,"ĠOn":1550,"Ġleast":1551,"Ġprom":1552,"Ġdr":1553,"Ġhist":1554,"ither":1555,"Ġest":1556,"iqu":1557,"17":1558,"son":1559,"Ġtell":1560,"Ġtalk":1561,"ohn":1562,"oint":1563,"lection":1564,"AN":1565,"Ġuntil":1566,"augh":1567,"Ġlater":1568,"Ġve":1569,"Ġview":1570,"ending":1571,"ived":1572,"Ġword":1573,"ware":1574,"Ġcost":1575,"Ġenough":1576,"Ġgive":1577,"ĠUnited":1578,"Ġtechn":1579,"arent":1580,"OR":1581,"Ġpar":1582,"ĠDr":1583,"Ġ2016":1584,"rist":1585,"ering":1586,"ĠÂ":1587,"Ġlarge":1588,"side":1589,"acy":1590,"ccess":1591,"Ġwin":1592,"Ġimportant":1593,"Ġ199":1594,"Ġdoesn":1595,"Ġ17":1596,"Ġbusiness":1597,"Ġclear":1598,"Ġrese":1599,"\",":1600,"ury":1601,"Ġequ":1602,"aster":1603,"alf":1604,"ĠAmerican":1605,"nect":1606,"Ġexpect":1607,"iversity":1608,"Ġocc":1609,"ĠFl":1610,"Ġkind":1611,"Ġmean":1612,"Ġpast":1613,"Ġdev":1614,"Ġbas":1615,"let":1616,"raft":1617,"Ġorgan":1618,"Ġdel":1619,"Ġperform":1620,"Ġstory":1621,"Ġseason":1622,"ĠCol":1623,"Ġclaim":1624,"Ġcame":1625,"Ġwithin":1626,"Ġline":1627,"Ġproject":1628,"ĠAt":1629,"Ġcontrol":1630,"ended":1631,"ĠSy":1632,"Ġair":1633,"ization":1634,"Ġ*":1635,"ley":1636,"Ġmoney":1637,"idd":1638,"You":1639,"for":1640,"Ġfamily":1641,"Ġmaking":1642,"Ġbit":1643,"Ġpolice":1644,"Ġhappen":1645,"Ġvers":1646,"ony":1647,"uff":1648,"ĠWhen":1649,"Ġsit":1650,"ideo":1651,"lf":1652,"ison":1653,"Ġsure":1654,"gin":1655,"Ġappear":1656,"Ġlight":1657,"Ġes":1658,"of":1659,"Ġwater":1660,"Ġtimes":1661,"not":1662,"Ġgrow":1663,"Ġcompany":1664,"ĠTe":1665,"ows":1666,"Ġmar":1667,"ource":1668,"iol":1669,"arm":1670,"br":1671,"Ġexample":1672,"Ġconc":1673,"Ġfore":1674,"ĠTo":1675,"pro":1676,"EN":1677,"ries":1678,"Ġ25":1679,"ĠCan":1680,"ney":1681,"Ġactually":1682,"Ġever":1683,"urity":1684,"aken":1685,"aps":1686,"Ġtax":1687,"Ġmajor":1688,"ama":1689,"Ġoften":1690,"eral":1691,"Ġhuman":1692,"Ġjob":1693,"ister":1694,"Ġavailable":1695,"ocr":1696,"enn":1697,"aid":1698,"ivid":1699,"Ġrecord":1700,"?\"":1701,"Ġsing":1702,"ĠAm":1703,"idence":1704,"Ġnews":1705,"ster":1706,"Ġeconom":1707,"Ġfollowing":1708,"ĠBr":1709,"ising":1710,"Ġhour":1711,"most":1712,"ument":1713,"Ġsex":1714,"Ġdesc":1715,"Ġbecome":1716,"ĠEd":1717,"Ġtook":1718,"Ġhaving":1719,"Ġproduct":1720,"ault":1721,"As":1722,"aring":1723,"Ġmeans":1724,"Ġhop":1725,"une":1726,"Ġcho":1727,"Ġcertain":1728,"Ġnon":1729,"Ġdeal":1730,"24":1731,"lement":1732,"oci":1733,"ene":1734,"Ġside":1735,"ĠPr":1736,"ĠMay":1737,"Ġreason":1738,"ued":1739,"ched":1740,"ulation":1741,"Ġelect":1742,"Ġofficial":1743,"Ġpossible":1744,"Ġhold":1745,"ands":1746,"ots":1747,"Ġcity":1748,"ories":1749,"Ġsever":1750,"Ġchildren":1751,"Ġonce":1752,"Ġactiv":1753,"ler":1754,"Ġnight":1755,"itions":1756,"ĠJohn":1757,"ape":1758,"play":1759,"Ġdone":1760,"Ġlim":1761,"Ġworking":1762,"ĠPres":1763,"orld":1764,"eb":1765,"ĠCo":1766,"Ġbody":1767,"ails":1768,"utes":1769,"ĠMr":1770,"Ġwhether":1771,"Ġauthor":1772,"rop":1773,"Ġproper":1774,"Ġseen":1775,");":1776,"Ġfac":1777,"ĠSu":1778,"Ġcond":1779,"iting":1780,"Ġcourse":1781,"Ġ}":1782,"----------------":1783,"aign":1784,"Ġevent":1785,"Ġeng":1786,"Ġpot":1787,"Ġintern":1788,"iam":1789,"Ġshort":1790,"empt":1791,"ãĤ":1792,"ĠGod":1793,"ilar":1794,"80":1795,"Ġorig":1796,"IS":1797,"ourn":1798,"ability":1799,"itive":1800,"Ġdam":1801,"Ġ100":1802,"Ġpress":1803,"Ġdoing":1804,"Ġprotect":1805,"ring":1806,"Ġthought":1807,"Ġquestion":1808,"rew":1809,"ĠWar":1810,"Ġseveral":1811,"ĠState":1812,"Ġgiven":1813,"Ġfund":1814,"ĠTw":1815,"Ġwent":1816,"ances":1817,"work":1818,"por":1819,"my":1820,"40":1821,"Ġarg":1822,"artment":1823,"ustom":1824,"Ġpolic":1825,"Ġmeet":1826,"Ġcreat":1827,"22":1828,"ĠStates":1829,"Ġgames":1830,"raw":1831,"uture":1832,"Ġunderstand":1833,"urs":1834,"ĠOb":1835,"lish":1836,"sy":1837,"Ġmakes":1838,"Ġwon":1839,"agon":1840,"Ġhtt":1841,"Ġlove":1842,"ential":1843,"Ġcomplete":1844,"par":1845,"ĠIm":1846,"AL":1847,"Ġaccount":1848,"Âł":1849,"ored":1850,"vert":1851,"Ġident":1852,"Ġ2015":1853,"Ġothers":1854,"ĠMin":1855,"iber":1856,"verage":1857,"There":1858,"itional":1859,"dd":1860,"Ġprob":1861,"Ġyoung":1862,"Ġalong":1863,"Ġaccording":1864,"Ġyet":1865,"Ġmembers":1866,"ĠWhat":1867,"oid":1868,"ĠMan":1869,"And":1870,"Ġamong":1871,"ai":1872,"Ġemploy":1873,"ĠRes":1874,"Ġ>":1875,"Ġinvol":1876,"Ġlow":1877,"af":1878,"ĠCar":1879,"Ġhig":1880,"ĠOne":1881,"ĠSec":1882,"ination":1883,"Ġlikely":1884,"Ġant":1885,"aged":1886,"ĠRuss":1887,"Ġben":1888,"Ġrele":1889,"For":1890,"back":1891,"ĠNot":1892,"Ġpresident":1893,"ball":1894,"Ġaccess":1895,"ividual":1896,"ĠDem":1897,"ĠEuro":1898,"60":1899,"Ġknown":1900,"irl":1901,"ĠGr":1902,"Ġearly":1903,"use":1904,"iety":1905,"âĢĵ":1906,"Ġfight":1907,"Ġsent":1908,"Ġtoday":1909,"Ġmarket":1910,"\".":1911,"Ġbased":1912,"Ġstrong":1913,"urther":1914,"Ġdeb":1915,"mber":1916,"Ġproblem":1917,"Ġdeath":1918,"Ġsocial":1919,"imate":1920,"AS":1921,"ortun":1922,"Ġcampaign":1923,"ery":1924,"Ch":1925,"Ġey":1926,"ially":1927,"Ġmus":1928,"wh":1929,"pos":1930,"Ġer":1931,"Ġsaf":1932,"Ġmonths":1933,"iron":1934,"Ġviol":1935,"Ġfive":1936,"Ġstre":1937,"Ġplayers":1938,"inc":1939,"ald":1940,"year":1941,"aun":1942,"Ġsuccess":1943,"Ġpresent":1944,"erence":1945,"Ġ2014":1946,"Ġsugg":1947,"Ġparticular":1948,"Ġtry":1949,"Ġsuggest":1950,"ĠChrist":1951,"ones":1952,"Ġpriv":1953,"23":1954,"Ġcrit":1955,"Ġland":1956,"Ġlocal":1957,"ify":1958,"29":1959,"Ġaut":1960,"ED":1961,"ĠGu":1962,"Ġmult":1963,"Ġpolitical":1964,"Ġasked":1965,"Ġformer":1966,"itter":1967,"ript":1968,"Ġclose":1969,"Ġpract":1970,"ĠYork":1971,"Ġgetting":1972,"Ġacross":1973,"Ġcomb":1974,"Ġbelieve":1975,"Ġz":1976,"Ġtoget":1977,"Ġtogether":1978,"ĠCent":1979,"irc":1980,"Ġindividual":1981,"ĠMc":1982,"27":1983,"isk":1984,"ĠEng":1985,"Ġface":1986,"Ġ24":1987,"Ġvalue":1988,"Ġarea":1989,"ev":1990,"Ġwrit":1991,"ĠPresident":1992,"Ġvot":1993,"Ġkey":1994,"Ġmom":1995,"put":1996,"Ġanything":1997,"Ġexperience":1998,"attle":1999,"Ġmind":2000,"aff":2001,"omm":2002,"Ġfuture":2003,"ged":2004,"Ġcut":2005,"Ġtot":2006,"itch":2007,"Ġvideo":2008,"Ġinvestig":2009,"Ġnet":2010,"ĠMy":2011,"rict":2012,"ien":2013,".)":2014,"Ġimpro":2015,"though":2016,"wards":2017,"Ġconnect":2018,"ĠMed":2019,"selves":2020,"ensive":2021,"mb":2022,"ober":2023,"ators":2024,"An":2025,"Ġ50":2026,"Ġredu":2027,"resent":2028,"Ġabove":2029,"Ġfre":2030,"ĠEurope":2031,"sw":2032,"Ġamount":2033,"ĠApp":2034,"Ġeither":2035,"Ġmilit":2036,"Ġanal":2037,"Ġfail":2038,"ĠEn":2039,"ales":2040,"Ġspecial":2041,"Ġblack":2042,"IT":2043,"cher":2044,"Ġlooking":2045,"Ġfire":2046,"yn":2047,"Ġalmost":2048,"oon":2049,"Ġstudy":2050,"Ġmiss":2051,"ches":2052,"rown":2053,"Ġtre":2054,"Ġcommunity":2055,"Ġmedia":2056,"Ġfood":2057,"Ġcomes":2058,"ĠUniversity":2059,"Ġsingle":2060,"What":2061,"uly":2062,"Ġhalf":2063,"ague":2064,"hod":2065,"ĠRepublic":2066,"Ġstarted":2067,"Ġquick":2068,"oto":2069,"book":2070,"Ġissue":2071,"itor":2072,"Ġelse":2073,"Ġconsider":2074,"26":2075,"rodu":2076,"Ġtaken":2077,"28":2078,"99":2079,"ĠWith":2080,"Ġtrue":2081,"Ġwa":2082,"Ġtrad":2083,"Ġago":2084,"Ġmess":2085,"ief":2086,"Ġadded":2087,"oke":2088,"Ġbad":2089,"Ġfav":2090,"33":2091,"Ġsimilar":2092,"ask":2093,"ĠDon":2094,"Ġcharacter":2095,"orts":2096,"ĠHouse":2097,"Ġreported":2098,"Ġtype":2099,"val":2100,"iod":2101,"ĠHowever":2102,"Ġtarg":2103,"Ġentire":2104,"pping":2105,"Ġhistory":2106,"Ġlive":2107,"ffic":2108,"........":2109,"ederal":2110,"Ġtrying":2111,"Ġdiscuss":2112,"ĠHar":2113,"aces":2114,"lished":2115,"Ġself":2116,"osp":2117,"rest":2118,"Ġroom":2119,"elt":2120,"Ġfall":2121,"olution":2122,"Ġet":2123,"Ġx":2124,"Ġisn":2125,"Ġidea":2126,"bo":2127,"Ġsound":2128,"ĠDep":2129,"Ġsomeone":2130,"cially":2131,"ully":2132,"Ġfoc":2133,"Ġobject":2134,"ift":2135,"aper":2136,"Ġplayer":2137,"Ġrather":2138,"Ġservice":2139,"ashing":2140,"ĠDo":2141,"ĠPart":2142,"rug":2143,"mon":2144,"ply":2145,"Ġmor":2146,"Ġnothing":2147,"Ġprovide":2148,"IC":2149,"ung":2150,"Ġparty":2151,"Ġexist":2152,"Ġmag":2153,"70":2154,"Ġrul":2155,"Ġhouse":2156,"Ġbehind":2157,"Ġhowever":2158,"ĠWorld":2159,"Ġsum":2160,"Ġapplic":2161,"Ġ;":2162,"Ġfunction":2163,"gr":2164,"ĠPol":2165,"Ġfront":2166,"200":2167,"Ġseries":2168,"Ġtem":2169,"Ġtyp":2170,"ills":2171,"Ġopt":2172,"Ġpoints":2173,"Ġbelow":2174,"itted":2175,"Ġspecific":2176,"Ġ2017":2177,"umb":2178,"Ġra":2179,"Ġprevious":2180,"Ġpret":2181,"reme":2182,"Ġcustom":2183,"Ġcourt":2184,"ĠMe":2185,"Ġrepl":2186,"Ġwhole":2187,"go":2188,"cer":2189,"Ġtreat":2190,"ĠAct":2191,"Ġprobably":2192,"Ġlearn":2193,"ender":2194,"ĠAss":2195,"Ġversion":2196,"now":2197,"Ġcheck":2198,"ĠCal":2199,"RE":2200,"minist":2201,"On":2202,"ources":2203,"Ġbenef":2204,"Ġdoc":2205,"Ġdeter":2206,"Ġenc":2207,"Ġsuper":2208,"Ġaddress":2209,"Ġvict":2210,"Ġ2013":2211,"Ġmeas":2212,"tr":2213,"Ġfield":2214,"When":2215,"Ġsignific":2216,"uge":2217,"Ġfeat":2218,"Ġcommon":2219,"load":2220,"Ġbegin":2221,"Ġbring":2222,"Ġaction":2223,"erman":2224,"Ġdescrib":2225,"Ġindust":2226,"Ġwanted":2227,"ried":2228,"ming":2229,"Ġattempt":2230,"45":2231,"fer":2232,"Ġdue":2233,"ression":2234,"##":2235,"Ġshall":2236,"Ġsix":2237,"oo":2238,"Ġstep":2239,"Ġpub":2240,"Ġhimself":2241,"Ġ23":2242,"Ġcop":2243,"Ġdest":2244,"Ġstop":2245,"AC":2246,"ibility":2247,"Ġlab":2248,"icult":2249,"Ġhours":2250,"Ġcreate":2251,"Ġfurther":2252,"ĠAmerica":2253,"ĠCity":2254,"Ġdou":2255,"head":2256,"ST":2257,"ĠNorth":2258,"cing":2259,"Ġnational":2260,"ule":2261,"ĠInst":2262,"Ġtaking":2263,"ĠQu":2264,"irt":2265,"Ġred":2266,"Ġresearch":2267,"viron":2268,"ĠGe":2269,"Ġbreak":2270,"ana":2271,"Ġspace":2272,"aterial":2273,"Ġrecent":2274,"ĠAb":2275,"Ġgeneral":2276,"Ġhit":2277,"Ġperiod":2278,"Ġeverything":2279,"ively":2280,"Ġphys":2281,"Ġsaying":2282,"anks":2283,"Ġcou":2284,"Ġcult":2285,"aced":2286,"eal":2287,"uation":2288,"Ġcoun":2289,"lu":2290,"Ġinclude":2291,"Ġposition":2292,"ĠAfter":2293,"ĠCanad":2294,"ĠEm":2295,"Ġimm":2296,"ĠRed":2297,"Ġpick":2298,"Ġcompl":2299,"Ġmatter":2300,"reg":2301,"ext":2302,"angu":2303,"isc":2304,"ole":2305,"aut":2306,"Ġcompet":2307,"eed":2308,"fect":2309,"Ġ21":2310,"ĠSen":2311,"ĠThese":2312,"asing":2313,"Ġcannot":2314,"Ġinit":2315,"Ġrelations":2316,"ached":2317,"Ġbar":2318,"Ġ40":2319,"ĠTH":2320,"Ġ2012":2321,"Ġvol":2322,"Ġground":2323,"Ġsecurity":2324,"Ġupd":2325,"ilt":2326,"35":2327,"Ġconcern":2328,"ĠJust":2329,"Ġwhite":2330,"Ġseems":2331,"ĠHer":2332,"pecially":2333,"ients":2334,"Ġannoun":2335,"Ġfig":2336,"ights":2337,"Ġstri":2338,"like":2339,"ids":2340,"Ġsus":2341,"Ġwatch":2342,"Ġâ":2343,"Ġwind":2344,"ĠCont":2345,"Ġitself":2346,"Ġmass":2347,"Al":2348,"yle":2349,"ique":2350,"ĠNational":2351,"Ġabs":2352,"Ġpack":2353,"Ġoutside":2354,"Ġanim":2355,"Ġpain":2356,"eter":2357,"Ġmanag":2358,"duct":2359,"ogn":2360,"Ġ]":2361,"ĠSept":2362,"sec":2363,"off":2364,"ĠJan":2365,"Ġfoot":2366,"ades":2367,"Ġthird":2368,"Ġmot":2369,"Ġevidence":2370,"inton":2371,"Ġthreat":2372,"apt":2373,"ples":2374,"cle":2375,"Ġlo":2376,"Ġdecl":2377,"Ġitem":2378,"medi":2379,"Ġrepresent":2380,"omb":2381,"amer":2382,"Ġsignificant":2383,"ograph":2384,"su":2385,"Ġcal":2386,"ires":2387,"0000":2388,"ID":2389,"AM":2390,"Ġsimply":2391,"Ġlonger":2392,"Ġfile":2393,"OT":2394,"che":2395,"So":2396,"ateg":2397,"org":2398,"ĠHis":2399,"Ġener":2400,"Ġdom":2401,"Ġupon":2402,"ili":2403,"\":\"":2404,"Ġthemselves":2405,"Ġcoming":2406,"Ġquite":2407,"Ġdifficult":2408,"ĠBar":2409,"ilities":2410,"rel":2411,"ends":2412,"cial":2413,"64":2414,"Ġwoman":2415,"rap":2416,"yr":2417,"Ġnecess":2418,"ips":2419,"Ġtext":2420,"Ġrequire":2421,"Ġmilitary":2422,"Ġreview":2423,"Ġrespons":2424,"75":2425,"Ġsubject":2426,"Ġinstead":2427,"Ġissues":2428,"Ġgen":2429,"\",\"":2430,"Ġminutes":2431,"Ġweap":2432,"ray":2433,"amed":2434,"time":2435,"bl":2436,"How":2437,"Ġcode":2438,"ĠSm":2439,"Ġhigher":2440,"ĠSte":2441,"ris":2442,"Ġpage":2443,"Ġstudents":2444,"ĠIntern":2445,"Ġmethod":2446,"ĠAug":2447,"ĠPer":2448,"ĠAg":2449,"Ġpolicy":2450,"ĠSw":2451,"Ġexec":2452,"Ġaccept":2453,"ume":2454,"ribut":2455,"Ġwords":2456,"Ġfinal":2457,"Ġchanges":2458,"ĠDemocr":2459,"Ġfriends":2460,"Ġrespect":2461,"Ġep":2462,"Ġcompan":2463,"ivil":2464,"Ġdamage":2465,"****":2466,"ogle":2467,"vironment":2468,"Ġneg":2469,"ental":2470,"Ġap":2471,"Ġtotal":2472,"ival":2473,"!\"":2474,"lim":2475,"Ġneeds":2476,"Ġagre":2477,"Ġdevelopment":2478,"Ġage":2479,"iple":2480,"21":2481,"Ġresults":2482,"ĠAf":2483,"Sh":2484,"Ġgun":2485,"ĠObama":2486,"roll":2487,"Ġ@":2488,"Ġrights":2489,"ĠBrit":2490,"Ġrunning":2491,"Ġwasn":2492,"Ġport":2493,"Ġrate":2494,"Ġpretty":2495,"Ġtarget":2496,"Ġsaw":2497,"Ġcirc":2498,"Ġworks":2499,"icro":2500,"alt":2501,"over":2502,"www":2503,"That":2504,"lier":2505,"Ġeveryone":2506,"ude":2507,"Ġpie":2508,"iddle":2509,"rael":2510,"Ġrad":2511,"Ġblock":2512,"Ġwalk":2513,"To":2514,"ãģ":2515,"nes":2516,"ĠAust":2517,"aul":2518,"rote":2519,"ĠSouth":2520,"ession":2521,"oph":2522,"Ġshows":2523,"Ġsite":2524,"Ġjo":2525,"Ġrisk":2526,"clus":2527,"lt":2528,"Ġinj":2529,"iding":2530,"ĠSpe":2531,"Ġchall":2532,"irm":2533,"Ġ22":2534,"itting":2535,"str":2536,"Ġhy":2537,"LE":2538,"key":2539,"Ġbegan":2540,"atur":2541,"ashington":2542,"lam":2543,"ĠDav":2544,"bit":2545,"Ġsize":2546,"ĠPar":2547,"38":2548,"ournal":2549,"face":2550,"Ġdecision":2551,"Ġlarg":2552,"Ġjud":2553,"rect":2554,"Ġcontinue":2555,"ĠOct":2556,"overed":2557,"ĠInt":2558,"========":2559,"Ġparent":2560,"ĠWill":2561,"Ġeasy":2562,"Ġdrug":2563,"anger":2564,"Ġsense":2565,"Ġdi":2566,"iday":2567,"Ġenergy":2568,"istic":2569,"Ġassoci":2570,"arter":2571,"obal":2572,"eks":2573,"ĠEl":2574,"urch":2575,"Ġgirl":2576,"oe":2577,"itle":2578,"Ġ28":2579,"ĠChe":2580,"Ġrequest":2581,"Ġsoon":2582,"Ġhost":2583,"ky":2584,"Ġstates":2585,"omes":2586,"Ġmaterial":2587,"lex":2588,"Ġmoment":2589,"Ġansw":2590,"onse":2591,"Ġespecially":2592,"Ġnorm":2593,"Ġservices":2594,"pite":2595,"ran":2596,"Ġrole":2597,"44":2598,"):":2599,"Ġcred":2600,"Cl":2601,"________":2602,"Ġmat":2603,"Ġlog":2604,"ĠClinton":2605,"OU":2606,"Ġoffice":2607,"Ġ26":2608,"Ġcharg":2609,"Ġtrack":2610,"ma":2611,"Ġheart":2612,"Ġball":2613,"Ġpersonal":2614,"Ġbuilding":2615,"na":2616,"set":2617,"body":2618,"ĠBlack":2619,"Ġincrease":2620,"itten":2621,"Ġneeded":2622,"36":2623,"32":2624,"=\"":2625,"Ġlost":2626,"Ġbecame":2627,"Ġgroups":2628,"ĠMus":2629,"Ġwrote":2630,"ĠPe":2631,"Ġprop":2632,"joy":2633,"é":2634,"ĠWhite":2635,"Ġdead":2636,".'":2637,"Ġhttp":2638,"Ġwebs":2639,"OS":2640,"Ġinside":2641,"Ġwrong":2642,"Ġstatement":2643,"Ġ...":2644,"yl":2645,"Ġfilm":2646,"Ġmusic":2647,"Ġshare":2648,"ification":2649,"Ġrelease":2650,"Ġforward":2651,"Ġstay":2652,"Ġcomput":2653,"itte":2654,"ser":2655,"Ġoriginal":2656,"Ġcard":2657,"Ġcand":2658,"Ġdiv":2659,"atural":2660,"Ġfavor":2661,"OM":2662,"Ġcases":2663,"uses":2664,"Ġsection":2665,"Ġleave":2666,"ging":2667,"oved":2668,"ĠWashington":2669,"39":2670,"ĠGl":2671,"Ġrequired":2672,"action":2673,"apan":2674,"oor":2675,"iter":2676,"ĠKing":2677,"Ġcountries":2678,"ĠGerman":2679,"lling":2680,"Ġ27":2681,"34":2682,"Ġquestions":2683,"Ġprim":2684,"Ġcell":2685,"Ġshoot":2686,"Ġanyone":2687,"ĠWest":2688,"Ġaffect":2689,"epend":2690,"Ġonline":2691,"ĠIsrael":2692,"ĠSeptember":2693,"Ġability":2694,"Ġcontent":2695,"ises":2696,"Ġreve":2697,"Ġlaun":2698,"Ġindic":2699,"Ġforce":2700,"cast":2701,"Ġsold":2702,"aving":2703,"fl":2704,"Ġsoft":2705,"Ġcompanies":2706,"ceed":2707,"Ġarticle":2708,"Ġaud":2709,"Ġrev":2710,"Ġeduc":2711,"Ġplaying":2712,"05":2713,"Ġheld":2714,"ctor":2715,"Ġreleased":2716,"Ġfederal":2717,"37":2718,"Ġadminist":2719,"Ġinterview":2720,"Ġinstall":2721,"Ġreceived":2722,"Ġsource":2723,"uk":2724,"Ph":2725,"Ġserious":2726,"Ġcreated":2727,"Ġcause":2728,"Ġimmedi":2729,"Ġdefin":2730,"uel":2731,"ĠDepartment":2732,"ctions":2733,"ĠCour":2734,"ĠNow":2735,"ze":2736,"ites":2737,"itution":2738,"Ġlate":2739,"Ġspeak":2740,"ners":2741,"Ġlegal":2742,"ari":2743,"ĠCor":2744,"Ġweeks":2745,"Ġmodel":2746,"Ġpred":2747,"Ġexact":2748,"BC":2749,"ĠBy":2750,"ING":2751,"osing":2752,"Ġtakes":2753,"Ġregard":2754,"Ġopportun":2755,"Ġprice":2756,"Ġ198":2757,"ĠApr":2758,"fully":2759,"Ġord":2760,"Ġproblems":2761,"ruction":2762,"ham":2763,"ĠCount":2764,"lege":2765,"Ġleaders":2766,"ET":2767,"lev":2768,"Ġdeep":2769,"ological":2770,"ese":2771,"haps":2772,"ĠSome":2773,"Ġpers":2774,"Ġcontract":2775,"Ġrelationship":2776,"sp":2777,"oud":2778,"Ġbase":2779,"48":2780,"mit":2781,"Ad":2782,"ancial":2783,"Ġconsum":2784,"Ġpotential":2785,"Ġlangu":2786,"rem":2787,"eth":2788,"Ġrelig":2789,"ressed":2790,"66":2791,"Ġlink":2792,"Ġlower":2793,"ayer":2794,"ĠJune":2795,"Ġfem":2796,"unt":2797,"erc":2798,"urd":2799,"Ġcontact":2800,"Ġill":2801,"Ġmother":2802,"Ġestab":2803,"htt":2804,"ĠMarch":2805,"ĠBro":2806,"ĠChina":2807,"Ġ29":2808,"Ġsqu":2809,"Ġprovided":2810,"Ġaverage":2811,"asons":2812,"Ġ2011":2813,"Ġexam":2814,"lin":2815,"55":2816,"ned":2817,"Ġperfect":2818,"Ġtou":2819,"alse":2820,"ux":2821,"Ġbuy":2822,"Ġshot":2823,"Ġcollect":2824,"Ġphot":2825,"Ġplayed":2826,"Ġsurpr":2827,"Ġofficials":2828,"Ġsimple":2829,"avy":2830,"Ġindustry":2831,"Ġhands":2832,"ground":2833,"Ġpull":2834,"Ġround":2835,"Ġuser":2836,"Ġrange":2837,"uary":2838,"Ġprivate":2839,"ops":2840,"ees":2841,"Ġways":2842,"ĠMich":2843,"Ġveh":2844,"Ġexcept":2845,"Ġterms":2846,"imum":2847,"pper":2848,"ION":2849,"ores":2850,"ĠDragon":2851,"oul":2852,"Ġden":2853,"Ġperformance":2854,"Ġbill":2855,"cil":2856,"47":2857,"Ġenvironment":2858,"Ġexc":2859,"add":2860,"Ġworth":2861,"Ġpict":2862,"Ġchance":2863,"Ġ2018":2864,"bor":2865,"Ġspeed":2866,"iction":2867,"Ġalleg":2868,"ĠJapan":2869,"atory":2870,"reet":2871,"Ġmatch":2872,"ĠII":2873,"Ġstru":2874,"order":2875,"Ġste":2876,"Ġliving":2877,"Ġstruct":2878,"ino":2879,"Ġsepar":2880,"hern":2881,"Ġresponse":2882,"Ġenjoy":2883,"Ġvia":2884,"AD":2885,"uments":2886,"acebook":2887,"Ġmember":2888,"ibr":2889,"izing":2890,"Ġtool":2891,"ĠMon":2892,"ĠWhile":2893,"hood":2894,"ĠAng":2895,"ĠDef":2896,"Ġoffer":2897,"Tr":2898,"aur":2899,"Ġturned":2900,"ĠJuly":2901,"down":2902,"anced":2903,"Ġrecently":2904,"ĠEar":2905,"Ġce":2906,"ĠStar":2907,"ĠCong":2908,"rought":2909,"Ġblood":2910,"Ġhope":2911,"Ġcomment":2912,"aint":2913,"Ġarri":2914,"iles":2915,"Ġparticip":2916,"ought":2917,"ription":2918,"08":2919,"49":2920,"Ġgave":2921,"Ġselect":2922,"Ġkilled":2923,"sych":2924,"Ġgoes":2925,"ij":2926,"Ġcoll":2927,"Ġimpact":2928,"atives":2929,"ĠSer":2930,"09":2931,"ĠAugust":2932,"Ġboy":2933,"de":2934,"ĠDes":2935,"Ġfelt":2936,"US":2937,"Ġexpected":2938,"Ġimage":2939,"ĠMark":2940,"ccording":2941,"oice":2942,"EC":2943,"ĠMag":2944,"ened":2945,"hold":2946,"ĠPost":2947,"Ġprevent":2948,"No":2949,"Ġinvolved":2950,"Ġeyes":2951,"Ġquickly":2952,"At":2953,"unk":2954,"Ġbehav":2955,"Ġur":2956,"Ġled":2957,"come":2958,"ey":2959,"Ġcandid":2960,"Ġearlier":2961,"Ġfocus":2962,"ety":2963,"Pro":2964,"ledge":2965,"ixed":2966,"illed":2967,"Ġpopular":2968,"AP":2969,"Ġsett":2970,"light":2971,"Ġvarious":2972,"inks":2973,"Ġlevels":2974,"Ġroad":2975,"ellig":2976,"ables":2977,"hel":2978,"ittee":2979,"ĠGener":2980,"ype":2981,"Ġheard":2982,"icles":2983,"Ġmis":2984,"Ġusers":2985,"ĠSan":2986,"Ġimprove":2987,"Ġfather":2988,"Ġsearch":2989,"They":2990,"vil":2991,"Ġprofess":2992,"Ġknew":2993,"Ġloss":2994,"Ġevents":2995,"65":2996,"Ġbillion":2997,"07":2998,"02":2999,"ĠNews":3000,"ĠAM":3001,"Ġcover":3002,"where":3003,"ension":3004,"Ġbott":3005,"Ġareas":3006,"ences":3007,"ope":3008,"ĠTwitter":3009,"ael":3010,"Ġgets":3011,"ĠGoogle":3012,"Ġsn":3013,"iant":3014,"Ġvote":3015,"Ġnearly":3016,"Ġincluded":3017,"Ġrecogn":3018,"zz":3019,"mm":3020,"aled":3021,"Ġhappened":3022,"04":3023,"Ġhot":3024,"Ġwhose":3025,"Ġcivil":3026,"Ġsuff":3027,"oes":3028,"itiz":3029,"ĠSyri":3030,"Ġrespond":3031,"Ġhon":3032,"Ġfeatures":3033,"Ġeconomic":3034,"ĠApril":3035,"rim":3036,"Ġtechnology":3037,"Ġoption":3038,"aging":3039,"Ġpurch":3040,"Re":3041,"Ġlat":3042,"chie":3043,"isl":3044,"Ġrecomm":3045,"uf":3046,"Ġtraining":3047,"Ġeffects":3048,"Ġfast":3049,"Ġ2010":3050,"Ġoccur":3051,"Ġwebsite":3052,"Ġemail":3053,"Ġsens":3054,"ech":3055,"Ġoil":3056,"Ġinflu":3057,"Ġcurrently":3058,"ĠSch":3059,"ĠAdd":3060,"Ġgoal":3061,"Ġscient":3062,"Ġconv":3063,"100":3064,"emy":3065,"Ġdecided":3066,"Ġtravel":3067,"Ġmention":3068,"LL":3069,"03":3070,"Ġelection":3071,"Ġphone":3072,"Ġlooks":3073,"Ġsituation":3074,"Ġcy":3075,"Ġhor":3076,"bed":3077,"ĠCourt":3078,"aily":3079,"aves":3080,"Ġquality":3081,"ĠComp":3082,"wise":3083,"Ġtable":3084,"Ġstaff":3085,"ĠWind":3086,"ett":3087,"Ġtried":3088,"idered":3089,"Ġaddition":3090,"Ġbox":3091,"Ġlack":3092,"arily":3093,"Ġwide":3094,"Ġmid":3095,"Ġboard":3096,"ysis":3097,"Ġanti":3098,"ha":3099,"Ġdig":3100,"ening":3101,"Ġdro":3102,"Con":3103,"68":3104,"Ġslow":3105,"based":3106,"sequ":3107,"Ġpath":3108,"Ex":3109,"aker":3110,"Ġworked":3111,"Ġpen":3112,"Ġengine":3113,"Ġlooked":3114,"ĠSuper":3115,"ĠServ":3116,"Ġvictim":3117,"Un":3118,"Ġproperty":3119,"Ġintrodu":3120,"Ġexecut":3121,"ĠPM":3122,"Le":3123,"Ġcolor":3124,"ĠMore":3125,"Ġ60":3126,"Ġnetwork":3127,"Ġdate":3128,"cul":3129,"idge":3130,"Ġextra":3131,"31":3132,"Ġsle":3133,"67":3134,"Ġwond":3135,"Ġreports":3136,"just":3137,"ĠAustral":3138,"Ġcapital":3139,"Ġens":3140,"Ġcommand":3141,"Ġallowed":3142,"Ġprep":3143,"Ġcapt":3144,"hib":3145,"Ġnumbers":3146,"chan":3147,"Ġfair":3148,"mp":3149,"oms":3150,"Ġreach":3151,"With":3152,"tain":3153,"Ġbroad":3154,"Ġcouple":3155,"ecause":3156,"lying":3157,"ĠFeb":3158,"Ġscreen":3159,"Ġlives":3160,"Ġprior":3161,"ĠCongress":3162,"Ar":3163,"Ġapproach":3164,"Ġemer":3165,"aries":3166,"ĠDis":3167,"serv":3168,"ĠNe":3169,"Ġbuilt":3170,"cies":3171,"Ġrepe":3172,"Ġrules":3173,"force":3174,"ĠPal":3175,"Ġfinancial":3176,"Ġconsidered":3177,"ĠChar":3178,"nces":3179,"ĠIS":3180,"Ġbrought":3181,"Ġbi":3182,"iers":3183,"ĠSim":3184,"OP":3185,"Ġproducts":3186,"Ġvisit":3187,"Ġdocument":3188,"Ġconduct":3189,"Ġcompletely":3190,"ining":3191,"ĠCalif":3192,"ibly":3193,"Ġwritten":3194,"ĠTV":3195,"ements":3196,"Ġdraw":3197,"One":3198,"Ġpublished":3199,"Ġsecret":3200,"rain":3201,"het":3202,"ĠFacebook":3203,"onday":3204,"ĠUp":3205,"Ġsexual":3206,"Ġthous":3207,"ĠPat":3208,"Ġess":3209,"Ġstandard":3210,"Ġarm":3211,"ges":3212,"ection":3213,"Ġfell":3214,"Ġforeign":3215,"ani":3216,"ĠFriday":3217,"Ġregular":3218,"inary":3219,"Ġincreased":3220,"Ġusually":3221,"Ġdemon":3222,"Ġdark":3223,"Ġadditional":3224,"rol":3225,"ĠOf":3226,"Ġproduction":3227,"!!":3228,"undred":3229,"Ġinternational":3230,"idents":3231,"ĠFree":3232,"roup":3233,"Ġrace":3234,"Ġmach":3235,"Ġhuge":3236,"All":3237,"lear":3238,"ovember":3239,"Ġtown":3240,"Ġattention":3241,"ĠOff":3242,"yond":3243,"ĠThen":3244,"field":3245,"Ġterror":3246,"raz":3247,"ĠBo":3248,"Ġmeeting":3249,"ĠPark":3250,"Ġarrest":3251,"Ġfear":3252,"Ġaw":3253,"ĠVal":3254,"oring":3255,"',":3256,"Ġextreme":3257,"arr":3258,"Ġworkers":3259,"After":3260,"Ġ31":3261,"net":3262,"ament":3263,"Ġdirectly":3264,"Ġpopulation":3265,"ube":3266,"ĠOctober":3267,"ĠIN":3268,"ĠJanuary":3269,"59":3270,"ĠDavid":3271,"Ġcross":3272,"cember":3273,"ĠFirst":3274,"Ġmessage":3275,"irit":3276,"Ġnation":3277,"Ġpoll":3278,"isions":3279,"Ġanswer":3280,"ny":3281,"isode":3282,"Ġcarry":3283,"ĠRussia":3284,"Ġhear":3285,"ength":3286,"roy":3287,"Ġnatural":3288,"inally":3289,"Ġdog":3290,"mitted":3291,"Ġtrade":3292,"Ġsubst":3293,"Ġmultiple":3294,"ĠAfric":3295,"Ġfans":3296,"Ġsort":3297,"Ġglobal":3298,"ication":3299,"ĠWed":3300,"ara":3301,"Ġachie":3302,"Ġlanguage":3303,"vey":3304,"Ġtal":3305,"Ġnecessary":3306,"Ġdetails":3307,"Ġsen":3308,"ĠSund":3309,"ĠReg":3310,"ĠRec":3311,"06":3312,"Ġsil":3313,"ressive":3314,"Ġmedical":3315,"unch":3316,"ornia":3317,"Ġund":3318,"fort":3319,"ocks":3320,"ĠMonday":3321,"uesday":3322,"craft":3323,"77":3324,"urt":3325,"Ġver":3326,"ĠHill":3327,"Ġreceive":3328,"Ġmorning":3329,"estern":3330,"Ġbank":3331,"Ġsat":3332,"irth":3333,"ĠHigh":3334,"Ġdevice":3335,"ĠTHE":3336,"ĠCenter":3337,"Ġsafe":3338,"Ġple":3339,"ĠCanada":3340,"Ġsystems":3341,"Ġassist":3342,"Ġsurv":3343,"Ġbattle":3344,"ĠSoc":3345,"vertis":3346,"She":3347,"Ġpaper":3348,"Ġgrowth":3349,"Ġcast":3350,"Sc":3351,"Ġplans":3352,"lled":3353,"Ġparts":3354,"Ġwall":3355,"Ġmovement":3356,"Ġpractice":3357,"imately":3358,"Ġdisplay":3359,"Ġsometimes":3360,"omp":3361,"ĠPaul":3362,"ĠYes":3363,"king":3364,"58":3365,"oly":3366,"Ġson":3367,"Ġavoid":3368,"okes":3369,"ĠJew":3370,"Ġtowards":3371,"asc":3372,"Ġ//":3373,"ĠKore":3374,"Ġtalking":3375,"Ġcorrect":3376,"Ġspent":3377,"icks":3378,"iable":3379,"eared":3380,"Ġterm":3381,"Ġwants":3382,"oming":3383,"Ġut":3384,"Ġdoub":3385,"Ġforces":3386,"Ġplease":3387,"69":3388,"ĠNovember":3389,"atform":3390,"ondon":3391,"Ġones":3392,"Ġimmediately":3393,"ĠRussian":3394,"ĠMet":3395,"Ġdeg":3396,"Ġparents":3397,"CH":3398,"ĠAmericans":3399,"aly":3400,"ĠMod":3401,"Ġshown":3402,"Ġconditions":3403,"Ġstuff":3404,"Ġreb":3405,"ĠYour":3406,"Ġincludes":3407,"nown":3408,"ĠSam":3409,"Ġexperien":3410,"mission":3411,"ĠEven":3412,"aught":3413,"Ġannounced":3414,"ĠRepublican":3415,"Ġdetermin":3416,"Ġdescribed":3417,"ĠCounty":3418,"()":3419,"Ġdoor":3420,"Ġchanged":3421,"Ġneigh":3422,"ĠHere":3423,"Ġclean":3424,"Ġpan":3425,"ĠDecember":3426,"ĠEuropean":3427,"iring":3428,"apter":3429,"Ġclub":3430,"ĠTuesday":3431,"Ġpaid":3432,"ĠNet":3433,"Ġattacks":3434,"Ġcharacters":3435,"Ġalone":3436,"Ġdirector":3437,"dom":3438,"Ġ35":3439,"Ġload":3440,"Ġrout":3441,"ĠCalifornia":3442,"Ġfinally":3443,"Ġrac":3444,"Ġcontr":3445,"Ġexactly":3446,"resh":3447,"pri":3448,"ĠIslam":3449,"Ġnature":3450,"Ġcareer":3451,"Ġlatest":3452,"Ġconvers":3453,"ĠSl":3454,"pose":3455,"cient":3456,"ĠInc":3457,"ivity":3458,"88":3459,"ĠAtt":3460,"ĠMor":3461,"nesday":3462,"Ġweight":3463,"ken":3464,"Ġnote":3465,"Ġteams":3466,"Ġ\\":3467,"airs":3468,"ĠGreen":3469,"Ġhundred":3470,"onent":3471,"Ġstreng":3472,"Ġconsist":3473,"icated":3474,"Ġregul":3475,"Ġlic":3476,"astic":3477,"Ġten":3478,"ursday":3479,"elligence":3480,"ously":3481,"ĠUK":3482,"BI":3483,"Ġcosts":3484,"Ġindepend":3485,"ĠAP":3486,"Ġnormal":3487,"Ġhom":3488,"Ġobvious":3489,"Ġswe":3490,"Ġstar":3491,"Ġready":3492,"acher":3493,"Ġimplement":3494,"gest":3495,"Ġsong":3496,"ĠGet":3497,"ĠLab":3498,"Ġinteresting":3499,"using":3500,"Ġgiving":3501,"ĠSunday":3502,"Ġetc":3503,"Ġmiddle":3504,"Ġremember":3505,"right":3506,"osition":3507,"utions":3508,"Ġmax":3509,"46":3510,"Ġyourself":3511,"Ġdemand":3512,"Ġtreatment":3513,"Ġdanger":3514,"ĠCons":3515,"Ġguy":3516,"ĠBritish":3517,"Ġphysical":3518,"Ġrelated":3519,"Ġremain":3520,"Ġcouldn":3521,"Ġrefer":3522,"Ġcitiz":3523,"box":3524,"ENT":3525,"board":3526,"Ġinn":3527,"IG":3528,"ero":3529,"ĠStreet":3530,"ospital":3531,"rench":3532,"chers":3533,"Ġstra":3534,"OL":3535,"ager":3536,"ĠAN":3537,"Ġeasily":3538,"IA":3539,"enge":3540,"iny":3541,"Ġclos":3542,"ocked":3543,"Ġuses":3544,"ĠCoun":3545,"Im":3546,"uild":3547,"??":3548,"more":3549,"Ġang":3550,"Ġwrite":3551,"olute":3552,"57":3553,"Ġleader":3554,"Ġreading":3555,"":3784,"Ġfigure":3785,"Ġdisapp":3786,"enty":3787,"Ġsoftware":3788,"Ġult":3789,"Ġofficers":3790,"New":3791,"Is":3792,"Ġremains":3793,"ĠIndia":3794,"Ġpsych":3795,"rief":3796,"Ġcat":3797,"esc":3798,"Ġobserv":3799,"Ġstage":3800,"ĠDark":3801,"Ġenter":3802,"change":3803,"Ġpassed":3804,"Ġdespite":3805,"ĠOut":3806,"Ġmovie":3807,"rs":3808,"Ġvoice":3809,"mine":3810,"ĠPlay":3811,"Ġtoward":3812,"ĠTer":3813,"Ġregion":3814,"Ġvalues":3815,"orters":3816,"Ġmount":3817,"Ġofficer":3818,"ĠOther":3819,"ban":3820,"Ġhous":3821,"wood":3822,"room":3823,"IV":3824,"ĠSun":3825,"see":3826,"ĠOver":3827,"rog":3828,"90":3829,"Ġlay":3830,"ĠTur":3831,"awn":3832,"Ġpressure":3833,"ĠSub":3834,"Ġbooks":3835,"edom":3836,"ĠSand":3837,"AA":3838,"ago":3839,"Ġreasons":3840,"ford":3841,"Ġactivity":3842,"UT":3843,"Now":3844,"ĠSenate":3845,"cell":3846,"night":3847,"Ġcalls":3848,"inter":3849,"Ġletter":3850,"ĠRob":3851,"ĠJe":3852,"Ġchoose":3853,"ĠLaw":3854,"Get":3855,"Be":3856,"Ġrob":3857,"Ġtypes":3858,"Ġplatform":3859,"Ġquarter":3860,"RA":3861,"ĠTime":3862,"Ġmaybe":3863,"ĠCr":3864,"95":3865,"pre":3866,"Ġmoving":3867,"Ġlif":3868,"Ġgold":3869,"Ġsom":3870,"Ġpatients":3871,"Ġtruth":3872,"ĠKe":3873,"urance":3874,"antly":3875,"mar":3876,"Ġcharge":3877,"ĠGreat":3878,"Ġcele":3879,"--------------------------------":3880,"Ġrock":3881,"roid":3882,"ancy":3883,"Ġcredit":3884,"aud":3885,"By":3886,"ĠEvery":3887,"Ġmoved":3888,"inger":3889,"ribution":3890,"Ġnames":3891,"Ġstraight":3892,"ĠHealth":3893,"ĠWell":3894,"Ġfeature":3895,"Ġrule":3896,"Ġsche":3897,"inated":3898,"ĠMichael":3899,"berg":3900,"41":3901,"iled":3902,"band":3903,"Ġclick":3904,"ĠAngel":3905,"onents":3906,"ÂŃ":3907,"ĠIraq":3908,"ĠSaturday":3909,"Ġaware":3910,"part":3911,"Ġpattern":3912,"OW":3913,"ĠLet":3914,"Ġgrad":3915,"igned":3916,"Ġassociated":3917,"Ġstyle":3918,"no":3919,"iation":3920,"aith":3921,"ilies":3922,"Ġstories":3923,"uration":3924,"Ġindividuals":3925,"ĠâĢ¦":3926,"miss":3927,"ĠAssoci":3928,"ishing":3929,"aby":3930,"Ġsummer":3931,"ĠBen":3932,"Ġ32":3933,"Ġarch":3934,"uty":3935,"ĠTexas":3936,"hol":3937,"Ġfully":3938,"Ġmill":3939,"Ġfollowed":3940,"ĠBill":3941,"ĠIndian":3942,"ĠSecret":3943,"ĠBel":3944,"ĠFebruary":3945,"Ġjobs":3946,"Ġseemed":3947,"ĠGovern":3948,"ipped":3949,"Ġreality":3950,"Ġlines":3951,"Ġpark":3952,"Ġmeasure":3953,"ĠOur":3954,"IM":3955,"Ġbrother":3956,"Ġgrowing":3957,"Ġban":3958,"Ġestim":3959,"Ġcry":3960,"ĠSchool":3961,"Ġmechan":3962,"ĠOF":3963,"ĠWindows":3964,"Ġrates":3965,"ĠOh":3966,"Ġpositive":3967,"Ġculture":3968,"istics":3969,"ica":3970,"Ġhar":3971,"ya":3972,"itely":3973,"ipp":3974,"Ġmap":3975,"encies":3976,"ĠWilliam":3977,"II":3978,"akers":3979,"56":3980,"ĠMart":3981,"ĠRem":3982,"Ġaltern":3983,"itude":3984,"Ġcoach":3985,"rowd":3986,"Don":3987,"Ġkids":3988,"Ġjournal":3989,"Ġcorpor":3990,"Ġfalse":3991,"Ġweb":3992,"Ġsleep":3993,"Ġcontain":3994,"Ġsto":3995,"Ġbed":3996,"iverse":3997,"ĠRich":3998,"ĠChinese":3999,"Ġpun":4000,"Ġmeant":4001,"known":4002,"Ġnotice":4003,"Ġfavorite":4004,"aven":4005,"Ġcondition":4006,"Ġpurpose":4007,"))":4008,"Ġorganization":4009,"Ġchalleng":4010,"Ġmanufact":4011,"Ġsusp":4012,"ĠAc":4013,"Ġcritic":4014,"unes":4015,"uclear":4016,"Ġmer":4017,"vention":4018,"Ġ80":4019,"Ġmist":4020,"ĠUs":4021,"ĠTor":4022,"http":4023,"olf":4024,"Ġlarger":4025,"Ġadvant":4026,"Ġresear":4027,"Ġactions":4028,"ml":4029,"Ġkept":4030,"Ġaim":4031,",'":4032,"col":4033,"Ġbenefits":4034,"ifying":4035,"Ġactual":4036,"ĠInternational":4037,"Ġvehicle":4038,"Ġchief":4039,"Ġefforts":4040,"ĠLeague":4041,"ĠMost":4042,"Ġwait":4043,"Ġadult":4044,"Ġoverall":4045,"Ġspeech":4046,"Ġhighly":4047,"Ġfemale":4048,"Ġerror":4049,"Ġeffective":4050,"54":4051,"Ġencour":4052,"well":4053,"Ġfailed":4054,"Ġconserv":4055,"Ġprograms":4056,"Ġtrou":4057,"Ġahead":4058,"500":4059,"vertisement":4060,"IP":4061,"ĠFound":4062,"pir":4063,"Ġ%":4064,"Ġcrime":4065,"ander":4066,"Ġlocation":4067,"ĠIran":4068,"Ġbehavior":4069,"azing":4070,"Ġrare":4071,"Ġemb":4072,"Ġcaused":4073,"Ġship":4074,"Ġactive":4075,"Ġcontribut":4076,"Ġgreen":4077,"Ġacqu":4078,"Ġreflect":4079,"venue":4080,"Ġfirm":4081,"Ġbirth":4082,"].":4083,"Ġclearly":4084,"Ġemot":4085,"Ġagency":4086,"riage":4087,"Ġmemory":4088,"98":4089,"SA":4090,"ĠSee":4091,"acing":4092,"CC":4093,"Ġbiggest":4094,"Ġrap":4095,"Ġbasic":4096,"Ġband":4097,"eat":4098,"Ġsuspect":4099,"ĠMac":4100,"Ġ90":4101,"mark":4102,"istan":4103,"Ġspread":4104,"ams":4105,"ki":4106,"asy":4107,"rav":4108,"ĠRober":4109,"Ġdemonstr":4110,"rated":4111,"Ġabsolute":4112,"Ġplaces":4113,"Ġimpl":4114,"ibrary":4115,"Ġcards":4116,"Ġdestroy":4117,"Ġvirt":4118,"vere":4119,"Ġappeared":4120,"yan":4121,"point":4122,"Ġbeg":4123,"Ġtemper":4124,"spe":4125,"anted":4126,"ears":4127,"ĠDirect":4128,"Ġlength":4129,"Ġblog":4130,"amb":4131,"Ġinteg":4132,"Ġresources":4133,"acc":4134,"iful":4135,"Ġspot":4136,"Ġforced":4137,"Ġthousands":4138,"ĠMinister":4139,"Ġqual":4140,"ĠFrench":4141,"atically":4142,"Ġgenerally":4143,"Ġdrink":4144,"Ġthus":4145,"IL":4146,"odes":4147,"Ġappropri":4148,"ĠRead":4149,"Ġwhom":4150,"Ġeye":4151,"Ġcollege":4152,"Ġ45":4153,"irection":4154,"Ġensure":4155,"Ġapparent":4156,"iders":4157,"Ġreligious":4158,"Ġminor":4159,"olic":4160,"Ġtro":4161,"ĠWhy":4162,"ribute":4163,"met":4164,"Ġprimary":4165,"Ġdeveloped":4166,"Ġpeace":4167,"Ġskin":4168,"ste":4169,"ava":4170,"Ġblue":4171,"Ġfamilies":4172,"Ġir":4173,"Ġapply":4174,"Ġinform":4175,"ĠSmith":4176,"CT":4177,"ii":4178,"Ġlimit":4179,"Ġresist":4180,"................":4181,"umn":4182,"Ġconflic":4183,"Ġtwe":4184,"udd":4185,"ĠTom":4186,"Ġliter":4187,"que":4188,"bon":4189,"Ġhair":4190,"Ġeventually":4191,"Ġpus":4192,"Ġhelped":4193,"Ġagg":4194,"orney":4195,"ĠApple":4196,"Ġfit":4197,"ĠSur":4198,"Ġprem":4199,"Ġsales":4200,"Ġseconds":4201,"Ġstrength":4202,"Ġfeeling":4203,"¿½":4204,"Ġtour":4205,"Ġknows":4206,"oom":4207,"Ġexerc":4208,"Ġsomew":4209,"�":4210,">>":4211,"Ġspokes":4212,"Ġideas":4213,"Ġregist":4214,"soft":4215,"ĠDel":4216,"ĠPC":4217,"Ġpropos":4218,"Ġlaunch":4219,"Ġbottom":4220,"TH":4221,"ĠPlease":4222,"vest":4223,"itz":4224,"ĠInter":4225,"Ġscript":4226,"Ġrat":4227,"arning":4228,"Ġil":4229,"ĠJer":4230,"ĠAre":4231,"Ġwhatever":4232,"oken":4233,"cience":4234,"Ġmode":4235,"Ġagree":4236,"Ġsources":4237,"Ġinitial":4238,"Ġrestrict":4239,"Ġwonder":4240,"usion":4241,"####":4242,"ĠSil":4243,"ville":4244,"Ġburn":4245,"tw":4246,"asion":4247,"Ġ£":4248,"Ġnor":4249,"uing":4250,"Ġreached":4251,"Ġsun":4252,"Ġcateg":4253,"igration":4254,"Ġcook":4255,"Ġpromot":4256,"Ġmale":4257,"Ġclimate":4258,"Ġfix":4259,"Ġalleged":4260,"UR":4261,"alled":4262,"Ġimages":4263,"Cont":4264,"ota":4265,"Ġschools":4266,"ios":4267,"Ġdrop":4268,"Ġstream":4269,"ĠMo":4270,"Ġpreviously":4271,"aling":4272,"Ġpet":4273,"Ġdouble":4274,"Ġ(@":4275,"annel":4276,"Ġdefault":4277,"ties":4278,"Ġrank":4279,"ĠDec":4280,"ĠCouncil":4281,"Ġweapon":4282,"Ġstock":4283,"Ġanaly":4284,"ĠStr":4285,"Ġpicture":4286,"ĠPolice":4287,"ference":4288,"Ġcentury":4289,"Ġcitizens":4290,"Ġonto":4291,"Ġexpand":4292,"Ġhero":4293,"ĠSol":4294,"Ġwild":4295,"Ġupdate":4296,"Ġcustomers":4297,"ront":4298,"def":4299,"Ġlik":4300,"Ġcriminal":4301,"ĠChristian":4302,"SP":4303,"76":4304,"Ġleaving":4305,"Ġotherwise":4306,"ĠDist":4307,"Ġbasis":4308,"52":4309,"53":4310,"icip":4311,"ĠBer":4312,"Ġrecommend":4313,"Ġfloor":4314,"Ġcrowd":4315,"oles":4316,"Ġ70":4317,"Ġcentral":4318,"ĠEv":4319,"Ġdream":4320,"Ġdownload":4321,"Ġconfir":4322,"ĠThom":4323,"Ġwindow":4324,"Ġhappens":4325,"Ġunit":4326,"Ġtend":4327,"Ġspl":4328,"Ġbecomes":4329,"Ġfighting":4330,"Ġpredict":4331,"ĠPress":4332,"ĠPower":4333,"Ġheavy":4334,"aked":4335,"Ġfan":4336,"orter":4337,"ategy":4338,"BA":4339,"izes":4340,"Ġspend":4341,"Here":4342,"Ġ2007":4343,"Ġadop":4344,"ĠHam":4345,"Ġfootball":4346,"ĠPort":4347,"oday":4348,"51":4349,"ampions":4350,"Ġtransfer":4351,"ht":4352,"Ġ38":4353,"term":4354,"acity":4355,"Ġbur":4356,"],":4357,"ternal":4358,"rig":4359,"but":4360,"Ġtherefore":4361,"ĠBecause":4362,"resp":4363,"rey":4364,"Ġmission":4365,"Some":4366,"Ġnoted":4367,"Ġassum":4368,"Ġdisease":4369,"Ġedit":4370,"Ġprogress":4371,"rd":4372,"ĠBrown":4373,"ocal":4374,"Ġadding":4375,"Ġraised":4376,"ĠAny":4377,"Ġtick":4378,"Ġseeing":4379,"ĠPeople":4380,"Ġagreement":4381,"Ġserver":4382,"Ġwat":4383,"Ġdebate":4384,"Ġsupposed":4385,"iling":4386,"Ġlargest":4387,"Ġsuccessful":4388,"ĠPri":4389,"ĠDemocratic":4390,"Ġjump":4391,"ĠSyria":4392,"Ġowners":4393,"Ġoffers":4394,"Ġshooting":4395,"Ġeffic":4396,"sey":4397,"Ġhaven":4398,"verse":4399,"tered":4400,"ĠLight":4401,"imal":4402,"ĠBig":4403,"Ġdefend":4404,"Ġbeat":4405,"Ġrecords":4406,"%)":4407,"Ġscen":4408,"Ġemployees":4409,"Ġdevices":4410,"hem":4411,"Ġcommer":4412,"ĠMex":4413,"Ġbenefit":4414,"ĠProf":4415,"Ġilleg":4416,"Ġsurface":4417,"ĠAlso":4418,"Ġharm":4419,"ingly":4420,"wide":4421,"ĠAlex":4422,"Ġshut":4423,"ĠCur":4424,"Ġlose":4425,"pm":4426,"Ġchallenge":4427,"semb":4428,"Ġstation":4429,"Ġintelligence":4430,"Ġaccur":4431,"ĠFlor":4432,"Ġrequires":4433,"ĠMal":4434,"bum":4435,"Ġhospital":4436,"Ġspirit":4437,"Ġoffered":4438,"Ġproduce":4439,"ĠCommun":4440,"Ġcreating":4441,"Ġcris":4442,"spect":4443,"Ġended":4444,"Ġdaily":4445,"Ġvoters":4446,"lands":4447,"ias":4448,"ih":4449,"ona":4450,"Ġsmart":4451,"ĠOffice":4452,"ĠLord":4453,"rial":4454,"ĠInternet":4455,"Ġcircum":4456,"Ġextremely":4457,"'.":4458,"Ġopinion":4459,"ĠMil":4460,"Ġgain":4461,"BS":4462,"ĠFin":4463,"yp":4464,"Ġuseful":4465,"Ġbudget":4466,"Ġcomfort":4467,"isf":4468,"Ġbackground":4469,"eline":4470,"Ġepisode":4471,"Ġenemy":4472,"Ġtrial":4473,"Ġestablish":4474,"date":4475,"ĠCap":4476,"Ġcontinues":4477,"Ġshowing":4478,"ĠUnion":4479,"with":4480,"Ġposted":4481,"ĠSystem":4482,"Ġeat":4483,"rian":4484,"Ġrise":4485,"ĠGermany":4486,"ils":4487,"Ġsigned":4488,"Ġvill":4489,"Ġgrand":4490,"mor":4491,"ĠEngland":4492,"Ġprojects":4493,"umber":4494,"Ġconference":4495,"za":4496,"Ġresponsible":4497,"ĠArab":4498,"Ġlearned":4499,"âĢĶâĢĶ":4500,"ipping":4501,"ĠGeorge":4502,"OC":4503,"Ġreturned":4504,"ĠAustralia":4505,"Ġbrief":4506,"Qu":4507,"Ġbrand":4508,"illing":4509,"abled":4510,"Ġhighest":4511,"Ġtrain":4512,"ĠCommission":4513,"while":4514,"Ġnom":4515,"ception":4516,"Ġmut":4517,"ĠBlue":4518,"Ġincident":4519,"vant":4520,"86":4521,"ĠID":4522,"Ġnuclear":4523,"74":4524,"ĠLike":4525,"ĠRE":4526,"ĠMicro":4527,"li":4528,"mail":4529,"Ġcharges":4530,"89":4531,"Ġadjust":4532,"ado":4533,"Ġearth":4534,"NA":4535,"Ġprices":4536,"PA":4537,"Ġdraft":4538,"Ġruns":4539,"Ġcandidate":4540,"enses":4541,"Ġmanagement":4542,"ĠPhil":4543,"ĠMiss":4544,"Ġteach":4545,"gram":4546,"Ġunderstanding":4547,"ait":4548,"icago":4549,"Add":4550,"ĠEp":4551,"secut":4552,"Ġseparate":4553,"Ġinstance":4554,"Ġeth":4555,"Ġunless":4556,"********":4557,"ĠFore":4558,"inate":4559,"Ġoperations":4560,"Sp":4561,"Ġfaith":4562,"gar":4563,"ĠChurch":4564,"ronic":4565,"Ġconfig":4566,"osure":4567,"Ġactivities":4568,"Ġtraditional":4569,"Ġ36":4570,"Ġdirection":4571,"Ġmachine":4572,"Ġsurround":4573,"Ġpush":4574,"unction":4575,"ĠEU":4576,"Ġeasier":4577,"Ġargument":4578,"GB":4579,"Ġmicro":4580,"Ġspending":4581,"izations":4582,"Ġtheory":4583,"adow":4584,"Ġcalling":4585,"ĠLast":4586,"Ġder":4587,"Ġinfluence":4588,"Ġcommit":4589,"Ġphoto":4590,"Ġunc":4591,"istry":4592,"gn":4593,"aste":4594,"acks":4595,"Ġdisp":4596,"ady":4597,"do":4598,"ĠGood":4599,"Ġ`":4600,"Ġwish":4601,"Ġrevealed":4602,"³³":4603,"lig":4604,"Ġenforce":4605,"ĠCommittee":4606,"Ġchem":4607,"Ġmiles":4608,"Ġinterested":4609,"Ġsolution":4610,"icy":4611,"inct":4612,"Ġ->":4613,"ĠDet":4614,"Ġremoved":4615,"Ġcompar":4616,"eah":4617,"Ġplant":4618,"ĠSince":4619,"Ġachieve":4620,"Ġadvantage":4621,"Ġslightly":4622,"bing":4623,"Ġplaced":4624,"under":4625,"2015":4626,"ĠMad":4627,"Ġtim":4628,"oses":4629,"Ġcru":4630,"ĠRock":4631,"Ġmostly":4632,"Ġnegative":4633,"Ġsetting":4634,"Ġproduced":4635,"Ġmur":4636,"Ġconnection":4637,"ĠMer":4638,"Ġdriver":4639,"Ġexecutive":4640,"Ġassault":4641,"Ġborn":4642,"ĠVer":4643,"tained":4644,"Ġstructure":4645,"Ġreduce":4646,"Ġdecades":4647,"Ġded":4648,"uke":4649,"ĠMany":4650,"idden":4651,"Ġleague":4652,"Se":4653,"Ġjoin":4654,"Ġdisco":4655,"Ġdie":4656,"cks":4657,"actions":4658,"Ġassess":4659,"agn":4660,"Ġgoals":4661,"ours":4662,"IR":4663,"Ġsenior":4664,"iller":4665,"mod":4666,"ipment":4667,"ocol":4668,"uy":4669,"ĠQue":4670,"Ġparties":4671,"irgin":4672,"Ġlearning":4673,"itable":4674,"Ġstreet":4675,"Ġcamera":4676,"App":4677,"Ġskills":4678,"bre":4679,"cious":4680,"Ġcelebr":4681,"ĠFranc":4682,"Ġexisting":4683,"Ġwilling":4684,"lor":4685,"Ġid":4686,"ĠSpace":4687,"Ġcritical":4688,"ĠLa":4689,"ortunately":4690,"Ġserve":4691,"Ġcold":4692,"Ġspecies":4693,"TS":4694,"Ġanimals":4695,"ĠBay":4696,"Ġolder":4697,"ĠUnder":4698,"estic":4699,"ĠTre":4700,"Ġteacher":4701,"Ġprefer":4702,"vis":4703,"Ġthread":4704,"ĠMatt":4705,"Ġmanager":4706,"ãĥ»":4707,"Ġprofessional":4708,"ĠVol":4709,"Ġnotes":4710,"These":4711,"ula":4712,"Ġfresh":4713,"ented":4714,"uzz":4715,"edy":4716,"clusion":4717,"ĠRel":4718,"Ġdoubt":4719,"EO":4720,"Ġopened":4721,"ĠBit":4722,"Advertisement":4723,"Ġguess":4724,"ĠUN":4725,"Ġsequ":4726,"Ġexplain":4727,"otten":4728,"Ġattract":4729,"aks":4730,"Ġstring":4731,"Ġcontext":4732,"ossible":4733,"ĠRepublicans":4734,"Ġsolid":4735,"Ġcities":4736,"Ġasking":4737,"Ġrandom":4738,"ups":4739,"uries":4740,"arant":4741,"dden":4742,"gl":4743,"ĠFlorida":4744,"Ġdepend":4745,"ĠScott":4746,"Ġ33":4747,"ĠiT":4748,"icon":4749,"Ġmentioned":4750,"Ġ2000":4751,"Ġclaimed":4752,"Ġdefinitely":4753,"ulf":4754,"Ġcore":4755,"Ġopening":4756,"ĠConst":4757,"which":4758,"ĠTra":4759,"AG":4760,"72":4761,"Ġbelieved":4762,"ada":4763,"Ġ48":4764,"ĠSecurity":4765,"yright":4766,"ĠPet":4767,"ĠLou":4768,"Ġholding":4769,"================":4770,"Ġice":4771,"Ġbrow":4772,"Ġauthorities":4773,"host":4774,"word":4775,"Ġscore":4776,"ĠDiv":4777,"Ġcells":4778,"Ġtransl":4779,"Ġneighbor":4780,"Ġremove":4781,"uct":4782,"Ġdistrict":4783,"ĠAccording":4784,"Ġworse":4785,"Ġconcerns":4786,"Ġpresidential":4787,"Ġpolicies":4788,"ĠHall":4789,"73":4790,"Ġhus":4791,"AY":4792,"Ġ2006":4793,"ĠJud":4794,"Ġindependent":4795,"ĠJustice":4796,"iliar":4797,"print":4798,"ighter":4799,"Ġprotection":4800,"zen":4801,"Ġsudden":4802,"house":4803,"ĠJes":4804,"PR":4805,"ĠInf":4806,"Ġbul":4807,"Ġ_":4808,"ĠService":4809,"ĠPR":4810,"Ġstrategy":4811,"ffect":4812,"Ġgirls":4813,"Ġmissing":4814,"oyal":4815,"ĠTeam":4816,"ulated":4817,"Ġdat":4818,"Ġpolitics":4819,"abor":4820,"According":4821,"Ġspell":4822,"Ġgraph":4823,"orthern":4824,"TC":4825,"Ab":4826,"Ġlabor":4827,"isher":4828,"Ġkick":4829,"ĠiTunes":4830,"Ġsteps":4831,"poses":4832,"Ġsmaller":4833,"En":4834,"bert":4835,"Ġroll":4836,"Ġresearchers":4837,"Ġclosed":4838,"Ġtransport":4839,"Ġlawy":4840,"________________":4841,"ĠChicago":4842,"Ġaspect":4843,"Ġnone":4844,"Ġmarriage":4845,"96":4846,"Ġelements":4847,"ĠFre":4848,"ĠSal":4849,"Ġdram":4850,"FC":4851,"top":4852,"equ":4853,"Ġhearing":4854,"Ġsupported":4855,"Ġtesting":4856,"cohol":4857,"Ġmassive":4858,"Ġstick":4859,"Ġguard":4860,"isco":4861,"phone":4862,"From":4863,"However":4864,"Ġborder":4865,"Ġcopy":4866,"ography":4867,"list":4868,"71":4869,"Ġowner":4870,"class":4871,"ruit":4872,"rate":4873,"ĠOnce":4874,"Ġdigital":4875,"Ġtask":4876,"ERS":4877,"Ġincred":4878,"tes":4879,"++":4880,"ĠFrance":4881,"Ġbreat":4882,"owl":4883,"Ġissued":4884,"ĠWestern":4885,"Ġdetect":4886,"Ġpartners":4887,"Ġshared":4888,"ĠCall":4889,"Ġcancer":4890,"ache":4891,"ribe":4892,"Ġexplained":4893,"Ġheat":4894,"{\"":4895,"Ġinvestment":4896,"ĠBook":4897,"Ġwood":4898,"Ġtools":4899,"ĠAlthough":4900,"Ġbelief":4901,"Ġcrisis":4902,"Ġge":4903,"ĠMP":4904,"Ġoperation":4905,"type":4906,"~~":4907,"ga":4908,"Ġcontains":4909,"anta":4910,"Ġexpress":4911,"ĠGroup":4912,"ĠJournal":4913,"ka":4914,"Ġamb":4915,"ĠUSA":4916,"Ġfinding":4917,"Ġfunding":4918,"how":4919,"Ġestablished":4920,"ideos":4921,"Ġdegree":4922,"Ġdangerous":4923,"anging":4924,"Ġfreedom":4925,"pport":4926,"outhern":4927,"Ġchurch":4928,"Ġcatch":4929,"ĠTwo":4930,"Ġpresence":4931,"ĠGuard":4932,"Up":4933,"Ġauthority":4934,"ĠProject":4935,"Ġbutton":4936,"Ġconsequ":4937,"Ġvalid":4938,"Ġweak":4939,"Ġstarts":4940,"Ġreference":4941,"ĠMem":4942,"\")":4943,"UN":4944,"orage":4945,"ĠOpen":4946,"Ġcollection":4947,"ym":4948,"gency":4949,"Ġbeautiful":4950,"ros":4951,"Ġtells":4952,"Ġwaiting":4953,"nel":4954,"Ġproviding":4955,"ĠDemocrats":4956,"Ġdaughter":4957,"Ġmaster":4958,"Ġpurposes":4959,"ĠJapanese":4960,"Ġequal":4961,"Ġturns":4962,"Ġdocuments":4963,"Ġwatching":4964,"Res":4965,"Ġran":4966,"2014":4967,"Ġreject":4968,"ĠKorea":4969,"Ġvictims":4970,"Level":4971,"erences":4972,"Ġwitness":4973,"Ġ34":4974,"Ġreform":4975,"coming":4976,"Ġoccup":4977,"Ġcaught":4978,"Ġtraffic":4979,"ading":4980,"Ġmodels":4981,"ario":4982,"Ġserved":4983,"Ġbatter":4984,"uate":4985,"ĠSecretary":4986,"Ġagreed":4987,"Ġtruly":4988,"ynam":4989,"ĠRet":4990,"Ġunits":4991,"ĠResearch":4992,"hand":4993,"azine":4994,"ĠMike":4995,"Ġvariety":4996,"otal":4997,"Ġamazing":4998,"Ġconfirmed":4999,"Ġentirely":5000,"Ġpurchase":5001,"Ġelement":5002,"Ġcash":5003,"Ġdetermine":5004,"De":5005,"Ġcars":5006,"ĠWall":5007,"âĸ":5008,"Ġviews":5009,"Ġdrugs":5010,"Ġdepartment":5011,"ĠStep":5012,"uit":5013,"Ġ39":5014,"asure":5015,"ĠClass":5016,"Ġcovered":5017,"ĠBank":5018,"Ġmere":5019,"uana":5020,"Ġmulti":5021,"Ġmix":5022,"Ġunlike":5023,"levision":5024,"Ġstopped":5025,"Ġsem":5026,"ĠGal":5027,"ules":5028,"Ġwel":5029,"ĠJohnson":5030,"la":5031,"Ġskill":5032,"Ġbecoming":5033,"rie":5034,"Ġappropriate":5035,"fe":5036,"ellow":5037,"ĠProt":5038,"ulate":5039,"ocation":5040,"Ġweekend":5041,"odies":5042,"Ġsites":5043,"Ġanimal":5044,"ĠTim":5045,"Ġscale":5046,"Ġcharged":5047,"Ġinstruct":5048,"illa":5049,"Ġmethods":5050,"Ġcert":5051,"Ġjudge":5052,"ĠHel":5053,"Ġdollars":5054,"Ġstanding":5055,"ĠSqu":5056,"Ġdebt":5057,"liam":5058,"Ġdriving":5059,"ĠSum":5060,"ĠEdition":5061,"Ġalbum":5062,"andon":5063,"IF":5064,"ĠUk":5065,"63":5066,"ader":5067,"Ġcommercial":5068,"esh":5069,"ĠGovernment":5070,"Ġdiscovered":5071,"Ġoutput":5072,"ĠHillary":5073,"ĠCarol":5074,"Ġ2005":5075,"Ġabuse":5076,"ancing":5077,"Ġswitch":5078,"Ġannual":5079,"Tw":5080,"Ġstated":5081,"agement":5082,"inner":5083,"Ġdemocr":5084,"Ġresidents":5085,"Ġallowing":5086,"Ġfactors":5087,"odd":5088,"Ġfuck":5089,"emies":5090,"Ġoccurred":5091,"oti":5092,"Ġnorth":5093,"ĠPublic":5094,"Ġinjury":5095,"Ġinsurance":5096,"CL":5097,"olly":5098,"ãĢ":5099,"Ġrepeated":5100,"Ġarms":5101,"anged":5102,"Ġconstruction":5103,"Ġfle":5104,"PU":5105,"icians":5106,"Ġforms":5107,"ĠMcC":5108,"antic":5109,"Ġmental":5110,"pire":5111,"Ġequipment":5112,"Ġfant":5113,"Ġdiscussion":5114,"Ġregarding":5115,"kin":5116,"arp":5117,"Ġchair":5118,"ogue":5119,"Ġproceed":5120,"ĠId":5121,"Our":5122,"Ġmurder":5123,"Man":5124,"Ġ49":5125,"asp":5126,"Ġsupply":5127,"Ġinput":5128,"Ġwealth":5129,"liament":5130,"Ġproced":5131,"orial":5132,"ĠStat":5133,"ĠNFL":5134,"hens":5135,"ĠInstitute":5136,"Ġputting":5137,"ournament":5138,"etic":5139,"Ġlocated":5140,"Ġkid":5141,"eria":5142,"run":5143,"Ġprinc":5144,"Ġ!":5145,"going":5146,"ĠBet":5147,"Ġclot":5148,"Ġtelling":5149,"Ġproposed":5150,"iot":5151,"orry":5152,"Ġfunds":5153,"gment":5154,"ĠLife":5155,"Ġbaby":5156,"ĠBack":5157,"Ġspoke":5158,"Image":5159,"Ġearn":5160,"ĠAT":5161,"gu":5162,"Ġexchange":5163,"ĠLin":5164,"oving":5165,"Ġpair":5166,"More":5167,"azon":5168,"Ġarrested":5169,"Ġkilling":5170,"can":5171,"ĠCard":5172,"yd":5173,"Ġidentified":5174,"Ġmobile":5175,"Ġthanks":5176,"onym":5177,"ĠForm":5178,"Ġhundreds":5179,"ĠChris":5180,"ĠCat":5181,"Ġtrend":5182,"hat":5183,"ĠAv":5184,"oman":5185,"Ġelectric":5186,"ĠWil":5187,"SE":5188,"Of":5189,"Ġrestaur":5190,"oted":5191,"Ġtrig":5192,"Ġnine":5193,"Ġbomb":5194,"Why":5195,"¯":5196,"Ġcoverage":5197,"Ġappeal":5198,"ĠRobert":5199,"ĠSup":5200,"Ġfinished":5201,"Ġflow":5202,"Ġdeliver":5203,"Ġcalcul":5204,"Ġphotos":5205,"Ġphil":5206,"Ġpieces":5207,"Ġappre":5208,"kes":5209,"Ġrough":5210,"Do":5211,"Ġpartner":5212,"Ġconcerned":5213,"Ġ37":5214,"ĠGen":5215,"Col":5216,"ctors":5217,"Ġ=>":5218,"state":5219,"Ġsuggested":5220,"ĠForce":5221,"CE":5222,"Ġherself":5223,"ĠPlan":5224,"works":5225,"ooth":5226,"rency":5227,"Ġcorner":5228,"Ġhusband":5229,"Ġinternet":5230,"ĠAut":5231,"ems":5232,"osen":5233,"ĠAtl":5234,"gen":5235,"Ġbalance":5236,"62":5237,"Ġsounds":5238,"text":5239,"Ġarr":5240,"oves":5241,"Ġmillions":5242,"Ġradio":5243,"Ġsatisf":5244,"ĠDam":5245,"Mr":5246,"Go":5247,"Spe":5248,"Ġcombat":5249,"rant":5250,"ĠGree":5251,"Ġfuel":5252,"Ġdistance":5253,"Ġtests":5254,"Ġdecre":5255,"ĠEr":5256,"Ġmanaged":5257,"DS":5258,"Ġtit":5259,"Ġmeasures":5260,"ĠLiber":5261,"Ġattend":5262,"ashed":5263,"ĠJose":5264,"ĠNight":5265,"dit":5266,"ĠNov":5267,"ĠEnd":5268,"outs":5269,"Ġgeneration":5270,"Ġadvoc":5271,"yth":5272,"Ġconversation":5273,"ĠSky":5274,"active":5275,"cel":5276,"rier":5277,"ĠFrank":5278,"Ġgender":5279,"Ġconcent":5280,"Ġcarried":5281,"anda":5282,"ĠVirgin":5283,"Ġarrived":5284,"icide":5285,"aded":5286,"Ġfailure":5287,"Ġminimum":5288,"lets":5289,"Ġworst":5290,"Ġkeeping":5291,"Ġintended":5292,"Ġillegal":5293,"Ġsubsc":5294,"Ġdetermined":5295,"Ġtrip":5296,"Yes":5297,"Ġraise":5298,"Ġ~":5299,"Ġfeels":5300,"Ġpackage":5301,"ĠJo":5302,"hi":5303,"2016":5304,"real":5305,"Ġfra":5306,"Ġsymb":5307,"Me":5308,"ucky":5309,"pret":5310,"ĠKh":5311,"ĠEdit":5312,"ĠWeb":5313,"emic":5314,"ĠColor":5315,"Ġjustice":5316,"Int":5317,"Ġfarm":5318,"cknow":5319,"\">":5320,"eless":5321,"Ġreduced":5322,"Ġ500":5323,"xx":5324,"ĠRad":5325,"ĠWood":5326,"Ġclin":5327,"Ġhyp":5328,"iler":5329,"ura":5330,"kins":5331,"85":5332,"61":5333,"ĠTheir":5334,"ĠMary":5335,"Ġsan":5336,"Ġnovel":5337,"ĠWho":5338,"Ġcapacity":5339,"Ġimpossible":5340,"Ġplays":5341,"Ġminister":5342,"ijuana":5343,"icate":5344,"ĠSet":5345,"Ġfram":5346,"Ġing":5347,"Ġcommunities":5348,"ĠFBI":5349,"ita":5350,"Ġbon":5351,"Ġstrateg":5352,"Ġinterests":5353,"lock":5354,"gers":5355,"mas":5356,"ĠAND":5357,"Ġconflict":5358,"Ġrequirements":5359,"Ġsac":5360,"Ġoperating":5361,"ini":5362,"related":5363,"Ġcommitted":5364,"Ġrelatively":5365,"Ġsouth":5366,"¯¯":5367,"Ġafford":5368,"Ġidentity":5369,"Ġdecisions":5370,"Ġaccused":5371,"place":5372,"Ġvictory":5373,"och":5374,"iat":5375,"Name":5376,"Com":5377,"tion":5378,"eds":5379,"Ġseek":5380,"Ġtight":5381,"ĠImages":5382,"Ġiniti":5383,"Ġhumans":5384,"Ġfamiliar":5385,"Ġaudience":5386,"Ġinternal":5387,"venture":5388,"Ġsides":5389,"ĠTO":5390,"Ġdim":5391,"Ġconclud":5392,"Ġappoint":5393,"Ġenforcement":5394,"ĠJim":5395,"ĠAssociation":5396,"Ġcircumst":5397,"ĠCanadian":5398,"Ġjoined":5399,"Ġdifferences":5400,"ĠLos":5401,"Ġprotest":5402,"Ġtwice":5403,"win":5404,"Ġglass":5405,"arsh":5406,"ĠArmy":5407,"Ġexpression":5408,"Ġdecide":5409,"Ġplanning":5410,"ania":5411,"Ġhandle":5412,"ĠMicrosoft":5413,"ĠNor":5414,"Ġmaximum":5415,"ĠRev":5416,"Ġsea":5417,"Ġeval":5418,"Ġhelps":5419,"ref":5420,"Ġbound":5421,"Ġmouth":5422,"Ġstandards":5423,"Ġclim":5424,"ĠCamp":5425,"ĠFox":5426,"cles":5427,"Ġarmy":5428,"ĠTechn":5429,"acking":5430,"xy":5431,"SS":5432,"Ġ42":5433,"Ġbug":5434,"ĠUkrain":5435,"ĠMax":5436,"ĠJones":5437,"ĠShow":5438,"lo":5439,"Ġplanet":5440,"Ġ75":5441,"Ġwinning":5442,"Ġfaster":5443,"Ġspect":5444,"Ġbroken":5445,"TR":5446,"Ġdefined":5447,"Ġhealthy":5448,"Ġcompetition":5449,"https":5450,"ĠIsland":5451,"ĠFe":5452,"Ġannounce":5453,"ĠCup":5454,"ĠInstead":5455,"Ġclient":5456,"Ġpossibly":5457,"section":5458,"ocket":5459,"look":5460,"Ġfinish":5461,"Ġcrew":5462,"Ġreserv":5463,"Ġeditor":5464,"Ġhate":5465,"Ġsale":5466,"Ġcontrovers":5467,"Ġpages":5468,"wing":5469,"Ġnumer":5470,"Ġopposition":5471,"Ġ2004":5472,"Ġrefuge":5473,"Ġflight":5474,"Ġapart":5475,"ĠLat":5476,"Americ":5477,"ĠAfrica":5478,"Ġapplications":5479,"ĠPalest":5480,"ĠBur":5481,"Ġgar":5482,"ĠSocial":5483,"Ġupgr":5484,"Ġshape":5485,"Ġspeaking":5486,"ansion":5487,"ao":5488,"ĠSn":5489,"Ġworry":5490,"ĠBritain":5491,"Please":5492,"roud":5493,"Ġhun":5494,"Ġintroduced":5495,"Ġdiet":5496,"Ind":5497,"ĠSecond":5498,"Ġfunctions":5499,"uts":5500,"ĠEach":5501,"ĠJeff":5502,"Ġstress":5503,"Ġaccounts":5504,"Ġguarant":5505,"ĠAnn":5506,"edia":5507,"Ġhonest":5508,"Ġtree":5509,"ĠAfrican":5510,"ĠBush":5511,"},":5512,"Ġsch":5513,"ĠOnly":5514,"Ġfif":5515,"igan":5516,"Ġexercise":5517,"ĠExp":5518,"Ġscientists":5519,"Ġlegislation":5520,"ĠWork":5521,"ĠSpr":5522,"ÃĤ":5523,"ĠHuman":5524,"Ġè":5525,"Ġsurvey":5526,"Ġrich":5527,"rip":5528,"Ġmaintain":5529,"Ġflo":5530,"Ġleadership":5531,"stream":5532,"ĠIslamic":5533,"Ġ01":5534,"ĠCollege":5535,"Ġmagic":5536,"ĠPrime":5537,"Ġfigures":5538,"2017":5539,"inder":5540,"xual":5541,"ĠDead":5542,"Ġabsolutely":5543,"Ġfourth":5544,"Ġpresented":5545,"respond":5546,"rible":5547,"Ġalcohol":5548,"ato":5549,"ĠDE":5550,"porary":5551,"Ġgrab":5552,"Ġvari":5553,"Ġquant":5554,"ĠPhoto":5555,"Ġplus":5556,"rick":5557,"arks":5558,"Ġalternative":5559,"Ġpil":5560,"Ġapprox":5561,"that":5562,"Ġobjects":5563,"ĠRo":5564,"ĠAndroid":5565,"Ġsignificantly":5566,"ĠRoad":5567,"kay":5568,"Read":5569,"avor":5570,"Ġacknow":5571,"ĠHD":5572,"ĠSing":5573,"Or":5574,"ĠMont":5575,"Ġuns":5576,"prof":5577,"Ġnegoti":5578,"ĠArch":5579,"iki":5580,"Ġtelevision":5581,"ĠJewish":5582,"Ġcommittee":5583,"Ġmotor":5584,"Ġappearance":5585,"Ġsitting":5586,"Ġstrike":5587,"ĠDown":5588,"comp":5589,"ĠHist":5590,"Ġfold":5591,"acement":5592,"ĠLouis":5593,"Ġbelong":5594,"ĠâĢ¢":5595,"Ġmort":5596,"Ġprepared":5597,"Ġ64":5598,"ĠMaster":5599,"Ġindeed":5600,"ĠDen":5601,"Ġrent":5602,"TA":5603,"ourney":5604,"arc":5605,"Su":5606,"97":5607,"Ġadvice":5608,"Ġchanging":5609,"Ġlisted":5610,"Ġlaunched":5611,"isation":5612,"ĠPeter":5613,"ishes":5614,"Ġlived":5615,"ĠMel":5616,"ĠSupreme":5617,"ĠFederal":5618,"Ġ);":5619,"ructure":5620,"Ġsets":5621,"Ġphilos":5622,"uous":5623,"ĠÂł":5624,"Ġapplied":5625,"ĠNOT":5626,"Ġhousing":5627,"ĠMount":5628,"Ġodd":5629,"Ġsust":5630,"DA":5631,"fficient":5632,"Ġ?":5633,"olved":5634,"Ġpowers":5635,"Ġthr":5636,"Ġremaining":5637,"ĠWater":5638,"LC":5639,"Ġcauses":5640,"ãģ®":5641,"Ġmanner":5642,"ads":5643,"Ġsuggests":5644,"Ġends":5645,"standing":5646,"fig":5647,"ĠDun":5648,"idth":5649,"Ġgay":5650,"Ġtermin":5651,"ĠAngeles":5652,"MS":5653,"Ġscientific":5654,"Ġcoal":5655,"apers":5656,"bar":5657,"ĠThomas":5658,"Ġsym":5659,"ĠRun":5660,"this":5661,"PC":5662,"igrants":5663,"Ġminute":5664,"ĠDistrict":5665,"cellent":5666,"Ġleaves":5667,"Ġcompleted":5668,"amin":5669,"Ġfocused":5670,"Ġmonitor":5671,"Ġvehicles":5672,"MA":5673,"ĠMass":5674,"ĠGrand":5675,"Ġaffected":5676,"itutional":5677,"Ġconstruct":5678,"Ġfollows":5679,"Ġton":5680,"reens":5681,"Ġhomes":5682,"ĠExt":5683,"ĠLevel":5684,"rast":5685,"ĠIr":5686,"Ġelim":5687,"Ġlargely":5688,"ĠJoe":5689,"Ġvotes":5690,"alls":5691,"Ġbusinesses":5692,"ĠFoundation":5693,"ĠCentral":5694,"Ġyards":5695,"Ġmaterials":5696,"ulner":5697,"Ġguide":5698,"Ġcloser":5699,"ums":5700,"Ġsports":5701,"eder":5702,"Just":5703,"Ġtaxes":5704,"84":5705,"ĠOld":5706,"Ġdecade":5707,"ola":5708,"Ġvir":5709,"Ġdropped":5710,"Ġdelay":5711,"itect":5712,"Ġsecure":5713,"stein":5714,"level":5715,"Ġtreated":5716,"Ġfiled":5717,"aine":5718,"Ġvan":5719,"Ġmir":5720,"Ġcolumn":5721,"icted":5722,"eper":5723,"Ġrot":5724,"Ġconsult":5725,"Ġentry":5726,"Ġmarijuana":5727,"ĠDou":5728,"Ġapparently":5729,"oking":5730,"clusive":5731,"Ġincreases":5732,"ano":5733,"Ġspecifically":5734,"Ġtele":5735,"ensions":5736,"Ġreligion":5737,"abilities":5738,"Ġframe":5739,"ĠNote":5740,"ĠLee":5741,"Ġhelping":5742,"Ġedge":5743,"oston":5744,"Ġorganizations":5745,"Ãĥ":5746,"ĠBoth":5747,"hips":5748,"Ġbigger":5749,"Ġboost":5750,"ĠStand":5751,"Ġrow":5752,"uls":5753,"abase":5754,"Ġrid":5755,"Let":5756,"aren":5757,"rave":5758,"Ġstret":5759,"PD":5760,"Ġvision":5761,"Ġwearing":5762,"Ġappreci":5763,"Ġaward":5764,"ĠUse":5765,"Ġfactor":5766,"war":5767,"ulations":5768,")(":5769,"Ġgod":5770,"Ġterrit":5771,"Ġparam":5772,"asts":5773,"87":5774,"Ġenemies":5775,"ĠGames":5776,"FF":5777,"Ġaccident":5778,"Well":5779,"ĠMartin":5780,"TER":5781,"Ġath":5782,"ĠHell":5783,"Ġforg":5784,"Ġveter":5785,"ĠMedic":5786,"free":5787,"Ġstars":5788,"Ġexpensive":5789,"Ġacad":5790,"rawn":5791,"ĠWhe":5792,"Ġlock":5793,"Ġformat":5794,"Ġsoldiers":5795,"sm":5796,"Ġagent":5797,"Ġresponsibility":5798,"ora":5799,"ĠScience":5800,"Ġrapid":5801,"Ġtough":5802,"ĠJesus":5803,"Ġbelieves":5804,"ML":5805,"Ġwear":5806,"lete":5807,"ÃĥÃĤ":5808,"ĠDri":5809,"Ġcommission":5810,"ĠBob":5811,"Oh":5812,"aped":5813,"Ġwarm":5814,"ÃĥÃĤÃĥÃĤ":5815,"Ġ2003":5816,"ortion":5817,"Ġhasn":5818,"uster":5819,"Ġunivers":5820,"ĠIll":5821,"Ġking":5822,"ologies":5823,"94":5824,"ĠTem":5825,"ĠMos":5826,"Ġpatient":5827,"ĠMexico":5828,"cean":5829,"ĠDeath":5830,"ĠSanders":5831,"you":5832,"ĠCast":5833,"ĠCompany":5834,"pty":5835,"Ġhappening":5836,"FP":5837,"ĠBattle":5838,"Ġbought":5839,"Am":5840,"Mod":5841,"Us":5842,"uters":5843,"ĠCre":5844,"ĠThose":5845,"Ġ44":5846,"iser":5847,"Ġsoul":5848,"ĠTop":5849,"ĠHarry":5850,"ĠAw":5851,"Ġseat":5852,"ffee":5853,"Ġrevolution":5854,"Ġ(\"":5855,"ĠDuring":5856,"ette":5857,"Ġring":5858,"Ġoffensive":5859,"Ġreturns":5860,"Ġvideos":5861,"Ġdiscl":5862,"Ġfamous":5863,"enced":5864,"ĠSign":5865,"ĠRiver":5866,"Ġ300":5867,"PM":5868,"ĠBus":5869,"ĠCH":5870,"Ġcandidates":5871,"arden":5872,"Ġpercentage":5873,"Ġvisual":5874,"Ġthank":5875,"Ġtrouble":5876,"nergy":5877,"Ġ2001":5878,"Ġprove":5879,"ashion":5880,"Ġenh":5881,"ĠLong":5882,"UM":5883,"Ġconnected":5884,"Ġpossibility":5885,"Over":5886,"Ġexpert":5887,"Ġlibrary":5888,"arts":5889,"ĠDirector":5890,"Ġfellow":5891,"92":5892,"irty":5893,"Ġdry":5894,"Ġsigns":5895,"ĠLove":5896,"Ġquiet":5897,"foot":5898,"Ġpure":5899,"ĠHun":5900,"Ġfilled":5901,"phas":5902,"ĠElect":5903,"endment":5904,"ĠExpl":5905,"Ġunable":5906,"ns":5907,"mo":5908,"Ġvast":5909,"obe":5910,"Ġidentify":5911,"apping":5912,"ĠCarolina":5913,"gress":5914,"Ġprote":5915,"Ġfish":5916,"Ġcircumstances":5917,"razy":5918,"ĠPhot":5919,"Ġbodies":5920,"ĠMur":5921,"Ġdeveloping":5922,"ĠAR":5923,"Ġexperienced":5924,"Ġsubstant":5925,"ĠBoard":5926,"esome":5927,"Ġdomestic":5928,"Ġcombined":5929,"ĠPut":5930,"Ġchemical":5931,"ĠChild":5932,"Ġpool":5933,"ĠCy":5934,"Ġegg":5935,"cons":5936,"sters":5937,"Ġhurt":5938,"Ġmarkets":5939,"Ġconservative":5940,"Ġsupporters":5941,"Ġagencies":5942,"idel":5943,"Ob":5944,"urb":5945,"Ġ43":5946,"ĠDefense":5947,"ye":5948,"ĠAp":5949,"dule":5950,"Ġtemperature":5951,"Ġconducted":5952,"ĠChief":5953,"Ġpulled":5954,"Ġfol":5955,"Last":5956,"onto":5957,"osis":5958,"VER":5959,"Des":5960,"ĠPan":5961,"First":5962,"Ġadvance":5963,"Ġlicense":5964,"rors":5965,"ĠJon":5966,"Ġimagine":5967,"Ġhell":5968,"Ġfixed":5969,"Ġincor":5970,"osite":5971,"ĠLog":5972,"icken":5973,"]:":5974,"Ġsurprise":5975,"hab":5976,"Ġcraft":5977,"olt":5978,"ĠJul":5979,"Ġdial":5980,"Ġrelevant":5981,"Ġentered":5982,"Ġleads":5983,"ĠAD":5984,"ĠClean":5985,"Ġpictures":5986,"essor":5987,"Ġalt":5988,"Ġpaying":5989,"Per":5990,"ĠMarket":5991,"Ġupdates":5992,"amily":5993,"ĠType":5994,"ĠHome":5995,"Ġ55":5996,"sembly":5997,"rome":5998,"83":5999,"Ġgreatest":6000,"Ġheight":6001,"Ġheav":6002,"aints":6003,"Ġlisten":6004,"aser":6005,"ĠSH":6006,"Ġcapable":6007,"acle":6008,"Ġperspect":6009,"inating":6010,"Ġoffering":6011,"rypt":6012,"ĠDevelop":6013,"abin":6014,"rc":6015,"Ġbright":6016,"alty":6017,"arrow":6018,"Ġsuppl":6019,"inding":6020,"acked":6021,"gypt":6022,"ĠAnother":6023,"pg":6024,"ĠVirginia":6025,"ĠLu":6026,"Ġplanned":6027,"Ġpit":6028,"Ġsweet":6029,"Type":6030,"ĠDi":6031,"Ġtypically":6032,"ĠFrancisco":6033,"Ġprospect":6034,"ĠDan":6035,"Ġteen":6036,"rees":6037,"Ġsched":6038,"Ġhol":6039,"Ġscr":6040,"Ġlots":6041,"life":6042,"Ġnewsp":6043,"Ġforget":6044,"ĠNone":6045,"ĠMiddle":6046,"ĠRyan":6047,"edd":6048,"Ġsevere":6049,"Ġsuit":6050,"ller":6051,"93":6052,"Ġcorrespond":6053,"Ġexplos":6054,"uations":6055,"Ġflag":6056,"game":6057,"rid":6058,"Ġprin":6059,"ĠData":6060,"Ġdeploy":6061,"ĠEnter":6062,"suit":6063,"ghan":6064,"ĠMen":6065,"Ġthoughts":6066,"Ġmatters":6067,"Ġadapt":6068,"ĠAri":6069,"Ġfill":6070,"Ġforth":6071,"Ġsam":6072,"Ġ41":6073,"Ġpayment":6074,"ĠHor":6075,"Ġspring":6076,"duc":6077,"Ġlosing":6078,"Ġbringing":6079,"FO":6080,"ala":6081,"Ġdistribution":6082,"hered":6083,"bour":6084,"ĠIsraeli":6085,"oma":6086,"Ġcombination":6087,"Ġplenty":6088,"VE":6089,"Can":6090,"ĠHaw":6091,"Ġperman":6092,"ĠSpecial":6093,"Ġtow":6094,"Ġseeking":6095,"Ġexamples":6096,"Ġclasses":6097,"cr":6098,"Ġbeer":6099,"Ġmoves":6100,"ĠIP":6101,"ĠKn":6102,"Ġpanel":6103,"Even":6104,"Ġproperly":6105,"Ġris":6106,"Ġplug":6107,"Ġestimated":6108,"Every":6109,"Ġdefensive":6110,"agraph":6111,"Ġpregn":6112,"Ġinstit":6113,"ĠVict":6114,"Ġvolume":6115,"Ġpositions":6116,"Ġlinks":6117,"ĠProgram":6118,"ĠWeek":6119,"agues":6120,"Ġtransform":6121,"ker":6122,"ĠCEO":6123,"Ġcas":6124,"Ġopponent":6125,"Ġtweet":6126,"ĠCode":6127,"Ġshop":6128,"Ġfly":6129,"Ġtalks":6130,"Ġbag":6131,"Phone":6132,"Ġaid":6133,"Ġplants":6134,"Ġ65":6135,"Ġattorney":6136,"arters":6137,"quest":6138,"ĠMagic":6139,"Ġbegins":6140,"Ġmyster":6141,"Ġenvironmental":6142,"Ġstorage":6143,"NN":6144,"Ġmarg":6145,"Ġske":6146,"Ġmetal":6147,"elly":6148,"Ġordered":6149,"Ġremained":6150,"Ġloved":6151,"Ġprompt":6152,"Ġupdated":6153,"Ġexperts":6154,"Ġwalking":6155,"Ġancient":6156,"Ġperformed":6157,"ATE":6158,"Ġneither":6159,"iency":6160,"Ġmanufacture":6161,"ĠPak":6162,"Ġselected":6163,"Ġmine":6164,"Ġultimately":6165,"Ġexplan":6166,"Ġlabel":6167,"ĠServices":6168,"ributed":6169,"Trump":6170,"Ġsyn":6171,"ĠUlt":6172,"SC":6173,"Ġmeat":6174,"Ġgiant":6175,"ĠWars":6176,"ĠON":6177,"Ġadm":6178,"Ġinterpret":6179,"Ġevening":6180,"Ġevil":6181,"ĠBoston":6182,"ĠWild":6183,"ĠÃ":6184,"ĠBitcoin":6185,"ĠAmazon":6186,"Dr":6187,"ĠInformation":6188,"Ġobviously":6189,"Ġadvanced":6190,"Photo":6191,"olar":6192,"Ġweather":6193,"Ġsymbol":6194,"Ġsole":6195,"Ġpotentially":6196,"oster":6197,"Ġoriginally":6198,"mun":6199,"300":6200,"aze":6201,"essions":6202,"Ġdeck":6203,"Ġstood":6204,"Ġyouth":6205,"ĠBern":6206,"Rep":6207,"ĠTest":6208,"Ġbasically":6209,"otic":6210,"Ġinvolve":6211,"olit":6212,"lyn":6213,"See":6214,"Ġaircraft":6215,"Ġconfirm":6216,"EW":6217,"Ġmessages":6218,"ĠRichard":6219,"Ġkit":6220,"Ġprohib":6221,"Ġvulner":6222,"isters":6223,"Ġexistence":6224,"Ġturning":6225,"ĠSP":6226,"Ġdesire":6227,"Ġflat":6228,"Ġment":6229,"season":6230,"anges":6231,"Ġneighborhood":6232,"ĠLake":6233,"ATION":6234,"Ġpointed":6235,"bur":6236,"Ġinnov":6237,"ucks":6238,"UL":6239,"Ġprofessor":6240,"Ġexpressed":6241,"AB":6242,"icious":6243,"Ġ2002":6244,"ĠDev":6245,"Ġsession":6246,"Ġbare":6247,"sen":6248,"Ġdiss":6249,"ĠCath":6250,"ĠPass":6251,"ĠPoint":6252,"Ġdoctor":6253,"orrow":6254,"ailed":6255,"ĠRub":6256,"ĠDC":6257,"ĠCharl":6258,"person":6259,"Ġwriter":6260,"ighters":6261,"ureau":6262,"Ġoblig":6263,"Ġrecorded":6264,"Ġbroke":6265,"Ġorders":6266,"ilty":6267,"Ġmotion":6268,"inity":6269,"law":6270,"adium":6271,"Ġimmigration":6272,"Ġcontrast":6273,"Ġbatt":6274,"Ġexcellent":6275,"Ġtechnical":6276,"ami":6277,"Ġtun":6278,"Ġcloud":6279,"ĠYear":6280,"geon":6281,"Ġcreation":6282,"Ġstrange":6283,"Ġauth":6284,"Ġfort":6285,"born":6286,"Ġextent":6287,"ĠToday":6288,"ĠClub":6289,"Ġrain":6290,"Ġsample":6291,"Ġaccepted":6292,"Ġtact":6293,"Ġfired":6294,"ĠSon":6295,"Ġstands":6296,"Ġboot":6297,"Ġ47":6298,"Ġstatements":6299,"Ġversions":6300,"Ġselling":6301,"ounded":6302,"Ġ1990":6303,"Ġweren":6304,"ĠWatch":6305,"Ġexperiment":6306,"Post":6307,"Ġretail":6308,"uled":6309,"Inst":6310,"unte":6311,"ãĥ¼":6312,"Ġdepart":6313,"Ġbond":6314,"ivery":6315,"ompl":6316,"Ġreaction":6317,"ĠSyrian":6318,"ĠPac":6319,"apped":6320,"aniel":6321,"DP":6322,"Ġresolution":6323,"Ġreact":6324,"Ġapproved":6325,"onom":6326,"mond":6327,"ĠOffic":6328,"---":6329,"Ġreplace":6330,"Ġtack":6331,"Ġsport":6332,"Ġchain":6333,"Ġemergency":6334,"rad":6335,"ĠPalestin":6336,"Ġ46":6337,"Ġautomatically":6338,"Ġroute":6339,"Ġpal":6340,"Ġbanks":6341,"ĠParis":6342,"ĠMedia":6343,"road":6344,"icing":6345,"ixt":6346,"isted":6347,"Ġgrew":6348,"Ġcoord":6349,"ĠWhere":6350,"omin":6351,"Ġsubs":6352,"��":6353,"Ġ±":6354,"Ġcorporate":6355,"Ġselection":6356,"noon":6357,"ĠReport":6358,"cs":6359,"cluding":6360,"orders":6361,"anche":6362,"ĠIts":6363,"Ġslowly":6364,"ĠEgypt":6365,"ĠAcc":6366,"Ġcolle":6367,"iques":6368,"EX":6369,"Ġattempts":6370,"url":6371,"ĠCross":6372,"Ġfindings":6373,"ĠSC":6374,"ĠOR":6375,"Ġindex":6376,"ensity":6377,"ĠWay":6378,"ĠLand":6379,"Ġshock":6380,"dis":6381,"Ġdynam":6382,"Ġcart":6383,"mosp":6384,"Since":6385,"iest":6386,"ĠBoy":6387,"Ġstorm":6388,"ĠContin":6389,"2013":6390,"hew":6391,"ilit":6392,"Ġessential":6393,"iquid":6394,"Other":6395,"ivered":6396,"Ġreasonable":6397,"Act":6398,"Ġsubsequ":6399,"ĠPack":6400,"ĠFort":6401,"Ġconsidering":6402,"Ġuniversity":6403,"log":6404,"Ġmarried":6405,"Ġillust":6406,"ĠTrue":6407,"£ı":6408,"Ġnumerous":6409,"rastructure":6410,"Ġseriously":6411,"Ġreferred":6412,"ua":6413,"Ġconsistent":6414,"onna":6415,"ĠReal":6416,"ruption":6417,"ciples":6418,"Ġfacts":6419,"91":6420,"otes":6421,"erg":6422,"Then":6423,"Ġaccompl":6424,"Note":6425,"Ġrevenue":6426,"Ġpassing":6427,"Ġmal":6428,"een":6429,"ĠYet":6430,"Ġgather":6431,"terday":6432,"ework":6433,"ĠAuthor":6434,"Pe":6435,"Ġoptim":6436,"Ġrub":6437,"Ġè£ı":6438,"Ġunknown":6439,"stone":6440,"Ġunion":6441,"olve":6442,"Ġopportunities":6443,"Ġbrowser":6444,"ĠWal":6445,"ĠCost":6446,"Ġreporting":6447,"sts":6448,"pet":6449,"Ġsand":6450,"Ġsuddenly":6451,"Ġsurprising":6452,"ĠVR":6453,"Ġsomewhat":6454,"ĠBas":6455,"ulture":6456,"izz":6457,"ĠCD":6458,"Ġchallenges":6459,"Ġsettings":6460,"Ġexperiences":6461,"ĠFull":6462,"Ġcann":6463,"Ġreceiving":6464,"EST":6465,"Ġjoint":6466,"Ġcultural":6467,"Ġast":6468,"82":6469,"astern":6470,"ceived":6471,"ĠCru":6472,"Ġbull":6473,"pired":6474,"amm":6475,"Ġfacing":6476,"power":6477,"Ġboss":6478,"ĠHol":6479,"Ġinstr":6480,"Ġincreasingly":6481,"Ġshift":6482,"Ġstreets":6483,"ĠWilliams":6484,"abb":6485,"Ġlie":6486,"Ġlaugh":6487,"ĠCa":6488,"PL":6489,"Ġadults":6490,"Ġcustomer":6491,"Ġobtained":6492,"Ġsupporting":6493,"html":6494,"fire":6495,"Ġdetailed":6496,"Ġpicked":6497,"ĠRight":6498,"lder":6499,"EE":6500,"stood":6501,"ĠKim":6502,"Ġwire":6503,"Ġsight":6504,"Ġdevelopers":6505,"Ġpersons":6506,"Ġsad":6507,"Ġcup":6508,"Ġwarning":6509,"Ġboys":6510,"long":6511,"Ġbird":6512,"fo":6513,"Ġwal":6514,"Ġobserved":6515,"Ġzone":6516,"iveness":6517,"Ġchannel":6518,"cript":6519,"Ġrefused":6520,"ĠAgain":6521,"Ġsuc":6522,"Ġspokesman":6523,"ĠRef":6524,"rite":6525,"ouston":6526,"ãĥ³":6527,"ĠSher":6528,"Ġacts":6529,"ĠName":6530,"Ġstruggle":6531,"arry":6532,"ometimes":6533,"Ġdiscrim":6534,"HT":6535,"Ġcategory":6536,"Ġrealize":6537,"Ġemployee":6538,"ĠAfghan":6539,"enger":6540,"Ġguns":6541,"ĠSteve":6542,"ĠMot":6543,"ĠOl":6544,"oked":6545,"Ġthick":6546,"Ġfairly":6547,"illy":6548,"Ġsurve":6549,"ĠMat":6550,"weight":6551,"âĶ":6552,"Ġtroops":6553,"Ġagents":6554,"Ġbattery":6555,"Ġmotiv":6556,"á":6557,"Sec":6558,"den":6559,"overy":6560,"LS":6561,"Ġflu":6562,"Ġconfident":6563,"ĠOper":6564,"Ġempty":6565,"Ġphen":6566,"Ġsector":6567,"Ġexcited":6568,"Ġremote":6569,"aph":6570,"oen":6571,"Ġdestroyed":6572,"Ġmoral":6573,"ĠHP":6574,"ĠRon":6575,"Ġdress":6576,"ĠBat":6577,"Ġlit":6578,"ĠMS":6579,"Ġaf":6580,"HL":6581,"rum":6582,"isms":6583,"Ġshouldn":6584,"Ġsympt":6585,"ĠToronto":6586,"hetic":6587,"Ġcarbon":6588,"Ġinstalled":6589,"Ġviolent":6590,"Ġsolar":6591,"ja":6592,"Ġpractices":6593,"Ġride":6594,"ĠPenn":6595,"Ġimproved":6596,"Ġaudio":6597,"Ġbehavi":6598,"ĠPS":6599,"Ġeating":6600,"Data":6601,"ĠReview":6602,"pass":6603,"claim":6604,"uated":6605,"angers":6606,"chen":6607,"Ġproperties":6608,"Ġanywhere":6609,"Another":6610,"Ġblow":6611,"ĠJackson":6612,"Ġproud":6613,"Ġplane":6614,"lines":6615,"Ġsquare":6616,"Ġproof":6617,"ansas":6618,"Ġtalked":6619,"makers":6620,"Ġsister":6621,"Ġholds":6622,"Ġresident":6623,"Ġ==":6624,"Ġresistance":6625,"Ġsplit":6626,"Ġprosecut":6627,"Ġconfidence":6628,"resents":6629,"Ġcuts":6630,"Ġexception":6631,"Ġzero":6632,"Getty":6633,"Ġcopyright":6634,"Ġtotally":6635,"ormal":6636,"ifications":6637,"ĠAustralian":6638,"Ġsick":6639,"Ġ150":6640,"Ġhousehold":6641,"Ġfees":6642,"Ġdrivers":6643,"ogen":6644,"ĠNY":6645,"Ġnecessarily":6646,"Ġregulations":6647,"earing":6648,"sl":6649,"Ġperspective":6650,"care":6651,"icial":6652,"His":6653,"Ġescape":6654,"Ġsurprised":6655,"ĠVan":6656,"urrent":6657,"Ġvac":6658,"81":6659,"ĠThus":6660,"Ġemphas":6661,"ĠChampions":6662,"ĠIce":6663,"Ġnarr":6664,"Ġheads":6665,"Ġcausing":6666,"bel":6667,"fortunately":6668,"ĠMa":6669,"Ġtargets":6670,"cipl":6671,"Ġafternoon":6672,"Ġadds":6673,"ĠMaybe":6674,"ĠFour":6675,"essed":6676,"plete":6677,"Ġusual":6678,"cho":6679,"ingu":6680,"Ġwithd":6681,"ĠEnergy":6682,"ĠEconom":6683,"OO":6684,"Ġarticles":6685,"Ġinjured":6686,"Ġmanage":6687,"Ġexplains":6688,"Ġdiagn":6689,"Rec":6690,"atures":6691,"Ġlinked":6692,"Ġdiscussed":6693,"Ġexplo":6694,"Ġoccasion":6695,"athan":6696,"Ġopposite":6697,"Ġfaces":6698,"Ġdenied":6699,"ĠKnight":6700,"Ġnut":6701,"Ġapproximately":6702,"Ġdisappoint":6703,"onymous":6704,"ĠBest":6705,"ĠLo":6706,"ĠHy":6707,"ĠAff":6708,"Ġvoting":6709,"anwhile":6710,"ĠIII":6711,"Ġinstitutions":6712,"agram":6713,"ĠDaily":6714,"Ġdrag":6715,"Ġnearby":6716,"Ġguilty":6717,"Ġconver":6718,"Pre":6719,"ship":6720,"Ġreward":6721,"Ġphilosoph":6722,"ĠSS":6723,"ugh":6724,"Ġapps":6725,"friend":6726,"Ġupper":6727,"Ġadvert":6728,"Ġsnow":6729,"Ġfrust":6730,"Ġourselves":6731,"Fr":6732,"ĠDie":6733,"ampion":6734,"Ġdismiss":6735,"Ġcere":6736,"Ġsignal":6737,"from":6738,"Ġ).":6739,"Ġ52":6740,"Ġcrimes":6741,"itors":6742,"estival":6743,"useum":6744,"Ġcouncil":6745,"ĠSaud":6746,"May":6747,"ĠGun":6748,"ician":6749,"ether":6750,"Ġsufficient":6751,"ĠHen":6752,"sole":6753,"Ġhistorical":6754,"ĠFar":6755,"ĠTurn":6756,"Ġpin":6757,"Ġsucceed":6758,"mat":6759,"lymp":6760,"Ġtradition":6761,"ĠOk":6762,"Ġcro":6763,"Ġdescription":6764,"alle":6765,"Ġsky":6766,"Te":6767,"Ġwidely":6768,"Ġwave":6769,"Ġdefinition":6770,"ĠJews":6771,"Ġcycle":6772,"Ġrefere":6773,"Ġbrings":6774,"usal":6775,"Ġalive":6776,"Ġfrequently":6777,"Ġintention":6778,"ĠControl":6779,"lv":6780,"ystem":6781,"Ġprivacy":6782,"gent":6783,"rence":6784,"ĠQuest":6785,"ĠChristmas":6786,"Ġrail":6787,"Ġcooper":6788,"Ġtested":6789,"ĠCapt":6790,"asks":6791,"Ġcomfortable":6792,"Ġdelivered":6793,"scape":6794,"Ġdepth":6795,"ĠGOP":6796,"Ġwrites":6797,"Ġassets":6798,"Ġsav":6799,"iments":6800,"Ġtransition":6801,"Ġartist":6802,"ĠLook":6803,"Ġlob":6804,"Ġcomponents":6805,"arity":6806,"Ġwalked":6807,"Ġroot":6808,"Ġparticipants":6809,"Ġnoticed":6810,"Ġresc":6811,"Ġnav":6812,"ĠAdminist":6813,"da":6814,"utral":6815,"plate":6816,"Ġimportance":6817,"Ġassert":6818,"iously":6819,"cription":6820,"Ġinjuries":6821,"ĠCheck":6822,"Ġregistered":6823,"Ġintent":6824,"Ġmissed":6825,"ographic":6826,"Ġsentence":6827,"ounter":6828,"Ġassistance":6829,"evin":6830,"Ġdatabase":6831,"Ġbuildings":6832,"Ġclassic":6833,"Ġthinks":6834,"ĠOhio":6835,"Pr":6836,"ugg":6837,"Ġfee":6838,"pan":6839,"Ġeffectively":6840,"Ġfacility":6841,"Ġbear":6842,"Ġchapter":6843,"Ġdogs":6844,"ĠColumb":6845,"Ġlatter":6846,"itial":6847,"Ġadmitted":6848,"TV":6849,"ĠGeorg":6850,"Ġposts":6851,"\\\\":6852,"Ġlawyer":6853,"Ġequival":6854,"Ġmand":6855,"Ġcontrolled":6856,"ĠWalk":6857,"ĠAndrew":6858,"Ġmenu":6859,"amental":6860,"Ġprotected":6861,"va":6862,"Ġadministr":6863,"oral":6864,"Ġrein":6865,"ĠSar":6866,"Ġamounts":6867,"Ġnative":6868,"ĠMoon":6869,"Ġrepresents":6870,"Ġabandon":6871,"Ġcarrying":6872,"Ġtank":6873,"mary":6874,"Ġdeclared":6875,"Tube":6876,"Ġhat":6877,"Ġpunish":6878,"ellect":6879,"mes":6880,"Ġuniverse":6881,"ĠRod":6882,"phy":6883,"Ġinfrastructure":6884,"Ġ51":6885,"Ġopposed":6886,"ownt":6887,"ca":6888,"ĠMake":6889,"Ġhardware":6890,"Ġcoffee":6891,"Rel":6892,"bal":6893,"world":6894,"ĠSaf":6895,"ĠSea":6896,"inals":6897,"Ġowned":6898,"Ġhall":6899,"ersion":6900,"Ġdescribe":6901,"ĠPot":6902,"Ġportion":6903,"Ġatmosp":6904,"Ġgovernments":6905,"Ġdepending":6906,"Ġoffense":6907,"Ġtrick":6908,"awa":6909,"ĠLine":6910,"ĠVis":6911,"ĠHard":6912,"ĠOrig":6913,"ĠClick":6914,"Ġdesk":6915,"ĠValley":6916,"ĠSov":6917,"Ġmovies":6918,"Ġremark":6919,"Ġmail":6920,"Ġconscious":6921,"Ġruling":6922,"ĠRights":6923,"Ġmedic":6924,"hent":6925,"ĠWomen":6926,"><":6927,"Ġreplaced":6928,"ĠPrem":6929,"ĠThanks":6930,"Ġrenew":6931,"ĠBall":6932,"iform":6933,"Ġshots":6934,"Comm":6935,"Ġarmed":6936,"Ġconstant":6937,"Ġtaste":6938,"Ġrealized":6939,"Ġbuff":6940,"Ġmo":6941,"Ġefficient":6942,"Most":6943,"oration":6944,"ifies":6945,"Ġcommunication":6946,"Ġflood":6947,"Ġconsequences":6948,"Ġanyway":6949,"igg":6950,"ĠGM":6951,"ĠThank":6952,"Ġiron":6953,"Ġevolution":6954,"ĠCop":6955,"twitter":6956,"Ġ95":6957,"Ġrelationships":6958,"adel":6959,"ĠYoung":6960,"Ġproposal":6961,"ayers":6962,"uilding":6963,"ĠHot":6964,"ORE":6965,"cos":6966,"Ġcollabor":6967,"PG":6968,"axy":6969,"Ġknowing":6970,"Ġsupports":6971,"owed":6972,"Ġcontrols":6973,"Ġmerely":6974,"umer":6975,"Ġathlet":6976,"Ġfashion":6977,"path":6978,"Ġgift":6979,"Ġera":6980,"AND":6981,"Ġkinds":6982,"ĠKorean":6983,"Ġlegit":6984,"ulous":6985,"Ġessentially":6986,"Ġtherap":6987,"nic":6988,"Ġsuffered":6989,"Ġhur":6990,"Ġpromise":6991,"Ġexcess":6992,"Ġoverw":6993,"Ġprime":6994,"ĠHouston":6995,"erry":6996,"ĠMs":6997,"RS":6998,"2012":6999,"Ġstores":7000,"ĠOlymp":7001,"Ġjourney":7002,"Although":7003,"Sub":7004,"ĠEduc":7005,"ĠChapter":7006,"Ġrequests":7007,"Ġconsumers":7008,"Ġtiny":7009,"Ġisol":7010,"ĠFair":7011,"ba":7012,"ĠYOU":7013,"Ġcrash":7014,"celer":7015,"Ġemotional":7016,"Ġgoods":7017,"Ġelected":7018,"Ġmoder":7019,"ĠLinux":7020,"Ġblocks":7021,"Ġisland":7022,"ĠSociety":7023,"Ġelections":7024,"Ġbroadcast":7025,"Ġcheap":7026,"Ġnations":7027,"Ġseasons":7028,"400":7029,"Ġwaste":7030,"ĠSat":7031,"Ġfields":7032,"employ":7033,"Ġprofile":7034,"Ġauthors":7035,"ALL":7036,"ĠGra":7037,"west":7038,"ĠTy":7039,"Ġdeaths":7040,"Ġvacc":7041,"Ġformed":7042,"Ġdu":7043,"Ġongoing":7044,"ĠMuslims":7045,"elf":7046,"igure":7047,"Ġassume":7048,"ĠUkraine":7049,"water":7050,"Ġcoast":7051,"Ġvoted":7052,"gor":7053,"ĠAS":7054,"ĠMichigan":7055,"aza":7056,"ĠArm":7057,"iro":7058,"Ġflex":7059,"asters":7060,"''":7061,"Ġwelcome":7062,"arl":7063,"Ġlocations":7064,"igation":7065,"ĠFil":7066,"Ġbuying":7067,"Ġarchitect":7068,"Ġharder":7069,"ĠCub":7070,"Ġinterface":7071,"Ġrestaurant":7072,"Ġdiscover":7073,"Ġexceed":7074,"Ġfavour":7075,"gery":7076,"Ġduty":7077,"Ġpitch":7078,"ador":7079,"ĠMach":7080,"boy":7081,"Ġresponded":7082,"Ġextended":7083,"hers":7084,"Many":7085,"raid":7086,"ifer":7087,"ĠIns":7088,"Ser":7089,"Ġmedium":7090,"she":7091,"ĠSports":7092,"Ġmagazine":7093,"utation":7094,"Ġlimits":7095,"ĠGall":7096,"Ġexternal":7097,"razil":7098,"Ġyounger":7099,"tle":7100,"Ġremind":7101,"ĠCON":7102,"Ġimmediate":7103,"Ġhidden":7104,"Ġvolunte":7105,"Ġsimpl":7106,"odcast":7107,"Ġphase":7108,"dr":7109,"Ġplot":7110,"Ġexposure":7111,"RI":7112,"ograp":7113,"vin":7114,"anish":7115,"ĠAcad":7116,"ĠEngine":7117,"Ġexpansion":7118,"ĠPay":7119,"Your":7120,"Ġpushed":7121,"ĠEll":7122,"ĠHead":7123,"Ġmarketing":7124,"ĠAC":7125,"ket":7126,"Ġhits":7127,"Ġgro":7128,"ĠAge":7129,"ĠScot":7130,"][":7131,"Ġstim":7132,"ĠiPhone":7133,"ĪĴ":7134,"Ġnarrow":7135,"ĠGetty":7136,"ĠTurkey":7137,"Ġperfectly":7138,"Ġenable":7139,"utch":7140,"Ġprecise":7141,"Ġregime":7142,"Ġshif":7143,"Ġcompens":7144,"gun":7145,"div":7146,"Ġchosen":7147,"ĠKen":7148,"Any":7149,"Ġtrees":7150,"Ġrecommended":7151,"ĠRen":7152,"uable":7153,"ĠHT":7154,"Follow":7155,"EG":7156,"ĠHand":7157,"ĠKenn":7158,"Ġarguments":7159,"Ġexists":7160,"Ġbike":7161,"ĠConserv":7162,"Ġbreaking":7163,"ĠGar":7164,"Ġcrazy":7165,"Ġvirtual":7166,"aylor":7167,"ixel":7168,"Ġ1980":7169,"Ġpermission":7170,"ĠSeries":7171,"Ġconsumer":7172,"Ġclosely":7173,"called":7174,"Ġ54":7175,"Ġhopes":7176,"Ġarray":7177,"ĠWin":7178,"ĠLabour":7179,"Ġspons":7180,"ĠIre":7181,"Ġpow":7182,"Ġreaders":7183,"Ġemployment":7184,"Ġcreature":7185,"Ġresulting":7186,"Ġaccurate":7187,"Ġmoments":7188,"Ġargued":7189,"Ġped":7190,"During":7191,"Ġ53":7192,"ĠTal":7193,"Ġsought":7194,"Ġsuffering":7195,"Ġicon":7196,"lee":7197,"Ġ($":7198,"alian":7199,"°":7200,"Ġpra":7201,"Ġbonus":7202,"(\"":7203,"ko":7204,"Ġacting":7205,"DE":7206,"fall":7207,"Ġcomparison":7208,"Ġsmooth":7209,"ĠNAS":7210,"upp":7211,"ĠJoseph":7212,"eping":7213,"ĠTake":7214,"ĠMid":7215,"Ġsending":7216,"fast":7217,"ĠFall":7218,"Ġdealing":7219,"user":7220,"ĠOrgan":7221,"Co":7222,"Ġattached":7223,"Ġsees":7224,"%.":7225,"Ġtypical":7226,"ART":7227,"Ġfinds":7228,"ĠAsia":7229,"umin":7230,"ĠCore":7231,"ĠEnt":7232,"inent":7233,"uce":7234,"ĠBlood":7235,"ĠNever":7236,"Ġemails":7237,"Ġhighlight":7238,"Ġconfront":7239,"atus":7240,"uted":7241,"Ġunus":7242,"Ġtopic":7243,"ĠAdam":7244,"Ġble":7245,"ati":7246,"Ġunderstood":7247,"Set":7248,"struct":7249,"TP":7250,"Ġmob":7251,"aa":7252,"ĠStart":7253,"pected":7254,"sell":7255,"Ġdedicated":7256,"ĠCA":7257,"uan":7258,"Ġsongs":7259,"escription":7260,"Ġtech":7261,"Ġrape":7262,"Ġaside":7263,"Ġgrant":7264,"Ġ56":7265,"sub":7266,"Ġargue":7267,"Ġcontaining":7268,"Ġschedule":7269,"Ġliberal":7270,"Ġpublicly":7271,"Ġheavily":7272,"ĠUt":7273,"iner":7274,"ĠSection":7275,"ĠCare":7276,"weet":7277,"ls":7278,"Dis":7279,"âĶĢ":7280,"ĠFollow":7281,"Back":7282,"ĠIT":7283,"Ġbes":7284,"ji":7285,"ĠHit":7286,"ested":7287,"Ġeverybody":7288,"ĠSwed":7289,"Ġfemin":7290,"Ġfacilities":7291,"Ġconven":7292,"Comp":7293,"ĠOS":7294,"core":7295,"Ġanx":7296,"Ġdivision":7297,"ĠCam":7298,"ĠStan":7299,"mates":7300,"Ġexplore":7301,"plom":7302,"Ġshares":7303,"pload":7304,"anes":7305,"Ġideal":7306,"eters":7307,"ĠBase":7308,"Ġplastic":7309,"Ġdistinct":7310,"ĠNetwork":7311,"ĠSeattle":7312,"Ġtrading":7313,"ensus":7314,"intend":7315,"Ġexhib":7316,"Ġinitially":7317,"ĠFood":7318,"Ġthousand":7319,"ĠBusiness":7320,"acter":7321,"Ġparagraph":7322,"Ġroughly":7323,"Ġwww":7324,"Ġcreative":7325,"ĠConf":7326,"Ġconsumption":7327,"Ġfilms":7328,"agan":7329,"Ġobtain":7330,"Ġtall":7331,"Ġtor":7332,"Ġacknowled":7333,"Ġgrown":7334,"alo":7335,"KE":7336,"Ġ400":7337,"enders":7338,"taining":7339,"UG":7340,"Ġsuicide":7341,"Ġwatched":7342,"ĠList":7343,"ali":7344,"rehens":7345,"Ġsurrounding":7346,"Ġpip":7347,"Ġflying":7348,"ĠJava":7349,"ordan":7350,"Ġserving":7351,"inations":7352,"post":7353,"Ġsho":7354,"Av":7355,"Ġjail":7356,"zy":7357,"Ġ1999":7358,"Ġ>":9609,"orous":9610,"Ġfirms":9611,"screen":9612,"una":9613,"Ġembarrass":9614,"ulse":9615,"Ġletting":9616,"Ġthrew":9617,"iley":9618,"Ġchannels":9619,"lan":9620,"ĠVegas":9621,"Ġsear":9622,"Ġfantastic":9623,"arre":9624,"uzzle":9625,"ĠDer":9626,"Those":9627,"Ġswing":9628,"Ġsheet":9629,"index":9630,"cover":9631,"ogan":9632,"Ġvariables":9633,"ĠTech":9634,"Ġspoken":9635,"achel":9636,"ĠDa":9637,"ĠMountain":9638,"Ġloaded":9639,"Ġfootage":9640,"version":9641,"Ġunl":9642,"ĠPhoenix":9643,"Ġthrowing":9644,"Ġfiring":9645,"Ġtracking":9646,"Ġwidth":9647,"Ġstruggling":9648,"rooms":9649,"otion":9650,"Ġmonthly":9651,"ĠServer":9652,"Ġeggs":9653,"open":9654,"MC":9655,"Ġ1993":9656,"Ġhired":9657,"Ġstayed":9658,"ĠAllen":9659,"Ġstro":9660,"Ġ98":9661,"step":9662,"ĠTurkish":9663,"Ġfabric":9664,"isting":9665,"ĠDom":9666,"Ġdates":9667,"Ġpron":9668,"Ġbasketball":9669,"Ġlucky":9670,"ĠArabia":9671,"Ġassumed":9672,"esty":9673,"Ġaffairs":9674,"Ġglad":9675,"ĠIndeed":9676,"ĠFA":9677,"ĠWord":9678,"Ġjoining":9679,"ifice":9680,"pread":9681,"irts":9682,"ĠSelect":9683,"Ġpopulations":9684,"aware":9685,"Ġnose":9686,"Ġcomplaints":9687,"start":9688,"Ġscoring":9689,"Thanks":9690,"Ġmining":9691,"Ġvisitors":9692,"SH":9693,"Ġdamaged":9694,"Ġcharacteristics":9695,"ĠPent":9696,"DC":9697,"Ġ83":9698,"ĠSix":9699,"rates":9700,"Ġflags":9701,"ĠBrew":9702,"dog":9703,"Mark":9704,"////":9705,"Ġexecution":9706,"Ġjoke":9707,"phones":9708,"Ġtestimony":9709,"Ġobst":9710,"QL":9711,"ĠCut":9712,"Ġstudied":9713,"ĠNintendo":9714,"icket":9715,"ĠNBC":9716,"Ġlad":9717,"ĠBra":9718,"ĠMoh":9719,"Ġkernel":9720,"Ġoverwhelming":9721,"Ġaged":9722,"Ġapplicable":9723,"ĠCond":9724,"Ġroads":9725,"ĠBlock":9726,"made":9727,"odge":9728,"Ġcommands":9729,"Ġoffices":9730,"veland":9731,"Ġtut":9732,"Ġreceiver":9733,"ĠFro":9734,"Ġshopping":9735,"ĠiP":9736,"ĠStre":9737,"ĠABC":9738,"Ġentertainment":9739,"ĠBow":9740,"orted":9741,"Mc":9742,"Ġreads":9743,"grad":9744,"ĠCollect":9745,"ĠâĪĴ":9746,"ĠCapital":9747,"ederation":9748,"Ġemployer":9749,"Ġinvolvement":9750,"Ġanxiety":9751,"alia":9752,"Ġroof":9753,"ĠAmong":9754,"ĠDemocrat":9755,"Ġstats":9756,"ĠVill":9757,"Ġconstitutional":9758,"Ġreferring":9759,"itty":9760,"Ġtackle":9761,"outube":9762,"Ġbacked":9763,"ĠHong":9764,"ĠBroad":9765,"Ġele":9766,"ĠOtt":9767,"Ġ1992":9768,"hour":9769,"achusetts":9770,"Cal":9771,"Ġdefeated":9772,"Ġ81":9773,"esp":9774,"Ġseemingly":9775,"was":9776,"ĠJenn":9777,"ĠKurd":9778,"Ġgene":9779,"Ġdiscount":9780,"Ret":9781,"ECT":9782,"();":9783,"Ġclubs":9784,"Ġsid":9785,"ĠMarsh":9786,"Check":9787,"Ġpp":9788,"ĠEag":9789,"idespread":9790,"Ġbeings":9791,"FT":9792,"Ġintroduction":9793,"ĠChange":9794,"ARD":9795,"Ġ110":9796,"adows":9797,"ierce":9798,"Ġmeal":9799,"author":9800,"ĠBang":9801,"lahoma":9802,"Ġranks":9803,"2011":9804,"????":9805,"max":9806,"Ġcollapse":9807,"Ġopens":9808,"Ġecho":9809,"Ġsoph":9810,"Ġracist":9811,"Ġenormous":9812,"Ġwaves":9813,"Ġtap":9814,"Ġcomprehensive":9815,".--":9816,"ĠRoy":9817,"Ġfarmers":9818,"Related":9819,"aired":9820,"rones":9821,"ĠCrim":9822,"Ġproportion":9823,"Ġdesigns":9824,"Ġnegotiations":9825,"Ġvirtually":9826,"ĠBatman":9827,"Ġwarn":9828,"Ġlegitimate":9829,"mate":9830,"Ġconvention":9831,",,":9832,"netic":9833,"ĠSD":9834,"Ġconsistently":9835,"Ġcompensation":9836,"Ġpunishment":9837,"Ġye":9838,"Ġtie":9839,"ĠBureau":9840,"irlf":9841,"ĠBu":9842,"ĠAren":9843,"ĠPhilipp":9844,"Ġknife":9845,"Ġmemories":9846,"ĠRoss":9847,"Ġangle":9848,"Ġ86":9849,"ĠThunder":9850,"Ġrend":9851,"ĠTour":9852,"Ġcounts":9853,"sung":9854,"ĠImp":9855,"Ġeducational":9856,"Ġaccessible":9857,"COM":9858,"Ġdrew":9859,"yer":9860,"Gl":9861,"amine":9862,"ORT":9863,"OB":9864,"IB":9865,"master":9866,"Ġtrials":9867,"ogy":9868,"har":9869,"ĠTrust":9870,"Ġpreferred":9871,"irlfriend":9872,"ĠNev":9873,"Ġbin":9874,"Ġcow":9875,"Page":9876,"Ġsignature":9877,"ĠBL":9878,"700":9879,"Ġretired":9880,"Ġbytes":9881,"Ġneighb":9882,"ĠLegend":9883,"Ġdevast":9884,"Ġsuspected":9885,"isons":9886,"ĠPokémon":9887,"scale":9888,"Ġcapabilities":9889,"Ġrevel":9890,"Ġcheese":9891,"dy":9892,"igrant":9893,"Ġfailing":9894,"bits":9895,"ĠHeroes":9896,"ĠGhost":9897,"ĠScient":9898,"Ġappointed":9899,"uri":9900,"Ġinstitution":9901,"Ġexpanded":9902,"greg":9903,"Ġmonitoring":9904,"Ġpodcast":9905,"Ġcoalition":9906,"Ġ96":9907,"Jo":9908,"Ġstolen":9909,"ĠSab":9910,"Ġstops":9911,"Ġholiday":9912,"Ġintr":9913,"Car":9914,"Black":9915,"ĠLGBT":9916,"Ġwarming":9917,"ĠAnderson":9918,"Ġ89":9919,"Ġproducer":9920,"Med":9921,"Ġaccuracy":9922,"ĠMarvel":9923,"izabeth":9924,"ĠPatrick":9925,"mony":9926,"Ġmini":9927,"acles":9928,"Ġovert":9929,"they":9930,"Ġmembership":9931,"ĠVen":9932,"Ġexch":9933,"Ġremoval":9934,"ĠDave":9935,"TY":9936,"mad":9937,"ĠFind":9938,"Ġadequ":9939,"Ġec":9940,"Ġteeth":9941,"Ġemotion":9942,"Ġperm":9943,"Ġsolely":9944,"db":9945,"Ġextraord":9946,"IGHT":9947,"cal":9948,"Ġguidelines":9949,"Ġdying":9950,"Ġsuspended":9951,"ĠPremier":9952,"ĠAnthony":9953,"elve":9954,"Ġdad":9955,"ĠEth":9956,"ĠFootball":9957,"Ġabandoned":9958,"Ġ<<":9959,"Ġmarch":9960,"Ġhorror":9961,"âĢ¦\"":9962,"Ġchildhood":9963,"Ġcampaigns":9964,"Ġlunch":9965,"ĠAlbert":9966,"block":9967,"âĸĪâĸĪ":9968,"ounding":9969,"Ġbone":9970,"organ":9971,"aders":9972,"ĠFlash":9973,"ĠDrive":9974,"Ġtonight":9975,"Ġwars":9976,"ĠFL":9977,"Ġformation":9978,"const":9979,"News":9980,"Ġcompe":9981,"orious":9982,"ĠStaff":9983,"Ġdiscussions":9984,"ĠProtection":9985,"ĠJam":9986,"Ġcriteria":9987,"Ġinstallation":9988,"Ġaccomplish":9989,"izza":9990,"Ġpublisher":9991,"Ġrescue":9992,"ĠTry":9993,"ULL":9994,"ĠSom":9995,"ĠHop":9996,"oret":9997,"ths":9998,"ordon":9999,"Ġpocket":10000,"ĠInv":10001,"Download":10002,"ĠCrime":10003,"Ġbene":10004,"ĠGuide":10005,"ĠAssembly":10006,"Ġparameters":10007,"IE":10008,"ĠAlexander":10009,"Ġconcert":10010,"ĠSche":10011,"Ġshoes":10012,"Ġvisiting":10013,"Ġrecall":10014,"Ġbub":10015,"Ġrural":10016,"Ġconcrete":10017,"ĠRos":10018,"Next":10019,"Russ":10020,"Ġloans":10021,"ĠShield":10022,"Ġtrem":10023,"hemat":10024,"kg":10025,"ĠHarris":10026,"isition":10027,"ĠMove":10028,"ĠFC":10029,"Ġfate":10030,"ĠCho":10031,"Ġtired":10032,"Ġprincipal":10033,"hist":10034,"iences":10035,"athy":10036,"Ġsevent":10037,"Ġmood":10038,"Ġstrategic":10039,"Ġdiseases":10040,"Ġforum":10041,"Ġtempor":10042,"Ġheadquarters":10043,"Par":10044,"ige":10045,"flix":10046,"Ġguitar":10047,"Ġ94":10048,"Only":10049,"Ġreleases":10050,"roph":10051,"================================":10052,"Ġ600":10053,"ĠContinue":10054,"igate":10055,"ĠCrit":10056,"system":10057,"Ġdisabled":10058,"Ġunexpected":10059,"ithub":10060,"Ġunclear":10061,"ĠEst":10062,"Ġcontrad":10063,"Ġstrategies":10064,"ventures":10065,"Ġpassage":10066,"AME":10067,"Ġimproving":10068,"Ġreveals":10069,"Ġdecrease":10070,"ova":10071,"Ġannoy":10072,"ĠShort":10073,"ĠLibrary":10074,"Ġcyber":10075,"nell":10076,"ĠHur":10077,"ĠCB":10078,"Ġphotograp":10079,"UI":10080,"Ġsed":10081,"Ge":10082,"Ġ87":10083,"Ġdiverse":10084,"Ġencouraged":10085,"Ġconspiracy":10086,"Ġbirds":10087,"Ġoperator":10088,"Ġhandful":10089,"Ġclassified":10090,"?)":10091,"Ġdramatic":10092,"Ġinvestigators":10093,"ito":10094,"Ġwidespread":10095,"ĠRoom":10096,"----------------------------------------------------------------":10097,"Ġcollective":10098,"Ġjournalist":10099,"String":10100,"Ġtemperatures":10101,"ila":10102,"Ġguid":10103,"Ġinspect":10104,"Ġmissile":10105,"ĠMayor":10106,"Ġmanual":10107,"Ġsimultane":10108,"Ġratings":10109,"Ġsuck":10110,"Ġ97":10111,"Ġuniversal":10112,"Ġpharm":10113,"Ġdisrupt":10114,"iano":10115,"AV":10116,"Ġft":10117,"Ġstatist":10118,"olds":10119,"ĠWalker":10120,"php":10121,"Ġundert":10122,"ĠLas":10123,"ishop":10124,"ntil":10125,"reshold":10126,"ĠWhether":10127,"Ms":10128,"Ġdeny":10129,"ĠCloud":10130,"Ġprovider":10131,"Ġsurviv":10132,"ĠUpdate":10133,"has":10134,"Ġmistakes":10135,"charge":10136,"pled":10137,"rity":10138,"Ġnode":10139,"ĠMassachusetts":10140,"ools":10141,"lication":10142,"Ġfails":10143,"emale":10144,"ori":10145,"backs":10146,"Ġshirt":10147,"Ġ''":10148,"ĠNAT":10149,"Ġwaters":10150,"elson":10151,"Ġease":10152,"Ġscar":10153,"Ġcontents":10154,"mind":10155,"Ġcontribution":10156,"Ġshr":10157,"Ġhanded":10158,"Ġstability":10159,"Ġtrave":10160,"Em":10161,"Ġmirror":10162,"123":10163,"Ġweigh":10164,"Ġfiction":10165,"ouver":10166,"istant":10167,"rition":10168,"ĠFed":10169,"Ġphysically":10170,"Ġstake":10171,"ĠArticle":10172,"ĠArc":10173,"ĠLewis":10174,"ĠMind":10175,"Ġdemonstrate":10176,"Ġprofits":10177,"vision":10178,"omic":10179,"olid":10180,"Ġbattles":10181,"Ġdrives":10182,"Ġeastern":10183,"ĠSony":10184,"!!!":10185,"aration":10186,"vard":10187,"ĠGL":10188,"portation":10189,"Ġ92":10190,"Ġlawmakers":10191,"Ġprotecting":10192,"ĠEPA":10193,"Ġyeah":10194,"Ġshame":10195,"olph":10196,"even":10197,"xit":10198,"Ġattach":10199,"Ġrepresenting":10200,"Ġobs":10201,"ĠUtah":10202,"iffs":10203,"ĠFreedom":10204,"ó":10205,"AK":10206,"Ġincidents":10207,"itage":10208,"Ġviewers":10209,"cd":10210,"Ġmouse":10211,"Ġclar":10212,"Ġaccordance":10213,"Ġbot":10214,"cor":10215,"ĠSummer":10216,"held":10217,"Ġinnocent":10218,"Ġinitiative":10219,"ols":10220,"________________________________":10221,"Ġspots":10222,"pace":10223,"Ġconventional":10224,"Ġcorporations":10225,"Ġblocked":10226,"HD":10227,"attered":10228,"Ġrefers":10229,"Ġbuck":10230,"ĠDigital":10231,"120":10232,"Ġtopics":10233,"TF":10234,"Äģ":10235,"brid":10236,"reement":10237,"Ġunderlying":10238,"ĠMember":10239,"Ġinvestigating":10240,"Ġpregnancy":10241,"Ġtouchdown":10242,"ĠBand":10243,"ĠCaller":10244,"Ġinstances":10245,"PP":10246,"wa":10247,"Good":10248,"Ġ1991":10249,"ĠCold":10250,"Ġfears":10251,"Ġremarks":10252,"ĨĴ":10253,"atal":10254,"Ġmit":10255,"Ġexperiments":10256,"ipt":10257,"Color":10258,"indu":10259,"Update":10260,"Ġ93":10261,"Ag":10262,"Ġå":10263,"ancouver":10264,"Both":10265,"Ġjudges":10266,"Object":10267,"Ġstere":10268,"umbn":10269,"Ġparticipation":10270,"ĠStars":10271,"ĠJere":10272,"Ġweekly":10273,"ĠBan":10274,"Ġconversations":10275,"ĠPitt":10276,"uz":10277,"ĠIndiana":10278,"ĠKick":10279,"Ġinfection":10280,"Ġheroes":10281,"Ġsettled":10282,"Ġstrip":10283,"Ġhal":10284,"Ġdump":10285,"ĠSci":10286,"Ġles":10287,"Ġreferences":10288,"ĠURL":10289,"ĠBridge":10290,"Ġwanting":10291,"Force":10292,"Ġexclus":10293,"Meanwhile":10294,"mn":10295,"Ġgentle":10296,"maker":10297,"senal":10298,"ĠGro":10299,"ouri":10300,"ĠRain":10301,"ĠAlliance":10302,"Ġlift":10303,"ela":10304,"SD":10305,"ĠCleveland":10306,"Ġranked":10307,"Ġstadium":10308,"Ġdeadly":10309,"ä¸":10310,"Ġriding":10311,"aria":10312,"ĠArmor":10313,"Ġdocumentation":10314,"ĠGreece":10315,"reek":10316,"Ġlens":10317,"ĠSa":10318,"Ġgross":10319,"ĠEmer":10320,"agers":10321,"ĠDub":10322,"ĠRh":10323,"ĠAMD":10324,"Ġarrival":10325,"Ġdesert":10326,"Ġsupplement":10327,"ĠResp":10328,"Ġknee":10329,"Ġmargin":10330,"font":10331,"ogg":10332,"2010":10333,"ĠPir":10334,"ĠProm":10335,"ivals":10336,"Ġintake":10337,"Ġdifferently":10338,"ugs":10339,"Ġbits":10340,"cluded":10341,"Ġsearching":10342,"ĠDu":10343,"umble":10344,"Ġfunctional":10345,"ĠBaltimore":10346,"ĠCould":10347,"Ġdesired":10348,"Ġcircuit":10349,"ĠLyn":10350,"ĠGO":10351,"ĠFalse":10352,"repre":10353,"':":10354,"alties":10355,"Ġminim":10356,"Ġdrove":10357,"ĠShould":10358,"Ġhip":10359,"Ġpros":10360,"Ġutility":10361,"ĠNature":10362,"ĠMode":10363,"President":10364,"opp":10365,"rat":10366,"formance":10367,"Ġconcentration":10368,"Ġfont":10369,"ĠBud":10370,"Ġamid":10371,"Ġrevers":10372,"ĠML":10373,"Bar":10374,"Ġinteraction":10375,"Ġjurisd":10376,"Ġspells":10377,"dep":10378,"fil":10379,"Ġcivilians":10380,"utter":10381,"ĠCooper":10382,"ĠBelow":10383,"Ġentrance":10384,"Ġconvert":10385,"Ġcontroversy":10386,"owered":10387,"Ġcontrary":10388,"Ġarc":10389,"ĠExecutive":10390,"ĠOfficer":10391,"Ġpackages":10392,"Ġprogressive":10393,"width":10394,"Ġreserved":10395,"vol":10396,"ĠSamsung":10397,"Ġprinted":10398,"Ġcenters":10399,"Ġintroduce":10400,"ĠKennedy":10401,"Ġodds":10402,"Ġsurely":10403,"Ġindependence":10404,"Ġpassengers":10405,"reprene":10406,"ĠBeh":10407,"Ġloves":10408,"ĠESPN":10409,"Ġfacilit":10410,"Ġidentical":10411,"Ġdoct":10412,"Ġpartnership":10413,"conf":10414,"ĠHide":10415,"Ġconfused":10416,"ĠCow":10417,"Men":10418,"Ġwrest":10419,"ĠIraqi":10420,"Ġholes":10421,"ĠStudies":10422,"Ġpregnant":10423,"hard":10424,"Ġsignals":10425,"IX":10426,"Ġpulling":10427,"Ġgraduate":10428,"Ġnominee":10429,"Date":10430,"Ġpermitted":10431,"ĠâĤ¬":10432,"ĠOklahoma":10433,"Start":10434,"Ġauthorized":10435,"Ġalarm":10436,"ĠCos":10437,"van":10438,"Ġgenerations":10439,"cular":10440,"Ġdragon":10441,"ĠSoftware":10442,"ĠEdward":10443,"Ġcontroller":10444,"Sen":10445,"gered":10446,"ĠVik":10447,"Ġapproached":10448,"Thank":10449,"Ġcance":10450,"Ġformula":10451,"ĠSmall":10452,"Ġweakness":10453,"Ġramp":10454,"itudes":10455,"jud":10456,"Ġbrilliant":10457,"Ġaccus":10458,"source":10459,"Ġ800":10460,"ĠEvil":10461,"Sw":10462,"Ġhomeless":10463,"week":10464,"iens":10465,"rics":10466,"ĠThird":10467,"TO":10468,"Ġorganic":10469,"Ġpresentation":10470,"agh":10471,"ĠDownload":10472,"vation":10473,"Ġassembly":10474,"orable":10475,"holders":10476,"ĠBernie":10477,"ĠHelp":10478,"Ġtong":10479,"ĠFight":10480,"Ġbeach":10481,"Book":10482,"ĠLic":10483,"Ġrush":10484,"ĠRound":10485,"oup":10486,"ĠMarx":10487,"Ġcalculated":10488,"ĠDevil":10489,"ĠSarah":10490,"Ġoccasionally":10491,"Ġbullet":10492,"Available":10493,"gate":10494,"Ġ91":10495,"Ġhosp":10496,"Ġpromises":10497,"ĠHIV":10498,"ĠStadium":10499,"ĠStock":10500,"ĠCorporation":10501,"gage":10502,"NG":10503,"ĠCredit":10504,"Ġsne":10505,"ibl":10506,"Ġaccum":10507,"such":10508,"Ġterrorists":10509,"Ġconsciousness":10510,"ĠZh":10511,"Ġdrama":10512,"oola":10513,"piration":10514,"Ġlabour":10515,"ĠNin":10516,"Ġutter":10517,"Ġdemocratic":10518,"Ġassass":10519,"ilation":10520,"Ġgest":10521,"Ġabroad":10522,"Ġmetab":10523,"Ġsorts":10524,"Ġflav":10525,"UB":10526,"Ġmg":10527,"ĠNothing":10528,"ĠOd":10529,"Ġmusical":10530,"2009":10531,"Ġdrops":10532,"ocated":10533,"ateral":10534,"000000":10535,"Ġgre":10536,"Ġequality":10537,"Ġburden":10538,"Ġvig":10539,"ĠLeader":10540,"------------":10541,"Ġceremony":10542,"Ġfighter":10543,"Ġactors":10544,"Ġæ":10545,"aman":10546,"Fi":10547,"Ġalign":10548,"puter":10549,"Ġelder":10550,"ĠNSA":10551,"Ġrepresentation":10552,"ĠOntario":10553,"ITH":10554,"usalem":10555,"Ġharassment":10556,"itzer":10557,"Ġsymp":10558,"Ġboxes":10559,"ĠDR":10560,"Ġmanifest":10561,"atre":10562,"Ġ^":10563,"Ġdies":10564,"leton":10565,"Ġmissions":10566,"ethe":10567,"Ġresolve":10568,"Ġfollowers":10569,"Ġasc":10570,"Ġkm":10571,"lord":10572,"ammed":10573,"Ġsilent":10574,"ĠAssociated":10575,"Ġtiming":10576,"Ġprisoners":10577,"ĠKings":10578,"ĠFive":10579,"Ġtower":10580,"Ġapproaches":10581,"Ġprecisely":10582,"Ġbureau":10583,"ĠMother":10584,"ĠIss":10585,"Ġkeyboard":10586,"itual":10587,"Ġfunded":10588,"Ġstaying":10589,"Ġpsychological":10590,"Ġmile":10591,"ĠLeon":10592,"ĠBarb":10593,"will":10594,"Ġwider":10595,"ĠAtlantic":10596,"Ġtill":10597,"ĠRome":10598,"rot":10599,"Ġaccompan":10600,"Ġflour":10601,"aco":10602,"World":10603,"ĠExpress":10604,"ĠYu":10605,"Cor":10606,"Ġpleased":10607,"party":10608,"Ġpointing":10609,"Ġinflation":10610,"Ġroy":10611,"Ġ),":10612,"ainer":10613,"Ġwedding":10614,"ormon":10615,"Ġrequiring":10616,"Ġqualified":10617,"Ġsegment":10618,"END":10619,"Ġsizes":10620,"eals":10621,"Ġcorrupt":10622,"assador":10623,"Ġceleb":10624,"Ġdreams":10625,"ĠMess":10626,"Ġchecking":10627,"ĠVersion":10628,"Ġpreparing":10629,"Ġactively":10630,"ĠDiff":10631,"Ġlux":10632,"ĠWinter":10633,"acteria":10634,"ĠNE":10635,"Ġdeputy":10636,"Ġtransgender":10637,"Ġsummary":10638,"Ġinher":10639,"eries":10640,"char":10641,"ĠYan":10642,"Ġknock":10643,"ĠPath":10644,"Ġlip":10645,"roller":10646,"Ġimpression":10647,"Ġcelebrate":10648,"Ġslide":10649,"Ġguests":10650,"Ġclip":10651,"FS":10652,"Ġsavings":10653,"Ġcaptain":10654,"Ġlegacy":10655,"ĠDenver":10656,"Ġwounded":10657,"taboola":10658,"ACT":10659,"Ġpursue":10660,"Ġoxy":10661,"Ġq":10662,"Ġsemi":10663,"ĠNeed":10664,"ĠAffairs":10665,"Ġobsc":10666,"Ġchecked":10667,"Ġdual":10668,"Code":10669,"ĠMD":10670,"lem":10671,"ulty":10672,"Ġ©":10673,"ĠElizabeth":10674,"Ġcenturies":10675,"arded":10676,"src":10677,"Ġevident":10678,"ennis":10679,"atin":10680,"Ġunemployment":10681,"ĠMario":10682,"Ġintim":10683,"Christ":10684,"Ġbiological":10685,"Ġsoldier":10686,"ĠAdded":10687,"Ġmath":10688,"ĠGil":10689,"Ġbias":10690,"Ġdating":10691,"ĠOcean":10692,"Ġmice":10693,"Mus":10694,"hire":10695,"ĠTes":10696,"Server":10697,"limited":10698,"Size":10699,"Ġmeters":10700,"Ġrocket":10701,"essee":10702,"Ġcertificate":10703,"ĠIranian":10704,"ASS":10705,"Ġgrid":10706,"Dec":10707,"Ġrolling":10708,"commun":10709,"ĠSweden":10710,"bury":10711,"Ġtissue":10712,"Ġracism":10713,"ĠLocal":10714,"Ġmystery":10715,"Ġexamine":10716,"Ġstem":10717,"Ġsits":10718,"Ġhoped":10719,"oting":10720,"Ġdialogue":10721,"Ġpersu":10722,"Watch":10723,"lay":10724,"MAN":10725,"Ġchronic":10726,"ĠPortland":10727,"market":10728,"ĠSEC":10729,"Ġparallel":10730,"Ġscandal":10731,"Ġcarries":10732,"Ġphenomenon":10733,"human":10734,"acker":10735,"ĠOx":10736,"Ġretirement":10737,"tainment":10738,"ovie":10739,"ĠGear":10740,"Ġduties":10741,"Ġdose":10742,"Ġscroll":10743,"MB":10744,"inf":10745,"Ġsauce":10746,"Ġlandscape":10747,"reddit":10748,"ĠChampionship":10749,"ĠReddit":10750,"alid":10751,"Ġcoin":10752,"Ġovers":10753,"Ġposting":10754,"about":10755,"Ġfel":10756,"andy":10757,"Ġbold":10758,"Ġfocusing":10759,"effect":10760,"GR":10761,"Ġdeemed":10762,"Ġrecommendations":10763,"Ġstepped":10764,"Ġvoter":10765,"ĠDeep":10766,"ĠInstagram":10767,"Ġmoderate":10768,"ĠMaryland":10769,"Ġrestricted":10770,"ĠMB":10771,"ĠChall":10772,"Ġtob":10773,"Ġcir":10774,"ĠOcc":10775,"ĠEver":10776,"Ġcollaps":10777,"INFO":10778,"=-":10779,"ĠPict":10780,"ĠAccount":10781,"nc":10782,"Ġought":10783,"Ġexport":10784,"Ġdrunk":10785,"('":10786,"Ġwise":10787,"ĠMort":10788,"necess":10789,"Ġancest":10790,"ĠIncre":10791,"Ġfrequent":10792,"mir":10793,"Ġinterpretation":10794,"Ġdependent":10795,"Ġcoins":10796,"ĠBol":10797,"Video":10798,"ĠJustin":10799,"Ġfatal":10800,"Ġcooking":10801,"Ġconfusion":10802,"ipher":10803,"Ġcustody":10804,"ĠMorgan":10805,"omach":10806,"ĠGovernor":10807,"Ġrestaurants":10808,"eling":10809,"Ġacknowledged":10810,"Ġther":10811,"Ġgenes":10812,"ching":10813,"Hey":10814,"Ġtactics":10815,"ĠMexican":10816,"Ġvend":10817,"Ġhes":10818,"quer":10819,"Ġnoting":10820,"ĠCameron":10821,"Ġtargeting":10822,"rock":10823,"Ġcredits":10824,"Ġemotions":10825,"Ġrepresentatives":10826,"news":10827,"Ġlegislative":10828,"Ġremoving":10829,"Ġtweeted":10830,"ĠCarter":10831,"ĠFixed":10832,"Ġforcing":10833,"Ġspeaker":10834,"Ġmales":10835,"ĠVietnam":10836,"lined":10837,"Ġconcepts":10838,"Ġvoices":10839,"oir":10840,"ĠTrib":10841,"Whe":10842,"ĠJerusalem":10843,"ĠSant":10844,"Ġcul":10845,"Ġlady":10846,"ĠHawai":10847,"Ġarts":10848,"ĠInn":10849,"ĠMachine":10850,"ĠEmperor":10851,"Ġslot":10852,"gly":10853,"ĠProcess":10854,"III":10855,"Ġathletes":10856,"ĠTemple":10857,"ĠRepresent":10858,"Ġpresc":10859,"Ġtons":10860,"Ġgolden":10861,"Ġpunch":10862,"ĠGR":10863,"iverpool":10864,"Ġenact":10865,"Ġlobby":10866,"Ġmos":10867,"Ġpicking":10868,"Ġlifetime":10869,"Ġcognitive":10870,"Each":10871,"zo":10872,"Ġdub":10873,"Ġconsists":10874,"oln":10875,"Ġfestival":10876,"amous":10877,"Ġintellig":10878,"words":10879,"ĠSmart":10880,"Ġdele":10881,"Ġlapt":10882,"Ġmagical":10883,"ĠSin":10884,"bus":10885,"urities":10886,"ighth":10887,"ĠRuby":10888,"ĠSure":10889,"olving":10890,"Ġjun":10891,"OST":10892,"Ġimposed":10893,"Ġastron":10894,"Ġcorrel":10895,"ĠNS":10896,"ĠKit":10897,"ĠFuture":10898,"burn":10899,"Ġimmune":10900,"ocus":10901,"Ġcourses":10902,"ĠString":10903,"Ġlean":10904,"Ġghost":10905,"Ġoutcomes":10906,"Ġexpense":10907,"Ġeveryday":10908,"Ġacceptable":10909,"Ah":10910,"Ġequipped":10911,"Ġorange":10912,"FR":10913,"ĠDutch":10914,"Though":10915,"ĠRank":10916,"QU":10917,"ĠRoberts":10918,"what":10919,"rend":10920,"Ġdisappear":10921,"Ġspawn":10922,"ĠLam":10923,"ois":10924,"Ġdeserve":10925,"Ġminimal":10926,"Ġnervous":10927,"ĠWould":10928,"Ġrook":10929,"ĠVancouver":10930,"Ġresign":10931,"shire":10932,"ĠWorks":10933,"ĠBuild":10934,"Ġaffordable":10935,"ĠGary":10936,"ĠArena":10937,"Ġhanging":10938,"Ġimplications":10939,"ĠSong":10940,"Ġmaintaining":10941,"Ġguards":10942,"CON":10943,"Ġderived":10944,"Ġexecuted":10945,"Ġtheories":10946,"Ġquoted":10947,"ĠAndre":10948,"oga":10949,"seless":10950,"info":10951,"ĠBelg":10952,"Ġtears":10953,"ĠSurv":10954,"Ġbirthday":10955,"igious":10956,"immer":10957,"Ġspectrum":10958,"Ġarchitecture":10959,"Ġrecruit":10960,"arma":10961,"Table":10962,"Ġmonsters":10963,"ĠGov":10964,"Ġdestination":10965,"Ġattractive":10966,"Ġfoss":10967,"ĠMoreover":10968,"Ġpresents":10969,"THE":10970,"Ġreply":10971,"pton":10972,"Ġcum":10973,"Ġdelight":10974,"Ġaffects":10975,"Ġdonations":10976,"ĠToy":10977,"ĠHim":10978,"MENT":10979,"Ġovercome":10980,"itched":10981,"ĠFantasy":10982,"ĠHat":10983,"ĠBeast":10984,"bott":10985,"Ġinvestigations":10986,"Run":10987,"Ġhunting":10988,"di":10989,"fund":10990,"Ġsessions":10991,"estyle":10992,"Ġportray":10993,"oids":10994,"Yeah":10995,"Ġcommunicate":10996,"Ġcomedy":10997,"ĠYang":10998,"Ġbelt":10999,"ĠMarine":11000,"Ġpredicted":11001,"Play":11002,"Ġimportantly":11003,"Ġremarkable":11004,"Ġeliminate":11005,"David":11006,"Ġbind":11007,"VID":11008,"Ġadvocates":11009,"ĠGaza":11010,"imp":11011,"DB":11012,"ĠNa":11013,"ĠSimilar":11014,"IES":11015,"Ġcharity":11016,"vas":11017,"math":11018,"Ġâĸ":11019,"oker":11020,"ndum":11021,"Ġcaps":11022,"ĠHal":11023,"2000":11024,"ean":11025,"Ġfleet":11026,"Ġrecre":11027,"Right":11028,"Ġsleeping":11029,"ijing":11030,"kind":11031,"Ġdesignated":11032,"ä":11033,"Ġanimation":11034,"kee":11035,"ĠIntrodu":11036,"Ġ/>":11037,"Ġdelayed":11038,"Ġtremend":11039,"Ġcurious":11040,"Use":11041,"Ġlect":11042,"dam":11043,"Ġinnovation":11044,"ĠPoints":11045,"Ġloading":11046,"Ġdispute":11047,"ctic":11048,"irds":11049,"ĠBY":11050,"Ġnurs":11051,"ĠValue":11052,"IONS":11053,"ĠHum":11054,"Ġtemplate":11055,"mers":11056,"Ġappearances":11057,"ĠEntertainment":11058,"Ġtranslation":11059,"Ġsake":11060,"Ġbeneath":11061,"Ġinhib":11062,"Ġeuro":11063,"abetes":11064,"Ġstudying":11065,"ĠMas":11066,"Ġperceived":11067,"Ġexamined":11068,"Ġeager":11069,"Ġcoaches":11070,"Ġimper":11071,"chi":11072,"Ġproduces":11073,"\").":11074,"ĠEveryone":11075,"Ġmunicip":11076,"Ġgirlfriend":11077,"Ġhire":11078,"ĠVice":11079,"Ġsuitable":11080,"opy":11081,"Ġinequ":11082,"ĠDuke":11083,"fish":11084,"first":11085,"ĠObs":11086,"Ġinterior":11087,"ĠBruce":11088,"ĠRy":11089,"Ġanalys":11090,"Ġconsiderable":11091,"Ġforecast":11092,"Ġfert":11093,"orship":11094,"ĠDrug":11095,"ĠALL":11096,":\"":11097,"thur":11098,"ĠMail":11099,"Ġballot":11100,"Ġinstantly":11101,"ĠChannel":11102,"Ġpicks":11103,"Ġ1989":11104,"Ġtent":11105,"oli":11106,"Ġcivilian":11107,"bling":11108,"ello":11109,"bu":11110,"Ġinch":11111,"Ġlogo":11112,"Ġcooperation":11113,"Ġwalks":11114,"Ġinvestments":11115,"Ġimprison":11116,"ĠFestival":11117,"ĠKy":11118,"Ġlegally":11119,"Ġgri":11120,"charg":11121,"Sl":11122,"Ġthreatening":11123,"duction":11124,"flow":11125,"Ġdismissed":11126,"ibraries":11127,"cap":11128,"ele":11129,"ĠMcG":11130,"ĠHarvard":11131,"ĠConservative":11132,"ĠCBS":11133,"png":11134,"Ġroots":11135,"ĠHaving":11136,"umbled":11137,"ĠFun":11138,"\\/":11139,"ĠSearch":11140,"plex":11141,"Ġdiscussing":11142,"Ġcontinu":11143,"ĠTai":11144,"ĠWik":11145,"Free":11146,"fit":11147,"Ġrefuse":11148,"Ġmanaging":11149,"Ġsynd":11150,"ipedia":11151,"walk":11152,"Ġprofessionals":11153,"Ġguidance":11154,"Ġuniversities":11155,"Ġassemb":11156,"untu":11157,"Finally":11158,"ASE":11159,"ĠAuto":11160,"ĠHad":11161,"Ġanniversary":11162,"LD":11163,"ĠDur":11164,"ĠUltimate":11165,"ihad":11166,"product":11167,"Ġtransit":11168,"Ġrestore":11169,"Ġexplaining":11170,"Ġasset":11171,"Ġtransferred":11172,"Ġburst":11173,"apolis":11174,"ĠMagazine":11175,"ĠCra":11176,"ĠBR":11177,"gged":11178,"ĠHE":11179,"Mich":11180,"bet":11181,"ĠLady":11182,"ylum":11183,"erves":11184,"Ġmeets":11185,"white":11186,"Log":11187,"Ġcorresponding":11188,"Ġinsisted":11189,"GG":11190,"Ġsurrounded":11191,"Ġtens":11192,"Ġlane":11193,"Ġcoinc":11194,"home":11195,"Ġexisted":11196,"ected":11197,"ĠDouble":11198,"lamm":11199,"Ġskept":11200,"exp":11201,"Ġperception":11202,"iev":11203,"ĠBeing":11204,"oft":11205,"Ġadopt":11206,".:":11207,"];":11208,"Windows":11209,"Ġsatellite":11210,"ASH":11211,"Ġinfant":11212,"description":11213,"ĠMeanwhile":11214,"cm":11215,"oca":11216,"ĠTreat":11217,"actor":11218,"Ġtobacco":11219,"ĠNorm":11220,"emption":11221,"Ġflesh":11222,"Ġje":11223,"oop":11224,"ĠHeaven":11225,"Ġbeating":11226,"anim":11227,"Ġgathering":11228,"Ġcultiv":11229,"GO":11230,"abe":11231,"ĠJonathan":11232,"ĠSafety":11233,"Ġbadly":11234,"prot":11235,"Ġchoosing":11236,"Ġcontacted":11237,"Ġquit":11238,"Ġdistur":11239,"Ġstir":11240,"Ġtoken":11241,"Det":11242,"ĠPa":11243,"Ġfunctionality":11244,"003":11245,"some":11246,"Ġlimitations":11247,"Ġmeth":11248,"build":11249,"config":11250,"NT":11251,"rell":11252,"blem":11253,"ĠMom":11254,"Ġveterans":11255,"ĠHu":11256,"Ġtrends":11257,"arer":11258,"ĠGiven":11259,"ĠCaption":11260,"may":11261,"AST":11262,"Ġwondering":11263,"ĠClark":11264,"normal":11265,"Ġseparated":11266,"Ġdesp":11267,"stic":11268,"brew":11269,"Ġrelating":11270,"ĠNik":11271,"ĠFarm":11272,"Ġenthusi":11273,"good":11274,"deb":11275,"Ġactivist":11276,"Ġmart":11277,"Ġexplosion":11278,"ĠEconomic":11279,"Link":11280,"Ġinsight":11281,"Ġconvenient":11282,"Ġcounterpart":11283,"support":11284,"ĠVirt":11285,"agen":11286,"ĠTennessee":11287,"ĠSimon":11288,"ĠAward":11289,"OCK":11290,"ĠFigure":11291,"Ġoverseas":11292,"Ġpride":11293,"ĠCas":11294,"note":11295,"mg":11296,"Current":11297,"Ġdisplays":11298,"content":11299,"Ġtraveling":11300,"Ġhospitals":11301,"ĠFinancial":11302,"ĠPast":11303,"Ġdefendant":11304,"Ġstreaming":11305,"mble":11306,"ĠBerlin":11307,"uki":11308,"Ġdistribut":11309,"Ġantib":11310,"Ġchocolate":11311,"ĠCastle":11312,"Ġinterrupt":11313,"ĠRow":11314,"Ġconversion":11315,"Ġbugs":11316,"ĠRather":11317,"liest":11318,"LY":11319,"ĠJean":11320,"common":11321,"akh":11322,"Ġ130":11323,"otton":11324,"ĠDean":11325,"Ġamendment":11326,"Ġgameplay":11327,"ĠWarren":11328,"oda":11329,"Ġhighlights":11330,"Ġirre":11331,"ĠNATO":11332,"Ġballs":11333,"Ġdemanding":11334,"URE":11335,"ĠLuke":11336,"Figure":11337,"stop":11338,"onia":11339,"zone":11340,"izers":11341,"ĠWR":11342,"Ġawarded":11343,"Ġregulatory":11344,"ĠHart":11345,"ĠSN":11346,"pling":11347,"Ġsour":11348,"ĠPixel":11349,"usive":11350,"Ġfet":11351,"ĠSent":11352,"Ġautomatic":11353,"Ġfer":11354,"vernment":11355,"ĠKhan":11356,"TON":11357,"father":11358,"Ġextraordinary":11359,"throp":11360,"ĠPython":11361,"ĠGPU":11362,"Ġsexually":11363,"Ġdesktop":11364,"itivity":11365,"ĠAntonio":11366,"Ġorient":11367,"Ġears":11368,"obby":11369,"ouses":11370,"vertisements":11371,"Ġmanufacturers":11372,"icient":11373,"minute":11374,"Ġconviction":11375,"Ġgarden":11376,"public":11377,"Ġsatisfied":11378,"fold":11379,"OK":11380,"Ġinhab":11381,"ĠThink":11382,"Ġprogramme":11383,"Ġstomach":11384,"Ġcoordin":11385,"Ġholy":11386,"Ġthreshold":11387,"Ġrhet":11388,"Ġserial":11389,"Ġemployers":11390,"ĠEverything":11391,"rah":11392,"Ġbother":11393,"Ġbrands":11394,"Value":11395,"ĠTed":11396,"ĠPlanet":11397,"Ġpink":11398,"ĠFurthermore":11399,"sa":11400,"PE":11401,"reck":11402,"ĠUSD":11403,"otte":11404,"Ġ&&":11405,"Ġlanded":11406,"gets":11407,"Ġproducers":11408,"Ġhealthcare":11409,"Ġdominant":11410,"Ġdestro":11411,"Ġamended":11412,"chron":11413,"Ġfits":11414,"ĠSyd":11415,"ĠAuthority":11416,"ATCH":11417,"Ġfights":11418,"ĠLLC":11419,"Ġ---":11420,"ĠCorp":11421,"Ġtoxic":11422,"specific":11423,"ĠCorn":11424,"ĠChel":11425,"Ġtelephone":11426,"ĠPant":11427,"Ġmysterious":11428,"aunch":11429,"odox":11430,"media":11431,"Ġwitnesses":11432,"agu":11433,"Ġquestioned":11434,"ĠBrexit":11435,"ĠRemember":11436,"enez":11437,"Ġendorse":11438,"iatric":11439,"ĠIdent":11440,"Ġridiculous":11441,"110":11442,"Ġprayer":11443,"Ġscientist":11444,"Ġ1950":11445,"ĠAqu":11446,"Ġunderground":11447,"ĠUFC":11448,"mare":11449,"ĠLater":11450,"wich":11451,"Ġsubscrib":11452,"Ġhosts":11453,"Ġerr":11454,"Ġgrants":11455,"antom":11456,"Ġsummon":11457,"early":11458,"ĠClear":11459,"ĠPrim":11460,"Ġsuspension":11461,"Ġguaranteed":11462,"apper":11463,"Ġrice":11464,"ĠSean":11465,"ĠShin":11466,"Ġreferendum":11467,"Ġfled":11468,"rust":11469,"Ġ360":11470,"tery":11471,"Ġshocked":11472,"BR":11473,"ĠOil":11474,"ĠAllah":11475,"Ġpartly":11476,"Ġignor":11477,"Ġtransmission":11478,"Ġhomosexual":11479,"iversal":11480,"Ġhopefully":11481,"ãĤ¤":11482,"Ġlesson":11483,"Leg":11484,"Ġ..":11485,"Yet":11486,"table":11487,"appropri":11488,"rett":11489,"Ġboards":11490,"Ġincorrect":11491,"Ġbacteria":11492,"aru":11493,"amac":11494,"Ġsnap":11495,".'\"":11496,"Ġparad":11497,"tem":11498,"heart":11499,"Ġavailability":11500,"Ġwisdom":11501,"Ġ(+":11502,"Ġpriest":11503,"ĠÂłĠÂł":11504,"Open":11505,"Ġspan":11506,"Ġparameter":11507,"Ġconvince":11508,"Ġ(%)":11509,"rac":11510,"Ġfo":11511,"Ġsafely":11512,"Ġconverted":11513,"ĠOlympic":11514,"Ġreserve":11515,"Ġhealing":11516,"ĠMine":11517,"Max":11518,"Ġinherent":11519,"ĠGraham":11520,"Ġintegrated":11521,"Dem":11522,"Ġpipeline":11523,"Ġapplying":11524,"Ġembed":11525,"ĠCharlie":11526,"Ġcave":11527,"2008":11528,"Ġconsensus":11529,"Ġrewards":11530,"Pal":11531,"ĠHTML":11532,"Ġpopularity":11533,"looking":11534,"ĠSword":11535,"ĠArts":11536,"')":11537,"Ġelectron":11538,"clusions":11539,"Ġintegrity":11540,"Ġexclusively":11541,"Ġgrace":11542,"Ġtorture":11543,"Ġburned":11544,"two":11545,"Ġ180":11546,"Produ":11547,"Ġentreprene":11548,"raphics":11549,"Ġgym":11550,"ricane":11551,"ĠTam":11552,"Ġadministrative":11553,"Ġmanufacturer":11554,"Ġvel":11555,"ĠNi":11556,"Ġisolated":11557,"ĠMedicine":11558,"Ġbackup":11559,"Ġpromoting":11560,"Ġcommander":11561,"Ġflee":11562,"ĠRussell":11563,"Ġforgotten":11564,"ĠMissouri":11565,"Ġresidence":11566,"mons":11567,"Ġresemb":11568,"Ġwand":11569,"Ġmeaningful":11570,"PT":11571,"Ġbol":11572,"Ġhelic":11573,"Ġwealthy":11574,"Ġrifle":11575,"strong":11576,"rowing":11577,"plan":11578,"asury":11579,"âĢ¦.":11580,"Ġexpanding":11581,"ĠHamilton":11582,"Ġreceives":11583,"SI":11584,"eatures":11585,"ĠAnim":11586,"REE":11587,"Put":11588,"Ġbriefly":11589,"rive":11590,"Ġstimul":11591,"Ġ``(":11592,"Ġ__":11593,"Ġchip":11594,"Ġhaz":11595,"Ġprize":11596,"ĠThings":11597,"ACE":11598,"ulin":11599,"dict":11600,"oku":11601,"Ġassociate":11602,"ockets":11603,"youtube":11604,"Story":11605,"ategory":11606,"Ġmild":11607,"ailing":11608,"ĠYe":11609,"Orig":11610,"ĠKa":11611,"orig":11612,"Ġpropaganda":11613,"Ġanonymous":11614,"Ġstruggled":11615,"Ġoutrage":11616,"ATED":11617,"ĠBeijing":11618,"rary":11619,"Ġleather":11620,"Ġworlds":11621,"Ġbroader":11622,"125":11623,"idal":11624,"ĠBetter":11625,"Ġtear":11626,"Ext":11627,"Ġproposals":11628,"Ġiter":11629,"ĠSquad":11630,"Ġvolunt":11631,"mi":11632,"Did":11633,"ĠPu":11634,"pin":11635,"Ġspeakers":11636,"Ġborders":11637,"Ġfigured":11638,"='":11639,"Ġsimultaneously":11640,"aeda":11641,"Ġcharging":11642,"Ġurged":11643,"Ġconj":11644,"256":11645,"ĠGordon":11646,"merce":11647,"Ġdocumentary":11648,"Share":11649,"itol":11650,"ONE":11651,"ĠGarden":11652,"hatt":11653,"ĠThompson":11654,"aneous":11655,"apore":11656,"Ġtanks":11657,"Ġlessons":11658,"track":11659,"Ġoutstanding":11660,"Ġvolunteers":11661,"Ġspray":11662,"Ġmanagers":11663,"large":11664,"Ġcamps":11665,"Ġartificial":11666,"ĠRu":11667,"Ġbags":11668,"thal":11669,"Ġcompatible":11670,"ĠBlade":11671,"Ġfed":11672,"Ġargues":11673,"FI":11674,"Ġunfair":11675,"Ġcorn":11676,"Ġoffset":11677,"Ġdirections":11678,"Ġdisappointed":11679,"ĠConvention":11680,"Ġviewing":11681,"ME":11682,"ocity":11683,"Ġtowns":11684,"Ġlayers":11685,"Ġrolled":11686,"Ġjumped":11687,"Ġattribute":11688,"Ġunnecess":11689,"incoln":11690,"Ġsuppose":11691,"ĠNether":11692,"cha":11693,"Ġburied":11694,"Ġsixth":11695,"Ben":11696,"ressing":11697,"OUR":11698,"Ġwound":11699,"Ġcycl":11700,"Ġmechanisms":11701,"Ġcongressional":11702,"ĠElement":11703,"Ġagreements":11704,"Ġdecor":11705,"Ġclosest":11706,"ĠMit":11707,"Google":11708,"}}":11709,"Ġmixture":11710,"Ġfluid":11711,"Sign":11712,"ĠScholar":11713,"Ġpist":11714,"asket":11715,"abling":11716,"Ġracing":11717,"hero":11718,"riel":11719,"assy":11720,"Ġcheaper":11721,"ben":11722,"Ġvertical":11723,"amacare":11724,"ĠReading":11725,"gments":11726,"Ġhelicop":11727,"Ġsacrifice":11728,"aya":11729,"paren":11730,"VA":11731,"ĠLes":11732,"ĠStudio":11733,"Ġviolations":11734,"ĠAnna":11735,"acer":11736,"é¾":11737,"ĠRat":11738,"ĠBeck":11739,"ĠDick":11740,"ĠACT":11741,"Ġcomposition":11742,"Ġtexture":11743,"ĠOwn":11744,"Ġsmartphone":11745,"ĠNA":11746,"Ġforb":11747,"import":11748,"Ġdefending":11749,"ilst":11750,"rer":11751,"Ġoh":11752,"ĠJeremy":11753,"Ġbanking":11754,"ceptions":11755,"Ġrespective":11756,"/.":11757,"Ġdrinks":11758,"ĠWi":11759,"Ġbands":11760,"ĠLiverpool":11761,"Ġgrip":11762,"ĠBuy":11763,"Ġopenly":11764,"Ġreviewed":11765,"pert":11766,"Ġverify":11767,"ĠCole":11768,"ĠWales":11769,"MO":11770,"Ġunpre":11771,"Ġshelter":11772,"ĠImperial":11773,"Ġgui":11774,"ĠDak":11775,"Ġsuggestions":11776,"Ġexplicitly":11777,"Ġslave":11778,"Ġblockchain":11779,"Ġcompeting":11780,"Ġpromising":11781,"SON":11782,"Ġsoccer":11783,"Ġconstitution":11784,"429":11785,"Ġdistract":11786,"ĠUser":11787,"esides":11788,"ĠMethod":11789,"ĠTokyo":11790,"Ġaccompanied":11791,"Client":11792,"sur":11793,"alog":11794,"Ġidentification":11795,"Ġinvasion":11796,"asma":11797,"Ġindustries":11798,"ppers":11799,"Ġsubtle":11800,"ĠUnit":11801,"natural":11802,"Ġsurvived":11803,"Ġflaw":11804,"ĺħ":11805,"ĠHoll":11806,"Ġdeficit":11807,"Ġtutorial":11808,"ĠChance":11809,"Ġarguing":11810,"Ġcontemporary":11811,"Ġintegration":11812,"forward":11813,"Ġtum":11814,"itis":11815,"Ġhiding":11816,"ĠDomin":11817,"ĠTan":11818,"ĠBuilding":11819,"ĠVin":11820,"Ġspokesperson":11821,"ĠNotes":11822,"Ġemerging":11823,"Ġpreparation":11824,"Ġprost":11825,"Ġsuspects":11826,"Ġautonom":11827,"Description":11828,"Ġdealt":11829,"ĠPear":11830,"Ġsteady":11831,"Ġdecreased":11832,"Ġsovere":11833,"ĠClin":11834,"Ġgradually":11835,"orses":11836,"ĠWAR":11837,"Serv":11838,"ãĤ¢":11839,"hr":11840,"Ġdirty":11841,"ĠBarn":11842,"ĠBC":11843,"Ġdil":11844,"Ġcalendar":11845,"Ġcompliance":11846,"Ġchamber":11847,"bb":11848,"Ġpassenger":11849,"ateful":11850,"ĠTitle":11851,"ĠSydney":11852,"ĠGot":11853,"Ġdarkness":11854,"Ġdefect":11855,"Ġpacked":11856,"assion":11857,"Ġgods":11858,"Ġharsh":11859,"ICK":11860,"leans":11861,"Ġalgorithm":11862,"Ġoxygen":11863,"Ġvisits":11864,"Ġblade":11865,"Ġkilomet":11866,"ĠKentucky":11867,"Ġkiller":11868,"Pack":11869,"enny":11870,"Ġdivine":11871,"Ġnomination":11872,"being":11873,"Ġengines":11874,"Ġcats":11875,"Ġbuffer":11876,"ĠPhill":11877,"Ġtraff":11878,"AGE":11879,"Ġtongue":11880,"Ġradiation":11881,"erer":11882,"mem":11883,"ĠExplicit":11884,"é¾į":11885,"Ġcouples":11886,"Ġphysics":11887,"ĠMcK":11888,"Ġpolitically":11889,"awks":11890,"ĠBloom":11891,"Ġworship":11892,"eger":11893,"uter":11894,"ĠFO":11895,"Ġmathemat":11896,"Ġsentenced":11897,"Ġdisk":11898,"ĠMarg":11899,"Ġ/*":11900,"PI":11901,"Ġoptional":11902,"Ġbabies":11903,"Ġseeds":11904,"ĠScottish":11905,"Ġthy":11906,"]]":11907,"ĠHitler":11908,"PH":11909,"ngth":11910,"Ġrecovered":11911,"inge":11912,"Ġpowder":11913,"Ġlips":11914,"Ġdesigner":11915,"Ġdisorders":11916,"Ġcourage":11917,"Ġchaos":11918,"\"},{\"":11919,"Ġcarrier":11920,"bably":11921,"High":11922,"ĠRT":11923,"esity":11924,"len":11925,"Ġroutes":11926,"uating":11927,"Fil":11928,"NOT":11929,"wall":11930,"sburgh":11931,"Ġengaging":11932,"ĠJavaScript":11933,"orer":11934,"lihood":11935,"Ġunions":11936,"ĠFederation":11937,"ĠTesla":11938,"Ġcompletion":11939,"ĠTa":11940,"Ġprivilege":11941,"ĠOrange":11942,"Ġneur":11943,"parency":11944,"Ġbones":11945,"Ġtitled":11946,"Ġprosecutors":11947,"ĠME":11948,"Ġengineer":11949,"ĠUniverse":11950,"ĠHig":11951,"nie":11952,"oard":11953,"Ġhearts":11954,"ĠGre":11955,"ussion":11956,"Ġministry":11957,"Ġpenet":11958,"ĠNut":11959,"ĠOw":11960,"ĠXP":11961,"instein":11962,"Ġbulk":11963,"System":11964,"icism":11965,"ĠMarketable":11966,"Ġpreval":11967,"Ġposter":11968,"Ġattending":11969,"urable":11970,"Ġlicensed":11971,"ĠGh":11972,"etry":11973,"ĠTradable":11974,"Ġblast":11975,"à¤":11976,"ĠTitan":11977,"elled":11978,"die":11979,"Have":11980,"ĠFlame":11981,"Ġprofound":11982,"Ġparticipating":11983,"Ġanime":11984,"ĠEss":11985,"Ġspecify":11986,"Ġregarded":11987,"ĠSpell":11988,"Ġsons":11989,"owned":11990,"Ġmerc":11991,"Ġexperimental":11992,"lando":11993,"hs":11994,"ĠDungeon":11995,"inos":11996,"Ġcomply":11997,"ĠSystems":11998,"arth":11999,"Ġseized":12000,"local":12001,"ĠGirls":12002,"udo":12003,"oned":12004,"ĠFle":12005,"Ġconstructed":12006,"Ġhosted":12007,"Ġscared":12008,"actic":12009,"ĠIslands":12010,"ĠMORE":12011,"Ġbless":12012,"Ġblocking":12013,"Ġchips":12014,"Ġevac":12015,"Ps":12016,"Ġcorporation":12017,"Ġox":12018,"Ġlighting":12019,"Ġneighbors":12020,"ĠUb":12021,"aro":12022,"Ġbeef":12023,"ĠUber":12024,"Facebook":12025,"armed":12026,"itate":12027,"ĠRating":12028,"ĠQuick":12029,"Ġoccupied":12030,"Ġaims":12031,"ĠAdditionally":12032,"ĠInterest":12033,"Ġdramatically":12034,"Ġheal":12035,"Ġpainting":12036,"Ġengineers":12037,"MM":12038,"ĠMust":12039,"Ġquantity":12040,"Paul":12041,"Ġearnings":12042,"ĠPosts":12043,"stra":12044,"ãĥ¼ãĥ":12045,"Ġstance":12046,"Ġdropping":12047,"script":12048,"Ġdressed":12049,"Make":12050,"Ġjustify":12051,"ĠLtd":12052,"Ġprompted":12053,"Ġscrut":12054,"Ġspeeds":12055,"ĠGiants":12056,"omer":12057,"ĠEditor":12058,"Ġdescribing":12059,"ĠLie":12060,"mented":12061,"Ġnowhere":12062,"ocaly":12063,"Ġinstruction":12064,"fortable":12065,"Ġentities":12066,"Ġcm":12067,"ĠNatural":12068,"Ġinquiry":12069,"Ġpressed":12070,"izont":12071,"forced":12072,"Ġraises":12073,"ĠNetflix":12074,"ĠSide":12075,"Ġouter":12076,"Ġamongst":12077,"ims":12078,"owski":12079,"Ġclimb":12080,"never":12081,"Ġcombine":12082,"ding":12083,"Ġcompr":12084,"Ġsignificance":12085,"Ġremembered":12086,"ĠNevada":12087,"ĠTel":12088,"ĠScar":12089,"ĠWarriors":12090,"ĠJane":12091,"Ġcoup":12092,"bas":12093,"Ġterminal":12094,",-":12095,"OH":12096,"Ġtension":12097,"Ġwings":12098,"ĠMyster":12099,"����":12100,"ĠUnlike":12101,"valid":12102,"vironments":12103,"ĠAli":12104,"Ġnaked":12105,"books":12106,"ĠMun":12107,"ĠGulf":12108,"Ġdensity":12109,"Ġdimin":12110,"Ġdesperate":12111,"Ġpresidency":12112,"Ġ1986":12113,"hy":12114,"IND":12115,"Ġunlock":12116,"imens":12117,"Ġhandled":12118,"ĠEb":12119,"Ġdisappeared":12120,"Ġgenre":12121,"Ġ1988":12122,"Ġdetermination":12123,"Stream":12124,"iko":12125,"apters":12126,"Ġacknowledge":12127,"Jan":12128,"Ġcapitalism":12129,"Pat":12130,"Ġ2020":12131,"Ġpainful":12132,"Ġcurve":12133,"Ġbombs":12134,"storm":12135,"ĠMetal":12136,"encer":12137,"ĠFig":12138,"ĠAaron":12139,"anches":12140,"Ġinspiration":12141,"Ġexhaust":12142,"tains":12143,"ashi":12144,"Ġdescript":12145,"Ġritual":12146,"ĠChelsea":12147,"Ġpromotion":12148,"ĠHung":12149,"ĠWard":12150,"iva":12151,"ĠET":12152,"Ġtoss":12153,"allow":12154,"ĠFrancis":12155,"Dep":12156,"Ġhappiness":12157,"ĠGlass":12158,"Ġbeta":12159,"Ġstrengthen":12160,"NE":12161,"oa":12162,"Ġbuttons":12163,"ĠMurray":12164,"Ġkicked":12165,"Quest":12166,"ĠTalk":12167,"ĠSeveral":12168,"ĠZero":12169,"Ġdrone":12170,"ulk":12171,"Ġcam":12172,"ĠMobile":12173,"Ġpreventing":12174,"Ġretro":12175,"ĠAx":12176,"Ġcruel":12177,"Ġfloat":12178,".),":12179,"Ġfiling":12180,"ĠGrant":12181,"ĠBor":12182,"Ġrib":12183,"Ġchampionship":12184,"ĠMerc":12185,"Ġstyles":12186,"Ġcake":12187,"Ġbuilds":12188,"ĠSelf":12189,"iox":12190,"Ġepic":12191,"oyd":12192,"Bel":12193,"ĠStew":12194,".(":12195,"ahu":12196,"ĠBeyond":12197,"Ġouts":12198,"Ġsolo":12199,"ĠTree":12200,"Ġpreserve":12201,"Ġtub":12202,"ARE":12203,"roc":12204,"ĠImpro":12205,"ĠWright":12206,"Ġbund":12207,"Ġtraged":12208,"Ġoccasional":12209,"bian":12210,"Second":12211,"rons":12212,"Ġinteractions":12213,"formed":12214,"sing":12215,"Ġowns":12216,"Ġhockey":12217,"General":12218,"Ġlogical":12219,"Ġexpend":12220,"Ġescal":12221,"ĠGriff":12222,"ĠCrown":12223,"ĠReserve":12224,"Ġstopping":12225,"Ġexcuse":12226,"second":12227,"Ġoperated":12228,"Ġreaches":12229,"ĠMalays":12230,"Ġpollution":12231,"ĠBrooklyn":12232,"Ġdelete":12233,"Ġhash":12234,"Block":12235,"aha":12236,"âĢ³":12237,"Ġshorter":12238,"piece":12239,">>>":13163,"ĠMormon":13164,"tor":13165,"Ġparticles":13166,"ĠBart":13167,"ryption":13168,"Ġadmin":13169,"Ġsquee":13170,"VIDIA":13171,"Ġcreator":13172,"iameter":13173,"icular":13174,"NBC":13175,"Ġgrabbed":13176,"Ġnodd":13177,"Ġrated":13178,"Ġrotation":13179,"Ġgrasp":13180,"Ġexcessive":13181,"ĠEC":13182,"ĠWhit":13183,"Ġinventory":13184,"aults":13185,"ĠFB":13186,"Ġecosystem":13187,"Ġbillions":13188,"Ġventure":13189,"named":13190,"Ġdefender":13191,"oute":13192,"Instead":13193,"irable":13194,"War":13195,"Ġassumption":13196,"Ġbite":13197,"Ġearthqu":13198,"tail":13199,"space":13200,"Ġgifts":13201,"boys":13202,"Ġinevitable":13203,"Ġstructural":13204,"Ġbeneficial":13205,"Ġcompelling":13206,"hole":13207,"ervation":13208,"Ġcoat":13209,"oj":13210,"incarn":13211,"ĠYears":13212,"Ġdetermining":13213,"Ġrhetoric":13214,"Ġboundaries":13215,"Ġwhites":13216,"Ant":13217,"addy":13218,")-":13219,"raham":13220,"etermin":13221,"Ġharvest":13222,"ĠConc":13223,"Ġlaptop":13224,"ĠMatch":13225,"Ġenjoying":13226,"cca":13227,"ollar":13228,"Ġtrips":13229,"Ġaddiction":13230,"ĠSak":13231,"Ġpowered":13232,"Ġcous":13233,"ĠRussians":13234,"iere":13235,"Ġretrie":13236,"quality":13237,"Ġdiffer":13238,"Ġkingdom":13239,"ĠLaur":13240,"ĠCapitol":13241,"Ġconclusions":13242,"ĠAltern":13243,"ĠNav":13244,"Ġtransparent":13245,"BER":13246,"Group":13247,"ĠComplete":13248,"Ġinfer":13249,"Ġintrig":13250,"Ġinsane":13251,"RO":13252,"ophob":13253,"isen":13254,"qual":13255,"Michael":13256,"Ġmuseum":13257,"ĠPope":13258,"Ġreset":13259,"rative":13260,"five":13261,"Ġaggreg":13262,"ittees":13263,"ository":13264,"Ġcarb":13265,"ĠRecord":13266,"Ġdecides":13267,"ĠFix":13268,"Ġexceptions":13269,"ĠCommissioner":13270,"uns":13271,"ĠEnvironmental":13272,"Ġlegendary":13273,"istence":13274,"Ġtunnel":13275,"km":13276,"Ġinsult":13277,"Ġtroll":13278,"Ġshake":13279,"Ġdetention":13280,"ques":13281,"ĠChrome":13282,"ĠFiles":13283,"Ġsubt":13284,"Ġprospects":13285,"Ġprol":13286,"render":13287,"proof":13288,"Ġperformances":13289,"Str":13290,"Ġhref":13291,"ername":13292,"Ġachievement":13293,"Ġfut":13294,"Full":13295,"ĠLeban":13296,"google":13297,"ãĥĪ":13298,"ampa":13299,"Maybe":13300,"Ġprojected":13301,"ĠEmb":13302,"Ġcolleg":13303,"Ġawards":13304,"ĠâĶ":13305,"Gold":13306,"ĠBlake":13307,"ĠRaj":13308,"ifting":13309,"Ġpending":13310,"Ġinstinct":13311,"Ġdevelopments":13312,"Connect":13313,"ĠMand":13314,"ĠWITH":13315,"ĠPhilippines":13316,"profile":13317,"Ġaltogether":13318,"ĠBund":13319,"ĠTD":13320,"oooo":13321,"amped":13322,"iph":13323,"Ġsteam":13324,"Ġoldest":13325,"Ġdetection":13326,"ulpt":13327,"Ġç":13328,"ĠWayne":13329,"2006":13330,"fa":13331,"Ġcircles":13332,"ĠFu":13333,"Ġdonors":13334,"appropriate":13335,"ĠDakota":13336,"jamin":13337,"Ġmotivated":13338,"Ġpurchases":13339,"ĠLouisiana":13340,"ĠSpl":13341,"Ġglobe":13342,"Ġ105":13343,"zip":13344,"call":13345,"Ġdepartments":13346,"Ġsustainable":13347,"105":13348,"ĠOP":13349,"ifiers":13350,"Ġprevented":13351,"Ġincomp":13352,"ĠCommander":13353,"Ġdominated":13354,"Ġ»":13355,"Ġinvested":13356,"Ġcomplexity":13357,"Ġincl":13358,"Ġensuring":13359,"Ġrealm":13360,"ync":13361,"ĠIndependent":13362,"rained":13363,"ĠJen":13364,"ĠFlight":13365,"Ġathe":13366,"Ġspeculation":13367,"ĠTE":13368,"ocate":13369,"tic":13370,"Ġplaint":13371,"herry":13372,"Ġtoy":13373,"Ġ111":13374,"Ġplates":13375,"status":13376,"ĠIsa":13377,"Ġdevoted":13378,"Cop":13379,"ĠES":13380,"255":13381,"urrency":13382,"Main":13383,"Ġslaves":13384,"Ġpepper":13385,"Ġquotes":13386,"Ġceiling":13387,"ĠFish":13388,"Ġtransformation":13389,"Ġfraction":13390,"Ġadvantages":13391,"Ġtoile":13392,"Ġstunning":13393,"Ġmoist":13394,"breaking":13395,"si":13396,"ĠLocation":13397,"ĠMedium":13398,"Ġtexts":13399,"Ġugly":13400,"Ġbio":13401,".âĢĶ":13402,"ĠBased":13403,"Ġtrains":13404,"ĠWing":13405,"ĠAncient":13406,"ĠRecords":13407,"ĠHope":13408,"Special":13409,"adesh":13410,"obi":13411,"[/":13412,"Ġtemporarily":13413,"Ver":13414,"hu":13415,"oser":13416,"Ġovernight":13417,"Ġmamm":13418,"ĠTreasury":13419,"ĠVenezuel":13420,"ĠMega":13421,"Ġtar":13422,"Ġexpects":13423,"black":13424,"orph":13425,"\\\\\\\\":13426,"Ġacceptance":13427,"Ġradar":13428,"sis":13429,"Ġjunior":13430,"Ġframes":13431,"Ġobservation":13432,"acies":13433,"Power":13434,"ĠAdvanced":13435,"Mag":13436,"ologically":13437,"ĠMechan":13438,"Ġsentences":13439,"Ġanalysts":13440,"aughters":13441,"forcement":13442,"Ġvague":13443,"Ġclause":13444,"Ġdirectors":13445,"Ġevaluate":13446,"Ġcabinet":13447,"Matt":13448,"ĠClassic":13449,"Ang":13450,"Ġcler":13451,"ĠBuck":13452,"Ġresearcher":13453,"Ġ160":13454,"Ġpoorly":13455,"Ġexperiencing":13456,"ĠPed":13457,"ĠManhattan":13458,"Ġfreed":13459,"Ġthemes":13460,"advant":13461,"Ġnin":13462,"Ġpraise":13463,"104":13464,"ĠLibya":13465,"best":13466,"Ġtrusted":13467,"Ġcease":13468,"Ġdign":13469,"Direct":13470,"Ġbombing":13471,"Ġmigration":13472,"ĠSciences":13473,"Ġmunicipal":13474,"ĠAverage":13475,"Ġglory":13476,"Ġrevealing":13477,"Ġarena":13478,"Ġuncertainty":13479,"Ġbattlefield":13480,"iao":13481,"God":13482,"Ġcinem":13483,"rape":13484,"elle":13485,"apons":13486,"Ġlisting":13487,"Ġwaited":13488,"Ġspotted":13489,"keley":13490,"ĠAudio":13491,"eor":13492,"arding":13493,"idding":13494,"igma":13495,"ĠNeg":13496,"Ġlone":13497,"Ġ----":13498,"exe":13499,"deg":13500,"Ġtransf":13501,"Ġwash":13502,"Ġslavery":13503,"Ġexploring":13504,"ĠWW":13505,"atson":13506,"Ġencl":13507,"lies":13508,"ĠCreek":13509,"Ġwooden":13510,"Manager":13511,"ĠBrand":13512,"ummy":13513,"ĠArthur":13514,"Ġbureaucr":13515,"Ġblend":13516,"arians":13517,"Further":13518,"Ġsupposedly":13519,"Ġwinds":13520,"Ġ1979":13521,"Ġgravity":13522,"Ġanalyses":13523,"ĠTravel":13524,"ĠVeter":13525,"Ġdumb":13526,"Ġalternate":13527,"gal":13528,"Ġconsumed":13529,"Ġeffectiveness":13530,".''":13531,"Ġpaths":13532,"onda":13533,"LA":13534,"ĠStrong":13535,"Ġenables":13536,"Ġescaped":13537,"Ġ\"\"":13538,"Ġ112":13539,"Ġ1983":13540,"Ġsmiled":13541,"Ġtendency":13542,"Fire":13543,"Ġpars":13544,"ĠRoc":13545,"Ġlake":13546,"Ġfitness":13547,"ĠAth":13548,"ĠHorn":13549,"Ġhier":13550,"Ġimpose":13551,"mother":13552,"Ġpension":13553,"icut":13554,"borne":13555,"iciary":13556,"._":13557,"ĠSU":13558,"Ġpolar":13559,"isy":13560,"engu":13561,"itialized":13562,"ATA":13563,"write":13564,"Ġexercises":13565,"ĠDiamond":13566,"otypes":13567,"Ġharmful":13568,"onz":13569,"Ġprinting":13570,"story":13571,"Ġexpertise":13572,"ĠGer":13573,"Ġtragedy":13574,"ĠFly":13575,"Ġdivid":13576,"ampire":13577,"stock":13578,"Mem":13579,"Ġreign":13580,"Ġunve":13581,"Ġamend":13582,"ĠProphet":13583,"Ġmutual":13584,"ĠFac":13585,"Ġreplacing":13586,"Har":13587,"ĠCircuit":13588,"Ġthroat":13589,"ĠShot":13590,"Ġbatteries":13591,"Ġtoll":13592,"Ġaddressing":13593,"ĠMedicaid":13594,"Ġpupp":13595,"ĠNar":13596,"olk":13597,"Ġequity":13598,"MR":13599,"ĠHispan":13600,"ĠLarge":13601,"mid":13602,"Dev":13603,"Ġexped":13604,"Ġdemo":13605,"ĠMarshall":13606,"ergus":13607,"Ġfiber":13608,"Ġdivorce":13609,"ĠCreate":13610,"Ġslower":13611,"ĠParker":13612,"ĠStudent":13613,"ĠTraining":13614,"Return":13615,"ĠTru":13616,"Ġcub":13617,"ĠReached":13618,"Ġpanic":13619,"Ġquarters":13620,"Ġrect":13621,"Ġtreating":13622,"Ġrats":13623,"ĠChristianity":13624,"oler":13625,"Ġsacred":13626,"Ġdeclare":13627,"ulative":13628,"eting":13629,"Ġdelivering":13630,"estone":13631,"Ġtel":13632,"ĠLarry":13633,"Ġmeta":13634,"accept":13635,"artz":13636,"ĠRoger":13637,"handed":13638,"Ġheader":13639,"Ġtrapped":13640,"ĠCentury":13641,"Ġknocked":13642,"ĠOxford":13643,"Ġsurvivors":13644,"bot":13645,"Ġdemonstration":13646,"Ġdirt":13647,"Ġassists":13648,"OME":13649,"ĠDraft":13650,"ortunate":13651,"folio":13652,"pered":13653,"usters":13654,"gt":13655,"ĠLock":13656,"Ġjudicial":13657,"verted":13658,"Ġsecured":13659,"outing":13660,"ĠBooks":13661,"Ġhosting":13662,"Ġlifted":13663,"length":13664,"Ġjer":13665,"Ġwheels":13666,"ĠRange":13667,"umbnails":13668,"Ġdiagnosis":13669,"tech":13670,"ĠStewart":13671,"ĠPract":13672,"Ġnationwide":13673,"Ġdear":13674,"Ġobligations":13675,"Ġgrows":13676,"Ġmandatory":13677,"Ġsuspicious":13678,"!'":13679,"Apr":13680,"Great":13681,"Ġmortgage":13682,"Ġprosecutor":13683,"Ġeditorial":13684,"ĠKr":13685,"Ġprocessed":13686,"ungle":13687,"Ġflexibility":13688,"Earlier":13689,"ĠCart":13690,"ĠSug":13691,"Ġfocuses":13692,"Ġstartup":13693,"Ġbreach":13694,"ĠTob":13695,"cycle":13696,"ãĢĮ":13697,"rose":13698,"Ġbizarre":13699,"ãĢį":13700,"Ġvegetables":13701,"$$":13702,"Ġretreat":13703,"oshi":13704,"ĠShop":13705,"ĠGround":13706,"ĠStop":13707,"ĠHawaii":13708,"ĠAy":13709,"Perhaps":13710,"ĠBeaut":13711,"uffer":13712,"enna":13713,"Ġproductivity":13714,"Fixed":13715,"control":13716,"Ġabsent":13717,"ĠCampaign":13718,"Green":13719,"Ġidentifying":13720,"Ġregret":13721,"Ġpromoted":13722,"ĠSeven":13723,"Ġeru":13724,"neath":13725,"aughed":13726,"ĠPin":13727,"ĠLiving":13728,"Cost":13729,"omatic":13730,"mega":13731,"ĠNig":13732,"ocy":13733,"Ġinbox":13734,"Ġempire":13735,"Ġhorizont":13736,"Ġbranches":13737,"Ġmetaph":13738,"Active":13739,"edi":13740,"ĠFilm":13741,"ĠSomething":13742,"Ġmods":13743,"incial":13744,"ĠOriginal":13745,"Gen":13746,"Ġspirits":13747,"Ġearning":13748,"Hist":13749,"Ġriders":13750,"Ġsacrific":13751,"MT":13752,"ĠVA":13753,"ĠSalt":13754,"Ġoccupation":13755,"ĠMi":13756,"Ġdisg":13757,"lict":13758,"Ġnit":13759,"Ġnodes":13760,"eem":13761,"ĠPier":13762,"Ġhatred":13763,"psy":13764,"ãĥī":13765,"Ġtheater":13766,"Ġsophisticated":13767,"Ġdefended":13768,"Ġbesides":13769,"Ġthoroughly":13770,"ĠMedicare":13771,"Ġblamed":13772,"arently":13773,"Ġcrying":13774,"FOR":13775,"priv":13776,"Ġsinging":13777,"ĠIl":13778,"Ġcute":13779,"oided":13780,"olitical":13781,"ĠNeuro":13782,"å¤":13783,"Ġdonation":13784,"ĠEagles":13785,"ĠGive":13786,"Tom":13787,"Ġsubstantially":13788,"ĠLicense":13789,"ĠJa":13790,"Ġgrey":13791,"ĠAnimal":13792,"ĠER":13793,"ĠUnd":13794,"Ġkeen":13795,"Ġconclude":13796,"ĠMississippi":13797,"Engine":13798,"ĠStudios":13799,"Press":13800,"overs":13801,"llers":13802,"Ġ350":13803,"ĠRangers":13804,"Ġrou":13805,"erto":13806,"Ep":13807,"issa":13808,"ivan":13809,"Ġseal":13810,"ĠRegist":13811,"display":13812,"Ġweaken":13813,"uum":13814,"ĠCommons":13815,"ĠSay":13816,"Ġcultures":13817,"Ġlaughed":13818,"Ġslip":13819,"Ġtreatments":13820,"izable":13821,"mart":13822,"ĠRice":13823,"Ġbeast":13824,"Ġobesity":13825,"ĠLaure":13826,"iga":13827,"Which":13828,"holder":13829,"Ġelderly":13830,"Ġpays":13831,"Ġcomplained":13832,"Ġcrop":13833,"Ġproc":13834,"Ġexplosive":13835,"ĠFan":13836,"ĠArsenal":13837,"Author":13838,"eful":13839,"Ġmeals":13840,"Ġ(-":13841,"idays":13842,"Ġimagination":13843,"Ġannually":13844,"Ġms":13845,"asures":13846,"Head":13847,"ikh":13848,"matic":13849,"Ġboyfriend":13850,"ĠComputer":13851,"Ġbump":13852,"Ġsurge":13853,"ĠCraig":13854,"ĠKirk":13855,"Del":13856,"mediate":13857,"Ġscenarios":13858,"ĠMut":13859,"ĠStream":13860,"Ġcompetitors":13861,"ÙĦ":13862,"ĠStanford":13863,"ĠResources":13864,"azed":13865,"bage":13866,"Ġorganis":13867,"ĠRelease":13868,"Ġseparately":13869,"Ġhabits":13870,"Ġmeasurements":13871,"ĠClose":13872,"Ġaccompany":13873,"Ġgly":13874,"Ġtang":13875,"ĠRou":13876,"Ġplugin":13877,"Ġconvey":13878,"ĠChallenge":13879,"oots":13880,"jan":13881,"Ġcurs":13882,"ĠRelations":13883,"keeper":13884,"Ġapproaching":13885,"ping":13886,"Speaking":13887,"Ġarrangement":13888,"ĠVI":13889,"arettes":13890,"Ġaffecting":13891,"Ġpermits":13892,"because":13893,"Ġuseless":13894,"ĠHus":13895,"!!!!":13896,"Ġdestroying":13897,"Unfortunately":13898,"Ġfascinating":13899,"Sem":13900,"Ġelectoral":13901,"Ġtransparency":13902,"ĠChaos":13903,"Ġvolunteer":13904,"Ġstatistical":13905,"Ġactivated":13906,"rox":13907,"Web":13908,"HE":13909,"ĠHampshire":13910,"isive":13911,"Map":13912,"Ġtrash":13913,"ĠLawrence":13914,"stick":13915,"Cr":13916,"Ġrings":13917,"EXT":13918,"Ġoperational":13919,"opes":13920,"Does":13921,"ĠEvans":13922,"Ġwitnessed":13923,"Port":13924,"Ġlaunching":13925,"econom":13926,"wear":13927,"ĠParticip":13928,"umm":13929,"cules":13930,"ĠRAM":13931,"ĠTun":13932,"Ġassured":13933,"Ġbinary":13934,"Ġbetray":13935,"Ġexploration":13936,"ĠFel":13937,"Ġadmission":13938,"itated":13939,"Sy":13940,"Ġavoided":13941,"ĠSimulator":13942,"Ġcelebrated":13943,"ĠElectric":13944,"¥ŀ":13945,"Ġcluster":13946,"itzerland":13947,"health":13948,"Line":13949,"ĠNash":13950,"aton":13951,"Ġspare":13952,"Ġenterprise":13953,"ĠDIS":13954,"cludes":13955,"Ġflights":13956,"Ġregards":13957,"ĠÃĹ":13958,"half":13959,"Ġtrucks":13960,"Ġcontacts":13961,"Ġuncons":13962,"ĠClimate":13963,"Ġimmense":13964,"NEW":13965,"occ":13966,"ective":13967,"Ġembod":13968,"Ġpatrol":13969,"Ġbeside":13970,"Ġviable":13971,"Ġcreep":13972,"Ġtriggered":13973,"verning":13974,"Ġcomparable":13975,"ql":13976,"Ġgaining":13977,"asses":13978,"Ġ();":13979,"ĠGrey":13980,"ĠMLS":13981,"sized":13982,"Ġprosper":13983,"\"?":13984,"Ġpolling":13985,"Ġshar":13986,"ĠRC":13987,"Ġfirearm":13988,"orient":13989,"Ġfence":13990,"Ġvariations":13991,"giving":13992,"ĠPi":13993,"ospel":13994,"Ġpledge":13995,"Ġcure":13996,"Ġspy":13997,"Ġviolated":13998,"Ġrushed":13999,"Ġstroke":14000,"ĠBlog":14001,"sels":14002,"ĠEc":14003,",''":14004,"Ġpale":14005,"ĠCollins":14006,"terror":14007,"ĠCanadians":14008,"Ġtune":14009,"Ġlaboratory":14010,"Ġnons":14011,"tarian":14012,"Ġdisability":14013,"ĠGam":14014,"Ġsinger":14015,"alg":14016,"ĠSenior":14017,"Ġtraded":14018,"ĠWarrior":14019,"Ġinfring":14020,"ĠFranklin":14021,"Ġstrain":14022,"ĠSwedish":14023,"Ġseventh":14024,"ĠBenn":14025,"ĠTell":14026,"Ġsyndrome":14027,"Ġwondered":14028,"iden":14029,"++++":14030,"igo":14031,"Ġpurple":14032,"Ġjournalism":14033,"Ġrebel":14034,"Ġfu":14035,"blog":14036,"Ġinvite":14037,"rencies":14038,"ĠContact":14039,"Israel":14040,"ĠContent":14041,"Ġcheer":14042,"Ġbedroom":14043,"ĠEngineering":14044,"ĠQueens":14045,"Ġdwell":14046,"ĠPlayStation":14047,"ĠDim":14048,"ĠColon":14049,"lr":14050,"Ġoperates":14051,"Ġmotivation":14052,"USA":14053,"astered":14054,"Core":14055,"ĠTruth":14056,"olo":14057,"OSE":14058,"ĠMemory":14059,"Ġpredec":14060,"Ġanarch":14061,"Ġ1920":14062,"ĠYam":14063,"è":14064,"bid":14065,"Ġgrateful":14066,"Ġexcitement":14067,"Ġtreasure":14068,"Ġlongest":14069,"ctive":14070,"Ġdeserves":14071,"Ġreserves":14072,"Ġcops":14073,"ĠOttawa":14074,"ĠEgyptian":14075,"anked":14076,"Ġartif":14077,"Ġhypothesis":14078,":/":14079,"Ġpurchasing":14080,"Ġlovely":14081,"HP":14082,"Ġdivide":14083,"Ġstrictly":14084,"Ġquestioning":14085,"Ġtaxpayers":14086,"ĠJoy":14087,"Ġrolls":14088,"ĠHeavy":14089,"Ġports":14090,"Ġmagnetic":14091,"Ġinflamm":14092,"Ġbrush":14093,"tics":14094,"âĪĴ":14095,"Ġbottles":14096,"ppy":14097,"Ġpadd":14098,"ãĤ¯":14099,"million":14100,"Ġdevastating":14101,"Ġcompiled":14102,"Ġmedication":14103,"Ġtwelve":14104,"ĠPerry":14105,"Space":14106,"imb":14107,"your":14108,"Ġleaked":14109,"ĠTar":14110,"Ġunity":14111,"Ġinfected":14112,"Ġtraveled":14113,"IDE":14114,"ĠMcDonald":14115,"txt":14116,"ĠPrinc":14117,"Ġinterven":14118,"ĠTaiwan":14119,"ĠPow":14120,"Ġbearing":14121,"ĠThread":14122,"Ġzones":14123,"izards":14124,"unks":14125,"Chapter":14126,"llor":14127,"Ġ·":14128,"Ġwounds":14129,"Ġdiscretion":14130,"Ġsucceeded":14131,"iking":14132,"Ġiconic":14133,"Call":14134,"Ġscreening":14135,"ĠMis":14136,"icts":14137,"Ġministers":14138,"Ġseparation":14139,"Player":14140,"Ġbip":14141,"Ġbeloved":14142,"Ġcounting":14143,"ĠEye":14144,"around":14145,"inging":14146,"Ġtablet":14147,"Ġoffence":14148,"inance":14149,"have":14150,"ĠInfo":14151,"ĠNinja":14152,"Ġprotective":14153,"ĠCass":14154,"Mac":14155,"ĠQuality":14156,"North":14157,"Ġic":14158,"ĠCuba":14159,"ĠChronicle":14160,"ĠProperty":14161,"Ġfastest":14162,"otos":14163,"ĠGerm":14164,"OWN":14165,"Ġboom":14166,"ĠStanley":14167,"erguson":14168,"Ġclever":14169,"Ġenters":14170,"mode":14171,"terior":14172,"ĠSens":14173,"Ġlinear":14174,"ARK":14175,"Ġcomparing":14176,"Ġpurely":14177,"Ġsafer":14178,"ĠPotter":14179,"Ġcups":14180,"RT":14181,"Ġgluc":14182,"Ġattributed":14183,"Ġdupl":14184,"ĠPap":14185,"Ġprecious":14186,"Ġpa":14187,"ictionary":14188,"ĠTig":14189,"ĠToo":14190,"olutions":14191,"stan":14192,"Ġrobots":14193,"Ġlobb":14194,"Ġstatute":14195,"Ġprevention":14196,"western":14197,"160":14198,"ĠActive":14199,"ĠMaria":14200,"hal":14201,"None":14202,"ellar":14203,"ĠKB":14204,"ĠPartners":14205,"ĠSingle":14206,"ĠFollowing":14207,"ango":14208,"acious":14209,"Ġthou":14210,"Ġkg":14211,"Ġinfluential":14212,"ĠFriends":14213,"Sur":14214,"ainted":14215,"Ġforums":14216,"Ġstarter":14217,"Ġcitizenship":14218,"ĠElection":14219,"onge":14220,"otation":14221,"osph":14222,";;;;":14223,"utical":14224,"pur":14225,"eren":14226,"Ġaccusations":14227,"bitious":14228,"abbit":14229,"ĠOrd":14230,"Posted":14231,"irk":14232,"Ġsensitivity":14233,"iche":14234,"ĠAmy":14235,"ĠFab":14236,"Ġsummit":14237,"Ġpedest":14238,"Ġrubber":14239,"Ġagricultural":14240,"Ġcancel":14241,"AE":14242,"Ġinaug":14243,"Ġcontam":14244,"Ġfirmly":14245,"iw":14246,"stage":14247,"ĠKan":14248,"Ġtier":14249,"Ġinvention":14250,"Ġtranslated":14251,"ĠRules":14252,"Box":14253,"Twitter":14254,"IDS":14255,"Ġpizza":14256,"Ġdebug":14257,"ĠDrop":14258,"vs":14259,"Ġhorses":14260,"big":14261,"Ġboring":14262,"Ġhood":14263,"ĠMcCain":14264,"atched":14265,"ĠBros":14266,"Ġskip":14267,"Ġessay":14268,"stat":14269,"ĠLegends":14270,"Ġammunition":14271,"auc":14272,"Ġshooter":14273,"Ġunh":14274,"Ġsupplied":14275,"Ġgeneric":14276,"ĠSK":14277,"iban":14278,"yrics":14279,"Ġ255":14280,"Ġclimbing":14281,"Former":14282,"Ġflip":14283,"Ġjumping":14284,"Ġfrustration":14285,"ĠTerry":14286,"Ġneighborhoods":14287,"Ġmedian":14288,"bean":14289,"Ġbrains":14290,"Following":14291,"Ġshaped":14292,"Ġdraws":14293,"Ġaltered":14294,"Jack":14295,"Ġrecipes":14296,"Ġskilled":14297,"wealth":14298,"achi":14299,"election":14300,"Ġbehaviors":14301,"deals":14302,"ĠUntil":14303,"Fe":14304,"Ġdeclaration":14305,"marks":14306,"ĠBetween":14307,"celona":14308,"Ġreson":14309,"Ġbubble":14310,"Among":14311,"Ġimperial":14312,"GS":14313,"Ġfeminist":14314,"2005":14315,"ĠKyle":14316,"Ġaccounting":14317,"ĠTele":14318,"ĠTyr":14319,"Ġconnecting":14320,"Ġrehab":14321,"ĠPred":14322,"sim":14323,"Ġmeantime":14324,"Ġphysician":14325,"MW":14326,"ĠCampbell":14327,"ĠBrandon":14328,"Ġcontributing":14329,"ĠRule":14330,"ĠWeight":14331,"ĠNap":14332,"Ġinteractive":14333,"Ġvag":14334,"Ġhelmet":14335,"ĠComb":14336,"four":14337,"Ġshipped":14338,"Ġcompleting":14339,"ĠPD":14340,"PDATE":14341,"Ġspreading":14342,"Ġscary":14343,"erving":14344,"ĠGas":14345,"Ġfrank":14346,"school":14347,"Ġromantic":14348,"Ġstabil":14349,"Rob":14350,"Ġaccurately":14351,"Ġacute":14352,"ĠHann":14353,"Ġsymbols":14354,"Ġcivilization":14355,"ĠAW":14356,"Ġlightning":14357,"Ġconsiders":14358,"Ġvenue":14359,"Ġ×":14360,"Ġoven":14361,"ĠSF":14362,"his":14363,"Ġnu":14364,"ĠLearn":14365,"Ġpeoples":14366,"Ġstd":14367,"Ġslee":14368,"Ġslic":14369,"ĠStatistics":14370,"Ġcorners":14371,"ĠBaker":14372,"Ġ:)":14373,"mentation":14374,"olver":14375,"Ġlaughing":14376,"ĠTodd":14377,"onde":14378,"ĠHills":14379,"Ġnuts":14380,"ĠWoman":14381,"plane":14382,"Ġliver":14383,"ĠInside":14384,"Sorry":14385,"Ġagrees":14386,"Ġfundament":14387,"ĠFisher":14388,"Ġauction":14389,"Ġthreads":14390,"glas":14391,"ĠBasic":14392,"ĠNat":14393,"Ġlacking":14394,"Ġcelebration":14395,"ju":14396,"Ġsilly":14397,"Euro":14398,"Ġtatt":14399,"ighty":14400,"controlled":14401,"Test":14402,"ĠSingh":14403,"Ġrage":14404,"Ġrhyth":14405,"offic":14406,"ĠPhantom":14407,"Ġheadlines":14408,"Ġresponding":14409,"ĠMorning":14410,"Ġvitamin":14411,"Ġboots":14412,"ĠSite":14413,"alin":14414,"pi":14415,"Ġviral":14416,"ĠUC":14417,"DER":14418,"ĠSex":14419,"Ġstocks":14420,"current":14421,"Ġchurches":14422,"ĠRare":14423,"ĠMurphy":14424,"Ġdenial":14425,"ĠGaming":14426,"Ġtoug":14427,"Ġnick":14428,"Ġmakers":14429,"ĠRonald":14430,"Ġgenerous":14431,"ĠDoc":14432,"ĠMorris":14433,"Ġtransformed":14434,"ĠNormal":14435,"Ġ104":14436,"ĠKickstarter":14437,"ĠUpon":14438,"Online":14439,"ĠIRS":14440,"Ġwrap":14441,"Ġloving":14442,"Ġarrives":14443,"ĠDue":14444,"Ġheter":14445,"ĠMade":14446,"Ġrental":14447,"Ġbelongs":14448,"Ġattorneys":14449,"Ġcrops":14450,"Ġmatched":14451,"ulum":14452,"oline":14453,"109":14454,"Ġdispar":14455,"Ġbuyers":14456,"ĠCambridge":14457,"Ġethics":14458,"roups":14459,"Ġjustified":14460,"Ġmarginal":14461,"Ġrespected":14462,"winning":14463,"Ġnodded":14464,"ĠSerge":14465,"ĠFormer":14466,"Craft":14467,"################":14468,"ĠWarner":14469,"Ġdash":14470,"ete":14471,"Ġentert":14472,"ĠEscape":14473,"outheast":14474,"Ġknees":14475,"ĠBomb":14476,"Ġrug":14477,"Pass":14478,"Ġattitudes":14479,"government":14480,"ĠPrior":14481,"Ġqualities":14482,"Ġnotification":14483,"ĠPhone":14484,"lie":14485,"Ġanticipated":14486,"ĠCombat":14487,"ĠBarry":14488,"Ġ1982":14489,"Users":14490,"oner":14491,"Ġcomputing":14492,"ĠConnecticut":14493,"Ġlesser":14494,"Ġpeers":14495,"ĠCu":14496,"Ġtechnically":14497,"Ġsubmission":14498,"ĠUniversal":14499,"Ġmanually":14500,"ourge":14501,"Ġrespondents":14502,"ĠBTC":14503,"ĠHost":14504,"Ġfare":14505,"ĠBird":14506,"Ġreceipt":14507,"also":14508,"Ġjack":14509,"Ġagriculture":14510,"Ġskull":14511,"Ġ!=":14512,"Ġpassive":14513,"ĠCI":14514,"Ġsocieties":14515,"Ġreminded":14516,"Ġinterference":14517,"Buy":14518,"Ġâľ":14519,"gon":14520,"Ġscrutiny":14521,"ĠWitch":14522,"Ġconducting":14523,"Ġãĥ":14524,"Ġexchanges":14525,"ĠMitchell":14526,"Ġinhabit":14527,"Ġtwist":14528,"BD":14529,"Ġwherever":14530,"groupon":14531,"Ġjokes":14532,"ĠBenjamin":14533,"ĠRandom":14534,"frame":14535,"ĠLions":14536,"Ġhighlighted":14537,"ĠArkansas":14538,"Ent":14539,"Ġpile":14540,"Ġprelim":14541,"gs":14542,"minded":14543,"Ġfelony":14544,"ĠGA":14545,"ĠLuck":14546,"Ġpractically":14547,"ĠBos":14548,"Ġactress":14549,"Dam":14550,"ĠBou":14551,"Ġvisa":14552,"Ġembedded":14553,"Ġhybrid":14554,"Ġearliest":14555,"Ġsooner":14556,"social":14557,"ĠHA":14558,"Ġsteep":14559,"Ġdisadvant":14560,"Ġexploit":14561,"ĠEgg":14562,"ĠUltra":14563,"Ġnecessity":14564,"Local":14565,"iege":14566,"Ġdated":14567,"Ġmasses":14568,"Ġsubscription":14569,"pless":14570,"Ġanonym":14571,"Ġpresumably":14572,"Blue":14573,"Their":14574,"asketball":14575,"ĠPhilip":14576,"Ġcomed":14577,"loaded":14578,"rane":14579,"Ġreflection":14580,"China":14581,"Ġextends":14582,"Ġforming":14583,"Ġunders":14584,"2001":14585,"Ġgrat":14586,"Ġconcentrations":14587,"Ġinsulin":14588,"Ġsecular":14589,"Ġwhilst":14590,"Ġwinners":14591,"Advertisements":14592,"Ġdeliberately":14593,"ĠWorking":14594,"Ġsink":14595,"etics":14596,"dale":14597,"Ġmandate":14598,"Ġgram":14599,"Ġvacation":14600,"Ġwarnings":14601,"ripp":14602,"ĠTHAT":14603,"Ġcommentary":14604,"Ġintu":14605,"Ġaest":14606,"Ġreasoning":14607,"Ġbreakdown":14608,"ĠZombie":14609,"Ġ-->":14610,"ĠPolitical":14611,"cott":14612,"Ġthrust":14613,"Ġtechnological":14614,"Ġdeciding":14615,"Ġtrafficking":14616,"Long":14617,"Welcome":14618,"prising":14619,"ĠCommunications":14620,"Ġendors":14621,"Ġswift":14622,"Ġmetabol":14623,"coins":14624,"resa":14625,"ĠHTTP":14626,"Ġenroll":14627,"ĠHappy":14628,"usr":14629,"intage":14630,"Ġ[\"":14631,"uably":14632,"ĠMaterial":14633,"Ġrepeal":14634,"Sept":14635,"kh":14636,"ĠModi":14637,"Ġunderneath":14638,"ĠIL":14639,"shore":14640,"Ġdiagnosed":14641,"aceutical":14642,"Ġshower":14643,"aux":14644,"ĠSwitch":14645,"ĠStrength":14646,"Ġjihad":14647,"national":14648,"Ġtrauma":14649,"ussy":14650,"oni":14651,"Ġconsolid":14652,"Ġcalories":14653,"ĠFlynn":14654,"agged":14655,"168":14656,"ĠPink":14657,"Ġfulfill":14658,"Ġchains":14659,"Ġnotably":14660,"ĠAV":14661,"Life":14662,"ĠChuck":14663,"mus":14664,"ĠUrban":14665,"ĠHend":14666,"Ġdeposit":14667,"ĠSad":14668,"Ġaffair":14669,"ORK":14670,"ieval":14671,"ĠFDA":14672,"Ġtrop":14673,"ĠOverall":14674,"Ġvirtue":14675,"Ġsatisfaction":14676,"aund":14677,"Ġlun":14678,"ĠSwitzerland":14679,"ĠOperation":14680,"process":14681,"Ġshook":14682,"Ġcounties":14683,"leased":14684,"ĠCharlotte":14685,"112":14686,"Ġtranscript":14687,"Ġredd":14688,"push":14689,"ĠHey":14690,"ĠAnalysis":14691,"[\"":14692,"Ġalternatives":14693,"ardless":14694,"Ġeleph":14695,"Ġprejud":14696,"ĠLeaf":14697,"Having":14698,"ĠHub":14699,"Ġexpressions":14700,"ĠVolume":14701,"Ġshocking":14702,"ĠReds":14703,"Ġreadily":14704,"Ġplanets":14705,"adata":14706,"Ġcollapsed":14707,"ĠMadrid":14708,"Ġirrit":14709,"ipper":14710,"ĠEnc":14711,"ĠWire":14712,"Ġbuzz":14713,"ĠGP":14714,"asha":14715,"Ġaccidentally":14716,"uru":14717,"Ġfrustrated":14718,"ĠSA":14719,"Ġhungry":14720,"ĠHuff":14721,"Ġlabels":14722,"anto":14723,"ĠEP":14724,"Ġbarriers":14725,")|":14726,"ĠBerkeley":14727,"ĠJets":14728,"Ġpairs":14729,"ĠLan":14730,"James":14731,"ĠBear":14732,"Ġhumor":14733,"ĠLiberty":14734,"Ġmagnitude":14735,"Ġaging":14736,"ĠMason":14737,"Ġfriendship":14738,"umbling":14739,"Ġemerge":14740,"Ġnewspapers":14741,"Ġambitious":14742,"ĠRichards":14743,"aternal":14744,"Ġ1981":14745,"Ġcookies":14746,"Ġsculpt":14747,"Ġpursuit":14748,"Location":14749,"Ġscripts":14750,"pc":14751,"Ġarrangements":14752,"Ġdiameter":14753,"Ġloses":14754,"amation":14755,"Ġliqu":14756,"ĠJake":14757,"arette":14758,"Ġunderstands":14759,"ĠZen":14760,"vm":14761,"Ġapprove":14762,"Ġwip":14763,"Ġultra":14764,"Ġintend":14765,"ĠDI":14766,"ascular":14767,"Ġstays":14768,"ĠKor":14769,"ĠKl":14770,"Ġinvesting":14771,"La":14772,"Ġbelieving":14773,"bad":14774,"mouth":14775,"Ġtaxpayer":14776,"ãĥĥ":14777,"ĠQuebec":14778,"Ġlap":14779,"ĠSwiss":14780,"drop":14781,"Ġdrain":14782,"iri":14783,"etc":14784,"ften":14785,"ĠNex":14786,"Ġstraw":14787,"Ġscreaming":14788,"Ġcounted":14789,"Ġdamaging":14790,"Ġambassador":14791,"century":14792,"Ġprox":14793,"Ġarrests":14794,"uv":14795,"ilateral":14796,"ĠCharg":14797,"Ġprescribed":14798,"Ġindependently":14799,"Ġfierce":14800,"ĠBaby":14801,"Ġbrave":14802,"Ġsuits":14803,"=>":14804,"Ġbaseline":14805,"ĠRate":14806,"Ġislands":14807,"Ġ((":14808,"green":14809,"ixels":14810,"Ġnamely":14811,"ĠVillage":14812,"than":14813,"amy":14814,"Version":14815,"gmail":14816,"entials":14817,"ĠSud":14818,"ĠMelbourne":14819,"Ġarriving":14820,"Ġquantum":14821,"eff":14822,"ropolitan":14823,"Tri":14824,"Ġfuneral":14825,"ĠIR":14826,"ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ":14827,"ĠCob":14828,"itably":14829,"Ġturb":14830,"Ġcombo":14831,"Review":14832,"Ġdeployment":14833,"uity":14834,"ĠBott":14835,"Ġinvisible":14836,"Ġrendering":14837,"Ġunlocked":14838,"Ġaqu":14839,"ĠVladimir":14840,"Ġpad":14841,"ĠBrain":14842,"ĠLegacy":14843,"dragon":14844,"ĠKurdish":14845,"Ġsounded":14846,"Ġdetained":14847,"ĠDM":14848,"gary":14849,"Ġdaughters":14850,"Ġdisturbing":14851,"uka":14852,"ĠParad":14853,"Ġtast":14854,"Ġunfortunate":14855,"Ġul":14856,"emin":14857,"Ġattendance":14858,"trl":14859,"Ġparks":14860,"ĠMemorial":14861,"ĠAlice":14862,"othy":14863,"guard":14864,"ĠDise":14865,"ĠShan":14866,"ĠForum":14867,"Rich":14868,"Ġshifted":14869,"uez":14870,"Ġlighter":14871,"ĠMagn":14872,"Ġcod":14873,"Sch":14874,"hammad":14875,"Pub":14876,"350":14877,"ĠPokemon":14878,"Ġprototype":14879,"Ġunre":14880,"Base":14881,"ĠStudents":14882,"ĠReply":14883,"ĠCommunist":14884,"Ġgau":14885,"ĠTyler":14886,"IZ":14887,"Ġparticipated":14888,"Ġsuprem":14889,"ĠDetails":14890,"Ġvessels":14891,"rod":14892,"Ġtribe":14893,"keep":14894,"Ġassumptions":14895,"Ġpound":14896,"Ġcrude":14897,"ĠAvailable":14898,"Ġswimming":14899,"Ġinclusion":14900,"Ġadvances":14901,"culation":14902,"Ġconservation":14903,"Ġoverd":14904,"ĠBuffalo":14905,"Article":14906,"edge":14907,"Ġawa":14908,"ĠMadison":14909,"Ġsidew":14910,"Ġcatast":14911,"ĠKrist":14912,"ucle":14913,"ĠHighway":14914,"ĠTerror":14915,"Ġactivation":14916,"Ġunconscious":14917,"ĠSatan":14918,"ĠSusan":14919,"illery":14920,"Ġarranged":14921,"iop":14922,"Ġrumors":14923,"urring":14924,"think":14925,"ĠKeith":14926,"ĠKind":14927,"Ġavoiding":14928,"byn":14929,"nut":14930,"ĠSpeaker":14931,"rus":14932,"names":14933,"Ġguilt":14934,"ĠOlympics":14935,"Ġsail":14936,"ĠMes":14937,"levant":14938,"ĠColumbus":14939,"aft":14940,"City":14941,"South":14942,"ĠHarvey":14943,"ĠPun":14944,"Several":14945,"Ġmentally":14946,"Ġimpress":14947,"mount":14948,"ĠUbuntu":14949,"âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ":14950,"ĠSuperman":14951,"ĠMPs":14952,"Ġintentions":14953,"ĠRacing":14954,"Ġlikelihood":14955,"Ġ240":14956,"Total":14957,"Ġtoys":14958,"ĠWatson":14959,"Ġurge":14960,"Lear":14961,"ĠPaper":14962,"Ġoccurring":14963,"ĠBeng":14964,"ĠCert":14965,"Ġstones":14966,"Tim":14967,"ĠTwin":14968,"zb":14969,"ĠDynam":14970,"Ġpolitician":14971,"kens":14972,"ĠEnterprise":14973,"UTERS":14974,"Ġabol":14975,"Ġrefresh":14976,"Ġarbitrary":14977,"pection":14978,"Ġtroubles":14979,"Ġ});":14980,"tv":14981,"Ġpilots":14982,"Ġdistribute":14983,"Ġaudit":14984,"Ġpause":14985,"original":14986,"Ġrivals":14987,"£":14988,"Fig":14989,"TL":14990,"abil":14991,"rying":14992,"Lin":14993,"ioned":14994,"lon":14995,"Ġfancy":14996,"Ġcrashed":14997,"Ġtract":14998,"Ġshed":14999,"Ġconsume":15000,"Based":15001,"download":15002,"init":15003,"Ġvoltage":15004,"Introdu":15005,"Ġcondemned":15006,"ĠFinance":15007,"respect":15008,"Ġexcluded":15009,"Ġestablishing":15010,"heric":15011,"Ġheritage":15012,"Ġspectacular":15013,"Ġunst":15014,"ĠSnowden":15015,"ĠLane":15016,"San":15017,"Ġprotections":15018,"struction":15019,"incinn":15020,"Ġmacro":15021,"Custom":15022,"iosity":15023,"Ġesp":15024,"Ġfunctioning":15025,"Ġmush":15026,"Ġpuzzle":15027,"Ġethical":15028,"Mal":15029,"Ġgoverning":15030,"ĠFerguson":15031,"Ġrestored":15032,"Ġstressed":15033,"ĠCounter":15034,"ĠKas":15035,"clip":15036,"ANS":15037,"Ġseiz":15038,"UK":15039,"byss":15040,"oldown":15041,"api":15042,"Ġpermanently":15043,"ounters":15044,"West":15045,"Through":15046,"Light":15047,"atoes":15048,"Ġneat":15049,"Ġcord":15050,"urer":15051,"Ġseverely":15052,"ĠAven":15053,"Ġinterrog":15054,"Ġtriple":15055,"Given":15056,"Number":15057,"Ġarise":15058,"Ġsher":15059,"plant":15060,"Ġflower":15061,"ĠCou":15062,"Ġate":15063,"Ġnewer":15064,"bul":15065,"Ġmeanwhile":15066,"ĠLair":15067,"Ġadjustment":15068,"ĠCopyright":15069,"Ġdivers":15070,"iological":15071,"Ġgamers":15072,"oat":15073,"Ġhistorically":15074,"Ġanalog":15075,"Ġlongtime":15076,"Ġprescription":15077,"ĠMist":15078,"ĠHyper":15079,"ĠMaine":15080,"ĠDeity":15081,"Ġmultipl":15082,"ĠReincarn":15083,"ĠHyd":15084,"ĠPic":15085,"Sil":15086,"rants":15087,"ĠCris":15088,".;":15089,"({":15090,"ependence":15091,"Ġrecy":15092,"ateur":15093,"Ġquad":15094,"Ġglob":15095,"Ġconced":15096,"team":15097,"Ġcapitalist":15098,"ĠLot":15099,"Ġroyal":15100,"ĠCyber":15101,"Ġblacks":15102,"metic":15103,"riv":15104,"ĠDanny":15105,"Ġspo":15106,"ĠRO":15107,"Ġanimated":15108,"rypted":15109,"ĠDeputy":15110,"Ġrendered":15111,"FE":15112,"Ġstreak":15113,"Ġclouds":15114,"ĠDoug":15115,"~~~~~~~~":15116,"Ġdiscour":15117,"ĠVeh":15118,"Ġpsychology":15119,"ĠJourney":15120,"Ġcrystal":15121,"ĠFrost":15122,"Ġsuspicion":15123,"Ġrelate":15124,"orus":15125,"ĠCrypt":15126,"ĠNVIDIA":15127,"comed":15128,"uting":15129,"incinnati":15130,"Ġvulnerability":15131,"ostic":15132,"Ġisolation":15133,"Ġcooling":15134,"ĠCoalition":15135,"Ġ119":15136,"Four":15137,"ĠDeal":15138,"Ġâī":15139,"semble":15140,"rament":15141,"ĠBarcelona":15142,"Ġ102":15143,"Ġcocaine":15144,"ocalypse":15145,"Feb":15146,"ogenic":15147,"Ġmutation":15148,"Ġcryptoc":15149,"ĠKel":15150,"ĠGit":15151,"ais":15152,"Ġsisters":15153,"ANK":15154,"Ġactivate":15155,"Ter":15156,"Ġdread":15157,"ylon":15158,"Ġpropri":15159,"Aust":15160,"ĠDefault":15161,"Ġoutdoor":15162,"Ġsheer":15163,"ceive":15164,"Ġgently":15165,"о":15166,"Program":15167,"ĠâĨĴ":15168,"Ġvegan":15169,"ĠCrus":15170,"Ġresponsibilities":15171,"ĠHR":15172,"OLD":15173,"Ġprevents":15174,"Ġstiff":15175,"ĠWere":15176,"Ġathletic":15177,"ĠScore":15178,"Ġ):":15179,"Ġcolumns":15180,"ĠLoc":15181,"available":15182,"ĠFram":15183,"ĠSessions":15184,"Ġcompanion":15185,"Ġpacks":15186,"140":15187,"ĠKnights":15188,"Ġfart":15189,"Ġstreams":15190,"Ġshore":15191,"Ġappeals":15192,"ĠPerformance":15193,"haul":15194,"ĠStra":15195,"ĠNag":15196,"103":15197,"ĠTransportation":15198,"BB":15199,"Ev":15200,"zan":15201,"Public":15202,"Ġtwin":15203,"ulsion":15204,"Mult":15205,"Ġelectro":15206,"Ġstatue":15207,"ationally":15208,"ĠNort":15209,"Ġinspection":15210,"/*":15211,"igue":15212,"Ġcompassion":15213,"ĠTales":15214,"ĠStein":15215,"ĠScreen":15216,"ĠBug":15217,"ĠLion":15218,"girl":15219,"Ġwithdrawal":15220,"Ġobjectives":15221,"Ġbloody":15222,"Ġpreliminary":15223,"Ġjacket":15224,"Ġdimensions":15225,"ĠCool":15226,"ĠOccup":15227,"Ġwreck":15228,"Ġdoubled":15229,"anking":15230,"Ġ1975":15231,"Ġglasses":15232,"ĠWang":15233,"prov":15234,"Path":15235,"connected":15236,"ĠMulti":15237,"ĠNorway":15238,"agonist":15239,"Ġfeared":15240,"Ġtouching":15241,"Ġarguably":15242,"¯¯¯¯¯¯¯¯":15243,"ĠNCAA":15244,"chem":15245,"Ġspat":15246,"ĠWWE":15247,"ĠCel":15248,"igger":15249,"Ġattacker":15250,"ĠJoin":15251,"object":15252,"etta":15253,"Ġeliminated":15254,"det":15255,"Ġdestruct":15256,"ĠLucas":15257,"ctuary":15258,"180":15259,"ĠBrady":15260,"ĠBlues":15261,"Bay":15262,"aukee":15263,"Ġtimeline":15264,"Ġdelegates":15265,"written":15266,"ufficient":15267,"Ġshapes":15268,"Copyright":15269,"ouble":15270,"service":15271,"Ġpione":15272,"Ġcolleges":15273,"Ġrows":15274,"Ġspite":15275,"Ġassessed":15276,"360":15277,"Ġlease":15278,"Ġconfidential":15279,"cker":15280,"ĠManning":15281,"ĠVoice":15282,"Ġsealed":15283,"Ġcalculate":15284,"NO":15285,"ĠAssistant":15286,"Ġteenager":15287,"ulent":15288,"atherine":15289,"Ġmock":15290,"Ġdiamond":15291,"Ġfest":15292,"Ġswitched":15293,"Ġresume":15294,"ĠPuerto":15295,"Ġlanes":15296,"iration":15297,"ĠSimilarly":15298,"Ġrod":15299,"ĠSel":15300,"ĠPalace":15301,"ĠLimited":15302,"eous":15303,"Ġvariant":15304,"Ġward":15305,"Ġ))":15306,"Show":15307,"OOK":15308,"Alex":15309,"ĠNep":15310,"bris":15311,"ĠWikipedia":15312,"Ġexceptional":15313,"Ġmanages":15314,"ĠDraw":15315,"Again":15316,"Ġcopper":15317,"utt":15318,"Ġexports":15319,"Ġportfolio":15320,"Ġelevated":15321,"Rated":15322,"ĠOtherwise":15323,"ĠTact":15324,"ĠShel":15325,"ĠTX":15326,"\"âĢĶ":15327,"Ġresur":15328,"ĠWa":15329,"venant":15330,"Ġmonetary":15331,"people":15332,"Email":15333,"Ġfifty":15334,"ĠSweet":15335,"ĠMalaysia":15336,"Ġconfusing":15337,"ĠRio":15338,"uda":15339,"utenant":15340,"\");":15341,"Ġpraised":15342,"Ġvolumes":15343,"turn":15344,"Ġmature":15345,"Ġnonprofit":15346,"Ġpassionate":15347,"ĠPrivate":15348,"Ġ103":15349,"Ġdescend":15350,"ç¥ŀ":15351,"uffy":15352,"headed":15353,"Whether":15354,"rien":15355,"zech":15356,"beit":15357,"Ġchrom":15358,"ĠMcM":15359,"Ġdancing":15360,"Ġeleg":15361,"ĠNoticed":15362,"115":15363,"Ġadvocacy":15364,"ENTS":15365,"ambling":15366,"ĠMinor":15367,"ĠFinn":15368,"Ġpriorities":15369,"Ġthereof":15370,"ĠStage":15371,"ĠRogers":15372,"Ġsubstitute":15373,"ĠJar":15374,"ĠJefferson":15375,"Ġlightly":15376,"102":15377,"ĠLisa":15378,"uits":15379,"ysical":15380,"Ġshifts":15381,"Ġdrones":15382,"Ġworkplace":15383,"Ġresid":15384,"ensed":15385,"ahn":15386,"Ġpreferences":15387,"server":15388,"Ġdebates":15389,"doc":15390,"ĠGods":15391,"Ġhelicopter":15392,"Ġhonour":15393,"Ġconsiderably":15394,"eded":15395,"ĠFemale":15396,"ĠAnne":15397,"Ġreun":15398,"ĠFace":15399,"ĠHallow":15400,"ĠBudget":15401,"Ġcondemn":15402,"Ġtender":15403,"Prof":15404,"ocratic":15405,"ĠTurner":15406,"ĠAgric":15407,"Ġ1976":15408,"Ġapt":15409,"disc":15410,"ĠFighter":15411,"ĠAur":15412,"Ġgarbage":15413,"input":15414,"ĠKarl":15415,"ĠOliver":15416,"ĠLanguage":15417,"kn":15418,"Non":15419,"ĠClar":15420,"Ġtraditions":15421,"Ġadvertisement":15422,"ĠSor":15423,"Ġarchive":15424,"Ġvillages":15425,"750":15426,"Ġimplementing":15427,"waukee":15428,"Ġdietary":15429,"Ġswitching":15430,"Republic":15431,"Ġvelocity":15432,"Ġcit":15433,"ĠAwards":15434,"Ġfinancing":15435,"Ġlasted":15436,")]":15437,"Ġreminder":15438,"Person":15439,"Ġprecision":15440,"Ġdesigners":15441,"ĠFried":15442,"ĠBorder":15443,"Ġtragic":15444,"Ġwield":15445,"Ġinitiatives":15446,"ĠTank":15447,"wer":15448,"Ġjoins":15449,"Ro":15450,"inery":15451,"Ġarrow":15452,"Ġgenerating":15453,"founder":15454,"Ġsearches":15455,"Ġrandomly":15456,"Access":15457,"Ġbatch":15458,"Ġposed":15459,"lat":15460,"Ġpursuing":15461,"asa":15462,"Ġtestified":15463,"forming":15464,"ĠShar":15465,"wiki":15466,"ĠEither":15467,"Sometimes":15468,"Ġsenators":15469,"ĠJohnny":15470,"ĠTaliban":15471,"ĠGPS":15472,"\":\"/":15473,"ãģ®å":15474,"Ġanalyzed":15475,"ĠRubio":15476,"ĠMovement":15477,"opard":15478,"iii":15479,"Stand":15480,"fight":15481,"Ġignoring":15482,"iang":15483,"ĠGN":15484,"soever":15485,"ĠSTAT":15486,"Ġrefusing":15487,"Ġsweat":15488,"Ġbay":15489,"PORT":15490,"irmed":15491,"aky":15492,"Ġdispro":15493,"Ġlabeled":15494,"Ġ108":15495,"Hello":15496,"Ġpleasant":15497,"aba":15498,"Ġtriumph":15499,"Ġaboard":15500,"Ġincom":15501,"ĠCrow":15502,"lett":15503,"Ġfolk":15504,"Ġchase":15505,"``":15506,"ĠBrus":15507,"Ġteens":15508,"cue":15509,"Ġterrain":15510,"hyd":15511,"ilight":15512,"ORY":15513,"Support":15514,"ews":15515,"lli":15516,"raints":15517,"ĠCand":15518,"Ġabused":15519,"achment":15520,"larg":15521,"Bas":15522,"ĠCancer":15523,"Ġ1978":15524,"Ġsupporter":15525,"access":15526,"ĠTermin":15527,"ĠTampa":15528,"ĠANY":15529,"Ġnewest":15530,"ĠCriminal":15531,"edu":15532,"Ġ1930":15533,"Ġadmits":15534,"Ġende":15535,"Ġfailures":15536,"urate":15537,"fulness":15538,"cycl":15539,"ĠSubject":15540,"Ġinfinite":15541,"three":15542,"WA":15543,"pit":15544,"ĠInstall":15545,"Rad":15546,"iliation":15547,"GM":15548,"Ġcontinent":15549,"Ġaccommodate":15550,"ĠClay":15551,"Ġpup":15552,"ĠFunction":15553,"Ġhammer":15554,"ĠAlberta":15555,"Ġrevised":15556,"Ġminorities":15557,"Ġmeasurement":15558,"Connell":15559,"Ġdisable":15560,"ĠMix":15561,"Incre":15562,"Ġfork":15563,"ĠRosen":15564,"Ġimplies":15565,"umblr":15566,"ANG":15567,"Ġproteins":15568,"Ġaggression":15569,"Ġfacilitate":15570,"SN":15571,"Ġillegally":15572,"uer":15573,"Ġacadem":15574,"Ġpuzz":15575,"ĠShift":15576,"pay":15577,"ollo":15578,"Ġaudiences":15579,"Build":15580,"Ġnoble":15581,"Ġsyntax":15582,"âĺħ":15583,"Ġbeam":15584,"ĠBed":15585,"ĠAld":15586,"Ġorigins":15587,"video":15588,"Ġ1977":15589,"ĠAssault":15590,"Ġgarage":15591,"Team":15592,"Ġverdict":15593,"Ġdwar":15594,"ĠVirtual":15595,"event":15596,"Keep":15597,"Ġsentiment":15598,"Ġwildlife":15599,"shirt":15600,"Ġburg":15601,"Ġrecommendation":15602,"represent":15603,"Ġgallery":15604,"owners":15605,"Ġscholar":15606,"Ġconvenience":15607,"ĠSwift":15608,"Ġconvinc":15609,"Cap":15610,"Ġwarfare":15611,"ĠVisual":15612,"Ġconstitute":15613,"Ġabort":15614,"ĠWeather":15615,"ĠLooking":15616,"ĠHem":15617,"Ġmartial":15618,"Ġincoming":15619,"etition":15620,"Ġtolerance":15621,"ĠCreated":15622,"Ġflows":15623,"ĠElder":15624,"Ġsouls":15625,"Ġfoul":15626,"ĠPain":15627,"ĠCAN":15628,"Ġ220":15629,"bc":15630,"hend":15631,"Ġgenius":15632,"Real":15633,"ĠWr":15634,"ometer":15635,"pad":15636,"Ġlimiting":15637,"ĠSi":15638,"ĠLore":15639,"ĠAdventures":15640,"Ġvaried":15641,"Disc":15642,"fin":15643,"ĠPersonal":15644,"Chris":15645,"Ġinvented":15646,"Ġdive":15647,"ĠRise":15648,"Ġoz":15649,"ĠComics":15650,"Ġexpose":15651,"ĠReb":15652,"letters":15653,"site":15654,"imated":15655,"Ġhacking":15656,"Ġeducated":15657,"ĠNobody":15658,"Ġdepri":15659,"Ġincentive":15660,"ãĤ·":15661,"Ġoversight":15662,"Ġtribes":15663,"ĠBelgium":15664,"Ġlicensing":15665,"ourt":15666,"Product":15667,"ahl":15668,"ĠGem":15669,"Ġspecialist":15670,"Ġcra":15671,"anners":15672,"ĠCorbyn":15673,"Ġ1973":15674,"READ":15675,"Ġsummar":15676,"Ġoverlook":15677,"ĠApplication":15678,"Ġinappropriate":15679,"Ġdownloaded":15680,"Que":15681,"ĠBears":15682,"Ġthumb":15683,"ĠCharacter":15684,"ĠReincarnated":15685,"ĠSid":15686,"Ġdemonstrates":15687,"sky":15688,"ĠBloomberg":15689,"ĠArray":15690,"ĠResults":15691,"ĠFourth":15692,"ĠEDT":15693,"ĠOscar":15694,"cend":15695,"Ġ106":15696,"ĠNULL":15697,"ĠHERE":15698,"match":15699,"ĠBrun":15700,"Ġglucose":15701,"ieg":15702,"egu":15703,"Ġcertified":15704,"Ġrelie":15705,"Ġhumanitarian":15706,"Ġprayers":15707,"King":15708,"Ġnan":15709,"hou":15710,"108":15711,"ulu":15712,"Ġrenewable":15713,"Ġdistinguish":15714,"Ġdense":15715,"ĠVent":15716,"ĠPackage":15717,"ĠBoss":15718,"Ġeditors":15719,"Ġmigr":15720,"Tra":15721,"ĠPeters":15722,"ĠArctic":15723,"2004":15724,"ĠCape":15725,"Ġlocally":15726,"Ġlasting":15727,"Ġhandy":15728,".).":15729,"Pan":15730,"ĠRES":15731,"Index":15732,"Ġtensions":15733,"Ġformerly":15734,"Ġideological":15735,"Ġsensors":15736,"Ġdealers":15737,"Ġdefines":15738,"Sk":15739,"Ġproceeds":15740,"Ġproxy":15741,"azines":15742,"ĠBash":15743,"ĠPad":15744,"ĠCraft":15745,"ealous":15746,"Ġsheets":15747,"ometry":15748,"June":15749,"clock":15750,"TT":15751,"ĠTheatre":15752,"ĠBuzz":15753,"Ġchapters":15754,"Ġmillenn":15755,"Ġdough":15756,"ĠCongressional":15757,"Ġimagined":15758,"avior":15759,"Ġclinic":15760,"Ġ1945":15761,"Ġholder":15762,"root":15763,"olester":15764,"Ġrestart":15765,"BN":15766,"ĠHamas":15767,"ĠJob":15768,"Ġorb":15769,"Ġram":15770,"Ġdisclose":15771,"Ġtranslate":15772,"Ġimmigrant":15773,"Ġannoying":15774,"Ġtreaty":15775,"anium":15776,"ĠTea":15777,"ĠLegion":15778,"Ġcrowds":15779,"ĠBec":15780,"ĠAer":15781,"ohyd":15782,"Bro":15783,"Looking":15784,"Ġlbs":15785,"Ġaggress":15786,"Ġseam":15787,"Ġintercept":15788,"ĠMI":15789,"mercial":15790,"activ":15791,"ĠCit":15792,"Ġdimension":15793,"Ġconsistency":15794,"Ġrushing":15795,"ĠDouglas":15796,"Ġtrim":15797,"Install":15798,"icker":15799,"Ġshy":15800,"106":15801,"Ġmentions":15802,"pelled":15803,"ĠTak":15804,"cost":15805,"Ġclassroom":15806,"Ġfortune":15807,"driven":15808,"Ġunle":15809,"ĠWheel":15810,"Ġinvestor":15811,"ĠMasters":15812,"kit":15813,"Ġassociations":15814,"ĠEvolution":15815,"oping":15816,"uscript":15817,"Ġprovincial":15818,"ĠWalter":15819,"avi":15820,"SO":15821,"Ġunlimited":15822,"English":15823,"ĠCards":15824,"ĠEbola":15825,"nered":15826,"Ġrevenge":15827,"Ġoutright":15828,"umper":15829,"Ġfitting":15830,"ĠSolid":15831,"Ġformally":15832,"Ġproblematic":15833,"Ġhazard":15834,"Ġencryption":15835,"Ġstraightforward":15836,"ĠAK":15837,"Ġpse":15838,"ĠOrb":15839,"ĠChamber":15840,"ĠMak":15841,"Contents":15842,"Ġloyalty":15843,"Ġlyrics":15844,"ĠSym":15845,"Ġwelcomed":15846,"Ġcooked":15847,"Ġmonop":15848,"Ġnurse":15849,"Ġmisleading":15850,"Ġeternal":15851,"Ġshifting":15852,"Ġ+=":15853,"Vis":15854,"Ġinstitutional":15855,"illary":15856,"Ġpant":15857,"VERT":15858,"ĠACC":15859,"ĠEnh":15860,"Ġincon":15861,"ĠREUTERS":15862,"Ġdonated":15863,"âĢ¦âĢ¦âĢ¦âĢ¦":15864,"Intern":15865,"Ġexhibit":15866,"Ġtire":15867,"ĠRic":15868,"ĠChampion":15869,"ĠMuhammad":15870,"NING":15871,"ĠSoccer":15872,"Ġmobility":15873,"Ġvarying":15874,"ĠMovie":15875,"Ġlord":15876,"oak":15877,"Field":15878,"Ġvector":15879,"usions":15880,"Ġscrap":15881,"Ġenabling":15882,"make":15883,"Tor":15884,".*":15885,"||":15886,"ĠWebsite":15887,"ĠNPC":15888,"Ġsocialist":15889,"ĠBilly":15890,"ĠAdditional":15891,"Ġcargo":15892,"Ġfarms":15893,"ĠSoon":15894,"ĠPrize":15895,"Ġmidnight":15896,"Ġ900":15897,"seen":15898,"ĠSpot":15899,"Ġsheep":15900,"Ġsponsored":15901,"ĠHi":15902,"ĠJump":15903,"Ġ1967":15904,"Microsoft":15905,"ĠAgent":15906,"Ġcharts":15907,"dir":15908,"Ġadjacent":15909,"Ġtricks":15910,"Ġmanga":15911,"Ġexagger":15912,"/>":15913,"football":15914,"ĠFCC":15915,"GC":15916,"ĠTier":15917,"andra":15918,"OUND":15919,"%),":15920,"Ġfruits":15921,"VC":15922,"ĠAA":15923,"Rober":15924,"Ġmidst":15925,"âĹ":15926,"anka":15927,"Ġlegislature":15928,"ĠNeil":15929,"Ġtourists":15930,"\"\"":15931,"ĠWarning":15932,"ĠNevertheless":15933,"ĠOfficial":15934,"ĠWhatever":15935,"Ġmold":15936,"Ġdrafted":15937,"Ġsubstances":15938,"Ġbreed":15939,"Ġtags":15940,"ĠTask":15941,"Ġverb":15942,"Ġmanufactured":15943,"comments":15944,"ĠPolish":15945,"Prov":15946,"Ġdetermines":15947,"Obama":15948,"kers":15949,"Ġutterly":15950,"Ġsect":15951,"sche":15952,"ĠGates":15953,"ĠChap":15954,"Ġaluminum":15955,"Ġzombie":15956,"ĠTouch":15957,"ĠUP":15958,"Ġsatisfy":15959,"Ġpredomin":15960,"ascript":15961,"Ġelaborate":15962,"Ġ1968":15963,"Ġmeasuring":15964,"ĠVari":15965,"anyahu":15966,"Ġsir":15967,"ulates":15968,"idges":15969,"ickets":15970,"ĠSpencer":15971,"TM":15972,"oubted":15973,"Ġprey":15974,"Ġinstalling":15975,"ĠCab":15976,"reed":15977,"reated":15978,"Supp":15979,"Ġwrist":15980,"ĠKerry":15981,"107":15982,"ĠKle":15983,"ĠRachel":15984,"Ġcotton":15985,"ĠARE":15986,"ĠEle":15987,"Control":15988,"Ġloads":15989,"ĠDod":15990,"anas":15991,"bone":15992,"Ġclassical":15993,"ĠRegional":15994,"ĠInteg":15995,"VM":15996,"Ġdesires":15997,"Ġautism":15998,"supported":15999,"ĠMessage":16000,"Ġcompact":16001,"writer":16002,"Ġ109":16003,"ĠHurricane":16004,"cision":16005,"Ġcycles":16006,"Ġdrill":16007,"Ġcolleague":16008,"Ġmaker":16009,"German":16010,"Ġmistaken":16011,"Sun":16012,"ĠGay":16013,"Ġwhatsoever":16014,"Ġsells":16015,"ĠAirl":16016,"liv":16017,"ĠOption":16018,"Ġsolved":16019,"Ġsectors":16020,"Ġhorizontal":16021,"Ġequation":16022,"ĠSkill":16023,"ĠBio":16024,"gement":16025,"ĠSnap":16026,"ĠLegal":16027,"Ġtrademark":16028,"Ġmakeup":16029,"Ġassembled":16030,"Ġsaves":16031,"ĠHalloween":16032,"ĠVermont":16033,"ĠFROM":16034,"Ġfarming":16035,"ĠPodcast":16036,"acceptable":16037,"ĠHigher":16038,"Ġasleep":16039,"ullivan":16040,"Ġreferen":16041,"ĠLev":16042,"Ġbullets":16043,"oko":16044,"HC":16045,"Ġstairs":16046,"Ġmaintains":16047,"ĠLower":16048,"ĠVi":16049,"Ġmarine":16050,"Ġacres":16051,"Ġcoordinator":16052,"ĠJoh":16053,"Ġcounterparts":16054,"ĠBrothers":16055,"Ġindict":16056,"bra":16057,"Ġchunk":16058,"Ġcents":16059,"Home":16060,"ĠMonth":16061,"Ġaccordingly":16062,"ifles":16063,"ĠGermans":16064,"ĠSyn":16065,"Hub":16066,"Ġeyeb":16067,"âĶĢâĶĢâĶĢâĶĢ":16068,"Ġranges":16069,"ĠHolland":16070,"ĠRobot":16071,"fc":16072,"Mike":16073,"Ġplasma":16074,"Ġswap":16075,"Ġathlete":16076,"ĠRams":16077,",'\"":16078,"Ġinfections":16079,"Ġcorrid":16080,"Ġvib":16081,"Ġpatches":16082,"Ġtraditionally":16083,"Ġrevelation":16084,"Ġsweep":16085,"Ġglance":16086,"Ġinex":16087,"2003":16088,"ĠRaw":16089,"working":16090,"osures":16091,"ĠDat":16092,"ĠLynch":16093,"Ġleverage":16094,"ĠReid":16095,"Ġcorrelation":16096,"iances":16097,"avascript":16098,"Ġrepository":16099,"retty":16100,"Ġ1972":16101,"240":16102,"Ġoun":16103,"pol":16104,"ĠReed":16105,"Ġtactical":16106,"isite":16107,"Apple":16108,"ĠQuinn":16109,"Ġraped":16110,"illo":16111,"Europe":16112,"Ġalgorithms":16113,"ĠRodrig":16114,"iu":16115,"Ġillum":16116,"Ġfame":16117,"Ġintroducing":16118,"Ġdelays":16119,"ĠRaiders":16120,"Ġwhistle":16121,"Ġnovels":16122,"ĠReally":16123,"Ġderiv":16124,"Ġpublications":16125,"ĠNeither":16126,"ĠCommerce":16127,"Ġaston":16128,"language":16129,"Notes":16130,"ĠRoth":16131,"ĠFear":16132,"Ġmate":16133,"Ġparade":16134,"ĠQB":16135,"Ġmaneu":16136,"ĠCincinnati":16137,"mitting":16138,"Ġwaist":16139,"ĠRew":16140,"Ġdiscont":16141,"а":16142,"Ġstaring":16143,"Ġalias":16144,"Ġsecurities":16145,"Ġtoilet":16146,"ĠJedi":16147,"Ġunlaw":16148,"vised":16149,"////////":16150,"](":16151,"ĠWeiss":16152,"Ġprest":16153,"ĠCompan":16154,"Ġmemo":16155,"ĠGrace":16156,"July":16157,"ĠElite":16158,"center":16159,"ĠStay":16160,"Ġgalaxy":16161,"Ġtooth":16162,"ĠSettings":16163,"Ġsubjected":16164,"ãĤ¦":16165,"Ġlineback":16166,"Ġretailers":16167,"ĠWant":16168,"Ġdangers":16169,"Air":16170,"Ġvoluntary":16171,"eway":16172,"Ġinterpreted":16173,"otine":16174,"ç":16175,"Ġpel":16176,"Service":16177,"ĠEventually":16178,"Ġcareers":16179,"Ġthreaten":16180,"Ġmemor":16181,"ĠBradley":16182,"ancies":16183,"sn":16184,"ĠUnknown":16185,"National":16186,"Ġshadows":16187,"ailand":16188,"ĠDash":16189,"Everyone":16190,"izzard":16191,"March":16192,"=(":16193,"Ġpulls":16194,"Ġstranger":16195,"Ġbackwards":16196,"ĠBernard":16197,"imensional":16198,"Ġchron":16199,"Ġtheoretical":16200,"ktop":16201,"Ġware":16202,"ĠInvestig":16203,"ĠIniti":16204,"ĠOperations":16205,"oven":16206,"ocide":16207,"*/":16208,"Ġflames":16209,"ĠCash":16210,"shit":16211,"Ġcab":16212,"ĠAnaly":16213,"ĠSeah":16214,"Ġdefining":16215,"Ġordering":16216,"Ġimmun":16217,"Ġpersistent":16218,"ACH":16219,"Russian":16220,"mans":16221,"Ġhind":16222,"Ġphotography":16223,"©":16224,"Ġhug":16225,"Ġ107":16226,"ĠHence":16227,"iots":16228,"udeau":16229,"Ġsubsidies":16230,"Ġroutinely":16231,"ĠDevice":16232,"itic":16233,"Ġdisgust":16234,"lander":16235,"Ġ1940":16236,"Ġassignment":16237,"ĠBesides":16238,"wick":16239,"ĠDust":16240,"usc":16241,"structed":16242,"111":16243,"develop":16244,"Ġfond":16245,"Ġintersection":16246,"Ġdignity":16247,"Ġcommissioner":16248,"Without":16249,"reach":16250,"Ġcartoon":16251,"Ġscales":16252,"ãĥŃ":16253,"FIG":16254,"Ġsurveys":16255,"ĠIndonesia":16256,"Ġartwork":16257,"Ġunch":16258,"Ġcycling":16259,"unct":16260,"auer":16261,"orate":16262,"ĠObviously":16263,"Ġcharacterized":16264,"feld":16265,"Ġaffirm":16266,"Ġinnings":16267,"Ġé":16268,"Ġaliens":16269,"Ġcloth":16270,"etooth":16271,"ĠCertain":16272,"§":16273,"Ġdigest":16274,"know":16275,"ĠXL":16276,"Ġpredictions":16277,"Ġdin":16278,"WAR":16279,"Ġaftermath":16280,"Example":16281,"ĠSuccess":16282,"ĠThr":16283,"IGN":16284,"Ġminer":16285,"Bus":16286,"Ġclarity":16287,"heimer":16288,"ĠOUT":16289,"ĠSend":16290,"ĠCircle":16291,"ĠDiet":16292,"Ġpronounced":16293,"Ġcreators":16294,"Ġearthquake":16295,"attery":16296,"geons":16297,"Ġod":16298,"Ġlaying":16299,"orp":16300,"Ult":16301,"project":16302,"Ġundermin":16303,"Ġsequel":16304,"Sam":16305,"ĠDarkness":16306,"Ġreception":16307,"bull":16308,"YS":16309,"ĠVir":16310,"Ġsequences":16311,"ĠCoin":16312,"Ġoutfit":16313,"ĠWait":16314,"119":16315,"Ġdelivers":16316,"......":16317,"Ġblown":16318,"ĠEsc":16319,"ĠMath":16320,"perm":16321,"ĠUl":16322,"Ġglim":16323,"Ġfacial":16324,"Ġgreenhouse":16325,"Ġtokens":16326,"/-":16327,"ĠAnnual":16328,"ĠONE":16329,"Ġteenage":16330,"ĠPhysical":16331,"ĠLang":16332,"ĠCelt":16333,"Ġsued":16334,"ividually":16335,"Ġpatience":16336,"chair":16337,"regular":16338,"Ġaug":16339,"inv":16340,"except":16341,"ĠLil":16342,"Ġnest":16343,"fd":16344,"sum":16345,"ĠChase":16346,"Russia":16347,"ĠJennifer":16348,"Ġoffseason":16349,"Overall":16350,"Fore":16351,"Ġriot":16352,"Aud":16353,"former":16354,"Ġdefenders":16355,"ĠCT":16356,"iotic":16357,"ribly":16358,"Ġautomated":16359,"Ġpenis":16360,"Ġinsist":16361,"Ġdiagram":16362,"ĠSQL":16363,"ĠGarc":16364,"Ġwitch":16365,"client":16366,"ierra":16367,"ambers":16368,"Ġrecount":16369,"far":16370,"Very":16371,"osterone":16372,"Ġappreciated":16373,"ĠPerfect":16374,"Section":16375,"Ġdoses":16376,"ocaust":16377,"Ġcostly":16378,"Ġgrams":16379,"ĠShi":16380,"Ġwrestling":16381,"Ġ1971":16382,"Ġtrophy":16383,"Ġnerve":16384,"ĠKaz":16385,"ĠExperience":16386,"Ġpledged":16387,"Ġplayback":16388,"Ġcreativity":16389,"bye":16390,"Ġattackers":16391,"Ġholders":16392,"ĠCoach":16393,"ĠPhD":16394,"Ġtransfers":16395,"Ġcolored":16396,"ĠHindu":16397,"Ġdrown":16398,"Ġlistened":16399,"ĠWA":16400,"iasm":16401,"PO":16402,"Ġappealing":16403,"Ġdisclosed":16404,"ĠChicken":16405,"agging":16406,"Ġpleaded":16407,"Ġnavigation":16408,"ĠReturns":16409,"Ġ[[":16410,"ROR":16411,"EA":16412,"Ġphotographer":16413,"ĠRider":16414,"ippers":16415,"Ġslice":16416,"Ġerect":16417,"Ġhed":16418,"issance":16419,"ĠVikings":16420,"urious":16421,"Ġappet":16422,"oubtedly":16423,"Child":16424,"Ġauthentic":16425,"oos":16426,"ĠMaking":16427,"Ġannouncing":16428,"Ġbod":16429,"Ġmeter":16430,"ĠNine":16431,"ĠRogue":16432,"Ġworkforce":16433,"Ġrenewed":16434,"Ġorganisations":16435,"acs":16436,"PLE":16437,"Short":16438,"Ġcompounds":16439,"ĠVisit":16440,"Ġenvelop":16441,"earth":16442,"Ġsupportive":16443,"ggle":16444,"ĠBrussels":16445,"ĠGuild":16446,"Create":16447,"REL":16448,"Ġaveraged":16449,"Ġ1969":16450,"riages":16451,"Ġlengthy":16452,"Ġforgot":16453,"Okay":16454,"ĠErd":16455,"Ġdealer":16456,"Ġrecession":16457,"DD":16458,"Ġdesperately":16459,"Ġhunger":16460,"Ġsticks":16461,"Ġmph":16462,"ĠFaith":16463,"Ġintentionally":16464,"Ġdemol":16465,"ueller":16466,"ĠSale":16467,"Ġdebris":16468,"spring":16469,"Ġleap":16470,">>>>":16471,"Ġcontainers":16472,"selling":16473,"ranean":16474,"attering":16475,"Ġcommented":16476,"ĠCM":16477,"onut":16478,"Ġwoods":16479,"especially":16480,"Ġorganize":16481,"ivic":16482,"ĠWoods":16483,"anga":16484,"squ":16485,"Ġmaj":16486,"amon":16487,"Ġaxis":16488,"Ġ1974":16489,"ĠDenmark":16490,"Ġwarrior":16491,"ĠPand":16492,"Ġoutlined":16493,"ĠBO":16494,"insula":16495,"zilla":16496,"ebook":16497,"Ġdare":16498,"Ġsearched":16499,"Ġnavigate":16500,"Sn":16501,"writing":16502,"Ġunited":16503,"Japan":16504,"ĠHebrew":16505,"Ġflame":16506,"Ġrelies":16507,"Ġcatching":16508,"ĠSho":16509,"Ġimprisonment":16510,"Ġpockets":16511,"Ġclosure":16512,"ĠFam":16513,"tim":16514,"adequ":16515,"Activity":16516,"Ġrecruiting":16517,"ĠWATCH":16518,"ĠArgentina":16519,"dest":16520,"Ġapologize":16521,"oro":16522,"Ġlacks":16523,"Ġtuned":16524,"ĠGriffin":16525,"Ġinfamous":16526,"Ġcelebrity":16527,"sson":16528,"Ġ----------------------------------------------------------------":16529,"ĠIsis":16530,"ĠDisplay":16531,"Ġcredibility":16532,"Ġeconomies":16533,"Ġheadline":16534,"ĠCowboys":16535,"Ġindef":16536,"Ġlately":16537,"Ġincentives":16538,"button":16539,"ĠMob":16540,"Aut":16541,"Ġresigned":16542,"ĠOm":16543,"camp":16544,"Ġprofiles":16545,"Ġschemes":16546,"olphins":16547,"ayed":16548,"Clinton":16549,"enh":16550,"ĠYahoo":16551,"Ġabst":16552,"Ġank":16553,"suits":16554,"Ġwished":16555,"ĠMarco":16556,"udden":16557,"Ġsphere":16558,"ĠBishop":16559,"Ġincorporated":16560,"ĠPlant":16561,"114":16562,"Ġhated":16563,"pic":16564,"Ġdonate":16565,"Ġlined":16566,"Ġbeans":16567,"Ġstealing":16568,"Ġcostume":16569,"Ġsheriff":16570,"Ġforty":16571,"Ġintact":16572,"Ġadapted":16573,"Ġtravelling":16574,"bart":16575,"Ġnicely":16576,"Ġdried":16577,"Ġscal":16578,"osity":16579,"NOTE":16580,"ĠBh":16581,"ĠBroncos":16582,"ĠIgn":16583,"Ġintimate":16584,"Ġchemistry":16585,"Ġoptimal":16586,"Deb":16587,"ĠGeneration":16588,"Ġ],":16589,"ichi":16590,"ĠWii":16591,"ĠYOUR":16592,"ventions":16593,"Write":16594,"Ġpopul":16595,"unning":16596,"ĠWor":16597,"Vol":16598,"Ġqueen":16599,"heads":16600,"KK":16601,"Ġanalyze":16602,"opic":16603,"earchers":16604,"Ġdot":16605,"legraph":16606,"astically":16607,"Ġupgrades":16608,"Ġcares":16609,"Ġextending":16610,"Ġfreeze":16611,"Ġinability":16612,"Ġorgans":16613,"Ġpretend":16614,"Ġoutlet":16615,"113":16616,"olan":16617,"ĠMall":16618,"uling":16619,"talk":16620,"Ġexpressing":16621,"ĠAlways":16622,"ĠBegin":16623,"files":16624,"Ġlicenses":16625,"%%":16626,"ĠMitt":16627,"Ġfilters":16628,"ĠMilwaukee":16629,"GN":16630,"Ġunfold":16631,"Mo":16632,"Ġnutrition":16633,"ppo":16634,"Bo":16635,"Ġfounding":16636,"Ġundermine":16637,"Ġeasiest":16638,"ĠCzech":16639,"ĠMack":16640,"Ġsexuality":16641,"ĠNixon":16642,"Win":16643,"ĠArn":16644,"ĠKin":16645,"ãĤ£":16646,"icer":16647,"Ġfortun":16648,"Ġsurfaces":16649,"aghd":16650,"Ġcarriers":16651,"ĠPART":16652,"ĠTib":16653,"Ġinterval":16654,"Ġfrustrating":16655,"ĠShip":16656,"ĠArmed":16657,"ffe":16658,"Ġboats":16659,"ĠAbraham":16660,"inis":16661,"Ġsuited":16662,"thread":16663,"iov":16664,"abul":16665,"ĠVenezuela":16666,"Ġtom":16667,"super":16668,"Ġcastle":16669,"although":16670,"ioxide":16671,"eches":16672,"Ġevolutionary":16673,"Ġnegotiate":16674,"Ġconfronted":16675,"Remember":16676,"Ġ170":16677,"Such":16678,"Ġ911":16679,"mult":16680,"ĠAbyss":16681,"urry":16682,"kees":16683,"spec":16684,"ĠBarbara":16685,"Ġbelonging":16686,"Ġvillain":16687,"istani":16688,"Ġaccountable":16689,"Ġportions":16690,"ĠDecl":16691,"Ur":16692,"ĠKate":16693,"gre":16694,"Ġmagazines":16695,"UCK":16696,"Ġregulate":16697,"omon":16698,"ĠAlmost":16699,"Ġoverview":16700,"Ġscram":16701,"Ġloot":16702,"ĠFitz":16703,"Ġcharacteristic":16704,"ĠSnake":16705,"say":16706,"ĠRico":16707,"Ġtrait":16708,"ĠJoined":16709,"aucus":16710,"Ġadaptation":16711,"ĠAirlines":16712,"Ġarchae":16713,"ĠIde":16714,"Ġbikes":16715,"Ġliterary":16716,"Ġinfluences":16717,"ĠUsed":16718,"Creat":16719,"Ġplea":16720,"ĠDefence":16721,"ĠAssass":16722,"Ġpond":16723,"ULT":16724,")\"":16725,"Ġevaluated":16726,"Ġobtaining":16727,"Ġdemographic":16728,"Ġvigil":16729,"aley":16730,"Ġspouse":16731,"ĠSeahawks":16732,"respons":16733,"ĠBelt":16734,"umatic":16735,"Ġrises":16736,"runner":16737,"ĠMichelle":16738,"Ġpotent":16739,"race":16740,"ĠPAC":16741,"Find":16742,"olesterol":16743,"ISS":16744,"ĠIntroduced":16745,"resses":16746,"ignment":16747,"Os":16748,"ĠTu":16749,"ĠDex":16750,"icides":16751,"Ġsparked":16752,"ĠLaura":16753,"ĠBryant":16754,"Ġsmiling":16755,"ĠNexus":16756,"Ġdefendants":16757,"ĠCatal":16758,"Ġdishes":16759,"shaped":16760,"Ġprolong":16761,"mt":16762,"($":16763,"ãĢĤ":16764,"Ġcalculations":16765,"ĠSame":16766,"Ġpiv":16767,"HH":16768,"Ġcancelled":16769,"Ġgrin":16770,"Ġterritories":16771,"istically":16772,"Come":16773,"ĠParent":16774,"Project":16775,"Ġneglig":16776,"ĠPrivacy":16777,"Ġammo":16778,"LECT":16779,"olutely":16780,"ĠEpic":16781,"Ġmisunder":16782,"wal":16783,"April":16784,"mos":16785,"pathy":16786,"ĠCarson":16787,"Ġalbums":16788,"ĠEasy":16789,"Ġpistol":16790,"<<":16791,"Ġ\\(":16792,"target":16793,"help":16794,"Ġinterpre":16795,"conscious":16796,"ĠHousing":16797,"ĠJoint":16798,"127":16799,"Ġbeers":16800,"science":16801,"ĠFirefox":16802,"effective":16803,"ĠCabin":16804,"ĠOkay":16805,"ĠApplic":16806,"Ġspacecraft":16807,"ĠSR":16808,"vet":16809,"ĠStrange":16810,"SB":16811,"Ġcorps":16812,"iberal":16813,"efficient":16814,"Ġprevalence":16815,"Ġeconomists":16816,"118":16817,"Thread":16818,"ordable":16819,"ODE":16820,"ĠCant":16821,"=-=-":16822,"ifiable":16823,"ĠAround":16824,"Ġpole":16825,"Ġwillingness":16826,"CLA":16827,"ĠKid":16828,"Ġcomplement":16829,"Ġscattered":16830,"Ġinmates":16831,"Ġbleeding":16832,"every":16833,"Ġqueue":16834,"ĠTrain":16835,"Ġhij":16836,"Ġmelee":16837,"pleted":16838,"Ġdigit":16839,"Ġgem":16840,"official":16841,"Ġlifting":16842,"е":16843,"Requ":16844,"itutes":16845,"Ġpackaging":16846,"ĠWorkers":16847,"hran":16848,"ĠLebanon":16849,"olesc":16850,"Ġpunished":16851,"ĠJuan":16852,"Ġjam":16853,"ĠDocument":16854,"Ġmapping":16855,"icates":16856,"Ġinevitably":16857,"Ġvanilla":16858,"ĠTon":16859,"Ġwatches":16860,"Ġleagues":16861,"Ġinitiated":16862,"degree":16863,"portion":16864,"Ġrecalls":16865,"Ġruin":16866,"Ġmelt":16867,"IAN":16868,"Ġhem":16869,"Exp":16870,"Ġbaking":16871,"ĠColomb":16872,"atible":16873,"Ġradius":16874,"plug":16875,"ĠIF":16876,"etically":16877,"Ġfict":16878,"HER":16879,"ĠTap":16880,"atinum":16881,"Ġink":16882,"Ġcoh":16883,"ĠWizard":16884,"both":16885,"tex":16886,"Ġspends":16887,"ĠCurrently":16888,"ĠPit":16889,"Ġneurons":16890,"ignt":16891,"Ġrall":16892,"Ġbuses":16893,"building":16894,"Ġadjustments":16895,"Ġcried":16896,"iblical":16897,"atted":16898,"ĠZion":16899,"ĠMatter":16900,"Ġmeditation":16901,"ĠDennis":16902,"Ġours":16903,"ĠTab":16904,"Ġrankings":16905,"ortal":16906,"Ġadvers":16907,"Ġsurrender":16908,"ĠGob":16909,"cium":16910,"omas":16911,"imeter":16912,"Ġmultiplayer":16913,"Ġheroin":16914,"Ġoptimistic":16915,"Ġindicator":16916,"ĠBrig":16917,"Ġgrocery":16918,"Ġapplicant":16919,"ĠRocket":16920,"vid":16921,"Exception":16922,"pent":16923,"Ġorganizing":16924,"Ġencounters":16925,"ĠTOD":16926,"Ġjewel":16927,"Save":16928,"ĠChristie":16929,"Ġheating":16930,"Ġlazy":16931,"ĠCP":16932,"Ġcousin":16933,"Config":16934,"Ġregener":16935,"Ġnearest":16936,"Ġachieving":16937,"ENS":16938,"throw":16939,"ĠRichmond":16940,"antle":16941,"2002":16942,"Ġanten":16943,"bird":16944,"133":16945,"Ġnarc":16946,"raint":16947,"unny":16948,"ĠHispanic":16949,"ournaments":16950,"Ġprophe":16951,"ĠThailand":16952,"ĠTi":16953,"Ġinjection":16954,"Ġinherit":16955,"ravis":16956,"Ġmedi":16957,"Ġwhoever":16958,"ĠDEBUG":16959,"GP":16960,"ĠHud":16961,"Card":16962,"prom":16963,"Ġpor":16964,"Ġoverhead":16965,"Law":16966,"Ġviolate":16967,"Ġheated":16968,"Ġdescriptions":16969,"Ġachievements":16970,"ĠBeer":16971,"ĠQuant":16972,"Was":16973,"Ġeighth":16974,"ĠIv":16975,"Ġspecialized":16976,"UPDATE":16977,"ĠDelta":16978,"Pop":16979,"Jul":16980,"ĠAsk":16981,"ophy":16982,"Ġnewsletters":16983,"ĠTool":16984,"Ġgard":16985,"ĠConfeder":16986,"ĠGMT":16987,"ĠAbbott":16988,"Ġimmunity":16989,"ĠVM":16990,"Islam":16991,"Ġimplicit":16992,"wd":16993,"Ġ1944":16994,"ravity":16995,"ometric":16996,"Ġsurviving":16997,"urai":16998,"ĠPrison":16999,"Ġrust":17000,"ĠSketch":17001,"Ġbees":17002,"ĠTheory":17003,"Ġmerit":17004,"Tex":17005,"chat":17006,"Ġmim":17007,"Ġpaste":17008,"ĠKoch":17009,"Ġignorance":17010,"ĠShoot":17011,"Ġbasement":17012,"United":17013,"ĠAdvis":17014,"height":17015,"Ġfoster":17016,"Ġdetain":17017,"information":17018,"Ġneural":17019,"';":17020,"Ġproves":17021,"allery":17022,"Ġinvitation":17023,"umbers":17024,"Ġcattle":17025,"Ġbicycle":17026,"zi":17027,"Ġconsultant":17028,"Ġapology":17029,"ĠTiger":17030,"Ġ123":17031,"999":17032,"Ġindividually":17033,"rt":17034,"igion":17035,"ĠBrazilian":17036,"Ġdisturb":17037,"Ġentrepreneurs":17038,"Ġforests":17039,"cerpt":17040,"plates":17041,"pher":17042,"clipse":17043,"Ġtwitter":17044,"Ġacids":17045,"ographical":17046,"hum":17047,"ĠBald":17048,"ifully":17049,"Ġcompiler":17050,"ĠDA":17051,"Ġdonor":17052,"asi":17053,"Ġtribal":17054,"lash":17055,"ĠConfig":17056,"Ġapplicants":17057,"Ġsalaries":17058,"135":17059,"Putin":17060,"ĠFocus":17061,"irs":17062,"Ġmisconduct":17063,"ĠHaz":17064,"Ġeaten":17065,"Mobile":17066,"Muslim":17067,"ĠMarcus":17068,"viol":17069,"Ġfavorable":17070,"Ġstub":17071,"adin":17072,"ĠHob":17073,"Ġfaithful":17074,"Ġelectronics":17075,"Ġvacuum":17076,"wait":17077,"backed":17078,"economic":17079,"dist":17080,"Ġtenure":17081,"Ġsincere":17082,"ĠTogether":17083,"ĠWave":17084,"Ġprogression":17085,"Ġdenying":17086,"Ġdistress":17087,"braska":17088,"third":17089,"Ġmixing":17090,"Ġcolonial":17091,"Ġprivately":17092,"Ġunrest":17093,"aternity":17094,"Ġpremises":17095,"anti":17096,"gregation":17097,"Ġlicence":17098,"ĠHind":17099,"ĠSamuel":17100,"Ġconvincing":17101,"ĠAce":17102,"ĠRust":17103,"ĠNetanyahu":17104,"Ġhandles":17105,"ĠPatch":17106,"oriented":17107,"aho":17108,"ĠGonz":17109,"Ġhackers":17110,"claimer":17111,"Ġcustoms":17112,"ĠGran":17113,"fighters":17114,"Ġluc":17115,"Ġmanuscript":17116,"arenthood":17117,"Ġdevil":17118,"Ġwarriors":17119,"Ġoffenders":17120,"William":17121,"Ġholidays":17122,"Ġnightmare":17123,"Ġlever":17124,"ifferent":17125,"Stat":17126,"Ġexhibition":17127,"puted":17128,"ĠPure":17129,"Ġalpha":17130,"Ġenthusiasm":17131,"ĠRepresentatives":17132,"EAR":17133,"ĠTyp":17134,"Ġwheat":17135,"ĠAlf":17136,"Ġcorrection":17137,"Ġevangel":17138,"ATT":17139,"Miss":17140,"Ġsoup":17141,"Ġimplied":17142,"param":17143,"Ġsexy":17144,"ĠLux":17145,"Ġrepublic":17146,"patch":17147,"ablish":17148,"Ġicons":17149,"Ġfathers":17150,"ĠGET":17151,"ĠCarib":17152,"Ġregulated":17153,"ĠCohen":17154,"ĠBobby":17155,"Ġner":17156,"Ġbent":17157,"ventory":17158,"ĠAlong":17159,"ĠEST":17160,"ĠWallace":17161,"Ġmurders":17162,"rise":17163,"kell":17164,"ĠCommonwealth":17165,"Ġnasty":17166,"eta":17167,"ĠMIT":17168,"Ġadministered":17169,"Ġgenuinely":17170,"Editor":17171,"nick":17172,"Ġhydro":17173,"********************************":17174,"ĠBle":17175,"Ġfines":17176,"Ġgorge":17177,"ausible":17178,"rh":17179,"Ġapple":17180,"mentioned":17181,"Ġrope":17182,"otyp":17183,"HR":17184,"Ġdisappointing":17185,"Ġcage":17186,"nik":17187,"Ġdoubts":17188,"ĠFREE":17189,"prints":17190,"ĠMUST":17191,"Ġvendors":17192,"ĠInqu":17193,"Ġliberals":17194,"Ġcontractor":17195,"Ġupside":17196,"children":17197,"Ġtricky":17198,"Ġregulators":17199,"charged":17200,"liter":17201,"Ġ***":17202,"Ġrebell":17203,"lang":17204,"Ġlocals":17205,"Ġphysicians":17206,"Ġhey":17207,"arse":17208,"tm":17209,"ĠLex":17210,"Ġbehavioral":17211,"successful":17212,"FX":17213,"Ġbrick":17214,"ovic":17215,"Ġconform":17216,"Ġreviewing":17217,"Ġinsights":17218,"Ġbiology":17219,"ĠRemove":17220,"ĠExtra":17221,"Ġcommitting":17222,"induced":17223,"ignty":17224,"igm":17225,"Ġatomic":17226,"Common":17227,"ĠEM":17228,"ĠPere":17229,"ĠItems":17230,"eh":17231,"Ġpreserved":17232,"ĠHood":17233,"Ġprisoner":17234,"Ġbankruptcy":17235,"Ġgren":17236,"ushes":17237,"Ġexploitation":17238,"Ġsignatures":17239,"Ġfinan":17240,"],\"":17241,"ĠMR":17242,"Ġmeg":17243,"remlin":17244,"Ġmusicians":17245,"Ġselecting":17246,"Ġexamining":17247,"INK":17248,"lated":17249,"Hi":17250,"Ġartic":17251,"Ġpets":17252,"Ġimpair":17253,"ĠMAN":17254,"Ġtablets":17255,"include":17256,"Range":17257,"Ġcaut":17258,"Ġlogs":17259,"Ġmounting":17260,"Ġunaware":17261,"Ġdynamics":17262,"ĠPalestine":17263,"ĠQuarter":17264,"ĠPurple":17265,"Ġma":17266,"ĠImport":17267,"Ġcollections":17268,"ciation":17269,"Ġsuccessor":17270,"Ġclone":17271,"Ġaiming":17272,"Ġpossessed":17273,"Ġsticking":17274,"Ġshaking":17275,"Ġlocate":17276,"ĠHockey":17277,"Turn":17278,"170":17279,"Ġfifteen":17280,"ĠHarrison":17281,"Ġcontinuously":17282,"ĠTC":17283,"ĠValent":17284,"ĠRescue":17285,"Ġbypass":17286,"amount":17287,"Ġmast":17288,"Ġprotects":17289,"Ġartistic":17290,"Ġsometime":17291,"Ġshoe":17292,"Ġshouted":17293,"ificant":17294,"etitive":17295,"ĠRegister":17296,"ĠJin":17297,"Ġconcentrated":17298,"lington":17299,"onies":17300,"Ġgenerator":17301,"yrim":17302,"ĠArmen":17303,"Ġclearing":17304,"ido":17305,"ĠTW":17306,"alph":17307,"Ġladies":17308,"Hard":17309,"Ġdialog":17310,"Ġinputs":17311,"æľ":17312,"Ġposes":17313,"Ġslots":17314,"ĠPremium":17315,"Ġleaks":17316,"Ġbosses":17317,"Ġ113":17318,"course":17319,"Acc":17320,"ĠNewton":17321,"ĠAustria":17322,"ĠMage":17323,"Ġteaches":17324,"abad":17325,"Ġwears":17326,"Ġcyl":17327,"Ġcurse":17328,"ĠSales":17329,"ĠWings":17330,"Ġpsy":17331,"Ġgaps":17332,"ĠIceland":17333,"ĠPinterest":17334,"Ġlandlord":17335,"Ġdefinitions":17336,"ĠKer":17337,"Ġsufficiently":17338,"ĠPence":17339,"ĠArchitect":17340,"Ġsurpass":17341,"Ġ114":17342,"Ġsuperhero":17343,"ĠDisease":17344,"Ġpriests":17345,"ĠCulture":17346,"Ġdefinitive":17347,"Ġsecretly":17348,"ĠDance":17349,"install":17350,"chief":17351,"ĠJessica":17352,"Would":17353,"Updated":17354,"Ġlocker":17355,"ĠKay":17356,"Ġmemorial":17357,"è¦":17358,"fat":17359,"Ġdisgu":17360,"Ġflavors":17361,"ĠBaseball":17362,"ĠResistance":17363,"Ġkicks":17364,"Ġenv":17365,"Ġteenagers":17366,"Dark":17367,"ĠCAR":17368,"Ġhalt":17369,"ĠLG":17370,"ĠGabriel":17371,"Ġfever":17372,"Ġsatur":17373,"Ġmall":17374,"Ġaffiliate":17375,"ĠSleep":17376,"ĠSpecific":17377,"ĠVel":17378,"Ġjar":17379,"ĠSacred":17380,"ĠEdwards":17381,"ĠACL":17382,"Ġretained":17383,"ĠGiant":17384,"Ġlimitation":17385,"inces":17386,"Ġrefusal":17387,"ĠTale":17388,"ĠButler":17389,"Ġaccidents":17390,"ĠCSS":17391,"Ġimported":17392,"ĠCopy":17393,"α":17394,"ERT":17395,"zel":17396,"Ġdivisions":17397,"hots":17398,"ĠAlb":17399,"ĠDS":17400,"Loader":17401,"Washington":17402,"atisf":17403,"ĠCreative":17404,"\\.":17405,"ĠAutom":17406,"redict":17407,"Ġreceptor":17408,"ĠCarlos":17409,"Method":17410,"oka":17411,"Ġmalicious":17412,"Ġstepping":17413,",[":17414,"ĠDad":17415,"Ġattraction":17416,"ĠEffects":17417,"ĠPirate":17418,"ĠCer":17419,"ĠIndustry":17420,"ĠRud":17421,"Ġcharter":17422,"Ġdining":17423,"Ġinsists":17424,"Ġconfigure":17425,"Ġ(#":17426,"ĠSimple":17427,"ĠScroll":17428,"UTC":17429,"175":17430,"ĠKon":17431,"Ġmarketplace":17432,"ĠãĤ":17433,"Ġrefres":17434,"Ġgates":17435,"erred":17436,"ĠPod":17437,"Ġbehave":17438,"Frank":17439,"node":17440,"Ġendorsed":17441,"hett":17442,"asive":17443,"ĠHomeland":17444,"Ġrides":17445,"ĠLeave":17446,"erness":17447,"Ġflooding":17448,"AFP":17449,"Ġrisen":17450,"Ġcontinually":17451,"Ġunanim":17452,"ĠContract":17453,"ĠPas":17454,"Ġguided":17455,"ĠChile":17456,"bd":17457,"Ġsucc":17458,"ptic":17459,"Ġcommittees":17460,"ĠLuther":17461,"ĠAnyone":17462,"Ġsab":17463,"124":17464,"Ġpixel":17465,"ĠBak":17466,"ĠTag":17467,"ĠBennett":17468,"Enter":17469,"small":17470,"ĠPresidential":17471,"Ġpul":17472,"Ġcontrace":17473,"archive":17474,"Ġcoastal":17475,"ĠKids":17476,"192":17477,"âĢ²":17478,"icky":17479,"INGTON":17480,"Ġwolf":17481,"ĠStalin":17482,"Tur":17483,"idget":17484,"amas":17485,"ĠUnless":17486,"Ġsponsor":17487,"Ġmorph":17488,"ĠChoose":17489,"Ġrunner":17490,"Ġunbel":17491,"Ġmud":17492,"ĠMana":17493,"Ġdubbed":17494,"Ġgodd":17495,"urers":17496,"window":17497,"Ġrelied":17498,"Ġcelebrating":17499,"osc":17500,"Ġ135":17501,"Ġlobbying":17502,"Ġincomplete":17503,"Ġrestriction":17504,"Ġincap":17505,"itus":17506,"Ġexpectation":17507,"ĠApollo":17508,"Ġintens":17509,"Ġsync":17510,"GH":17511,"Ġmanipulation":17512,"BY":17513,"Ġspear":17514,"Ġbreasts":17515,"Ġvolcan":17516,"ilia":17517,"Material":17518,"Ġformats":17519,"ĠBast":17520,"Ġparliamentary":17521,"Ġsnake":17522,"Ġservants":17523,"ĠTrudeau":17524,"ĠGrim":17525,"ĠArabic":17526,"ĠSCP":17527,"ĠBoys":17528,"station":17529,"Ġprospective":17530,"orde":17531,"initialized":17532,"Ġbored":17533,"ABLE":17534,"Ġaccessed":17535,"Ġtaxi":17536,"ĠShell":17537,"aiden":17538,"ursed":17539,"inates":17540,"ĠInsurance":17541,"ĠPete":17542,"September":17543,"650":17544,"Ġadventures":17545,"ĠCover":17546,"Ġtribute":17547,"Ġsketch":17548,"Ġempower":17549,"ĠØ":17550,"ĠGlenn":17551,"ĠDaw":17552,"=\\\"":17553,"ĠPolitics":17554,"Ġguides":17555,"Ġdioxide":17556,"ĠGore":17557,"ĠBright":17558,"ĠSierra":17559,"Ġvalued":17560,"cond":17561,"Ġpointer":17562,"Select":17563,"Ġrisky":17564,"Ġabsorb":17565,"images":17566,"Ġrefuses":17567,"Ġbonuses":17568,"___":17569,"Ġhilar":17570,"ĠFeatures":17571,"220":17572,"ĠCollector":17573,"Foot":17574,"Ġ1964":17575,"culus":17576,"Ġdawn":17577,"Ġworkout":17578,"ĠLO":17579,"Ġphilosophical":17580,"ĠSandy":17581,"ĠYouth":17582,"Ġliable":17583,"Af":17584,"blue":17585,"Ġoverturn":17586,"lessness":17587,"ĠTribune":17588,"ĠIng":17589,"Ġfactories":17590,"Ġcatches":17591,"Ġprone":17592,"Ġmatrix":17593,"Ġlogin":17594,"Ġinacc":17595,"Ġexert":17596,"sys":17597,"Ġneedle":17598,"ĠQur":17599,"Ġnotified":17600,"oulder":17601,"tx":17602,"Ġreminds":17603,"Ġpublishers":17604,"Ġnort":17605,"Ġgit":17606,"Ġflies":17607,"ĠEmily":17608,"Ġflowing":17609,"ĠAlien":17610,"ĠStrateg":17611,"Ġhardest":17612,"Ġmodification":17613,"API":17614,"ĠMY":17615,"Ġcrashes":17616,"stairs":17617,"number":17618,"Ġurging":17619,"channel":17620,"ĠFalcon":17621,"Ġinhabitants":17622,"Ġterrifying":17623,"Ġutilize":17624,"Ġbanner":17625,"Ġcigarettes":17626,"Ġsenses":17627,"ĠHolmes":17628,"Ġpractition":17629,"ĠPhillips":17630,"otto":17631,"Ġcompile":17632,"Model":17633,"ĠKo":17634,"Ġ[]":17635,"Americans":17636,"ĠTerms":17637,"Ġmedications":17638,"ĠAna":17639,"Ġfundamentally":17640,"ĠNotice":17641,"Ġweaker":17642,"Ġ0000":17643,"Ġgarlic":17644,"Ġoutbreak":17645,"Ġeconomist":17646,"ĠBirth":17647,"Ġobstacles":17648,"arcer":17649,"ĠOrthodox":17650,"Ġplacebo":17651,"ĠCrew":17652,"aspberry":17653,"ĠAngels":17654,"Ġdischarge":17655,"Ġdestructive":17656,"117":17657,"ĠRising":17658,"Ġdairy":17659,"late":17660,"Ġcollision":17661,"ĠTigers":17662,"eanor":17663,"ocumented":17664,"ĠInvalid":17665,"Ġdont":17666,"ĠLiter":17667,"ĠVa":17668,"Ġhydrogen":17669,"Ġvariants":17670,"ĠBrowns":17671,"Ġ1965":17672,"Ġindigenous":17673,"Ġtrades":17674,"Ġremainder":17675,"Ġswept":17676,"ĠImpact":17677,"Ġredist":17678,"Ġunint":17679,"graduate":17680,"ãĥķ":17681,"ĠWILL":17682,"ãģ®ç":17683,"ĠCritical":17684,"Ġfisher":17685,"Ġvicious":17686,"Ġreversed":17687,"Year":17688,"ĠSox":17689,"Ġshootings":17690,"Ġfilming":17691,"Ġtouchdowns":17692,"aires":17693,"mel":17694,"Ġgrandfather":17695,"Ġaffection":17696,"ingle":17697,"Ġoverly":17698,"Additional":17699,"Ġsupreme":17700,"ĠGrad":17701,"Ġsporting":17702,"Ġmercy":17703,"ĠBrooks":17704,"ounty":17705,"Ġperforms":17706,"Ġtightly":17707,"Ġdemons":17708,"Ġkillings":17709,"Ġfaction":17710,"ĠNova":17711,"auts":17712,"Ġundoubtedly":17713,"arin":17714,"Ġunderway":17715,"rak":17716,"Ġliv":17717,"ĠRegion":17718,"Ġbriefing":17719,"sers":17720,"cloud":17721,"ĠMik":17722,"usp":17723,"Ġprediction":17724,"azor":17725,"Ġportable":17726,"ĠGand":17727,"Ġpresenting":17728,"Ġ1080":17729,"»":17730,"ushi":17731,"ĠSpark":17732,"thereum":17733,"Ġjustification":17734,"ĠNy":17735,"Ġcontractors":17736,"mingham":17737,"ĠStyle":17738,"åħ":17739,"ĠChronicles":17740,"ĠPicture":17741,"Ġproving":17742,"Ġwives":17743,"sett":17744,"Ġmolecules":17745,"ĠFairy":17746,"Ġconsisting":17747,"Ġpier":17748,"alone":17749,"inition":17750,"Ġnucle":17751,"json":17752,"Ġgotta":17753,"Ġmobil":17754,"Ġverbal":17755,"arium":17756,"Ġmonument":17757,"ucked":17758,"Ġ256":17759,"Tech":17760,"minecraft":17761,"ĠTrack":17762,"Ġtile":17763,"Ġcompatibility":17764,"asis":17765,"Ġsadd":17766,"Ġinstructed":17767,"ĠMueller":17768,"Ġlethal":17769,"Ġhormone":17770,"Ġorche":17771,"else":17772,"Ġskelet":17773,"Ġentertaining":17774,"Ġminimize":17775,"again":17776,"Ġundergo":17777,"Ġconstraints":17778,"Ġcigarette":17779,"ĠIslamist":17780,"Ġtravels":17781,"ĠPanthers":17782,"lings":17783,"Care":17784,"Ġlawsuits":17785,"uras":17786,"Ġcryst":17787,"Ġlowered":17788,"Ġaerial":17789,"Ġcombinations":17790,"Ġhaun":17791,"Ġcha":17792,"Ġvine":17793,"Ġquantities":17794,"Ġlinking":17795,"bank":17796,"Ġsoy":17797,"Bill":17798,"ĠAngela":17799,"Ġrecipient":17800,"ĠProtest":17801,"Ġsocket":17802,"Ġsolidarity":17803,"ĠâĨ":17804,"mill":17805,"Ġvaries":17806,"ĠPakistani":17807,"Dragon":17808,"Ġune":17809,"Ġhorizon":17810,"³³³³³³³³":17811,"Ġprovinces":17812,"Ġfrankly":17813,"Ġenacted":17814,"notes":17815,"['":17816,"Ġ192":17817,"ocracy":17818,"Ġendorsement":17819,"Ġovertime":17820,"True":17821,"Lab":17822,"licted":17823,"ĠDNC":17824,"Ġbeats":17825,"ĠJamie":17826,"152":17827,"ĠINT":17828,"Contact":17829,"Ġaccounted":17830,"hash":17831,"ĠPackers":17832,"pires":17833,"Ġlesbian":17834,"Ġamendments":17835,"Ġhopeful":17836,"ĠFinland":17837,"Ġspotlight":17838,"Ġconfigured":17839,"Ġtroubled":17840,"Ġgaze":17841,"ĠCalgary":17842,"Ġreliability":17843,"Ġinsurg":17844,"swer":17845,"buy":17846,"ĠSkin":17847,"Ġpixels":17848,"Ġhandgun":17849,"Ġparas":17850,"Ġcategor":17851,"ĠEL":17852,"ĠRex":17853,"Indeed":17854,"Ġkinda":17855,"Ġconjunction":17856,"ĠBryan":17857,"ĠManufact":17858,"yang":17859,"Plus":17860,"SQL":17861,"ishment":17862,"Ġdominate":17863,"Ġnail":17864,"Ġoath":17865,"Ġerupt":17866,"ĠFine":17867,"itbart":17868,"ĠChip":17869,"ĠAbd":17870,"ĠNam":17871,"Ġbuyer":17872,"Ġdissent":17873,"Leaks":17874,"Contin":17875,"Ġrider":17876,"ĠSomeone":17877,"Ġillusion":17878,"cin":17879,"ĠBoeing":17880,"Ġinadequ":17881,"ovation":17882,"iants":17883,"Ġrebuild":17884,"450":17885,"ĠDestiny":17886,"SW":17887,"ĠTill":17888,"Hit":17889,"iaz":17890,"ĠBangl":17891,"achers":17892,"ĠReform":17893,"Ġsegments":17894,"Ġsystematic":17895,"dc":17896,"ĠConservatives":17897,"Ġportal":17898,"hor":17899,"ĠDragonbound":17900,"Ġdragged":17901,"omo":17902,"Ġthee":17903,"advert":17904,"ĠReports":17905,"ĠEt":17906,"Ġbarrels":17907,"August":17908,"Ġcomparisons":17909,"Ġhex":17910,"Ġanthrop":17911,"\"[":17912,"borough":17913,"abi":17914,"Ġpictured":17915,"playing":17916,"ĠAddress":17917,"ĠMirror":17918,"Smith":17919,"Ġtires":17920,"ĠNPR":17921,"AAAA":17922,"Ġclassification":17923,"ĠThan":17924,"ĠHarm":17925,"ĠRA":17926,"Ġrejection":17927,"mination":17928,"Ġranged":17929,"ĠFalls":17930,"DI":17931,"Host":17932,"ãĤ´":17933,"ĠExample":17934,"listed":17935,"thirds":17936,"Ġsafegu":17937,"brand":17938,"Ġprobable":17939,"Canada":17940,"ITION":17941,"ĠQaeda":17942,"Ġchick":17943,"Ġimports":17944,"hit":17945,"loc":17946,"WW":17947,"Ġblew":17948,"Ġanytime":17949,"Ġwholes":17950,"iked":17951,"Ġcalculation":17952,"create":17953,"ĠOri":17954,"Ġupgraded":17955,"Ġappar":17956,"utory":17957,"ĠMol":17958,"Brit":17959,"ĠJong":17960,"INAL":17961,"ĠStarting":17962,"Ġdice":17963,"urtle":17964,"Ġrelying":17965,"closure":17966,"Ġprofitable":17967,"Ġslaughter":17968,"ĠManual":17969,"caster":17970,"Ġ\"$":17971,"Ġfeather":17972,"ĠSimply":17973,"ieves":17974,"Ġdeterior":17975,"ĠPCI":17976,"Ġstamp":17977,"Ġflaws":17978,"Ġshade":17979,"hammer":17980,"Ġpassport":17981,"Ġconting":17982,"amel":17983,"Ġobservers":17984,"Ġneglect":17985,"ĠRB":17986,"ĠBrotherhood":17987,"Ġskeptical":17988,"family":17989,"usk":17990,"Ġemotionally":17991,"âĻ":17992,"ĠBeta":17993,"asonable":17994,"idity":17995,"ĠMul":17996,"Ġkicking":17997,"ĠCarm":17998,"ollah":17999,"VERTIS":18000,"ĠAthen":18001,"Ġladder":18002,"ĠBullet":18003,"å£":18004,"0001":18005,"ĠWildlife":18006,"ĠMask":18007,"ĠNan":18008,"Rev":18009,"Ġunacceptable":18010,"legal":18011,"Ġcrowded":18012,"agi":18013,"ĠCox":18014,"je":18015,"Ġmorality":18016,"Ġfuels":18017,"Ġcables":18018,"Ġmankind":18019,"ĠCaribbean":18020,"Ġanchor":18021,"Ġbyte":18022,"ĠOften":18023,"ĠOz":18024,"Ġcrafted":18025,"Ġhistorian":18026,"ĠWu":18027,"Ġtowers":18028,"ĠCitizens":18029,"Ġhelm":18030,"Ġcredentials":18031,"Ġsingular":18032,"ĠJesse":18033,"Ġtackles":18034,"Ġcontempt":18035,"Ġafore":18036,"ĠShadows":18037,"Ġnil":18038,"Ġurgent":18039,"apple":18040,"blood":18041,"Ġvon":18042,"Ġoffline":18043,"Ġbreathe":18044,"Ġjumps":18045,"Ġirrelevant":18046,"oxic":18047,"omal":18048,"important":18049,"Jim":18050,"Ġgloves":18051,"arming":18052,"depth":18053,"Ġtalents":18054,"ookie":18055,"ĠSB":18056,"Ġpalm":18057,"uffs":18058,"esta":18059,"IGH":18060,"Ġcanon":18061,"ĠVerizon":18062,"ĠPle":18063,"Ġcoupled":18064,"velt":18065,"Ġfundraising":18066,"ĠGetting":18067,"ĠDLC":18068,"Ġmathematical":18069,"ĠHS":18070,"ĠCardinals":18071,"telling":18072,"Ġsponsors":18073,"ĠÏ":18074,"ĠBulls":18075,"option":18076,"Ġpropose":18077,"Ġmemorable":18078,"Ġembraced":18079,"Ġdeclining":18080,"Health":18081,"eda":18082,"Ġ};":18083,"Ġspam":18084,"mile":18085,"Ġpitcher":18086,"ĠEight":18087,"Ġcaring":18088,"utic":18089,"role":18090,"Ġairline":18091,"ernandez":18092,"ĠAthlet":18093,"Ġcertification":18094,"uxe":18095,"riger":18096,"Ġempir":18097,"Ġsensation":18098,"Ġdism":18099,"Ġbolt":18100,"Ġevolve":18101,"House":18102,"Ġconsultation":18103,"ĠDuty":18104,"Ġtouches":18105,"ĠNathan":18106,"Ġfaint":18107,"had":18108,"\"(":18109,"ĠConsumer":18110,"ĠExtreme":18111,"Ġ127":18112,"ĠHerm":18113,"ĠSacrament":18114,"izoph":18115,"Ġanxious":18116,"ulously":18117,"Ġsocially":18118,"ĠUTC":18119,"Ġsolving":18120,"ĠLetter":18121,"History":18122,"educ":18123,"Price":18124,"));":18125,"Ġreload":18126,"amic":18127,"Ġpork":18128,"Ġdiscourse":18129,"Ġtournaments":18130,"airo":18131,"ĠKur":18132,"ĠCosta":18133,"Ġviolating":18134,"Ġinterfere":18135,"Ġrecreational":18136,"uffle":18137,"Ġspeeches":18138,"Ġneeding":18139,"Ġremembers":18140,"Ġcredited":18141,"nia":18142,"focused":18143,"amera":18144,"Ġbru":18145,"umbs":18146,"ĠCuban":18147,"Ġpreceding":18148,"Ġnonsense":18149,"acial":18150,"Ġsmartphones":18151,"ĠStories":18152,"Sports":18153,"ĠEmergency":18154,"ouncing":18155,"efined":18156,"Ġber":18157,"Ġconsulting":18158,"Ġmasters":18159,"heastern":18160,".\"[":18161,"ĠRunning":18162,"Ġsuscept":18163,"ĠFeng":18164,"America":18165,"prises":18166,"stitial":18167,"ĠWeekly":18168,"ĠGreater":18169,"modules":18170,"ifter":18171,"Graphics":18172,"uler":18173,"Ġwholly":18174,"Ġsuppress":18175,"Ġconcealed":18176,"Ġhappily":18177,"Ġaccepts":18178,"ĠEnjoy":18179,"Ġrivers":18180,"ĠExcept":18181,"225":18182,"ĠNHS":18183,"ĠMcConnell":18184,"Ġpussy":18185,"ferred":18186,"utable":18187,"Ġattain":18188,"Ġ>=":18189,"Ġdeposits":18190,"rophic":18191,"Ġnotorious":18192,"ĠShaw":18193,"ilitation":18194,"Ġepidemic":18195,"allic":18196,"Ġsmallest":18197,"ovich":18198,"Ġaccessories":18199,"perties":18200,"Ġsurplus":18201,"ĠMech":18202,"Ġambig":18203,"ĠImmigration":18204,"Ġchim":18205,"eval":18206,"Ġpracticing":18207,"ĠMystery":18208,"Ġdomains":18209,"ĠSilicon":18210,"apps":18211,"Ġkilometers":18212,"ea":18213,"ĠSmash":18214,"Ġwarranty":18215,"Ġnost":18216,"sil":18217,"rev":18218,"Jon":18219,"ĠDublin":18220,"Ġtastes":18221,"Ġbout":18222,"great":18223,"error":18224,"Ġswitches":18225,"ĠBapt":18226,"DO":18227,"oki":18228,"Ġsourced":18229,"produ":18230,"Ġattachment":18231,"ĠIssue":18232,"ĠQuestion":18233,"Join":18234,"Ġfitted":18235,"Ġunlawful":18236,"^^":18237,"erek":18238,"Ġauthentication":18239,"Ġstole":18240,"Ġaccountability":18241,"label":18242,"Search":18243,"Ġalbeit":18244,"atican":18245,"funded":18246,"ĠAdding":18247,"ĠIQ":18248,"Ġsubmar":18249,"lit":18250,"aque":18251,"ĠLearning":18252,"Ġinteger":18253,"Master":18254,"ĠChrom":18255,"Ġpremier":18256,"Op":18257,"ĠLiu":18258,"Ġblessed":18259,"ĠGlobe":18260,"ĠResponse":18261,"Ġlegitim":18262,"ĠMerkel":18263,"Ġdisposal":18264,"´":18265,"Ġgauge":18266,"peat":18267,"Ġinduced":18268,"Ġquestionable":18269,"arthy":18270,"ĠVit":18271,"ĠFeed":18272,"Until":18273,"Ut":18274,"worthy":18275,"RY":18276,"ĠHerald":18277,"ĠHammer":18278,"Ġmedal":18279,"ĠRivers":18280,"ĠHack":18281,"Ġclarify":18282,"Ġtracked":18283,"Ġautonomous":18284,"Ġtenant":18285,"ĠQatar":18286,"erie":18287,"Ġgrim":18288,"ĠMonitor":18289,"Ġresistant":18290,"ĠSpec":18291,"ĠWells":18292,"NAS":18293,"148":18294,"Ġminers":18295,"iotics":18296,"Ġmisses":18297,"116":18298,"gian":18299,"git":18300,"ĠEyes":18301,"pres":18302,"Ġgraduated":18303,"Ġangel":18304,"Ġsynchron":18305,"Ġefficiently":18306,"Ġtransmitted":18307,"Harry":18308,"Ġglobally":18309,"ENCE":18310,"ĠMontana":18311,"raged":18312,"ĠPrevention":18313,"Ġpiss":18314,"ĠLl":18315,"Ġshelf":18316,"ĠBJP":18317,"ĠTestament":18318,"ĠLate":18319,"iker":18320,"ĠHapp":18321,"ĠJulian":18322,"hall":18323,"Ġspont":18324,"Ġshutdown":18325,"Ġinconsistent":18326,"Ġsubscribers":18327,"Ġskeleton":18328,"ĠNebraska":18329,"Ġinspire":18330,"ĠVoid":18331,"Feed":18332,"Ġangles":18333,"ĠSprings":18334,"Ġbenchmark":18335,"Ġvaccines":18336,"izophren":18337,"sexual":18338,"uffed":18339,"Ġshine":18340,"ĠKath":18341,"Ġgesture":18342,"inea":18343,"Ġrip":18344,"Ġoppression":18345,"Ġconscience":18346,"bt":18347,"ĠLum":18348,"Ġincidence":18349,"ĠFa":18350,"wr":18351,"Ġmineral":18352,"ĠSpurs":18353,"alky":18354,"Ġthunder":18355,"Ġopio":18356,"Being":18357,"ĠPalm":18358,"Ġwasted":18359,"Ġlb":18360,"iaries":18361,"ĠInitiative":18362,"Ġcurric":18363,"Ġmarker":18364,"ĠMcL":18365,"Ġextensions":18366,"ĠPv":18367,"ĠArms":18368,"Ġofferings":18369,"Ġdefenses":18370,"Ġvendor":18371,"Ġcontradict":18372,"ĠColin":18373,"Ġreddit":18374,"Ġperipher":18375,"122":18376,"Ġsins":18377,"Edit":18378,"ICT":18379,"Soft":18380,"ĠShah":18381,"Ġadministrator":18382,"ĠTrip":18383,"Ġpornography":18384,"Ġtuition":18385,"inence":18386,"ĠProgress":18387,"Ġcatalog":18388,"Ġsuite":18389,"Ġhike":18390,"Ġreproductive":18391,"engine":18392,"Ġdrought":18393,"ĠNoah":18394,"Ġ230":18395,"Ġdude":18396,"Ġrelaxed":18397,"Ġpartition":18398,"Ġparticipant":18399,"Ġtelesc":18400,"Ġfeas":18401,"ĠFF":18402,"owner":18403,"Ġsweeping":18404,"Ġlenses":18405,"Ġmatchup":18406,"ĠRepl":18407,"ournals":18408,"Ġcredible":18409,"Ġgrandmother":18410,"Ġthermal":18411,"Ġsubscribing":18412,"Ġidentities":18413,"colm":18414,"UCT":18415,"Ġreluctant":18416,"users":18417,"ĠCort":18418,"Ġassisted":18419,"OSS":18420,"ATIONS":18421,"ISH":18422,"Ġpharmaceutical":18423,"icable":18424,"adian":18425,"ĠSonic":18426,"ĠFury":18427,"ĠMong":18428,"AH":18429,"ĠPsychology":18430,"Ġphosph":18431,"Ġtreats":18432,"ŃĶ":18433,"Ġsteadily":18434,"ĠHello":18435,"Ġrelates":18436,"Ġclue":18437,"Expl":18438,"auth":18439,"Ġrevision":18440,"Ġeld":18441,"osion":18442,"Ġbron":18443,"144":18444,"rikes":18445,"Ġmines":18446,"Ġblanket":18447,"ĠFail":18448,"eled":18449,"ĠImagine":18450,"ĠPlanned":18451,"aic":18452,"Request":18453,"Mad":18454,"ĠHorse":18455,"ĠEagle":18456,"Ġcapac":18457,"157":18458,"Ġling":18459,"ĠNice":18460,"ĠParenthood":18461,"minster":18462,"ogs":18463,"ensitive":18464,"Nothing":18465,"Ġcarn":18466,"Fin":18467,"ĠPE":18468,"Ġrifles":18469,"ĠLP":18470,"Sand":18471,"ĠguiActive":18472,"Ġtourist":18473,"CNN":18474,"Ġunveiled":18475,"Ġpredecessor":18476,"}{":18477,"uber":18478,"Ġoffshore":18479,"Ġoptical":18480,"ĠRot":18481,"ĠPearl":18482,"eton":18483,"Ġstared":18484,"Ġfarther":18485,"atility":18486,"contin":18487,"ĠGy":18488,"ĠFoster":18489,"ĠCoc":18490,"rients":18491,"Ġdesigning":18492,"ĠEconomy":18493,"ONG":18494,"Women":18495,"ĠNancy":18496,"erver":18497,"Ġmascul":18498,"Ġcasualties":18499,"Ġ225":18500,"ĠSullivan":18501,"ĠChoice":18502,"Ġaster":18503,"ws":18504,"Ġhotels":18505,"Ġconsiderations":18506,"Ġcouch":18507,"ĠStrip":18508,"ĠGn":18509,"Ġmanipulate":18510,"lied":18511,"Ġsynthetic":18512,"Ġassaulted":18513,"Ġoffenses":18514,"ĠDrake":18515,"Ġimpe":18516,"October":18517,"ĠHeritage":18518,"hl":18519,"ĠBlair":18520,"Unlike":18521,"Ġgrief":18522,"Ġ450":18523,"Ġopted":18524,"Ġresignation":18525,"ilo":18526,"Ġverse":18527,"ĠTomb":18528,"Ġupt":18529,"Ġaired":18530,"ĠHook":18531,"ĠMLB":18532,"Ġassumes":18533,"outed":18534,"ĠVers":18535,"Ġinferior":18536,"Ġbundle":18537,"ĠDNS":18538,"ographer":18539,"Ġmultip":18540,"ĠSouls":18541,"Ġillustrated":18542,"Ġtactic":18543,"Ġdressing":18544,"Ġduo":18545,"Conf":18546,"Ġrelent":18547,"Ġcant":18548,"Ġscarce":18549,"Ġcandy":18550,"ĠCF":18551,"Ġaffiliated":18552,"Ġsprint":18553,"ylan":18554,"ĠGarcia":18555,"Ġjunk":18556,"Print":18557,"exec":18558,"Crit":18559,"Ġportrait":18560,"iries":18561,"ĠOFF":18562,"Ġdisputes":18563,"WR":18564,"Love":18565,"ãģĦ":18566,"ĠReyn":18567,"Ġhipp":18568,"opath":18569,"Ġfloors":18570,"ĠFeel":18571,"Ġworries":18572,"Ġsettlements":18573,"ĠPos":18574,"Ġmosque":18575,"Ġfinals":18576,"Ġcrushed":18577,"ĠProbably":18578,"ĠBot":18579,"ĠMans":18580,"ĠPeriod":18581,"Ġsovereignty":18582,"Ġseller":18583,"Ġapost":18584,"Ġamateur":18585,"Ġdorm":18586,"Ġconsuming":18587,"Ġarmour":18588,"ĠRoose":18589,"Ġintensive":18590,"Ġeliminating":18591,"ĠSunni":18592,"ĠAleppo":18593,"jin":18594,"Ġadvise":18595,"pal":18596,"ĠHalo":18597,"Ġdescent":18598,"Ġsimpler":18599,"Ġbooth":18600,"STR":18601,"Later":18602,"ĠCave":18603,"===":18604,"Ġmol":18605,"Ġfist":18606,"Ġshotgun":18607,"supp":18608,"Ġrobbery":18609,"Effect":18610,"Ġobscure":18611,"ĠProfessional":18612,"Ġembassy":18613,"Ġmilitant":18614,"Ġincarcer":18615,"Ġgenerates":18616,"Ġlaunches":18617,"Ġadministrators":18618,"Ġshaft":18619,"Ġcircular":18620,"Ġfreshman":18621,"ĠWes":18622,"ĠJoel":18623,"ĠDrew":18624,"ĠDuncan":18625,"ĠApparently":18626,"sight":18627,"ĠInternal":18628,"ĠIndividual":18629,"ĠFE":18630,"Ġbore":18631,"ĠMt":18632,"Ġbroadly":18633,"ĠOptions":18634,"ountain":18635,"ipes":18636,"ĠVideos":18637,"204":18638,"Ġhills":18639,"Ġsimulation":18640,"Ġdisappointment":18641,"itan":18642,"ĠLaboratory":18643,"Ġupward":18644,"Ġboundary":18645,"Ġdarker":18646,"hart":18647,"Ġdominance":18648,"Cong":18649,"ĠOracle":18650,"ĠLords":18651,"Ġscholarship":18652,"ĠVincent":18653,"ede":18654,"ĠRah":18655,"Ġencourages":18656,"rov":18657,"Ġquo":18658,"Ġpremise":18659,"ĠCrisis":18660,"ĠHolocaust":18661,"Ġrhythm":18662,"Ġmetric":18663,"club":18664,"Ġtransported":18665,"Ġnod":18666,"ĠPist":18667,"Ġancestors":18668,"ĠFreder":18669,"thumbnails":18670,"ĠCE":18671,"OND":18672,"Phil":18673,"venge":18674,"ĠProducts":18675,"castle":18676,"Ġqualifying":18677,"ĠKaren":18678,"VERTISEMENT":18679,"Ġmighty":18680,"Ġexplanations":18681,"Ġfixing":18682,"Di":18683,"Ġdeclaring":18684,"Ġanonymity":18685,"Ġjuven":18686,"ĠNord":18687,"ĠDoom":18688,"ĠActually":18689,"Ok":18690,"phis":18691,"ĠDesert":18692,"Ġ116":18693,"IK":18694,"ĠFM":18695,"Ġincomes":18696,"VEL":18697,"okers":18698,"Ġpecul":18699,"Ġlightweight":18700,"gue":18701,"Ġaccent":18702,"Ġincrement":18703,"ĠChan":18704,"Ġcomplaining":18705,"ĠBaghd":18706,"Ġmidfielder":18707,"Ġoverhaul":18708,"Process":18709,"ĠHollow":18710,"ĠTitans":18711,"Small":18712,"manuel":18713,"ĠUnity":18714,"ĠEvents":18715,"Sty":18716,"Ġdisproportion":18717,"nesty":18718,"enes":18719,"ĠCod":18720,"Ġdemonstrations":18721,"ĠCrimson":18722,"ĠOH":18723,"Ġenrolled":18724,"Ġcel":18725,"ĠBrett":18726,"Ġaide":18727,"Ġheels":18728,"Ġbroadband":18729,"Ġmarking":18730,"Ġwizard":18731,"ĠNJ":18732,"ĠChiefs":18733,"Ġingredient":18734,"Ġdug":18735,"ĠShut":18736,"urchase":18737,"endor":18738,"Ġfarmer":18739,"ĠGoldman":18740,"129":18741,"155":18742,"Order":18743,"Ġlion":18744,"iably":18745,"Ġstain":18746,"array":18747,"ilitary":18748,"ĠFAQ":18749,"Ġexploded":18750,"ĠMcCarthy":18751,"ĠTweet":18752,"ĠGreens":18753,"eking":18754,"ln":18755,"ensen":18756,"Ġmotorcycle":18757,"Ġparticle":18758,"Ġcholesterol":18759,"Bron":18760,"Ġstair":18761,"Ġoxid":18762,"Ġdesirable":18763,"ibles":18764,"Ġtheor":18765,"forcing":18766,"Ġpromotional":18767,"ovo":18768,"boot":18769,"ĠBonus":18770,"rawling":18771,"Ġshortage":18772,"ĠPsy":18773,"Ġrecruited":18774,"Ġinfants":18775,"Ġtestosterone":18776,"Ġdeduct":18777,"Ġdistinctive":18778,"Ġfirmware":18779,"built":18780,"145":18781,"Ġexplored":18782,"Ġfactions":18783,"Ġvide":18784,"Ġtattoo":18785,"Ġfinancially":18786,"Ġfatigue":18787,"Ġproceeding":18788,"constitutional":18789,"Ġmiser":18790,"Ġchairs":18791,"gging":18792,"ipple":18793,"Ġdent":18794,"Ġdisreg":18795,"çĶ":18796,"stant":18797,"llo":18798,"bps":18799,"akening":18800,"Ġabnormal":18801,"ĠERA":18802,"士":18803,"ĠHBO":18804,"ĠMAR":18805,"Ġconcess":18806,"Ġservant":18807,"Ġaspir":18808,"lav":18809,"ĠPanel":18810,"amo":18811,"Ġprecip":18812,"Ġrecordings":18813,"Ġproceeded":18814,"Ġcolony":18815,"ĠTang":18816,"ablo":18817,"Ġstripped":18818,"Left":18819,"too":18820,"Ġpotatoes":18821,"Ġfinest":18822,"%).":18823,"Ġcrap":18824,"ĠZach":18825,"abases":18826,"ĠGoth":18827,"Ġbillionaire":18828,"wolf":18829,"Ġsanction":18830,"SK":18831,"Ġlogged":18832,"Po":18833,"eyed":18834,"unal":18835,"Ġcricket":18836,"Ġarmies":18837,"Ġuncovered":18838,"Cloud":18839,"ón":18840,"Ġrebounds":18841,"Ġmes":18842,"Oper":18843,"Pac":18844,"Ġnationally":18845,"Ġinserted":18846,"pict":18847,"Ġgovernance":18848,"и":18849,"Ġprivileges":18850,"GET":18851,"Ġfavorites":18852,"imity":18853,"Ġlover":18854,"them":18855,"empl":18856,"Ġgorgeous":18857,"Ann":18858,"Ġslipped":18859,"Ġveto":18860,"Bob":18861,"Ġslim":18862,"ucc":18863,"ĠFame":18864,"uddenly":18865,"Ġdenies":18866,"ĠMaur":18867,"Ġdistances":18868,"Ġwanna":18869,"tar":18870,"ĠSER":18871,"ĠâĪ":18872,"Ġlemon":18873,"athetic":18874,"Ġliteral":18875,"Ġdistinguished":18876,"Ġanswering":18877,"GI":18878,"Ġreligions":18879,"ĠPhilos":18880,"ĠLay":18881,"Ġcompos":18882,"irements":18883,"ĠKos":18884,"inez":18885,"rolling":18886,"Ġyoungest":18887,"andise":18888,"ĠBorn":18889,"Ġaltar":18890,"amina":18891,"ĠBoot":18892,"voc":18893,"Ġdigging":18894,"Ġpressures":18895,"Ġlen":18896,"264":18897,"Ġassassination":18898,"ĠBirmingham":18899,"ĠMyth":18900,"Ġsovereign":18901,"ĠArtist":18902,"ĠPhotograph":18903,"Ġdepicted":18904,"Ġdispens":18905,"orthy":18906,"Ġambul":18907,"integ":18908,"ĠCele":18909,"ĠTibet":18910,"Ġhierarchy":18911,"Ġcu":18912,"Ġpreseason":18913,"ĠPeterson":18914,"Ġcolours":18915,"Ġworrying":18916,"Ġbackers":18917,"ĠPalmer":18918,"Ġμ":18919,"Ġcontributor":18920,"Ġhearings":18921,"Ġurine":18922,"ĠÙ":18923,"ourgeois":18924,"Similar":18925,"ĠZimmer":18926,"something":18927,"ĠUSC":18928,"Ġstrengths":18929,"ĠFI":18930,"Ġlogging":18931,"Asked":18932,"ĠThai":18933,"inqu":18934,"ĠWalt":18935,"Ġcrews":18936,"itism":18937,"301":18938,"Ġsharply":18939,"umed":18940,"Ġredirect":18941,"rators":18942,"Inf":18943,"ĠWeapons":18944,"Ġteasp":18945,"1999":18946,"Live":18947,"ĠEspecially":18948,"ĠSter":18949,"ĠVeterans":18950,"Ġintro":18951,"otherapy":18952,"Ġmalware":18953,"Ġbreeding":18954,"Ġmolecular":18955,"ĠRoute":18956,"ĠComment":18957,"ochem":18958,"Ġain":18959,"Season":18960,"Ġlinebacker":18961,"Ä«":18962,"ĠEconomics":18963,"esar":18964,"ĠLives":18965,"ĠEmma":18966,"Ġkin":18967,"ĠTerrit":18968,"Ġplanted":18969,"oton":18970,"ĠButter":18971,"ĠSpons":18972,"PER":18973,"Ġdungeon":18974,"Ġsymbolic":18975,"Ġfilmed":18976,"Ġdiets":18977,"Ġconcludes":18978,"Ġcertainty":18979,"ĠFormat":18980,"Ġstrangers":18981,"format":18982,"ĠPhase":18983,"Ġcopied":18984,"Ġmetres":18985,"lda":18986,"ĠUsers":18987,"Ġdeliberate":18988,"Ġwashed":18989,"ĠLance":18990,"imation":18991,"Ġimproper":18992,"ĠGenesis":18993,"ickr":18994,"ĠKush":18995,"Ġrealise":18996,"Ġembarrassing":18997,"alking":18998,"bucks":18999,"Ġverified":19000,"Ġoutline":19001,"years":19002,"ĠIncome":19003,"202":19004,"Ġzombies":19005,"Final":19006,"ĠMillenn":19007,"Ġmodifications":19008,"ĠVision":19009,"ĠMoses":19010,"verb":19011,"iterranean":19012,"ĠJet":19013,"Ġnaval":19014,"ĠAgg":19015,"Ġurl":19016,"Ġvictories":19017,"Ġnonetheless":19018,"Ġinjust":19019,"ĠFact":19020,"çļ":19021,"Ġinsufficient":19022,"review":19023,"facebook":19024,"Ġnegotiating":19025,"Ġguarantees":19026,"imen":19027,"utenberg":19028,"Ġgambling":19029,"Ġcongr":19030,"Loading":19031,"Ġnevertheless":19032,"Ġpresidents":19033,"ĠIndustrial":19034,"Ġ118":19035,"Ġpoured":19036,"ĠTory":19037,"Ġ175":19038,"Ġ:=":19039,"Scott":19040,"angered":19041,"Tok":19042,"Ġorganizers":19043,"Mat":19044,"ĠGrowth":19045,"Ġadul":19046,"Ġensures":19047,"Ġ117":19048,"é¾įå":19049,"Ġmassacre":19050,"Ġgrades":19051,"before":19052,"ADVERTISEMENT":19053,"ĠSlow":19054,"ĠMMA":19055,"âĢĶ\"":19056,"ĠVatican":19057,"Qaeda":19058,"Ġowe":19059,"6666":19060,"ĠSorry":19061,"ĠGrass":19062,"Ġbackgrounds":19063,"Ġexhausted":19064,"Ġclan":19065,"Ġcompromised":19066,"ĠElf":19067,"ĠIsaac":19068,"enson":19069,"Invest":19070,"IFA":19071,"Ġinterrupted":19072,"ãĥīãĥ©":19073,"Ġtwisted":19074,"ĠDragons":19075,"Mode":19076,"ĠKremlin":19077,"Ġfertil":19078,"heres":19079,"phan":19080,"ĠNode":19081,"fed":19082,"ĠOrc":19083,"Ġunwilling":19084,"Cent":19085,"Ġpriorit":19086,"Ġgraduates":19087,"Ġsubjective":19088,"Ġissuing":19089,"ĠLt":19090,"Ġviewer":19091,"Ġwoke":19092,"Thus":19093,"brook":19094,"Ġdepressed":19095,"Ġbracket":19096,"ĠGor":19097,"ĠFighting":19098,"Ġstriker":19099,"Report":19100,"ĠPortugal":19101,"Ġneo":19102,"wed":19103,"199":19104,"Ġfleeing":19105,"shadow":19106,"identified":19107,"USE":19108,"Steam":19109,"Ġstretched":19110,"Ġrevelations":19111,"arted":19112,"ĠDw":19113,"Ġalignment":19114,"eston":19115,"ĠJared":19116,"Sep":19117,"Ġblogs":19118,"update":19119,"gom":19120,"risk":19121,"Ġclash":19122,"ĠHour":19123,"Ġruntime":19124,"Ġunwanted":19125,"Ġscam":19126,"Ġrack":19127,"Ġenlight":19128,"onest":19129,"ĠFerr":19130,"Ġconvictions":19131,"Ġpiano":19132,"Ġcirculation":19133,"ĠWelcome":19134,"Ġbacklash":19135,"ĠWade":19136,"Ġreceivers":19137,"otive":19138,"Jeff":19139,"Ġnetworking":19140,"ĠPrep":19141,"ĠExplorer":19142,"Ġlecture":19143,"Ġuploaded":19144,"ĠMeat":19145,"BLE":19146,"ĠNazis":19147,"ĠSynd":19148,"stud":19149,"roots":19150,"rians":19151,"Ġportrayed":19152,"Ġ??":19153,"ĠBuddha":19154,"sun":19155,"Robert":19156,"ĠComplex":19157,"Ġoversee":19158,"Ġstealth":19159,"Title":19160,"ĠJobs":19161,"ĠKum":19162,"Ġappreciation":19163,"ĠMOD":19164,"Ġbasics":19165,"Ġclips":19166,"Ġnursing":19167,"Ġproposition":19168,"Ġrealised":19169,"ĠNYC":19170,"Ġallocated":19171,"rium":19172,"aran":19173,"ĠProduction":19174,"ĠVote":19175,"Ġsmugg":19176,"Ġhunter":19177,"azer":19178,"ĠChanges":19179,"Ġfluct":19180,"yon":19181,"Array":19182,"Ġkits":19183,"Water":19184,"Ġuncommon":19185,"Ġresting":19186,"ells":19187,"would":19188,"Ġpursued":19189,"Ġassertion":19190,"ometown":19191,"ĠMosul":19192,"ĠPlatform":19193,"iolet":19194,"Ġshareholders":19195,"Ġtrails":19196,"Pay":19197,"ĠEnforcement":19198,"types":19199,"ĠAnonymous":19200,"Ġsatisfying":19201,"ilogy":19202,"Ġ('":19203,"wave":19204,"city":19205,"Steve":19206,"Ġconfrontation":19207,"ĠEld":19208,"Capt":19209,"ahan":19210,"htm":19211,"ĠCtrl":19212,"ONS":19213,"230":19214,"ifa":19215,"holding":19216,"Ġdelicate":19217,"Ġjaw":19218,"ĠGoing":19219,"orum":19220,"Sal":19221,"Ġdull":19222,"ĠBeth":19223,"Ġprisons":19224,"Ġego":19225,"ĠElsa":19226,"avorite":19227,"ĠGang":19228,"ĠNuclear":19229,"Ġspider":19230,"atsu":19231,"Ġsampling":19232,"Ġabsorbed":19233,"ĠPharm":19234,"ieth":19235,"Ġbucket":19236,"ĠRecomm":19237,"OF":19238,"ĠFactory":19239,"ANCE":19240,"Ġbacter":19241,"Has":19242,"ĠObserv":19243,"121":19244,"Ġpremiere":19245,"Develop":19246,"Ġcurrencies":19247,"Cast":19248,"Ġaccompanying":19249,"ĠNashville":19250,"Ġfatty":19251,"ĠBrend":19252,"Ġlocks":19253,"Ġcentered":19254,"ĠUT":19255,"aughs":19256,"orie":19257,"ĠAffordable":19258,"vance":19259,"DL":19260,"emet":19261,"Ġthrone":19262,"ĠBluetooth":19263,"Ġnaming":19264,"ifts":19265,"ADE":19266,"Ġcorrected":19267,"Ġpromptly":19268,"ĠSTR":19269,"Ġgenome":19270,"Ġcope":19271,"Ġvalley":19272,"Ġrounded":19273,"ĠKend":19274,"alion":19275,"pers":19276,"Ġtourism":19277,"Ġstark":19278,"vl":19279,"Ġblowing":19280,"ĠSchedule":19281,"std":19282,"Ġunhappy":19283,"Ġlitigation":19284,"cedes":19285,"Ġandroid":19286,"Ġintegral":19287,"erers":19288,"uded":19289,"tax":19290,"Ġreiter":19291,"ĠMotors":19292,"ociated":19293,"Ġwonders":19294,"ĠApost":19295,"ucking":19296,"ĠRoosevelt":19297,"fram":19298,"Ġyields":19299,"Ġconstitutes":19300,"awk":19301,"Interest":19302,"Ġinterim":19303,"Ġbreakthrough":19304,"ĠCher":19305,"Ġprosec":19306,"ĠDj":19307,"ĠMT":19308,"Resp":19309,"ĠPT":19310,"Ġsperm":19311,"edit":19312,"BT":19313,"Linux":19314,"country":19315,"league":19316,"Ġdick":19317,"Ġoct":19318,"Ġinserting":19319,"Ġscra":19320,"ĠBrewing":19321,"Ġ1966":19322,"Ġrunners":19323,"Ġplun":19324,"idy":19325,"ĠDian":19326,"Ġdysfunction":19327,"Ġexclusion":19328,"Ġdisgr":19329,"Ġincorporate":19330,"Ġreconc":19331,"Ġnominated":19332,"ĠArcher":19333,"draw":19334,"achelor":19335,"Ġwritings":19336,"Ġshallow":19337,"Ġhast":19338,"ĠBMW":19339,"ĠRS":19340,"Ġthigh":19341,"Ġ1963":19342,"Ġlamb":19343,"Ġfavored":19344,"agle":19345,"Ġcooler":19346,"ĠHours":19347,"ĠGU":19348,"ĠOrigin":19349,"Ġglimpse":19350,"--------------------":19351,"Lim":19352,"Ġcheek":19353,"Ġjealous":19354,"-'":19355,"Ġharness":19356,"ĠPoison":19357,"Ġdisabilities":19358,"neapolis":19359,"Ġoutlook":19360,"Ġnotify":19361,"ĠIndianapolis":19362,"Ġabrupt":19363,"nsic":19364,"Ġencrypted":19365,"Ġforfe":19366,"reath":19367,"Ġrabb":19368,"Ġfoundations":19369,"Ġcompliment":19370,"ĠInterview":19371,"ĠSwe":19372,"Ġadolesc":19373,"Ġmonitors":19374,"ĠSacramento":19375,"Ġtimely":19376,"Ġcontempl":19377,"Ġpositioned":19378,"Ġposters":19379,"phies":19380,"iovascular":19381,"void":19382,"ĠFifth":19383,"Ġinvestigative":19384,"OUN":19385,"Ġintegrate":19386,"ĠINC":19387,"isha":19388,"iblings":19389,"ĠRequest":19390,"ĠRodriguez":19391,"Ġslides":19392,"ĠDX":19393,"Ġfeminism":19394,"Ġdatas":19395,"Ġbend":19396,"irus":19397,"ĠNigeria":19398,"Fox":19399,"Change":19400,"Ġairplane":19401,"ĠLaden":19402,"Ġpublicity":19403,"ixty":19404,"Ġcommitments":19405,"Ġaggregate":19406,"Ġdisplaying":19407,"ĠArrow":19408,"Ġ122":19409,"Ġrespects":19410,"android":19411,"six":19412,"ĠSha":19413,"Ġrestoration":19414,")\\":19415,"WS":19416,"oys":19417,"Ġillustrate":19418,"without":19419,"126":19420,"ĠâĶĤ":19421,"Ġpickup":19422,"nels":19423,"Ġ....":19424,"food":19425,"ĠFen":19426,")?":19427,"Ġphenomena":19428,"Ġcompanions":19429,"ĠWrite":19430,"Ġspill":19431,"Ġbridges":19432,"ĠUpdated":19433,"ĠFo":19434,"Ġinsects":19435,"ASHINGTON":19436,"Ġscare":19437,"iltr":19438,"ĠZhang":19439,"Ġseverity":19440,"Ġindul":19441,"149":19442,"ĠCoffee":19443,"Ġnorms":19444,"Ġpulse":19445,"ĠFT":19446,"Ġhorrific":19447,"ĠDestroy":19448,"ĠJSON":19449,"Ġolive":19450,"Ġdiscusses":19451,"Rest":19452,"Elect":19453,"ĠWinn":19454,"ĠSurviv":19455,"ĠHait":19456,"Sure":19457,"oped":19458,"Ġrooted":19459,"ĠSke":19460,"ĠBronze":19461,"Ġlol":19462,"Default":19463,"Ġcommodity":19464,"redited":19465,"Ġlibertarian":19466,"Ġforbidden":19467,"Ġgran":19468,"à¨":19469,"Ġlag":19470,"enz":19471,"drive":19472,"Ġmathematics":19473,"Ġwires":19474,"Ġcritically":19475,"Ġcarbohyd":19476,"ĠChancellor":19477,"ĠEddie":19478,"Ġbanning":19479,"ĠFri":19480,"Ġcomplications":19481,"etric":19482,"ĠBangladesh":19483,"Ġbandwidth":19484,"Stop":19485,"ĠOriginally":19486,"Ġhalfway":19487,"ynasty":19488,"shine":19489,"Ġtales":19490,"rities":19491,"avier":19492,"Ġspinning":19493,"ĠWHO":19494,"Ġneighbourhood":19495,"bach":19496,"Ġcommerce":19497,"ĠSle":19498,"BU":19499,"Ġentrepreneur":19500,"Ġpeculiar":19501,"ĠComments":19502,"fre":19503,"320":19504,"ICS":19505,"Ġimagery":19506,"ĠCanon":19507,"ĠElectronic":19508,"short":19509,"((":19510,"Dig":19511,"Ġcommem":19512,"uced":19513,"Ġinclined":19514,"ĠSummon":19515,"Ġcliff":19516,"ĠMediterranean":19517,"Ġpoetry":19518,"Ġprosperity":19519,"ĠRece":19520,"Ġpills":19521,"member":19522,"Ġfinale":19523,"unc":19524,"ĠGig":19525,"ä½":19526,"Ġlod":19527,"Ġbackward":19528,"-+":19529,"ĠForward":19530,"Ġthri":19531,"sure":19532,"Ġsoap":19533,"ĠFX":19534,"RES":19535,"ĠSexual":19536,"oulos":19537,"Ġfoolish":19538,"Ġrighteous":19539,"Ġcoff":19540,"terrorism":19541,"ustain":19542,"oter":19543,"Ġabuses":19544,"next":19545,"Ġabusive":19546,"Ġthereafter":19547,"Ġprohibition":19548,"ĠSUP":19549,"Ġdip":19550,"Ġripped":19551,"Ġinherited":19552,"Ġbats":19553,"stru":19554,"GT":19555,"Ġflawed":19556,"phabet":19557,"Ġfog":19558,"doors":19559,"Ġimaging":19560,"Ġdigits":19561,"ĠHungary":19562,"Ġarrog":19563,"Ġteachings":19564,"Ġprotocols":19565,"ĠBanks":19566,"à¸":19567,"pound":19568,"ĠCurt":19569,".\")":19570,"./":19571,"Ġexemption":19572,"endix":19573,"ĠMull":19574,"Ġimproves":19575,"ĠGamer":19576,"dimensional":19577,"Icon":19578,"ĠMargaret":19579,"Status":19580,"dates":19581,"Ġintends":19582,"Ġdepict":19583,"Ġparked":19584,"Joe":19585,"ĠMarines":19586,"chnology":19587,"!).":19588,"Ġjudged":19589,"Ġweights":19590,"Ray":19591,"Ġapartments":19592,"hester":19593,"Ġreinforce":19594,"Ġoffender":19595,"occup":19596,"Ġsore":19597,"ept":19598,"ĠPHP":19599,"ĠBrow":19600,"Ġauthorization":19601,"ĠRisk":19602,"ĠDelaware":19603,"ĠQU":19604,"Ġnotifications":19605,"Ġsunlight":19606,"Ġexclude":19607,"dat":19608,"Ġmesh":19609,"ĠSudan":19610,"Ġbelonged":19611,"Ġsubway":19612,"Ġnoon":19613,"ĠInterior":19614,"olics":19615,"ĠLakers":19616,"Ġcoding":19617,"Disclaimer":19618,"Calif":19619,"Old":19620,"Ġdisl":19621,"?????":19622,"Ġconfirms":19623,"Ġrecruitment":19624,"Ġhomicide":19625,"Consider":19626,"ĠJeffrey":19627,"fty":19628,"};":19629,"Ġobjection":19630,"doing":19631,"ĠLeo":19632,"Want":19633,"Ġglow":19634,"ĠClarke":19635,"ĠNorman":19636,"Ġverification":19637,"Ġpacket":19638,"ĠFormula":19639,"Ġplag":19640,"esville":19641,"Ġshouting":19642,"Ġov":19643,"ĠREC":19644,"ĠBub":19645,"Ġninth":19646,"Ġenerg":19647,"Ġvalidity":19648,"Ġups":19649,"jack":19650,"Ġneighboring":19651,"ĠNec":19652,"eworks":19653,"ĠHab":19654,"arez":19655,"Ġspine":19656,"Ġeventual":19657,"ĠLeaders":19658,"ĠCarn":19659,"Ġprobation":19660,"Ġromance":19661,"msg":19662,"ĠMechanical":19663,"ERY":19664,"Rock":19665,"Ġpartisan":19666,"Node":19667,"assets":19668,"minent":19669,"Ġforeigners":19670,"Ġtestify":19671,"ĠUsually":19672,"lords":19673,"ĠGren":19674,"ĠPowell":19675,"BIL":19676,"Ġsr":19677,"Ġaddict":19678,"Ġshells":19679,"Ġsigh":19680,"ĠYale":19681,"ternity":19682,"Ġ750":19683,"EU":19684,"ĠRifle":19685,"Ġpatron":19686,"ema":19687,"ĠBannon":19688,"anity":19689,"Ġtropical":19690,"ĠVII":19691,"cross":19692,"Everything":19693,"ĠISO":19694,"Ġhumble":19695,"assing":19696,"ĠFIG":19697,"Ġupdating":19698,"yson":19699,"Ġcalcium":19700,"Ġcompetent":19701,"Ġsteering":19702,"Prot":19703,"ĠSY":19704,"ĠFinals":19705,"ĠRug":19706,"159":19707,"137":19708,"ĠGolf":19709,"Ġ126":19710,"Ġaccommodation":19711,"ĠHughes":19712,"Ġaesthetic":19713,"artisan":19714,"ĠTwilight":19715,"Ġprince":19716,"ĠAgriculture":19717,"ĠDisco":19718,"Ġprecedent":19719,"Ġtyping":19720,"authorized":19721,"Option":19722,"ĠAub":19723,"lishes":19724,"acht":19725,"mag":19726,"Peter":19727,"ĠUFO":19728,"monton":19729,"ĠLith":19730,"Ġarom":19731,"Ġsecuring":19732,"Ġconfined":19733,"private":19734,"Ġswords":19735,"Ġmarkers":19736,"Ġmetabolic":19737,"select":19738,"ĠCurse":19739,"ĠOt":19740,"gressive":19741,"Ġincumb":19742,"ĠSaga":19743,"Ġpriced":19744,"Ġclearance":19745,"Content":19746,"Ġdrilling":19747,"Ġnotices":19748,"Ġbourgeois":19749,"Ġvest":19750,"Ġcookie":19751,"ĠGuardians":19752,"rys":19753,"inyl":19754,"Ġ124":19755,"Ġplausible":19756,"ongh":19757,"ĠOdin":19758,"Ġconception":19759,"ĠYuk":19760,"ĠBaghdad":19761,"ĠFlag":19762,"Austral":19763,"ĠIBM":19764,"Ġinternationally":19765,"ĠWikiLeaks":19766,"IED":19767,"Ġcyn":19768,"Ġchooses":19769,"ĠPill":19770,"Ġcombining":19771,"Ġradi":19772,"ĠMohammed":19773,"defense":19774,"atching":19775,"Subject":19776,"iciency":19777,"Frame":19778,"Ġ{\"":19779,"Ġchess":19780,"Ġtimer":19781,"190":19782,"Ġtin":19783,"Ġordinance":19784,"emetery":19785,"Ġaccusing":19786,"Ġnoticeable":19787,"Ġcentres":19788,"Ġlid":19789,"ĠMills":19790,"imgur":19791,"Ġzoom":19792,"ergic":19793,"Ġcompression":19794,"prim":19795,"find":19796,"Ġsurg":19797,"Ġpand":19798,"ĠKee":19799,"ĠChad":19800,"cellence":19801,"oyle":19802,"Ġsocialism":19803,"ĠTravis":19804,"ĠMHz":19805,"Ġguild":19806,"ALLY":19807,"ĠSubscribe":19808,"ĠRelated":19809,"Ġoccurrence":19810,"itching":19811,"Ġfictional":19812,"Ġcrush":19813,"ĠEA":19814,"cod":19815,"mix":19816,"ĠTriple":19817,"Ġretrieve":19818,"Ġstimulus":19819,"Ġpsychiat":19820,"ĠDoor":19821,"Ġhomosexuality":19822,"Ġelementary":19823,"Ġcellular":19824,"idian":19825,"ĠLaun":19826,"Ġintriguing":19827,"Ġfoam":19828,"ĠBass":19829,"idi":19830,"itsu":19831,"Ġassure":19832,"Ġcongrat":19833,"Ġbusinessman":19834,"ĠBoost":19835,"close":19836,"Ġlied":19837,"Ġsciences":19838,"ĠOmega":19839,"ĠGraphics":19840,"Ġ<=":19841,"spoken":19842,"Ġconnectivity":19843,"Saturday":19844,"ĠAvengers":19845,"Ġtoggle":19846,"Ġankle":19847,"Ġnationalist":19848,"model":19849,"ĠPool":19850,"ophobia":19851,"Var":19852,"ĠMons":19853,"atories":19854,"Ġaggressively":19855,"Clear":19856,"Forge":19857,"acters":19858,"Ġhedge":19859,"Ġpipes":19860,"Ġblunt":19861,"Ġsq":19862,"Ġremotely":19863,"Wed":19864,"asers":19865,"Ġrefriger":19866,"Ġtiles":19867,"Ġrescued":19868,"Ġcomprised":19869,"insky":19870,"Ġmanif":19871,"avanaugh":19872,"Ġprolifer":19873,"Ġaligned":19874,"xml":19875,"Ġtriv":19876,"Ġcoordination":19877,"ĠPER":19878,"ĠQuote":19879,"134":19880,"bf":19881,"ĠSaw":19882,"Ġtermination":19883,"Ġ190":19884,"Ġadditions":19885,"Ġtrio":19886,"Ġprojections":19887,"Ġpositively":19888,"Ġinclusive":19889,"Ġmembr":19890,"1990":19891,"older":19892,"Ġpracticed":19893,"inkle":19894,"Arch":19895,"Ġstarters":19896,"arius":19897,"Ġintermediate":19898,"ĠBenef":19899,"ĠKiller":19900,"Ġinterventions":19901,"ĠKil":19902,"ĠFlying":19903,"Inv":19904,"Ġpremature":19905,"Ġpsychiatric":19906,"Ġindie":19907,"Ġcollar":19908,"ĠRainbow":19909,"afi":19910,"Ġdisruption":19911,"ĠFOX":19912,"casting":19913,"Ġmisdem":19914,"cro":19915,"Ġwipe":19916,"ardon":19917,"Ġbast":19918,"ĠTommy":19919,"ĠRepresentative":19920,"Ġbelly":19921,"ĠPO":19922,"ĠBreitbart":19923,"132":19924,"Ġmessaging":19925,"Should":19926,"References":19927,"ĠGRE":19928,"istical":19929,"LP":19930,"ĠCav":19931,"ĠCrazy":19932,"Ġintuitive":19933,"keeping":19934,"ĠMoss":19935,"Ġdiscontin":19936,"ĠModule":19937,"Ġunrelated":19938,"ĠPractice":19939,"ĠTransport":19940,"Ġstatistically":19941,"orns":19942,"Ġsized":19943,"pu":19944,"Ġcaf":19945,"ĠWorlds":19946,"ĠRodgers":19947,"ĠLun":19948,"ĠComic":19949,"living":19950,"Ġcared":19951,"Ġclimbed":19952,"){":19953,"Ġconsisted":19954,"Ġmedieval":19955,"folk":19956,"Ġhacked":19957,"Ġdire":19958,"ĠHermione":19959,"Ġtended":19960,"ceans":19961,"Daniel":19962,"went":19963,"Ġlegislators":19964,"Ġredes":19965,"games":19966,"Ġgn":19967,"amiliar":19968,"Ġ++":19969,"ggy":19970,"threat":19971,"Ġmagnet":19972,"Ġperceive":19973,"Ġzip":19974,"Ġindictment":19975,"Ġcritique":19976,"gard":19977,"ĠSafe":19978,"ĠCream":19979,"Ġadvent":19980,"oba":19981,"Ġvowed":19982,"ousands":19983,"Ġski":19984,"Ġabortions":19985,"uart":19986,"Ġstunned":19987,"Ġadvancing":19988,"Ġlacked":19989,"Ġ\\\"":19990,"Ġschizophren":19991,"Ġelegant":19992,"Ġconferences":19993,"Ġcanceled":19994,"ĠHudson":19995,"ĠHopefully":19996,"Ġtrump":19997,"Ġfrequencies":19998,"Ġmeteor":19999,"ĠJunior":20000,"ĠFleet":20001,"ĠMalcolm":20002,"ĠTools":20003,"Ġ........":20004,"Ġhobby":20005,"ĠEuropeans":20006,"Ġ1500":20007,"ĠInto":20008,"Ġsway":20009,"ĠAppro":20010,"ĠCompl":20011,"Community":20012,"Ġtide":20013,"ĠSummit":20014,"ä»":20015,"Ġintervals":20016,"ĠEther":20017,"Ġhabitat":20018,"ĠStevens":20019,"lishing":20020,"ĠDomain":20021,"Ġtriggers":20022,"Ġchasing":20023,"Ġcharm":20024,"ĠFlower":20025,"itored":20026,"Ġblessing":20027,"Ġtextures":20028,"Five":20029,"Ġliquor":20030,"RP":20031,"FIN":20032,"Ġ1962":20033,"CAR":20034,"Unknown":20035,"Ġresil":20036,"ĠLily":20037,"Ġabundance":20038,"Ġpredictable":20039,"rar":20040,"Ġbullshit":20041,"leen":20042,"chet":20043,"Mor":20044,"Much":20045,"ä¹":20046,"Ġemphasized":20047,"Ġcrust":20048,"Ġprimitive":20049,"Ġenjoyable":20050,"ĠPictures":20051,"Ġteammate":20052,"pler":20053,"ĠTol":20054,"ĠKane":20055,"Ġsummoned":20056,"thy":20057,"rama":20058,"ĠHonda":20059,"Ġrealizing":20060,"Ġquicker":20061,"Ġconcentrate":20062,"clear":20063,"Ġ210":20064,"ĠErdogan":20065,"aris":20066,"Ġresponds":20067,"ĠBI":20068,"Ġeligibility":20069,"Ġpushes":20070,"ĠIdaho":20071,"Ġaggrav":20072,"Ġruins":20073,"urations":20074,"Ġbans":20075,"Ġanat":20076,"share":20077,"Ġgrind":20078,"hin":20079,"umen":20080,"Ġutilities":20081,"ĠYankees":20082,"Ġdatabases":20083,"ĠDD":20084,"Ġdisplaced":20085,"Ġdependencies":20086,"Ġstimulation":20087,"hun":20088,"houses":20089,"ĠPretty":20090,"ĠRavens":20091,"ĠTODAY":20092,"Ġassociates":20093,"Ġtherape":20094,"cled":20095,"Ġdeer":20096,"Ġrepairs":20097,"rentice":20098,"Ġreceptors":20099,"Ġremed":20100,"ĠCe":20101,"Ġmarriages":20102,"Ġballots":20103,"ĠSoldier":20104,"Ġhilarious":20105,"opl":20106,"138":20107,"Ġinherently":20108,"Ġignorant":20109,"Ġbounce":20110,"ĠEaster":20111,"RELATED":20112,"ĠCurrency":20113,"EV":20114,"ãĥŀ":20115,"ĠLead":20116,"Ġdeceased":20117,"Brien":20118,"ĠMusk":20119,"JS":20120,"Ġmerge":20121,"hearted":20122,"creat":20123,"mitt":20124,"mund":20125,"ĠâĢĭ":20126,"ĠBag":20127,"Ġprojection":20128,"Ġjava":20129,"ĠStandards":20130,"ĠLeonard":20131,"Ġcoconut":20132,"ĠPopulation":20133,"Ġtraject":20134,"Ġimply":20135,"Ġcuriosity":20136,"ĠDB":20137,"ĠFresh":20138,"ĠPor":20139,"Ġheavier":20140,"neys":20141,"gomery":20142,"Ġdeserved":20143,"Ġphrases":20144,"ĠGC":20145,"Ġyeast":20146,"desc":20147,"Death":20148,"Ġreboot":20149,"Ġmetadata":20150,"ICAL":20151,"Ġrepay":20152,"ĠIndependence":20153,"Ġsuburban":20154,"icals":20155,"Ġatop":20156,"Ġallocation":20157,"generation":20158,"ĠGram":20159,"Ġmoisture":20160,"Ġpine":20161,"ĠLiberals":20162,"Ġaides":20163,"Ġunderest":20164,"ĠBerry":20165,"Ġceremon":20166,"370":20167,"astrous":20168,"ĠPirates":20169,"Ġtense":20170,"ĠIndustries":20171,"ĠAppeals":20172,"ĠNear":20173,"Ġè£ıç":20174,"Ġlovers":20175,"ĠCAP":20176,"ĠCraw":20177,"Ġgiants":20178,"Ġefficacy":20179,"Element":20180,"ĠBehavior":20181,"ĠToyota":20182,"Ġintest":20183,"Priv":20184,"AI":20185,"Ġmaneuver":20186,"Ġperfection":20187,"Ġbang":20188,"paper":20189,"rill":20190,"George":20191,"border":20192,"inters":20193,"ĠSeth":20194,"Ġclues":20195,"ĠLevi":20196,"ĠRevenue":20197,"147":20198,"Ġvapor":20199,"Ġfortunate":20200,"Ġthreatens":20201,"Ġvet":20202,"Ġdependency":20203,"ersed":20204,"article":20205,"ĠBlizzard":20206,"Ġchlor":20207,"Ġminus":20208,"ĠBills":20209,"Ġcryptocurrency":20210,"Ġmetabolism":20211,"tering":20212,"Ġpestic":20213,"steps":20214,"ĠTreasure":20215,"racted":20216,"ĠConstant":20217,"Ġtemp":20218,"139":20219,"ĠDetective":20220,"urally":20221,"Ġrecovering":20222,"Ġcortex":20223,"Ġ144":20224,"closed":20225,"Ġprejudice":20226,"aunted":20227,"Ġstorms":20228,"ĠNOW":20229,"Ġmachinery":20230,"Address":20231,"Ġcompelled":20232,"270":20233,"Ġdespair":20234,"bane":20235,"Ġvegetable":20236,"Ġbeds":20237,"Learn":20238,"Ġcolorful":20239,"Ġspike":20240,"Ġmargins":20241,"Ġsympathy":20242,"Ġworkshop":20243,"ĠCBC":20244,"Sat":20245,"Ġburns":20246,"ĠGender":20247,"Ġ129":20248,"ĠCable":20249,"Ġdebts":20250,"ĠTheresa":20251,"Ġreflecting":20252,"Ġairst":20253,"Ġrim":20254,"ramid":20255,"Ġweaknesses":20256,"Writ":20257,"oggle":20258,"ti":20259,"ĠCharge":20260,"Ġweighed":20261,"Ġ(.":20262,"Ġlaughter":20263,"Ġrouter":20264,"ĠDemocracy":20265,"Dear":20266,"Ġhasht":20267,"Ġdy":20268,"Ġhints":20269,"running":20270,"Ġfinishes":20271,"arus":20272,"Mass":20273,"result":20274,"ascus":20275,"Ġvintage":20276,"Ġconqu":20277,"Ġwildly":20278,"acist":20279,"Ġlingu":20280,"Ġprotagonist":20281,"strom":20282,"teenth":20283,"ĠSolo":20284,"mac":20285,"filled":20286,"Ġrenown":20287,"itives":20288,"Ġmotive":20289,"ĠAntar":20290,"ĠMann":20291,"ĠAdjust":20292,"Ġrockets":20293,"Ġtroubling":20294,"ei":20295,"Ġorganisms":20296,"assis":20297,"Christian":20298,"Ġ145":20299,"ĠHass":20300,"Ġswall":20301,"Ġwax":20302,"ĠSurvival":20303,"VS":20304,"ĠMurd":20305,"vd":20306,"standard":20307,"Ġdragons":20308,"Ġacceleration":20309,"rational":20310,"final":20311,"Ġpaired":20312,"ĠEthereum":20313,"Ġinterfaces":20314,"Ġresent":20315,"Ġartifacts":20316,"Å«":20317,"arel":20318,"Ġcompetitor":20319,"ĠNicholas":20320,"ĠSurface":20321,"cpp":20322,"ĠTot":20323,"Ġeconomically":20324,"Ġorganised":20325,"Ġenforced":20326,"inho":20327,"Ġvarieties":20328,"Ġabdom":20329,"ĠBailey":20330,"idav":20331,"ĠSalv":20332,"paid":20333,"Ġaltitude":20334,"essert":20335,"ĠGutenberg":20336,"area":20337,"opoulos":20338,"Ġprofessors":20339,"iggs":20340,"ĠFate":20341,"hey":20342,"Ġ3000":20343,"Dist":20344,"Ġtwins":20345,"cill":20346,"ĠMaps":20347,"Ġtraps":20348,"Ġweed":20349,"ĠKiss":20350,"Ġyoga":20351,"Ġrecipients":20352,"ĠWestminster":20353,"Ġpools":20354,"ĠWalmart":20355,"188":20356,"ĠSchools":20357,"attack":20358,"ĠARM":20359,"paragraph":20360,"Warning":20361,"jl":20362,"Ġselfish":20363,"anchez":20364,"ĠHeights":20365,"Fre":20366,"ĠSoph":20367,"Ġ--------------------------------":20368,"tml":20369,"333":20370,"Ġraids":20371,"Ġsatellites":20372,"KEY":20373,"Ġlasts":20374,"ÑĤ":20375,"Ins":20376,"ĠDame":20377,"Ġunpredict":20378,"///":20379,"ghai":20380,"Ġartillery":20381,"Ġcruise":20382,"Ġgel":20383,"ĠCabinet":20384,"Ġblows":20385,"ĠEsp":20386,"Ġproximity":20387,"othe":20388,"ĠSkills":20389,"ĠUpper":20390,"obo":20391,"ĠNDP":20392,"Ġenjoys":20393,"Ġrepeating":20394,"ĠConstruction":20395,"ĠQuestions":20396,"Hillary":20397,"Ġuint":20398,"Ġprocessors":20399,"ĠGibson":20400,"ĠMultiple":20401,"qa":20402,"ĠBom":20403,"ĠMiles":20404,"ventional":20405,"Ġhurts":20406,"skin":20407,"ĠAIDS":20408,"Ġadvisers":20409,"ĠRoot":20410,"Ġmethodology":20411,"ĠDale":20412,"Ġdeton":20413,"ĠKnowledge":20414,"sequently":20415,"Ġ121":20416,"Ġconnects":20417,"Cy":20418,"ĠDanger":20419,"Ġcontributors":20420,"ĠBent":20421,"Ġbrass":20422,"ĠGuns":20423,"into":20424,"ĠFortune":20425,"Ġbroker":20426,"balance":20427,"Ġlengths":20428,"Ġvic":20429,"Ġaveraging":20430,"Ġappropriately":20431,"ĠCamera":20432,"Ġsandwich":20433,"ĠCDC":20434,"Ġcoordinate":20435,"Ġnavig":20436,"Ġgoodness":20437,"laim":20438,"Ġbrake":20439,"Ġextremist":20440,"ĠWake":20441,"ĠMend":20442,"ĠTiny":20443,"ĠCOL":20444,"ĠRF":20445,"ĠDual":20446,"ĠWine":20447,"Case":20448,"Ġrefined":20449,"Ġlamp":20450,"Lead":20451,"Ġbapt":20452,"ĠCarb":20453,"ĠSadd":20454,"ĠMinneapolis":20455,"PDF":20456,"Early":20457,"ĠHidden":20458,"Its":20459,"ĠTIME":20460,"Ġpap":20461,"Ġcommissioned":20462,"ĠFew":20463,"ĠColts":20464,"ĠBren":20465,"Ġbothered":20466,"Ġlikewise":20467,"Exper":20468,"ĠSchw":20469,"cry":20470,"nn":20471,"ĠMitch":20472,"imon":20473,"MG":20474,"bm":20475,"UMP":20476,"rays":20477,"Ġregistry":20478,"Ġ270":20479,"achine":20480,"rella":20481,"anting":20482,"00000":20483,"Ġruined":20484,"spot":20485,"Ġta":20486,"Ġmaximize":20487,"Ġinconven":20488,"Dead":20489,"Human":20490,"Enabled":20491,"ĠMarie":20492,"Ġchill":20493,"ĠParadise":20494,"Ġstarring":20495,"ĠLatino":20496,"ĠProtocol":20497,"ĠEVER":20498,"Ġsuppliers":20499,"message":20500,"ĠBrock":20501,"Ġserum":20502,"âĸĪâĸĪâĸĪâĸĪ":20503,"Ġencomp":20504,"Ġambition":20505,"uese":20506,"Ġarrows":20507,"Andrew":20508,"Ġantenna":20509,"Ġ1961":20510,"ĠBark":20511,"Ġbool":20512,"ãĤª":20513,"ĠStorage":20514,"Ġrailway":20515,"Ġtougher":20516,"ĠCad":20517,"Ġwashing":20518,"Py":20519,"']":20520,"embed":20521,"ĠMemphis":20522,"ackle":20523,"Ġfamously":20524,"ĠFortunately":20525,"ovies":20526,"Ġmindset":20527,"Ġsneak":20528,"ĠDh":20529,"RAW":20530,"ĠSimpson":20531,"Ġlivest":20532,"Ġlandmark":20533,"Ġcement":20534,"Low":20535,"Ġthrilled":20536,"ĠCourse":20537,"inel":20538,"Ġchuck":20539,"idate":20540,"global":20541,"Ġwhit":20542,"Ġ�":20543,"adays":20544,"ski":20545,"ĠSV":20546,"Ġviruses":20547,"306":20548,"ĠRespons":20549,"Ġtheaters":20550,"ĠBranch":20551,"ĠGeneva":20552,"ĠMK":20553,"Ġunbeliev":20554,"Ġcommunist":20555,"Original":20556,"ĠReceived":20557,"ĠTransfer":20558,"ĠArg":20559,"Input":20560,"ĠStrategy":20561,"Ġpalace":20562,"thening":20563,"Dri":20564,"Ġsentencing":20565,"umbnail":20566,"Ġpins":20567,"recy":20568,"Ġsiblings":20569,"Getting":20570,"ĠBU":20571,"ĠNorthwest":20572,"Ġprolonged":20573,"ĠSakura":20574,"Comb":20575,"ĠBour":20576,"Ġinadequate":20577,"ĠKash":20578,"Ġusername":20579,"ĠImprove":20580,"Ġbattling":20581,"ĠMAC":20582,"Ġcurriculum":20583,"Ġsoda":20584,"ĠCannon":20585,"Ġsensible":20586,"spons":20587,"December":20588,"Ġwicked":20589,"ĠPengu":20590,"Ġdictators":20591,"ĠHearts":20592,"ogyn":20593,"Ġsimilarities":20594,"ĠStats":20595,"Ġhollow":20596,"itations":20597,"\":[":20598,"Ġhover":20599,"ĠListen":20600,"sch":20601,"Sund":20602,"Ġcad":20603,"ĠParks":20604,"Ġlur":20605,"Ġhype":20606,"ĠLem":20607,"NAME":20608,"isure":20609,"Friday":20610,"Ġshoots":20611,"Ġcloses":20612,"Ġdb":20613,"ĠRidge":20614,"ĠDifferent":20615,"Ġreplies":20616,"ĠBroadway":20617,"opers":20618,"Ġintoler":20619,"ĠZeus":20620,"akespe":20621,"Ġproprietary":20622,"Ġrequesting":20623,"Ġcontrollers":20624,"ĠMIN":20625,"imedia":20626,"becca":20627,"Ġexpans":20628,"Ġoils":20629,"Bot":20630,"ĠChand":20631,"Ġprinter":20632,"Ġtopped":20633,"ĠPOL":20634,"ĠEarlier":20635,"Social":20636,"avin":20637,"Ġdecreases":20638,"ĠSeb":20639,"Ġspecifications":20640,"ĠBlast":20641,"ĠKurt":20642,"Ġfreel":20643,"Brown":20644,"Ġdilig":20645,"roe":20646,"ĠProblem":20647,"ĠQuad":20648,"Ġdecentral":20649,"ĠVector":20650,"anut":20651,"Ġplugins":20652,"ĠGregory":20653,"Ġfucked":20654,"elines":20655,"ĠAmbassador":20656,"take":20657,"Ġcleans":20658,"ongyang":20659,"Anonymous":20660,"stro":20661,"\"}":20662,"aline":20663,"ĠOdd":20664,"ĠEug":20665,"216":20666,"Ġboil":20667,"ĠPowers":20668,"Ġnurses":20669,"Obviously":20670,"ĠTechnical":20671,"Ġexceeded":20672,"ORS":20673,"Ġextremists":20674,"Ġtraces":20675,"expl":20676,"Ġcomr":20677,"ĠSach":20678,")/":20679,"Ġmasks":20680,"Ġsci":20681,"Bon":20682,"Ġregression":20683,"wegian":20684,"Ġadvisor":20685,"itures":20686,"ĠVo":20687,"example":20688,"ĠInstruct":20689,"Ġsiege":20690,"Ġreductions":20691,"ptr":20692,"Ġstatutory":20693,"Ġremoves":20694,"Ġpuck":20695,"redits":20696,"Ġbee":20697,"Ġsalad":20698,"Ġpromotions":20699,"ĠJoshua":20700,"withstanding":20701,"ETH":20702,"ĠCha":20703,"imus":20704,"Ġexpenditure":20705,"aunting":20706,"Ġdelighted":20707,"Ġ155":20708,"beh":20709,"Ġcarpet":20710,"ĠSpart":20711,"Ġjungle":20712,"lists":20713,"Ġbullying":20714,"ĠNobel":20715,"ĠGlen":20716,"Ġreferenced":20717,"Ġintroduces":20718,"sein":20719,"Ġchopped":20720,"glass":20721,"ĠWrest":20722,"Ġneutrality":20723,"ĠâĻ":20724,"Ġinvestigator":20725,"Ġshelves":20726,"Ġunconstitutional":20727,"Ġreproduction":20728,"Ġmerchant":20729,"mia":20730,"Ġmetrics":20731,"Ġexplosives":20732,"ĠSonia":20733,"Ġbodily":20734,"Ġthickness":20735,"Ġpredominantly":20736,"ĠAbility":20737,"Ġmonitored":20738,"ICH":20739,"Ġ].":20740,"ĠMartinez":20741,"Ġvisibility":20742,"Ġqueries":20743,"Ġgenocide":20744,"ĠWarfare":20745,"Query":20746,"Ġstudios":20747,"Ġembry":20748,"Ġcorridor":20749,"Ġcleaned":20750,"complete":20751,"ĠMH":20752,"Ġenrollment":20753,"INGS":20754,"Ġimpacted":20755,"Ġdisastrous":20756,"ĠYun":20757,"ĠClaire":20758,"ĠBasically":20759,"yt":20760,"usterity":20761,"Ġindirectly":20762,"wik":20763,"Ġdod":20764,"ĠCarr":20765,"Ġamp":20766,"Ġprohibit":20767,"ĠInitial":20768,"ĠRd":20769,"iji":20770,"Ġeducate":20771,"corn":20772,"iott":20773,"ĠBeauty":20774,"Ġdetective":20775,"ĠConn":20776,"since":20777,"Ġstagger":20778,"Ġobese":20779,"Ġbree":20780,"ologic":20781,"isse":20782,"walker":20783,"Ġblades":20784,"Ġlawful":20785,"func":20786,"ĠBehind":20787,"Ġappetite":20788,"Ġ(*":20789,"Ġtennis":20790,"Ġoffspring":20791,"Ġjets":20792,"Ġstructured":20793,"Ġaforementioned":20794,"Nov":20795,"Ġscaling":20796,"fill":20797,"Ġstew":20798,"Ġcurb":20799,"ĠStephan":20800,"edIn":20801,"SF":20802,"obic":20803,"éŃĶ":20804,"oug":20805,"ĠMM":20806,"Ġgenetically":20807,"opez":20808,"136":20809,"Ġumb":20810,"ancers":20811,"Ġcohort":20812,"Ġmerchandise":20813,"Ġimposing":20814,"ĠLegislature":20815,"ĠArchive":20816,"ivia":20817,"ĠNaval":20818,"Ġoffences":20819,"Ġmiracle":20820,"Ġsnapped":20821,"Ġfoes":20822,"Ġextensively":20823,"ĠRaf":20824,"Ġcater":20825,"edience":20826,"Kit":20827,"ĠBin":20828,"Ġrecommends":20829,"ĠCities":20830,"Ġrigid":20831,"ĠREAD":20832,"ĠNoble":20833,"ĠTian":20834,"Ġcertificates":20835,"antis":20836,"oiler":20837,"ĠBuddhist":20838,"did":20839,"Ġsurveyed":20840,"Ġdownward":20841,"Ġprints":20842,"ĠMotion":20843,"ronics":20844,"ĠSans":20845,"ossibly":20846,"uctions":20847,"Ġcolonies":20848,"ĠDanish":20849,"unit":20850,"Ġspoil":20851,"Ġadvisory":20852,"berries":20853,"Plan":20854,"Ġspecification":20855,"ophers":20856,"ĠResource":20857,"Ġshirts":20858,"prisingly":20859,"communications":20860,"Ġtrivial":20861,"Ġmentioning":20862,"isexual":20863,"Ġsupplements":20864,"Ġsupervision":20865,"BP":20866,"vor":20867,"Ġwit":20868,"Ġcooldown":20869,"Ġplaintiff":20870,"ĠReviews":20871,"ĠSri":20872,"ĠMint":20873,"ĠSugar":20874,"Ġafterward":20875,"ĠPriest":20876,"ĠInvestment":20877,"ogene":20878,"ĠTaking":20879,"Ġstretching":20880,"Ġinflammation":20881,"ĠTehran":20882,"Ġlining":20883,"Ġfreezing":20884,"ĠEntity":20885,"Ġinspiring":20886,"special":20887,"price":20888,"Ġsue":20889,"ĠPorter":20890,"ounge":20891,"ETA":20892,"ĠDerek":20893,"ĠLuis":20894,"uo":20895,"ymph":20896,"Ġexterior":20897,"ihil":20898,"ĠAshley":20899,"inator":20900,"Ġnutrients":20901,"ĠThrones":20902,"Ġfinances":20903,"ĠInspect":20904,"Ġspecially":20905,"ĠRequired":20906,"ĠPTS":20907,"ĠViolence":20908,"ointed":20909,"shots":20910,"Ġexcerpt":20911,"coon":20912,"INS":20913,"ĠGri":20914,"Ġrecognised":20915,"Week":20916,"Young":20917,"Ġvom":20918,"isle":20919,"ĠCurry":20920,"ĠBuddh":20921,"Ġnotebook":20922,"Ġdurable":20923,"/?":20924,"ĠGad":20925,"ĠPupp":20926,"Ġforgive":20927,"park":20928,"Ġpersonalities":20929,"analysis":20930,"clamation":20931,"Ġelevator":20932,"Ġwarehouse":20933,"ĠRole":20934,"unn":20935,"Ġillustration":20936,"ĠScan":20937,"Ġatmospheric":20938,"Import":20939,"ANC":20940,"ricted":20941,"fu":20942,"010":20943,"Ġarche":20944,"Ġrewarded":20945,"akespeare":20946,"Ġinternally":20947,"ĠRBI":20948,"alker":20949,"Ġelephant":20950,"owitz":20951,"ĠPizza":20952,"Ġbipartisan":20953,"és":20954,"Ġslowed":20955,"ĠStark":20956,"Ġoverride":20957,"OUS":20958,"Ġ320":20959,"undreds":20960,"ĠDeck":20961,"ĠCensus":20962,"bee":20963,"146":20964,"otor":20965,"Ġip":20966,"Ġub":20967,"ocations":20968,"ĠButton":20969,"rice":20970,"Ġcripp":20971,"fff":20972,"Ġoriginated":20973,"Ġoverwhelmed":20974,"appa":20975,"Ġforemost":20976,"âĢij":20977,"ĠLEG":20978,"release":20979,"eatured":20980,"atches":20981,"Ġreps":20982,"Ġlending":20983,"ĠReference":20984,"ĠClient":20985,"165":20986,"venth":20987,"Complete":20988,"ĠPatrol":20989,"Ġsworn":20990,"cam":20991,"Ġshuttle":20992,"ĠRalph":20993,"Ġhometown":20994,"-,":20995,"onal":20996,"ĠBP":20997,"åı":20998,"Ġpersuade":20999,"ĠAlexand":21000,"Ġcombines":21001,"Ġvivid":21002,"ĠLag":21003,"Ġencoding":21004,"Ġsalvation":21005,"wen":21006,"ĠRecovery":21007,"iya":21008,"University":21009,"ĠBiden":21010,"Ġbudgets":21011,"ĠTexans":21012,"fits":21013,"Ġhonored":21014,"Ġpython":21015,"TD":21016,"###":21017,"clone":21018,"Ġblink":21019,"ĠLiquid":21020,"Ġunemployed":21021,"Ġclashes":21022,"ĠCounsel":21023,"Ġdirecting":21024,"Ġpunct":21025,"ĠFalcons":21026,"Ġshark":21027,"ĠDamascus":21028,"Ġjeans":21029,"Ġembark":21030,"Ġseize":21031,"Ġupwards":21032,"280":21033,"ĠEz":21034,"ĠAnything":21035,"Ġexotic":21036,"lower":21037,"ĠCreator":21038,"ĠUm":21039,"Ġsuburbs":21040,"berger":21041,"ĠWend":21042,"Ġmint":21043,"ĠXX":21044,"ĠDro":21045,"Ġsuffers":21046,"Ġherb":21047,"tree":21048,"Ġfragile":21049,"Ġflooded":21050,"ĠAlcohol":21051,"olean":21052,"nyder":21053,"ĠKO":21054,"Fram":21055,"Ġ136":21056,"Ġowed":21057,"ĠMelee":21058,"ĠHash":21059,"Ġwhisk":21060,"Ġsudo":21061,"rr":21062,"Quick":21063,"appro":21064,"Ġii":21065,"ĠExamples":21066,"hee":21067,"Ġpromotes":21068,"perature":21069,"kar":21070,"ĠHonor":21071,"Ġsodium":21072,"ĠLif":21073,"rosso":21074,"intendent":21075,"Ġcorrespondent":21076,"Found":21077,"secret":21078,"Ġidentifies":21079,"agne":21080,"Ġlou":21081,"ĠPP":21082,"Ġcoincidence":21083,"move":21084,"Ġmilitia":21085,"Ġinfiltr":21086,"ĠPrimary":21087,"Ġpitching":21088,"ĠIb":21089,"ĠGOOD":21090,"ãĤ¸":21091,"ĠWizards":21092,"iral":21093,"ĠVenus":21094,"RR":21095,"ĠâĢķ":21096,"ĠCasey":21097,"Ġsadly":21098,"Ġadmire":21099,"Ġembarrassed":21100,"cb":21101,"Mel":21102,"Ġtubes":21103,"Ġbeautifully":21104,"ĠQueensland":21105,"Below":21106,"rez":21107,"quet":21108,"pleasant":21109,"Ġ«":21110,"Camp":21111,"Ġdecisive":21112,"1998":21113,"ĠLamb":21114,"utton":21115,"hn":21116,"ĠJagu":21117,"aunder":21118,"ĠCord":21119,"Ġclerk":21120,"Ġcaffe":21121,"Ġwiped":21122,"Ġreim":21123,"ĠMountains":21124,"Ġimprisoned":21125,"Ġdevelops":21126,"ĠPra":21127,"Ġmodeling":21128,"Anyone":21129,"ancel":21130,"ĠSit":21131,"Ġshields":21132,"Ġlawn":21133,"Ġcardiovascular":21134,"Ġdemonstrating":21135,"Ġparse":21136,"ĠIsraelis":21137,"Ġeuros":21138,"143":21139,"Ġglorious":21140,"inski":21141,"ecd":21142,"Ġconditioning":21143,"Ġhelpless":21144,"Ġmicrosc":21145,"ĠHarbor":21146,"Ġstakes":21147,"Ġ260":21148,"Ġunequ":21149,"ĠFloyd":21150,"Ġdamp":21151,"Ġapparatus":21152,"ĠLaws":21153,"Ġcounters":21154,"Ġinduce":21155,"atable":21156,"ĠAhmed":21157,"Ġslam":21158,"November":21159,"Ġpersist":21160,"Ġimminent":21161,"án":21162,"Ġshred":21163,"Ġphases":21164,"ĠEdmonton":21165,"ĠArmstrong":21166,"ĠMeet":21167,"ĠKitty":21168,"ÑĢ":21169,"circ":21170,"ĠAdult":21171,"Ġarose":21172,"ĠXen":21173,"Dan":21174,"gow":21175,"Ġsuperf":21176,"ĠAdmir":21177,"Ġendure":21178,"Ġkeyword":21179,"yrus":21180,"Ġyarn":21181,"Ġpathway":21182,"ĠHopkins":21183,"midt":21184,"Ġcensorship":21185,"dependent":21186,"Ġinstructor":21187,"Sources":21188,"Ġtoe":21189,"Ġballoon":21190,"Nob":21191,"Ġswear":21192,"ĠCastro":21193,"Ġgloss":21194,"ĠKavanaugh":21195,"Ġremarkably":21196,"Photos":21197,"ĠNom":21198,"ĠSoutheast":21199,"yers":21200,"Ġvalidation":21201,"Ġcannon":21202,"ĠVictory":21203,"ĠPierre":21204,"Ġcautious":21205,"Audio":21206,"Ġfetch":21207,"ĠGift":21208,"ĠHyp":21209,"Ġremedy":21210,"ZE":21211,"Ġscent":21212,"Ġbeard":21213,"ĠRut":21214,"-\"":21215,"Ġpatents":21216,"Hy":21217,"Ġunjust":21218,"Ġpotato":21219,"Ġforthcoming":21220,"Ġchef":21221,"ĠRift":21222,"affe":21223,"ĠROM":21224,"ĠLaunch":21225,"Ġpads":21226,"ĠNeo":21227,"Ġonset":21228,"Ġsqueeze":21229,"safe":21230,"Ġprefix":21231,"ĠTM":21232,"ĠNearly":21233,"ĠClinical":21234,"ĠMental":21235,"otiation":21236,"ĠUnic":21237,"antry":21238,"ĠCir":21239,"Ġepit":21240,"æ":21241,"Ġextracted":21242,"versely":21243,"riad":21244,"Ġstrains":21245,"Ġtops":21246,"Ġpoem":21247,"ĠRandy":21248,"ĠMaple":21249,"THER":21250,"upiter":21251,"ĠSSD":21252,"ļé":21253,"Ġuncon":21254,"pering":21255,"Ġslept":21256,"iners":21257,"Ġunderwater":21258,"ĠEvidence":21259,"gone":21260,"205":21261,"Ġhistorians":21262,"Ġsynthesis":21263,"Ġfrog":21264,"basketball":21265,"Ġvibrant":21266,"Ġsubord":21267,"Ġ365":21268,"ĠDial":21269,"Ġcooperate":21270,"HAHA":21271,"Ġgreeted":21272,"158":21273,"Ġjazz":21274,"Ġintox":21275,"ĠWalking":21276,"Ġsupervisor":21277,"ĠFusion":21278,"ĠMercedes":21279,"send":21280,"Ham":21281,"sd":21282,"nl":21283,"Ġtours":21284,"ĠFIFA":21285,"Ġculp":21286,"gd":21287,"304":21288,"Ġpleas":21289,"Ġillustrates":21290,"ĠColombia":21291,"Ġhighlighting":21292,"ĠSummary":21293,"Ġexposing":21294,"ĠDru":21295,"Ġirony":21296,"ritional":21297,"ĠCarroll":21298,"ĠEllis":21299,"Pict":21300,"ĠRapt":21301,"Ġadapter":21302,"Ġunm":21303,"Ġcorpse":21304,"Ġcelebrities":21305,"Den":21306,"atum":21307,"ĠApocalypse":21308,"ĠWag":21309,"lining":21310,"Ġhormones":21311,"Rub":21312,"ĠXi":21313,"ĠVaults":21314,"208":21315,"alkyrie":21316,"inosaur":21317,"Ġfeeds":21318,"vity":21319,"Ġdefeating":21320,"Wait":21321,"Ġemphasize":21322,"ĠSteelers":21323,"yrinth":21324,"leys":21325,"ĠWhenever":21326,"Currently":21327,"ĠClock":21328,"Ġcollectively":21329,"anyon":21330,"ĠJP":21331,"Ġmentality":21332,"Ġdownloads":21333,"Ġsurroundings":21334,"ĠBarnes":21335,"Ġflagship":21336,"Ġindicators":21337,"Ġgrapp":21338,"January":21339,"ĠElemental":21340,"ĠAthena":21341,"ibal":21342,"Ġsights":21343,"Ġcapita":21344,"ĠTreaty":21345,"Ġvoiced":21346,"ĠGaz":21347,"lette":21348,"Ġya":21349,"Ġexpired":21350,"Legend":21351,"Hot":21352,"nature":21353,"Ġunstable":21354,"Ġ280":21355,"ú":21356,"Comment":21357,"ALE":21358,"Ġquests":21359,"Ġhandler":21360,"nis":21361,"Ġversatile":21362,"Ġconceal":21363,"engeance":21364,"ĠInteractive":21365,"Ġobsessed":21366,"ĠDogs":21367,"Ġcracked":21368,"Sound":21369,"sv":21370,"ĠDylan":21371,"roads":21372,"fx":21373,"ĠCatholics":21374,"ĠHag":21375,"Ġslammed":21376,"Ġglowing":21377,"sale":21378,"Ġtissues":21379,"ĠChi":21380,"nee":21381,"Ġcher":21382,"sic":21383,"urrection":21384,"Ġbacon":21385,"ulatory":21386,").\"":21387,"Ġirregular":21388,"FORM":21389,"assed":21390,"Ġintentional":21391,"Ġcompensate":21392,"ĠSpeaking":21393,"ĠSets":21394,"153":21395,"Ġconventions":21396,"bands":21397,"emade":21398,"Ġecc":21399,"ĠWinston":21400,"ĠAssassin":21401,"ĠBelgian":21402,"Ġdependence":21403,"Ġniche":21404,"Ġbark":21405,"ĠJazz":21406,"Ġdisadvantage":21407,"Ġgasoline":21408,"Ġ165":21409,"çļĦ":21410,"essa":21411,"module":21412,"angular":21413,"OY":21414,"ĠTreatment":21415,"itas":21416,"olation":21417,"ĠArnold":21418,"Ġfeud":21419,"ĠNest":21420,"Ġtheatre":21421,"ewater":21422,"Ġminors":21423,"olicy":21424,"ĠHaven":21425,"division":21426,"Ġtrunk":21427,"Far":21428,"ĠPull":21429,"Ġcapturing":21430,"Ġ1800":21431,"ĠTeen":21432,"Ġexempl":21433,"Ġclinics":21434,"ĠBurg":21435,"Ġsubstit":21436,"Ġpayload":21437,"ĠLav":21438,"ĠTroy":21439,"ĠWitness":21440,"Ġfragments":21441,"Ġpasswords":21442,"Ġgospel":21443,"ĠGin":21444,"Ġtenants":21445,"olith":21446,"Six":21447,"Previous":21448,"ĠAges":21449,"ĠDarwin":21450,"Ġblat":21451,"Ġempathy":21452,"smith":21453,"bag":21454,"ĠEcho":21455,"ĠCamb":21456,"ĠMadd":21457,"ĠBoo":21458,"Ġrede":21459,"ĠBurning":21460,"Ġsmoothly":21461,"ĠAdrian":21462,"ĠVampire":21463,"ĠMonsters":21464,"steam":21465,"Style":21466,"Ma":21467,"rea":21468,"ĠDwar":21469,"alyst":21470,"ursor":21471,"Ġelimination":21472,"Ġcrypto":21473,"cht":21474,"ĠEternal":21475,"âĢ¦]":21476,"ĠSorce":21477,"Ill":21478,"NER":21479,"Ġuh":21480,"Conclusion":21481,"wage":21482,"Ġrespir":21483,"Ġreminis":21484,"hetical":21485,"Ġgy":21486,"Ġutilized":21487,"icidal":21488,"Ġ1900":21489,"Ġhunters":21490,"ĠSwan":21491,"ĠReact":21492,"Ġvisitor":21493,"ĠThanksgiving":21494,"308":21495,"Posts":21496,"Ġhips":21497,"1997":21498,"omers":21499,"Ġknocking":21500,"ĠVehicle":21501,"Ġtil":21502,"Ġ138":21503,"Ġmi":21504,"ĠInvestigation":21505,"ĠKenya":21506,"Ġcasino":21507,"Ġmotives":21508,"Ġregain":21509,"rex":21510,"Ġweekends":21511,"Ġstabbed":21512,"boro":21513,"Ġexploited":21514,"ĠHAVE":21515,"ĠTelevision":21516,"cock":21517,"Ġpreparations":21518,"Ġendeav":21519,"ĠRemote":21520,"ĠMaker":21521,"ĠProdu":21522,"ĠEvan":21523,"Ġinformational":21524,"ĠLouisville":21525,"154":21526,"ĠDreams":21527,"Ġplots":21528,"ĠRunner":21529,"Ġhurting":21530,"Ġacademy":21531,"ĠMontgomery":21532,"nm":21533,"ĠLanc":21534,"ĠAlz":21535,"210":21536,"elong":21537,"Ġretailer":21538,"Ġarising":21539,"Ġrebellion":21540,"Ġblonde":21541,"played":21542,"Ġinstrumental":21543,"Cross":21544,"Ġretention":21545,"Ġtherapeutic":21546,"Ġseas":21547,"Ġinfantry":21548,"ĠClint":21549,"Ġprompting":21550,"Ġbitch":21551,"Ġstems":21552,"ĠKra":21553,"Ġthesis":21554,"ĠBog":21555,"rued":21556,"Ġkings":21557,"Ġclay":21558,"ificent":21559,"ĠYES":21560,"ĠThing":21561,"ĠCubs":21562,"veyard":21563,"elsh":21564,"inarily":21565,"ĠEy":21566,"ĠRolling":21567,"Ġevolving":21568,"India":21569,"Ġrecognizes":21570,"Ġgraduation":21571,"isers":21572,"Ġfertility":21573,"ĠMilan":21574,"Command":21575,"Ġboxing":21576,"Ġ1943":21577,"Ġgluten":21578,"ĠEmir":21579,"Ġidol":21580,"Ġconceived":21581,"ĠCreation":21582,"Merit":21583,"uddy":21584,"ussions":21585,"ĠLieutenant":21586,"ietal":21587,"Ġunchanged":21588,"ĠScale":21589,"ĠCrimea":21590,"balls":21591,"atorial":21592,"Ġdepths":21593,"Ġempirical":21594,"Ġtransm":21595,"Ġunsafe":21596,"missible":21597,"comfort":21598,"156":21599,"Ġmechanic":21600,"002":21601,"lins":21602,"Ġsmoked":21603,"Pos":21604,"Ġslowing":21605,"Ġlav":21606,"Texas":21607,"Ġcheating":21608,"ĠMetropolitan":21609,"ethyl":21610,"Ġdiscovering":21611,"asse":21612,"Ġpencil":21613,"ĠPyongyang":21614,"Ġcloset":21615,"ĠSheet":21616,"ĠEntry":21617,"oustic":21618,"Ġmyst":21619,"erate":21620,"ariat":21621,"Ġminerals":21622,"Ġmusician":21623,"ĠPul":21624,"ĠMaz":21625,"249":21626,"Ġpermissions":21627,"Ġiv":21628,"enary":21629,"ickers":21630,"ĠBing":21631,"hea":21632,"enable":21633,"Ġgriev":21634,"Ġasserted":21635,"ĠColonel":21636,"Ġaffidav":21637,"wo":21638,"Ġseated":21639,"ĠRide":21640,"Ġpaintings":21641,"ĠPix":21642,"Ġ137":21643,"ishi":21644,"umbai":21645,"gotten":21646,"ĠEarl":21647,"Ġinning":21648,"Ġcensus":21649,"Ġtravelled":21650,"ĠConsult":21651,"185":21652,"bind":21653,"Ġsimplicity":21654,"Ġoverlooked":21655,"ĠHelpful":21656,"Ġmonkey":21657,"Ġoverwhelmingly":21658,"Blood":21659,"ĠFlint":21660,"ĠJama":21661,"ĠPresent":21662,"ĠRage":21663,"ĠTA":21664,"ptive":21665,"Ġturnout":21666,"wald":21667,"ĠDolphins":21668,"ĠVPN":21669,"Ġonion":21670,"Ġcrafting":21671,"mma":21672,"ĠMercury":21673,"Ġarrange":21674,"Ġalerts":21675,"ĠOT":21676,"zbollah":21677,"Ġgases":21678,"ĠRichardson":21679,"sal":21680,"lar":21681,"Ġfrost":21682,"Ġlowering":21683,"Ġacclaim":21684,"Ġstartups":21685,"ĠGain":21686,"essment":21687,"Ġguardian":21688,"人":21689,"ĠPie":21690,"ĠLinks":21691,"Ġmerits":21692,"Ġawake":21693,"Ġparental":21694,"Ġexceeds":21695,"Ġidle":21696,"ĠPilot":21697,"ĠeBay":21698,"ĠAccept":21699,"ipeg":21700,"Cam":21701,"ĠKot":21702,"Ġtraders":21703,"olitics":21704,"unker":21705,"ĠPale":21706,"osi":21707,"anmar":21708,"Ġ1947":21709,"ĠFell":21710,"estial":21711,"itating":21712,"GF":21713,"ĠSr":21714,"ifted":21715,"Ġconnector":21716,"ĠBone":21717,"illes":21718,"260":21719,"hma":21720,"Ġoverlap":21721,"ĠGitHub":21722,"Ġcleaner":21723,"ĠBaptist":21724,"ĠWAS":21725,"Ġlungs":21726,"Ñģ":21727,"ĠBUT":21728,"Ġcite":21729,"Ġpitched":21730,"reatment":21731,"Ġtrophies":21732,"ĠNu":21733,"386":21734,"ĠPride":21735,"Ġattendees":21736,"[]":21737,"179":21738,"Ġspatial":21739,"Ġprizes":21740,"ĠReligion":21741,"Ġshowcase":21742,"ĠCategory":21743,"vidia":21744,"Target":21745,"Property":21746,"?,":21747,"Ġfusion":21748,"pie":21749,"ĠUCLA":21750,"Ġsoundtrack":21751,"Ġprincess":21752,"ĠCaval":21753,"should":21754,"Ġlimbs":21755,"Background":21756,"Ġlonely":21757,"Ġcores":21758,"ĠTail":21759,"sheet":21760,"Ġ132":21761,"Ra":21762,"ãĤ«":21763,"ĠBolt":21764,"Ġbooked":21765,"Ġadminister":21766,"Ġequals":21767,"wy":21768,"Ġobserving":21769,"ĠBaron":21770,"ĠAdobe":21771,"Ġvirgin":21772,"ĠSocialist":21773,"Move":21774,"ghazi":21775,"ĠLinda":21776,"212":21777,"Ġbrewing":21778,"Ġmerchants":21779,"burse":21780,"Ġdivor":21781,"Ġmetals":21782,"ĠNer":21783,"Ġsums":21784,"ĠEnemy":21785,"Ġenvision":21786,"Ġgranting":21787,"ĠHoney":21788,"ĠSkyrim":21789,"Ġsocio":21790,"graded":21791,"Ġselective":21792,"WASHINGTON":21793,"Ġ1948":21794,"ĠSirius":21795,"ĠGross":21796,"activity":21797,"ĠIvan":21798,"Ġfurious":21799,"BSD":21800,"ĠPrevious":21801,"Ġresponsive":21802,"Ġcharitable":21803,"Ġleaning":21804,"ĠPew":21805,"Ġviolates":21806,"\\\\\\\\\\\\\\\\":21807,"ĠComing":21808,"wire":21809,"Ġpoet":21810,"Ġresolutions":21811,"command":21812,"ĠPortuguese":21813,"Ġnickname":21814,"Ġdeaf":21815,"February":21816,"Ġrecognise":21817,"Ġentirety":21818,"Ġseasonal":21819,"placed":21820,"ĠTelegraph":21821,"Ġmicrophone":21822,"ouring":21823,"Ġgrains":21824,"Ġgoverned":21825,"Ġpostp":21826,"ĠWaters":21827,"inement":21828,"Ġundocumented":21829,"ĠComcast":21830,"Ġfox":21831,"Ġassaults":21832,"reon":21833,"many":21834,"ĠJenkins":21835,"ĠAnyway":21836,"Ġassessments":21837,"Ġdowns":21838,"ĠMouse":21839,"Ġsuperb":21840,"kt":21841,"ĠDow":21842,"Ġtaxation":21843,"401":21844,"Ġsmiles":21845,"Ġundertaken":21846,"Ġexh":21847,"Ġenthusiastic":21848,"Ġtwent":21849,"Ġgovernmental":21850,"Ġautonomy":21851,"ĠTechnologies":21852,"ĠChain":21853,"Ġprevalent":21854,"fb":21855,"Ġnicotine":21856,"ogram":21857,"job":21858,"Ġawaiting":21859,"ĠMenu":21860,"Ġdeputies":21861,"kov":21862,"ishops":21863,"Button":21864,"ĠShanghai":21865,"Ġdiesel":21866,"ĠDuck":21867,"Ryan":21868,"ĠPCs":21869,"NF":21870,"jury":21871,"ente":21872,"Ġinaccurate":21873,"eddy":21874,"Whatever":21875,"Ġshowc":21876,"ĠNad":21877,"odus":21878,"etr":21879,"Ġplaintiffs":21880,"ĠWOR":21881,"ĠAssange":21882,"Ġprivat":21883,"Ġpremiums":21884,"Ġtam":21885,"URL":21886,"Ġelites":21887,"ĠRanger":21888,"ottenham":21889,"ĠHoff":21890,"ĠAthens":21891,"Ġdefinite":21892,"Ġsighed":21893,"Ġevenly":21894,"211":21895,"ĠAmber":21896,"akia":21897,"Ġmailing":21898,"Ġcrashing":21899,"ĠConfederate":21900,"rugged":21901,"Wal":21902,"ĠDepths":21903,"Ġjuvenile":21904,"Ġreactor":21905,"Introduction":21906,"ĠDeluxe":21907,"1995":21908,"ĠSanchez":21909,"ĠMead":21910,"ivable":21911,":-":21912,"ĠPlanning":21913,"ĠTrap":21914,"quin":21915,"ĠProtect":21916,"vered":21917,"Information":21918,"Ġkidney":21919,"innamon":21920,"las":21921,"Ġpolicing":21922,"Ġtolerate":21923,"ĠQi":21924,"Ġbiased":21925,"Fort":21926,"ĠKi":21927,"save":21928,"Ġprivileged":21929,"Ġbeasts":21930,"ĠGlas":21931,"ĠCinem":21932,"Ġcomeback":21933,"Sunday":21934,"Ġextinction":21935,"hops":21936,"Ġtransmit":21937,"Ġdoubles":21938,"ĠFlat":21939,"167":21940,"Ġdisputed":21941,"Ġinjustice":21942,"foo":21943,"Vict":21944,"roleum":21945,"ĠJulie":21946,"Context":21947,"ĠRarity":21948,"issue":21949,"Component":21950,"Ġcounseling":21951,"anne":21952,"dark":21953,"Ġobjections":21954,"uilt":21955,"Ġgast":21956,"Ġplac":21957,"Ġunused":21958,"ãĥĩ":21959,"ĠTrial":21960,"ĠJas":21961,"hedral":21962,"obb":21963,"Ġtemporal":21964,"ĠPRO":21965,"ĠNW":21966,"ĠAnniversary":21967,"Large":21968,"Ġtherm":21969,"Ġdavid":21970,"Ġsystemic":21971,"ĠShir":21972,"mut":21973,"ĠNept":21974,"address":21975,"Ġscanning":21976,"Ġunderstandable":21977,"Ġcanvas":21978,"Cat":21979,"ĠZoo":21980,"Ġangels":21981,"LO":21982,"ĠStatement":21983,"ĠSig":21984,"ovable":21985,"ĠAway":21986,"sharing":21987,"ocrats":21988,"stated":21989,"Ġweighing":21990,"Nor":21991,"wild":21992,"Bey":21993,"Ġastonishing":21994,"ĠReynolds":21995,"Ġopener":21996,"Ġtrainer":21997,"Ġsurgical":21998,"pn":21999,"Ġadjusting":22000,"wheel":22001,"Ġfrown":22002,"ervative":22003,"Ġsuspend":22004,"Within":22005,"tein":22006,"Ġobstacle":22007,"Ġliberties":22008,"ymes":22009,"Ġuranium":22010,"ansom":22011,"anol":22012,"uba":22013,"ĠLoss":22014,"Ġarous":22015,"ĠHenderson":22016,"Wow":22017,"spl":22018,"cur":22019,"ĠÂŃ":22020,"Ġtheirs":22021,"Damage":22022,"Ġdownloading":22023,"Ġdiscern":22024,"ĠSto":22025,"ĠFla":22026,"Ġhath":22027,"ĠAj":22028,"Ġunpleasant":22029,"European":22030,"expensive":22031,"Ġscreenshot":22032,"ĠUV":22033,"Ġallied":22034,"ĠPersian":22035,"Ġmonopoly":22036,"Ġatom":22037,"ĠRedskins":22038,"\"><":22039,"Ġcancell":22040,"Ġcinema":22041,"131":22042,"fair":22043,"ĠAlfred":22044,"Ġduck":22045,"args":22046,"223":22047,"ĠISI":22048,"Ġsignaling":22049,"inar":22050,"Ġlaughs":22051,"Ġforwards":22052,"Ġreckless":22053,"Ġlisteners":22054,"ativity":22055,"Ġvastly":22056,"nant":22057,"Less":22058,"ĠHunting":22059,"ĠScientific":22060,"ITED":22061,"Ġknight":22062,"ĠHTC":22063,"usa":22064,"tmp":22065,"Ġrude":22066,"ĠLegendary":22067,"Ġarises":22068,"Bad":22069,"ĠClaim":22070,"peg":22071,"Ġrealities":22072,"Think":22073,"Ġ°":22074,"Ġrode":22075,"Ġstrive":22076,"Ġanecd":22077,"Ġshorts":22078,"Ġhypothes":22079,"Ġcoordinated":22080,"ĠGandhi":22081,"ĠFPS":22082,"RED":22083,"Ġsusceptible":22084,"Ġshrink":22085,"ĠChart":22086,"Help":22087,"Ġion":22088,"deep":22089,"ribes":22090,"ĠKai":22091,"ĠCustomer":22092,"Summary":22093,"Ġcough":22094,"wife":22095,"Ġlend":22096,"Ġpositioning":22097,"Ġlottery":22098,"ĠCanyon":22099,"Ġfade":22100,"Ġbronze":22101,"ĠKenny":22102,"Ġboasts":22103,"ĠEnhanced":22104,"record":22105,"Ġemergence":22106,"Ġakin":22107,"ĠBert":22108,"itous":22109,"âĸij":22110,"Ġstip":22111,"Ġexchanged":22112,"omore":22113,"alsh":22114,"Ġreservoir":22115,"Ġstandpoint":22116,"WM":22117,"Ġinitiate":22118,"Ġdecay":22119,"Ġbrewery":22120,"Ġterribly":22121,"Ġmortal":22122,"levard":22123,"Ġrevis":22124,"NI":22125,"elo":22126,"Ġconfess":22127,"ĠMSNBC":22128,"Ġsubmissions":22129,"Controller":22130,"Ġ202":22131,"ĠRuth":22132,"});":22133,"ĠAzure":22134,"Ġ.\"":22135,"206":22136,"ĠMarketing":22137,"Ġlaund":22138,"iencies":22139,"Ġrenowned":22140,"ĠTrou":22141,"ĠNGO":22142,"blems":22143,"Ġterrified":22144,"Ġwarns":22145,"Ġpert":22146,"Ġunsure":22147,"480":22148,"alez":22149,"ultz":22150,"ĠOutside":22151,"Ġstyl":22152,"ĠUnderground":22153,"Ġpanc":22154,"Ġdictionary":22155,"Ġfoe":22156,"riminal":22157,"ĠNorwegian":22158,"Ġjailed":22159,"Ġmaternal":22160,"ée":22161,"ĠLucy":22162,"cop":22163,"Cho":22164,"Ġunsigned":22165,"ĠZelda":22166,"ĠInsider":22167,"ĠContinued":22168,"Ġ133":22169,"ĠNaruto":22170,"ĠMajority":22171,"169":22172,"ĠWo":22173,"ãĤĵ":22174,"Ġpastor":22175,"Ġinformal":22176,"н":22177,"anthrop":22178,"join":22179,"ãģĹ":22180,"itational":22181,"NP":22182,"ĠWriting":22183,"fn":22184,"ĠBever":22185,"195":22186,"Ġyelling":22187,"Ġdrastically":22188,"Ġeject":22189,"Ġneut":22190,"Ġthrive":22191,"ĠFrequ":22192,"oux":22193,"Ġpossesses":22194,"ĠSenators":22195,"ĠDES":22196,"ĠShakespeare":22197,"ĠFranco":22198,"ĠLB":22199,"uchi":22200,"Ġincarn":22201,"Ġfounders":22202,"Function":22203,"Ġbrightness":22204,"ĠBT":22205,"Ġwhale":22206,"ĠTheater":22207,"mass":22208,"ĠDoll":22209,"Something":22210,"Ġechoed":22211,"ĠHex":22212,"crit":22213,"afia":22214,"Ġgoddess":22215,"Ġeleven":22216,"ĠPreview":22217,"ĠAurora":22218,"Ġ401":22219,"ulsive":22220,"ĠLogan":22221,"inburgh":22222,"ĠCenters":22223,"ĠONLY":22224,"ĠAid":22225,"Ġparadox":22226,"Ġhurd":22227,"ĠLC":22228,"Due":22229,"court":22230,"Ġoffended":22231,"Ġevaluating":22232,"ĠMatthews":22233,"Ġtomb":22234,"Ġpayroll":22235,"Ġextraction":22236,"ĠHands":22237,"ifi":22238,"Ġsupernatural":22239,"ĠCOMM":22240,"]=":22241,"dogs":22242,"Ġ512":22243,"ĠMeeting":22244,"Richard":22245,"ĠMaximum":22246,"Ġideals":22247,"Things":22248,"mand":22249,"ĠRegardless":22250,"Ġhumili":22251,"buffer":22252,"Little":22253,"ĠDani":22254,"ĠNak":22255,"Ġliberation":22256,"ĠAbe":22257,"ĠOL":22258,"Ġstuffed":22259,"aca":22260,"inda":22261,"raphic":22262,"Ġmosqu":22263,"Ġcampaigning":22264,"Ġoccupy":22265,"Squ":22266,"rina":22267,"ĠWel":22268,"ĠVS":22269,"Ġphysic":22270,"Ġpuls":22271,"rint":22272,"oaded":22273,"ETF":22274,"ĠArchives":22275,"Ġvenues":22276,"hner":22277,"ĠTurbo":22278,"Ġlust":22279,"Ġappealed":22280,"quez":22281,"ilib":22282,"ĠTimothy":22283,"Ġomn":22284,"dro":22285,"Ġobsession":22286,"ĠSavage":22287,"1996":22288,"Global":22289,"Jes":22290,"214":22291,"Ġsliding":22292,"Ġdisappro":22293,"ĠMagical":22294,"Ġvoluntarily":22295,"gb":22296,"aney":22297,"Ġprophet":22298,"ĠRein":22299,"ĠJulia":22300,"ĠWorth":22301,"aurus":22302,"Ġbounds":22303,"ieu":22304,")))":22305,"Ġcrore":22306,"ĠCitizen":22307,"Sky":22308,"Ġcolumnist":22309,"Ġseekers":22310,"ondo":22311,"ISA":22312,"ĠLength":22313,"Ġnostalg":22314,"Ġnewcom":22315,"Ġdetrim":22316,"entric":22317,"375":22318,"ĠGE":22319,"Ġautop":22320,"Ġacademics":22321,"AppData":22322,"ĠShen":22323,"Ġidiot":22324,"ĠTransit":22325,"Ġteaspoon":22326,"Wil":22327,"KO":22328,"ĠComedy":22329,">,":22330,"Ġpopulated":22331,"WD":22332,"Ġpigs":22333,"ĠOculus":22334,"Ġsympathetic":22335,"Ġmarathon":22336,"198":22337,"Ġseizure":22338,"sided":22339,"Ġdop":22340,"irtual":22341,"Land":22342,"ĠFloor":22343,"osaurs":22344,"...]":22345,"Ġlos":22346,"Ġsubsidiary":22347,"EY":22348,"ĠParts":22349,"ĠStef":22350,"ĠJudiciary":22351,"Ġ134":22352,"Ġmirrors":22353,"Ġket":22354,"times":22355,"Ġneurolog":22356,"Ġcav":22357,"ĠGuest":22358,"Ġtumor":22359,"scill":22360,"ĠLloyd":22361,"Est":22362,"Ġclearer":22363,"Ġstereotypes":22364,"Ġdur":22365,"nothing":22366,"Reddit":22367,"Ġnegotiated":22368,"------------------------":22369,"235":22370,"Ġflown":22371,"ĠSeoul":22372,"ĠResident":22373,"ĠSCH":22374,"Ġdisappearance":22375,"ĠVince":22376,"grown":22377,"Ġgrabs":22378,"ril":22379,"ĠInfinite":22380,"ĠTwenty":22381,"Ġpedestrian":22382,"Ġjersey":22383,"ĠFur":22384,"ĠInfinity":22385,"ĠElliott":22386,"Ġmentor":22387,"Ġmorally":22388,"Ġobey":22389,"secure":22390,"iffe":22391,"Ġantibiotics":22392,"angled":22393,"ĠFreeman":22394,"ĠIntroduction":22395,"Jun":22396,"Ġmarsh":22397,"icans":22398,"ĠEVENTS":22399,"ochond":22400,"Wall":22401,"iculty":22402,"Ġmisdemeanor":22403,"Ġly":22404,"Thomas":22405,"ĠResolution":22406,"Ġanimations":22407,"ĠDry":22408,"Ġintercourse":22409,"ĠNewcastle":22410,"ĠHog":22411,"ĠEquipment":22412,"177":22413,"Ġterritorial":22414,"Ġarchives":22415,"203":22416,"Filter":22417,"ĠMunich":22418,"Ġcommanded":22419,"ĠWand":22420,"Ġpitches":22421,"ĠCroat":22422,"Ġratios":22423,"ĠMits":22424,"Ġaccumulated":22425,"ĠSpecifically":22426,"Ġgentleman":22427,"acerb":22428,"Ġpenn":22429,"Ġaka":22430,"ĠFuk":22431,"Ġintervene":22432,"ĠRefuge":22433,"ĠAlzheimer":22434,"Ġsuccession":22435,"ohan":22436,"does":22437,"Lord":22438,"Ġseparat":22439,"Ġcorrespondence":22440,"Ġshiny":22441,"Prior":22442,"Ġsulf":22443,"Ġmiserable":22444,"Ġdedication":22445,"().":22446,"Ġspecialists":22447,"Ġdefects":22448,"ĠCult":22449,"ĠXia":22450,"Ġjeopard":22451,"ĠOre":22452,"Ability":22453,"Ġlear":22454,"Ġambitions":22455,"ĠBMI":22456,"ĠArabs":22457,"Ġ1942":22458,"Ġpreservation":22459,"ificate":22460,"Ġashamed":22461,"loss":22462,"ĠRestaur":22463,"Ġresemble":22464,"Ġenrich":22465,"ĠKN":22466,"ĠClan":22467,"float":22468,"Ġplayable":22469,"ITT":22470,"Ġharmony":22471,"arrison":22472,"ĠWeinstein":22473,"were":22474,"Ġpoisoning":22475,"ĠComput":22476,"ĠWordPress":22477,"major":22478,"ĠValve":22479,"Fan":22480,"ĠThrow":22481,"ĠRomans":22482,"ĠDepression":22483,"ados":22484,"Ġtortured":22485,"Ġbalancing":22486,"bottom":22487,"Ġacquiring":22488,"ĠMonte":22489,"ardi":22490,"Ġaura":22491,"Ġ##":22492,"ĠStanding":22493,"ĠAtlas":22494,"CF":22495,"Ġintrins":22496,"ĠBenghazi":22497,"Ġcamping":22498,"Ġtapped":22499,"blade":22500,"strous":22501,"ĠRabb":22502,"ĠWritten":22503,"tip":22504,"ĠNeigh":22505,"sterdam":22506,"ĠAllow":22507,"ĠHealing":22508,"ĠRhod":22509,"num":22510,"Ġcaffeine":22511,"ĠPercent":22512,"Ġboo":22513,"Ġapples":22514,"305":22515,"Ġwelcoming":22516,"Ġapplaud":22517,"Ġausterity":22518,"±":22519,"ĠReality":22520,"efe":22521,"å®":22522,"Ġsucks":22523,"Ġtabs":22524,"ĠPayPal":22525,"Ġbackpack":22526,"Ġgifted":22527,"abulary":22528,"ĠScout":22529,"irteen":22530,"Ġchin":22531,"Ġomitted":22532,"Ġnegatively":22533,"Ġaccessing":22534,"ĠEarn":22535,"Ġambulance":22536,"Ġheadphones":22537,"Ġ205":22538,"ĠRefresh":22539,"president":22540,"ĠKitchen":22541,"ĠEntered":22542,"ĠSnyder":22543,"005":22544,"omical":22545,"Ġborrowed":22546,"ĠNem":22547,"Ġaviation":22548,"Ġstall":22549,"rimination":22550,"Ġuniforms":22551,"itime":22552,"ĠSimmons":22553,"energy":22554,"ablished":22555,"yy":22556,"qualified":22557,"Ġrallies":22558,"ĠStuart":22559,"flight":22560,"Ġgangs":22561,"rag":22562,"Ġvault":22563,"lux":22564,"ĠCompar":22565,"Ġdesignation":22566,"209":22567,"ĠJos":22568,"dollar":22569,"zero":22570,"Ġwells":22571,"303":22572,"Ġconstituents":22573,"Ġheck":22574,"Ġcows":22575,"Ġcommanders":22576,"Ġdifferential":22577,"ĠCatherine":22578,"299":22579,"Ġvalve":22580,"Ġbrace":22581,"Ġperspectives":22582,"cert":22583,"fact":22584,"icularly":22585,"ĠMcN":22586,"planes":22587,"Ġintric":22588,"Ġpeas":22589,"ovan":22590,"Ġtossed":22591,"retch":22592,"ĠLopez":22593,"Ġunfamiliar":22594,"death":22595,"ĠApart":22596,"ĠChang":22597,"Ġrelieved":22598,"rophe":22599,"Ġairports":22600,"Ġfreak":22601,"util":22602,"Mill":22603,"ĠChin":22604,"ĠOwen":22605,"male":22606,"ĠBroken":22607,"ĠWinds":22608,"rob":22609,"rising":22610,"Ġfirefighters":22611,"Ġauthoritarian":22612,"Ġ148":22613,"Bitcoin":22614,"external":22615,"Ġbrowsers":22616,"ichever":22617,"orian":22618,"Ġunb":22619,"Ġpoke":22620,"ĠZot":22621,"Mid":22622,"ĠPopular":22623,"Ġcovert":22624,"Ġcontributes":22625,"Ġ650":22626,"Ġcontention":22627,"Gate":22628,"Ġconsoles":22629,"Ġchromos":22630,"ĠIX":22631,"Ġvisually":22632,"ĠEisen":22633,"Ġjewelry":22634,"Ġdelegation":22635,"Ġaccelerate":22636,"ĠRiley":22637,"Ġslope":22638,"Ġindoor":22639,"itially":22640,"Ġhugely":22641,"Ġtunnels":22642,"Ġfined":22643,"Ġdirective":22644,"Ġforehead":22645,"ustomed":22646,"Ġskate":22647,"Music":22648,"gas":22649,"Ġrecognizing":22650,"ambo":22651,"Ġoverweight":22652,"ĠGrade":22653,"ÙĬ":22654,"Ġsounding":22655,"Ġlocking":22656,"ĠREM":22657,"Store":22658,"Ġexcav":22659,"ĠLikewise":22660,"ĠLights":22661,"Ġelbow":22662,"ĠSupply":22663,"wic":22664,"Ġhandsome":22665,"1994":22666,"Coll":22667,"Ġadequately":22668,"ĠAssociate":22669,"Ġstrips":22670,"Ġcrackdown":22671,"Ġmarvel":22672,"ĠKun":22673,"Ġpassages":22674,"@@@@":22675,"ĠTall":22676,"Ġthoughtful":22677,"namese":22678,"Ġprostitution":22679,"business":22680,"Ġballistic":22681,"personal":22682,"cig":22683,"izational":22684,"Round":22685,"ĠÂłĠÂłĠÂłĠÂł":22686,"ĠColeman":22687,"Ġadmitting":22688,"ĠPlug":22689,"Ġbitcoins":22690,"ĠSuz":22691,"Ġfairness":22692,"Ġsupplier":22693,"Ġcatastrophic":22694,"ĠHelen":22695,"oqu":22696,"Marc":22697,"ĠArticles":22698,"gie":22699,"Ġendangered":22700,"Ġdestiny":22701,"ĠVolt":22702,"olia":22703,"axis":22704,"Ġcheat":22705,"Ġunified":22706,"ICO":22707,"quote":22708,"302":22709,"ĠSed":22710,"Ġsuppression":22711,"Ġanalyzing":22712,"Ġsquat":22713,"Ġfiguring":22714,"Ġcoordinates":22715,"Ġchunks":22716,"Ġ1946":22717,"Ġsubp":22718,"Ġwiki":22719,"ĠForbes":22720,"ĠJupiter":22721,"ĠErik":22722,"imer":22723,"ĠCommercial":22724,"\\)":22725,"Ġlegitimacy":22726,"Ġdental":22727,"ĠMean":22728,"Ġdeficits":22729,"550":22730,"Originally":22731,"ĠHorror":22732,"Ġcontamination":22733,"llah":22734,"Ġconfisc":22735,"ĠClare":22736,"TB":22737,"ĠFailed":22738,"aned":22739,"Ġruler":22740,"ĠController":22741,"Ġfeminists":22742,"Fix":22743,"gay":22744,"207":22745,"Ġrabbit":22746,"Third":22747,"owntown":22748,"Ġglue":22749,"Ġvolatile":22750,"Ġshining":22751,"Ġfoll":22752,"Ġimpaired":22753,"Ġsupers":22754,"æĪ":22755,"Ġclutch":22756,"ļéĨĴ":22757,"Ġprolet":22758,"Ġ(!":22759,"Ġyelled":22760,"ĠKiev":22761,"ĠErn":22762,"ĠShock":22763,"KB":22764,"Ġsituated":22765,"query":22766,"ĠNas":22767,"Ġannex":22768,"character":22769,"ĠHoliday":22770,"Ġautomation":22771,"ĠJill":22772,"ĠRemastered":22773,"Ġlinem":22774,"Ġwilderness":22775,"ĠHorizon":22776,"ĠGuinea":22777,"AZ":22778,"Ġmainland":22779,"Ġsecrecy":22780,"LEASE":22781,"Ġpunk":22782,"ĠProvince":22783,"(),":22784,"Speed":22785,"Ġhanding":22786,"ĠSebast":22787,"Sir":22788,"rase":22789,"Ġjournals":22790,"Ġcongest":22791,"ĠTut":22792,"irrel":22793,"Ġschizophrenia":22794,"Ġmisogyn":22795,"healthy":22796,"Iron":22797,"Ġreacted":22798,"-$":22799,"252":22800,"Ġplural":22801,"Ġplum":22802,"Ġbargain":22803,"Ġgrounded":22804,"finder":22805,"Ġdisse":22806,"ĠLaz":22807,"OOD":22808,"Ġatroc":22809,"Factory":22810,"Ġminions":22811,"Ġori":22812,"ĠBrave":22813,"ĠPRE":22814,"ĠMyanmar":22815,"ĠHod":22816,"Ġexpedition":22817,"Ġexplode":22818,"ĠCoord":22819,"Ġextr":22820,"ĠBrief":22821,"ĠADHD":22822,"Ġhardcore":22823,"feeding":22824,"Ġdile":22825,"ĠFruit":22826,"Ġvaccination":22827,"ĠMao":22828,"osphere":22829,"Ġcontests":22830,"-|":22831,"Ġfren":22832,"isphere":22833,"Rom":22834,"ĠSharp":22835,"ĠTrend":22836,"Ġdisconnect":22837,"âĢ¢âĢ¢":22838,"Ġpersecution":22839,"Earth":22840,"Ġhealthier":22841,"384":22842,"Ġcob":22843,"ĠTrinity":22844,"OWS":22845,"ANN":22846,"Ġspecialty":22847,"Ġgru":22848,"Ġcooperative":22849,"why":22850,"Starting":22851,"ĠIssues":22852,"stre":22853,"ensor":22854,"Ġ185":22855,"Adv":22856,"!?":22857,"ĠRevel":22858,"emia":22859,"ĠHulk":22860,"Ġcelebrations":22861,"ĠSou":22862,"raud":22863,"ĠKlein":22864,"Ġunreal":22865,"context":22866,"Ġpartnerships":22867,"Ġadopting":22868,"tical":22869,"Ġsplash":22870,"ĠHezbollah":22871,"category":22872,"cyclop":22873,"xton":22874,"ĠDot":22875,"urdy":22876,"tz":22877,"Ġenvelope":22878,"ĠNL":22879,"âķ":22880,"Ġwherein":22881,"Spec":22882,"184":22883,"Ġtelev":22884,"aliation":22885,"Ġmyths":22886,"å°":22887,"Ġrigorous":22888,"Ġcommunicating":22889,"Ġobserver":22890,"Ġrehe":22891,"ĠWash":22892,"Ġapologized":22893,"ĠTin":22894,"Ġexpenditures":22895,"workers":22896,"document":22897,"Ġhesitate":22898,"ĠLenin":22899,"Ġunpredictable":22900,"Ġrenewal":22901,"cler":22902,"okia":22903,"ĠCONT":22904,"Ġpostseason":22905,"Tokens":22906,"Ġexacerb":22907,"Ġbetting":22908,"Ġ147":22909,"Ġelevation":22910,"Wood":22911,"ĠSolomon":22912,"194":22913,"004":22914,"output":22915,"Ġredund":22916,"ĠMumbai":22917,"ĠpH":22918,"Ġreproduce":22919,"ĠDuration":22920,"MAX":22921,"Ġbog":22922,"CBS":22923,"ĠBalance":22924,"ĠSgt":22925,"ĠRecent":22926,"Ġcd":22927,"Ġpopped":22928,"Ġincompet":22929,"prop":22930,"ayan":22931,"guy":22932,"Pacific":22933,"Ġtyr":22934,"Ġ{{":22935,"ĠMystic":22936,"ĠDana":22937,"Ġmasturb":22938,"Ġgeometry":22939,"â":22940,"ĠCorrect":22941,"Ġtrajectory":22942,"Ġdistracted":22943,"Ġfoo":22944,"ĠWelsh":22945,"Luc":22946,"mith":22947,"Ġrugby":22948,"Ġrespiratory":22949,"Ġtriangle":22950,"Ġ215":22951,"Ġundergraduate":22952,"ĠSuperior":22953,"changing":22954,"_-":22955,"Ġrightly":22956,"Ġreferee":22957,"Ġlucrative":22958,"Ġunauthorized":22959,"Ġresembles":22960,"ĠGNU":22961,"ĠDerby":22962,"Ġpathways":22963,"ĠLed":22964,"Ġendurance":22965,"Ġstint":22966,"Ġcollector":22967,"Fast":22968,"Ġdots":22969,"Ġnationals":22970,"ĠSecurities":22971,"Ġwhip":22972,"Param":22973,"Ġlearns":22974,"Magic":22975,"Ġdetailing":22976,"moon":22977,"Ġbroadcasting":22978,"Ġbaked":22979,"265":22980,"holm":22981,"ĠSah":22982,"ĠHussein":22983,"ĠCourtesy":22984,"174":22985,"Ġ146":22986,"Ġgeographic":22987,"peace":22988,"Ġjudging":22989,"ĠStern":22990,"Bur":22991,"Ġstoryline":22992,"Gun":22993,"ĠStick":22994,"245":22995,"307":22996,"ãĤ´ãĥ³":22997,"ĠAdministrator":22998,"Ġburnt":22999,"Ġpave":23000,"choes":23001,"Exec":23002,"Ġcampuses":23003,"Result":23004,"Ġmutations":23005,"ĠCharter":23006,"Ġcaptures":23007,"Ġcompares":23008,"Ġbadge":23009,"Scient":23010,"Ġerad":23011,"iery":23012,"oi":23013,"ettes":23014,"ĠEstate":23015,"Ġstrap":23016,"Ġproudly":23017,"Ġfried":23018,"Ġwithdrawn":23019,"ĠVoy":23020,"phony":23021,"Items":23022,"ĠPierce":23023,"bard":23024,"Ġannotation":23025,"anton":23026,"illon":23027,"Impro":23028,"...)":23029,"Ġhappier":23030,"------":23031,"adjust":23032,"Ġstaffers":23033,"Ġactivism":23034,"Ġperf":23035,"Ġalright":23036,"Need":23037,"Ġcommence":23038,"Ġopioid":23039,"ĠAmanda":23040,"Es":23041,"ĠPars":23042,"ĠKaw":23043,"Works":23044,"248":23045,"Ġindo":23046,"tc":23047,"endant":23048,"ĠMoto":23049,"Ġlegalization":23050,"OTE":23051,"Ġtasked":23052,"Ġtsp":23053,"ĠACTIONS":23054,"166":23055,"Ġrefreshing":23056,"ĠNR":23057,"ĠPerez":23058,"Ġinfringement":23059,"SY":23060,"Listen":23061,"inning":23062,"ku":23063,"Ġrotate":23064,"program":23065,"arah":23066,"Design":23067,"Ġ(£":23068,"Ġstoring":23069,"Ġwarrants":23070,"Ġjudgement":23071,"ĠBrist":23072,"usually":23073,"photo":23074,"ĠRan":23075,"ĠPine":23076,"Ġoutrageous":23077,"ĠValentine":23078,"luence":23079,"ĠEverybody":23080,"Altern":23081,"Ġrelevance":23082,"Ġterminated":23083,"Ġdessert":23084,"Ġfulfilled":23085,"Ġprosecuted":23086,"ĠWords":23087,"Ġmigrant":23088,"Ġcultivation":23089,"ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ":23090,"idelity":23091,"ĠVern":23092,"ĠLogin":23093,"Ġmetaphor":23094,"ĠTip":23095,"Ġrecruits":23096,"ĠPig":23097,"ribing":23098,"Ġenthusiasts":23099,"exper":23100,"Ġfrightening":23101,"ĠHair":23102,"anson":23103,"strate":23104,"Ġhi":23105,"Height":23106,"Ġowning":23107,"none":23108,"Ġdislike":23109,"Ġknives":23110,"pherd":23111,"Ġloudly":23112,"ĠAPIs":23113,"Display":23114,"ĠLac":23115,"ĠUSS":23116,"abl":23117,"verages":23118,"Jew":23119,"Ġ172":23120,"ĠHistorical":23121,"atoon":23122,"ĠPhysics":23123,"intern":23124,"Ġwarmth":23125,"Ġtopp":23126,"DM":23127,"Ġgunman":23128,"Ġemperor":23129,"odi":23130,"ãĥ£":23131,"inatory":23132,"ĠRib":23133,"Ġ131":23134,"ĠSaturn":23135,"ĠShining":23136,"Ġwaking":23137,"Quotes":23138,"Ġcomedian":23139,"enberg":23140,"½":23141,"Ġbelievers":23142,"Ġpaperwork":23143,"custom":23144,"Ġlev":23145,"Ġlament":23146,"Ġpouring":23147,"222":23148,"political":23149,"ĠSupplement":23150,"maid":23151,"Ġcruelty":23152,"Ġtread":23153,"ysics":23154,"Aw":23155,"rites":23156,"Ġmodifier":23157,"ĠPosition":23158,"Adam":23159,"lb":23160,"ubs":23161,"Ġimperfect":23162,"Ġclusters":23163,"ĠEngineer":23164,"ĠCherry":23165,"Ġinauguration":23166,"ĠSau":23167,"Ġembodiment":23168,"ĠUncle":23169,"Ġoverr":23170,"Ġexplosions":23171,"cule":23172,"ĠPrinceton":23173,"ĠAndrea":23174,"Ġincorrectly":23175,"Ġearnest":23176,"Ġpilgr":23177,"ĠSprint":23178,"Ġsleeve":23179,"Ġhears":23180,"ĠAmazing":23181,"Ġbrowsing":23182,"agin":23183,"Ġhomeland":23184,"Ġhaw":23185,"Ġdiving":23186,"istered":23187,"178":23188,"Ġbargaining":23189,"ĠArcade":23190,"Ġdelegate":23191,"terson":23192,"................................................................":23193,"ĠJacksonville":23194,"275":23195,"Ġstagn":23196,"Ġadam":23197,"ĠSherman":23198,"CB":23199,"Ġsuburb":23200,"ĠFoods":23201,"Ġconverting":23202,"ĠArist":23203,"Ġchambers":23204,"love":23205,"Ġamino":23206,"ĠGan":23207,"Ġmadness":23208,"mc":23209,"ĠUSE":23210,"defined":23211,"Ġultr":23212,"indust":23213,"Ġwolves":23214,"lance":23215,"Additionally":23216,"Ġcracks":23217,"asia":23218,"ĠReason":23219,"ĠPump":23220,"Ġaccidental":23221,"ĠLaser":23222,"ĠRid":23223,"Ġinitialized":23224,"elli":23225,"Ġunnamed":23226,"Ġnoun":23227,"ĠPassed":23228,"Ġhostage":23229,"ĠEthiop":23230,"shirts":23231,"Ġunrel":23232,"ĠEmbassy":23233,"Ġ1941":23234,"Ġatoms":23235,"Ġpurported":23236,"164":23237,"ĠFi":23238,"Ġgallons":23239,"ĠMonica":23240,"Ġpg":23241,"enment":23242,"Ġsorted":23243,"ĠGospel":23244,"Ġheights":23245,"Ġtraced":23246,"Ġundergoing":23247,"Shell":23248,"Ġsacks":23249,"Ġproportions":23250,"Ġhalluc":23251,"Font":23252,"acet":23253,"Ġwarmer":23254,"ĠINTER":23255,"Ġgrabbing":23256,"Plug":23257,"Ġrealization":23258,"ĠBurke":23259,"Ġenchant":23260,"ATER":23261,"ĠSeed":23262,"Ġabundant":23263,"FM":23264,"Ġcivic":23265,"Vs":23266,"isi":23267,"Ġvow":23268,"Ġreper":23269,"ĠPartnership":23270,"Ġpenetration":23271,"Ġaxe":23272,"Ġshattered":23273,"ĠZombies":23274,"Ġvinyl":23275,"ĠAlert":23276,"eon":23277,"Ġobliged":23278,"ĠIllust":23279,"ĠPlaza":23280,"ĠFrontier":23281,"Ġdavidjl":23282,"ĠSerial":23283,"ĠHav":23284,"ĠNutrition":23285,"Bi":23286,"ĠâĸĪ":23287,"ĠJays":23288,"linux":23289,"Ġhurry":23290,"Ġvoy":23291,"Ġhopeless":23292,"ĠStealth":23293,"Ġãģ":23294,"essors":23295,"ttle":23296,"borg":23297,"ĠSafari":23298,"fell":23299,"Ġwary":23300,"due":23301,"ĠAbove":23302,"Ha":23303,"ELL":23304,"Ġnotor":23305,"ĠWon":23306,"Too":23307,"Ġoccupations":23308,"Ġpossessions":23309,"Ġinviting":23310,"Ġpredators":23311,"Ġaccelerated":23312,"Ġ157":23313,"uterte":23314,"ĠCube":23315,"east":23316,"account":23317,"Give":23318,"Ġtransplant":23319,"redients":23320,"idable":23321,"Ġscreenshots":23322,"ĠGund":23323,"ĠFS":23324,"Ġtravelers":23325,"Ġsensory":23326,"ĠFiat":23327,"ĠRockets":23328,"İĭ":23329,"_{":23330,"Friend":23331,"Ġcharming":23332,"ALS":23333,"Ġenjoyment":23334,"mph":23335,"Ġ5000":23336,"ĠREG":23337,"ÙĨ":23338,"bia":23339,"Ġcompilation":23340,"rost":23341,"ĠVP":23342,"ĠSchne":23343,"2019":23344,"Ġcopying":23345,"MORE":23346,"ĠFlore":23347,"falls":23348,"215":23349,"total":23350,"Ġdisciples":23351,"double":23352,"Ġexceeding":23353,"Ġsmashed":23354,"Ġconceptual":23355,"ĠRomania":23356,"ĠBrent":23357,"ĠICE":23358,"ĠTou":23359,"Ġgrap":23360,"Ġnails":23361,"189":23362,"ãĥĺ":23363,"Ġprocure":23364,"eur":23365,"Ġconfirming":23366,"ĠCec":23367,"awi":23368,"ĠEden":23369,"Ġng":23370,"Ġengineered":23371,"atics":23372,"Ġhooked":23373,"Ġdisgusting":23374,"ĠMurder":23375,"ãĤ¿":23376,"Library":23377,"Ġ168":23378,"Almost":23379,"hematic":23380,"Menu":23381,"ĠNotre":23382,"ĠJur":23383,"Ġkidnapped":23384,"Ġhacker":23385,"ĠJade":23386,"Ġcreepy":23387,"Ġdrawings":23388,"ĠSponsor":23389,"Ġcyclists":23390,"ĠGoblin":23391,"Ġoptimized":23392,"Ġstaged":23393,"ĠMcD":23394,"between":23395,"Age":23396,"eno":23397,"Sex":23398,"ĠWide":23399,"nings":23400,"avis":23401,"Ġincapable":23402,"ĠKob":23403,"Ġrewarding":23404,"ĠLone":23405,"olescent":23406,"Ġcontracted":23407,"Ġsticky":23408,"Jose":23409,"Ball":23410,"fest":23411,"ĠInput":23412,"ĠRecently":23413,"Ġtomat":23414,"square":23415,"Application":23416,"Ġnitrogen":23417,"Ġduplicate":23418,"ĠRecon":23419,"ĠDear":23420,"London":23421,"Ġintra":23422,"Ġdock":23423,"Ġoutreach":23424,"ĠMillion":23425,"Ġmammals":23426,"ampton":23427,"VAL":23428,"Ġsnaps":23429,"Ġdos":23430,"ĠWhole":23431,"ĠReady":23432,"Try":23433,"ĠWinnipeg":23434,"earance":23435,"Ġincurred":23436,"renched":23437,"ĠNSW":23438,"ilot":23439,"raine":23440,"Ġcube":23441,"got":23442,"Ġrunway":23443,"etermined":23444,"ĠHawks":23445,"Ġsurvivor":23446,"ĠWish":23447,"ĠDin":23448,"ĠDEF":23449,"ĠVault":23450,"187":23451,"Ġmushrooms":23452,"Ġcrisp":23453,"bey":23454,"ĠDiscovery":23455,"Ġdevelopmental":23456,"Ġparadigm":23457,"Ġchaotic":23458,"ĠTsu":23459,"Ġ333":23460,"bons":23461,"Ġbacterial":23462,"Ġcommits":23463,"Ġcosmic":23464,"Ġmega":23465,"ocative":23466,"ĠPaint":23467,"ophobic":23468,"Ġvain":23469,"Ġcarved":23470,"ĠThief":23471,"ĠGul":23472,"owship":23473,"Ġcites":23474,"ĠEdinburgh":23475,"Ġdiminished":23476,"Ġacknowledges":23477,"ĠKills":23478,"Ġmicrow":23479,"ĠHera":23480,"Ġseniors":23481,"Ġwhereby":23482,"Hop":23483,"atron":23484,"Ġunavailable":23485,"ĠNate":23486,"Ġ480":23487,"Ġslated":23488,"ĠRebecca":23489,"ĠBattery":23490,"Ġgrammar":23491,"Ġheadset":23492,"Ġcursor":23493,"Ġexcluding":23494,"anye":23495,"aundering":23496,"ebin":23497,"Ġfeasible":23498,"ĠPublishing":23499,"ĠLabs":23500,"ĠCliff":23501,"ĠFerrari":23502,"Ġpac":23503,"visible":23504,"marked":23505,"pell":23506,"Ġpolite":23507,"Ġstaggering":23508,"ĠGalactic":23509,"Ġsuperst":23510,"Ġparan":23511,"ĠOfficers":23512,"ãĢģ":23513,"Ġspecifics":23514,"ulus":23515,"239":23516,"ĠPaste":23517,"AMP":23518,"ĠPanama":23519,"ĠDelete":23520,"anguard":23521,"restrial":23522,"Ġheroic":23523,"ĠDy":23524,"اÙĦ":23525,"Ġincumbent":23526,"Ġcrunch":23527,"tro":23528,"Ġscoop":23529,"Ġblogger":23530,"Ġsellers":23531,"uren":23532,"Ġmedicines":23533,"ĠCaps":23534,"ĠAnimation":23535,"oxy":23536,"Ġoutward":23537,"Ġinquiries":23538,"229":23539,"Ġpsychologist":23540,"ĠSask":23541,"evil":23542,"Ġcontaminated":23543,"ãĤ¨":23544,"herence":23545,"Ġbranded":23546,"ĠAbdul":23547,"zh":23548,"Ġparagraphs":23549,"Ġmins":23550,"Ġcorrelated":23551,"erb":23552,"Ġimpart":23553,"Ġmilestone":23554,"ĠSolutions":23555,"otle":23556,"Ġundercover":23557,"Ġmarched":23558,"ĠChargers":23559,"fax":23560,"ĠSecrets":23561,"Ġruth":23562,"weather":23563,"Ġfeminine":23564,"Ġsham":23565,"Ġprestigious":23566,"iggins":23567,"Ġsung":23568,"history":23569,"ettle":23570,"ggie":23571,"Ġoutdated":23572,"oland":23573,"Ġperceptions":23574,"ĠSession":23575,"ĠDodgers":23576,"uj":23577,"ĠEND":23578,"Doc":23579,"Ġdeficiency":23580,"Grand":23581,"ĠJoker":23582,"Ġretrospect":23583,"Ġdiagnostic":23584,"Ġharmless":23585,"Ġrogue":23586,"ĠAval":23587,"Equ":23588,"Ġtransc":23589,"ĠRobertson":23590,"ĠDepending":23591,"ĠBurns":23592,"ivo":23593,"Ġhostility":23594,"Features":23595,"ĵĺ":23596,"Ġdiscomfort":23597,"ĠLCD":23598,"specified":23599,"ĠExpect":23600,"340":23601,"Ġimperative":23602,"ĠRegular":23603,"Chinese":23604,"Ġstatewide":23605,"Ġsymm":23606,"Ġloops":23607,"Ġautumn":23608,"Nick":23609,"Ġshaping":23610,"Ġquot":23611,"Ġcherry":23612,"ĠCrossref":23613,"è¦ļéĨĴ":23614,"Standard":23615,"heed":23616,"ĠDell":23617,"ĠVietnamese":23618,"Ġost":23619,"ĠValkyrie":23620,"OA":23621,"Assad":23622,"Ġrebound":23623,"ĠTraffic":23624,"places":23625,"æĺ":23626,"ĠBuc":23627,"172":23628,"Ġshelters":23629,"Ġinsisting":23630,"ĠCertainly":23631,"ĠKenneth":23632,"ĠTCP":23633,"Ġpenal":23634,"ĠReplay":23635,"heard":23636,"Ġdialect":23637,"iza":23638,"ĠFY":23639,"itcher":23640,"ĠDL":23641,"Ġspiral":23642,"Ġquarterbacks":23643,"Ġhull":23644,"Ġgoogle":23645,"Ġtodd":23646,"ĠSterling":23647,"ĠPlate":23648,"Ġspying":23649,"mbol":23650,"ĠRealm":23651,"ĠProced":23652,"ĠCrash":23653,"Ġterminate":23654,"Ġprotesting":23655,"Center":23656,"guided":23657,"Ġuncover":23658,"Ġboycott":23659,"Ġrealizes":23660,"sound":23661,"Ġpretending":23662,"ĠVas":23663,"1980":23664,"Ġframed":23665,"Ġ139":23666,"Ġdescended":23667,"Ġrehabilitation":23668,"Ġborrowing":23669,"ĠBuch":23670,"Ġblur":23671,"Ron":23672,"ĠFrozen":23673,"enza":23674,"Chief":23675,"ĠPoor":23676,"Ġtranslates":23677,"MIN":23678,"Ġ212":23679,"JECT":23680,"Ġerupted":23681,"Ġsuccesses":23682,"SEC":23683,"Ġplague":23684,"Ġgems":23685,"doms":23686,"Ġstretches":23687,"ĠSpy":23688,"Ġstorytelling":23689,"Credit":23690,"ĠPush":23691,"Ġtraction":23692,"Ġineffective":23693,"ĠLuna":23694,"Ġtapes":23695,"Ġanalytics":23696,"ercise":23697,"Ġprogrammes":23698,"ĠCarbon":23699,"Ġbehold":23700,"heavy":23701,"ĠConservation":23702,"ĠFIR":23703,"Ġsack":23704,"termin":23705,"ricks":23706,"Ġhoused":23707,"Ġunusually":23708,"Ice":23709,"Ġexecuting":23710,"ĠMoroc":23711,"eday":23712,"Ġeditions":23713,"Ġsmarter":23714,"ĠBA":23715,"Ġoutlaw":23716,"Ġvanished":23717,"iba":23718,"ALSE":23719,"ĠSilva":23720,"238":23721,"Could":23722,"Ġphilosopher":23723,"Ġevacuated":23724,"Secret":23725,"142":23726,"Ġvisas":23727,"ãĤ¬":23728,"ĠMalt":23729,"ĠClearly":23730,"ĠNiger":23731,"ĠCairo":23732,"ĠFist":23733,"380":23734,"ĠXML":23735,"auto":23736,"itant":23737,"Ġreinforced":23738,"Record":23739,"ĠSurvivor":23740,"GHz":23741,"Ġscrews":23742,"parents":23743,"Ġoceans":23744,"mares":23745,"Ġbrakes":23746,"vasive":23747,"Ġhello":23748,"ĠSIM":23749,"rimp":23750,"Ġore":23751,"ĠArmour":23752,"247":23753,"Ġterrific":23754,"Ġtones":23755,"141":23756,"ĠMinutes":23757,"Episode":23758,"Ġcurves":23759,"Ġinflammatory":23760,"Ġbatting":23761,"ĠBeautiful":23762,"Lay":23763,"Ġunpop":23764,"vable":23765,"Ġriots":23766,"ĠTactics":23767,"baugh":23768,"ĠCock":23769,"Ġorgasm":23770,"ĠSas":23771,"Ġconstructor":23772,"etz":23773,"Gov":23774,"Ġantagon":23775,"Ġtheat":23776,"Ġdeeds":23777,"hao":23778,"cuts":23779,"ĠMcCl":23780,"Ġum":23781,"ĠScientists":23782,"Ġgrassroots":23783,"yssey":23784,"\"]=>":23785,"Ġsurfaced":23786,"Ġshades":23787,"Ġneighbours":23788,"Ġadvertis":23789,"oya":23790,"Ġmerged":23791,"Upon":23792,"Ġgad":23793,"Ġanticipate":23794,"Anyway":23795,"Ġslogan":23796,"Ġdisrespect":23797,"Iran":23798,"ĠTB":23799,"acted":23800,"Ġsubpoen":23801,"mediately":23802,"OOOO":23803,"Ġwaiver":23804,"Ġvulnerabilities":23805,"ottesville":23806,"ĠHuffington":23807,"Josh":23808,"ĠDH":23809,"Monday":23810,"ĠEllen":23811,"Know":23812,"xon":23813,"items":23814,"228":23815,"Ġfills":23816,"ĠNike":23817,"Ġcumulative":23818,"andals":23819,"Ir":23820,"Ġì":23821,"Ġfriction":23822,"igator":23823,"Ġscans":23824,"ĠVienna":23825,"ldom":23826,"Ġperformers":23827,"Prim":23828,"Ġbidding":23829,"Mur":23830,"Ġleaned":23831,"ĠPrix":23832,"alks":23833,"Ġ[âĢ¦]":23834,"ĠTwitch":23835,"ĠDeveloper":23836,"ĠGir":23837,"Ġcallback":23838,"Abstract":23839,"Ġaccustomed":23840,"Ġfreedoms":23841,"ĠPG":23842,"uracy":23843,"Ġlump":23844,"isman":23845,",,,,":23846,"1992":23847,"ĠRED":23848,"Ġworm":23849,"Match":23850,"ĠPlatinum":23851,"IJ":23852,"ĠOwner":23853,"Trivia":23854,"compl":23855,"Ġnewborn":23856,"Ġfantas":23857,"Own":23858,"Ġ1959":23859,"Ġsympath":23860,"Ġubiqu":23861,"Ġoutputs":23862,"Ġallev":23863,"Ġprag":23864,"Kevin":23865,"Ġfavors":23866,"Ġburial":23867,"Ġnurt":23868,"solete":23869,"cache":23870,"Ġ156":23871,"Ġunlocks":23872,"techn":23873,"Making":23874,"Ġconquer":23875,"adic":23876,"æĸ":23877,"Ġelf":23878,"Ġelectorate":23879,"ĠKurds":23880,"ĠStack":23881,"ĠSamurai":23882,"Ġâĺħ":23883,"Ġ{}":23884,"ĠSaid":23885,"ĠFallout":23886,"Ġkindness":23887,"ĠCustoms":23888,"ĠBoulevard":23889,"Ġhelicopters":23890,"otics":23891,"ĠVeget":23892,"comment":23893,"Ġcriticised":23894,"Ġpolished":23895,"ĠRemix":23896,"ĠCultural":23897,"Ġrecons":23898,"Ġdoi":23899,"atem":23900,"Screen":23901,"Ġbarred":23902,"Comments":23903,"ĠGenerally":23904,"Ġslap":23905,"720":23906,"Vari":23907,"pine":23908,"Ġempt":23909,"Ġhats":23910,"ĠPlaying":23911,"lab":23912,"average":23913,"forms":23914,"ĠCotton":23915,"Ġcans":23916,"ĠDON":23917,"ĠSomalia":23918,"Crypt":23919,"ĠIncreases":23920,"Ever":23921,"modern":23922,"Ġsurgeon":23923,"3000":23924,"Ġrandomized":23925,"================================================================":23926,"Bern":23927,"impl":23928,"ĠCOR":23929,"Ġproclaim":23930,"thouse":23931,"Ġtoes":23932,"Ġample":23933,"Ġpreserving":23934,"Ġdisbel":23935,"grand":23936,"Besides":23937,"Ġsilk":23938,"ĠPattern":23939,"hm":23940,"Ġenterprises":23941,"Ġaffidavit":23942,"ĠAdvisory":23943,"Ġadvertised":23944,"ĠReligious":23945,"sections":23946,"psych":23947,"ĠFields":23948,"aways":23949,"Ġhashtag":23950,"ĠNightmare":23951,"Ġvampire":23952,"Ġforensic":23953,"rossover":23954,"nar":23955,"Ġnavy":23956,"Ġvacant":23957,"ĠDuel":23958,"Ġhallway":23959,"Ġfacebook":23960,"identally":23961,"ĠNRA":23962,"Ġmatt":23963,"Ġhurricane":23964,"ĠKirby":23965,"ĠPuzzle":23966,"Ġskirt":23967,"oust":23968,"dullah":23969,"Ġanalogy":23970,"inion":23971,"Ġtomatoes":23972,"ĠNV":23973,"ĠPeak":23974,"ĠMeyer":23975,"Ġappointments":23976,"Ġmasc":23977,"Ġalley":23978,"rehend":23979,"Ġcharities":23980,"Ġundo":23981,"Ġdestinations":23982,"ĠTesting":23983,"\">\"":24618,"cats":24619,"*.":24620,"Ġgestures":24621,"general":24622,"League":24623,"Ġpackets":24624,"ĠInspector":24625,"ĠBerg":24626,"Ġfraudulent":24627,"Ġcriticize":24628,"Fun":24629,"Ġblaming":24630,"ndra":24631,"Ġslash":24632,"ĠEston":24633,"Ġproposing":24634,"Ġwhales":24635,"Ġtherapist":24636,"Ġsubset":24637,"Ġleisure":24638,"ELD":24639,"ĠCVE":24640,"ĠActivity":24641,"Ġculmin":24642,"shop":24643,"ĠDAY":24644,"ischer":24645,"ĠAdmiral":24646,"ĠAttacks":24647,"Ġ1958":24648,"Ġmemoir":24649,"Ġfolded":24650,"Ġsexist":24651,"Ġ153":24652,"ĠLI":24653,"Ġreadings":24654,"Ġembarrassment":24655,"ĠEmployment":24656,"wart":24657,"chin":24658,"Ġcontinuation":24659,"lia":24660,"Recently":24661,"Ġduel":24662,"Ġevacuation":24663,"ĠKashmir":24664,"Ġdisposition":24665,"ĠRig":24666,"Ġbolts":24667,"Ġinsurers":24668,"467":24669,"Mex":24670,"Ġretaliation":24671,"Ġmisery":24672,"Ġunreasonable":24673,"raining":24674,"Imm":24675,"ĠPU":24676,"emer":24677,"Ġgenital":24678,"ãĤ³":24679,"ĠCandy":24680,"Ġonions":24681,"ĠPatt":24682,"liner":24683,"Ġconceded":24684,"Ġfa":24685,"Ġforc":24686,"ĠHernandez":24687,"ĠGeoff":24688,"debian":24689,"ĠTeams":24690,"Ġcries":24691,"Ġhomeowners":24692,"237":24693,"ABC":24694,"Ġstitch":24695,"Ġstatistic":24696,"Ġheaders":24697,"ĠBiology":24698,"Ġmotors":24699,"ĠGEN":24700,"ĠLip":24701,"Ġhates":24702,"Ġheel":24703,"Self":24704,"ipl":24705,"EDIT":24706,"orting":24707,"Ġannot":24708,"ĠSpeech":24709,"oldemort":24710,"ĠJavascript":24711,"ĠLeBron":24712,"Ġfootprint":24713,"Ġfn":24714,"Ġseizures":24715,"nas":24716,"hide":24717,"Ġ1954":24718,"ĠBee":24719,"ĠDeclaration":24720,"ĠKatie":24721,"Ġreservations":24722,"NR":24723,"female":24724,"Ġsaturated":24725,"Ġbiblical":24726,"Ġtrolls":24727,"Device":24728,"photos":24729,"Ġdrums":24730,"ãĥīãĥ©ãĤ´ãĥ³":24731,"Night":24732,"fighter":24733,"ĠHak":24734,"riber":24735,"Ġcush":24736,"Ġdisciplinary":24737,"baum":24738,"ĠGH":24739,"ĠSchmidt":24740,"ilibrium":24741,"Ġsixty":24742,"ĠKushner":24743,"rots":24744,"Ġpund":24745,"ĠRac":24746,"Ġsprings":24747,"Ġconve":24748,"Business":24749,"Fall":24750,"Ġqualifications":24751,"Ġverses":24752,"Ġnarciss":24753,"ĠKoh":24754,"ĠWow":24755,"ĠCharlottesville":24756,"edo":24757,"Ġinterrogation":24758,"ĠWool":24759,"365":24760,"Brian":24761,"Ġâľĵ":24762,"Ġalleges":24763,"onds":24764,"idation":24765,"ĠJackie":24766,"yu":24767,"Ġlakes":24768,"Ġworthwhile":24769,"Ġcrystals":24770,"ĠJuda":24771,"Ġcomprehend":24772,"Ġflush":24773,"Ġabsorption":24774,"ĠOC":24775,"Ġfrightened":24776,"ĠChocolate":24777,"Martin":24778,"Ġbuys":24779,"Ġbucks":24780,"Ġappell":24781,"ĠChampionships":24782,"Ġlistener":24783,"ĠDefensive":24784,"Ġcz":24785,"uds":24786,"ĠMate":24787,"Ġreplay":24788,"Ġdecorated":24789,"Ġsunk":24790,"ĠVIP":24791,"ĠAnk":24792,"Ġ195":24793,"aaaa":24794,"Nobody":24795,"ĠMilk":24796,"ĠGur":24797,"ĠMk":24798,"ĠSara":24799,"Ġseating":24800,"ĠWid":24801,"Track":24802,"Ġemploys":24803,"Ġgigantic":24804,"APP":24805,"ãĤ§":24806,"inventory":24807,"Ġtowel":24808,"atche":24809,"lasting":24810,"ĠTL":24811,"Ġlatency":24812,"Ġkne":24813,"Ber":24814,"meaning":24815,"Ġupheld":24816,"Ġplayground":24817,"Ġmant":24818,"Side":24819,"Ġstereo":24820,"Ġnorthwest":24821,"Ġexceptionally":24822,"Ġrays":24823,"Ġrecurring":24824,"Drive":24825,"Ġupright":24826,"Ġabduct":24827,"ĠMarathon":24828,"Ġgoodbye":24829,"Ġalphabet":24830,"hp":24831,"Ġcourtroom":24832,"rington":24833,"othing":24834,"Tag":24835,"Ġdiplomats":24836,"Ġbarbar":24837,"ĠAqua":24838,"183":24839,"3333":24840,"Ġmaturity":24841,"Ġinstability":24842,"ĠApache":24843,"Ġ===":24844,"Ġfasting":24845,"ĠGrid":24846,"ModLoader":24847,"Ġ152":24848,"Abs":24849,"ĠOperating":24850,"etti":24851,"Ġacquaint":24852,"Donnell":24853,"ĠKem":24854,"ĠForge":24855,"Ġarmored":24856,"Mil":24857,"Ġphilosophers":24858,"invest":24859,"Players":24860,"âĪ":24861,"Ġmyriad":24862,"Ġcomrades":24863,"Rot":24864,"Ġremembering":24865,"Ġcorresponds":24866,"Ġprogrammers":24867,"ĠLynn":24868,"Ġolig":24869,"Ġcoherent":24870,"ynchron":24871,"ĠChemical":24872,"Ġjugg":24873,"pair":24874,"posts":24875,"Eye":24876,"ĠInner":24877,"Ġsemester":24878,"ottest":24879,"ĠEmirates":24880,"ricanes":24881,"orously":24882,"mits":24883,"ĠWis":24884,"Ġdodge":24885,"location":24886,"Ġfaded":24887,"Amazon":24888,"ĠProceed":24889,"ĠINFO":24890,"journal":24891,"ĠTruck":24892,"Ten":24893,"Ġ217":24894,"Ġstatutes":24895,"mobile":24896,"ĠTypes":24897,"Recomm":24898,"buster":24899,"pex":24900,"Ġlegends":24901,"Ġheadache":24902,"faced":24903,"ĠWiFi":24904,"ifty":24905,"ĠHER":24906,"Ġcircuits":24907,"ERROR":24908,"226":24909,"olin":24910,"Ġcylinder":24911,"ospace":24912,"ikers":24913,"Prem":24914,"Quant":24915,"Ġconflicting":24916,"Ġslightest":24917,"Ġforged":24918,"ionage":24919,"Stephen":24920,"ĠKub":24921,"ĠOpportun":24922,"ĠHeal":24923,"Ġblo":24924,"Ġrulers":24925,"Ġhuh":24926,"Ġsubmarine":24927,"fy":24928,"asser":24929,"Ġallowance":24930,"ĠKasich":24931,"ĠTas":24932,"ĠAustralians":24933,"ForgeModLoader":24934,"ĠâĨij":24935,"ĠMatrix":24936,"amins":24937,"Ġ1200":24938,"ĠAcqu":24939,"236":24940,"Document":24941,"ĠBreaking":24942,"193":24943,"ĠSubst":24944,"ĠRoller":24945,"ĠProperties":24946,"ĠNI":24947,"tier":24948,"Ġcrushing":24949,"Ġadvocating":24950,"Furthermore":24951,"keepers":24952,"Ġsexism":24953,"xd":24954,"Ġcaller":24955,"ĠSense":24956,"chieve":24957,"ĠTF":24958,"Ġfueled":24959,"Ġreminiscent":24960,"Ġobsess":24961,"urst":24962,"Ġuphold":24963,"ĠFans":24964,"hetics":24965,"ĠâĹ":24966,"ĠBath":24967,"Ġbeverage":24968,"Ġoscill":24969,"254":24970,"Ġpoles":24971,"Ġgradual":24972,"Ġexting":24973,"ĠSuff":24974,"ĠSuddenly":24975,"Ġliking":24976,"Ġ1949":24977,"unciation":24978,"amination":24979,"ĠOmar":24980,"ĠLV":24981,"ĠConsequently":24982,"Ġsynthes":24983,"ĠGIF":24984,"Ġpains":24985,"Ġinteracting":24986,"uously":24987,"incre":24988,"Ġrumor":24989,"ĠScientology":24990,"197":24991,"ĠZig":24992,"Ġspelling":24993,"ĠASS":24994,"Ġextingu":24995,"mson":24996,"Ġgh":24997,"Ġremarked":24998,"ĠStrategic":24999,"ĠMON":25000,"å¥":25001,"gae":25002,"ĠWHAT":25003,"Eric":25004,"ĠCampus":25005,"Ġmethane":25006,"Ġimagin":25007,"JUST":25008,"ĠAlm":25009,"XT":25010,"iq":25011,"ĠRSS":25012,"Ġwrongdoing":25013,"atta":25014,"Ġbigot":25015,"Ġdemonstrators":25016,"ĠCalvin":25017,"ĠVilla":25018,"Ġmembrane":25019,"ĠAwesome":25020,"Ġbenefic":25021,"268":25022,"Ġmagnificent":25023,"ĠLots":25024,"Greg":25025,"ĠBoris":25026,"Ġdetainees":25027,"ĠHerman":25028,"Ġwhispered":25029,"Ġawe":25030,"Professor":25031,"funding":25032,"Ġphysiological":25033,"ĠDestruction":25034,"Ġlimb":25035,"Ġmanipulated":25036,"Ġbubbles":25037,"Ġpseud":25038,"Ġhydra":25039,"ĠBristol":25040,"Ġstellar":25041,"ĠExpansion":25042,"ĠKell":25043,"ĠInterestingly":25044,"Ġmans":25045,"Ġdragging":25046,"Ġecological":25047,"ĠFit":25048,"Ġgent":25049,"Ġbenefited":25050,"ĠHaiti":25051,"Ġpolyg":25052,"ãĥİ":25053,"Ġ2030":25054,"Ġprow":25055,"Ġreconstruction":25056,"Ġwast":25057,"Ġpsychic":25058,"ĠGreeks":25059,"Handler":25060,"162":25061,"ĠPulse":25062,"Ġsolicit":25063,"Ġsys":25064,"Ġinflux":25065,"ĠGentle":25066,"percent":25067,"Ġproliferation":25068,"Ġtaxable":25069,"Ġdisregard":25070,"Ġescaping":25071,"Ġginger":25072,"Ġwithstand":25073,"Ġdevastated":25074,"ĠDew":25075,"series":25076,"Ġinjected":25077,"elaide":25078,"Ġturnover":25079,"heat":25080,"ĻĤ":25081,"Happy":25082,"ĠSilent":25083,"ãĤŃ":25084,"ivism":25085,"Ġirrational":25086,"AMA":25087,"Ġreef":25088,"rub":25089,"Ġ162":25090,"Ġbankers":25091,"ĠEthics":25092,"vv":25093,"Ġcriticisms":25094,"Kn":25095,"186":25096,"Movie":25097,"ĠTories":25098,"Ġnood":25099,"Ġdistortion":25100,"False":25101,"odore":25102,"Ġtasty":25103,"Research":25104,"ĠUID":25105,"-)":25106,"Ġdivorced":25107,"ĠMU":25108,"ĠHayes":25109,"ĠIsn":25110,"iani":25111,"ĠHQ":25112,"Ġ\"#":25113,"ignant":25114,"Ġtraumatic":25115,"ĠLing":25116,"Hun":25117,"Ġsabot":25118,"online":25119,"random":25120,"Ġrenamed":25121,"rared":25122,"KA":25123,"dead":25124,"ét":25125,"ĠAssistance":25126,"Ġseaf":25127,"++++++++":25128,"Ġseldom":25129,"ĠWebb":25130,"Ġboolean":25131,"ulet":25132,"Ġrefrain":25133,"ĠDIY":25134,"rule":25135,"Ġshutting":25136,"Ġutilizing":25137,"loading":25138,"ĠParam":25139,"coal":25140,"ooter":25141,"Ġattracting":25142,"ĠDol":25143,"Ġhers":25144,"agnetic":25145,"ĠReach":25146,"imo":25147,"Ġdiscarded":25148,"ĠPip":25149,"015":25150,"ür":25151,"Ġmug":25152,"Imagine":25153,"COL":25154,"Ġcursed":25155,"ĠShows":25156,"ĠCurtis":25157,"ĠSachs":25158,"speaking":25159,"ĠVista":25160,"ĠFramework":25161,"ongo":25162,"Ġsubreddit":25163,"Ġcrus":25164,"ĠOval":25165,"Row":25166,"growing":25167,"Ġinstallment":25168,"Ġglac":25169,"ĠAdvance":25170,"ECK":25171,"ĠLGBTQ":25172,"LEY":25173,"Ġacet":25174,"Ġsuccessive":25175,"ĠNicole":25176,"Ġ1957":25177,"Quote":25178,"Ġcircumstance":25179,"ackets":25180,"Ġ142":25181,"ortium":25182,"Ġguessed":25183,"ĠFrame":25184,"Ġperpetrators":25185,"ĠAviation":25186,"ĠBench":25187,"Ġhandc":25188,"Ap":25189,"Ġ1956":25190,"259":25191,"rand":25192,"NetMessage":25193,"din":25194,"urtles":25195,"hig":25196,"ĠVIII":25197,"ffiti":25198,"ĠSwords":25199,"bial":25200,"Ġkidnapping":25201,"device":25202,"Ġbarn":25203,"ĠEli":25204,"aucas":25205,"Send":25206,"Constructed":25207,"Ġ½":25208,"Ġneedles":25209,"Ġadvertisements":25210,"Ġvou":25211,"Ġexhibited":25212,"ĠFortress":25213,"Ask":25214,"Berry":25215,"TYPE":25216,"Ġcancers":25217,"umping":25218,"ĠTerritory":25219,"Ġprud":25220,"Ġnas":25221,"Ġatheist":25222,"Ġbalances":25223,"ãģŁ":25224,"ĠShawn":25225,"&&":25226,"Ġlandsc":25227,"ĠRGB":25228,"Ġpetty":25229,"Ġexcellence":25230,"Ġtranslations":25231,"Ġparcel":25232,"ĠChev":25233,"East":25234,"ĠOutput":25235,"imi":25236,"Ġambient":25237,"ĠThreat":25238,"Ġvillains":25239,"Ġ550":25240,"ICA":25241,"Ġtaller":25242,"Ġleaking":25243,"cup":25244,"Ġpolish":25245,"Ġinfectious":25246,"ĠKC":25247,"Ġ@@":25248,"background":25249,"Ġbureaucracy":25250,"ĠSai":25251,"unless":25252,"itious":25253,"ĠSkype":25254,"Atl":25255,"IDENT":25256,"008":25257,"Ġhypocr":25258,"Ġpitchers":25259,"Ġguessing":25260,"ĠFINAL":25261,"Between":25262,"Ġvillagers":25263,"Ġ252":25264,"fashion":25265,"ĠTunis":25266,"Beh":25267,"ĠExc":25268,"ĠMID":25269,"288":25270,"ĠHaskell":25271,"196":25272,"ĠNOR":25273,"Ġspecs":25274,"Ġinvari":25275,"Ġglut":25276,"ĠCars":25277,"Ġimpulse":25278,"Ġhonors":25279,"gel":25280,"Ġjurisdictions":25281,"ĠBundle":25282,"ulas":25283,"California":25284,"ĠIncrease":25285,"Ġpear":25286,"Ġsingles":25287,"Ġcues":25288,"Ġunderwent":25289,"ĠWS":25290,"Ġexaggerated":25291,"Ġdubious":25292,"Ġflashing":25293,"LOG":25294,")].":25295,"Journal":25296,"tg":25297,"Van":25298,"ĠIstanbul":25299,"ĠInsp":25300,"ĠFranken":25301,"Draw":25302,"Ġsadness":25303,"Ġironic":25304,"ĠFry":25305,"xc":25306,"Ġ164":25307,"isch":25308,"Way":25309,"ĠProtestant":25310,"horn":25311,"Ġunaff":25312,"ĠViv":25313,"illas":25314,"ĠProductions":25315,"ĠHogan":25316,"Ġperimeter":25317,"ĠSisters":25318,"Ġspontaneous":25319,"Ġdownside":25320,"Ġdescendants":25321,"Ġorn":25322,"worm":25323,"Japanese":25324,"Ġ1955":25325,"Ġ151":25326,"ĠDoing":25327,"elsen":25328,"umbles":25329,"Ġradically":25330,"ĠDrum":25331,"ĠBach":25332,"Ġliabilities":25333,"ĠOB":25334,"ĠElementary":25335,"Ġmeme":25336,"ynes":25337,"Ġfingerprint":25338,"ĠGrab":25339,"Ġundertake":25340,"Members":25341,"ĠReader":25342,"ĠSims":25343,"god":25344,"Ġhypothetical":25345,"scient":25346,"ĠAJ":25347,"Ġcharism":25348,"Ġadmissions":25349,"ĠMissile":25350,"trade":25351,"Ġexercising":25352,"ĠBackground":25353,"Written":25354,"Ġvocals":25355,"whether":25356,"Ġvi":25357,"ĠWinner":25358,"Ġlitter":25359,"ĠShooting":25360,"STEM":25361,"ãĤ¡":25362,"ĠAFL":25363,"Ġvariability":25364,"Ġeats":25365,"ĠDPS":25366,"brow":25367,"Ġelephants":25368,"Ġstrat":25369,"ĠÅ":25370,"Ġsettlers":25371,"Matthew":25372,"Ġinadvert":25373,"HI":25374,"ĠIMF":25375,"ĠGoal":25376,"Ġnerves":25377,"Johnson":25378,"eye":25379,"ablishment":25380,"Thursday":25381,"BILITY":25382,"Had":25383,"amoto":25384,"hetamine":25385,"eps":25386,"Ġmitochond":25387,"Ġcompressed":25388,"ĠTrevor":25389,"ĠAnimals":25390,"Tool":25391,"Lock":25392,"Ġtweak":25393,"Ġpinch":25394,"Ġcancellation":25395,"Pot":25396,"Ġfocal":25397,"ĠAstron":25398,"173":25399,"ĠASC":25400,"ĠOTHER":25401,"umni":25402,"Ġdemise":25403,"dl":25404,"Ùħ":25405,"Semitism":25406,"Ġcracking":25407,"Ġcollaborative":25408,"Ġexplores":25409,"sql":25410,"Ġherbs":25411,"Ġconfigurations":25412,"mis":25413,"ĠResult":25414,"acey":25415,"ĠSmoke":25416,"Ġsanct":25417,"elia":25418,"Ġdegener":25419,"Ġdeepest":25420,"Ġscreamed":25421,"Ġnap":25422,"Software":25423,"ĠSTAR":25424,"EF":25425,"ĠXin":25426,"sponsored":25427,"manship":25428,"233":25429,"Ġprimaries":25430,"Ġfiltering":25431,"Ġassemble":25432,"mil":25433,"ĠMyers":25434,"bows":25435,"Ġpunched":25436,"Mic":25437,"Ġinnovations":25438,"Ġfunc":25439,"ando":25440,"Ġfracking":25441,"ĠVul":25442,"оÐ":25443,"oshop":25444,"ĠImmun":25445,"Ġsettling":25446,"Ġadolescents":25447,"Ġrebuilding":25448,"Ġtransforming":25449,"Ġparole":25450,"Ġharbor":25451,"Ġbooking":25452,"otional":25453,"ongevity":25454,"ĠYo":25455,"bug":25456,"Ġemerges":25457,"ĠMethods":25458,"ĠChu":25459,"Pres":25460,"ĠDungeons":25461,"Ġtrailing":25462,"ĠRum":25463,"ĠHugh":25464,"天":25465,"ĠEra":25466,"ĠBattles":25467,"Results":25468,"ĠTrading":25469,"Ġversa":25470,"css":25471,"axies":25472,"heet":25473,"Ġgreed":25474,"1989":25475,"Ġgardens":25476,"Ġcontingent":25477,"Park":25478,"ĠLeafs":25479,"hook":25480,"robe":25481,"Ġdiplomacy":25482,"ĠFuel":25483,"ĠInvasion":25484,"Ġupgrading":25485,"Male":25486,"Ġelic":25487,"Ġrelentless":25488,"ĠCovenant":25489,"apesh":25490,"ĠTrop":25491,"Ty":25492,"production":25493,"arty":25494,"Ġpunches":25495,"ako":25496,"cyclopedia":25497,"ĠRabbit":25498,"ĠHDMI":25499,"Ġ141":25500,"Ġfoil":25501,"ItemImage":25502,"ĠFG":25503,"Ġimplementations":25504,"ĠPom":25505,"ixtures":25506,"Ġawait":25507,"Ġ330":25508,"amus":25509,"Ġumbrella":25510,"Ġforesee":25511,"separ":25512,"Ġcircumcision":25513,"Ġperipheral":25514,"Say":25515,"ĠExpert":25516,"Inc":25517,"Ġwithdrew":25518,"ĠAnders":25519,"fried":25520,"Ġradioactive":25521,"ĠOpening":25522,"Ġboarding":25523,"ĠND":25524,"Ġoverthrow":25525,"Activ":25526,"WP":25527,"ĠActs":25528,"×Ļ":25529,"Ġmotions":25530,"vic":25531,"ĠMighty":25532,"ĠDefender":25533,"aer":25534,"Ġthankful":25535,"ĠKilling":25536,"ĠBris":25537,"moil":25538,"Ġpredicting":25539,"266":25540,"choice":25541,"Ġkillers":25542,"Ġincub":25543,"ĠChest":25544,"athering":25545,"Ġproclaimed":25546,"flower":25547,"ossom":25548,"umbledore":25549,"ĠCycling":25550,"ĠOccupy":25551,"AGES":25552,"Pen":25553,"ĠYug":25554,"Ġpackaged":25555,"Ġheightened":25556,"cot":25557,"stack":25558,"Cond":25559,"Ġstamps":25560,"mage":25561,"Ġpersuaded":25562,"Ġensl":25563,"ĠCardinal":25564,"Ġsolitary":25565,"Ġpossessing":25566,"ĠCork":25567,"Ġevid":25568,"ĠTay":25569,"Ġblues":25570,"Ġextremism":25571,"Ġlunar":25572,"Ġclown":25573,"Techn":25574,"Ġfestivals":25575,"ĠPvP":25576,"ĠLar":25577,"Ġconsequently":25578,"present":25579,"Ġsomeday":25580,"çİĭ":25581,"ĠMeteor":25582,"Ġtouring":25583,"culture":25584,"Ġbeaches":25585,"Ship":25586,"cause":25587,"ĠFlood":25588,"ãĥ¯":25589,"Ġpurity":25590,"those":25591,"Ġemission":25592,"bolt":25593,"Ġchord":25594,"ĠScripture":25595,"Lu":25596,"Ġ${":25597,"created":25598,"Others":25599,"258":25600,"Ġelemental":25601,"Ġannoyed":25602,"ĠAE":25603,"dan":25604,"ĠSag":25605,"Researchers":25606,"Ġfairy":25607,"âĢĵâĢĵ":25608,"============":25609,"Smart":25610,"GGGG":25611,"Ġskeletons":25612,"Ġpupils":25613,"linked":25614,"Ġurgency":25615,"enabled":25616,"ĠFuck":25617,"Ġcouncill":25618,"rab":25619,"UAL":25620,"TI":25621,"Ġlifes":25622,"Ġconfessed":25623,"Bug":25624,"Ġharmon":25625,"ĠCONFIG":25626,"ĠNeutral":25627,"Double":25628,"Ġstaple":25629,"ĠSHA":25630,"British":25631,"ĠSNP":25632,"ATOR":25633,"oco":25634,"Ġswinging":25635,"gex":25636,"oleon":25637,"plain":25638,"ĠMissing":25639,"ĠTrophy":25640,"vari":25641,"ranch":25642,"Ġ301":25643,"440":25644,"0000000000000000":25645,"Ġrestoring":25646,"Ġhaul":25647,"ucing":25648,"nerg":25649,"Ġfutures":25650,"Ġstrategist":25651,"question":25652,"Ġlateral":25653,"ĠBard":25654,"Ġsor":25655,"ĠRhodes":25656,"ĠDowntown":25657,"?????-":25658,"ĠLit":25659,"ĠBened":25660,"Ġcoil":25661,"street":25662,"ĠPortal":25663,"FILE":25664,"ĠGru":25665,"*,":25666,"231":25667,"neum":25668,"Ġsucked":25669,"Ġrapper":25670,"Ġtendencies":25671,"ĠLauren":25672,"cellaneous":25673,"267":25674,"Ġbrowse":25675,"Ġoverc":25676,"header":25677,"oise":25678,"Ġbeet":25679,"ĠGle":25680,"Stay":25681,"Ġmum":25682,"Ġtyped":25683,"Ġdiscounts":25684,"Talk":25685,"ĠOg":25686,"existing":25687,"ĠSell":25688,"uph":25689,"CI":25690,"ĠAustrian":25691,"ĠWarm":25692,"Ġdismissal":25693,"Ġaverages":25694,"camera":25695,"Ġallegiance":25696,"LAN":25697,"=\"#":25698,"Ġcommentators":25699,"ĠSetting":25700,"ĠMidwest":25701,"Ġpharmac":25702,"ĠEXP":25703,"Ġstainless":25704,"Chicago":25705,"Ġtan":25706,"244":25707,"Ġcountryside":25708,"ĠVac":25709,"295":25710,"Ġpinned":25711,"Ġcrises":25712,"Ġstandardized":25713,"Task":25714,"ĠJail":25715,"ĠDocker":25716,"colored":25717,"forth":25718,"\"},":25719,"Ġpatrons":25720,"Ġspice":25721,"Ġmourn":25722,"ĠMood":25723,"Ġlaundry":25724,"Ġequip":25725,"ĠMole":25726,"yll":25727,"ĠTHC":25728,"nation":25729,"ĠSherlock":25730,"Ġissu":25731,"ĠKre":25732,"ĠAmericas":25733,"ĠAAA":25734,"Ġsystematically":25735,"Ġcontra":25736,"ĠSally":25737,"Ġrationale":25738,"Ġcarriage":25739,"Ġpeaks":25740,"Ġcontradiction":25741,"ensation":25742,"ĠFailure":25743,"Ġprops":25744,"Ġnamespace":25745,"Ġcove":25746,"fields":25747,"ãĤĭ":25748,"Ġwool":25749,"ĠCatch":25750,"Ġpresumed":25751,"ĠDiana":25752,"ragon":25753,"igi":25754,"Ġhamm":25755,"Ġstunt":25756,"ĠGUI":25757,"ĠObservatory":25758,"ĠShore":25759,"Ġsmells":25760,"annah":25761,"Ġcockpit":25762,"ĠDuterte":25763,"850":25764,"Ġoppressed":25765,"breaker":25766,"ĠContribut":25767,"ĠPeru":25768,"ĠMonsanto":25769,"ĠAttempt":25770,"Ġcommanding":25771,"Ġfridge":25772,"ĠRin":25773,"ĠChess":25774,"uality":25775,"Ġol":25776,"Republican":25777,"ĠGlory":25778,"ĠWIN":25779,".......":25780,"agent":25781,"reading":25782,"Ġinh":25783,"Jones":25784,"Ġclicks":25785,"alan":25786,"Ġ[];":25787,"ĠMajesty":25788,"ĠCed":25789,"opus":25790,"atel":25791,"ê":25792,"ARC":25793,"ĠEcuador":25794,"ãĥł":25795,"ĠKuro":25796,"Ġrituals":25797,"Ġcaptive":25798,"Ġounce":25799,"Ġdisagreement":25800,"Ġslog":25801,"fuel":25802,"Pet":25803,"Mail":25804,"Ġexercised":25805,"Ġsolic":25806,"Ġrainfall":25807,"Ġdevotion":25808,"ĠAssessment":25809,"Ġrobotic":25810,"options":25811,"ĠRP":25812,"ĠFamilies":25813,"ĠFlames":25814,"Ġassignments":25815,"007":25816,"akedown":25817,"Ġvocabulary":25818,"Reilly":25819,"Ġcaval":25820,"gars":25821,"Ġsuppressed":25822,"ĠSET":25823,"ĠJohns":25824,"Ġwarp":25825,"broken":25826,"Ġstatues":25827,"Ġadvocated":25828,"Ġ275":25829,"Ġperil":25830,"omorph":25831,"ĠFemin":25832,"perfect":25833,"Ġhatch":25834,"Lib":25835,"512":25836,"Ġlifelong":25837,"313":25838,"Ġcheeks":25839,"Ġnumbered":25840,"ĠMug":25841,"Body":25842,"ravel":25843,"Weight":25844,"ĠJak":25845,"ĠHeath":25846,"Ġkissing":25847,"ĠJUST":25848,"Ġwaving":25849,"upload":25850,"Ġinsider":25851,"ĠProgressive":25852,"ĠFilter":25853,"tta":25854,"ĠBeam":25855,"Ġviolently":25856,"ipation":25857,"Ġskepticism":25858,"Ġ1918":25859,"ĠAnnie":25860,"ĠSI":25861,"Ġgenetics":25862,"Ġonboard":25863,"atl":25864,"ĠFriedman":25865,"ĠBri":25866,"ceptive":25867,"Ġpirate":25868,"ĠReporter":25869,"278":25870,"Ġmythology":25871,"Ġeclipse":25872,"Ġskins":25873,"Ġglyph":25874,"ingham":25875,"Files":25876,"Cour":25877,"women":25878,"Ġregimes":25879,"Ġphotographed":25880,"Kat":25881,"ĠMAX":25882,"Officials":25883,"Ġunexpectedly":25884,"Ġimpressions":25885,"Front":25886,";;;;;;;;":25887,"Ġsupremacy":25888,"Ġsang":25889,"Ġaggravated":25890,"Ġabruptly":25891,"ĠSector":25892,"Ġexcuses":25893,"Ġcosting":25894,"idepress":25895,"Stack":25896,"ĠRNA":25897,"obil":25898,"Ġghosts":25899,"ldon":25900,"atibility":25901,"Topics":25902,"Ġreimburse":25903,"ĠHM":25904,"ĠDeg":25905,"Ġthief":25906,"yet":25907,"ogenesis":25908,"leaning":25909,"ĠKol":25910,"ĠBasketball":25911,"Ġfi":25912,"ĠSeeing":25913,"Ġrecycling":25914,"Ġ[-":25915,"Congress":25916,"Ġlectures":25917,"Psy":25918,"Ġnep":25919,"Ġmaid":25920,"Ġoriented":25921,"AX":25922,"Ġrespectful":25923,"rene":25924,"flush":25925,"ĠUnloaded":25926,"request":25927,"grid":25928,"ĠAlternatively":25929,"ĠHugo":25930,"Ġdecree":25931,"ĠBuddhism":25932,"andum":25933,"Android":25934,"ĠCongo":25935,"ĠJoyce":25936,"Ġacknowledging":25937,"hesive":25938,"ĠTomorrow":25939,"ĠHiro":25940,"thren":25941,"ĠMaced":25942,"Ġhoax":25943,"ĠIncreased":25944,"ĠPradesh":25945,"Wild":25946,"______":25947,"161":25948,"Ġaunt":25949,"Ġdistributing":25950,"ĠTucker":25951,"ĠSSL":25952,"ĠWolves":25953,"Building":25954,"oult":25955,"ĠLuo":25956,"ĠYas":25957,"ĠSpir":25958,"ĠShape":25959,"ĠCambod":25960,"ĠIPv":25961,"Ġml":25962,"Ġextrad":25963,"390":25964,"ĠPenny":25965,"dream":25966,"Ġstationed":25967,"optional":25968,"eworthy":25969,".":26700,"ĠWorkshop":26701,"ĠRetail":26702,"ĠAvatar":26703,"625":26704,"Na":26705,"ĠVC":26706,"ĠSecure":26707,"MY":26708,"1988":26709,"ossip":26710,"Ġprostate":26711,"Ġunden":26712,"Ġgamer":26713,"ĠContents":26714,"ĠWarhammer":26715,"ĠSentinel":26716,"310":26717,"Ġsegregation":26718,"ĠFlex":26719,"ĠMAY":26720,"Ġdrills":26721,"ĠDrugs":26722,"Islamic":26723,"Ġspur":26724,"Ġcafe":26725,"Ġimaginary":26726,"Ġguiding":26727,"Ġswings":26728,"ĠTheme":26729,"oby":26730,"Ġnud":26731,"Ġbegging":26732,"Ġstrongh":26733,"Ġrejecting":26734,"Ġpedestrians":26735,"ĠProspect":26736,"Rare":26737,"sle":26738,"Ġconcessions":26739,"ĠConstitutional":26740,"Ġbeams":26741,"Ġfibers":26742,"poon":26743,"Ġinstincts":26744,"property":26745,"ĠBIG":26746,"Sanders":26747,"imates":26748,"Ġcoating":26749,"Ġcorpses":26750,"ĠTRUE":26751,"checked":26752,"Ġ166":26753,"Ash":26754,"ĠJS":26755,"ĠFiction":26756,"Ġcommunal":26757,"Ġenergetic":26758,"oooooooo":26759,"Ġnowadays":26760,"ILD":26761,"ibo":26762,"ĠSUV":26763,"Ren":26764,"Ġdwelling":26765,"Silver":26766,"Ġtally":26767,"ĠMoving":26768,"Ġcoward":26769,"Ġgenerals":26770,"Ġhorns":26771,"Ġcirculated":26772,"Ġrobbed":26773,"ĠUnlimited":26774,"Ġharassed":26775,"Ġinhibit":26776,"Ġcomposer":26777,"ĠSpotify":26778,"Ġspreads":26779,"364":26780,"Ġsuicidal":26781,"Ġnoises":26782,"ĠStur":26783,"Ġsaga":26784,"ĠKag":26785,"iso":26786,"Ġtheoretically":26787,"Money":26788,"Ġsimilarity":26789,"Ġsliced":26790,"utils":26791,"inges":26792,"\"-":26793,"Ġanth":26794,"Ġimped":26795,"Module":26796,"Throughout":26797,"Ġmenus":26798,"committee":26799,"andi":26800,"obj":26801,"inav":26802,"fired":26803,"ĠAbdullah":26804,"Ġundead":26805,"Ġfonts":26806,"Hold":26807,"ENG":26808,"Ġsustainability":26809,"Ġflick":26810,"Ġrazor":26811,"ĠFest":26812,"ĠCharacters":26813,"Ġwording":26814,"Ġpopulist":26815,"Ġcriticizing":26816,"Ġmuse":26817,"vine":26818,"Ġcardboard":26819,"Ġkindly":26820,"Ġfringe":26821,"ĠTheft":26822,"icultural":26823,"Ġgovernors":26824,"Ġ����":26825,"Ġ163":26826,"Ġtimeout":26827,"ĠAuth":26828,"Children":26829,"AU":26830,"Ġredemption":26831,"ĠAlger":26832,"Ġ1914":26833,"Ġwaved":26834,"Ġastronauts":26835,"ograms":26836,"Ġswamp":26837,"ĠFinnish":26838,"Ġcandle":26839,"Ġtonnes":26840,"utm":26841,"Ġray":26842,"Ġspun":26843,"Ġfearful":26844,"articles":26845,"Ġcaus":26846,"orically":26847,"ĠRequires":26848,"ĠGol":26849,"Ġpope":26850,"Ġinaugural":26851,"Ġgle":26852,"ADA":26853,"ĠISIL":26854,"ĠOffensive":26855,"Ġwatchdog":26856,"Ġbalcon":26857,"entity":26858,"ĠHoo":26859,"Ġgallon":26860,"ACC":26861,"Ġdoubling":26862,"Ġimplication":26863,"ĠSight":26864,"Ġdoctr":26865,"-------":26866,"Ġ\\\\":26867,"Ġmalt":26868,"Roll":26869,"Ġâī¥":26870,"Ġrecap":26871,"adding":26872,"uces":26873,"ĠBend":26874,"figure":26875,"Ġturkey":26876,"Ġsocietal":26877,"ĠTickets":26878,"Ġcommercially":26879,"Ġspicy":26880,"Ġ216":26881,"ĠRamp":26882,"Ġsuperiority":26883,"ï":26884,"ĠTracker":26885,"Carl":26886,"ĠCoy":26887,"ĠPatriot":26888,"Ġconsulted":26889,"Ġlistings":26890,"Ġslew":26891,"reenshot":26892,"ĠGone":26893,"Ġ[...]":26894,"309":26895,"Ġhottest":26896,"ر":26897,"Ġrocky":26898,"ĠDiaz":26899,"Ġmassage":26900,"Ġparaly":26901,"Ġpony":26902,"Az":26903,"Ġcartridge":26904,"ĠNZ":26905,"Ġsnack":26906,"ĠLamar":26907,"plement":26908,"ĠLeslie":26909,"Ġmater":26910,"Ġsnipp":26911,"246":26912,"Ġjointly":26913,"ĠBrisbane":26914,"ĠiPod":26915,"Ġpumping":26916,"Ġgoat":26917,"ĠSharon":26918,"ealing":26919,"Ġcoron":26920,"Ġanomal":26921,"rahim":26922,"ĠConnection":26923,"Ġsculpture":26924,"Ġscheduling":26925,"ĠDaddy":26926,"athing":26927,"Ġeyebrows":26928,"Ġcurved":26929,"Ġsentiments":26930,"Ġdrafting":26931,"Drop":26932,"([":26933,"Ġnominal":26934,"ĠLeadership":26935,"ĠGrow":26936,"Ġ176":26937,"Ġconstructive":26938,"ivation":26939,"Ġcorrupted":26940,"gerald":26941,"ĠCros":26942,"ĠChester":26943,"ĠLap":26944,"ãģª":26945,"OTH":26946,"DATA":26947,"Ġalmond":26948,"probably":26949,"Imp":26950,"Ġfeast":26951,"ĠWarcraft":26952,"Flor":26953,"Ġcheckpoint":26954,"Ġtranscription":26955,"Ġ204":26956,"Ġtweaks":26957,"Ġrelieve":26958,"Science":26959,"Ġperformer":26960,"Zone":26961,"Ġturmoil":26962,"igated":26963,"hibit":26964,"ĠCafe":26965,"themed":26966,"Ġfluor":26967,"bench":26968,"Ġdecom":26969,"ĠUnt":26970,"ĠBarrett":26971,"ĠFacts":26972,"Ġtasting":26973,"ĠPTSD":26974,"ĠSeal":26975,"ĠJudaism":26976,"ĠDynamic":26977,"ĠCors":26978,"Ve":26979,"ĠMing":26980,"ĠTransform":26981,"von":26982,"ĠDefenders":26983,"ĠTactical":26984,"ĠVon":26985,"ĠUnivers":26986,"Ġdistorted":26987,"ĠBreath":26988,"?'\"":26989,"Ġagon":26990,"ĠDeadly":26991,"Ġlan":26992,"ĠCycle":26993,"orned":26994,"Ġreliably":26995,"Ġglor":26996,"ĠMonkey":26997,"ãĥ¡":26998,"Ġadren":26999,"Ġmicrowave":27000,"ĠAlban":27001,"ircraft":27002,"digit":27003,"smart":27004,"ĠDread":27005,"¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯":27006,"{{":27007,"ĠRochester":27008,"Ġsimplified":27009,"Ġinflicted":27010,"Ġtakeover":27011,"Ġyourselves":27012,"aditional":27013,"Ġmuscular":27014,"KS":27015,"Ġingen":27016,"Tax":27017,"ĠFeature":27018,"277":27019,"Ġcruc":27020,"Ġcrate":27021,"Ġunidentified":27022,"Ġacclaimed":27023,"ĠManga":27024,"ĠFrances":27025,"ĠNepal":27026,"ĠGerald":27027,"ĠKuwait":27028,"Ġslain":27029,"ĠHeb":27030,"ĠGoku":27031,"ãģ®æ":27032,"286":27033,"Mrs":27034,"ĠCody":27035,"ĠSanctuary":27036,"016":27037,"Ġdismant":27038,"Ġdataset":27039,"ĠHond":27040,"buck":27041,"ĠPatterson":27042,"Ġpalette":27043,"ĠGD":27044,"icol":27045,"ĠLodge":27046,"Ġplanetary":27047,"akin":27048,"ĠRegistered":27049,"abwe":27050,"ĠPetersburg":27051,"Ġhailed":27052,"ĠPiece":27053,"Sche":27054,"ĠDOJ":27055,"Ġenumer":27056,"181":27057,"ĠObserver":27058,"ĠBold":27059,"founded":27060,"commerce":27061,"Ġexploits":27062,"ĠFinding":27063,"URN":27064,"ĠSne":27065,"ĠAcid":27066,"ayette":27067,"ĠValues":27068,"Ġdrastic":27069,"Ġarchitectural":27070,"Ġ\".":27071,"×ķ":27072,"umped":27073,"Ġwrapping":27074,"Ġwidow":27075,"ĠSlayer":27076,"lace":27077,"once":27078,"Germany":27079,"avoid":27080,"Ġtemples":27081,"PAR":27082,"ô":27083,"ĠLucifer":27084,"ĠFlickr":27085,"lov":27086,"forces":27087,"Ġscouting":27088,"Ġlouder":27089,"tesy":27090,"Ġbeforehand":27091,"Äĵ":27092,"ĠNeon":27093,"ĠWol":27094,"ĠTypically":27095,"ĠPolitico":27096,"-+-+":27097,"Ġbuilder":27098,"Ġderive":27099,"Kill":27100,"Ġpoker":27101,"Ġambiguous":27102,"Ġlifts":27103,"Ġcyt":27104,"Ġribs":27105,"oodle":27106,"ĠSounds":27107,"hair":27108,"ĠSyndrome":27109,"tf":27110,"Ġproportional":27111,"uid":27112,"Ġpertaining":27113,"ĠKindle":27114,"ĠNegro":27115,"Ġreiterated":27116,"ĠTonight":27117,"oths":27118,"ĠCornell":27119,"Ġowing":27120,"Ġ208":27121,"elfare":27122,"ocating":27123,"ĠBirds":27124,"Subscribe":27125,"Ġessays":27126,"Ġburdens":27127,"Ġillustrations":27128,"arious":27129,"ERAL":27130,"ĠCalcul":27131,"Ġxen":27132,"ĠLinkedIn":27133,"ĠJung":27134,"Ġredesign":27135,"Connor":27136,"296":27137,"Ġreversal":27138,"ĠAdelaide":27139,"ĠLL":27140,"Ġsinking":27141,"Ġgum":27142,"USH":27143,"capt":27144,"ĠGrimm":27145,"Ġfootsteps":27146,"ĠCBD":27147,"ispers":27148,"Ġprose":27149,"Wednesday":27150,"ĠMovies":27151,"edin":27152,"Ġoverturned":27153,"Ġcontentious":27154,"USB":27155,"~~~~~~~~~~~~~~~~":27156,"ĠCopper":27157,"Ġpointless":27158,"NV":27159,"values":27160,"olphin":27161,"dain":27162,"Ġdeposited":27163,"ĠGW":27164,"Ġpreceded":27165,"ĠCla":27166,"ĠGolem":27167,"ĠNim":27168,"Ġβ":27169,"ĠEngineers":27170,"middle":27171,"Ġflatt":27172,"operative":27173,"Ġcouncils":27174,"imbabwe":27175,"elin":27176,"Ġstressful":27177,"ĠLD":27178,"Ġresh":27179,"lake":27180,"Ġwheelchair":27181,"ĠAlternative":27182,"Ġoptimize":27183,"operation":27184,"Ġpeek":27185,"Ġoneself":27186,"igil":27187,"Ġtransitions":27188,"opathy":27189,"blank":27190,"Ġ169":27191,"171":27192,"________________________________________________________________":27193,"Ġlaundering":27194,"Enc":27195,"ĠDEC":27196,"Ġworkouts":27197,"Ġspikes":27198,"Ġdinosaurs":27199,"Ġdiscriminatory":27200,"Pool":27201,"Rather":27202,"385":27203,"RNA":27204,"testers":27205,"eto":27206,"ĠIdentity":27207,"Ġvein":27208,"ĠBurton":27209,"Ġarcade":27210,"420":27211,"Ultimately":27212,"ĠSadly":27213,"ð":27214,"pill":27215,"Ġcubic":27216,"ĠSpectrum":27217,"these":27218,"states":27219,"Ġunofficial":27220,"hawks":27221,"ĠEVERY":27222,"Ġrainbow":27223,"Ġincarceration":27224,"anding":27225,"Ġsyll":27226,"ĠEverton":27227,"Ġ179":27228,"ĠSerbia":27229,"Ġ189":27230,"meter":27231,"ĠMickey":27232,"Ġantiqu":27233,"Ġfactual":27234,"neck":27235,"ĠNare":27236,"norm":27237,"must":27238,"Ġhighways":27239,"Ġglam":27240,"Ġdividing":27241,"ĠSquadron":27242,"ĠMartha":27243,"Ġbirths":27244,"Cover":27245,"////////////////":27246,"ĠWong":27247,"Phot":27248,"ĠALS":27249,"rio":27250,"ĠNonetheless":27251,"ĠLemon":27252,"Ġ206":27253,"ĠEE":27254,"Ġderivative":27255,"ĠWWII":27256,"vote":27257,"Ġtherein":27258,"Ġseparating":27259,"446":27260,"sync":27261,"ĠStreets":27262,"Ġratt":27263,"Ġmunicipality":27264,"ĠShortly":27265,"Ġmonk":27266,"),\"":27267,"Ġscrub":27268,"Ġoperatives":27269,"Neither":27270,"Place":27271,"ĠLimit":27272,"Female":27273,"ĠActor":27274,"Character":27275,"Ġconstituted":27276,"357":27277,"Ġprotested":27278,"ĠStraw":27279,"ĠHeight":27280,"ilda":27281,"ĠTyph":27282,"Ġfloods":27283,"Ġcosmetic":27284,"WAY":27285,"perture":27286,"upon":27287,"tons":27288,"essing":27289,"ĠPocket":27290,"Ġrooft":27291,"ĠCaucas":27292,"Ġantidepress":27293,"Ġincompatible":27294,"ECD":27295,"Ġopera":27296,"ĠContest":27297,"Ġgenerators":27298,"lime":27299,"Defense":27300,"1987":27301,"forum":27302,"Ġsavage":27303,"ĠHungarian":27304,"nz":27305,"Ġmetallic":27306,"Ġexpelled":27307,"Ġresidency":27308,"Ġdresses":27309,"666":27310,"ĠClement":27311,"fires":27312,"Category":27313,"Ġgeek":27314,"alis":27315,"Ġcemetery":27316,"educated":27317,"Ġcrawl":27318,"ĠUnable":27319,"ĠTyson":27320,"akis":27321,"Ġpardon":27322,"ĠWra":27323,"Ġstrengthened":27324,"ĠFors":27325,"335":27326,"ĠHC":27327,"ĠMond":27328,"Ġvisuals":27329,"ĠBeatles":27330,"ettlement":27331,"Ġï":27332,"gro":27333,"Ġbash":27334,"Ġpoorest":27335,"Ġexcel":27336,"Ġaspirations":27337,"ĠMunicip":27338,"ensible":27339,"Ġceremonies":27340,"Ġintimidation":27341,"ĠCONTR":27342,"beck":27343,"ĠKap":27344,"asu":27345,"Ġtrademarks":27346,"ĠSew":27347,"ĠCompetition":27348,"network":27349,"ĠArri":27350,"ĠTet":27351,"Roaming":27352,"WC":27353,"Dat":27354,"Ġsob":27355,"Ġpairing":27356,"Ġoverdose":27357,"SAY":27358,"aber":27359,"Ġrevolt":27360,"ĠFah":27361,"acting":27362,"eq":27363,"estation":27364,"Fight":27365,"ĠMarks":27366,"273":27367,"Ġ178":27368,"Raw":27369,"ãģĭ":27370,"349":27371,"blocks":27372,"Ġverge":27373,"estine":27374,"ĠPodesta":27375,"Ġinvasive":27376,"Ġprofoundly":27377,"ĠAo":27378,"each":27379,"Ġlest":27380,"interpret":27381,"Ġshrinking":27382,"Ġerrone":27383,"Ġchees":27384,"lys":27385,"ĠIvy":27386,"ĠDirectory":27387,"Ġhinted":27388,"VICE":27389,"Ġcontacting":27390,"ĠGent":27391,"hei":27392,"Ġlabeling":27393,"Ġmercury":27394,"ĠLite":27395,"Ġexpires":27396,"Ġdestabil":27397,"ritis":27398,"cu":27399,"Ġfeathers":27400,"Ġsteer":27401,"Ġprogrammed":27402,"ĠVader":27403,"Going":27404,"ĠElim":27405,"Ġyo":27406,"ĠMiche":27407,"Ġ203":27408,"Ġsleeves":27409,"Ġbully":27410,"ĠHumans":27411,"368":27412,"Ġcompress":27413,"ĠBanner":27414,"ARS":27415,"Ġawhile":27416,"Ġcalib":27417,"Ġsponsorship":27418,"ĠDifficulty":27419,"ĠPapers":27420,"Ġidentifier":27421,"}.":27422,"Ġyog":27423,"ĠShia":27424,"Ġcleanup":27425,"Ġvibe":27426,"introdu":27427,"imming":27428,"Australia":27429,"Ġoutlines":27430,"ĠYoutube":27431,"train":27432,"ĠMakes":27433,"Ġdeported":27434,"Ġcentr":27435,"ĠDug":27436,"ĠBoulder":27437,"ĠBuffy":27438,"Ġinjunction":27439,"ĠHarley":27440,"ĠGroups":27441,"ĠDumbledore":27442,"ĠClara":27443,"Ġ\"-":27444,"Ġsacrificed":27445,"eph":27446,"Shadow":27447,"ibling":27448,"Ġfreelance":27449,"Ġevidently":27450,"phal":27451,"Ġretains":27452,"Mir":27453,"Ġfinite":27454,"dar":27455,"ĠCous":27456,"Ġrepaired":27457,"Ġperiodic":27458,"Ġchampionships":27459,"Ġasteroid":27460,"blind":27461,"Ġexpressly":27462,"ĠAstros":27463,"Ġscaled":27464,"Ġgeographical":27465,"ĠRapids":27466,"Enjoy":27467,"Ġelastic":27468,"ĠMohamed":27469,"Market":27470,"begin":27471,"Ġdiscovers":27472,"Ġtelecommunications":27473,"Ġscanner":27474,"Ġenlarge":27475,"Ġsharks":27476,"Ġpsychedel":27477,"ĠRouge":27478,"Ġsnapshot":27479,"isine":27480,"XP":27481,"Ġpesticides":27482,"ĠLSD":27483,"ĠDistribution":27484,"really":27485,"Ġdegradation":27486,"Ġdisguise":27487,"Ġbiom":27488,"ĠEXT":27489,"Ġequations":27490,"Ġhazards":27491,"ĠCompared":27492,")*":27493,"Ġvirtues":27494,"Ġelders":27495,"Ġenhancing":27496,"ĠAcross":27497,"eros":27498,"angling":27499,"Ġcombust":27500,"ucci":27501,"Ġconcussion":27502,"Ġcontraception":27503,"ĠKang":27504,"Ġexpresses":27505,"Ġaux":27506,"ĠPione":27507,"Ġexhibits":27508,"Debug":27509,"OTAL":27510,"ĠAlready":27511,"ĠWheeler":27512,"Ġexpands":27513,"?:":27514,"Ġreconciliation":27515,"Ġpirates":27516,"Ġpurse":27517,"Ġdiscourage":27518,"Ġspectacle":27519,"Rank":27520,"Ġwraps":27521,"ĠThought":27522,"Ġimpending":27523,"Opp":27524,"ĠAnglo":27525,"ĠEUR":27526,"Ġscrewed":27527,"retched":27528,"Ġencouragement":27529,"models":27530,"Ġconfuse":27531,"mmm":27532,"ĠVitamin":27533,"âĸijâĸij":27534,"Cru":27535,"Ġknights":27536,"Ġdiscard":27537,"Ġbishops":27538,"ĠWear":27539,"ĠGarrett":27540,"kan":27541,"ãĥŁ":27542,"Ġmasculine":27543,"capital":27544,"ĠAus":27545,"Ġfatally":27546,"thanks":27547,"ĠAU":27548,"ĠGut":27549,"1200":27550,"Ġ00000000":27551,"Ġsurrog":27552,"ĠBIOS":27553,"raits":27554,"ĠWatts":27555,"Ġresurrection":27556,"ĠElectoral":27557,"ĠTips":27558,"4000":27559,"Ġnutrient":27560,"Ġdepicting":27561,"Ġsprink":27562,"Ġmuff":27563,"ĠLIM":27564,"ĠSample":27565,"psc":27566,"ibi":27567,"generated":27568,"Ġspecimens":27569,"Ġdissatisf":27570,"Ġtailored":27571,"Ġholdings":27572,"ĠMonthly":27573,"ĠEat":27574,"poons":27575,"Ġnec":27576,"ĠCage":27577,"ĠLotus":27578,"ĠLantern":27579,"Ġfrontier":27580,"Ġpensions":27581,"Ġjoked":27582,"ĠHardy":27583,"=-=-=-=-":27584,"rade":27585,"UID":27586,"Ġrails":27587,"Ġemit":27588,"Ġslate":27589,"Ġsmug":27590,"Ġspit":27591,"ĠCalls":27592,"ĠJacobs":27593,"feat":27594,"ĠUE":27595,"Ġrestruct":27596,"Ġregeneration":27597,"Ġenergies":27598,"ĠConnor":27599,"OHN":27600,"ĠCheese":27601,"Ġger":27602,"Ġresurrect":27603,"management":27604,"NW":27605,"Ġpresently":27606,"ĠBruins":27607,"Member":27608,"ĠMang":27609,"idan":27610,"Ġboosting":27611,"wyn":27612,"+.":27613,"requisite":27614,"ĠNYPD":27615,"ĠMegan":27616,"ĠConditions":27617,"Ġpics":27618,"nesium":27619,"ĠRash":27620,"Ġ174":27621,"ĠDucks":27622,"Ġembro":27623,"zu":27624,"onian":27625,"religious":27626,"Ġcraz":27627,"ĠACA":27628,"ĠZucker":27629,"EMA":27630,"ĠPros":27631,"Weapon":27632,"ĠKnox":27633,"ĠArduino":27634,"Ġstove":27635,"Ġheavens":27636,"ĠPurchase":27637,"Ġherd":27638,"Ġfundraiser":27639,"Digital":27640,"5000":27641,"Ġproponents":27642,"/âĢĭ":27643,"Ġjelly":27644,"ĠVisa":27645,"Ġmonks":27646,"Ġadvancement":27647,"ĠWer":27648,"Ġ187":27649,"eus":27650,"ertility":27651,"Ġfetal":27652,"Ġ1936":27653,"Lo":27654,"Ġoutfits":27655,"Ġstaircase":27656,"bomb":27657,"Ġcustomized":27658,"clair":27659,"Tree":27660,"Ġmapped":27661,"ĠConsidering":27662,"ĠTorres":27663,"Ġmethyl":27664,"Ġapproximate":27665,"Ġdoom":27666,"ĠHansen":27667,"Ġcrossover":27668,"Ġstandalone":27669,"ä¼":27670,"Ġinvites":27671,"Ġgraveyard":27672,"Ġhp":27673,"DonaldTrump":27674,"Ġescort":27675,"Gar":27676,"Ġpredecessors":27677,"Ġhay":27678,"Ġenzyme":27679,"ĠStraight":27680,"visors":27681,"Ing":27682,"aneously":27683,"ĠApplied":27684,"Ġfec":27685,"ĠDurant":27686,"Ġoutspoken":27687,"orb":27688,"Ġzeal":27689,"Ġdisgrace":27690,"').":27691,"ĠCheng":27692,"289":27693,"ĠRena":27694,"ĠSuicide":27695,"294":27696,"Ġoutraged":27697,"ĠNewman":27698,"ĠNvidia":27699,"ĠAber":27700,"ĠBers":27701,"Ġrecreation":27702,"Window":27703,"ĠDP":27704,"xe":27705,"Ġpedoph":27706,"Ġfallout":27707,"amboo":27708,"Ġpresentations":27709,"ĠApps":27710,"Ġhtml":27711,"345":27712,"ĠXXX":27713,"Ġrubbing":27714,"ĠLeather":27715,"Ġhumidity":27716,"seys":27717,"established":27718,"ĠUnits":27719,"646":27720,"Ġrespectable":27721,"Auto":27722,"Ġthriving":27723,"ĠInnovation":27724,"angs":27725,"Extra":27726,"regulation":27727,"298":27728,"pick":27729,"Examples":27730,"ĠCJ":27731,"Attack":27732,"Ġdracon":27733,"LT":27734,"Ġsticker":27735,"rers":27736,"Ġsunny":27737,"Iss":27738,"regulated":27739,"dim":27740,"ĠAbstract":27741,"Ġhusbands":27742,"Office":27743,"omination":27744,"itars":27745,"ANGE":27746,"ascal":27747,"ĠKris":27748,"ĠInfantry":27749,"Ġmalf":27750,"ĠAthe":27751,"ĠRally":27752,"balanced":27753,"........................":27754,"OUP":27755,"Ġmolecule":27756,"metics":27757,"ĠSplit":27758,"ĠInstructions":27759,"ĠNights":27760,"cards":27761,"Ġtug":27762,"Ġcone":27763,"åŃ":27764,"Ġtx":27765,"ĠDiscussion":27766,"Ġcatastrophe":27767,"ppe":27768,"gio":27769,"Ġcommunism":27770,"Ġhalted":27771,"ĠGuant":27772,"clean":27773,"ĠSched":27774,"ĠKanye":27775,"Ġwander":27776,"ĠSeriously":27777,"Ġ188":27778,"ennial":27779,"follow":27780,"productive":27781,"ĠFlow":27782,"ĠSail":27783,"Ġcraw":27784,"Ġsimulations":27785,"oru":27786,"angles":27787,"ĠNolan":27788,"Ġmenstru":27789,"470":27790,"Ġ207":27791,"aja":27792,"Ġcasually":27793,"boarding":27794,"Ġ222":27795,"ovy":27796,"ĠNumbers":27797,"umat":27798,"OE":27799,"287":27800,"ĠClemson":27801,"Ġcerts":27802,"Ġslid":27803,"ĠTribe":27804,"Ġtoast":27805,"Ġfortunes":27806,"Ġfals":27807,"ĠCommittees":27808,"Ġgp":27809,"Ġfiery":27810,"ĠNets":27811,"ĠAnime":27812,"Package":27813,"ĠCompare":27814,"laughter":27815,"infect":27816,"Ġatrocities":27817,"Ġjustices":27818,"Ġinsults":27819,"ĠVernon":27820,"Ġshaken":27821,"Ġpersona":27822,"estamp":27823,"367":27824,"brain":27825,"Ġexperimenting":27826,"Ken":27827,"ĠElectronics":27828,"Ġ161":27829,"domain":27830,"Ġgraphical":27831,"bishop":27832,"Ġwhopping":27833,"ĠEvangel":27834,"Ġadvertisers":27835,"ĠSpear":27836,"Ġbids":27837,"Ġdestroys":27838,"utz":27839,"Ġundersc":27840,"ĠADD":27841,"Ġants":27842,"ĠCum":27843,"ipples":27844,"ĠFill":27845,"Ġglanced":27846,"Ġindicted":27847,"ĠEff":27848,"Ġmiscon":27849,"ĠDesktop":27850,"Ġabide":27851,"ãĥĢ":27852,"ĠIo":27853,"ĠCoul":27854,"Ġcapsule":27855,"ĠChrys":27856,"MON":27857,"Ġundes":27858,"ĠIRA":27859,"Ġcitation":27860,"Ġdictate":27861,"ĠNetworks":27862,"ĠConflict":27863,"ĠStuff":27864,"xa":27865,"isec":27866,"ĠChemistry":27867,"Ġquarterly":27868,"Williams":27869,"anan":27870,"Opt":27871,"ĠAlexandria":27872,"outheastern":27873,"ĠSpringfield":27874,"ĠBlacks":27875,"Ġgeography":27876,"242":27877,"Ġutmost":27878,"ĠExxon":27879,"abouts":27880,"EVA":27881,"ĠEnable":27882,"ĠBarr":27883,"Ġdisagreed":27884,"ĠCyprus":27885,"Ġdementia":27886,"Ġlabs":27887,"Ġubiquitous":27888,"ĠLOVE":27889,"Ġconsolidated":27890,"sr":27891,"Ġcreamy":27892,"ĠTimber":27893,"Regardless":27894,"ĠCertificate":27895,"Ġ\"...":27896,"ogenous":27897,"Captain":27898,"Ġinsulting":27899,"ĠSoros":27900,"ĠInstr":27901,"ĠBulgaria":27902,"better":27903,"Ġsucking":27904,"ĠDavidson":27905,"atz":27906,"Ġcollateral":27907,"gif":27908,"Ġplagued":27909,"ĠCancel":27910,"ĠGardner":27911,"RB":27912,"Ġsixteen":27913,"Remove":27914,"uristic":27915,"cook":27916,"Rod":27917,"Ġcomprising":27918,"fle":27919,")âĢĶ":27920,"ĠViking":27921,"growth":27922,"agonal":27923,"Ġsrf":27924,"afety":27925,"mot":27926,"Nearly":27927,"stown":27928,"ĠFactor":27929,"Ġautomobile":27930,"Ġprocedural":27931,"mask":27932,"ampires":27933,"Ġdisappears":27934,"jab":27935,"315":27936,"Ġ1951":27937,"needed":27938,"Ġdaring":27939,"leader":27940,"Ġpodium":27941,"Ġunhealthy":27942,"Ġmund":27943,"Ġpyramid":27944,"ocre":27945,"Ġkissed":27946,"Ġdreamed":27947,"ĠFantastic":27948,"ĠGly":27949,"åĬ":27950,"Ġgreatness":27951,"Ġspices":27952,"Ġmetropolitan":27953,"Ġcompuls":27954,"iets":27955,"1016":27956,"ĠSham":27957,"ĠPyr":27958,"flies":27959,"ĠMidnight":27960,"Ġswallowed":27961,"Ġgenres":27962,"ĠLucky":27963,"ĠRewards":27964,"Ġdispatch":27965,"ĠIPA":27966,"ĠApply":27967,"Ġaven":27968,"alities":27969,"312":27970,"things":27971,"Ġ().":27972,"Ġmates":27973,"ĠSz":27974,"ĠCOP":27975,"olate":27976,"OFF":27977,"Ġrecharge":27978,"caps":27979,"ĠYorker":27980,"icone":27981,"Ġgalaxies":27982,"ileaks":27983,"Dave":27984,"ĠPuzz":27985,"ĠCeltic":27986,"ĠAFC":27987,"276":27988,"ĠSons":27989,"Ġaffirmative":27990,"Hor":27991,"Ġtutorials":27992,"ĠCITY":27993,"ĠRosa":27994,"ĠExtension":27995,"Series":27996,"Ġfats":27997,"Ġrab":27998,"lis":27999,"Ġunic":28000,"Ġeve":28001,"ĠSpin":28002,"Ġadulthood":28003,"typ":28004,"Ġsectarian":28005,"Ġcheckout":28006,"ĠCycl":28007,"Single":28008,"Ġmartyr":28009,"Ġchilling":28010,"888":28011,"oufl":28012,"Ġ];":28013,"Ġcongestion":28014,"mk":28015,"ĠWhereas":28016,"Ġ1938":28017,"urrencies":28018,"erion":28019,"Ġboast":28020,"ĠPatients":28021,"Ġchap":28022,"ĠBD":28023,"realDonaldTrump":28024,"Ġexamines":28025,"hov":28026,"Ġstartling":28027,"ĠBabylon":28028,"wid":28029,"omew":28030,"brance":28031,"ĠOdyssey":28032,"wig":28033,"Ġtorch":28034,"ĠVox":28035,"ĠMoz":28036,"ĠTroll":28037,"ĠAns":28038,"Similarly":28039,"ĠFul":28040,"006":28041,"Unless":28042,"ĠAlone":28043,"stead":28044,"ĠPublisher":28045,"rights":28046,"tu":28047,"ĠDoesn":28048,"Ġprofessionally":28049,"Ġclo":28050,"icz":28051,"Ġsteals":28052,"Ġá":28053,"1986":28054,"Ġsturdy":28055,"ĠJohann":28056,"Ġmedals":28057,"Ġfilings":28058,"ĠFraser":28059,"done":28060,"Ġmultinational":28061,"Ġfeder":28062,"Ġworthless":28063,"Ġpest":28064,"Yesterday":28065,"ankind":28066,"Ġgays":28067,"Ġborne":28068,"ĠPOS":28069,"Picture":28070,"Ġpercentages":28071,"251":28072,"rame":28073,"Ġpotions":28074,"AMD":28075,"ĠLebanese":28076,"Ġrang":28077,"ĠLSU":28078,"ongs":28079,"Ġpeninsula":28080,"ĠClause":28081,"ALK":28082,"oha":28083,"ĠMacBook":28084,"Ġunanimous":28085,"Ġlenders":28086,"Ġhangs":28087,"Ġfranchises":28088,"orers":28089,"ĠUpdates":28090,"Ġisolate":28091,"andro":28092,"Soon":28093,"Ġdisruptive":28094,"ĠSurve":28095,"Ġstitches":28096,"ĠScorp":28097,"ĠDominion":28098,"Ġsupplying":28099,"Arg":28100,"Ġturret":28101,"ĠLuk":28102,"Ġbrackets":28103,"*)":28104,"ĠRevolutionary":28105,"ĠHonest":28106,"Ġnoticing":28107,"ĠShannon":28108,"Ġafforded":28109,"Ġtha":28110,"ĠJanet":28111,"!--":28112,"ĠNarendra":28113,"ĠPlot":28114,"Hol":28115,"sever":28116,"eenth":28117,"Ġobstruction":28118,"Ġ1024":28119,"staff":28120,"jas":28121,"orget":28122,"scenes":28123,"laughs":28124,"ĠFargo":28125,"crime":28126,"Ġorchestr":28127,"Ġdelet":28128,"iliary":28129,"rieved":28130,"Ġmilitar":28131,"ĠGreene":28132,"âĹı":28133,"ãģ¦":28134,"ĠGuards":28135,"Ġunleashed":28136,"ĠWeber":28137,"Ġadjustable":28138,"Ġcaliber":28139,"Ġmotivations":28140,"ĠÃł":28141,"mAh":28142,"ĠLanka":28143,"handle":28144,"Ġpent":28145,"ĠRav":28146,"ĠAngular":28147,"ĠKau":28148,"umbing":28149,"Ġphilanthrop":28150,"Ġdehyd":28151,"Ġtoxicity":28152,"eer":28153,"ĠYORK":28154,"witz":28155,"å¼":28156,"ĠIE":28157,"community":28158,"ĠAH":28159,"Ġretali":28160,"Ġmassively":28161,"ĠDaniels":28162,"ĠDEL":28163,"Ġcarcin":28164,"Url":28165,"Ġrouting":28166,"ĠNPCs":28167,"ĠRAF":28168,"ryce":28169,"Ġwaived":28170,"ĠGuatem":28171,"Everybody":28172,"Ġcovenant":28173,"Ġ173":28174,"Ġrelaxing":28175,"Ġquart":28176,"almost":28177,"Ġguarded":28178,"ĠSoldiers":28179,"ĠPLAY":28180,"Ġoutgoing":28181,"LAND":28182,"Ġrewrite":28183,"ĠMOV":28184,"ĠImper":28185,"ĠSolution":28186,"Ġphenomenal":28187,"Ġlongevity":28188,"Ġimpat":28189,"ĠNissan":28190,"irie":28191,"Ġodor":28192,"ĠZar":28193,"oks":28194,"Ġmilitias":28195,"ĠSPEC":28196,"Ġtolerated":28197,"arser":28198,"ĠBradford":28199,"+,":28200,"Ġsurreal":28201,"sf":28202,"Canadian":28203,"Ġresemblance":28204,"Ġcarbohydrate":28205,"VIEW":28206,"Ġaccessory":28207,"meal":28208,"largest":28209,"iegel":28210,"Someone":28211,"Ġtoughest":28212,"oso":28213,"Ġfunnel":28214,"Ġcondemnation":28215,"luent":28216,"Ġwired":28217,"ĠSunset":28218,"Jesus":28219,"ĠPST":28220,"ĠPages":28221,"ĠTycoon":28222,"ĠPF":28223,"Ġselections":28224,"Ġà¤":28225,"partisan":28226,"Ġhighs":28227,"ĠRune":28228,"Ġcrafts":28229,"lead":28230,"ĠParents":28231,"Ġreclaim":28232,"eker":28233,"ĠAllied":28234,"aeper":28235,"Ġlooming":28236,"Ġbeneficiaries":28237,"ĠHull":28238,"Students":28239,"Jewish":28240,"dj":28241,"Ġpact":28242,"template":28243,"ĠOfficials":28244,"ĠBaylor":28245,"Ġhemp":28246,"Ġyouths":28247,"ĠLevels":28248,"ĠXiao":28249,"ĠChes":28250,"Ġendeavor":28251,"ĠRemoved":28252,"Ġhippocamp":28253,"Hell":28254,"ãĤĬ":28255,"805":28256,"Ġdinosaur":28257,"ĠWrath":28258,"ĠIndonesian":28259,"Ġcalculator":28260,"ĠDictionary":28261,"Ġ420":28262,"ĠMAG":28263,"(_":28264,"!,":28265,"tarians":28266,"Ġrestricting":28267,"racuse":28268,"Ġweekday":28269,"OUNT":28270,"Ġshrugged":28271,"leground":28272,"Ġbald":28273,"ĠDoctors":28274,"Ġtouted":28275,"ĠMaxwell":28276,"Ġ214":28277,"Ġdiplomat":28278,"Ġrepression":28279,"Ġconstituency":28280,"vice":28281,"ranked":28282,"ĠNapoleon":28283,"gang":28284,"ĠForever":28285,"tun":28286,"Ġbulb":28287,"ĠPDT":28288,"ĠCisco":28289,"VEN":28290,"Ġresumed":28291,"Steven":28292,"ĠManitoba":28293,"Ġfabulous":28294,"ĠAgents":28295,"1984":28296,"Ġamusing":28297,"ĠMysteries":28298,"Ġorthodox":28299,"floor":28300,"Ġquestionnaire":28301,"Ġpenetrate":28302,"Ġfilmmakers":28303,"ĠUnc":28304,"Ġstamped":28305,"Ġthirteen":28306,"Ġoutfield":28307,"Ġforwarded":28308,"Ġappra":28309,"Ġaided":28310,"try":28311,"Ġunfocused":28312,"ĠLiz":28313,"ĠWendy":28314,"ĠScene":28315,"Charg":28316,"Ġrejects":28317,"Ġleftist":28318,"ĠProvidence":28319,"ĠBrid":28320,"regn":28321,"Ġprophecy":28322,"ĠLIVE":28323,"499":28324,"Ġforge":28325,"ĠFML":28326,"Ġintrinsic":28327,"ĠFrog":28328,"Ġwont":28329,"ĠHolt":28330,"Ġfamed":28331,"CLUS":28332,"aepernick":28333,"ĠHate":28334,"ĠCay":28335,"Ġregistering":28336,"ortality":28337,"ropy":28338,"ocalyptic":28339,"aan":28340,"nav":28341,"Ġfascist":28342,"IFIED":28343,"Ġimplicated":28344,"ĠResort":28345,"ĠChandler":28346,"ĠBrick":28347,"Pin":28348,"ysc":28349,"Usage":28350,"ĠHelm":28351,"usra":28352,"âĺħâĺħ":28353,"ĠAbbas":28354,"Ġunanimously":28355,"Ġkeeper":28356,"Ġaddicted":28357,"???":28358,"Ġhelmets":28359,"Ġantioxid":28360,"apsed":28361,"808":28362,"giene":28363,"Ġwaits":28364,"Ġminion":28365,"raved":28366,"ĠPorsche":28367,"Ġdreaming":28368,"Ġ171":28369,"ĠCain":28370,"Ġunfor":28371,"asso":28372,"ĠConfiguration":28373,"kun":28374,"hardt":28375,"Ġnested":28376,"ĠLDS":28377,"LES":28378,"Ġtying":28379,"enos":28380,"Ġcue":28381,"ĠMarqu":28382,"skirts":28383,"Ġclicked":28384,"Ġexpiration":28385,"ĠAccordingly":28386,"ĠWC":28387,"Ġblessings":28388,"Ġaddictive":28389,"ĠNarr":28390,"yx":28391,"ĠJaguars":28392,"Ġrents":28393,"ĠSiber":28394,"Ġtipped":28395,"ousse":28396,"ĠFitzgerald":28397,"Ġhierarch":28398,"outine":28399,"Ġwavelength":28400,">.":28401,"chid":28402,"ĠProcessing":28403,"/+":28404,"ranking":28405,"Easy":28406,"ĠConstruct":28407,"Ġtet":28408,"insured":28409,"HUD":28410,"Ġquoting":28411,"Ġcommunicated":28412,"inx":28413,"Ġinmate":28414,"Ġerected":28415,"ĠAbsolutely":28416,"ĠSurely":28417,"Ġunim":28418,"ĠThrone":28419,"heid":28420,"Ġclaws":28421,"Ġsuperstar":28422,"ĠLenn":28423,"ĠWhis":28424,"Uk":28425,"abol":28426,"Ġsket":28427,"ĠNiet":28428,"Ġperks":28429,"Ġaffinity":28430,"Ġopenings":28431,"phasis":28432,"Ġdiscriminate":28433,"Tip":28434,"vc":28435,"Ġgrinding":28436,"ĠJenny":28437,"Ġasthma":28438,"holes":28439,"ĠHomer":28440,"Ġregisters":28441,"ĠGlad":28442,"Ġcreations":28443,"Ġlithium":28444,"Ġapplause":28445,"until":28446,"Justice":28447,"ĠTurks":28448,"Ġscandals":28449,"Ġbake":28450,"tank":28451,"Mech":28452,"ĠMeans":28453,"ĠMaid":28454,"Republicans":28455,"isal":28456,"windows":28457,"ĠSantos":28458,"Ġvegetation":28459,"338":28460,"tri":28461,"Ġflux":28462,"insert":28463,"Ġclarified":28464,"Ġmortg":28465,"ĠChim":28466,"ĠTort":28467,"Ġdisclaim":28468,"metal":28469,"ĠAside":28470,"Ġinduction":28471,"Ġinfl":28472,"Ġatheists":28473,"amph":28474,"Ġether":28475,"ĠVital":28476,"ĠBuilt":28477,"Mind":28478,"Ġweaponry":28479,"SET":28480,"Ġ186":28481,"admin":28482,"gam":28483,"contract":28484,"afa":28485,"Ġderivatives":28486,"Ġsnacks":28487,"Ġchurn":28488,"Econom":28489,"Ġcapped":28490,"ĠUnderstanding":28491,"ĠHers":28492,"ĠIz":28493,"Ġduct":28494,"IENT":28495,"aughty":28496,"ĠâľĶ":28497,"ĠNP":28498,"Ġsailing":28499,"Initialized":28500,"Ġted":28501,"Ġreactors":28502,"ĠLomb":28503,"Ġchoke":28504,"ĠWorm":28505,"Ġadmiration":28506,"Ġswung":28507,"ensibly":28508,"Ġrash":28509,"ĠGoals":28510,"ĠImportant":28511,"Shot":28512,"ĠRas":28513,"Ġtrainers":28514,"ĠBun":28515,"Working":28516,"Ġharmed":28517,"ĠPandora":28518,"ĠLTE":28519,"Ġmushroom":28520,"ĠCHAR":28521,"ĠFee":28522,"ĠMoy":28523,"Born":28524,"oliberal":28525,"ĠMartial":28526,"Ġgentlemen":28527,"Ġlingering":28528,"Official":28529,"Ġgraffiti":28530,"ĠNames":28531,"Der":28532,"Ġquint":28533,"istrate":28534,"azeera":28535,"ĠNOTICE":28536,"ĠFlorence":28537,"Ġpayable":28538,"Ġdepicts":28539,"ĠSpecies":28540,"Heart":28541,"âĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢ":28542,"Ġenclosed":28543,"Increases":28544,"Daily":28545,"ĠLis":28546,"Ġenactment":28547,"ĠBacon":28548,"ĠSteele":28549,"demand":28550,"Ġ183":28551,"Ġmouths":28552,"Ġstranded":28553,"Ġenhancement":28554,"011":28555,"ĠWhats":28556,"Ġhealed":28557,"eny":28558,"ĠRab":28559,"Ġ340":28560,"ĠLabyrinth":28561,"roach":28562,"ĠYosh":28563,"ĠClippers":28564,"Ġconcerts":28565,"Internet":28566,"355":28567,"Ġstickers":28568,"Ġtermed":28569,"ĠAxe":28570,"Ġgrandparents":28571,"France":28572,"ĠClim":28573,"ĠUh":28574,"ulic":28575,"Ġthrill":28576,"centric":28577,"ĠOverview":28578,"ĠConduct":28579,"Ġsubstantive":28580,"Ġ182":28581,"mur":28582,"Ġstray":28583,"ĠCoff":28584,"Ġrepetitive":28585,"ĠForgotten":28586,"Ġqualification":28587,"ewitness":28588,"ĠZimbabwe":28589,"Ġsimulated":28590,"ĠJD":28591,"253":28592,"ĠWare":28593,"Ġunsc":28594,"Times":28595,"Ġsummons":28596,"Ġdisconnected":28597,"Ġ184":28598,"cius":28599,"ĠGujar":28600,"odka":28601,"Ġerase":28602,"ĠTobacco":28603,"elected":28604,"Ġuncont":28605,"ĠShepard":28606,"ĠLamp":28607,"Ġalerted":28608,"Ġoperative":28609,"arna":28610,"uint":28611,"Ġnegligence":28612,"acements":28613,"Ġsupra":28614,"Ġprevail":28615,"ĠShark":28616,"Ġbelts":28617,"ãģ«":28618,"Ġtighter":28619,"Engineers":28620,"Ġinactive":28621,"Ġexponent":28622,"ĠWillie":28623,"aples":28624,"Ġheir":28625,"ĠHits":28626,"iann":28627,"ĠSays":28628,"Ġcurrents":28629,"ĠBengal":28630,"Ġarist":28631,"Buffer":28632,"Ġbreeze":28633,"ĠWesley":28634,"Cola":28635,"Ġpronoun":28636,"Ġdeed":28637,"ĠKling":28638,"Ġoft":28639,"Ġinflict":28640,"Ġpunishing":28641,"Ġnm":28642,"iku":28643,"ODUCT":28644,"014":28645,"Ġsubsidy":28646,"ĠDEA":28647,"ĠHerbert":28648,"ĠJal":28649,"Bank":28650,"Ġdeferred":28651,"Ġshipment":28652,"Bott":28653,"Ġalle":28654,"bearing":28655,"HTML":28656,"Offline":28657,"Ġ213":28658,"Ġscrolling":28659,"Ġscanned":28660,"ĠLibyan":28661,"ĠTOP":28662,"chrom":28663,"dt":28664,"column":28665,"PsyNetMessage":28666,"Zero":28667,"Ġtorso":28668,"050":28669,"âķIJ":28670,"Ġimperson":28671,"ĠSchwartz":28672,"udic":28673,"Ġpissed":28674,"ĠSapp":28675,"257":28676,"ĠISPs":28677,"ogl":28678,"Ġsupervised":28679,"Ġadolescent":28680,"Ġattained":28681,"ĠDelivery":28682,"ĠBunny":28683,"Ġ1937":28684,"Ġminiature":28685,"Ġos":28686,"Ġ370":28687,"608":28688,"ĠMourinho":28689,"Ġinnate":28690,"Ġtempo":28691,"ĠNM":28692,"ĠFallen":28693,"009":28694,"Ġprovocative":28695,"Streamer":28696,"ĠBenedict":28697,"ĠBolshe":28698,"Ġturtle":28699,"ĠPCB":28700,"ĠEqual":28701,"Director":28702,"ĠRend":28703,"Ġfluids":28704,"Authorities":28705,"Ġcousins":28706,"requency":28707,"ĠNeighbor":28708,"sets":28709,"shared":28710,"Charles":28711,"password":28712,"Ġgears":28713,"Ġ211":28714,"ĠHardware":28715,"rika":28716,"Ġupstream":28717,"Hom":28718,"Ġdisproportionately":28719,"ivities":28720,"Ġundefined":28721,"Ġelectrons":28722,"Ġcommemor":28723,"Eventually":28724,"Ġ><":28725,"Ġirresponsible":28726,"218":28727,"ĠReleased":28728,"ĠOVER":28729,"ĠIGN":28730,"ĠBread":28731,"stellar":28732,"ĠSage":28733,"tted":28734,"damage":28735,"edition":28736,"ĠPrec":28737,"Ġlime":28738,"Ġconfinement":28739,"Ġcalorie":28740,"weapon":28741,"Ġdiffering":28742,"ĠSina":28743,"mys":28744,"amd":28745,"Ġintricate":28746,"kk":28747,"ĠPAT":28748,"ão":28749,"stones":28750,"links":28751,"Ġranch":28752,"Semitic":28753,"Ġdifferentiate":28754,"ĠSinger":28755,"occupied":28756,"Ġfortress":28757,"cmd":28758,"Ġinterception":28759,"ĠAnkara":28760,"Ġrept":28761,"ĠSolitaire":28762,"Ġremake":28763,"pred":28764,"Ġdared":28765,"autions":28766,"ĠBACK":28767,"Running":28768,"Ġdebugging":28769,"Ġgraphs":28770,"399":28771,"ĠNigel":28772,"Ġbun":28773,"Ġpillow":28774,"Ġprogressed":28775,"fashioned":28776,"Ġobedience":28777,"ERN":28778,"Ġrehears":28779,"Cell":28780,"tl":28781,"Sher":28782,"Ġherald":28783,"ĠPayment":28784,"ĠCory":28785,"ĠDept":28786,"Ġrepent":28787,"ĠWeak":28788,"uckland":28789,"Ġpleasing":28790,"Ġshortages":28791,"Ġjurors":28792,"ĠKab":28793,"qqa":28794,"Anti":28795,"Ġwow":28796,"ĠRCMP":28797,"Ġtsun":28798,"ĠSic":28799,"Ġcomprises":28800,"Ġspies":28801,"Ġprecinct":28802,"nu":28803,"Ġurges":28804,"Ġtimed":28805,"Ġstripes":28806,"ĠBoots":28807,"Ġyen":28808,"Advanced":28809,"Ġdiscrete":28810,"ĠArchangel":28811,"employment":28812,"Diff":28813,"Ġmonuments":28814,"Ġ209":28815,"worker":28816,"Ġ196":28817,"ĠIg":28818,"utterstock":28819,"TPS":28820,"Jac":28821,"Ġhomelessness":28822,"Ġcommentator":28823,"Ġracially":28824,"fing":28825,"seed":28826,"Ele":28827,"ellation":28828,"Ġethanol":28829,"Ġparish":28830,"ĠDong":28831,"ĠAwakening":28832,"Ġdeviation":28833,"ĠBearing":28834,"ĠTsuk":28835,"Ġrecess":28836,"Ġlymph":28837,"ĠCannabis":28838,"åľ":28839,"ĠNEWS":28840,"Ġdra":28841,"ĠStefan":28842,"ĠWrong":28843,"ĠSAM":28844,"Ġloosely":28845,"Ġinterpreter":28846,"ĠPlain":28847,"Government":28848,"Ġbigotry":28849,"Ġgrenades":28850,"avez":28851,"pictured":28852,"Ġmandated":28853,"ĠMonk":28854,"ĠPedro":28855,"Ġlava":28856,"274":28857,"Ġcynical":28858,"ĠScrolls":28859,"locks":28860,"Mp":28861,"Ġcongregation":28862,"ornings":28863,"phil":28864,"ĠIbid":28865,"Ġferv":28866,"Ġdisappearing":28867,"Ġarrogant":28868,"syn":28869,"ĠMaver":28870,"ĠSuit":28871,"241":28872,"Ġabbre":28873,"ackers":28874,"Pa":28875,"ĠYel":28876,"Whenever":28877,"Ġ235":28878,"ĠVine":28879,"ĠAnat":28880,"Ġextinct":28881,"LET":28882,"Ġexecutable":28883,"VERS":28884,"oxide":28885,"DNA":28886,"ĠPrel":28887,"Ġresentment":28888,"Ġcomprise":28889,"ĠAviv":28890,"Ġinterceptions":28891,"Ġprolific":28892,"INA":28893,"ĠErin":28894,"thought":28895,"219":28896,"ĠPsychiatry":28897,"unky":28898,"chemist":28899,"Ho":28900,"ĠMcCoy":28901,"Ġbricks":28902,"Los":28903,"rily":28904,"ĠUSSR":28905,"Ġrud":28906,"Ġlaud":28907,"ĠWise":28908,"ĠEmerald":28909,"Ġrevived":28910,"Ġdamned":28911,"ĠRepair":28912,"idem":28913,"ctica":28914,"Ġpatriarch":28915,"ĠNurs":28916,"meg":28917,"Ġcheapest":28918,"reements":28919,"empty":28920,"ĠCelebr":28921,"Ġdeprivation":28922,"chanted":28923,"ĠThumbnails":28924,"Energy":28925,"ĠEthan":28926,"ĠQing":28927,"Ġopposes":28928,"WIND":28929,"vik":28930,"ĠMau":28931,"ĠSUB":28932,"667":28933,"GRE":28934,"ĠVolunte":28935,"nton":28936,"Cook":28937,"åIJ":28938,"esque":28939,"Ġplummet":28940,"Ġsuing":28941,"Ġpronounce":28942,"Ġresisting":28943,"ĠFishing":28944,"ĠTrials":28945,"Ġyell":28946,"Ġ310":28947,"Ġinduct":28948,"Ġpersonalized":28949,"often":28950,"Reb":28951,"EMBER":28952,"Ġviewpoint":28953,"Ġexistential":28954,"())":28955,"remove":28956,"MENTS":28957,"lasses":28958,"Ġevapor":28959,"Ġaisle":28960,"meta":28961,"Ġreflective":28962,"Ġentitlement":28963,"Ġdevised":28964,"music":28965,"ascade":28966,"Ġwinding":28967,"offset":28968,"Ġaccessibility":28969,"kered":28970,"Better":28971,"ĠJohnston":28972,"thinking":28973,"Snow":28974,"ĠCroatia":28975,"ĠAtomic":28976,"271":28977,"348":28978,"Ġtextbook":28979,"ĠSixth":28980,"ĠاÙĦ":28981,"Ġslider":28982,"ĠBurger":28983,"bol":28984,"Sync":28985,"Ġgrandchildren":28986,"Ġcerv":28987,"+)":28988,"Ġeternity":28989,"Ġtweeting":28990,"Ġspeculative":28991,"Ġpivotal":28992,"ĠWP":28993,"ĠTER":28994,"ynamic":28995,"Ġupl":28996,"ĠCats":28997,"perhaps":28998,"Ġclassmates":28999,"Ġblatant":29000,"'-":29001,"Ġlakh":29002,"antine":29003,"ĠBorg":29004,"iom":29005,"/(":29006,"ĠAthletic":29007,"Ġsar":29008,"OTA":29009,"ĠHoffman":29010,"Nevertheless":29011,"Ġadorable":29012,"Ġspawned":29013,"Associated":29014,"ĠDomestic":29015,"Ġimplant":29016,"ĠLuxem":29017,"ĠKens":29018,"Ġpumps":29019,"ĠSAT":29020,"Attributes":29021,"509":29022,"avour":29023,"Ġcentralized":29024,"ĠTN":29025,"Ġfreshly":29026,"ĠAchieve":29027,"Ġoutsiders":29028,"herty":29029,"ĠRee":29030,"ĠTowers":29031,"ĠDart":29032,"akable":29033,"Ġmp":29034,"ĠHeavenly":29035,"Ġripe":29036,"ĠCaroline":29037,"ryan":29038,"Ġclassics":29039,"Ġretiring":29040,"Ġ228":29041,"Ġah":29042,"Ġdealings":29043,"Ġpunching":29044,"ĠChapman":29045,"Options":29046,"maxwell":29047,"volume":29048,"Ġstal":29049,"Ġexported":29050,"ĠQuite":29051,"Ġnumerical":29052,"Burn":29053,"Fact":29054,"ĠKeystone":29055,"Ġtrending":29056,"Ġaltering":29057,"ĠAfricans":29058,"478":29059,"ĠMN":29060,"ĠKnock":29061,"Ġtemptation":29062,"Ġprestige":29063,"Overview":29064,"ĠTraditional":29065,"ĠBahrain":29066,"Private":29067,"ĠHOU":29068,"Ġbarr":29069,"ĠTat":29070,"Cube":29071,"USD":29072,"ĠGrande":29073,"ĠGat":29074,"ĠFlo":29075,"Ġresides":29076,"Ġindec":29077,"volent":29078,"Ġperpetual":29079,"ubes":29080,"Ġworldview":29081,"ĠQuantum":29082,"Ġfiltered":29083,"Ġensu":29084,"orgetown":29085,"ERSON":29086,"ĠMild":29087,"379":29088,"OTT":29089,"Ã¥":29090,"Ġvitamins":29091,"Ġribbon":29092,"Ġsincerely":29093,"ĠHin":29094,"Ġeighteen":29095,"Ġcontradictory":29096,"Ġglaring":29097,"Ġexpectancy":29098,"Ġconspir":29099,"Ġmonstrous":29100,"Ġ380":29101,"reci":29102,"Ġhandic":29103,"Ġpumped":29104,"Ġindicative":29105,"Ġrapp":29106,"Ġavail":29107,"ĠLEGO":29108,"ĠMarijuana":29109,"1985":29110,"erton":29111,"Ġtwentieth":29112,"################################":29113,"ĠSwamp":29114,"Ġvaluation":29115,"Ġaffiliates":29116,"adjusted":29117,"ĠFacility":29118,"262":29119,"Ġenzymes":29120,"itudinal":29121,"Ġimprint":29122,"Site":29123,"Ġinstaller":29124,"ĠTRA":29125,"mology":29126,"linear":29127,"ĠCollective":29128,"igating":29129,"ĠToken":29130,"Ġspeculated":29131,"KN":29132,"ĠCly":29133,"ority":29134,"Ġdefer":29135,"Ġinspectors":29136,"approved":29137,"RM":29138,"ĠSuns":29139,"Ġinforming":29140,"ĠSyracuse":29141,"ibli":29142,"765":29143,"Ġglove":29144,"Ġauthorize":29145,"âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦":29146,"ĠCruise":29147,"Ġcontracting":29148,"shell":29149,"IFE":29150,"ĠJewel":29151,"pract":29152,"ĠPhotoshop":29153,"ĠKnowing":29154,"harm":29155,"Ġattractions":29156,"adan":29157,"etus":29158,"018":29159,"wagen":29160,"Alt":29161,"Ġmultiply":29162,"Ġequilibrium":29163,":{":29164,"ĠFighters":29165,"ĠEdgar":29166,"Ġfourteen":29167,"Govern":29168,"Ġmisuse":29169,"Ġabusing":29170,"Ġancestry":29171,"ramer":29172,"644":29173,"Ġworms":29174,"Ġthicker":29175,"ĠCombine":29176,"Ġpeasants":29177,"Ġvind":29178,"Ġconquest":29179,"Ġmocked":29180,"Ġcinnamon":29181,"ĠCald":29182,"ĠGallup":29183,"Ġavoidance":29184,"Ġincarnation":29185,"ĠStrat":29186,"Ġtasted":29187,"enta":29188,"ĠNeal":29189,"pared":29190,"Ġterminology":29191,"jection":29192,"Scientists":29193,"ĠINS":29194,"ĠDee":29195,"Ġdirectories":29196,"Road":29197,"ĠShap":29198,"bright":29199,"ĠDirectors":29200,"ĠColumn":29201,"Ġbob":29202,"Ġpreferably":29203,"Ġglitch":29204,"furt":29205,"Ġeg":29206,"idis":29207,"CBC":29208,"Ġsurrendered":29209,"Ġtestament":29210,"336":29211,"uggest":29212,"ĠNil":29213,"another":29214,"Ġpathetic":29215,"ĠDonna":29216,"Ġ218":29217,"ĠAvery":29218,"Ġwhiskey":29219,"Ġfixture":29220,"ĠConquest":29221,"Ġbets":29222,"Occ":29223,"ĠLeicester":29224,"].\"":29225,"Ġ));":29226,"Ġflashes":29227,"456":29228,"Ġmasked":29229,"gebra":29230,"Ġcomputed":29231,"chel":29232,"auder":29233,"Ġdefeats":29234,"ĠLiberation":29235,"ĠOsama":29236,"ĠVive":29237,"Changes":29238,"Channel":29239,"Ġtariffs":29240,"Ġmage":29241,"ĠSax":29242,"Ġinadvertently":29243,"ĠCRE":29244,"ĠReaper":29245,"inky":29246,"grading":29247,"Ġstereotyp":29248,"Ġcurl":29249,"ĠFANT":29250,"Ġframeworks":29251,"Mom":29252,"ĠAnch":29253,"Ġflavour":29254,"carbon":29255,"Ġpermitting":29256,"letcher":29257,"ĠMozilla":29258,"ĠParking":29259,"ĠChamp":29260,"Scroll":29261,"Ġmurderer":29262,"Ġrested":29263,"Ġowes":29264,"ĠPoss":29265,"ADD":29266,"IFF":29267,"resolution":29268,"ĠMining":29269,"Ġcomparative":29270,"Dim":29271,"Ġneighbouring":29272,"ĠAST":29273,"ĠToxic":29274,"Ġbiases":29275,"Ġgunfire":29276,"urous":29277,"ĠMoment":29278,"1983":29279,"Ġpervasive":29280,"ttp":29281,"ĠNormally":29282,"rir":29283,"Sarah":29284,"ĠAlbany":29285,"Ġunsett":29286,"ĠSMS":29287,"ipers":29288,"layer":29289,"ĠWhites":29290,"uple":29291,"Ġturbo":29292,"ĠLeeds":29293,"Ġthats":29294,"ĠMiner":29295,"MER":29296,"ĠReign":29297,"Ġperme":29298,"ĠBlitz":29299,"Ġ1934":29300,"Ġintimidating":29301,"tube":29302,"Ġeccentric":29303,"abolic":29304,"boxes":29305,"ĠAssociates":29306,"votes":29307,"Ġsimulate":29308,"umbo":29309,"astery":29310,"Ġshipments":29311,"FFFF":29312,"anth":29313,"Ġseasoned":29314,"Ġexperimentation":29315,"âĸł":29316,"laws":29317,"Meet":29318,"iddles":29319,"antics":29320,"Rating":29321,"ISIS":29322,"hift":29323,"Ġfronts":29324,"buf":29325,"017":29326,"Ġunatt":29327,"ĠDil":29328,"leases":29329,"ĠGardens":29330,"777":29331,"touch":29332,"vell":29333,"458":29334,"Ġ=====":29335,"saving":29336,"Ġerosion":29337,"ĠQuin":29338,"Ġearns":29339,"Ġaccomplishment":29340,"ĠWei":29341,"Ġ<[":29342,"_____":29343,"Ġirrig":29344,"ĠTeddy":29345,"Ġconquered":29346,"ĠArmored":29347,"Ġasserts":29348,"Ġmanipulating":29349,"ré":29350,"Ġtranscripts":29351,"Gallery":29352,"Ġplotting":29353,"Neil":29354,"Ġbetrayal":29355,"loader":29356,"ĠSul":29357,"Ġdisplacement":29358,"Ġroyalty":29359,"ĠWI":29360,"heit":29361,"ĠDevices":29362,"allel":29363,"Ġmunicipalities":29364,"Ġcanal":29365,"Stars":29366,"ĠUAE":29367,"Ġ\"âĢ¦":29368,"ĠCU":29369,"above":29370,"Ġresonance":29371,"ĠguiActiveUn":29372,"added":29373,"ĠBraves":29374,"ĠIbn":29375,"Ġhereby":29376,"ĠBRE":29377,"Ġshareholder":29378,"ĠHir":29379,"ĠJi":29380,"Ġstrangely":29381,"Ġadmired":29382,"Ġplight":29383,"Ġbachelor":29384,"ĠPole":29385,"ciplinary":29386,"Tony":29387,"ĠArmenian":29388,"Ġunman":29389,"ĠZionist":29390,"Stage":29391,"iscover":29392,"Ġautomotive":29393,"Ġsidelines":29394,"Ġslick":29395,"ĠRenaissance":29396,"ĠFUN":29397,"Images":29398,"ĠHaj":29399,"Ġping":29400,"Ġshortcut":29401,"ĠBlvd":29402,"ĠLooks":29403,"Ġbursts":29404,"Ġclamp":29405,"Ġmish":29406,"Ġsorting":29407,"Ġpatriot":29408,"Ġcorrectness":29409,"ĠScandinav":29410,"ĠCavaliers":29411,"python":29412,"azar":29413,"Ġ375":29414,"ĠJaune":29415,"409":29416,"Ġdetrimental":29417,"Ġstabbing":29418,"Ġpoisoned":29419,"Ġfountain":29420,"ocent":29421,"orst":29422,"ĠMari":29423,"Ġrains":29424,"ĠOvers":29425,"ĠInstitution":29426,"udget":29427,"AMY":29428,"tale":29429,"ĠKR":29430,"ĠPrices":29431,"Ġheadaches":29432,"Ġlandsl":29433,"ĠAura":29434,"Bonus":29435,"ĠZhao":29436,"ĠHip":29437,"Ġhops":29438,"ĠKurdistan":29439,"Ġexploiting":29440,"ryn":29441,"Ġhypocrisy":29442,"opening":29443,"Ġgunshot":29444,"Ġwed":29445,"interstitial":29446,"Interstitial":29447,"Ġamen":29448,"Breaking":29449,"Ġmarketed":29450,"Wire":29451,"ĠCrowd":29452,"Continue":29453,"ĠKnown":29454,"ĠEffective":29455,"orean":29456,"izons":29457,"Joseph":29458,"Ġescalation":29459,"username":29460,"Ġcurtain":29461,"ATES":29462,"ĠPAR":29463,"ĠMiy":29464,"Ġcounterfe":29465,"lene":29466,"Ġcontenders":29467,"daily":29468,"ĠAsc":29469,"ĠPhillip":29470,"mostly":29471,"Ġfilename":29472,"hene":29473,"Ġresembling":29474,"Ġstaging":29475,"ĠChloe":29476,"Ġwiring":29477,"Hon":29478,"ĠRenew":29479,"ottage":29480,"ĠHybrid":29481,"much":29482,"Ġstrokes":29483,"Ġpolicymakers":29484,"APTER":29485,"ĠArkham":29486,"plot":29487,"Ġassistants":29488,"Ġdeport":29489,"ĠSega":29490,"Ġinfluenza":29491,"ĠCursed":29492,"ĠKobe":29493,"Ġskinny":29494,"Provider":29495,"ĠRip":29496,"Ġincremental":29497,"products":29498,"BF":29499,"Ġdome":29500,"ĠCredits":29501,"Ġlosers":29502,"ints":29503,"ĠBetty":29504,"ĠTalent":29505,"ĠDAM":29506,"Lv":29507,"Ess":29508,"Ġdens":29509,"temp":29510,"Judge":29511,"odic":29512,"Ġ'(":29513,"URES":29514,"etsk":29515,"VO":29516,"Ġretrieved":29517,"Ġarchitects":29518,"Ùĩ":29519,"Ġethic":29520,"ĠSecondary":29521,"stocks":29522,"adia":29523,"Ġ325":29524,"ĠOpinion":29525,"Ġsimultaneous":29526,"Ġdizz":29527,"ulp":29528,"Ġsmuggling":29529,"ippery":29530,"Random":29531,"facing":29532,"ĠDas":29533,"Ġstockp":29534,"Ġdisclosures":29535,"pointer":29536,"Ġcoral":29537,"ĠSelection":29538,"ĠPike":29539,"ivalent":29540,"Ġruthless":29541,"ĠRim":29542,"Ġensuing":29543,"ĠExperiment":29544,"Ġcongressman":29545,"Ġbeliever":29546,"Ġunspecified":29547,"ĠMord":29548,"Ġknowledgeable":29549,"ĠVERY":29550,"TX":29551,"Ġstraps":29552,"Ġturf":29553,"apeshifter":29554,"Ġmarital":29555,"Ġflock":29556,"ãģĨ":29557,"263":29558,"AMES":29559,"ĠOpposition":29560,"Ġtreasures":29561,"ĠGOD":29562,"Ġmodeled":29563,"ĠWORLD":29564,"Ġ([":29565,"ĠUsage":29566,"HF":29567,"Ġ$(":29568,"ussed":29569,"Ġpioneer":29570,"Eight":29571,"parse":29572,"bread":29573,"ritz":29574,"ĠMiranda":29575,"ĠKant":29576,"++)":29577,"oren":29578,"Ġprovoked":29579,"Ġbreeds":29580,"ĠIncludes":29581,"ĠPastebin":29582,"ĠFlip":29583,"Java":29584,"Ġbrink":29585,"Ġrumored":29586,"Ġunseen":29587,"Ġgarnered":29588,"ĠDefin":29589,"alted":29590,"Ġtattoos":29591,"Ġhesitation":29592,"isitions":29593,"ĠWeaver":29594,"ĠReporting":29595,"Ġtherapies":29596,"Ġconsultants":29597,"Ġresidual":29598,"ĠMali":29599,"ĠRoma":29600,"iago":29601,"ĠResidents":29602,"ubi":29603,"Ġremedies":29604,"Ġadaptive":29605,"ĠAlive":29606,"ĠBarcl":29607,"Ġwallets":29608,"crypt":29609,"etermination":29610,"ĠPelosi":29611,"Ġslipping":29612,"otonin":29613,"Ġalliances":29614,"patrick":29615,"iris":29616,"Ġorth":29617,"ĠPerkins":29618,"ĠDeV":29619,"ĠGets":29620,"Ġdrying":29621,"gee":29622,"forest":29623,"ĠForget":29624,"orem":29625,"339":29626,"Ġvaguely":29627,"ĠDion":29628,"ĠPorn":29629,"ĠHOW":29630,"Ġpneum":29631,"Ġrubble":29632,"ĠTaste":29633,"encia":29634,"ĠGel":29635,"Ġdst":29636,"Ġ245":29637,"ĠMorocco":29638,"inflamm":29639,"ĠTwins":29640,"Ġbots":29641,"daughter":29642,"ĠBalk":29643,"Ġbrethren":29644,"Ġlogos":29645,"Ġgobl":29646,"fps":29647,"Ġsubdivision":29648,"Ġpawn":29649,"Ġsqueezed":29650,"Ġmorale":29651,"ĠDW":29652,"'\"":29653,"Ġknot":29654,"ooky":29655,"Ġdivisive":29656,"Ġboosted":29657,"chy":29658,"ãĥIJ":29659,"ifact":29660,"Ġnewcomers":29661,"ĠWrestling":29662,"Ġscouts":29663,"wolves":29664,"Rat":29665,"Ġnineteenth":29666,"ĠOsborne":29667,"Stats":29668,"Ġempowered":29669,"Ġpsychopath":29670,"ĠOEM":29671,"uggage":29672,"ĠPK":29673,"ĠMohammad":29674,"Pak":29675,"Ġanarchists":29676,"ĠExtract":29677,"esthes":29678,"ĠStockholm":29679,"loo":29680,"ĠGraph":29681,"Ġdeploying":29682,"ĠStranger":29683,"ĠMold":29684,"Ġstaffer":29685,"Ġdiscounted":29686,"uckle":29687,"please":29688,"ĠLanding":29689,"ÃŃa":29690,"Ġ193":29691,"Ġante":29692,"Ġrepetition":29693,"Ġ+/-":29694,"Ġparody":29695,"Ġlively":29696,"AAA":29697,"ĠHorus":29698,"Ġpits":29699,"inders":29700,"LOC":29701,"ĠVenice":29702,"406":29703,"ĠDiscover":29704,"âĨ":29705,"ellectual":29706,"Ġpens":29707,"Ġeyel":29708,"iguous":29709,"Impl":29710,"Ġjoking":29711,"Ġinval":29712,"ĠBelfast":29713,"Ġcreditors":29714,"ĠSkywalker":29715,"ovsky":29716,"Ġceasefire":29717,"Ġseals":29718,"isoft":29719,")).":29720,"ĠFelix":29721,"ITS":29722,"Ġtresp":29723,"ĠBlockchain":29724,"eware":29725,"ĠSchwar":29726,"enne":29727,"mounted":29728,"ĠBeacon":29729,"lesh":29730,"Ġimmensely":29731,"Ġcheering":29732,"Employ":29733,"scene":29734,"ishly":29735,"atchewan":29736,"ĠNicolas":29737,"Ġdrained":29738,"ĠExit":29739,"ĠAzerb":29740,"jun":29741,"Ġfloated":29742,"uania":29743,"Deep":29744,"Ġsuperv":29745,"Ġmystical":29746,"ĠDollar":29747,"ĠApostle":29748,"ĠREL":29749,"ĠProvided":29750,"ĠBucks":29751,"ãĥ´":29752,"cutting":29753,"Ġenhancements":29754,"ĠPenguins":29755,"ĠIsaiah":29756,"Ġjerk":29757,"ĠWyn":29758,"Ġstalled":29759,"Ġcryptocurrencies":29760,"ĠRoland":29761,"single":29762,"Ġlumin":29763,"ĠFellow":29764,"ĠCapacity":29765,"ĠKazakh":29766,"WN":29767,"Ġfinanced":29768,"389":29769,"Ġtid":29770,"Ġcollusion":29771,"ĠMyr":29772,"îĢ":29773,"Senator":29774,"Ġpediatric":29775,"Ġneatly":29776,"Ġsandwiches":29777,"ĠArchitecture":29778,"Ġtucked":29779,"Ġbalcony":29780,"Ġearthquakes":29781,"quire":29782,"Future":29783,"Ġhefty":29784,"éĹ":29785,"Ġspecializes":29786,"Ġstresses":29787,"Ġsender":29788,"Ġmisunderstanding":29789,"Ġepile":29790,"Ġprovoke":29791,"ĠColors":29792,"Ġdismay":29793,"uko":29794,"[_":29795,"586":29796,"neutral":29797,"Ġdonating":29798,"ĠRandall":29799,"Multi":29800,"Ġconveniently":29801,"ĠSung":29802,"ĠCoca":29803,"Ġtents":29804,"ĠAcceler":29805,"Ġpartnered":29806,"272":29807,"irming":29808,"ĠBAS":29809,"sometimes":29810,"Ġobjected":29811,"ubric":29812,"posed":29813,"LCS":29814,"grass":29815,"Ġattributable":29816,"VIS":29817,"Israeli":29818,"Ġrepeats":29819,"ĠRM":29820,"vag":29821,"uta":29822,"inous":29823,"Ġinert":29824,"ĠMiguel":29825,"æŃ":29826,"ĠHawaiian":29827,"Board":29828,"Ġartific":29829,"ĠAzerbai":29830,"asio":29831,"ĠRent":29832,"AIN":29833,"Ġappliances":29834,"Ġnationality":29835,"Ġasshole":29836,"ĠNeb":29837,"Ġnotch":29838,"hani":29839,"ĠBride":29840,"Availability":29841,"Ġintercepted":29842,"Ġcontinental":29843,"Ġswelling":29844,"ĠPerspect":29845,"bies":29846,".<":29847,"ithmetic":29848,"ĠLara":29849,"Ġtempting":29850,"addr":29851,"Ġoverseeing":29852,"clad":29853,"ĠDV":29854,"ĠGingrich":29855,"Ġmun":29856,"ĠAppropri":29857,"Ġalterations":29858,"ĠPatreon":29859,"Ġhavoc":29860,"Ġdisciplines":29861,"Ġnotoriously":29862,"akuya":29863,"ieri":29864,"?).":29865,"ĠWent":29866,"Ġsilicon":29867,"Ġtremb":29868,"Container":29869,"Known":29870,"Ġmortar":29871,"este":29872,"icka":29873,"Arthur":29874,"ĠPreviously":29875,"ĠMarty":29876,"Ġsparse":29877,"gins":29878,"Ġinward":29879,"ĠParticipant":29880,"Copy":29881,"ĠMisc":29882,"Ġantibiotic":29883,"ĠRetro":29884,"Ġelusive":29885,"Ġassail":29886,"ĠBattalion":29887,"ĠBought":29888,"Ġdiminish":29889,"ĠEuropa":29890,"session":29891,"ĠDangerous":29892,"iesel":29893,"Ġdisbelief":29894,"Ġblasts":29895,"extreme":29896,"ĠBoyd":29897,"ĠProjects":29898,"ĠGuys":29899,"Ġundergone":29900,"Ġgrill":29901,"ĠDwight":29902,"Ġ197":29903,"USER":29904,"Ġfilesystem":29905,"Ġclocks":29906,"Taylor":29907,"Ġwrapper":29908,"Ġfolding":29909,"ousand":29910,"ĠPhilippine":29911,"ATIONAL":29912,"ĠPerth":29913,"Ġashes":29914,"Ġaccumulate":29915,"ĠGateway":29916,"Shop":29917,"orkshire":29918,"Han":29919,"ĠBarrel":29920,"ĠLeh":29921,"ĠXV":29922,"Ġwhim":29923,"Ġrepo":29924,"ĠCG":29925,"ĠMam":29926,"Ġincorporating":29927,"Ġbailout":29928,"Ġlinguistic":29929,"Ġdisinteg":29930,"CLE":29931,"Ġcinematic":29932,"ĠFiber":29933,"Syn":29934,"ilion":29935,"ĠCompos":29936,"chens":29937,"Ġneoc":29938,"Ġboiled":29939,"FINE":29940,"ono":29941,"uncle":29942,"iken":29943,"ĠBM":29944,"ι":29945,"Ġreceipts":29946,"Ġdisposed":29947,"ĠThirty":29948,"ĠRough":29949,"ĠABS":29950,"Ġnotwithstanding":29951,"ollen":29952,"#$":29953,"Ġunreliable":29954,"Ġbloom":29955,"Ġmediocre":29956,"Ġtram":29957,"ĠTasman":29958,"Ġshakes":29959,"Ġmanifesto":29960,"ĠMW":29961,"Ġsatisfactory":29962,"Ġshores":29963,"Ġcomputation":29964,"Ġassertions":29965,"ormons":29966,"arag":29967,"abit":29968,"Democrats":29969,"ĠLoot":29970,"ĠVolks":29971,"haired":29972,"Ġgravitational":29973,"Sing":29974,"ĠMiz":29975,"Ġthrottle":29976,"Ġtyranny":29977,"ĠViews":29978,"Ġrobber":29979,"ĠMinority":29980,"Ġshrine":29981,"scope":29982,"purpose":29983,"Ġnucleus":29984,"ourcing":29985,"ĠUSDA":29986,"ĠDHS":29987,"wra":29988,"ĠBowie":29989,"Scale":29990,"ĠBEL":29991,"xi":29992,"Iter":29993,"Ġ(),":29994,"wright":29995,"Ġsailors":29996,"oused":29997,"NASA":29998,"ĠProof":29999,"ĠMineral":30000,"token":30001,"ĠFD":30002,"Rew":30003,"Ġell":30004,"630":30005,"Ġchancellor":30006,"ĠGos":30007,"Ġamounted":30008,"ĠRecre":30009,"omez":30010,"ĠOptim":30011,"ĠOlive":30012,"Ġtracker":30013,"owler":30014,"ĠUnique":30015,"Root":30016,"Ġmaritime":30017,"ĠQuran":30018,"ĠAdapt":30019,"Ġecosystems":30020,"ĠRepeat":30021,"ĠSoy":30022,"ĠIMP":30023,"Ġgraduating":30024,"andem":30025,"Pur":30026,"ĠReset":30027,"ĠTrick":30028,"ĠPhilly":30029,"ĠTue":30030,"ĠMalaysian":30031,"Ġclimax":30032,"Ġbury":30033,"Ġconspic":30034,"ĠSouthampton":30035,"ĠFlowers":30036,"Ġescorted":30037,"ĠEducational":30038,"ĠIRC":30039,"Ġbrutally":30040,"eating":30041,"Ġpillar":30042,"ĠSang":30043,"ĠJude":30044,"arling":30045,"ĠAmnesty":30046,"Ġreminding":30047,"ĠAdministrative":30048,"hesda":30049,"Ġflashed":30050,"ĠPBS":30051,"perate":30052,"feature":30053,"Ġswipe":30054,"Ġgraves":30055,"oultry":30056,"261":30057,"breaks":30058,"ĠGuer":30059,"Ġshrimp":30060,"ĠVoting":30061,"quist":30062,"Ġanalytical":30063,"Ġtablespoons":30064,"ĠSOU":30065,"Ġresearched":30066,"Ġdisrupted":30067,"Ġjour":30068,"Ġreplica":30069,"Ġcartoons":30070,"bians":30071,"})":30072,"copy":30073,"Got":30074,"ouched":30075,"PUT":30076,"Ġswarm":30077,"notations":30078,"said":30079,"Ġrebuilt":30080,"Ġcollaborate":30081,"Ġraging":30082,"Ġnar":30083,"Ġdemographics":30084,"ĠDDR":30085,"Ġdistrust":30086,"ossier":30087,"ĠKro":30088,"Ġpumpkin":30089,"Ġregrets":30090,"Ġfatalities":30091,"ĠLens":30092,"ĠOle":30093,"pd":30094,"Ġpuppet":30095,"ĠOutlook":30096,"ĠStam":30097,"Ol":30098,"Fair":30099,"UU":30100,"Ġrewritten":30101,"ı":30102,"Ġfascinated":30103,"Ġvectors":30104,"Ġtribunal":30105,"uay":30106,"ĠMats":30107,"ĠCoins":30108,"[[":30109,"Ġ181":30110,"Ġrenders":30111,"ĠKaepernick":30112,"Ġespionage":30113,"Ġsumm":30114,"Ġditch":30115,"Account":30116,"Ġspreadsheet":30117,"Ġmutant":30118,"past":30119,"407":30120,"Ġdye":30121,"Ġinitiation":30122,"Ġ4000":30123,"Ġpunishable":30124,"Ġthinner":30125,"ĠKhal":30126,"Ġintermedi":30127,"Dun":30128,"ĠGotham":30129,"Ġeagerly":30130,"Ġvaginal":30131,"powers":30132,"VW":30133,"ĠWATCHED":30134,"Ġpredator":30135,"amsung":30136,"Ġdisparity":30137,"Ġ[*":30138,"Ġamph":30139,"Ġoutskirts":30140,"ĠSpirits":30141,"Ġskeletal":30142,"л":30143,"ĠRear":30144,"Ġissuance":30145,"ĠLogic":30146,"released":30147,"ZZ":30148,"ĠBound":30149,"Entry":30150,"Ġexits":30151,"isol":30152,"ĠFounder":30153,"Ġwre":30154,"ĠGreenland":30155,"ĠMMO":30156,"taker":30157,"INC":30158,"ãģ¾":30159,"Ġhourly":30160,"henko":30161,"Ġfantasies":30162,"Ġdisob":30163,"Ġdemolition":30164,"ãĥĭ":30165,"Ġenlisted":30166,"ratulations":30167,"Ġmisguided":30168,"Ġensured":30169,"Ġdiscouraged":30170,"mort":30171,"Ġflank":30172,"Ġcess":30173,"Ġreacts":30174,"ĠSere":30175,"sensitive":30176,"ĠSerpent":30177,"assad":30178,"Ġ247":30179,"Ġcalmly":30180,"busters":30181,"Ġbleed":30182,"ĠStro":30183,"Ġamusement":30184,"ĠAntarctica":30185,"Ġscept":30186,"ĠGaw":30187,"aq":30188,"asonic":30189,"Ġsprawling":30190,"native":30191,"aturated":30192,"ĠBattlefield":30193,"IVERS":30194,"EB":30195,"ĠGems":30196,"ĠNorthwestern":30197,"ĠFilms":30198,"ĠAutomatic":30199,"Ġapprehend":30200,"ãģ¨":30201,"ĠguiName":30202,"Ġbackend":30203,"Ġevidenced":30204,"geant":30205,"012":30206,"ĠSiege":30207,"ĠexternalTo":30208,"ĠunfocusedRange":30209,"ĠguiActiveUnfocused":30210,"ĠguiIcon":30211,"ĠexternalToEVA":30212,"ĠexternalToEVAOnly":30213,"Fri":30214,"chard":30215,"enaries":30216,"Ġchiefs":30217,"Ġcf":30218,"ĠHUD":30219,"Ġcorrobor":30220,"ĠdB":30221,"ĠTaken":30222,"ĠPatricia":30223,"rail":30224,"ĠCharm":30225,"ĠLibertarian":30226,"rieve":30227,"Personal":30228,"ĠOUR":30229,"geries":30230,"Ġdumping":30231,"Ġneurological":30232,"itimate":30233,"ĠClintons":30234,"rafted":30235,"ĠMolly":30236,"Ġterminals":30237,"register":30238,"Ġflare":30239,"Ġencoded":30240,"Ġautopsy":30241,"pel":30242,"machine":30243,"Ġexemptions":30244,"ĠRoyals":30245,"distance":30246,"Ġdrafts":30247,"Ġlame":30248,"ĠCunning":30249,"Ġspouses":30250,"ĠMarkets":30251,"ĠCarrier":30252,"Ġimplying":30253,"ĠYak":30254,"sid":30255,"Ġloser":30256,"Ġvigilant":30257,"Ġimpeachment":30258,"Ġaugmented":30259,"ĠEmployees":30260,"Ġunintended":30261,"ternally":30262,"ĠWatt":30263,"Ġrecognizable":30264,"essim":30265,"æĿ":30266,"Ġcoated":30267,"rha":30268,"Ġlieutenant":30269,"ĠLegislation":30270,"published":30271,"444":30272,"013":30273,"Ġideally":30274,"ĠPassword":30275,"Ġsimplify":30276,"ĠMeta":30277,"ĠMRI":30278,"Ġpleading":30279,"organized":30280,"handler":30281,"Ġunravel":30282,"correct":30283,"Ġicy":30284,"Ġparanoid":30285,"Ġpasser":30286,"Ġinspections":30287,"ofer":30288,"ĠHealthcare":30289,"283":30290,"ĠBrut":30291,"iola":30292,"forge":30293,"ĠMedieval":30294,"MSN":30295,"ievers":30296,"ĠProgramming":30297,"åī":30298,"Ġ223":30299,"mu":30300,"ĠCLE":30301,"uga":30302,"Ġshoppers":30303,"Ġinformative":30304,"ĠPlans":30305,"Ġsupplementation":30306,"ĠTests":30307,"tyard":30308,"ocytes":30309,"ĠVega":30310,"ĠGujarat":30311,"ermanent":30312,"Except":30313,"ĠLOT":30314,"alla":30315,"ĠCumm":30316,"ĠOsw":30317,"Ġvenom":30318,"ĠDebt":30319,"ĠDOWN":30320,"Ġreunion":30321,"Ġmuc":30322,"ĠRelief":30323,"Ġgeop":30324,"ĠðŁĺ":30325,"alogue":30326,"Anth":30327,"echo":30328,"Ġcorros":30329,"Ġreplication":30330,"ĠBlazing":30331,"ĠDaughter":30332,"Ġinflic":30333,"ĠLindsey":30334,"ÙĪ":30335,"284":30336,"Exit":30337,"Ġgloom":30338,"TAIN":30339,"Ġundermining":30340,"Ġadvising":30341,"hidden":30342,"Ġoverflow":30343,"Ġgor":30344,"urdue":30345,"Ġechoes":30346,"enhagen":30347,"Ġimpuls":30348,"drug":30349,"cash":30350,"Ġasync":30351,"Ġmirac":30352,"atts":30353,"punk":30354,"Ġpivot":30355,"ĠLegislative":30356,"Ġbloggers":30357,"ĠClaw":30358,"sburg":30359,"dyl":30360,"ĠRecommend":30361,"Ġverte":30362,"Ġprohibiting":30363,"ĠPanther":30364,"Jonathan":30365,"Ġomin":30366,"Ġhateful":30367,"281":30368,"ĠOrche":30369,"ĠMurdoch":30370,"downs":30371,"Ġasymm":30372,"GER":30373,"Always":30374,"Ġinforms":30375,"ĠWM":30376,"ĠPony":30377,"ĠAppendix":30378,"ĠArlington":30379,"Jam":30380,"Ġmedicinal":30381,"ĠSlam":30382,"ITIES":30383,"Ġreaff":30384,"ĠRi":30385,"FG":30386,"Spring":30387,"bool":30388,"Ġthighs":30389,"Ġmarkings":30390,"ĠRaqqa":30391,"ĠLak":30392,"poll":30393,"tsky":30394,"ĠMorty":30395,"ĠDefinition":30396,"Ġdebunk":30397,"endered":30398,"ĠLeone":30399,"avers":30400,"Ġmortgages":30401,"Apparently":30402,"Nic":30403,"haus":30404,"ĠThousands":30405,"auld":30406,"Ġmash":30407,"shoot":30408,"Ġdiarr":30409,"Ġconsciously":30410,"Hero":30411,"eas":30412,"ĠNaturally":30413,"ĠDestroyer":30414,"Ġdashboard":30415,"services":30416,"Rog":30417,"Ġmillennials":30418,"Ġinvade":30419,"-(":30420,"Ġcommissions":30421,"ĠAuckland":30422,"Ġbroadcasts":30423,"Ġfrontal":30424,"Ġcrank":30425,"ĠHistoric":30426,"Ġrumours":30427,"CTV":30428,"Ġsteril":30429,"Ġbooster":30430,"rocket":30431,"ãĤ¼":30432,"utsche":30433,"ĠPI":30434,"Ġ233":30435,"ĠProducer":30436,"ĠAnalytics":30437,"Ġinvaluable":30438,"Ġunintention":30439,"ĠCY":30440,"Ġscrutin":30441,"Ġgigg":30442,"Ġengulf":30443,"Ġproletariat":30444,"Ġhacks":30445,"ĠHew":30446,"arak":30447,"ĠSlime":30448,"ielding":30449,"agher":30450,"ĠElliot":30451,"Ġtelecom":30452,"Ġ219":30453,"ultan":30454,"ĠArbor":30455,"ĠScouts":30456,"Ban":30457,"Ġlifespan":30458,"Ġblasp":30459,"388":30460,"Ġjudiciary":30461,"ĠContinental":30462,"asking":30463,"McC":30464,"LED":30465,"Ġbaggage":30466,"ĠSorcerer":30467,"Ġremnants":30468,"ĠGriffith":30469,"etsu":30470,"ĠSubaru":30471,"ĠPersonality":30472,"designed":30473,"ushima":30474,"agnar":30475,"Ġrecoil":30476,"Ġpassions":30477,"\\\":":30478,"Ġtee":30479,"Ġabolition":30480,"ĠCreating":30481,"jac":30482,"Ġ194":30483,"019":30484,"Ġpillars":30485,"riched":30486,"/\"":30487,"tk":30488,"Ġlivelihood":30489,"Ġroasted":30490,"ahon":30491,"ĠHutch":30492,"assert":30493,"Ġdividend":30494,"Ġknit":30495,"Ġdaunting":30496,"Ġdisturbance":30497,"Ġshale":30498,"Ġcultivated":30499,"Ġrefrigerator":30500,"LB":30501,"ĠNET":30502,"Ġcommercials":30503,"Ġthinkers":30504,"455":30505,"Ġchop":30506,"Broad":30507,"Ġsuspicions":30508,"Ġtagged":30509,"lifting":30510,"Ġstylish":30511,"ĠShields":30512,"Shortly":30513,"Ġtails":30514,"Auth":30515,"STE":30516,"ĠGAME":30517,"Ġseism":30518,"ĠKis":30519,"ologne":30520,"Ġcowork":30521,"Ġforcibly":30522,"Ġthyroid":30523,"ĠPB":30524,"ANE":30525,"married":30526,"horse":30527,"Ġpolymer":30528,"ĠChal":30529,"odor":30530,"DEBUG":30531,"ĠContext":30532,"Ġbliss":30533,"Ġpinpoint":30534,"ĠMathemat":30535,"legram":30536,"ĠWeekend":30537,"Ġlabelled":30538,"Ġbart":30539,"itles":30540,"Ġestrogen":30541,"âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ":30542,"\"'":30543,"Ġvisibly":30544,"Ġoutsider":30545,"aida":30546,"Area":30547,"Ġdissemin":30548,"Ġdishonest":30549,"ĠClosed":30550,"ĠBulletin":30551,"ĠRamsey":30552,"sword":30553,"ĠXI":30554,"ourced":30555,"Same":30556,"346":30557,"ĠRepe":30558,"ĠKou":30559,"cake":30560,"emis":30561,"Cache":30562,"ĠMeaning":30563,"ĠEnlight":30564,"onomy":30565,"Ġmanifestation":30566,"sworth":30567,"Jay":30568,"Ġchore":30569,"ör":30570,"Dream":30571,"Ġsanctioned":30572,"Ġculturally":30573,"ĠAra":30574,"Nav":30575,"Ġtheological":30576,"Ġstrut":30577,"ĠVO":30578,"ĠHandbook":30579,"Ġconstructing":30580,"Ġ¶":30581,"ĠBenefits":30582,"ĠPsychological":30583,"sac":30584,"å¸":30585,"policy":30586,"ĠMatters":30587,"ĠReported":30588,"ĠByte":30589,"Ġvitro":30590,"ĠMaiden":30591,"Ġlam":30592,"ĠJennings":30593,"Ġgarment":30594,"ĠRutgers":30595,"ĠStafford":30596,"ĠWellington":30597,"Ġintermitt":30598,"Ġnpm":30599,"Ġordeal":30600,"Ġplugged":30601,"ooming":30602,"inished":30603,"framework":30604,"Ġtimber":30605,"Ġcass":30606,"Ġ850":30607,"iless":30608,"ĠRedux":30609,"768":30610,"Stre":30611,"Ġsurpassed":30612,"whel":30613,"Ġparallels":30614,"Ġveil":30615,"ĠGI":30616,"ĠREST":30617,"Ġreadiness":30618,"sort":30619,"Ġmodifying":30620,"ĠSlate":30621,"ruff":30622,"Ġmarble":30623,"Ġinfrared":30624,"Ġauditor":30625,"ĠFANTASY":30626,"ĠPoverty":30627,"ĠSPD":30628,"Ġ\"(":30629,"Ky":30630,"RAY":30631,"Ġexecutions":30632,"ĠBeverly":30633,"ĠMarxism":30634,"ĠBurst":30635,"ĠKali":30636,"estones":30637,"Clearly":30638,"Ell":30639,"ãģ§":30640,"ĠProceedings":30641,"Token":30642,"IFIC":30643,"ña":30644,"Central":30645,"ĠHaley":30646,"ĠDrama":30647,"Ġformations":30648,"ORN":30649,"Books":30650,"Ġdominating":30651,"ĠFlyers":30652,"ĠCompanion":30653,"Ġdisciplined":30654,"ĠYugoslav":30655,"ĠSpells":30656,"Ġvengeance":30657,"Ġlandlords":30658,"Len":30659,"ĠOgre":30660,"anoia":30661,"Ġpiercing":30662,"Ġcongreg":30663,"Ġscorer":30664,"obia":30665,"Ġnickel":30666,"ĠLearns":30667,"Ġrejo":30668,"Ġmasterpiece":30669,"Flash":30670,"Ġinhabited":30671,"ĠOpenGL":30672,"ĠDud":30673,"ĠICO":30674,"Ġarter":30675,"Ġplur":30676,"Ġmastery":30677,"Ġlongstanding":30678,"sted":30679,"Ġwines":30680,"Ġtelevised":30681,"ĠShrine":30682,"ĠBayern":30683,"Ġâĵĺ":30684,"Ġenclosure":30685,"john":30686,"Ġprophets":30687,"ĠResurrection":30688,"ĠOrders":30689,"Ġuneven":30690,"rals":30691,"Ġdwind":30692,"ĠLah":30693,"ĠSloven":30694,"378":30695,"Ġinsistence":30696,"affle":30697,"ĠClone":30698,"Ġhardship":30699,"ĠCongressman":30700,"Ġplead":30701,"Ġreviewers":30702,"Ġcured":30703,"Ġ1935":30704,"asley":30705,"fake":30706,"ĠThinking":30707,"ydia":30708,"PART":30709,"ĠDota":30710,"oit":30711,"Ġwhipped":30712,"Ġbouncing":30713,"ĠHispanics":30714,"comings":30715,"Ġcannabin":30716,"ĠChambers":30717,"ĠZack":30718,"Optional":30719,"Ġcoats":30720,"Ġprowess":30721,"ĠNorton":30722,"Ġplainly":30723,"Ġfreight":30724,"Ġinhibition":30725,"Ġclam":30726,"Ġ303":30727,"kef":30728,"aleigh":30729,"Luke":30730,"Ġpsycho":30731,"atorium":30732,"MED":30733,"Ġtreaties":30734,"Ġindisc":30735,"Ġdc":30736,"OPS":30737,"Ġresilient":30738,"ĠInterstate":30739,"Ġslack":30740,"Ġmundane":30741,"Ġestablishes":30742,"359":30743,"Ġstrained":30744,"Ġnond":30745,"Sus":30746,"Ġcaste":30747,"arate":30748,"ieving":30749,"Ġunfairly":30750,"Ġparser":30751,"onial":30752,"ursive":30753,"Via":30754,"ĠOtto":30755,"ĠAuthorities":30756,"stroke":30757,"KR":30758,"ĠMercy":30759,"Ġfurnished":30760,"Ġoutset":30761,"Ġmetic":30762,"1982":30763,"olithic":30764,"ĠTent":30765,"ogical":30766,"ĠAircraft":30767,"Ġhides":30768,"ĠBecame":30769,"Ġeducators":30770,"reaching":30771,"Ġvolatility":30772,"Ġtoddler":30773,"ĠNASCAR":30774,"ĠTwelve":30775,"ĠHighlights":30776,"Ġgrape":30777,"Ġsplits":30778,"Ġpeasant":30779,"Ġreneg":30780,"ĠMSI":30781,"Temp":30782,"stars":30783,"Ġtrek":30784,"ĠHyde":30785,"binding":30786,"Ġrealism":30787,"Ġoxide":30788,"ĠHos":30789,"Ġmounts":30790,"Ġbiting":30791,"Ġcollapsing":30792,"Ġpostal":30793,"Ġmuseums":30794,"Ġdetached":30795,"Ġrespecting":30796,"Ġmonopol":30797,"Ġworkflow":30798,"ĠCake":30799,"Template":30800,"ĠOrganisation":30801,"Ġpersistence":30802,"369":30803,"Coming":30804,"Brad":30805,"Ġredundant":30806,"ĠGTA":30807,"Ġbending":30808,"Ġrevoked":30809,"Ġoffending":30810,"Ġframing":30811,"Ġprintf":30812,"Commun":30813,"members":30814,"Outside":30815,"Ġconstrued":30816,"Ġcoded":30817,"FORE":30818,"Ġchast":30819,"Chat":30820,"Indian":30821,"ĠYard":30822,"?!\"":30823,"ĠPorts":30824,"ĠXavier":30825,"ĠRET":30826,"'.\"":30827,"ĠBoat":30828,"ivated":30829,"icht":30830,"umerable":30831,"Ds":30832,"ĠDunn":30833,"Ġcoffin":30834,"Ġsecurely":30835,"ĠRaptors":30836,"ĠBes":30837,"Installation":30838,"Ġinception":30839,"ĠHealthy":30840,"endants":30841,"Ġpsychologists":30842,"ĠSheikh":30843,"cultural":30844,"ĠBlackBerry":30845,"shift":30846,"Fred":30847,"oche":30848,"Ġcakes":30849,"ĠSEO":30850,"ĠGian":30851,"ĠAsians":30852,"ogging":30853,"element":30854,"Ġpundits":30855,"ĠVaugh":30856,"ĠGavin":30857,"Ġhitter":30858,"Ġdrowned":30859,"Ġchalk":30860,"ĠZika":30861,"Ġmeasles":30862,"802":30863,"âĢ¦..":30864,"ĠAWS":30865,"]\"":30866,"Ġdistort":30867,"ĠMast":30868,"Ġantibodies":30869,"ĠMash":30870,"Memory":30871,"ĠUganda":30872,"ĠProb":30873,"Ġvomiting":30874,"ĠTurns":30875,"Ġoccupying":30876,"Ġevasion":30877,"ĠTherapy":30878,"Ġpromo":30879,"Ġelectr":30880,"Ġblueprint":30881,"ĠDre":30882,"priced":30883,"ĠDepot":30884,"Ġalleviate":30885,"ĠSomali":30886,"marg":30887,"nine":30888,"Ġnostalgia":30889,"ĠShepherd":30890,"Ġcavalry":30891,"Ġtorped":30892,"ĠBloody":30893,"xb":30894,"Ġsank":30895,"Ġgoalt":30896,"reportprint":30897,"embedreportprint":30898,"cloneembedreportprint":30899,"ĠInitially":30900,"ĠFischer":30901,"Ġnoteworthy":30902,"cern":30903,"Ġinefficient":30904,"rawdownload":30905,"rawdownloadcloneembedreportprint":30906,"cation":30907,"ĠDynasty":30908,"lag":30909,"DES":30910,"Ġdistinctly":30911,"ĠEstonia":30912,"Ġopenness":30913,"Ġgossip":30914,"ruck":30915,"Width":30916,"ĠIbrahim":30917,"Ġpetroleum":30918,"Ġavatar":30919,"ĠHed":30920,"atha":30921,"ĠHogwarts":30922,"Ġcaves":30923,"678":30924,"Ġsafeguard":30925,"ĠMog":30926,"isson":30927,"ĠDurham":30928,"slaught":30929,"ĠGraduate":30930,"Ġsubconscious":30931,"ĠExcellent":30932,"ĠDum":30933,"-----":30934,"Ġpiles":30935,"ĠWORK":30936,"ĠGarn":30937,"ĠFol":30938,"ĠATM":30939,"Ġavoids":30940,"ĠTul":30941,"Ġbleak":30942,"ELY":30943,"ivist":30944,"lightly":30945,"Pers":30946,"ĠDob":30947,"ĠLS":30948,"Ġinsanity":30949,"ε":30950,"atalie":30951,"Enlarge":30952,"Ġtwists":30953,"Ġfaulty":30954,"Ġpiracy":30955,"Ġimpover":30956,"Ġrugged":30957,"ĠFashion":30958,"Ġsands":30959,"'?":30960,"swick":30961,"Ġnatives":30962,"Ġhen":30963,"ĠNoise":30964,"ãĥĹ":30965,"Ġgreens":30966,"Ġfreezer":30967,"Ġdynasty":30968,"ĠFathers":30969,"ĠNewark":30970,"Ġarchaeological":30971,"Ġot":30972,"obar":30973,"Ġblockade":30974,"Ġallerg":30975,"LV":30976,"Ġdebit":30977,"ĠRFC":30978,"ĠMilton":30979,"ĠPressure":30980,"Ġwillingly":30981,"Ġdisproportionate":30982,"Ġoppressive":30983,"Ġdiamonds":30984,"Ġbelongings":30985,"1970":30986,"Ġbells":30987,"Ġimperialism":30988,"Ġ227":30989,"Ġexploding":30990,"ĠEclipse":30991,"Ġ1919":30992,"Ġrant":30993,"Ġnominations":30994,"347":30995,"Ġpeacefully":30996,"rica":30997,"ĠFUCK":30998,"Ġvibration":30999,"malink":31000,"Ġropes":31001,"ĠIvanka":31002,"ĠBrewery":31003,"ĠBooker":31004,"ĠOwens":31005,"goers":31006,"Services":31007,"ĠSnape":31008,"Ġ191":31009,"395":31010,"Ġ299":31011,"justice":31012,"Ġbri":31013,"Ġdiscs":31014,"Ġprominently":31015,"Ġvulgar":31016,"Ġskipping":31017,"lves":31018,"Ġtsunami":31019,"374":31020,"ĠUrug":31021,"ĠEid":31022,"recated":31023,"phen":31024,"Ġfaults":31025,"ĠStarted":31026,"950":31027,"Ġpi":31028,"Ġdetector":31029,"Ġbastard":31030,"Ġvalidated":31031,"SpaceEngineers":31032,"OURCE":31033,"Ġ(~":31034,"Ġunsur":31035,"Ġaffirmed":31036,"Ġfascism":31037,"Ġresolving":31038,"ĠChavez":31039,"ĠCyn":31040,"Ġdetract":31041,"Lost":31042,"Ġrigged":31043,"Ġhomage":31044,"ĠBruno":31045,"555":31046,"eca":31047,"Ġpresses":31048,"Ġhumour":31049,"Ġspacing":31050,"Ġ'/":31051,"olkien":31052,"Coun":31053,"OPER":31054,"Tre":31055,"Son":31056,"ĠCambodia":31057,"ierre":31058,"mong":31059,"ozy":31060,"Ġliquidity":31061,"ĠSoviets":31062,"ĠFernando":31063,"Ġ229":31064,"Ġslug":31065,"ĠCatalan":31066,"electric":31067,"Ġscenery":31068,"ĠHearth":31069,"Ġconstrained":31070,"Ġgoalie":31071,"ĠGuidelines":31072,"ĠAmmo":31073,"ĠPearson":31074,"Ġtaxed":31075,"Ġfetus":31076,"Response":31077,"ĠAlexis":31078,"thia":31079,"Guy":31080,"Ġreconstruct":31081,"Ġextremes":31082,"Ġconcluding":31083,"ĠPeg":31084,"ooks":31085,"Ġdeductions":31086,"Rose":31087,"Ġgroundbreaking":31088,"ĠTarg":31089,"ãĥģ":31090,"ĠReve":31091,"resource":31092,"Ġmoons":31093,"Ġelectromagnetic":31094,"Ġamidst":31095,"ĠViktor":31096,"NESS":31097,"BACK":31098,"Ġcommute":31099,"ĠAnaheim":31100,"Ġfluctuations":31101,"640":31102,"Ġnoodles":31103,"ĠCopenhagen":31104,"ĠTide":31105,"ĠGrizz":31106,"ĠSEE":31107,"Ġpipelines":31108,"Ġscars":31109,"endo":31110,"agus":31111,"ĠETF":31112,"/#":31113,"ĠBecome":31114,"448":31115,"Ġvisc":31116,"ĠRecommended":31117,"Ġjumper":31118,"Ġcognition":31119,"Ġassassin":31120,"Ġwitnessing":31121,"ĠSetup":31122,"Ġlac":31123,"vim":31124,"ISM":31125,"pages":31126,"SSL":31127,"358":31128,"Ġadject":31129,"industrial":31130,"lore":31131,"chery":31132,"Ġglitter":31133,"Ġcalf":31134,"Florida":31135,"Ġspoilers":31136,"Ġsucceeds":31137,"Ġchanting":31138,"Ġslogans":31139,"ĠTracy":31140,"Visit":31141,"rology":31142,"Ġmornings":31143,"Ġlineage":31144,"Ġsip":31145,"Ġintensely":31146,"Ġflourish":31147,"ĠSleeping":31148,"ĠFem":31149,"orpor":31150,"ĠKlan":31151,"ĠDarth":31152,"hack":31153,"ĠNielsen":31154,"Ġtumors":31155,"Ġprocurement":31156,"ĠYorkshire":31157,"Ġraided":31158,"KY":31159,"Anna":31160,"Ġ//[":31161,"ĠDisorder":31162,"ĠMustang":31163,"ĠWen":31164,"ĠTrying":31165,"sq":31166,"Ġdeliveries":31167,"Ġshutter":31168,"Ġcerebral":31169,"Ġbipolar":31170,"ĠCN":31171,"lass":31172,"jet":31173,"Ġdebating":31174,">:":31175,"Ġeagle":31176,"grades":31177,"ĠDixon":31178,"UGC":31179,"MAS":31180,"ĠDraco":31181,"ĠMachines":31182,"affer":31183,"Ġeman":31184,"²":31185,"pron":31186,"ĠGym":31187,"Ġcomparatively":31188,"ĠTribunal":31189,"PRO":31190,"Ġlex":31191,"Ġfertile":31192,"Ġdepressing":31193,"Ġsuperficial":31194,"essential":31195,"ĠHunters":31196,"gp":31197,"Ġprominence":31198,"Liber":31199,"ĠAncest":31200,"otechnology":31201,"Ġmocking":31202,"ĠTraff":31203,"ĸļ":31204,"Medium":31205,"Iraq":31206,"Ġpsychiatrist":31207,"Quantity":31208,"ĠLect":31209,"Ġnoisy":31210,"520":31211,"GY":31212,"Ġslapped":31213,"ĠMTV":31214,"Ġpara":31215,"pull":31216,"Multiple":31217,"asher":31218,"Ġnour":31219,"ĠSeg":31220,"Spell":31221,"vous":31222,"ordial":31223,"Senior":31224,"ĠGoldberg":31225,"ĠPlasma":31226,"need":31227,"Ġmessenger":31228,"eret":31229,"Ġteamed":31230,"Ġliteracy":31231,"ĠLeah":31232,"ĠDoyle":31233,"Ġemitted":31234,"UX":31235,"Ġevade":31236,"Ġmaze":31237,"Ġwrongly":31238,"ĠLars":31239,"Ġstereotype":31240,"Ġpledges":31241,"Ġaroma":31242,"ĠMET":31243,"Ġacre":31244,"ĠOD":31245,"Ġff":31246,"Ġbreweries":31247,"ĠHilton":31248,"undle":31249,"ĠKak":31250,"ĠThankfully":31251,"ĠCanucks":31252,"inctions":31253,"ĠAppears":31254,"Ġcoer":31255,"Ġundermined":31256,"rovers":31257,"Andre":31258,"Ġblaze":31259,"umers":31260,"Ġfamine":31261,"amphetamine":31262,"ulkan":31263,"Amount":31264,"Ġdesperation":31265,"wikipedia":31266,"development":31267,"ĠCorinth":31268,"ussia":31269,"Jackson":31270,"LI":31271,"Native":31272,"Rs":31273,"Ohio":31274,"ĠKathleen":31275,"Fortunately":31276,"Ġattendant":31277,"ĠPreferred":31278,"ĠDidn":31279,"ĠVs":31280,"Mis":31281,"Ġrespondent":31282,"Ġboun":31283,"stable":31284,"Ġpaved":31285,"Ġunexpl":31286,"ĠCheney":31287,"LM":31288,"ĠCull":31289,"blown":31290,"Ġconfronting":31291,"ocese":31292,"serving":31293,"Wi":31294,"ĠLithuania":31295,"anni":31296,"Ġstalk":31297,"hd":31298,"Ġvener":31299,"APH":31300,"ynchronous":31301,"URR":31302,"umably":31303,"historic":31304,"Half":31305,"Hay":31306,"Ġresilience":31307,"spection":31308,"Ġabandoning":31309,"Obs":31310,"ĠDebbie":31311,"Ġgradient":31312,"ĠPlaint":31313,"ĠCanal":31314,"ARCH":31315,"Ġexpansive":31316,"Ġfung":31317,"Ġbounced":31318,"Und":31319,"Ġprecautions":31320,"Ġclarification":31321,"Ġdagger":31322,"Ġgrips":31323,"Ġµ":31324,"ĠRivera":31325,"ĠUndead":31326,"isites":31327,"ĠFIRST":31328,"ño":31329,"audi":31330,"Ġhostages":31331,"Ġcompliant":31332,"Ġalumni":31333,"Seven":31334,"Ġcybersecurity":31335,"either":31336,"Collect":31337,"Ġinvariably":31338,"ĠSoci":31339,"Ġlawmaker":31340,"Ġale":31341,"ĠPersonally":31342,"Nazi":31343,"Ġcustomization":31344,"ĠProc":31345,"ĠSaskatchewan":31346,"eaturing":31347,"Ġspared":31348,"Ġdiscontinued":31349,"Ġcomputational":31350,"ĠMotorola":31351,"Ġsupremacist":31352,"governmental":31353,"Ġparadise":31354,"ĠDowning":31355,"ĠNikon":31356,"Ġcatalyst":31357,"berra":31358,"Toronto":31359,"875":31360,"beta":31361,"ĠMacron":31362,"Ġunrealistic":31363,"vector":31364,"ĠVehicles":31365,"itiveness":31366,"ĠRV":31367,"ĠColbert":31368,"sin":31369,"oji":31370,"entin":31371,"ĠKrish":31372,"hello":31373,"ffield":31374,"oky":31375,"ĠTate":31376,"Ġmaple":31377,"Ġaids":31378,"chemical":31379,"334":31380,"nuts":31381,"ĠWarp":31382,"Ġxx":31383,"ĠRobb":31384,"umerous":31385,"_-_":31386,"ftime":31387,"ĠVW":31388,"Ġwinger":31389,"ĠDome":31390,"tools":31391,"ĠPV":31392,"ĠGeorgetown":31393,"Ġgeared":31394,"Ġjihadists":31395,"Ġcp":31396,"Ġsteroids":31397,"Mother":31398,"clerosis":31399,"ĠDRM":31400,"nesia":31401,"Ġlinger":31402,"Ġimmersive":31403,"ĠCOUN":31404,"Ġoutweigh":31405,"ensual":31406,"Band":31407,"Ġtransforms":31408,"matched":31409,"psons":31410,"ĠJudicial":31411,"factor":31412,"Ġreferral":31413,"Ġoddly":31414,"ĠWenger":31415,"Bring":31416,"ĠBows":31417,"602":31418,"ICLE":31419,"Ġlions":31420,"ĠAcademic":31421,"ĠThorn":31422,"ĠRaider":31423,"kefeller":31424,"Storage":31425,"Lower":31426,"ĠOrt":31427,"ĠEquality":31428,"ALT":31429,"ĠSOC":31430,"Types":31431,"Ġlyn":31432,"ĠAsset":31433,"coat":31434,"TPP":31435,"CVE":31436,"ĠPioneer":31437,"application":31438,"Modern":31439,"ĠHK":31440,"Environment":31441,"Alright":31442,"Rain":31443,"IPP":31444,"ĠShiite":31445,"Ġmound":31446,"ĠAbilities":31447,"condition":31448,"Staff":31449,"Ġcompetence":31450,"ĠMoor":31451,"ĠDiablo":31452,"Ġwithheld":31453,"Ġostensibly":31454,"ĠBrom":31455,"Ġmsg":31456,"Ġdenomin":31457,"ĠReferences":31458,"ĠFP":31459,"Ġplunged":31460,"Ġpamph":31461,"moving":31462,"central":31463,"Ġdownright":31464,"Ġfading":31465,"Tal":31466,"Typ":31467,"ĠThy":31468,"ukes":31469,"ithe":31470,"Ġove":31471,"Ġbattled":31472,"Ġseafood":31473,"Ġfigur":31474,"ĠRD":31475,"crop":31476,"Ġsquads":31477,"{\\":31478,"à¹":31479,"ĠEh":31480,"Ġinterviewing":31481,"ĠQin":31482,"Ġaspiring":31483,"PLIC":31484,"Ġclauses":31485,"ĠGast":31486,"ĠNir":31487,"Ġluggage":31488,"Ġhose":31489,"Ġsystemd":31490,"Ġdescending":31491,"ĠRevised":31492,"ĠRails":31493,"align":31494,"709":31495,"337":31496,"Ġfug":31497,"charging":31498,"tags":31499,"Ġuter":31500,"kish":31501,"WARNING":31502,"490":31503,"profits":31504,"Ġvoyage":31505,"Ġace":31506,"ĠVanguard":31507,"ĠTanks":31508,"ĠMuk":31509,"Ġ226":31510,"Safe":31511,"Armor":31512,"Ġvolcanic":31513,"Ġwomb":31514,"ĠMIL":31515,"Ġbeginner":31516,"ĠRecogn":31517,"ĠAAP":31518,"PLAY":31519,")!":31520,"Ġdetecting":31521,"cn":31522,"Ġbreaches":31523,"Basically":31524,"ĠPag":31525,"ĠMunicipal":31526,"ĠIndie":31527,"ĠLaf":31528,"ĠDisable":31529,"ĠOlson":31530,"Ġrestrained":31531,"Ġrulings":31532,"Ġhumane":31533,"events":31534,"ĠCinema":31535,"displayText":31536,"ĠHatch":31537,"actionDate":31538,"onnaissance":31539,"Ġassaulting":31540,"ĠLug":31541,"CHAT":31542,"Ġvigorous":31543,"ĠPerse":31544,"Ġintolerance":31545,"ĠSnapchat":31546,"ĠSharks":31547,"Ġdummy":31548,"ĠDiagn":31549,"ĠGuitar":31550,"imeters":31551,"403":31552,"REG":31553,"Ax":31554,"Ġseparates":31555,"ĠMahm":31556,"Ġtv":31557,"jah":31558,"OOL":31559,"Circ":31560,"ĠWindsor":31561,"ussian":31562,"Ġintuition":31563,"Ġdisdain":31564,"ĠDonovan":31565,"Ġ221":31566,"Emb":31567,"Ġcondemning":31568,"Ġgenerosity":31569,"zzy":31570,"Ġpanties":31571,"ĠPrevent":31572,"ActionCode":31573,"ANA":31574,"342":31575,"externalActionCode":31576,"Ġspecifying":31577,"Ġcrystall":31578,"Jere":31579,"Ġrupt":31580,"ĠApprentice":31581,"Ġprofiling":31582,"к":31583,"Strike":31584,"Ġsideline":31585,"Ġobligated":31586,"Ġoccult":31587,"Ġbureaucratic":31588,"antically":31589,"rupted":31590,"negative":31591,"ĠEthiopia":31592,"ĠCivic":31593,"Ġinsiders":31594,"eligible":31595,"ĠTVs":31596,"ĠBAR":31597,"ĠTI":31598,"iologist":31599,"ĠAIR":31600,"Ġsubstituted":31601,"Arab":31602,"ĠSaul":31603,"ĠYog":31604,"prem":31605,"Ġbuilders":31606,"Ġstationary":31607,"Ġdoubtful":31608,"Ġvigorously":31609,"Ġthrilling":31610,"Physical":31611,"ĠCarey":31612,"ĠHydra":31613,"geoning":31614,"ĠSly":31615,"yton":31616,"Ġborrowers":31617,"ĠParkinson":31618,"Ġë":31619,"ĠJamaica":31620,"Ġsatir":31621,"Ġinsurgents":31622,"ĠFirm":31623,"Ġisot":31624,"ĠKarn":31625,"ourning":31626,"akens":31627,"docs":31628,"little":31629,"ĠMonaco":31630,"CLASS":31631,"Turkey":31632,"Ly":31633,"ĠConan":31634,"assic":31635,"Ġstarred":31636,"ĠPacers":31637,"eties":31638,"Ġtipping":31639,"Moon":31640,"ĠRw":31641,"same":31642,"Ġcavity":31643,"Ġgoof":31644,"ĠZo":31645,"Shock":31646,"ummer":31647,"Ġemphasizes":31648,"Ġregrett":31649,"Ġnovelty":31650,"Ġenvy":31651,"ĠPassive":31652,"rw":31653,"505":31654,"Ġindifferent":31655,"ĠRica":31656,"ĠHimself":31657,"ĠFreddie":31658,"Ġadip":31659,"ä¸Ģ":31660,"Ġbreakout":31661,"Ġhurried":31662,"ĠHuang":31663,"ĠDisk":31664,"Ġroaming":31665,"?????-?????-":31666,"UV":31667,"ĠRicky":31668,"ĠSigma":31669,"Ġmarginalized":31670,"Ġedits":31671,"Ġ304":31672,"memory":31673,"Ġspecimen":31674,"293":31675,"ãģ¯":31676,"Ġvertically":31677,"Ġaudition":31678,"ĠHeck":31679,"Ġcaster":31680,"ĠHoldings":31681,"adal":31682,"ĠCron":31683,"ĠLiam":31684,"Ġdeflect":31685,"Pick":31686,"ĠDebug":31687,"REF":31688,"Ġversatility":31689,"othes":31690,"classified":31691,"ĠMahar":31692,"ĠHort":31693,"Counter":31694,"stasy":31695,"noticed":31696,"331":31697,"ĠShim":31698,"fuck":31699,"ĠBie":31700,"Ġairing":31701,"ĠProtein":31702,"ĠHolding":31703,"Ġspectators":31704,"iliated":31705,"ĠThatcher":31706,"nosis":31707,"ãĥ¼ãĥ³":31708,"Tele":31709,"Boston":31710,"ĠTempl":31711,"stay":31712,"Ġdeclarations":31713,"479":31714,"Volume":31715,"ĠDesigner":31716,"ĠOverwatch":31717,"idae":31718,"Ġonwards":31719,"Ġnets":31720,"ĠManila":31721,"particularly":31722,"Ġpolitic":31723,"oother":31724,"Ġportraits":31725,"Ġpavement":31726,"cffff":31727,"Ġsaints":31728,"Ġbeginners":31729,"ESPN":31730,"Ġshortcomings":31731,"âķIJâķIJ":31732,"Ġcomet":31733,"ĠOrganic":31734,"quel":31735,"Ġhospitalized":31736,"Break":31737,"Ġpeel":31738,"dylib":31739,"aspx":31740,"urances":31741,"ĠTIM":31742,"Pg":31743,"Ġreadable":31744,"ĠMalik":31745,"Ġmuzzle":31746,"Ġbenchmarks":31747,"dal":31748,"ĠVacc":31749,"ĠHicks":31750,"609":31751,"ĠBiblical":31752,"heng":31753,"Ġoverload":31754,"ĠCivilization":31755,"Ġimmoral":31756,"Ġfries":31757,"ãĤĴ":31758,"Ġreproduced":31759,"Ġformulation":31760,"jug":31761,"irez":31762,"gear":31763,"Ġcoached":31764,"MpServer":31765,"ĠSJ":31766,"ĠKw":31767,"Init":31768,"deal":31769,"ĠOro":31770,"ĠLoki":31771,"ĠSongs":31772,"Ġ232":31773,"ĠLouise":31774,"asionally":31775,"Ġuncond":31776,"ollywood":31777,"Ġprogressives":31778,"ĠEnough":31779,"ĠDoe":31780,"Ġwreckage":31781,"Ġbrushed":31782,"ĠBaseType":31783,"Ġzoning":31784,"ishable":31785,"hetically":31786,"ĠCaucus":31787,"ĠHue":31788,"Ġkarma":31789,"ĠSporting":31790,"Ġtrader":31791,"Ġseeming":31792,"ĠCapture":31793,"430":31794,"bish":31795,"Ġtunes":31796,"Ġindoors":31797,"ĠSphere":31798,"ĠDancing":31799,"TERN":31800,"Ġnob":31801,"ĠGST":31802,"maps":31803,"Ġpeppers":31804,"Fit":31805,"Ġoversees":31806,"ĠRabbi":31807,"ĠRuler":31808,"vertising":31809,"office":31810,"xxx":31811,"Ġraft":31812,"Changed":31813,"Ġtextbooks":31814,"Links":31815,"ĠOmn":31816,"ãĢij":31817,"Ġinconvenience":31818,"ĠDonetsk":31819,"=~":31820,"Ġimplicitly":31821,"Ġboosts":31822,"ĠBones":31823,"ĠBoom":31824,"Courtesy":31825,"Ġsensational":31826,"ANY":31827,"Ġgreedy":31828,"eden":31829,"Ġinexper":31830,"ĠLer":31831,"ĠVale":31832,"Ġtighten":31833,"ĠEAR":31834,"ĠNum":31835,"Ġancestor":31836,"Sent":31837,"ĠHorde":31838,"urgical":31839,"allah":31840,"Ġsap":31841,"amba":31842,"ĠSpread":31843,"twitch":31844,"Ġgrandson":31845,"Ġfracture":31846,"Ġmoderator":31847,"ĠSeventh":31848,"ĠReverse":31849,"Ġestimation":31850,"Choose":31851,"Ġparach":31852,"Ġbarric":31853,"ãĢIJ":31854,"Ġcompass":31855,"Ġallergic":31856,"âĢķ":31857,"OTHER":31858,"errilla":31859,"Ġwagon":31860,"Ġzinc":31861,"Ġrubbed":31862,"ĠFuller":31863,"ĠLuxembourg":31864,"ĠHoover":31865,"Ġliar":31866,"ĠEvening":31867,"ĠCobb":31868,"esteem":31869,"Ġselector":31870,"ĠBrawl":31871,"isance":31872,"ĠEk":31873,"Ġtroop":31874,"Ġguts":31875,"ĠAppeal":31876,"ĠTibetan":31877,"Ġroutines":31878,"ĠMent":31879,"Ġsummarized":31880,"steamapps":31881,"Ġtranqu":31882,"Ġ1929":31883,"oran":31884,"ĠAuthent":31885,"Ġgmaxwell":31886,"Ġapprehens":31887,"Ġpoems":31888,"Ġsausage":31889,"ĠWebster":31890,"urus":31891,"Ġthemed":31892,"Ġlounge":31893,"Ġcharger":31894,"Spoiler":31895,"Ġspilled":31896,"hog":31897,"ĠSunder":31898,"ĠAin":31899,"ĠAngry":31900,"Ġdisqual":31901,"ĠFrequency":31902,"ĠEthernet":31903,"Ġhelper":31904,"Percent":31905,"Ġhorrifying":31906,"Ġail":31907,"ĠAllan":31908,"EEE":31909,"ĠCrossing":31910,"449":31911,"Ġholog":31912,"ĠPuzzles":31913,"ĠGoes":31914,"erenn":31915,"604":31916,"ãģı":31917,"ĠRafael":31918,"Ġatten":31919,"ĠEmanuel":31920,"Ġupro":31921,"ĠSusp":31922,"Psych":31923,"ĠTrainer":31924,"ĠNES":31925,"ĠHunts":31926,"becue":31927,"Ġcounselor":31928,"Rule":31929,"Ġtoxins":31930,"Ġbanners":31931,"rifice":31932,"Ġgreeting":31933,"Ġfrenzy":31934,"Ġallocate":31935,"Ġ*)":31936,"expr":31937,"503":31938,"ĠChick":31939,"ĠTorn":31940,"Ġconsolidation":31941,"ĠFletcher":31942,"switch":31943,"frac":31944,"clips":31945,"ĠMcKin":31946,"ĠLunar":31947,"Month":31948,"ITCH":31949,"Ġscholarly":31950,"raped":31951,"398":31952,"Ġ1910":31953,"Ġegreg":31954,"Ġinsecure":31955,"Ġvictorious":31956,"cffffcc":31957,"Ġsingled":31958,"Ġelves":31959,"ĠWond":31960,"burst":31961,"Ġcamoufl":31962,"ĠBLACK":31963,"Ġconditioned":31964,"çī":31965,"answered":31966,"Ġcompulsory":31967,"ascist":31968,"Ġpodcasts":31969,"ĠFrankfurt":31970,"bnb":31971,"Ġneoliberal":31972,"ĠKeyboard":31973,"ĠBelle":31974,"warm":31975,"Ġtrusts":31976,"Ġinsured":31977,"ĠBucc":31978,"usable":31979,"607":31980,"ĠPlains":31981,"Ġ1890":31982,"Ġsabotage":31983,"Ġlodged":31984,"felt":31985,"Ġga":31986,"ĠNarc":31987,"ĠSalem":31988,"Ġseventy":31989,"ĠBlank":31990,"pocket":31991,"Ġwhisper":31992,"Ġmating":31993,"omics":31994,"ĠSalman":31995,"ĠKad":31996,"Ġangered":31997,"Ġcollisions":31998,"Ġextraordinarily":31999,"Ġcoercion":32000,"Ghost":32001,"birds":32002,"èĢ":32003,"kok":32004,"Ġpermissible":32005,"avorable":32006,"Ġpointers":32007,"Ġdissip":32008,"aci":32009,"Ġtheatrical":32010,"ĠCosmic":32011,"Ġforgetting":32012,"Ġfinalized":32013,"大":32014,"yout":32015,"library":32016,"Ġbooming":32017,"ĠBelieve":32018,"ĠTeacher":32019,"ĠLiv":32020,"ĠGOODMAN":32021,"ĠDominican":32022,"ORED":32023,"ĠParties":32024,"Ġprecipitation":32025,"ĠSlot":32026,"Roy":32027,"ĠCombined":32028,"Ġintegrating":32029,"Ġchrome":32030,"Ġintestinal":32031,"ĠRebell":32032,"Ġmatchups":32033,"Ġblockbuster":32034,"ĠLoren":32035,"ĠLevy":32036,"Ġpreaching":32037,"ĠSending":32038,"ĠPurpose":32039,"rax":32040,"fif":32041,"Ġauthoritative":32042,"ĠPET":32043,"astical":32044,"Ġdishon":32045,"Ġchatting":32046,"Ġ\"$:/":32047,"Connection":32048,"Ġrecreate":32049,"Ġdelinqu":32050,"Ġbroth":32051,"ĠDirty":32052,"ĠAdmin":32053,"zman":32054,"Ġscholarships":32055,"Ġ253":32056,"contact":32057,"alsa":32058,"767":32059,"creen":32060,"abbage":32061,"Ġ1915":32062,"Ġblended":32063,"Ġalarmed":32064,"Language":32065,"356":32066,"Ġblends":32067,"ĠChanged":32068,"Wolf":32069,"Ġhepat":32070,"Creating":32071,"Ġpersecut":32072,"Ġsweetness":32073,"arte":32074,"Ġforfeiture":32075,"ĠRoberto":32076,"impro":32077,"NFL":32078,"ĠMagnet":32079,"Detailed":32080,"Ġinsignificant":32081,"ĠPOLIT":32082,"ĠBBQ":32083,"ĠCPS":32084,"Ġseaw":32085,"aminer":32086,"mL":32087,"endif":32088,"finals":32089,"Ġ265":32090,"uish":32091,"Ġ})":32092,"ĠProblems":32093,"Ġemblem":32094,"Ġseriousness":32095,"Ġparsing":32096,"Ġsubstitution":32097,"Ġpressured":32098,"Ġrecycled":32099,"aleb":32100,"Ruby":32101,"Ġproficiency":32102,"Driver":32103,"ĠWester":32104,":'":32105,"AFTA":32106,"Ġmantle":32107,"ĠClayton":32108,"flag":32109,"Ġpractitioner":32110,"covered":32111,"ĠStruct":32112,"addafi":32113,"425":32114,"ĠTownship":32115,"ĠHydro":32116,"Louis":32117,"343":32118,"Ġcondo":32119,"ĠTao":32120,"Ġutilization":32121,"Ġnausea":32122,"ĠDems":32123,"ridges":32124,"pause":32125,"Ġformulas":32126,"Ġchallenger":32127,"376":32128,"Ġdefective":32129,"ĠRailway":32130,"ĠPubMed":32131,"Ġyogurt":32132,"lbs":32133,"ĠNorfolk":32134,"OPE":32135,"ĠMoody":32136,"Ġdistributor":32137,"Ġscrolls":32138,"Ġextracts":32139,"Stan":32140,"Ġviability":32141,"Ġexposes":32142,"Ġstarvation":32143,"ĠSteps":32144,"ĠDodd":32145,"few":32146,"STD":32147,"332":32148,"Ġclosures":32149,"Ġcomplementary":32150,"ĠSasha":32151,"umpy":32152,"Ġmonet":32153,"Ġarticulate":32154,"ĠDoct":32155,"killer":32156,"Ġscrim":32157,"Ġ264":32158,"Ġprostitutes":32159,"Ġsevered":32160,"Ġattachments":32161,"Ġcooled":32162,"Lev":32163,"ĠFalk":32164,"fail":32165,"Ġpoliceman":32166,"ĠDag":32167,"Ġprayed":32168,"ĠKernel":32169,"Ġclut":32170,"Ġcath":32171,"Ġanomaly":32172,"Storm":32173,"emaker":32174,"ĠBreakfast":32175,"uli":32176,"oire":32177,"JJ":32178,"hz":32179,"Operation":32180,"ĠSick":32181,"354":32182,"ĠGuatemala":32183,"Rate":32184,"Ġexposures":32185,"faces":32186,"ĠArchae":32187,"raf":32188,"ĠMia":32189,"Ġ2025":32190,"Ġopaque":32191,"Ġdisguised":32192,"ĠHeadquarters":32193,"Sah":32194,"Ġpots":32195,"978":32196,"ĠMalf":32197,"Ġfrowned":32198,"Ġpoisonous":32199,"ĠConvers":32200,"eeks":32201,"Ġcrab":32202,".\"\"":32203,"Ġtreason":32204,"Ġranc":32205,"Ġescalating":32206,"Ġwarr":32207,"Ġmobs":32208,"Ġlamps":32209,"ĠSunshine":32210,"ĠBrunswick":32211,"Phones":32212,"Ġspelled":32213,"ĠSkip":32214,"Ġ2050":32215,"Ġ1911":32216,"ĠPluto":32217,"ĠAmend":32218,"Ġmeats":32219,"387":32220,"Ġstomp":32221,"ĠZhou":32222,"ĠLeviathan":32223,"ĠHazard":32224,"adv":32225,"ĠOrwell":32226,"Ġaloud":32227,"Ġbumper":32228,"ĠAnarch":32229,"ubuntu":32230,"ĠSerious":32231,"fitting":32232,"ĠOptional":32233,"ĠCecil":32234,"REAM":32235,"Ġserotonin":32236,"Ġcultivate":32237,"agogue":32238,"}\\":32239,"Ġmosques":32240,"ĠSunny":32241,"Ġreactive":32242,"revolution":32243,"ĠLup":32244,"ĠFedora":32245,"Ġdefenseman":32246,"ĠVID":32247,"istine":32248,"Ġdrowning":32249,"ĠBroadcasting":32250,"Ġthriller":32251,"ĠScy":32252,"Ġaccelerating":32253,"Ġdirects":32254,"odied":32255,"bike":32256,"duration":32257,"Ġpainfully":32258,"Redd":32259,"Ġproductions":32260,"Ġgag":32261,"Ġwhist":32262,"Ġsock":32263,"Ġinfinitely":32264,"ĠConcern":32265,"ĠCitadel":32266,"Ġlieu":32267,"Ġcandles":32268,"ogeneous":32269,"arger":32270,"Ġheavenly":32271,"inflammatory":32272,"Performance":32273,"Cs":32274,"ructose":32275,"azaki":32276,"Ġpessim":32277,"Ġinference":32278,"Ġpowd":32279,"ĠZoe":32280,"Ġpaints":32281,"Ġdazz":32282,"pta":32283,"-----------":32284,"Ġinspir":32285,"ĠExperimental":32286,"ĠKnife":32287,"regor":32288,"bors":32289,"Ġshowers":32290,"romeda":32291,"Ġsaint":32292,"Ġbenign":32293,"ĠJiang":32294,"Ġenvisioned":32295,"Ġshroud":32296,"IFT":32297,"HO":32298,"Ġshuff":32299,"ĠICC":32300,"Ġsegreg":32301,"Ġrevisit":32302,"ighthouse":32303,"Li":32304,"Ġsubstrate":32305,"ĠSeas":32306,"ĠReward":32307,"ĠHep":32308,"ĠBrass":32309,"sbm":32310,"Ġeliminates":32311,"Ġstamina":32312,"ĠVAT":32313,"ĠLoan":32314,"Ġconstraint":32315,"Ġappropriated":32316,"Ġpes":32317,"ĠALE":32318,"ranging":32319,"Ġ404":32320,"392":32321,"Ġintellectuals":32322,"achu":32323,"Ġrestructuring":32324,"ĠLevin":32325,"Ġrunes":32326,"Ġdelightful":32327,"Ġcarbohydrates":32328,"ĠModels":32329,"ĠExpo":32330,"Ġtransporting":32331,"alloc":32332,"Ġringing":32333,"Samsung":32334,"Ġscarcely":32335,"ĠURLs":32336,"ĠMAS":32337,"Ġprototypes":32338,"Ġnarrator":32339,"ĠCPUs":32340,"cdn":32341,"ĠBarton":32342,"Ġdecidedly":32343,"ĠShu":32344,"ixir":32345,"ocious":32346,"ĠMyst":32347,"Nintendo":32348,"Ġreuse":32349,"Ġforgiven":32350,"Few":32351,"inical":32352,"nat":32353,"Ġseamless":32354,"ĠEva":32355,"ĠEVE":32356,"ĠJO":32357,"landers":32358,"Ġsofter":32359,"negie":32360,"Ġtransient":32361,"Ġorbital":32362,"Ġfulfil":32363,"ĠKom":32364,"Hopefully":32365,"Ġdynamically":32366,"ĠHunger":32367,"åĽ":32368,"ĠArmenia":32369,"elman":32370,"berto":32371,"Ġpige":32372,"ĠIDs":32373,"limit":32374,"Ġveins":32375,"Ġsoaring":32376,"packs":32377,"Golden":32378,"ĠCrab":32379,"istor":32380,"ĠRPM":32381,"Ġ$$":32382,"gression":32383,"Ġjihadist":32384,"Ġgamble":32385,"Ġcareg":32386,"Ġinflated":32387,"Face":32388,"ĠFirearms":32389,"ĠEmmanuel":32390,"âĿ":32391,"Ġshocks":32392,"grab":32393,"Ġsplend":32394,"ĠHPV":32395,"abortion":32396,"Above":32397,"Entity":32398,"players":32399,"Ġcommenced":32400,"ulence":32401,"Ġfulfillment":32402,"Ġembodiments":32403,"ĠWelfare":32404,"Ġhail":32405,"Ġ<@":32406,"tten":32407,"Ġcatcher":32408,"ĠJazeera":32409,"Ġvolcano":32410,"Ġstabilize":32411,"ĠHandler":32412,"Ġintensified":32413,"ĠAbrams":32414,"Ġhumiliation":32415,"paced":32416,"605":32417,"ĠCentOS":32418,"Specific":32419,"Ġheed":32420,"ĠCAM":32421,"ĠGalile":32422,"Die":32423,"Ġabolished":32424,"ĠThomson":32425,"ĠTeachers":32426,"ĠWass":32427,"jong":32428,"ĠISBN":32429,"ĠAllies":32430,"shake":32431,"å·":32432,"vict":32433,"Howard":32434,"Ġdeem":32435,"Ġexceedingly":32436,"ĠSmartstocks":32437,"ibe":32438,"Ġdoorway":32439,"Ġcompeted":32440,"igmat":32441,"Ġnationalists":32442,"Ġgroom":32443,"ĠKeen":32444,"Ġdisposable":32445,"decl":32446,"ĠTolkien":32447,"ĠScheme":32448,"Ġbiod":32449,"Ġavid":32450,"ĠElon":32451,"agar":32452,"ĠTSA":32453,"Roman":32454,"Ġartificially":32455,"Ġadvisors":32456,"XL":32457,"ĠInferno":32458,"366":32459,"Ġtedious":32460,"ĠPhotography":32461,"ĠCarrie":32462,"Ġtrope":32463,"ĠSandra":32464,"Ġdecimal":32465,"Queen":32466,"ĠGundam":32467,"ĠOM":32468,"otech":32469,"NBA":32470,"Ġ1932":32471,"Ġentrenched":32472,"ĠMarion":32473,"Ġfraternity":32474,"Labour":32475,"Henry":32476,"Ġlatitude":32477,"Either":32478,"Ġenhances":32479,"ĠPotential":32480,"Ġshines":32481,"idad":32482,"Ġbreadth":32483,"Ġcapacities":32484,"ĠðŁĻĤ":32485,"ĠBronx":32486,"Ġsexes":32487,"Ġdifferentiation":32488,"Ġheavyweight":32489,"ĠTaj":32490,"dra":32491,"Ġmigrate":32492,"Ġexhaustion":32493,"ĠRUN":32494,"elsius":32495,"ĠCuomo":32496,"Ġguitars":32497,"Ġclones":32498,"ĠSomew":32499,"ĠPry":32500,"-------------":32501,"Ġwarranted":32502,"cycles":32503,"Ġsalvage":32504,"Ġdisks":32505,"RANT":32506,"ĠNGOs":32507,"ĠMartian":32508,"\":[{\"":32509,"Ġaddicts":32510,"ojure":32511,"illet":32512,"Ġamazingly":32513,"artments":32514,"pixel":32515,"ĠGPUs":32516,"Layout":32517,"è£":32518,"ĠTamil":32519,"ĠBasil":32520,"Ġimpartial":32521,"ĠStructure":32522,"fork":32523,"bryce":32524,"Ġridge":32525,"ĠHamburg":32526,"rious":32527,"Ġblitz":32528,"cigarettes":32529,"Ġcanned":32530,"402":32531,"Ġironically":32532,"Ġcompassionate":32533,"ĠHawkins":32534,".#":32535,"ĠCathedral":32536,"Ġrallied":32537,"internal":32538,"Ġquota":32539,"stakes":32540,"TEXT":32541,"mom":32542,"Ġcompletes":32543,"Ġ238":32544,"Ġshrug":32545,"ãĥij":32546,"ĠNinth":32547,"Ġrevise":32548,"ĠProvider":32549,"Ġtreacher":32550,"Ġquasi":32551,"ĠPRES":32552,"Ġdeposition":32553,"Ġconfidentiality":32554,"issors":32555,"Ġimbalance":32556,"Ġspanning":32557,"Ġangular":32558,"ĠCul":32559,"communication":32560,"ĠNora":32561,"ĠGenius":32562,"opter":32563,"Ġsacked":32564,"Spot":32565,"Ġfinely":32566,"ĠCHR":32567,"282":32568,"waves":32569,"Palest":32570,"ĠRohing":32571,"NL":32572,"è¿":32573,"Ġshitty":32574,"ĠScalia":32575,"475":32576,"Progress":32577,"Ġreferencing":32578,"Ġclassrooms":32579,"abee":32580,"Ġsod":32581,"hesion":32582,"708":32583,"ĠZuckerberg":32584,"ĠFinish":32585,"ĠScotia":32586,"ĠSavior":32587,"ĠInstallation":32588,"antha":32589,"(-":32590,"Ġ302":32591,"ĠPunk":32592,"Ġcrater":32593,"youtu":32594,"Ġroast":32595,"Ġinfluencing":32596,"Ġdup":32597,"ĠJR":32598,"ĠGrav":32599,"Ġstature":32600,"Ġbathrooms":32601,"Aside":32602,"Wiki":32603,"mean":32604,"ĠZak":32605,"ĠOnes":32606,"ĠNath":32607,"Ġhypert":32608,"Ġcommencement":32609,"Civil":32610,"Ġmoderately":32611,"Ġdistributors":32612,"Ġbreastfeeding":32613,"Ġ980":32614,"ĠSik":32615,"ĠCig":32616,"ĠAMER":32617,"RIP":32618,"ĠCareer":32619,"usting":32620,"Ġmessed":32621,"Ġeh":32622,"ĠJensen":32623,"/$":32624,"Ġblackmail":32625,"Ġconversions":32626,"Ġscientifically":32627,"Ġmantra":32628,"paying":32629,"Ġivory":32630,"ĠCourts":32631,"OUGH":32632,"auntlet":32633,"Serial":32634,"Brow":32635,"ĠHundreds":32636,"323":32637,"Ġpee":32638,"Ġlinux":32639,"Ġsubmer":32640,"ĠPrincipal":32641,"485":32642,"ĠDSL":32643,"ĠCousins":32644,"Ġdoctrines":32645,"ĠAthletics":32646,"Ġ315":32647,"ĠKarma":32648,"Ġattent":32649,"urger":32650,"Ġprescribe":32651,"Ġencaps":32652,"ĠCame":32653,"Ġsecretive":32654,"ĠCrimes":32655,"dn":32656,"Clean":32657,"ĠEgyptians":32658,"ĠCarpenter":32659,"Ġll":32660,"Hum":32661,"ĠMilo":32662,"Ġcapitalists":32663,"Ġbriefed":32664,"Twe":32665,"ĠBasin":32666,"elvet":32667,"Mos":32668,"Ġplunge":32669,"ĠKaiser":32670,"ĠFuj":32671,"illin":32672,"Ġsafeguards":32673,"Ġoste":32674,"ĠOpportunity":32675,"ĠMafia":32676,"ĠCalling":32677,"apa":32678,"urban":32679,"brush":32680,"illard":32681,"cé":32682,"intelligence":32683,"ĠLob":32684,"ĠDruid":32685,"Ġsmoother":32686,"Ġfooting":32687,"Ġmotorists":32688,"arcity":32689,"Ġmasculinity":32690,"Ġmism":32691,"Ġabdominal":32692,"ĠTavern":32693,"ĠRoh":32694,"Ġescapes":32695,"signed":32696,"Anthony":32697,"Ġsacrificing":32698,"Ġintimacy":32699,"Ġanterior":32700,"ĠKod":32701,"Ġmotif":32702,"Ġgraz":32703,"Ġvisualization":32704,"Ġguitarist":32705,"ĠTrotsky":32706,"magic":32707,"Dar":32708,"ĠMori":32709,"Ġwards":32710,"Ġtoilets":32711,"lest":32712,"Ġteleport":32713,"ĠSundays":32714,"ĠPlat":32715,"ETS":32716,"ĠeSports":32717,"Patrick":32718,"ĠKatherine":32719,"enko":32720,"Ġhassle":32721,"ĠMick":32722,"ggles":32723,"Ġhob":32724,"aintain":32725,"Ġairborne":32726,"Ġspans":32727,"Ġchili":32728,"Ġaperture":32729,"Ġvolunteered":32730,"ĠIncident":32731,"ĠFres":32732,"ĠVeteran":32733,"aughtered":32734,"ingo":32735,"Ġuninsured":32736,"CLOSE":32737,"Ġfuse":32738,"Ġerotic":32739,"Ġadvertise":32740,"raising":32741,"Texture":32742,"Ġattends":32743,"ĠREAL":32744,"uddled":32745,"Ġsmoot":32746,"Ġ305":32747,"ĠWillis":32748,"Ġblond":32749,"Analysis":32750,"ĠVT":32751,"onica":32752,"Ġstronghold":32753,"RF":32754,"NM":32755,".>>":32756,"Ġprosperous":32757,"Ġboasted":32758,"292":32759,"ĠManufacturing":32760,"PRESS":32761,"gren":32762,"Ġpharmacy":32763,"ĠRockefeller":32764,"kai":32765,"Ġthumbs":32766,"ĠHut":32767,"Ġmotherboard":32768,"Ġguardians":32769,"ĠAlter":32770,"llular":32771,"Ġshack":32772,"Ġwisely":32773,"Ġbackbone":32774,"erva":32775,"Ġsuicides":32776,"ĠMcGregor":32777,"ijah":32778,"Emer":32779,"ĠBrav":32780,"Ġdesignate":32781,"POST":32782,"produced":32783,"Ġcleansing":32784,"irlwind":32785,"existent":32786,"ĠHumph":32787,"ĠPayne":32788,"Ġvested":32789,"Å¡":32790,"Ġstringent":32791,"iona":32792,"Ġunsub":32793,"Ġsummed":32794,"ĠHercules":32795,"subject":32796,"ĠRagnar":32797,"ĠNos":32798,"Ġcharacterization":32799,"Ġsavvy":32800,"ĠDawson":32801,"ĠCasino":32802,"Ġfri":32803,"ĠBarrier":32804,"Ġmisinformation":32805,"Ġinsulation":32806,"Ġcorridors":32807,"Ġairplanes":32808,"ĠNoct":32809,"ahi":32810,"Ġ1916":32811,"kb":32812,"armac":32813,"Ġshun":32814,"Ġschema":32815,"Ġhorrified":32816,"Ġ239":32817,"aunders":32818,"NB":32819,"iates":32820,"erity":32821,"ĠShard":32822,"Ġrarity":32823,"Ġgrouped":32824,"ĠGhana":32825,"against":32826,"ĠBiological":32827,"ĠAware":32828,"owell":32829,"ÏĦ":32830,"ĠBeau":32831,"shaw":32832,"Hack":32833,"ĠJulius":32834,"USS":32835,"olson":32836,"auna":32837,"cru":32838,"ĠMaurice":32839,"ĠIk":32840,"Ġsequencing":32841,"Ġradicals":32842,"Ġ(?,":32843,"virtual":32844,"Ġanyways":32845,"Ġreperc":32846,"Ġhandlers":32847,"Ġhesitant":32848,"éĥ":32849,"ĠMF":32850,"plementation":32851,"associated":32852,"Ġcampaigned":32853,"ĠYue":32854,"utations":32855,"ĠYoga":32856,"Ġsimmer":32857,"Ġrods":32858,"Ġmelody":32859,"Ġconvoy":32860,"videos":32861,"Ġscreened":32862,"Neg":32863,"ochemical":32864,"Ġ())":32865,"Ġultras":32866,"Ġantip":32867,"ĠIslanders":32868,"704":32869,"Ġfetish":32870,"Ġridiculously":32871,"ĠKart":32872,"Ġmitochondrial":32873,"Ġinterfering":32874,"Builder":32875,"Ġoverfl":32876,"Ġacne":32877,"ĠMud":32878,"ĠKerr":32879,"flex":32880,"ĠPostal":32881,"ĠBaltic":32882,"477":32883,"ĠPersons":32884,"ourage":32885,"HB":32886,"ĠMuse":32887,"ĠImmortal":32888,"ĠDriving":32889,"Ġpetitions":32890,"Ġsubscript":32891,"Ġsorce":32892,"ĠProcessor":32893,"uton":32894,"Sony":32895,"Ġphon":32896,"Ġraced":32897,"ĠAnthrop":32898,"Ġdaytime":32899,"ĠExercise":32900,"Adding":32901,"Ġengages":32902,"ĠQualcomm":32903,"Ġmiracles":32904,"Ġmemes":32905,"ĠDrink":32906,"ĠOrioles":32907,"Ġhairs":32908,"ĠPolar":32909,"athom":32910,"Ġslippery":32911,"ĠRemy":32912,"Ġcaramel":32913,"ĠYEAR":32914,"Ġalk":32915,"Ign":32916,"aution":32917,"ĠMerlin":32918,"ĠCran":32919,"Ġapologies":32920,"Ġ410":32921,"Ġouting":32922,"ĠMemories":32923,"appointed":32924,"Ġcountered":32925,"uld":32926,"posing":32927,"Ġfirewall":32928,"ĠWast":32929,"ĠWet":32930,"worked":32931,"seller":32932,"Ġrepealed":32933,"ereo":32934,"assuming":32935,"BLIC":32936,"mite":32937,"ĠCEOs":32938,"ĠChapel":32939,"elligent":32940,"________________________":32941,"Dog":32942,"Ġwart":32943,"Ġsubscriber":32944,"sports":32945,"Ġbegged":32946,"ĠMV":32947,"Ġsemif":32948,"ethical":32949,"Ġpreach":32950,"Ġrevital":32951,"Ġpunitive":32952,"Ġshortcuts":32953,"Ġinstituted":32954,"ĠWarsaw":32955,"Ġabdomen":32956,"ĠKING":32957,"Ġsuperintendent":32958,"Ġfry":32959,"ĠGeo":32960,"TOR":32961,"Ġcontradictions":32962,"aptic":32963,"Ġlandscapes":32964,"bugs":32965,"Ġclust":32966,"Ġvolley":32967,"cribed":32968,"Ġtandem":32969,"Ġrobes":32970,"WHAT":32971,"Ġpromoter":32972,"Ġeloqu":32973,"reviewed":32974,"ĠDK":32975,"ĠPlato":32976,"Ġfps":32977,"Tank":32978,"ĠDerrick":32979,"Ġprioritize":32980,"asper":32981,"ĠHonduras":32982,"ĠCompleted":32983,"nec":32984,"Ġmog":32985,"nir":32986,"ĠMayo":32987,"DEF":32988,"stall":32989,"inness":32990,"ĠVolkswagen":32991,"Ġprecaution":32992,"ĠMell":32993,"iak":32994,"istries":32995,"Ġ248":32996,"Ġoverlapping":32997,"Senate":32998,"ĠEnhance":32999,"resy":33000,"racial":33001,"ORTS":33002,"ĠMormons":33003,"Strong":33004,"ĠCoch":33005,"Mexico":33006,"ĠMaduro":33007,"Ġjars":33008,"Ġcane":33009,"Wik":33010,"olla":33011,"ifference":33012,"Ġphysicist":33013,"ĠMaggie":33014,"Ġ285":33015,"Ġdepiction":33016,"ĠMcLaren":33017,"Ju":33018,"Ġslows":33019,"Ġcommissioners":33020,"ĠWillow":33021,"ĠExplos":33022,"hovah":33023,"Ġtechnician":33024,"Ġhomicides":33025,"ĠFlav":33026,"ĠTruman":33027,"Ġ10000":33028,"uctor":33029,"Ġshader":33030,"Newsletter":33031,"457":33032,"Ġrever":33033,"Ġhardened":33034,"Ġwhereabouts":33035,"Ġredevelop":33036,"Ġcarbs":33037,"Ġtravers":33038,"Ġsquirrel":33039,"Ġfollower":33040,"Ġsings":33041,"508":33042,"Ġrabbits":33043,"emonium":33044,"Ġdocumenting":33045,"Ġmisunderstood":33046,")'":33047,"Rick":33048,"ggies":33049,"Ġpremie":33050,"Ġskating":33051,"Ġpassports":33052,"Ġfists":33053,"ageddon":33054,"Haw":33055,"ACP":33056,"080":33057,"ĠThoughts":33058,"ĠCarlson":33059,"Ġpriesthood":33060,"hua":33061,"Ġdungeons":33062,"ĠLoans":33063,"Ġantis":33064,"Ġfamiliarity":33065,"ĠSabb":33066,"opal":33067,"ĠInk":33068,"strike":33069,"Ġcram":33070,"Ġlegalized":33071,"Ġcuisine":33072,"Ġfibre":33073,"Travel":33074,"ĠMonument":33075,"ODY":33076,"ethy":33077,"Ġinterstate":33078,"ĠPUR":33079,"emporary":33080,"ĠArabian":33081,"developed":33082,"Ġsaddle":33083,"Ġgithub":33084,"ĠOffer":33085,"ĠISP":33086,"rolet":33087,"ĠSUPER":33088,"ĠDenis":33089,"Ġmultiplier":33090,"Ġstirred":33091,"Interestingly":33092,"Ġcustomary":33093,"Ġbilled":33094,"hex":33095,"Ġmultiplied":33096,"Ġflipping":33097,"ĠCrosby":33098,"Ġfundamentals":33099,"iae":33100,"ĠPlayed":33101,"ĠAtom":33102,"amazon":33103,"ĠFlam":33104,"eez":33105,"activated":33106,"Ġtablespoon":33107,"Ġliberalism":33108,"ĠPalin":33109,"ĠPatel":33110,"Num":33111,"ĠTAM":33112,"Ġsurn":33113,"ĠReloaded":33114,"Ġcoined":33115,"\"],":33116,"ĠClash":33117,"ĠAgu":33118,"Ġpragmatic":33119,"ĠActivate":33120,"Ġ802":33121,"Ġtrailers":33122,"Ġsilhou":33123,"Ġprobes":33124,"Ġcircus":33125,"ĠBain":33126,"ĠLindsay":33127,"ĠAbbey":33128,"Delivery":33129,"Ġconcession":33130,"Ġgastro":33131,"ĠSprite":33132,"ÄŁ":33133,"andel":33134,"Ġgimm":33135,"Ġautobi":33136,"ĠTurtle":33137,"Ġwonderfully":33138,"ĠHaram":33139,"ĠWorldwide":33140,"ĠHandle":33141,"Ġtheorists":33142,"Ġsleek":33143,"ĠZhu":33144,"ographically":33145,"EGA":33146,"ĠOwners":33147,"aths":33148,"ĠAntarctic":33149,"natal":33150,"=\"\"":33151,"flags":33152,"````":33153,"Ġsul":33154,"Kh":33155,"Ġpotassium":33156,"Ġlineman":33157,"Ġcereal":33158,"ĠSeasons":33159,"Ġ2022":33160,"Ġmathematic":33161,"Ġastronomers":33162,"professional":33163,"Ġfares":33164,"cknowled":33165,"Ġchi":33166,"Ġyoungsters":33167,"Ġmistakenly":33168,"Ġhemisphere":33169,"ĠDivinity":33170,"rone":33171,"Ġ\",":33172,"rings":33173,"Ġattracts":33174,"vana":33175,"å¹":33176,"CAP":33177,"Ġplaylist":33178,"Ġporch":33179,"ãģ£":33180,"Ġincorporates":33181,"Ġsoak":33182,"Ġasserting":33183,"ĠTerrorism":33184,"ĠPablo":33185,"Ja":33186,"cester":33187,"Ġfearing":33188,"ĠPrayer":33189,"Ġescalated":33190,"GW":33191,"Ġrobe":33192,"ĠBrighton":33193,"acists":33194,"ĠSymphony":33195,"ĠDwarf":33196,"ĠParade":33197,"ĠLego":33198,"Ġinexpl":33199,"Ġlords":33200,"leaf":33201,"RAG":33202,"liber":33203,"Ġcigars":33204,"ĠJehovah":33205,"606":33206,"WINDOWS":33207,"ĠLiberia":33208,"ebus":33209,"Heavy":33210,"Ġlubric":33211,"ĠRW":33212,"anguages":33213,"Ġnarrowed":33214,"computer":33215,"ĠEmber":33216,"Ġmurdering":33217,"Ġdownstream":33218,"ĠTuls":33219,"ĠTables":33220,"Topic":33221,"ĠAccuracy":33222,"=/":33223,"lost":33224,"ĠRei":33225,"Ġprogresses":33226,"bear":33227,"Ġestablishments":33228,"Justin":33229,"ĠPeach":33230,"ĠGomez":33231,"å¿":33232,"ĠTriangle":33233,"Ident":33234,"ĠHive":33235,"Resources":33236,"Ġmixes":33237,"ĠAssuming":33238,"Mu":33239,"Ġhypoc":33240,"Ġsane":33241,"ĠWan":33242,"idious":33243,"Success":33244,"Ġio":33245,"Angel":33246,"Ġdangerously":33247,"ĠCreature":33248,"WORK":33249,":[":33250,"ĠKatrina":33251,"Listener":33252,"Miller":33253,"ĠIdlib":33254,"hang":33255,"Ġcircumvent":33256,"href":33257,"Ġcelestial":33258,"ĠWeeks":33259,"ĠPug":33260,"ĠDalton":33261,"Ġsubpoena":33262,"uku":33263,"Ġpersisted":33264,"pei":33265,"olding":33266,"ĠDocuments":33267,"ĠHast":33268,"ĠCENT":33269,"Ġprimer":33270,"Ġsynonymous":33271,"Ġnib":33272,"ombs":33273,"Ġnotation":33274,"ĠDish":33275,"ĠAtmosp":33276,"Ġforbid":33277,"ĠANG":33278,"pattern":33279,"los":33280,"Ġprojectiles":33281,"brown":33282,".\",":33283,"ĠVenom":33284,"Ġfiercely":33285,"ublished":33286,"ĠUran":33287,"ĠNicarag":33288,"410":33289,"ĠCAL":33290,"OTOS":33291,"ĠMiracle":33292,"ĠEnchant":33293,"Ġguarding":33294,"append":33295,"Attach":33296,"Ġleveled":33297,"Ġcondoms":33298,"ihilation":33299,"649":33300,"Ġnightmares":33301,"ĠTHEY":33302,"ĠSTART":33303,"ĠKinn":33304,"Ġroommate":33305,"Ġhygiene":33306,"opping":33307,"Job":33308,"Ġlvl":33309,"ĠVER":33310,"ĠKeeping":33311,"abetic":33312,"Ġformatting":33313,"erala":33314,"Ġrevisions":33315,"Ġresurg":33316,"Tel":33317,"ĠGoodman":33318,"353":33319,"pod":33320,"Ġindisp":33321,"ĠTranslation":33322,"Ġgown":33323,"ĠMund":33324,"Ġcis":33325,"Ġbystand":33326,"collect":33327,"ĠPunjab":33328,"actively":33329,"ĠGamb":33330,"tell":33331,"Ġimporting":33332,"gencies":33333,"Ġlocom":33334,"ĠBrill":33335,"Holy":33336,"ĠBerger":33337,"Ġshowdown":33338,"Ġresponders":33339,"ILY":33340,"Ġtakedown":33341,"leted":33342,"Ġmattered":33343,"Ġpredictive":33344,"Ġoverlay":33345,"GPU":33346,"ĠVick":33347,"Ġconveyed":33348,"Tab":33349,"peer":33350,"Scan":33351,"Ġdefensively":33352,"vae":33353,"Ġapproving":33354,"Ġtiers":33355,"ĠVia":33356,"querade":33357,"ĠSaudis":33358,"Ġdemolished":33359,"ĠProphe":33360,"Ġmono":33361,"Ġhospitality":33362,"HAM":33363,"ĠAriel":33364,"MOD":33365,"ĠTorah":33366,"Ġblah":33367,"ĠBelarus":33368,"erential":33369,"ĠTuc":33370,"Ġbanker":33371,"397":33372,"Ġmosquit":33373,"ĠScientist":33374,"ĠMusical":33375,"Ġhust":33376,"Shift":33377,"Ġtorment":33378,"Ġstandoff":33379,"Educ":33380,"ĠFog":33381,"Ġamplifier":33382,"Shape":33383,"Instance":33384,"ĠCritics":33385,"Ġdaemon":33386,"Houston":33387,"Ġmattress":33388,"ĠIDF":33389,"Ġobscene":33390,"ĠAmer":33391,"hetti":33392,"Ġcompiling":33393,"352":33394,"verett":33395,"ĠReduction":33396,"istration":33397,"ĠBlessed":33398,"ĠBachelor":33399,"316":33400,"Ġprank":33401,"ĠVulcan":33402,"dding":33403,"Ġmourning":33404,"ĠQuint":33405,"ĠBlaster":33406,"testing":33407,"Ġsediment":33408,">>>":33409,"ĠEternity":33410,"ĠWHERE":33411,"ĠMaze":33412,"Ġreacting":33413,"ĠAlv":33414,"omsday":33415,"ĠCRA":33416,"Ġtranslator":33417,"Ġbogus":33418,"atu":33419,"Website":33420,"olls":33421,"Ġbaptism":33422,"Ġsibling":33423,"ĠAutumn":33424,"vez":33425,"ãģ®é":33426,"guards":33427,"Georg":33428,"assadors":33429,"ĠFreud":33430,"Ġcontinents":33431,"ĠRegistry":33432,"Bernie":33433,"ĸļ士":33434,"Ġtolerant":33435,"ĠUW":33436,"Ġhorribly":33437,"995":33438,"ĠMIDI":33439,"Ġimpatient":33440,"ocado":33441,"eri":33442,"ĠWorst":33443,"ĠNorris":33444,"ĠTalking":33445,"Ġdefends":33446,"ensable":33447,"Ġ2021":33448,"Ġanatomy":33449,"Lew":33450,"Ġdrawer":33451,"ĠCanberra":33452,"Ġpatriotic":33453,"é¾įåĸļ士":33454,"ĠAvg":33455,"ARM":33456,"Ġundisclosed":33457,"Ġfarewell":33458,"459":33459,"bable":33460,"ĠAllison":33461,"OLOG":33462,"Ġconco":33463,"tight":33464,"ĠACPI":33465,"ĠMines":33466,"lich":33467,"ĠâĶľ":33468,"represented":33469,"200000":33470,"Ġenthusiast":33471,"OTS":33472,"bil":33473,"ĠIngredients":33474,"Ġinventor":33475,"ĠMySQL":33476,"³³³":33477,"ĠABOUT":33478,"within":33479,"Ġmk":33480,"Bul":33481,"ĠFake":33482,"Ġdraconian":33483,"Wa":33484,"helm":33485,"ĠTerran":33486,"erville":33487,"Ġcommonplace":33488,"SIZE":33489,"Ġ\"<":33490,"replace":33491,"ographs":33492,"ĠSELECT":33493,"incible":33494,"ĠMostly":33495,"ĠSheffield":33496,"ĠIDE":33497,"uggle":33498,"Ġcitations":33499,"hurst":33500,"ĠUnix":33501,"Ġunleash":33502,"ĠPiper":33503,"ĠNano":33504,"Ġsuccumb":33505,"Ġreluctance":33506,"Ġ2500":33507,"ĠMerchant":33508,"Ġwiret":33509,"Ġcombos":33510,"ĠBirthday":33511,"Ġcharcoal":33512,"ĠUPS":33513,"ĠFairfax":33514,"Ġdriveway":33515,"ĠTek":33516,"ĠPitch":33517,"overe":33518,"Ġtechnicians":33519,"ĠActual":33520,"flation":33521,"ĠFiscal":33522,"ĠEmpty":33523,"anamo":33524,"Ġmagnesium":33525,"Ġslut":33526,"Ġgrowers":33527,"Investigators":33528,"():":33529,"ĠSatellite":33530,"ĠKeynes":33531,"missive":33532,"lane":33533,"Ġborough":33534,"344":33535,"ĠTEAM":33536,"ĠBethesda":33537,"CV":33538,"hower":33539,"ĠRAD":33540,"Ġchant":33541,"ĠRiy":33542,"Ġcompositions":33543,"Ġmildly":33544,"Ġmeddling":33545,"Ġagility":33546,"aneers":33547,"501":33548,"Ġsynth":33549,"linger":33550,"291":33551,"Ġexclaimed":33552,"Party":33553,"Ġcontamin":33554,"ĠManor":33555,"ĠRespond":33556,"Ġpraising":33557,"Ġmanners":33558,"fleet":33559,"Summer":33560,"ĠLynd":33561,"ĠDefinitely":33562,"grim":33563,"Ġbowling":33564,"stri":33565,"çĽ":33566,"ynt":33567,"Ġmandates":33568,"DIV":33569,"Ġreconcile":33570,"views":33571,"ĠDamon":33572,"vette":33573,"Flo":33574,"ĠGreatest":33575,"ilon":33576,"icia":33577,"Ġportrayal":33578,"Ġcushion":33579,"504":33580,"1979":33581,"ossal":33582,"Applic":33583,"scription":33584,"Ġmitigation":33585,"ATS":33586,"pac":33587,"Ġerased":33588,"Ġdeficiencies":33589,"ĠHollande":33590,"ĠXu":33591,"Ġbred":33592,"Ġpregnancies":33593,"femin":33594,"Ġemph":33595,"Ġplanners":33596,"Ġoutper":33597,"uttering":33598,"Ġperpetrator":33599,"Ġmotto":33600,"ĠEllison":33601,"ĠNEVER":33602,"Ġadmittedly":33603,"ARI":33604,"ĠAzerbaijan":33605,"Ġmillisec":33606,"Ġcombustion":33607,"ĠBottle":33608,"ĠLund":33609,"ĠPs":33610,"ĠDress":33611,"Ġfabricated":33612,"Ġbattered":33613,"Ġsidel":33614,"ĠNotting":33615,"Foreign":33616,"ĠJerome":33617,"020":33618,"ĠArbit":33619,"Ġknots":33620,"ĠRIGHT":33621,"Moving":33622,"ãģĻ":33623,"Ġsurgeries":33624,"Ġcourthouse":33625,"Ġmastered":33626,"Ġhovering":33627,"ĠBran":33628,"ĠAlison":33629,"Ġsafest":33630,"military":33631,"Ġbullied":33632,"Ġbarrage":33633,"Reader":33634,"ESE":33635,"ĠGeographic":33636,"Tools":33637,"314":33638,"ĠGeek":33639,"roth":33640,"glers":33641,"ĠFIN":33642,"Ïģ":33643,"ĠAston":33644,"altern":33645,"488":33646,"Ġveterin":33647,"Gamer":33648,"Ġintel":33649,"renches":33650,"Shield":33651,"Ġamnesty":33652,"ĠBhar":33653,"Ġpiled":33654,"Ġhonorable":33655,"ĠInstitutes":33656,"Ġsoaked":33657,"Ġcoma":33658,"ĠEFF":33659,"341":33660,"bytes":33661,"ĠGmail":33662,"lein":33663,"ĠCanadiens":33664,"material":33665,"Il":33666,"Ġinstructors":33667,"ĠKY":33668,"Ġconceive":33669,"ubb":33670,"ĠPossible":33671,"Ġeasing":33672,"ĠChristina":33673,"Ġcaric":33674,"ĠHDR":33675,"ROM":33676,"Ġshovel":33677,"delete":33678,"Ġpuff":33679,"ĠChanging":33680,"Ġseamlessly":33681,"Attribute":33682,"Ġacquisitions":33683,"akery":33684,"ĠEF":33685,"Ġautistic":33686,"ĠTakes":33687,"ĠPowder":33688,"ĠStir":33689,"510":33690,"ĠBubble":33691,"settings":33692,"ĠFowler":33693,"Ġmustard":33694,"Ġmoreover":33695,"Ġcopyrighted":33696,"ĠLEDs":33697,"1500":33698,"æī":33699,"ĠHIS":33700,"enf":33701,"Ġcustod":33702,"ĠHuck":33703,"Gi":33704,"Ġimg":33705,"Answer":33706,"Ct":33707,"jay":33708,"ĠInfrastructure":33709,"Ġfederally":33710,"Loc":33711,"Ġmicrobes":33712,"Ġoverrun":33713,"dds":33714,"otent":33715,"adiator":33716,">>>>>>>>":33717,"Ġtornado":33718,"Ġadjud":33719,"Ġintrigued":33720,"Ġsi":33721,"ĠRevelation":33722,"progress":33723,"Ġburglary":33724,"ĠSaiyan":33725,"ĠKathy":33726,"Ġserpent":33727,"ĠAndreas":33728,"Ġcompel":33729,"essler":33730,"ĠPlastic":33731,"ĠAdvent":33732,"ĠPositive":33733,"ĠQt":33734,"ĠHindus":33735,"registered":33736,"ularity":33737,"Ġrighteousness":33738,"Ġdemonic":33739,"uitive":33740,"ĠBDS":33741,"ĠGregg":33742,"cia":33743,"ĠCrusade":33744,"ĠSinai":33745,"WARE":33746,"+(":33747,"Ġmell":33748,"Ġderail":33749,"yards":33750,"Ast":33751,"Ġnoticeably":33752,"ĠOber":33753,"Ram":33754,"Ġunnoticed":33755,"Ġseq":33756,"avage":33757,"Ts":33758,"Ġ640":33759,"Ġconcede":33760,"Ġ])":33761,"Fill":33762,"Ġcaptivity":33763,"ĠImprovement":33764,"ĠCrusader":33765,"araoh":33766,"MAP":33767,"æĹ":33768,"Ġstride":33769,"always":33770,"Fly":33771,"Nit":33772,"Ġalgae":33773,"ĠCooking":33774,"ĠDoors":33775,"Malley":33776,"Ġpolicemen":33777,"ãģį":33778,"Ġastronaut":33779,"accessible":33780,"495":33781,"ĠRAW":33782,"cliffe":33783,"udicrous":33784,"Ġdepended":33785,"alach":33786,"Ġventures":33787,"rake":33788,"Ġtits":33789,"ĠHou":33790,"Ġcondom":33791,"ormonal":33792,"Ġindent":33793,"Ġuploading":33794,"Footnote":33795,"Important":33796,"Ġ271":33797,"Ġmindful":33798,"Ġcontends":33799,"Cra":33800,"Ġcalibr":33801,"ĠOECD":33802,"plugin":33803,"Fat":33804,"ĠISS":33805,"ĠDynamics":33806,"ansen":33807,"686":33808,"'),":33809,"Ġsprite":33810,"Ġhandheld":33811,"ĠHipp":33812,"=~=~":33813,"Trust":33814,"Ġsemantics":33815,"ĠBundes":33816,"ĠReno":33817,"ĠLiterature":33818,"sense":33819,"Gary":33820,"ĠAeg":33821,"ĠTrin":33822,"EEK":33823,"Ġcleric":33824,"ĠSSH":33825,"Ġchrist":33826,"Ġinvading":33827,"ibu":33828,"Ġenum":33829,"aura":33830,"Ġallege":33831,"ĠIncredible":33832,"BBC":33833,"Ġthru":33834,"Ġsailed":33835,"Ġemulate":33836,"Ġinsecurity":33837,"Ġcrou":33838,"Ġaccommodations":33839,"Ġincompetent":33840,"Ġslips":33841,"ĠEarthqu":33842,"sama":33843,"ILLE":33844,"ĠiPhones":33845,"asaki":33846,"Ġbye":33847,"Ġard":33848,"Ġextras":33849,"Ġslaughtered":33850,"Ġcrowdfunding":33851,"resso":33852,"Ġfilib":33853,"ĠERROR":33854,"ĠTLS":33855,"egg":33856,"ĠItal":33857,"Ġenlist":33858,"ĠCatalonia":33859,"ĠScots":33860,"Ġsergeant":33861,"Ġdissolve":33862,"NH":33863,"Ġstandings":33864,"rique":33865,"IQ":33866,"Ġbeneficiary":33867,"Ġaquarium":33868,"YouTube":33869,"ĠPowerShell":33870,"Ġbrightest":33871,"ĠWarrant":33872,"Sold":33873,"Writing":33874,"Ġbeginnings":33875,"ĠReserved":33876,"ĠLatinos":33877,"heading":33878,"Ġ440":33879,"Ġrooftop":33880,"ATING":33881,"Ġ390":33882,"VPN":33883,"Gs":33884,"kernel":33885,"turned":33886,"Ġpreferable":33887,"Ġturnovers":33888,"ĠHels":33889,"Sa":33890,"ĠShinji":33891,"veh":33892,"ĠMODULE":33893,"Viol":33894,"Ġexiting":33895,"Ġjab":33896,"ĠVanilla":33897,"Ġacron":33898,"ĠGap":33899,"bern":33900,"Ak":33901,"ĠMcGu":33902,"Ġendlessly":33903,"ĠFarage":33904,"ĠNoel":33905,"Va":33906,"MK":33907,"Ġbrute":33908,"ĠKru":33909,"ĠESV":33910,"ĠOlivia":33911,"âĢł":33912,"ĠKaf":33913,"Ġtrusting":33914,"Ġhots":33915,"324":33916,"Ġmalaria":33917,"Ġjson":33918,"Ġpounding":33919,"ortment":33920,"Country":33921,"Ġpostponed":33922,"Ġunequiv":33923,"?),":33924,"ĠRooney":33925,"udding":33926,"ĠLeap":33927,"urrence":33928,"shapeshifter":33929,"ĠHAS":33930,"osate":33931,"Ġcavern":33932,"Ġconservatism":33933,"ĠBAD":33934,"Ġmileage":33935,"Ġarresting":33936,"Vaults":33937,"Ġmixer":33938,"Democratic":33939,"ĠBenson":33940,"Ġauthored":33941,"8000":33942,"Ġproactive":33943,"ĠSpiritual":33944,"tre":33945,"Ġincarcerated":33946,"ĠSort":33947,"Ġpeaked":33948,"Ġwielding":33949,"reciation":33950,"×Ļ×":33951,"Patch":33952,"ĠEmmy":33953,"Ġexqu":33954,"tto":33955,"ĠRatio":33956,"ĠPicks":33957,"ĠGry":33958,"phant":33959,"Ġfret":33960,"Ġethn":33961,"Ġarchived":33962,"%-":33963,"cases":33964,"ĠBlaze":33965,"Ġimb":33966,"cv":33967,"yss":33968,"imony":33969,"Ġcountdown":33970,"Ġawakening":33971,"ĠTunisia":33972,"ĠRefer":33973,"ĠMJ":33974,"Ġunnatural":33975,"ĠCarnegie":33976,"izen":33977,"ĠNuggets":33978,"hess":33979,"Ġevils":33980,"647":33981,"Ġintroductory":33982,"loving":33983,"ĠMcMahon":33984,"Ġambiguity":33985,"Label":33986,"ĠAlmighty":33987,"Ġcoloring":33988,"ĠClaus":33989,"setting":33990,"NULL":33991,"ĠFavorite":33992,"ĠSIG":33993,">(":33994,"ĠShiva":33995,"ĠMayer":33996,"Ġstormed":33997,"ĠCoverage":33998,"weapons":33999,"igham":34000,"Ġunanswered":34001,"Ġleve":34002,"Ġcoy":34003,"cas":34004,"bags":34005,"asured":34006,"Seattle":34007,"ĠSantorum":34008,"serious":34009,"Ġcourageous":34010,"ĠSoup":34011,"Ġconfiscated":34012,"Ġ///":34013,"Ġunconventional":34014,"Ġmoms":34015,"ĠRohingya":34016,"ĠOrchestra":34017,"ĠPotion":34018,"Ġdiscredit":34019,"ĠFIL":34020,"fixed":34021,"ĠDeer":34022,"doi":34023,"ĠDimension":34024,"Ġbureaucrats":34025,"eteen":34026,"ĠactionGroup":34027,"ohm":34028,"Ġbumps":34029,"ĠUtility":34030,"Ġsubmarines":34031,"renheit":34032,"research":34033,"ĠShapiro":34034,"Ġsketches":34035,"Ġdeceptive":34036,"ĠVil":34037,"esame":34038,"ĠEssentially":34039,"Ġrampage":34040,"isky":34041,"Ġmuttered":34042,"thritis":34043,"Ġ236":34044,"fet":34045,"bars":34046,"Ġpupil":34047,"ĠThou":34048,"oS":34049,"song":34050,"Ġfractured":34051,"Ġrevert":34052,"picture":34053,"Ġcriterion":34054,"usher":34055,"Ġrepercussions":34056,"ĠVintage":34057,"ĠSuperintendent":34058,"Officers":34059,"Ġflagged":34060,"Ġblames":34061,"Ġinverse":34062,"ographers":34063,"Ġmakeshift":34064,"Ġdevoid":34065,"Ġfossils":34066,"ĠAristotle":34067,"ĠFunds":34068,"Ġdepleted":34069,"ĠFlu":34070,"ĠYuan":34071,"Ġwoes":34072,"Ġlipid":34073,"Ġsitu":34074,"requisites":34075,"Ġfurnish":34076,"ĠSamar":34077,"Ġshameful":34078,"Ġadversely":34079,"Ġadept":34080,"Ġremorse":34081,"Ġmurderous":34082,"uckles":34083,"ĠESL":34084,"Ġ314":34085,"sent":34086,"Ġredef":34087,"ĠCache":34088,"ĠPurs":34089,"igans":34090,"Ġ460":34091,"Ġprescriptions":34092,"Ġfres":34093,"Fuck":34094,"ocrates":34095,"Twenty":34096,"ĠWeird":34097,"ĠToggle":34098,"ĠCalled":34099,"itizens":34100,"Ġpoultry":34101,"Ġharvesting":34102,"ãĤ¦ãĤ¹":34103,"Bottom":34104,"Ġcautioned":34105,"tn":34106,"396":34107,"ĠNikki":34108,"Ġevaluations":34109,"Ġharassing":34110,"Ġbindings":34111,"ĠMonetary":34112,"Ġhitters":34113,"Ġadversary":34114,"unts":34115,"Ġsetback":34116,"Ġencrypt":34117,"ĠCait":34118,"Ġlows":34119,"enges":34120,"ĠNorn":34121,"Ġbulbs":34122,"Ġbottled":34123,"ĠVoyager":34124,"317":34125,"Ġspheres":34126,"politics":34127,"Ġsubtract":34128,"Ġsensations":34129,"Ġappalling":34130,"Ġ316":34131,"Ġenvironmentally":34132,"ĠSTEM":34133,"Ġpublishes":34134,"560":34135,"Ġdiligence":34136,"484":34137,"Ġadvises":34138,"Ġpetrol":34139,"Ġimagining":34140,"Ġpatrols":34141,"ĠInteger":34142,"ĠAshes":34143,"actus":34144,"ĠRadiant":34145,"ĠLT":34146,"itability":34147,"htaking":34148,"Setting":34149,"Ġnuanced":34150,"ĠReef":34151,"ĠDevelopers":34152,"Ni":34153,"pieces":34154,"990":34155,"License":34156,"Ġlowers":34157,"ĠOttoman":34158,"327":34159,"ooo":34160,"Ġquitting":34161,"markets":34162,"Behind":34163,"Ġbasin":34164,"Ġdocs":34165,"anie":34166,"flash":34167,"ctl":34168,"Ġcivilized":34169,"ĠFukushima":34170,"\"],\"":34171,"ĠKS":34172,"ĠHonestly":34173,"arat":34174,"Ġconstructs":34175,"ĠLans":34176,"ĠDire":34177,"ĠLIKE":34178,"ĠTrouble":34179,"Ġwithholding":34180,"ĠOblivion":34181,"Ġsanity":34182,"anya":34183,"Const":34184,"Ġgrocer":34185,"ĠCelsius":34186,"Ġrecounted":34187,"ĠWife":34188,"Border":34189,"atered":34190,"happy":34191,"Ġspoiler":34192,"Ġlogically":34193,"Hall":34194,"Ġsucceeding":34195,"Ġpolymorph":34196,"Ġaxes":34197,"ĠShotgun":34198,"ĠSlim":34199,"ĠPrinciples":34200,"ĠLeth":34201,"arta":34202,"Ġscor":34203,"Screenshot":34204,"Ġrelaxation":34205,"#$#$":34206,"Ġdeterrent":34207,"iddy":34208,"Ġpowerless":34209,"Ġlesbians":34210,"Ġchords":34211,"ĠEdited":34212,"selected":34213,"Ġseparatists":34214,"0002":34215,"Ġairspace":34216,"Ġturnaround":34217,"Ġcunning":34218,"PATH":34219,"Poly":34220,"Ġbombed":34221,"Ġtion":34222,"xs":34223,"Ġwithhold":34224,"Ġwaged":34225,"ĠLiberties":34226,"Flag":34227,"Ġcomforting":34228,"454":34229,"ĠIris":34230,"arers":34231,"Ġrag":34232,"Ġrelocated":34233,"ĠGuarant":34234,"Ġstrategically":34235,"Ġgamma":34236,"uberty":34237,"ĠLockheed":34238,"gres":34239,"Ġgrilled":34240,"ĠLowe":34241,"stats":34242,"ĠRocks":34243,"Ġsensing":34244,"Ġrenting":34245,"ĠGeological":34246,"اØ":34247,"otrop":34248,"Ġsew":34249,"Ġimproperly":34250,"486":34251,"Ġâĸł":34252,"Ġstarving":34253,"ĠBj":34254,"Discussion":34255,"328":34256,"ĠCombo":34257,"ĠFixes":34258,"NAT":34259,"Ġstriving":34260,"thora":34261,"Ġharvested":34262,"ĠPing":34263,"Ġplayful":34264,"Ġavenues":34265,"Ġoccupational":34266,"Ġwakes":34267,"ĠCourier":34268,"Ġdrummer":34269,"ĠBrowser":34270,"ĠHouth":34271,"itu":34272,"Ġapparel":34273,"paste":34274,"Ġhunted":34275,"ĠSecondly":34276,"lain":34277,"XY":34278,"ĠPIN":34279,"icons":34280,"Ġcocktails":34281,"Ġsizable":34282,"Ġhurdles":34283,"estinal":34284,"ĠRecreation":34285,"Ġeco":34286,"648":34287,"ĠDied":34288,"mint":34289,"Ġfingerprints":34290,"Ġdispose":34291,"ĠBosnia":34292,"tsy":34293,"2200":34294,"Ġinspected":34295,"ĠFou":34296,"Ġfuss":34297,"Ġambush":34298,"ĠRak":34299,"Ġmanifested":34300,"Prosecut":34301,"Ġsuffice":34302,"rences":34303,"Ġcompensated":34304,"ĠCyrus":34305,"Ġgenus":34306,"ĠWolverine":34307,"ĠTrends":34308,"Ġhikes":34309,"ĠSeen":34310,"Ġenrol":34311,"Cold":34312,"Ġpolitely":34313,"ĠSlav":34314,"ĠRupert":34315,"Ġeyewitness":34316,"ĠAlto":34317,"Ġuncomp":34318,"Ġposterior":34319,"Must":34320,"ĠHerz":34321,"Ġprogressively":34322,"Ġ234":34323,"Ġindifference":34324,"ĠCunningham":34325,"Ġacademia":34326,"Ġsewer":34327,"Ġastounding":34328,"ĠAES":34329,"rather":34330,"Ġeldest":34331,"Ġclimbs":34332,"ĠAdds":34333,"Ġoutcry":34334,"Ġcontag":34335,"ĠHouses":34336,"Ġpept":34337,"ĠMelania":34338,"interested":34339,"ĠUCH":34340,"ĠRoots":34341,"ĠHubbard":34342,"ĠTBD":34343,"ĠRomanian":34344,"filename":34345,"Stone":34346,"ĠImpl":34347,"Ġchromosome":34348,"Cle":34349,"dx":34350,"Ġscrambled":34351,"ĠPt":34352,"Ġ242":34353,"OPLE":34354,"Ġtremendously":34355,"Street":34356,"Ġcraving":34357,"Ġbundled":34358,"ĠRG":34359,"pipe":34360,"Ġinjuring":34361,"Ġarcane":34362,"Particip":34363,"ĠHeroic":34364,"sty":34365,"Ġtopping":34366,"ĠTempest":34367,"rentices":34368,"bh":34369,"Ġparanoia":34370,"ĠUnicode":34371,"Ġegregious":34372,"Ġ\\'":34373,"ĠOswald":34374,"Ġgravel":34375,"ĠSimpsons":34376,"Ġbland":34377,"ĠGuantanamo":34378,"Writer":34379,"liners":34380,"ĠDice":34381,"JC":34382,"Ġparity":34383,"Ġsided":34384,"Ġ237":34385,"ĠPyrrha":34386,"atters":34387,"dk":34388,"Fine":34389,"compan":34390,"Ġformulated":34391,"ĠIdol":34392,"ilers":34393,"hemoth":34394,"ĠFav":34395,"Ġintrusion":34396,"Ġcarrots":34397,"ĠLayer":34398,"ĠHacker":34399,"Ġ----------------":34400,"Ġmoderation":34401,"éģ":34402,"ococ":34403,"Ġcharacterize":34404,"ĠTeresa":34405,"Ġsocioeconomic":34406,"Ġperk":34407,"ĠParticipation":34408,"training":34409,"ĠPaulo":34410,"phys":34411,"Ġtrustworthy":34412,"Ġembodied":34413,"ĠMerch":34414,"currency":34415,"ĠPriority":34416,"Ġteasing":34417,"Ġabsorbing":34418,"Ġunfinished":34419,"ĠComparison":34420,"Ġdisple":34421,"writers":34422,"Ġprofessions":34423,"ĠPenguin":34424,"Ġangrily":34425,"ĠLINK":34426,"688":34427,"ĠCorrespond":34428,"Ġprevailed":34429,"Ġcartel":34430,"lp":34431,"asms":34432,"ĠRedemption":34433,"ĠIslamists":34434,"effects":34435,"dose":34436,"ĠLatter":34437,"ĠHalifax":34438,"Ġvas":34439,"ĠTopics":34440,"ĠNamed":34441,"advertising":34442,"zza":34443,"ICES":34444,"Ġretarded":34445,"achable":34446,"ĠPuppet":34447,"ĠItemLevel":34448,"Ġretract":34449,"Ġidentifiable":34450,"Aaron":34451,"ĠBuster":34452,"sol":34453,"helle":34454,"assemb":34455,"Hope":34456,"ranged":34457,"Ba":34458,"ĠPurch":34459,"éĢ":34460,"ĠSiri":34461,"Ġarrivals":34462,"Ġ1912":34463,"Ġshortened":34464,"Ġ312":34465,"Ġdiscrepancy":34466,"ĠTemperature":34467,"ĠWalton":34468,"Ġkinderg":34469,"polit":34470,"Ġremix":34471,"Ġconnectors":34472,"ãĥĺãĥ©":34473,"ĠKazakhstan":34474,"dominated":34475,"Ġsugars":34476,"imble":34477,"ĠPanic":34478,"ĠDemand":34479,"ĠColony":34480,"onen":34481,"ĠMER":34482,"775":34483,"uria":34484,"azaar":34485,"ĠDegree":34486,"Pri":34487,"Ġsunshine":34488,"Ġ251":34489,"Ġpsychedelic":34490,"Ġdigitally":34491,"ĠBraun":34492,"Ġshimmer":34493,"Ġshave":34494,"ĠTelesc":34495,"ĠAstral":34496,"ĠVenezuelan":34497,"ĠOG":34498,"Ġcrawling":34499,"Integ":34500,"ĠFeather":34501,"Ġunfolding":34502,"Ġappropriation":34503,"Ġè£ıè":34504,"ĠMobility":34505,"ĠNey":34506,"-.":34507,"bilt":34508,"LIN":34509,"ĠTube":34510,"ĠConversely":34511,"Ġkeyboards":34512,"ĠCao":34513,"Ġoverth":34514,"Ġlaure":34515,">>\\":34516,"ĠViper":34517,"acha":34518,"Offset":34519,"ĠRaleigh":34520,"ĠJae":34521,"Jordan":34522,"jp":34523,"Ġtotalitarian":34524,"Connector":34525,"Ġobserves":34526,"ĠSpartan":34527,"ĠImmediately":34528,"ĠScal":34529,"Cool":34530,"Ġtaps":34531,"Ġroar":34532,"Past":34533,"Ġchars":34534,"ĠBender":34535,"ĠSheldon":34536,"Ġpainter":34537,"Ġbeacon":34538,"ĠCreatures":34539,"Ġdownturn":34540,"Ġhinder":34541,"ĠAndromeda":34542,"ÃĽ":34543,"ccoli":34544,"ĠFitness":34545,"etrical":34546,"Ġutilizes":34547,"Ġsenate":34548,"Ġensemble":34549,"Ġcheers":34550,"TW":34551,"Ġaffluent":34552,"kil":34553,"rylic":34554,"ordering":34555,"Computer":34556,"Ġgruesome":34557,"ostics":34558,"ĠUbisoft":34559,"ĠKelley":34560,"Ġwrench":34561,"Ġbourgeoisie":34562,"IBLE":34563,"ĠPreston":34564,"worn":34565,"arist":34566,"reating":34567,"Ġstained":34568,"arine":34569,"Ġslime":34570,"ENN":34571,"Ġchests":34572,"Ġgroundwater":34573,"annot":34574,"ĠTray":34575,"ĠLocke":34576,"ĠCTR":34577,"Ġdudes":34578,"ĠExternal":34579,"ĠDecoder":34580,"Ġparamed":34581,"ĠMedline":34582,"809":34583,"ĠDinner":34584,"rupal":34585,"gz":34586,"ĠGum":34587,"ĠDemo":34588,"jee":34589,"Ġdh":34590,"berman":34591,"archs":34592,"Ġenqu":34593,"ĠEpstein":34594,"Ġdevastation":34595,"Ġfriendships":34596,"ĠArd":34597,"Ġ231":34598,"ĠRubin":34599,"ĠDistance":34600,"Ġspurred":34601,"Ġdossier":34602,"Ġoverlooking":34603,"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":34604,"Forest":34605,"ĠComes":34606,"\\\",":34607,"ĠIranians":34608,"Ġfixtures":34609,"Laughs":34610,"Ġcurry":34611,"ĠKingston":34612,"Ġsquash":34613,"Ġcatalogue":34614,"Ġabnormalities":34615,"Ġdigestive":34616,".........":34617,"Ġsubordinate":34618,"ogly":34619,"Ġ249":34620,"Middle":34621,"Ġmassac":34622,"Ġburgers":34623,"Ġdownstairs":34624,"Ġ1931":34625,"394":34626,"ĠVG":34627,"Ġlasers":34628,"ĠSikh":34629,"ĠAlexa":34630,"derived":34631,"Ġcyclist":34632,"ãģ®éŃĶ":34633,"oneliness":34634,"!!!!!!!!":34635,"Ġbuffs":34636,"legate":34637,"Ġraping":34638,"Ġrecommending":34639,"rored":34640,"Ġmulticultural":34641,"unique":34642,"Ġbusinessmen":34643,"Ġuneasy":34644,"ĠMAP":34645,"Ġdispersed":34646,"cipline":34647,"Jess":34648,"ĠKerala":34649,"å§":34650,"Ġabstraction":34651,"Surv":34652,"Uh":34653,"Ġprinters":34654,"ija":34655,"owder":34656,"Ġanalogous":34657,"ĠASP":34658,"afer":34659,"Ġunfolded":34660,"Ġleveling":34661,"Ġbreached":34662,"ĠHearing":34663,"Ġnat":34664,"Ġtranslating":34665,"critical":34666,"Ġantagonist":34667,"ĠYesterday":34668,"Ġfuzzy":34669,"wash":34670,"mere":34671,"Ġbewild":34672,"ĠMae":34673,"Virgin":34674,"phrase":34675,"Ġsignaled":34676,"ĠHIGH":34677,"Ġprotester":34678,"Ġgarner":34679,"unknown":34680,"Ġkay":34681,"Ġabducted":34682,"Ġstalking":34683,"amn":34684,"Ġdeserving":34685,"ĠRiv":34686,"ĠJorge":34687,"Ġscratching":34688,"ĠSaving":34689,"iping":34690,"Ġtease":34691,"Ġmissionary":34692,"ĠMorrow":34693,"TIME":34694,"Present":34695,"Ġchemotherapy":34696,"terness":34697,"ĠHomes":34698,"ĠPurdue":34699,"Ġstaunch":34700,"ĠWhitney":34701,"ĠTHERE":34702,"μ":34703,"iatus":34704,"ĠErnest":34705,"ĠDeploy":34706,"Ġcoveted":34707,"FML":34708,"ĠDialogue":34709,"Ġexited":34710,"fruit":34711,"Ġnerd":34712,"\":\"\",\"":34713,"Ġvivo":34714,"ruly":34715,"460":34716,"ĠAmen":34717,"rehensible":34718,"Ġâĺ":34719,"DIR":34720,"Ġadherence":34721,"Ġchew":34722,"ĠCoke":34723,"ĠSergei":34724,"digital":34725,"ĠNeck":34726,"gently":34727,"enthal":34728,"/)":34729,"Ġweary":34730,"Ġguise":34731,"ĠConcord":34732,"ĠOnion":34733,"atcher":34734,"Ġbinge":34735,"ĠDirective":34736,"Ġmanned":34737,"ansk":34738,"Ġillusions":34739,"Ġbillionaires":34740,"383":34741,"olyn":34742,"odynamic":34743,"ĠWheat":34744,"ĠAlic":34745,"Ġcoloured":34746,"ĠNAFTA":34747,"abo":34748,"Ġmacros":34749,"independent":34750,"sweet":34751,"Ġspac":34752,"ĠKabul":34753,"ĠÄ":34754,"eme":34755,"Ġdictated":34756,"Ġshouts":34757,"={":34758,"Ġripping":34759,"ĠShay":34760,"ĠCricket":34761,"directed":34762,"Ġanalysed":34763,"ĠWARRANT":34764,"agons":34765,"ĠBlazers":34766,"Ġcheered":34767,"Ġarithmetic":34768,"ĠTanz":34769,"373":34770,"ĠFlags":34771,"Ġ295":34772,"Ġwitches":34773,"ĠIncluded":34774,"ĠGained":34775,"ĠBlades":34776,"Gam":34777,"ĠSamantha":34778,"ĠAtlantis":34779,"ĠPratt":34780,"Ġspoiled":34781,"ĠIB":34782,"ĠRamirez":34783,"Probably":34784,"rero":34785,"ĠNg":34786,"ĠWarlock":34787,"tp":34788,"Ġoverhe":34789,"Ġadministrations":34790,"Ġtint":34791,"Ġregiment":34792,"Ġpistols":34793,"Ġblankets":34794,"Ġepist":34795,"Ġbowls":34796,"Ġhydraulic":34797,"Ġdean":34798,"Ġjung":34799,"Ġascend":34800,"705":34801,"ĠSantiago":34802,"î":34803,"Ġunavoid":34804,"ĠShaman":34805,"reb":34806,"Ġstemming":34807,"998":34808,"ĠMG":34809,"sticks":34810,"esthesia":34811,"ERO":34812,"Ġmorbid":34813,"ĠGrill":34814,"ĠPoe":34815,"anyl":34816,"Ġdeleting":34817,"ĠSurveillance":34818,"Ġdirectives":34819,"Ġiterations":34820,"ĠRox":34821,"ĠMilky":34822,"Father":34823,"Ġpatented":34824,"447":34825,"Ġprecursor":34826,"Ġmaiden":34827,"ĠPhen":34828,"ĠVegan":34829,"ĠPatent":34830,"Kelly":34831,"Redditor":34832,"Ġnods":34833,"Ġventilation":34834,"ĠSchwarz":34835,"Ġwizards":34836,"Ġominous":34837,"ĠHeads":34838,"ĠBG":34839,"Ġlumber":34840,"ĠSpiel":34841,"ĠisEnabled":34842,"Ġancestral":34843,"ĠShips":34844,"Ġwrestler":34845,"phi":34846,"Ġyuan":34847,"ĠRebellion":34848,"Ġiceberg":34849,"Ġmagically":34850,"Ġdiversion":34851,"arro":34852,"ythm":34853,"ĠRiders":34854,"ĠRobbie":34855,"ĠKara":34856,"ĠMaintenance":34857,"ĠHerb":34858,"Ġharms":34859,"packed":34860,"ĠFeinstein":34861,"Ġmarrying":34862,"Ġblending":34863,"ĠRates":34864,"Ġ1880":34865,"Ġwrink":34866,"ĠUnch":34867,"ĠTorch":34868,"described":34869,"Ġhumanoid":34870,"ilitating":34871,"ĠConv":34872,"ĠFeld":34873,"IGHTS":34874,"Ġwhistleblower":34875,"ortmund":34876,"etsy":34877,"arrett":34878,"ĠMono":34879,"ĠIke":34880,"ĠCNBC":34881,"ĠWAY":34882,"ĠMDMA":34883,"ĠIndividuals":34884,"Ġsupplemental":34885,"Ġpowerhouse":34886,"ĠStru":34887,"Focus":34888,"aphael":34889,"ĠColleg":34890,"atti":34891,"ZA":34892,"Ġperenn":34893,"ĠSignature":34894,"ĠRodney":34895,"Ġcubes":34896,"iddled":34897,"ĠDante":34898,"ĠINV":34899,"ilingual":34900,"ĠCth":34901,"Ġsofa":34902,"Ġintimidate":34903,"ĠRoe":34904,"ĠDiplom":34905,"ĠCountries":34906,"ayson":34907,"Ġextradition":34908,"Ġdisabling":34909,"ĠCardiff":34910,"Ġmemorandum":34911,"ĠTrace":34912,"Ġ???":34913,"sector":34914,"ĠRouhani":34915,"ĠYates":34916,"ĠFreeze":34917,"Ġbladder":34918,"Motor":34919,"ĠPromise":34920,"antasy":34921,"Ġforeseeable":34922,"ĠCologne":34923,"container":34924,"ĠTrees":34925,"ĠGors":34926,"ĠSinclair":34927,"Ġbarring":34928,"keye":34929,"Ġslashed":34930,"ĠStatistical":34931,"éĩ":34932,"Ġâĸº":34933,"Allows":34934,"Ġhumility":34935,"Ġdrilled":34936,"ĠFurn":34937,"443":34938,"Ġsewage":34939,"Ġhomepage":34940,"Ġcourtyard":34941,"Ġvile":34942,"Ġsubsidiaries":34943,"ajo":34944,"directory":34945,"Ġammon":34946,"Vers":34947,"charges":34948,"Ġ}}":34949,"ĠChains":34950,"Ġ246":34951,"nob":34952,"Ġpercept":34953,"Ġgrit":34954,"Ġfishermen":34955,"ĠIraqis":34956,"ĠDISTR":34957,"ĠFULL":34958,"ĠEvaluation":34959,"graph":34960,"atial":34961,"Ġcooperating":34962,"Ġmelan":34963,"Ġenlightened":34964,"Ġali":34965,"tailed":34966,"Ġsalute":34967,"Ġweakest":34968,"ĠBulldogs":34969,"UA":34970,"ĠAlloy":34971,"Ġsemen":34972,"ocene":34973,"ĠWilliamson":34974,"spr":34975,",âĢĶ":34976,"ĠGF":34977,"ittens":34978,"Beat":34979,"ĠJunk":34980,"iphate":34981,"ĠFarmers":34982,"ĠBitcoins":34983,"igers":34984,"dh":34985,"ĠLoyal":34986,"payer":34987,"Ġentertained":34988,"Ġpenned":34989,"Ġcoupon":34990,"Queue":34991,"Ġweakening":34992,"carry":34993,"Ġunderestimate":34994,"Ġshootout":34995,"Ġcharismatic":34996,"ĠProcedure":34997,"Ġprudent":34998,"inances":34999,"Ġriches":35000,"Ġcortical":35001,"Ġstrides":35002,"Ġdrib":35003,"ĠOilers":35004,"540":35005,"ĠPerform":35006,"ĠBangkok":35007,"Ġeuth":35008,"SER":35009,"Ġsimplistic":35010,"tops":35011,"campaign":35012,"Quality":35013,"Ġimpoverished":35014,"ĠEisenhower":35015,"Ġaugment":35016,"ĠHarden":35017,"Ġintervened":35018,"Ġlistens":35019,"ĠKok":35020,"Ġsage":35021,"Ġrubbish":35022,"ĠDed":35023,"Ġmull":35024,"pelling":35025,"Ġvideot":35026,"Production":35027,"DJ":35028,"miah":35029,"Ġadaptations":35030,"Ġmedically":35031,"Ġboarded":35032,"Ġarrogance":35033,"Ġscrapped":35034,"Ġoppress":35035,"FORMATION":35036,"Ġjunction":35037,"415":35038,"EEEE":35039,"Skill":35040,"Ġsubdu":35041,"ĠSuggest":35042,"ĠPett":35043,"Ġlett":35044,"ĠManip":35045,"ĠCaf":35046,"ĠCooperation":35047,"Ther":35048,"Ġregained":35049,"¶æ":35050,"reflect":35051,"Ġthugs":35052,"ĠShelby":35053,"Ġdictates":35054,"ĠWeiner":35055,"ĠHale":35056,"Ġbattleground":35057,"schild":35058,"Ġcondol":35059,"hunt":35060,"ositories":35061,"Ġaccuses":35062,"Filename":35063,"Ġshri":35064,"Ġmotivate":35065,"Ġreflections":35066,"Null":35067,"ĠLobby":35068,"¥µ":35069,"ĠSATA":35070,"ĠBackup":35071,"Ñĥ":35072,"nin":35073,"ĠCorrection":35074,"Ġjuicy":35075,"utra":35076,"ĠPric":35077,"Ġrestraining":35078,"ĠAirbnb":35079,"ĠArrest":35080,"Ġappropriations":35081,"Ġslopes":35082,"Ġmanslaughter":35083,"Ġworkings":35084,"ĠHuss":35085,"ĠFrey":35086,"Leave":35087,"ĠHarmony":35088,"ĠFeder":35089,"Ġ430":35090,"Ġtrench":35091,"Ġgladly":35092,"Ġbullpen":35093,"ĠGau":35094,"bones":35095,"Ġgroove":35096,"Ġpretext":35097,"ãħĭ":35098,"Ġtransmitter":35099,"ĠComponent":35100,"Ġunderage":35101,"ĠEmpires":35102,"Tile":35103,"Ġoy":35104,"ĠMarvin":35105,"ĠCAS":35106,"Ġbloss":35107,"Ġreplicated":35108,"ĠMariners":35109,"Marcus":35110,"ĠBlocks":35111,"Ġliberated":35112,"Ġbutterfly":35113,"Feel":35114,"Ġfermentation":35115,"Ġyoutube":35116,"Ġoffend":35117,"ĠTerm":35118,"resist":35119,"Ġcessation":35120,"Ġinsurgency":35121,"Ġbir":35122,"ĠRaise":35123,"595":35124,"Ġhypotheses":35125,"502":35126,"Ġplaque":35127,"ocrat":35128,"Ġjackets":35129,"ĠHuffPost":35130,"among":35131,"Ġconfer":35132,"487":35133,"ĠLilly":35134,"Ġadapting":35135,"ĠFay":35136,"Ġshoved":35137,"vec":35138,"Ġrefine":35139,"Ġgon":35140,"Ġgunmen":35141,"zai":35142,"ĠShuttle":35143,"ĠIzan":35144,"Ġ1913":35145,"Ġplethora":35146,"··":35147,"Ġ510":35148,"Ġpuberty":35149,"Ġ241":35150,"ĠWealth":35151,"ĠAlma":35152,"ĠMEM":35153,"ĠAdults":35154,"Cas":35155,"prison":35156,"Race":35157,"Ġwaterproof":35158,"Ġathleticism":35159,"Ġcapitalize":35160,"ĠJuice":35161,"Ġilluminated":35162,"ĠPascal":35163,"Ġirritation":35164,"ĠWitnesses":35165,"adle":35166,"ĠAstro":35167,"Ġfax":35168,"ĠElvis":35169,"Primary":35170,"ĠLich":35171,"ĠElves":35172,"Ġresiding":35173,"Ġstumble":35174,"319":35175,"ĠPKK":35176,"Ġadversaries":35177,"DOS":35178,"ĠRitual":35179,"Ġsmear":35180,"Ġarson":35181,"idental":35182,"Ġscant":35183,"Ġmonarchy":35184,"Ġhalftime":35185,"Ġresidue":35186,"Ġindign":35187,"ĠShaun":35188,"ĠElm":35189,"auri":35190,"Aff":35191,"WATCH":35192,"ĠLyon":35193,"helps":35194,"361":35195,"Ġlobbyist":35196,"Ġdiminishing":35197,"Ġoutbreaks":35198,"Ġgoats":35199,"favorite":35200,"ĠNah":35201,"sonian":35202,"ĠBooster":35203,"Ġsandbox":35204,"ĠFare":35205,"ĠMalta":35206,"ĠattRot":35207,"ĠMOR":35208,"lde":35209,"Ġnavigating":35210,"Touch":35211,"Ġuntrue":35212,"ĠDisaster":35213,"Ġludicrous":35214,"Password":35215,"ĠJFK":35216,"blogspot":35217,"416":35218,"ĠUNDER":35219,"ernal":35220,"Ġdelaying":35221,"TOP":35222,"Ġimplants":35223,"ĠAVG":35224,"ĠHuge":35225,"attr":35226,"Ġjournalistic":35227,"ĠPeyton":35228,"ĠIA":35229,"Rap":35230,"goal":35231,"ĠProgramme":35232,"Ġsmashing":35233,"wives":35234,"println":35235,"ĠPlague":35236,"inus":35237,"EEP":35238,"Ġcruiser":35239,"ĠParish":35240,"uminium":35241,"Ġoccupants":35242,"ĠJihad":35243,"mop":35244,"Ġpint":35245,"Ġhect":35246,"ĠMecca":35247,"director":35248,"ĠFunding":35249,"ĠMixed":35250,"Ġstag":35251,"Tier":35252,"Ġgust":35253,"Ġbrightly":35254,"orsi":35255,"Ġuphill":35256,"RD":35257,"Ġlesions":35258,"ĠBundy":35259,"livious":35260,"Ġbiologist":35261,"ĠFaculty":35262,"ĠAuthorization":35263,"Ġ244":35264,"Allow":35265,"ï¸":35266,"ĠGiul":35267,"Ġpertinent":35268,"otaur":35269,"esse":35270,"ĠRoof":35271,"Ġunmanned":35272,"351":35273,"ĠShak":35274,"ĠOrient":35275,"Ġendanger":35276,"Dir":35277,"Ġreplen":35278,"edient":35279,"Ġtailor":35280,"Ġgadgets":35281,"Ġaudible":35282,"âĺĨ":35283,"Nice":35284,"Ġbombard":35285,"ĠRape":35286,"Ġdefiance":35287,"ĠTWO":35288,"ĠFilipino":35289,"Ġunaffected":35290,"ervatives":35291,"Ġsoared":35292,"ĠBolton":35293,"Ġcompromising":35294,"ĠBrewers":35295,"RAL":35296,"ĠAHL":35297,"icycle":35298,"Ġvampires":35299,"Ġdipped":35300,"oyer":35301,"ĠXIII":35302,"Ġsideways":35303,"ĠWaste":35304,"ĠDiss":35305,"ĠâĶľâĶĢâĶĢ":35306,"$.":35307,"Ġhabitats":35308,"ĠBeef":35309,"truth":35310,"trained":35311,"split":35312,"Rus":35313,"Andy":35314,"ĠBram":35315,"REP":35316,"pid":35317,"è£ħ":35318,"ĠMutant":35319,"Anim":35320,"ĠMarina":35321,"Ġfutile":35322,"highest":35323,"frequency":35324,"Ġepilepsy":35325,"Ġcoping":35326,"Ġconcise":35327,"Ġtracing":35328,"ĠSUN":35329,"panel":35330,"ĠSophie":35331,"ĠCrowley":35332,"ĠAdolf":35333,"ĠShooter":35334,"Ġshaky":35335,"ĠIG":35336,"ĠLies":35337,"ĠBarber":35338,"pkg":35339,"Ġuptake":35340,"Ġpredatory":35341,"ULTS":35342,"/**":35343,"Ġintoxicated":35344,"ĠWestbrook":35345,"odder":35346,"hement":35347,"Ġbaseman":35348,"APD":35349,"storage":35350,"ĠFifty":35351,"editor":35352,"GEN":35353,"UTION":35354,"irting":35355,"Ġsewing":35356,"rift":35357,"Ġagony":35358,"ĠSands":35359,"Ġ254":35360,"Cash":35361,"Ġlodge":35362,"Ġpunt":35363,"Natural":35364,"ĠIdeas":35365,"Ġerroneous":35366,"ĠSensor":35367,"ĠHannity":35368,"Ġ1921":35369,"Ġmould":35370,"ĠGon":35371,"kaya":35372,"Ġanonymously":35373,"ĠKEY":35374,"Ġsimulator":35375,"Winter":35376,"Ġstreamed":35377,"507":35378,"?\",":35379,"Ġteased":35380,"Ġcoefficient":35381,"Ġwartime":35382,"ĠTHR":35383,"''.":35384,"ĠBanking":35385,"mpire":35386,"Ġfandom":35387,"Ġlia":35388,"Ga":35389,"Ġdownhill":35390,"Ġinterpreting":35391,"Individual":35392,"Norm":35393,"Ġjealousy":35394,"bitcoin":35395,"Ġpleasures":35396,"ĠToys":35397,"ĠChevrolet":35398,"ĠAdvisor":35399,"IZE":35400,"Ġreceptions":35401,"706":35402,"Cro":35403,"Ġ262":35404,"Ġcitrus":35405,"iru":35406,"Reviewer":35407,"jected":35408,"UES":35409,"anz":35410,"1981":35411,"ĠWorker":35412,"Ġcomplied":35413,"orescent":35414,"continental":35415,"Ton":35416,"ĠPrism":35417,"ĠSheep":35418,"Ġ288":35419,"nox":35420,"ĠVog":35421,"Ord":35422,"Ġrealms":35423,"tek":35424,"Ġirrigation":35425,"Ġbicycles":35426,"Ġelectronically":35427,"poly":35428,"tall":35429,"());":35430,"Ġaesthetics":35431,"ĠIntegrated":35432,"Explore":35433,"Ġdunk":35434,"476":35435,"pain":35436,"ĠJacques":35437,"ĠDmit":35438,"Frames":35439,"Ġreunited":35440,"Ġhumid":35441,"Dro":35442,"Political":35443,"Ġyouthful":35444,"Ġentails":35445,"Ġmosquito":35446,"363":35447,"species":35448,"Ġcoordinating":35449,"ĠMayhem":35450,"ĠMagnus":35451,"Mount":35452,"Improved":35453,"ĠSTATE":35454,"ATTLE":35455,"Ġflowed":35456,"Ġtackled":35457,"Ġfashioned":35458,"Ġreorgan":35459,"ivari":35460,"finger":35461,"Ġreluctantly":35462,"etting":35463,"ĠVand":35464,"young":35465,"ĠGarland":35466,"Ġpresumption":35467,"Ġamenities":35468,"ĠPleasant":35469,"onential":35470,"ĠOxy":35471,"Ġmorals":35472,"ĠYah":35473,"Ready":35474,"Simon":35475,"Enh":35476,"Demon":35477,"Ġclich":35478,"Monitor":35479,"ĠDU":35480,"Ġwelcomes":35481,"Ġstandout":35482,"Ġdreadful":35483,"Ġbananas":35484,"Ġballoons":35485,"hooting":35486,"basic":35487,"Ġsuffix":35488,"Ġduly":35489,"cano":35490,"Chain":35491,"atos":35492,"Ġgeopolitical":35493,"Ġ(&":35494,"ĠGemini":35495,"ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ":35496,"Ġacquitted":35497,"Luck":35498,"protect":35499,"1024":35500,"Ġscarcity":35501,"Ġmindfulness":35502,"ecided":35503,"DN":35504,"prime":35505,"ĠPresidents":35506,"ĠVIDEO":35507,"Ġ(âĪĴ":35508,"addock":35509,"NOR":35510,"ĠPru":35511,"pun":35512,"ĠLOL":35513,"))))":35514,"ĠLiqu":35515,"ĠSAS":35516,"Ġstyling":35517,"Ġpunishments":35518,"Ġnumb":35519,"Ġascertain":35520,"ĠRockies":35521,"flu":35522,"Thumbnail":35523,"Ġperpetrated":35524,"ĠSemi":35525,"Ġdisarm":35526,"ĠOlder":35527,"ĠException":35528,"Ġexponentially":35529,"ĠCommunities":35530,"Ġabolish":35531,"ĠPartner":35532,"ptoms":35533,"Ġ777":35534,"ĠFoley":35535,"ĠCases":35536,"Ġgrease":35537,"ĠRebirth":35538,"Ground":35539,"Ġ;)":35540,"ĠDoctrine":35541,"ikini":35542,"Ye":35543,"ĠBlossom":35544,"Ġpersists":35545,"bill":35546,"Ġinfusion":35547,"Ġbuddies":35548,"911":35549,"ĠPatient":35550,"Ġdemos":35551,"Ġacquaintance":35552,"ĠPaw":35553,"atari":35554,"Ġxml":35555,"Ġfascination":35556,"ĠServe":35557,"ÏĤ":35558,"branded":35559,"Ġaz":35560,"Returns":35561,"Ġovershadow":35562,"Ġroam":35563,"Ġspeedy":35564,"numbered":35565,"helial":35566,"Ġdisciple":35567,"Ġassurances":35568,"given":35569,"pecting":35570,"ĠNatalie":35571,"çĶ°":35572,"Ġmosquitoes":35573,"rotein":35574,"Ġnumeric":35575,"Ġindependents":35576,"Ġtransitional":35577,"Ġreactionary":35578,"ĠMechdragon":35579,"doctor":35580,"Ġshortest":35581,"Ġsequential":35582,"ĠBac":35583,"ĠAccounts":35584,"ãģĮ":35585,"achy":35586,"ractive":35587,"ĠRegiment":35588,"Ġbreathtaking":35589,"fficiency":35590,"ĠBates":35591,"Ġ311":35592,"Ġwardrobe":35593,"fts":35594,"ĠBerk":35595,"Simply":35596,"ĠRiverside":35597,"ivering":35598,"idential":35599,"lucent":35600,"Ġenriched":35601,"ĠConver":35602,"ĠGiving":35603,"ãĥĻ":35604,"Ġlegalize":35605,"ĠFTC":35606,"Ġfreaking":35607,"Mix":35608,"Ġterrestrial":35609,"esian":35610,"cients":35611,"Wing":35612,"LOAD":35613,"Ġledge":35614,"ĠViolent":35615,"ĠMetall":35616,"Ġ308":35617,"Ġsoutheastern":35618,"hetto":35619,"Meat":35620,"Ġslowdown":35621,"Ġretreated":35622,"Jeremy":35623,"endas":35624,"*****":35625,"eric":35626,"Ġreins":35627,"oppable":35628,"ĠHumanity":35629,"earances":35630,"rigan":35631,"Camera":35632,"Ġwaivers":35633,"soc":35634,"Ġalteration":35635,"transform":35636,"ĠCemetery":35637,"506":35638,"Ġindefinite":35639,"Ġstimulating":35640,"yg":35641,"603":35642,"ĠSop":35643,"Ġdescriptive":35644,"Phase":35645,"ĠEdmund":35646,"Ġpneumonia":35647,"ventus":35648,"Amb":35649,"Ġlaboratories":35650,"ĠExclusive":35651,"ugar":35652,"Were":35653,"Ġmalfunction":35654,"Ġhomosexuals":35655,"Ġ-------":35656,"uni":35657,"Ġturbines":35658,"ĠEquity":35659,"Du":35660,"Ġminded":35661,"ĠRH":35662,"ĠBlackhawks":35663,"Ġfeats":35664,"Ġ1700":35665,"repl":35666,"362":35667,"laden":35668,"Ġindispensable":35669,"lyss":35670,"tti":35671,"Ġreel":35672,"Ġdiverted":35673,"Ġlikeness":35674,"Ġsubscriptions":35675,"Ġfingert":35676,"Ġfilthy":35677,"destruct":35678,"draft":35679,"ĠBernardino":35680,"launch":35681,"Ġperplex":35682,"ĠSUM":35683,"carb":35684,"Ġsweater":35685,"ĠVenture":35686,"ĠJag":35687,"ĠCeleb":35688,"ĠVoters":35689,"Ġsteadfast":35690,"Ġathletics":35691,"ĠHanson":35692,"ĠDrac":35693,"Tracker":35694,"Ġcommend":35695,"ĠPresidency":35696,"ĠDID":35697,"informed":35698,"Ġwebpage":35699,"Pretty":35700,"Ġforcefully":35701,"ãĥĥãĤ¯":35702,"Ġrelocation":35703,"Ġsatire":35704,"âī":35705,"ĠSunderland":35706,"æĦ":35707,"Voice":35708,"????????":35709,"Ġinformant":35710,"Ġbowel":35711,"ĠUniform":35712,"Ġ...\"":35713,"Ġpurge":35714,"Ġpicnic":35715,"ĠUmb":35716,"ĠUPDATE":35717,"ĠSapphire":35718,"ĠStall":35719,"learn":35720,"Ġobjectively":35721,"Ġobliter":35722,"Ġloophole":35723,"Ġjourneys":35724,"Ġomission":35725,"Pros":35726,"ĠSidney":35727,"ploma":35728,"Ġsprayed":35729,"Ġguru":35730,"Ġtraitor":35731,"Ġtimet":35732,"Ġsnapping":35733,"ĠSevent":35734,"urnal":35735,"ĠUkip":35736,"Ġbowed":35737,"poral":35738,"liberal":35739,"Ros":35740,"Questions":35741,"iOS":35742,"Ġsummarize":35743,"STAT":35744,"Ġ1850":35745,"apest":35746,"Ġlender":35747,"ĠVariable":35748,"bringing":35749,"ĠLORD":35750,",)":35751,"Ġcollapses":35752,"xiety":35753,"ĠNed":35754,"YD":35755,"ĠScha":35756,"Ġantibody":35757,"Ġdisband":35758,"yre":35759,"illusion":35760,"Ġrover":35761,"shed":35762,"ĠHirosh":35763,"cci":35764,"Ġcalam":35765,"ĠMorton":35766,"Pinterest":35767,"Ġ1928":35768,"ĠEuras":35769,"ordes":35770,"Ġfences":35771,"ĠInventory":35772,"ĠValencia":35773,"ĠUd":35774,"ĠTiff":35775,"Ġsque":35776,"Ġquotation":35777,"Ġtroublesome":35778,"erker":35779,"QUEST":35780,"ĠKingdoms":35781,"south":35782,"Ġlevy":35783,"Prince":35784,"ĠSting":35785,"Ġnicknamed":35786,"Ġappe":35787,"Ġphotographic":35788,"Ġcorpus":35789,"reference":35790,"ĠTrog":35791,"Unt":35792,")=(":35793,"ĠLatvia":35794,"Ġactivating":35795,"Ġlicensee":35796,"Ġdisparities":35797,"ĠNewsletter":35798,"ãĥĥãĥĪ":35799,"Ġfreeing":35800,"ĠJeep":35801,"ĠPerception":35802,"insk":35803,"Ġsilicone":35804,"ĠHayden":35805,"Lean":35806,"ĠSuzuki":35807,"ibrarian":35808,"668":35809,"Ġspor":35810,"Ġcorrelations":35811,"aghetti":35812,"Ġtuber":35813,"ĠIPCC":35814,"ilus":35815,"ĠVu":35816,"Ġwealthiest":35817,"ĠCarbuncle":35818,"anza":35819,"Ġfooled":35820,"ĠZur":35821,"Ġdaddy":35822,"rano":35823,"ilian":35824,"Ġknockout":35825,"fman":35826,"required":35827,"ĠWikileaks":35828,"ĠDuffy":35829,"ONT":35830,"Ġinsol":35831,"ĠObjects":35832,"Ġbou":35833,"ĠNordic":35834,"ĠInsert":35835,"scan":35836,"Ġdancers":35837,"Ġidiots":35838,"majority":35839,"ĠNeville":35840,"ĠFreeBSD":35841,"Ġtart":35842,"panic":35843,"690":35844,"Ġcocoa":35845,"Ġsampled":35846,"Ġlookup":35847,"Indust":35848,"Ġinjections":35849,"genre":35850,"Ġau":35851,"Ġroadway":35852,"Ġgenitals":35853,"Kind":35854,"ĠExaminer":35855,"ĠYaz":35856,"Fresh":35857,"Ġparalysis":35858,"ĠAluminum":35859,"Ġreap":35860,"oké":35861,"Ġsloppy":35862,"ĠTunnel":35863,"posium":35864,"nery":35865,"enic":35866,"Ġherbal":35867,"ĠOuter":35868,"ĠBuilder":35869,"Ġincur":35870,"Ġideologies":35871,"Ġbackups":35872,"consuming":35873,"ĠDetect":35874,"deck":35875,"ĠKNOW":35876,"ĠGret":35877,"ĠMIC":35878,"Ġtoughness":35879,"ĠExhibit":35880,"Ġhive":35881,"Les":35882,"ĠSCHOOL":35883,"ĠAtari":35884,"alde":35885,"ĠNull":35886,"andestine":35887,"mouse":35888,"Ġbrigade":35889,"489":35890,"Ġrevol":35891,"ĠLawson":35892,"ĠWah":35893,"opoly":35894,"ebted":35895,"ĠSaunders":35896,"Ġ313":35897,"ĠWinc":35898,"Ġtaboo":35899,"ĠHelmet":35900,"Ġwedge":35901,"chip":35902,"ĠTina":35903,"bg":35904,"Ġinfuri":35905,"rn":35906,"Ġanomalies":35907,"ĠSync":35908,"ĠExam":35909,"ĠCommit":35910,"ĠDiary":35911,"ĠALSO":35912,"ĠDebor":35913,"omedical":35914,"Ġcomprehension":35915,"655":35916,"Ġempowering":35917,"Ġire":35918,"Ġjuices":35919,"ĠETH":35920,"ĠBoxing":35921,"=\"/":35922,"Ġfacilitated":35923,"poke":35924,"ĠParsons":35925,"ĠModer":35926,"travel":35927,"Ġcivilizations":35928,"Ġlibertarians":35929,"Ġrune":35930,"ĠClarks":35931,"athed":35932,"Ġcampaigners":35933,"ĠDispatch":35934,"ĠFahrenheit":35935,"ĠCapcom":35936,"----------":35937,"Ġlace":35938,"Ġdraining":35939,"Ġliner":35940,"ĠArtificial":35941,"én":35942,"task":35943,"]).":35944,"ĠGMO":35945,"ĠOperator":35946,"ordinary":35947,"ĠInfluence":35948,"ĠUps":35949,"Ġpotency":35950,"ussen":35951,"ospons":35952,"ĠSwim":35953,"ĠDeadline":35954,"Unity":35955,"Ġculinary":35956,"Ġenlightenment":35957,"Ġwearer":35958,"Ġmined":35959,"Ġply":35960,"Ġincest":35961,"ĠDVDs":35962,"Walk":35963,"BTC":35964,"Trade":35965,"Ġdeval":35966,"iband":35967,"ĠOversight":35968,"Palestinian":35969,"Ġdart":35970,"Ġmul":35971,"LR":35972,"Ġremovable":35973,"ĠRealms":35974,"ìĿ":35975,"Ġmiscar":35976,"ĠVulkan":35977,"685":35978,"ère":35979,"ĠSap":35980,"Ġmerging":35981,"ĠCarly":35982,"chester":35983,"Ġbrisk":35984,"Ġluxurious":35985,"ĠGenerator":35986,"Ġbitterness":35987,"Ġedible":35988,"Ġ243":35989,"TG":35990,"Ġrectangle":35991,"WithNo":35992,"below":35993,"Jenn":35994,"Ġdarkest":35995,"Ġhitch":35996,"Ġdosage":35997,"Ġscaven":35998,"ĠKeller":35999,"ĠIllustrated":36000,"Certainly":36001,"ĠMavericks":36002,"Marginal":36003,"Ġdiarrhea":36004,"Ġenormously":36005,"Ġ999":36006,"shr":36007,"quart":36008,"Ġadamant":36009,"ĠMew":36010,"Ġrenovation":36011,"Ġcervical":36012,"ĠPercentage":36013,"eners":36014,"ĠKimber":36015,"Ġfloats":36016,"Ġdex":36017,"ĠWitcher":36018,"ĠSwansea":36019,"dm":36020,"Ġsalty":36021,"yellow":36022,"Ġcape":36023,"ĠDrain":36024,"ĠPaula":36025,"ĠToledo":36026,"lesi":36027,"Magazine":36028,"ĠWick":36029,"ĠMn":36030,"ĠAck":36031,"ĠRiding":36032,"ASON":36033,"Ġhomophobic":36034,"ARP":36035,"Ġwandered":36036,"CPU":36037,"oodoo":36038,"ĠPipe":36039,"Ġtightening":36040,"ĠButt":36041,"318":36042,"Ġdeserted":36043,"Session":36044,"Ġfacilitating":36045,"Jump":36046,"Ġemergencies":36047,"OWER":36048,"Ġexhaustive":36049,"ĠAFTER":36050,"Ġheartbeat":36051,"ĠLabel":36052,"acky":36053,"ĠCertified":36054,"iltration":36055,"Ze":36056,"ĠUtt":36057,"Ġ1300":36058,"Ġpresume":36059,"ĠDisp":36060,"Ġsurged":36061,"Ġdolls":36062,"Columb":36063,"Ġchimpan":36064,"ĠRazor":36065,"Ġticks":36066,"Ġcouncillor":36067,"Ġpilgrimage":36068,"ĠRebels":36069,"ĠQC":36070,"ĠAuction":36071,"xia":36072,"ikk":36073,"bred":36074,"Ġinsertion":36075,"Ġcoarse":36076,"dB":36077,"SEE":36078,"ĠZap":36079,"ĠFoo":36080,"Ġcontempor":36081,"ĠQuarterly":36082,"otions":36083,"ĠAlchemist":36084,"ĠTrey":36085,"ĠDuo":36086,"Sweet":36087,"804":36088,"ĠGiov":36089,"Ġfunn":36090,"Nin":36091,"hoff":36092,"Ġramifications":36093,"Ġ1922":36094,"ĠExperts":36095,"azes":36096,"Ġgarments":36097,"arial":36098,"ĠNab":36099,"Ġ257":36100,"ĠVed":36101,"Ġhumorous":36102,"ĠPompe":36103,"Ġnylon":36104,"Ġlurking":36105,"ĠSergey":36106,"ĠMattis":36107,"Ġmisogyny":36108,"ĠComponents":36109,"ĠWatching":36110,"ĠFolk":36111,"ractical":36112,"Bush":36113,"Ġtaped":36114,"Ġgrouping":36115,"Ġbeads":36116,"Ġ2048":36117,"Ġcondu":36118,"querque":36119,"Reading":36120,"Ġgrievances":36121,"Ultra":36122,"Ġendpoint":36123,"Hig":36124,"ĠStatic":36125,"ĠScarborough":36126,"Lua":36127,"ĠMessi":36128,"aqu":36129,"ĠPsyNet":36130,"ĠRudd":36131,"Ġavenue":36132,"vp":36133,"Jer":36134,"Ġshady":36135,"ĠResist":36136,"ĠArtemis":36137,"Ġcareless":36138,"Ġbrokers":36139,"Ġtemperament":36140,"Ġ520":36141,"Tags":36142,"ĠTurning":36143,"Ġuttered":36144,"Ġpedd":36145,"Ġimprovised":36146,"Ġ:(":36147,"Ġtabl":36148,"Ġplains":36149,"1600":36150,"pressure":36151,"ĠEssence":36152,"margin":36153,"friends":36154,"ĠRestoration":36155,"Ġpollut":36156,"ĠPoker":36157,"ĠAugustine":36158,"ĠCIS":36159,"ĠSEAL":36160,"orama":36161,"Ġthwart":36162,"seek":36163,"Ġpagan":36164,"º":36165,"cpu":36166,"Ġgarn":36167,"Ġassortment":36168,"ĠILCS":36169,"tower":36170,"Recommended":36171,"Ġunborn":36172,"ĠRandomRedditor":36173,"ĠRandomRedditorWithNo":36174,"Ġparalyzed":36175,"Ġeruption":36176,"Ġintersect":36177,"ĠStoke":36178,"ĠSco":36179,"Bind":36180,"å¾":36181,"ĠPNG":36182,"ĠNegative":36183,"ĠNOAA":36184,"Leon":36185,"Ġalloy":36186,"ĠLama":36187,"ĠDiversity":36188,"575":36189,"Ġunderestimated":36190,"ĠScor":36191,"Ġmural":36192,"Ġbusted":36193,"soon":36194,"lif":36195,"Ġnonex":36196,"Ġallergy":36197,"ĠUnderworld":36198,"ĠRays":36199,"ĠBlasio":36200,"Ġhrs":36201,"ĠDir":36202,"Ġ327":36203,"byter":36204,"Ġreplacements":36205,"Ġactivates":36206,"rived":36207,"MH":36208,"Ġpans":36209,"ĠHI":36210,"Ġlongitudinal":36211,"Ġnuisance":36212,"aler":36213,"Ġswell":36214,"ĠSigned":36215,"sci":36216,"ĠIsles":36217,"ĠAGA":36218,"Ġdefiant":36219,"Ġsonic":36220,"ocon":36221,"KC":36222,"ĠAim":36223,"tie":36224,"ahah":36225,"ĠmL":36226,"DX":36227,"Ġbisc":36228,"ĠBillboard":36229,"ĠSYSTEM":36230,"NEY":36231,"gaard":36232,"Ġdistressed":36233,"formerly":36234,"Alan":36235,"Ġchefs":36236,"Ġoptics":36237,"ĠComet":36238,"ĠAMC":36239,"Ġredesigned":36240,"irmation":36241,"Ġsightings":36242,"382":36243,"311":36244,"ĠWB":36245,"Ġcontraction":36246,"ĠTOTAL":36247,"Dual":36248,"Ġstartled":36249,"Ġunderstandably":36250,"Ġsunglasses":36251,"ETHOD":36252,"Ġdocker":36253,"Ġsurfing":36254,"ĠHEL":36255,"ĠSlack":36256,"tones":36257,"Ġshalt":36258,"Visual":36259,"498":36260,"Department":36261,"cussion":36262,"Ġunrestricted":36263,"Ġtad":36264,"Ġrename":36265,"employed":36266,"Ġeducating":36267,"Ġgrinned":36268,"bedroom":36269,"ĠActivities":36270,"ĠVelvet":36271,"ĠSWAT":36272,"Ġshuffle":36273,"igor":36274,"Ġsaturation":36275,"Finding":36276,"cream":36277,"icter":36278,"Ġvodka":36279,"tracking":36280,"tec":36281,"Ġforeground":36282,"iesta":36283,"Ġvehement":36284,"ĠECB":36285,"ĠTie":36286,"Ey":36287,"Ġturtles":36288,"ĠRailroad":36289,"ĠKatz":36290,"ĠFrames":36291,"Ġmenace":36292,"ĠFellowship":36293,"ĠEssential":36294,"uggish":36295,"Ġdrip":36296,"chwitz":36297,"ĠKyoto":36298,"sb":36299,"ĠNina":36300,"Parameter":36301,"Ġalarms":36302,"ĠClaud":36303,"Ġpioneering":36304,"Ġchiefly":36305,"ĠScream":36306,"Collection":36307,"Ġthankfully":36308,"ĠRonaldo":36309,"åŃIJ":36310,"strip":36311,"ĠDisneyland":36312,"commercial":36313,"Seeing":36314,"Soul":36315,"Ġevacuate":36316,"Ġciv":36317,"ĠAshe":36318,"Ġdivides":36319,"ĠDagger":36320,"rehensive":36321,"Ġberries":36322,"ĠDF":36323,"Ġsushi":36324,"Ġplurality":36325,"WI":36326,"Ġdisadvantaged":36327,"Ġbattalion":36328,"obiles":36329,"451":36330,"Ġcling":36331,"Ġundeniable":36332,"ĠLounge":36333,"Ġhaunt":36334,"phe":36335,"Ġquantify":36336,"Ġdiffered":36337,"Ġ[*]":36338,"ĠViz":36339,"cum":36340,"slave":36341,"Ġvideog":36342,"Ġquar":36343,"Ġbundles":36344,"ĠAlonso":36345,"tackle":36346,"Ġneuronal":36347,"Ġlandslide":36348,"confirmed":36349,"ĠDepth":36350,"Ġrenewables":36351,"Bear":36352,"ĠMacedonia":36353,"Ġjerseys":36354,"Ġbunk":36355,"ĠSpawn":36356,"ĠControls":36357,"ĠBuchanan":36358,"Ġrobotics":36359,"Ġemphasizing":36360,"ĠTutorial":36361,"hyp":36362,"iston":36363,"Ġmonumental":36364,"æ°":36365,"ĠCarry":36366,"Ġtbsp":36367,"enance":36368,"Hill":36369,"arthed":36370,"Ġrotten":36371,"Dean":36372,"Ġtwisting":36373,"Ġgoodwill":36374,"Ġimmersion":36375,"Living":36376,"Ġbrushes":36377,"ĠCGI":36378,"ĠAtk":36379,"traditional":36380,"Ġphantom":36381,"ĠStamina":36382,"Ġexpansions":36383,"ĠMarin":36384,"Ġembarked":36385,"ĠEg":36386,"intestinal":36387,"ĠPEOPLE":36388,"ĠBooth":36389,"ĠAppalach":36390,"Ġrelegated":36391,"VT":36392,"MIT":36393,"Ġmuster":36394,"Ġwithdrawing":36395,"Ġmicroscope":36396,"ĠGathering":36397,"ĠCrescent":36398,"ĠArgentine":36399,"ĠDecre":36400,"ĠDominic":36401,"Ġbuds":36402,"antage":36403,"ĠIon":36404,"Ġwidened":36405,"ONSORED":36406,"ĠGloves":36407,"iannopoulos":36408,"razen":36409,"feel":36410,"Ġrepayment":36411,"Ġhindsight":36412,"ĠREALLY":36413,"ĠPistol":36414,"ĠBrah":36415,"Ġwatts":36416,"Ġsurvives":36417,"Ġflurry":36418,"issy":36419,"Alert":36420,"ĠUruguay":36421,"Phoenix":36422,"Slow":36423,"ĠGrave":36424,"ĠFir":36425,"Ġmanageable":36426,"Ġtariff":36427,"ĠUDP":36428,"ĠPistons":36429,"ĠNigerian":36430,"Ġstrikeouts":36431,"Ġcosmetics":36432,"whelming":36433,"fab":36434,"cape":36435,"proxy":36436,"Ġrethink":36437,"Ġovercoming":36438,"simple":36439,"Ġwoo":36440,"Ġdistracting":36441,"ĠStanton":36442,"ĠTulsa":36443,"ĠDock":36444,"659":36445,"Ġdiscord":36446,"ĠEmacs":36447,"ĠVes":36448,"ĠROB":36449,"Ġreassuring":36450,"Ġconsortium":36451,"Muslims":36452,"321":36453,"Ġprompts":36454,"sei":36455,"ĠHitch":36456,"imposed":36457,"ĠFool":36458,"Ġindiscrim":36459,"wrong":36460,"buquerque":36461,"Davis":36462,"!]":36463,"Ġtimeless":36464,"ĠNEED":36465,"Ġpesticide":36466,"Ġrallying":36467,"ĠCalder":36468,"Ġå¤":36469,"Ġxp":36470,"ĠUnle":36471,"ĠExport":36472,"luaj":36473,"Buff":36474,")[":36937,"Ġsqor":36938,"Saudi":36939,"Ġistg":36940,"Ġindulge":36941,"proc":36942,"Ġdisgusted":36943,"Ġcompounded":36944,"Ġnem":36945,"Ġschooling":36946,"ĠCure":36947,"processing":36948,"Sol":36949,"Ġproverb":36950,"itized":36951,"ĠAlvarez":36952,"Ġscarf":36953,"Ġrectangular":36954,"reve":36955,"Ġhormonal":36956,"ĠStress":36957,"itizen":36958,"Ġ425":36959,"girls":36960,"ĠNoir":36961,"ĠRapp":36962,"Ġmarches":36963,"church":36964,"ĠUses":36965,"Ġ405":36966,"ĠBerm":36967,"Ġordinances":36968,"ĠJudgment":36969,"Charges":36970,"ĠZin":36971,"Ġdusty":36972,"Ġstrawberries":36973,"Ġperce":36974,"ĠThur":36975,"ĠDeborah":36976,"netflix":36977,"ĠLambert":36978,"Ġamused":36979,"ĠGuang":36980,"YOU":36981,"RGB":36982,"ĠCCTV":36983,"Ġfiat":36984,"rang":36985,"Ġfederation":36986,"ĠMant":36987,"ĠBust":36988,"ĠMare":36989,"respective":36990,"ĠMigration":36991,"ĠBIT":36992,"590":36993,"Ġpatriotism":36994,"Ġoutlining":36995,"region":36996,"ĠJosé":36997,"Ġblasting":36998,"ĠEzra":36999,"Bs":37000,"Ġundermines":37001,"ĠSmooth":37002,"Ġclashed":37003,"radio":37004,"Ġtransitioning":37005,"ĠBuccaneers":37006,"ĠOwl":37007,"Ġplugs":37008,"Ġhiatus":37009,"ĠPinball":37010,"Ġmig":37011,"ĠNutr":37012,"ĠWolfe":37013,"Ġintegers":37014,"Ġorbits":37015,"ĠEdwin":37016,"ĠDirectX":37017,"bite":37018,"Ġblazing":37019,"vr":37020,"Edge":37021,"ĠPID":37022,"exit":37023,"ĠComed":37024,"ĠPathfinder":37025,"ĠGuid":37026,"ĠSigns":37027,"ĠZer":37028,"ĠAgenda":37029,"Ġreimbursement":37030,"Mesh":37031,"iPhone":37032,"ĠMarcos":37033,"ĠSites":37034,"hate":37035,"enburg":37036,"Ġsockets":37037,"pend":37038,"Batman":37039,"vir":37040,"ĠSHOW":37041,"Ġprovisional":37042,"conn":37043,"ĠDeaths":37044,"ATIVE":37045,"Profile":37046,"sym":37047,"JA":37048,"Ġninja":37049,"installed":37050,"idates":37051,"ebra":37052,"ĠOmaha":37053,"Ġseizing":37054,"ĠBeasts":37055,"Ġsalts":37056,"Mission":37057,"Generally":37058,"ĠTrilogy":37059,"heon":37060,"legates":37061,"Ġdime":37062,"Ġfaire":37063,"parable":37064,"Graph":37065,"Ġtotaling":37066,"Ġdiagrams":37067,"ĠYanuk":37068,"plet":37069,"ĠMeh":37070,"Ġmythical":37071,"ĠStephens":37072,"autical":37073,"ochemistry":37074,"Ġkilograms":37075,"Ġelbows":37076,"ancock":37077,"ĠBCE":37078,"ĠPrague":37079,"Ġimprov":37080,"ĠDevin":37081,"Ġ\"\\":37082,"paralle":37083,"Ġsupremacists":37084,"ĠBillion":37085,"Ġregimen":37086,"innacle":37087,"Ġrequisite":37088,"angan":37089,"ĠBurlington":37090,"ainment":37091,"ĠObjective":37092,"omsky":37093,"GV":37094,"Ġunilateral":37095,"Ġtc":37096,"Ġhires":37097,"mental":37098,"Ġinvoluntary":37099,"Ġtranspl":37100,"ĠASCII":37101,"¨":37102,"Events":37103,"Ġdoubted":37104,"ĠKaplan":37105,"ĠCourage":37106,"igon":37107,"ĠManaging":37108,"ĠTart":37109,"Ġfalsehood":37110,"ĠViolet":37111,"Ġairs":37112,"Ġfertilizer":37113,"Britain":37114,"Ġaquatic":37115,"ouf":37116,"Words":37117,"ĠHartford":37118,"Ġevenings":37119,"ĠVengeance":37120,"quite":37121,"Gall":37122,"ĠPret":37123,"Ġpdf":37124,"ĠLM":37125,"ĠSochi":37126,"ĠIntercept":37127,"920":37128,"Ġprofitability":37129,"ĠIdle":37130,"ĠMacDonald":37131,"ĠEstablishment":37132,"umsy":37133,"Ġgatherings":37134,"ĠNaj":37135,"Charlie":37136,"Ġascent":37137,"ĠProtector":37138,"Ġalgebra":37139,"Ġbios":37140,"forums":37141,"ELS":37142,"Introduced":37143,"Ġ335":37144,"Ġastronomy":37145,"Contribut":37146,"ĠPolic":37147,"Platform":37148,"Ġcontainment":37149,"wrap":37150,"Ġcoronary":37151,"ĠJelly":37152,"manager":37153,"Ġheartbreaking":37154,"cair":37155,"ĠChero":37156,"cgi":37157,"Medical":37158,"ĠAccountability":37159,"!!\"":37160,"ophile":37161,"Ġpsychotic":37162,"ĠRestrict":37163,"Ġequitable":37164,"issues":37165,"Ġ1905":37166,"ĠNek":37167,"cised":37168,"ĠTracking":37169,"Ġozone":37170,"Ġcooker":37171,"rosis":37172,"Ġreopen":37173,"Ġinfinity":37174,"ĠPharmaceutical":37175,"ensional":37176,"Attempt":37177,"ĠRory":37178,"Marco":37179,"Ġawaits":37180,"HOW":37181,"treated":37182,"Ġbolst":37183,"Ġrevered":37184,"Ġpods":37185,"oppers":37186,"0010":37187,"Ġamplitude":37188,"rican":37189,"SPONSORED":37190,"Ġtrousers":37191,"Ġhalves":37192,"ĠKaine":37193,"ĠCutler":37194,"ĠAUTH":37195,"Ġsplendid":37196,"Ġpreventive":37197,"ĠDudley":37198,"ifacts":37199,"uminati":37200,"ĠYin":37201,"Ġadmon":37202,"ĠVag":37203,"Ġinverted":37204,"Ġhastily":37205,"ĠHague":37206,"Lyn":37207,"Ġledger":37208,"Ġastronomical":37209,"getting":37210,"Ġcirca":37211,"ĠCic":37212,"ĠTennis":37213,"Limited":37214,"Ġdru":37215,"ĠBYU":37216,"Ġtravellers":37217,"Ġpane":37218,"ĠIntro":37219,"Ġpatiently":37220,"Ġaiding":37221,"Ġloos":37222,"ĠTough":37223,"Ġ293":37224,"Ġconsumes":37225,"SourceFile":37226,"Ġ\"\"\"":37227,"Ġbonding":37228,"Ġtilted":37229,"Ġmenstrual":37230,"ĠCelestial":37231,"ULAR":37232,"Plugin":37233,"Ġrisking":37234,"Naz":37235,"ĠRiyadh":37236,"Ġaccredited":37237,"Ġskirm":37238,"éĽ":37239,"Ġexaminer":37240,"Ġmessing":37241,"Ġnearing":37242,"ĠChern":37243,"ĠBeckham":37244,"Ġswapped":37245,"Ġgoose":37246,"Kay":37247,"Ġlofty":37248,"ĠWallet":37249,"Ġ['":37250,"Ġapocalypse":37251,"Ġbamboo":37252,"ĠSPACE":37253,"ĠElena":37254,"Ġ306":37255,"acons":37256,"Ġtightened":37257,"Ġadolescence":37258,"Ġrainy":37259,"Ġvandalism":37260,"ĠNewtown":37261,"Ġconject":37262,"cakes":37263,"Ġcheated":37264,"Ġmoderators":37265,"params":37266,"EFF":37267,"Ġdeceit":37268,"ĠSTL":37269,"ĠTanzania":37270,"ĠRI":37271,"Ġ1923":37272,"ĠExile":37273,"thel":37274,"Ġtheolog":37275,"Ġquirky":37276,"ĠIrvine":37277,"Ġneedy":37278,"oris":37279,"Um":37280,"Ka":37281,"Ġmailbox":37282,"322":37283,"Ġbos":37284,"ĠPetra":37285,"KING":37286,"Ġenlarged":37287,"Often":37288,"Ġbadass":37289,"Ġ343":37290,"ĠPlaces":37291,"ĠCAD":37292,"Ġpristine":37293,"Ġintervening":37294,"direction":37295,"Ġlaz":37296,"ĠDSM":37297,"Ġprojecting":37298,"ĠFunk":37299,"agog":37300,"payment":37301,"nov":37302,"Ġchatter":37303,"ARB":37304,"Ġexaminations":37305,"ĠHousehold":37306,"ĠGus":37307,"Ford":37308,"414":37309,"Boss":37310,"Ġmystic":37311,"Ġleaps":37312,"ĠBav":37313,"ulz":37314,"budget":37315,"Football":37316,"Ġsubsidized":37317,"Ġfirsthand":37318,"Ġcoincide":37319,"ocular":37320,"Conn":37321,"ĠCollabor":37322,"Ġfools":37323,"amura":37324,"ahar":37325,"rists":37326,"Ġswollen":37327,"Ġexpended":37328,"ĠPau":37329,"sup":37330,"Ġspar":37331,"Ġkeynote":37332,"suff":37333,"Ġunequal":37334,"Ġprogressing":37335,"strings":37336,"ĠGamergate":37337,"Disney":37338,"ĠEleven":37339,"omnia":37340,"Ġscripted":37341,"Ġearners":37342,"brother":37343,"ĠEnabled":37344,"æ³":37345,"Ġlarvae":37346,"ĠLOC":37347,"mess":37348,"Wilson":37349,"ĠTemplate":37350,"successfully":37351,"Ġparamount":37352,"Ġcamouflage":37353,"Ġbinds":37354,"ĠQuiet":37355,"ĠShutterstock":37356,"rush":37357,"Ġmascot":37358,"fortune":37359,"ĠColt":37360,"ĠBeyon":37361,"habi":37362,"Ġhairc":37363,"Ġ267":37364,"ĠDeus":37365,"Ġtwitch":37366,"Ġconcentrating":37367,"Ġnipples":37368,"cible":37369,"Ġgir":37370,"NZ":37371,"Math":37372,"nih":37373,"Required":37374,"Ġponder":37375,"ĠSAN":37376,"Ġweddings":37377,"Ġloneliness":37378,"NES":37379,"ĠMahjong":37380,"695":37381,"addle":37382,"ĠGarner":37383,"ĠCOUR":37384,"Bridge":37385,"Ġspree":37386,"ĠCaldwell":37387,"Ġbribery":37388,"Ġ��������":37389,"plugins":37390,"Ġracket":37391,"Ġchampagne":37392,"versible":37393,"Vote":37394,"Ġmodifiers":37395,"Mayor":37396,"680":37397,"Ġassemblies":37398,"ĠSultan":37399,"ĠNing":37400,"ĠLadies":37401,"Ġsulfur":37402,"Ġorbs":37403,"Ġ-----":37404,"_______":37405,"ĠJournalism":37406,"Ġesports":37407,"Ġlush":37408,"Ġhue":37409,"Ġspectral":37410,"Honest":37411,"ãĥı":37412,"Ġbushes":37413,"Ġreinforcement":37414,"Ġreopened":37415,"ĠWheels":37416,"ĠMorg":37417,"rieving":37418,"Ġauxiliary":37419,"ĠjQuery":37420,"ĠBAT":37421,"tesque":37422,"Ġvertex":37423,"pure":37424,"frey":37425,"ãĤº":37426,"dos":37427,"Ġtyph":37428,"Ġcull":37429,"Ġeq":37430,"Ġdecon":37431,"Ġtossing":37432,"Ġdisparate":37433,"ĠBrigham":37434,"printf":37435,"ledged":37436,"Ġsund":37437,"Ġcozy":37438,"Ġhepatitis":37439,"performing":37440,"Ġaval":37441,"ĠGG":37442,"future":37443,"Ġpetertodd":37444,"ĠKosovo":37445,"Ġmagnets":37446,"Already":37447,"ĠEdison":37448,"ĠCeres":37449,"ĠRAID":37450,"Ġbrilliance":37451,"576":37452,"Ġderives":37453,"Ġhypertension":37454,"ĠÎĶ":37455,"Ġlambda":37456,"Ġflair":37457,"Ġmissionaries":37458,"Ġrapes":37459,"ĠStarter":37460,"ĠMonths":37461,"Ġdefy":37462,"Ġseismic":37463,"ĠRaphael":37464,"Ġeurozone":37465,"656":37466,"zsche":37467,"Ġscratched":37468,"Ġbows":37469,"ĠLennon":37470,"ĠGaia":37471,"Ġdripping":37472,"facts":37473,"Ale":37474,"Ġfrogs":37475,"ĠBreast":37476,"ogeneity":37477,"ĠProsecutor":37478,"Ġamplified":37479,"ĠHodg":37480,"ĠFn":37481,"Thousands":37482,"ĠNIH":37483,"ĠMonitoring":37484,"FTWARE":37485,"ĠPriebus":37486,"ĠGrowing":37487,"hunter":37488,"Ġdiagnose":37489,"ĠMald":37490,"ĠLR":37491,"Ġcrowned":37492,"Ġbursting":37493,"Ġdissolution":37494,"javascript":37495,"Ġusefulness":37496,"ĠExecution":37497,":(":37498,"ĠIvory":37499,"aah":37500,"Ġpersecuted":37501,"violence":37502,"istas":37503,"ĠCrate":37504,"Ġimpulses":37505,"ĠSpani":37506,"edes":37507,"Handle":37508,"ĠZerg":37509,"thinkable":37510,"Lastly":37511,"Ġspontaneously":37512,"Ġinconvenient":37513,"Ġdismissing":37514,"Ġplotted":37515,"Ġeighty":37516,"Ġ737":37517,"rish":37518,"ĠThornton":37519,"atham":37520,"Ġsitcom":37521,"Ven":37522,"Recipe":37523,"tel":37524,"lund":37525,"Ġclears":37526,"ĠSasuke":37527,"Ġ258":37528,"Ġopting":37529,"Ġenraged":37530,"esthetic":37531,"ĠAe":37532,"uchs":37533,"Prep":37534,"Flow":37535,"Ġrunoff":37536,"ĠEating":37537,"ĠGiles":37538,"ĠActing":37539,"resources":37540,"ibaba":37541,"Ġrpm":37542,"Ġskewed":37543,"ĠBlanc":37544,"ĠSakuya":37545,"Ġhotter":37546,"Ġ1924":37547,"opian":37548,"cko":37549,"Ġcrumbling":37550,"Ġcaptains":37551,"ĠAppropriations":37552,"leaders":37553,"dropping":37554,"anuts":37555,"Ġreversing":37556,"ĠPose":37557,"ĠSek":37558,"Scot":37559,"ĠIdea":37560,"cise":37561,"ĠSlovenia":37562,"Ġ317":37563,"Doctor":37564,"Ġcrocod":37565,"aldi":37566,"Sea":37567,"ĠFarrell":37568,"Ġmercenaries":37569,"ĠRNC":37570,"ĠGuess":37571,"Ġpacing":37572,"Machine":37573,"StreamerBot":37574,"ĠCharity":37575,"Ġ298":37576,"Ġcannons":37577,"ĠToby":37578,"TPPStreamerBot":37579,"ĠPassion":37580,"cfg":37581,"Thom":37582,"Ġbadges":37583,"ĠBernstein":37584,".âĢĵ":37585,"ĠPOP":37586,"ĠConj":37587,"Ġinitialization":37588,"Ġbiodiversity":37589,"Dub":37590,"Ġfeudal":37591,"Ġdisclaimer":37592,"Ġcrow":37593,"Ġignition":37594,"arf":37595,"SHA":37596,"ĠkHz":37597,"hazard":37598,"ĠArtists":37599,"oeuv":37600,"679":37601,"ĠRudy":37602,"Nine":37603,"ĠRamadan":37604,"å½":37605,"itto":37606,"Ġadrenaline":37607,"Cert":37608,"Ġsmelled":37609,"Ġimpunity":37610,"Ġagendas":37611,"ĠReborn":37612,"ĠConcent":37613,"ĠSeems":37614,"Ġomega":37615,"ĠDustin":37616,"Ġbacker":37617,"ĠSauce":37618,"ĠBoyle":37619,"WIN":37620,"Ġspins":37621,"Ġpauses":37622,"upt":37623,"Ġshredded":37624,"Ġstrapped":37625,"ĠCorruption":37626,"Ġscratches":37627,"Ġni":37628,"Ġattire":37629,"ĠSAF":37630,"FactoryReloaded":37631,"ĠIPS":37632,"Ġ(%":37633,"Ġseminar":37634,"focus":37635,"civil":37636,"Ġ1860":37637,"intosh":37638,"Ġcontinual":37639,"Ġabbrevi":37640,"ĠSok":37641,"ocobo":37642,"XM":37643,"Ġfrantic":37644,"Ġunavoidable":37645,"Ġartery":37646,"Ġannotations":37647,"bath":37648,"Climate":37649,"Ġdors":37650,"ĠSlide":37651,"coord":37652,"ĠReload":37653,"ĠLDL":37654,"ĠLovecraft":37655,"Ġunimagin":37656,"Ġresembled":37657,"Ġbarracks":37658,"np":37659,"Ġsurrogate":37660,"Ġcategorized":37661,"ãĤ©":37662,"Ġvaccinated":37663,"Ġdrainage":37664,"Ġindist":37665,"ĠWhatsApp":37666,"Ġ1870":37667,"olerance":37668,"invoke":37669,"amorph":37670,"Ġreconnect":37671,"Ġemanc":37672,"Ġblindness":37673,"Ġ1280":37674,"internet":37675,"collar":37676,"Ġaltru":37677,"Ġabyss":37678,"ĠTRI":37679,"657":37680,"Ġinfused":37681,"HEAD":37682,"Ġforestry":37683,"ĠWoody":37684,"ĠCi":37685,"wi":37686,"sam":37687,"784":37688,"holiday":37689,"Ġmogul":37690,"ĠFees":37691,"ĠDEN":37692,"Internal":37693,"urbed":37694,"fusc":37695,"atom":37696,"ĠIllusion":37697,"Ġpolled":37698,"Ġflap":37699,"Ġcoax":37700,"LGBT":37701,"Analy":37702,"ĠSections":37703,"ĠCaliforn":37704,"emn":37705,"Ġhither":37706,"ĠNIGHT":37707,"Ġnailed":37708,"ĠPipeline":37709,"391":37710,"oof":37711,"ĠPrimal":37712,"verend":37713,"Ġslashing":37714,"Ġretri":37715,"aviour":37716,"Ġdeparting":37717,"gil":37718,"ISC":37719,"Ġmidway":37720,"Ġultrasound":37721,"Ġbehaving":37722,"ĠTara":37723,"classes":37724,"Virtual":37725,"ĠColonial":37726,"Ġstripping":37727,"Ġorchestrated":37728,"ĠGraves":37729,"452":37730,"ĠIronically":37731,"ĠWriters":37732,"Ġlends":37733,"ĠManz":37734,"Ġraven":37735,"Ġoxidative":37736,"Ġ266":37737,"ELF":37738,"actually":37739,"ascar":37740,"Draft":37741,"Ġfavourable":37742,"Ġhumiliating":37743,"Ġfidelity":37744,"ĠHof":37745,"ĠXuan":37746,"496":37747,"Ġlayered":37748,"atis":37749,"790":37750,"Ġpaycheck":37751,"iton":37752,"Kar":37753,"ĠVMware":37754,"ĠFarmer":37755,"Ġservic":37756,"glomer":37757,"Ġslump":37758,"ĠFabric":37759,"ĠDOC":37760,"esting":37761,"Ġreassure":37762,"Ġphyl":37763,"volt":37764,"itory":37765,"Rules":37766,"Ġoxidation":37767,"Ġprized":37768,"Ġmistress":37769,"ĠDjango":37770,"WARN":37771,"åij":37772,"Ġencode":37773,"ĠFeedback":37774,"Ġstupidity":37775,"Ian":37776,"ĠYugoslavia":37777,"ר":37778,"acl":37779,"UTE":37780,"1977":37781,"Ġqualifies":37782,"Ġpulses":37783,"pretty":37784,"Ġfroze":37785,"Ġss":37786,"Iterator":37787,"Ġurgently":37788,"Ġmailed":37789,"ĠCham":37790,"Ġsustaining":37791,"Ġbasil":37792,"Ġpuppies":37793,"ilant":37794,"ĠPLEASE":37795,"lap":37796,"aceous":37797,"Fear":37798,"ĠMastery":37799,"automatic":37800,"ĠTAG":37801,"Ġantim":37802,"agles":37803,"473":37804,"frames":37805,"Ġwhispers":37806,"ĠWhoever":37807,"Ġbravery":37808,"ĠUKIP":37809,"ractions":37810,"\"\"\"":37811,"Ġtame":37812,"Ġparted":37813,"everything":37814,"CONT":37815,"Ġindebted":37816,"Ġaddr":37817,"rek":37818,"IRED":37819,"Ġeminent":37820,"clinton":37821,"Ġousted":37822,"Ġreviewer":37823,"Ġmeltdown":37824,"Ġrearr":37825,"ĠYao":37826,"thereal":37827,"abyte":37828,"Ġstumbling":37829,"Ġbatches":37830,"Ġ259":37831,"Ġcontraceptive":37832,"Ġprostitute":37833,"ensis":37834,"Decl":37835,"ĠStrikes":37836,"Military":37837,"ĠOath":37838,"vacc":37839,"ppings":37840,"052":37841,"ĠpartName":37842,"amping":37843,"Reports":37844,"KI":37845,"CHR":37846,"Ġsubtly":37847,"swers":37848,"Blake":37849,"usual":37850,"Ġcontestants":37851,"Ġcartridges":37852,"ĠGREAT":37853,"Ġblush":37854,"ĠâĢº":37855,"472":37856,"Ġreasoned":37857,"ãĥ¤":37858,"paralleled":37859,"Ġdyn":37860,"agate":37861,"Ġnightly":37862,"åĨ":37863,"556":37864,"Ġsemantic":37865,"ĠAdvoc":37866,"Ġ!!":37867,"Ġdisagrees":37868,"ĠBW":37869,"Veh":37870,"Ġharming":37871,"Ġembraces":37872,"Ġstrives":37873,"Ġinland":37874,"ĠKard":37875,"Ġheats":37876,"ĠGinny":37877,"utan":37878,"ernaut":37879,"ylene":37880,"ĠElev":37881,"JD":37882,"Ġhars":37883,"ĠStarr":37884,"Ġskysc":37885,"Ġcollaborators":37886,"Usually":37887,"Ġrevolutions":37888,"ĠSTATS":37889,"Ġdismantle":37890,"Ġconfidently":37891,"Ġkinetic":37892,"Ali":37893,"Ġpercentile":37894,"Ġextracting":37895,"illian":37896,"estead":37897,"Ġphysicists":37898,"ĠMarshal":37899,"Ġfellowship":37900,"Ġdashed":37901,"ĠUR":37902,"ĠSioux":37903,"ĠCompact":37904,"amide":37905,"Python":37906,"ĠLeigh":37907,"ĠPharmac":37908,"istrates":37909,"herical":37910,"Ġfue":37911,"ĠEmin":37912,"Ġ({":37913,"ĠNeighborhood":37914,"Ġdisrupting":37915,"ĠDup":37916,"Ġgland":37917,"ĠSev":37918,"ĠMarian":37919,"argon":37920,"ĠDund":37921,"Ġ":46904,"ĠPhilips":46905,"ĠKafka":46906,"Ġupheaval":46907,"Ġsentimental":46908,"Ġsax":46909,"ĠAkira":46910,"serial":46911,"Matrix":46912,"Ġelecting":46913,"Ġcommenter":46914,"ĠNebula":46915,"plets":46916,"ĠNadu":46917,"ĠAdren":46918,"Ġenshr":46919,"ĠRAND":46920,"financial":46921,"ĠClyde":46922,"utherford":46923,"Ġsignage":46924,"Ġdeline":46925,"Ġphosphate":46926,"roversial":46927,"fascist":46928,"ĠVall":46929,"ĠBethlehem":46930,"Ġfors":46931,"Ġenglish":46932,"Solid":46933,"Nature":46934,"Ġva":46935,"ĠGuests":46936,"Ġtantal":46937,"Ġautoimmune":46938,";;;;;;;;;;;;":46939,"ĠTotally":46940,"ĠOv":46941,"Ġdefences":46942,"ĠCoconut":46943,"Ġtranquil":46944,"Ġploy":46945,"Ġflavours":46946,"ĠFlask":46947,"ãĤ¨ãĥ«":46948,"ĠWeston":46949,"ĠVolvo":46950,"870":46951,"Ġmicrophones":46952,"verbal":46953,"RPG":46954,"Ġiii":46955,";}":46956,"028":46957,"Ġheadlined":46958,"Ġprimed":46959,"Ġhoard":46960,"ĠShad":46961,"ĠENTER":46962,"Ġtriangular":46963,"Ġcapit":46964,"lik":46965,"ĠAncients":46966,"Ġlash":46967,"Ġconvol":46968,"Ġcolonel":46969,"enemy":46970,"Gra":46971,"Ġpubs":46972,"utters":46973,"Ġassigns":46974,"ĠPenet":46975,"ĠMonstrous":46976,"ĠBowen":46977,"ilver":46978,"Haunted":46979,"ĠDing":46980,"started":46981,"plin":46982,"Ġcontaminants":46983,"ĠDOE":46984,"ffen":46985,"ĠTechnician":46986,"Ry":46987,"Ġrobbers":46988,"Ġhotline":46989,"ĠGuardiola":46990,"ĠKaufman":46991,"rower":46992,"ĠDresden":46993,"ĠAlpine":46994,"Elf":46995,"Ġfmt":46996,"ĠSard":46997,"urses":46998,"gpu":46999,"Unix":47000,"Ġunequivocally":47001,"ĠCitizenship":47002,"quad":47003,"mire":47004,"ĠSweeney":47005,"Battery":47006,"615":47007,"Ġpancakes":47008,"Ġoats":47009,"Maps":47010,"ĠContrast":47011,"mbudsman":47012,"ĠEPS":47013,"Ġsubcommittee":47014,"Ġsourcing":47015,"Ġsizing":47016,"ĠBuffer":47017,"ĠMandatory":47018,"Ġmoderates":47019,"ĠPatterns":47020,"ĠChocobo":47021,"ĠZan":47022,"ĠSTATES":47023,"ĠJudging":47024,"ĠInher":47025,"*:":47026,"Ġbil":47027,"ĠYen":47028,"Ġexhilar":47029,"ollower":47030,"zers":47031,"Ġsnug":47032,"maximum":47033,"Ġdespicable":47034,"ĠPACK":47035,"ĠAnnex":47036,"Ġsarcastic":47037,"Ġlatex":47038,"Ġtamp":47039,"ĠSao":47040,"bah":47041,"ĠReverend":47042,"ĠChinatown":47043,"ĠAUT":47044,"documented":47045,"ĠGABA":47046,"ĠCanaan":47047,"ĠÙħ":47048,"Ġgoverns":47049,"prev":47050,"Esc":47051,"ĠEstimates":47052,"OSP":47053,"Ġendeavour":47054,"ĠClosing":47055,"ometime":47056,"everyone":47057,"Ġworsen":47058,"Ġscanners":47059,"Ġdeviations":47060,"ĠRobotics":47061,"ĠCompton":47062,"Ġsorcerer":47063,"Ġendogenous":47064,"Ġemulation":47065,"ĠPiercing":47066,"ĠAph":47067,"ĠSocket":47068,"Ġbould":47069,"ĠOU":47070,"ĠBorderlands":47071,"Ġ1863":47072,"Gordon":47073,"ĠWTO":47074,"Ġrestricts":47075,"Ġmosaic":47076,"Ġmelodies":47077,"çĦ":47078,"Tar":47079,"Ġdisson":47080,"ĠProvides":47081,"Ġ......":47082,"bek":47083,"FIX":47084,"Ġbroom":47085,"anship":47086,"Doctors":47087,"Ġnerds":47088,"ĠRegions":47089,"naissance":47090,"Ġmete":47091,"Ġcrept":47092,"plings":47093,"Ġgirlfriends":47094,"knit":47095,"igent":47096,"owe":47097,"Ġushered":47098,"ĠBaz":47099,"Mobil":47100,"434":47101,"ĠPresents":47102,"origin":47103,"Ġinsomnia":47104,"ĠAux":47105,"439":47106,"ĠChili":47107,"irsch":47108,"GAME":47109,"Ġgestation":47110,"algia":47111,"romising":47112,"$,":47113,"crow":47114,"ĠInspection":47115,"atomic":47116,"Relations":47117,"JOHN":47118,"roman":47119,"ĠClockwork":47120,"ĠBakr":47121,"mone":47122,"MET":47123,"Ġthirsty":47124,"Ġbc":47125,"Ġfaculties":47126,"Rum":47127,"Ġnuance":47128,"ĠDarius":47129,"pleting":47130,"fters":47131,"etchup":47132,"Registration":47133,"ĠKE":47134,"Rah":47135,"Ġpreferential":47136,"ĠLash":47137,"ĠHH":47138,"Valid":47139,"ĠNAV":47140,"Ġstarve":47141,"ĠGong":47142,"zynski":47143,"ĠActress":47144,"Ġwik":47145,"Ġunaccompanied":47146,"lvl":47147,"Bride":47148,"ADS":47149,"ĠCommando":47150,"ĠVaughn":47151,"Wallet":47152,"Ġhopping":47153,"ĠVie":47154,"Ġcaveats":47155,"Ġalas":47156,"ifled":47157,"abuse":47158,"661":47159,"Ġibn":47160,"Ġgul":47161,"Ġrobbing":47162,"til":47163,"ILA":47164,"Ġmitigating":47165,"Ġaptly":47166,"Ġtyrant":47167,"Ġmidday":47168,"ĠGilmore":47169,"ĠDecker":47170,"Ġ§§":47171,"partial":47172,"Exactly":47173,"Ġphenotype":47174,"Ġ[+]":47175,"ĠPlex":47176,"ĠIps":47177,"versions":47178,"Ġebook":47179,"Ġchic":47180,"gross":47181,"\":\"\"},{\"":47182,"ĠSurprisingly":47183,"Morgan":47184,"Ġresidues":47185,"ĠConfederation":47186,"infeld":47187,"Ġlyr":47188,"moderate":47189,"Ġperpendicular":47190,"VK":47191,"Ġsynchronized":47192,"Ġrefreshed":47193,"Ġadore":47194,"ĠTorment":47195,"olina":47196,"Ġ2600":47197,"ItemTracker":47198,"Ġpies":47199,"ĠFAT":47200,"ĠRHP":47201,"048":47202,"ĠRESP":47203,"ĠBJ":47204,"allows":47205,"Pand":47206,"Ġunwelcome":47207,"ĠVoc":47208,"ĠBastard":47209,"ĠOW":47210,"ĠLAR":47211,"ĠHealer":47212,"Environmental":47213,"ĠKenyan":47214,"ĠTrance":47215,"ĠPats":47216,"Ġaliases":47217,"ĠGarfield":47218,"Ġcampaigner":47219,"Ġadvancements":47220,"ĠOkinawa":47221,"ĠCoh":47222,"owsky":47223,"Ġstarved":47224,"Ġsizeable":47225,"Ġ:-)":47226,"ĠmRNA":47227,"Ġsuspensions":47228,"istar":47229,"Scotland":47230,"Prin":47231,"------------------------------------------------":47232,"Ġ502":47233,"Ġteaspoons":47234,"Ġ1050":47235,"Ġcoercive":47236,"ĠMasonic":47237,"edded":47238,"ĠPassenger":47239,"Ġlatt":47240,"Ġbraces":47241,"ĠSteal":47242,"ĠNYT":47243,"ĠKats":47244,"ĠCelest":47245,"aez":47246,"Tu":47247,"ĠCoulter":47248,"ðŁĺ":47249,"Flickr":47250,"ĠWilmington":47251,"iths":47252,"++;":47253,"Ġvending":47254,"Ġnegro":47255,"ĠPhi":47256,"ĠYellowstone":47257,"Callback":47258,"Ġshampoo":47259,"ĠShades":47260,"wat":47261,"Ġsuperhuman":47262,"Ġridiculed":47263,"Ġholiest":47264,"ombo":47265,"Ġinterns":47266,"Ġhone":47267,"ĠParagu":47268,"URI":47269,"Ġdangling":47270,"ãĤ»":47271,"sov":47272,"ictional":47273,"availability":47274,"Ġrevocation":47275,"Ġdow":47276,"inic":47277,"ĠTHEIR":47278,"Ġiso":47279,"Ġoutings":47280,"ĠLethal":47281,"Ġ)))":47282,"Ġinaccur":47283,"Ġoutlandish":47284,"Ġanus":47285,"letico":47286,"idon":47287,"lol":47288,"Ġunregulated":47289,"Ġsuccumbed":47290,"Ġcuff":47291,"ĠWasteland":47292,"letal":47293,"Ġsubstr":47294,"Ġcoffers":47295,"Ġautomakers":47296,"ovi":47297,"ĠXue":47298,"ĠDaytona":47299,"Ġjarring":47300,"Ġfumes":47301,"Ġdisbanded":47302,"zik":47303,"itton":47304,"Ġstrikingly":47305,"Ġspores":47306,"Adapter":47307,".):":47308,"ĠLyndon":47309,"ivalry":47310,"Ġorally":47311,"Ġtumultuous":47312,"Ġdispleasure":47313,"Ġcones":47314,"orrect":47315,"Ġappease":47316,"Ġderby":47317,"ĠTripoli":47318,"ĠAless":47319,"Ġpoked":47320,"ĠGuilty":47321,"vP":47322,"Enough":47323,"Ġoriginals":47324,"699":47325,"Ġrabbi":47326,"Ġproverbial":47327,"Ġpostpone":47328,"elope":47329,"ĠMisty":47330,"Ġstaffed":47331,"ĠUnemployment":47332,"reditary":47333,"Ġdiligent":47334,"recomm":47335,"measures":47336,"asin":47337,"825":47338,"Ġponds":47339,"Ġmmol":47340,"ĠSAR":47341,"ĠCARE":47342,"Ġ371":47343,"Ġclenched":47344,"ĠCorsair":47345,"Ġcaricature":47346,"zn":47347,"attach":47348,"ĠSchro":47349,"speak":47350,"painted":47351,"ĠSuc":47352,"ĠENT":47353,"Ġcellul":47354,"ĠPaid":47355,"diagn":47356,"WHERE":47357,"Ġtexted":47358,"Barn":47359,"Ġretracted":47360,"ĠReferred":47361,"Sav":47362,"Ġupkeep":47363,"Ġworkplaces":47364,"ĠTokens":47365,"Ġamplify":47366,"clinical":47367,"Ġmultic":47368,"mberg":47369,"Ġconvoluted":47370,"Region":47371,"565":47372,"ĠTopic":47373,"Ġsnail":47374,"Ġsaline":47375,"Ġinsurrection":47376,"ĠPetr":47377,"forts":47378,"BAT":47379,"ĠNavajo":47380,"Ġrudimentary":47381,"ĠLaksh":47382,"ONDON":47383,"Measure":47384,"Ġtransformer":47385,"ĠGoddard":47386,"Ġcoincides":47387,"irin":47388,"Rex":47389,"ĠBok":47390,"quit":47391,"Ġshotguns":47392,"Ġproletarian":47393,"Ġscorp":47394,"ĠAda":47395,"514":47396,"Ġslander":47397,"recorded":47398,"Ġembell":47399,"risome":47400,"Ġapologizing":47401,"ĠMulcair":47402,"ĠGibraltar":47403,"Cla":47404,"Ġallot":47405,"ĠAttention":47406,"Ġ433":47407,"leave":47408,"Ġwhine":47409,"ĠIssa":47410,"ĠFaust":47411,"ĠBarron":47412,"heny":47413,"Ġvictimized":47414,"Jews":47415,"Ġnurturing":47416,"ettel":47417,"Winged":47418,"ĠSubtle":47419,"Ġflavorful":47420,"ĠReps":47421,"enged":47422,"callback":47423,"Ġdirectional":47424,"Ġclasp":47425,"ĠDirections":47426,"planet":47427,"iculture":47428,"Helper":47429,"icion":47430,"acia":47431,"Ġç¥ŀ":47432,"Ġsurges":47433,"Ġcanoe":47434,"ĠPremiership":47435,"been":47436,"Ġdefied":47437,"ĠTrooper":47438,"Ġtripod":47439,"Ġgasp":47440,"ĠEuph":47441,"ĠAds":47442,"vernight":47443,"highly":47444,"Role":47445,"Ġentangled":47446,"ĠZeit":47447,"618":47448,"ĠRusty":47449,"Ġhavens":47450,"ĠVaughan":47451,"HAEL":47452,"ĠSERVICE":47453,"/,":47454,"Ġstricken":47455,"Ġdelusions":47456,"Ġbis":47457,"ĠHaf":47458,"Ġgratification":47459,"Ġenticing":47460,"UNCH":47461,"Adams":47462,"ĠOLED":47463,"ĠBeetle":47464,"Ġ1899":47465,"ĠSOFTWARE":47466,"ategor":47467,"VL":47468,"ĠTotem":47469,"ĠGators":47470,"ATURES":47471,"Ġimpedance":47472,"Registered":47473,"ĠCary":47474,"ĠAerial":47475,"onne":47476,"enium":47477,"Ġdred":47478,"ĠBeg":47479,"Ġconcurrently":47480,"Ġsuperpower":47481,"ĠXan":47482,"jew":47483,"imester":47484,"ĠDickinson":47485,"âĶģ":47486,"Fla":47487,"Ġpree":47488,"ĠRollins":47489,"©¶æ":47490,"Ġdenomination":47491,"ĠLana":47492,"516":47493,"Ġinciting":47494,"scribed":47495,"juries":47496,"ĠWonders":47497,"approximately":47498,"Ġsuspending":47499,"Ġmountainous":47500,"ĠLaugh":47501,"oidal":47502,"Ns":47503,"Detect":47504,")=":47505,"ĠLuthor":47506,"ĠSchwarzenegger":47507,"ĠMuller":47508,"ĠDevi":47509,"ecycle":47510,"Jar":47511,"613":47512,"ĠLongh":47513,"Bah":47514,"ĠSPORTS":47515,"nw":47516,"Ġrefinement":47517,"Ġwaterways":47518,"Ġdiner":47519,"Blade":47520,"683":47521,"Fac":47522,"Ġinitials":47523,"Ġrog":47524,"Ġparanormal":47525,"BUT":47526,"Ġ[(":47527,"ĠSwanson":47528,"ĠMesh":47529,"âĸ¬":47530,"Improve":47531,"ĠRadiation":47532,"ĠEsther":47533,"ĠEsk":47534,"ĠAly":47535,"iky":47536,"Ġirrad":47537,"ĠBuckingham":47538,"Ġrefill":47539,"Ġ._":47540,"Repe":47541,"CONCLUS":47542,"Ġdifferentiated":47543,"Ġchirop":47544,"ĠAtkins":47545,"Pattern":47546,"Ġexcise":47547,"Ġcabal":47548,"NSA":47549,"ĠSTA":47550,"ĠSIL":47551,"ĠParaly":47552,"Ġrye":47553,"ĠHowell":47554,"ĠCountdown":47555,"nesses":47556,"alysed":47557,"Ġresize":47558,"ãĤ½":47559,"Ġbudgetary":47560,"ĠStras":47561,"wang":47562,"Ġapiece":47563,"Ġprecincts":47564,"Ġpeach":47565,"Ġskyline":47566,"Ġ353":47567,"popular":47568,"Appearances":47569,"ĠMechanics":47570,"ĠDevOnline":47571,"Sullivan":47572,"Zen":47573,"Ġpu":47574,"opolis":47575,"544":47576,"Ġdeform":47577,"Ġcounteract":47578,"ĠLange":47579,"Ġ417":47580,"Console":47581,"774":47582,"Ġnodding":47583,"Ġpopulism":47584,"Ġhep":47585,"Ġcounselling":47586,"compliance":47587,"UFF":47588,"Ġundeniably":47589,"Ġrailing":47590,"ĠHorowitz":47591,"ĠSimone":47592,"ĠBungie":47593,"Ġak":47594,"ĠTalks":47595,"xff":47596,"flake":47597,"Crash":47598,"Ġsweaty":47599,"Ġbanquet":47600,"ĠOFFIC":47601,"Ġinventive":47602,"Ġastronomer":47603,"ĠStamford":47604,"ĠScare":47605,"ĠGREEN":47606,"olicited":47607,"Ġrusher":47608,"Ġcentrist":47609,"ighting":47610,"Ġsubclass":47611,"Ġdisav":47612,"Ġdefund":47613,"ĠNanto":47614,"ociate":47615,"mast":47616,"Ġpacif":47617,"Ġmend":47618,"eers":47619,"immigration":47620,"ESSION":47621,"Ġnumbering":47622,"Ġlaughable":47623,"ĠEnded":47624,"viation":47625,"emark":47626,"Pitt":47627,"Ġmeticulous":47628,"ĠLF":47629,"Ġcongratulated":47630,"ĠBirch":47631,"Ġswayed":47632,"Ġsemifinals":47633,"Ġhumankind":47634,"matter":47635,"ĠEquip":47636,"opausal":47637,"Said":47638,"ĠLayout":47639,"Ġvoicing":47640,"Ġthug":47641,"Ġpornographic":47642,"IPS":47643,"Ġmoaning":47644,"Ġgrievance":47645,"Ġconfessions":47646,"escal":47647,"TEXTURE":47648,"Authent":47649,"osaurus":47650,"Purchase":47651,"Ġrelegation":47652,"alter":47653,"Ġ³³":47654,"Ġriddled":47655,"Ġogre":47656,"ĠLowell":47657,"Occup":47658,"Eat":47659,"ĠHyder":47660,"ĠAdviser":47661,"Commerce":47662,"Hunt":47663,"ĠOrth":47664,"ĠCompetitive":47665,"ĠCLA":47666,"CDC":47667,"Ġsalads":47668,"Fle":47669,"Ġindustrialized":47670,"`,":47671,"ĠOWN":47672,"Ġbeck":47673,"ĠParticularly":47674,"oubt":47675,"ĠmM":47676,"ĠHussain":47677,"ĠChennai":47678,"Ġ920":47679,"Ġappointing":47680,"ĠCullen":47681,",,,,,,,,":47682,"Ġpores":47683,"verified":47684,"Ġbiochemical":47685,"emate":47686,"Ġcowardly":47687,"ĠHelsinki":47688,"ĠEthiopian":47689,"SOURCE":47690,"ERC":47691,"estro":47692,"Ġbiotech":47693,"ĠSour":47694,"Ġbrewer":47695,"Bloomberg":47696,"Ġintensify":47697,"Glass":47698,"anco":47699,"ĠFDR":47700,"greSQL":47701,"ĠFires":47702,"©¶æ¥µ":47703,"eco":47704,"1001":47705,"ĠHomeless":47706,"Ġinstantaneous":47707,"ĠHaste":47708,"igel":47709,"Diamond":47710,"Ġpaving":47711,"Ġlandfill":47712,"Ġdads":47713,"houn":47714,":]":47715,"Ġincendiary":47716,"ĠLivingston":47717,"ĠHilbert":47718,"ĠChecks":47719,"styles":47720,"inators":47721,"ĠClive":47722,"phrine":47723,"Ġchimpanzees":47724,"Ġpall":47725,"ĠJM":47726,"ĠAadhaar":47727,"ðĿ":47728,"Ġachievable":47729,"disabled":47730,"PET":47731,"OOOOOOOO":47732,"Mot":47733,"Ġintangible":47734,"Ġballet":47735,"ĠWebs":47736,"ĠEstimated":47737,"Effects":47738,"Ġbailed":47739,"Joshua":47740,"Ġturbulence":47741,"Ġoccupant":47742,"ĠDaylight":47743,"Ġ361":47744,"meet":47745,"Ġstatically":47746,"Ġonlook":47747,"Ġki":47748,"illegal":47749,"Ġvelvet":47750,"Ġdehydration":47751,"Ġacquies":47752,"ĠRez":47753,"akura":47754,"ĠUpton":47755,"atro":47756,"Ġincomprehensible":47757,"Ġbackdoor":47758,"ĠRhino":47759,"727":47760,"Ġmaths":47761,")+":47762,"Ġheresy":47763,"Ġdf":47764,"ĠRoche":47765,"ĠLydia":47766,"Ġpancreat":47767,"reply":47768,"arrell":47769,"Ġsolicitation":47770,"Ġcircadian":47771,"BIP":47772,"Ġforay":47773,"Ġcryptic":47774,"izu":47775,"imeo":47776,"ĠTomato":47777,"ĠHoms":47778,"examination":47779,"Ġquarry":47780,"ĠValiant":47781,"ĠJericho":47782,"ĠINCLUD":47783,"Ġ1840":47784,"519":47785,"Ġresists":47786,"Ġsnapshots":47787,"ĠSpur":47788,"ĠAntiqu":47789,"Login":47790,"Ġbestselling":47791,"Ġantic":47792,"ĠSutherland":47793,"ãĤ¢ãĥ«":47794,"Ġ~/":47795,"ĠParm":47796,"èĥ":47797,"Pages":47798,"intensity":47799,"Ġimmobil":47800,"Ġ1865":47801,"zzo":47802,"Ġnifty":47803,"Ġfentanyl":47804,"ĠPreservation":47805,"ophen":47806,"Ġdarts":47807,"ĠDinosaur":47808,"pointers":47809,"ĠRite":47810,"suggest":47811,"awareness":47812,"ĠSheridan":47813,"Ġstances":47814,"Ġsorcery":47815,"Ġperjury":47816,"ĠNikola":47817,"iever":47818,"Ġfiance":47819,"ĠJordanian":47820,"ĠBalloon":47821,"Ġnab":47822,"Ġkb":47823,"Ġhumanities":47824,"ĠTanaka":47825,"hillary":47826,"Ġconsultancy":47827,"ĠZub":47828,"Ġremission":47829,"Ġconfid":47830,"CHQ":47831,"ĠFug":47832,"Ġimprovis":47833,"Yep":47834,"/_":47835,"Ġunwillingness":47836,"Ġportfolios":47837,"055":47838,"ĠInstructor":47839,"aiman":47840,"Ġclaimants":47841,"Mbps":47842,"ĠBye":47843,"received":47844,"Tweet":47845,"Ġindemn":47846,"riz":47847,"amara":47848,"Nat":47849,"Ġevaluates":47850,"ĠLur":47851,"epad":47852,"FOX":47853,"ĠThro":47854,"Ġrusty":47855,"Ġbedrock":47856,"ĠOprah":47857,"JB":47858,"Ġmanipulative":47859,"Ġwillful":47860,"Ġrelapse":47861,"Ġextant":47862,"Theme":47863,"Sensor":47864,"ĠStability":47865,"govern":47866,"Ġpoppy":47867,"Ġknack":47868,"Ġinsulated":47869,"ĠTile":47870,"ĠExtrem":47871,"Ġuntold":47872,"Ġconverge":47873,"Ġrefuel":47874,"igroup":47875,"Ġdistortions":47876,"Ġravaged":47877,"Ġmechanically":47878,"ĠReilly":47879,"ĠNose":47880,"ĠIncarnation":47881,"ĠBecky":47882,"abbling":47883,"Ġtaco":47884,"Ġrake":47885,"Ġmelancholy":47886,"Ġillustrious":47887,"ĠDartmouth":47888,"Guide":47889,"ĠRazer":47890,"ĠBenz":47891,"Ultimate":47892,"ĠSurprise":47893,"Ġpageant":47894,"offer":47895,"Whoever":47896,"Ġwiser":47897,"Ġchemist":47898,"ĠHELL":47899,"ĠBulk":47900,"Ġplutonium":47901,"ĠCOVER":47902,"Ö¼":47903,"failed":47904,"Ġtirelessly":47905,"Ġinfertility":47906,"ĠTrident":47907,"ĠShowtime":47908,"ĠCiv":47909,"Vice":47910,"requires":47911,"ittance":47912,"Ġuncontrolled":47913,"interesting":47914,"561":47915,"Ġinnovate":47916,"ategic":47917,"Lie":47918,"ĠSelling":47919,"Ul":47920,"Ġsavior":47921,"ĠTosh":47922,"Ġswast":47923,"PASS":47924,"Ġrink":47925,"Ġcardio":47926,"ĠIro":47927,"udi":47928,"Ġvantage":47929,"Ġvans":47930,"ĠNiño":47931,"+=":47932,"Ġpropagate":47933,"":49029,"Ġleukemia":49030,"Ġeluc":49031,"Ġannouncer":49032,"ĠLithuan":49033,"ĠArmageddon":49034,"åĩ":49035,"Lenin":49036,"ĠRuk":49037,"Ġpepp":49038,"ĠRomantic":49039,"ĠPIT":49040,"ĠInterstellar":49041,"ĠAtkinson":49042,"Raid":49043,"Js":49044,"Goal":49045,"Course":49046,"Ġvanishing":49047,"esley":49048,"ĠRounds":49049,"Elsa":49050,"593":49051,"Ġredundancy":49052,"ĠSTAND":49053,"Ġprophetic":49054,"Ġhabitable":49055,"ryu":49056,"Ġfaintly":49057,"MODE":49058,"Ġflanked":49059,"IRC":49060,"Awesome":49061,"Ġspurious":49062,"ĠZah":49063,"ĠMSG":49064,"Ġshading":49065,"Ġmotivational":49066,"ĠSantana":49067,"ĠSPR":49068,"Ġexcruciating":49069,"omial":49070,"ĠMiko":49071,"ĠLeopard":49072,"Abyss":49073,"Ġ[|":49074,"dirty":49075,"Ġbaths":49076,"Ġdemoral":49077,"andre":49078,"PB":49079,"Ġunification":49080,"Ġsacrament":49081,"Ġ[&":49082,"Ġpriceless":49083,"Ġgelatin":49084,"Ġemanating":49085,"ĠAllaah":49086,"986":49087,"Ġoutburst":49088,"Ġeras":49089,"ĠXVI":49090,"ĠSPI":49091,"Ott":49092,"ĠLazarus":49093,"PLIED":49094,"Flying":49095,"blogs":49096,"Wisconsin":49097,"Raven":49098,"Ġrebate":49099,"Ġcreeps":49100,"ĠSpan":49101,"ĠPainter":49102,"ĠKira":49103,"ĠAmos":49104,"ĠCorvette":49105,"Consumer":49106,"ĠRecover":49107,"cki":49108,"Ġpesky":49109,"ĠInvention":49110,"Companies":49111,"Ġchallengers":49112,"ademic":49113,"ĠUkrainians":49114,"ĠNeurolog":49115,"ĠForsaken":49116,"Ġentrants":49117,"Ġembattled":49118,"Ġdefunct":49119,"ĠGlacier":49120,"Ġpoisons":49121,"ĠHorses":49122,"makes":49123,"ĠDirt":49124,"Ġ423":49125,"hhh":49126,"ĠTransformation":49127,"QUIRE":49128,"..................":49129,"Ġtraveller":49130,"ĠSexy":49131,"ĠKern":49132,"ipolar":49133,"Ġransomware":49134,"oooooooooooooooo":49135,"Ec":49136,"ruby":49137,"Professional":49138,"ĠOutbreak":49139,"argument":49140,"Grey":49141,"ĠFifa":49142,"ĠCHO":49143,"ĠFORM":49144,"ĠAmtrak":49145,"-[":49146,"Ġcradle":49147,"Ġantioxidants":49148,"ãģ®å®":49149,"736":49150,"ĠNASL":49151,"ĠContributions":49152,"Indiana":49153,"ĠSTEP":49154,"CSS":49155,"Ġsalient":49156,"Ġallocations":49157,"yrights":49158,"Ġmashed":49159,"ĠCutter":49160,"Sexual":49161,"Ġpounded":49162,"Ġfanbase":49163,"Ġcasc":49164,"ĠTransparency":49165,"Ġanalytic":49166,"ĠSummoner":49167,"×ŀ":49168,"ĠADC":49169,"detail":49170,"Ġvanquished":49171,"Ġcrabs":49172,"arie":49173,"Destroy":49174,"ĠSack":49175,"Ġtransistor":49176,"Alabama":49177,"ĠKoen":49178,"ĠFisheries":49179,"cone":49180,"Ġannexed":49181,"ĠMGM":49182,"esa":49183,"Ġfaked":49184,"ĠCongratulations":49185,"Ġhindered":49186,"Ġcorrectional":49187,"ĠITV":49188,"leeve":49189,"Ġinappropriately":49190,"licks":49191,"Ġtrespass":49192,"Ġpaws":49193,"Ġnegotiator":49194,"ĠChristensen":49195,"limits":49196,"ĠDianne":49197,"Ġelegance":49198,"ĠContracts":49199,"anke":49200,"Obj":49201,"Ġvigilance":49202,"Ġcastles":49203,"ĠNAD":49204,"ĠHolo":49205,"Ġemphatically":49206,"ĠTitus":49207,"ĠServing":49208,"ĠRichie":49209,"ĠPigs":49210,"568":49211,"Ġanimosity":49212,"ĠAttributes":49213,"ĠUriel":49214,"MQ":49215,"myra":49216,"ĠApplicant":49217,"Ġpsychiatrists":49218,"ĠVij":49219,"ĠAbby":49220,"agree":49221,"Push":49222,"ĠkWh":49223,"hiba":49224,"Ġincite":49225,"ĠWeasley":49226,"ĠTaxi":49227,"ministic":49228,"hyper":49229,"ĠFarn":49230,"Ġ601":49231,"ĠNationwide":49232,"Fake":49233,"952":49234,"Ġmaize":49235,"Ġinteracted":49236,"Ġtransitioned":49237,"Ġparasitic":49238,"Ġharmonic":49239,"Ġdecaying":49240,"Ġbaseless":49241,"nsics":49242,"Ġtranspired":49243,"Ġabundantly":49244,"ĠForensic":49245,"Ġtreadmill":49246,"ĠJav":49247,"aband":49248,"Ġsshd":49249,"Ġfrontman":49250,"ĠJakarta":49251,"oller":49252,"drops":49253,"ĠSERVICES":49254,"romptu":49255,"ophical":49256,"hospital":49257,"bledon":49258,"645":49259,"Ġmidrange":49260,"ĠEVENT":49261,"culated":49262,"rawled":49263,"Ġperched":49264,"Ġoverboard":49265,"ĠPeel":49266,"ĠPwr":49267,"ĠCarth":49268,"ĠCOMPLE":49269,"coe":49270,"shall":49271,"Ġdeterrence":49272,"METHOD":49273,"ĠAbsent":49274,"MEN":49275,"Ġsill":49276,"ĠLEVEL":49277,"York":49278,"Ġsinners":49279,"ĠOPEC":49280,"ĠNur":49281,"ĠDesigns":49282,"selection":49283,"Ġunworthy":49284,"CHA":49285,"Ġstrengthens":49286,"883":49287,"edly":49288,"Ġslicing":49289,"Ġmalnutrition":49290,"Ġfilmmaking":49291,"ĠPolk":49292,"urated":49293,"Ġ421":49294,"breakers":49295,"!'\"":49296,"Ġwetlands":49297,"ĠDiscrimination":49298,"Ġallowable":49299,"Ġsteered":49300,"ĠSicily":49301,"SAM":49302,"Ġmustache":49303,"Ġmids":49304,"Ġclipped":49305,"Ġcirculate":49306,"Ġbrittle":49307,"ĠBuildings":49308,"raised":49309,"ĠRoundup":49310,"Ġwealthier":49311,"Ġoverwrite":49312,"Ġoverpowered":49313,"ĠGerrard":49314,"sites":49315,"PDATED":49316,"Ġacutely":49317,"ĠGamble":49318,"Ġpim":49319,"ĠKus":49320,"Typically":49321,"Deploy":49322,"ĠMoroccan":49323,"potion":49324,"combe":49325,"Ġvigilante":49326,"Ġ363":49327,"Stew":49328,"ĠBagg":49329,"Ġresided":49330,"ĠSpo":49331,"Ġremnant":49332,"Ġemptiness":49333,"brainer":49334,"Ġoutpatient":49335,"priority":49336,"Ġleptin":49337,"ĠPayton":49338,"ĠGleaming":49339,"ĠShed":49340,"ĠPolo":49341,"ĠMormonism":49342,"restricted":49343,"arlane":49344,"wx":49345,"Ġcreatine":49346,"ĠAnon":49347,"ĠSTUD":49348,"ĠJUL":49349,"ĠTee":49350,"528":49351,"089":49352,"Ġhatched":49353,"Dispatch":49354,"ĠComposite":49355,"Ġ451":49356,"puff":49357,"ĠXCOM":49358,"ĠOrn":49359,"ĠTHANK":49360,"ENDED":49361,"ĠAsheville":49362,"ĠÃľ":49363,"Ġmango":49364,"ĠSlightly":49365,"worldly":49366,"ĠWander":49367,"ĠExpand":49368,"ĠChr":49369,"Mist":49370,"Ġorthodoxy":49371,"ĠUNESCO":49372,"regate":49373,"Elsewhere":49374,"kie":49375,"irled":49376,"Ġtopple":49377,"Ġadoptive":49378,"ĠLegs":49379,"dress":49380,"ĠSagan":49381,"bare":49382,"ĠGlou":49383,"Crunch":49384,"Ġhelpers":49385,"Ġchronically":49386,"ĠHuma":49387,"10000":49388,"Ġaccommodating":49389,"äºĶ":49390,"Ġwrinkles":49391,"Ġdodged":49392,"fourth":49393,"Ġprecon":49394,"Ġcompressor":49395,"ĠKare":49396,"Ġevict":49397,"ĠWarwick":49398,"imar":49399,"Ġmodernization":49400,"Ġbandwagon":49401,"Ġrefuted":49402,"Ġnetted":49403,"ĠNaples":49404,"ĠGenie":49405,"perors":49406,"Ġfielded":49407,"Ġdere":49408,"ĠParables":49409,"lees":49410,"Ġtrout":49411,"aspers":49412,"Ġnihil":49413,"Ġhappiest":49414,"Ġfloppy":49415,"ĠLoft":49416,"ĠHeard":49417,"Ġunison":49418,"Ġlug":49419,"ĠRedmond":49420,"classic":49421,"Supporters":49422,"SHIP":49423,"GMT":49424,"Ġfuelled":49425,"çIJ":49426,"Ġdd":49427,"ĠEminem":49428,"Ġ1897":49429,"NYSE":49430,"Ġsecretaries":49431,"ĠFIA":49432,"ĠCanaveral":49433,"Favorite":49434,"Ġpomp":49435,"Ġdetainee":49436,"ership":49437,"aimon":49438,"iour":49439,"ĠApex":49440,"Ġplantations":49441,"amia":49442,"acion":49443,"Rust":49444,"Ġtowed":49445,"ĠTruly":49446,"577":49447,"Ġsheltered":49448,"rider":49449,"Wo":49450,"Ġlair":49451,"ĠIntelligent":49452,"improve":49453,"matically":49454,"Ġetiquette":49455,"adra":49456,"allo":49457,"ĠJuno":49458,"anything":49459,"ĠStruggle":49460,"ĠPredict":49461,"ĠGrimes":49462,"ĠAMERICA":49463,"ctx":49464,"ĠSituation":49465,"WOOD":49466,"Ġsoluble":49467,"meier":49468,"Ġintolerable":49469,"angering":49470,"Ġuninterrupted":49471,"Ġtooltip":49472,"Ġinterrogated":49473,"Ġgunned":49474,"ĠSneak":49475,"æѦ":49476,"Ġtether":49477,"Ġcrumble":49478,"Lens":49479,"Ġclustered":49480,"ĠSyl":49481,"ĠHasan":49482,"Ġdystopian":49483,"wana":49484,"Ġjoystick":49485,"ĠThib":49486,"ammu":49487,"Tomorrow":49488,"546":49489,"Ġovercame":49490,"Ġminimized":49491,"ceptor":49492,"Runner":49493,"ENGTH":49494,"ĠBrenda":49495,"ĠAchievements":49496,"Ġtorches":49497,"Ġrapport":49498,"ĠInvestigator":49499,"ĠHandling":49500,"relation":49501,"grey":49502,"815":49503,"Ġkcal":49504,"ĠCommands":49505,"dq":49506,"Ġcurls":49507,"Ġbearer":49508,"Ġcynicism":49509,"itri":49510,"ĠUseful":49511,"Bee":49512,"DCS":49513,"Ġabras":49514,"Pract":49515,"BILITIES":49516,"712":49517,"Ġdebugger":49518,"Ġdebtor":49519,"ĠLia":49520,"ĠKers":49521,"Ġexacerbate":49522,"ĠStacy":49523,"ĠBland":49524,"ĠScenes":49525,"Ġbranching":49526,"âĸĪâĸĪâĸĪâĸĪâĸĪâĸĪâĸĪâĸĪ":49527,"apeake":49528,"Ġsalsa":49529,"Ġmishand":49530,"ĠKonami":49531,"ĠNib":49532,"Ġanecdote":49533,"Ġagreeable":49534,"Ïī":49535,"ĠNathaniel":49536,"ĠHeisman":49537,"ĠBeware":49538,"Ġ1886":49539,"spective":49540,"691":49541,"522":49542,"Ġinhibits":49543,"Ġhashing":49544,"Ġ1889":49545,"å°Ĩ":49546,"vich":49547,"Pure":49548,"Ġsolidly":49549,"Ġaspirin":49550,"imaru":49551,"Ġstreetcar":49552,"ĠUCS":49553,"ĠJudd":49554,"Ġflashbacks":49555,"pins":49556,"Ġ1440":49557,"ĠUNHCR":49558,"ĠSymptoms":49559,"TIT":49560,"538":49561,"Fra":49562,"%);":49563,"Ġooz":49564,"Ġcurfew":49565,"Ġcalmed":49566,"Ġparticipates":49567,"TeX":49568,"Ġnonsensical":49569,"Ġfullback":49570,"ĠDeL":49571,"monkey":49572,"hari":49573,"Ġmetabolites":49574,"Ġlooted":49575,"ĠALWAYS":49576,"ĠBCC":49577,"Lt":49578,"ochet":49579,"Bone":49580,"Ġvetoed":49581,"Ġgcc":49582,"ĠCLICK":49583,"Ġ1888":49584,"saf":49585,"Ġstiffness":49586,"Ġlowly":49587,"ĠGeh":49588,"verson":49589,"orset":49590,"Ġunforeseen":49591,"Ġanesthesia":49592,"ĠOptical":49593,"Ġreconstructed":49594,"ĠTup":49595,"shows":49596,"NEWS":49597,"ĠNewspaper":49598,"ĠASA":49599,"tera":49600,"Numbers":49601,"Ġinexplicable":49602,"×ij":49603,"Ġhardness":49604,"untarily":49605,"ĠAcer":49606,"gradient":49607,"ARDIS":49608,"Ġwoodland":49609,"Ġmetaphors":49610,"ĠWembley":49611,"ĠPavel":49612,"philis":49613,"Ġrewriting":49614,"Ġperceptual":49615,"Ġ1070":49616,"worms":49617,"ĠDowns":49618,"Ġunsurprisingly":49619,"Ġtagging":49620,"flame":49621,"Ġlitres":49622,"Ġbounces":49623,"ĠBabe":49624,"shut":49625,"Ġoverdoses":49626,"ĠSheila":49627,"ĠChau":49628,"ĠBless":49629,"Capture":49630,"ĠSignificant":49631,"ĠScion":49632,"Ġ389":49633,"ĠMcH":49634,"ĠTitanium":49635,"ĠMeal":49636,"ameda":49637,"agents":49638,"aggressive":49639,"Billy":49640,"763":49641,"ĠSaying":49642,"DERR":49643,"itone":49644,"Collins":49645,"Bound":49646,"Ġbolted":49647,"ĠDMCA":49648,"953":49649,"Ġuniqueness":49650,"Ġepigen":49651,"unci":49652,"antam":49653,"Ġreckoning":49654,"chairs":49655,"OGR":49656,"ĠSenegal":49657,"Ġ1862":49658,"relevant":49659,"Ġ¯":49660,"Ġpharmacies":49661,"ĠGeral":49662,"vier":49663,"Yan":49664,"ORPG":49665,"Ġrabid":49666,"bending":49667,"ĠUNITED":49668,"Ġ465":49669,"Assembly":49670,"Ġweep":49671,"Ġbehest":49672,"ĠMothers":49673,"ĠJace":49674,"hid":49675,"Ġwhirlwind":49676,"ĠUNIVERS":49677,"Ġutopian":49678,"Ġkidnap":49679,"Philipp":49680,"Kin":49681,"893":49682,"Ġlivestream":49683,"ĠMISS":49684,"Ġsubversive":49685,"ĠTechniques":49686,"ĠJUSTICE":49687,"ĠBASE":49688,"Ġ387":49689,"Ġassailants":49690,"ĠHardcore":49691,"Ġsprinkled":49692,"ĠPse":49693,"éļ":49694,"printed":49695,"ĠHau":49696,"ORGE":49697,"ĠTOUR":49698,"Ġlaced":49699,"Ġitch":49700,"Giving":49701,"Ġported":49702,"781":49703,"////////////////////////////////":49704,"breeding":49705,"Ġlogger":49706,"ĠHOL":49707,"innie":49708,"Firstly":49709,"Ġembryonic":49710,"Ġdelegated":49711,"pai":49712,"OIL":49713,"Ġcentrally":49714,"ĠRx":49715,"ĠScouting":49716,"Dutch":49717,"Ġhereditary":49718,"ĠCruiser":49719,"sat":49720,"529":49721,"ĠMarriott":49722,"othermal":49723,"Ġprohibitions":49724,"Earn":49725,"ĠStab":49726,"ĠColleges":49727,"ĠBelief":49728,"stretched":49729,"ĠLH":49730,"ĠEntityItem":49731,"CIA":49732,"Ġunrem":49733,"Ġlaureate":49734,"Ġdenominations":49735,"summary":49736,"hler":49737,"Spect":49738,"ĠKlaus":49739,"ĠBeans":49740,"Ġinsur":49741,"ĠPAX":49742,"Ġfielder":49743,"ĠVet":49744,"ĠSparrow":49745,"zie":49746,"ĠSQ":49747,"ĠMondays":49748,"ĠOffline":49749,"ĠLerner":49750,"ĠExtensions":49751,"Ireland":49752,"Ġpatronage":49753,"Ġcontrasted":49754,"ĠMania":49755,"hirt":49756,"Moscow":49757,"Ġcondemns":49758,"ĠAnge":49759,"Ġcomposing":49760,"ĠPepe":49761,"ĠPaddock":49762,"Ġheterogeneity":49763,"Ġideologically":49764,"Ġfishes":49765,"Ġcursing":49766,"ĠRutherford":49767,"ĠFloating":49768,"ĠAmelia":49769,"Tea":49770,"Synopsis":49771,"Ġstunts":49772,"Ġbead":49773,"Ġstocking":49774,"ĠMILL":49775,"obook":49776,"massive":49777,"\\<":49778,"Ġhump":49779,"ĠPreferences":49780,"EngineDebug":49781,"geist":49782,"ĠNieto":49783,"omever":49784,"ishy":49785,"evaluate":49786,"colonial":49787,"Alternative":49788,"ĠGoPro":49789,"ĠVortex":49790,"ĠNETWORK":49791,"ansky":49792,"Secure":49793,"ĠThrust":49794,"Snake":49795,"Ġparcels":49796,"Ġsamurai":49797,"Ġactresses":49798,"Nap":49799,"MF":49800,"iferation":49801,"Beer":49802,"523":49803,"ĠIly":49804,"ointment":49805,"Ping":49806,"Ġstriped":49807,"ĠMellon":49808,"ossession":49809,"Ġneutron":49810,"endium":49811,"Ġaph":49812,"ĠFlavoring":49813,"Ġ383":49814,"Ġresponsiveness":49815,"ĠJindal":49816,"ĠHitchcock":49817,"Denver":49818,"ĠDRAGON":49819,"smanship":49820,"ĠDupl":49821,"Ġsly":49822,"Ġwebcam":49823,"ĠTwain":49824,"ĠDarling":49825,"iliate":49826,"consumer":49827,"DIT":49828,"Ġnamesake":49829,"Ġunorthodox":49830,"Ġfuner":49831,"ĠPLoS":49832,"ĠCONTROL":49833,"ozyg":49834,"oglobin":49835,"FACE":49836,"ERG":49837,"ĠDia":49838,"ĠFiesta":49839,"cele":49840,"034":49841,"Ġenclave":49842,"âĸ¬âĸ¬":49843,"onement":49844,"alist":49845,"Mand":49846,"Ġhomegrown":49847,"ĠFancy":49848,"Ġconceptions":49849,"ĠContains":49850,"ureen":49851,"Ġreiterate":49852,"Ġmeager":49853,"Ġinstallments":49854,"Spawn":49855,"627":49856,"Ġphotoc":49857,"ĠCabrera":49858,"ĠRosenthal":49859,"ĠLansing":49860,"isner":49861,"Ġinvests":49862,"ĠUFOs":49863,"EXP":49864,"Hardware":49865,"Ġtragically":49866,"Ġconcedes":49867,"ieft":49868,"cham":49869,"borgh":49870,"ĠSchr":49871,"ĠMelanie":49872,"ĠHoy":49873,"Ġvisitation":49874,"Ġidiosyncr":49875,"Ġfractions":49876,"Ġforeskin":49877,"obos":49878,"Ġpoaching":49879,"ĠVIEW":49880,"Ġstimulates":49881,"ĠGork":49882,"canon":49883,"MIC":49884,"ĠNemesis":49885,"ĠIndra":49886,"ĠDMV":49887,"Ġ529":49888,"Ġinspecting":49889,"Ġgrandma":49890,"ĠWhedon":49891,"ĠShant":49892,"ĠPurg":49893,"ikan":49894,"ĠTeg":49895,"ĠCLR":49896,"zac":49897,"Victoria":49898,"ĠVerify":49899,"ionics":49900,"Ġpartying":49901,"ĠMou":49902,"colour":49903,"Ġtestimonies":49904,"lations":49905,"Ġpressuring":49906,"hiro":49907,"acers":49908,"Ġfid":49909,"angler":49910,"ĠCSI":49911,"Ġhereafter":49912,"Ġdissidents":49913,"reporting":49914,"iphany":49915,"chev":49916,"Ġsolitude":49917,"Ġlobe":49918,"Ġindis":49919,"Ġcredential":49920,"recent":49921,"adult":49922,"ĠNirvana":49923,"ĠFranchise":49924,"Layer":49925,"Hyp":49926,"ĠBerkshire":49927,"Ġwills":49928,"tif":49929,"Ġtotem":49930,"ĠJudah":49931,"repair":49932,"Instant":49933,"548":49934,"Ġembassies":49935,"Ġbottleneck":49936,"Ġbount":49937,"Ġtypew":49938,"ĠAlvin":49939,"jing":49940,"imilar":49941,"Rush":49942,"Ġbrim":49943,"ĠHELP":49944,"Aim":49945,"]'":49946,"Ġpassively":49947,"Ġbounded":49948,"ĠRated":49949,"Ġcriminality":49950,"Ġbiomark":49951,"Ġdispatcher":49952,"ĠTowards":49953,"Ġ+++":49954,"righteous":49955,"frog":49956,"ĠPanc":49957,"Carter":49958,"032":49959,"æ©Ł":49960,"Ġultraviolet":49961,"ĠLicensed":49962,"ĠTata":49963,"ĠBlessing":49964,"ĠGAM":49965,"Ġchemically":49966,"ĠSeaf":49967,"ĠRELE":49968,"ĠMercenary":49969,"capitalist":49970,"Ġformulations":49971,"Ġannihilation":49972,"ĠVerb":49973,"ĠArgon":49974,"Ġunloaded":49975,"Ġmorphed":49976,"Ġconquering":49977,"backer":49978,"IELD":49979,"Ġthefts":49980,"Ġfrontrunner":49981,"ĠRoyale":49982,"ĠFundamental":49983,"elight":49984,"Chip":49985,"necessary":49986,"ayn":49987,"ĠSlip":49988,"Ġ448":49989,"cerned":49990,"Pause":49991,"Ġshockingly":49992,"ĠABV":49993,"Ġcomposure":49994,"733":49995,"ĠMotorsport":49996,"ahime":49997,"Murray":49998,"Mach":49999,"Ġgrids":50000,"Ġdebian":50001,"Ġfurthermore":50002,"Ġdexterity":50003,"ĠCollections":50004,"oslov":50005,"ilage":50006,"bj":50007,"ĠMonteneg":50008,"ĠstrutConnector":50009,"Ġmassacres":50010,"Ġbriefs":50011,"fetched":50012,"uvian":50013,"olition":50014,"Failure":50015,"emonic":50016,"Ġflared":50017,"Ġclaimant":50018,"Ġcures":50019,"Ġgiveaways":50020,"ĠSubstance":50021,"alions":50022,"Ġcringe":50023,"ĠKul":50024,"Ġaristocracy":50025,"ĠUlster":50026,"olated":50027,"housing":50028,"ĠMIS":50029,"Ġglared":50030,"ĠWilhelm":50031,"needs":50032,"lambda":50033,"builders":50034,"ĠVIS":50035,"Ġradiator":50036,"ĠGhostbusters":50037,"Ġ436":50038,"actual":50039,"Ġherds":50040,"ça":50041,"watching":50042,"Ġcountering":50043,"Charge":50044,"Ġcharred":50045,"Ġwarheads":50046,"Ġiodine":50047,"ĠMacy":50048,"041":50049,"Ġdepartures":50050,"ĠSins":50051,"Ġdyed":50052,"ĠConcepts":50053,"gado":50054,"713":50055,"Ġquotations":50056,"Ġgist":50057,"ĠChristy":50058,"Ġantigen":50059,"ĠHemp":50060,"ĠDrawn":50061,"ĠBarg":50062,"ezvous":50063,"Ġpaternity":50064,"Ġardu":50065,"ĠAnchorage":50066,"ĠRik":50067,"Ġoverloaded":50068,"ĠUsername":50069,"ĠTammy":50070,"ĠNau":50071,"ĠCellular":50072,"Ġwaning":50073,"Ġrodent":50074,"ĠWorcester":50075,"ilts":50076,"ĠTad":50077,"Ġdwellings":50078,"Ġbullish":50079,"431":50080,"Ġretaliate":50081,"Ġmigraine":50082,"ĠChevron":50083,"CHECK":50084,"Ġdonkey":50085,"crim":50086,"SPA":50087,"ĠAnalog":50088,"Ġmarquee":50089,"ĠHaas":50090,"Bir":50091,"ĠGDDR":50092,"ĠDownloads":50093,"Ġwillpower":50094,"ĠForth":50095,"ĠRecorded":50096,"Ġimpossibility":50097,"ĠLogged":50098,"ĠFranks":50099,"ĠRatt":50100,"initions":50101,"Ġcleaners":50102,"Ġsorely":50103,"Ġflickering":50104,"ĠExamination":50105,"catching":50106,"alloween":50107,"Msg":50108,"Ġdunno":50109,"Fa":50110,"Ġdysph":50111,"crazy":50112,".''.":50113,"Ġmainline":50114,"Ġcs":50115,"Ġptr":50116,"ĠWally":50117,"igun":50118,"951":50119,"ĠBigfoot":50120,"fights":50121,"Ġretrieving":50122,"Jr":50123,"Ġduplication":50124,"ĠExplan":50125,"Ġrelational":50126,"Ġquaint":50127,"Ġbiscuits":50128,"Ġado":50129,"Ġshudder":50130,"Ġantidote":50131,"blooded":50132,"ksh":50133,"Ġsauces":50134,"Ġreinvest":50135,"Ġdispensary":50136,"ĠDiver":50137,"Ġ9000":50138,"student":50139,"Ġinsepar":50140,"escap":50141,"Ġtoddlers":50142,"ĠGPIO":50143,"ĠAssignment":50144,"headers":50145,"Ġlackluster":50146,"Ġaback":50147,"956":50148,"Ġtoolbar":50149,"745":50150,"Ġoust":50151,"Ġcontemplation":50152,"ĠPRESIDENT":50153,"Ġ458":50154,"======":50155,"Ġguaranteeing":50156,"ĠHeist":50157,"ĠCannes":50158,"Ļ½":50159,"Ġcollaborator":50160,"ĠAmp":50161,"Ġgou":50162,"ĠSHALL":50163,"stories":50164,"783":50165,"Ġmobilized":50166,"Ġbrood":50167,"ĠLU":50168,"ĠðŁij":50169,"Ġrefin":50170,"ĠAnthropology":50171,"vind":50172,"illi":50173,"Ġwarranties":50174,"ĠBabel":50175,"Ġswath":50176,"Ġcaches":50177,"Ġantagonists":50178,"artifacts":50179,"Ġhotly":50180,"ĠStarts":50181,"ĠGö":50182,"zag":50183,"!!!!!":50184,"Ġscourge":50185,"Ġconspiring":50186,"ruits":50187,"reverse":50188,"ĠSheen":50189,"ĠJesuit":50190,"ĠGiovanni":50191,"adies":50192,"Ġbuttocks":50193,"earcher":50194,"acan":50195,"Ġvolleyball":50196,"Ġshrouded":50197,"Ġscoreboard":50198,"bats":50199,"ĠIPM":50200,"Ġasses":50201,"Ġderegulation":50202,"ĠTelegram":50203,"ĠReboot":50204,"Ġ7000":50205,"ĠCanary":50206,"Ġkernels":50207,"ĠFrançois":50208,"ĠDuff":50209,"ĠPon":50210,"ĠLeica":50211,"ĠGarmin":50212,"Ġorphans":50213,"ĠClaudia":50214,"Ġcalendars":50215,"ĠLeilan":50216,"ento":50217,"Rocket":50218,"Ġbrunch":50219,"ĠHawking":50220,"ainers":50221,"Ġsensibilities":50222,"ĠkW":50223,"ĠKand":50224,"Ġreclaimed":50225,"Ġinterestingly":50226,"ש":50227,"romy":50228,"JM":50229,"ĠEnhancement":50230,"bush":50231,"Skip":50232,"Ġrappers":50233,"Ġgazing":50234,"pedia":50235,"athlon":50236,"Revolution":50237,"Ġsnipers":50238,"Ġreverted":50239,"Ġconglomerate":50240,"Terry":50241,"794":50242,"Ġharsher":50243,"Ġdesolate":50244,"ĠHitman":50245,"Commission":50246,"Ġ(/":50247,"âĢ¦.\"":50248,"Compar":50249,"Ġamplification":50250,"ominated":50251,"Ġregress":50252,"ĠCollider":50253,"Ġinformants":50254,"Ġgazed":50255,"<|endoftext|>":50256} \ No newline at end of file diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/mel_filters.npz b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/mel_filters.npz new file mode 100644 index 0000000000000000000000000000000000000000..9793b084efeb26bf24cb2ba1f30d0989b2c94aa1 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/mel_filters.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd2cc75e70e36fcbdd8ffbc2499062f30094093e6bf2cbafa9859f59972b420b +size 2048 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/added_tokens.json b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/added_tokens.json new file mode 100644 index 0000000000000000000000000000000000000000..8c47192e3c147457973fb41368cad0d8ff3ee0ea --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/added_tokens.json @@ -0,0 +1 @@ +{"<|endoftext|>": 50257} diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/merges.txt b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..e8e5d4ef516711af08314d10f4d066a1363b7107 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/merges.txt @@ -0,0 +1,50000 @@ +Ġ t +Ġ a +Ġt h +i n +e r +Ġ w +Ġ s +o u +Ġth e +r e +o n +a t +e n +Ġ c +i t +i s +Ġ b +n d +Ġ d +Ġ m +Ġ h +Ġ o +in g +e s +Ġ p +Ġt o +a n +Ġ f +o r +l l +Ġ I +Ġ l +Ġ y +a r +Ġ g +Ġy ou +e d +Ġa nd +Ġ in +Ġo f +a s +Ġ n +o m +i c +Ġth at +u s +e t +v e +a l +o w +l e +Ġ is +Ġ e +Ġ it +o t +' s +Ġb e +i on +Ġ T +Ġw h +Ġ A +en t +Ġ S +Ġ re +a y +Ġw e +Ġ on +er e +Ġh a +u t +a c +i d +i g +o s +k e +v er +i m +Ġ Ð +ĠT h +a m +a ll +Ġf or +e l +c h +r o +Ġth is +Ġs t +Ġ W +Ġ u +a d +ou t +i r +l d +c t +Ġ k +i f +Ġg o +. . +Ð ¾ +it h +l y +h t +q u +Ġ - +Ġd o +Ġ j +Ġha ve +Ġ B +Ġa n +Ġw ith +Ġa re +Ġ r +Ġd e +Ġs e +Ġs o +Ġ v +s t +i ll +u r +Ġl i +Ġ M +es t +o d +all y +' t +us t +Ġa s +Ġ C +c e +Ġm e +Ð ° +Ð µ +i l +Ġ H +Ġw as +t er +t h +Ġc an +an t +Ġc om +ou r +ig ht +Ġ Y +at ion +ĠA nd +o l +Ġs h +Ñ Ĥ +o p +s e +Ġn ot +ĠS o +Ġn e +u n +Ġa b +Ġli ke +Ġa t +Ġ D +i e +Ġh e +Ġc on +Ġc h +o re +Ġa l +Ġo r +Ġ qu +Ġ O +om e +r a +u l +Ġ N +p p +Ġyou r +ou ld +Ġ P +Ġf r +g e +er s +' re +Ð ¸ +Ġthe y +Ġwh at +us e +Ġa ll +ĠTh e +Ġ L +es s +e m +Ġk n +Ġj ust +ar t +Ġp ro +ver y +u m +Ġl o +Ġ ì +Ġm y +o k +Ġe x +a b +Ġth ere +Ġb ut +Ġkn ow +Ġs u +Ġ G +Ñ ģ +Ġ E +Ġm a +о Ð +Ġ en +Ġab out +ĠI t +is t +Ġw or +r i +in d +Ġon e +at e +a nd +in k +Ġl e +or t +' m +Ġ F +ic h +Ñ Ģ +id e +Ġg et +Ġ out +.. . +Ġw ill +ã ģ +i ve +Ð ½ +Ġfr om +a in +ĠW e +Ġu p +p e +re s +c a +Ġ R +Ġ if +Ġp l +Ġd on +ac k +Ġ 1 +Ġ " +Ġt r +Ġ us +ĠW h +it y +Ġ J +ĠY ou +Ġh ere +h er +Ġs ome +ou g +a k +ar d +Ġgo ing +Ġu n +m ent +Ġth ink +Ġp e +en d +Ġ ( +ca use +Ġt im +as t +à © +Ġ our +Ġw ant +am e +i es +Ġ ë +u d +in e +Ġre ally +Ġt e +Ġse e +c i +Ġb y +s o +u re +os e +Ġ [ +a re +Ġm ore +a h +on e +c k +op le +а Ð +Ġthe n +Ġth ing +Ġthe m +v en +ou nd +os t +on g +e ct +Ġr ight +a g +Ġin t +Ġpe ople +Ġwh en +ou s +p l +Ġtim e +Ġ im +Ġwh o +Ġ 2 +a p +Ġbe cause +h ing +Ġn o +ic e +Ġlo ok +Ġh as +Ġw ould +Ġh ow +ac t +Ġf e +n t +oug h +Ġp r +ĠB ut +Ġs ay +Ñ ĥ +Ġn ow +Ġm an +Ġ very +Ġwor k +i z +Ġ K +i v +it t +Ġa r +e p +Ġc l +Ġwh ich +Ġc o +an s +' ve +Ġs a +f f +' ll +Ġan y +Ġa ct +Ġy e +b er +ac h +a ge +p er +Ġal so +f er +Ġthe se +Ġa d +е Ð +th er +ac e +ic k +a ke +re at +i re +u e +Ġa g +Ġ U +u ch +ion s +r y +0 0 +n a +Ġd id +Ġqu e +Ġha d +Ġe very +ĠH e +Ġl a +Ġw ay +Ġs p +b le +ĠTh is +as s +Ġthe ir +it e +Ġne ed +Ġp art +Ġw ere +Ġb ack +i p +ow n +om et +b e +as e +Ġma ke +ir st +i a +en ce +an g +an k +Ġg ot +Ġp re +Ġcon t +Ġo ther +p t +ĠTh at +o g +Ġgo od +Ġint o +al k +Ġbe en +Ġa m +Ġo ver +u ally +Ġ â +ì Ŀ +Ġu nd +h e +w ay +Ġg r +Ñ Į +Ġd if +Ġp er +Ñ ı +ĠI n +Ġt w +on d +ar s +in t +or m +Ġl ot +Ġwh ere +Ġ à +Ġ V +Ġs omet +Ð » +en s +Ġg u +Ġa c +u g +Ñ ĭ +Ä ± +Ġf irst +re e +Ġh is +itt le +Ġim p +Ġm o +a v +Ġl ittle +ĠWh at +Ġm uch +Ġ z +Ġ ê +ab le +ĠÐ ¿ +Ġp o +Ġcom p +n e +Ġd is +Ġl et +an ce +Ġh er +Ġthing s +Ġst art +ul t +Ġa pp +Ġre s +Ġf o +Ġc ould +Ġin ter +Ġth ose +Ġd es +Ġwe ll +Ġtw o +Ġk ind +x t +res s +el y +à ¤ +Ġb r +Ġth r +ĠÐ ² +Ġ i +is h +Ġdif fer +Ġ ro +ĠS t +Ġsomet hing +Ġt ake +Ġb o +y s +Ġsh e +Ġt alk +l o +Ñ ĩ +Ġe ven +Ð º +ã Ģ +ĠÐ ½ +Ġb u +ĠI f +Ġd own +ĠC h +ad e +ation s +Ġ use +or d +Ġof f +Ġact ually +Ġs pe +d u +at ed +at er +os s +n ing +à ¼ +Ġdo es +Ġ Ñģ +Ġne w +Ġb et +ve l +c ess +p le +Ġha pp +t ing +on na +Ġ es +Ġd ay +Ġon ly +ig n +k ay +s el +ent s +ou nt +i ld +i le +Ġs c +Ġh im +Ġag ain +v ing +Ġg onna +Ġcom m +Ġh el +ot her +Ġ ke +ic al +Ġ 3 +Ġe l +Ġthr ough +Ġcom e +ar k +d ay +i er +à ³ +Ġth an +ĠThe y +Ġm ay +Ġs er +í ķ +Ġc all +Ġdiffer ent +Ġsh ould +ĠTh ere +ar y +ĠN ow +ã Ĥ +th ing +w e +or y +f ter +Ġp ut +or s +i al +ë ĭ +Ġund er +Ġin c +ĠY e +u b +f orm +Ġv ide +à ¸ +ver s +Ġfe el +à ¡ +od y +f t +f ore +Ġe m +g et +Ġsa id +it ion +Ġre c +i ous +at ch +Ġtr y +Ġhel p +Ġsh ow +Ð ´ +Ġb it +u ll +Ð ² +ÑĤ о +g r +Ġpl ay +if e +a il +ĠYe ah +Ġqu est +Ġman y +Ġp ers +Ġg reat +Ã Ń +Ġ est +n g +Ġâ Ļ +t y +l a +ĠO h +Ġ × +à ® +ĠB e +ad y +Ġm ost +ct ion +ĠN o +Ġdo ing +Ġbe ing +Ġto o +c es +Ġb l +. " +Ġre m +is s +on s +> > +r u +w n +on t +i b +e ll +Ġs m +ot h +u al +Ġ >> +Ġp h +l es +o c +f ul +Ġse c +is e +Ġad d +ig h +er t +Ġs ame +â Ģ +Ġme an +Ġf ind +e k +Ġen d +- - +Ð ¼ +Ġst ill +a z +Ġ ' +Ġm in +Ġye ars +ur n +Ġar ound +sel f +Ġw r +b s +oug ht +ĠâĻ ª +Ġf l +an ge +Ġa fter +Ġpo int +m er +v ed +Ġl ong +o y +ä ¸ +Ġc r +way s +Ġs y +Ġt ra +Ġ2 0 +a ve +Ġch e +Ġ ent +Ġbe fore +p h +Ġat t +i an +i ly +Ġpers on +Ġb ig +Ġs ch +Ġre al +Ġne xt +Ġlo ve +Ġvide o +ĠL et +Ġf in +Ġma k +i ble +Ġto day +er m +ĠA l +ow er +an n +i x +Ġp ar +Ġst ud +à ¶ +Ġimp ort +t e +Ġg ive +v es +Ġd ie +Ġde c +Ġte ll +ĠÐ º +Ñģ ÑĤ +Ġwh y +ic ally +ic t +re d +Ġb as +Ġsu re +Ġbe l +at ing +Ġt ak +Ġs et +Ġl ife +Ġdid n +Ø § +o b +u nd +at h +Ġo p +ĠÐ ¾ +a it +Ġwor ld +Ġsu pp +i o +Ġc our +ĠÐ ¸ +w ard +е н +Ġal ways +u p +Ġha nd +ĠH ow +ci al +Ġcon s +Ġ Ñ +Ġin d +Ġ 4 +ĠA s +Ġf un +j ect +Ġimport ant +Ġs ur +e w +at es +Ġ 5 +Ġd i +Ġm ade +Ġin s +Ġas k +Ġ et +Ġn um +Ġc ar +ĠO kay +Ġs im +i k +Ġl ast +ĠG o +Ġm us +Ġre l +ul ar +´ ì +ĠWe ll +pe ct +ĠTh ank +Ġth ree +à £ +ã ĥ +Ġin v +Ġg en +l ic +Ġhapp en +ë Ĭ +i en +e ver +оР² +Ġst r +ĠA ll +Ġin st +Ġâ Ģ +Ġde f +Ġs l +Ġm ight +un g +Ġye ar +Ġo wn +Ġke ep +b ody +d er +Ġ ÑĤ +ĠÐ ´ +Ġan other +Ġm od +Ġe v +Ġgu ys +Ġab le +ã o +qu e +id ent +ĠY es +Ġit s +Ġpl ace +Ġpro du +ar n +ĠÐ ¼ +Ġre p +Ġex per +Ġf am +it ies +if ic +Ġh igh +i ed +o ol +ie w +е ÑĤ +re n +Ġdon e +Ġ ... +ëĬ Ķ +st em +ĠS e +Ġbet ter +c ome +Ġd el +Ġt y +Ġu m +Ġh o +ĠA n +Ġm on +ing s +Ġs k +Ġo b +c om +ble m +op e +st and +' d +ment s +Ġe le +ĠI s +Ġd a +Ġre g +le ase +i ke +al s +iz e +ê ° +Ġc are +Ġne ver +ìĿ ´ +es e +Ġm et +ol og +ĠWh en +u ck +е ÑĢ +Ġ é +Ġd at +à § +Ġex am +il ity +Ġd et +c ri +Ġus ed +ĠD o +Ġtr ans +e g +t en +Ñ İ +c us +Ġsec ond +Ġb est +Ġh ard +Ġ ide +Ġpro blem +ê ³ +ĠU n +Ñ ħ +Ġ Î +Ġw atch +ĠS h +at ter +Ġpre t +Ġd er +Ġcour se +Å Ł +at ive +ic s +Ġquest ion +ut e +ì Ĺ +ĠF or +at her +Ġc ol +i end +Ġ í +Ġ Z +Ġdoes n +ar ch +Ġinter est +Ġp ol +Ġc or +i ence +Ġp res +Ġe ach +Ġsy stem +Ġf act +i el +ab ly +Ġ er +Ġr un +Ġì Ŀ +Ġto p +n er +Ġth ought +Ġe as +i ent +Ġc re +Ñ Ī +Ġcomm un +y e +re ady +ll ow +Ġevery thing +om m +Ġm ed +ļ Ķ +Ġc ount +it s +Ġcom pl +h ip +Ù Ħ +o ok +Ġto get +Ġtoget her +am p +Ġg ame +Ġal ready +аР» +Ġcall ed +al e +Å Ĥ +ĠM y +Ġunder stand +Ġd r +Ġm om +it ed +оР» +Ġus ing +z y +Ġnum ber +ãĢ ģ +c ed +Ġc le +н о +ëĭ ¤ +in ce +Ġlook ing +Ġpret ty +Ġpro b +ĠS he +Ġ ve +Ġget ting +Ġwe ek +Ġe ff +u ff +a ir +u es +er n +Ġ Q +ou p +ent ion +Ġs ide +оР¼ +Ġfor m +Ġb us +Ġas s +Ġ ed +as on +we en +âĢ ¦ +Ġt urn +Ġc ur +Ġco ll +Ġd ire +ĠG od +Ġ1 0 +Ġe qu +ĠÐ ± +Ġop en +Ġsu ch +ir d +аРº +Ġe ar +Ä Ļ +g an +Ġpart ic +Ġfr iend +Ġex p +Ġex t +Ġh ome +Ġw ater +ĠO n +ÑĤ ÑĮ +or k +Ġп ÑĢ +Ġmo ve +n ess +en se +h o +Ġch ar +c o +in s +Ġb oth +Ġ1 9 +Ġg ra +Ġbet ween +á » +Ġì ķ +as h +ĠR e +a i +al th +u res +em ber +Ġa v +Ġ ver +à ª +one y +Ġth ank +Ġmay be +u c +im e +ê³ ł +Ġa way +Ġn ame +ou se +Ġac c +Ġmus ic +Ġch ange +Ġp ass +g er +Ġbu ild +Ġv al +in ess +an y +Ġfe w +´ ë +t a +Ġl ist +à ¥ +Ġo ld +Ġì ŀ +Ġs ort +Ġme m +Ġc a +ce pt +Ġgen er +Ġye ah +Ġwh ile +Ġany thing +r ic +gr am +Ġe in +c y +ur ing +ĠD e +Ġp ower +Ġcom ing +Ġwor d +Ġ- - +Ġbel ie +Ġf ound +t o +Ð ¿ +Ġme ans +Ġin form +Ġ Ø +Ġ Ñĩ +Ġsm all +00 0 +Ġc ame +Ġ íķ +w h +Ġwork ing +Ġexam ple +Ġp os +Ġde p +ê ² +ä º +ot e +Ġde m +ì § +t s +Ġv ar +a ut +Ġt ri +ch n +Ġhe ad +Ġwho le +× Ļ +z e +Ġtry ing +Ġt em +Ġc ou +et s +Ġ 6 +Ġf il +vel op +Ġc ase +à ¯ +Ġprob ably +Ġo kay +Ġpl an +Ġs it +Ġsch ool +ĠTh en +¸ ë +m e +Ġpro cess +Ġf ar +Ġre ad +Ġp oss +Ġb re +Ġso l +ic ht +Ġsupp ort +ĠT o +ert ain +Ġstart ed +Ġc ap +Ġle ft +Ġdat a +Ġtim es +еР» +Ġwant ed +а н +Ġtalk ing +Ġis t +Ġha ving +um p +Ġcont in +Ġsu b +ĠÐ · +p r +ëĭ Ī +in a +Å ¼ +Ġc reat +od e +× ķ +æ ĺ +! ! +Ġt erm +is m +оР´ +ĠBe cause +Ġw ent +id er +Ġpro v +Ġch ild +Ġd en +Ġl ight +b r +³ о +o h +Ġbo ok +Ġ Ù +ut ion +ĠJ ust +en e +Ġf our +Ġv is +ê° Ģ +Ġh ope +Ġmak ing +ĠL e +ì ķ +Ġo pp +a u +Ġm oney +Ġpro gram +à ¨ +Ġst and +I N +Ġs ign +Ġle arn +à ł +ĠD on +Ġte am +Ġн а +l ud +Ġre st +ic es +æ ľ +Ġ ÑĢ +Ġa ut +Ġle ad +ation al +d e +g y +Ġn ice +Ġd as +Ġd ist +Ġh um +ĠO ne +æ Ī +Ġcom es +Ġj o +Ġc ent +Ġex pl +Ġm ark +re en +l ed +g in +ì ļĶ +Ġle vel +Ġcon f +us h +Ġde velop +Ġt est +en g +v ious +at ure +еР¼ +re t +Ġj e +Ġst uff +Ġcl ass +ow s +Ġê · +Ġs i +Ġl es +ro p +ç ļ +Ġp or +Ġw ar +ìĹ IJ +Ġevery one +Ġg e +Ġche ck +ot t +Ġs ing +Ġar t +Ġfo llow +Ġ20 1 +ĠF r +a is +ì ĸ +Î ± +å ° +Ġà ł +im es +Ġre t +Ġch ang +Ġp ub +Ġin f +Ġte chn +ad a +iv es +Ġbe h +æĺ ¯ +Ġlook s +ãĢ Ĥ +Ð · +ĠWh y +çļ Ħ +Ġen ough +Ġb ra +it ch +ä » +Ġad v +Ð ± +Ġwith out +w er +mer ic +d en +Ġcompl et +Ġide a +ter s +o ck +Ġdef in +Ġe ver +Ġg l +Ġon ce +Ġbr ing +Ġsay ing +Ġan s +Ġhe ar +n ect +Ġl ess +g o +re am +ad o +ì ŀ +Ġm ind +ent e +Ġf ull +Ġb ad +Ġw om +Ġsome one +Ġd u +Ġw on +Ġcont ro +ort un +Ġhe alth +Ġch o +ĠA r +Ġcon c +Ġinform ation +Ġst op +at t +at ely +ä ½ +Ġgr oup +Ġ Ñĥ +Ġqu ite +Ġres p +E R +ug ht +ê ¸ +m an +iz ed +ĠB r +Ġrem ember +Ġfam ily +Ġbus iness +a w +Ġspe c +Ġa u +ĠO r +Ä ħ +Ġse en +Ġl ar +Ġ 7 +g g +b ers +Ġd ra +Ġmon th +Ġsay s +Ġis s +Ġli ve +Ġl ine +Ġmom ent +Ġex c +el s +Ġs ound +Ġco ol +Ġlo c +Ġc ertain +Ġd ri +о ÑĤ +am es +Ġm ust +n y +и ÑĤ +Ġk id +Ġinc lud +ìĿ Ħ +at or +Ä Ł +h a +are d +Ġse em +Ð ¹ +ì Ħ +Ġel se +Ġì ł +ir l +Ġ 8 +Ġv o +Ġquest ions +in es +e e +æĪ ij +ü r +ĠA meric +Ġst ory +Ġser v +ver n +ag es +l and +ĠâĢ ĵ +er a +ĠC an +Ġp op +et her +Ġn a +Ġor der +Ġmak es +Ġs ince +c on +ct or +Ġth ough +Ġprodu ct +л и +Ġle g +Ġme et +al f +Ñģ Ñı +un ch +it er +o ve +×ķ × +i et +аР¼ +it al +Ġsu per +l ing +Ġp ay +Ġpar a +Ġj ob +ĠH ere +Ġs w +k s +pt ion +m a +Ġbelie ve +¬ ë +Ġw ait +оР¹ +Ġun t +Ġqu ick +h r +ĠÑ į +ĠP ro +Ġm en +à ¹ +Ġday s +Ġgo es +Ġspe ak +ĠA t +em ent +Ġm iss +Ġa w +Ġdes ign +Ġpro ject +о ÑĢ +i j +ant s +at s +ĠCh r +Ġ 9 +Ġc ut +Ġre qu +Ġн е +ĠN ot +as ter +Ġm ill +Ġpartic ular +Ġp ie +Ġstud ents +Ġf ive +ou n +ĠN e +Ġg i +Ġp as +Ġf ree +ĠS p +l ich +Ġpro f +Ġen g +Ġpr ot +ĠL ike +os ed +Ġcon nect +a pp +Ġë § +it ing +Ġb lo +Ġl os +ist s +Ġexper ience +re nt +Ġst ay +Ġfo od +t on +ru ct +Ġh ist +v iew +in ing +m ost +i vers +b o +ãģ Ħ +ĠT r +g en +Ġp lease +Ġcommun ity +Ġc e +A N +n o +Ġb ody +Ġh our +Ġ vers +á º +c er +Ġê ° +Ġre ason +ĠR ight +Ġl ater +Ï Ħ +Ġh ouse +Ġ X +оР½ +Ġst ate +f ic +å ¤ +Å Ľ +iel d +Ġp ri +Ġp ast +Ġw alk +olog y +er ing +an na +Ġt er +Ġho ld +Ġor gan +b en +Î ¿ +ó n +Ġeff ect +Ġyour self +Ġpl us +a j +and o +ur al +Ġro om +le ct +ê² Į +? " +s ide +Ġbe come +Ñ Ĩ +Ġ  +o od +Ġcon st +Ġn ight +ut es +Ð ¶ +Ġbre ak +Ġp ain +Ġst ep +ire d +Ġnot hing +Ġunt il +Ñ ĸ +аР² +Ù Ĭ +Ġd uring +ì§ Ģ +l ess +o ll +н Ñĭ +Î ¹ +f ect +i ver +ı Ħ +ith er +y ing +Ġbe gin +×Ļ × +iv id +Ġà § +Ġs al +Ġt a +Ġp ot +Ġ $ +Ġm ar +Ġcle ar +Ġf ace +Ġgr ow +Ġ * +Ġins ide +Ġfriend s +Ġle ave +en n +Ġeas y +Ġare a +al ity +ou d +Ġe at +Ù Ĩ +Ġp ur +or n +Ġsa w +Ġans wer +Ġfr ont +Ġbe aut +¼ ë +Ġm atter +Ġs on +ĠN ew +Ġres ult +id es +ch e +Ġf ut +p s +Ġfo cus +Ġinterest ing +å ¥ +Ġa p +" . +Ġcre ate +о Ñģ +Ġp ress +r oss +Ġp ick +l ine +Ġto ok +ĠM ay +r ow +Ġ ich +ĺ ë +Ġre f +Ġm or +r act +are nt +A R +Ġex act +Ġsp ace +w ork +н и +Ġb ir +Ġde v +Ð ³ +Ġto ld +Ġpub lic +ci ally +Ġv iew +ĠHe y +m ed +ll o +c c +Ġf ac +Ġcou ple +Ġhe art +l er +Ġre ady +Ġal most +ar ing +Ġh alf +ĠM e +av or +i que +Ġchar ac +Ġpr act +O N +an e +Ġ il +н а +Ġv i +l ish +he ad +Ġle ast +Ġbas ically +as ed +r ight +Ġy et +Ġtak ing +Ġcount ry +Ġw in +Ġis n +Ġposs ible +Ġc am +Ġinc re +Ġp at +Ġw anna +Ġcons ider +Ġab s +Ġwith in +Ġhum an +Ġthink ing +Ġo h +¡ ľ +Ġqu i +as es +Ġ 0 +it ely +ä¸ į +Ġk ill +Ġm il +Ġinv est +is ter +Ġsu c +ion al +el f +Ġwh ether +Ġcontro l +Ġagain st +ot s +ëĭĪ ëĭ¤ +i or +Ġpres ent +Ġ ا +Ġwatch ing +u be +er v +Ġn icht +Ġgo vern +ĠTh ese +Ġ : +u it +ug h +Ġwork s +o o +Ġw ir +Ġa ir +ĠT e +аР· +is ion +wh ere +Ġto t +j oy +ì ĭ +Ġv ol +ĠÐ µ +Ġcl ose +ĠA d +Ñ ī +in ed +Ġun a +Ġê· ¸ë +° ë +or ry +Ġb ro +Ġfil m +if t +2 0 +Ġty pe +Ġhappen ed +ĠA m +Ġg irl +ĠA re +ward s +Ġp our +Ġcol or +el t +а Ñģ +Ġs ense +le x +ĠW ith +us s +ri b +Ġre se +Ġn orm +Ġfut ure +Ġde al +end ing +e y +Ġ x +er o +ĠC l +u k +Ġwhat ever +sel ves +Ġyou ng +ì Ĭ +ĠM ar +ĠChr ist +Ġgu ess +Ġper form +Ġen er +r on +Ġh it +Ġw ond +Ġdire ct +ĠE very +Ġof ten +Ġf a +Ġal ong +Ġcl ick +ĠL ook +Ġsit u +Ġhapp y +e ad +Ġag o +Ġen c +Ġmy self +Ġco ver +оР± +Ġm id +Ġc ost +Ġt en +ĠS ch +Ġex pect +Ġwas n +Ġstr ong +if ul +Ġopp ortun +in al +y le +Ġsh are +Ġtr ue +Ġapp ro +Ġch all +Ġmin utes +Ġch ann +Ġë Ĥ +Î µ +l i +Ġm ess +or ies +pe cially +Ġwr ong +Ġy es +Ġì Ĺ +ir on +Ġall ow +Ġsu bs +Ġf ore +Ġf ight +Ġso cial +Ġc ra +an a +Ġa ff +Ġ ess +Ġway s +Ġsh ort +Ġf all +Ġla w +ĠWh o +Ġen joy +Ġc al +Ġac cess +f e +Ġn on +Ġac ross +er y +vious ly +ĠE x +id ed +Ġl ink +ĠP r +Ġterm s +ac es +Ġl and +az ing +Ġ1 5 +Ġm ult +Ġspe cial +å Ģ +iv ing +ìĿ Ģ +Ġty p +Ġst e +Ġ Ä +Ġfor ward +å ı +Ġf re +å¥ ½ +Ġrese arch +௠į +а ÑĤ +Ġma in +Ġrec ord +Ġh u +Ġdefin itely +Ġe ither +Ġlist en +Ġke y +Ġmark et +ĠÑĩ ÑĤо +iz ation +Ġvide os +Ġgu y +Ġf ig +Ġst ra +ĠP l +ull y +am os +Ġm ention +Ġs ong +Ġinter n +r al +ur s +Ġh on +Ġval ue +Ġb ar +c le +оР¶ +Ä ĩ +ľ ë +Ġz u +и м +ä½ ł +Ġsing le +Ġa uch +cus s +Ġget s +Ġsomet imes +å ¾ +am b +m m +c ing +Ġper fect +ĠB l +out h +ì ł +Ġs ci +p ar +Ġre d +Ġp ost +Ġm ot +Ġele ct +ĠE u +it ive +ĠS ome +Ġdes cri +Ġcur rent +é s +Ġt re +ĠE n +Ġm it +E N +Ī ë +i um +Ġhe ard +Ġsim ple +l ar +Ġevery body +il ar +Ġneed s +Ġdif fic +ĠGo od +um ent +c ent +Ġo per +а ÑĤÑĮ +et y +Ġbl ack +Ġgi ven +on es +Ġwe l +é Ģ +Ġìķ Ħ +Ġ3 0 +A T +Ġst at +ou ch +ĠM r +а ÑĢ +Ġsh o +Ġcon d +× Ķ +m y +Ġchild ren +Ġe u +еР´ +ìķ Ħ +ter n +Ġu h +Ġh ar +Ġpr om +Ġp ull +re w +Ġcomp any +Ġbeaut iful +ust om +íķ ĺ +к и +Ġst re +Ġam azing +ri es +Ġsuc cess +Ġm ach +n ot +Ġdis cuss +Ġn at +¦ ¬ +Ġun e +Ġdiffic ult +Ġr is +Î ½ +Ġc amp +Ġbu y +ä¸ Ģ +Ġma g +p o +ĠY our +Ġbeh ind +ic a +ı n +ĠO K +Ġl ang +Ġwom en +Ġen v +Ġre ce +Ġchann el +i ally +u le +Ġ1 2 +th ers +Ġb ott +Ġrep ort +ent ly +f ully +T he +Ġs ent +Ġev ent +Ġener gy +l t +Ġword s +ar r +d le +Ġa head +ard s +Ø ± +äº Ĩ +Ġto ol +con om +е Ñģ +Ġexact ly +Ġf avor +Ġl ow +Ġpro per +Ġìŀ Ī +Ġ ! +Ġrel ations +Ġm as +Ġkid s +Ġent ire +ud e +Ù ħ +ĠWh ere +Ġon es +Ġc ity +ol ut +Ġs ix +ab ility +ö r +il i +ĠE s +Ġhapp ens +ain s +Ġmod el +Ġp ict +Ġes pecially +Ġ1 00 +k t +Ġso on +b y +ro du +Ġan n +Ġsubs cri +ĠQ u +Ġav ail +im ent +Ġv oc +k a +Ġ2 00 +ap er +ĠI nd +Ġì § +h or +į ° +j or +и л +Ġs qu +A U +ar ning +ĠÐ ³ +I S +ĠÐ » +еР¹ +y es +å ħ +ĠÐ Ĵ +Ġor ig +оР³Ð¾ +Ġask ed +il t +оР³ +Ġcontin ue +Ġì ĺ +r am +Ġo thers +E S +oh n +Ġl ay +Ġbas ed +Ġp u +Ġapp e +Ġl im +Ġpro p +Ģ ë +m in +Ġh ot +ĠL a +Ġf ast +Ġprot ect +Ġam ount +Ġa qu +Ġf und +Ġc ustom +Ġc ult +Ġhand s +Ġha ven +Ġa ud +Ġout side +ĠA fter +ap s +Ġan im +pl oy +Ġh at +ĠF irst +Ġt reat +Ġe p +Ġm ater +Ġbuild ing +Ġë ° +å IJ +ìĦ ľ +z a +ught er +ĠP e +ne y +et er +at ic +Ġed uc +ê¸ ° +Ġmo v +ĵ ¤ +am a +r ation +Ġs n +Ù Ī +Ġs um +Ġph ot +ĠÐ Ŀ +Ġ . +æľ ī +Ġfin ish +itt ing +å ® +Ġlar ge +Ġì ĸ +Ġwh ite +ar a +Ġma is +ĠH i +Ġd am +Ġا ÙĦ +Ġbo x +ĠHe llo +Ġs le +Ġo pt +ri ed +¥ ¼ +Ġact iv +Ġn ão +ĠC om +Ġplay ing +T h +Ġavail able +Ġp ort +å Ī +ĠA h +Ġl as +Ġear ly +Ġwond er +± ° +Ġ1 8 +c ul +Ġfun ction +Ġmor ning +ll e +i ents +u x +Ġc ir +it ions +Ġde ep +Ġpol it +y or +m p +ak ing +Į ë +ĠM an +Ġmill ion +Ġ / +Ġind ivid +Ġp an +Ġgovern ment +Ġwr ite +ĠT od +am ent +Ġ Ï +Ġw ind +ĠE ng +ch en +W h +ì ľ +Ġ ident +ãģ § +v ent +ur ch +Ġh y +Ġy a +Ġtr ad +Ġrelations hip +à º +Ġd ou +O R +Ġs we +Ġne g +in ation +Ġte xt +i pp +Ġf ine +á s +ĠD r +ĠC ome +Ġmonth s +, " +ен и +Ġhour s +Ġp od +ir t +Ġinv ol +Ġcoll ect +Ġau f +Ġp a +Ġhist ory +m b +if y +Ġ ? +Ġbel ow +as ure +ab y +Ġlang u +Ġan t +Ġcom b +at o +Ġex ist +Ġë ĭ +Ġtak es +Ġcharac ter +a ff +Ġf ield +Ġe conom +ie f +Ġpie ce +å ľ +Ġre ach +Ġê ² +on y +Ġmater ial +Ġd ig +Ġph ys +Ġimp ro +Ġsim ilar +I C +Ġn et +y n +Ġpos ition +à Ł +Ġb ene +re ad +Ġle arning +um e +Ġcle an +ÑĤо ÑĢ +Ġco ok +Ġseem s +Ġo l +ĠU S +ĠJ es +Ġ à® +ent ial +ivers ity +ac y +Ġ Ñı +olut ely +re ct +ĠP lease +Ġrep res +Ġt ouch +m en +ĠÐ ° +i ón +ĠThank s +Ġan g +Ġma jor +Ġit self +ill s +" , +i ans +Ġsc reen +Ġh or +Ġknow n +Ġenv iron +Ġfin al +Ġfig ure +ĠT w +Ġe yes +Ġim ag +Ġsee ing +Ġha ir +re m +Ġapp lic +end s +p ut +Ġnew s +Ġcomplet ely +ugh s +Ġkn ew +if ied +ĠJ e +ĠD id +Ġsitu ation +Ġf lo +m s +Ġph one +Ġb all +d o +Ġp arent +Ġs orry +ur y +и н +ip s +аР´ +Ġinst ead +Ġhu ge +Ġt u +Ġ ãģ +ĠG r +Ġdet ail +ĠÐ Ł +Ġindivid ual +Ġf ire +Ġcl os +Ġw er +un e +Ġrun ning +Ġcon vers +Ġrec omm +Ġcom o +Ġsome body +ĠJ ohn +ĠìĿ ´ +ĠO ur +pl es +ĠP h +Ġan al +Ġ5 0 +Ġof fer +Ġ < +ition al +g est +Ġv ous +l et +ic y +Ġfeel ing +L E +r os +Ġth ird +оРº +Ġser ies +ĠAn y +is ed +o ld +Ġdra w +Ġserv ice +Ġcan not +b al +ãģ Ĩ +Ġli ving +ı m +Ġdiffer ence +Ġopportun ity +Ġne ar +or th +k en +Ġloc al +Ø ª +ĠC on +Ġob ject +Ġd ass +ãģ Ļ +IJ × +Ġquick ly +ra ph +Ġiss ues +éĢ Ļ +ĠAmeric an +Ġpre p +en ces +Ġprof ess +ll ing +o f +Ġfo ot +b re +Ġus ually +Ġgener al +d a +an ces +Ġd est +Ġo cc +Ġmem bers +Ġd ans +Ġequ al +z t +Ġbe com +Ġmo ving +Ġspec ific +ÃŃ a +Ġf ur +Ġne cess +Ġcomm on +Ġatt ack +ĠÑį ÑĤо +ĠTod ay +Ġun s +ĠG u +i od +Ġacc ount +Ġgra nd +Ġs elf +ĠE l +Ġt ast +Ġcont ent +Ġc u +Ħ ë +ĠMay be +ĠJes us +ore s +p ort +© ´ +Ġg ives +Ġnorm al +ÑĢ Ñĥ +Ġimp act +ä r +Ġd ies +Ġl ab +s h +i os +ĠP res +ĠU nd +ĠO f +Ġfin ally +Ġdo ll +Ġvoc ê +p ly +ĠA g +Ġtak en +Ġgr ound +f ort +Ġg ave +ĠIn st +Ġl ost +Ġwork ed +Ġl iter +Ġiss ue +Ġind ust +Ġret urn +Ġhappen ing +Ġwant s +и в +Ġproblem s +ĠC ar +Ŀ ¼ +ĠAl so +Ġs ize +Ġob viously +ĠS u +ĠS c +Ġrecomm end +our ces +ast ic +.. .. +Ġm i +l ier +ĠE ven +ci a +Ġh ur +v a +Ġm ass +Ġwould n +un t +ck s +Ġf elt +os p +l ight +ол ÑĮ +n ie +Ġbott om +Ġб Ñĭ +ore d +is on +Ġgr ad +Ġum a +Ġv a +Ġì Ĥ +ress ion +ul ation +I D +id ence +Ġb ur +Ġg one +l u +ìĸ ´ì +Ġre du +Ġj a +ìĿ ĺ +it a +Ġso ft +Ġç a +ic o +er al +à ± +a f +Ġpoint s +g u +Ġd é +ap t +a x +ĠAl right +Ġcam era +Ġa ch +Ġп о +Ġse ver +5 0 +Ġs ie +Ï ģ +Ġm al +Ġcomp ut +Ġmid dle +Ġcould n +m ing +Ġì ĭ +ĠH is +Ġg ames +Ġint rodu +Ġc ell +p or +Ġsle ep +Ġë ³ +id ing +Ġ ou +Ġde g +Ġdr ink +Ġenviron ment +ĠUn ited +Ġtalk ed +Ġcho ose +Ġj our +e ge +ĠM in +Ġint e +Ġr ather +Ġoff ic +к а +ac hing +Ġmention ed +Ġf ill +Ġtr ack +Ġn ie +Ġ ut +Ġв Ñĭ +ib ility +Ġv ac +Ġr ad +Ġp ack +Ġs end +ĠD as +ĠA b +Ġeng ine +ãģ Ĺ +Ġcomp et +à ´ +Ġв Ñģ +Ġdo or +Ġlong er +å° į +Ġlangu age +Ġext ra +pl ay +Ġwe bs +um b +ro om +ç ľ +Ġbegin ning +Ġre fer +A M +n en +ig her +f ace +er c +Ġfor get +Ġcom ment +еРº +л Ñı +r or +ż e +ĠG e +Ġd ark +Ġany one +ant e +g es +ìĬ µ +Ñ ij +b ed +j e +ruct ure +Ġpr im +id a +è ¦ +ãģ ¾ +Ġm ix +Ġstart ing +ĠìĿ ´ë +Ġprov ide +act ion +Ġm other +Ġper iod +Ġst ick +ĠYou T +Ġtechn ology +ê ¹ +Ġb ed +Ġg iving +Ġexpl ain +z en +im ate +Ġrepres ent +lo ad +ĠHow ever +Ġli ves +ut h +ir it +og n +Ġli k +Ġresp ons +Ġpri v +Ġto m +ç ão +i am +Ġexc ited +Ġc ard +gr ound +Ġ× Ķ +Ġs ens +Ġte ach +id o +h od +Ġep is +Ġwel come +Ġw all +ä ¹ +Ġch ance +h en +ĠÐ ¡ +ĠÄ ij +Ġsim ply +ĠÑĤ ак +r ing +j a +b ook +Ġsever al +st e +Ġcreat ed +Ġо ÑĤ +Ġp ush += = +Ġh igher +u f +our ce +o ke +Ġon line +Ġre le +Ġt on +ens ive +Ġfavor ite +Ñĥ д +Ġlook ed +Ġv on +âĢ Ķ +Ġf ür +Ġbut ton +Ġb ill +Ġchang es +! " +Ġsl ow +ab les +Ġde ath +and s +ate g +Ġthem selves +ãģ £ +Ġc op +ãģ ® +Ġperson al +ug hing +Ġ1 1 +g ar +ad es +Ġneed ed +Ġstud y +ag ed +ÑģÑĤ в +in o +Ġdis c +k i +Ġadd ress +× ¨ +itt en +es ome +ĠÐ ¶ +¤ ë +ur a +Ġm u +Ġcontin u +f or +Ġm atch +ãģ ¦ +Ġstra ight +IJ ë +n ers +Ġdo g +Ġde b +ĠC O +Ġo s +g ed +c ame +Ġcor rect +et te +ĠSe e +Ġinclud ing +ĠEu ro +est er +Ġj ump +ĠWh ich +Ġк ак +s on +y a +IN G +Ġe ine +os h +en cy +Ġmed ia +Ġsubscri be +é Ĥ +Ġpr in +Ġha b +ĠP er +ĠW as +Ġp age +it or +Ġto wards +Ġtri ed +en ge +art ment +Ġvar i +Ġp aper +Ġpict ure +Ġvers ion +Ġbr ought +w are +ĠSt ates +Ġs ich +led ge +Ġper cent +Ġgo d +e c +ĠC omm +Ġdec ided +Ġse lect +íķ ľ +) . +ur ity +Ġfur ther +Ġcom ments +le ment +Ġd ream +Ġcent er +m i +Ġc as +Ġwom an +Ġro ad +Ġf ail +Ġbe came +l us +il ities +ãģ ¯ +ĠC o +Ġman age +Ġrec ogn +Ġact ion +Ġbene f +Ġear lier +× ľ +Ġspe ed +Ġm ent +Ġso ci +Ġsho ot +u i +Ġà ¤ +Ġapp ly +v o +x im +Ġca use +Ġsur pr +Ġha ben +D I +Ġf ather +ĠNe xt +ĠYouT ube +Ġc ode +Ġro le +g ress +Ġg reen +et t +Ġbu ilt +Ġfl ow +Ġb ase +Ġtra ining +Ġr ound +ĠW ill +Ġp ath +ĠR o +Ġinterest ed +ìĸ ´ +Ġres pect +Ġchang ed +iss ion +Ġstud ent +og raph +Ġappro ach +Ġshow s +å° ± +Ġt ar +Ġcr it +Ġg lo +ìĬµ ëĭĪëĭ¤ +Ġde ad +ĠPres ident +Ġth ous +Ġb al +st er +e x +Ġabs olutely +Ġm ic +Ġpract ice +Ġqu ality +Ġl ower +og le +Ġse par +b all +med i +Ġre view +ĠA pp +Ġo k +âĢ ĭ +Ġexper ien +Ġconc ern +ent ially +m ore +ĠJ o +ap an +ĠI ch +ist ic +Ġf air +Ġwebs ite +i res +ĠB y +Ġtra vel +Ġris k +Ġm ir +Ġbo ard +Ġs en +Ġparent s +ĠW ow +Ġfe ed +Ġsa ve +Ġser ious +Ġin it +E L +und red +A S +Ġv an +or row +Ġwor th +Ġse arch +Ġ1 6 +Ġpart s +ÑģÑĤ ÑĮ +Ġcomp an +Ġmov ie +Ġmet hod +Ġ ill +Ġw ish +d y +Ġit em +Ġmin us +ang er +Ġvo ice +Ġsk in +Ġare as +Ġe ight +Ġo bs +Ġ , +аР¹ +Ġo il +Ġc y +Ġb aby +s y +Ġem ploy +ĠK e +Ġpl aces +Ġf ix +Ġest á +ãģ ¨ +iv ed +Ġlot s +Ġse ason +un k +al t +Ġt able +ĠÐ ¢ +à ¢ +Ġatt ention +ãģ ª +ĠH er +Ġa ge +Ġp ra +b ack +c il +Ġnet work +r it +Ġdo c +Ġare n +ig en +Ġë Ħ +Ø ¯ +end er +Ġtot al +Ġpr ice +Ġcra zy +ì ļ +i qu +th ough +Y ou +Ù ĩ +ãĤ ĵ +Ï ħ +Ġs at +Ġb i +ĠD ie +Ġsh a +Ġthank s +u h +Ġst age +аР¶ +ĠF l +Ġle av +Ġbo y +Ġa f +ö n +ĠG et +Ġac cept +Ġent er +Ġt ur +Ġsi ÄĻ +Ġhon est +ãĢ Į +Ġs am +Ġre pl +g ing +Ġdevelop ment +ĠA ct +or a +ãĢ į +ä ¾ +Ġknow s +Ġim age +ĠL ord +и ÑĤÑĮ +Ġweek s +Ġse x +Ķ ë +Ġh undred +Ġsound s +Ġlearn ed +Ġb ud +ĠÑģ ÑĤ +Ġinc red +â Ļ +Ġn os +Ġd rop +Ġb en +ĠÐ ĺ +Ġsa fe +at a +Ġf uck +so ci +Ġd an +Ġcr oss +1 0 +m o +ver t +Ġ1 7 +z ie +å ķ +Ġd om +ĠB o +Ġset ting +Ġinvol ved +ar ily +Ġs ind +Ġs us +Ġwor ry +et h +ê¹ Į +Ġs un +Ġh ier +Ġcertain ly +ou l +ort s +ĠE r +ĠU m +Ġca us +Ġnat ural +Ġà ¼ +Ġc ry +ĠSe c +Ġs om +æ ² +Ġeduc ation +а еÑĤ +Ġmult ip +Ġal one +Ġe ye +Ġr ate +ĠEuro pe +è ¿ +m on +Ġf it +iz ing +pp ed +Ġpress ure +th e +и Ñģ +it es +ĠA f +re ci +att le +Ġserv ices +ĠGo ogle +é ģ +Ġc ases +Ġdri ve +Ġchall eng +u z +ĠM o +ìľ ¼ë +v al +åĢ ĭ +Ġf ol +Ġì ¢ +ff ic +Ġr a +Ġs in +Ġbl ue +Ġaff ect +Ġm is +Ġsh ot +Ġо б +as ing +Ġsign ific +ĠC he +Ġê ³ +Ġpos itive +ì £ +Ġw ie +Ġ4 0 +ord ing +ĠFr om +ê µ +Ġbra nd +Ġtr ust +Ġp le +Ġcommun ic +Ġwe ight +Ġask ing +Ġta x +ĠJ apan +ãģ Ł +Ġíķ ĺ +op s +Ï Ĥ +Ġput ting +Ġro ll +ĠAmeric a +re g +ŀ × +at ures +ens ion +ĠS omet +Ġorig inal +p ing +Ġ ÅŁ +Ġproduct s +ãĥ ¼ +Ġcont act +ol ution +Ġgo al +Ġp ow +Ġperform ance +Ġblo od +at ors +ĠM ich +Ġtem per +ĠD an +Ġsu gg +ÑĤ и +Ġim m +Ġoff ice +Ġar ri +Ġcom fort +ĠÐ Ķ +Ġsugg est +Ġpl at +Ĥ ĺ +1 9 +Ġo m +Ġse ven +ĠC ent +ill e +Ġcon cept +Ġb ag +ü n +ive ly +Ġd iv +m os +æ ī +Ġfeel s +Ġ ir +ak es +le y +Ġpartic ip +ĠÐ ļ +f l +j ust +Ġs il +ĠP a +A L +Ġgot ta +Ġf an +Ġchall enge +Ġcompan ies +ĠPe ople +< / +оР· +Ġp en +is ing +Ġa us +em ic +am ente +Ġmeet ing +Ġvis it +Ġsupp osed +ĠOn ce +д а +or ld +3 0 +U S +Ġvi ol +Ġnot ice +ĠÐ IJ +h an +p ed +ì ĺ +h h +Ġtr ou +Ġmin ute +ĠP ar +r ay +Ġt it +Ġup d +Ġblo ck +Ġd ue +a ur +Ġfor ce +Ġcou n +ĠâĢ Ķ +Ġtyp es +ë § +Ġl ate +Ġimpro ve +Ġì Ī +Ġa ve +ul es +c l +am ed +Ġaw esome +ĠO k +Ġv ot +Ġmach ine +Ġfollow ing +Ġme asure +ac ión +u el +ch an +Ġab ility +Ġt out +Ġide as +Ġincre ase +Ġen s +ĠÑ ħ +Ġë ª +Ġj est +ĠÐ ľ +Ġtr uth +h y +Ġsp end +Ġsci ence +et e +Ġ1 4 +Ġepis ode +Ġal g +end ed +ãģ ĵ +ar i +ll a +Ġf ish +Ġthr ow +m it +å ¹ +Ġcir c +ĠC al +Ġt our +Ġdire ction +Ġno ch +еР² +é n +Ġcount ries +Ġindust ry +in y +ic le +Ġfe et +I t +Ġlead ers +et zt +Ġst aff +ç Ķ +Ġpur p +it o +? ! +ĠJ a +Ġst ore +et ic +ĠCh ina +Ġë IJ +ĠUn iversity +Ġ # +Ġdec ision +Ġach ie +Ġact ual +u ly +Ġse ction +Ġresult s +Ġst ar +Ġm ist +ib ly +Ġd ad +Ġnum bers +om b +è ª +ĠS pe +Ġm er +Ġ2 5 +Ġaut om +Ġco ld +Ø ¨ +Ħ ľ +ag er +ĠT V +ĠS ie +ĠH ave +Ġ że +ug g +ain ed +Ġup on +Ġlo g +Ġcomplet e +Ġbra in +ag ing +ĠM us +o ver +Ġeas ier +Ġinte gr +Ġm ás +Ġturn ed +Ġst ri +iv al +Ġhe av +ĠT H +Ġwr iting +ÑĢ а +åľ ¨ +å¤ § +Ġcl a +d ing +Ġtell ing +и д +ic ated +ä» ¥ +ac ht +ãģ Ĥ +h aps +ĠSt e +Ġres ources +Ġd ann +Ġpart y +Ġ ÏĦ +Ġsa f +is es +t re +o int +Ġknow ledge +Ġany more +Ġf ly +Ġma int +и к +å ij +Ġse ll +la ughs +ĠY ork +Ġb ien +Ġo d +Ġeas ily +Ġr ange +Ġo ption +Ø ¹ +Ġapp reci +oc r +Ġdet erm +Ñ Ħ +Ġmean ing +Ġs ite +Ġdis co +ver age +Ġl ose +Ġinst all +Ġem ot +ant ly +ä t +Ġt amb +ĠW ar +ĠH o +ĠG en +em y +еР· +ĠP ol +Ġmess age +Ġnot e +Į Ģ +Ġh et +Ġim medi +Ġav o +Ġbook s +Ġbecom es +res h +è s +as ons +Ġhim self +ut s +Ġj u +Ġaw are +Ġrequ ire +Ġsystem s +ĠH ar +Ġam ong +Ġh om +Ġb reat +Ġwe ird +Ġë ¶ +Î » +Ø © +if f +or ing +Ġplat form +ĠT ake +Ġhelp s +ut ions +Ġfor g +Ġl uck +ĠEng lish +Ġwe b +Ġneg ative +Ġt ut +Ġab ove +ng th +Ġê ±° +Ġst ories +Ġlo ad +Ġback ground +Ġsw itch +g a +Ġprin ci +Ġfin an +Ġvar ious +Ġl Ãł +Ġkind s +ain ing +Ġn ature +ĠÐ ŀ +c z +Ġpr ay +Ġg ar +ir m +Ġ & +Ġì ĥ +n s +ĠR ep +ĠF e +Ġre v +ra nd +Ġlike ly +Ġunderstand ing +ı r +ãģ ĭ +Ġf al +Ġ1 3 +ÑĨ и +Ġsu d +Ġbr other +Ġpl ant +Ġthrough out +w ise +p re +Ġcult ure +ĠÙ ħ +Ġwonder ful +Ġa h +pp er +Ġso ld +Ġstart s +Ġwr itten +Î ¯ +n i +Ġ×Ķ × +ĠD av +Ġu lt +Ġar m +Ġro ck +Ġwe ar +ë į° +an o +ra g +Ġsqu are +ан и +c ast +le br +Ġliter ally +Ġplay ed +Ġhe at +on se +r ict +Ġins p +id s +Ġpop ular +ë ıĦ +Ġc atch +Ġm ount +Ġj ud +Wh at +еР± +R A +a ud +к о +Ġsur face +Ġcon v +Ġpie ces +O h +æ Ģ +Ġst yle +pp ing +Ġread ing +Ġconvers ation +оР¿ +ä¾ Ĩ +ĠAg ain +Ġb ank +t ime +Ñĥ ÑĤ +er ve +ĠG reat +Ġcap t +аР± +ay s +ĠF in +ific ation +Ġä r +а Ñİ +Ġe gg +ĠW el +Ġtar get +ul a +ch es +an i +O O +ic ious +n ow +Ï ĥ +bo ard +Ġg ente +Ġd ro +ĠE t +Ġd in +Ġc os +Ġaut hor +Ø ³ +Ġo ch +Ġem ail +Ġsp irit +Ġs itting +m as +Ġstre ngth +Ġbig ger +ĠW ait +Ġm at +Ġpol ice +ress ed +Ġwait ing +is hing +Ġdoll ars +ho od +s s +Ġimag ine +in i +Ġm es +Ġdis e +id ge +ab or +Ġp et +Ġh op +ĠK ing +Ġcomput er +Ġgo ld +Ġn u +Ġf ing +) , +Ġsec urity +ru ction +Ġsol ution +e xt +Ġp atter +ick en +ure d +Ġstand ard +ìĭ ľ +Ġdou ble +Î · +Ġw ife +is a +Ġdirect ly +ac ed +Ġb unch +Ġ ¿ +ал ÑĮ +Ġreg ard +Ġswe et +Ġun ique +ĠâĻ « +Ġtra in +ĠG erm +Î ¬ +R E +Ġbeh av +Ġpre d +ì ĥ +s et +Ġdescri ption +é e +Ġc at +å ĵ +Ġcoll ege +ì Ľ +Ġapplic ation +ĠS en +as k +Ġc red +ub lic +Ġmultip le +Ġn i +Ġpres ident +Ġadd ed +Ġro b +Ġaqu i +Ġh osp +Ġtool s +Ġg un +Ġbas ic +Ġl ines +Ġst ructure +ĠR uss +Ġtot ally +Ġbig gest +Ġe en +Ġar g +Ġ× ľ +Ġp ark +ĠD es +Ġce lebr +Ġf ait +ен ÑĮ +Ġsu ff +Ġreg ular +¨ ë +Ġm ine +ĠK ore +Ġpre vious +Ġp i +Ġse g +Ġpol icy +Ġк о +ĠTr ump +Ġvac c +ó w +ĠS y +и Ñĩ +it ter +Ġpolit ical +r as +Ġal s +ел ÑĮ +Ġsha pe +an z +Ġon to +Ġar ch +Ġam b +ag ram +ĠS m +ct ions +Ġjo in +b or +å Ľ +Ġfr ame +ł ĩ +Ġcho ice +௠ģ +Ñĥ Ñİ +ĠC or +ĠS w +I T +Ġt end +ĠE ar +Ġto r +Ġev ents +Ġcla im +ĠD a +ĠM ark +Ġgroup s +Ġe ating +ĠW orld +Ġrec ently +Ġtast e +Ġsur v +à ¤ +Ġsk ills +Ġи з +itt ed +Ġsh op +ìĿ ´ì +Ġest ab +ĠëĤ ĺ +Ġsecond s +ĠTh ose +ĠE nt +Ġì Ħ +ers on +Ġto wn +Ġc and +Ġopt ions +Ġ ing +V ID +Ġenc our +Ġr é +âĻ ª +Ġent re +Ġmove ment +ĠB en +Ġbir th +Ġwh e +Ġh ang +ĠE m +ig e +ro ll +Ġun f +ì Ĥ +Ġr id +Ġsp read +Ġh ost +al d +ĠE d +Ġcons um +U N +Ġop in +it ar +ĠM ed +Ġsub ject +Ġp al +Ġcar ry +Ġag ree +ĠWh ile +Ġcare er +Ġsci ent +Ġsud den +Ġf ile +z i +Ġex cept +é º +Ġpot ential +ĠAn other +Ġcomp lex +ĠS im +end o +Ġr ais +Ġphys ical +Ġd ate +ak er +ĠC ol +Ġpower ful +Ġmem ber +ra p +Ġsp ot +Ġs ource +Ġf em +é m +Ġem p +j i +iet y +Ġinf lu +Ġd ry +Ġlo ck +Ġz ero +ĠU h +Ġr out +Ġpor que +Ġ2 4 +Ġt al +Ġfol ks +Ġla unch +Ġcomp on +ĠWel come +Ġk ann +ä n +ĠÑį ÑĤ +e es +ĠÙ Ī +Ġany way +Ġaud ience +äº º +Ġsl ight +on a +Ġu r +Ġrel ig +Ġext rem +ı z +ĠM a +Î ¼ +Ġà ¶ +Ġall ows +Ġf at +ĠF ace +Ġn ational +Ġinter view +ĠM c +é t +Ġc ute +el a +Ġsec ret +ĠW est +ĠD ep +Ġex erc +Ġhist or +Ġpri or +Ġ6 0 +av a +ac her +y ond +ĠH a +Ġest e +in ary +ĠN orth +on st +Ġsm art +am s +ал и +Ġd ar +er ed +Ġfun ny +ĠO b +ĠBl ack +Ġrel ated +ĠB u +Ġsome where +ĠR em +n es +ment e +ĠRe ally +Ġcreat ing +Ġfam il +Ġsoci ety +Ġg el +Ġtrans form +Ä ĥ +Ġinclud e +Ġh ol +l ike +k o +air s +Ġп од +Ġpers pect +Ġb es +Ġparticular ly +Ġshow ing +ĠP art +Ġqu al +lo ck +Ġreal ity +ho ld +ict ion +o on +Ġv ir +ãģ « +it ary +Ġdr ug +Ġfe ature +Ġre asons +Ġ× © +Ġwr ote +Ġf ant +Ġb and +Ù ĥ +en a +ke y +Ġear th +d om +Ġfe atures +Ġflo or +Ġspeak ing +Ġt ip +ĠA ust +Ġst ock +Ġch urch +Ġr ac +ìľ¼ë ¡ľ +ภĻ +ãĤ Į +k y +Ġresp onse +Û Į +ul ations +Ġsl ide +Ġgrad u +ci ous +Ġme ant +Ġ == +Ġ× IJ× +ã ħ +Ġkind a +Ġsc ene +Ġm uit +Ġê° Ģ +r ast +re st +Ġplay ers +w a +Ġbro ad +Ġtom orrow +oc ol +ĠÑģ в +ĠB ar +ı k +Ġse a +Ġrem ove +Ġrem ind +ом Ñĥ +ĠS ince +Ġave c +ce ll +и Ñħ +Ġdoc ument +Ġê·¸ë Ł +Ġne igh +be at +Ġp Ã¥ +Ġas pect +Ġd ed +lish ed +il s +Ġour selves +u ce +Ġhe y +ĠпÑĢ о +ent y +Ġas soci +ad os +um ber +Ġ ] +éĤ £ +no v +Ġì Ļ +Ñĥ Ñĩ +Ġcond ition +ëĬĶ ëį° +Ġval ues +Ġsc en +min ist +Ġc ast +Ġgrow ing +Ġus er +Ġresp ond +l im +é r +y m +çľ ĭ +os es +sy ch +ĠÑĢ аз +Ġappe ar +Ġpro gress +eng th +Ġj ak +ĠD is +Ġpat ients +ĠS er +Ġg as +è re +ìĸ´ì ļĶ +Ġre ci +ìĿ ¸ +Ġs ca +ep end +Ñģ к +аР¿ +Ġb atter +Ġve h +ð Ł +Ġac com +Ġbe at +Ġpain t +Ġcont rib +Ġs ad +Æ ° +al es +Ġt ree +b a +Ġb orn +ic ed +à® ķ +b and +Ġme chan +ĠD et +Ġcap ital +Ġdel iver +Ġfe ar +ŀ ĺ +ĠS outh +Ġb ought +Ġst ress +Ġv or +? ? +i h +ìķ ¼ +Ġer a +ìĿ´ ë +а Ñı +is ions +iv ity +Ġhelp ed +Ġass ist +Ġplay er +r an +Ġimmedi ately +Ġmo ved +c ie +ê ± +Ġann oun +å ¿ +ìŀ IJ +Ġprodu ction +Ġsum mer +Ġt un +Ġprogram s +G H +al ing +ir a +el ess +. ) +Ġa verage +è¦ ģ +Ġgl ass +om an +if ically +Ġëĭ ¤ +ĠC ong +ĠV er +Ġtr ick +Ġbe gan +Ġv ill +ê ±° +h ow +æ Ń +Ġt ill +Ġ9 0 +ber t +Ġê ¸ +Ġtemper ature +à ² +๠Ī +Ġgra ph +Ġê· ¸ +Ġr ot +Ġmo b +A Y +a el +Ġre pe +Ġdev ice +Ġ19 9 +Ġte le +Ġke pt +p a +æ ĸ +ver se +Ġst ream +е Ñĩ +ess ion +Ġstr ugg +z z +Ġdeg ree +Ġhelp ing +Ġsm ell +Ġper haps +p ro +Ġcont ext +Ġi k +Ġп еÑĢ +Ġcal cul +éº ¼ +b ing +Ġreal ize +l am +ĠCh ar +y t +ĠìĿ ´ì +Ġd anger +ĠI m +a a +Ġlo ved +Ġpurp ose +Ġfinish ed +Ġpe ace +Ġo t +Ġglo bal +Ï Ģ +Ġab er +ĸ Ī +Ġcharac ters +Ġn ur +Ġdam age +Ġem er +Ġpre c +ĠW ir +Ġinst it +ij × +Ġallow ed +b on +Ġto d +еР³Ð¾ +Ġj etzt +Ġmed ic +Ġsmall er +ce ed +Ġlevel s +Ġint ell +W e +Ġse m +Ġcurrent ly +Ġmod ern +Ġcont ract +Ġdetail s +ortun ately +O S +Ġst ates +Ġad just +ant age +e z +ĠV ery +Ġsc ale +Ġre lease +Ġf az +Ġ ic +it ude +A C +ĠP at +id en +Ń IJ +Ġpre fer +olog ical +ĠFace book +Ġê° Ļ +Ġ .. +ĠM ake +Ġко ÑĤоÑĢ +ĠDav id +ĠAf ric +Ġmod e +ĠC ity +Ġsh all +ĠÑ Ħ +im in +Ġз а +r om +u a +Ġbe yond +Ġdist rib +к Ñĥ +ĠDo es +Ġv ict +r ate +Ġv ai +Ġsuccess ful +Ġh ous +ah a +est s +ĠE st +Ġdisco ver +Ġthere fore +ch a +Ġc up +Ġpop ulation +ĠI l +s c +Ġsp ent +re l +Ġuse ful +Ġt ab +æ Ŀ +Ġ Å +Ġìł ľ +Ġcon se +Ġqu ant +ay a +Ġb on +åı ¯ +ĠCh in +Ġê² ĥ +ound s +е ÑĪ +ell e +Ġ ice +2 1 +Ġk ick +ä¸ ĭ +Ġstep s +Ġton ight +нÑĭ й +ren ch +. ' +Ġgra b +Ġimp lement +ĠìĪ ĺ +Ġmiss ion +Ġclear ly +Ġappreci ate +è Ģ +Ġf resh +ar m +ĠTw o +Ġex ec +Ġproject s +Ġcommun ities +ri ble +Ġreg ion +Ġfre qu +ro y +Ġhow ever +Ġpart ners +an c +Ġmin im +Ġl at +Ġfamil ies +Ġev idence +Ġp un +ra ft +Ġl oss +Ġma p +Ġany body +Ġchang ing +Ġr ules +Ġorgan ization +Ġess entially +ĠR ed +Ġele ment +æ Ĺ +Ġv irt +r at +Ġpr int +and er +are n +em os +ο Ïħ +Ġcond itions +ab e +Ġd ance +и ÑĢ +Ġd os +о Ñĩ +ĠQ ue +Ġwalk ing +Ġt ro +Ġ id +Ġadd itional +Ġfull y +Ġf ans +Ġadd ition +Ġlik ed +Ġü ber +Ġb ow +d i +Ġm aster +o ff +) : +m ber +Ġë ¬ +å ¯ +åĪ ° +la use +Ġo der +Ġsaf ety +Ġre act +à® ¿ +b t +Ġdis app +Ġgirl s +S t +ĠA ng +Ġfa ith +Ġturn s +Ġt ight +Ġm outh +am i +z er +Ġwe ap +Ġб Ñĥд +Ġhosp ital +ra id +Ġmic ro +ĠSt ate +ĠM ost +ag n +Ġdec ide +Ġpat ient +Ġcor ner +Ġdi ed +N o +ĠSt ud +re nd +em pt +Ġli e +Ġl if +ĠBe fore +t ó +ĠSu per +Ġbe ll +6 0 +Ġpriv ate +ĠPa ul +Ġg ib +Ġag re +´ì Ħľ +Ġs ig +Ġinvest ig +Ñı ÑĤ +en ing +Ġdist ance +Ġwar m +Ġdig ital +å¾ Ī +in er +Ġp and +ĠCO VID +Ð ³Ð¾ +g n +Ġr ace +Ġpr oud +Ġte aching +Ġ ÑĤо +ìŀ ¥ +ĠAll ah +I n +Ġw ood +Ġcol ors +Ġw ird +u j +id ad +Ġcustom ers +Ġconnect ed +Ġlay er +Ġachie ve +Ġperspect ive +ĠC oll +Ù Ĥ +Ġcl oud +!! ! +Ġend ed +łĩ ê²Į +Ġmanage ment +Ġr ich +Ġsub st +Ġrem o +Ġser ve +Ġres ist +Ġthought s +Ġgrow th +ili ar +Ġright s +Ġchar ge +Ġcons ist +Ġwer den +Ġem b +and om +Ġhur t +Ġk an +i as +л о +Ġsh it +Ġbe g +Ġrece ived +it ation +Ġme at +Ġis so +ff ee +Ġfam ous +Ġcomfort able +I L +ĠB ye +èª ª +åĢ ij +oth es +Ġmed ical +Ġenjoy ed +Ġhealth y +Ġw y +c ies +Ġeff ort +Ġdo ctor +Ġmil itary +L AU +Ġg ro +Ġb attle +Ġf ed +Ġcap ac +Ġaf raid +iv il +ĠвÑģ е +Ġl ength +ys is +Ġbe i +¤ í +Ġorgan iz +or g +in c +Ġinter act +ĠChin ese +Ġacc ording +Ġincred ible +Ġkill ed +Ġda ughter +ĠÏ Ģ +Ñĭ в +Ġschool s +Ġ « +ll er +Ġshould n +n al +Ġcr is +Ġch icken +Ġf aster +Ġextrem ely +Ġopp os +Ġn ous +Ġ + +ri a +Ġfinan cial +Ġexc iting +Ġjour ney +×Ļ× Ŀ +ł ë +Ġdis play +Ġmem ory +Ġheav y +н е +Ġpass ed +ÑĢ и +il es +Ġp sych +Ġspec ifically +Ġeng age +Ġl ed +or ge +ĠD em +ord er +Ġ8 0 +Ġcre am +ester day +Ġed ge +Ġп ол +Ġbu ll +Ġind ic +Ġk tó +Ġhope fully +um ents +ag en +н ого +Ġh ate +ch t +8 0 +Ġeff ic +Ġì§ Ģ +Ġintern et +Ġbud get +Ġproper ty +id ay +Ġì ļ +Ġм ож +ol a +Ġshow ed +ĠM on +Ġthous and +A P +Ġpo or +us ed +ĠJ ack +Ġs Ã¥ +ĥ ½ +Ġes c +Ġsoft ware +Ġqu ar +ĠØ ¨ +Ġnecess arily +om en +i y +Ġevent ually +ish ed +Ġbr ight +E D +Ġs pl +Ġdem and +Ġth reat +Ġs ir +Ġrele ased +ck et +ĠâĢ « +Ġrequ ired +Ġv ote +ì ¹ +à® ¤ +Ġdevelop ed +ĠìĤ ¬ +at ory +Ġd ir +ca pe +Ġslight ly +à ¬ +๠ī +re et +Ġdise ase +Ġcour t +Ġitem s +ĠEar th +ÑģÑĤ и +ж е +ì ² +Ġchalleng es +ĠBr it +Ġdesign ed +1 2 +Ġhear ing +Ġlisten ing +z o +ĠÑģ л +ãģ§ ãģĻ +Ġper o +Ġwe aring +pl ic +Ġch em +Ġbal ance +Ġb a +Ġrece ive +im a +Ġsignific ant +Ġм Ñĭ +an ch +ĠC r +ĠC oun +ê¸ Ī +Ġjo bs +Ġoffic ial +Ġper m +om s +Ġopportun ities +Ġover all +Ġh us +od es +Ġn ation +ĠR eg +Ġor d +Ġrest aur +Ġì Ĩ +Ġm el +v in +Ġw enn +Ġk ön +æ ĥ +Ġopin ion +ãĤ Ĥ +è ¬ +ĠSomet imes +ç Ĥ +Ñī е +as c +O U +Ġ20 20 +Ġdel icious +ig er +Ġìķ Ī +o le +Ġhand le +Ġc it +Ġíķ ľ +Ġf ör +o oth +Ġnecess ary +Ġind epend +æ Ħ +ist en +h am +Ġé t +ãĥ ³ +Ġmult i +Ï Į +? ) +Ġcamp us +Ġtop ic +Ġr ain +Ġpan el +ĠS am +Ġlar ger +aud ience +Ġpa id +Ġeconom ic +ol t +Ġstre et +ĠC ont +Ġdri ving +Ġìł Ģ +Ġh ay +Ġprofess ional +ĠIn tern +å ¸ +Ġin put +Ġc ateg +Ġc ro +Ġ ll +E T +Ñĭ й +* * +ĠZ e +B LE +Ġì ¤ +re es +ĠÐ ¯ +ed e +ier t +Ġfo ld +Ġd ur +ĠN ational +Ġìĸ ´ë +an ced +Ġfa ire +ut ed +Ġk ing +Ġw ild +o i +up beat +Ġpre vent +i us +Ġà ¨ +Ġw ide +Ġr ing +Ġtit le +Ġstand ing +Ġal though +Ġh i +Ġsa uce +Ġs ides +Ġanim als +il ing +at ives +ìĹIJ ìĦľ +ĠO ver +Ġdes p +Ġconsider ed +ar ies +i ers +Ġein en +Ġs ister +Ġë ķ +ĠS ure +ãĤ ĭ +ri end +a ign +Ġsh own +Ġs ac +Ġs ont +Ġcent ury +Ġt ien +ĠÎ º +ĠS T +åķ Ĭ +Ġold er +ie m +Ġtr uly +ĠS i +Ġwind ow +iqu es +ar io +æ² Ĵ +Ġloc ation +Î º +Ġì ľ +v i +ag ue +ĠS orry +Ġdis p +Ġhe ll +Ġà ī +Ġtr ade +Ġcrit ical +Ġê ± +Ġn amed +Ġprep ared +ĠH ouse +al u +Ġt ough +Ġtri p +Ġs and +c el +ü z +ĠP ut +Ġap art +is f +v is +Ġli br +a ven +Ġv ie +Ġeffect ive +ภ² +Ġmag n +Ġmuit o +Ġê µ +h al +Ġlim it +Ġn ine +Ġwill ing +ı ÅŁ +s p +еР³ +h i +Ġal t +ĠJ an +Ġorig in +ĠU s +Ġele ments +Ġus es +Ġhelp ful +Ġfl at +Ġfam iliar +ĠP ark +Ġc ore +Ġclos er +Ġact ive +Ġad minist +C E +нÑĭ е +ç Ħ +Ġrel ative +Ġment al +Ġr andom +Ġpart ner +Ġut il +ph one +Ġr ule +w w +Ġìł ķ +Ġsch on +Ġco ffee +H A +Ġconnect ion +Ġun it +la ughing +l og +Ġapp l +л а +us ic +ĠB ra +Ġany where +AU DI +Ġsepar ate +bo x +Ġd ivid +Ġtest ing +Ġs ick +Ġwer en +ä» ĸ +Ġ׾ × +Ġadv antage +Ġtrans fer +' . +Ġë ¹ +Ġfind ing +н ой +Ġì¢ ĭ +Ġfor t +Ġeconom y +Ġl ack +Ġleav ing +Ġd im +å İ +ĠR es +Ø Ń +Ġdiscuss ion +еР¿ +Ġg es +du ct +Ġch ain +Ġus ers +e ch +ÅĤ a +Ġdis h +Ġcare ful +Ġte acher +Ġopt im +Ġfl u +at ically +Ġref lect +Ġtreat ment +e ed +i ÄĻ +à ¹ +à® ¾ +Ġequ ip +Ġplan ning +Ġsol ve +ãģ Ŀ +ĠT om +Ġavo id +Ġp ou +Ġgreat er +l in +O L +ĠL u +ĠM ore +Ġatt ract +ê n +un a +Ġphot o +er ation +Ġplan et +Ġcop y +Ġvis ual +ir ing +Ġintern ational +Ġla ughing +Ġth ick +Ġhold ing +Ġbring ing +Ġlet ter +Ġb urn +Ġeffect s +it é +our s +O T +ê me +ĠSch ool +×ķ× ª +rop ri +l ig +α ι +Ġad ult +Ġsu gar +Ġr ide +Ġhigh light +Ġno body +Ġ2 1 +Ġch at +ĠпÑĢ и +Ġin nov +ung en +Ġatt ach +ed om +å Ĭ +y l +Ġleg al +Ġr ice +Ġcoll abor +k ing +d own +æ Ļ +ãĤ Ĭ +Ġi h +ĠA c +ous ly +Ġr ap +Ġsol id +Ġgener ally +Ġpatter n +al i +à¸ Ń +Ġtrans l +in ter +a ult +Ġë ¨ +Ġexp ress +Ġexam ples +Ġch ose +Ġtell s +ÃŃ s +ain t +ĠT ell +ĠMich ael +æ ¨ +ĠN umber +Ġt ap +Ġexper iment +Ġbenef it +Ġì ° +Ġse qu +Ġexp ensive +Ġgener ation +ĠM any +Ġadd ing +Ġk il +Ġcamp aign +ĠA nt +ra w +omm en +Ġs oul +j o +ĠAct ually +am m +ê² ł +Ġma xim +Ġsal t +Ġc ru +Ġcall ing +ãģ Į +Ġbas is +b an +Ġkeep ing +ĠM or +ed s +ì Ĩ +Ġto do +ам и +н Ñı +Ġli ved +ĠD u +ãĤ ī +å® ¶ +for ce +å¹ ´ +fer ence +al a +Ġocc ur +s k +Ġrec ent +Ġc ars +Ġtrad itional +ent le +² Ī +Ġhel d +Ġn ach +ĠCent er +er en +Ġb in +Ù ģ +Ġcomm e +Ġre ve +Ġìĺ ¤ +Ġexpect ed +ab il +Ġfocus ed +o v +Ġi P +or ial +i ro +Ġet c +am ing +ĠS on +Ġy esterday +Ġstr ate +ĠÑ Ĩ +Ġë ı +p es +Ġactiv ity +Ġadv ice +Ġopen ing +f in +Ġre la +é ĸ +Ġinst ance +ĠEvery one +b l +p en +Ġvis ion +ĠA lex +if orn +Ġt ick +H e +Ġstrate gy +Ġk om +P E +ĠG l +Ġelect ric +1 5 +Ġda ily +Ġhus band +Ġst ation +Ġanal ysis +yn am +Ġatt empt +Ġbill ion +v ant +Ġfor th +Ġm ath +al y +Ġbehav ior +ĠM as +k an +ĠD ay +Ġbl ess +Ġg ut +ĠH igh +o x +Ġd ress +Ġj ed +è ¯ +å ĸ +Ġexperien ces +ist a +Ġfight ing +å · +ĠÑģ к +Ġmost ly +a use +Ġpict ures +ен ÑĤ +Ġm ad +Ġmod els +ÑĪ е +ĠC ount +Å Ħ +ÅĤ o +ep t +O M +ĠA N +Ġtrou ble +4 0 +Ġb ird +ul ate +Ġm ur +Ġprodu ce +Ġmar ried +b it +Ġthe ory +í ĺ +Ġlead er +ĠL ast +A A +è µ +Ġim ages +Ġexp and +ĠP or +Ġpur ch +ĠS an +ĠChrist mas +ĠAust ral +Ġw id +ĠM iss +Ġknow ing +Ġz e +s hip +k u +Ñħ од +ĠInst agram +ĠInd ia +Ġest a +ĠCal iforn +Ġ7 0 +Ġdra g +Ġbr ush +Ġn ames +A nd +Ġy o +ill a +Ġsch ed +Ġdest roy +ye ar +Ġv amos +Ġ ÙĦ +ç a +Ġforg ot +и е +Ġra ise +re me +íķ ´ +ĠG ive +Ġcont ain +ra b +Ġg ift +ĠÑģ п +Ġrequ est +Ġsh ut +Ġdeg rees +Ġbenef its +Ñĭ е +Ġstud ies +Ġend s +Ġevery where +Ġher o +op h +er ry +Ġmaterial s +en ed +N A +å į +Ġmu y +Ġwor se +ä» Ģ +ĠM ad +Ġdec isions +ion e +Ġfore ign +la ughter +i ber +ени Ñı +ãħ ĭ +Ġreal ized +Ġ ign +Ġwe ak +ĠÎ ¼ +Ġsca red +Ġass um +A K +ï ¿ +ï¿ ½ +Ġcover ed +ĠS at +Ġо н +Ġindividual s +Ġcomp ared +1 1 +ĠAd d +ic les +Ġc ert +r ar +Ġbr ief +Ġactiv ities +Ġf ab +b ar +Ġa st +ĠO ther +Ġclass es +Ġo g +Ġmiss ing +ãģ ł +é Ŀ +w ers +× © +Ġintrodu ce +Ġequ ation +ãģ¾ ãģĻ +Ġn om +Ġpain ting +us hing +ĠA P +Ġencour age +Ġsh ip +itt ee +iver se +ot a +n am +ãĥ » +Ġexerc ise +ĠÐ Ń +Ġn as +Ġthous ands +ĠCaliforn ia +Ġs es +Ġr ow +ŀ Ī +Ġpand emic +Ġsk ill +b el +Ġdire ctor +Ġmil k +Ġn ut +Ġmot ion +Ġcl osed +è ¨ +Ġcred it +ah r +Ġche ese +Ġal tern +im ately +Ġs ust +ĠT ra +Ġgl ad +Ġhigh ly +Ġw a +Ġredu ce +Ġb le +ad or +in ated +ion es +ci ent +Ġdep ending +Ġsh aring +Ġca ught +ra el +Ġme hr +Ġpass ion +ç Ľ +Ġr u +Ġfar m +T I +av es +ĠR ob +ĠB ro +Ġmot iv +ret ch +ru pt +ĠB ig +Ġall e +Ġet t +ub s +ĠJapan ese +ĠH all +и ли +AUDI BLE +ç ¬ +Ġcell s +ik a +el ine +il er +Ġì £ +Ġsk y +IN AUDIBLE +end e +ap ter +Ġp in +Ġg ather +h ol +le ction +Ġsy n +Ġpl ug +r ound +Ġun iversity +h ib +Ġfant astic +k n +Ġho le +ĠRem ember +in ct +ak s +C H +Ġbro ken +Ġstr ateg +Ġal ive +Ġt ank +Ġc art +r ated +r ie +ĠSt ep +ĠEvery thing +Ġb ound +Ġso bre +Ġcustom er +¡ Į +ur g +ĠB ill +L a +wh at +Ġre action +Ġs ession +Ġpl ans +ĠìĿ´ë łĩê²Į +Ġdown load +ì Ļ +u er +Ġc ab +Ġinst r +if ying +ĠN ice +Ġteam s +ı l +Ġgo als +is ch +Ġtrans port +Ġanim al +Ġcost s +Ġcall s +Ġse hr +ì Ī +ri an +Ġd ial +Ġwe ather +๠Ģ +Ġв оÑĤ +ĠPl ay +Ġsh ared +Ġsm ooth +ab a +Ġleav es +à® © +Ġconc ent +Ġsh ift +ĠëIJ ĺ +ĠGo vern +Ġdem onst +Ġbut ter +ĠìĹ ¬ +Ġsat isf +Īë ¬ +Ġrecogn ize +ĠF rench +Ġvol ume +ä nd +Ñĥ м +Ġì§ Ħ +ĠKe ep +ow a +ipp ed +ÑģÑĤ ÑĢ +Ġdet ect +ĠÏ ĥ +Ġl ift +Ġcl othes +ĠSt op +à µ +m et +Ġcl in +Ġar r +f riend +Ġst uck +Y e +h and +um a +Ġsc ri +Ġfuck ing +ct ors +× ª +Ġjo ining +Ġc ette +ĠØ £ +ĠWh ite +Ġi hr +Î Ń +ãģ Ń +Ġinclud ed +ess o +Ġac ad +b um +Ġs ab +Ġд лÑı +è¿ Ļ +uf act +ĠRep ublic +r im +Ġye llow +Ġlim ited +T ER +ĠT y +Ġnot es +v est +и з +al ed +Ġph ase +and a +ĠM om +R I +Ġim mer +m al +Ġin j +Ġy ang +ud ible +аР³ +Ġset t +Ġmag ic +Ġens ure +Ġsp ring +Ġsh ock +Ġwhe el +ог да +ãĤ Ī +Ġcan cer +Ġro ot +Ð IJ +gen cy +Ġë į +i i +Ġout put +Ġcomm it +Ġwork ers +ìķĦ ìļĶ +ĠÑģ ам +ve y +Ġpe u +Ġc ivil +is c +Ġbr ings +ÑĢ ав +an ia +Ä ģ +c raft +mb ol +Ġintell ig +b i +ac ing +y ou +Ġbecom ing +ĠD er +em a +å°± æĺ¯ +Ġing red +Ġcomm and +Ġupd ate +Ġpre m +Ġopen ed +Ħ ¤ +ени е +Ġg ard +Ġstat ement +Ġsc rew +Ġpr ote +Ġc ards +Ġt ask +Ġeven ing +Ġst itch +in en +ĠB er +m ark +ĠD ad +Ġе ÑģÑĤÑĮ +Ġ× ŀ× +ìĹ Ī +Ġb an +Ġcl im +Ġfre edom +Ġnorm ally +еÑģ ÑĮ +å ¦ +Ġprov ided +Ġìŀ IJ +ĠìķĦ ëĭĪ +ĠK im +ied er +ìĿ Į +Ġcit iz +Ġb ike +Ġb ak +Ġno ise +Ġcl imate +iz es +å¾ Į +Ġincre asing +ĠTH E +Ġli qu +Ġperson ally +e f +res p +Ġleg s +ind er +Ġp ed +Ġë§ İ +Ġdep end +Ġvar iety +ĠIs rael +Ġwas h +å Ĩ +Ġqu iet +ĠJ ames +ĠJ ew +Ġfore ver +ĠI nt +Ġcoun ter +ur ance +ĠAny way +ca re +ĠOn ly +ci ón +ad i +ĠE v +ëĭĪ ê¹Į +ĠÎ ± +Ġslow ly +Ġо д +Ġnot iced +ier en +Ġfe ll +ĠÐ ij +Ġm ême +Ġwhen ever +! ) +ĠH y +å ¼ +ord s +us ion +ĠSt ar +Ġí ĺ +ĠM ac +ä¸ Ĭ +i ven +Ġìĭ ľ +ĠìĹ Ĩ +ĠT ur +Ġg er +r is +Ġve z +Ġл Ñİ +Ġvers us +ا Ø +ocol ate +Ġplan e +Ġz o +Ġsu it +Th is +Ġn erv +ĠA cc +Ñĥ ж +ìĤ ¬ +n h +em e +Ġa uss +Ġme as +Ġtr ès +Ï ī +Ñģ ли +ĠAr t +ĠSec ond +олÑĮ ко +ch o +it ect +е ÑģÑĤ +Ġb oss +Ġinc ome +ł ¤ +Ġsh ad +Ġapp ropri +ĠM al +op t +Ġart ist +Ġplay s +oth ers +ĠIn ter +Ġvir us +Ġh ung +Ġconst ant +Ġscri pt +Ġsn ow +ul f +k et +Ġdev ices +Ġmet al +ight s +ìĦ ¸ +Ġsal es +Ġve get +Ġcollect ion +Ġv ia +k er +Ġgot ten +O W +i én +Ġacc ur +Ġw ave +ult y +ĠA ir +Ġlead ing +ic ing +Ġcent ral +ĠChrist ian +f r +ĠAl though +Ġsong s +Ġf if +нÑĭ Ñħ +Ġbel ong +oss ible +ì ° +Ġphot os +is l +Ġrela x +s a +US IC +ê · +Ġman ufact +ĠTw itter +Ġdanger ous +Ġhy d +le ar +i ant +ĠâĢ ¦ +Ġsudden ly +Ġla ugh +Ġang le +ĠG ot +Ġwor ried +о е +Ġp ap +ĠM art +en o +Ġbatter y +Ġп оÑģ +Ġlight s +Ġar ms +ĠA bs +m es +âĢ ĵ +use um +Ġte a +ĠM ic +Ġfor mer +ograph y +Ġapplic ations +ĠD ire +çĦ ¶ +Ġfeed back +itch en +yor um +u ed +ig t +Æ° á» +os ition +ĠD el +Ġíķ ĺë +ĠB ack +ad s +Ġpr ime +ì£ ¼ +ì£ ł +× ij +Ġm ut +] . +ĠÐ Ĺ +lo c +k in +Ġexper t +Ġal right +ung s +Ġsupp ly +Ġleaders hip +ĠF ra +Ġtyp ically +Ġs el +Ġtre es +Ġ2 2 +h ar +Ġwor st +Ġbus y +ant o +ĠU p +ĠB as +Ġpresent ation +Ġstr ange +Ġth in +ÑĤ е +Ġveh icle +Ġд о +cell ent +7 0 +Ġt ired +Ġcris is +Ġt iny +as y +Ġr an +é ĩ +Ġfor ces +Ġо Ñĩ +Ġident ify +Ġass ess +иÑĤ е +S E +Ġcreat ive +ç Ł +Ġdep artment +Ġinit ial +æĪij åĢij +ĠD am +ak t +v ere +Ġinf ect +Ġp ump +Ạ¡ +Ġv iel +Ġr are +Ġd ot +ash ion +em pl +Ġf lex +Ġk on +Ġtr uck +Ġle ct +Ġpl astic +la w +Ġlik es +Ġr ough +ĠM AT +í ŀĪ +Ġcomm er +Ġas se +Ġc ake +Ġact ions +Ġad m +Ġother wise +ĠHe alth +Ġcoll e +à¹Ģ ภ+Ġr ub +å¾ Ĺ +æ Ķ +Ġsc r +Ġz um +ĠH im +Ġch amp +Ġconcern ed +Ġ5 00 +Ġpl ate +ĠO ut +Ġdon c +Ġequip ment +Ġta ught +ll ed +Ġí Ļ +iv a +Ġmot or + » +Ġgu ide +å ī +Ġstop ped +Ġr at +Ġlab or +Ġa im +Ġprep are +ĠÑ Ī +Ġshoot ing +ann ed +cri pt +Ġen emy +Ġdep ends +Ġn av +Ġb er +Ġland s +Ġun ivers +i u +Ġfact or +ok ing +Ġcar bon +b ut +ĠL ove +el d +ĠÎ µ +Ġg a +Ġé s +Ġbre ad +Ġvol t +í Ĭ +Ġwas te +Ġkeep s +æī Ģ +Ġst or +Ġhon or +Ġun less +Ġcol um +Ġë ĮĢ +Ġpl ants +Ye ah +Ġinclud es +ä¸ Ń +Ġo x +Ġpe ut +ë§ Į +ìĥ ģ +ist ry +ภ± +ĠDep artment +ant a +Ġfing er +Ġst retch +Ġsy mbol +Ġneigh bor +æ ¬ +ê° Ħ +~ ~ +ĠÑĤ Ñĭ +ĠA ber +k es +Ġmass ive +ĠC H +ĠS al +× ł +ãĤ Ĵ +Ġd ynam +ach e +ĠP re +Ġmon itor +ent ed +E O +Ġrais ed +ist ics +Ú © +Ġv ou +it en +¡ ° +Ġbusiness es +Ġe arn +Ġmob ile +id ade +Ġha be +y r +l ict +Ġcon duct +Ġfed eral +Ġw o +b u +Ġn one +Ġteach ers +ĠاÙĦ Ø +éģ ĵ +id ents +ا ÙĦ +Ġtre nd +еР¶ +Ġal bum +Ġm ich +b ased +ภµ +Ġtrans ition +Ġн о +õ es +h ost +ed y +ĠPro f +p an +ij n +Ġcapac ity +und o +Ġ× ij× +Ġbreat h +Ġм ен +Ġm ü +í Ļ +ĠA ut +hing ton +Ġn or +Ġg ain +po int +Y es +ĠØ ª +ĠN a +Ã¥ r +Ġi ç +ĠM ary +Ġsp in +Ġant i +åIJ § +Ġsome how +Ġlaw s +Ġmom ents +Ġg re +Ġmo ves +ĠW ould +Ġpred ict +Ġv ra +Ġ201 9 +¶ Ħ +Ġfund ament +2 5 +Ġp ure +Ġw ow +Ġis land +Ġinvest ment +Ġb ath +ĠY a +Ġhard er +Ġt ips +å Ĺ +Ġelect ron +ĠB ob +Ġb ond +od ies +ĠA ug +Ġgib t +Ġch air +Ġtw ice +w ood +Ġcl ar +Ġmas k +Ġhonest ly +Ġ201 8 +t ies +' , +Ġp ens +Ġsurpr ised +Ġcommunic ation +ãģ£ ãģ¦ +Ġsp r +Ġwh ose +Ġst ars +× IJ× +ĠâĢ ĭ +Ġproper ly +Ġg rew +os ing +Ġdi vers +A D +Ġem pt +Ġexp ression +Ạ¿ +ĠP al +ãģ Ĭ +Ġjust ice +Ġp air +w o +Ġse at +or ter +Ġlink s +ĠM er +Ġre nd +но е +up id +ĠH el +ĠM arch +ĠL o +Ñģ ÑĮ +Ġhas n +Ġev alu +ãģ ı +å¤ © +il os +Ġfund ing +Ġv en +u an +ĠM aster +ĠO l +ĠF re +Ġy ap +ĠS ir +s ch +Ġmist ake +am an +Ġdin ner +ĠWas hington +Ġorganiz ations +Ġж е +av ing +Ġv ÃŃ +Ġbirth day +Ġbe ar +ĠÙ ģ +Ġaff ord +Ġre ven +Ġrelationship s +r ough +ĠT ime +Ġt ag +ĠS un +u ary +ĠP o +c ar +ab ilities +Ġpr ison +Ġl ic +ìł ķ +id den +Ġspec ies +é » +Ġf irm +Ġsc ore +Ġd it +Ġspe ct +Ġp el +Ġcompl icated +æ¨ £ +Ġr ank +Ġoppos ite +Ġpick ed +Ġк он +el er +Ġm ig +ĠS l +ĠN et +Ġne ck +ĠFr ance +Ġtechn ical +ภ¡ +Ġmil es +Ġprim ary +Ġse in +s es +Ġla ughs +b ra +ÅĽ ci +ri age +Ġn ic +et ers +Ġà ª +olog ies +ĠI S +r ad +ud o +ı nd +m ar +Ġex ch +Ġcompet ition +Ġauss i +ĠS erv +Ġre nt +Ġch ocolate +Ġw ieder +Ġnear ly +Ġspe ech +Ġun c +Ġpar am +ĠBrit ish +Ġrem ain +ภģ +ur t +ĠØ ¹ +Ġcr ack +ail s +Ġprom ise +Ġpay ing +i ÃŁ +Ġad apt +ал а +Ġmov ies +Ġw ire +Ł ¬ +æľ ĥ +Ġter rible +Ġs ó +Ġperfect ly +åij ¢ +ord in +Ġj á +Ġimp ossible +ĠTh ree +Ġn h +Ġtur ning +r um +ĠB el +ig g +Ġrespons ible +и й +Ġincred ibly +w i +ian o +Ġhum ans +Ġà ĩ +Ġsetting s +Ġj oy +o ot +Ġdeal ing +ill ed +Ġsur round +Ġfollow ed +Ġposs ibly +Ġinit i +st en +Ġpr os +Ġcand id +Ġass ign +Ġviol ence +W ell +Ġr ise +P S +Ġtamb ém +Ġë ĵ¤ +i ance +y an +Ġaud io +ĠB et +ĠAmeric ans +ĠAs s +is chen +ìŀ ħ +Ġult imately +Ġpol ic +Ġmajor ity +éĢĻ åĢĭ +ĠFin ally +er ap +Ġgu ard +ĠMAT T +Ġbr own +м и +Ġch a +ĠHo ly +Ġnerv ous +ipp ing +ÄĻ d +ĠS a +ĵ ľë +¶ Ģ +l ie +çľ Ł +Ġn uc +ĠA pr +é Ľ +ĠKore a +eg o +ĠCan ada +Ġkön nen +Ġcomp ar +Ġg anz +ĠM ais +Ġthem e +Ġk i +Ġdraw ing +az on +ĠO ff +t t +ĠW ind +Ġtod os +Ġob vious +на Ñı +I M +ĠÐ ł +we ll +Ġbl ow +Ġho ok +Ġcir cle +Ġë³ ´ +Ġarch itect +ĠK r +Ġc ó +Ġprotect ion +eg a +å ĩ +Ġwatch ed +Ġans wers +Ġdi et +iv o +Ġpow der +Ġyour s +Ġhigh est +çĤ º +F F +å º +Ġbo ys +ö yle +Ġl unch +è¬ Ŀ +ĠI I +Ġset s +Ġmo le +Û ģ +Ġwin ter +Ġluck y +Ġrespons ibility +Ġsign al +Ġwond ering +Ġa x +Ġcook ing +ов оÑĢ +le g +Ġп оÑĤ +Ġsurpr ise +Ġdem ocr +Ġlo op +Ġj ag +Ġcur ious +Ġmarket ing +Ð Ŀ +ar on +ĠApp le +Ġvirt ual +Ġ19 8 +no on +ĠM et +оÑģ ÑĤо +об Ñĭ +it u +ĠA w +Ġbu ying +Ġrestaur ant +ĠB ud +Ġdou bt +Ġgr ant +Ġver d +Ġc ash +Ġfac ulty +Th at +ĠE in +å¤ ļ +Ġw ed +it ness +ĠM ag +n el +Ġn arr +Ġacc ident +Ġmed ium +em ents +Ġcr ow +n ight +ìĿ ¼ +ä¹ Ł +Ġlibr ary +аÑİ ÑĤ +Ġtamb ién +Ġrefer ence +Ġfour th +h ouse +v ention +Ġfill ed +ĠC our +ib r +Ġn g +Ġdevelop ing +Ġprov ides +Ġpo ll +Ġtra ffic +arent ly +à® Ł +Ġform s +Ġcl ient +Ġg entle +Ġmus s +ĠCong ress +ĠInd ian +ce an +Ġp il +Ġc zy +st ood +ut y +Ġn ä +Ġsp ending +Ġconst ruction +ina udible +Ġë§ Ī +Īë¬ ´ +Ġìĥ Ŀ +om a +os en +ag o +Ġlar gest +ãħĭ ãħĭ +Ġun iverse +b es +os a +Ġе го +Ġd ude +ĠM AR +Ġind eed +ε ι +Ġman aged +ĠSh ould +S o +Ġappl ied +Ġfair ly +ĠD en +Ġanal y +Ġconst antly +Ñģ п +H ow +ĠS ay +en cies +ĠP C +Ġegg s +à® ° +Ġet h +ĠEnt ão +in ar +i ot +Ġc z +ĠEurope an +ãģ Ī +ĠA M +Ġc á +Ġrad io +§ Į +Ġh ide +ä» Ĭ +ĠSt art +Ġcl ub +ĠH ope +Ġeff orts +lus ion +Ġc ities +h one +Ġreach ed +Ġgu id +ro id +Ġhar m +Ġcut ting +Ġb ul +1 8 +i est +ĠMe x +Ġ iron +çŁ ¥ +Ġafter noon +Ġha ll +Ġpr zy +Ġg osh +Ġinflu ence +Ġв ид +Ġincre ased +ĠMin ister +Ġdis ci +ĠP eter +Ġver t +Ġmen u +Ġse lling +ur ally +Ġqu ote +Ġ ¡ +Ġcontin ues +mp re +ĠÅŁ ey +it ution +Ġна Ñģ +c les +ĠGerm an +c zy +ĠÐ £ +B e +Ġk itchen +ĠT ry +i pe +Ġic on +ar p +Ġprov iding +ĠTr ans +Ġtechn ique +Ġh är +Ġinf rast +Ġsus p +ü ck +ic ip +ĠÐ ķ +Ġc in +ìĸ ´ë +Ġpr z +Ġcompon ent +Ġby e +ĠB ible +iz er +C h +Ġsol utions +Ġaccom pl +Ġ201 6 +I E +ĠT a +Ġass ume +Ġliqu id +Ġë¨ ¹ +Ġquar ter +Ġfem ale +ĠTh ink +Ġstat us +it ute +Ġco ach +Ġre in +Ġcomb ination +è · +ĠT er +Ġobject s +Ġdist rict +Ġmake up +Ġmur der +w as +f en +Ġbow l +Ġpub lished +Ġsp orts +ãģ ¡ +Ġident ity +Ġseem ed +Ġact ing +л Ñİ +ri x +Ġup load +Ġh ast +Ġbo at +ĠM od +ri o +Ġ = +Ġcy cle +¯ ¸ +Ġl oud +ust ed +com ing +Ġ201 7 +Ġon t +Ġleg isl +Ġst ruct +ĠSomet hing +Ġconf lict +Ġu pper +Ġman ager +Ġm ort +Ġf ra +ĠÄ ° +ĠM ike +ĠW ork +Ġn ó +ph ere +ĠìĤ ¬ë +ĠL and +Ġfil ter +Ġprom ot +æ ° +æĻ Ĥ +ķ ¼ +Ġrecord ing +× Ŀ +Ġassoci ated +Ġf uel +und er +Ġele ction +Ġemploy ees +ĠCom p +ÑĢÑĥ г +ĠW o +ro l +Ġsa ved +ĠH on +ĠV i +åĪ Ĩ +ac a +p ret +Ġw et +Ġst upid +Ġl ad +Ġf est +Ġw ake +Ġи н +Ġgreat est +ĠJ im +Ġserious ly +Ġì ¹ +Ġfeel ings +Ġ3 00 +i ation +Ġbeaut y +Ġìŀ ĺ +Ġs an +ĵ ł +Ġ- ( +Ġcons cious +Ġд ел +b ye +ç Ļ +M an +Ġlet s +Ġsho es +y d +ä¹ Ī +Ġdisapp e +ĠCount y +ĠSc ott +Ġbut t +Ġaqu ÃŃ +Ġconf ig +resp ond +LAU GH +© ëĭĪëĭ¤ +Ġdivid ed +Ġac qu +Ġz one +Ġk omm +a ção +ì§ ľ +c ut +Ġ2 3 +Ġmaxim um +ro g +Ġrun s +Ġcompon ents +Ġarri ved +Ġconf ident +ÑĢ ов +Ġhe ight +Ġpro ced +E M +ĠÐŃ ÑĤо +ĠM en +Ġtalk s +Ġconf idence +ĠChr is +Ġlead s +Ġn ose +f all +b b +ĠNot hing +is er +Ġindepend ent +Ġmin or +Ġsy m +l en +ci ence +Ġf ashion +Ġsex ual +Ġb un +h ere +Ġso il +Ġdies e +Ġsh ap +Ġempt y +Ġjour nal +ag on +ĠThe ir +Ġweek end +ÃŃ t +Ġer ror +Ġn ar +à ¸ +è © +an cy +Ġìķ Ĭ +Ġfore st +Ġha cer +Ġmiss ed +ãģ ķ +åı¯ 以 +Ġev il +Ġstor age +Ġsing ing +in ha +Ġkn ock +Ġimp ress +ĠоÑĩ енÑĮ +ĠGo ld +ĠS ur +ĠP ort +åİ » +ĠL ond +Ġfaz er +ot y +ot o +Ġan x +ĠWill iam +Ġexist ing +pl ace +ĠC D +Î ³ +ĠColl ege +l or +ĠE ast +s en +f ach +o ft +Ġexperien ced +Ġlo ves +im m +Ġpo ly +Ġes se +ì ¤ +ĠG rand +è § +ch er +Ġvict im +ĠG es +л ÑĮ +v ision +Ġt all +Ġl ens +Ġз на +ĠB oth +Ġì ² +Ġsust ain +Ġarg ument +Ġfact ors +Ġautom atically +Ġfr uit +Ġli ber +Ġa le +ĠP ress +ĠB a +ĠÐ ³Ð¾ +Ġhundred s +th at +ĠR ich +Ġreci pe +ĠI T +è ĩ +Ạ¥ +Ġdescri be +Ġdri ver +ĠO ct +ĠM at +д е +Ġme al +Ġlat est +Ġth erap +Ġcomp are +ĠAm azon +Ġì¢ Ģ +ĠRuss ia +Ġstr ing +Ġk a +ĠComm un +Ġd ia +I s +Ġmill ions +Ġcor por +Ġcor respond +Ġfix ed +ĠJo e +Ù İ +Ġview s +Ġr iver +Ġstud io +ig ger +Ġfl avor +Ġpres ence +Ġun its +Ġsa ving +av our +Ġp esso +or ith +Ġh ers +ĠN at +as ion +ĠFr ank +о ÑĪ +ÅĤ y +í Ħ +Ġein em +Ġfun ctions +um an +Ġn orth +Ġìł Ħ +Ġhor se +v id +Ġple asure +а ÑĪ +é es +ind a +Ġt ail +Ġexpl ore +S T +Ġcommer cial +ĠD uring +ar l +] : +f it +Ġr ates +æ ³ +M USIC +Ġhous ing +Ġein er +Ġsitu ations +æ ĭ +Ġdec re +Ġappropri ate +ен но +% . +Ġb ac +Ġw at +ens ity +ä h +kn own +it z +Ġemot ional +erv ation +Ġbl ind +1 6 +í ĥ +大 家 +Ġjo ined +Ġloc ated +ĠÑģ м +ad as +ber g +Ġd ess +Ġde ar +ed en +c os +Ġad opt +1 00 +ow e +ĠChe ck +ism o +Ġsim pl +Ġang ry +Ġмен Ñı +ĠC am +Ġp ad +Ġatt end +Ġsam ple +æĹ ¥ +Ġì Ľ +ĠI N +ul ous +ĠS ar +ĠSh ow +Ġinfrast ructure +ĠAug ust +Ġless on +Ġn iet +æ İ +Ġfo i +Ġbro ke +t r +ç ķ +Ġ4 5 +Ġg ew +Ñĥ п +at i +Ġmaint ain +Ġart ists +ing er +æĿ ¥ +er ved +I A +Ġequ als +Ġoper ation +ill y +ĠëĤ ´ +Ġcrow d +Ġintern al +Ġtest s +ĠR ock +ĠC ons +ĠëĦ Ī무 +w ar +Ġs ou +Ġch art +ĠJ une +ĠApr il +g ent +Ġv ent +Ġqu and +ĠKore an +im o +ç ī +id ers +Ġmount ain +ÑģÑĤ ав +æľ Ī +ij k +Ġdiscover ed +ĠS und +ĠS il +Ġso lo + ´ +Ġsch ol +ĠE ach +ç µ +Ġb are +Ġí Į +ĠvÃŃ de +Ġingred ients +ĠIt s +Ŀ¼ ê³ł +Ġì Ĭ +Ï į +ĠLe e +Ġsc ary +Ġprinci p +Ġspirit ual +ì ħ +ĠH old +æ²Ĵ æľī +Ġdef ine +ĠL es +ĠN or +ĠE nd +Ġbl og +ĠG reen +аеÑĤ ÑģÑı +p art +el es +äº ĭ +ĠUnd er +Ġpart e +Ġ3 5 +Ġse ctor +ĠS ept +Ġaut h +à® ® +om in +Ġcl ients +Ġc i +ĠFr iday +er as +Ġtw e +ul ated +Ġcult ural +ĠÑģв о +Ġëį Ķ +Ġà º +Ġpar ce +à® ² +Ġtrad ition +Ġjud ge +ĠGen eral +Ġdeterm ine +ĠIs n +ĠP L +ne ath +Ġmatter s +íķ ´ì +! ] +а Ñħ +Ġpo ol +Ġvari able +Ġvacc ine +Ġcaus ed +Ġw est +ĠY ep +f ast +Ġph ilos +hor a +Ġcontinu ed +Ġunf ortunately +ãģ į +æ ķ +Ġfl ight +Ġw rap +Ġhu h +ĠAbs olutely +Ġp ink +Ġrem ains +Ġn é +Ġf le +ĠS ol +Ġlos ing +Ġalg orith +Ġrequ ires +Ġfound ation +ĠB ur +Ġprofess ion +ĠM id +Ġë ŃIJ +c an +ĠM il +Ġyoung er +Ġappe ars +ter m +íķĺ ê³ł +ac le +ĠLond on +Ġengine ering +ภ¢ +Ġadv ent +ìĦ¸ ìļĶ +Ġê¸ ° +ĠM aj +ÑĢ ем +ing u +ĠU K +u ro +s pe +Ġt ent +Ġreport ed +ĠA L +H ey +Ġë§ IJ +Ġd ent +ĠAustral ia +ĠJan uary +³ ´ +ag ues +ars h +r ig +Ġtien e +ภ£ +Î ® +Ġmach en +un te +Ñĥ Ñģ +Ġelect r +Ġtut orial +Ġpl aced +ĠìĿ´ ê±° +ĠCoun cil +í ĸĪ +°ë ¦¬ +ah ren +Ġê·¸ë ŀĺ +Ġpro ve +f ol +Ġqu er +Ġche ap +ĠF ather +ĠP ower +ĵ ľ +Ġpur s +Ġes p +ĠB re +ê¸ °ë +om as +æĥ ³ +ил ÑĮ +Ġge ht +os ter +ê³ ¼ +Ġfil es +ĠÐ § +be ll +Ġwh om +Ġë ĺ +Ġex cellent +Ġdat ab +Ġg ö +Ġì§Ħ ì§ľ +Ġbelie f +j et +Ġj ack +Ġsw im +ri al +um in +a uc +Ġso ll +Ġess ential +íķĺ ëĬĶ +Ġev ol +cha ft +ain e +th let +Ġinc or +Ġreport s +Ġdefin ition +ke l +Ġcirc um +Ġprodu ced +Ġ× Ľ +ant ic +n et +Ġa ward +Ġd urch +Ġtrans p +Ġm ale +¦ ¬ë +Ġmo on +ĠGe orge +Ġfly ing +i ó +Ġs ources +Ġpl enty +ĠDem ocr +R O +Ġ 00 +Ġsec ure +ĠB ir +ra in +Ġz ur +Ġeffic ient +Ġrepe at +Ġmethod s +Ġcal m +Ġdiscuss ed +ĠìŀĪ ëĬĶ +Ġser ver +an ie +ĠInst ead +Ġide al +Ġcon ven +Ġhop ing +ĠT or +Ġdep th +Ġhe aven +EN CE +Ġhab it +gr ad +Ġfl ag +Ġin e +Ġk h +ĠL I +Ġfac ing +ĠA U +ĠT im +Ġg em +ĠJ ul +Ġel a +iz za +Ġfe llow +Ġqu el +Ġsp oke +Ġcitiz ens +u ge +é ĥ½ +Ġp ages +Ġf asc +Ġrelig ious +at en +Ġch apter +ĠV al +Ġcons ult +ĠM ill +g l +op er +Ġinf in +Ġmar riage +Ġmedic ine +Ġд в +Ġdog s +Ġinstr ument +ĠEx act +á n +Ġ20 21 +Ġf er +Ġwe alth +Ġgr ade +Ñĭ Ñħ +Ġcr ime +Ġth read +Ġess a +Ġw ine +co hol +ph a +ภĩ +og ue +Ġins urance +arr ator +ĠSept ember +Ġv id +ĠSp irit +Ġg est +ĠRuss ian +Ġproper ties +Ġart icle +Ġunder neath +y er +Ġjo int +Ġrelative ly +Ġin ch +Ġdesp ite +ĠG ree +Ġclass ic +Ġsupport ing +Ġinst ruct +lus ive +Ġdi agn +æ Ĭ +Ġadminist ration +аб оÑĤ +ĠO pen +æīĢ 以 +Ġп ок +Ġdoll ar +Ġconse qu +o ber +ĠGerm any +Ġter r +ĠQ U +ĠÐ ĵ +ç ¾ +Ġstrong er +É Ļ +ĠÙ Ĭ +ĠiP hone +Ġfab ric +ü h +Ġen em +æ ¯ +Ġsub t +E E +ond e +Ġcre w +Ġremo ved +Ġl ady +Ġpot entially +ĠÐĿ о +y al +Ġsym pt +Ġar my +Ġintrodu ced +t es +Ġaspect s +1 4 +ĠL ou +Ġ ) +Ġde ploy +p et +Ġh an +ĠW atch +Ġweap ons +Ġph en +Ġreg ister +Ġein fach +Ġsp ort +Ġbr idge +Ġin ner +Ġminim um +Ġw itness +Ġes o +Ġvill age +Ġown er +¦¬ ê³ł +Ġsc ream +il ed +Ġp itch +b ru +Ġadv ance +ä¸į æĺ¯ +Ġsupp ose +ĠAt t +еÑĤ ÑģÑı +Ġdiffer ences +ak ed +Ġinter pret +à ¦ +iend o +Ġabs ol +ĠбÑĥд еÑĤ +Ġë ² +Ġtri al +Ġthink s +ly ing +cept ion +ĠAfric an +Ġchem ical +Ġta pe +Ġconvers ations +Ġdistrib ution +t i +ĠA I +Ġfl ash +Ġunder stood +ĠGovern ment +å° ı +! ? +ĠS k +ê± °ë +ri er +T S +ĠAcc ording +Ñİ ÑĤ +Ġsp ons +ÑĤ обÑĭ +Ġval u +ere m +icht ig +Ġresist ance +ĠG al +ger y +Ġbeg ins +Ġadv anced +Ġrele vant +Ġpolit ics +ĠF am +Ġç ok +ĠN ever +ill ing +Ġfoot ball +и и +ĠI D +ĠAfric a +Ġfing ers +Ġб олÑĮ +Ġà ¡ +Ġcl ip +ĠL at +ãĤ Ħ +Ġì§Ģ ê¸Ī +es se +Ġvo or +Ġas ide +æ ŀ +Ġto ward +Ġb at +Ġval id +ĠM ens +Ġcomplet ed +ı ÄŁ +Ġpod cast +ĠB on +Û Ĵ +ĠJ uly +il a +Ġpack age +Ġpull ed +ch ar +ĠM el +o is +Ġs outh +Ġë Ķ +Ġimport ance +Ġp ushing +Ġis ol +Ġstand s +c ill +ä ¼ +Ġ ðŁ +or i +ê° ģ +Ġhom es +Ġconcern s +Ġb iz +å ½ +b ie +Ġb is +Ġge ar +ĠM S +Ġh un +ĠM att +Ạ£ +se y +ĠSec ret +Ġod d +ĠM ax +oll y +f ord +ĠS H +Ġrepl ace +Ġnav ig +Ġin i +и Ñı +Ġgi ant +Ġma nd +ĠH app +TI ON +g un +iam o +ìŀħ ëĭĪëĭ¤ +Ġg ap +Ġê tre +Ġclass room +Ġhy p +ak i +è ® +is ters +ack s +ĠÑģ о +Ġb ug +Ġgra v +am in +Ġevery day +Ġì ¡° +Ġgard en +ce mber +Ġest o +åĹ İ +Ø ¬ +Ł ° +å ģ +Ġr om +Ġìłľ ê°Ģ +Ġfall ing +Ġfa ult +ell y +Ġch est +Ġл и +Ġpot ato +Ġbuild ings +Ġoper ating +Ġp are +w r +D on +ĠF our +Ġv ul +Ġl á +Ġfr ust +ĠD ann +ol es +ny a +Ġì ¶ +ĠÑĢ аÑģ +× Ľ +Ġa ÃŃ +w ord +Ġweap on +Ġob t +ĠF all +ĠSte ve +Ġmix ed +Ġp ode +ĠA S +ĠL eg +Ġdes c +Ġspl it +Ġemer gency +ĠS ing +Ġprof it +Ġtyp ical +ĠDon c +Ġannoun ce +ĠTe x +Ġsac r +tern al +Ġcomm ittee +ig o +Ġdi am +ph as +Ġdef e +ĠProf ess +Ġdec l +Ñĥ ÑĢ +2 2 +ol f +ĠM ond +u y +Ġa y +Ġl em +Ġlove ly +ĠC ould +Ġgu ar +H H +Ġcare fully +ĠL isten +Ġк ÑĢ +Ġyou th +ĠThere fore +Ġdream s +ĠJe ff +? ] +Ġë Ī +D A +Ġb odies +au x +Ġtechn iques +Ġmechan ism +× ĵ +Ġо ни +Ġdes ire +à ® +ĠV o +qu es +ĠÑĥ же +ĠWho a +ĠG ame +Ġh al +an ish +Ġpract ices +5 00 +Ġsort s +up s +ate ful +Ġhers elf +Ġgu itar +Ġprop os +Ġsit es +Ġbe ach +Ġ× ¢ +ç¬ ¬ +н Ñĥ +Ġdr am +ĠNo ve +V E +r ant +Ġpl ot +ĠìŬ 기 +ĠC a +Ġestab lished +Ġ201 5 +Ġinsp ired +Ġannoun ced +ä¸ ª +ĠÑĤ ÑĢ +Ġ2 6 +Ġv oy +Ġte ch +ìł ģ +Ġprocess es +ont o +ĠP an +Ġrap id +ist an +Ġ19 7 +Ġrelig ion +Ġ2 8 +Ġsm ile +Ġb ab +Ġ Ú© +ĠV ir +Ġsched ule +Ġexec ut +Ġpr on +Ñ į +ĠÐĿ Ñĥ +m usic +ìĽ IJ +Ġg an +ìĭ ł +Ġdef ault +Ġbe m +Ù ī +Ġfor ced +ĠOb viously +Ġst one +Ġt ie +Ġdrink ing +Ġser ved +C ause +Ġcon ference +ĠExact ly +ãĥ Ī +ł ľ +ìĻ Ģ +ĠR a +Ġf ake +Ġdif f +ãģ © +Ġchalleng ing +Ġì¤ ij +Ï ĩ +ä»Ģ 麼 +Ġintellig ence +re te +Ġstud ying +Ġapp oint +Ġt an +Ġи м +Ġcur ve +ĠTe am +ĠA z +Ġз д +ĠMus ic +f ield +ir ation +Ġfail ed +Ġno vel +Ġdifferent ly +Ġes cape +ĠY o +ĠOct ober +ı yor +Ġdescri bed +Ġcon vert +ac ement +Ġhot el +is ation +Ġsu is +ãģ ij +å ŃIJ +æĢ İ +Ġwalk ed +2 00 +Ġneighbor hood +is p +ĠL os +Ġh idden +Ġ2 7 +л е +Ġph r +ĠIs land +ĠSt reet +end a +hip s +os ure +Ġdefin ed +ภ§ +Ġv ida +Ġlab el +ĠEvery body +Ġjo ke +ia o +ا ÙĨ +Ġa thlet +... " +ĠF ire +D o +Ġdef ense +Ġent ertain +á t +Ġpolic ies +Ġal cohol +ĠEng ine +Ġg al +ĠJ ud +Ġvol unte +ick s +et a +ag t +Ġ× ķ +Ġm ö +1 3 +Ġenc oun +Ġe h +Ġor ange +Ġabs or +Ġsp aces +ĠNove mber +êµ ¬ +i at +Ġt am +ck now +Ġst orm +ĠDire ctor +Ġpre gn +ĠìĿ ¼ +Ġо п +Ġres ource +Ġb ard +ne w +ĠDe cember +u its +Ġwe il +Ġconst ruct +s i +n ic +Ġfl our +Ġrest rict +ü t +Ġentire ly +Ġbreak ing +ent lich +Ġtw enty +Ġcaus es +Ġele v +ĠS pr +ĠIntern et +Ġk iss +Ġoper ations +s zy +Ġë Ĭ +Ġscient ists +Ġgr own +Ġown ers +out s +Ġcour ses +Ġus ual +Ġin n +Ġtrans m +ñ o +Ġnu est +к ов +Ġcateg ory +ĠL ife +ĠPl us +Ġat mos +wh ile +Ġrecord s +Ġde ÄŁ +ëĭ¤ ê³ł +ĠìĤ¬ë ŀ +Ġrequire ments +in n +Ġimm ig +Ġdeep er +ç ´ +Ġapp s +Ġcolle agues +ż y +Ġoff ers +Ġt á +Ġcolum n +la ud +I R +ĠM s +Ġexch ange +l as +ĠL aw +ĠJ on +is se +ro gen +Ġmo i +× Ĺ +Ġs ending +Ġhe llo +е е +ÅĽ Äĩ +Ġsuc ceed +Ġsuff ering +Ġad vert +Ġì£ ¼ +çŁ¥ éģĵ +Ġrec o +ın ı +Ġк ом +all ey +Ġfail ure +ie j +Ġëķ Į +Ġdrug s +Ġcu ando +Ġìĸ´ë ĸ +ĠAb out +Ġqu ando +9 0 +ĠF ed +1 7 +S h +in ho +ĠSund ay +ĠPh il +Ġacad emic +ĠIn c +Ġmaint en +åĩ º +Ġre ward +er d +Ġcomm itted +ìĬ ¤ +г ÑĢ +Ġstand ards +Ġk al +Ġint ention +ĠZ h +Ġa cknow +ä ¿ +Ġ== = +og y +å § +Ġfilm s +is k +Ġte eth +Ġstrugg le +r d +u en +Ġdis s +ĠD ar +am y +Ġenem ies +Ġve loc +ĠC all +um bs +иÑĤ елÑĮ +Ġo cean +é d +ìļ ° +Ġtre m +ient o +еÑĪ ÑĮ +ffic ient +Ġbott le +Ġinstit ution +est y +ĠH an +h ab +ëĬ ĺ +Ġar rest +éĤ Ħ +Ġlet ters +oun ce +í Į +A n +Ġcreat es +Ġcl ock +Ġdeb t +Ġan cient +ific ations +g i +B ut +ĠT u +k l +Ġb order +Ġo ok +ĠB ay +est a +Ġë³ ´ì +Ġw ra +pre ne +Ġê² Į +ang le +Ġbelie ved +ien cy +ak a +Ġcrit ic +Ġb omb +Ġha m +ĠÐ Ľ +êµ Ń +ĠGu ys +ros oft +Ġcr im +et ch +AR R +Ġs ight +и на +Ġa in +á» ij +is che +Ġau x +Ġnum er +Ġsurv ive +A ll +B C +Ġs z +Ł ¬ë +Ġj am +ĠCour t +Ġall es +Ġtr igger +Ð ŀ +Ġform at +Ġdec ades +Ġc es +Ġsign s +Ġrob ot +ĠCh urch +Ġa z +Ġs oup +ĠTex as +ut en +ĠÑĩ ÑĤобÑĭ +Ġneigh b +ĸ ×Ķ +Ġcommunic ate +Å ¡ +Ġel imin +Ġfrequ ency +her n +id os +Ġem phas +Ġmess ages +Ġg ender +ĠW enn +Ġв о +Ġpr ices +ol o +Ġп он +w ing +ĠF il +а ем +ĠC ur +Ġfal se +Ġfield s +Ġs é +2 4 +Ġm ac +u ÅŁ +Ġlay ers +Ġadv oc +w an +Ġk ar +ĠÅ ŀ +Ġdec or +Ġwall s +o e +iss ions +Ġres ol +× ¢ +ĠCar ol +ĠV ide +le ep +ĠY OU +Ġfl ip +Ġsur gery +Ġch op +U R +. , +Ġag ency +Ġwant ing +Ġsol ar +Ġhor iz +ĠAd am +Ġstay ing +ol ic +Ġgr ateful +Ġrem ark +Ġtechn ologies +Ġprote in +å¿ ĥ +д ел +ĠM ont +Ġshould er +Ġz a +re y +ĠO oh +Ġst y +ic ar +оÑĤ ÑĢ +Ġrout e +ĠT urn +Ġb om +Ġdeb ate +Ġposs ibility +Ġíķ ´ì +ap a +Ġinv ent +ür lich +Ġprof ile +Ġsen ior +pp y +v as +Ġm undo +ate ver +Ġapp arently +en er +× IJ +ç Ń +Ġprec is +Ġal ign +Ġkn ife +ĠRo bert +å ĭ +Ġfo ol +Ġinv ite +us ing +Ġcircum st +Ġcapt ure +Ġd ough +ĠS and +Ġse u +ĠNew s +Ġb ite +Ġne ut +w ide +Ġlect ure +Ġëĺ IJ +Ġorigin ally +Ġcho ices +ĠG ar +Ġver se +Ġl it +Ġ19 6 +íķ ł +Ġmeas ures +ç ões +w ater +ri ve +Ġz ijn +í ģ +ĠB us +Ġhe b +е Ñħ +ĠK ar +ĠN ão +Ġkill ing +à® ª +Ġmir ror +m od +Ġm ol +Ġcre ation +Ġest im +Ġatmos phere +Ġg am +Ġt ables +is i +ĠL ittle +Ġt as +ĠE le +é l +Ġscen es +Ġt one +Ġaffect ed +ĠAU DI +ĠBr own +I f +ĠÙ ĩ +ĠDan iel +羣 çļĦ +qu er +ch i +íķ ĺë +Ġmist akes +Ġs la +ãĤ ¤ +Ġent r +Ġе Ñģли +Ġsh out +Ġport ion +Ñ Ĺ +Ġpre viously +á» Ļ +ĠпÑĢ ед +оÑģ ÑĮ +Ġhead s +ç İ +å Ń +åľ ĭ +Ġgr ass +ภ° +cri be +Ġqu é +ĠSp anish +Ġoffer ed +ĠбÑĭ ло +ĠCl oud +Ġve ctor +ĠH uh +Ġk ad +if ts +ĠÎ ½ +Ġhung ry +Ð ¡ +Ġpar all +AN D +ĠvÃŃde o +iz z +Ġocc up +Ġí Ķ +Ġsee k +h es +Ġdo ors +Ġhous es +Ġconsider ing +Ġgradu ate +Ġf ulf +è ¡Į +è £ +Ġext reme +Ġflow ers +it ate +ĠP ri +Ġfundament al +Ñĩ аÑģ +è¯ ´ +Ġtext ure +į ĺ +ĠAN D +à® ± +ĠT em +Ġn ada +ì§ Ħ +Ġcelebr ate +um s +Ġp ill +Ġи ли +go ing +Ġh ip +Ġsupport ed +Ġper man +Ġagre ement +Ġty m +Ġë ij +ĵ¤ ìĿ´ +Ġpurch ase +í Ķ +ĠPl an +eg en +Ġrec over +P U +ĠMic rosoft +du c +Ġhol es +Ġdro pped +Ġp ig +Ġend ing +Ġattack s +be c +Ġre n +Ġr app +Ġìļ °ë¦¬ +Ġter ror +Ġ× Ļ +Ġed it +Ġa o +. +Ġhero es +ĠB oston +Ġdepend ent +Ġmotiv ation +fl ix +Ġse am +ки е +Ġdra in +od ed +Ġgu ilty +ĠJ enn +ing en +Ġgrant ed +ĠK elly +ĠS av +ĠUn cle +ĠHon estly +EL I +Ġnavig ate +Ġbless ed +c ore +Ġear ning +Ġsign als +Ġdis k +ial s +Ġag es +æ ħ +Ġpartic le +ĠÑĩ еÑĢ +Ġcan n +Ġt ier +Ġstat ements +ê³ł ìļĶ +ĠëķĮ문 ìĹIJ +ĠCh o +Ġpol ar +an ç +ĠK enn +ĠN i +ĠF ight +or gan +é ķ +ĠCh a +ĠS ÃŃ +ãĥ ª +Ġs lic +Ġcert ific +Ġtempl ate +ĠFed eral +Ġconsider ation +Ġexpl o +ĠM ain +ĠN E +Ġalong side +Ġd ressed +ĠP oint +Ġenviron ments +Ġpró xim +Ġda ar +Ġprom pt +Ġpurs ue +Ġentertain ment +Ġth roat +Ġproblem a +Ġm art +ì ¼ +Ġprov ider +Ø Į +Ġ× Ĺ +int e +m aking +Ġstro ke +Ġtiss ue +U n +Ġpre cious +ĠAr ts +ink ing +ĠÐŀ н +Ġи Ñģ +n ah +ĠÐķ Ñģли +Ġcor ners +Ġtrick y +in ch +l ijk +Ġpress ing +le vel +AN G +Ġrad iation +ìĦ ł +Ġconf ront +Ġv et +Ġrepresent ative +Ġprop ag +Ġcra p +ĠDe c +Ġr amp +еп еÑĢÑĮ +u és +ess en +cri ption +Ġb ills +ĠMatth ew +Ġan ime +ấ t +Ġlow est +h as +sc reen +og rap +ал о +int on +ĠJ ah +èĢ ħ +it Ãł +Ġk ay +Ġrot ation +ĠW ere +abe i +Ġtri als +Ġle ver +ight y +Ġsp oon +Ġh unt +c ling +Ġdis m +ĠболÑĮ ÑĪ +Ġass ault +Ġíĺ ķ +Ġweek ly +Ġm ismo +Ġgen etic +ul pt +ĠStud ent +Ġreal istic +Ġauthent ic +æī ĵ +ast a +Ġarrest ed +Ġguid elines +Ġ×ľ× IJ +Ġд ав +ĠCom ing +f ür +Ġrequ ests +ĥ IJ +Ġanaly ze +Ġinter ess +Ġh alt +ĠO per +on om +Ġd uck +Ġwith d +s er +ĠÏ Į +ĠHist ory +Ġyout ube +ãĤ į +Ġsab er +w alk +f ont +Ġover view +3 9 +ü y +ett i +Ġfro zen +Ġf lesh +ÄŁ i +ĠP M +ĠìĻ Ģ +é ¢ +ÑĨи и +Ġê¸ °ë +íģ ¬ +Ġpr ose +oo oo +r ates +W S +Ġautom atic +Ġcollect ing +Å ij +Ġneighb ors +» . +ĠEx pl +Ġcir cul +co ver +we g +Ġstick s +Ġe ller +Ġw ww +Ġd orm +ĠEx per +Ġstat istics +Ġemail s +Ġgra ve +im iz +H S +Ġu it +, ' +Ġlas er +è ī +ĠÑĤ ем +Ñĭ ÑĪ +Ñī Ñij +Ġgen au +Ġtien en +Ġmed itation +ĠOr gan +Ġest imate +Ġë¬ ´ì +l ets +Ġn Ãły +Ġmind set +Ġres on +Ġm és +Ġnumer ous +Ġvie lleicht +ĠTh ird +u ous +ĠDe ad +ан д +H N +Ġrac ing +Ġag ents +ĠU t +Ġte ar +ĠH P +Ġchem istry +Ġsurv ival +æĸ ° +Ġconvin ced +Ġ ; +Ġreg ulations +ĠE S +åĴ Į +3 00 +Ġen se +Ġì µ +Ġd ict +G A +Ġah ÃŃ +åĭ ķ +Ġte j +Ġо ÑģÑĤ +ĠE lect +Ġintellect ual +Ġbi as +Ġbur den +çĤ ¹ +Ġìĸ´ëĸ » +Ġche er +Ġso ph +Ġportfol io +ub a +Ġest os +T V +F or +Ġas h +Ġkom mer +Ġcollect ive +Ġw rest +ĠJ etzt +ĠW at +re ich +Ġprim er +act ive +Ġm ie +ick ed +Ġhun ting +Ġtest im +Ġcompass ion +ĠØ ± +Ġbr ut +Ġsal ad +об Ñīе +Ġsol ving +Ġflo ating +ç · +Ġattract ive +ÙĪ ÙĦ +Ġper d +if fer +Ġsc ulpt +hh h +ĠWe ek +Ġent hus +Ġn ad +Ġmer ch +ĠíĻ ķ +Ġm ile +好 äºĨ +ĠÎ ¸ +ĠëĤ ĺë +éĩ į +3 8 +Ġch ains +ĠAl most +Ġtick ets +r in +ĠC C +Ġdistrib uted +abet es +Ġtemper atures +Ġg ained +Ġflex ibility +Ġscream ing +Ġab road +un o +Ġentreprene urs +ĠNet work +ĠCanad ian +Ġpre v +Ġs ö +ĠÑĤеб Ñı +ĠP oke +ĠP od +ĠTur key +çı¾ åľ¨ +Ġabst ract +Ġsn ake +ĠAm y +ĠëĬIJëĤ Į +Ġbra ve +ĠìŀĪ ìĸ´ìļĶ +ĠK al +Ġ200 7 +á rio +Ġmark ed +gin es +Ġall oc +ON G +Ġscient ist +Ġes ca +Ġrac ism +× ij× +ĠS ams +ĠP enn +Ġload s +Ġà® ¨ +ü ber +M e +ix ò +Ġper ò +an ne +Ġexp ressed +м еÑĢ +Ġmo et +Ġret urning +n ia +Ġexp on +P ro +Ġlo yal +M L +Ġl amp +Ġsh y +Ġcomp osition +ĠL y +Ġmagn etic +Ġprem ier +Ġmeasure d +Ġsumm ary +Ġattack ed +Ġfin ishing +Ð Ĺ +ç ¥ +Ġs its +Ġhyd rogen +Ġma i +ĠDeuts ch +as ı +Ġobt ain +v ie +Ġso it +Ġë° Ķ +Ġl ane +Ġconse gu +в о +Ġe ase +ak in +ĠF a +Ġunt uk +Ġbur st +Ġc um +al ım +ú blic +id i +ĠRoy al +ĠK on +Ġcommon ly +Ġremo ving +Ġj ur +il ib +Ġan ch +íĸ ī +Æ°á» £ +ĠÐľ Ñĭ +ĠAn th +ĠS Ã¥ +Ġinter rupt +Ġst ere +ĠO S +ony m +ter y +ĠMar ia +ê² ĥ +Ġexpl oring +Ġtransp arent +Ġf ate +ĠJ ung +Ġgr up +Ġdark er +ĠD oug +Ġman e +æĶ ¾ +ạ i +d ri +lo ok +ĠDes ign +Ġtut aj +Ġhorizont al +re on +ort e +ĠCor rect +ĠSte ven +Ġv ine +0 2 +i Äĩ +Ġsie mpre +ĠK ey +åĥ ı +ĠG ames +Ġna ar +Ġshock ed +el ve +ĠR ose +ìĭ ¬ +Ġstop ping +oh l +ĠM ix +Ġsuff ered +Ġsig ma +Ġweak ness +ĠO w +ี à¹Ī +I F +Ġà® ħ +ad ed +ĠNet flix +an es +Ġrem ained +ir y +Ġr ip +ell t +Ġsil ent +Ġpro ven +Ġtox ic +Ġal umin +Ġmulti pl +al and +Ġ3 4 +0 6 +ĠB ru +Ġìłķ ë§IJ +J ust +b oy +Ġsho e +Ġcreat ure +Ġhead ed +ĠоÑĤ к +æ ± +Ġess ence +Ġremark able +Ġnú mer +Ġd rew +Ġpu zzle +ĠLibr ary +ĠF u +ash es +k k +ĠI st +¦ ° +ĠB ry +Ġc eremony +Ġà® İ +Ġc ri +e qu +ãĤ ¢ +Ġpri ze +Ġdim ensions +og ram +Ġle ather +Ġpop ulations +u um +Ġve gan +Ñı д +Ġcó mo +å Ħ +Ġstri p +å £ +Ġvac ation +ħ ķ +Ġme als +ili pp +Ġ ents +ar am +ric ht +Ġgra in +ĠSp ain +Ġche ek +ĠA ff +I ON +ĠBr ing +Ġ3 8 +iel en +ul u +ĠболÑĮ ÑĪе +Ġannounce ment +ĠÑĤ ÑĥÑĤ +ĠPro phet +ard o +3 7 +Ġw oke +Ġtransl ation +ĠN OT +ĠC L +Ġd Ã¼ÅŁ +ÑĨ Ñĸ +ac er +ĠL oc +Ġper ception +N O +Ġdies en +L ook +he art +av ed +Ġbound ary +Ġfl ows +Ñij м +Ġarg uments +Ġelect ions +ı s +Ġhe ck +Ġsuit able +Ġf iber +ĠSt ra +x y +ĠH um +Ġmonth ly +u per +Ġgol f +Ġl ately +ĠG ard +ĠR en +ĠA st +ĠF ant +аÑģ Ñģ +Ġobs er +ë ¡ľ +Ġeas iest +į Ķë +Ġwebs ites +p ol +Ġco con +Ġà® ĩ +ĠV eg +Ġwalk s +Ġint ro +Ġdirect ed +ĠAn na +Ġëĵ¤ ìĸ´ +ĠEaster n +ĠS aint +ĠB ow +Ġro ast +ĠU RL +Ġjed en +ur as +aj a +Ġse mi +Ġrapid ly +Ġtarget s +ĠCont rol +Ġb ah +Ġref lection +Ġcreat ivity +hold ers +Ġìĺ ¬ë +Ġamong st +Ġfeed ing +ÑįÑĤ омÑĥ +Ġвид е +Ġë§Įë ĵ¤ +ĠSm art +Ġrel iable +Ġvez es +Ġ× ¨ +ch uckles +az ione +ĠWilliam s +Ġa ç +Ġsle e +е Ñī +Ġtim eline +Ġthor ough +á» į +ĠO t +ạ n +Ġimag ination +Ġmechan ics +r ist +Ġclaim ed +ÏĦ η +ê te +ĠHur ry +ĠiP ad +Ġconst ru +ĠC la +ĠAl s +ä¼ ļ +ut z +Ġcult ures +Ġìĸ´ëĸ» ê²Į +Ġbelong s +Ġy er +ĠDoes n +Ġge omet +Ġb id +Ġfo am +Ġh ob +ĠBrit ain +Ġsubst ance +Ġann iversary +ĠëĦ Ī +Ġnot ed +Ġgovern or +Ġstock s +3 1 +Ġdi ye +ìĬ ¤ë +Ġre b +z el +Ġmultip ly +Ġoper ator +Ħ¤ ìļĶ +Ġwat ers +Ġd är +Ġuns er +ĠEliz abeth +é« ĺ +Ġincreasing ly +ĠG ro +Ġen gines +ir s +Ø « +Ġtre asure +P C +in ction +ir i +Ġacc um +Ġvari ation +Ġp om +Ġtit les +ĠF est +ó s +Ġeld er +ny m +r un +Ñı в +Ġinnov ative +Ġnom bre +Ġco inc +Ġfr anch +Ġent onces +Ġnicht s +Ġexc lusive +ĠChe ers +ĠB i +u je +æŃ ¡ +Ġp ok +ĠP rem +Ġrock et +ELI PE +Ġhosp itals +ri um +Ġjust e +Ġham mer +Ġquant um +Ġrespons es +ll y +end i +Ġact ively +Ġfr idge +i ate +l ong +Ġqu em +Ġdeath s +Ġsuper ior +ck en +ìĿ´ì ĹIJ +kt op +Ġgather ed +£ ¨ +Ġd azu +Ġreci pes +Ġbu zz +c en +Ġany time +ons ense +Ġcirc les +Ġsol ved +Ġìĭ ł +Ġcoron avirus +ĠLu ke +Ġbu bb +Ġcont empor +r zy +ĠJ ane +Ġд ом +Ġscrew s +Ġhy brid +Ġcas ual +Ġsel bst +be ing +ĠÄ IJ +ĠCol umb +ĠÑħ оÑĩ +Ġbu cket +Ġevalu ate +Ġid ol +Ġrep utation +ĠìĨ Įë +ÙĪ ر +Ġhe cho +Ġpo em +Ġsubject s +pl ant +ĠBe h +ĠSpe aking +Ġbatter ies +Ġfollow ers +ö l +Ġg ently +Ġsi xt +Ġparam eter +Ġik ke +ĠT our +ĠD J +ot te +ĠJ ahren +Ġprepar ation +Ġд Ñĥм +Ġ8 00 +c op +ik ing +Ġë¬ ¸ +Ġн Ñĥ +Ġл еÑĤ +åIJ Į +ĠI de +Ġì¡° ê¸Ī +Ġla ughter +Ġmole cules +ĠR est +Ġobs erved +d zie +Ġadvert ising +ert o +Ġmo ins +ĠM IT +Ġexc it +Ġt um +Ġty l +Ġinvest ed +Ġph arm +Ġunex pected +Ġph i +oty pe +we ise +Ġge ç +jour d +Ġhors es +n Äħ += " +ĠS M +Ġf ib +Ġcl ips +çķ ¶ +å¦Ĥ æŀľ +Ġreg ime +Ġrot ate +r ou +n ik +Ġarm or +ðŁ ĺ +еÑĢ а +åº ¦ +ĠO ch +Ġr ichtig +üz el +ane ously +m ek +éĮ ¯ +ĠX iao +Ġexist ed +w orth +ãģ£ ãģ¨ +Ġna ught +Ġhe iÃŁt +ĠB al +Ġres id +iv ot +om atic +Ġh ired +Ġgrad ually +Ġon ions +Ġcomp at +Ġint im +Ġj ew +Ġcontrib ution +ĠI re +ac ji +Ġsl ice +Ġimm un +ĠR us +Ġgr ows +ĠSimilar ly +Ġhard est +Ġst ruck +Ġmeasure ment +... ] +th ey +Ġìł Ģë +Ġsne ak +Ġappl ies +Ġн ем +æ ĵ +×ij ר +ĠЧ ÑĤо +Ġout ro +Ġinnoc ent +Ġm og +ĠSams ung +Ġmer cy +Ġhand ling +Ġinter vention +id ays +g ot +Ġcur ric +Ġbound aries +Ġconf using +Ŀ¼ ëĬĶ +æ ĩ +Ġstitch es +ÃŃ vel +Ġtun nel +it ä +Ġg ost +im y +Ġcz as +Ġm é +Ġcat al +ĠSim on +ĠLI AM +m ic +ĠÐ ¤ +Ġey el +is as +ĠC PU +ĠD ou +Ġnä ch +Ġinfin ity +Ġr if +ĠPe ace +ĠC u +Ġminim al +Ġlisten ed +Ġpo le +hal b +Ġload ed +Ġste ady +ĠBes ides +ê m +Ġl ap +Ġco op +Ġfriends hip +w orld +Ġge h +Ġtyl ko +ĠLa ura +Ġsurround ed +ĠE vent +Ġch ap +ĠW onder +bre ak +Ġdro ve +Ġbroad er +Ġch i +F i +Ġge hen +Ġwest ern +Ġintellig ent +Ġpers ist +Ġfound ed +ãģĵ ãģ¨ +Ġhistor ic +Ġfr Ã¥ +cks Ã¥ +Ġhand y +Ġsy mp +Ġr ows +Ġnut ri +b ur +ĠLe on +Ġsist ema +Ġext ensive +ĠÑĥ в +í ı +Ġnight s +Ġcá c +Ġcount ing +ĠM ust +all ow +еÑģ Ñģ +M om +Ġнад о +Ġbar rel +ãĥ ŀ +AR D +Ġinstall ation +Ġin sect +Ġëħ ¸ë +uj Äħ +ĠÄij i +Ġpack ed +Ġf iction +N ow +ĠY ay +Ġper t +r ons +und e +ach es +Ġsty les +Ġapr ès +ok u +ĠV ice +ın ız +com m +Ġassign ed +Ġinteract ions +Ġac ab +F ELIPE +Ġresc ue +Ġindust ries +ĠAnd y +Ġpra ise +Ġfl ame +Ġsn ack +í Ĥ +ç ģ +Ġsw o +rend er +Ġbo ards +ĠÑĤ ом +en ne +Ġpast a +Ġdev il +ĠF el +Ġhat te +Ġcoll eg +e h +ì » +ãģĵ ãģ® +Ġproduct ive +for ward +и п +Ġsmart phone +Ġinv is +Ġb um +Ġwho a +ìŀ Ħ +Ġo cksÃ¥ +ĠL ang +ĠSy ria +Ġses i +ί α +Ġappro val +4 8 +Ġод ин +Ġë ĸ +ĠH arr +ĠAd minist +Ġ× ¤ +ĠDe an +f i +Ġcitiz en +Ġsh ark +0 5 +Ġbo il +Ġindic ate +å ¡ +A re +Ġlay out +Ġref r +ĠPac ific +AA AA +ĠAustral ian +g ression +V oice +ал ÑģÑı +Ġshel ter +T o +au pt +Ġevalu ation +ap or +Ġcur rency +Ġм ного +ig os +ãģ ° +Ġo ct +Ġro yal +è ³ +as il +ĠChild ren +Ġr ien +Ġë ĵľë +Ġbar rier +Ġej emplo +Ġe k +N D +es p +ен а +Ġp ic +Ġkill er +Ġintegr ate +Ġfew er +Ġdis abilities +Ġ .... +Ġtri angle +Ġfe es +Ġwid ely +em i +Ġoverwhel ming +Ġz omb +Ġb ere +Ġho od +ĠA ye +ĠHar vard +e v +ĠÏĦ οÏħ +Ġcup s +ĠA uch +z ona +Ġ199 0 +Ġwe iÃŁ +Ġcr unch +æ ¥ +Ġз ав +Ġmeas uring +Ġst ations +ĠStep hen +Ġshort ly +Ġsig ning +Ġcom edy +om o +Ġsuggest ions +Ġsign ature +ĠпÑĢ ив +Ġdis order +as ka +Ġworld s +Ġprecis ely +n orm +ra v +ĠC ivil +In ter +ĠC ertain +Ġinj ured +Ġsuggest s +ĠGold en +Ġcy ber +ĠØ ´ +Ġtempor ary +Ġco oper +Ġvot ed +Ġ ought +ấ y +x ual +Ġpan els +Ġ9 5 +Ġhands ome +ĠпÑĢ ов +Ġper mit +Ġke in +Ġbad ly +Ġnot ifications +iz a +ĠNot ice +Ġinc lusive +Ġanswer ing +Ġí Ĺ +u ld +íħ Į +Ġnow adays +Ġ3 7 +Ġb olt +Ġstat ic +ĠH op +Ġav ant +aj o +Ġ맼 ìŀĪ +Ġfif ty +ĠF inal +Ġsc ores +ĠT ap +Ġcy l +Ġconv ince +Ġany ways +od a +Ġìķ ¼ +Ġser ves +ĠÑĤак ой +ĠZo om +Ġsaving s +ul o +Ġs outhern +view er +Ġho je +Ġse ja +Ġrepresent ing +Īë įĺ +l ik +ĠSome body +Ġbe ast +Ġstick ing +Ġins ist +Ġtal ented +Ġexplain ing +Ġatt orney +éĥ ¨ +Ġst airs +ĠD og +í ĭ +Ġc ig +Ġshap ed +Ġs ons +Ïģ ι +ut t +Ġì Ķ +Ġpar ad +ìĿ¸ë į° +Ġh orn +ĠJ our +ann o +Ġworld wide +åĬ Ľ +Ġparticip ation +¦ Ħ +Ġm ów +Ġburn ed +Ġwrit ers +all ah +ĠF und +Ġcle ver +ĠLe ute +b in +Ġbe ating +f oot +ĠìĽ IJ +ĠStud io +Ġv ag +be y +r ze +Ġoppos ition +Ġж из +w ho +Ġê± ´ +Ġtr ace +Ġд енÑĮ +Ġep id +Ġges ch +ĠN ar +ĠB E +Ñĥ й +ĠS ign +ed ly +Ġcl ay +Ġinst antly +Ġgather ing +ĠGal axy +Ġb ored +ĠBudd h +c é +Ġm am +Ġsl ope +Ġëĭ¤ ìĿĮ +Ġsch ön +Ġp ir +ge f +am er +Ġh ö +Ġcolle ague +Ġpres ents +ad ium +Ġà® µ +Ġfal ar +be ep +Ġdri ed +ism s +Ġro pe +Ġworks hop +Ġest ud +Ġb ands +Ġthem es +åħ ¬ +ÙĬ ر +åIJ İ +Ġremind er +ÑĤ Ñĥ +ĠB h +Ġcocon ut +ĠÑģ ÑĤо +ĠCh annel +Ġimmig ration +ä s +.. ... +ä¸ » +çĻ ½ +st op +Ġк аÑĢ +Ġco ins +ĠÑĩ аÑģ +Ġdest ruction +l ined +Ġbar riers +ant ine +Ġprint ed +Ġcongrat ulations +ĠHe art +Ġin qu +th a +Ġhard ly +ĠA ven +Ġt inha +ĠS ony +ĠN F +Ġgradu ates +Ġsque eze +ere my +ÏĦ ι +Ġep ic +ĠJ u +Ġol m +ĠLa ughter +Ġbelief s +ĠC ru +ĠTr ue +ĠS oul +owe en +Ġrom antic +Ġз в +Ġan os +ĠY up +éĺ ¿ +d im +Ġin fer +Ġз ам +Ġso c +uk a +Ġprec ise +Ġdro pping +Ġcl ue +Ġer rors +char ge +ĠP u +omet er +Ġlamb da +ac ional +ĠD ong +Ġcham ber +Ġthank ful +ĠN u +ĠHaw ai +Ġinf o +Ġactiv ate +ĠQ ual +Ġqu ed +Ñĥ лÑĮ +Ġcl oth +åĸ ľ +Ġw ichtig +5 5 +Ġot ra +ograp her +Ġcur ios +Ġ19 80 +Ġemp res +d ess +e ur +Ġcl uster +ar ter +ob ile +ĠY an +ĠAd v +Ġdiscipl ine +Ġìłķ ëıĦ +ĠPl ace +ĠSe lect +T E +ĠбÑĭ ла +Ġwh is +Ġb ay +ĠD or +en cing +Ġrep et +Ġf icar +p ad +Ġf og +u yor +Ġsn ap +ib t +Ġso bie +Ġappoint ment +ĠR y +Ġce iling +our se +Ġwr ites +ĠAfghan istan +Ġm os +az e +Ġpen al +Ġcry stal +IC E +ê° IJ +é Ł +ĠTes la +Ġthe ories +Ġappe al +Ġnewsp aper +Ġcook ies +æ © +ĠاÙĦ ÙĦ +Ġma j +ĠGet ting +k ommen +ĠHe aven +ell s +Ġdiv ine +Ä « +Ġa kt +Ġhop es +ĠCh en +we gen +** * +ĠFra ge +Ġн и +ภ¹ +min ister +nes ota +wh ich +Ġexpl icit +Ġverd ad +Ġgradu ated +ĠPh ilipp +Q L +ĠM I +Ġdev ot +Ġc ure +Ġclos est +Ġà Ħ +Ġsex y +ãģ Ľ +ĠDe ath +ok o +ug u +ĠAn ne +itar ian +es a +ег од +ĠD ur +Ġ 000 +ze it +Ġtour nament +Ġmel hor +ภª +Ġin du +Ġf law +Ġw ars +ĠM ind +ĠI ron +ÑĤ ак +ĠV R +Ġs iz +ĠS outhern +Ġê·¸ëŁ ¬ë +Ġaw ak +Ġìķ ŀ +Ġc ube +believ able +if all +d is +Ġabandon ed +m ind +Ġpar l +Ġclass ical +è ĭ +á»Ļ t +ĠAut o +ĠB or +ç © +4 00 +ĠSoci ety +Ġsubt le +Ġmiss ions +Ġremember ed +ĠE ither +Ġda für +OR D +Ġint ensity +ES IN +ĠC up +Ġrare ly +Ġto ys +ĠChar lie +á» Ł +Ġgla ube +Ġround s +T IN +Ġcap ability +Ġderiv ative +Ġrefer ring +Ġd Ã¥ +ĠT ALI +Ġcott on +Ġcon fer +Ġcolum ns +Ġliber al +Ġnun ca +Ġμ ε +Ġind o +ib en +ĠBe ispiel +Ġê·¸ë łĩ +ĠÑĥ Ñĩ +Ġh oy +Ġfr y +ĠScott ish +è Ĭ +Ġc iv +Ġconserv ative +Ġair pl +Ġs ar +r us +Ġinvest ments +Ġinfin ite +Ġà® ķ +ĠTALI ESIN +ĠG ary +ue ll +Ġа к +ĠC ir +Ġrit ual +Ġ>> > +Ġtem pt +ĠTe ch +ĠPoke mon +Ġimprove ments +Ġsp are +Ġtransl ate +Ġson ra +ĠFil m +w ort +Ġм и +Ġperiod s +Ġje alous +ãģĦ ãģĦ +Ġt ir +M I +Ġconduct ed +ĠìķĪë ħķ +0 9 +ĠPol it +ĠWhere as +Ġmoist ure +Ġs ins +Ġk ap +ĠÑį к +Ġben im +Ġelimin ate +Ġathlet es +ĠMan ager +Ġfeature d +ap ore +äº Ľ +Ġë° ľ +Ġper f +ĠTh us +Ġdeb ut +об ÑĢ +Ġse ñ +Ġmyster ious +w ords +Ķ ê°Ģ +Ġcheck s +Ġvolunte er +Ġwas hing +ĠMar vel +ĠA B +iss ors +! ' +ĠF ull +ye on +Ġwe igh +ĠJO HN +Ġv os +Ġproced ures +Ġaddress ed +ĠBer lin +put er +ĠB an +Ġmedic ation +Ġdr one +ĠÑĥ б +ĠJe an +Ġcap s +Ġdisappoint ed +Ġw ore +Ġêµ Ń +Ġorgan ize +ĠHall oween +Ġfant asy +y ard +Ġnos otros +Ġjump ed +Ġphot ography +ĠN ame +re c +A B +Ġbless ing +ĠSh ut +Ġbit ter +p op +ãģĿ ãĤĮ +Ġde i +Ġfulf ill +çIJ Ĩ +Ġden gan +Ġbe lo +ĠMean while +Ġdep ois +Ġdi abetes +Ġbu nd +ĠZe aland +Ġdig est +Ġt ires +Ġdo d +ag ne +ế t +Ġpe el +Ġз аб +Ġn odes +Ġtrend s +ĠSw itch +ĠA ward +ĠOr ig +ĠH al +Ġest as +Ġ3 60 +Ġsim ult +Ġcom ic +Ġm Ãł +Ġbal anced +ĠPrin cess +Ġkilomet ers +á» © +Ġpart ir +ì¤ ij +so ft +ĠV iew +Ġbi ological +in st +4 4 +Ġman era +Ġcompreh ensive +ĠS ab +Ġcr imes +y ers +ĠComp any +ĠPh ot +Ġpou co +i ac +Ġbe im +in ate +Ġsub sequ +ĠMay or +Ġcent uries +è res +ìŀĸ ìķĦìļĶ +Ġê·¸ëŁ ¼ +ĠFra u +ĠO H +Ġëģ Ŀ +ĠN ah +ĠSer ies +Ġover night +íĴ Ī +ĠâĢ ¢ +Ġtra ve +atter ed +Ġwar ri +ĠGru nd +ĠInd ones +Ġsc ra +ob y +ĠBro ok +Ġcur s +Ġë ¸ +Ġexpl ains +ram atic +Ġparticip ating +Ġmin ut +Ġcontract s +Ġg egen +Ġdisappe ared +ĠS N +Ġrob ust +ap h +Ġsh rim +Ġdev ast +c ope +Ġme ets +Ġpeace ful +m ate +Ġwe ld +Ġ× ª +d on +Ñĥ ÑĤÑĮ +Ġregister ed +ĠN ik +j in +Ġc av +Ġe cht +io x +Ġflow ing +но ÑģÑĤи +Ġto e +Ġent ity +ов а +f its +ĠPat rick +ÑĤ ÑĢ +Ġle verage +Ġcor rel +i ah +Ġstr ings +ist inct +Ġg ue +arch y +Ġteng o +ım ız +Ġor bit +ä¸ º +Ġе ÑīÑij +ca ke +Ġ׾ ×Ķ +ĠMin nesota +Ġbra ke +ow ie +Ġcra w +ê¸°ë ¥¼ +Ġprogram me +ĠÑģл ÑĥÑĩ +åı ª +ien ces +ĠO ui +ĠP ers +im iento +ĠIn vest +Ġsl ower +æĻĤ åĢĻ +ĠB eth +Ġnur se +ĠSpr ing +S p +Ġun employ +д и +Ġgen ius +ĠA aron +Ġê·¸ëŁ ¬ +Ġe i +ãģĹ ãĤĩ +Ġtank s +Ġau jourd +Ġcomplex ity +ĠÑĢ еÑĪ +Ġold est +Ġlet z +åħ ¥ +Ġphenomen on +pr int +ĠBund es +it at +ê» ĺ +Ġ4 2 +ĠW i +Ġinc om +Ġg ek +Ġembr ace +Ġt ies +out e +Ġd ose +ĠF riends +Ñĭ ÑĤ +егод нÑı +Ġor g +Ħë ¡ľ +ó g +Ġex ceed +Ġgod s +Ġê±° ìĺĪìļĶ +Ġsoci et +ĠUn ivers +it ät +Ġword en +Ġsm oking +Ġint ens +ab ul +em ia +è ij +4 7 +f ly +Ġ200 6 +ĠSer iously +Ġprze z +æ ¼ +c re +Ġn an +Ġmod es +ов аÑĤÑĮ +ĠH ang +em en +Ġbenefic ial +Ġvot ers +ĠBro ad +Ġb ent +W ow +Ġm ul +åĵ ¥ +ĠU C +Ġdam aged +ĠUk raine +Ġw ipe +Ġst ones +Ġman agers +Ġr ab +ÑģÑĤÑĢ о +l at +Ġde ce +Ġgraph ic +Ġf oss +Ġdisag ree +ĠAm en +Ġsec rets +ho le +ink le +Ġfortun ate +Ġì ± +ìľ Ħ +èIJ ¬ +Ġhab its +Ġbur ied +Ġh in +Ġvirt ually +ol as +ĠR P +ĠT ab +l ow +Ġsacr ific +Ġestim ated +ol n +Ù ĭ +c ur +ĠFe el +Ġcast le +Ġus eless +Ġdis g +ĠJac ob +Ġga an +Ġup side +Ġpare ce +ãĥ³ ãĥ +Ġsh ipping +ĠC R +Ġdis rupt +ac ter +UN D +f u +å® Į +ĠP ick +ĠChar l +ĠB ull +Ġenter prise +Ġpunish ment +ack ing +Ġfr action +Ġtab let +Ġch ord +Ġsimilar ly +åħ¶ 實 +ĠTor onto +Ġcour ts +ÄŁ l +esz cze +Ġpron oun +ĠS ister +ĠM P +Ġgreat ly +ĠD ank +ic op +Ġgar bage +Ġresol ve +ĠS af +ĠG un +Ġcomp ound +Ġë° ° +ĠMus ik +âĻ « +Ġcha os +ĠWhen ever +Ġe uros +Ġor chest +Ġrefr iger +al an +ภ· +ĠAm azing +Ġp ud +ag an +Ġj eszcze +is y +Ġaccur acy +ĠA ma +is ode +ë ĮĢ +Ġinterpret ation +ĠL iber +æ · +c am +Ġevol ved +ĠK ay +ÑĨ Ñĭ +Ġcreat or +it as +Ġal arm +Ġcelebr ation +z ent +Ġfun cion +Ġo v +umb ling +Ġ % +ภĪ +Ġrestrict ions +Ġн ав +ĠK inder +Ġban ana +ÑĮ Ñı +Ġdiam eter +Ġnor thern +ur ers +ĠP as +æĪij çļĦ +Ġwork force +Ġj ung +Ġguar ante +Ġequ ilib +Ġsu ite +Ġeu ro +Ġdel iber +S te +Ġdownt own +Ġch in +Ġc odes +ed ia +Ġshe ep +res hold +wn ie +ó b +Ġunder lying +l ia +j er +ÏĢ ÏĮ +ç Ŀ +th rop +Ġz ap +Ġvac uum +ĠH ab +Ġwra pped +ì ¢ +Ġinvent ory +м а +Ġco ord +Ġpl ates +Ġsy mm +T e +ĠwÅĤa ÅĽnie +Ġreach es +Ġlon ely +S cript +le e +ess er +Ġê± ¸ +ĠGes ch +ĠMo ving +Ġré p +ĠV ill +åIJ Ī +ĠR achel +Ġtem os +ON E +Ġstra in +Ġang el +Ġf Ã¥ +T r +Ġach o +Ġhighlight s +ĠW er +ĠCar l +Ġbl ur +Ġreg ards + · +ил ÑģÑı +Ġrec re +ĠY ani +U CK +ł ¸ +Ġelectr ons +ĠSp iel +Ġv ed +Ú ¾ +Ġbe am +Ġid iot +ë ĵ¤ +на Ñĩ +id d +Ġsk i +it ative +Ġhyp othes +ãģ§ãģĻ ãģŃ +ent er +ĠìķĦëĭĪ ë +Ġih re +Ġpre view +ang el +Ġdem on +Ġd us +Ġd ic +ĠK om +LE Y +... ! +Ġsie ht +ĠSon ic +Ġten ho +an as +Ġdig it +ĠMa ar +Ġunder grad +oun cer +uff y +Ġconvers ion +Ġdis connect +Ġe cho +om er +Ġcurric ulum +Ġper ché +Ġw and +.. ? +Ġroll ed +Ġentreprene ur +Ġtheore t +ĠÑī о +Ġins ights +Ġzus ammen +o in +ret t +p rodu +Ġvisit ors +e ous +Ġgrand mother +Ġhum or +Ġн иÑħ +zen ia +ins on +Ġres et +Ġbase ball +Ġmatch ing +ëĭ¤ ê°Ģ +Ġpun to +ì ¡ +Ġre de +Ġaddress ing +Ġfore cast +ĠB ol +Ġcol ored +Ġdocument ation +Ġexpect ation +ĠNor thern +Ġcre o +Ġà® ļ +f on +Ġuns ere +U M +Ġcop ies +Ġexpand ed +Ġveter ans +ĠAl m +Ġво обÑīе +Ġpsych ological +Ġnos so +Ġpay ments +im eters +Ġ-- > +ĠJenn ifer +Ġvolunte ers +os se +or ious +ĠбÑĭ ли +è Ĥ +ĠEs s +w s +ĠB C +ĠI C +W oman +Ġv ont +Ġeth nic +EN N +им о +Ġlo b +Ġou i +c s +Ġre he +Ġìł ģ +Ġch ick +ús ica +Ġk ont +ĠDist rict +Ġp ile +Ġа в +ей ÑģÑĤв +Ġ £ +Ġiss ued +Ġком п +Ġpros per +Ġprof ound +ĠDe ar +Ġãģ ĵ +Ġfund ed +Ġb isa +ŀ ĺë +× Ł +ĠìĿ ĺ +Ġtw elve +ĠChamp ions +éĿŀ 常 +Ñģ л +Ġ200 5 +p m +Ġon de +Ġdiff é +ĠCh all +Ġdifficult ies +Ġgar age +Ġd á +ün k +Ġë¬ ¼ +Ġtr an +Ġsubm itted +z w +ÙĪ ا +Ġar k +ĠìĦ ± +Ġgrocer y +он а +i ere +Ġa est +Ġexhib ition +Ġr és +Ġconsist ency +Ġcook ie +н ей +Ġrepl acement +æ² ¹ +ĠS em +ĠìĤ¬ ìļ© +8 00 +Ġgen es +Ġtrans action +ĠE L +Ġdur ante +ib les +ĠE at +t ail +iss ance +Ġto ss +Ġsurv ived +Ġoff ices +Ġsupport ive +Wh ere +Ġtout es +Ġë§ ī +Ġj okes +ier on +ap ers +Ġm ature +ĠM arsh +Ġs ido +k ind +Ġreal mente +ĠChe f +Ġquel que +Ġjud ges +e ft +ER S +Ġj et +Ġpers ons +è » +iz ations +ri k +Ġsh ops +ĠW y +Ġele g +qu è +qu oi +Ġjug a +Ġíķľë ²Ī +ĠQuest ion +ĠGlo bal +Ġìķ½ ê°Ħ +ĠSt ation +æİ ¥ +ĠOh io +Ġstick y +Ġst ressed +Ġg ün +Ġí Ŀ +ÑģÑĤ Ñĥп +é ¡Į +ĠPh D +im mer +Ġment or +Ġinv ented +Ġre un +Ġine vit +Ġpol ÃŃt +Ġexec ute +ĠSt ory +Ġout standing +Ġgu er +ĠR ain +Ġch oses +ĠT it +ĠÑģ еÑĢ +ĠSing apore +ĠN one +Ġch ronic +°ë į° +Ġe go +æł · +ES T +ãģĤ ãĤĬ +ĠW ang +ĠN AT +Ġa ug +Ġdes ktop +Ġetern al +ĠìĤ¬ ìĭ¤ +ĠConst itution +ìĤ ¬ë +×Ļ× ľ +p res +ĠТ Ñĭ +Ġinter f +Ġlist s +Ġfight s +ft en +ĠI owa +Ġmotiv ated +ĠH osp +Ġelse where +Ġpath s +Ġinst ances +B l +r ange +á» ± +ĠS it +man a +Ġìĭľ ìŀij +Ġm ình +ans as +Ġs na +Ġphilos oph +Ġpas se +Æ°á» Ŀi +ak h +ent al +Ġih n +ru ctor +Ġв аÑĪ +Ġgener ous +Ġp ivot +п ол +Ġjam ais +Ġcom ent +ĠL ew +od zi +ĠX box +Ġв од +Ġcons ent +ī ìŀ¥ +Ġdis par +l ass +ĠGovern or +Be ifall +Ġê° ľ +Ġbelo ved +׳ ×ķ +se ll +Ġhon ored +le h +Ġw äre +un ting +Ġfra ud +ĠR AM +ê± ¸ +Ġkill s +Ġeconom ics +0 4 +п еÑĢ +Ġco isas +Ġи гÑĢ +ÃŃ m +Ġmö chte +Ġìµ ľ +Ġstim ul +Ġfast est +l v +Ġg én +ĠS ounds +Ġ19 70 +Ġhome work +spe aking +Ġencour aging +Ġqu ery +Ġre vers +pro fit +Ġd y +Ġìŀ ij +ëĬĶëį° ìļĶ +Ġso ap +ĠG all +ĠC N +ĠAn s +Ġf ic +ank s +Ġdess ert +ĠìłĢ íĿ¬ +ĠM aking +Ġcome ç +ê³ Ħ +Ġassoci ation +D ad +he e +Ġh ogy +Ġap ro +Ġinvis ible +Americ an +í İ +Ġvi be +Ġem issions +Ġadvoc ate +Ġkick ed +Ġ vel +Ġsum mar +Ġfre aking +ch ron +Ġpin ch +Ġwszyst k +isc al +Ġpro ved +Ġmind ful +Ġt ä +Ġno ises +Ġisol ated +Ġcross ed +Ġê° ķ +Ġvo ilÃł +Ġch ore +ĠR A +C om +Ġrelax ed +at ro +Ġpre vention +Voice over +O D +ĠCo vid +Ġsepar ation +Ġ- [ +иÑĩ его +çĻ ¼ +ĠS D +ble ep +Ġindepend ence +Ġpart ial +Ġalgorith ms +ĠAny one +Ġassoci ate +h um +ic ular +Ġb ạn +Ġbatt les +G ood +App lause +Ġbast ante +Ġadv ant +ĠS weet +Ġref used +ãĤ ¸ +ĠÑĤеб е +pl et +Ġencour aged +åĵ ¦ +Ġmir acle +ĠB un +ĠV ar +rim ination +e lect +ĠM ult +Ġdeliver ing +e ing +Ġc m +ne hmen +ĠL ine +Ġë§ Į +en ced +ĠS ound +ĠCont in +ij d +UN G +k le +Ġth reshold +Ġcomp act +ad t +Ġto es +ĠP ur +own ed +ment ed +Ġdes igning +Ġvacc inated +Ġexha ust +Ġbas ics +Ġcons ists +ĠGu y +ac zy +Ġm ÃŃ +w on +å® ³ +Ġ8 5 +æ Ĥ +Ġm um +Ġign or +Ġprint ing +ac ular +p ow +Ġexpand ing +Ġg ir +ĠC ab +íĺ ¸ +ÑĤÑĮ ÑģÑı +ĠìĹ¬ëŁ¬ë ¶Ħ +Ġang les +Ġterm inal +ĠW on +ĠInter esting +Ġcross ing +Ġbond s +Ġpu eden +Ġor b +lar ın +Ġcreep y +Ġnutr ition +Ġall ies +Ġwire less +Ġdes ired +Ġcomp ute +ĠAri zona +ĠBeaut iful +Ġprodu ces +Ġnuest ro +t ed +Ġel igible +ĠÑģ оз +ic ial +ĠH ero +Ġcons ume +Ġrob ots +Ġpurch ased +c ción +Ġ iz +ượ c +ίν αι +ĠØ£ ÙĨ +Ġshad ows +ĠMed ia +Ġprin cess +Ġk lar +Ġwood en +Ġus ar +Ġg üzel +Ġsl ot +r ade +Ġë Ĵ +Ġhar mon +Ġingred ient +ors hip +ek i +Ġgrand father +Ġexcit ement +Ġpolit icians +.. ! +Ġout s +Ġsepar ately +ĠÑı к +ĠW elt +ĠP ow +j an +Ġorient ation +åı ĭ +L C +age m +ÛĮ Úº +åIJ Ĺ +Ġbran ches +ad en +rent e +ĠI hr +as m +Ġest ão +ĠN ic +Ġsla ve +Ġcomp ress +c rowd +Ġclim bing +ĠMan agement +ĠB ah +Ġpan ic +Ġk or +Ġcool ing +Ġb ind +Ġз ад +Ġr ack +Ġent it +Ġs ends +Ġyour selves +d es +ĠMuslim s +Ġí ļ +ism a +cy cle +un kt +ĠC ore +Ġinj uries +Ġident ical +ка Ñı +ĠDeutsch land +Ġе е +is an +Ġtr uc +let on +Ġback up +Ġult ra +Ġab und +ille urs +Ġby ÅĤo +åħ ĥ +ort ed +Ġearth qu +Ġк л +Ġobs ervation +Ġmainten ant +el en +Ġsett led +Ġp ela +ĠE conom +Ġ Õ +Ġste ering +ĠAL L +ĠC her +Ġpat ience +ĠS now +Ġb or +Ġworth y +Ġcá i +Ġ× § +Ġκ α +d og +ĠK aren +ill es +Î ² +Ġagric ulture +×ķ× Ł +ĠSe an +Ġsens ors +íķ ´ë +ag h +Ġpublic ly +Ġpe ux +ĠAlex ander +Ġprior it +Ġla zy +ard on +atter ing +Ġcost ume +س ت +è¿ ĺ +Ġun w +Ð Ľ +Ġthick ness +qu ito +g unt +ist as +ne ys +ĠëIJĺ ê²Į +ĠBr asil +Ġto ken +Ġaff ili +l on +Ġf Ã¥r +ĠBe ach +Ġw itch +ĠSe ven +Ġp ant +λ λ +Ġcapt ain +å Ŀ +Ġve ut +Ġpou voir +ac z +ĠBar b +Ġut ility +Ġcontempor ary +Ġobt ained +Ġpainting s +e ar +Ġpe an +ĠO g +Ġc ust +л ем +Ĥ ĺë +ĠIs so +Ġac onte +ĠTe le +ĠAss istant +à ī +íĸĪ ìĬµëĭĪëĭ¤ +Ġcount s +Ġbu ck +ĠDe ep +Ġtack le +Ġh arsh +Ġdec ides +éĹ ľ +. âĢĭ +éĤ Ĭ +ĠAng el +Ġlay ing +Ġcal ories +Ġcontro lling +Ġadvant ages +ĠÑįÑĤ ой +Ġappro aching +Ġthreat s +ak an +em atic +m ann +ê³ µ +m umbles +ac ió +Ġmaint aining +Ġfound er +l ah +f ight +Ġadm itted +âĢ¦ . +ķ Į +ab ol +Ġus age +Ġn onsense +ĠPal est +Ġcont re +ĠDemocr atic +ĠE R +j ekt +Ġar bit +Ġг ол +ĠMich elle +ich er +es h +ĠP ho +к ом +4 9 +ĠEner gy +ο Ïį +Ġc ents +Ġref ers +Ġg ospel +ĠSh a +ĠSh are +×Ļ× ł +Ġclin ic +ĠëĦ £ +Ġequ ality +ug s +Ġsh ed +Ġplan es +Ġtout e +re ck +Ġstra nd +Ġbi ology +Ġle ague +ĠP ok +Ġnúmer o +ĠCo ast +Ġconsist ently +Ġnuc le +OO OO +Ġob jet +Ġch or +Ġg inger +Ġd abei +Ġcoop eration +à¯į . +nt en +ç ¤ +l Ãł +ìĸ ij +r ado +Ġpass ive +Ġglo ves +Ġunder ground +Ġlog ical +Ġk et +Ġfunction ality +¸ë ¦¬ +Ġport al +ell er +×Ļ× ¨ +ĠT ed +ĠG re +IJ ľ +Ġperson nel +Ġemer ging +ĠF ür +Ġmeant ime +usal em +ĠC lear +Ġtra pped +Ġìļ ° +Ġdis pl +Ġmet tre +Ġmun icip +Ġwithd raw +Ġsp at +un es +Ġaccess ibility +æĪij 们 +Ġap are +Ġpros pect +Ġн аз +Ġcop per +ĠP RO +Ïħ ÏĦ +Ġattack ing +ĠV in +ĠSt one +Ġinvestig ate +st yle +ĠÎ » +ë ¡Ŀ +ë§ Ī +Ġins pect +Ġli ver +ал иÑģÑĮ +Ġser a +hal ten +em an +Ġmin istry +' ' +Ġd ots +ãħĭãħĭ ãħĭãħĭ +Ñĥ ÑģÑĤ +ĠJ ak +AK E +Ġg aps +uck er +ĠинÑĤеÑĢ еÑģ +ĠEm ily +Ġinter val +Ġt ender +ĠTechn ology +g ame +Ġtri b +ÙĦ ا +ĠDevelop ment +Ùħ ا +Ġwr ist +Ġf ires +Ġtarget ed +ìł IJ +Ġso d +íļ Į +Ġoldu ÄŁ +Ġse asons +vent ions +Ġн его +Ġsomet ime +ли в +n é +Ġt ú +ĠDe us +Ġexec ution +á p +ĠCh ange +ĠInd eed +Ġreg ulation +ĠH ung +é is +Ġwish es +Ġj azz +Ġstruct ural +Ġblow ing +Ġby Äĩ +Ġtherm al +ph ant +ÑĢÑĥ з +ан ÑĤ +ĠP ull +Ġconf usion +нÑĭ ми +Ġscen arios +ìłģ ìľ¼ë¡ľ +Ġд еÑĤ +Ġtatto o +Ġaut re +Ġhe ating +Ġtreat ing +Ġпон им +Ġexc lus +ĠL OL +we ar +ag le +Ġzur ück +Ġr ational +s u +Ġdet er +ĠN ative +à®ķ ள +ach ed +Ġ ãĥ +ĠEnt onces +Ġhor a +ìĿ´ìĹIJ ìļĶ +Ġl ite +à « +Ġsix th +Ġбол ее +act or +Ġpsych ology +çĽ ¸ +Ġdem ands +Ġpe er +Ġnew ly +ĠWW E +Don ald +ĠBo x +Ġp ine +Ġload ing +ĠN ico +Ġs ÅĤ +omm e +AR T +Ġrecru it +Ġbug s +arent s +ĠпÑĢ об +ĠIn side +ipp er +d ramatic +Ġplan ets +ord e +Ġy oga +ch ild +ĠMar ie +Ġãģ Ĥ +ĠB L +Ġfil med +Ġref resh +Ġtomato es +Ġf et +Qu é +Ġ !! +ĠëĤ ´ë +r ine +Ġinteract ive +s al +ann ah +pe z +ç¶ ĵ +Ġunderstand s +ĠTok yo +Ġlibr aries +Ġread er +ij IJ +o z +ĠEnd e +ĠF lo +Ġm ild +Ġpo etry +Ġж ив +æĦ Ľ +Ġbeh ave +Ġdo en +ĠSus an +p age +ra ham +Ġcommunic ations +Ġtun ing +Ġp ac +Ġanx ious +I O +M ark +Ġhi ç +book s +Ġp iss +Ġen abled +achel or +ĠF OR +Ġé c +ĠT R +il st +h at +ĠìĿ Į +Ġty ch +Ġj ar +Ġbuild s +ĠAr gent +Ġinter medi +Ġl ou +Ġa ra +Ġassign ment +Ġcabin et +Ġretire ment +ãģ » +Ġdis abled +ric a +Ġa wards +Ġbo ots +Ġacknow led +Ġth y +Ġêµ ¬ +Ġsy nd +ни й +il ton +Ġprob l +ĠF al +Ġverd ade +Ġ7 00 +ĠLe arning +oc us +Ġpal ace +N ot +t ain +c m +Ġmagn et +inc oln +Ġfig uring +ĠL yn +ĠB oss +ĠV O +Ġdiagn osis +Ġequ ipped +w atch +in os +ad ers +Ġsh elf +Ġorgan is +Ġn od +Ġk ız +pp ers +Ġrest ore +Ġart ic +ĠVo ice +ı yorum +ê² © +Ġspread ing +Ġh ips +Ġw ard +ure au +Ġinter section +6 6 +Ġ3 9 +ç ³ +Ġwait ed +ì ´ +hh hh +Ġd ys +ĠE N +Ġb atch +Ġca f +Ġmark er +大家 好 +or able +ó ria +Ġste pped +Ġcelebr ating +ан а +Ġwor n +ĠF ol +Ġpl a +Ġattempt s +Ġtwe et +Ġr ust +g ence +í Ĩµ +Ġre vel +Ġre cept +en ess +Ġ( ( +ãĥ¼ ãĥ +! âĢĭ +ĠìĨ IJ +Ġinfluen ced +и ж +Ġкон еÑĩно +Ġcolleg es +ion i +Ġs ag +An n +ol ar +Ġexpress ions +Ġsu its +Ġowners hip +el and +pie ce +æĢİ ä¹Ī +Ġdesp ués +Ġt el +Ġins ult +Ġêµ īìŀ¥ +ĠSm all +ĠF R +ok a +ber ries +ĠAnt on +ел Ñı +Ñı Ñģ +Ġval ve +act s +Ġwood s +à® £ +Ġcult iv +Ġf á +ãģ¨ ãģĦãģĨ +Ġche ers +Ġassum ption +Ġfit ness +ÃŃ cul +Ġpod r +Ġwe it +ĠH ind +Ġd ign +Ġз н +Ġsqu ad +Ġdest ro +c ere +sh irt +imm t +eng ers +Ġs ä +k ÅĤad +Ġ ÈĻ +Ġocc as +Ġì¤ Ħ +Ġprocess or +ĠD M +ĠDad dy +Ġsoon er +Ġstraight forward +Ġdepart ments +ĠChr ome +Ġwork place +ĠPy thon +Ġm eng +ĠD AN +ĠI ce +ĠëĪ Ī +ĠG i +Ġh iring +Ġland ed +Ġdemocr atic +ied z +ãģĺ ãĤĥ +Ġse v +ic ia +Ġespe cial +ĠN ous +Ġh ät +Ġb ou +per t +ies z +åij Ģ +Ġv il +ÅĽ li +Ġî n +Ġloss es +éķ · +Ġto ast +Ġreal m +ĠAust in +ĠIn formation +Ġres ume +Ġch ase +Ġsal ary +Ġë¶ Ħ +ли Ñĩ +ĠÑģл ед +ĠFur ther +Ġcar ing +Ġv ig +Ġval or +è¿Ļ 个 +ĠÑĩ а +Ġanalyt ics +Ġglo be +ĠM AN +Ġn el +ìĿ´ì ķ¼ +Ł ¼ +Ġo y +íķĺ ìĦ¸ìļĶ +j en +Ġtrou bles +ah aha +Ġchurch es +u et +Ġmeasure ments +b il +ì ½ +if ully +ин Ñĥ +ĠWil son +¦ ´ +ĠíĮ Į +Ġì° ¨ +Ġp úblic +ĠJer usalem +Ġn ails +Ġsp ine +Ġhe mos +Ġz n +qu is +ĠLe ben +Ġrefer ences +IT H +i per +ĠÑģеб Ñı +ì ģ +ĠW a +st ate +§ Ŀ +åħ ± +ĠGen er +Ġact ress +ĠEn joy +๠ĥ +Ġ× Ĵ +Ġinfect ed +Ġsh aking +Ġn ick +ภ¸ +Ġf ot +Ġaccompl ished +u ke +Ġshe ets +Ġf ence +Ġnurs ing +Ġintrodu cing +Ġfe at +O ne +T O +Ġcl ubs +ĠBru ce +on ge +ch ange +ĠBat man +åı ° +ĠOffic er +Ġhyd ro +Ġsupp lement +Ġc ela +Ġlong est +Ġcompet ing +Ġcon he +g iving +Ġbra ins +Ġlo ans +Ġw age +ĠCl inton +Ġs Äĥ +ane ous +Ġl ord +ÑĢÑĥ ж +Ġqu iz +Ġst iff +ĠL GB +s z +M E +m are +th ere +Ġn är +ĠM and +l ast +Ġd ag +Ġhalf way +ĠB and +Ġëĭ¤ ìĭľ +ĠA ren +Ġi le +P N +ent o +Ġalg um +Ġsoc cer +Ġblock ed +ĠJon athan +Ġse w +ĠTest ament +Ġv ale +Ġbehav i +å§ ĭ +Ġcon na +IC H +Ġaud iences +m l +amm ad +ĠìĤ ´ì +I GH +Ġr aces +em ed +Ġm á»Ļt +à ¯ +Ġover s +Ġdecl ared +Ġs ana +ĠU na +ĠÑĢ е +uck s +Ġp airs +Ġan ge +N e +Ġup s +av y +ø r +ree k +Ġbehav iors +Ġreflect ed +Ġprior ities +Ġcon du +Ġret reat +Ġexp enses +Ġë´ IJ +Ġtri ple +Ġêµīìŀ¥ íŀĪ +ä lt +Ġind igenous +Ġmin ing +Ġaccept able +Ġru in +C A +u ine +Ġpip eline +ct ic +ê t +ĠвÑģ его +Ġb oun +ĠDig ital +ĠBo om +ÑĨ е +Ġл ÑĥÑĩ +Ġas c +ĮĢë ¡ľ +ĠGood bye +Ġrend er +ene z +ar re +ĠTH AT +b our +ic ión +ãĤ Ń +E very +Ġw ires +ĠPar liament +n ung +ate ur +ĠS ave +ĠPh ys +Ġam or +ĠE ve +Ġfr ight +Ġgam ma +Ġmic ros +m itt +ĠC ode +ĠBe y +pl ed +ĠиÑģп олÑĮз +ç Ĺ +ìĥ ī +å¥ ¹ +Ġmon et +ĠJah re +Ġlux ury +Ġde af +Ġbet ray +Ġê² ° +и ки +Ġdefe ated +Ġunder t +Ġwe g +Ġcool er +ãģķ ãĤĵ +iam i +éĤĦ æľī +ĠJess ica +ĠJ oy +Ġsoph istic +ени и +ðĿ ĺ +Ġch ili +ĠTy pe +Ġprote ins +Ġpresent ing +al ia +ìļ ¸ +ĠMaj or +Ġmolec ule +um er +Ġcoll apse +ĠAny ways +ĠMount ain +ant ed +ãĢ IJ +Ġвиде о +æ° ´ +A ud +Ġcon qu +Ġvo ll +Ġkn it +Ġmem br +ĠMark et +Ġd ari +Ġcalcul ated +г и +Ġshrim p +ĠM u +ĠпÑĢ оÑĤ +Ġìĺģ ìĥģ +Ġproduct ivity +Ġcogn itive +ĠHe b +ict ions +ê² ½ +Ġcr é +f ör +Ġpray ing +ash i +ĠT ik +ó r +w en +ÑĮ Ñİ +ix o +Ġ( " +ĠÑĤ ел +Ġìĸ´ëĸ ¤ +ĠпеÑĢ ед +ĠD rive +ãĢ ij +ĠE qu +Ġequilib rium +Ġdescri bes +не е +4 2 +ĠCur rent +y y +Ġabsor b +Ġsold ier +d ers +Ġtestim ony +Ġdec line +ľë ¡ľ +g age +Ġinsp ire +la pping +Ġspin ning +Ġsla very +Ġfac ial +Ġtrad itions +ári os +ĠHosp ital +Ġn est +ĠëĪ Ħ +Ġto i +Ġfe ars +ìħ ¨ +ĠM uh +Ġgradu ation +Ġimpact ed +Ġa unt +ĠLet s +Ġalumin um +Ġdomin ant +ĠDav is +ĠNav y +Ġcom pt +op les +Ġest ava +è ¥ +Ġsc al +Ġpres erve +ĠO pp +Ġpract ically +Ġmagn itude +Ġf itting +Ġcoordin ate +Ġfurn iture +ĠFam il +Ġexplos ion +Ġdocument ary +ĠS cript +Ġport ray +m at +Ġschedul ed +Ġdynam ics +ph y +ak y +ĠU I +C he +Ġcontinu ously +ĠPro v +å° ij +Ñĥ з +ra h +Ġger ne +pro of +Ġsecret ary +ĠPat reon +sc ream +ĠK ids +á»ĵ i +Ġk g +Ġuncertain ty +Ġк ажд +Ġmit ig +Ġread s +å· ² +ĠR u +Ġpri est +Ġн ед +Ġlimit ations +Ġflo at +6 00 +ĠT oy +ĠJim my +Ġoff ensive +en i +ĠX i +Ġeye br +ĠTur k +Ġaccident ally +Ġoh ne +ĠS aud +9 5 +ĠD utch +ан Ñģ +ĠSe attle +Ġëĵ ± +che ck +k ÄĻ +Ġcontrib utions +Ġbes ide +Ġqu indi +Ġfle w +æĹ ¶ +Ø° ا +ĠL O +Ġwa ist +ĠE V +Ġhol idays +j on +Ġmis under +Ñı н +Ġb out +Ġd imin +Ạ½ +ó l +ĠGr ace +Ġinput s +Ġden y +Ġform ing +ĠB ild +Ġad equ +Ġfol k +Ġreject ed +se mb +Ġfrust rated +op en +ĠBet ter +il on +Ġtow el +Ġdifferent ial +Ġsac red +Ġsa il +éĩ Į +ent imes +Ġgentle man +Ġicon ic +Ġcomp aring +Ġs agt +Ġtext s +Ġgrand ma +Ġroll s +Ġcont ents +ä¸į 好 +оÑģ Ñģ +Ġsusp ension +ro it +¦ ¼ +Ġasse z +Ġd ort +ĠM ath +ĠVict or +ĠJava Script +ä¸į å°į +Ġen han +Å Ļ +ĠB ush +Ġpromot ion +Ġk in +Ġmon sters +ĠColor ado +ĠÎ ² +íķ´ì ļĶ +æŃ £ +iffer ent +Ġn aked +Ġpro d +et ics +ĠW oman +Ġtreat ments +Ġest oy +v é +Ġlif ting +Ġy apt +ĠRo ber +Ġì¹ ľ +Ġsubst itute +ak u +r idge +Ġê± °ë +Ġrespond ed +Ġb é +ĠEngine er +Ġtransfer red +ë ² +Ġha ber +o op +ĠW E +Ġv est +Ġfor ty +ĠD S +Ġ200 4 +Ġco aching +n om +ĠB ab +Ġn ossa +ĠJ ake +Ġg y +Ġde leg +Ġìŀ ł +ĠкÑĢ аÑģ +Ġstand point +Ġdis ad +Ġart work +A d +ill o +ĠÄij ược +ĠPr om +ĠL ib +Ġcritic ism +Ġcontact s +ÑĢ ам +Ġachieve ment +ÐĶ а +Ġdiss ol +ĠVeg as +Ġstream s +ĠK ent +ĠعÙĦ Ùī +Ġrad ius +Ġsu cks +ĠA ch +Ġf i +ou st +ĠлÑİд и +Ġpal ette +ĠH az +ĠAnth ony +Ġtem a +ĠC os +Ġsa fer +α ÏĤ +Ġcont rad +Ġma ior +Ġinfl ation +ĠSil ver +Ġatt ending +íķľ íħĮ +art o +Ġapplaud ing +Ġcomput ing +ĠH at +æ » +k now +mak ers +Ġcon oc +Ġeduc ated +Ġmod ified +Ġinc lusion +ment al +ŀ IJ +is ia +ĠÏĢ οÏħ +Ġa un +ĠIre land +Ġk ö +Ġcompl iance +Ġinsp iring +иÑĤелÑĮ но +Ġdisp os +ì° ¨ +Ġw ip +r ical +raw d +Ġt res +Ġmob il +olut ions +B O +Ġb ounce +Ġassum ed +ĠMed ical +Ġf iscal +Ġng Æ°á»Ŀi +ition ally +Ġst olen +ĠB M +Ġmechanism s +ε ί +Ġqual ified +Ġìŀ IJë +ught ers +ĠH IV +ĠL ots +Ġser vers +Ġcar r +ĠT ogether +Ġattract ed +Ġk r +æĪij æĺ¯ +th ur +in in +ĠH alf +È Ľ +ĠP ap +Ġremind ed +AL L +Ġhel met +Ġbott les +Ġprofess ors +Ġse ine +ÅĤ Äħ +ãĥ ı +Ġê±° ìķ¼ +Ġ×¢ ׾ +f un +ĠB ird +Ġfight er +ĠëĶ °ë +ĠT ool +Ġt in +ino is +ë ¶Ħ +×Ļ× Ł +ĠC AR +åIJ į +irst y +Ġout door +ĠN S +ãħ İ +ff en +Ġl ud +H ello +Ġroll er +ie le +ĠPol and +Ġap a +ex p +Ġcertific ate +ĠT own +аÑİÑĤ ÑģÑı +ild e +Ġdeterm in +P R +Ġfree ze +Ġmain stream +Ġobject ives +b lo +Ġtak ie +åĵĪ åĵĪ +Ġë°Ķë ¡ľ +el et +ĠI V +ĠF ast +Ġd ere +em p +ĠD ra +ĠìŀĪ ìĹĪ +Ġdisc rimination +Ġε ίναι +ne cess +æ ® +ıģ ı +Ġpost ing +wi ÅĽcie +Ġl ub +Ġol ive +Ġr im +Ġmodel ing +Ġa ño +ĠPak istan +Ġover l +Ġinf lam +N E +ìĹIJ ê²Į +Ġatt ended +Ġdeal t +ĠAl t +ĠL incoln +Ġaw ake +Ġfil ters +ĠWith in +czy wiÅĽcie +Ġs û +ĠJohn ny +Ġintegr ity +Ġisol ation +ĠE asy +ĠпÑĢ ин +ĠAl ice +Ġsm iling +en ix +, ... +Î ¶ +Ġbeg un +Ġjew el +Ġconvention al +Ġstat ist +Ġhand ed +Ġir re +Ġpro hib +Ġsatell ite +é¦ Ļ +ĠInd ust +Ġtra ged +Ġtra va +Ġih m +Ġcru el +ĠAg ora +ĠD oc +Ġz ones +Ġm all +Ġtr ay +×ķ× ł +Ġir rit +Ġk ans +ĠBe at +ud ge +ie lle +Ġtrust ed +Ġb ikes +ĠÑĥ п +ĠM ember +w ick +Ġcreat ors +Ġher itage +ind istinct +Ġres ur +enn en +C ome +Ġf iring +ĠBu eno +ĠТ о +ik an +ett es +Ġk es +Ġtri ps +Ġdivor ce +ĠK l +Ġcons ol +ke ep +기 ê°Ģ +ĠRep ort +Ġhost ing +Ġdiam ond +Ġcompl ic +Ġhel icop +Ġdep uis +d s +ĠCh an +Ñı л +Ġsc issors +il ation +Ġprop ortion +ER E +ĠÙĪ اÙĦ +int a +Ġmuch as +u ation +it is +æĬ Ĭ +Ñı Ñī +Ġni in +Ġemphas ize +uel a +Ġprodu cers +Ġr ze +änd er +ET H +æ º +Ġconst itu +åĽ ½ +Ġperform ances +ist le +go v +ĠL iter +Ġincorpor ate +Ġeduc ate +ĠN in +ì ª½ +Ùĩ Ùħ +el eration +×ķ× ij +Ġya ÅŁ +or ous +ĠC as +Ġgr ants +ëĬ ¥ +am el +Ġê·¸ë łĩê²Į +ĠE ste +Ñħод иÑĤ +ĠпоÑģ ле +Ġg ent +Ġfocus es +al ities +ĠR h +ë ³´ +æ° ij +ĠD ance +r r +Ġam er +Ġutil ize +Ġl ÃŃ +ĠAm ong +Ġpregn ancy +Ġlo ops +ал оÑģÑĮ +ĠM oh +Ġcatch ing +Ġglo b +Ġa jud +Ġ[ ? +ĠAn al +lo oking +Ġsurf aces +Ġprogress ive +Ġvir al +0 8 +Î ¾ +K A +Ġ ży +Ġpick s +ann on +Ġbul k +ĠR oss +Ġdescri bing +ĠG el +Ġloc ally +Ġend less +Ġmass age +Ġclean ed +Ġtravel ed +ен Ñĭ +Ġsent iment +ig ma +ĠN as +Ġchemical s +Ġright eous +ĠMag ic +Ġrel ates +Ġtruck s +Ġ19 60 +åĪ ¥ +Ġapp et +Ġsn acks +ĠSum mer +Ġy üz +Ġpr is +ĠMex ican +Ġtransp aren +Ġminor ity +Ġver te +Ġl assen +4 6 +л ек +é p +ĠÑĦ илÑĮ +Ġi yi +Ġsp an +íķĺ ì§Ģ +Ġind icated +qu ar +Ġscholars hip +ĠLGB T +Ġhistor ically +ó ÅĤ +Ġmin ist +Ġpen et +ĠR ap +Ġcons ervation +çĽ ´ +ĠH oney +ĠBe i +id el +Ġrespons ibilities +Ġmess y +ĠEx cept +OR E +Ġiniti atives +Ġjun ior +Ġdesign ers +Ġexpl oration +Ġspons or +Ġmob ility +Ġint eg +land o +Ġb ark +Ġindic ates +à ¶ +Ġemploy er +å® ī +Ġcous in +Ġbo iling +Ġch rom +Ġç al +Ġper pet +Ġcont ained +Ġpark s +Ð « +ĠEngine ering +P lease +ĠStart ing +her o +Ġlaw yers +è¥ ¿ +Ġz d +Ġfranch ise +ra ge +Ġint uit +ĠG L +re ach +ĠE lle +Ġnh Æ° +ĠN ord +Ġbe an +0 7 +Ġple asant +å½ ĵ +v iron +Ġgrad ient +z us +ĠE M +Ġess ay +ìĹIJ ìļĶ +ế n +n u +á» « +ĠÃī s +Ġden omin +ĠGirl s +Ġperson nes +ĠاÙĦØ £ +b ild +ĠSt at +Ġcompl iment +ĠK ate +Ġoptim al +Ġh id +د ÙĬ +Ġquick er +w all +E n +IN E +?? ? +ì² ´ +ĠA ction +å Ł +Ġpenal ty +ĠK az +' ? +Ġc ried +Ġcan vas +ft e +Ġexc lud +¸ë ¡ľ +Ġemphas is +Ġen zy +ĠH ou +Ġoverse as +ÃŃ amos +å¸ « +ö glich +Ġhead phones +c n +ĠA ge +Ġa kan +Ġcharacter istic +íķĺë ©´ +get s +Ġë¶ Ī +Ġr ival +Ġb orders +em ente +em ás +Ġy ol +Ġcom pe +end ers +ınd an +Ġmö glich +Ġbubb les +nat ural +Ġar med +Ġel abor +ĠìĿ´ë ²Ī +Ġwash ed +οÏħ με +è« ĭ +Ġfl avors +Ġexist e +Ġpre st +ĠThe ma +оп ÑĢоÑģ +er on +U E +er i +Ġconc er +Ġa ixò +åħ © +Ġprotect ive +Ġзна Ñİ +ĠëĤ ł +ĠII I +Ġme er +ĠSh op +ll i +ĠOr der +ĠM Y +ĠG host +ãĤĤ ãģĨ +ad el +Ġst ole +Ġrele asing +ĠCom ment +Ġtra ins +ë ªħ +Ġw issen +ens ed +Ġdesc end +Ġf ier +Ġrad i +Ġpers u +ç ¢ +Ġм н +ĠD est +Ġwor ries +it et +b as +Ġst ab +n ame +or ic +ĠCl ose +Ġalum ni +ĠS elf +ff e +it ating +ather ine +ĠRight s +Ġell os +Ġwar rant +Ġn erve +Ġveget able +ĠTe il +Ġê°Ļ ìĿ´ +R Y +Ġsustain ability +Ġste ht +Ġbr id +ada ÅŁ +Ġt v +Ġdur ation +Ġpesso a +Ġmet rics +Ġad am +c as +аÑĢ и +Ġev ident +Ġdisplay ed +Ø§Ø ¦ +Ġre ck +ĠBudd ha +Ġde le +ĠDie go +os ph +Ġb la +ĠM ik +ul ator +Ġ200 1 +Ġpromot ing +y ch +ĠE X +Ġlast ly +Ġout line +Ġspir its +Ġve ux +Ġsubt ract +ĠÅŁ imdi +Ġp ins +Ġbur ger +Ġmol to +Ġhab ÃŃa +Ġë° ĺ +ig u +er st +Ġn en +Ġbac on +it ious +Ġcar ries +Ġprom ises +nd e +ĠLe ft +ĠL im +æ £ +Ġ4 4 +Ġcare ers +Ġì£ ¼ë +Ġspeed s +qu é +m ad +mark et +is me +Ġ200 3 +Ġre cess +ĠJ UD +Ġrac ist +ĠSch l +Ġpar ler +Ġot ros +ish es +Ġconvert ed +aa aa +ани и +ĠAr k +ĠCh ance +Ġelement ary +ε ν +ink s +Inter viewer +Ġfre ely +al ah +Ġëĭ¤ë ¥¸ +Ġrequest ed +Ġtor que +no ÅĽci +ou red +ĠSt aff +Ġst ain +ĠAl an +Ġv ere +ĠW inter +Ġdef ect +ied y +Ġbe ats +Ġh á +um n +o ons +it udes +Ġse it +o ly +Ġres erv +Ġext r +Ġphys ician +vis or +Ġhand ful +ĠN ations +Ġì¢ĭ ìĿĢ +uc cess +Ġup stairs +ĠSqu are +Ġhe in +ĠSe ason +ol is +Ġpr ince +Ġdef ensive +ç ½ +Ġм еÑģÑĤ +Ñĸ й +Ġا ÙĨ +um ble +ê¹Į ìļĶ +Ġass ass +Ġcirc ular +Ġqual ities +Ġh mm +Ġbl own +ĠL iz +ĠK ur +ĠS A +Ġfind ings +Ġcol ours +Ġde lle +ĠI R +ĠA th +ĠD ub +ĠO x +ĠØ ® +Ġpo ckets +Ġgr ill +Ġswitch ing +Ġprefer red +ĠW ales +Ġex emplo +Ġchop ped +Ġvacc ination +Ġne uro +Ġspec ify +iv os +Ġser á +Ġz ie +Ġà® ® +Ġresult ing +ĠU gh +Ġmess ed +C D +Ġpa ar +Ġcom er +Ġcou ch +ĠFest ival +Ġ4 9 +v ous +z ens +ç¨ ® +ĠKenn edy +ĠT s +Ġë³´ì Ĺ +Ġdemonst ration +Ġun to +Ġfrust rating +Ġlabor atory +Ġe gy +Ġbeaut ifully +Ġìŀ ¬ë +Ġal gu +Ġö yle +ä½ł çľĭ +ĠP H +Ġfort une +Ġclean er +ĠRob in +Ġsa us +ĠG eld +Ġk at +o bs +Ġol ur +Ġm att +Ġquest a +Ġsuggest ion +en cer +о ÑģÑĤ +Ġrad ar +Ġìŀ ¡ +ish a +à® ¨ +ãĤĵ ãģª +j es +Ġve el +ìĤ ° +Ġauth ors +ãĢ İ +pl an +Ġcollabor ative +Ġinst inct +Ġfar ming +au ge +E du +Ġmembers hip +Ġsimult aneously +Ġb ake +Ġk ä +Ġlect ures +Ñĩ еÑģ +Ġprend re +Ġcoll aps +ĠS aya +ĠF ut +Ġy og +ĠR ather +ر ÙĬ +Ġcamp s +ол од +Ġsim ulation +ĠM ak +La ughs +Ġgre y +Ġsent ences +y en +ĠUn less +J e +ĠSat an +ĠÑĤак же +ĠN A +Ġbr on +Ġ? ] +Ġsoul s +Ġlight ning +Ġimag ined +Ġczy li +ps ilon +et ta +Ġbelie ving +Ġstrong est +ĠC ON +Ġquel ques +Ġimmig rants +Ġwall et +éĢĻ æĺ¯ +ĠJer sey +Ġimplic ations +Ġfor b +ãĢ ı +Ġun believable +Ø§Ø ¡ +Ġoper ational +ü s +ĠG M +Ġê·¸ëŁ °ëį° +Ġgrac ias +Ġent end +ĠReg ard +ro b +ĠÑĤ еÑħ +è ı +ĠRev olution +Ġwa ar +ĠB iz +th eless +Ġspons ored +qu ier +ĠìĿ ¼ë +Ġte k +ĠëIJ ł +ig keit +ĠL uck +ĠCertain ly +Ġto ll +Ġн иÑĩего +ĠM oney +ĠÑģ ÑĤоÑĢ +ĠDou ble +ĠW olf +Ġch unk +ά ν +it és +on ing +M ar +Ġgrand es +Ġcollect ions +ĠEurop a +Ġа ÑĢ +ĠâĢĭâĢĭ âĢĭ +Ġê·¸ëŁ¬ë ©´ +Ġоб ÑĬ +Ġãģ ª +Ġìĭľ ê°Ħ +ĠC ustom +Ġì² ĺ +Ñĸ лÑĮ +Ġindivid ually +í Ĺ +Ġdo zen +Ġo we +ĠVict oria +åı¯ èĥ½ +Ġbe et +ur b +Ġanal og +i ção +Ĥ ľ +so ever +Ġmod o +Ġsubscri bed +ìŀ ¬ +Ġent ities +çī ĩ +Ġclos et +Ġrespond ing +Ġprin ter +ĠStep han +Ġby ÅĤ +ĠD om +ĠF ern +ĠP ier +ĠwiÄĻ c +Ġh ence +Ġmod ules +ãĥ ¬ +ĠëĶ ± +ĠDann y +ĠÑģеб е +Ġv ad +ĠìĹ Ħ +Ġs ous +Ġsp here +B Y +ĠP ed +ign ed +Ġwhe at +Ġund ers +Ġevol ve +Ġdec lar +Ġlight ly +Ġident ifying +æĦı æĢĿ +Ġlegend ary +Ġgen uine +Ġgr ind +ĠU ne +ge ben +Ġb icy +Ġjump s +Ġprov ince +zi ÄĻ +Ġ×IJ× ł×Ļ +Ġh oc +Ġб л +ĠGr ad +Ġreven ge +ĠاÙĦ ت +o oh +æĭ ľ +аÑĨи и +å¹ ³ +Ġelect ro +ĠëIJ IJ +ãģ§ ãģ¯ +Ġf als +ri el +ok er +ĠEx cellent +ĠMor gan +Ġbr ick +Ġsubstant ial +Ġpoll ution +ĠT ür +ĠEv et +Ġl ung +ãģ ĸ +×Ļ× © +omm es +Ġreal izing +Ġhum ble +ĠL ock +Ġb od +Ġìĸ ¸ +Ġpe ers +uz z +Ġembed ded +Ġclar o +Ġag greg +Ġemploy ers +ĠR aj +Ġãģ ¨ +ĠY i +Ġje u +at ers +Ġstri kes +n os +aut res +d r +op her +ĠApp arently +íĺ Ħ +Ġinf ant +ا ب +ÑĤ Ñĭ +í Ľ +Ú ¯ +Ġred es +acaÄŁ ım +ĠDA VID +ĠCh icken +Ġperspect ives +Ġview er +Ġsh ar +ĠпÑĢо из +lig t +er os +it able +ил оÑģÑĮ +Ġdif ÃŃ +´ë į° +Ġret ired +Ġthat s +zen ie +be iten +Ġmy cket +ĠR ab +Ġinflam m +ì° ® +Ġd um +Ġdad dy +æľ Ł +Ġimm ers +Ġplay list +௠Ĩ +Ġtra um +Ġref use +st ep +à® ļ +c up +Ġpop s +r imin +ay ım +Ġa ld +Ġun necess +Ġd ah +ĠIr ish +Ġcomp r +la ÅŁ +T P +Ġtransl ated +S c +ce ÄŁim +´ IJ +Ġd rei +ĠлÑİд ей +Ġqu iero +Ġhe le +z lich +Ġapp les +Ġdistrict s +Ġcred its +Ġas p +Ġëĭ ¨ +or al +å½ ± +Ġste pping +ĠV a +Ġg ains +6 5 +Ġnuest ra +ed ay +ass ador +ĠL ind +Ġcrop s +ci endo +ig ue +Ġb ana +A m +Ġp ent +Ġadd iction +Ġpack aging +ä d +ª ¨ +Ġper què +Ġcampaign s +Ġste ep +Ġne ue +Ġembarrass ed +Ġdist inction +it zer +åij Ĭ +Ġregist ration +Ġll am +ĠAlm ighty +li est +Ġu z +n ak +ç º +Ġter az +iam ente +Ġtrans actions +Ġc ôt +Ġswitch ed +Ġcom bo +Ġpray ers +Ġintern ship +Ġaddress es +Ġchar ity +ĠW OO +Ġb ait +è¿ ĩ +Ġ � +Ġf ica +ĠTy ler +ar u +Ġat oms +ĠLe vel +ĠпоÑĤ ом +Ġf ame +ul k +Ġteach es +Ġre build +ед ÑĮ +ĠIndones ia +ush i +ĠSh ort +Ġens uring +f s +e le +Ġmargin al +Ġconclud e +am t +Ġver ify +ĠMc Donald +Ġsk al +Ġrec onst +ĠM ann +Ġbas ement +Ġtransform ed +Ġoccasion ally +z one +ĠD ans +Ġкак ой +Ġdiagn osed +ĠÏĦ α +Ġcomm ands +Ġpresident ial +Ġab b +Ġbrack et +ĠL em +Ã¥ ng +Ġfavor ites +Ġrev ol +ĠíĬ ¹ +Ġhar ass +é ħ +Ġcle ans +st änd +Ġknock ed +Ġpe oples +Ġmusic ians +Ġmut ual +ĠC old +8 8 +ze j +at ie +ĠHon or +Ġobs essed +ĠM USIC +ĠBre ak +ú ng +Ġmod ify +Ġs öyle +Ġ×ŀ ×Ķ +ĠOn line +f o +ĠMill er +Ġlik ing +Ġin hab +Ġgrat itude +ĠJour nal +arn ess +J ohn +ĠG it +åī Ľ +Ġsin cere +ĠS ci +ĠE li +Ġsymbol s +Ġman ually +ε ÏĤ +Ġв Ñĸд +ĠF at +Ġlab els +Ġsophistic ated +ump s +Ġrele ases +Ġ4 7 +ĠO M +ê°Ģ ë +ĠB ien +ĠRe f +è¨ ĺ +ĠSt a +ĠE gg +Ġindic ator +ps on +Ġnas ıl +R ight +Ġcon vey +Ġkn ot +Ġconnect s +ul as +Ġpre ced +Ġine quality +am iento +Ġrep ly +O Y +Ġdism iss +ĠëIJ ľ +çĦ ¡ +ĠÑħоÑĢоÑĪ о +Ġm éd +Ġrandom ly +ĠO nt +u ard +Ġpull s +ĠÑĤ епеÑĢÑĮ +ĠNe ed +ĠSo ft +Ġstrength s +Ġgo ed +um en +æŃ » +Ġíİ ¸ +Ġд об +Ġclar ity +ĠA i +Ġball oon +ĠP and +ĠìķĦ ëĭ +Ġsh iny +Ġsmall est +on ia +h ill +ot ing +Ġe ing +Ġmere ly +Ġse us +Ġн еп +Ġí Ĩµ +Ġgu ides +Ġspecial ist +Ġste ak +ãĤĪ ãģĨ +Ġmig ration +que le +Ġru ined +Ġpu pp +å¥ ³ +Ġk end +ang an +Ġpal m +Ġunf air +Ġz m +ĠD V +ch ester +и Ñİ +Ġo oh +er g +AT H +° © +åĵ ª +r ison +Ġinvol ving +Ġpart ly +anç ais +Ġv ow +Ġprom inent +Ġcry st +ib a +Ġdes erves +Ġover t +Ġsens it +ĠWh e +Ġtight en +Ġintim id +Ġal iment +w ill +Ġstrength en +ĠT an +åı Ī +ãģĹ ãģ¾ãģĻ +on i +ĠM un +Ġpro ph +Ġrehe ars +ĠK le +Ġve ces +Ġwonder ed +ok i +Ġsens es +´ì ĭ +Æ°á» Ľ +ĠÈĻ i +Ġmuch os +Ġwatch es +ortun ate +ĠJ uan +ìŀĸ ìķĦ +ÑĢ е +e i +ion en +Ġexperiment al +Ġda ughters +ภĽ +Ġment ally +bec ca +aw are +ìĦ Ŀ +Ġwhat soever +Ġen ables +ĠL ow +o id +ภĬ +ó d +Ø º +Ġconstruct ed +ĠLad ies +Ġaccus ed +Ġа н +D an +Ġsp awn +Ġcontain ers +Ġart istic +ı p +Ġdisc l +Ġaut res +in as +ĠN ation +Ġn ag +be an +w he +ľë ıĦ +ĠSe oul +Ġíı ¬ +ĠN ich +Ġcomp lement +Ġinter ven +ĠMod el +ĠOr ange +nam on +Ġcalcul ation +se e +Ġusted es +Ġle b +Ġdo ct +Ñĸ н +Ġf oster +Ġel astic +ĠAh h +Ġa ce +ĠP ink +ĠJ eg +Ġde er +ãģĹ ãģĦ +s is +Ġjak o +ĠEm ma +ÑģÑĤв енно +Ġport rait +Ġmak er +Ġa ument +ÑĢ об +Ġairpl ane +Ġtransparen cy +Ġadjust ment +ĠCD C +ç on +Ġupload ed +Ġд ейÑģÑĤв +Ġго ÑĤов +Ġit er +Ġcur se +ô n +mer ce +ar an +Ġle ak +çµ IJ +Ġabs ence +Ñģ кий +Ġread ers +al er +Ġbene ath +ang o +h etic +Ġfin ns +Ġpo op +Ġdu plic +H i +ig s +olog ically +op p +Ġd izer +ĠAll en +Ġgl i +Ġacc eleration +Ġvit amin +ãĥ Ń +v ä +ĠAc cess +à® Ļ +r ás +Ġappreci ated +Ġn ah +Ġpos ter +Ġt ale +Ġhighlight ed +æĸ ĩ +ż eli +Ġblock chain +Ġmic row +Ġcin ema +ĠCh ang +ĠSe arch +ust ers +ĠZ ero +ĠDiv ision +ÑĢ аÑģ +Ġsca re +Ġj elly +ĠAdminist ration +S O +Ġl ined +Ġê° Ħ +Ġge ben +Ġso da +Ġwin ners +³ ¼ +Ù Ĵ +ĠAm b +åķı é¡Į +å Ķ +Ġpe g +å· ± +4 3 +Ġra us +Ġre wards +Ġinc lus +Ġhigh way +Ġha h +Ġmultipl ied +Ġs ẽ +Ġdisci ples +Ġn ing +Ġdress ing +Ġattrib utes +ĠM osc +ĠGree ce +Ġse k +ĠLe arn +Ġj us +rend re +Ġperson ne +pl ete +Ġpl acing +Ġl uego +ill ance +Ġоб Ñī +Ġprov ision +Ġl ion +t ra +bo ards +Ġbehavi our +he y +Ġsubscri ption +Ġprot agon +ãĥ £ +Ġvar a +ĠÅŁ u +Ġha ha +Ġteas poon +æ Ł +av oir +Ġcrypt o +ĠÑģÑĤ аÑĢ +ĠSt ore +ab s +ĠStud ents +Ġla und +int o +Ġapproach ed +° ľ +ÑĥÑİ Ñī +ĠL abor +ot es +iat ric +Ġgro ÃŁ +ut ive +Ġи д +ĠG ib +Ġpl acement +ĠdifÃŃ cil +Ġf rog +ĠвÑģе Ñħ +ĠJ r +az ed +Ñĥ Ñī +Ġê ¼ +fr ame +а еÑĪÑĮ +Ġlock down +åij ³ +Ġmed i +Ġ×Ķ× ŀ× +ени й +em ale +ì¢ ħ +ater al +Ġdist ant +Ġbe ars +Ġjournal ist +è§ £ +ĠMarsh all +ĠIh nen +uet ooth +b ag +ĠÄij ã +ĠHigh ness +Ġì° į +и ка +ĠW u +ĠFr an +Ġp eng +Ġf on +Ġhypothes is +ĠÑĢ Ñĥ +Ġl y +× ļ +ìĽ Ķ +ĠRad io +ภŀ +D av +Ġembarrass ing +ĠìŀĪ ìĸ´ +Ġcast ing +Ġc age +ĠP sych +ĠìĿ¼ ëĭ¨ +ĠÅ ¾ +im b +Ġdirect ors +S H +ĠÏĦη ν +á»ģ u +Ġkon uÅŁ +Ġoption al +quar ters +ik er +ĠS ant +Ġvers es +ë ¶Ģ +Ġo lar +ĠÏ ĩ +ãĥ ķ +Ġγ ια +ĠI mm +Ġcontrovers ial +Ġer sten +Ġreci p +ĠChristian ity +Ġê´ ľ +ord on +×ķ× © +Ġsl ash +ĠP f +Ñĥд ÑĮ +×ķ× Ŀ +ĠPer ry +Ġm amy +Ġbackground s +Ġà®İ ன +Ġpend ant +ĠColumb ia +Ġin verse +ĠÑĩеÑĢ ез +Ġs v +Ġdig ging +4 1 +ch em +Ġnavig ation +ĠSh in +ĠFr ont +P D +Ġbe aring +ĠW asser +Ġw ax +ĠCH RIS +ch ing +Ġpress ed +E l +ĠD al +ons in +Ġb inding +Ñģк ой +po ons +Ġmo ck +are st +к ÑĢа +M M +Ġcor rupt +st orm +Ġref res +ĠCo ach +ll ä +ĠTH IS +Ġpar ag +Ġìĵ ° +p ool +Ġbill ions +Ġê¹ Ģ +gr oup +Ġwel coming +cell ence +ĠDu ke +ê¸ ´ +Ġprim era +ìł ¸ +Ġp ond +Ġstat ue +Ġêµ ¬ë +Ġh atch +Ġinstrument al +Ġresident ial +ì» ¤ +Ġaccept ing +osh i +d ate +ĠìĶ ¨ +Ġplant ed +Ġj oking +Ġì Ħľ +Ġh ated +ĠÑĢаÑģ Ñģк +Ġsle pt +Ġpack ages +Ġisland s +es en +ÄŁ ı +Ġdi agon +ĠO sc +Ġmes h +Ġsc ales +ar ity +ĠDef ense +ãģ¡ ãĤĩ +ĠLew is +ĠÑģ егоднÑı +Ġfl ies +uin ely +ĠCons ider +Ġst ark +he w +ĠAs ÃŃ +³ ´ë +Ġprop ose +Ġíķĺë ©´ +od o +ĠNorm ally +Ġhe eft +ĠHarr is +g ro +ĠBlo od +b ase +Ġi OS +Ġtouch es +Ġinsp ir +Ġ× ĵ +Ġb inary +Ġì¶ Ķ +Ġser ial +Ġ ion +Ġunemploy ment +Ġodd s +ĠF ab +ĠF BI +BR UN +Ġweight s +ν ο +at ile +Ġnurs es +Ġinvolve ment +ĠíĶ ¼ +Ġgovern ance +Ġâ Ĥ¬ +ÑĢÑĥ п +ier ra +íĺ ķ +ĠJ erry +Ġbe ard +Ġsal vation +ĠAl ong +g entle +ĠK i +b ol +ĠPl at +Ġhas ht +è¿ ij +Ġw are +Ġpart ie +y cz +Ġint r +F ih +n ent +Ġche at +il en +Ġë ¯ +or ie +Ġfá cil +et ric +Ġaffect ing +unci ation +Ġaff airs +Ġbe e +Ġview ing +Ġor ang +ĠL an +ĠС ÑĤ +ä¸ ĸ +ĠM es +ĥ ģ +er ie +Ġes pa +Ġinter pre +Ġposs ess +Ġpure ly +rit o +f ound +as ma +ìłģ ìĿ¸ +Ġexam ine +ĠÑĥ м +Ġbes ch +ĠTom orrow +ĠB lock +Ġvari ant +Ġprefer ence +Ġcoach es +Ġmedic ations +Ġíĺ Ħ +Ġemp ire +ë Ħ¤ +ĠIll inois +Ġcris py +Ġth ì +Ġbe es +7 7 +Ġgl ow +è º +ĠStud ies +åIJ Ħ +ĠChall enge +Ġunlike ly +Ð § +ıy orsun +DI E +Ġminim ize +iz ard +Ġú n +Ġencont rar +ĠK ill +å » +Ġvan illa +ĠGr ant +ĠG T +se a +Ġs ought +в од +Ġnä m +ĠA unt +OW N +Ġpump kin +st ellen +Ġr ag +ег да +Ġstory t +Ġfor um +æ© Ł +Ġestab a +uch e +Ġcon gress +ĠRe y +Ġdram atically +ĠSp ort +ĠYe llow +Ġê³Ħ ìĨį +Ġdisg usting +ĠRe cent +Ġacqu ired +Ġc ables +çĶ ļ +d in +Ġv isto +Ġcommunic ating +ÑģÑĤав лÑı +еÑģ ÑĤо +ãĥ»ãĥ» ãĥ» +Ġré g +Ġso cks +Ġpro ces +be cause +Ġut ter +Ġcoloc ar +Ġnew est +Ġgr amm +è¡ ¨ +ä¸į çŁ¥éģĵ +Ġsh ifting +Ġcar rier +ĠÑģк оÑĢ +ĠSch w +Ġexec uted +Ġmaint ained +ĠÏ Ĩ +ĠM oses +Ġdis se +Ġhor r +ãĢ ľ +Ġr ally +Ġall em +ĠEvent ually +Ġdi yor +lv ania +Ġsch nell +Ġê³ ¼ +Ġë§ ¤ +Ġstrugg les +l ate +Ġclar ify +é ment +Ġmulti plic +иб о +Ġjour n +Ġfra gr +Ġsurprising ly +Ġdesper ate +5 2 +Ġs ul +ĠRe ad +ĠF ried +Ġm ond +w oo +Ġorgan izing +ãģĹãĤĩ ãģĨ +ĠSo on +Ġв опÑĢоÑģ +ĠN ur +ĠÐĹ Ð´ +Ġsp ider +е ÑģÑı +Ġtutorial s +Ġnutri ents +or er +Ġcoe fficient +Ġarrange ment +Ġpr icing +n an +y u +B L +Ġtri be +ĠHow ard +un ks +Ġnew er +Ġprov in +Ġpred iction +h os +Ġol sun +ĠAr ound +Ġv ier +ĠÑģÑĤоÑĢ он +Ġv alley +ĠE la +if i +Ġgal axy +Ġtran qu +Ġad vers +ĠTem ple +iff s +ig ence +èĩª å·± +Ġkön nte +ĠÄij ó +D id +Ġphotograph s +ĠA WS +ÑĨи Ñı +Ġgu ards +Ġappoint ed +ĠG il +Ġм ом +Ġc od +ĠUn like +Ġeven ly +isc onsin +Ġest ou +Ġm nie +ĠEx ec +ĠM V +ĠE ine +ä¿ ¡ +ĠRog er +ĠF ac +ĠL ist +Ġf uer +аеÑĤ е +om ed +Ġattract ion +èī ² +Ġter rain +ĠD rop +Ġcorpor ations +Ġsci ences +Ġthr one +ãģĦ ãģŁ +Ġa j +ĠR ot +çī ¹ +Ġsupp orters +ĠB ere +H ere +Ġdifer entes +Ġsignific ance +Ïĥ η +æĪij 覺å¾Ĺ +Ġcl amp +Ġë ĮĢë +Ġfab ulous +re z +æĮ ģ +Ġassum ptions +ut her +w id +p ot +è¿ İ +Ġy an +ul in +ÑĢ Ñĭв +ĠSl ow +ĠPenn sy +Ġíķ ´ìĦľ +Ġme io +Ġwealth y +ĠE ight +Ġpul se +Ġfr iction +id ity +ĠH oll +i yorum +Ġsound ed +ĠC arr +Ġfor k +â ĺ +ĠP A +Ġcons pir +Ġc oding +r t +ĠTy p +Ġìĸ ij +Ġп ог +Ġmis er +ĠÑģм оÑĤÑĢ +ĠSw eden +Ġolar ak +ĠZh ang +ĠCh i +ĠT itan +Ġscreen ing +ĠSp ider +ĠÅŀ imdi +Ġobst acles +lar a +Ġchalleng ed +p se +T ON +á» ¥ +ĠP i +Ġlag i +ie urs +Ġhur ting +Ġneg lect +Ġgener ating +Ġyoung est +Ġaud it +ĠÑĢ ез +Ïģ ά +Ġdon ate +ĠPD F +Ġvis its +Ġcru ise +P P +as er +Ġw sp +back s +iv als +ãģĨ ãĤĵ +Ġde ve +Ġprop ort +Ġc ath +ĠE ffect +Ġwind s +ĠìĻ Ķ +Ġchart s +Ġs ama +Ġautom ation +Ġпок а +Ġol an +Ġbo ats +Ġca fe +Ġden ied +ĠM ama +Ġblock ing +ĠTh or +Ġphenomen al +Ġstake holders +Ġun os +Ñĥ еÑĤ +ĠAb raham +ãģ§ ãĤĤ +Ġdetect ion +Ġjur is +Ġpower ed +z ial +Ġwel fare +Ġup grad +Ġmoż na +ĠC ase +c ular +Ķ ìĿ´ +ãĥ ģ +ĠGu ess +Ġcy cles +ä¾ ĭ +çµ ¦ +ro ck +um i +Ġel ite +Ġqu è +åł ± +ÑĤ ом +Ġsh ore +gun ta +Ġk u +Ġfaith ful +ĠJ eremy +a id +à · +ug al +å°į åķĬ +ĠV el +Ġvra i +st ell +¨ ¸ +Ġk ol +è ½ +Ġquant o +Ġз аÑĢ +Ġ200 2 +es y +Ġres erve +Ġмом енÑĤ +Ġdeploy ed +Ġdefin ing +Ġsa u +Ġga at +" ) +Ġtrans mit +Ġpubl ishing +Ġrank ing +Ġoff ense +Ġ4 6 +p in +ĠT aking +Ġentit led +Ġgen uinely +Ġvari ations +Ġfind e +Ġt au +Ġunf ortunate +ĠR ah +port s +Ġc Å +Ġmon key +Ġbr ac +we i +l ung +Ġart if +Ġsy rup +ĠÐĶ ав +Ġlift ed +Ġche z +ĠAd vent +ĠSt ock +Ġdo l +м ен +иÑĪ ÑĮ +Ġy n +g io +d et +Ġdes se +Ġg ri +ĠChair man +ç ħ +Ġcu enta +an im +Ġcra b +Ġesc al +Ġpremi ère +ĠGe f +Ġd ining +Ġsevent h +Ġch asing +ĠT ower +Ġbrut al +Ġfundament ally +ãģ¨ ãģĨ +л ениÑı +st age +Ġacqu is +Ġcyl inder +Ġcomm ander +m em +ĠU V +ha ppy +Ġe psilon +Ġinv itation +Ġfar mer +ch air +Ġdest iny +Ġso vere +ĠHeb rew +Ġserv ant +Ġbe w +Ġg ast +ut ies +Ġadministr ative +ĠComm and +é ta +Ġnit rogen +ê· ¼ +Ġab i +Ġvill ain +Ġblank et +ĠS end +Ġbeat en +² Ħ +Ġvol unt +Ġschol ar +ĠEm peror +Ġ4 3 +v able +ĠD us +ĠG U +Ġtarget ing +ww w +Ġamend ment +ìĨ Įë +Ġt ing +Ġn asty +Ġg auge +ĠÑĢ од +ĠH ans +Y our +α ν +Ġpro jet +ĠHawai i +Ġsusp icious +Ġsch w +Ġremo val +Ġint rig +ĠM U +Ġp onto +ठ¾ +Ġоб ÑĢаз +Ġguess ing +p ace +Ġm others +Ġmill imeter +л ение +没 æľī +Ġavail ability +ic z +æŃ ¤ +Ġfr act +Ġbas es +k m +ĠB TS +ĠF ield +Ġd zie +Ġseg undo +ĠëĤĺ ëĬĶ +Ġlegit imate +im as +Ġв н +Ġcor ruption +Ġsm ash +ĠVal ent +Ġalign ed +ĠPennsy lvania +Ġg ab +ĠE un +ent h +ĠMor ning +Ġcand le +Ġback pack +ĠIslam ic +a ções +Ġenc ry +Ġmushroom s +íĮ Į +d it +Ġtrans it +ĠW isconsin +Ġparticip ated +ĠIl s +Ġunf old +¶ Ģë +Ġprof its +Ġwar ming +ĠG ang +Ġnetwork ing +Ġme ga +Ġthorough ly +le ments +ĠH m +Ġdec iding +Ġemotion ally +Ġexha usted +ĠÐŁ оÑĤ +c ido +ĠHT ML +Ġcopy right +Ġmel ody +y im +Ġand ers +osh op +Ġë³ ¼ +Ġathlet e +ĠG E +Ġfrequ ent +Ġdes ires +Ġneed ing +ĠY un +Ġrif le +Ġlo ver +' T +Ġd ense +Ġt ão +Ġnot ified +Ġid i +ìĹ Ń +í Ĩ +Ġinteract ing +Ġrapp ort +еÑĢ и +s ki +Ġb esser +Ġmanufact urer +ĠK yle +Ġaccount able +ĠS ak +ĠP il +ĠD omin +Ġpres um +ĠÐĴÑģ е +Ġvine gar +Ġguarante ed +çľĭ åĪ° +Ġhand led +éŁ ³ +c at +Ġcivil ization +Ġaccom p +ĠV M +é mon +Ġde ze +Ġgrad es +Ġsoll te +Ġst aring +×IJ× ª +ar nt +Ġhoriz on +Ġtrav ail +h our +第 ä¸Ģ +ĠE D +ĠD ak +Ġn y +Ġcon ve +ĠCh am +Ġfir ms +ĠL iu +ĠÑģÑĤ ÑĢан +Ġli bert +Ġlens es +Ġint ake +ĠвÑĭ б +Ġmens en +h el +Ġpract ition +Ġ3 50 +ãĤ ³ +F O +Ġbed s +Ġancest ors +ĠìĹĦ ì²Ń +Ġdistur b +ĠLast ly +ĠSupp ort +ี à¹ī +ĠCor ona +Ġenthus i +Ġвоз м +ĠìĤ¬ëŀ Įë +Ġ5 2 +b ird +Ġredu ces +ĠìŀĪ ìĿĦ +ĠG ene +êµ IJ +ÄĻ p +ĠÃľ ber +Ġconcer ning +us er +Ġconcent rate +ĠWH AT +ish op +onym ous +no ld +Ġsuggest ing +© ° +ĠF ish +.... .... +Ġvess el +Ġtrabaj o +ãģ µ +ĠO cean +å§ IJ +y g +Ġtown s +d el +Ġterr ifying +Ġçal Ä±ÅŁ +Ġs ino +Ġe ats +Ġge z +Ġg eme +ĠìĻ Ħ +Ġcomp art +Ġimplement ing +ĠPot ter +ĠGerm ans +Ġg ÅĤ +Ġt ennis +Ġcar pet +au er +ĠSaud i +ye ong +Ġcur ry +ĠFore st +Ñĭ л +Ġfif teen +Ġbol ts +Ġ{ \ +¬ ´ +Ġsett lement +Ġl ange +Ġb am +G et +íķ Ļ +Ġsw ap +ĠK han +Ġcomm ence +Ġquar antine +Ġsc ored +ç ĸ +Ġ19 50 +Ġthick er +Ġsû r +åı £ +ĠLar ry +Ġall ez +ìĭľ ëĬĶ +Ġg ü +Ġspect acular +/ / +b oth +Ġst ats +å¦ ³ +ĠN ancy +Ġbun u +Ġcr ust +Ġactiv ated +Ġê·¸ë ŀ +out he +Ġport s +Ġne ural +Ġj aw +Ġobserv ations +Ġvo it +ab an +ả i +¦¬ë ¥¼ +om es +௠ĭ +qu i +Ġkind ness +Ð ij +Ġ4 1 +Ġmoder ate +Ġang els +ĠT amb +è t +Ġch lor +ĠBill y +ì² ĺë +ac on +Ġselect ing +ĠDel ta +Ġn ull +den ly +Ġci ud +Ġtend ency +Ġbreak down +Ġm int +ÑĦ оÑĢм +or ph +Ġda wn +s pr +ĠW ILL +äch lich +Ġpu ppy +7 00 +Ġà® ¤ +Ġfail s +ĠCon c +Ġrel atives +Ġinv iting +Ġaut onom +Ġcomp osed +Ġun ity +Ġdec is +Ġaccess ories +ĠC ass +Ġb ist +ĠT ip +ì§ ¸ +Ġp unt +Ġr áp +éĢ ² +AN K +ãģ ļ +ex ist +Ġcompat ible +Ġn er +Ġе мÑĥ +Ġa plic +Ġb apt +Ġfail ing +ĠTam am +Ġos cill +Ġletz ten +Ġrepeated ly +Ġjung le +ĠP ush +h ai +ĠÎ · +Ġdead ly +Ñı ж +wi Äħ +ĠComm on +ĠÎ ķ +Ġsk ate +T C +ĠMin i +Ġhob by +ầ n +Ġrout es +Ġam igos +Ġcon jun +Ġpartners hips +Ġno vo +Ġa ver +Ġpou vez +br idge +Ġpre oc +h im +Ġtur b +Ġso b +ĠSn ap +Ġì° ¸ +min ute +Ġtra ject +uj ÄĻ +Ġe ager +Ġregul atory +Ġbank ing +b ling +ÑĪ ÑĮ +a ż +Ġbiz arre +it ated +d ire +Ġthreat ened +Ġsh ining +Ġn esse +Ġcor ps +ĠÑģ Ñĥ +Ġt eles +Ġtem p +t em +Ġк ан +Ġfe ver +N ew +Ġheav ier +ĠS ah +b ud +Ġout ros +Ġì° ¾ +Ġëª ħ +arr ing +Ġê´ľ ì°® +ĠN ap +Ġse min +ĠTh an +if s +Ġdes en +ĠÑĤак ое +Ġlos es +ĠB alt +k on +Ġнап ÑĢ +Ġvo is +ĠMosc ow +Ġch airs +h is +Ġrefuge es +k g +Ġk ole +į ¨ +аÑģ ибо +¦ ½ +ĠUn iverse +ĠDire ct +Ġche ating +ĠC in +Ġpat ri +Ġadv ise +ĠN ether +Ġprime iro +Ġmention ing +n ut +5 6 +ar ı +Ġpet ite +b led +Ġpens ar +ic io +IN D +Ġveter an +Ġlad der +Ġconsequ ence +ож ал +ĠB urn +Ġr ug +ĠM ade +Ġg it +" ... +Ġcompet itors +Ġprz ed +Ġapp arent +ĠArgent ina +ĠWork ing +Ġcollabor ate +w oman +Ġret ain +Ġle urs +Ġdash board +×Ļ× ĵ +ĠEar ly +B M +Ġе Ñij +ол ог +Ġsatisf ying +Ġoft entimes +Ġma pping +ünk ü +ar th +f old +Ġlaunch ing +Ġa ura +Ġprec ision +work s +G od +Ġstra p +ĠIm per +Ġr ivers +Ġ | +Ġcu er +reg on +Ġarri val +ка Ñħ +ĠM iami +ан Ñĭ +Ġsurviv ors +ĠSen ior +Dav id +Ġest ado +Ġse ctors +Ġpop ping +Ġch im +ay ı +Ġkun nen +Ġgall ery +Ġsun light +ese hen +Ġye lling +ĠMe in +ĠPho enix +Ġman o +Ġhistor ia +Ġoccur ring +æ¬ ¸ +ì ¸ +ад и +å¾ ħ +Ġinstitution al +ĠT ut +ç ² +Ġsl aves +ãģ© ãģĨ +Ġforg iveness +Ġtw in +ĠHy un +н ÑĮ +ĠK omm +and ra +sh ot +ss ä +ĠÑĨ е +at ta +Ġexp ense +ĠG PU +ĠP ast +rib ly +ĠëŃIJ ìķ¼ +Ġгод а +Ġresp ir +æĿ ± +ĠQue ens +h ops +Ġs érie +Ġpre f +Ġcom ed +Ġpl ut +ĠOver all +Ġãģ Ŀ +Ġc ush +Ġring ing +Ġincor rect +ĠÑģÑĤ ÑĢ +Ġgeomet ry +Ġadvert is +ĠÐ ¨ +Ġreview ed +ãģĤ ãģĤ +Ġdo zens +Ġdeterm ination +ĠPh ill +Ġcontrib uted +ĠC it +Ġpass engers +Ġcôt é +Ġre ver +Ġtechn ological +Ġall en +Ġr aining +av i +Ġsal ty +Ġtyp ing +ĠÑĤ е +Ġt ilt +Ġì¹ ĺ +Ġо ÑĢ +ĠпÑĢ Ñıм +Ġr ou +Ġare na +ar at +åĪ « +HH HH +Ġmanufact urers +ĠEd ward +Ġt uck +Ġbl ows +ing o +ĠMar c +ìķĦ ìĦľ +M ich +ĠCle an +è ´ +est o +ĠP ack +Ġsha ft +BRUN O +Ġa ven +u ur +Ñģк олÑĮко +ê´ Ģ +Ġautom ated +Ġvent ure +Ġsurve illance +ĠG row +ĠE mer +Ġд оÑĢ +Ġinvest or +ĠY ok +Ġl atter +ĠN I +Ġfunction ing +ĠHam ilton +Ġ5 1 +Ġmurder ed +Ġanch or +Ġc uc +ĠSC P +ĠMad am +Ġconstra ints +Ġb arn +ank en +Ġë§İ ìĿĢ +ĠMot or +ĠDo ing +Ġam en +et ts +Ġinst ructor +eg t +ak o +Ġpost ure +iv ia +ĠPol ish +Ġдв а +Ġcolor ful +Ġel bow +Ġpar le +Ġpass er +Ġcond em +ort al +Ġfert il +ا د +ĠCol omb +Ġalign ment +Ġastron aut +ĠM ut +Ġsal mon +Ġstructure d +ŀ ר +Ġclick s +Ġm iej +æĶ ¿ +ãģĦ ãĤĦ +ĠR ound +Ġrain bow +ĠV A +ãģĶ ãģĸ +ì§ Ī +ot z +, +Ġch ords +ĠSand ers +Ġë¶ Ħë +B en +Ġdar über +ili ans +Ġorder ing +ĠMan h +Ġkil ogram +Ġkar ÅŁ +Ġgr asp +Ġghost s +al en +ĠJ edi +Ġб ли +Ġdownload ed +Ġconduct ing +ĠH ak +Ġresearch er +il an +go od +ĠH annah +ĠdÃ¼ÅŁ ün +ĠMess iah +u ity +ion a +Ġprob able +ĠY E +Ġindepend ently +Ġbuff er +b urn +our d +ĠMc K +Ġl ingu +uj emy +еÑĢ ÑĤ +Ġintuit ive +Ġcrack s +app ropri +nt y +Ġge en +Ġl end +Ġcert ification +ID S +un ter +pe es +Ġtr ump +Ġbank rupt +Ġfe as +è Ĺ +Ġdu ż +æ¸ ħ +Ġvirus es +Ġ5 8 +g od +Ġж ел +Ġst alk +I nd +ach i +ĠC F +ĠC ond +Ġsan ct +Ġcont en +Ġfre ed +ĠR T +Ġment ors +ì¡ ± +Ġport able +ĠPaul o +r ane +HA HA +ĠS ection +ç Ĩ +hy un +ĠÎŃ Ïĩ +ĠP ub +ĠInd epend +Ġcomp ounds +ĠÑģ Ñĭ +Ġmess aging +Ġded ication +Ġnot icing +Ġdevot ed +ÑİÑĤ ÑģÑı +Ġsn akes +Ġbattle field +p ers +Ġdel a +9 2 +Ġha i +ill ä +ér er +e very +Ġrespons ive +×Ļ ×ķ +op f +é ī +Ĭ ¸ +Be cause +Ġtour ism +Ġê·¸ ê²Į +×ķ× ¦ +Ġcan s +st üt +Ġdon ne +ĠD ios +ĠU ber +act ory +Ġorient ed +ĠH erm +Ġpat ron +ur f +be i +Ġprogram a +ĠOh h +gen er +Ġf ist +ĠW endy +Ġand a +Ġguess ed +Ġfre ak +ä¸Ń åľĭ +ĠK ings +ch ool +Ġoff line +ĠIndian a +ĠAll iance +Ġ5 3 +Ġpartic ul +ĠF ocus +Ġinhab it +Ġê°ĻìĿĢ ëį° +ĠMc G +ows ki +ĠìĿ´ ê±´ +Ġpa ÅĦst +он и +itt a +Ġconfirm ation +ĠBrook lyn +Ġnood le +f und +it ud +Ġgrand parents +Ġbar becue +ει ÏĤ +Ġ á +Ġball ot +ĠV eter +Ġpip es +ig ious +ĠG raph +est ed +Ġë¸ Įë +ĠK E +ãģ¡ãĤĩ ãģ£ãģ¨ +Ġe ins +Ġhat red +ãģij ãģ© +Ġd ang +ee ee +Ġarch ae +ĠJes se +Ġdetect ed +Ġsen i +burg h +Ġdispl acement +Ġdo p +Ġcondition ing +Ġне ÑģколÑĮко +Ġdistur bing +P H +Ġthin ner +Ġwound ed +ĠCu ando +Ġcush ion +Ġwh ites +Ġprefer ences +Ġì¤Ģë ¹Ħ +Ġka ż +ĠG ate +ĠP ath +d les +à¸Ħ ร +im ore +Ġë³´ìĹ ¬ +Ġdiscipl ines +á» ı +Ġmes ma +Ġìĥ Īë +Ġìĭ ¬ +Ġg ing +Ġumbre lla +IGH T +Ġp ension +Ġcomb ining +S S +Ġrect angle +á»ĩ t +Ġpro xim +ĠC ow +¸ Į +Ġintention al +æķ Ļ +Ġdec id +ĠÑģк аж +ĠU ma +ias m +b uz +Ġdebr is +Ġc ass +ĠP rop +is ka +ë ł¥ +ester ol +uss ian +ìĿ´ë ŀij +Ġun limited +Ġadm ire +Ġtight ly +Ġgen ome +ĠJun ior +ven ir +g us +Ġc Äĥ +ĠV lad +Ġí Ĥ +Ġrel ativ +in ci +Ġaun que +ĠBo ys +ÑĨи он +ĠSw iss +Ġphys icians +Ġíı ī +ĠP ET +Ġw ounds +ab out +Ãł i +on z +ur ities +ĠÑĥв ид +å· ¦ +Ġment ality +Ġvari ance +Ġseg unda +Ġvol cano +al ie +ॠĩ +Ġt iles +ĠT erry +ĠاÙĦÙĦ Ùĩ +Ġcan on +Ġsc attered +pt on +Ġdefin itions +Ġal gebra +ot en +ab lo +ij uana +Ġwra pping +Ġses ame +ĠнаÑĩ ина +ĠAl f +ĠÐł оÑģÑģ +or no +Ġan kle +Ġspecial ty +Ġattempt ing +ili ation +Ġ19 20 +Ġphen omena +ĠPro duct +ĠB uck +ĠA ww +se en +Ġvo id +ĠFrank lin +Ġadvoc acy +ĠS ep +Ġcool est +ĠÑģ ÑĢазÑĥ +ĠQu and +Ġ9 00 +ĠTr ad +d ies +Ġhas h +æĪij å°± +ä¹Ł æĺ¯ +Ġpot s +Ġsad ly +Ġvi able +ĠT iger +ĠON E +Ġneur ons +ow anie +Ä Ĺ +ĠSh ar +ĠLand es +Ġconfer ences +è© ² +Ġcred ential +Ġl ime +ine e +x it +p ay +Ġinc ons +Ġ>> : +èª į +Ġí ŀĺë +Ġless er +Ġsp ill +Ġprem ise +Ġ36 5 +ĠH ost +Ġtom ar +×IJ× ľ +ë ²Ī +ĠWhat s +Ġlight weight +ĠM ap +f ia +ells chaft +Ġvend ors +uest o +ĠM ister +ĠÐŁ ÑĢи +åı ³ +h ma +Ġintention ally +ĠT ang +éĹ ® +Ġident ification +Ġetc etera +ĠN ee +ĠÑĤ ÑĢи +ê· ¸ +Ġcrypt ocur +Ġin hale +Ġadd ict +åIJĦ ä½į +Ġma u +ĠÑĤак аÑı +Ġë² Ħ +Ġcomp rar +ied zieÄĩ +ĠоÑĤ но +Ġbegin ner +Ġм Ñĥж +Ġobs c +Ġlim iting +asc ular +Ġins pection +ac i +Ġre jo +M us +Ġz aten +Ġsz cz +ĠMad rid +Ġvar ieties +Ġest Ãł +ĠSh akes +Ġk its +Ġad minister +Ġla va +Ġg Ã¥ +è© ¦ +ת ×Ļ +ĠWay ne +Ġinst agram +Ġr ated +p aper +Ġb ild +Ġpret ending +Ġobser ving +ĠÑģам ом +Ġtr or +Ġorgan isms +Ġfal ta +Ġh ometown +ç ± +Ġí ĭ +Ġche g +Ġì ¡ +Ġcomm a +is é +Ġlike lihood +av ored +Ġgel di +ни ков +Ġmed io +Ġjak ie +ĠJ up +Ġgreen house +Ġsp it +ко е +Ġк аж +ĠG ram +ĠCon ference +Ġdef icit +s ın +in se +u ÄŁ +Ġr icht +Ġcoinc idence +åı į +Ġeu rop +Ġbutter fly +p read +Ġìĸ ¼ +èĢ ¶ +Ġwa vel +ĠIn fin +ĠPlan et +Ġself ie +ient ras +Ġar rog +os er +id al +ł×Š׳×ķ +üt ün +Ġfresh man +ĠMach ine +Ïĥ ÏĦ +ĠD ia +ìĿ´ ëĭ¤ +ãģĵ ãģĨ +ne a +Ġlist ing +Ġconfig ure +ut or +U p +ts chaft +ri ère +Ġup wards +ĠÑħоÑĩ Ñĥ +Ġswe ep +B r +Ġexpress ing +Ġun happy +Ġmand atory +g ender +ĠA ÃŃ +Ġindic ators +Ġoil s +n ote +Ġseg ur +ож еÑĤ +yn asty +Ġdist ances +Ġmer ge +BER T +Ġsur render +Ġbu at +ĠA wards +Ġseñ or +od ox +Ġfl avour +Ġab dom +Ġconfig ur +8 6 +ĠDI Y +Ġrig id +° ĺ +Ġcorpor ation +Ġg room +j aw +ĠNe ar +ил о +Ġoper a +ĠIn nov +и ÑĢа +ĵ ± +Ġspec ified +Ġcos m +ĠFre edom +Ġcl own +ĠN em +Ġв ол +Ñij н +Ġchar ger +à¹ģ ล +Ġinflu ential +äs ident +é ¤ +ĠìĦ łë +Ġvol umes +æ IJ +Ġout ras +ĠTw itch +Ġfound ing +Ġa while +Ġco il +ê° Ļ +Ġc ả +ĠTh row +ĠH ence +omm t +ĠBen jamin +глÑı д +T ime +ob ic +Ġm our +Ġd read +ĠL Ãł +ĠCh ile +Ġpre val +Ġv ain +Ġart ık +Ġpres erved +ĠоÑĤ д +Ġware house +Ġbest e +ĠSever al +ĠS ituation +Ġcard board +T od +er na +Ġgar ant +Ġgest ure +Ġh en +Ġspe lling +ose xual +Ġan ne +Ġm ice +ĠMe ine +c ard +Ġre bell +Ġcert o +Ġìľ łë +Ġvers chied +ĠB os +Ġinv ention +Ġtr ze +Ġman ière +ĠCh ad +Ġsp re +Ġorganis ations +Ġpoor ly +Ġan terior +Ġst air +к ÑĢ +Ġatom ic +Ġsymp ath +Ġcontin ually +Ġkle ine +è te +и Ñī +ο ÏĤ +pe ut +Ġrep osit +Ġent ra +E m +Ġfinan cing +Ġмн ог +Ġthe sis +ĠCom puter +e au +ĠT ree +Ġbr ide +ons ieur +sh ire +w ic +D E +ĠìĪ ĺë +Ġac om +ĠP O +ers ch +Ġпом оÑī +ĠAr men +Ġì£ ½ +Ġz or +Ġprint s +ĠD ass +æ¸ ¯ +Ġdur able +ĠTrans port +ìŀIJ ê°Ģ +Ġл ег +Ġdé t +ô le +am ous +Y N +Ġcl iff +Ġgramm ar +ĠÐŁÐ¾ ÑįÑĤомÑĥ +ĠlÃł m +es ch +Ġmiser able +Ġvol ts +ĠC ad +uk an +ÑĤ ив +r ust +Ġìĺ¬ë Ŀ¼ +Ġver k +Ġchick ens +ĠY oo +Ġout fits +c ode +Ġhier archy +net es +Ġcounter part +Ġt ôi +Ġt ed +ĠB art +Ġë Ŀ¼ +ĠGen au +Ġinc oming +ĠA BC +ri que +ĠоÑĤ п +qu al +Ġincent ive +Ġih ren +׳ ×Ļ +lo e +Ġ19 30 +Ġbar g +Ġd iction +Ġön ce +IN S +Ġre h +isia j +m outh +Ġsc oring +l ık +ĠìķĦ 주 +OR IA +ĠEst ados +Ġcompan ion +Ġasse mble +Ġpun ished +Ġit al +Ġprev ents +ist es +ĠKent ucky +Ġloc ate +Ġfast ing +ãģ¨ æĢĿ +ĥ Ģ +ĠSe b +ĠCr own +op ia +Ġwh ip +us z +к ами +Ġdatab ases +åŃ Ĺ +Ġprose c +Ġ199 7 +ĠìĤ´ì §Ŀ +ĠSol ar +ĠP ues +ĠZ en +oll o +ĠG uru +Ġsque ez +ĠÐĹ Ð° +ĠÄ į +cept ions +c ca +iz able +m and +Ġbreak through +Ġtables poon +ĠS EC +ik h +ĠS ão +Ġп ло +am en +Ġpr ac +Ġdar ling +Ġtall er +Ġrend ering +Ġìļ°ë¦¬ ê°Ģ +ĠÏĦη ÏĤ +Ġm ã +Ġes os +uer do +ĠÑģ ÑĩиÑĤ +all er +ìĹĪ ìĸ´ìļĶ +Ġmill ones +ler in +Ġpe gar +on ne +Ġenroll ment +Ġli egt +Ġbo a +w iÄĻ +bs p +Ġcy cling +ĠBern ie +Ġ198 9 +Ġд алÑĮ +ĠDak ota +ĠÑģв Ñıз +ĠC P +Ġst are +íĤ ¤ +Ġprosper ity +Ġarrange ments +Ġarri ving +m ä +Ġkay ak +ip t +Ġp ardon +Ġrel at +Ġver ste +ĠF ig +Ġfo il +ĠTalk ing +pe are +Ġno i +ĠпÑĢи ÑĪ +Ġhoc key +Ġad o +ĠO UT +6 7 +Ġhorm ones +ĠAven ue +ĠSuper man +Ġpres cription +uber netes +C L +ot ive +N IS +ien en +Ġsad ness +ĠV it +T y +Ġstar ter +Ġbed e +Ġfound ations +Ġso re +åº Ĺ +Ñīе ÑģÑĤв +ìļ °ë +ĠÑĩ Ñĥв +l ink +Ġmane u +work ing +Ãł n +ĠAtt ack +ĠC art +ve is +ĠRes p +ens ing +Ġì¢ĭ ìķĦìļĶ +Ġesc uch +ĠR NA +Ĥ ´ +Ġad op +Ġb ending +ع د +Ġman ages +us p +Ġt art +Ġrout er +B o +Ġestab lishing +Ġbal ancing +Ġathlet ic +ĠS lo +Ġf ills +Ġн аб +Ġд ал +Ġpos so +ĠV ielen +Ġcrit ics +Ġlaws uit +ĠIsa ac +ĠÑĦилÑĮ м +Ġtr as +Ġpra w +ĠCra zy +Ġne u +Ġk ull +Ġtum or +ĠAP P +g ate +ĠA RE +9 8 +ĠSte am +Ġfuck ed +l age +ĠâĻ ¬ +ĠM D +f y +Ġshell s +ĠSe ems +iz ers +Ġr anges +ĠAnton io +AT ION +ĠB aba +Ġìĥ ī +k un +Ġpray ed +ÑĢ Ñı +ĠпÑĢоÑĤ ив +Ġse as +b ury +Ġ×Ķ× © +Ġtra it +ĠDep ending +Ġd re +Ġkön nt +ÑĨ Ñĥ +Ġlip stick +ee z +ĠпÑĢ имеÑĢ +Ġassign ments +B ob +Ġmet als +Ġspe cially +å°į ä¸įå°į +Ġìĺ Īë +ĠÅ ¡ +Ġv ista +ĠÎ ¬ +Ġtw ins +Ġnot able +ĠS au +Ġdé velop +Ġç ek +Ġpoly nom +av am +Ġtamb é +он ом +Ġpl asma +Ġe fect +Ġlä ng +Ġcas i +Ñģ а +ım ı +ãģĻ ãĤĭ +ĵ¤ ìĿĢ +Ġlab our +oss en +ĠP un +r if +Ġd oses +Ġoper ates +ил ли +Ġja ar +st aw +ĠìĤ¬ëŀ ij +Ġat m +Ġprotect s +Ġimp ed +H O +Ġc ima +Ġto ch +ab is +Ġsend o +la us +Ġcur l +ĠN um +Ġspons ors +Ġdé but +ĠAlex a +ĠB ür +ĠA mer +Ġc ope +Ġиз в +j al +Ġ199 5 +ap at +res se +ĠPri ze +ĠCla ire +ĠBrand on +Ġwszyst ko +Ġval ued +à¸Ļ ะ +Ġse ct +Ġsecret ly +Ġdiam onds +ĠEv an +ĠRP G +ãģ« ãģª +Īë ıĦ +ĠUnivers al +Ġdoub ts +ĠP in +wiÄħ z +ļ © +Ġal bo +Ġbra ucht +AU L +ĠM obile +gr ades +Ġsch em +wh y +ĠN icht +p i +g le +Ġchor us +Ġg ly +Ġrein force +Ġm uff +ĠSh en +ĠH ola +Ñĥ г +vid emment +v ial +ac ious +laim ed +ĠR ico +Ġve gg +Ġillust ration +ĠBut ter +ow ad +Ġeu x +Ġenf ants +ĠLe ader +ĠVill age +et ically +ÙĨ ÙĬ +Ġst ew +Ġsurpr ises +Ġc ue +ĠGrand ma +ĠC elsius +ĠR icht +en c +Ġpet ition +Ġher b +Ġw icked +Ġsch le +oc aly +Ġtrans f +Ġtok ens +ĠGr ay +ĠB BC +I K +Ġ15 00 +z n +ĠNe v +Ġk oy +Ġz ar +Ġbull shit +ĠColomb ia +ul ative +Ġwides pread +y ect +k it +Ġempres a +Ġn our +Ġburn s +at in +a ired +Ġrevolution ary +Ġгод Ñĥ +ĠLog an +Ġ199 6 +ĠGra ham +re b +ĠN HS +æľ Ľ +Ġcost umes +Ġnaw et +Ġlo vers +ĠLuc y +ĠInd igenous +íķĺ 기 +Ġimmun ity +¥ ´ë +uit o +Ġexcess ive +Ġdon ations +Ġ×Ķ ר +Ġì² « +éī Ħ +Ġdry ing +mel on +Ġsurve ys +Ġ무ì Ĭ¨ +é¢ ¨ +aa a +Ġpro be +an cial +Ġlou der +Ġhot els +ü ÄŁ +ag ner +Ġorig ins +Ġë§Ī ì§Ģë§ī +Ġ* * +Ġstr angers +ĠHa us +com ed +Ġan throp +Ġus o +ĠìķĦ ì§ģ +ĠY uan +ĠíķĦ ìļĶ +pl er +ress ive +Ġsp raw +ĠSt ew +Ġ199 4 +Ġeld ers +Ġme inen +Ġj unt +Ġac oust +ĠW ohn +Ġban anas +Ġproject ion +ĠSt ick +leg t +spe ed +ĠcÅ ©ng +ĠW ort +ĠBalt imore +ĠÑĨ ел +Ġdun no +å¼ · +? , +ãĥī ãĥ³ +ĠLoc al +ost o +Ð Ń +од а +ĠPort uguese +Ġtheir s +Ġdé m +åı ¦ +Ġdra uf +ĠBuddh ist +ert a +G e +Ġcar rot +ĠWonder ful +Ġso ak +Ġchair man +gg i +IC A +f ried +Ġfl ick +ĠThrough out +Ġìļ °ë +Ġc ough +Ġfl uffy +sch ool +Ġr ipped +---- ---- +ĠZuk unft +Ġн еб +Ġst o +ĠB O +p ent +ĠLaw rence +Ïī ÏĤ +st icks +ĠE ins +ĠÑĢ Ñĭ +ĠStr ong +Ġcar amel +Ġsp ite +az ar +éĥ½ æĺ¯ +Ġcrit ically +Ġob ra +ow itz +ĠZ one +ĠÑĢ ек +Ġsu g +ard ed +Ġg ì +ff entlich +an che +Ø Ł +ast ically +ìĿ ¼ë +л ав +Ġsimpl est +ĠF riend +Ġque llo +Ġamb ition +Ġabb iamo +åº ķ +ĠÑĦ оÑĢм +ĠEs sa +Ġeduc ators +Ġstatist ical +éĢĻ éĤĬ +Ġchang er +Ġat au +éta is +ĠShakes peare +ë IJĺ +Ġtr iggers +Ġreal iz +Ġcel ui +whe el +Ġloyal ty +Ġscream s +ke hr +ĠM ega +e ast +Ġtop s +ĠTot ally +ount ain +l ord +Ġviol ation +ĠG A +Ġnic er +ĠF resh +ĠMel issa +fun ction +Ġra pe +Ġexcept ions +Ġsil icon +Ġliber ty +Ġhousehold s +ãģį ãģ¾ãģĻ +ĠC A +ĠÐŀ б +Ġli b +ŀ Į +c ific +Ġtrop ical +Ġinvestig ating +H D +Ġad apter +ĠP itt +an cia +ĠShe ll +friend ly +Ġconclus ions +Ġtur tle +Ġdec omp +Ġanim ations +ĠÑģ ек +ins i +Ġret ention +k ie +Ġinject ion +ĠMad ison +ì° ° +Ġv ient +Ġvar ied +Ġviol in +ĠB il +Ġluck ily +Ġh tt +l ä +Ġr anch +çľĭ çľĭ +Ġsó lo +ìķ ħ +ĠD erek +ĠScript ure +оÑĢ а +Ġclassroom s +av il +form ed +Ġbefore hand +ĠG em +pre ch +Ġl in +Ġgre ens +ÑĨ ев +ĠMer cedes +Ġdr ought +gas ps +Ġab ortion +Ġter ribly +Ġspos ób +Ġsec ured +Ġat rás +Ġwavel ength +Ġgra ins +ect ive +Ġspace craft +Ġtour s +Ġprof es +Ġsur geon +ĠP ie +Ġide ally +arn er +U P +op ard +s ce +Ġimm ense +ĠOr t +roll er +ĠD allas +ĠNich olas +Ġs ulf +ĠToy ota +Ġquant ities +ce ans +Ġcu i +an ça +ĠC AN +itzer land +åĦ ¿ +Ġz ou +ĠCy ber +le gen +ĠIn it +ed u +Ġa pert +Ġad jac +ou v +èĢĮ ä¸Ķ +r s +Ġcab bage +Ġwheel chair +iny l +ĠD ynam +ĠìķĦëĭĪë Ŀ¼ +Ġl ing +h l +Ġмог Ñĥ +Ġcris p +Ġm ij +Ġd ug +n in +Ġbl oss +Ġbelong ing +Ġloud ly +Ġminer als +Ġconclud ed +Ġsearch ed +9 6 +ĠMe et +ĠS EO +ĠС к +ĠH ob +ot ta +Ġpropag anda +Ġcin namon +Ġhun ter +Ġgeme ins +Ġsculpt ure +uls ion +Ġv äl +Ġmagaz ines +Ġcontrovers y +ä¸Ģ 樣 +Ġsequ ences +ãģĦ ãĤĭ +Ġíļ Į +Ġdel eted +ä½ ¿ +IJë ıĦ +Ġvary ing +ãĥ Ĩ +Ġmount ing +Ġaff air +Ġpath ways +æ ¦ +Ġdig o +äº ® +Ġд ок +A lex +Ġtob acco +ĠC V +Ġbother ed +Ġamb ient +ink y +ĠS L +Ġh ates +Ġje żeli +Ġcon greg +Ġel as +Ġde uts +ĠStud ios +ch ÄĻ +Ġdocument ed +ĠCru z +ĠL en +ĠDoug las +ĠPort ugal +ent i +Ġsp ouse +Ġanal ys +av ia +Ġed ited +Ġl ại +bu ilt +Ġv ille +ad ora +Ġbrac elet +Ġs ushi +Ġp m +Ġtra ils +Ġl ug +Ġö ver +Ġs orrow +Ġcol ony +ado x +Ġser ie +any ak +ĠØ · +ĠG ulf +æĺ¯ ä¸įæĺ¯ +ĠP V +ĠSam uel +ĠK it +ĠR al +ont in +ex pl +Ġent ries +Ġactiv ists +P s +Ġs ant +ĠÑĤо Ñĩ +ĠBr uno +ke ley +Ġtut to +é Ķ +Ġv intage +Ġterr ified +Ġпо Ñħ +us ive +ow ers +ай ÑĤ +ë ıĻ +Ġtwist ed +ĠTh ought +Ġt ah +Ġshr ink +Ġshe er +l it +Ġdal am +Ġd ib +Ġv ard +ow ane +Ġdo br +ĠR ena +ĠÑģво Ñİ +ĠpaÃŃs es +ĠE ra +ãģ® ãģ§ +ĠB UT +s ighs +Ġê·¸ ê±° +Ġgro ÃŁen +Ġë¹ ¨ë¦¬ +Ġn erves +Ġconst it +Ġpreoc up +ĠG ay +ĠX u +keep er +he ure +.. ) +ĠCal m +ĠUn idos +ĠìĿ´ ê²ĥ +ĠAqu i +Ġìłľ ìĿ¼ +d ır +ì¦ ĺ +y our +ĠÑįÑĤ им +20 20 +Ġr und +ĠH O +ĠC atherine +iel i +Ġf usion +Ġide ology +Ġfor am +sh aped +ĠíĽ Ħë +Ġw t +Ġret r +Ġpr éc +Ġê° ij +Ġopen ly +v ity +구 ìļĶ +Ġobst acle +Ġbo o +Ġse iner +ic orn +Ġeigen lijk +Ġhead er +are mos +Ġso fter +ĠÐŁ од +Ġpre jud +Ġdefin es +ier te +Ġbl ending +Ġbelie vers +ĠWo chen +Ġник ак +ĠÐļ огда +ĠTyp ically +Ġíģ ¬ +ç® ¡ +ci os +Ġmiss iles +Ġsp onge +ĠK itchen +Ġt ren +ning en +Ġsc rap +Ġser ait +´ì ł +ç ¹ +Ġë° ĺë +Ġrest ored +Ġprzy kÅĤad +ĠK ubernetes +Ġsa it +Ġu w +Ġen abling +Ġtra vers +amp s +åı Ĺ +ĠOM G +ens or +Ġz osta +Ġpronoun ced +A ng +norm al +Ġeconom ies +t in +ĠChamp ion +iz en +Ġar beiten +ĠG ospel +ĠZ u +ng a +Ġliter acy +ĠM ans +Ġcircul ation +Ġad ap +ĠTot al +Ġmere ka +Ġol acak +ÑģÑĤ аÑĤи +J ack +Ġm und +Ġth ief +b ies +Ġê² ģ +a que +ĠÚ© ÛĮ +ĠSc ar +å ² +Ġab ol +Ġdev ote +Ġ0 1 +Ġs itten +ĠVis ual +we ek +s ome +ing t +Ġjournal ism +ĠH ir +ĠB achelor +in ery +Ãľ ND +ãĥ Ł +ç» Ļ +Ġcolor ing +ĠCr ist +Ġcelebr ities +ĠÑĩ иÑģ +ĠC rit +Ġdifferent iate +ĠÐľ не +el im +Ġse afood +Ġalgum as +otherap y +æĪ ° +Ġgla ub +Ġarbitr ary +g ens +ĠбÑĥд ем +Ġt av +Ġcream y +ĠCount ry +a ñ +м еÑĤ +Ġh inter +Ġm ism +Ġillust rate +ÃľND NIS +Ġdecre asing +Ġwen iger +AK I +ix on +Ġн ей +Ġfat to +Ġn erd +ç ł +Ġb itte +P er +Ġt ane +Ġgö z +Ġfor te +ĠE y +Ġнав еÑĢ +è¢ « +ĠWord Press +ĠM is +Å ¯ +z äh +Ġinté ress +osa urs +ĠFall s +Ġn essa +9 7 +Ġmuseum s +Ġcorrespond s +Ġs ings +f our +Ġed er +ĠCommun ist +o a +ne k +ĠWH O +Ġcor po +Ġmess ing +ÏĦ αι +Ġbrush es +Ġb isc +ĠAr beits +ĠT ax +Ġse le +Ġflag s +ou pe +Ġanticip ated +ãĥ ij +ĠN ad +Ġpou red +Ġm l +Ġll ama +Ġvisual ize +Ġlisten ers +ÙĦ Ùĥ +al ten +Mich ael +Ġcos ì +Õ¡ Õ +op us +Ġíķ´ì £¼ +Ġh ike +ĠAtt orney +ĠHill ary +ud ed +Ġíķĺ ì§Ģë§Į +Ġdo ve +Ġstorm s +ак Ñģ +Ġdoct rine +Ġhe x +ik s +no ÅĽÄĩ +Ġscript s +Ġδ εν +ĠÑįÑĤи Ñħ +ĠÐ Ĩ +ab er +ĠV as +Ġcent imeters +×ŀ ×Ķ +ни б +Ġrid ers +ĠT rib +åĮ ħ +Ġtak że +Ġn oun +Ġic ons +Ġsole ly +mind ed +Ġdisp on +ĠSw itzerland +Ġcl usters +Ġqu eda +ail ing +Ġman ga +Ġ6 8 +Ħ Ī +Ġt et +g ins +ha us +ç© º +å· ¥ +ĠO P +ot ed +Ġnouve au +AL LY +ÙĪ د +ò n +Ġmort ality +ĠGit Hub +d rop +Ġdis gu +Ġrec om +Ġloc als +Ġhome made +amb a +Ġpron unciation +Ġal phabet +ан ÑĮ +ow any +ir as +id ency +OM E +ĠÑĢаÑģ Ñģ +ar ak +v iamente +Ġnon profit +ĠYouT uber +Ġp arenth +ĠB oo +v at +ĠSt ir +Ġpre cip +Ġan ts +Ġall y +ĠMa ori +ĠëĮĢ íķľ +åı¯ æĺ¯ +og ene +ĠLab our +aret te +Ġrecy cling +ens a +Ġpurs uit +Ġs ak +ĠÐĹд еÑģÑĮ +Ġtoler ance +Ġsa at +Ġclick ed +âĻ ¥ +Ġface book +ĠInt o +Ġincent ives +기 ëĬĶ +ĠD ennis +ĠW ik +ges ch +à¹ĢภĽ +ĠÏĢ α +ĠWh oo +Ġround ed +Ġdo pe +Ġcapt uring +ĠWar ri +Ġcivil ian +Ġchar ming +Ġes as +Ġsust ained +Ġle aning +Ġabund ance +ÃŃ lia +алÑĮ нÑĭй +Ġph ải +ac ja +Ġê°Ļ ìķĦ +act iv +า ย +Ġ9 7 +Ġм ой +c ro +ĠJack ie +itt ees +br acht +ul ent +Ġìł ľë +Ġplug in +v antage +part y +Ġsu as +Ġan te +Ñĥ л +ÐĿ ÐIJ +æĤ ¨ +ĠÏĥ Ïħ +Ġmet h +Ġenthus iasm +ÑıÑĤ ÑģÑı +íĻ Ķë +Ġsynth etic +Ġseason ing +ĠL ost +on omy +ĠSp ark +Ġb ure +Ġass ured +Ġimag in +Ġcar ro +S ha +Äħ t +нÑĥ ÑĤÑĮ +át ica +T Y +Ġk ern +ĠBrazil ian +à ° +Ġsusp ended +ĠCar ib +Ġbiz im +ĠOl iver +ãģ ¶ +T om +Ġпл ан +Ġn ope +omet hing +Ġbe iden +ÑĨ ен +Ġflu ct +Ġμ οÏħ +Ġf athers +ĠBl ake +Ġup ward +ĠD ash +ĠL il +ĠìĪ ĺëıĦ +Ġrevel ation +Ġelev ated +ĠJi ang +LE D +ĠThom pson +Ġмог ÑĥÑĤ +ÑģÑĤ ÑĢÑĥ +if iers +Ġcome back +Ġbuy ers +ê² ° +ĠS ales +иÑĩ е +c iones +Ġwh istle +Ġd ull +LE X +Ġíķĺ ê²łìĬµëĭĪëĭ¤ +Ġcrimin als +Ġdes cent +ipp le +mas ı +Ġfool ish +ĠдÑĥм аÑİ +t ar +Ġman go +Ġchore ography +M att +Ġterr itor +Ġac aba +ĠEin stein +ĠI BM +ĠMet al +ĠCry stal +Ġr ah +Ġf oul +ĠIsland s +Ġint act +ĠR ail +. : +Ġac á +ĠпÑĢ оп +еÑĢ е +ĠWr ite +he he +ĠF O +ĠÏĥ ÏĦη +Ġdo in +h eld +Ġappropri ately +Ġdeliber ately +Ġarch ive +Ġgive away +ãģĵ ãģĵ +Ġfin ale +л аÑģ +ен о +Æ¡ n +æ£ Ĵ +og o +çī © +ĠAud ience +ãħ ł +Ġsub ur +Ġhead ache +ан нÑı +ĠW itch +ĠSwed ish +ĠB I +Ġer ase +Ġk hi +Ġcomment ary +ĠS ultan +íĥ Ŀ +ĠLe ban +Ġë³´ì ĭ +ĠP am +pe kt +mon th +Ġground ed +ê ¾ +ĠÅŁek ilde +2 50 +ĠS CH +ios o +Ġin aug +he imer +Ġreflect ing +ĠR uth +ĠO il +Ġtrou ver +u ep +.. ] +Ġìŀ Īë +Ġol ha +Ġreason ably +Ġgl itch +U B +ĠGr an +Ġad alah +Ġl ent +ر ا +Ġtr action +Ġadjust ing +´ ¤ +ниб ÑĥдÑĮ +Ġд оп +Ġstretch ed +Ġor t +Ġcos ine +vi ol +Ġì ħ +c ir +Ġbast ard +ä¸ ĩ +ĠÑħ од +Ġqu ier +Ġpress ures +ĠAn h +å¹ ¾ +Ġell es +Ġд ÑĢÑĥз +ĠможеÑĤ е +Ġch á» +ĠM é +ö k +ầ u +ìł Ī +z in +Ġca ution +ib an +Ġjud ging +ÑĥÑİ ÑĤ +Ġb aj +ĠС ейÑĩаÑģ +ĠPo or +ĠNaz i +Ġup beat +y ang +Ġweek ends +ĠEss entially +Ġol uyor +Ġspat ial +ack er +Ġsell er +Ġ×IJ ×ķת +ij ׾ +Ġv ivid +ĠB ond +ê ¶Į +is kt +ãĤ µ +Ġgo at +dri ver +Ġm ug +ict ional +Ġall t +ĠIn iti +ĠR and +Ġfinish es +Ġê° Ī +Ġvit am +Ġteen agers +ĠMor ris +ì¤ Ħ +ĠO ri +i ya +Ġmy ös +St ep +ĠK re +è¾ ¦ +Ġdin osaur +Ġëª ĩ +aff e +ĠëIJ ©ëĭĪëĭ¤ +Ġz eg +åĪ ĩ +ĠManh attan +Ġsu jet +ue lle +st off +Ġd ür +Ġsub mar +es es +Ġa quele +Ġn ou +ĠFa ith +t z +ĠÑĤ омÑĥ +ace ut +li ers +Ġband width +Æ°á» Ŀ +Ġrespect ive +ĠA ve +Ġspread she +ĠS ent +ic amente +Ġinf ra +Ġlearn ers +Ġà® ī +ai ah +ren al +Ġmust ard +Ġhab t +ç ĥ +ĠQu é +Ġanaly zing +æ¯ ı +Ġso lic +Ġ×Ķ ×ķ×IJ +Ġcaus a +Ġwel comed +ĠS uccess +Ġfac ile +ĠÐŁÐ¾ÑĤ омÑĥ +sche in +Ġf etch +Ġstr at +ĠÑģÑĤо иÑĤ +ìĹIJìĦľ ëĬĶ +ĠÑģп оÑģоб +m am +Ġser ÃŃa +nam ents +wr iter +Ġconsult ing +íĺ Ģ +ĠBer keley +e u +as ive +U U +ĠAnal yt +Ġsubm ission +Ġmagnific ent +en za +Ġe con +Ġprof iles +Ġinc ar +A b +ĠN un +Ġh ic +scream ing +Ġresil ient +åĪ © +gr und +Ġconc ur +Ġbere its +L D +Ġnur t +ì ī +Ġfe ast +Ġenc uent +ĠMich el +Ġsup rem +" ] +Ġfeed s +ĠKoll egen +iss er +ĠF eng +ĠW en +m un +Ġten ÃŃa +ĠW rest +Ġìĺ¤ëĬĺ ìĿĢ +Ġst ead +Ġrest oration +Ġdon ated +Ġdel s +Ġc ensus +Ġdesper ately +worth y +H E +ĠSp a +ĠBry an +Ġh j +ĠR aw +ìķĦ ë +ĠCam era +Ġz ien +Ġst yl +ĠT W +ĠChe ese +bor ne +Ġob l +ĠAl ready +Ġunst able +Ġfl ames +p ost +H a +rom agn +ĠìĹ Ħë§Ī +d est +Ġkole j +Ġtempor arily +Ġdeterm ining +ĠGl ass +ÑĢ он +ol an +Ġdom inated +åĮ ĸ +__ __ +ĠÙĩ ذا +ĠD ana +Ġdin heiro +a qu +ë ¯¼ +ĠÃł s +ĠJo ey +ĠGr iff +Ġatt ain +Ġtrans itions +ĠLiter ally +ен д +ĠHa ven +Ġgrab bing +Ġcryst als +ĠFour th +Ġcand les +ĠÑģлÑĥÑĩ а +ric o +Ġ5 000 +et to +Ġund o +Ġk to +Ġdi vert +Ġch ir +Ġper sec +Ġh iking +Ġannounce ments +çĶ ± +з Ñĭ +Ġa uc +Ġsystem ic +ĠR M +Ïĥ α +ĠÐĶ ж +Ġy ar +ĠW ard +Ġpiss ed +Ġcar n +Ġautonom ous +ãħİ ãħİ +so ver +æ²Ĵ éĮ¯ +å¾Ī 好 +Ġref lex +Ġgard ens +Ġd ated +ì ± +ami ÄĻ +Ġcontinu ity +Ġcitizens hip +Ġsch wer +Ġz ak +t able +ĠÑģ Ñĩ +è§ ģ +ĠÏĥ ε +Ġgener ates +구ë Ĥĺ +ö h +ó m +al am +ĠJUD Y +ĠB ug +Ġãģ ¦ +Ġdr ones +Ġá gua +ac aks +æ ļ +ĠÐļ он +× ĸ×Ķ +Ġstri ve +ĠAl tern +Ġne arest +Ġpro yect +ter a +ĠASH LEY +Ġwor m +Ġre play +Ġt ara +ĠInd ians +ãĤ ° +ica id +ĠìĪ ľ +Ġappe aling +ĠW es +Ġment ions +Ġдел е +Ġk w +Ġfrag ile +is z +k ów +h ang +col or +Ġpresident e +8 7 +е ÑĦ +çĪ ¸ +Ġдоб ав +ĠN elson +á fic +ĠMIC HAEL +Ġmechan ic +Ġmet res +Ġo czywiÅĽcie +ĠC ind +Ġog sÃ¥ +Ġlands ca +AC E +Ġhead lines +Ġcat alyst +ĠC atch +ink les +Ġp ills +ord o +Ġimmig rant +Ġexam ination +Ġacc idents +zÄħ d +Ġqui ere +Ġne lla +Ġ6 7 +Ġpass a +Ġsuper fic +ist or +Ġno v +ëĭ µ +Ġmand ate +is ons +ĠVirt ual +Ġsel ber +Ġcounsel ing +ĠN BA +Ġse pt +Ġbelie ver +Ġmar vel +ĠInte gr +Ġм Ñĸ +Ġor ph +Ġback ward +ĠGen eration +ĠP ict +ĠÑĤо ÑĤ +Ġtap i +pro chen +Ġhall way +ht e +ĠÛģ ÛĴ +ĠZ um +èĢģ 師 +ach ment +iqu er +fol g +ĠEd die +ĠK il +Ġwell ness +st ock +è¼ ĥ +Ġka ç +Ġterror ism +Ġpo inter +O f +her ic +ĠUlt imately +Ġmes es +ĠTr ade +Ġp int +Ġtu ition +Ġdisag re +Ġê²Į ìŀĦ +Ġmanus cript +Ġro omm +Ġoutput s +е ÑĨи +Ġr ies +Ġsal ud +otz dem +Ġmass es +Ġby ÅĤa +Ġclear ing +Ġdisc ourse +ats on +Ġfold ed +ĠJ ar +ÙĦ Ùī +9 00 +ĠÑĥ Ñģп +Ġprophe cy +Ġinterf ere +иÑħ од +๠Į +Ġth ri +Ġ×ŀ× © +Ġlaz ım +Ġ199 2 +Ġfut uro +Ġlock ing +Ġembar go +ĠNe ither +iv amente +ĠmÃ¥ ste +Ġm ik +Ġcollect or +еко ÑĤоÑĢ +ĠG and +Ġsent ir +ĠM ight +å¡ Ķ +Ġgan zen +U C +Ġrel ating +S D +Ġmos quito +G R +Ġho llow +âĺ ħ +ĠWalk er +Ġaffili ate +Ġduplic ate +н ем +Ġgra pe +ĠOrgan ization +Ġsy nt +J oe +Ġg eg +Ġreve aling +ĠEth an +out er +Ġy ay +é« Ķ +л аÑĢ +Ġreported ly +Ġihr er +Ġrecogn ise +Ġbum per +ĠR andy +ĠVen us +t les +Ġappet ite +Ġgluc ose +Ġch odzi +ĠFurther more +t ir +Ġcont a +Ġint uition +Ġalt itude +Ġch unks +ĠJosh ua +ıģ ım +ry lic +le ans +ĠíĶ ¼ë +L L +Q ue +Ġg or +Ġзна ÑĩиÑĤ +Ġpo ems +Ġexc el +Ġexpl ored +Ġpop ul +Ġinclus o +st ä +ĠG avin +all ing +ĠÏĦο ν +é © +ar beit +ĠG as +Ġgl orious +rie ben +Ġsp am +Ġindo or +Ġthr ust +ĠA ld +ĠPri or +Ġon board +ãģł ãģķãģĦ +o ca +AS H +£ ł +ĠChrist ine +Ġdra wer +Ġno on +Ġìŀ ĺë +Ġperman ently +æ· ± +ĠнапÑĢ имеÑĢ +Ġpodcast s +era peut +pr it +Ġstain less +ĠÚ© ÛĴ +Ġfamil ia +ĠÑĢаз ÑĢ +un to +ĠÑģÑĤ ол +Ġh ä +ĠH ai +ĠP B +iz on +Ġkon nte +Ġbüy ük +Ġutil izar +Ú Ĩ +Ġaqu esta +Ġmix er +ud ent +лек Ñģ +ÅĤ u +ĠÑģиÑģÑĤ ем +Ġн оÑĢм +Ġfat al +Ġconsider ations +Ġvalid ation +Ġo li +Ġk ardeÅŁ +ĠGL ORIA +Ġp all +еÑģÑĤ е +Ġrect ang +Ġmed ieval +allah i +ast i +ĠSy rian +Ġshe ar +Ġdeb ug +ĠM ai +Ġknock ing +ĠLe x +ard an +ro v +Ġmem orial +æ° £ +ook y +Ġstuff ed +Ġpass é +Ġw ig +Ĥ ł +Ġpróxim a +Ġ199 1 +Ġм еждÑĥ +Ġnuest ros +ĠBe ast +Ġsm o +atch ed +olog ia +Ġм од +Ġge e +Ġconcept ual +Ġà ´ +Ġdecre ases +Ġquer ies +олÑĮ ÑĪ +ĠA part +Ġex empl +å± ± +Ġfl ed +ĠO FF +gg ak +Ġbe ad +h ir +l ies +ĠClear ly +ı lar +Ġch ess +Ġwhich ever +Ġ9 6 +Ạ± +Ġrespect s +Ġм оÑĢ +Ġorgan ism +Ġgrand pa +ĠV ie +è·Ł ä½ł +Ġflo oding +Ġupgrad ed +Ñij ÑĢ +Ġcheek s +Ġcon quer +Ġstub born +Ġpuzz les +Ġau ction +Ġre lying +ĠPRO F +ĠEs per +ĠÐľ У +Ġhy pe +Ġposs ibil +Ġimp rison +ĠEr n +ìĹĪ ìĬµëĭĪëĭ¤ +Ġenv ie +Ġresur rection +ä¸į è¡Į +Ġs per +ĠVenez uela +s om +Ġìŀł ê¹ +Ġnouve lle +Ġclos es +Ġ19 40 +Ġqu a +ĠJ ared +ĠP ir +Ġind e +Ġscr ub +uk u +Ġrequ iring +Ġв ами +Ġconsider able +åIJ Ľ +il ia +Ġin ne +Ġmein em +Ġhard ship +Ġtra ps +ro c +ĠìĦ ¤ë +Ġresearch ing +ĠMarg aret +Ġpen ny +Ġbı rak +Ñij л +Ġw ool +Ġr het +Ġflat ten +ç ĩ +à¹Ģภ£ +Ġp ied +ĠCh ap +Ġunder m +Ġf ret +Ġcrash ed +ĠFra uen +Ø° Ùĩ +iv an +Ġliter ary +late go +Ġsp äter +Ġsimilar ities +â Ĩ +ĠCor on +ĠC reek +Ġboss es +Ġaccompan ied +Ġdeb ates +Ġassemb led +Ġà ģ +ĠV ai +Ġtr act +Ġsimple ment +ĠAr in +Ġvulner ability +Ġhorm one +I EL +OO K +Ġrel ay +ĠAnd rea +r il +Ġnecess ity +aceut ical +Ñİ Ñī +ous ing +nah men +Ġfoot print +m ap +ĠT ier +ann ya +int end +åĸ ® +å ¢ +Ġdecor ate +Ġzomb ies +ĠHy d +ĠSu z +Ġcampus es +ĠE mb +Ġthr ottle +Ġad min +Ġop ortun +Ġmir rors +Ġident ities +ĠCl in +Ġë¹ Ħë +á¹ £ +ĠO tt +Ġbl ues +Ġimpress ions +- , +Ġv ague +a fe +Ġinfer ior +eral d +Ġmedic ines +Ġpre gunta +os ely +Ġt élé +ĠMon th +ĠLe aders +ĠEgypt ian +Ġr ation +k ers +he its +Ġre cht +P lay +Ġe g +Ġpoll s +ĠWOO DR +Ġsl ots +j am +B oth +ĠR at +ÑĢ аж +ĠBr ight +ä¸Ģ å®ļ +á»ij i +ur ious +Ġsing ers +Ġlo gin +Ġt êm +l ation +ĠM um +Æ°á»Ŀ ng +ĠEd itor +åIJ ij +Ġinnov ations +h ave +ĠS ek +Ġwe aker +ĠG ob +A fter +´ì §Ģ +Ġ문 ìłľ +ãĥ¼ ãĥ¼ +Ġdisad vantage +ç¢ º +Ġg aze +ĠM ack +Ïģ ί +ĠK iss +ĠH olo +ĠBir th +iz i +b ab +ä¿ Ŀ +ìĭľ ê³ł +д еÑĢж +Ġsqu at +кÑĥ Ñģ +un i +ĠComm e +ĠWOODR UFF +ĠChampions hip +Ġwel che +ĠY outh +z em +Ġod pow +Ġpersist ent +r ut +ìĶ © +íĸ ¥ +la ir +ik u +Ġvend or +Ġch úng +Ġfinan ci +Ġover ly +â u +Ġgl uten +Ġ18 00 +Ġdiv isions +Ġciud ad +Ġob ed +Ġwar um +Ġe her +Ġel im +ĠÐĴ о +Ġpeu vent +ĠW anna +Ġattend ance +Ġassess ments +ĠB og +Ġimag ery +Ġcollect ively +Ġinform al +ĠSch we +Ġde utlich +ĠCh el +ĠP E +ow ed +Ġb anner +Ġshel ves +ĠRet urn +æĭ ¿ +LAUGH S +Ġcongrat ulate +ĠNor way +Ġd well +ĠCarib bean +Ġnorm s +ĠAn imal +ĠValent ine +Ġext ending +ĠV ou +or r +ĠCh eng + ¡ +ĠдоÑĢ ог +Ġve g +Ġh Ã¥ +ĠX in +Ġì¹ ´ë +em et +Ġhyp oth +Ġinteress ante +ric es +I Z +ĠUS D +Ġrun ner +ĠB ag +Ġê ½ +Ġcomeç ar +Ġpig s +Ġweakness es +P h +ĠVi ol +ä¸į çĶ¨ +Ġdra gging +ĠAqu ÃŃ +ĠCS S +Ġmill imeters +Ġest ás +Ġac ute +Ġde jar +i ÄŁ +ob ra +L ove +Ġsil k +** ** +Ġjo ins +Ġpro l +Ġê°IJìĤ¬ íķ©ëĭĪëĭ¤ +æĶ ¯ +ØŃ Ø¯ +agh etti +än ner +Ġstr ang +Ġdoub led +Ġdescri ptions +Ġst ellen +Ġpart i +ç« ĭ +² Ħë +Ġö ÄŁ +ig hing +Ġang ular +Ġnat uur +ĠSh el +Æ° Æ¡ +Ġr ays +Ġse per +st art +v ised +Ġrush ed +Ġinternation ally +Ġnive l +Ġbox ing +fall en +á»ij c +Ġse inen +plic ity +Ġcarb oh +ĠTra vis +us o +ĠPh ase +Ġactiv ation +Ġop io +· ¨ +Ġdecre ased +C ar +Ġbund le +Ġexp end +orm al +Ġadjac ent +Ġme e +ĠоÑĢ г +Ġtrans cript +ĠLang uage +G S +è§ ī +Ġse ul +Ãł nh +Ġn ya +ning s +Ġìĭ ľë +ĠëĶ°ë Ŀ¼ +ĠA gr +ÃŃ d +çķ Ļ +Ġab y +ĠNe o +ıyor uz +ĠThink ing +a ime +Ġv ite +Ġtrav és +Ġ×ij× ¢ +Ġм ед +O ur +ho ot +Ġl iner +ĠP izza +Ġhy g +fl ies +ĠContin ue +Ġdent al +ĠT ib +Ġreg ulate +lie ÃŁ +AL K +ĠTa e +ê¸ ¸ +ĠBre xit +ĠG ut +Ġoccup ation +Ġz robi +â m +Ġwh isk +ä¸ĸ çķĮ +Ġkans ke +om on +ro be +Ġwar fare +Ġth á»ĥ +Ġjak i +Ġstro kes +Ġpe as +ĠDam it +H AN +Ġinter ference +Ġмин ÑĥÑĤ +N ER +out ing +Ġtext ures +Ł ī +ow i +Ġíķ Ļ +Ġd ens +Ġprotagon ist +än n +Ġgod dess +Ġwoll te +ij o +ĠWo che +ĠV PN +st ory +Ġkind erg +Ġfun nel +Ġdist ress +ноÑģÑĤÑĮ Ñİ +Ġno isy +ĠпÑĢод олж +Ġdar an +Ġenzy me +л ож +Ġm ute +Ġd war +Ġا س +Ġkom pl +Ġmer it +Ġf osse +ĠDr ink +Ġfor a +Ġw ohl +Ġbree ze +Ġsan it +Ġdr in +ĠìĿ´ê±° ëĬĶ +Ġ6 2 +Ġì° ¨ë +aby tes +Ġde eds +ĠÐ ¹ +i ème +igg ling +Ġ" ' +ĠÑĩа ÑģÑĤÑĮ +ĠAns wer +Ġev angel +Ġ10 80 +ĠVis it +ic ient +Ġreli ability +Ñİ ÑģÑĮ +ĠEar lier +Ġf id +çŃī ä¸Ģä¸ĭ +Ġslee ves +iy orsun +Ġb ib +ĠAcc ount +Ñı ли +cipl inary +z as +Ġб еÑĢ +Ġneck lace +Ġbl ender +ĠPhill ips +et i +ĠJup iter +Ġprov oc +ĠYe ars +ent re +ac io +Ġk ü +Ġanten na +Ġnovel s +Ġf art +ĠS ugar +ĠJud y +Ġcollaps ed +ç ° +rit is +Ġìĥģ íĻ© +ÐĹ Ð« +ĠVer f +rane an +ere um +ĠTar get +Ġ8 8 +ĠÐĺ з +ide o +Ġreg ression +ì¶ ľ +Ġmów i +Ġstud ios +i ens +ip h +Ġfr ying +Ġfasc inated +ĠW ah +b ucks +m aya +ĠSat urn +ĠM ommy +Ġrating s +Ġaut umn +Æ°Æ¡ ng +Ġlos er +Ġcent ro +érie ur +ĠF old +Ġsuper visor +ĠNo bel +Ġunder est +ob ia +Ġв ÑģÑı +Ġver w +Ġfu els +Ġartif acts +Ġë¶ Ļ +ĠAut om +çļĦ æĺ¯ +Û Ķ +×ķ× ¡ +Ġih nen +Ġ5 9 +ound ing +еÑĢ Ñĭ +in ars +ch ant +Ġadd icted +Ġexplos ive +Ġdisp ers +â ĸĪ +ax is +AR Y +Ġl um +ĠÑĥ Ñģл +ĠØ Į +Ġru pees +ĠPe arl +c amp +t v +oy a +Ġconclud es +Ġcoll ision +Ġbuy er +Ġplay ground +Ġspr ings +Ġfemin ine +ĠR as +Ġincar cer +íĹ ĺ +Ġdial ect +Ġclos ure +Ġchat ting +Ġb abe +Ġspot light +Ġnot ation +è· ¯ +St ar +i ão +Ġt ête +Ġt ide +Ġjun to +Ġsen ator +Ð ¥ +Ġexcus es +Ġbl ink +Ġadm ission +ĠL ily +Ñĭ ми +Ġam igo +Ġl ust +ëĭ ¬ +Ġam ino +äºĭ æĥħ +Ġconsult ant +ĠElect ric +Ġëħ¸ë ŀĺ +uj ah +Ġshoot er +icht en +ĠUkrain ian +Ġaim s +ĠEnter tain +Ġmir acles +èŃ ° +Ġze igen +Ġl am +Ġres s +ĠJ ill +yl an +Ġro ok +Ġh aya +Ġpass port +ad ata +Ġju icy +con f +л ей +ĠS z +Ġinter cept +ãģĤãĤĬãģĮãģ¨ãģĨ ãģĶãģĸ +ĠTe ams +Ġmak en +ir rel +ĠLI KE +áºŃ y +êµ ° +Ġshort age +Ġparad igm +Ġpap el +Ġast ero +ãģ¾ ãģŁ +Ġsoll en +ĠMic key +ĠOr leans +Ġchol esterol +Ġgo ose +ÑĨи Ñİ +ãģĤ ãĤĭ +ĠF L +Ġгол ов +Ġtrib ute +ĠG am +Ġé videmment +Ñı Ñħ +å® ŀ +çĶ ° +Ġin appropri +uh an +Ġorganiz ational +ail ed +Ġend ure +Ġ7 6 +Ġshot gun +Ġliv re +Ġsu ited +Ġwarm th +ĠS IM +Ġenv ision +Ġde grad +î ne +La ughing +ĠWho ever +ĠBuddh ism +Ġspr inkle +ceÄŁ iz +Ġru ins +Ġst arch +ĠHer z +Ġinjust ice +Ġhum idity +ожал Ñĥй +ĠOb ject +ĠI gn +ĠEx am +ig ers +Ġth ou +ĠSo y +iv as +Ġpol es +m ath +Ġв ним +ING ING +ed ral +Ġexpl or +Ġroast ed +Ġcraw l +Ġco ff +Ġan om +Ġw ij +Ġimpro ves +Ġtreat y +Ġdiscover ing +Ġstat ute +Ġmerc ado +ĠÑģ ил +Ġint el +ĠChance llor +ĠMed icaid +ug i +Ġver bal +Ġd ön +Ġscript ure +Ġit eration +ek s +ĠOx ford +Ġw äh +ĠV ad +ĠA K +ĠìķĦ ìĿ´ë +Ġi ets +Ġneed les +Ùĥ Ùħ +Ġpas ado +Ġalbum s +Ġye a +et zen +Ħë ıĦ +Ġdeterm ines +Ġthe e +ĠPlay ing +är t +Ġ× ¦ +c led +Ġdown ward +al one +Ġsol u +Ġpart ition +Ġw z +d d +Ġpesso al +å ª½ +Ġfact ories +Ġble ibt +ม า +als a +ĠNF L +Ġfu era +Ġres erved +ĠE arn +Ġhel t +Ġshort cut +Ġconvin cing +sp ace +Ġen force +Ġc ores +Ġe fter +Ġrecess ion +x ico +Ġprop osition +ar ians +rop ol +Ġëª °ë +ĠÎ ľ +ĠìļĶ ì¦ĺ +Ġactiv ist +Ġconv iction +Ġz ab +Ġcancel ed +ÑĤо Ñĩно +ĠÎ ® +éĢĻ樣 åŃIJ +n ite +Ġfund ra +buz zer +ел о +ic ations +Ġz ona +Ġte ens +Ġmethod ology +Ġì¤ij ìļĶ +th an +ĠU l +ĠG rey +Ġh og +IN K +ĠS ung +ĠC laud +ĠCN N +Ġdel ivers +al in +ĠAd obe +ot he +ĠDes wegen +ภ³ +Ġwer de +Ġgre ase +Ġup grades +ĠFin land +ac cept +Ġinter rog +be e +Ġãģ « +Ġpre de +ĠN ep +ĠCam bridge +Ġgraph s +Ġha unted +Ñģ ем +æ § +åħ ĭ +S ome +ĠM all +Ġrehears al +ĠUr ban +ĠL ag +Ġn im +ê° ķ +Ġposition ed +Ġavo ided +EM A +Ġlleg ar +Ġráp ido +Ġgou vern +Ġh ing +Ġdeal er +Ġreform s +Ġfat ty +к ол +ĠA ce +Ġne p +Ġì² Ń +Ġcomput ation +ĠSt ream +bour ne +t ur +P or +Ġsleep y +Ġbang et +ãģĤ ãģ® +Ġwe ighs +Ġble iben +ĠG ren +Ġun ions +Ġêµ IJ +Ġap render +uit ar +ĠJ est +um ing +ĠPlay er +ĠExt rem +Ġinteg er +аÑĩ е +Ġconcert s +×ķ× Ľ +Ġtro chÄĻ +ĠRe pe +éĩį è¦ģ +๠Ĥ +ż en +Ġsound ing +Ġan onymous +Ġex ca +ĠIran ian +Ġener getic +Ġw ives +ĠÑĨ веÑĤ +Ġa is +ãģĭ ãģª +Ġsud ah +Ġunder wear +Ġcrunch y +ĠP ain +Ġger çek +red ict +Ġm isma +Ñĸ ÑĤ +Ġsurv iving +ÎŃ ÏĤ +Ġparticip ant +ĠH essen +ári as +Ġsub way +ist ä +Ġcor al +Ġmar ijuana +ĠMem orial +ÑĪ ий +ri z +Ġsatell ites +Ġle ase +ĠCam eron +um ph +Ġclass mates +äh än +ÑģÑĤв е +Ġh ue +ĵ¤ ìĿĦ +Ġproport ional +Ġn oss +Ġl aps +r Ã¥ +Ġbit coin +ÐĹЫ ÐļÐIJ +Ġì¶ © +ĠÙĦ ÙĦ +ĠM ort +ĠEs p +arn os +ĠÑģказ ал +Ġä nd +åħ Ħ +×Ļ ×Ļ×Ŀ +ĠGe b +ge hen +I naudible +bor ough +ÑĦ ÑĦ +Ġfellow ship +ĠP aper +Ġcur ved +ĠGE OR +Ġcalcul ator +ĠCat al +ĠvÃł o +Ġby pass +л еÑĤ +à ³ +tr ans +ren cies +ì ¡Į +ig ent +Ġtast ed +Ġo ceans +u ft +erv ice +ĠÐľÐ£ ÐĹЫÐļÐIJ +ĠClass ic +Ġrespect ively +~ ) +î tre +ĠN ash +Ġz it +ĠìĽ ĥ +ĠëĨ Ĵ +qu ote +ĠUn s +Ġt ac +Ġpro ves +ĠPort land +b ly +Ġ ere +ì¶ Ķ +Ġépo ca +ĠÑĤÑĭ ÑģÑıÑĩ +7 6 +Ġhad e +ĠF ro +ĠpolÃŃt ica +t ag +Ġíķ Ń +Ġsch ö +are tt +Ġprov isions +Ġmot ors +Ġimag ing +Ġdo k +ul ously +Ġme ille +çİ° åľ¨ +ë IJ +ĠIS O +ĠST EM +ĠBow l +Ġto wers +ĠE e +ĠPerform ance +Ġlo in +cuss ion +Ġcoast al +ial e +com pass +Ġspell s +Ġdisappoint ing +Ġë²Ī 째 +E ER +Ġvers atile +as ury +Ġen fin +Ġdown side +Ġgu iding +ĠاÙĦ ÙĤ +Ġnin ety +char ged +ĠF ans +Ġphilosoph ical +Ġg arn +ĠmÃ¥ nga +Ġwilling ness +Ġport ions +ab en +Ġ ï + ¿ +ra ul +Ġspr int +if en +ıy la +Ġк Ñĥп +ãģı ãģłãģķãģĦ +Ġens uite +ĠCap itol +Ġ6 3 +ĠговоÑĢ иÑĤ +Ġappoint ments +æī ¾ +omi ast +Ġcare g +Ġpubl isher +Ġher aus +Ġε ί +ĠV S +ãģĿ ãģĹãģ¦ +ä¸Ń åħ± +Ġsacrific es +th ird +Ġhuman itarian +ĠëĤ ´ì +im on +Ġine qu +Ġz ob +Ġcomfort ably +ĠD inge +Ġcancell ed +ĠPS AKI +ĠRob inson +Ġfin s +) ? +ĠHist or +ĠÑĩеловек а +Ġt bsp +te xt +k im +Ġupd ating +Ġgel d +f eld +ı ¼ +Ġm ä +Ġcaf é +Ö Ģ +ĠS ri +ĠReg ion +ĠH ahaha +Ġfin ances +ĠاÙĦØ ´ +Ġb unk +ru k +ha ft +Ġlater al +Ġext ensions +ĠìķĦ ìĿ´ +Ġdefin ite +ĠZ hao +ĠLu is +st y +Ġcas os +ĠK lim +Ġ199 3 +Ġreal ization +Ġhistor ian +Ġcrack ed +ëĤ ´ +Ġsyst ème +ĠC IA +ĠÑĤ во +osp heric +Ġfle e +Ġr ất +ĠRegard less +Ġrel uct +Ġtim ely +ĠJul ian +G M +é Ĵ +ad ura +é£ Ł +Ġdress es +çģ £ +ĠëĶ Ķ +Ġnom inated +Ġadvoc ates +ym ph +Ġrecord ings +Ġdev iation +Ġpriorit ize +Ġspir al +ĠYOU R +Ġtransp ose +amp oo +ĠìĽIJë ŀĺ +ĠV ision +Ġpol ite +Ġha mb +ĠPat ient +æ¯Ķ è¼ĥ +íģ ¬ë +Ġs ia +Ġê³ ³ +Ġž e +è§ Ģ +Ġsuper market +ë ¹ +ĠS ierra +Ġgr illed +ĠUp on +Ġabs ent +Ġme c +ĠAp ollo +Ġp unk +ĠPa ÅĦst +ĠÑģв ой +Ġê±° 기 +G irl +Ġskin ny +ĠPrem ier +Ġterrit ories +Ġli ability +Ġj erk +r atic +Ġdan cers +ĠÑĥ ÑĢов +Ġê´ Ģë +on ly +ĠSt u +Ġske leton +ĠëŃ IJë +Ġзак он +ı kt +ĠMI KE +Ġl ö +m ie +Ġre iter +ãģĵãĤĮ ãģ¯ +ĠKoll eg +ĠAd ams +lich er +Ġçoc uk +Ñı г +Ġbl ush +Ġsun shine +Ġe z +ĠDev il +Ġê¸ ¸ +Ġãģ Ĭ +ad d +Ġlic ensed +Ġv inyl +ĠC zech +im ag +Ġcrack ing +Ġì º +Ġud ah +Ġs ommes +Ġìĸ¼ êµ +wa Äĩ +Ġf res +åij ½ +ĠWal mart +ĠТ епеÑĢÑĮ +at isf +C I +l ang +Ġdiff usion +çĶ · +Ġsom os +ĠM akes +æĪij æĥ³ +ĠRick y +Ġmuch a +íķ ¨ +Ġhorse power +as ia +Ġfib ers +Ġ erm +Ñģ кие +Ġjest e +Ġfire fight +Ġcu isine +Ġbesond ers +d ig +Ġì¢ ħ +ĠÑĥ ж +Ġtr acing +Ġcertain s +ĠApp ly +Ñĭв аÑĤÑĮ +ç Į +Ġbr u +ĠY ES +ĠB ai +ĠD it +ĠB is +Ġun le +ÑģÑĤа ÑĤоÑĩно +ĠAw ak +.. " +Ġ12 5 +Ġroot ed +Ġcaut ious +con st +Ġorchest ra +çľ ¼ +Ġвн ÑĥÑĤ +Ġquel qu +ĠоÑĤ веÑĤ +ĠMet hod +ì¹ ľ +Ġμ αÏĤ +l ü +ĠìķĦ ê¹Į +Ġn aming +C har +ĠS icher +Ġprivile ged +ĠF ly +Ġãģ ĭ +áºŃ t +Ġadv ances +ĠZel da +Ġand ra +Ġgr inding +ĠEd ition +p f +Ġwarri ors +Ġh edge +Ġuns eren +ĠÑģÑİ Ð´Ð° +el iness +Ġpersonal ities +Ġf ö +' M +ĠÑĤо Ñĩно +Ġsh ipped +Ġmete or +Ġsurround ings +ĠF ill +u esta +ĠPerson al +ĠAll e +OR T +ä¹ ħ +ĠS che +V I +Ġcompar able +dam n +Ġd itch +Y AN +ism us +Ġpick up +Ġd ak +ĠE P +b est +ĠS ue +äll t +Ġpop corn +Ġfold ing +h ome +ив аеÑĤ +å·² ç¶ĵ +Ġan not +ch uck +Ġfier ce +Ġdam aging +Ġfl op +Ġpas ar +Ġre ef +ĠÑģво ей +Ġz oo +o vers +j ets +Ġpr ès +ĠSil icon +te ok +ĠS eth +at amente +Ġtransm itted +Ġrepl icate +Ġsl im +ĠC ream +æĦŁ ãģĺ +Ġside walk +ìĪ ĺë +Ġжиз нÑĮ +ĠMon ica +ä¾Ĩ äºĨ +Ġcop ied +ĠTer ra +ist ent +ç³ » +Ġо но +Ġwh ale +ĠW ITH +л ÑĥÑĪ +å½± çīĩ +ĠE en +ĠÑģво и +Ġord in +Ġpl ural +Ġsp okes +Ġdisp ute +Ġsens ible +Ġpre aching +Ġktó rzy +pt ed +av ier +Ġpist ol +ĠTap i +Ġ ÅĤ +ff ff +Ġac rylic +Ġignor ance +ĠZ iel +r ans +Ġweld ing +m id +æĪij ä¸į +Ġзан им +Ġlan es +Ġmin es +Ġmom s +×ķ× Ĺ +ĠCham ber +t ier +Ġmod est +ĠìĹ¬ê¸° ìĦľ +Ġun as +Ġw rench +hand ed +Ġsatur ated +ĠF ang +ĠCommission er +ठ° +Ġ× ĸ +ĠLouis iana +ĠM ask +Ġcub es +ìĶ ¨ +Ġvidé os +ĠnÃ¥ gon +Ġr ider +Ġì¶ ľ +Ġs ón +ĠLat ino +b ank +íķ´ì £¼ +ĠB rend +Ġsexual ity +... , +Ġforget ting +Ġ ÛĮ +ĠAven gers +ĠBon jour +cess or +кÑĢа ÑĹ +c ence +Ġge ograph +cul o +о ÑģÑĤÑĮ +Ġswe ating +íĥ Ģ +Ġsymm etry +ts Ã¥ +Ġj an +ĠFer r +é¦ ĸ +Ġamb assador +ziÄĻ k +Ġmus un +ĠÑĥ ÑĤ +ĠL G +iss ent +comm un +Ġcour s +Ġdevelop s +Ġbron ze +Ġsubst ances +dri ven +주 ìĦ¸ìļĶ +Ġa os +åĦ Ħ +ĠPROF ESS +h alf +Ġsort ed +ĠB omb +л аг +ĠMalays ia +ĠChrist ina +Ġteam mate +èģ ŀ +F T +Ġk ı +heart ed ++ + +ogen ic +Ġbell s +ĠOu ais +Ġspecial ists +б Ñĭ +dep th +lass es +g ies +ĠCo ffee +Ġmark ing +Ġfo ll +ul i +Ġad hesive +ĠB ot +ĠP unkt +e ye +ĠB ub +el ong +åĪ ¶ +ĠпÑĢ ик +Ġdon or +8 4 +Ġen for +Ġcatch es +Ġbr icks +Ġkn itting +ĠKnow ing +ok s +H Y +r ide +ĠFant asy +im an +Ġp se +Ġìĺ ¨ +Ġв д +Ġrest ra +Ġevalu ated +ÑĢ ев +Ġfortun ately +Ġche gar +ر ب +Ġdom ains +ib i +ar ry +Ġshut ter +Ġfic ou +M ike +Ġinc lu +Ġdon ors +Ġa pl +ĠL ower +Ġimport ed +Ġacad emy +Ġfin als +Ġdisappe ars +ÙĬ ا +Ġadministr ator +j s +Ġcut ter +Ġr anging +ör per +Ġconstra int +ĠT able +ĠSh an +v ic +ĠF ix +ĠSw ift +oun ces +ĠWar um +Ġlett uce +app elle +Ġsh ave +Ġb ás +Ġ7 7 +ĠO oo +a o +ĠMc M +ĠD rew +Ġl ump +Ġl ashes +schein lich +R ep +in is +ĠC ette +Ġcompos ite +emet ery +Ġsort e +ĠFin ancial +он е +ron es +ĠV oy +Ġt éc +ł ¹ +ĠNin ja +ĠCor in +ен нÑı +ìĿ´ìĹ Ī +Ġn ich +Ġdetect ive +âĢ¦ " +Ïĥ ε +Ŀ¼ë ıĦ +Ġë³ Ģ +Ġë¸ Ķë +Ġpro pe +ĠW right +Ġ×Ķ× ª +ĠSh i +Ġãģ Ł +Ġinvestig ations +éĤĦ æĺ¯ +ĠPower Point +ĠCh u +Ġìĺ ¤í +ĠìĻĦ ìłĦ +ĠFra gen +un ning +Ġpour rait +Ġtext book +м Ñĭ +Ġf ahren +Ġ ÑĤоÑĢ +Ġl akes +ünd e +I nt +ĠMet ro +Ġmans ion +Ġа б +ĠZh ou +Ġcorrid or +Ġesc ol +Ġindic ating +ia ÅĤa +Ġm ommy +Ġarch ives +Ġfound ers +eng ine +ĠDie u +Ġsick ness +Ġë³´ ëĭĪê¹Į +Ġar b +Ġn ed +ĠCh op +Ġco vid +Ġsl am +Ġpublic ations +D C +Ġsp ends +æ ¾ +Ġrefuge e +Ġd ile +Ġ×IJ× ĸ +ific ar +ĠS ach +G u +Ġre load +?? ?? +Ġje ÅĽli +ĠÑģ оÑģÑĤо +Ġsim plicity +Ġbull ying +Ġм ол +Ġreal idad +Ġuncle ar +app a +le vant +ĠIS IS +ĠW atson +Ġde in +ĠMic ro +íķ ľë +ü g +Ġdev am +Ġtwe eted +å° İ +Ġunderstand able +at an +Ġvers a +Ġpre ca +Ġv á»ģ +ĠCop y +ĠOr acle +Ġmindful ness +Ġdisc ret +ern en +ĠP le +H ave +Ġisol ate +Ġde u +Ġsevent y +ĠH ills +Ġarc ade +ĠÑģп еÑĨи +Ġsigu iente +ĠB ÃľNDNIS +lig a +ĠвÑģÑĤÑĢ еÑĩ +ô m +Ġtwe ets +Ġsch auen +Ġcrit ique +ĠðŁİ µ +Ġst att +ĠÑģам ое +ân cia +Ġsuper natural +Ġplug ged +F l +yn ı +ĠTamb ién +Ġencourage ment +ĠSer ver +ëĤ ľ +up a +Ġast on +Ġhe ars +ÑĢа Ñħ +Ġsch e +Ġr ats +Ġrec uper +Ġun ten +ĠFight ing +Ġacadem ics +ç¤ º +ĠS ü +Ñģ киÑħ +Ġpa ired +Ģ ìĿĦ +Ġá rea +Ġsweet ness +åı Ĭ +Ġde fer +Ġmuit as +ĠAud io +Ġlock er +ÙĬ د +ĠÑģÑĤ ав +Ġbu ena +AN S +Ġdetect or +av o +be k +Ġα ν +íİ ¸ +Ġdra gged +Ġдолж ен +à ĸ +ر Ø© +ìĿ´ì §Ģ +Ġcell e +ck ing +ĠاÙĦØ ¬ +ĠCan vas +Ġespa ñ +Ġgl imp +Ġspread s +ong o +ĠM ason +ĠIn g +Ġê°Ģ ëĬ¥ +ÏĦ ικ +Ġsec ular +Ġb ater +Ġinqu iry +Ġenerg ies +Ġmanufact ured +Ġveget arian +Ġpine apple +ÑıÑĤ а +Ġpractition ers +2 000 +Ġíķ´ì ļĶ +ĠìĹ¬ëŁ¬ë ¶Ħëĵ¤ +Ġë¶ Īë +ĠJeff erson +ĠJo an +Ġtr am +å® ¹ +ch mal +ĠH ait +á¹ ĩ +Ġun real +Ġsymbol ic +Ġste alth +Ġspl ash +ĠEntertain ment +Ġmetall ic +?" . +è¶ Ĭ +ar ound +Ġdesp air +ĠNev ada +ĠFin ance +Ġk rie +ĠL ux +ĠSm ash +ke eping +Ġз аг +Ġnarc iss +Ġdz isiaj +Ġtoler ate +o ard +Ġlink ing +ĠEconom ic +Ġì ¼ +Ġmor ph +ĠN ak +ĠB aker +at on +r ings +ĠP eng +ĠAir port +ãģĭ ãģ£ãģŁ +íķĺ ëĭ¤ +§ ģ +pr ints +Ġhad i +Ġemp ir +ĠL ives +ann ers +Ġн им +ĠPROFESS OR +Ġpositive ly +ant om +Ġbad ge +ke lt +Ġinter fer +Ġfulf illing +Ġvisual ization +éĹľ ä¿Ĥ +ĠPr ice +� � +Ġscen ery +Ġpr one +Ġw izard +Ġb anyak +ver b +s ky +Ġwish ed +Ġrail way +Ġü zer +Ġalgu ien +ĠA W +Ġкол иÑĩе +Ġreact ing +ĠB uch +ภ¶ +Ġan th +Ġsi h +Ġh ust +ĠSc reen +il ant +ah o +Ġfragr ance +Ġelev ation +ĠMed iter +Ġë ¿ +Ġé qu +Ġwra ps +Ġin ert +Ġrecre ate +л аÑĤ +Ġbo leh +Ġharass ment +unk y +Ġglimp se +reg ierung +Ġfut ur +Ġreposit ory +Ġeng ra +Ġtraff icking +ass is +ĠTre k +Ġë² Į +Ġë§ Īë +ĠK ab +ani u +g ive +Ġdin osaurs +Ġfe ather +Ġatt itudes +Ġpl um +ĠR S +ĠAn fang +ill ery +ĠìĬ ¤ +M Y +Ġtrze ba +Ġsk ies +ĠA j +ur able +C U +ĠSh ane +Ġdepart ure +ĠT ON +iet en +r ats +æ° Ĺ +is u +Ġb ord +Ġinteresting ly +çĻ » +oug hing +Ġr ushing +Ġvol atility +Ġp yt +Ġform ats +Ġз аÑĤ +Ġê¼ Ń +Ġwhat not +Ġcomp ort +s w +ore an +ĠRel ax +Ġcl an +ĠA H +Ġpe w +Ġdiction ary +T ake +sh irts +ĠH ugh +ĠعÙĦ ÙĬ +ĠP ic +Ġenroll ed +Ġjed nak +Ġoffer ings +Ġcor az +L ife +Ġ !!! +Ġcl er +ĠVide os +ĠRod rig +ĠId ent +ĠP os +ĠSt age +ĠR ace +Ġen act +ãģĦ ãģ¾ãģĹãģŁ +ĠG y +ĠHis pan +Ġdef ence +ĠCamp bell +m atic +Ġrele v +Ġpe ach +Ħ¸ ìļĶ +Ġparad ise +Ġcere mon +Ġannoy ed +æĮ ĩ +la x +Ġexplo it +Ġcla use +ek er +ĠBlo om +n ant +ate urs +Ġhe ights +E ven +Ñģ он +Ġoutra ge +ĠVietnam ese +ãģ¯ ãģ¯ +T R +Ġe er +Ġcann on +ĠCom b +IJë §Į +è» Ĭ +Ġê²ĥ ëıĦ +Ġaccomplish ments +ĠAnalyt ics +Ġshap ing +re iben +Ġb achelor +Ġfing ert +ack ed +Ġpyram id +ĠStew art +á st +Ġsurviv or +Ġdu ct +Ġdeal ers +æ´ » +ع Ùħ +ли н +Ġed e +×ķ× ¢ +ĠÙĥ اÙĨ +ĠÏĦ ι +Ġcho oses +ĠO wn +го ÑĤов +h ire +алÑĮ нÑĭе +ĠÐĽ Ñİ +Ġо ÑģÑĤав +te ch +Ġdro it +Ġsubject ive +en es +Ġdiv is +ave z +Ġmaneu ver +à¹Ħ à¸Ķ +ade ce +ĠEn s +ac ial +ĠProt ection +ĸ ´ +Ġform ally +Ġwy d +ingu ém +Ġz iem +Ġrecru iting +×Ļ× ļ +n em +Ġforb idden +ĠB apt +×IJ× ł×Ļ +Ġsubs et +ĠMag az +n ement +Ġaqu ela +rag on +Ġcomm ittees +Ġéta ient +ud i +ĠDa wn +Ġb ore +Ġcompos er +ĠwiÄĻ cej +ang a +Ġdis like +ĠD ays +åŁ º +Ġpar al +Ġm ientras +Ġheaven s +ãģ Ĵ +he id +Ġtrad ers +on ce +Ġmasc ara +ĠÏĢ Ïģο +Ġwhis per +ĠMus k +éĽ Ĩ +ĠFamil ie +All ah +ĠOl ivia +ĠPr os +Ġol ika +il im +Ġrép ond +ĠP eters +Ġ å¾Ī +Ġbit es +Ġv ic +ĠN Y +em ption +Ġ4 50 +Ġvisual s +Ġlie u +ück en +ĠSte el +ĠG P +w ait +Ġnotice able +uch a +Ġreh abil +Ġreject ion +ĠÑģлед ÑĥÑİÑī +Ġsl ider +Ġregard ed +Ġgrav it +ĠRes erve +c ount +Ġbre eding +Ġlon ge +ale b +Ġkn ight +Ġв ой +Ġprés ent +Ĥĺ ìļĶ +ĠSpec ifically +Ġpos es +Ġve ure +ok ay +em as +Ġ ãģ§ãģĻ +Ġma jÄħ +Ġweb inars +Ġcann abis +Ġdam als +ĠNorth west +Ġp ada +Ġcrowd s +Ġfut ures +Ġä n +Ġciv ilians +ĠS achen +æ į +Ġtr aces +Ġ먹 ê³ł +Q U +é¡ĺ ãģĦ +ĠI F +an ın +ìĤ ´ +Ġb iblical +ĠV ed +Ġst oring +ÑĢав лÑı +æĩī 該 +Ġn ast +Ġd ö +ÑĢ оп +el ia +Ġside ways +ĠUnder stand +ĠQ ur +Ġper pend +ĠMill ionen +Ġwater melon +ĠDiv ine +ult ur +ab ord +Ġsuccess es +Ġhom bre +Ġcar p +Ġsus cept +ung kin +Ġk ij +ul us +Ø§Ø ¬ +Ġnot ch +Ġpolynom ial +å¹ ² +å © +Ġún ico +Ġteles cope +Ġpolit ique +k iem +ĠÎŃ Î½Î± +Ġaggreg ate +ĠGe off +Ġtr il +ĠG RA +Ġsubscri ber +im et +Ġдол лаÑĢ +op ing +Ġth erapeut +ĠCan cer +Ġpar ade +Ġir rig +âĻª âĻª +Ġclear er +Ġb og +ĠM aur +า à¸ĩ +ĠShang hai +acht e +ĠK ol +el ujah +Ġha v +ĠCr ime +se k +Ġë ¡ľ +ien na +ĠG or +è Ľ +ĠпоÑĤ ÑĢ +Ġкаж еÑĤÑģÑı +ĠL ift +ĠS ort +ĠP sal +Ġp ing +ĵ Ŀ +ph is +ĠF UCK +ĠS yn +Ġbam boo +¬ ìĺģ +c uts +Ġm mm +Ġfunktion iert +Ġ _ +ÃŃ cio +St op +Ġimag inary +Ġnot amment +ĠIniti ative +ãĥ ¥ +ĠK urt +Ġlo osen +Ġbus car +çģ « +Ġz elf +Ġpro ps +åĽ ī +Ġmoet en +Ġmill i +Ġhall s +ĠM atch +Ġbrack ets +ĠC ou +æ¦ Ĥ +ĠÐľ аÑĢ +IS A +Ġcig arette +Ġcompet itions +ĠM IN +Ġbeh ö +vo or +Ġ ust +ĠZ i +ĠO cc +ul ates +Ġball oons +Ġpr onto +ĠM iy +ĠF ile +Ġкл аÑģÑģ +нÑĥ л +Ġcere al +Ġincre ment +Ġref ined +åı¦ å¤ĸ +pr ising +ĠR F +Ġrespect ful +Ġlo ot +ask et +Ġdeix a +ing le +Ġfuncion a +ĠRe vel +Ġso ber +Ġperform s +ĠG entle +ãĤ ¨ +Ġrecip ient +ĠHa use +Ġë ĥ +F rom +Ġmin isters +Ġpar adox +å°±æĺ¯ èªª +Ġtast ing +Ġ×Ķ× Ĺ +Ġre use +ĠL ane +ĠÑģов еÑĢÑĪ +Ġremem bers +Ġfemin ist +Ġcommit ments +Ġproject ed +Ġg az +iyor uz +Ġoblig ations +R o +z ar +Ġch w +ĠJ AM +ĠbÄĻd Äħ +asp berry +Ġм еÑģÑĤо +ë² ķ +Ġreg ulated +Ġw icht +ĠTre vor +Ġsecond ly +ĠIh re +els h +Ġrep orters +ÑĤоÑĢ а +oy o +G I +Ġinter connect +é IJĺ +OS H +æŃ ² +Ġbr ass +Ġign oring +ä»Ĭ æĹ¥ +in fect +Ġpro jekt +ore t +ÏĦα ν +ĠÑĤ ип +Ġmut ta +Ġunbox ing +Ħ ° +å¡ Ĭ +Ġadv ised +ĠDen ver +Ġsevere ly +ĠM hm +Ġfl ipped +Ġp ien +Ġkomm un +ĠF RE +Ġà®ĩ à®° +aint ed +Ġkn ives +Ġhab l +Ġgew orden +arett es +C S +Ġмал енÑĮ +Ġgal ax +Ġnin ete +ê±°ë Ĥĺ +Ġs is +Ġadvis ory +Ġdr illing +ĠWould n +ün f +gest ellt +ĠHel en +Ġ×ŀ× IJ +ap olis +Ġrze czy +Ġter ra +Ġhe p +Ġalg ún +ik k +Ġastron om +ĠStar bucks +k Äħ +Ġpat rol +Ġì½ Ķ +Ġg on +Ġ ãĢIJ +Ġson st +Ġencoun ters +Ġret rou +Ġshark s +Ġd or +ĠR ever +Ġev apor +Ġreserv oir +Ġalleg ed +ul er +Ġver m +Ġcommer ce +Ġf itted +ge m +Ġtact ical +Ġl ith +éīĦ å¡Ķ +h ad +è® Ĭ +Ġcarboh yd +Ġlength s +ι ο +Ġdem ographic +R ob +ĠS kin +cc oli +Ġsimpl ified +Ġread ily +ĠC um +ades h +ĠD Ã¥ +us st +ig ne +et on +Ġmen or +q i +OO M +à¸Ń à¸Ļ +Ġpsych iat +Ġeight y +Ġм илли +ĠT ob +ed o +ç¶ ² +ĠÄij ến +Ġcirc uits +ĠLAU GH +ic ism +em or +Ġreg ener +eg ree +Ġbure auc +ĠAl ber +ä¹ĭ å¾Į +ĠW or +å¤ « +Ġres in +Ġby ÅĤy +ĠI G +à¯į , +Ġ7 8 +Ġwe eds +ĠMy th +9 3 +æ ¿ +ĠëĤĺ ìĻĶ +é v +á ½ +ö ren +ç ar +ĠP AUL +Ġdisad vant +Ġposition ing +Ġcock tail +Ġagre es +n n +ĠS ally +M s +Ġinher ent +Ġmonet ary +Ġnat ur +ĠN h +ĠImp ort +Ġle ben +Ġw i +uss y +Ġob es +Ġwand ering +Ġìĭ łë +Äħ da +etch up +Ġdispos al +ĠJ A +ĠC er +z illa +Ġvir gin +ĠSl ide +and el +Ġrighteous ness +ĠÎ £ +Ġide ia +ä½ł 好 +иÑĢов аÑĤÑĮ +ר ×IJ +Com ment +Ġpre lim +ĠV ale +Ġì§Ģë Ĥľ +ĠV anc +OM AN +Ġп Ñĸд +Ġy um +st re +ce m +Ġpo cz +Ġfrag ment +ĠÑģлÑĥÑĩа е +Ġunder go +ĠH ank +ce ks +ĠF PS +Ġoc ur +Ġdeter ior +æ³ ¨ +Ġempres as +Pa ul +Ġ) )) +ĠвÑĢем ени +Ġsc old +×Ļ× ¢ +Ġsuspect ed +Ġaccess ing +Ġsubst it +Ġhistor ians +ä» » +Ġдел о +Ġsoci ed +r one +Ġre den +Ġext ends +epher d +Ġbal con +ä¸į èµ· +ĠSol o +Ġpolit ician +олÑĮ но +Ġirgend w +Ġtraum atic +Ġrapp er +ĠRO BERT +Re ally +æģ ¯ +Ġline up +AS E +Ġcontract or +ĠCorpor ation +g or +ĠTod o +ÑģÑĤÑĢ ой +F BE +Ġnews letter +Ġko ÅĦ +alt ies +ĠпÑĢ иÑĩ +ĠHe avy +Ġsw ords +Ġmanip ulation +Ġfun k +Ġv Ã¥r +ĠTal iban +Ġë° ¥ +Ġac ne +ür ü +Ġdes wegen +ĠD ust +Ġsil ic +Ġhook s +Ġbl ij +Ġpet its +Ġfil me +ĠBere ich +ĠSa id +Ġimp osed +Ġdi ary +Ġго ÑĢ +ĠG ates +Ġal ta +å¸ Į +Ġch cia +ple asant +Ġë° Ŀ +Ġmoż emy +ĠAust ria +Ġbro ker +Ġsuck ed +èĢ ĥ +Ġcomp artment +Ġcl one +Ġ×Ķ× ¢ +ĠDan ke +Ġnoch mal +ез д +Ġad renal +Ġkle inen +ãģ¾ ãģĹãĤĩãģĨ +Ġsubsequ ently +Ġdecent ral +Ġgen etics +Ġê´ ij +Ġmon itors +ĠApp lic +ĠRep orter +w ert +Ġwie m +ĠMove ment +Ġinterview ing +Ġhair s +Ġpu ò +ĠChel sea +Ġco her +Ġc ot +Ġz as +Ġpatch es +Ġl ah +Ñĥн к +ĠRe agan +ĠMar co +c ity +Ġdef ender +Ġdecor ation +ij i +Ġl itter +Ð ¨ +Ġj ego +RE W +ĠP ik +ĠHe e +ĠI v +Ġи де +ĠThe ater +ĠÑĩаÑģ ÑĤо +Ġswe ater +Ġhighlight ing +Ġa insi +Ġdipl omatic +ĠNever theless +å ³ +AS ON +Ġpúblic o +Ġf erm +reat ed +c od +Ġë¬ ¼ë +Ġm ister +ĠVanc ouver +Ġrecogn izes +ec d +Ġcomplic ations +en cial +ãģĹ ãģı +Ġê°Ģ ì§Ģ +ĠUlt imate +Ġva ig +ĠM erry +×ķ× Ĵ +ĠMar cus +ç¸ ½ +ow ego +Ġm ente +S m +Ġa ja +ĠTa o +Ġjud icial +Ġentrepreneurs hip +Ġнем ного +Ġp is +Ġer g +Ġch rist +ĠC urt +ĠÑĢаÑģ п +λ ε +ens ch +ÃŃ re +Ġfo cal +ĠDiam ond +av ÃŃa +Ġh anno +ĠSqu ad +Ġassoci ations +ĠCreat ive +Ġmess enger +Ġbe gging +Ġdec imal +Ġd Ä±ÅŁ +Ġmet adata +sel s +ĠÄ° ÅŁ +ữ a +Ġdiffic ile +d ı +Ġs laughter +ĠVer g +Ġ×Ĵ ×Ŀ +ç° ¡ +æĮ ī +ĠTe a +ass es +O k +Ġsynth es +ot iation +Ġpain ter +Ġel bows +Ġarchitect ural +ĠÑĢ ад +Ġgl or +im age +amp a +cul iar +ł ¨ +Ġte ve +ĠSt elle +ĠB am +Ġì´ Ī +as is +ip edia +ĠG I +ĠAct ive +çĦ¶ åIJİ +az i +ãĤĮ ãģ¦ +ĠL ucky +íķ © +ĠпÑĢ иÑħод +Ġrun way +Ġauthent ication +Ġpos ible +Ġsupp lements +Ġsurg ical +G en +Ġfeas ible +D O +Ġout look +Ġinter vals +Ġan ecd +Ãł ng +Ġstra ps +ĠSh u +ud d +iss enschaft +Ġport e +Ġcomm itting +Ġall ey +Ġco venant +ĠPed ro +less ness +ĠSol id +ĠM olly +Ġн екоÑĤоÑĢ +Ġcooper ate +åĮ Ĺ +oll en +Ġtun a +Ġkinderg arten +ĠS iz +Ġduż o +ĠM BA +ĠGEOR GE +ĠF isher +å¿ ĺ +ĠCa esar +ĠкÑĢаÑģ ив +ĠDel hi +zy m +Ġexpl icar +ê°Ģ ì§Ģ +un s +gr ow +ĠпÑĢ иÑģ +Ġ8 6 +Ġst ating +Ġmass a +ch ter +Ġì»¬ë Ł¬ +Ġdep uty +S M +n oc +Ġge ography +ĠEnter prise +ĠC ant +ö z +Ġun pack +ĠíĻ Ķë +Ġsearch es +Ġpres idency +Ġtri vial +Ġp ige +ou bt +ãĤ ļ +ì¼ ĢìĿ´ +Ġbudget s +Ġu b +Ġp ne +ĠY ale +ĠÅŁ öyle +reg ular +Ġimper fect +AR A +Ġfam ÃŃlia +ur m +ĠAdvent ure +ãĥ Ĭ +c is +em ark +Ġne go +Ġinappropri ate +ĠпÑĢи з +ĠÑĢ ол +Ġdream ed +B ry +Ġshut tle +Ġpill ars +Ġb ik +in um +ĠÑĥ Ñģ +ĠNe br +Ġperpend icular +Ġbook ed +ber y +Ġv ikt +be ar +es us +Ġвозм ожно +¨ ¹ +Ġpresum ably +ĠMem phis +Ġambul ance +×ķ× ŀר +Ġthumbna il +Ġmod ification +éĩ ı +Ġinterpret ed +Ġprom o +Ġκ ά +Ġε ÏĢ +Ġacoust ic +ĠD B +åĵ İ +Ġnon etheless +ou le +Ġpe qu +Ġkn ob +ãĤ £ +ĠëıĮ ìķĦ +Ġpurch ases +ĠÃĩ ünkü +Ġdivid ing +per form +ract ion +health y +ĠTit le +Ġu k +Ġcer ca +Ġargu ably +Ġf ale +ë³ µ +Ġgam ers +Ġutil izing +Ġoff ended +Ġt ava +al ı +Ġmed ian +Ġinfect ious +ĠAn nie +Ġsmart phones +Ġpar ole +åĸ Ŀ +ĠEp ic +z za +Ġun ified +Ġê·¸ë ķĮ +Ġcur tain +ĠÄ ĥ +Ġsex ually +Ġuns erem +ĠCon vention +Ġalleg edly +Y a +ĠH oo +en ment +æĢ ª +íĽ Ħ +Ġgig antic +Ġnot ing +Ġre bo +ĠJ ama +ĠAl z +Ġborrow ed +ì¹ ¨ +Ġper ipher +оÑĤ а +ĠG B +ĠGe ar +Ġeconom ically +Ġtele fon +Ġqu eremos +ĠдалÑĮ ÑĪе +Ġr as +ĠTe ach +ic ios +at os +Ġpl edge +b au +ĠHim self +L ink +Ġesper o +Ġchrom os +ĠP ER +Ġer le +Ġpod ium +ç os +Ġnie u +Ġf en +ĠGO D +ĠCh ocolate +wer k +Ġt ừ +Ġsupp ress +λ η +Ġ24 0 +Ġsit ä +Ġhonest y +ĠB io +ĠB ard +ĠобÑī ем +Ġм Ñĥз +Ġmar ble +ĠÑĨ енÑĤ +Ġproc ure +Ġrot or +ber n +Ġtu h +Ġhead set +at em +Ġwarrant y +à® ´ +Ġfil ing +ι ά +Ġcomp rendre +Ġimp ulse +Ġsal v +wr itten +Ġinstit ute +K im +ĠLGBT Q +fic iente +H is +ĠαÏħÏĦ ÏĮ +Ġteen age +or us +ĠÑĢаз б +S ee +ĠCons erv +á»ģ n +ful ness +Ġstraw berries +ĠAb u +и он +Ġo lla +NO ISE +ĠEm ploy +Ġwip ed +ur ger +Ġmod ifications +Ġíķĺ ì§Ģ +Ġfoot steps +Ġhon ors +Ġad ul +Ġfl ipping +ĠH U +Z Y +Ġintegr ating +ب ر +ull a +Ġnatuur lijk +ĠíĹ Ī +ĠEth ereum +ÙĬ ÙĦ +w ed +Ġpe aks +ĠK es +Ġblo om +Ġcr ashing +Ġ9 11 +ĠоÑĤ лиÑĩ +Ġcontro llers +ĠD od +Ġвм еÑģÑĤе +Ġsort ir +å¥ ĩ +ĠStra ight +ĠGrac ias +Ġgro ove +Ġto gg +Ġìĭ¶ ìĿĢ +é ro +Ġout ward +ĠW A +ĠRock y +Ġsc am +Ġhay at +ig nty +â Ħ +pl ings +Ġantibiot ics +Ġ ä¸Ģ +Ġnever theless +j ang +com merce +Ġspo iler +Ġglo ve +Ġch atter +ĠB Y +~ ? +Ġíĺ ¸ +Ġdem ol +we chsel +im ir +Ġra id +еÑĢ Ñħ +ìŀIJ 기 +en f +Ġcomment ed +Ġoptim ized +Ġconv icted +Ġb ats +ĠS B +ĠA ur +ĠT ong +Ġimplic it +ĠJan et +Ġre ag +ãģ ² +ĠAdv anced +Ġimp ose +ש ×Ķ +Ġschem es +oug her +ab olic +Ġê±° ì£ł +Ġslow ing +Ġwt edy +Ġdest ructive +Ġоп ÑĢед +Ġland mark +Ġëı Ī +ĠWalk ing +Ạ¹ +Ġt ijd +ĠK N +ĠQu ant +ìĺ ¤ë +Ġк ÑĢÑĥ +Ġper der +Ġno ve +änd e +Ġãģ Ĺ +b ia +Ġcust ody +Ġb iod +æĿ± 西 +Ġdirect ing +... âĢĭ +Ġre loc +Ġdemand e +ãĤĵ ãģł +Ġo ÄŁlum +Ġод на +ĠMil k +åı · +ĠK ra +ĠH onda +Ġp ue +Ġele kt +Ġbegin ners +Ġspe ar +ÃŃ nh +ĠLu ft +Ġn ig +ĠSchool s +Ġfor ums +ĠQ in +pp o +Ġz ag +ĠÐ ® +Ġtooth p +ĠSt yle +ì´ Ī +Ġpun ct +Ġrep s +ĠA ly +Ġamend ments +Ġö z +Ġdig its +ur ai +Ġcha otic +ĠMas ters +e on +ĠC ash +ĠC uz +Ġbede utet +Ġscan ning +Ġж д +н еÑĤ +Ġcertain ty +j ek +Ġdi jo +ĠCl imate +Ġr inse +Ġk rij +vel and +Ġsound track +ĠSa fe +ĠNo va +9 4 +Ġa the +ĠVer b +ol er +ìĿ´ì £ł +Ġv in +Ġrespir atory +ĠStud y +ĠC AM +Ġav ocado +ĠZ hen +Ġlat ency +Ġfe athers +Ġcont ar +Ġв еÑī +Ġf ark +Ġbl ended +Ġexpl oded +ĠX X +ĠBen im +Ġalgu ém +isto ire +Ġconfident ial +Ġm ast +Ġì ¿ +ge h +Ġdis respect +ĠSystem s +Æ° a +E d +Ġw ys +Ġex otic +Ġgl owing +ù ng +oun ge +è Ħ +ани з +Ġpal av +ĠSw ord +Ġg im +ĠC row +Ġpot ent +b ish +Ġab used +ĠJ ed +Ġg ambling +ĠS pect +Ġinvestig ators +æĻ ļ +Ġr att +Ġdo b +ĠD ES +h og +ĠоÑĤк ÑĢÑĭ +íĮ ħ +ĠденÑĮ ги +Ġíĺ ¹ +Ġë¨ ¸ë¦¬ +Ġsat uration +Ġinher ited +ĠInnov ation +ìĹ Īëįĺ +Ġtang ible +Ġdep ri +h ed +Ġпом ог +Ġslic ed +ॠį +Ġth ế +Å ¥ +6 8 +Ġcor ona +Ġgift ed +Ġso ir +Ġhum ility +ĠìĿ´ 걸 +Ġflaw s +ĠпÑĢ акÑĤи +Ġk ald +wa ż +y w +ãĤĵ ãģ§ãģĻ +ir teen +Ġcroch ets +¦¬ ê°Ģ +ĠìłĦ ìĹIJ +Ġdes e +æ¥ Ń +Ġм аг +Ġdz iaÅĤ +Ġl ég +ch anging +Ġlle v +ÅĦ sk +çĶ » +Ġ198 4 +orn s +ĠW elsh +Ġpharm aceutical +Ġpump ing +ĠSh aw +p unk +Ġva ult +Ġkin etic +Ġhur ricane +ĠInc luding +ứ c +ĠGrand pa +ans hip +é¦Ļ 港 +ĠвÑĭ Ñħод +н ож +ľ ł +ut ta +Ġê²ģ ëĭĪëĭ¤ +Ġb az +Ġпо ÑĪ +Ġpe culiar +zy Äĩ +ĠEll ie +Ġlearn s +ĠKr ishna +Ġconse cut +Ġemp ath +ĠD in +Ġtrad ed +ĠBor is +ugg age +oll a +Ġназ в +Ġetern ity +Ġв п +è mes +Ġgra pp +b é +ĠпÑĢед ÑģÑĤав +ĠF C +į ëĭĪëĭ¤ +e ven +ĠNebr aska +ortun e +Ġk arena +ĠAg ent +Ġst ing +ĠP I +Ġmunicip al +power ed +Ġconse gue +ĠMan chester +Ġrain y +Ġbl i +Ġk ost +Ġhal ten +ĠAh hh +ins ula +er ting +ĠاÙĦ Ùģ +Ġrel acion +Ġk omen +Ġd ome +Ġpri ests +ĠInt rodu +rop he +sh ore +vel t +clip se +ĠÑĢ ÑĥÑģ +×Ļ× ¡ +Ġsab emos +ĠHoll and +og i +ank i +ĠM ats +Ġsm oked +ull ie +Ġeuro pe +ĠдейÑģÑĤв иÑĤелÑĮно +Ġbard ziej +Ġtransform ing +ĠE z +op ath +Ġìĸ¸ ëĭĪ +ĠÑģÑĤ ан +ằ ng +ั à¹ī +ĠO uch +Ġclear ance +ust ain +Ġsolid arity +Ġpro ving +ĠÐĺ н +ĠÑģ ÑĬ +Ġpro long +ад но +Ġs os +ĠDe al +Ġ17 0 +m ons +Ġз ем +Ġlo gged +Ġlif elong +Ġsens ory +Ġbe hold +ĠF AR +èt ement +ĠFed eration +Ġdod ge +ĠSh ir +Ġdrag ons +ĠAr ctic +Äħ ż +Å į + º +Ġden ke +Ġpodr ÃŃa +co le +ÑĥлÑĮÑĤ аÑĤ +Ġsystem atic +ам а +ch os +Ġclin ics +ĠB S +Ġtal es +us ions +Ġí Ī¬ +Ġpres ervation +Ġl ore +ĠProt est +á» Ľ +å¸ Ĥ +Ġacknowled ged +ĠIs aiah +ĠëķĮ ëĬĶ +Ġ× ĺ +Ġcompet itor +Ġadv ancing +z ip +Ġtent h +ĠLa ure +Ġh ints +Ġexerc ising +ŀ ľë +ĠIntell igence +u ated +OU T +op ed +Ġaut onomy +Ġbrand ing +ĠMediter ranean +Ñĸ к +Ġscrew driver +Ġsu pre +Ġst ap +Ġjurisd iction +ĠSetting s +Ġfore front +ĠF emale +com fort +Ġmultiplic ation +ĠMur ray +Ġbo b +ĠT as +Ġt ahu +Ġon un +et ter +Ġproph ets +l ag +Ġreven ues +Ġpr á +Ġupload ing +Ġmach inery +asc al +ĠEst á +ĠG oth +ĠB ald +ĠS aw +Ġstri pes +ìł ij +Ġpow in +æĹ¥ æľ¬ +Ġhost ile +Ġdar um +Ġprevent ed +ожалÑĥй ÑģÑĤа +Ġalgun as +Ġhop eless +Ġz naj +Ġread ings +Ġcra ving +t at +ĠP ig +Ġli ar +çĪ ± +Ġmulti player +Ġd ale +ĠCour se +íģ ¼ +ĠK ita +Ġcustom s +Ġrespond s +end ra +è¦ ĸ +Ġmet ro +Ñģ ол +Ġmitig ate +Ġopp ression +Ġ æĪijåĢij +qu inho +Ġam mo +Ġen fer +Ġp ony +Ġ ounces +° Ķ +ĠìĪĺ ê°Ģ +Ġdich o +ĠDe b +Ġwond ers +ĠRo ose +Ġpri zes +ĠA LEX +Ġthank fully +Ġtiss ues +ĠÑĢав но +ĠL una +intell igible +ĠìĻ ¸ +ê° ij +ĠHe at +ĠÑģ ид +ĠQu i +Ġ ions +Ġaccommod ation +ä¾ ¿ +ĠK art +ien st +Ġt arde +Ġso aked +ĠCase y +Ġì´ Ŀ +ĠÑĢ Ñĥб +Ġdifferent i +Ġleft over +Ġexch anges +sec ond +Ġfirst ly +Ġbuild er +ri en +Ġd w +Ġboun cing +? < +olog ÃŃa +we alth +Ġmed itate +ĵ¤ ìĿĺ +ĠC raft +è§ī å¾Ĺ +æĻ ® +ri v +ĠAgain st +Ġcer amic +esp ère +Ġcompet ent +ĠHop kins +Ġkil os +Ġgra vel +Ġpist on +Ġfriends hips +Ġesc re +Ġvo z +ĠGes ellschaft +Ġunter stüt +Ġmu j +Ġwarning s +p os +ĠProfess ional +w szy +od le +b ands +Ġteam work +stell ung +Ġd x +åį Ĭ +Ġatt orneys +Ġweit ere +ãħĭãħĭ ãħĭ +ĠOrig inal +×Ļ× Ĺ +Ġbroadcast ing +ĠпеÑĢв Ñĭй +uch i +Ġhe ure +Ġgra bs +ĠW OR +ĠPla id +M in +Ġp az +ĠP uis +um u +it ates +Ġco ats +Ġbu en +Ġhe ir +Ġpne um +ש ר +ens er +ĠJUD GE +Ġbl onde +á¹ Ľ +Ġg ak +Ġs ık +Ġquot ed +Ġequip o +Ġw ishing +ÃŃ cia +Ġver bs +çµ Ħ +ĠCanad ians +Ġgover ning +ĠEv ans +E uro +Ġgen res +Ġunters chied +ĠBeck y +³¼ ê²ĮìļĶ +Ġe inge +ĠRa ise +ol and +ĠStr ateg +Ġer es +ĠVeter ans +Ġbreak out +Ġsant é +Ġad el +Ġinvestig ated +Ġpe ur +Ġag ile +Ġrail road +ans ka +Ġе й +Ġexp os +ator ies +ĠCont ent +Ġtruth s +ĠTra il +Ġgu a +Ġp ores +Ġwrit ings +ĠU hr +ĠThat s +Ġic ing +O C +ĠProdu ction +Ġcar ne +IS S +Ġn inguém +n on +Ġv icious +×ķ× Ķ +Ġrecon nect +Ġcent res +ĠK em +Ġcre ase +ĠìĿ´ë ¯¸ +айÑĤ еÑģÑĮ +Ġб оÑĢ +ĠHay ır +ĠÑģ Ñĥд +Ġún ica +owa ÅĤ +Ġad her +h ua +Z Z +Ġprecis o +Ġcurrent s +Ġseason ed +ĠIo T +ĠB ishop +è¨ Ī +st ed +ĠBern ard +ì¤ ĺ +æ² » +ĠGl enn +Ġktóry m +ื à¹Ī +Ġast rolog +ĠK ot +å¤ ľ +Ġparf ois +Ġfor wards +ĠW iÄĻ +ĠÎ ĺ +Ġn ano +è» į +s ub +ĠBr ill +Ġgr it +Ġc ited +g ado +Ġmel ts +Ġfor cé +âĸĪ âĸĪ +Ġb ajo +Ġdiscret ion +° ° +at ivity +Ġsitu ated +ãĥ« ãĤ¯ +Ñīе е +åľ° æĸ¹ +ĠпÑĢин ÑĨип +am az +Ġaqu arium +Ġdissol ve +ĠGod s +S uper +Ġam id +z k +Ġ ãģĦ +éł IJ +amp f +Ġhel a +' ! +Ġdevelopment al +ĠD ise +ĠÑĢабоÑĤ аеÑĤ +Ġsnaps hot +好 好 +Õ ¸ +ĠY ue +ĠH ulk +ĠDo om +ĠFel ix +Ġré f +M ale +ç· Ĭ +ph ants +EN S +ĠMe chan +ĠG olf +åĨį è¦ĭ +Ġgener osity +ät ze +Ġunlock ed +Ġ ãĤĴ +íĥ ģ +ocaly pse +Al right +Ġê° ľë +Ġ×IJ× ij׾ +ĠKeep ing +Ġcollabor ating +ch ief +ĠFern ando +Ġchef s +ĠíĶ¼ë ¶Ģ +Ġsk ipped +Ġperson n +Ġax e +che z +Ġextract ion +ĠA V +ĠGib bs +Ġí ľ +Ġs ı +I AM +V iew +ĠGR ANT +Ġëª ¸ +Ġver ification +Ġdep icted +ĠMo z +ou x +Ġt ul +Ġsc anner +Ġcomed ian +ĠVol ks +ĠJE FF +è¨Ĥ éĸ± +§ Ħ +Ġdistract ion +r á +ĠIN TER +Ġsin cer +Ġ×ŀ× ª +Ġש ׳ +Ġconstruct ive +ar f +ĠëĪ Ħë +Ġe co +r amos +Ġrenew ed +in ement +ĠU b +ĠPe pper +ì§Ģ ê°Ģ +ĠDar win +Ġmerch and +Ġv árias +è ce +N G +ĠìľĦ íķ´ìĦľ +Ġак ÑĤив +ĠUn ters +ع ÙĦ +Ġint ric +omm a +ie ving +ĠCarol ine +åĵ ģ +ĠPR ES +Ġperform er +Ġaut our +ãģ¾ãģĽ ãĤĵ +Ġutter ly +Ġsynth esis +Ġles bian +Ġretrie ve +Ġmane ira +Ġimp air +Ġment oring +ĠSoul s +ĠGo Pro +ÑĢ аÑĤÑĮ +Ġc ose +ĠSS D +I RE +Ġup front +ĠA un +Ġgam er +Ġl itt +Ġag gression +ĠLike wise +ĠBet ty +ĠD art +ĠD LC +ish ment +ìŀ¥ ìĿĦ +Ġ 对 +ç» ı +c ream +ĠBaby lon +Ġn ug +br ar +Ġa ynı +am ily +b ike +ahah aha +lo yd +Ġmir a +Ġper me +ĠG aming +Ġfirm ware +M a +Ġassist ed +at ics +Ġìķŀ ìľ¼ë¡ľ +ĠM ental +niej s +ĠI z +ow Äħ +Ġt ougher +Ġde ed +èĭ ¦ +Ġsty lish +ĠTool s +ĠH amp +Ġsun screen +Ġartic ulate +i ye +и ÑĦ +ĠSp read +ĠHA VE +Ġsw irl +Ġspons oring +ä» ĭ +iov ascular +mes i +Ġrelax ation +ĠÑģво иÑħ +Ġmar gins +Ġsa ÄŁ +ĠPr ide +ĠÏĦοÏħ ÏĤ +и ÑĨи +en ci +Do es +Ġcor pse +Ġend urance +Ġí ŀĺ +ì¹ ´ +Ġhair cut +Ġinterrupt ed +Ġwind y +ĠC aleb +Ïģ Ïĩ +ĠPour quoi +Ġhol istic +uc lear +ĠWho le +å£ « +A ct +Ġgall on +c ade +ĠReg ional +ro ads +ĠSch ne +á ng +Ġиз мен +ãĤĪ ãģŃ +Ġmen us +Ġspl itting +Ġpr iced +ĠÎ ĵ +Ġus ername +ĠÐŀ Ñĩ +Ġcomp ressed +y in +Ġguard ian +Ġgo of +Ġcheck list +Ġinter change +Ġexped ition +Ġex tern +Ġinfra red +eng o +Ġden ying +Ġpack ets +on ent +B B +ĠInc re +Ġsin i +ÃŁ er +è g +ma al +gen eration +Ġminor ities +Ġlle var +Ġnom ination +Ġcons id +Ġ×ľ× ¢ +m uÅŁ +ĠEs c +Ġnumer ator +Ġka ik +Ġktóry ch +ies en +Ġv ê +ĠUS S +ĠPri vate +Ġод но +Ġal ém +ÃŃt ulo +Ġlim b +Ġforg iven +Ġdiscl osure +ÏĦ ί +Ġning ún +Ġtherapeut ic +Ġnegoti ating +ĠN ike +ense ful +Ġin cap +Ġflag ship +t own +â Ī +ĠÏĢ ολ +Ġwol ves +Ġviol ations +ĠAr nold +Ġinterven e +Ġhe ater +Ġrecurs os +Ġma id +ê² ¼ +Ġдав айÑĤе +ĠCe lebr +Ġca pe +ĠSt y +ain en +s ite +b ij +Ġп олÑĮз +Ġfr amed +Ġpublish ers +ĠÑĩ ÑĥÑĤÑĮ +Ġtempt ation +Ġcert eza +Ġex empt +ìĬ ¹ +se lling +ĠT ask +ho on +ĠC oc +ĠPark s +Ġrepet ition +ĠÑĤ Ñĥда +Ġens l +ĠdeÄŁ iÅŁ +ĠOr lando +ĠMain ten +æŃ ¢ +oc ument +ĠH C +Ġscoot er +Ġнап иÑģ +Ġtight er +Ġte ase +Ġremo ves +Ġkij ken +ĠÑģÑĥ ÑīеÑģÑĤв +Ġth é +ĠвÑĭ глÑıд +Ġrel ieve +Ġmit ä +Ġstation ary +ö ff +p able +Ġar ter +Ġdé f +r ative +Ġcon ect +Ġsad dle +ĠD iane +Ġcomm emor +fend im +S ÃŃ +Ġíģ ´ë +Ġman ge +at te +Ġarrog ant +Ġrobot ic +Ġgi Ãł +æĺ¯ çļĦ +Ġneighbour hood +iss on +Ġдв иж +ĠR I +ĠNorm an +b rand +am ation +Ġraz or +Ġmur ders +ĠÑĤ Ñĥ +Ġwszystk im +Ġut ilities +Ġmicros cop +ê ¿ +Ġda qui +oll ar +ĠÐĶав айÑĤе +Ġann ée +Ġkilomet res +Ġhom osexual +Ġarchitect s +ãģ¡ ãģ¯ +Ġni ye +L ER +Ġmicro phones +ĠSt unden +Ġconsecut ive +iend a +v änd +D ER +Ġlif ts +ĠMe at +Ġsave z +íĸ Īëįĺ +M en +Ġdism ant +ê±°ë ¥¼ +Ġins ulation +Ġsc all +Ġsp ooky +Ġpar c +Ġball et +ĠWhats App +Ġfr anc +Ġdeliber ate +Ġíħ Į +Ġm ars +ĠZ ur +P r +dis ciplinary +Ġobs ession +м е +Ġmarch ing +ĠEmer gency +ig uous +Ġs zy +ĠL ands +Ġboard ing +ĠпоÑĩ ÑĤи +Ġenv y +Ġcompassion ate +Ġmer ci +Ġdes irable +d ale +Ġcan ım +ĠAnt ar +tem ps +Ġconfig ured +ĠComp ared +ne h +ic ating +Ġnic kel +ÙĪ ÙĤ +Ùĥ ÙĪÙĨ +op es +Ġform ulas +ĠÐķ ÑģÑĤÑĮ +Ġpo bl +ĠP J +ĠL ud +ä»Ĭ åĽŀ +ĠBr id +ĠH og +ĠBr is +J en +Ġshad ing +ĠY as +Ġdistur bed +Ġrecomm ending +Ġc é +ĠH OW +ìĹĪ ìĸ´ +Ġrevers ed +ĠInteresting ly +iox id +åħ Ń +Ġìĺ¤ ì¼ĢìĿ´ +ế u +x x +Ġou ais +ĠYouT ubers +ĠR osa +ĠH aupt +j adi +Ġvlog s +Ġcult ura +ĠLeaders hip +ĠH ep +Ġill um +´ë ıĻ +Ġcustom ized +Ġmar ca +Ġqu atro +Ġн аг +ĠSpace X +ĠE igen +ast ing +ĠolduÄŁ u +Ġfor ts +ãģ ī +r iment +ien cia +Ġten ir +ro ffen +Ġ197 9 +Ġc ie +ĠëIJĺ ê³ł +Ġes cri +ÏĮ ÏĤ +íı ¬ +uz zy +C ong +ìĿ¸ ìĿ´ +G reat +s il +é ch +ãģ¨ ãģĭ +Ġmult ic +ĠDis k +² ķ +Ġfaz la +Ġle vant +Ġab ajo +ur ry +st ru +Ġ먹 ëĬĶ +Ġaccess ory +Ġдв иг +ĠR id +20 19 +Ġdown stream +æķ ¸ +Ġk az +ut an +Ġchar coal +Ġa fect +w u +Ġcontext s +Ġfe ared +ĠìĦ ¤ +Ġhist ories +Ġf as +ens ible +Ġcoco a +ill ar +ge ons +Ġspiritual ity +ĠP ew +Ġpharm acy +Ġpass ions +Ġb os +Ġall á +Ġthri ving +ĠRe act +Ġoccup y +Ġwithdraw al +Ġallow ance +ĠFra ktion +Ġbud dies +Ġid le +Ġdissol ved +Ġpreval ent +Ġmil itar +Ġsens ing +Ġpo jaw +Ġanc ora +Ġabund ant +Ġha irst +ãģĤ ãĤĮ +Ġtw ee +Ġnäch ste +ĠMöglich keit +Ġho o +uff icient +Ġfant ast +Ġed ible +Ġëĸ¨ ìĸ´ì +ìĽ ĥ +Ġve in +uc ci +Ġdevot ion +Ġconce aler +in come +Ġrecy cled +ĠìĬ¤í ĥĢ +Ġpont os +Ġdess us +Ġvé rit +Ġreflect ions +ĠA A +Ġtake away +b are +ĠCont act +e il +ĠHe ar +Ġmir ac +ĠGer ilim +ĠÑģам Ñĭй +Ġv ivo +Ġkilogram s +ĠCr im +û t +7 8 +Ġsincere ly +ra z +Ġë³ µ +Ġarri v +Ġconcept ion +ĠPers ian +Ġsj äl +Ġst arring +ĠìķĦë ¬´ +ĠFore ver +е ÑģÑĤÑĮ +Ġve il +Ġsubt it +od ka +ĠоÑĤно ÑĪ +Ġcook s +ен Ñı +K ay +Ġni ños +ĠPh one +Ġstitch ing +Ġfinger print +é¢ ĺ +λ ά +Ġded icate +ĠL ob +Ġblack s +ĠB le +b out +ĠÄij ang +Ġe ks +Ġsqu ash +ĠK ü +od i +Ġn Æ°á»Ľc +Ġvoy age +Ġplay ful +ĠØ¥ ÙĦÙī +an ic +Ġcondem n +ĠB öyle +ĠPol ize +ãĤ¿ ãĥ¼ +Ġay uda +Ġp am +à¹Ħ à¸Ľ +ĠK athy +ед ин +нов а +Ġbr ig +eg er +Ġe agle +Ġvis ions +ĠíķŃ ìĥģ +Ġsh itty +Ġh ott +ĠBr itt +ut ors +ENT E +æĽ ² +Ġph on +ĠB ing +Ġпод деÑĢж +spr ing +æĸ ¯ +et ten +Ġpil gr +Ġed iyor +енÑĤ Ñĭ +ag gio +Ġj ul +Ġcomp rend +te il +ĠØ ² +Ġperform ers +Ġinf amous +ĠM K +ç ª +æ³ ģ +ot le +e ff +ĠH ash +Ġcow ard +ĠB RA +ĠD D +Ġcom ida +Ġpl ata +Ġfl ap +ĠMe hr +rib ution +ĠY emen +Ġmyster ies +ĠÄ° yi +Ġst ell +Ġeyel iner +Ġdel es +Ġnail ed +Ġillness es +Ġst acks +Ġtrabaj ar +fl ower +ci u +Ġcr ude +Ġsubstant ially +Ġhome m +Ġnep hew +Ġstamp s +Ġcar bs +ÑĮ ÑĤе +mo oth +Ġtun nels +ac ie +æ³ ¢ +ĠSe ñ +ĠH era +ĠìķĦëĭĪ ìĹIJìļĶ +ĠWy oming +ĠHD MI +ĠL is +u ción +Ġste er +о Ñİ +иÑĤ а +N T +Ġìĸ¼êµ ´ +Ġpal ms +Ġne on +ов аниÑı +Ġfilter ing +Ġjou er +ĠH ö +Ġне Ñģ +ê²ł ìĸ´ìļĶ +Ġ8 1 +Ġstory line +Ġprz ep +Ġthank ing +ĠBo eing +Ġsoft ly +j em +алÑĮ нÑĭÑħ +Ġflash light +Ġп Ñĥ +ĠW OMAN +ắ c +ÃŃ ch +Ġlux urious +Ġw ün +Ġimpact ful +Ġcons on +re u +ir ring +if ter +Ġconstitu ents +èIJ ½ +Ġ9 4 +ĠT ou +g om +ĠìĥĿê°ģ ìĿĦ +Ġstere otypes +Ġmoż li +åĪĨ 享 +Ĥ ¨ +Ġpencil s +ĠÑģл ож +Ġih rem +ĠBes ch +ĠK oh +ĠEnt scheid +Ġle k +Ġför s +Ġtotal mente +Ġlive ly +Ġent ropy +Ġdisc ern +ĠÐĹ Ð½Ð° +Ġdo v +Ġmyth ology +è¨ĺ å¾Ĺ +apan ese +Ġapprox imate +аÑĤ ив +if iable +ĠSe o +åĢ Ĵ +´ìĭ¬ íŀĪ +Ġìĺ · +Ġtempor al +Ġi T +Ġest at +к им +Ġspr ink +Ġgr und +Ġinfant ry +Ġsch affen +ç´ Ħ +Ġan k +ri ages +ĠYe on +ĠMor oc +Ġinv asive +ģ Ķ +Ġparent ing +ĠR is +ib ile +Ġmod s +å½ ¢ +ĠпÑĢов еÑĢ +ĠTh ing +ĠWhere ver +Ġacknowled ging +Ġpa wn +um mer +or b +6 9 +Ġretr ouve +Ġrel ies +ĠHigh way +Ġa we +ãģ§ãģĻ ãģĭ +ita ire +Ġapplic ant +Ġais le +w orm +Ġpay load +Ġcar re +ĠB ach +æł ¼ +Ġì¹ľ 구ë +ни е +Ġit ÃŃs +onna ise +s ol +èı ¯ +alg ia +Ġrock ing +Ġbest en +rit es +^ ^ +ин ой +Ġba ixo +Ġ기 ìĸµ +оÑĤ ÑĢи +s im +Ġinc arn +ëĭ¤ ìĿĮ +Ġl ick +s ided +Ġ7 1 +f order +Ġreson ance +Ġte gen +Ġmet aph +ows er +Ġ×IJ× ł×Ĺ׳×ķ +? ãĢį +Ġsp ielen +Ġvoll ey +ĶìĿ´íģ¬ ìĹħ +lo oked +Ġsent enced +Ġmultip lying +Ġide als +Ġwahr scheinlich +Ġdepos its +bil ir +Ġeff et +ill on +Īë §Į +Ġtestim on +Ġz awsze +ĠпÑĢоÑĨ еÑģÑģ +ĠL av +ä¸į éĮ¯ +Ġtrava iller +Ġla isse +ĠMount ains +ĠÑĢ об +Ġexam ined +it us +W as +л Ñĭ +Ġattrib uted +ĠìĬ ¹ +ĠBar on +Ġg ep +Ġatt ent +ĠColl ection +Ġthe at +ĠC ai +Ġwell s +Ġhuman o +çĹ ħ +ĠH ast +ĠÑħоÑĤ Ñı +cz as +Ġperm its +Ġle gg +Ġe po +ĠF en +Ġth i +ĠF oi +Ġé lect +Ġ8 3 +Ġover th +Ġ è¬Ŀè¬Ŀ +Ġten ant +è² · +N ext +Ġpra ised +sec urity +ĠImp act +为 ä»Ģä¹Ī +Ġv ouch +Ġneg ó +Ġun ve +Ġcritic ize +ĠKen ya +Ġtact ic +Ġlo gr +Ġpo is +Ġpap a +spe aks +ðŁ ij +isp ers +Ġsur plus +Ġcold er +åį Ĺ +åIJ ¬ +pl ets +ĠV ienna +ĠLe ad +Ġaer ial +ĠT ah +енÑĤ ов +ĠGree ks +C am +Ġmá xim +Ġk uin +ch io +Ġdemonst rates +an os +ĠC ert +ĠÑį н +Ġblog s +ĠìĦľ ìļ¸ +Ġbe ams +ик ов +Ġprompt ed +Ġfright ening +ĠPors che +ãģĪ ãģ¦ +lar ını +Ġch illing +is phere +Ġfl ashing +ĠK ard +b read +Ġex h +Ġty cker +Ġec ological +ĠMa e +Ġ×ŀ×IJ ×ķ×ĵ +ĠëĤ ĺëıĦ +л он +ys s +Ġper gunt +Ġpri x +izz ard +Ġcan cers +Ġ9 1 +s usp +ĠIt em +ÅŁ a +Ġp est +Ġtak Äħ +Ġl ymph +ĠPat ri +f ill +Ġrec onna +Ġoptim ism +Ġmim ic +Ġì² ľ +ĠMad ame +oc y +l ining +åijĬ 訴 +erm e +Ġfold ers +Ġcz ÅĤ +uch ar +Ġcur so +Ġbre ach +ни ÑĤÑĮ +Ġp amiÄĻ +Ġel ig +Ġaut op +F low +Ġprogram med +ĠPro cess +Ġfig ur +ĠS F +ĠE les +Ġprogram mes +Ġdiz zy +ìĭľ ê°Ħ +Ġли бо +Ġsn iff +ĠSeb astian +ĠH ye +Ġ4 000 +Ġperm ite +æ¢ Ŀ +Ġза Ñī +Ġgu it +ĠD ais +Ġaccord ance +Ġmod ular +ogene ous +æĭ į +Ġpou quinho +Ġart illery +Ġlub ric +Ġvol can +ĠN H +ðŁ ¤ +Ġde an +R h +Ġminist re +åĿ IJ +ĠIn v +ĠBul gar +ĠD aten +è İ +I m +Ġorigin ated +ĠN ixon +inte gr +Ġlack s +ĠN acht +ìĸ´ë Ĥĺ +cam era +Ġrad ish +ki ye +Ġang es +Ġpré f +j uk +ĠBe e +ĠB U +ĠвоÑģ п +ĠB T +ê mes +ĠSt ück +ĠIn k +æĪĸ èĢħ +ĠSerge ant +ĠMult ip +Ġhiç bir +ĠС ам +ĠD é +ol ph +ìĸ ¸ +Ġimp at +ĠìķĬ ê³ł +ĠÑĤак ого +ĠнавеÑĢ ное +Ġunpredict able +Ġm end +ĠìĹĨ ìĸ´ìļĶ +Ġjakie ÅĽ +Ġann i +Ġdon né +ĠK irsty +Ġrectang ular +Ġempez ar +ĠEx change +ê° Ķ +Ġé conom +ãģĵ ãĤĵ +el in +re ibt +Ġ×Ķ× ¤ +Ġc emetery +Ġespañ ol +ol in +лÑİ Ð´ +Ġgr âce +all en +ĠPh ilos +ĠEr st +Ġìĥ Ī +ĠV id +G ive +O H +μ ο +ĠP are +Ġmetabol ism +Ġma ple +Ġax le +ĠD y +Ġkomm e +Ïİ Î½ +Ġgreat ness +Ġver ified +Ġsp é +ĠFahren heit +ĠB ren +ĠConf eder +Ġhist oire +Ġelimin ating +ĠAd ding +ĠAb i +æĿ İ +Ġhospital ity +t im +Ġbon ito +Ġpart es +ĠдÑĢÑĥг иÑħ +ĠSh ay +ĠS ed +Ġreg rets +Ñı ми +Ġten ants +éĢ Ł +ĠP TS +Ġdev i +ĠL ate +ue z +Ġsö yl +ãĤ » +Ġìŀ¬ë °Į +Ġtogg le +Ġmas king +алÑĮ ного +Ġpers ön +Ġamer ican +f ik +ĠR GB +ens on +ĠK A +ww ww +ĠÑĢ ег +met ics +Ġeduc ator +ãĤ· ãĥ«ãĤ¯ +p ark +елÑĮ зÑı +ar us +ÑĢ еÑĤ +Ġfe ito +Ġcho ir +Ġlar go +Ġe ens +Ġwat ts +ĠSing le +Ġsuscept ible +ic er +Ġв клÑİÑĩ +Ġp us +íĻ ĺ +E ng +Ġfant as +Ġspecific ation +Ġconfront ed +ĠColumb us +ив еÑĤ +ar ım +Ġcaffe ine +mun ition +Ġmig rants +l ide +it ations +ĠG eme +Ạ« +Ġpl anner +Ġstim ulate +Ġapro xim +ce u +ĠN om +Ġv og +ĠÑĢ аÑģÑĤ +Ġense ñ +Ġsell ers +Ġgut en +z d +C al +Ġdescri pt +Ġrecon ciliation +z inho +á¹ĩ a +ãģĺãĤĥ ãģĤ +acy j +ĠCO L +s aw +ĠíĻķ ìĿ¸ +Ġvar it +Ġpartner ing +Ġdet ention +Ġbomb ing +c lapping +ien cies +ond u +AM E +Ġê°Ļ ìĬµëĭĪëĭ¤ +c ÃŃa +ĠпоÑģ ÑĤо +ĠAS MR +Ġhome page +Ġsi è +an tha +ĠP oll +Ġ igen +cy ch +Ġê°ij ìŀIJ기 +Ġconsider ably +ä»ĸ çļĦ +ĠAr ist +Ġwith stand +Ġqual itative +ĠK raft +ĠÑį лекÑĤ +ĠBe ad +екÑĤ ив +Ġcr ushing +ì³ IJ +Ġnav y +ÙĪ Úº +s ho +Ġo ak +ipp ers +Ġso ils +Ġpig ment +Ġev itar +ãĥ ĩ +Ġf use +ĠD ale +: " +Ġcompl ètement +Ġke l +๠Ĩ +Ġqu atre +ĠU M +Ġë§ IJë +æł ¹ +ÃŃ r +Ġle isure +ĠH ousing +Ġfold s +est ion +AR S +Ġm ash +urp ose +Ġaccum ulated +ĠSt uff +èª ŀ +Ġtap es +ĠÑģ илÑĮно +ĠLO VE +Ġ198 2 +Ġsc ars +Ġcapital ist +ĠN ed +Ġsoft en +Ġnot ably +Ġforcé ment +ĠRa um +Ġнеоб Ñħод +Ġtrad emark +Ġfert ig +Ġ? ! +æĹ ł +Ġreinfor ced +Ġre charge +ĠPut ting +Ġvill ains +Ġhand ic +Ġadvertis ement +ت ÙĬ +ĠÑģ Ñĥм +ĠR iley +×ķ× ij× +äº ¬ +O s +Ø§Ø ² +B oy +Ġsqu ish +ock et +Ġtest ify +æ¼ Ķ +Ġ×ľ× ŀ× +Ġм аÑģÑģ +man uel +ĠArk ansas +if fe +Ġanalyst s +ĠDe af +Ġj ó +Ġgrocer ies +ĠWhe el +ĠÑĢ иÑģ +Ġc òn +ĠC ob +Ġpris ons +è ve +ĠCab inet +Ġpos ed +Ġguer re +ĠL loyd +Ġcl erk +Ġcr ises +ĠSh o +ĠO re +ĠFoot ball +ĠAd vis +ĠZh eng +è į +ĠAM Y +Ġun for +Ġmon aster +Ġcomp ile +Ġimm ortal +at able +Ġpar ano +Ġt iver +ĠStep h +ĠFu ÃŁ +Ġdisc ontin +Ġr ipe +Ġhack ing +Ġs iendo +Ġsegu ro +alt res +Ġand eres +Ġë ¦¬ë +Ġexp orts +æŃ ¥ +Ġtab ii +Ġ기 ëĭ¤ë +Ġbother ing +Ġpick le +ĠBRI AN +Ġalt ar +ĠпÑĢи б +Ġtransfer ring +ĠV ors +ĠÙĩ ÙĪ +ĠZ a +ĠFr ances +Ġbrow se +em it +Ġche wing +ĠFred dy +Ġedit ors +ä lle +Ġí ĮĢ +ĠS que +ĠC ultural +aw k +ĠS ache +ĠCar bon +ắ t +F L +ĠN GO +pe ÅĤ +ĠS ou +Ġh vor +un intelligible +Ġë² ķ +Ġ ° +i in +Ġ×¢ ×Ŀ +Ġder rière +Ġczy m +ĠAp ost +Ġregard er +Ġag rade +ĠC andy +Ġma re +Ġintrodu ces +bird s +Ġuniqu ely +Ġm uk +Ġcook er +Ġcrew s +Ġje ito +ER T +¶ Ħë +n isse +Ġe f +Ġcart e +ĠY ak +ĠP AT +и но +bok ki +Ġm ates +Ġdist int +Ġì½Ķë¡ľ ëĤĺ +Ġy ıl +Ġκ άν +Ġconfigur ations +eng a +re cht +H appy +ãĤĦ ãģ£ãģ¦ +in vest +Ġreconst ruct +ĠÑįÑĤ омÑĥ +Ġmos que +ra um +Ġvoy ez +ĠN BC +ĠìŀIJ ìĭł +Ġstur dy +Ġк ап +Ġans ch +al id +Ġmas ih +ĠR EP +Ġì½ Ķë +Ġded uct +Ġsal ir +w urf +il ot +ĠM utter +old s +ĠF EMA +ĠB ib +Ġneighb oring +Ġbl iss +Ġíĺ ¼ +ли ÑģÑĮ +ĠÑĤÑĢ еб +Ġ å°±æĺ¯ +Ġgren ade +Ġe gal +Ġfin ely +Ġpet als +Ġke er +Ġch yba +Ġsk ipping +Ġth irteen +Ġgrav y +ĠS AT +6 1 +Ġн ог +Ġmin s +IT E +Ġso zial +íķĺë ©´ìĦľ +rukt ur +Ġвозм ож +Ġоп ÑıÑĤÑĮ +Ġar th +ĠCub an +Ġtre asures +Ġfertil izer +Ġawak ening +Ġë°± ìĭł +Ġr all +Ġdep ict +ĠP ablo +Ġninete en +Ġw att +Ġentire ty +K S +ĠWood s +S ch +ĠÚ© ÙĪ +ĠD ry +ãģ ŀ +u ve +Ġreconst ruction +Ġanat omy +Īë ¥¼ +Ġb aba +Ġlisten er +Ġshar pen +ĠPer u +ĠвÑĭ з +Ġrecre ation +Ġiniti ate +Ġcal or +ĠN aj +ge e +ĠFe els +ĠSnap chat +ĠT et +ĠN est +ĠD af +ĠFin ish +ĠÑĤак им +ú c +iz ens +Ġsp ins +Ġemb ry +Ġpass ages +Ġc ient +Ġjust ification +ä»ĸ 說 +Ġolm az +Ġflood ed +Ġemo ji +Ġembr acing +Ġdisc ard +ĠBas ic +ag og +ĠìľĦ íķ´ +Ġas ylum +er in +Ġf im +Ġnin ja +Ġautom ate +Ġaller gic +ÿÿ ÿÿ +am am +Ġм аÑĢ +ĠO i +ä us +Ġin duct +ĠB EN +Ġz ÅĤ +Ġkaż dy +ĠAM P +n ÄĽ +S ure +Ġqu il +Ġespe c +ro k +BS CRI +Ġlie be +p us +ach sen +Ġcr icket +ëĬ IJ +ĠFr ame +ekk ür +ar b +Ġp ÅĻ +иÑģ Ñģ +Ġzeg gen +Ġdou bles +ĠD re +t est +ins p +bo ys +Ġm ão +ĠVer se +Ġmus cular +ĠMA LE +Ġd ulu +Ġoccas ional +L o +conom ic +Ġv ak +Ġrem edy +å¤ ł +ĠâĻªâĻª âĻª +ve m +Ġön em +ĠkarÅŁ ı +ĠSh arp +h ur +Ġë°© ë²ķ +Ġgrand son +Ġakt iv +ĠTh rones +ĠìķĪ ìĹIJ +Ġto ts +Ġsub d +ĠPa ula +Ġgra ves +ĠB rent +Ġник ÑĤо +Ġsö z +Ġcre c +ĠVlad imir +çĸ « +Ġп ой +Ġ" - +Ġp sy +at ri +id an +Ġa ún +Ġstandard ized +ì¹ ĺë +Ġк ÑĢов +ĠZh u +s omething +Ġ7 50 +Ġmuj eres +Ġa it +éĹ ´ +ag u +Ġcorrect ed +ik ka +el ed +ĠCare er +ow ym +Ġroomm ate +Ġdescend ants +ĠNapole on +ĠÐĶ о +íĸĪ ìĸ´ìļĶ +Ġbun un +ĠMich a +ç· ļ +Ġdesc ob +P I +Ġpalab ra +Ġtrack ed +Ġdepend ence +ĠBar ack +åģ ĩ +Ġfert ility +ĠSouth west +Ġincom plete +Ġcomun ic +Ġcomp ris +ĠRest aur +Ġac ron +κ α +Ġapprent ices +Ġmus st +ĠA br +Ġpent ru +ĠCons ort +ĠAve c +Ġdum plings +L R +Ġwszystk ie +Ġsw amp +н ев +ugg le +Ġwater color +Ġprot on +ĠEspa ña +ock ing +ов ал +Ġtak im +V ery +Ġdement ia +ĠÅŁey i +J ac +ĠMac Book +ĠL iv +ffic ients +ĠH unt +Ġover lay +æĦŁ 覺 +ĠSky pe +p unkt +Ġconf ined +ĠAd rian +ر Ùĥ +ĠJe ep +Ġenqu anto +Ġan est +оÑĤ веÑĤ +Ġм енÑĮ +Ġirrig ation +á»ij n +Ġeight een +ĠP on +Ġresc ued +Ġ198 3 +r ü +ja e +ĠJe ong +Ġamazing ly +ĠF DP +Ġback stage +c ue +ĠÏĥÏĦη ν +ĠاÙĦØ µ +Ġlivest ock +ĠW arner +Ġmaj ors +ãĥģ ãĥ£ +Ġcooper ative +ĠBr ady +ra ined +rie b +Ġ×ij× ŀ× +Ġдов олÑĮно +ĠF E +Ġle aked +ĠMerc ury +Ġpersu ade +Ġtransform er +ĠNor weg +ĠìĹ¬ë Ł¬ +Ġzrobi Äĩ +Ġcard iovascular +ĠCr ash +Ġg ossip +а ÑģÑĤÑĮ +Ġì ª½ +Ġsw ept +ĠH orn +ĠAt é +Ġbu kan +ĠK aw +K Y +ĠSt ories +G ary +Ġgard ening +ĠQuick ly +ĠFal con +Ġov at +c ı +ĠCom plet +ĠD ate +ĠпÑĢ им +Ġlä uft +ĠAud rey +ĠW ent +Ġpel ÃŃcul +Ġcar riage +Ġun acceptable +ny mi +ĠÑģл ÑĭÑĪ +Ġter re +uell ement +EE EE +Ġpharm ac +h ões +Ġz ich +Ġmig rate +ĠF ry +ñ ana +ĠM uito +EO VER +Ġfort ress +ĠCom pan +ĠJ SON +ord nung +Ġw arto +Ġun gef +ìħĶ ìĦľ +ĠÑĢ ок +Ġpad dle +J ared +Ġsubm itting +Ġl atch +Ġf ug +Ġк оÑģ +ĠE f +Ġlaunch es +Ġf t +ote chn +Ġtrave lled +ا Ùģ +éģ ķ +Ġpro ch +Ġded im +8 3 +Ġreb ound +ĠL U +p ath +ĠÑģп ÑĢав +Ġö l +ĠíĤ ¤ +Ġpriv at +Ġtr actor +ĠAtt ention +S er +Ġcos es +á ria +p al +ĠìĿ Ģ +Ġsuccess or +Ġconnect ors +ĠÑĥÑģÑĤ анов +Ġgen ocide +Ġsufficient ly +ĠA ixò +Ġstabil ize +Ġcon gest +Ġcar ving +Ġz ost +ĠбÑĭ ÑģÑĤÑĢо +Ġshort est +Ġli vel +Ġ8 9 +éģ Ĭ +Ġer k +Ġport raits +ॠĢ +è ĺ +bo at +ll ah +AN C +Ġempir ical +ĠE cho +ĠNeder land +è¿Ļ ä¹Ī +N et +Ġcuid ado +ĠR oma +Ġc alf +Ġgi ants +ĠExpl orer +ĠColl ect +al ition +ĠDest iny +Ġaus ge +ĠE du +ĠC lo +Ġear rings +ĠTr ack +ĠR OS +ĠBe lle +çĻ ¾ +Ġpu eda +Ġday time +Ġsupp lier +ĠS V +ĠEx hale +Ġgal era +c ourse +Ġcent imeter +ĠB ast +m ud +Ġsang at +ĠPhys ical +Ġpriv ately +Ġtr ata +lyn n +ill i +Ġë© ĶìĿ´íģ¬ìĹħ +Ġcryst all +Ġpod s +ả n +in ator +ĠRec ords +å® ĺ +ÄŁim iz +isse ment +h are +h adow +ĠD K +ĠìķĮ ê³ł +Ġw yn +Ġrequest ing +ĠD onna +ĠìĹ ´ìĭ¬íŀĪ +ine a +Ġex ert +ĠDun can +Ġв еÑĩ +ĠH ah +ठĤ +ĠL if +ĠF inding +ĠNo v +Ġзн ак +Ġо ÑĦ +ĠQu è +Ġquarter back +ĠÑĦ ак +Ġbipart isan +ÄŁ in +Ġné cess +Ġrefer endum +Ġcomp iler +Ġprob abil +ед и +Ġtrad er +æĺ ĵ +ĠR um +ge me +Ġd io +ĠbÄĻdzie my +ĠÏĢ ά +ê¾ ¸ +×ķ× ĺ +Ġठķ +Ġбл аг +Ġscal p +ĠPa use +Ġcapt ion +Ġend anger +Ġen lar +Ġrot ten +ãĥĥ ãĥĪ +Ġw ah +èĤ ī +Ġd zi +ĠInst all +A y +Ġcre ar +енÑĤ а +Ġwe ighing +Ġbutter flies +ĠG ast +äº ķ +h orn +war z +IC EOVER +Ġнай ÑĤи +Ġcoe fficients +ç°¡ åĸ® +ĠSp encer +ĠH igher +Ġcow ork +å¨ ĺ +ĠкоÑĤоÑĢ ое +Ġmon it +Ġdys function +ĠÑģÑĤ анов +Ġtour naments +Ġoy ster +B N +Ġtr ud +sl ow +ĠPen ny +ĠOd ys +æ r +Ġf ou +Ġenjoy ment +аÑĤ Ñĭ +Ġwygl Äħda +алÑĮ наÑı +ĠProt ect +Ġmo y +Ġcl aw +Ġsusp icion +Ġsacrific ed +Ġgost o +B ig +Ġaggress ively +Ġvor ne +ãĥ ł +Ġbl amed +ĠSe hr +פ ר +c ito +Ġse als +Ġmu jer +ĠWe ird +Ġfore ns +Ġcontrib utes +est ra +Ġp og +L OL +Ġhacer lo +о ÑĤÑĮ +f iction +7 9 +λ ο +大 æ¦Ĥ +å£ ° +ĠÑĤ об +ĠG S +ĠCl ara +ite z +Ġadvoc ating +ĠíĶ Ħë +s ung +Ġvert ices +Ġnavig ating +Ġeurop é +çļ Ĩ +Ġslow ed +Ġfore ground +ĠIndust rial +Ġad ore +ìĭ Ń +Ġcré er +æŀ Ĺ +chn itt +Ġun aware +Ġcur ly +ent ar +Ġl er +Ġprohib ited +ĠHero es +ĠRe ed +u ca +Ġsm ok +Ġkun na +zeit ig +im men +ĠL un +Ġаб ÑģолÑİÑĤ +Ġdeg li +Ġvill agers +Ġpres et +z ept +ud s +Ġem it +ä½ł è¦ģ +Ġë ī +ëĬĶ ì§Ģ +нак о +Ġos ób +Ġ196 9 +ĠÐIJ ÑĢ +Ġman chmal +ĠBro ck +Ġmant ra +ĠW IL +b ach +in ä +el as +kel n +Ġdisci ple +Ġqual c +Ġde hyd +ìĿ´ë Ŀ¼ëĬĶ +A f +ìĦ± ìĿ´ +R yan +Ġpupp et +ĠдÑĢÑĥг ие +Ġr ud +Ġp ending +P lus +ĠìķĬ ìĿĦ +Ġb á»ĭ +ĠSe ga +ç e +Ġprogram mer +b li +Ġun l +Ġensl aved +Ġsoci été +Äģ h +Ġinherit ance +ĠBang l +erm aid +Ġpractition er +ĠSt alin +ĠUs er +ci ble +Ġcard iac +ĠKore ans +Ġdump ed +Ġ×Ķ ×Ļ×Ķ +á is +Ġhydraul ic +oubt edly +ĠP it +Ġpic nic +Ġbehö ver +ĠÑģм ог +Ġbra king +é» ij +ut ar +ĠìĦ ¸ë +ub l +Ġü z +Ġmaj esty +Ġb ers +ut able +Ġhot ter +çħ § +ÛĮ ÙĨ +Ġbi ases +Ġsubject ed +Ġnaught y +Ġcir cus +ãģĹ ãģĭ +ĠIm medi +ĠSte fan +ĠTri ple +en k +Ġw it +Ġrecy cle +em ie +d ated +Ġun load +Ġpop ula +ch in +Ġyield s +Ġeng lish +ĠBon nie +Ġsp iders +à ģ +Ġer osion +éĥ¨ åĪĨ +ĠN ICK +иÑı Ñħ +Ġimp art +Ġк ни +Ġres olutions +Ġlith ium +Ġconver gence +ĠT ara +Ġдв е +th s +ĠCind y +æĪij è¦ģ +å¹ « +ĠD IE +Ġass urance +Ġоп иÑģ +Ġbu ckets +Ġc ues +ĠQu iet +Ġsimilar ity +Ġfound ational +ĠMin ist +æ» ¿ +Ġp ian +Ġcent r +Ġnum b +Ġmon ks +uj ourd +en zie +Ġskate board +Ġd latego +ĠÑģ оÑĤ +ĠA E +Ġmaster piece +ĠSol omon +ĠRed dit +Ġr iot +ab l +ĠJ azz +Ġelectromagn etic +Ġinsec ure +ĠComp et +ger ies +об од +ł ×ķ +ðŁ Ĵ +Ġsen ators +ĠBris bane +ĠAl b +utter ing +ĠAll ow +z ero +Ġp ai +ĠÐIJ лекÑģ +ĠDis play +ĠBl ade +ĠApp s +Ġp ä +Ġд еÑģÑı +Ġque lla +ĠGa o +ен нÑĭÑħ +Ġspoil ers +Ġgall ons +ĠÙĦ ÙĬ +ĠZ ion +æľī ä¸Ģ +on ie +rag t +ĠCh and +Ġë³ ij +Ġbl unt +Ġus u +ĠK ad +ra kt +Ġcin ematic +Ġam munition +re ne +Ġfour teen +ĠC arn +c rit +Ġten ure +v u +Ġprincipal mente +Ġalle en +éĢĻ ä¸Ģ +Ġkompl ett +Ġdü ny +J ames +Ġrecept or +Ġones elf +g uru +Ġmerch ant +l iness +Ġover looked +Ġharmon ic +éķ ¿ +ies o +×ķ× ŀ +col m +ĠпÑĢо екÑĤ +ĠAd a +ا س +T im +Ġrecur ring +Ġproceed s +ĠPart icularly +ĠDown load +et rical +Ġmat rices +Ġproyect o +anc ies +ĠUh m +Ġc aves +Ġìĸ´ë ł¤ +ĠLe af +Ġоб ÑĭÑĩ +ĠìĿ´ì ľł +Euro pe +Ġt Äħ +Ġpul s +Ġtak iego +ÐĿ е +G U +Ġfor s +Ïģ γ +Ġfot os +Ġ) ) +Ġë© ¤ë +Ġaqu ilo +ĠK urd +ï¸ ı +pt ic +ĠD ort +Ġmis ery +aus o +åĬ Ł +chuck ling +ĠR idge +ĠíĸĪ ìĬµëĭĪëĭ¤ +Ġ* ** +å® ¢ +ĠHmm m +Ġge ographic +Ġany s +Ġtal vez +Ġske let +Ġsign atures +Ġlit ers +IJë ©´ +ĠÑģво его +Ġski ing +ĠÐľ оÑģ +Ġadop ting +Ġha ft +Ġsymm etric +ĠL iqu +Ġthy roid +Ġmis in +lud e +Ġh ull +ĠX D +ĠG ust +ze ich +Ġvibr ations +Ġes emp +ĠвÑģ Ñİ +ĠQu em +Ġü brig +ĠS ke +ĠLyn ch +room s +art et +f est +Ġfr üher +Ġl ure +ä¸į好 æĦıæĢĿ +ĠìķĮ ìķĦ +ĠW IN +ĠR YAN +ĠкоÑĤоÑĢ ÑĥÑİ +ĠK ash +Ġ×Ķ× ŀ +Ġsaf eg +ĠHall elujah +Ġдв ÑĥÑħ +Ġstap le +Ġsed iment +ĠAct s +Ġbl aming +Ġmain land +Ġsport ing +Ġdecor ations +Ġexecut ing +Ġpar an +ĠDoll ar +Ġproject ions +Ġcommission ed +Ġb our +ö m +Ġste amed +ĠëŃ ĺ +Ġpet rol +Ġcel ular +å¸ ¶ +ĠHung ary +Ġrent ed +Ġв аÑĢи +bb ie +Ġsé cur +ü ll +Ġsw ings +bet ween +Ġи ÑĤ +est ro +Ġnie mand +ĠìĤ ¼ +ĠP ardon +ess es +ĠM ID +Ġcentral ized +ĠAl ien +cul os +Ġcr ise +裡 éĿ¢ +Ġcl asse +beit et +i ÄŁi +Ġwh ales +Ġper imeter +Ġty ing +Ġstr ony +Ġlike wise +ĠP unch +D a +ĠBapt ist +Ġsort ing +Ġ iv +Ġíķ © +Ġre hab +Ġet a +ri ver +Ġsa i +ãģĦãģŁ ãģł +od us +ãģĬé¡ĺãģĦ ãģĹãģ¾ãģĻ +Ġess ayer +Ġtur tles +ĠHaz rat +Ġfab rics +Ġcav ity +Ġpon ieważ +Ġschle cht +Ġs alsa +ÅŁ ekkür +Ġse ating +Ġeconom ists +Ġman g +Ġsegu inte +Ġr ang +Ġrat ios +Ġconst ell +Ġlong temps +u ating +Ġspo iled +Ġrecip ients +Ġsn iper +ä¹ĭ åīį +ìĬµ ëĭĪê¹Į +Ġw p +ĠLIN KE +Ġfl are +ĠAd ri +ñ as +Ġback l +mä ÃŁ +ĠB end +Ġworkload s +ĠÑģ Ñĥп +Ġ197 5 +им ÑģÑı +ан е +Ġм он +Ġaspir ations +ĠA er +ĠговоÑĢ иÑĤÑĮ +ĠQ ian +å¦ Ī +Ġcomprom ised +Ġyol k +ла ÑģÑĤ +Ġhe men +ro ve +d ens +Ġком менÑĤ +Ġ- -- +Ġflu ores +но Ñģ +ĠLiver pool +ĠÑģоб ой +ĠZ we +Ġl umin +ĠO G +á ¸ +hol m +pro fits +S N +Ġproport ions +Ġm ica +ĠB oh +ĠAt las +Ġuns ure +Ġtour ing +Ġn ied +Ġt ÄĻ +Ġimper ative +Ġdem ek +ĠSher iff +r ance +Ġhom eland +ĠH ail +ĠG anz +y mm +M on +åĨ · +v ida +Ġdesar roll +æĬ Ģ +Ġintrig uing +ĠH ugo +Ġ ãĤĤ +é ¬ +а ÑĨ +ĠWiÄĻ c +att ed +ĠìķĦëĭĪ ê³ł +ĠV ari +á d +Ġsur real +Ġdispar ities +Ġm ó +ull en +ĠìŀĪ ëĭ¤ê³ł +Ġп ожалÑĥйÑģÑĤа +Ġma ins +Ġe ject +Ġmeth ane +Ġmarginal ized +Ġchill i +r ès +Ġy em +ä½ł æĺ¯ +ĠCh un +Ġdeb ts +Ġdownload ing +ĠAth ens +is ierung +ry n +Ġte kn +ĠQu indi +éľ Ģ +Ġtara f +Ġh é +Ġconscious ly +Ġfix es +uck le +may ın +Ġfre i +Ġsp a +Ġì§Ħ íĸī +ĠاÙĦØ ° +ĠÑĥ к +let t +Ġolm uÅŁ +Ġche esy +า à¸ģ +na ire +Ġw iden +Ġli en +Ġesca ping +igg s +ĠBl ick +c Äħ +ĠìĦ ľë +Ġ×Ķ× ¡ +Ġв пеÑĢ +oph one +ie ll +ĠSU BSCRI +Ġl ions +Ġê·¸ ê²ĥ +Ġinsp ires +Ġguarante es +Ġcome ça +ĠGrow ing +Ġneg lig +ĠFrank f +Ġge geben +ĠÄij ầu +Ġend lich +Ġì į¨ +ĠT T +ĠL ith +ÏĢ α +aster n +ĠA zer +Ġlun ar +h ic +Ġна ÑĢод +Ġnen hum +è· ij +ĠSalv ador +ĠPro gress +Ġprivile ges +ĠëıĻ ìķĪ +Ġant agon +ĠImp f +Ġdesc ub +ĠLe i +ĠìĥĪë ¡ľ +Ñĩ е +Ġdó lares +ĠMeg han +ĠW ire +to o +ay ing +us c +Ġt ud +Ġappe als +ed uc +Ġp ane +Ġj i +Ġde cks +ĠAl ter +Ġ å°± +ìĦ ¤ +åĪĨ éIJĺ +Ġproduct ions +ĠWILL IAM +Ġimpl ied +Ġfulfill ment +ĠA ah +Ġsa ja +x us +ĠÎļ αι +Ãł s +uc ch +ок о +ĠDisc ord +ĠS Y +j sk +ĠWall ace +un ction +Dan iel +Ġk öt +ij ah +Ġmarch e +Ġdis gr +Ġm ungkin +Ġal ma +³ µ +Ġextensive ly +ĠFl oren +ĠAll ison +ãĤ ± +ÙĬ Ùħ +Ġju ven +ĠRena issance +Ġfundra ising +ĠCha os +Ġpar aly +Ġnarr ator +Ġecosystem s +A sh +Ġmitig ation +ĠA ujourd +ĠIde e +! , +Ġ ½ +Ġland lord +Ġdefect s +Ġac re +uls ive +Ġalg ae +pe k +Ġem ba +ĠR oc +éĽ ¢ +ks om +ä che +Ġle uk +Ġlever aging +Ġê·¸ëłĩ ì§Ģ +ĠPal m +Ġä ven +Ġl is +ĠIn sp +ĠR ita +ĠAb b +ith m +Ġsuper vision +Ġrevis it +Ġpi ÄĻ +Ġeu h +Ġf ades +Ġmot to +åį ¡ +ез ж +ĠSh im +Ġrelev ance +Ġo o +Ġo stat +n ica +Ġcho ix +ĠFac ulty +Ġì¤ij ìĹIJ +ĠAb ove +Ġнеб олÑĮÑĪ +Ġsequ encing +Ġnutri ent +Ġconqu ered +Ġdigest ive +Ġback drop +ĠL ori +ail able +G ame +Ġneglect ed +om orph +ill ah +Ġkn e +Ġsi itä +Ġworks pace +ĠVen ice +ĠK ne +Ñī о +ħ Ģ +ĠH ass +Ġv ita +Ŀ¼ë ©´ +Ġlay s +ên cias +é rica +ĠL l +æ± Ĥ +ĠCo ca +ĠWH Y +èĪ ŀ +Ġrout ing +Ġperm issions +Ġd ings +pre nd +pro gram +Ġcro cod +br al +AAAA AAAA +ag it +ĠN ä +Ġgek ommen +at ten +Ġrefer enced +Ġpair ing +ĠPart ner +ĠCoron avirus +Ñĸ Ñģ +è½ ī +Ġ×Ķ× ĵ +Ġespec ÃŃfic +ars i +qu elle +Ġspont aneous +çĨ ± +Ġê²ĥ ìĿĦ +ĠÐŁÐ¾Ñģ ле +ĠاÙĦ د +ĠSh out +Ġн ал +Ġdisgu ise +ĠJ ord +Ġwe e +Ġmiej sc +Ġser um +Ġplais ir +Ġcred ible +Ġb Ã¥ +ĠA J +ma res +Ġrod s +Ġer an +ãģ¾ ãģĤ +Ġp ää +ĠU A +ĠUn known +ĠÙĦ Ùħ +ĠRab bi +Ġla at +Ġhairst yle +ĠØ º +éģ ĭ +Ġc ach +ĠWr iting +оÑĩ ки +ab ad +Ġstraight en +-- " +w ife +Ġhott est +Ġpun ya +ĠF ashion +gr iff +ĠQ R +ot ch +ĠÐľ ожеÑĤ +Cl oud +ĠStri ke +ĠHe in +Ġ 羣çļĦ +Ġle i +ĠFl ow +weg s +Ġha br +åīĽ åīĽ +nah me +Ì ģ +Ġple asing +op ping +Ġ구ë ıħ +Ġdr an +Ġbang s +Ġ7 9 +Ġsk et +Ġcav al +ĠMac ron +Ġweight ed +Ġm uted +Ġnuest ras +EE P +Ġmath ematic +ĠM RI +ag us +Ġtherap ies +θ ε +Ġun pl +Ġcomm encer +f ull +Ġtow els +Ġpr ue +Ġlic enses +׼ ×ķ׾ +ĠÐŁ оÑĩемÑĥ +Ġpoint less +B ye +Ġelig ibility +Ġscra pe +Ġab usive +ĠM ant +Ġje unes +t al +ĠPrin cip +ĠOrth odox +Ġmel od +ĠмаÑĤ еÑĢи +Ġprosecut or +Ġopio id +ĠÑĥ веÑĢ +ĠBe en +Ġìłij ì¢ħ +Ġd ynasty +Ġajud a +Ġent reg +Ġweigh ed +Ġe ure +ĠB em +Ġab normal +8 2 +ĠJ R +ĠA kt +ĠB ri +ú t +Ġst agn +! * +Ġwe gen +Ġle aking +ĠW ords +ĠM au +Ġv ue +ĠL iam +ани ем +Ġclin icians +ĠP ump +Ġför st +? ... +Ġautom otive +ĠOw en +zus agen +ĠH undred +Ġdecentral ized +Ġbul bs +Ġ×ľ× Ľ +Ġprovin ces +ĠMil an +8 1 +k as +Ġëĵ £ +Ġfor ça +Ġright ly +å³ ¶ +r Äħ +Ġven ues +Ġw ai +Ġpred icting +ĠWi Fi +Ġê¶ģ ê¸Ī +ر ÙĪ +Ġ×Ķ× ĸ +cent ury +Ġgrad ual +ĠProblem e +ĠìĹ ħ +Ġcop ing +ĠBr us +Ġpean uts +irts chaft +Ġз ал +ĠT roy +Ġsper m +ĠM itar +ĠTür kiye +g rand +¦ Ń +Ġ×ŀ× ¡ +Ġp ans +ĠKnow ledge +ber ly +ĠÐķ го +Ġdan ced +ĠFr ost +ĠB urg +Ġbit ing +ìłķ ìĿĦ +me al +Ġhero ic +Ġmother board +ĠL icht +ãģ£ ãģ +ll an +ай н +ĠÑĢ Ñıд +Ġ à¹Ģภ+on en +ir ie +Ar t +r ang +ν η +Ġnew born +Ġam is +Ġا ÙĪر +Ġsoph om +ĠCare ful +Ġprospect s +ens en +Ġthr ill +ĠVi á»ĩt +A dam +r ition +ent ric +ud en +Ġcertific ates +Ġas hes +èª ¿ +play ing +Ġs adece +Ġo st +Ġairpl anes +ÑĢ ок +on er +Ġmagnes ium +Ġgod damn +Ġ197 2 +ĠSch ule +Ġtem at +Ġpart out +௠Ĥ +Ġin ve +ĠScient ists +ĠHud son +win ning +ceks in +Ġcongress ional +or u +Ġro pes +в ед +Ġmad re +Ġf erry +ĠCoh en +ĠP red +Ġvag y +Ġб еÑģп +Ġmult im +Ġdrain age +Ġsim ulator +g iggles +ĠSt adium +об Ñī +Ġnot ices +Ġcraw ling +Ġgr oupe +åı ¸ +Ġkto ÅĽ +ĠY oga +Ġmed ida +ĠÑħ ваÑĤ +ĠL ite +Ġr av +or ama +Ġdisc ord +ĠDI RE +Ġte h +ĠN urs +ç² ī +Ġpitch ed +Ġbark ing +ĠC oke +wi ad +Ġpop ulated +éĻ ¤ +pe lled +Ġб ог +Ġpe wno +ĠC ube +Ġrecru ited +éĢĻ 種 +ĠC ara +ıģ ını +im ated +ĠÑĪ кол +ic ional +ĠпÑĢо ÑĦ +Ġcontam ination +Ġúlt imos +Ġfear ful +Ġele phants +us i +ĠiT unes +ĠSw ami +ê ¼ +ĠìĦ¤ë ªħ +ĠRich ards +Ġmagn ets +ĠRicht ung +ĠLeg ion +èı ľ +Ġk itty +Ġkiss ed +Ġwater ing +Ġcon o +ĠPalest ine +id ir +Ġma ze +Ġflu ids +ĠProdu cer +ĠKr sna +好 åķ¦ +la f +Ġ×IJ ×ķ +Ġm iesz +ĠX ing +oint ed +se in +ĠF uk +ĠDep ression +ĠD uty +ĠPan ther +Ġsu nd +Ġref ere +Ġexc lusion +Ġnav al +ĠWin ston +Ġsl ogan +Ġhypoth etical +Ġelev ate +ë ł¹ +Ġcabe ça +ĠGes und +m eter +ĠìķĦëĭĪë ©´ +Ġcloud y +âĢ¦ ? +ĠSch ritt +ĠJ S +ì į +ĠSpr ings +ĠB atter +· ° +Ġtail or +ĠPTS D +ĠG ent +Ġba ÄŁ +Ġspat ula +Ġcr ay +ĠLeg isl +Ġs ú +Ġle ve +า ม +Ġer ad +Ġdon g +Ġd erm +ĠBank s +ich o +åħĪ çĶŁ +ĠFr anz +ra vel +éģ Ķ +ол о +Ġfl ute +ĠE k +Ġjoy ful +Ġch ased +ĠLar ge +O ver +Ġentrepreneur ial +Ġcons iders +Ñĥ ем +op a +Ġdorm ir +ĠElement ary +Ġprzy pad +ÑĥÑģ ка +ĠоÑĩ еÑĢ +ug ene +Ġten ido +Ġlug ares +ë ¥ +ĠÑĩ аÑģÑĤ +Ġsa o +Ġbra id +ĠV ere +ĠRe ich +ĠP oss +Ġin an +w and +re f +Ġmont rer +Ġ198 1 +çķ ª +as ında +Ġch rome +ĠTr inity +Ġexplo itation +ĠS ense +ĠC MS +ĠNo ble +ĠìĦł íĥĿ +Ġswe lling +elect ronic +] ? +Ġbr ushing +Ġliquid ity +ĠH ook +ĠCon nor +ĠAl um +Ġgu cken +su ite +Ġwie le +Ġbarrel s +ĠReg el +ĠM ent +ĠT rip +ĠBr ush +ĠE rik +ur ate +ÉĻ r +ĠC yr +ou ble +ĠBe cca +Ġpass words +Å ± +bor g +Ġv endo +ĠCla us +ĠF az +ind est +Ġdece ased +Ġcompar isons +ĠL CD +ĠP ork +Ġevent ual +Ġpat reon +Ġin ability +Ġext inction +Ġì¢ĭìķĦ íķĺëĬĶ +ĠÑģ оÑģ +aj u +Ġ×ij× IJ× +Ġso fort +Ġdest ined +ĠR in +Ġmouth s +ĠNat ürlich +Ġpres erving +Ġlim p +é» ¨ +oc used +ин г +Ġexp osing +ĠÎ ¾ +ë į +la ugh +Ġhis s +ãģł ãģĭãĤī +Ġind ie +Ġdet al +ÑĢав ÑģÑĤв +Ġtr ên +æķ ° +Ġog ni +Ġsimple mente +Ġ197 8 +Ġgo o +Ġ196 7 +Ġgen ug +h ö +Ġhist ó +å® Ł +Ġlob ster +c endo +Ġte il +Ġalle vi +00 00 +OL D +Ġpes os +Ġbon uses +Ġam i +Ġrev ival +ĠHor se +Ġs ack +T alk +Ġmul her +ĠпоÑģÑĤо Ñıн +ĠH ood +H uh +Ġë¶ ģ +Ġhy ung +ĠMe eting +Ġimport a +Ġì°¾ ìķĦ +ĠV ern +Ġstri pped +Ġref uses +Ġqual ifications +op l +Ģë ıĦ +ix ÃŃ +Ġdi ab +it ime +fl ows +Ġin ac +ĠG ong +Ġmeaning less +Ġcourage ous +Ġmicro bi +az y +h ist +Ġvolunte ering +V IE +Ġviol ated +Ġsymp athy +ĠEd it +好 åĥı +elect ric +produ ct +Ġpand emia +Ġgeomet ric +ĠCon vers +g re +Ġgl ut +ist ed +ĠاÙĦ Ùĥ +ĠCh ain +ĠPres ent +ĠY in +ĠÑģ ог +ĠV log +Ġìĸ´ë ¨¸ +Ġdon n +Ġh itch +uck ing +ãģĬ ãģĦ +w ald +ris k +Ġhar i +ĠK ens +ĠId ol +Ġвним ание +Ġtod d +Ġsm ashed +Ġinv ari +Ġкон ÑĤÑĢ +Ġaut istic +ìŀ¥ ëĭĺ +R es +д Ñĭ +ch au +Ġsel v +Ġhät ten +ठ¿ +Ġexpect s +Ïģ η +Ġaç ık +ĠHT TP +le ÅŁ +Ġswe eping +ĠBet a +Ġcounterpart s +ab ile +ĠSim s +C s +Ġrep ar +s qu +Ġprovin cial +Ġshare holders +Ġrun ter +Ġged acht +ĠTe en +Ġgrand s +çĶ ¢ +ag les +Ġrock y +ven s +Ġr ivals +un al +Ġreact s +ë © +Ġmerc ury +ĠLu igi +Ġо г +ĠJ UST +Ġl od +Ġcort ex +w ig +Ġl akh +ì¤ij ìĹIJ +ĠV ic +ĠM und +Ġma pped +ĠD ell +ĠD ruck +Ġlif es +алÑĮ ное +ivid ual +ad ım +Ġat rav +ĠFl ug +ĠKle in +ê±° ìķ¼ +ห à¸Ļ +Ġapp li +ா ? +ü yorum +ĠинÑĤеÑĢеÑģ но +Ġdis infect +> - +Ġchamp agne +Ġk la +op ers +Tr ans +ĠDes ert +Ġcultiv ate +ĠFuck ing +idel ity +ĠÑĤ ан +Ġinc ub +Ġtem u +Ġlearn er +found er +ĠSy l +ãĤ Ģ +Ġf ato +z ier +ĠìĹĨ ìĿ´ +ĠìĪ ¨ +Ġpsych o +ĠÑĤел еÑĦ +Ġregard e +Ġrepresent ations +Ġlit igation +Ġsp ann +ult s +b ior +è¦ĭ ãģ¦ +ä¸į å¤ļ +ĠSur vey +ĠLED s +Ġtr ä +Ġl ên +Ġant ioxid +еÑĢ ом +Ġindu ction +Ġfool ed +ät zlich +ĠговоÑĢ ÑıÑĤ +ĠF act +umb ai +Ġw iggle +NO UN +Ġdévelop p +ĠCl aro +Ġì ¸ +ë ¬ +ãģªãĤĵ ãģł +Ġaccum ulate +Ġmaint ains +ë Ħ +ĠFight er +íĨ ł +Ġmat in +Ġcoup on +Ġst unt +Ġdeb uted +å¾ħ ãģ£ãģ¦ +Ġpra g +ив аем +7 3 +Ġexp res +Ġìĺ¤ë ¹ł +ĠпеÑĢ Ñģон +Ġcalcul us +Ġab rupt +ĠInspect or +our t +æĸ Ļ +ź niej +int ense +B a +Ġl ounge +Ġast hma +ĠHi ç +ª » +Ġeditor ial +Ġse ize +Ġk ır +Ġm ouve +Ġtier ra +Ġtestoster one +Ġr h +ĠKing ston +EL LE +ĠRepresent ative +Ġ197 4 +Ġi ba +T s +Ġsort a +Ġ( ?) +Ġت ÙĪ +ĠëĤ´ë ł¤ +Ġbek ommt +Ġspirit ually +Ġdist orted +M ad +Ġre im +á nh +ĠOtt oman +ĠRel ig +ĠEl s +Ġret ained +ĠLa ughs +æĢ » +ĠS AS +ĠколиÑĩе ÑģÑĤво +×ķת ר +Ġinnov ate +Ġk ork +ĠÑĢаÑģÑģк азÑĭв +ond ere +iv i +ay e +ount y +ĠполÑĥÑĩ аеÑĤÑģÑı +Ġbun s +åħ « +Ġyüz den +Ġsur geries +Ø£ ÙĨ +Ġbankrupt cy +w elt +Ġsi amo +Ġdark est +ĠH ann +gg a +Ġform as +ĠD j +n amed +Ġshield s +ue ller +ĠF ew +Ġl ace +Ġfur ious +ĠY U +Ġsociet al +Ġjudge ment +ĠD os +Ġj ab +law s +Ġrein vent +ĠK atherine +ĠCh oi +ad ows +Ġr ans +od en +ĠMid west +n ın +Ġdep ort +ĠD ip +ç´ ħ +Ġaten ción +ĠCourt ney +ivid ad +ĠÚ© Ûģ +Ġeffic acy +ĠBrook s +Ġrefer ral +Ġкон ÑĨ +Ġmal icious +Ġk ir +ĠGod dess +Ġfun ky +Ġinter im +ĠK örper +Ġìĸ¼ë § +k ur +Ġк ли +Ġtruc s +ges etz +Ġz ug +ĠGl ück +ĠMin ute +Ġprest igious +Ġnie z +Ġconcent rations +ла ÑģÑĤи +ĠS is +ĠVit amin +ko v +ĠP BS +Ġне е +Ġretail ers +Ġcon ventions +ĠSam antha +Ġproud ly +J ordan +ĠJ ASON +at k +Ġtr iste +Ġst är +Ġreiter ate +Ġpos terior +Ġ197 3 +ĠP ine +ĠJul iet +Ġped ir +k il +Ġover lapping +Ġexclud e +Ġecon óm +Ġaccept s +ĠS ter +æ± º +Ġìļ ´ëıĻ +est ab +Ġt ug +ar g +Ġliv ro +Ø§Ø µ +Ġse ams +Ġbur aya +Ġe llo +ĠT M +ĠP aw +ĠInd ex +Ex c +Ġinspir ational +Ġd unk +è° ģ +ak ter +Ġcondition er +ĠSal ut +ÅĤ ec +Ġìī ½ +ĠÑĥз на +ĠRome o +f ruit +ĠY O +Ġchá» ī +б Ñĥ +b ons +Ġreprodu ctive +Ġor ada +Ġíļ ¨ +Ġtent ar +Ġma ñana +ãĤ ¬ +Ġsol vent +Jess ica +ĠLeg al +Ġtu a +Ġs ic +ĠE Q +au kee +ìĭľ ëĭ¤ +ĠÅŀ u +Ġad here +ĠT ul +Ġà® Ĩ +Ġtext books +ĠFif th +Ġexper i +Ġch ic +Ġhe ap +in ely +at ra +T wo +Ġhele maal +Ġf ren +æİ ¨ +Ġbis her +Ø§Ø ´ +ĠìĦł ìĥĿ +ĠT ages +Ġs á»± +Ġbull ied +Ø ¤ +Ġbenef ited +ĠPre viously +ĠÑį ÑĦÑĦ +Ù į +Ġsen ate +ĠM orm +ij ke +ĠF lu +Ġincorpor ating +j ack +Ġп иÑĤ +Ġimp ly +Ġha cks +ĠR ICH +Ġк ваÑĢ +ĠпÑĢек ÑĢаÑģ +Ġdepend ency +Ġìļ © +Ġì± ħ +Ġwäh rend +Ġsu lla +ĠPitts burgh +Ġesemp io +¼ë ¡ľ +pr ot +ĠR osen +ĠIndepend ence +Ġpars ley +ie gen +Ġha w +Ġaqu ell +ĠC AP +ĠÑĢабоÑĤ аÑĤÑĮ +ĠCl iff +ion ar +Ġsec uring +æĪijåĢij çļĦ +ν ε +Ġutil is +Ġcou le +ĠP ing +Ġtre k +Ġf ak +Ġenorm e +Ġìĭ « +è® © +Ġdoub ling +ĠнÑĢав иÑĤÑģÑı +Ġh ed +ho ven +ĠStand ing +Ġm ÃŃn +ĠJ imin +Ġmon arch +Ġco ke +Ġm r +Ġcl ic +à į +Ġimpe achment +Ġdur ability +Ġvar ios +Ġcommercial s +Ġgreet ings +ĠR i +ĠApp reci +ìŀĪ ëĬĶ +Ġrés ult +ér t +Ġsal ute +Ġpoder ia +Ġsun rise +ve ck +Ġreluct ant +Ġcommission er +å¿ µ +â te +ĠKen ny +ĠSir i +ãĥĥ ãĥĹ +ĠëĬ ĺ +ĠE E +Ġun ch +к он +ĠاÙĦØ ¥ +Ġbel ts +Ġhas s +Ġмо Ñı +Ġdispl aced +Ġab ra +ÎŃ Î» +Ġscratch es +Ġcom et +Ġauthor ization +ĠL LC +Ġprodu k +Ġrehabil itation +å ŀ +Ñĸ Ñĩ +ud ing +ol it +Ġ10 5 +Ġexp ands +Ġalt ri +ĠKom ment +Ġan f +P l +ĠM ana +f ed +Ġb ri +Ġor a +G s +ĠG ur +uck land +Ġjun ction +Ġiron ic +ĠFe ed +Ġpra kt +ĠHam mer +Įë ıĦ +ĠTr acy +çµ ± +ĠAs ide +н его +ĠиÑģполÑĮз оваÑĤÑĮ +Ġz aj +Ġequ itable +Ġcur b +Ġãģĵ ãĤĮ +Ġderiv atives +Ġpupp ies +ĠKenn eth +ĠCom pl +ig ram +ĠGar cia +) " +ĠHar bor +est ial +Ġ ä¾Ĩ +Ġ ers +æ ¹ +Ġunw anted +Ġbel ang +аР³Ð¾ +em b +d os +ĠìĻ ľë +ĠBud get +Ġbatt ling +ØŃ Øª +k ok +наÑĩ ала +Ġpl ag +Ġcant idad +Ġgrup os +Ġplug ins +ler ini +Ġиме еÑĤ +Ġso zusagen +ol ics +Ġpue blo +Ġrem inis +r än +ĠMor rison +Ġl inha +Ġbreath s +ĠT aste +Ġenf rent +ĠDo cker +Ġд ен +Ġethnic ity +Ġw ob +Ġsuff ers +Ġtransition ing +ĠR ange +ÄĻd zy +Ġк аÑĤ +Ġsy ner +Ġdon ut +Ġprob abilities +ĠO mar +Wh ich +u ish +is in +Ġdem os +ĠìłĢ 기 +Ġëĺij ê°Ļ +Ġед ин +Ġc erve +Ġj oka +I AN +Ġkilomet er +Ġhorizont ally +ĠBh ag +Ġ- > +ĠMon itor +Ġknowledge able +Ġf av +Ġpin ned +Ġe Bay +ick er +Ġìŀłê¹ IJë§Į +ĠXia omi +Ġcap it +Ġn p +Ġ196 5 +ho e +Ġn ok +ĠS age +Ġн елÑĮзÑı +ĠT ow +g am +Ġdic en +ĠSUBSCRI BE +Ġrebo ot +Ġp aj +Ġë³´ìĹ ¬ë +Ġth icken +ĠRe ality +id än +N a +Ġê²ĥ ìĿĢ +!! ) +Ġrout ines +Ġод ного +Ġex ting +Ġì¦ Ŀ +Ġsulf ur +Ġcar ve +Ġastero id +ĠWarri or +Ġphotograph ers +Ġpe ll +Ġcros sover +æĪij çŁ¥éģĵ +Ġhace mos +ĠNe j +Ġsett ling +Ġir m +ĠBook s +ient ôt +Ġesp acio +ĠSchol ars +Ġdo omed +ĠIR S +w ohl +Ġseg ue +ĠëĪĦ ê°Ģ +Ġpr atic +B T +ĠConsider ing +ĠBuff alo +Ġtrain ings +Ġge bru +ĠG leich +Ġpir ates +Ġen velop +Ġre open +im at +Ġte e +Ġsu ed +fe h +Ġ×Ķ× § +Ġdi ets +Ġjunt os +ast o +Ġmisunder stood +Ġru im +Ġclass ify +ĠпÑĢод Ñĥк +Ġin se +Ġillust rated +Ġcorros ion +Ġacc red +ĠAunt ie +ĠпÑĢив еÑĤ +ĠLI VE +Ġre k +Ġrece ipt +åĪ° åºķ +ĠBar bie +ĠSn ake +t urn +Je ff +ãģĬ ãģĬ +ķ Ħ +VO ICEOVER +co ll +Ġrun ners +ìł ľë +os os +mo on +Ġkey note +ĠInst it +S PEAK +Ġplug s +Ġcur v +ĠY uri +ĠTh eres +ĠP s +Ġμ ÏĢο +Ġconver ter +Ġref ine +Ġbad ass +Ġο ι +Ġreg en +az zi +ÙĬ Ùģ +Ġse ized +Ġiç er +ile e +Ġup stream +Ġbud s +Ġp im +Ġíķĺë £¨ +Ġall uded +Ġthem ed +Ġconsist ing +Ġb ons +un uz +ĠпÑĢов од +ĠLove ly +ॠĭ +Ġpar ach +ĠSta ats +éļ Ĭ +Ġselect ive +Ġf ase +ĠGeor get +Ġcoc aine +Ġreprodu ction +ĠL ara +ĠL D +Ġg h +J on +Ġl Ã¥ +Ġëij IJë +Ġtyp ed +ĠB ana +ë ĵľë +Ġsav ory +ĠZ omb +stand en +Ġpedest rian +Ġdifférent s +Ġìĭ ¸ +èī ¯ +Ġcompl ained +ç¦ ı +ĠÐļ ÑĤо +Ġ×ľ× ¤ +ali ÅĽmy +Ġmort ar +Ġverd ict +Ġsu ficiente +ĠMill ion +mitt el +in als +ĠاÙĦØ ® +аÑİ ÑģÑĮ +Ġmi ÄĻdzy +ĠO le +Ġin vert +czy Äĩ +озм ожно +star ter +Ġaud itor +ĠSc out +ch ien +ĠSver ige +uff led +Ġze hn +ĠA uckland +Ġarg ent +Ġ197 6 +ĠHo e +Ġboth ers +Ġsocial ist +Ġpl iers +Ġemer gen +ĠX P +еÑĢ ов +M ore +ĠLe vi +ĠAnd ers +ibil idad +ĠP arents +Ġindu ced +ìĸ´ì ¤ +Ġbal ances +ĠвÑĭ ÑĪ +Ġsubmar ine +St art +Ġdri es +Ġvol ver +Ġtick ing +c ott +Ġf aj +pr és +ĠS abb +Ġза Ñĩ +Ġпок Ñĥп +Ġbapt ized +ĠBrill iant +ĠÐij ог +Ġm ots +b its +Ġlatt ice +æĪij è·Łä½ł +Ġcor iander +Ġresid ency +yn c +Ġpier wszy +ĠKn ock +ĠZ ap +ĠÐķ в +ê² ¬ +å°ı å¿ĥ +Ġune ven +ĠJ as +od or +ç¿ Ĵ +7 4 +ĠS ite +Ġacontece u +ym pt +Ġtril ogy +Ġlan tern +ĠZ ucker +v ari +we lling +ĠPot ato +gom ery +Ġreact ed +ĠChr on +Ġj ede +be eld +Ġtw ent +Ġl act +æ¨ Ĥ +Ġré se +Ġrel ent +Ġfurn ace +Ġwid get +Ġearthqu akes +ĠAd just +il it +ĠØ£ ÙĪ +Ġhear ings +Ġdefend ant +irs iniz +Ġbas k +c ja +ľ ¨ +Ġrif les +Ġinst al +ĠFor give +p ical +ĠÐŀÑĩ енÑĮ +Ġpet ites +Ġh p +Ġren owned +ĠIn n +Ġ주 ìĦ¸ìļĶ +Ġemphas ized +éĹ® é¢ĺ +ĠìŀĪ ì£ł +Ġê²ĥ ìľ¼ë¡ľ +ãĤ Ĩ +Å ĵ +g ili +D ave +Ġexha usting +ÅĤ ug +Ġsch ema +μ ά +cy cl +Ġaut ant +Ġpar cel +Ġmater ia +ĠB erry +ĠÑģ ами +Ġextract ed +ĠSay ing +ism atic +Ġпоп ÑĢоб +Ġneur on +g raph +ľë ©´ +Ġencl osure +ĠJoh ann +Ġafter math +ÑĤ об +Ġu ży +Ġs amp +3 60 +ĠMe i +Ġt aco +Ġrecept ors +Ġpunch es +ĠHo je +ĠÙĩ ÙĨا +=" # +ĠAng ular +Ġmus ique +Ġro l +Ġà ± +ster reich +Ġcl am +ĠTre asury +chem ical +Ġap ar +Ġapp end +Ġforb id +ĠHamb urg +ак ов +Ġê¸ Ī +ild a +Ġprepar ations +Ġmog Äħ +Ġcam ino +E ric +ĠBl ind +èĪ ĩ +å¹´ çļĦ +ĠDis covery +ì¸ ł +çĪ ¶ +Ġinterpre ter +Ġb red +ĠPsal m +Ġdef ended +ìī ¬ +ĠEr fahr +ĠPe ach +Ġmo ons +ĠO st +Ġspé cial +Ġarri ver +ĠW is +u ci +Ġrobot ics +I VE +Ġsie ge +ar la +Ġsepar ates +ĠT C +íı ° +quis ite +Ġparenth eses +ик е +ç« Ļ +Ġtr ous +å» º +ĠÑģ илÑĮ +Ġbe ers +Ġпл аÑĤ +ãģĻãģĶ ãģĦ +Ġso la +Ġd ès +ming ham +ik te +Ġo ops +Ġtw itch +å° ĩ +Ï Ī +ĠShould n +uv re +Ġle er +cript ions +Ġeyes hadow +ĠGu o +ĠPow ell +Ġsup uesto +Ġan a +r als +ĠMont real +Ġsurf ing +ĠÐŁÐµÑĢ в +×ŀ ×ķ +Ġmillise conds +Ġsubur bs +Ġplanet a +ÑĥÑĪ ка +hr lich +ĠH Y +Ġس ÛĴ +ĠM M +ĠE ff +åı¯ æĦĽ +ĠH S +ans on +Ġì§ģ ìłij +Ġsu o +Ġdeploy ing +Ġk unt +ter ing +Ġere ct +ìŀ¥ ìĿ´ +ĠìĿĮ ìĭĿ +Ġspec imen +! ... +æĪij 說 +Ġlig ne +Ġk onst +ade qu +Ġìĥģ íĥľ +Ġaccess ed +ĠP ole +k ill +Ġë² Ħë +Ġauthentic ity +Ġapp elle +ull e +Ġrev ision +Ġgo ats +г ли +Ġp au +ĠR anger +ĠIm ag +aut hor +Ġe ve +ĠMess enger +Ġn ay +Ġwh oles +ät te +Ġon wards +ĠDep ois +Ġíijľ íĺĦ +ĠSAR S +Ġwszystk ich +Ġdest ru +umb ing +Ġcompat ibility +Ġmis information +od ore +ĠF avor +ek o +ı Į +w aukee +ĠTe aching +ĠK O +Ġbet ting +Ġquest s +Ġviv re +ĠмÑĥз Ñĭ +Ġs aga +Ġswe ll +Ġge he +æĢİ麼 樣 +ĠоÑĢг аниз +Ġg ide +ĠG ross +Ġdale j +Ġcl aws +á»Ļ c +Ġprejud ice +Ġins ign +i hood +Ġpl ed +Ġdó nde +ĠPolit ical +Ġprem ises +und ert +ع ت +on nen +Ġespa ço +Ġf é +ĠHarr ison +ĠC ensus +Ġcard io +Ġdi y +Ġmil ieu +Ġjourn ée +ĠRe lease +N IE +ĠM uk +id ée +á»į i +Ġiç inde +ŀ Ļ +Ġreson ate +Ġm oles +ĠF lying +ĠGl oria +ĠPast or +ĠAre na +好 ä¸į好 +N ON +ол ов +Ġall ÃŃ +om at +ìĸ´ë ıĦ +Ġcaracter ÃŃst +Ġdecl ining +Ñĸ Ñı +an co +ĠIn form +Ġbarg ain +Ġbus hes +ĠNat urally +Ġre chts +ĠT ensor +ĠPat ricia +Ġprincip io +ĠM umbai +Ġwom b +Ġnost ra +Ġdile mma +Ġirgendw ann +Ġ196 4 +Ġenerg ÃŃa +Ġна ÑĢ +Ġseg regation +ĠA thlet +Ġ» , +Ġy eni +ĠSe it +Ġven om +Ġdak ika +Ġëı Įë +ĠÃī l +Ġf us +ĠM og +¦½ ëĭĪëĭ¤ +Ġrem ar +ĠTed dy +Ġbreast s +ic ans +æĶ¶ çľĭ +k ap +Ġh Æ¡n +ĠJ P +ãĥ³ ãĤ¿ +Ġresur rect +ĠìĿ ¸ë +her ical +Ġfot ograf +ĠJos é +Ġlivel ihood +Ġbib li +ter i +Ġvor stellen +ĠA AA +Ġassess ing +Y A +Ġspl end +Ġexca v +Ġbapt ism +y ll +w ow +M ac +Ġpl astics +teok bokki +Ġintéress ant +Ġcommand ed +Ġfamous ly +ĠÐĺ ли +ĠMan uel +Ġsouth west +Ġde formation +ÃŃcul o +ĠнаÑħод иÑĤÑģÑı +ĠP atter +d egree +ĠczÄĻ sto +" - +Ġìħ ĭ +Ġman ger +ĠTrust ee +Ģë ¦¬ +Ġpunt os +iv able +Ġvol atile +ĠëĬ IJ +Ġinst ability +Ġc iel +ci Äħ +Ġpur ity +но ÑģÑĤ +S il +ed ar +åĻ ¨ +NOUN CER +Ġspe lled +G ER +Ġsanct uary +Ġacceler ating +Ġsc out +ĠпÑĢ ев +f ahren +ãģĵ ãģ¡ãĤī +ĠëĤĺìĺ ¨ +Ġpocz Äħt +ĠMe u +ka ar +³´ ê³ł +ak ra +D own +ĠÃĦ r +ĠEl ite +Ġall ons +Ġmay onnaise +ĠS ustain +prising ly +Ġsuper vis +Ġê·¸ëłĩ ì£ł +Ġunemploy ed +Ġfresh ly +Ġ×ŀ× ¢ +ĠD h +Ġtack ling +Ġo gr +Ġì´ Īë +ãĤĪ ãĤį +Ġlo ft +ar ah +ĠA irl +ĠD ir +ĠÐľ ожно +Ġbook ing +ĠC RA +Ġhtt ps +Ġcho ke +Ġg own +Ġno ite +Ġz ac +ist ol +Ġsec re +Ġresemb les +Ġcu ad +ìĤ¬ ê°Ģ +sh ow +Ġbl anc +Ġag u +ĠPr int +ast ed +ĠWe ather +i pl +Ġobsc ure +Ġcont e +ough s +) ; +ĠD ame +ä¸Ģ 缴 +Ġclar ification +Ġintim acy +Ġup hold +ĠMir ror +Ġw agon +x ide +Ġcl og +app er +ĠImmedi ately +ú de +Ġtouch down +Ġro oft +аÑĪ а +Ġç ıkt +Ġla isser +ĠUn real +ens itive +Ġ12 3 +Ġpl aster +Ġduck s +Ġet me +Ġb ishop +bre vi +Ġb ic +ä¸ĭ åİ» +Ġrun time +Ġamb itions +м аÑĤ +ĠWe in +ĠMar i +ĠíĬ ¸ë +Ġresol ver +Ġng Ãły +ĠR ise +ãĤĪãģĨ ãģ« +ĠCr us +Ġmerchand ise +Ġel i +Ġstate wide +Ġow l +éģ ł +æĶ ¹ +Ġtwist ing +Ġcontam inated +ĠCom merce +hy thm +Ġà Ī +Ġìĭ ¤ë +Ġmus ste +u ir +Ġsum s +ĠSome where +ãĥ İ +Ġk ami +Ġa ired +ĠAND REW +Ġê º +Ġv iendo +Ġantib ody +Ġabsol ument +Ġprotest ers +ĠQué bec +st adt +Sha un +Ġcham bers +ĠWe ar +ĠEffect s +Ġhaz ards +Ġne i +Ġcoraz ón +Ġá ¼ +ĠS G +Ķ © +ĠìĹŃ ìĭľ +Ġcom fy +ĠC ody +Ġpens ando +Ġg anska +ĠAc ross +öll ig +aby te +Ġwed ge +Ġkal ian +Ġsig ue +end es +ĠGro ÃŁ +Ġutil iser +Ġfl own +ани Ñİ +Ġle var +rest rial +Ġillust rations +Ġas lında +BLE EP +Ġдо ÑģÑĤ +Ġtur ret +Ġsuit case +ziÄĻ ki +Ġsket ches +Ġac red +ĠRe i +Ġt sun +ĠS ag +Ġthird s +ĠKIR BY +ra i +Ġhuman os +Ġrecomm ends +Ġextraordin arily +Ġcommence ment +K N +ope z +Ġ×ij× © +Ġlet hal +ĠEst amos +Ġinspect or +ĠSe ok +e un +Ġoff shore +Ġget tin +ye ars +ĠSil ence +ĠNat ur +up un +Ġtr zy +Ġno get +Ġhamb urger +ĠPra ise +é nd +Ġ197 1 +yl ie +k rit +ĠìĥĿê°ģ ìĿ´ +çļ ® +Ġmoment os +Ġest é +Ġdisse min +Ġgig s +Ġdes af +Ġav is +ĠZ oo +ĠìķĬ ìĿĢ +h äng +åı ¥ +h ake +ĠB ism +Ġre think +ĠMal colm +Ġident ifies +l ower +ix el +Ġtv Ã¥ +k ed +ier z +Ġö ffentlich +Ġproc laim +so on +l ol +Ġlo i +Ġb itten +ro llo +Ġser mon +Ġes qu +Ġjack ets +Ġgr áfic +Ġпок азÑĭв +Ġcabe za +ch odzi +Ġpel vis +Ġnost algia +Ġbre w +Ġshort cuts +ĠAd emás +Ġsuperfic ial +åħ© åĢĭ +Ġbo ca +ĠæĪij æĺ¯ +iment os +åĽł 为 +Ġspr outs +é£ Ľ +ĠJon as +ĠFloren ce +st atic +da ughter +* ) +ÅĤ by +f ashion +ĠG inger +Ġë§ ¤ë +Ġhust le +ut os +ĠÑĤ Ñıж +ĠL ös +ש ×Ļ×Ŀ +any ch +tu ber +Ġtid y +Ġfront al +Ġwhis key +Ġhum id +ĠÎ Ł +Ġr idge +Ġmar in +Ġb ientôt +ĠCarr ie +ch w +Ġtah un +ĠEr geb +F R +Ġìłķ ë¶Ģ +ĠSold ier +Ġenlight enment +Ġexam ining +ĠNot re +Ġer am +ĠSun ny +Ġlay ered +ĠD azu +r ades +好 åIJĥ +ĠнаÑĪ ей +Ġtim ber +Ġman ners +ĠBir mingham +Ġmini ature +omet ers +Ġfill er +ĠR ip +ĠK omb +own er +ì ¿ +id ian +Ġdem ás +ĠÙĪ ت +Ġpreca utions +Ġgovern o +z elf +ĠCom plete +å¸ ĥ +ĠPh antom +ãģ¾ ãģļ +Ġн ез +ĠкаÑĢ ÑĤ +ĠAnt wort +ĠPf izer +ĠFran co +Ġw ÅĤ +Ġfr ig +es per +Ġk ale +Ġfilm maker +Ġk urt +Ġinv alid +å± Ģ +are lla +Äĥ ng +ram ento +Ġnutr itional +Ġdict ators +Ġaf in +Ġf uzzy +ĠG ina +ó t +ĠExtrem adura +Ġdemonst rations +ĠMont gomery +íķ´ì Ħ¤ +ĠGand hi +ãĥ Ŀ +ç½ ® +Ġreun ion +Ġjaki ÅĽ +ĠZ ug +OU GH +l ifting +Ġ ಠ+á¹Ľ á¹£ +e b +ĠW OW +ĠSh iva +omet ry +Ġwild ly +Ġt ended +Ġmeg ap +ì² ĺ +Ġna use +Ġg erek +ãĥ ĭ +ĠMar cel +Ġn este +Ø® ر +Ġfe h +åĨ ħ +susp enseful +ĠWrest le +ĠPalestin ians +ĠG ORD +iy et +ĠÑĢ ади +Ġvers uchen +Ġtrans istor +ĠÐŁÑĢ оÑģÑĤо +Ġпон ÑĢав +Ġrhy me +ĠVerm ont +pl atz +è® ° +ĠÄ°ÅŁ te +ĠH ag +ĠÐĺ м +ĠÑĢаÑģÑģк аз +Ġmet ros +ĠInfin ity +w olf +ib al +ft ig +Ġ ÚĨ +Ġíĺ¹ ìĭľ +Ġo ggi +Ġdisp osit +ĠпÑĢ ил +ĠвÑĭ пол +Ġth ôi +ĠK ENN +Ġhand ing +act us +Ġtac os +Ġformer ly +ĠCorinth ians +ãģ« ãģ¯ +ÑĨÑĸ ÑĹ +Ġpad re +Ġcongreg ation +æ ij +fer t +Ġsub ir +ais er +qu a +ara oh +ĠCur ry +ĠìķĬ ëĬĶ +ел Ñİ +Ġf uss +Ġbo oty +Ġl ows +Ġh ommes +ĠM H +ĠDisney land +w ent +Ġresid ue +Ġbe eping +è¼ ķ +ät ta +Ġm ould +ĠPro jekt +st alk +Ġartif act +ĠAnt rag +ĠAM D +ĠCry pt +Ġë© Ķ +ĠFel ipe +ĠCO B +el u +Ġself ies +ĠS anti +ch utz +ĠУ кÑĢаÑĹ +ges amt +Ġflo ck +j az +pl ain +Ġwr inkles +Ġre ais +Ġpal jon +Ġempower ment +Ġattend ees +pp a +Ġn eden +он Ñĭ +Ġtime frame +ĠCher ry +Ġid ée +Ġg ag +Ġdon key +Ġô ng +ĠH are +éļ Ľ +ĠK ara +Ġacom pan +pl aces +im ientos +ĠH amm +б и +ub en +ili yor +Ġth irst +Ġk ry +ĠGeorget own +׳ ×Ķ +Ġor ch +Ġheart beat +Ġtransform ations +est ones +ĠK H +Ġcart oons +Ġan ci +Ġworth less +Ġtail ored +p u +Americ ans +Ġp iles +ĠMon key +Ġbas in +ĠTem per +ĠP aint +Ġpunch ing +Ġba ik +ĠOak land +v re +ÅŁ allah +yd d +Ġcas ually +od u +Ġc oded +ĠNorweg ian +ĠV ince +Ġprem ature +ĠProm ise +ек ÑģÑĤ +Ġdevast ated +ĠPrem ium +ĠPar am +ĠÃĸ yle +um uz +P O +r ators +Ġlamp s +Ġterritor ial +Ġback bone +list ed +D Y +ĠاÙĦ ر +Ġpurs ued +ĠComm ons +Ġê³ ¡ +lo cks +ed or +Ġconce ived +g ere +Ġdisappe aring +ĠS ull +ĠìĹ °ë +Ġho ffe +Ġdet ox +íĶ Į +Ġret ir +ĠëģĿ ëĤ +Ġper gunta +ĠB OY +ç² ¾ +Ġp enn +æĿ¥ äºĨ +h és +h on +Ġcatastroph ic +Ġa ust +Ġtor so +Ġìĸ´ ëĬIJ +ĠìĤ¬ëŀĮë ĵ¤ìĿ´ +Ġmarvel ous +ĠHar ley +ach ine +Ġti ế +itt o +ĠI ÃŃm +yl on +Ġshut down +.' ' +Ġap ologies +ĠCommun ication +ĠговоÑĢ Ñİ +ãģĤ ãĥ¼ +âĦ ¢ +ÃŃ veis +ac un +Ġret aining +Ġcontrad iction +ĠAD AM +C OM +Bry an +ĠM onsieur +Ġadap ting +Ш ÐIJ +ĠSc r +änd ert +Ġpl aus +ä»Ĭ天 çļĦ +Ġon set +Ġassist ants +Ġval ves +Ġsc atter +ĠR ust +aw ia +Ġread iness +Ġp ais +Ġb ible +Ġamb iente +Ġа меÑĢик +Ġunc ond +Ġk alk +åĬ ¨ +Ġmo c +un n +Ġact u +Ġhum ming +iss imo +ĠPat rol +g ow +ãĥ ¤ +ĠTHE Y +ĠBod en +ĠB ie +Ġre el +ĠÑĥÑģл ов +Ġende avor +ĠPer iod +ustom ed +m als +al on +B ox +ĠÏĥ αÏĤ +Ġom dat +Ġal tre +ĠHe h +k ad +Ġprotect or +Ġdomin ance +odynam ic +Ġcommunic ated +k ö +Ġprede cessor +ĠL uk +ĠFl ower +Ġãģ © +po que +ÑĤи ÑĢов +Ġret rospect +Ġdecis ive +Ġexem pel +{ \ +ĠR ück +r ite +ĠZe us +Ġcal orie +Ġattract ions +ĠH inter +Ġuh m +ĠíĮ IJ +Ġrul ers +Ġdiscour aged +Ġaconte cer +Ġacc ents +ĠOpt im +ĠAl g +k ids +20 21 +ĠLind say +Ġfilm makers +pr owad +Ġter ug +ëĭ ´ +ĠSom mer +20 18 +Ġborrow ing +ĠTrans fer +н оп +ari as +Ġhead phone +ì¼ ľ +Ġtransl ating +Ġauf ge +ப à®Ł +we is +av ant +pa id +b aby +Ġtough est +Ġrepe ats +ĠTer esa +L ord +Ġacab ar +ĠR ide +d ir +Ġl eng +Ġd wa +Ġhead aches +Ġn ữa +ĠнаÑģ ÑĤоÑıÑī +Ġbo ils +Ġlong ing +ri as +ó rio +ĠParad ise +ĠSeñ or +erd em +Ġrein st +Ġsal aries +Ġinsec urity +ÅĤo ÅĽci +ĠабÑģолÑİÑĤ но +ink en +ĠEd dy +ud os +Ġd ummy +Ðļ ак +s ix +Ġin box +Ạ© +Pe ople +á»ĵ ng +Ġorganiz ers +f ind +Ġü l +ĠCO M +ż a +we ile +Comment ary +íĬ¸ë ¥¼ +ĠMitt el +k us +èĽ ĭ +ठ¨ +ir al +Ġgar ment +ικ ά +Ġst ool +pay ers +Ġsh immer +ĠO llie +ĠJe żeli +è¿ĺ æľī +Ġ197 7 +Ġje ux +Ġext inct +ĠTransport ation +ĠM aker +Ġj ohn +Ġrich est +Ġtraum at +Ġli egen +´ë ¥¼ +è¿Ļ éĩĮ +Ġun rest +ĠSt raw +æĭľ æĭľ +Ġcom a +ĠKr isten +ĠÐļон еÑĩно +ĠBry ce +ĠÑıк Ñĸ +Ġpearl s +Ġпоним аÑİ +Ġadd itions +Ġas ympt +ĠменÑĮ ÑĪе +Ġsc ans +Ch ild +ĠH ide +к ÑĥÑİ +et as +Ġd ank +Ġple as +Ġess ays +Ġj ets +åħ Ĵ +Ġв ед +Ġposit ives +ho f +- ) +zz o +Ġstar ters +Ġsm iled +Ġ194 4 +qu iera +Ġro k +Ġpu esto +N ico +Ġsim ulations +Ġ ච+Ġintrig ued +ĠOver watch +åĸ Ĥ +s igh +b ai +Ġë§IJ ê³ł +id é +Ġcra bs +áºŃ p +ĠIraq i +ìĿ´ë ¥¼ +ÑĤ Ñı +ĠSoph ia +ĠDN S +Ġönem li +ĠLu o +Ŀ ¤ +ĠCoun sel +l igen +анÑĮ ÑĪе +Ġtrump et +Ġd apat +ĠJ M +ĠEVER Y +Ġå°į ä¸įå°į +å¤ ¢ +ĠL ayer +Ġc ô +н ал +ĠJ oo +ĠH ack +Ġs unt +ĠLeon ard +ĠFire base +äng er +Ġexpl oding +v oy +Ġì¦ IJ +ĠÑģ еÑĢÑĮ +Ġsever ity +Ġbest imm +çµIJ æŀľ +Ġt iring +Ġprocure ment +Ġdiplom acy +Ġdecor ative +ĠÙĬ ا +Ġpenet ration +Õ « +Ġout right +EN E +ĠUn i +od les +Ġz eros +Ġdelight ful +j m +Ġdo po +没 äºĭ +Ġposit ivity +ĠVIS TA +ĠRes ource +íĥ Ģë +ÑĪ ие +C arl +Ġpip ing +Ġchop ping +ĠGan ze +ü ss +ĠA o +Ġsh attered +ĠDet ective +Ġund oubtedly +Ġhall uc +Ġen ch +Ñĭ Ñĩно +ÑĥлÑı ÑĢ +is esti +Ġped als +Ġdur um +¤í Ķ +la imer +Ġprop re +C u +Ġtransl ator +Ġca ÅĤ +Ġê·¸ 걸 +Ġca ÅĤy +U A +Ġrev ised +Ġпод об +ĠArt icle +ĠHait i +Ġà ĵ +ĠC trl +Ġroz m +la it +Ġletz te +is pering +dis play +Ġalumin ium +Ġpalab ras +Ġconoc er +Ġz itten +Ġdir ig +åıª æľī +Ġbrain storm +Ġw ifi +ĠPart icip +Ġview point +ĠQu an +Ġhier arch +W elcome +å¯ ¾ +Ġoff en +ĠRe covery +gan o +W ould +Ġrep ro +Ġper ceptions +Ġdem asi +ĠBangl adesh +ĠIncred ible +Ġlet zt +Ġbehav ing +Ġaston ishing +Ġâ Ĩ +ĠëĤ¨ ìŀIJ +èµ° äºĨ +ãĥ Ķ +ĠGORD ON +C AR +? !" +ĠP rest +Ġë§ŀ ìķĦìļĶ +Ġt and +Ġl ash +ç Ĭ +ific ant +Ġint oler +Ġг еÑĢо +Ġte u +as o +ĠÑģов еÑĤ +Ġtravel ers +ĠSy nd +ĠвеÑĢ Ñģ +F onda +ad ı +Ġtrans cription +Ġtit anium +Ġtw ists +Ġgear box +ens ation +f at +C oll +ĠCommon wealth +z on +ĠPolize i +ĠAPP LAUSE +f ry +ĠJud a +este em +Ġso ck +ĠJug end +Ġк ÑģÑĤаÑĤи +ĠD ro +Ġproch aine +ãĥ¼ ãĥ« +Ġli ksom +ĠEner gie +ĠMar ina +Ġ2 30 +Ġê°Ģ ìĦľ +ump ing +Ġl one +ç´ ļ +Ġfont s +Ġbusiness man +Ġp ly +Ġdo e +gr id +ĠMil waukee +ĠE den +! ". +ĠÛĮ Ûģ +og ens +Ġteas er +Ġqui én +Ġincent iv +go vern +Ġchild care +Ġsneak ers +Ġimprison ed + ® +иÑĤ еÑģÑĮ +an bul +Ġreg ain +Ġtranqu il +Red ner +éĽ ¨ +IF A +Ġide ological +Ġmayor ÃŃa +Ġb ureau +et erm +ĠD ID +ìĬ · +Ġw aving +Ġbe b +Ġá r +Ġк в +Ġenv oy +an ut +ик Ñĥ +ĠEnviron ment +ĠAss ass +ãĤĵ ãģ§ +ĠB read +ĠТ ÑĥÑĤ +Ġstair case +ĠDise ase +Ġauc un +Ġëĭ Ī +Ġconfront ation +Ġ194 1 +Ġiron y +Ġwor sh +ãĤĮ ãĤĭ +Ġf ick +ĠNa omi +Ġback side +ie ux +K ap +Ġved ere +Ġlength y +Ġbreak er +ĠRoll e +Ġpred ator +Ġnoss os +Ġadvert ise +è³ ĩ +ÑĢод е +Redner wechsel +re ten +Ġcollect ors +ıģ ımız +Ġtr ig +Ġax es +in ters +Ġpen alties +ĠOs man +ĠJen na +Ġfl akes +Ġtrain ers +Ġstun ned +ĠSc roll +ĠP ip +Ġна ÑģÑĤ +Ġnh Ãł +ĠSm ack +ẫ n +rat os +ĠÑĢабоÑĤ Ñĭ +Ġu cz +ĠLem on +ĠS ind +Ġpsych ic +ĠAb g +Ġmamm als +Ġimmers ive +Ġb ots +Ġverschied ene +Ġg eral +Ġfoll ower +Ġ ä»ĸ +Ġsegur idad +Ġimmers ed +fe ito +c ross +Ġö ld +íĥ Ħ +Ġãģĵ ãģ® +Ġ×Ķ ×Ļ×IJ +ĠJ ian +Ġbili yor +are a +Ġk af +Ġgod t +缸 ä¿¡ +Ġë°© ìĨ¡ +Ġdet riment +æ¥ ļ +Ñĸ л +ĠÄij âu +Ġchlor ide +ø re +le i +Ġmont e +Ġdifférent es +à¯ģ . +Ġcareg ivers +Ġin adequ +Ġfare well +ĠÑĤип а +ont ec +ĠE ph +HH H +ĠTod os +ĠС ШÐIJ +Ġtro v +Ġl ige +Ġc ông +ĠC iv +Ġcap az +ĠV allahi +Ġquest e +Ġrepl ica +س ب +z na +ĠÑģл Ñĥж +ĠP T +w ave +ien i +Ġrel ied +de velop +Ġdem e +ĠA man +Ġ[ ...] +Ġcompl iments +u ais +ĠíĮ ¨ +Ġsmell ing +Ġdad urch +ÙĪ ت +Ġor anges +Ġл ай +Ġstabil ization +åĢ į +ãĤĮ ãģŁ +æ¥ ½ +Ġappl iances +Ġh m +ĥ IJë©´ +odynam ics +Ġc iÄĻ +ĠC ott +M ON +ĠM ang +æĶ¯ æĮģ +Ġall erdings +ικ ή +sh ots +Ġt s +ĠG ör +ĠCH AR +Ġ: ( +Ġwr ath +Ġf ique +Ġfüh ren +Ġtest ament +Ġ^ ^ +á¹Ľá¹£ á¹ĩa +AL D +Ġtext o +ĠDog s +Ġs ib +Ġpath etic +ock s +Ġrad ically +ĠM ORE +ĠJAM ES +Ġing l +ĠTechn ical +Ġpor ch +ĠU T +ĠобÑıз аÑĤелÑĮно +Ġrenew al +Ġaesthet ics +ik um +Ġbe verage +der n +Ġpredict ive +Ġch uy +ĠRegard ing +ĠFor ward +ĠÙĪ ÙĦ +Ġcontext ual +Ġdwar f +Ġpre he +Ġgovern ed +ħ Ħ +Ġtrabal har +Ġnegó cio +ĠболÑĮÑĪ ой +еÑĩ аÑĤ +Ġд ÑĥÑħ +Ġflood s +Ġbow ling +ĠO B +ĠH är +Ġgrad ing +주 ëĬĶ +Ġg ars +d ling +Ġr ak +ë Ī +c reat +ĠÑī е +Ġneighb ours +f ood +Qu ery +Ġhero in +ice ps +ĠK inda +N ET +Ġmar i +Ġim itate +Ġach ter +Ġsettle ments +ra re +cc iones +Ġë ĵľ +Ġf ik +it ung +Ġм акÑģим +Ġel f +Ġd alla +ĠPol sce +ĠP ul +Ч ÑĤо +ĠMor gen +ØŃ Ùħ +Ġsuprem acy +Ġk ys +ĠHur ricane +ĠG TA +ĠFe h +Ġfinal mente +m und +ĠK rie +é poque +ĠT ucker +IT T +Ġl ur +Ġdi pping +ä v +Ġeer ste +ĠFl int +bild ung +ู à¹ī +Ġto im +Ġpr acy +Ġtransform s +Ġspeed ing +Ġpresent er +Ġfellow s +f illed +ie za +Ġadv ising +ĠInter view +и гÑĢ +we hr +ĠD ante +pt ure +Īë¬ ¸ +¯ ¸ë +IJ IJ +ĠCoun ter +Ġcr ist +Ġì§ ľ +Ġje une +ĠÑģÑĤ ÑĢаÑĪ +Ġmie Äĩ +Ġtut or +Ġmas ala +Ġpowder ed +Ġn au +ĠFreder ick +Ġbill ing +ĠE isen +Ġд обÑĢ +Ġm est +æ ½ +Ġsn ipp +Ġmon o +ĠA lo +ĠMer cy +éri ence +Ġcasual ties +ĠAN NOUNCER +ä» İ +Ġto car +Ġbacter ial +H o +Ġstre ak +ĠJ ENN +Ġpl ast +Ñģ лед +Ġre app +Ġpay check +Ġmin ers +hab t +ĠJ ap +н ÑĥÑĤ +Ġred emption +Ġqu ir +hn lich +Ġaccum ulation +Ġsh ove +Ġadrenal ine +M ake +ĠH ern +oss ing +ĠV il +ub by +her tz +bre aks +Ġsp ur +ĠD aha +US TIN +Ġcontinu er +ĠSa ul +ãģ® ãģ¯ +Ġíı Ń +ĠëIJĺë ©´ +Ġë§IJìĶ Ģ +Ġо ж +Ġsuspect s +Ġla quelle +ĠMuch as +Ġv öllig +ul en +Ġimp res +Ġlo bb +ene e +Ġн аж +T a +Ġréal ité +ĠRe x +Ġharvest ing +Ġest r +æ ¶ +osp ace +OS S +Ġdisturb ance +ass ic +ĠIs ab +Ġdéc ouv +ĠHamp shire +Ġor nament +Ġlu ôn +ĠU W +Ġj Äħ +éĤ£ ä¹Ī +Ġrespect o +Ġcomun idad +Ġcom igo +ag na +Ġintrins ic +ĠAlum ni +Ġses leri +Ġestim ation +âĢĶ âĢĶ +Ġprodu it +ãĢĤ ãĢį +Ġв ÑĢ +Ġwh irl +Ġac ces +ç u +Ġvari ability +Ġv odka +its u +Ġinternship s +Ġalloc ate +R R +íĽ Ī +Ġinstruction al +t ant +Ġà®ħ த +Ġinv ites +Ġha k +Ġsca res +Ġe clipse +п ов +к олÑĮ +ativ as +Ġstab bed +ĠD OM +ä¸į åĪ° +ro ots +ĠPict ure +íĺ ¼ +ĠC HA +ie c +ı ı +han ol +Ġmisunder stand +R ay +Ġroad map +ocument ed +iz ione +ĠOl ive +r ift +Ġ×Ķ× ł +æ¯ į +l est +; ; +ĠE A +éľĢ è¦ģ +од Ñĥ +Ġhob bies +Ġbur ial +ãģ« ãģ¡ãģ¯ +Ð ¤ +le ge +ĠH J +Ġobject ion +Ġãģ Ń +ct ory +Ġincre mental +Ġgym n +Ġepid emi +Ñģ Ñĭл +à ij +Ġadvance ment +Ġpar ch +New s +Ġa yr +л ам +Ġ×ľ× © +Ġdipl oma +ãģ¡ãĤĥ ãĤĵ +Ġrob bed +On ly +Ġinc ur +Ġch anting +Ġíķ´ë ıĦ +Ġrich es +ĠCar men +Ġnost ro +λ ÎŃ +ĠPow der +à¹Ģภ« +ĠìŀĪ ìľ¼ë©´ +Ġgerçek ten +ĠPik achu +ем он +OL L +Ġplanet ary +Ġsl ows +Ġclock wise +al ion +Ġì Į +Ġver n +Ġh omme +Ġend point +Ġinnoc ence +Ġelement os +Ġsophom ore +Ġnot ions +ĠCould n +p ur +Ġz at +Ġobs ess +Ġmotiv o +ĠK ub +ĠDr ug +A nt +ĠPlay ers +ĠHum ans +Ġme lee +ĠWild life +ĠV P +Ġvolcan ic +Ġcom in +ĠGu ang +ĠÏĦι ÏĤ +ĠоÑģоб енно +ĠS ize +L isten +ĠA aa +app ro +Ġbar bar +ĠPark inson +нÑı ÑĤÑĮ +å į° +Ġunderest imate +Ġsubst itution +Ġcosm etic +ä¸ĭ 次 +Ġwill en +Ġbe ide +ann i +Ġcondition ed +ĠDe bbie +Ġis to +ĠEd wards +ìĽĮ ìļĶ +ĠÑĤ ов +Ġab brevi +ĠM ün +ĠPr inc +ĠLi ang +Ġst ink +Ġradio active +ãģĨ ãĤı +Ġac ontec +Ġun con +ĠTur bo +ãģ IJ +Ġkiss es +æĺ¯ ä»Ģ麼 +еÑĤ ÑĢов +Ġfront ier +ĠSp y +ĠBel arus +ĠC BS +á» Ĺ +am oto +íķľë į° +ĠÑģÑĤ ÑĢо +ĠEn fin +Ġbread th +éĺ ² +ĠCa fe +ĠDaf ür +ĠB our +ar as +Ġbl ueprint +an ı +Ġconst ants +Ġattack er +ĠForm ula +za Äĩ +Ġs owie +Ġeyebr ow +ob ook +Ġset zen +第 ä¸ī +ons ider +aw ning +Ġsöyle ye +Ġinv aded +Ġpronoun s +Ġdob ry +S i +ĠÐ¥ оÑĤ +Ġvolley ball +Ġl ament +is ches +ar me +ap i +ĠW iki +ли ÑĪ +Ġkas ih +Ġp ess +ĠÑĦ оÑĤ +ĠS ul +å¾ · +Ġpse udo +Ġmem o +ĠìĹ° ìĬµ +ĠдоллаÑĢ ов +ĠпеÑĢ ем +ĠRe ach +mir al +alt ed +Ġstat ut +read ing +Ġsöy led +ĠLind sey +ĠAh mad +ë ¶Ģë +ĠС егоднÑı +Ġprzy got +Ġhy ster +U RE +ĠNe igh +Rep orter +ĠB unu +ĠTreat y +ĠR ank +ĠF ame +in ished +Ġge ared +Ġcomp ose +od ia +ĠL on +Ġjeste ÅĽmy +ĠDIRE CTOR +Ġel kaar +ĠV iel +×IJ× © +ynth ia +ä¸ ¦ +Ġm ère +ĠTom ato +Ġex atamente +ni ÄĻ +ĠFre i +ĠD if +Ġopen ings +Ġgraph ical +ĠÑĥд об +ĠвÑģ п +ĠWeek ly +ев а +Ġhang s +Ġuns afe +Ġem blem +ĠKolleg innen +al ay +Ġk si +Ġh ides +Ġol may +Ġent ste +Ġarth ritis +ÃŁ erdem +Ġbin nen +Ġlist ens +ĠH ess +åĨį ä¾Ĩ +ĠLou ise +ld en +ен Ñģ +ĠVers ion +ĠAgric ulture +ìĬ¤ë ¥¼ +м ан +ë Ħ¤ìļĶ +Ġw ines +ĠIN F +r ul +ĠJ K +ıyor lar +sh ield +reat h +Ġter us +ĠL um +Ġanticip ation +Ġacc ustomed +ĠM ina +Ġw ield +io è +mer a +Ġcount down +Ġcl ing +Ġcomm end +Ġfakt iskt +Ġdef enses +Ġcock pit +Ġком анд +Ġdish was +ĠThan os +Ġkid neys +Ġse he +Ġmicro bes +Ġc uff +ĠвÑĭÑģ ок +ĠSp icy +çŃī çŃī +வ à®° +cul us +or c +ç¾ ħ +ix es +ĠC redit +Ġr aj +Ġbring t +ĠN iss +Ġgr im +ĠS OL +Ġten im +ĠSud an +ĠSp art +Ġpromot es +ĠN ossa +ĠÑģоÑģÑĤо Ñıни +Ġì° © +Ġunc ont +ĠLiber al +ĠТ олÑĮко +ĠV iele +Ġktóre j +Ġ* *** +M ax +ĠЧ ÑĤобÑĭ +3 50 +Ġíĺ¼ ìŀIJ +Ġë¶Ħë ĵ¤ìĿ´ +Ġwar p +Ġteng a +Ġsympath etic +Ġbiz i +ĠZ ack +ied o +Ġëī ´ì +p iel +ĠÑĤ ол +Ġsc aled +ĠPET ER +ĠCO MM +ĠC ame +Ġcatast rophe +Ġsweat y +ig ration +Ġstuff ing +ĠÏĢολ Ïį +ĠDri ver +zy st +T ech +Ġassess ed +ĠSur face +ır ım +s ur +ler weile +Ġд ог +Ġshut ting +Ġfr actions +ĠÑģ ол +every one +Ġer n +ĠÐĿ ов +Ġdefend ers +Ġvers ucht +ãĥ³ãĥ Ģ +Ġpol ity +ĠÐŁ он +ver ständ +Ġbrows ers +Ġtransform ative +Ġdict ate +ĠLE GO +Ġning una +ê´ ij +Ġp izz +ĠHar old +ĠL opez +Ú¾ ÛĮ +an ız +atch et +ÙĬ ت +Ġl ernen +Ġê·Ģ ìŬ +Ġhous ed +Ġclean se +ĠW AT +lar ation +Ġby tes +Ġtuck ed +Ġfault s +д о +F X +Ġìĸ¼ë§ ĪëĤĺ +Ġde form +Ġcontract ing +ĠTIM E +ir se +Ġne ben +Ġc erc +ĠArm strong +Ġtest er +Ġparf ait +Ġjealous y +Ġtox ins +Ġdis bel +ÑĥÑĢ Ñĭ +imp ression +Ġprost ate +Ġfire wall +Ġclass ics +еÑĩ ÑĮ +Ġsocial ism +Ġgrac ious +ĠÑģ нова +Ġд нÑı +Ġburn er +ĠMin or +Ġìļ°ë ¦¬ë +Ġjed es +Ġcontinu um +Ġh ots +Ġoccur rence +Ġadminister ed +Ġзам еÑĤ +Ġhes itation +Ġdr ills +er ca +ĠвÑĤоÑĢ ой +Ġstead ily +Ġinsan lar +Ġi han +í ij +Ġhel per +ĠSen in +åģ ľ +ов ание +ĠER IC +b la +ĠAcad emic +Ġhuman ities +bl ack +ump y +ort ex +Ġìł Īë +ĠØ¥ ÙĨ +Ġdiscl ose +ĠEl ijah +Ġλ ÎŃ +ĠQu er +ب ÙĦ +ãĤ ¡ +T ell +ar le +Ñĸ ÑĢ +Ġaug mented +Ġë¹Ħ ìĬ· +Ġand roid +ठ¤ +ar ma +Ġs zer +ge ord +Ġge ek +Ġye ux +Ġp ong +ĠãģĿ ãģĨ +Ġtort ured +ĠB ath +z ig +ason able +Ġn ets +Ġbar u +ĠFl at +ĠV ater +ĠTer ror +ĠA vo +Ġceremon ies +ro e +Ùģ س +O ps +Ġhy vin +Ġap resent +ol or +ĠигÑĢ Ñĭ +ort on +Ġê·¸ëŀ ¬ +Ġlook in +ĠT Y +ĠM int +Ad d +Ġm ite +ĠSm oke +Ġnot a +Ġm oss +ĠAb end +Ġì» ¨ +Ġexagger ated +f ires +Ġred ist +ff iti +Ġopen ness +ê°IJ ìĿ´ +ende u +ен ной +W atch +Ġav atar +ĠP ey +ur un +Ġsen za +Ġì§Ģ ìĹŃ +ĠNat omiast +Ġemer gence +ray s +Ġcraft ed +g ary +ãģł ãģij +ü ng +- " +Ġhack ed +Ġstr ay +en cie +em o +Ġcom en +ĠK ız +ĠJ asmine +ĠH indi +man as +Ġinfin itely +em on +ìĿ¸ëį° ìļĶ +j ak +Ġro aring +éri que +s weise +ĠRo lex +åł± å°İ +ĠStu art +bn b +Ġdiagn ose +Ġcoher ent +ĠM J +æºĸ åĤĻ +Ġp ike +l av +Ġorchest ral +а ÑģÑĤи +Ġterm inar +Ġgather ings +Ġcompl iant +Ġupgrad ing +Ġregul ator +Ġlan ç +éĢ £ +Ġmerch ants +ta wa +Ġmonit ored +Ġrend re +ä¸ ¤ +Ġunter wegs +ang uard +g ard +ĠBel ow +du ino +ĠЦ е +Ġimped ance +ìľ ¡ +ä» ½ +Ġakt uell +ĠV atic +åŃ © +Ġste wards +Ġbright est +Ġk enn +Ġk au +ĠMat rix +ĠB ark +ĠðŁ ij +Ġt aper +Ġcas ino +ר ×Ķ +ys ical +Ġbuild ers +ĠczÅĤ owie +ĠNep al +Ġ! " +Ġterm e +Ġin nych +Ġmath s +Ġdraft ed +ĠB alk +Ġhesit ant +Ġvolt ar +Ġrev ive +ĠÑĦилÑĮ ма +Ġassass in +ĠS olutions +Ġdu el +Ġbear ings +à¸Ħ ะ +Ġrook ie +ik at +Ġbisc uits +Ġc ords +Ñĥв аÑĤи +AR IN +Ġprogress ing +ĠG ir +Ġpenet rate +ĠSt orage +e ight +ĠÑĤ ÑĢÑĥ +Ġdon ÃŃt +Ġsiz in +Ġout dated +ĠнаÑĪ и +Ġaff ir +Ġspo ons +Ġon i +Ġfl ank +ĠG ol +h ã +Ġp éri +Ġhonor able +ĠBreat he +sc enes +Ġob viamente +ик Ñģ +Ġש ×ŀ× +Ġsmooth ie +ŀ Īë +Ġd ime +ĠíĸĪ ìĸ´ìļĶ +Ġapp el +ĠCath olics +Ġsing les +Ġlat en +Ġç ünkü +ĠV ader +æı Ľ +Ġvard ı +ĠIst anbul +gr é +ĠEl sa +ë l +Ġinve ce +Ġcr ane +Ġo be +ĠSh ark +Ġsm ack +Ġrest oring +. \ +Ġë¹ łë +Ġf aded +um bers +S inging +Ġdep ressing +th est +ĠW ahr +Ġmult itude +ÑĢавÑģÑĤв ÑĥйÑĤе +rij k +ek a +Ġcomplet es +ĠWell s +Ġro y +ĠPr ay +ĠKal au +iz in +iaÅĤ em +Ġlo com +ĠNash ville +ĠPent agon +ë ¯¸ +ĠNE W +Äħ Äĩ +ÃŃ ss +Ġmarry ing +Ġfe ud +íĻ ķ +æĢ ¥ +) ! +ĠOper ations +Ñĥ ÑĶ +Ġmo je +Ġinstruct ed +ĠëĪĦ 구 +Ġ×Ķ× Ĵ +ĠпомоÑī ÑĮÑİ +Ġsab ia +ìķĺ ìĸ´ìļĶ +pl ane +p ri +Ġпол ноÑģÑĤÑĮÑİ +ĠK itty +Ġpróp rio +ed ere +Ġinteres ante +Ġд е +Ġcond ensed +Ġav ent +T OR +Ġgre asy +AR K +ort a +A J +Ġdis reg +Ġcorrect ions +Ġst ero +Ġinfluen za +Ġdess es +Ġball ots +Ġme get +Ġma fia +Ġb öl +n ost +ĠÑģÑĤ аÑĤÑĮ +Ġrespond er +Ġhint en +g rav +à¸Ń ะ +yn chron +Ġvi ens +Ġsam o +Ġd t +pan nt +ĠÅĽwi at +Ġзап иÑģ +Ġmer ged +Ġke p +Ġmis leading +Ġdig amos +Ġam mon +è¾ Ľ +ch et +Ġê°Ģ ìł¸ +Ġun i +ĠëIJĺ ëĬĶëį° +Ġнап ÑĢав +ĠкоÑĤоÑĢ ого +Ġanim ate +×ķ× IJ× +еÑĢ в +Ġmin ced +Ġka um +ãģĤ ãģģ +ÏĢ ε +л ег +exist ing +Ġplata form +ĠK RIS +ìĽ ł +ĠFamil ien +ĠLib ya +Ġbiod iversity +Ġidi ots +ird i +Ġszy b +ĠRoll ing +ü cht +ĠÑĥд ив +Ñģ Ñĥд +Ġreal izar +Ġcan ned +ĠÑĢ ан +Ġmet abolic +ĠBe ef +Ġkil ka +лÑİ Ñģ +Ġreg istry +моÑĤÑĢ иÑĤе +Ġviel ä +Ġod c +Ġcondem ned +æ© ĭ +f al +ĠD il +wo ÅĽci +A w +Ġstatist ically +Ġso gen +ĠB ETH +Ġsh aving +å¹ ¸ +oc al +ĠFun ny +Ġpeace fully +Ġaddict ive +ĠIns ert +la uf +Ġexperien cia +é¦ĸ åħĪ +иÑĤ елÑı +ÃŃ gen +ág ina +Ġabdom en +íķľ ëĭ¤ +ic us +im ana +ì į¨ +arch ing +Ġkonk ret +ìķ ĺë +ек а +ou fl +ive l +Ġn ude +èt res +Ġm onsieur +Ġcl ash +Ġtherap ists +Ġcub ed +Ġretrou ver +Ġwave form +Ġpot em +ĠForm er +is ión +åº ľ +Ġ×IJ× Ŀ +und os +ĠMein ung +ص ÙĦ +ĠJ ude +Ġn Ã¥r +ĠLeon ardo +ĠCr isto +ĠG OT +ÑģÑĤÑĢÑĥ к +L AN +Ġg Ã¥ng +Ġdé b +ĠFrankf urt +Ġcra ppy +Ġli l +ann ée +ĠмеÑģÑĤ е +RE T +ĠN er +ĠCO STA +Ġjed em +Ġcurt ains +Ġiter ations +Ġun av +Ġpla que +or um +ĠÎ ¶ +Ġnúmer os +Ġdes ap +² ½ +Ġcomp iled +Ġref le +Ġrank ings +Ġrep aired +ĠÐĿап ÑĢ +Ġdownload s +Ġarm our +Ġ×Ļ ×ķתר +Ġlonge vity +ĠTON ER +ĠкомменÑĤ аÑĢ +Ġcz ego +Ġnot ify +Ġairport s +Ġend uring +let te +Ġapp arat +Ġhab il +á»ĩ c +n ad +IC O +ĠBra h +Ġseg ún +Ġgovern ors +k aha +ĠSchl uss +Ġodpow ied +ir ting +Ġrem pl +ĠAb original +ident ally +Ġenhan cing +lic ting +ĠHawai ian +Ġstri ving +ĠN iet +Ġzn aczy +Ġobed ience +ĠnÃ¥ got +Ġexp ired +Ġ19 18 +pres ented +Ġpr owad +ĠTer r +ĠPrinc eton +Ġmor gen +Ġattract ing +ĠS igma +ign er +ĠRe chts +ĠP eki +Ġmet hy +Ġha mm +Ġdire ito +Ġdeleg ation +ив аÑİÑĤ +Ġg in +You ng +Ġdepend encies +ĠBrad ley +bud s +Ġf is +Ġpyt anie +Ġinterconnect ed +Ġemba ixo +ĠS as +Ġr uh +ĠS icht +S ur +Ġsuper b +ĠSabb ath +ĠD anger +k ol +Ġh ou +s upp +ĠN acional +Ġsuccess ion +Ġv á +ĠMaÃŁ nahmen +ĠJess ie +ĠId aho +fore st +ħ ĺ +Ġ×ŀ× ĵ +ĠØ£ ÙĬ +Ġsweet heart +Ġneat ly +ĠEv angel +ê³ ¡ +ĠSu ite +úblic a +ĠÑĥ ли +ĠAnn ouncer +l igh +Ġsens ations +Ġshel ters +Ġh art +Ġsqueez ing +ĠR ivers +ĠCook ing +ì± ħ +person al +Ġman os +ÑijÑĤ ÑģÑı +w ij +Ġgo gg +ĠMill i +ĠF P +ün st +ĠL S +Ġspray ing +Ġf aux +Ġaut ograph +olog ic +Ġtor ment +Ġencry pted +á» ħ +Ġest re +ç¹ ¼ +à ± +Ġst umbled +Ġa ider +Ġsab en +x ter +ĠC ities +ĠTür k +ëĭ ¥ +ch ine +Ġto pping +Ġpoison ed +ĠRoman ia +×ĵ ×Ļ +Ģë ¡ľ +ĠпоÑĢ Ñıд +Ġchir ping +ĠìĻ Ħë +×ij× ¢ +Ġcu anto +Ġdon ating +ĠReg ent +ĠBer uf +Ġdistract ing +Ġstam ina +ĠDar ren +Ġì¶ ķ +l ists +d al +ch uss +Ġeconom ist +ãģĪ ãĥ¼ +org t +Ġist iyorum +è¿ Ľ +ĠSur prise +ĠHa o +Ġìµľ ê³ł +ĠG W +ĠIn ner +Ġqu ieren +Ġmind ed +Ġsupercom puter +Ġdiagram s +íĬ ľë +ê²ł ìĸ´ +ĠобÑĬ ÑıÑģ +Ġestab an +Ġdestro ys +ĠBre aking +Ġkar Ä±ÅŁ +Ġrebuild ing +ľë ĮĢ +ли во +ĠSau ce +ĠF usion +×ķ× ŀ× +ĠQu inn +Ġga uche +ĠÙĪ Ø£ +Ġ È +ç ĵľ +Ġtechn o +Ġdisp atch +ĠaÅŁ k +Ġein zel +ĠG mail +ç ŀ +Ġê°ľ ìĿ¸ +ĠÑģем ÑĮ +Ġjour neys +Ġi ht +Ġfib re +Ġdram as +ouch ed +Ġren ame +Ġоп еÑĢ +Ġpo o +ĠD ru +ĠиÑĤ ог +Ġz ast +Ġco z +Ġz ucch +Ġobt aining +Ġcomm ute +Ġsub mer +ĠV ish +ĠR abb +og g +Ġh ut +íĸĪ ìĸ´ +æ¯Ķ å¦Ĥ +ere mi +Ġμ α +Ġdisk ut +Ġб Ñĥк +Ġimp aired +d epend +ĠÙĪ ا +ĠÑĢ Ñĥк +Ġб аÑĢ +Ġoxid ation +Ġsitu ação +ÉĻ n +u ção +Ġsag te +ĠS ER +ĠC ake +Ġtur meric +ĠK ak +b ung +ĠK á¹Ľá¹£á¹ĩa +Ġpoison ing +Ġsl ipping +ĠS ays +å°± åı¯ä»¥ +ò ng +çŁ ³ + « +ĠClaud ia +ĠChar acter +ни ÑĨ +co at +Ġprogress ed +ĠFer gus +Ġìĺ¤ ëĬ +Ġo at +ord able +ĠLe y +ĠHera us +Ġresult ados +ĠKay la +Ġr iff +Ġcheg ou +Ġx i +Ġsp acious +Ġrecogn ised +Ġe ch +ĠT ie +Ġlaunch er +J im +Ġsupp ression +ĠImp ossible +Ġguit ars +ĠFour ier +иÑĩеÑģ кий +ĠTh erap +ĠK af +cent ered +ĠÑģо оÑĤвеÑĤ +Ġk lim +Ġcarbohyd rates +ign ant +ĠAst ron +Ġem ple +Ġdr astic +ĠмиÑĢ е +в ин +u w +Ġpret tier +Ġdon uts +ĠAth ena +Ġdiss ert +Ġpl ante +Ġur anium +ìĿ Įë +ar é +Ġrze cz +Ġdisplay ing +æĪ ² +Ġsar c +r ão +Ġtamp oco +Ġphilosoph ers +ĠRe cht +æĵ ļ +Ġcoment arios +y se +Ġìľ ¤ +Ġm ise +ĠG in +Ġн ом +ĠFR OM +l iner +at if +Ġspo ÅĤec +x a +ĠÑĤ ÑĢÑĥд +Ġw ag +기 ìĹIJ +ĠM G +Ġoff spring +ĠUnder standing +åıª æĺ¯ +OR A +Ġwh irring +Ġsur rend +Ġpok er +Ġmon uments +ĠâĻ © +Ġorgan ised +ĠSo zial +ĠF actory +Ñħ а +Ġrese mble +з д +Ġexplos ions +Ġpay roll +Ġom n +ĠJ orge +ι Ïĥ +Ġfract ure +Ġpersec ution +Ġdem ais +E CH +, ) +Ġcri ar +ĠJ OSH +Ġdem ographics +Ġ16 00 +Ġcur rencies +ĠT ips +Ġ éĢĻåĢĭ +ĠRe fer +ĠDan cing +Ġincons istent +Ġde h +Ġimm ens +Ġme ist +Ġimpat ient +Ġbehav es +æĿ ¾ +ĠëĤ´ì ļ© +Ġback story +Ġagree ing +ĠÅ ģ +ih in +Ġtemper atura +ĠBack ground +Ġnut zen +Ġëħ ¹ +ĠM änner +Ġcollabor ations +ĠK os +éģİ åİ» +Ġnight mares +ë ĵ± +ĠQueens land +Ġassoci ates +ĠK ok +Ġfact orial +ĠHy ung +Ġê·¸ ëĭ¤ìĿĮ +Ġfil ho +Ġel ét +Ġíĸī ë³µ +° ± +Ġgef unden +Ġsemic ondu +Ġcounsel ors +ĠU pper +ĠA ub +ick ers +V er +Ġnorth west +ĠMainten ant +ĠL akes +аÑı в +int é +ì° ½ +Ġг аз +Ġgi orn +Ġdigit ally +ĠCirc uit +ì¼ Ģ +ãĤĬ ãģ¾ãģĹãģŁ +Ġcheer ful +ĠPet erson +ĠDan ish +ativ os +Ġli ken +Ġhar bor +али ÑģÑĤ +x e +Ġcur ls +ĠR hod +E nd +ĠE T +Ġacqu aint +ĠKel vin +Ġtr if +ĠA way +ìŀIJ ëĬĶ +v s +Ġp ágina +Ġin let +ĠSant os +Ġìļ° ìĻĢ +Ġyap ıyorsun +th eme +Ġsou ff +Ġinject ed +Ġpó źniej +iver so +amp ed +Ġda her +Ġd agger +ĠлÑİб им +Ġt ummy +Ġenlight ened +c ents +ĠD ah +Ġcu est +ä¾Ĩ 說 +IL Y +Ġ×ij ר +Ġbang ing +ĠEm il +ĠC ler +ĠB order +иж Ñĥ +Ġpresent ers +ĠST UD +co ins +ĠíĻ į +Ġper ks +Ġpar ap +Ġcertain es +ĠL ore +ö st +ĠMAR TIN +Ġb ios +Ġwhere by +ver ts +ĠMir anda +Ġst ip +æ¾ ¤ +and ez +׼ ׾ +uj in +Ġê ¾ +Ġaller gies +pl ate +Ġyap ıl +Ġundert ake +ĠëĤĺ ê°Ģ +P art +Ġkız ım +h guru +ãģĤ ãģ¨ +ĠJohn s +Ġeyel ashes +Ġdra ined +Ġst Ã¥r +ãģĤãĤĬ ãģ¾ãģĻ +ĠJ ade +Ġcal end +fil m +Ġmes a +Ġlud zie +Ġattract s +Ġju ices +Ġк ил +Ġnieu we +Ġmen cion +Ġign ition +Ġbl adder +anda ag +ĠExt ension +íĤ ¨ +fe ed +ĠÙĪ Ùĩ +Ġsp un +Ġt ät +оÑĢ оÑĤ +ty ard +ron ics +ĠH uge +Ñĥж д +st ring +Ġun just +Ġpra wn +Ġfrost ing +Ġdisappear ance +ios a +Ġcard i +ĠPri est +Ġcient ÃŃfic +åĵª 裡 +ĠÐĴ аÑģ +Ġë¶Ģ íĥģ +Ġth ieves +Ġphys ique +ĠE ugene +Ġбли з +Ġmon opoly +Ġbi ography +Ġho ÅŁ +Ġt ö +m ac +Ġshock s +ìĦ ¸ë +h it +Ġsn ug +Ġinc l +Ġded ic +Ġult ras +Ġизв еÑģÑĤ +Ġutil ization +ĠÑģовеÑĢÑĪ енно +Ġserv i +st ag +1 80 +Ġse wer +ĠCh oice +Ġdis charged +ĠJ D +ол еÑĤ +ĠкваÑĢ ÑĤи +Ġteles cop +ĠJe ÅĽli +ĠN ana +c ale +ĠÑĤ он +mm m +äºĨ åIJ§ +Ġge habt +ëĤ ł +æĬ ķ +à¸Ļ à¸Ļ +Ġet her +Ġz en +Ġresearch ed +ĠCzy li +å®Į åħ¨ +work ers +Ġê²½ ì°° +Ġsher iff +all o +Ġtip os +Ġprosec ution +Ġfrog s +Ġf alt +j d +ĠíĮ Ķ +Ġfilter ed +ĠO ft +Ġì į +Ġdis fr +ĠMust ang +Ġwo ah +ĠRE ALLY +Ġмог ли +Ġentr ada +Ġиг ÑĢа +Ġmix es +ĠавÑĤом об +Ð Ļ +Ġsh in +Ġparan ormal +Ġsome place +Ġdish on +eta an +Ġfu erte +Ù ¹ +Ġdo om +ìĪ ľ +Ġexist ential +Ġbu ld +ĠSD K +ĠпÑĢав да +Ġturn over +ĠìĹ¬ê¸° ìĹIJ +Ġठ¹ +Ġmodel ed +Ġbug ün +Ġexperiment ation +Ġmorning s +Ġmed o +Ste vie +Ġplay able +Ġairl ines +g ments +Ġê¸°ë ¶Ħ +ĠT omb +ĠMV P +AUDI ENCE +Ġcheck out +Ġpas st +Ġbe ispiel +ĠLink s +he avy +Ġquestion able +Ġìĵ °ë +Ġs ill +Ġmanip ulated +ĠL oren +Ġìľ ¼ +Ġver ge +á k +I ES +Ġsab ot +ĠCustom er +ale ży +Ġnom inee +ĠG ad +Ġnouve lles +ĠS PE +ist ling +Ġo val +обÑĢ аж +if ty +éĩ İ +Ġbez el +y et +Ġfre ight +ĠHan ım +r ÃŃa +Ġz oning +Ġind em +ĠB ü +Ġfemin ism +Ġvo ix +Ġof icial +Ġdi yorum +» IJ +Ġar ose +Ġpar ar +ìĿ¸ ì§Ģ +ĠMart ine +ĠL ect +Ġrest er +Ġdrown ing +u ya +c ida +ĠAri el +Ġ0 2 +Ġ×Ķ ×Ķ +ç´ ł +ĠW ert +Т Ñĭ +Ġwid ow +Ġparch ment +Ġcott age +ĠX L +ĠSl ack +ĠN ES +Ġro be +Ġg imm +Ġcam inho +ĠHar per +Ġcit rus +Ġfirefight ers +Ġdop amine +el ets +Ġdemocr at +ìł ľë¡ľ +Ġplay back +o j +ĠпÑĢ ок +ĠSull ivan +se mble +ĠW orth +ĠMust afa +า ร +Ġmet s +éĸ Ģ +л оÑģÑĮ +Ġinert ia +Ġuniform s +è¶ ³ +é rio +×ķר ×Ķ +é nt +Ġà® Ĵ +ĠÑģам ÑĭÑħ +Ġvou lais +ĠZ immer +ê² łë +Ġн оÑģ +en cias +Ġrel ación +Ġê± ¸ë +Ġfact ion +Ġg osp +пол ож +n ap +h ak +Ġproceed ings +ĠìĨ Ķ +ìķĦ ëĭĪ +ĠìŀIJ 기 +Ġwer d +Ġso f +Ġsch lim +Ġfl avored +Ġquad ratic +ĠBo ot +Ġpublic ity +ĠCar o +Ġ ?" +ни ÑĨа +man ia +ĠS UR +ĠB UR +l ance +ét ica +Ġzob aczy +Ġtri o +s ama +Ġta ÅŁ +Ġas ymm +ress er +Ġت ع +Ġп еÑģ +Ġbeginning s +lad ım +ĠбÑĭ ÑģÑĤÑĢ +Ġmo o +ĠGene va +Ġ åľ¨ +er us +bor ah +Ġref using +b ull +ĠWait ing +ĠInd ividual +Ġan onym +im ens +Ġmed idas +Ġfragr ant +Ġdirect ement +ĠìķĦ ë§Ī +ur ia +Ġsp herical +Ġab ge +ĠVictor ian +Ġspect acle +ĠRodrig uez +Ġoc up +ĠN är +mark s +ng ulo +ĠLu ci +Ġshout ed +Ġregul ators +ÄŁ ini +Ġdis ent +ĠÑĢÑĭ н +ëĤ ¨ +ĠìĤ ´ë +Ġprobl èmes +ĠF inger +asse mble +Ġpe ar +Ġdro ite +ĠEvery where +t am +оÑĤ ив +в ой +ordin ate +ĠL ak +Ġm Ỽi +ĠTele vision +Ġexpon entially +av as +Ġble v +ĠM T +ä¿ º +Con nell +ĠêµŃ 민 +ĠÑģво им +Ġach a +ĠD ynasty +J in +Ġto re +Ġfl or +Ġмног ие +æ²Ĵ äºĭ +ow an +b ah +Ġì£ Ħ +ĠC ela +Ġìµľ ê·¼ +Ġpermett re +Ġab ras +Ġverste hen +Ġesc ort +ĠThe m +är ke +por ter +Ġkah kaha +Ġhe ct +Ġda u +w ah +ol ve +ĠAg es +s chaft +ĠSt ell +ne lle +ĠEn suite +ĠÐĴÑģ ем +Ġcr éd +ĠP P +l ords +gr unting +Ġcontract ion +G ot +Ġacqu iring +Ġso pr +Ġpoison ous +R NA +Ġan ar +ĠH of +' ) +Ġremark ably +Ġintern acional +ü cke +in qu +Ġdu y +Ġbeast s +ĠL AN +Ġpreced ent +ĠRP M +åij ¨ +Ġsel on +Ġmort e +Ġcomeç ou +Ñı ла +Ġinterpre ting +ĠBur ke +ÑĤ ÑĢа +ĠìĿ´ë Ł¬ +Ġpess im +ĠN ok +íĮ Ŀ +F emale +Ġìĭ ¤í +Ļ Ģ +Ġstim ulation +Ġsl ick +Ġê°Ģ ëĬĶ +Ġк аз +ĠH BO +Ġpap ier +Ġkön nten +Ñĥб ли +ĠConst ant +SPEAK ING +Ġktó rÄħ +Ġcos metics +ĠT rend +Ġrob bery +Ġt itt +Ġgj ort +Ġdiet ary +ł Į +ĠKir by +ĠпÑĢимеÑĢ но +Ġqual ification +Ġìķ ī +Ġcabin ets +Ġhtt p +ĠEric a +ç¾ © +Ġdisadvant ages +Ġch attering +y z +fe it +Ġgu ild +ĠE TF +ĠDrag ons +ĠH ERE +vent h +ÙĦ اÙħ +Ġmarch é +D am +Ġphot on +Ġest able +M ag +Ġol har +Ġcou pling +ĠHil fe +ĠW izard +Ġм ало +hel p +ĠlÃŃ nea +Ġì « +Ġstand alone +Ġmor ale +Ġzwe ite +ãĤĪãĤį ãģĹãģı +ähr t +Ġd otted +Ġdri pping +ĠFl ag +éĿ Ĵ +ro cket +rate gy +ir im +Ġíķĺë ©´ìĦľ +Ġsogen an +ĠUn o +ĠSch utz +Ġest ilo +ĠS ubs +ĠDais y +ÐĿ еÑĤ +' ... +Ġplat inum +Ġb irl +ĠSo vi +Ġviol ate +Ñĥ еÑĤÑģÑı +r ill +Ġtra z +Ġsn ip +Ġcum pl +à¸Ń à¸ģ +Ġc uk +éħ Ĵ +ĠParl ament +Ġhyper t +Ġpul p +Ġtong ues +at to +Ġbus ca +ih n +ER O +ĠÙĬ ع +Ġvari as +ĠMar ian +Ġbound ed +Ġpitch ing +Ġdefic iency +ĠBless ed +ĠEx erc +uch s +ĠnhÆ° ng +æľ¬ å½ĵ +Ġrap ed +h ales +Ġmal a +p ic +Ġ40 1 +ÅĽ niej +ar ina +ëĵ¤ ìĿĦ +ott i +Ġдол го +Ġtrack er +ĠShel by +Ġvan ished +Ġbak ery +Kap ı +J esus +ĠK R +J O +ħ ¸ +Ġdisc s +ìĦ ¯ +ì§Ģ ë +×Ļ× ¦ +em ary +K endra +Ġy ük +ück t +Ġv az +Ġk up +akt u +ĠÑģп аÑģибо +Ġa ik +Ġnurs ery +Ġendanger ed +êm ement +emat ics +Ġrespond ers +ĠRepresent atives +Ġsculpt ures +ig keiten +Ġde pl +Ġinterpret ations +Ġdead lines +Ġ194 2 +Ã Ĺ +Ġsug ars +em u +l ively +Ġrecre ational +Ġdist ort +Ġunders core +Ġun quote +Ġsaf est +Ġsw ollen +Ġanalys es +Ġcommen cé +å¦ ¹ +and in +ĠÐ¥ оÑĢоÑĪо +Ġdi arr +ãģ¾ ãģģ +zi est +Ġtooth brush +éł» éģĵ +u ations +Ġc ade +Ġbackl ash +h ind +Ġris que +z ess +ĠìĿ´ìķ¼ 기 +Ġesper ar +Ġtransl ations +ion ed +gro ans +Ġп ÑĥÑĤ +Ġgen etically +éĢ ł +Ġhapp iest +Ġwer k +ato on +Ġmus i +Ġfun ção +Ġìŀħ ëĭĪëĭ¤ +ĠÑĢ ай +Ġbe vor +BL ANK +Ġrepent ance +P ut +Ġpotrze b +Ġsal a +Ġcamp a +W ER +Ġdec ÃŃa +Ġsécur ité +ĠAppreci ate +Ñĩ и +ĠR andom +ë³ Ħ +k ah +Ġmö j +Ġsä ger +Ġ×Ļ ׼×ķ׾ +Ġ19 0 +xt ures +E u +Ġg ä +Ġ×ij× ª +ĠC roat +ap o +P LE +Ġpersist ence +åĬ © +Ġbl ends +Ġtre ffen +ĠSanti ago +yd ia +al do +ĠTensor Flow +ĠD ual +ãĥ ľ +Ġch iff +ìĹ ´ +Ġcontract ed +Ġseg reg +ĠFair y +Ġwis ely +Ġvulner abilities +Ġhand held +Ġgad gets +Ġbo ÅŁ +ĠPop ular +Ġcurv ature +ë ¬¸ +ĠMAR Y +ìĿ´ì Ĭ +Ġform ulation +Ġcel ery +Ġblur ry +ĠT S +ale z +Ġw s +Ġprogram m +ĠSt ack +ĠJ IM +ов али +ı ll +Ġp ère +ĠKan ye +ĠDel aware +Ġãģ ł +Ġda unting +Ġб еÑģ +ĠSt upid +b ig +ffic ial +Ġprecip itation +Ġpl ung +ụ c +bur se +Ġdar le +Ġcri pp +Ġpione er +Ġdis put +Ġse an +ãģĵ ãĤĵãģª +Ġresist or +Ġalle in +ipp les +are l +Ġend ors +z ust +ĠÑĢеб ÑıÑĤа +ed ed +Ġì¹´ë ©Ķë +Ġlle va +Ġken nt +Ġб ал +ĠDoc ument +ĠKn ights +Ġbuck le +Ġìī ¬ +Ġal k +ĠEvery day +atter s +Ġtoil ets +Ġj ugar +ĠìŀĪ ì§Ģ +Ġgen auso +ĠLandes regierung +ãģ£ãģ ± +ij e +Ġtrail ers +ĠT igers +Ġg itti +Ġforg iving +Ġconcur rent +ĠV u +ĠíĬ¹ íŀĪ +ĠBR OWN +ound ed +" ; +Ġtre mb +Ġt iet +ĠÑĢеж им +Ġnuts hell +ел иÑĩ +Ġlos ers +ric ting +Ġrede em +def ined +N ice +Ġbroad band +K O +Ġte asing +Ġpart isan +ı ma +Ġìŀ¬ë ¯¸ +ĠJour ney +Ġslop es +un ing +gr unts +Ġt äll +Ġuncover ed +Ġmy ÅĽlÄĻ +ĠEst her +äº İ +ĠHealth y +Ġë° ij +r ée +Ġpolar ization +Ġfl av +Ġcambi ar +Ġy r +ĠR anch +Ġspl its +Ġtrou vé +åľĭ 家 +Ġrecord er +Ġdé part +ÙĪ ب +ĠK ry +Ġinteress ant +Ġeder im +ÅĽ wiad +il ateral +w right +Ġpour ra +ê ter +Ġcam el +á ŀ +Ġrapid ement +Ġme j +Ġstiff ness +AD AS +Ġdiff ers +Ġal ot +ĠS ig +ÑıÑĤ елÑĮ +Ġabstract ion +åľ ĺ +Ġke iner +gr upp +ĠSher lock +íĺ Ķ +Ġc ite +Ġover flow +Ġt ại +ú car +b ula +Ġconjun to +ĠC I +Ġmoder ator +Ġindirect ly +Ġalle ine +â Ĥ +ÑĪ иб +Ġб аб +Ġdan ach +Ġ19 39 +Ġpr omet +Ġdest inations +ĠIll ust +ικ ÏĮ +Ġsab es +Ġhe h +ĠGesetz ent +ĠM iz +ен ко +ĠM ys +Ð ¬ +ĠJuda ism +Ġmust ache +Ġst immt +ĠG aza +Ġvol te +Ġnu o +Ġm ón +ĠCom put +ู à¹Ī +ĠR adi +Ġexception ally +Ġassum es +éĸĭ å¿ĥ +ãģĪ ãģ° +in form +Ġshr ine +æĵ Ĭ +Ġimplic ation +ĠF itz +æ²Ĵ éĹľä¿Ĥ +! . +Ġl t +Ġall oy +Ġeth ic +Ġmonaster y +ìĭľ ì£ł +ica ção +Ġcoordin ating +ĠM oto +Ġover look +Ġcho is +Ġantibiot ic +ĠMin ne +ĠB J +ĠA pa +or ian +Ġsp illed +J am +Ġhus bands +Ġcre ations +Ġa ñ +üs sel +ĠìĿ´ì ļ© +Ġanaly se +r ose +Ġpunch ed +Ġpres que +Ġastron omy +Ġschwier ig +ĠEb ola +Ġc is +Ġac et +ĠF X +end re +ĠìĿĮ ìķħ +Ġweb page +Ġfre aked +Ġlat te +Ġì¿ ł +Ġë¨ ¸ë +N ever +G ra +íĻĶë ¥¼ +ey ed +Ġë°ľë Ŀ¼ +Ġesper a +Ġapare ce +ra ção +Ġdisrupt ive +ĠJo int +ur ous +re as +Ġquer ÃŃa +Ġdistrib utions +Ġexpon ent +ì¹ ĺ를 +Ġd l +z hou +ĠHe aring +å·® ä¸įå¤ļ +ĠC raw +Ġflo ats +oun ced +L ab +W orld +Ġbur dens +Ġauthor itarian +ĠB olt +Ġод нÑĥ +Ġpige on +Ġdistract ions +ĠHeraus forder +Ġz est +es c +Ġsh akes +at as +ĠÙħ Ø´ +hol es +Ġthink ers +al ta +Ġar che +ĠS uk +an ha +Ġtempt ing +Ġyou tuber +Ġv ì +Ġdz iaÅĤa +ĠVatic an +P ark +Ġsup ers +ĠNik ki +ëĬ IJë +or ang +ram ient +é ¬¼ +Ġê°ĸ ê³ł +Ġdessert s +Ġav ere +ĠGreg ory +Ġëĵ¤ìĸ´ì ĺ +Ġcost ing +ĠClin ic +Ġreb els +ĠM ob +Ġbun lar +ĠYour s +ert ime +Ġret ali +m ara +at us +all es +Ġд ÑĢ +Ġд иÑģ +Ġdiscount s +ĠGU Y +Ġкак ое +ĠExper iment +re ment +ĠXi ang +Ġb ate +W E +Ġspecial ize +Ġde ity +ĠL oki +m ag +ĠN it +W est +Ġmater nal +Ġqu is +åŁº æľ¬ +bro ken +Ġlas ers +Ġha kk +ĠAng els +Ġmaster y +ant is +T iffany +ee e +ç ij +ore m +Ġin acc +Ġjurisd ictions +ĠKard ash +æľ º +I l +ĠS inn +åĭķ çĶ» +Ġathlet ics +c ÄĻ +Ġlo osely +Ġdiet a +A g +Ġ? ? +ĠëĮĢ íijľ +Ġsuper v +Ġnut rit +Ġdr ifting +ĠìĦłìĥĿ ëĭĺ +Ġпон Ñıл +ĠVict ory +ÙĦ Ø© +×ķ׳ ×Ķ +Ġп иÑĪ +Ġsh aved +Ġmes ure +ond en +Ùĥ ر +Ġex ile +ĠDes de +ĠP interest +Ġattach ments +Ġh ombres +Ġfin es +ĠìĦ¸ ìĥģ +Ġsleep s +ĠT aco +ĠI RA +ri os +Ġo ll +et es +Ġun ut +fashion ed +Ġtre ball +ĠNear ly +ĠÑĢе алÑĮно +Ġch il +éĢ ± +ÄŁ a +ĠM EL +ros cop +ĠC G +Ġv enge +Ġdishwas her +al gic +Ġmod ifier +Ġemb assy +t imer +em ics +Ġintric ate +Ġev et +ĠëĮĢë °ķ +Ġis ot +Ġна ÑĥÑĩ +ĠQu iz +res o +δ Ïİ +Ġye lled +Ġfed er +ELL ER +Ġexceed ed +on as +ic ano +Ġжив оÑĤ +ĠMa o +ĠKaz uto +Ġ ãħĭãħĭãħĭãħĭ +Ġfront line +ĠHung arian +Ġüber all +aw at +Ġgri ps +i ções +arn ya +ĠÍ ¡ +Ġse id +Ġan ak +Ġacab ou +íķ ij +Ġnot orious +ĠGod zilla +Ġover coming +ĠP end +Ġol abilir +ül me +Ġer halten +ãĤī ãģĦ +ê· ¹ +ĠM eter +Ġsta an +O l +Ġch ats +ĠBu enos +ÃŃ ve +alu able +Ġstrateg ically +Ġcompr ised +ĠпеÑĢÑģон аж +Ġw ann +ĠC en +н иÑĤе +Ł ģ +ĠÑĤоб ой +i ad +ĠkardeÅŁ im +ĠCongress man +ream ing +h omme +Ġcommun aut +Ġalcohol ic +Ġpick led +Ġac ord +p osition +eg ól +Ġtrou bling +ĠMarch eg +Ġzum indest +Ġseam lessly +Ġol un +ĠTV s +ĠпÑĢакÑĤи ÑĩеÑģки +Ġback end +ãģĵãĤĵ ãģ«ãģ¡ãģ¯ +id able +Ġgad get +Ġfa ço +ĠMarcheg iani +Ġë° ¤ +Ġaccident al +ĠL P +Ġeld est +ĠAd miral +Ġn Äĥm +le ver +Ġpast el +Ġfond o +Con nie +Ġter cer +Ġp act +ĠMont e +Ġme ats +ĠS MS +ĠAustral ians +ç ¼ +Rh ett +Ġexact ement +Ġë¹ ¼ +ĠM OD +ç ¡ +ĠR apt +ĠNo ch +Ġab ort +ĠNav al +ĠFu ji +IN TER +Ġнов Ñĭй +Ġmiej sce +ĠIC U +ĠGrad uate +ĠGl en +ard i +ĠÈ ĺ +Ġsold er +Ġprofess ions +Ġorth og +om n +int rodu +ĠDen ise +ìŀIJë ¥¼ +Ġcorrespond ence +AM A +Ġinf lict +Ġf and +ĠG ü +ĠÑĩ еÑĤ +Ġtr aced +Ġpat ents +Ġamb ush +Ġlot ta +ff er +ĠW agner +Ġimp erson +Ġextr êmement +ÙĤ ت +cond uct +A tt +ĠM ueller +ĠAl icia +Ġcy c +Ġha cker +Ġt ys +Ġha il +Ġз аÑıв +Ġpas so +Ġì¶ Ķê°Ģ +ĠÎ Ī +Ġpack aged +ĠC ynthia +he et +ä¸Ń åĽ½ +ĠNiss an +ĠQuest o +é ¨ +d id +Ġμ ια +ĠEll is +ĠAnal ysis +ce mos +Ġas eg +ĠMy ster +ĠCa o +Ġtu v +ĠIndust ry +주 ê³ł +ot al +Ġpeque ño +br as +Ġcompreh end +ĠSim pson +ÑģÑĤв ие +ocr acy +иÑĩеÑģ ки +ĠM ush +ĠLaur ie +Ġtriang ular +ĠPres ents +ĠK unden +ç´ ¹ +æŃ ¦ +ĠIs s +ĠDe ck +á»ĥ n +ĠDark ness +Ġinflamm atory +eremi ah +Ġwar med +vey ard +ĠMem ory +et ty +Ġtax payers +ภĵ +Ø ¡ +Ġpract ise +ëĭ ¬ë +Ġdr illed +m Ã¼ÅŁ +log o +ĠF ach +¤ë ¡ľ +Ġübrig ens +Ġkon nten +Ġnormal mente +Ġarg ues +iling ual +°ë ¥¼ +eg al +Ġtrava ill +ov y +а ÑĤо +Ġr uth +ĠL ights +Ġconsist ed +×ijר ×Ļ×Ŀ +Ġstere otype +Ġpay er +ĠRe e +ĠAir bnb +Ġdr owned +ĠZ oe +Ġcan opy +Ġbar r +Ġн оÑĩ +Ġpag an +Ġj ars +Ġr ê +er ver +æĪ ¿ +ie ben +Ġes pect +ĠF i +Ġunw illing +Ġtechn ician +ặ t +m ember +ĠCan al +س Ùħ +Ġlie ber +Ġin ference +Ġhon oring +åij µ +ĠCamp aign +Ġline age +ĠSt ress +Ġvict ories +Ġde ja +× £ +ê tes +bl ick +Ġмен ее +oth s +ĠCou ple +J ason +ĠNic olas +ек Ñģ +l ib +Ġher ramient +Ġ×IJ ×ķ×ŀר +Ġвид им +mill imeter +Ġsil houette +Ġdrive way +Ġcher ish +ãħł ãħł +Ġrans om +Ġinter disciplinary +ĠPort al +Ġtra g +th ood +Ġted ious +Ġgloss y +Ġpré par +ĠC ay +ĠT ook +ĠBott om +Ġz ig +å « +åį ± +re presented +à¹Ģล ย +Ġdesar rollo +ìĦ ľë +Ġvis cos +Ġmill igram +ĠG und +Ġfer ment +d rum +Ġdraw ers +La ugh +Ġpel os +Ġpave ment +Ġmem oir +av ait +Ġ20 50 +¤ë ¥¼ +Ġraz ón +Ġflour ish +Ġst ern +ä¸ Ī +ĠCh ung +Ġser pent +ĠGentle men +羣çļĦ å¾Ī +k ook +Ġl ut +import e +p arent +Ġw sz +Ġsc ree +ĠMitar beiter +å· ´ +m ut +Ġìĸĺ 기를 +Ġsem ble +ĠO W +Ġinvestig ator +ĠCher yl +ĠG erald +Ġpr ere +Ġcomp ares +ny t +Ġdiferen ça +? - +Ġqu á +ר ×Ļ +S en +Ġhe ps +Ġgrat uit +Ġcons ort +ĠST OP +ĠProtest ant +Ġelectro de +â Ĺ +Ġsecure ly +иÑĩеÑģ кой +Ġt ää +Ġreg isters +ĠHeaven ly +og ly +iss ä +ĠPhys ics +ĠMer kel +Ġré v +éĻ ¢ +Ġer ased +ĠSac ramento +Ġcoff in +Ġex acer +Ġl anz +Ġpo ets +ul if +Ġì¹ ĺë +ĠN erd +ĠN CT +ĠH our +neh mer +ŀ ĺëıĦ +ĠPrin ci +S w +m ies +ar med +ĠBeat les +Ġpropag ation +Ġexch anged +Ġcum ulative +Ġì§ij ìĹIJ +Ġdefe ating +æĬ ± +b els +Ġw es +ĠOdys sey +ä½ł æĥ³ +av ior +ĠìľĦ ìĹIJ +Ġbr it +Ġhij o +D AY +ĠاÙĦت ÙĬ +ĠС еÑĢг +Ñĥ ка +eds iÄĻ +Ġimp os +Ġell as +Ġfire arms +ĠN R +Ġ×ij× IJ +ĠÐŁ ока +aw i +ĠìĦ± ê³µ +Ġpup ils +ĠT ack +Ġfr ase +ĠSh ip +Ġst ad +ä¸ ľ +ĠGreat er +un un +imm ung +gr own +ĠN XT +ĠAmeric as +f ox +Ġmant en +éłIJ åĤĻ +ĠÑģ ок +Ġr ikt +lect ric +de ep +Ġзна еÑĪÑĮ +Ġben ut +ĠInf rast +ĠEm ir +ĠоÑĤп ÑĢав +ĠKim chi +ĠFinn ish +´ìł ģ +ina ire +Ġo ike +æ¸ħ æ¥ļ +Ġhost age +ĠBut ton +ÙĤ ÙĬ +ek ing +ĠKaz akh +Ġcomfort ing +Ġso g +Ġgreet ed +g uitar +p ayer +Ġrel ational +Ġconstru ir +çī¹ åĪ¥ +op ian +ĠVol ume +iet h +ÑģÑĤв ом +ur rection +li ÅĽmy +Ġhem isphere +ĠBe an +IG N +Ġköt ü +ĠFall out +Ġbr ace +ç¹¼ çºĮ +ÏĢ ά +ĠH AS +Ġg é +Ġcharacter ize +ặ c +ĠMil ky +Ġtum ors +Ġn uit +ĠG az +ĠìŀĪ ëĭ¤ëĬĶ +Ġг аÑĢ +ess ment +ĠA be +Ġë½ ij +ĠEins atz +J IN +j ä +C ry +ĠProm ised +ĠÑģеÑĢ д +ok us +Ġscal able +ĠпоÑģмоÑĤÑĢ еÑĤÑĮ +ück lich +Ġreal ism +Ġmay o +Ġjuven ile +Ġhead lights +Ġgör Ã¼ÅŁ +ĠRe form +Ġhal ves +cz ne +Ġbreak up +że j +Ġr ätt +D ay +ĠìĿ¼ë ³¸ +Ġmu erte +Ġtun es +ĠSm ile +rec ord +Ġrecher che +atisf ied +Ġpo zi +Ġcelebr ations +ise xual +ĠRO B +third s +ĠF ortune +ĠÑĤ ой +Ġbrand ed +lo o +Ġd ud +Ġrandom ized +Ġcomb in +ä¸Ģ äºĽ +ier an +c zenia +į ãĥ« +Ġcur ator +Ġar tery +ĠÑĥ ÑĪ +ĠÑĩ иÑĤ +Ġsubsid ies +Ġbloss om +ĠTw ilight +Ġhy vä +ĠPom pe +ĠC isco +ĠÐŁÑĢ о +Ġbir i +Ġg ern +Ġre built +Ġw cze +Ġbenefic i +Ġdrum mer +Ġsol ids +Ġdi yorsun +ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸ ãģĦãģ¾ãģĹãģŁ +l ated +Ġmud dy +Ġh olog +Ġcl aps +ĠR ings +ĠO key +ĠBra ve +Ġvalu ation +Ġmig rant +Ġinter mitt +Ġeig ene +ili ary +ãĥ¼ ãĥĪ +mark t +k r +ĠR ib +á»Ļ i +Ġaccus ations +Ġa rab +w ash +ĠBard zo +Ġu gh +est ers +oph ren +Ġaliment os +ĠU z +Ö Ĥ +Ġ6 50 +ĠпÑĢи еÑħ +F I +Ġsamp ai +Ġparl é +hes ion +Ġs ır +Ġapparat us +Ġcor related +ĠPrincip al +Ġcor r +ĠOffic ial +иÑĩеÑģ кие +Ġtermin als +Sh ould +Ġvac un +Ġst ellt +Ġmo oi +etz ung +Ġк ÑĢа +Ġda i +Ġп ож +Te am +ĠP PE +ĠÐŀ Ñģ +ĠLe ah +ĠI vy +y st +Ġuh hh +Ġnight time +Ġtrend y +Ġsec urities +Ġcontin ents +Ġfirst hand +ĠVer on +ĠëĤ ® +Ġbrows ing +ĠC ada +t ro +Ġtr amp +re ib +Ġerst mal +irl er +Ġps ic +Ġget ir +ĠN P +Ġdzie ci +об ÑĢаз +Ġmagic ian +Ġscrut iny +Ġsl ab +ĠO T +ist y +ir ies +ore st +Ġtask ed +Ġmor ally +ìķ¼ ì§Ģ +ust ered +Ġfool s +Ġir respons +Ġein f +Ġvi á»ĩc +Ġsc or +Ġpill ows +ĠG egen +Ġtut te +Ġquarter ly +Ġdid nt +ĠG ym +ĠE ther +ĠØ « +лиÑĪ ком +Ġsign aling +ĠN ode +ĠDonc s +Ġy ah +ĠKan al +Ġf ading +et in +Ġinfluen cers +Ġmed als +Ġengine ered +Ġfer mented +ê²ł ì§Ģë§Į +ĠBeet hoven +×ŀ× © +inent al +ĠìķĮë ł¤ +üt fen +al nya +Ġo vere +Ġden kt +ак ÑĤеÑĢ +Ġâ ĺ +Ġneces it +Ġgener ators +gr ass +Ġпод Ñĥм +lie ÃŁen +B ar +ľë ıĻ +ĠдеÑĤ ей +Ġsuck ing +Ġsten cil +Ġprim o +ĠBreat h +st rom +Ġimmens ely +Ġapp reh +ìłķ ìĿ´ +P op +Ġj ong +ĠGi ul +ĠAD HD +Ġhö ren +Ġe lo +iv ent +Ġr us +Ġoutrage ous +Ġmaster ed +Ġì» ¤ +ÙĪ Ùģ +ip es +ĠRud y +Jac ob +Ġbull ish +Ġt apped +Ġfa ud +iz ophren +ĠÑģо Ñħ +ĠDar ling +Ġ196 3 +ĠPre vention +² Ķ +Ġabdom inal +st ones +Ġav aient +á»ķ i +m ake +Ġs are +ĠInst ant +к ам +Ġkeep er +Ġblank ets +ãģ§ ãģĹãĤĩãģĨ +Ġswe ats +ĠMinne apolis +åħ¨ éĥ¨ +Ġgen ommen +Ġfast en +ĠBrus sels +åij ¼ +Ġcaf eter +Ġabsor bing +Ġha go +ĠEl mo +Ġgust o +ĠY ap +M úsica +Ġt ert +Ġband a +Ġm ily +Ġthere after +ĠStock holm +ĠC arson +Ġcalib ration +ava ÅŁ +ans a +ik ke +Ġfore see +Ġqual che +Ġdest e +æ ¤ +ün üz +Ġfor ge +D is +est en +Ġδ ια +Ġenca ps +ĠGes pr +Ġcher cher +ick ets +ÑĤоÑĢ Ñĭ +C r +ĠТак же +Ġrabb its +ĠD ot +he iten +Ġcaus al +ĠF oster +ajÄħ c +Ġbere it +Ġayud ar +é« Ļ +ãģ ³ +s ong +com b +Ġfr inge +Ġcyber security +Ġëľ ¨ +Ġk ier +Ġbesch äft +Ġкон ÑĨе +Ġfacil it +ĠNam en +Ġbil ateral +t x +ĠW issenschaft +Ġnu ances +Ġr ipping +Ġf y +ĠSicher heit +ĠGh ana +ol on +Ġto pped +ĠMoroc co +Ġrad ial +ĠL EE +ĠAndre as +ed d +ĠìĹ ´ë +ĠAirl ines +ãģĵ ãĤį +Ġval ores +ê· ľ +H y +Ġзад аÑĩ +ĠKend all +ĠÑħ аÑĢ +ĠV amp +Ġpy thon +Ġmanage able +ĠG ente +o ise +ici ary +Ġimp oss +ĠBun ny +iest a +And rew +Ġser t +ĠC ec +zz arella +Ġautom obile +ĠT iere +all ows +åĨ Ĩ +Ġë° Ģ +ĠSc orp +ĠJ elly +ag ara +ĠSt retch +Ġrede f +Ġexacer b +ĠS HA +é f +ors a +Ġflaw ed +ĠNo el +?! ? +Ġpro cent +Ġmen stru +ĠпÑĢо Ñĩ +Ġinf ants +ðŁİ µ +pa use +ĠR acing +Ġ194 8 +Ġsuper intendent +id ores +id y +bra him +Ġunl ucky +Ġper k +an ci +Ġë§Įë Ĥĺ +ĠÐľÐ¾Ñģ кв +Ġfin ans +Ġdiferen cia +łĪ ìĿ´ +éħ į +OR Y +ĠT ac +ÛĮ ا +Ġdes em +Ġваж но +ĠJ U +ĠìŀĪ ìŀĸìķĦìļĶ +ĠÎ Ŀ +Ġinform ations +ĠH EL +h st +Ġпог овоÑĢ +Ġvo iture +Ġre us +änd ig +ĠпоÑħ ож +j ing +Ġd ru +alt ra +Ġprodu its +Ġk ite +Ġeye ball +ĠB elt +ĠRestaur ant +Ġg amb +Ġpor ridge +it ters +Ġconver ts +Ġyard ım +Ġmáxim o +w irtschaft +Ġíķĺë Ĥĺë +Ġì¤ Ģ +Ġice berg +Ġvor bei +Ġ25 6 +ocr atic +Ġreck less +on ner +Ġm ús +Ġlog ically +ĠPr ison +ĠNet z +Ġvac ant +Ġn immt +ĠH ARR +Ġз ов +ĠDe e +ring e +ni est +ĠR ules +ìĬ¤ë Ł½ +cuss ions +Ġfl oral +Ġconstra ined +Ġdifferent iation +ĠQue bec +ĠÛģ ÛĮÚº +Ġpúblic a +it el +Ġaccommod ations +ĠGr ü +í ľ +Ġpick les +иÑĩеÑģ киÑħ +Ġcomm issions +ĠBa ek +Ġçoc uÄŁ +ĠMed ium +Ġperiod ically +Ġwonder fully +Ġstaff ing +ìĽ IJë +ri re +f le +ĠMc L +ĠÑĤ еп +ĠпеÑĢ ек +н олог +Ġíģ¬ ê²Į +çĻ¼ çı¾ +Ġprosper ous +ĠSpirit ual +ĠCh ick +DI A +ĠÐŁÑĢ ивеÑĤ +Ġper ÃŃ +ÑĮ ÑİÑĤ +Ġconsult ants +ĠEar l +ä»Ĭ å¹´ +Ġru ining +оÑĢ е +Ġpens er +Ġtak iej +Ġstrength ened +ĠLiqu id +он еÑĨ +ав аÑĤÑĮ +Ġcam er +Ġdisagre ement +Ġbat hing +ĠY osh +a al +pre chen +RIS ADAS +Ġsuper star +æģ Ń +лÑı ÑĤÑĮ +Ġn ib +ĠTh erm +ĠDAN IEL +Ġp aw +Ġliqu ids +Ġcapac it +ark en +Ġvag ina +Ġm ashed +Ġemer ges +ys cy +Ġun related +ĠGu ild +Ġin verted +it ives +T ra +Ġbe gr +Ġal te +ì§ ķ +ãĤģ ãģ¦ +ĠÑĢазÑĢ абоÑĤ +f inder +Ġдал ее +Ġблаг одаÑĢ +walk er +Ġcr ater +ass adors +ren ces +ins ki +ĠK IM +ĠEll iot +20 17 +ĠS r +ink a +ano v +Ġìŀĺë ª» +Ġpropriet ary +display style +ĠÑģ им +Ġиз б +ĠPan el +Ġinstinct s +ĠCommun ications +éº » +mid t +Ġë§Įëĵ¤ ìĸ´ +ĠÑģл ова +ĠGil bert +缮 åīį +Т ак +voor beeld +е ÑİÑģÑĮ +ary n +que z +Ġd art +Ñĸ ÑĪ +ĠH ut +S al +Ġs outheast +Ġpestic ides +Ġhelicop ters +Ġend ured +i ada +Ġbre wing +ìĹ ¬ë +ĠÑģв обод +ĠS aints +ĠFr ançais +ĠEconom ics +Ġdis loc +oph obia +C amer +Ġnegoti ated +ĠÑģÑĤ али +ìĬ¤í ģ +og ie +Ġtsun ami +Ġpeel ed +Ġmotiv ations +è¨ Ń +ost at +fl an +ĠD AC +Ġk av +' RE +ĠPe arson +b be +c zenie +Ġaten ção +íĨµ ëł¹ +ãģ£ ãģ¡ +ĠÑĥд аÑĢ +Ġintrodu ctory +ĠI ci +ë ĮĢë +ak at +Ġt rench +Ġproceed ed +ĠCo in +Ġdere cho +ĠRed e +æ¯ Ľ +ан нÑĭй +Ġincarcer ated +ĠRich mond +R ock +ĠP av +ĠKar ma +ug es +Ġconte ú +ë ¹Ħ +Ġê·¸ë §Į +ĠG one +Ġwsp óÅĤ +ĠRah men +un ken +Ġì¤ijìļĶ íķľ +Ġi b +Ġatt aching +H ay +Ġsu ka +ìį ¹ +Ġpivot al +ĠRes pect +ÃŃ da +I B +ĠVer antwort +w iet +Ġforens ic +ÑĢи ÑģÑĤ +ĠпÑĢинÑĨип е +Ġmark ings +Ġk ettle +ĠOper a +ĠDo ctors +Ġshred ded +Ġrec uer +Ġvig il +ĠF ail +Ġentre v +Ġд ÑĥÑĪ +Ġout breaks +èµ° åIJ§ +ĠÏĢ ο +Ġro gue +ang led +Ġyear ly +ĠCre ed +Ġw am +Ġlot us +ê³ ¼ë +ãĢģ ãĢģ +ĠSp it +ĠIt u +Ġstra ins +Ġstamp ed +Ġpl aint +Ġpot ion +Ġconsolid ation +è© ķ +оÑĩ кÑĥ +Ġvlog ging +Ġsl ate +ĠAu ft +ĠInc or +ừ ng +§ IJ +en h +Ġhe iÃŁ +Ġdom est +ĠSt rom +åį ³ +ak is +Ġfra gen +Ġfin er +ĠS ug +Ġup hill +Ġé én +âĢ¦ ) +ĠÑģ оп +ĠCore y +Ġsie bie +Ġm use +Ġclo ves +Ġp ous +ĠFin anz +ĠR oute +am at +Ġmut ually +ĠвнÑĥÑĤ ÑĢи +ĠSel ena +ë Ķ +ĠGa ussian +ë ¶ĢíĦ° +Ġ×ij× Ľ +Ġej erc +å¾ ® +ke a +ĠG erry +ĠS ic +大 çļĦ +Ġ196 6 +ies e +Ġfoss ils +Ġest ad +ĠK ane +ci Äĩ +Ġìľł íĬľë +Ġп ам +ĠCru ise +int érieur +Ġbe kannt +ĠP ode +Ġdem ander +R em +Ġinv ade +Ġdecor ating +rop ic +Ġcow boy +ĠPh oto +opol it +Ġì»¬ë Ł¬ë +Ġre ap +Ġhand writing +à¹Ħ ร +Ġë ļ +Ġب عد +ĠM t +Ù Ģ +Ġspaces hip +Ġnational ism +Ġcouncil s +ĠGriff in +ĠAh med +Ġcl ich +ĠO L +w l +ĠPil ot +å® ® +Ġacron ym +Ġg els +Ġelectro ly +è ĵ +Ġм ной +Ġepis od +ĠDies es +ĠAT P +Ġed iyorum +Ġexpress es +Ġexhib its +C omm +Ġк ÑĢÑĥп +Ġmat ar +Ġ20 25 +ĠArt em +vas ive +r Ãł +Ġbe ÅŁ +é» ĥ +Ġliz ard +Ġfill e +Ġì§ Ī문 +Ġмо Ñī +Ġt ür +Ġcul prit +Ġwo ven +ĠAN Y +n im +Ġt ay +Ġprom in +Ġacom pa +Ġid é +Ġbo iler +ĠThe men +Ġaven ue +ĠM ud +Ġнов Ñĭе +Ġwitness ing +Ġl ance +ĠCH AN +ĠBe ver +ت Ùħ +Ġchem otherapy +K ing +ĠbÄĻd ÄĻ +Ġat ual +Ġt ive +Ġtalk in +Ġqued ar +ie ÃŁ +ed el +Ġìĸ´ì łľ +Ġjog ar +Ġö r +Ġundert aking +ĠStre ngth +Ġmil hões +ĠW ine +ĠM olt +è® ² +ãģij ãĤĮ +Ġunderm ine +ĠArch ives +v ana +mer cial +M C +Ġcast e +п ÑĢ +Ġlegisl ators +ul ators +ên io +Ġëį °ë +ĠÑħоÑĤ иÑĤе +Ġн ек +Ġs urn +Ġcons ci +ĠP OW +Ġcul inary +ĠK AT +ĠFol ks +Ñĭв аем +Ġв ок +ãģij ãĤĭ +s ervice +pt s +Ġпоб ед +æĺ¯ åķĬ +Ġt ents +Ġn ord +ST E +Ġrepublic an +Ġwy k +Ġmin ions +èĻ ķ +Ġmem ang +j est +Ġcompar ative +Ġty le +car bon +bed ingt +ks en +Ġneg ativity +Ġsjäl v +Ġd ú +æīĢ æľī +Ġrec alled +c ra +ĠT ada +ĠÑĢÑĥ ки +ĠопÑĢед ел +Ġproc rast +Ġjog os +ĠO o +ĠHe arts +Ġé ch +Ġksi Äħż +Ġco arse +ĠT ube +ĠG reens +Ġé n +Ġdumb bell +ĠÑĤ и +Ġquer er +ا ØŃ +Ïĥ ει +ĠпÑĢав илÑĮно +Ġп ап +Ġcomp ra +Ġt ér +ĠAnt es +Ġoptim um +Ġbisc uit +κ ι +acz ego +Ġìĭľê°Ħ ìĿ´ +ĠMar ines +ver o +Ġvacc inations +Ġpet ty +rit ers +Ġа л +count ry +Ġcoun ters +Ġattend ant +ĠH ui +ãģ¨ãģĦãģĨãģĵãģ¨ ãģ§ +ck a +ÑģÑĤвен нÑĭй +gu y +Ġtrick ed +ĠR ED +Ġthr illing +ÏĢο ι +Ġpig gy +Ġan unci +OR TER +ĠVal ue +Ġr ond +ĠA DA +Ġpos er +h ores +ĠR oland +ĵ ¯ +Ġno ir +Ġש ×IJ× +ë° ľ +iem and +ĠпоÑĤ еÑĢ +ê³ ³ +Ġê± ± +Ġformat ting +ĠL ed +è§Ģ çľ¾ +Ġkill ers +ĠÄij ấy +Ġha ar +ag ain +! > [ +min ster +Ġв ли +Ġident ifier +ĠLamb da +Ġtr os +Ġflaw less +Ġdetriment al +Ġbun ları +W ar +Ġreg ião +羣çļĦ æĺ¯ +ĠB ike +cess ors +Ġc ùng +ĠR N +Ġê½ ĥ +Ġküç ük +ĠBegin ning +íĺ ¸ë +Ġge we +Ġden ote +ĠAlber to +Ġprob iot +Ġo de +Ġmol ar +Ġburst ing +ass umed +Ġfoot prints +ved a +Ġstero ids +Ġfl aming +ĠE ller +Ġerk ennen +ät zen +Ġlife cycle +ĠD OU +ĠK arena +ĠGuer ra +è¿ĺ æĺ¯ +Ġsin ister +Ġpod éis +Ġpar ab +Ġok o +Ġmat éri +Ġcar ic +son aro +Ġpratic amente +ÑĥÑģ а +Ġcomun que +Ġvig ilant +Ġreg imes +ĠShoot ing +Ġra ids +ĠN ora +ĠW ieder +m ens +ĠÑģ од +Ġê²½ìļ° ìĹIJëĬĶ +Ġв Ñħод +Ġaut obi +ĠS chn +ĠRob bie +ĠF itness +Ġкон ÑĦ +Ġpeng uin +моÑĤÑĢ Ñı +Ġми ним +play s +Ġdeleg ates +M er +Ġsist em +ĠMicha els +m ale +ا ع +Ġcá ch +ĠH ä +Ġ×Ļ ×ķ×ĵ×¢ +Ġsuper power +Ġstr on +Ġro ver +Ġdé pend +éĻ ³ +Ġret iring +Ġvamp ires +Ġmer de +ĠCh anging +Ġt ame +Ġspokes person +Ġc ay +Ġfl irting +ĠGr ö +Ġw är +Ġwy b +Ġcoe ur +ạ nh +ĠìĻĢ ìĦľ +Ġconna is +ĠHundred s +ĠBe a +Ġα ÏĢ +pr uch +Ġsocied ade +ĠWh ilst +ĠK ait +esp ace +Ġch ia +ĠEr m +Ġë°Ķ ê¿ +Ġf ences +ĠM ortal +ê² ģ +Ġг ÑĢаÑĦ +ĠHom eland +ĠJ UN +is st +Ġpar lar +Ġsport y +é o +Ġdeep en +ĠBeh avior +éĢ ı +åĵĪåĵĪ åĵĪ +Ġer rand +Ġrot ary +ĠWell ington +W ind +Ġmes ela +ả ng +iend e +Ġex cell +ĠGen ius +ĠEdu ardo +æľī 人 +ĠÅŁ unu +ĠÄ° stanbul +Ġprod uto +Ġ ãħİãħİ +O FF +Ġwoll t +çĪ Ĩ +Ġëī´ì Ĭ¤ +Ġl ass +Ġher tz +Ġar omatic +Ġзв он +Ġaut oc +ĠL ust +Ġ11 2 +ĠÎ Ĺ +Ġreview ers +Ġrecept ive +å°į äºĨ +â nd +og lo +ĠìķĦëĭ Ļ +Ġn go +Ñĸ ÑĤи +Ã¥ t +con o +Ġtek rar +Ġ주 ê³ł +Ġgel miÅŁ +Ġbed time +ĠAr gh +AD A +ĠгоÑĢод а +ĠÄ ĩ +Ġall iances +g iggling +Ġyer de +Ġsp ies +Ġg utes +ç i +Ġallt id +ĠL ah +ŀ IJë +Ġdo kÅĤad +ÙĪ ÙĬ +Ġtoxic ity +Ġcancell ation +Ġ195 8 +d ro +Ġìŀij ìĿĢ +ĠMotor ola +Ġmult in +Ġenthusi asts +ĠM ighty +ĠCoc onut +: ãĢĮ +ĠPict ures +Ġsang re +Ġbl inking +ol esome +ĠìĬ¤íĥĢ ìĿ¼ +F P +Ġboom ing +ĠдеÑģÑı ÑĤ +Ġr atchet +Ġtim elines +len ess +Ġc ages +ĠGood night +omet imes +Ġc unning +ĠR isk +ul ed +d ade +Ġpr ata +Ġgust arÃŃa +am us +ĠJin ping +Ġest rut +Ġdescob rir +ĠM Äģ +ĠAll an +Ġ åĪĨ +Ġ×ľ× § +Ġpres erv +ĠStraw berry +Ä ı +L u +Ġk ro +ĠRep orts +ìħĶ ìķ¼ +Ġval t +Ġpouv ait +Ġapp ar +ĠB one +Ġprefer ably +ĠRep ública +å°± åĪ° +Ġher zlich +Ġchim ney +Ġç ev +Ġvis as +Ġver r +Ġcultiv ation +ĠArmen ia +Ġвд ÑĢÑĥг +Ġcock ro +retch ed +art z +ĠлÑİд Ñıм +ĠpolÃŃt icas +ĠP anz +ĠA KA +ĠëĪ Į룬 +Ġer ro +Ġcam per +Ġ10 2 +ठ¸ +d one +Ġho ard +ĠÐŁÐ¾ÑĤ ом +je ong +Ġdest a +p ak +Ġin im +Ġgrow ers +ĠMess age +Ġele ctor +eng age +ĠFor bes +ĠCincinn ati +Ġdiffé rence +d f +Ġsp ar +Ġawait s +ĠUSS R +ĠR ising +ĠHo ÅŁ +Ġfoot ing +Ġcond iciones +ÑĤоÑĢ ов +Ġclin ician +ĠDisk uss +å£ ĵ +ר ×Ĵ +× ¥ +ite it +g ren +Ġchar isma +Ġle uke +Ġirrit ating +Ġcir ca +ĠRhod es +Ġp ior +Ġhandic ap +roy able +Ġv ull +O G +Ġin ÃŃcio +ier i +Ġspl ashing +Ġdem ise +Ġassist ir +Ñĩ ÑĤо +Ġcover t +ĠG ud +ภī +kl är +ĠìŀIJ 꾸 +Ġver ändert +ĠR EM +ĠCon ven +at ge +Ġpierws ze +Ġcler gy +ling ton +l iv +V PN +ĠÑģ ожал +ĠH ate +ãģ¨ ãģĵãĤį +ÏĨ ο +ĠResp ons +оз д +Ġet mek +Ġchem in +Ùħ Ø© +Ġê°Ģ 족 +T re +Ġum as +ĠBur ton +Ġpatri arch +ĠSmithson ian +¥ ĺ +M oon +A ir +Ġmed ios +Ġer aser +Ġwoll ten +Ġpare il +ĠBill ie +æĬ ½ +еÑĢÑĤ в +Ġparl ament +Ġag ony +ĠQU E +sequ ently +An other +ĠWh ew +ĠAnn ual +Ġse ben +ìĥģ ìĿĦ +val ues +ŀľë §Į +Ġsin on +ere al +ĠEn light +ĠChem istry +ĠCatal unya +Ġdoct r +ant on +Ġst uk +ĠPl ate +ĠKardash ian +Ġfil os +ĠW et +Ġпоп ÑĭÑĤ +Ġunknown s +ĠSch on +ĠBald win +Ġtelescop es +ĠG ucci +ox ide +ĠConserv ative +ìĦ± ìĿĦ +Ġhina us +P ower +Ġê±´ ê°ķ +Ġprev ail +orm an +m achine +Ġ194 6 +Ġun bel +Ġsch aut +Ġp iel +e enth +Ġobject ively +Ġch akra +aud io +Ġch icos +ĠV ault +å° Ī +Ġmedic inal +ĠT ail +Wh ile +Ġas phalt +Ġfro ze +ĠE K +unch ing +n osis +20 15 +ĠG ri +Ġodd ly +ĠM är +ĠA eg +c olo +P ar +Ġëĵ¤ ìĸ´ë +Ġv inden +ĠO VER +Ġ iced +Ġsc orp +Ġha c +qual ified +ĠÑĥвид еÑĤÑĮ +erm o +H EN +Ġso i +Ġmulti ples +Ġlay outs +Ġblind ness +ĠB owser +Ġпод ÑĤ +ĠÃ İ +vention al +Ġm ata +mad ı +Ġge ez +Ġcad ence +Ġważ ne +ĠChrist ie +ven ge +C all +Ġturn around +Ġblo b +ĠЯ к +ĠVoice over +Ġper il +ĠJa ime +ĠH OY +l ane +Ġse bel +ĠDu o +ĠHistor ical +Ġd ni +Ġg ema +y k +Ġsab em +ắ ng +Ġv ars +ĠRon nie +ĠRon aldo +ĠPer què +ns inn +h air +Ġrelent less +Ġl yn +Ġtravel er +æĢİ麼 äºĨ +n ine +Ġant im +Ġì¼ Ģ +Ġsnow ball +ĠÑħаÑĢ акÑĤеÑĢ +Ġintern s +Ġconstitu ency +ĠÐĿ ам +׾ ׾ +V EL +Ġvikt igt +Ġap oyo +ÙĦ ب +Ġj ard +Ġheight ened +ÑĢо ÑģÑĤ +ĠSM ITH +Ġдел а +Ġrepair ing +Ġr igt +ĠShe ikh +ĠBrit ney +Ġevery time +Ġadvent urous +oc key +er nt +Ġat aque +ĠAltern atively +e ffect +Ġpalav ras +ĠElli ott +Ġréuss i +Ġhypert ension +ĠMan ual +Ġproph etic +Ġhand c +ÑĮ е +Ġref rain +ĠSqu id +ìŀ ¡ +Ġком ан +äll en +Ġlleg ó +Ġbas h +ion y +ĠÑģк лад +Ġк аб +Ġcare less +ĠP ool +Ġtr ás +Ġfil s +ĠSch r +Ġsp rawd +ĠMon aten +Ġunfor gettable +ĠCott on +Ġinconven ient +ĠR X +or is +Ġhum bled +ת ×Ĺ +ĠØ¢ Ù¾ +Ġincre ÃŃ +ĠKomment are +èĪ Ĵ +r ación +Ġv antage +ĠSe al +ĠìĿ´ 거를 +Ġjou e +ãģĿãģĨ ãģ§ãģĻãģŃ +Ġìĺ¤ë ŀĺ +ĠиÑģп ÑĭÑĤ +ob en +Ġgr ate +Ġcontro le +ĠPer cy +ÅĤ ada +Ġsimult aneous +Ġprot oty +ĠgroÃŁ er +Ġbew usst +iniz i +Ġpass ieren +ĠHapp iness +åī ĩ +sh i +ge ht +Ġstation ed +ĠErgeb nis +Ġdirect amente +Ġsurv ives +Ġperson es +BER G +Ġvom iting +Ġconhe cer +Ġad jour +ĠCiv ic +pe i +bur st +Ġëĭ¤ ëĭĪ +é ı +Ġsl ed +Ġplataform a +ĠS ect +ĠDe fin +çĻ» éĮ² +én om +chn et +Ġprofit ability +Ġerre icht +á»ı i +c ation +Ġì§Ģ ê¸ +Ġperd re +Ġfel ony +Ġ195 7 +æĪij å¾Ī +Ġunsuccess ful +Ġnag yon +Ġelastic ity +Ġfac ade +Ġearth ly +ĠамеÑĢик ан +Ġcon n +c la +D u +Ġpolit iques +Ġhal o +iant es +Ġмо ей +ãĥ³ ãĥī +ton es +el ier +è® ļ +ht aking +Ġwicht ige +Ġan no +ĠL ok +ill ions +Ġv iver +Ġsol chen +Ġsu f +ĠSal z +ĠN vidia +z uge +ĠSp ike +V ideo +Ġtw or +ĠA la +èij ī +Ġh anya +ĠAd m +ìĿ µ +ĠPatient en +ĠOn ion +ĠKo be +ĠSc ene +ĠR ash +æ¨ Ļ +ÑĢа ÑģÑĤ +ist ani +Gen eral +le ye +imb ap +Ġconce aled +ĠFr idays +ĠW ool +Ġнов ÑĭÑħ +Ø´ ر +Ġê²° ê³¼ +Ġjed och +´ìĭ ľ +ĵ¤ ëıĦ +Ġìŀ¥ ëĤľ +uk t +L ou +Ġ먹 ìĸ´ +ĠEx pect +Ġдом ой +Ġirrespons ible +Ġac erca +ĠZ ust +ר ×ĺ +U I +Ġyout ubers +ĠPos itive +Ġsoci oe +Ġsn atch +èĥ Į +Ġrefresh ed +Ġnom inations +ĠP att +Ġobsol ete +Ġdem iÅŁ +åı ¤ +orm uÅŁ +ĠìĨĶì§ģ íŀĪ +Ġf la +Ġcra ziest +ĠZ ie +ĠT ú +z ep +ic em +Ġë©ĭ ìŀĪ +Ġcyn ical +ãģĿ ãĤĵãģª +Ġt resp +Ġcra z +Õ¥ Õ +Ġne lle +Ġm ph +ĠN ered +ĠK ob +ĠE ck +¨¸ ëĭĪ +J an +ĠТ огда +Ġde ci +ĠV og +Ġbubb ling +éĢ Ģ +ú a +Ġproduct os +iber al +Ġrepl icated +ĠImp rove +ill ary +C ha +Ġré du +ĥIJ íķĺë©´ +Ġcon not +ĠK rit +ĠдÑĥÑħ ов +Ġtread mill +ĠP W +Ġзов ÑĥÑĤ +Ġcl ams +Ġdra fting +Ġ195 6 +un ta +Ġexpend itures +ĠHoo ver +W OO +ÑĪе е +Ġded uction +mon ary +Ġreci b +Ġpo vo +Ġëį Ķë +ĠP AL +ĠBl ow +Ġwy p +Ġdest ac +de al +Gra eme +Ġnécess aire +Ġdamn ed +Ġ19 38 +Ġìĭ¤ ìłľë¡ľ +Ġtro op +Ġinsight ful +ĠT J +ĠоÑģ в +Ġf idelity +ĠSk ip +ĠMay o +ë§ Ŀ +app e +Ġbl as +ĠW Y +ĠG N +ct ar +S u +Ġcu ent +he ws +Ġcorps es +A bs +Ġwaste water +Ġc iek +ĠOn u +Ġexplos ives +Ġar ma +ĠSTEP HAN +polit ik +ĠOs aka +ta ÅĤ +Ġyap ıyor +Ġiz quier +Ġbele za +ĠWy att +åIJ ¸ +Ġsu k +Ġspec jal +Ġdan ke +wh istle +ĠfÃŃs ica +ĠHar riet +ĠìķĦ íĮĮ +Ġwill kommen +ip ing +ĠÑģмоÑĤÑĢ иÑĤе +Ġмож еÑĪÑĮ +Ġinacc urate +Ġarrog ance +ĠRem o +γ ά +ass ed +Ġdeliver ies +Ġst inky +ĠпеÑĢ еж +j ay +Ġtrans itional +Ġr ere +ĠNGO s +ĠAT M +Ø® ت +i ology +Ġв лад +Ġsch me +ĠSh ine +ìķ ¡ +p ants +Ġser ge +Ġsen hor +Ġab duct +ĠBry ant +V ES +Ġawak ened +ĠL az +rop olis +ĠLa o +è¾Ľ èĭ¦ +Ġvill a +Ġsumm ers +Ġent hal +Ġ194 9 +V ia +Ġìĸ´ì ¨ +Ġtend on +Ġviol et +Ġintellect ually +Ġboun ced +ara us +Ġ19 19 +Ġvra ag +Ġsp el +ĠSch war +Sc ott +ĠInd o +Ġë§ Ŀ +Ġcanon ical +ĠI KE +Ġthat ÃŃs +Ġme llan +æ¯ Ĵ +ig mat +C ould +... ?) +Ġfo arte +ĠKum ar +rend o +Ġél é +à ´ +val uation +c ases +Ġintuit ively +h ong +ett ed +Ġsou ven +Ġmor b +Ġc ors +ĠN V +ĠHas an +æĥħ åĨµ +ie ved +Ġì§Ģê¸Ī ìĿĢ +Ġdum pling +Ġcontr ôle +Ġambigu ity +æ©Ł æľĥ +Ġco g +ĠScript ures +Ġc ai +Ġbe ver +大家 éĥ½ +Ġhu is +Ġa ime +Ġerkl ären +ĠL M +ĠF ey +éļ ¾ +à®± த +Ġsuper vised +Ġje we +s pl +ĠÑĨенÑĤ ÑĢ +Ġcoll isions +ÙĦ Ùģ +ĠHog warts +ĠDur ham +×ķ× £ +Ġphosph ate +Ġoverse e +Ġinspect ions +Ġbr inc +ĠZ ak +Ġpay off +Ġch aud +ĠHung er +ã os +v ir +Ġf iance +Ġb oug +l ived +c ry +åĽŀ ä¾Ĩ +Ġjoint ly +Ġgirl friends +ĠNe xus +¦¬ ê²łìĬµëĭĪëĭ¤ +ĠK wang +åĵĪ åĽī +å§ ij +ÅĤ ÄĻ +ĠN eden +ie ce +Ġins erting +æŁ ĵ +ĠM ummy +ĠGlo be +Ġle e +Ġg erman +Ġcre ams +ach o +Ġch Æ°a +ĠGal ile +Ġfür s +Ġest iver +c idos +Christ ian +Ġlors qu +Ġcut est +v ale +ĠкÑĢ еп +Ġw ary +Ġslic ing +Ġesper ando +ĠV ander +ĠDe ixa +Ġ195 4 +Ġmów iÄħ +Ñĸ ÑĶ +Ġtool ing +Ġrest or +Ġpos ición +Ġintent ar +ĠAp ache +OU L +ĠÙĪ ب +Ġmat ière +ãĥ¼ ãĤĵ +Ġl inen +Ġestrat ég +ĠMut ta +é¡ ¯ +è¡Į äºĨ +Ġpart ing +Ġminim izing +Ġapp rendre +æľ Ŀ +Ġан глий +ĠDo o +ĠFire fox +c ómo +Ġge opolit +Ġmak an +Ġmog elijk +ĠÏĢε Ïģι +Ġcá» © +Ġinstall er +Ġdib uj +ĠHe ath +lo op +ĠBro ken +HY UN +sh elf +Ġf izer +Ġenh ances +ä¾ĭ ãģĪãģ° +Ġдо ÑģÑĤи +ĠP UB +ĠKolleg in +Ġatt ained +Ä ¾ +Ġmist ress +ĠOft entimes +×ŀ ×Ļ×Ŀ +Ġbe we +ĠS ora +ra uen +ba um +Ġroll ers +Ġm ering +ĠP AC +Ġн Ñĸ +ĠRép ublique +ĠÑĤ ÑĢав +ĠV anguard +uc iones +Ġ무ë ĮĢ +Ġg our +¯ ¤ +ĠÏ ī +Ġsa una +Ġpe ine +ĠVal erie +ĠS ikh +fend imiz +ber o +ĠÑĩ и +Ġdo ÅĽwiad +ĠE uros +Ġcomment aires +Ġtwe aks +ĠF aster +ĠÑĢаÑģ к +Ġprogress ively +ĠE uch +bor o +ĠIng red +C ap +Ġun check +Ġìĺ¤ë ¥¸ +Ġw re +ĠF T +ör ung +Ġmemor ized +ĠD inner +ĠP hew +ou bl +Ġput a +Ġadm its +ез де +op od +Ġpand a +Ġhing es +ci pe +Ġtrans act +Ġpod ia +Ġp ics +Ġcriter ion +ĠOrchest ra +ĠBl og +Ġsolem n +ĠPix ar +Th ree +Ġв низ +ĠVol unte +ĠSav age +ĠPV C +ĠC af +Ġwy kon +Ġgrad ers +Ġcr ouch +Ġcl iche +Ġsoy beans +ĠM UR +ĠGonz alez +ĠM imi +ĠBol sonaro +Ġdi aphrag +Ġbil ang +ëIJĺ ëĬĶ +éĤ£ æĪijåĢij +Ġregul ating +M c +J udge +Ġн ож +Ġjak Äħ +ites se +ĠW ij +Ġl ata +gro aning +POS ING +Ġ×IJ×ķת ×ķ +Ġha ga +Ġground ing +Ġviol ently +Ġt ills +Ġeng ag +ĠHo llow +Ġпоп ÑĥлÑıÑĢ +Ġw prowad +Ġrepl aces +Ġfluores cent +urg ical +igg ly +ĠTrad itional +t te +ĠÙĦ Ùĩ +Ġphosph orus +Ġapr on +ĠWat ers +ĠK ultur +ав ай +Ġol ives +Ġ×Ķ×IJ× ľ +Ġteil weise +Ġsen cill +Ġprend s +Ġnarr ower +Ġj ätte +ĠInformation en +ìĥģ ìĿ´ +Ġstar ve +Ġfr ick +ĠBe weg +ठ² +Ġdolph in +ĠLAUGH TER +ĠINTER VIE +åĶ ī +Ġyan lÄ±ÅŁ +Ġtor pedo +Ġshort ages +ìĿ´ë ĵľ +ıld ı +Ġp aws +Ġo zone +Ġcultiv ated +ĠF ot +Ġnot or +н оз +Ġко ÑĪ +Ġtouch screen +ĠAll y +æľĢ è¿ij +Ġ맼ìŀĪ ìĸ´ìļĶ +ĠС еÑĢ +Ġв полне +Ġpap rika +ĠDust in +Ġefect o +Ġop ini +Ġmu ut +Ġhá»į c +Ġinter ject +ÄĻ t +Ġbut ts +ure z +ĠP ike +ĠH ok +ĠGu inea +ĠCath edral +Ġ14 00 +C ra ++ , +ë§ Ľ +³´ë ıĦë¡Ŀ +aby rin +Ġvide og +Ġо ÑĢÑĥж +Ġu ž +Ġbus cando +ĠAss istance +éĻ ½ +Ġmel hores +ì¡ ´ +Ġëģ ¼ +ĠR J +Ġت Ùħ +Ġo min +Ġmotor cycles +ĠS app +Ġsupply ing +ĠAl gun +Ġaer ospace +×¢ ׾ +oc cup +le ist +Ġê±° ëĬĶ +Ġcomplet a +b res +! ( +ĠÐŁÑĢ ед +Ġdisadvant aged +ĠAtt end +ĠJud ah +á»ĭ ch +yl ene +act ly +Ġset ups +Ġammon ia +ĠSchwe iz +ĠSh ame +Ġband e +ĠF uel +Ġtroubles ome +Ġnum ero +ĠM OM +ĠпÑĢед лаг +ment ioned +ĠболÑĮÑĪ ое +ĠVikt or +ĠSty les +Ġcruc ified +ructure d +en viron +Ġmor als +Ġmed itating +Ġax ial +is ance +ĠAb st +G reen +Ġê± ´ì +Ġquad rant +Ġper gi +Ġcamer aman +ĠSe qu +Ġpa used +ĠLa ughing +ê· Ģ +? .. +ĠÅ» e +Ġpermit ir +Ġdetect ors +ĠH UD +av al +ĠìĹ¬ê¸° ê¹Įì§Ģ +Ġh ubs +Ġbest immt +ĠбÑĥдеÑĤ е +INTER POSING +Ġten gan +Ġcra ve +ĠBundes regierung +ĠBlo ody +Ġus ability +ĠE as +ĠÄijá»Ļ ng +Ġ195 5 +Ġkrie gen +Ġhabit ual +Ġessential s +rim inal +Ġroomm ates +éĤ£ å°± +ĠпеÑĢе Ñħод +Ġng hi +Ġmen ing +ĠSym phony +ĠH ug +ag gi +Ġw ied +Ġmit ad +ãģ£ãģ¦ ãģĦãģĨ +te enth +ida Äĩ +S ave +Ġrob iÄĩ +Ġboun ces +° ĸìĹIJ +st ars +Ġprag matic +Ġcogn ition +Ġwra pper +Ġw arten +ad h +Ġpens a +ĠHert z +Ġn ÄĽ +ĠRe id +ĠPC s +ĠMo le +Ġ.. ... +Ġpre cio +ĠChampions hips +ê°Ģë Ŀ½ +Ġv ér +Ġcorrid ors +ĠElect ronic +S l +Ġа ле +Ġoverth row +Ġk abul +ĠR ES +ĠCyber punk +ог од +ĠÐĿ ав +Ġw an +Ġmanifest ations +Ġcual es +ĠW ise +ĠLös ung +Ġex fol +Ġearn s +ÑĥÑģÑĤ иÑĤÑĮ +Ġsa pp +ĠBra un +ĠBRAND ON +ì¹ Ļ +Ġs ano +ĠF EL +Ñĭв айÑĤеÑģÑĮ +ожд ениÑı +Ġse wn +F un +Ġrecipro cal +Ġexpans ive +ĠTra ffic +Ġktóre go +ĠÙĪ س +æĺ ¥ +Ġë¹ ¨ +pro ve +ig are +Ġlo h +Ø§Ø ¶ +H ope +Ġdevote es +ĠG om +Ġste als +ĠU ms +ĠTw ice +ãĤ ² +iy im +Ġrhythm ic +ĠV orte +Ġpref ix +om ination +Ġdat o +Ġcust ard +ĠVO ICE +å· ŀ +Ġmen y +ist ors +Ġíĺ ij +ĠìĤ´ì ķĦ +Ġíĥ Ħ +Ġk ort +Ġab a +ĠV era +ep y +Ġì¹´ë©Ķë Ŀ¼ +Ġsubmer ged +ĠC lock +Ġthumbna ils +Ġbo ast +ĠF are +!! ] +ĠÅĽ m +Ġkaik ki +ĠTechn ologies +ìĻ ¸ +ãĥ Ĵ +иÑĤ ай +å°ı æĻĤ +Ġа ÑĤ +Ġkn obs +Ġre icht +ượ ng +gl io +Ġ맼 ìĿ´ +ê°IJ ìĿĦ +Ġjot ka +ĠHand y +ĠHab en +n ous +Ġin land +Ġam azon +ho oting +S L +Ġle isten +~ " +Ġprov oke +ĠTw ist +Ġ×ij× Ĺ +Ġdepart ed +ê° ľë¥¼ +Ġk onse +ĠCar wyn +íķĺ ìĭł +ident al +ES CO +Ġt teokbokki +Ġdiz endo +ç· ´ +ınd aki +imas u +af ar +Ġland fill +Ġcorrect ing +Ġcle ars +ĠNum mer +H AM +Ġcart ridges +ĠDies el +p aced +Ġobl iv +Ġmoy ens +ĠSin ne +ĠPre is +il iz +ĠÑģм ож +Ġbroad en +ä»ĸ æĺ¯ +x es +Ġcarbohyd rate +íĺ ¹ +se ok +Ġecho es +Ġc ess +ë° Ķ +Ġб изнеÑģ +Ġllam ado +Ġess ent +ĠìĿ¼ë °ĺ +ĠA ires +ph en +Ġze bra +Ġsymbol ism +On ce +Ġr acks +ĠKaf ka +ĠÑģеÑĢÑĮ ез +Ġsin n +p icious +ka a +Ġmotherf ucker +Ġapprentices hip +Ġr pm +Ġtax ation +Ġfur ry +ĠSac red +ĠÑĢаз м +por a +eng es +ĠíĹ Īë +ĠÑģ ин +Ġsanit izer +Ġcr inge +ĠS ca +оÑĩ но +Ġof ere +Ġmel odies +ĠVel vet +ĠIhr er +ĠHy brid +ĠG iov +Ġirgend was +Ġdep ende +ĠUs ers +Ġh ump +dri ving +Ġs f +Ġruth less +à¹ĢภĦ +Ġlem ons +Ġfö ret +ĠO j +Ġм ама +Ġinter personal +Ġge v +Ġab norm +иÑģ л +Ġин д +Ġkont roll +Ġreg res +Ġled ge +Ġerzäh lt +ĠT act +Ġarri vé +Ġsubstant ive +Ġspoon ful +zw ischen +oooo o +Ġconten ido +Ġbes l +á»ĥ m +k ten +Jam ie +Ġsand y +ä¸į åIJĮ +â ĭ +Ġp ase +Ġdet te +ĠBelg ian +ê° ľë +ula res +r ud +ig or +ĠíĮ ¬ë +Ġremed ies +Ġblast ing +ĠS ich +Ġож ид +Ġmon str +Ġmanif old +Ġglaub en +ĠE ST +Ġstream line +Ġlobb ying +ĠGoth ic +to ire +.. ' +Ġdém ocr +Ġнаб лÑİд +Ġwsp ól +ĠczÄĻ ÅĽÄĩ +ä¸ĭ éĿ¢ +is és +g angen +Ġbez pie +rem lin +ê° Ŀ +St ill +Ġres ides +Ġgele cek +Ġtélé phone +Ġpe wn +Ġle opard +Ġcompliment ary +Ġc rib +ĠAnim als +Ġge il +ess el +Ġgard er +Ġcatch y +æ¨ ¹ +ĠE ts +ĠCom mercial +ĠD ENNIS +ĠCoordin ator +ĠAb igail +ffff ff +ấ p +Ġpeque ña +Ġinject ions +ce kt +Ġphilanthrop y +Ġp uck +Ġcelebr ates +ĠD unk +ĠD latego +ãģ¾ ãģł +δ ή +grad uate +ĠM obil +t ill +ac am +Ġyol ks +Ġtang led +Ġman iac +Ġoblig ed +ĠLa ink +Ġver der +ĠDam on +Ġmut ant +Ġhop ping +Ġre ins +Ġinver ter +Ġcont empt +׳ ס +le arning +M iss +ĠÐĵ оÑģ +ĠMe yer +ê»ĺ ìĦľ +é£ İ +×ķ׳ ×Ļ×Ŀ +ask ing +Ġtrim ming +Ġtre asury +Ġs ente +A ust +ĠUnterstüt zung +ĠCom edy +ĠAn akin +é ¹ +ÑĢÑĥ ÑĤ +ĠH ari +ograph ers +Ġoat meal +ĠB ots +ä¸į äºĨ +Ġп алÑĮ +Ġacknowledge ment +x ic +Ġê´Ģ ìĭ¬ +gas ping +Ġãģ ķ +Ġterr ace +Ġor naments +ĠM ER +comm ittee +ĠìĹĨ ìĬµëĭĪëĭ¤ +Ġr ij +é ³ +צ ×Ŀ +le me +Ġlibert ies +Ġfell as +ĠCop per +ben ch +ĠIde a +á»į n +ÑĪ а +Ġvers ión +ÏĦο Ïį +ĠÐľ и +ĠпÑĢил ож +Ġbox er +ĠT anner +ĠM oy +ì¹ĺ ëĬĶ +T hr +Ġtin ham +Ġpol ishing +Ġconsequ ently +Ġamen ities +ĠK I +ĠGRE EN +ĠFrank ie +н иÑĤ +itt el +Ñģ кое +urs ed +Ġup bringing +Ġth ứ +ĠìĭĿ ìľ¼ë¡ľ +Ġwh im +Ġchin ese +conf idence +ĠJ eder +ãģª ãģ®ãģ§ +aj cie +ĠT ous +ĠPow ers +ừ a +other mal +ĠвÑĭ ÑĪе +r ale +Ø§Ø ® +Ġì§Ģ ìĽIJ +Ġép isode +Ġsul ph +Ġenc ara +k raft +alar ı +ĠCom es +Ġdiv ul +ĠRud olph +ĠM use +Ġut ens +ĠìŀIJ 주 +Ġp ana +ĠVeget a +ĠPH P +ĠN SA +ent in +ĠCarne gie +ا ÙĬ +iÄĻ cy +H arry +Ġf ır +С п +Ġglad ly +Ġaver aging +íķĺ ê²łìĬµëĭĪëĭ¤ +лÑı ÑİÑĤÑģÑı +ĠÐľ енÑı +Ġquot ation +ri res +itch ens +ay ed +Ġun att +ĠP erez +ĠоÑĤ меÑĤ +Ġtact ile +ĠEu h +is ini +b uh +Ġhat ır +ĠìŀĪ ìľ¼ +Ġpolicy makers +³´ì Ħ¸ìļĶ +ac ı +Ġκ ι +Ġregister ing +re to +ĠSpr inkle +ĠGram my +ax ter +Ġб и +Ġsit ter +Ġpred ic +Ġthin ly +Ġstr um +Ġag grav +Ġa ha +ر ج +m ellow +Ġconst ante +ĠL aut +ist on +Ġtransition ed +ĠCamb odia +ãģĦ ãģįãģ¾ãģĻ +è·Ł 大家 +art ed +Ġmis f +ĠPunk te +Įë ĵł +Ġtremb ling +Ġges pannt +ĠعÙĦÙĬ Ùĩ +Ġникак иÑħ +Ġë¶Ģë ĵľë +ĠÑĢазв иÑĤ +Ġit chy +Ġc iento +Ġpl ains +Ġk ittens +Ġback log +ĠPres iding +pt a +Ġha voc +ĠDarr in +ĠÐĽÑİ Ð± +Ġsegreg ated +Ġg hetto +Ġerle bt +Ġdrug iej +ĠSi xt +åı ĥ +ร ะ +uen cia +Ġíķĺ 기 +ĠëĨ į +Ġrob i +Ġpione ers +Ġmilli ards +ĠWitch er +Ġ무ìĹ ĩ +or ro +m ass +Ġdiver gence +ĠRiver a +ĠNo odles +Ġend roit +ĠK osten +ĠдÑĢÑĥг а +ĠmÃŃn imo +ĠKazakh stan +ت Ùĩ +Ġвоз дÑĥ +Ġgesch rieben +ĠN il +Ñģ ки +ĠFr üh +Ġbever ages +æº IJ +ĠG on +æĺ ¨ +Ar in +ĠInt ro +ocaly ptic +Ġexhaust ion +ĠStat us +ĠBatter y +és z +£ ¼ë +air y +Ġë³´ìŬë ĵľë +Ġdispar ity +Ù Į +ĠTuc son +Ġbright ly +pro blem +Ġbiom ass +éĻ į +§ ī +Ġhur dle +Ġwavelength s +Ġ< < +Ġteam ed +FF FF +ĠS lim +om ial +Ġunve iled +ĠVere in +ÙĤ Ø· +est ry +Ġcl ás +Ġch eddar +Ġaccus ing +ĠScient ific +ĠбÑĥд е +ĠCyr us +ε ÏĦε +Ĩĵ ê³ł +Ġë³ Ħ +Ġcur d +Ġrefer rals +sh ift +åį ķ +nik ów +Ġm ier +Ġconf ronting +ê²ĥ ëıĦ +aw l +Ġtry in +Ġê·¸ëŀĺ ìļĶ +Ġch iar +Ġìĺ¤ëĬ ĺëıĦ +æĶ¿ æ²» +es que +Ġmism os +ĠSh ak +Ġsoci aux +Ġpi ÅŁ +ĠkiÅŁ i +Ġcy an +h ay +be w +b od +ĠÎ ¹ +ĠMain ly +Ñİ ÑĤÑĮ +hab itude +ĠÑģп окой +è·Ł æĪij +Ġpre con +ĠM andy +ðŁ¤ £ +ill os +Ġgr upp +Ġcr umble +Ġconstru ctor +erv ices +Ġlight house +ĠCon cept +ан ÑĤи +alt ro +h ope +ĠAll eg +ìĸ´ë ¥¼ +pie ces +oun ter +Ġíķĺ ëĭĪê¹Į +ĠìĿ¸ íĦ°ë +Ġvérit able +Ġthread ed +bl ind +Ĥĺë Ŀ¼ +Ġtr ays +ĠEd ison +ĠÃĸ z +ĠSte vie +Ġl ender +Ġbrig ade +Ġdeuts che +m uffled +b art +Ġinsan ity +Ġsav vy +Ġsens ational +Ġdere chos +ĠM X +ĠпÑĢ еп +Ġthreat ens +Ġrealt Ãł +Ġindic ative +Ġch ops +Ġbenef iting +ĠVern on +ĠSt rand +n un +qu ently +10 1 +Ġe el +ìĪ Ļ +r ints +ĠÙħ س +Ġب د +Ġпо ÑģÑĤÑĢо +Ġyap mÄ±ÅŁ +Ġol ması +Ġi edereen +ol é +ke f +Ġë°ľ ìĥĿ +Ġr ained +Ġalm ighty +ĠвÑĭ д +ĠC PR +F re +Ġinhab ited +Ġarb ets +Ġa kin +а ÑģÑĤв +v ania +Ġhäuf ig +ĠMat te +s orry +Jen ny +ĠгÑĢ ад +Ġwh it +Ġbro kers +å¯ Ł +Ġh ine +ast en +Ġг ÑĢÑĥ +M B +ĠP RI +S ab +Ġwrest ler +Ġfacil itating +Ġeh kä +ĠC red +Ġ12 7 +Ġnot hin +Ġmand ated +å¯ Į +ÑĥÑĤ ÑģÑĤв +F rank +Ġwor s +Ġdzie ÅĦ +ĠUnder ground +Ġznaj du +ĠB ä +ĠPrin zip +аÑĤ елей +Ġveter inar +Ġsplend id +Ġroz p +Ġpsych opath +ig on +Ġh ops +Ġc ần +ĠX ian +Ġtro isième +Ġproduct o +ĠdeÄŁ er +ĠContin uing +ив ал +c ık +Ġmoistur izer +Wh ite +Ġsi is +ĠEver est +ien ced +Ġcả m +ĠJ apon +´ìł Ħ +Ġten ÃŃan +Ġenc anta +M m +Ġdrop down +ĠI ya +³´ë ©´ +Ġword ing +ĠSque eze +ĠMap le +Ġclar ified +ĠMun icip +ĠRou ge +ĠNick i +ĠGo o +v olt +t ek +fect ure +f red +ar rive +ãĥ¼ ãģĦ +te z +E p +Ġob ras +ĠV ID +ĠR iv +ĠMod i +i be +Ġacontec endo +Ġim itation +Ġcamoufl age +Ġspan ning +ĠSEC RET +ĠOre o +ìĨĮë ¦¬ +Ġh unch +Ġca ÅĤe +Ġspont aneously +ĠPer d +Ġet ap +ĠHo le +ĠDis ability +Ġafter life +æģ © +Ġtest ified +Ġpres up +Ġpet roleum +Ġcontr ario +ĠAss essment +ÄŁ lu +Ġp ests +Ġdil ig +ĠвÑģÑĤÑĢ еÑĤ +Ġcons équ +Ġcann ons +Ġcan oe +ĠM ile +Ġcit oy +Ġbe gged +ĠMin nie +ÅĤy ch +Ġprinci pe +ÏĢÏĮ ν +m niej +Ġw ert +Ġëĭ¤ë ĵ¤ +an se +Ġunc les +Ġprovoc ative +Ġinter sections +Ġdemocr ats +ĠJul ius +ин ки +yg usal +Ġ׾ ×ķ +Ġgj orde +Ġg asket +ĠB ock +ĠÄ° n +b reat +ĠEqu ity +ard ı +Ġкан але +Ġд ней +Ġt Ỽi +Ġfi xture +Ġab uses +Ġv aya +Ġou vert +Ġmultic ultural +Ġcontext o +ĠSes ame +Ġdé pl +Ġcons omm +ĠPart e +Ġp em +ĠCon an +Ġб ÑĸлÑĮ +Ġpersu aded +Ġdra ins +M oo +F ORE +Ġб аÑĤ +Ġf od +ĠProduct s +ì§Ħ ì§ľ +Ġ" [ +ĠW ick +ĠNar uto +н али +ry w +Ġl odge +Ġin h +Ġvont ade +Ġdi j +ĠJes ús +Look ing +Ġfore arm +ĠIntegr ation +ĠHARR IS +Ġtool bar +le ader +Ġsel dom +Ġб ÑĢоÑģ +ĠK ook +он д +Ġmon opol +Ġmill et +Ġl ira +ĠAs ians +Ġ18 90 +ci ÄŁim +Ġed en +ĠIKE A +ĠNeigh bor +ĠKazu ya +ü d +Ġpsych edel +Ġenvision ed +åĿ Ĺ +Ġï· » +Ġw under +ĠBulgar ia +B rid +Ġmar row +Ġdep iction +ĠT in +ĠPhar ise +Ġeinz ige +Ġblind ly +ãģĽ ãģ¦ +Ġdef ens +D ire +Ġvibr ating +Ġtroll s +Ġdisrespect ful +Ġw od +Ġstimul i +Ġcreep ing +Ġcla irement +Ġsc ariest +Ġdécouv rir +Ġ10 4 +ĠвеÑĢ Ñħ +ĠÅĤ at +Ġróż ne +Ġbar ley +ĠRe pl +ĠT we +k ke +ĠãģĿ ãĤĮ +ĠRed mi +ĠMet roid +Ġή ÏĦαν +Che ck +ĠS EN +Ġ ido +ÑĤоÑĢ ии +ó p +UN KNOWN +Ġänd ern +ĠJu ice +ĠGes icht +å°± æľĥ +ĠнаÑģÑĤ олÑĮко +íĥ ķ +Â Ń +ex hales +Ġì´ ī +Ġj sem +ÏĢ ÏīÏĤ +Ġit t +ëªħ ìĿ´ +Ġrem ix +Ġbloss oms +ĠR enee +is ations +ìĬ¤í Ħ° +Ġë³´ ìĿ´ëĬĶ +uest as +op edia +ĠA im +ìĿ´ì¦ Ī +sc ene +Ġleak age +uck t +S ad +A sk +Ġsusp ense +Ġimp ost +ĠStrateg ic +ĠIt ÃŃs +âĢ Į +Ġkey boards +Ġam using +og r +id erman +ŀ ĸ +Ġв ижÑĥ +Ġd ips +Ġapolog ized +ĠST AR +Ġesc uela +ĠC hing +н ениÑı +Ġë¶Ģë¶Ħ ìĿ´ +ĠFle et +Ġs amb +Ġentsprech end +Ġelectrod es +ĠFrei heit +æĪij ä¸įçŁ¥éģĵ +ĠSh rim +iÃŁ e +Ġselect ions +Ġfor di +Ġd oss +Ñı Ñĩ +Ġdiscrimin ate +ĠAu ÃŁerdem +Ġdesenvol v +ĠIntern al +ĠBened ict +å¯ Ĩ +ĠSh iv +M issy +Ġоб наÑĢÑĥж +Ġна ÑģÑĤÑĢо +Ġcontrol ar +ĠL ia +Ġopio ids +ant u +Ġcup board +æģ IJ +г е +acht s +Ġcur ated +Ġx em +Ġwe ary +Ġbre thren +Ġbudget ing +Ġpour tant +éļ » +ais ia +ĠоÑĤв еÑĩ +ĠG IS +μ αι +Ġש×Ķ ×ķ×IJ +Ġsa ud +Ġl Ỽ +Ðķ Т +ub ine +ĠнÑĥж ен +Ġkidna pping +Ġbr at +ĠTer re +ĠMon et +Ġë§Ī ìĬ¤íģ +Ġflash y +ĠIS BN +Ġfreel ance +i age +Ġjun ge +ì¶ © +cer al +ĠÑĤоÑĩ ки +Ġform ulate +ĠF ER +ĠDart mouth +ìľ¼ë ©´ìĦľ +å¢ ĥ +ow iÄħ +ĠëĶĶ ìŀIJ +Ġreg iment +Ġmetabol ismo +ĠP arr +Ġ충 ë¶Ħ +Ġsan ity +ĠL al +ĠG ö +ĠG la +Ġprot o +Ġmicroscop ic +Ġk ang +ĠSc alia +Ġp ug +ĠSc ore +ĠSav annah +Ġgard e +ĠN OR +å°į åIJ§ +Ġsche int +Ġp óÅĤ +Ġcor ri +Ġbr ute +Ġ ÅĤad +ä»ĸ 们 +Ġsucceed ing +Ġbicy cles +N on +Ġseek ers +Ġuncond itional +Ġrhy mes +ĠGar age +Ġinv oice +Ġcan vi +ne ck +Ġcustom izable +irit ual +Que en +íķĺ ìĭľëĬĶ +Ġpower less +Ġcs ak +ä¸į ä¼ļ +is oft +Ġìłķ íĻķ +Ġnh ân +ĠM AND +ĠH af +Ġrevol ves +ä¹Ł åı¯ä»¥ +ov an +ar oo +ĠGr ind +éĽ ª +Ġindispens able +Ġconsult ed +ĠClin ical +A cc +Ġol hos +Ġmon ter +ĠH ana +et ah +Ġva an +Ġt igers +Ġcau cus +ðŁĺ Ĥ +³´ì ŀIJ +pow ers +ium s +ĠíĨ łë +Ġtrad icional +Ġreson ated +Ġìĭł 기 +th em +Ro bert +Ġelement o +Ġant id +Ġоб Ñģ +Ġnat ives +Ġlo ca +ow ment +ĠT ight +Ġ æĢĿ +Ġmel an +ĠN ue +am is +Ġsor gen +as ına +H ome +ĠPUB G +Ġaw fully +ĠSh ore +ĠPer ché +ĠL au +ĠCind erella +ĠCh est +Ġsem antic +Ġdesert ed +ĠMom o +ĠHern andez +gen es +ĠAd ult +иÑĩеÑģ кого +osh ima +ĠcaracterÃŃst icas +ĠK L +´ìŀ ¥ +oc ar +Ġfeh lt +Ġd ruk +ĠPop py +EN GLISH +ĠVerg leich +B rien +Ġrec omp +ĠÑģ д +Ġmer ger +Ġmarket ers +Ġhoney moon +Ġpen so +Ġbell i +еÑĤ Ñĥ +Ġbank er +Cam era +ĠSt all +ĠSt amp +ĠB ite +еж де +Ġs ür +Ġgü ç +ĠPas sover +ĠBug ün +ĠÑģожал ениÑİ +Ġн из +Ġman ure +Ġglac ier +è« ĩ +RA Y +ter ror +Ġsal ads +Ġhur ricanes +ĠDesign er +ator io +Ġfact ual +ĠTam my +Ġзв ÑĥÑĩ +Ġintrodu ctions +Ġhouse keeping +Ġh anger +ëĭ ĺë +ak te +ĠCol a +' ] +ĠG ender +оÑĢ он +ip se +ic ias +Ġsuccess ive +Ġpolit ic +Ġhö her +ĠQ iao +ĠG imme +Ġл ож +Ġse b +ĠWe iter +ĠSak ura +ĠB oulder +ĠAm érica +peÅĤ nie +Ġtecn ologÃŃa +ish ops +f ur +Ġmoon light +Ġdispers ed +Ġre z +ен ное +алÑĮ нÑĥÑİ +ĠTw elve +ĠH OR +ìĭ¤í ŀĪ +il age +Ġshad ed +Ġres umes +ĠPe anut +ĠM ILL +ap ons +ĠU FC +ĠSo le +Ġjoy stick +ĠOliv ier +war ming +Ġsyll abus +Ġоб Ñīе +Ġhi á»ĩn +Ġfest a +Ġcr adle +ĠZ ac +Ġremem brance +Ġê°Ļ ìķĦìĦľ +ĠpiÄĻ k +Ġco exist +ĠV II +Ġá reas +Ġu waż +Ġobser vers +Ġmännisk or +co on +ĠD AM +Ġnas zym +Ġall igator +ĠFree ze +ĠEst ate +ĠÑĤÑĢ ади +Ġunder cover +Ġn ies +ĠFeh ler +pl in +ĠK abul +il ate +Ġê³ł ìĸij +Ġm op +ìĦ ¼ +Ġand erer +ĠK ELL +ок и +Ġж еÑģÑĤ +Ġgra zing +Ġda ÃŃ +Ġcapital ize +Ġa pex +Ġnurt uring +Ġcort ar +Ġcontr ac +ımız ı +Ġtand em +éĥ½ æľī +ge ment +ĠÑģиÑģÑĤем а +Ġman que +ia jÄħ +W OR +Ġا ب +Ġcart s +AN O +Ġë°Ľ ê³ł +ĠC ena +ĠBi ology +id ar +Ġa ż +er ne +an u +Ġthank ed +Ġsubmar ines +Ġman ic +Ġм оз +ä¼ Ĭ +inst ant +ess ential +Ġsam urai +Ġpast i +Ġal an +Ġbro ch +Ġb aker +ĠGu ill +¨ ¼ +Ġwithd rawn +ëĭ Ŀ +Per fect +qu ency +Ġstream lined +Ġ13 00 +´ë ıĦ +Ġëĸ łë +Ġãģ¯ ãģĦ +Ġh vad +ä¸Ģå®ļ è¦ģ +Ġverb ally +ĠK ons +Ġì¡° ìĭ¬ +Ġdie z +æİ° æİ° +Ġchuck ling +ĠM ih +Ġrall ies +Ġman ter +Ġearn est +s uper +Ġge ce +ĠR end +ĠGer ade +jen igen +ĠV all +Ġìŀ ĪëĤĺ +ĠÑģказ ала +Ġtrabal h +ĠнаÑĪ ем +Ġм еÑħ +ik it +Ġnoun s +Ġneurolog ical +Ġmotiv ational +ĠMcM ahon +ĠFin ished +Ġë³´ ìĿ´ +ĠField s +Ġadoles cents +ĠT isch +ĠNe ben +ĠFl owers +ĠEner g +Ġdire t +ĠTh i +ĠP icas +æĥ ľ +æĢİä¹Ī æł· +Ġav ete +ĠF ors +ĠChap el +N ão +E t +ĠÑģод еÑĢж +ren o +Ġs ven +Ġdost ÄĻp +ne e +ĠSnap dragon +ĠID s +ìķĺ ëĬĶëį° +ר ×ļ +Ġsun flower +Ġperpet ual +ç³ ĸ +Ġkn ights +Ġg ird +ĠTo ld +Ġvolcano es +Ġadvers ary +ĠEconom y +Ġextra pol +Ġbl uetooth +Ġzoom ing +Ġsk ys +Ġgen ial +ÃŃcul os +amb re +Ġм еÑĢ +Ġteen y +Ġstress ing +ìķ Į +ON Y +Ġtransluc ent +Ġround ing +Ġgr ues +×Ļ׳ ×Ķ +ap rès +Ġprue ba +Ġpoly gon +Ġblue berry +ĠProgram m +Ġtren ches +Ġse bagai +Ġpal ate +Ġla ude +Ġbehav ed +Ġlongitud inal +ĠMod ule +Ġadm ir +λ ι +G reg +Ġwy st +Ġpropag ate +Ġmold s +ĠT ub +ĠL oud +ust o +Ġun stoppable +Ġreinfor cing +éĿŀ常 çļĦ +ĠпÑĢоблем а +Ġpot encial +Ġhe mp +ìŀ Ķ +ठ¯ +Ġopt ic +Ġerfolg reich +Ñģ Ñĭ +олÑĮ ÑĪе +ur st +ĠPo is +Ġrespond ents +Ġneh me +ĠEx ternal +ol ate +H yun +Ġquart z +Ġmathematic ian +Ġbás icamente +Ġa il +ìł ľë¥¼ +att utto +Ġno oit +Ġaff lict +ĠOl ga +èŃ · +Ġна ÑĤ +Ġd ites +Ġreal idade +Ġk än +Ġuniqu eness +Ġpad res +Ġsubs idi +Ġpige ons +β α +st ad +Ġder en +ĠС лед +d oo +ĠопиÑģ ании +Ġam ber +Ġgoose bumps +ĠfrÃ¥ gor +ĠV ital +ĠIsrael ites +w asser +Is n +Ġcomm its +ĠSTE VEN +ĠBev ölker +uit ive +Ġleg en +Ġbr uk +иÑĢов ан +yn en +hel m +Ġgener ational +ĠL ändern +οι ÏĢÏĮν +uz u +Ġcall er +он ÑĮ +üm ü +Ġbes ar +Ġpl ats +Ġmig rated +Ġj ap +ĠW AR +Ġdis sect +ĠZus ch +ĠZe iten +ĠL ions +ĠD F +â Ķ +ки в +Ġpedest rians +ĠMar ilyn +d ock +Ġy ht +Ġre incarn +ĠSon o +ĠGrow th +ÑĥÑģ ов +Ġdun geons +Ġbag us +k ich +ĠÑĥ кÑĢаÑĹ +éĨ « +ĠK eller +chem istry +J apanese +Ġwill st +Ġdecomp osition +ĠÑģÑĤ ен +Ġrev ived +íķĻ êµIJ +ĠÅ ĵ +ä½ IJ +ìĭ ¸ +ipp y +Ġhour ly +j än +ĠWork shop +Ŀ¼ ìĦľ +Ġcu arto +Ġpat rim +ĠB urch +ĠìŀĪ 기 +Ġhe pat +Ġh Ãłng +ĠëĮĢ íķ´ +ĠваÑĪ и +Ġre work +Ġpar se +Ġçıkt ı +ĠS ax +ĠMong o +ĠAa ah +ram ble +D J +Ġstabil ized +ĠSpe ech +Book s +Ġhur dles +ĠW O +ĠLamb org +Ġ19 33 +Ġvor bere +Ġclin ically +Ġbreat htaking +ĠGate way +пеÑĢв ÑĭÑħ +ut ers +Ġë¹ µ +Ġyet er +Ġpull ey +Ġmuff in +ĠPre fer +ĠP ence +Ġinform ação +ìĬ¤í Ĭ¸ë +ãĤ¸ ãĥ£ +ĠTur tle +ĠReg ina +ĠLo ad +do es +pan ze +¸ Ķ +Ġmin a +ĠLatin os +amm ers +ĠT ort +ĠBey once +имо ÑģÑĤи +ĠвопÑĢоÑģ Ñĭ +Ġbul un +èĢĮ å·² +ine k +bere ich +Ġpast ure +ĠO A +ĠM elt +ĠEt t +ĠD Y +Ġob wohl +Ġle agues +ÑĤ еÑģÑĮ +Ġк ÑĥÑģ +Ġv ors +Ġto pp +ograph ical +as st +Ġl indo +Ġë°Ŀ íĺĶ +Ġré fl +Ġclim bs +Ġv arsa +Ġmethy l +ĠKar ere +Æ°á» Ł +R ad +Ġprepared ness +он Ñĩ +ĠO D +ĠC GI +Ġठ® +Ġspeech less +Ġlas ci +Ġbol ag +ĠÑħоÑĩ еÑĤÑģÑı +Ġgr ieving +ĠJohann es +ĠCar roll +ad aki +Ī ¬ë +ĠsÅĤ u +Ġinner halb +Ġgymn astics +п ÑĢи +if iques +Ġkar ate +Ġdom u +ãģĿãĤĮ ãģ§ +OTH ER +Ġdemand é +Ġbook let +ĠKy oto +Ġw oh +ĠMar ÃŃa +viol ent +J E +Ġl óg +Ġbrut ally +c ot +ĠÙħ ÛĮ +ĠWars z +å® Ī +w ol +Ġmik ä +ĠPron ounce +ĠBrend an +Ġr oup +Ġital iano +å¦Ĥ æѤ +Ġкомп ÑĮÑİÑĤ +Ġur ging +ed es +Ġcarbon o +ĠRichards on +ĠÐĿ аÑĩ +ĠTra iner +ĠCrime a +Ġdi apers +Ġco vet +ĠMah ar +ĠH utch +ĠAus w +ber ty +Ġind ifferent +кÑĢ еÑĤ +uld ade +Ġhar ms +¢ ÙĨ +les ia +Ġg io +ĠMist ress +ĠK nox +ĠFRE E +Ġë £¨ë +ĠнаÑĪ а +Ġinvinci ble +Ġma iden +ĠJ eez +Ġbre ve +po le +Ġcritic isms +ĠRus ia +ठ® +ph in +ĠComp are +ĠB ON +Ġsne aking +ĠR ails +ĠG eral +Ġ195 3 +H ola +Ġоп ÑĭÑĤ +Ġrain forest +Ġbel um +ĠOb i +ĠIS S +ãĤĮ ãģªãģĦ +ĠС в +Ġbl ond +Ġwz gl +Ġpowiedz iaÅĤ +Ġch oking +ĠSong s +ĠBir az +Ġyell s +Ġstyl ist +ÏĮ ÏĦε +Ġsch reiben +ĠJ aw +ĠEle ven +ĠR if +/ . +Ġìĺ¤ë ŀľë§Į +Ġtreat ies +uff ed +ĠâĪ Ĵ +Ġroof s +à¹Ģภª +Ġë » +Ġspark le +ĠK iev +ĠAr gu +ere cht +ĠÐĿад о +ĠF IL +Ġmol ta +ĠDe vi +Ġcam pe +Ġbene vol +ĠT ough +Ġmo im +Ġevac uate +Ġer rado +å© Ĩ +ÑĢÑĥ го +Ġíİ ĺ +ĠÎĵ ια +Ġweak en +Ġillum inated +Ġsig lo +ĠV acc +и ей +al is +ĠÑĥ ÑģÑĤÑĢой +Ġdon a +ÅĤ os +ü man +Ġprodu cción +Ġcl ot +ĠM ango +Ġune asy +Ġsh uts +ĠExam ples +ve ll +e be +Ġprompt ly +ĠT eles +ĠпÑĢоÑĪ л +Ġpu erta +Ġüber zeug +Ġco ch +so cial +ĠB enson +ĠM eth +ĠEx ped +Ġsupplement al +Ġconce ive +Ġ×ĺ ×ķ×ij +Ġcapt ivity +ıĻ ìķĪ +ĠÑħ Ñĥд +form ing +Ġupload s +Ġturbul ence +j oint +Ġsatisf actory +ĠAn ime +Ġwash es +Ġliber als +ĠSun shine +ĠRE AL +ub lik +b inary +T ony +Ġpolar ized +Ġenrich ed +t aking +ĠëģĿ ëĤĺ +Ġple asures +Ġex termin +in ese +at l +v är +аÑĢ Ñĭ +Ġmy ÅĽ +n arrator +Ġод ном +Ġnaj wiÄĻ +Ġmobil ize +Ġmill or +Ġat a +æ· · +ĠpolÃŃt ico +Ġple ad +Ġpain ters +ĠS ow +о ÑĦ +ĠìĺĽ ëĤł +ĠÑĩ ÑĤоб +Ġs abor +ĠUnd ert +ĠJER RY +Å¡ ÃŃ +Ġë° ĸìĹIJ +Ġpréc éd +Ġannot ation +ĠI naudible +Ġtext ured +Ġfisher man +v ordan +icher ung +Ġìłģ ìĿ´ +Ġge zeigt +Ġmand ates +Ġbe ak +ĠTW O +ĠAk bar +il ian +Ġtiế p +Ġsuperior ity +ink u +Ġl ys +ĠF CC +ĠC PA +ust ering +nic os +an ja +Ġch ills +ĠC age +Ġse aling +Ġsa ç +Ġded ans +ĠAl ger +Ġspe zie +Ġcol oss +ıy ı +clock wise +Ġexact amente +Ġ iemand +am ı +Ġmand ar +ra j +f aced +ag ua +Ġê¹ Ķë +Ġins besondere +Ġdri zzle +Ġdimin ish +ĠY oda +A I +Ġbil miyorum +ĠM MA +ateg ory +ĠпеÑĢ еп +Ġparticip ar +Ġnormal ized +Ġcomplex ities +æ´ ² +æİ § +аÑĢ ов +m ist +ich a +Gr oup +Ġresil iency +Ġnog le +ĠCN C +pr ü +Ġphysic ists +н ок +L I +Ġstuff s +Ġsist emas +Ġinterfer ing +ĠMar vin +ér cito +ĠìĹĨ ê³ł +Ġson ic +Ġequ iv +Ġab ord +ĠRam en +Ġ0 9 +med im +at iques +Ġдел аÑİÑĤ +Ġunanim ously +Ġsk irts +ĠíĬ¹ ë³Ħ +ĠP rix +k ami +Ġfr uition +Ġbirthday s +ик ом +Ġinaug ural +Ġcorrel ate +ĠT ory +ĠëĤĺ ìģ +Ġde w +ĠPre cis +ih i +Ġë¬¸ìłľ ê°Ģ +Ġc iting +ĠL ana +ĠK ag +Ġplay through +ĠProt ocol +fr ist +hov ah +Ġmerc iful +Ġb ilingual +ĠG uitar +r h +Ġglam orous +ĠVik ings +ĠOoo oh +íķĺ ëĬĶëį° +ĠUg anda +Ġcollaps es +ent ry +Ġantioxid ants +ëĤ ĺë +ÑĪ аÑı +Ġtri via +Ġgä ller +Ġfun gi +Ġmil ks +Ġd icht +μ η +po ke +ĠвÑĭп ÑĥÑģк +Ġfeed er +ĠAl cohol +h ower +Ġdes erving +ĠRe bel +ios is +Ġ10 3 +Ġhand out +Ġen m +Ġland lords +Ġge ology +r ils +Ġco bra +ĠV old +ĠP anch +ĠGRE G +Ġpr oss +Ġbrac elets +ĠV ega +Ġroz um +æ¬ ¾ +аз д +ĠLy nd +ĠHon ors +Ġsurrend ered +Ġlibr arians +12 5 +ĠÑģ иг +Ġuniform ly +ĠE agles +ìķ Ļ +иÑĤ ан +and id +ĠìłĪë ĮĢ +ĠØ ¶ +Ġarrest s +ĠCS V +ĠAzerbai jan +ort ic +ĠD X +ĠAdvent ures +Ġab us +ĠF au +Ġschlim m +Ġratt ling +Ġconsum es +ĠTol kien +Ġresurrect ed +ĠX Y +íĬ¸ ê°Ģ +ĠвÑĭ ÑģÑĤÑĥп +ĠAng ie +żen ia +M ic +ĠShe ila +acht et +Ġover st +Ġl â +Ġine ffective +æĿ ¡ +æĢİä¹Ī äºĨ +å¿ Ļ +Ġwicht iger +Ġv ino +Ġp um +Ġang led +ĠP ione +ĠM ỹ +ãģĿãĤĮ ãģ¯ +wo ÅĽÄĩ +d raw +ั à¹Ī +mark ets +Ġcaf es +ĠC em +â Ŀ¤ +ĠS uit +M K +Ġemphas izes +Ġtort illa +Ġmejor ar +ĠSur viv +cast ing +Ġeduc ación +ĠG um +u ely +ĠìĹ¬ê¸° ëĬĶ +Ġstretch y +en ça +Ġwith hold +Ġex iting +Ġenthal py +ĠTrans it +ıl mÄ±ÅŁ +al ies +Ġsal var +Ġlean ed +ĠgroÃŁ es +Ġf itt +ак и +S arah +Ġhost el +Ġfinger na +Ġnadzie jÄĻ +w ives +R ec +Ġsp ool +аÑĤ ов +ĠEn emy +Ġf ury +Ġdet ta +ĠF ay +éļ ¨ +Ñı ÑİÑĤ +Ġaproxim adamente +Ġsil os +Ġmag ist +Ġc ree +ĠKr ank +ĠD OWN +Ġstart led +Ġre born +ĠUm welt +ĠSuz anne +ни ÑĨÑĭ +out ez +ĠJ AC +y ards +rad as +ra u +ip ts +h ail +Ġparagraph s +Ġme glio +Ġisol ating +Ġace ite +ĠH arsh +Ġcy st +ĠBlock chain +ĠÑħоÑĢоÑĪ ий +Ġvirt uous +Ġinvestig ación +Ġdev oir +Ġmast urb +ĠS ale +ÙĬر Ø© +ĠÎ § +ĠStra ÃŁen +Ġdi kk +Ġa fore +ĠJung kook +Ġcho ciaż +ĠDebat te +Ġweird ly +Ġvia je +reg ist +H elp +Ġkind eren +Ġform ulated +Ġenf im +ĠTow ards +ко ÑĹ +iver ing +ĠдеÑĤ и +char ger +Ġpur l +Ġacadem ically +ĠNur se +Ġdel eting +ay o +Ġref usal +Ġdepict s +ĠDr acula +Ġtoast ed +ĠZomb ie +ĠSuper ior +ĠB old +Ġquizz es +Ġg le +4 50 +Ġcome ço +yn n +Ġver st +ĠO laf +Ġpom oc +ĠS ask +ë ĺ +ĠT CP +ĠProper ty +íķĺ ì£ł +à¸ľ ม +bo om +ar os +ĠÑĢоÑģÑģ ий +ĠбÑĭв аеÑĤ +åĩº åİ» +ĠìĿ´ìķ¼ 기를 +Ġcomb ien +v acc +Ġeben falls +par a +Ġз м +Ġdesper ation +ord re +Ġש׾ ×Ļ +Ġgener ously +ĠÐŀ к +Ġorb iting +> ", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"} \ No newline at end of file diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/tokenizer_config.json b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..0235ccff90efbc022fbe30360220375c5bad68da --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/tokenizer_config.json @@ -0,0 +1 @@ +{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "multilingual", "errors": "replace", "tokenizer_class": "GPT2Tokenizer"} \ No newline at end of file diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/vocab.json b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..406c3f2600089389bd8bf3920c2fa3770d11789a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/assets/multilingual/vocab.json @@ -0,0 +1 @@ +{"!": 0, "\"": 1, "#": 2, "$": 3, "%": 4, "&": 5, "'": 6, "(": 7, ")": 8, "*": 9, "+": 10, ",": 11, "-": 12, ".": 13, "/": 14, "0": 15, "1": 16, "2": 17, "3": 18, "4": 19, "5": 20, "6": 21, "7": 22, "8": 23, "9": 24, ":": 25, ";": 26, "<": 27, "=": 28, ">": 29, "?": 30, "@": 31, "A": 32, "B": 33, "C": 34, "D": 35, "E": 36, "F": 37, "G": 38, "H": 39, "I": 40, "J": 41, "K": 42, "L": 43, "M": 44, "N": 45, "O": 46, "P": 47, "Q": 48, "R": 49, "S": 50, "T": 51, "U": 52, "V": 53, "W": 54, "X": 55, "Y": 56, "Z": 57, "[": 58, "\\": 59, "]": 60, "^": 61, "_": 62, "`": 63, "a": 64, "b": 65, "c": 66, "d": 67, "e": 68, "f": 69, "g": 70, "h": 71, "i": 72, "j": 73, "k": 74, "l": 75, "m": 76, "n": 77, "o": 78, "p": 79, "q": 80, "r": 81, "s": 82, "t": 83, "u": 84, "v": 85, "w": 86, "x": 87, "y": 88, "z": 89, "{": 90, "|": 91, "}": 92, "~": 93, "¡": 94, "¢": 95, "£": 96, "¤": 97, "¥": 98, "¦": 99, "§": 100, "¨": 101, "©": 102, "ª": 103, "«": 104, "¬": 105, "®": 106, "¯": 107, "°": 108, "±": 109, "²": 110, "³": 111, "´": 112, "µ": 113, "¶": 114, "·": 115, "¸": 116, "¹": 117, "º": 118, "»": 119, "¼": 120, "½": 121, "¾": 122, "¿": 123, "À": 124, "Á": 125, "Â": 126, "Ã": 127, "Ä": 128, "Å": 129, "Æ": 130, "Ç": 131, "È": 132, "É": 133, "Ê": 134, "Ë": 135, "Ì": 136, "Í": 137, "Î": 138, "Ï": 139, "Ð": 140, "Ñ": 141, "Ò": 142, "Ó": 143, "Ô": 144, "Õ": 145, "Ö": 146, "×": 147, "Ø": 148, "Ù": 149, "Ú": 150, "Û": 151, "Ü": 152, "Ý": 153, "Þ": 154, "ß": 155, "à": 156, "á": 157, "â": 158, "ã": 159, "ä": 160, "å": 161, "æ": 162, "ç": 163, "è": 164, "é": 165, "ê": 166, "ë": 167, "ì": 168, "í": 169, "î": 170, "ï": 171, "ð": 172, "ñ": 173, "ò": 174, "ó": 175, "ô": 176, "õ": 177, "ö": 178, "÷": 179, "ø": 180, "ù": 181, "ú": 182, "û": 183, "ü": 184, "ý": 185, "þ": 186, "ÿ": 187, "Ā": 188, "ā": 189, "Ă": 190, "ă": 191, "Ą": 192, "ą": 193, "Ć": 194, "ć": 195, "Ĉ": 196, "ĉ": 197, "Ċ": 198, "ċ": 199, "Č": 200, "č": 201, "Ď": 202, "ď": 203, "Đ": 204, "đ": 205, "Ē": 206, "ē": 207, "Ĕ": 208, "ĕ": 209, "Ė": 210, "ė": 211, "Ę": 212, "ę": 213, "Ě": 214, "ě": 215, "Ĝ": 216, "ĝ": 217, "Ğ": 218, "ğ": 219, "Ġ": 220, "ġ": 221, "Ģ": 222, "ģ": 223, "Ĥ": 224, "ĥ": 225, "Ħ": 226, "ħ": 227, "Ĩ": 228, "ĩ": 229, "Ī": 230, "ī": 231, "Ĭ": 232, "ĭ": 233, "Į": 234, "į": 235, "İ": 236, "ı": 237, "IJ": 238, "ij": 239, "Ĵ": 240, "ĵ": 241, "Ķ": 242, "ķ": 243, "ĸ": 244, "Ĺ": 245, "ĺ": 246, "Ļ": 247, "ļ": 248, "Ľ": 249, "ľ": 250, "Ŀ": 251, "ŀ": 252, "Ł": 253, "ł": 254, "Ń": 255, "Ġt": 256, "Ġa": 257, "Ġth": 258, "in": 259, "er": 260, "Ġw": 261, "Ġs": 262, "ou": 263, "Ġthe": 264, "re": 265, "on": 266, "at": 267, "en": 268, "Ġc": 269, "it": 270, "is": 271, "Ġb": 272, "nd": 273, "Ġd": 274, "Ġm": 275, "Ġh": 276, "Ġo": 277, "ing": 278, "es": 279, "Ġp": 280, "Ġto": 281, "an": 282, "Ġf": 283, "or": 284, "ll": 285, "ĠI": 286, "Ġl": 287, "Ġy": 288, "ar": 289, "Ġg": 290, "Ġyou": 291, "ed": 292, "Ġand": 293, "Ġin": 294, "Ġof": 295, "as": 296, "Ġn": 297, "om": 298, "ic": 299, "Ġthat": 300, "us": 301, "et": 302, "ve": 303, "al": 304, "ow": 305, "le": 306, "Ġis": 307, "Ġe": 308, "Ġit": 309, "ot": 310, "'s": 311, "Ġbe": 312, "ion": 313, "ĠT": 314, "Ġwh": 315, "ĠA": 316, "ent": 317, "ĠS": 318, "Ġre": 319, "ay": 320, "Ġwe": 321, "Ġon": 322, "ere": 323, "Ġha": 324, "ut": 325, "ac": 326, "id": 327, "ig": 328, "os": 329, "ke": 330, "ver": 331, "im": 332, "ĠÐ": 333, "ĠTh": 334, "am": 335, "all": 336, "Ġfor": 337, "el": 338, "ch": 339, "ro": 340, "Ġthis": 341, "Ġst": 342, "ĠW": 343, "Ġu": 344, "ad": 345, "out": 346, "ir": 347, "ld": 348, "ct": 349, "Ġk": 350, "if": 351, "Ġgo": 352, "..": 353, "о": 354, "ith": 355, "ly": 356, "ht": 357, "qu": 358, "Ġ-": 359, "Ġdo": 360, "Ġj": 361, "Ġhave": 362, "ĠB": 363, "Ġan": 364, "Ġwith": 365, "Ġare": 366, "Ġr": 367, "Ġde": 368, "Ġse": 369, "Ġso": 370, "Ġv": 371, "st": 372, "ill": 373, "ur": 374, "Ġli": 375, "ĠM": 376, "est": 377, "od": 378, "ally": 379, "'t": 380, "ust": 381, "Ġas": 382, "ĠC": 383, "ce": 384, "Ġme": 385, "а": 386, "е": 387, "il": 388, "ĠH": 389, "Ġwas": 390, "ter": 391, "th": 392, "Ġcan": 393, "ant": 394, "Ġcom": 395, "our": 396, "ight": 397, "ĠY": 398, "ation": 399, "ĠAnd": 400, "ol": 401, "Ġsh": 402, "ÑĤ": 403, "op": 404, "se": 405, "Ġnot": 406, "ĠSo": 407, "Ġne": 408, "un": 409, "Ġab": 410, "Ġlike": 411, "Ġat": 412, "ĠD": 413, "ie": 414, "Ġhe": 415, "Ġcon": 416, "Ġch": 417, "ore": 418, "Ġal": 419, "Ġor": 420, "Ġqu": 421, "ĠO": 422, "ome": 423, "ra": 424, "ul": 425, "ĠN": 426, "pp": 427, "Ġyour": 428, "ould": 429, "ĠP": 430, "Ġfr": 431, "ge": 432, "ers": 433, "'re": 434, "и": 435, "Ġthey": 436, "Ġwhat": 437, "use": 438, "Ġall": 439, "ĠThe": 440, "ĠL": 441, "ess": 442, "em": 443, "Ġkn": 444, "Ġjust": 445, "art": 446, "Ġpro": 447, "very": 448, "um": 449, "Ġlo": 450, "Ġì": 451, "Ġmy": 452, "ok": 453, "Ġex": 454, "ab": 455, "Ġthere": 456, "Ġbut": 457, "Ġknow": 458, "Ġsu": 459, "ĠG": 460, "Ñģ": 461, "ĠE": 462, "Ġma": 463, "оÐ": 464, "Ġen": 465, "Ġabout": 466, "ĠIt": 467, "ist": 468, "Ġwor": 469, "ri": 470, "ind": 471, "Ġone": 472, "ate": 473, "and": 474, "ink": 475, "Ġle": 476, "ort": 477, "'m": 478, "ĠF": 479, "ich": 480, "ÑĢ": 481, "ide": 482, "Ġget": 483, "Ġout": 484, "...": 485, "Ġwill": 486, "ãģ": 487, "ive": 488, "н": 489, "Ġfrom": 490, "ain": 491, "ĠWe": 492, "Ġup": 493, "pe": 494, "res": 495, "ca": 496, "ĠR": 497, "Ġif": 498, "Ġpl": 499, "Ġdon": 500, "ack": 501, "Ġ1": 502, "Ġ\"": 503, "Ġtr": 504, "Ġus": 505, "ĠWh": 506, "ity": 507, "ĠJ": 508, "ĠYou": 509, "Ġhere": 510, "her": 511, "Ġsome": 512, "oug": 513, "ak": 514, "ard": 515, "Ġgoing": 516, "Ġun": 517, "ment": 518, "Ġthink": 519, "Ġpe": 520, "end": 521, "Ġ(": 522, "cause": 523, "Ġtim": 524, "ast": 525, "é": 526, "Ġour": 527, "Ġwant": 528, "ame": 529, "ies": 530, "Ġë": 531, "ud": 532, "ine": 533, "Ġreally": 534, "Ġte": 535, "Ġsee": 536, "ci": 537, "Ġby": 538, "so": 539, "ure": 540, "ose": 541, "Ġ[": 542, "are": 543, "Ġmore": 544, "ah": 545, "one": 546, "ck": 547, "ople": 548, "аÐ": 549, "Ġthen": 550, "Ġthing": 551, "Ġthem": 552, "ven": 553, "ound": 554, "ost": 555, "ong": 556, "ect": 557, "Ġright": 558, "ag": 559, "Ġint": 560, "Ġpeople": 561, "Ġwhen": 562, "ous": 563, "pl": 564, "Ġtime": 565, "Ġim": 566, "Ġwho": 567, "Ġ2": 568, "ap": 569, "Ġbecause": 570, "hing": 571, "Ġno": 572, "ice": 573, "Ġlook": 574, "Ġhas": 575, "Ġwould": 576, "Ġhow": 577, "act": 578, "Ġfe": 579, "nt": 580, "ough": 581, "Ġpr": 582, "ĠBut": 583, "Ġsay": 584, "Ñĥ": 585, "Ġnow": 586, "Ġman": 587, "Ġvery": 588, "Ġwork": 589, "iz": 590, "ĠK": 591, "iv": 592, "itt": 593, "Ġar": 594, "ep": 595, "Ġcl": 596, "Ġwhich": 597, "Ġco": 598, "ans": 599, "'ve": 600, "Ġsa": 601, "ff": 602, "'ll": 603, "Ġany": 604, "Ġact": 605, "Ġye": 606, "ber": 607, "ach": 608, "age": 609, "per": 610, "Ġalso": 611, "fer": 612, "Ġthese": 613, "Ġad": 614, "еÐ": 615, "ther": 616, "ace": 617, "ick": 618, "ake": 619, "reat": 620, "ire": 621, "ue": 622, "Ġag": 623, "ĠU": 624, "uch": 625, "ions": 626, "ry": 627, "00": 628, "na": 629, "Ġdid": 630, "Ġque": 631, "Ġhad": 632, "Ġevery": 633, "ĠHe": 634, "Ġla": 635, "Ġway": 636, "Ġsp": 637, "ble": 638, "ĠThis": 639, "ass": 640, "Ġtheir": 641, "ite": 642, "Ġneed": 643, "Ġpart": 644, "Ġwere": 645, "Ġback": 646, "ip": 647, "own": 648, "omet": 649, "be": 650, "ase": 651, "Ġmake": 652, "irst": 653, "ia": 654, "ence": 655, "ang": 656, "ank": 657, "Ġgot": 658, "Ġpre": 659, "Ġcont": 660, "Ġother": 661, "pt": 662, "ĠThat": 663, "og": 664, "Ġgood": 665, "Ġinto": 666, "alk": 667, "Ġbeen": 668, "Ġam": 669, "Ġover": 670, "ually": 671, "Ġâ": 672, "ìĿ": 673, "Ġund": 674, "he": 675, "way": 676, "Ġgr": 677, "ÑĮ": 678, "Ġdif": 679, "Ġper": 680, "Ñı": 681, "ĠIn": 682, "Ġtw": 683, "ond": 684, "ars": 685, "int": 686, "orm": 687, "Ġlot": 688, "Ġwhere": 689, "ĠÃ": 690, "ĠV": 691, "Ġsomet": 692, "л": 693, "ens": 694, "Ġgu": 695, "Ġac": 696, "ug": 697, "Ñĭ": 698, "ı": 699, "Ġfirst": 700, "ree": 701, "Ġhis": 702, "ittle": 703, "Ġimp": 704, "Ġmo": 705, "av": 706, "Ġlittle": 707, "ĠWhat": 708, "Ġmuch": 709, "Ġz": 710, "Ġê": 711, "able": 712, "Ġп": 713, "Ġpo": 714, "Ġcomp": 715, "ne": 716, "Ġdis": 717, "Ġlet": 718, "ance": 719, "Ġher": 720, "Ġthings": 721, "Ġstart": 722, "ult": 723, "Ġapp": 724, "Ġres": 725, "Ġfo": 726, "Ġcould": 727, "Ġinter": 728, "Ġthose": 729, "Ġdes": 730, "Ġwell": 731, "Ġtwo": 732, "Ġkind": 733, "xt": 734, "ress": 735, "ely": 736, "ä": 737, "Ġbr": 738, "Ġthr": 739, "Ġв": 740, "Ġi": 741, "ish": 742, "Ġdiffer": 743, "Ġro": 744, "ĠSt": 745, "Ġsomething": 746, "Ġtake": 747, "Ġbo": 748, "ys": 749, "Ġshe": 750, "Ġtalk": 751, "lo": 752, "Ñĩ": 753, "Ġeven": 754, "к": 755, "ãĢ": 756, "Ġн": 757, "Ġbu": 758, "ĠIf": 759, "Ġdown": 760, "ĠCh": 761, "ade": 762, "ations": 763, "Ġuse": 764, "ord": 765, "Ġoff": 766, "Ġactually": 767, "Ġspe": 768, "du": 769, "ated": 770, "ater": 771, "oss": 772, "ning": 773, "ü": 774, "Ġdoes": 775, "ĠÑģ": 776, "Ġnew": 777, "Ġbet": 778, "vel": 779, "cess": 780, "ple": 781, "Ġhapp": 782, "ting": 783, "onna": 784, "Ġes": 785, "Ġday": 786, "Ġonly": 787, "ign": 788, "kay": 789, "sel": 790, "ents": 791, "ount": 792, "ild": 793, "ile": 794, "Ġsc": 795, "Ġhim": 796, "Ġagain": 797, "ving": 798, "Ġgonna": 799, "Ġcomm": 800, "Ġhel": 801, "other": 802, "Ġke": 803, "ical": 804, "Ġ3": 805, "Ġel": 806, "Ġthrough": 807, "Ġcome": 808, "ark": 809, "day": 810, "ier": 811, "ó": 812, "Ġthan": 813, "ĠThey": 814, "Ġmay": 815, "Ġser": 816, "íķ": 817, "Ġcall": 818, "Ġdifferent": 819, "Ġshould": 820, "ĠThere": 821, "ary": 822, "ĠNow": 823, "ãĤ": 824, "thing": 825, "we": 826, "ory": 827, "fter": 828, "Ġput": 829, "ors": 830, "ial": 831, "ëĭ": 832, "Ġunder": 833, "Ġinc": 834, "ĠYe": 835, "ub": 836, "form": 837, "Ġvide": 838, "à¸": 839, "vers": 840, "Ġfeel": 841, "á": 842, "ody": 843, "ft": 844, "fore": 845, "Ġem": 846, "get": 847, "Ġsaid": 848, "ition": 849, "Ġrec": 850, "ious": 851, "atch": 852, "Ġtry": 853, "Ġhelp": 854, "Ġshow": 855, "д": 856, "Ġbit": 857, "ull": 858, "в": 859, "ÑĤо": 860, "gr": 861, "Ġplay": 862, "ife": 863, "ail": 864, "ĠYeah": 865, "Ġquest": 866, "Ġmany": 867, "Ġpers": 868, "Ġgreat": 869, "ÃŃ": 870, "Ġest": 871, "ng": 872, "ĠâĻ": 873, "ty": 874, "la": 875, "ĠOh": 876, "Ġ×": 877, "à®": 878, "ĠBe": 879, "ady": 880, "Ġmost": 881, "ction": 882, "ĠNo": 883, "Ġdoing": 884, "Ġbeing": 885, "Ġtoo": 886, "ces": 887, "Ġbl": 888, ".\"": 889, "Ġrem": 890, "iss": 891, "ons": 892, ">>": 893, "ru": 894, "wn": 895, "ont": 896, "ib": 897, "ell": 898, "Ġsm": 899, "oth": 900, "ual": 901, "Ġ>>": 902, "Ġph": 903, "les": 904, "oc": 905, "ful": 906, "Ġsec": 907, "ise": 908, "Ġadd": 909, "igh": 910, "ert": 911, "Ġsame": 912, "âĢ": 913, "Ġmean": 914, "Ġfind": 915, "ek": 916, "Ġend": 917, "--": 918, "м": 919, "Ġstill": 920, "az": 921, "Ġ'": 922, "Ġmin": 923, "Ġyears": 924, "urn": 925, "Ġaround": 926, "self": 927, "Ġwr": 928, "bs": 929, "ought": 930, "ĠâĻª": 931, "Ġfl": 932, "ange": 933, "Ġafter": 934, "Ġpoint": 935, "mer": 936, "ved": 937, "Ġlong": 938, "oy": 939, "ä¸": 940, "Ġcr": 941, "ways": 942, "Ġsy": 943, "Ġtra": 944, "Ġ20": 945, "ave": 946, "Ġche": 947, "Ġent": 948, "Ġbefore": 949, "ph": 950, "Ġatt": 951, "ian": 952, "ily": 953, "Ġperson": 954, "Ġbig": 955, "Ġsch": 956, "Ġreal": 957, "Ġnext": 958, "Ġlove": 959, "Ġvideo": 960, "ĠLet": 961, "Ġfin": 962, "Ġmak": 963, "ible": 964, "Ġtoday": 965, "erm": 966, "ĠAl": 967, "ower": 968, "ann": 969, "ix": 970, "Ġpar": 971, "Ġstud": 972, "ö": 973, "Ġimport": 974, "te": 975, "Ġgive": 976, "ves": 977, "Ġdie": 978, "Ġdec": 979, "Ġtell": 980, "Ġк": 981, "ÑģÑĤ": 982, "Ġwhy": 983, "ically": 984, "ict": 985, "red": 986, "Ġbas": 987, "Ġsure": 988, "Ġbel": 989, "ating": 990, "Ġtak": 991, "Ġset": 992, "Ġlife": 993, "Ġdidn": 994, "ا": 995, "ob": 996, "und": 997, "ath": 998, "Ġop": 999, "Ġо": 1000, "ait": 1001, "Ġworld": 1002, "Ġsupp": 1003, "io": 1004, "Ġcour": 1005, "Ġи": 1006, "ward": 1007, "ен": 1008, "Ġalways": 1009, "up": 1010, "Ġhand": 1011, "ĠHow": 1012, "cial": 1013, "Ġcons": 1014, "ĠÑ": 1015, "Ġind": 1016, "Ġ4": 1017, "ĠAs": 1018, "Ġfun": 1019, "ject": 1020, "Ġimportant": 1021, "Ġsur": 1022, "ew": 1023, "ates": 1024, "Ġ5": 1025, "Ġdi": 1026, "Ġmade": 1027, "Ġins": 1028, "Ġask": 1029, "Ġet": 1030, "Ġnum": 1031, "Ġcar": 1032, "ĠOkay": 1033, "Ġsim": 1034, "ik": 1035, "Ġlast": 1036, "ĠGo": 1037, "Ġmus": 1038, "Ġrel": 1039, "ular": 1040, "´ì": 1041, "ĠWell": 1042, "pect": 1043, "ĠThank": 1044, "Ġthree": 1045, "ã": 1046, "ãĥ": 1047, "Ġinv": 1048, "Ġgen": 1049, "lic": 1050, "Ġhappen": 1051, "ëĬ": 1052, "ien": 1053, "ever": 1054, "ов": 1055, "Ġstr": 1056, "ĠAll": 1057, "Ġinst": 1058, "ĠâĢ": 1059, "Ġdef": 1060, "Ġsl": 1061, "Ġmight": 1062, "ung": 1063, "Ġyear": 1064, "Ġown": 1065, "Ġkeep": 1066, "body": 1067, "der": 1068, "ĠÑĤ": 1069, "Ġд": 1070, "Ġanother": 1071, "Ġmod": 1072, "Ġev": 1073, "Ġguys": 1074, "Ġable": 1075, "ão": 1076, "que": 1077, "ident": 1078, "ĠYes": 1079, "Ġits": 1080, "Ġplace": 1081, "Ġprodu": 1082, "arn": 1083, "Ġм": 1084, "Ġrep": 1085, "Ġexper": 1086, "Ġfam": 1087, "ities": 1088, "ific": 1089, "Ġhigh": 1090, "ied": 1091, "ool": 1092, "iew": 1093, "еÑĤ": 1094, "ren": 1095, "Ġdone": 1096, "Ġ...": 1097, "ëĬĶ": 1098, "stem": 1099, "ĠSe": 1100, "Ġbetter": 1101, "come": 1102, "Ġdel": 1103, "Ġty": 1104, "Ġum": 1105, "Ġho": 1106, "ĠAn": 1107, "Ġmon": 1108, "ings": 1109, "Ġsk": 1110, "Ġob": 1111, "com": 1112, "blem": 1113, "ope": 1114, "stand": 1115, "'d": 1116, "ments": 1117, "Ġele": 1118, "ĠIs": 1119, "Ġda": 1120, "Ġreg": 1121, "lease": 1122, "ike": 1123, "als": 1124, "ize": 1125, "ê°": 1126, "Ġcare": 1127, "Ġnever": 1128, "ìĿ´": 1129, "ese": 1130, "Ġmet": 1131, "olog": 1132, "ĠWhen": 1133, "uck": 1134, "еÑĢ": 1135, "Ġé": 1136, "Ġdat": 1137, "ç": 1138, "Ġexam": 1139, "ility": 1140, "Ġdet": 1141, "cri": 1142, "Ġused": 1143, "ĠDo": 1144, "Ġtrans": 1145, "eg": 1146, "ten": 1147, "Ñİ": 1148, "cus": 1149, "Ġsecond": 1150, "Ġbest": 1151, "Ġhard": 1152, "Ġide": 1153, "Ġproblem": 1154, "ê³": 1155, "ĠUn": 1156, "Ñħ": 1157, "ĠÎ": 1158, "Ġwatch": 1159, "ĠSh": 1160, "atter": 1161, "Ġpret": 1162, "Ġder": 1163, "Ġcourse": 1164, "ÅŁ": 1165, "ative": 1166, "ics": 1167, "Ġquestion": 1168, "ute": 1169, "ìĹ": 1170, "ĠFor": 1171, "ather": 1172, "Ġcol": 1173, "iend": 1174, "Ġí": 1175, "ĠZ": 1176, "Ġdoesn": 1177, "arch": 1178, "Ġinterest": 1179, "Ġpol": 1180, "Ġcor": 1181, "ience": 1182, "Ġpres": 1183, "Ġeach": 1184, "Ġsystem": 1185, "Ġfact": 1186, "iel": 1187, "ably": 1188, "Ġer": 1189, "Ġrun": 1190, "ĠìĿ": 1191, "Ġtop": 1192, "ner": 1193, "Ġthought": 1194, "Ġeas": 1195, "ient": 1196, "Ġcre": 1197, "ÑĪ": 1198, "Ġcommun": 1199, "ye": 1200, "ready": 1201, "llow": 1202, "Ġeverything": 1203, "omm": 1204, "Ġmed": 1205, "ļĶ": 1206, "Ġcount": 1207, "its": 1208, "Ġcompl": 1209, "hip": 1210, "ÙĦ": 1211, "ook": 1212, "Ġtoget": 1213, "Ġtogether": 1214, "amp": 1215, "Ġgame": 1216, "Ġalready": 1217, "ал": 1218, "Ġcalled": 1219, "ale": 1220, "ÅĤ": 1221, "ĠMy": 1222, "Ġunderstand": 1223, "Ġdr": 1224, "Ġmom": 1225, "ited": 1226, "ол": 1227, "Ġusing": 1228, "zy": 1229, "Ġnumber": 1230, "ãĢģ": 1231, "ced": 1232, "Ġcle": 1233, "но": 1234, "ëĭ¤": 1235, "ince": 1236, "Ġlooking": 1237, "Ġpretty": 1238, "Ġprob": 1239, "ĠShe": 1240, "Ġve": 1241, "Ġgetting": 1242, "Ġweek": 1243, "Ġeff": 1244, "uff": 1245, "air": 1246, "ues": 1247, "ern": 1248, "ĠQ": 1249, "oup": 1250, "ention": 1251, "Ġside": 1252, "ом": 1253, "Ġform": 1254, "Ġbus": 1255, "Ġass": 1256, "Ġed": 1257, "ason": 1258, "ween": 1259, "âĢ¦": 1260, "Ġturn": 1261, "Ġcur": 1262, "Ġcoll": 1263, "Ġdire": 1264, "ĠGod": 1265, "Ġ10": 1266, "Ġequ": 1267, "Ġб": 1268, "Ġopen": 1269, "Ġsuch": 1270, "ird": 1271, "ак": 1272, "Ġear": 1273, "ÄĻ": 1274, "gan": 1275, "Ġpartic": 1276, "Ġfriend": 1277, "Ġexp": 1278, "Ġext": 1279, "Ġhome": 1280, "Ġwater": 1281, "ĠOn": 1282, "ÑĤÑĮ": 1283, "ork": 1284, "ĠпÑĢ": 1285, "Ġmove": 1286, "ness": 1287, "ense": 1288, "ho": 1289, "Ġchar": 1290, "co": 1291, "ins": 1292, "Ġboth": 1293, "Ġ19": 1294, "Ġgra": 1295, "Ġbetween": 1296, "á»": 1297, "Ġìķ": 1298, "ash": 1299, "ĠRe": 1300, "ai": 1301, "alth": 1302, "ures": 1303, "ember": 1304, "Ġav": 1305, "Ġver": 1306, "ê": 1307, "oney": 1308, "Ġthank": 1309, "Ġmaybe": 1310, "uc": 1311, "ime": 1312, "ê³ł": 1313, "Ġaway": 1314, "Ġname": 1315, "ouse": 1316, "Ġacc": 1317, "Ġmusic": 1318, "Ġchange": 1319, "Ġpass": 1320, "ger": 1321, "Ġbuild": 1322, "Ġval": 1323, "iness": 1324, "any": 1325, "Ġfew": 1326, "´ë": 1327, "ta": 1328, "Ġlist": 1329, "Ã¥": 1330, "Ġold": 1331, "Ġìŀ": 1332, "Ġsort": 1333, "Ġmem": 1334, "Ġca": 1335, "cept": 1336, "Ġgener": 1337, "Ġyeah": 1338, "Ġwhile": 1339, "Ġanything": 1340, "ric": 1341, "gram": 1342, "Ġein": 1343, "cy": 1344, "uring": 1345, "ĠDe": 1346, "Ġpower": 1347, "Ġcoming": 1348, "Ġword": 1349, "Ġ--": 1350, "Ġbelie": 1351, "Ġfound": 1352, "to": 1353, "п": 1354, "Ġmeans": 1355, "Ġinform": 1356, "ĠØ": 1357, "ĠÑĩ": 1358, "Ġsmall": 1359, "000": 1360, "Ġcame": 1361, "Ġíķ": 1362, "wh": 1363, "Ġworking": 1364, "Ġexample": 1365, "Ġpos": 1366, "Ġdep": 1367, "ê²": 1368, "äº": 1369, "ote": 1370, "Ġdem": 1371, "ì§": 1372, "ts": 1373, "Ġvar": 1374, "aut": 1375, "Ġtri": 1376, "chn": 1377, "Ġhead": 1378, "Ġwhole": 1379, "×Ļ": 1380, "ze": 1381, "Ġtrying": 1382, "Ġtem": 1383, "Ġcou": 1384, "ets": 1385, "Ġ6": 1386, "Ġfil": 1387, "velop": 1388, "Ġcase": 1389, "à¯": 1390, "Ġprobably": 1391, "Ġokay": 1392, "Ġplan": 1393, "Ġsit": 1394, "Ġschool": 1395, "ĠThen": 1396, "¸ë": 1397, "me": 1398, "Ġprocess": 1399, "Ġfar": 1400, "Ġread": 1401, "Ġposs": 1402, "Ġbre": 1403, "Ġsol": 1404, "icht": 1405, "Ġsupport": 1406, "ĠTo": 1407, "ertain": 1408, "Ġstarted": 1409, "Ġcap": 1410, "Ġleft": 1411, "Ġdata": 1412, "Ġtimes": 1413, "ел": 1414, "Ġwanted": 1415, "ан": 1416, "Ġtalking": 1417, "Ġist": 1418, "Ġhaving": 1419, "ump": 1420, "Ġcontin": 1421, "Ġsub": 1422, "Ġз": 1423, "pr": 1424, "ëĭĪ": 1425, "ina": 1426, "ż": 1427, "Ġcreat": 1428, "ode": 1429, "×ķ": 1430, "æĺ": 1431, "!!": 1432, "Ġterm": 1433, "ism": 1434, "од": 1435, "ĠBecause": 1436, "Ġwent": 1437, "ider": 1438, "Ġprov": 1439, "Ġchild": 1440, "Ġden": 1441, "Ġlight": 1442, "br": 1443, "³Ð¾": 1444, "oh": 1445, "Ġbook": 1446, "ĠÙ": 1447, "ution": 1448, "ĠJust": 1449, "ene": 1450, "Ġfour": 1451, "Ġvis": 1452, "ê°Ģ": 1453, "Ġhope": 1454, "Ġmaking": 1455, "ĠLe": 1456, "ìķ": 1457, "Ġopp": 1458, "au": 1459, "Ġmoney": 1460, "Ġprogram": 1461, "è": 1462, "Ġstand": 1463, "IN": 1464, "Ġsign": 1465, "Ġlearn": 1466, "Ãł": 1467, "ĠDon": 1468, "Ġteam": 1469, "Ġна": 1470, "lud": 1471, "Ġrest": 1472, "ices": 1473, "æľ": 1474, "ĠÑĢ": 1475, "Ġaut": 1476, "Ġlead": 1477, "ational": 1478, "de": 1479, "gy": 1480, "Ġnice": 1481, "Ġdas": 1482, "Ġdist": 1483, "Ġhum": 1484, "ĠOne": 1485, "æĪ": 1486, "Ġcomes": 1487, "Ġjo": 1488, "Ġcent": 1489, "Ġexpl": 1490, "Ġmark": 1491, "reen": 1492, "led": 1493, "gin": 1494, "ìļĶ": 1495, "Ġlevel": 1496, "Ġconf": 1497, "ush": 1498, "Ġdevelop": 1499, "Ġtest": 1500, "eng": 1501, "vious": 1502, "ature": 1503, "ем": 1504, "ret": 1505, "Ġje": 1506, "Ġstuff": 1507, "Ġclass": 1508, "ows": 1509, "Ġê·": 1510, "Ġsi": 1511, "Ġles": 1512, "rop": 1513, "çļ": 1514, "Ġpor": 1515, "Ġwar": 1516, "ìĹIJ": 1517, "Ġeveryone": 1518, "Ġge": 1519, "Ġcheck": 1520, "ott": 1521, "Ġsing": 1522, "Ġart": 1523, "Ġfollow": 1524, "Ġ201": 1525, "ĠFr": 1526, "ais": 1527, "ìĸ": 1528, "α": 1529, "å°": 1530, "ĠÃł": 1531, "imes": 1532, "Ġret": 1533, "Ġchang": 1534, "Ġpub": 1535, "Ġinf": 1536, "Ġtechn": 1537, "ada": 1538, "ives": 1539, "Ġbeh": 1540, "æĺ¯": 1541, "Ġlooks": 1542, "ãĢĤ": 1543, "з": 1544, "ĠWhy": 1545, "çļĦ": 1546, "Ġenough": 1547, "Ġbra": 1548, "itch": 1549, "ä»": 1550, "Ġadv": 1551, "б": 1552, "Ġwithout": 1553, "wer": 1554, "meric": 1555, "den": 1556, "Ġcomplet": 1557, "Ġidea": 1558, "ters": 1559, "ock": 1560, "Ġdefin": 1561, "Ġever": 1562, "Ġgl": 1563, "Ġonce": 1564, "Ġbring": 1565, "Ġsaying": 1566, "Ġans": 1567, "Ġhear": 1568, "nect": 1569, "Ġless": 1570, "go": 1571, "ream": 1572, "ado": 1573, "ìŀ": 1574, "Ġmind": 1575, "ente": 1576, "Ġfull": 1577, "Ġbad": 1578, "Ġwom": 1579, "Ġsomeone": 1580, "Ġdu": 1581, "Ġwon": 1582, "Ġcontro": 1583, "ortun": 1584, "Ġhealth": 1585, "Ġcho": 1586, "ĠAr": 1587, "Ġconc": 1588, "Ġinformation": 1589, "Ġstop": 1590, "att": 1591, "ately": 1592, "ä½": 1593, "Ġgroup": 1594, "ĠÑĥ": 1595, "Ġquite": 1596, "Ġresp": 1597, "ER": 1598, "ught": 1599, "ê¸": 1600, "man": 1601, "ized": 1602, "ĠBr": 1603, "Ġremember": 1604, "Ġfamily": 1605, "Ġbusiness": 1606, "aw": 1607, "Ġspec": 1608, "Ġau": 1609, "ĠOr": 1610, "Äħ": 1611, "Ġseen": 1612, "Ġlar": 1613, "Ġ7": 1614, "gg": 1615, "bers": 1616, "Ġdra": 1617, "Ġmonth": 1618, "Ġsays": 1619, "Ġiss": 1620, "Ġlive": 1621, "Ġline": 1622, "Ġmoment": 1623, "Ġexc": 1624, "els": 1625, "Ġsound": 1626, "Ġcool": 1627, "Ġloc": 1628, "Ġcertain": 1629, "Ġdri": 1630, "оÑĤ": 1631, "ames": 1632, "Ġmust": 1633, "ny": 1634, "иÑĤ": 1635, "Ġkid": 1636, "Ġinclud": 1637, "ìĿĦ": 1638, "ator": 1639, "ÄŁ": 1640, "ha": 1641, "ared": 1642, "Ġseem": 1643, "й": 1644, "ìĦ": 1645, "Ġelse": 1646, "Ġìł": 1647, "irl": 1648, "Ġ8": 1649, "Ġvo": 1650, "Ġquestions": 1651, "ines": 1652, "ee": 1653, "æĪij": 1654, "ür": 1655, "ĠAmeric": 1656, "Ġstory": 1657, "Ġserv": 1658, "vern": 1659, "ages": 1660, "land": 1661, "ĠâĢĵ": 1662, "era": 1663, "ĠCan": 1664, "Ġpop": 1665, "ether": 1666, "Ġna": 1667, "Ġorder": 1668, "Ġmakes": 1669, "Ġsince": 1670, "con": 1671, "ctor": 1672, "Ġthough": 1673, "Ġproduct": 1674, "ли": 1675, "Ġleg": 1676, "Ġmeet": 1677, "alf": 1678, "ÑģÑı": 1679, "unch": 1680, "iter": 1681, "ove": 1682, "×ķ×": 1683, "iet": 1684, "ам": 1685, "ital": 1686, "Ġsuper": 1687, "ling": 1688, "Ġpay": 1689, "Ġpara": 1690, "Ġjob": 1691, "ĠHere": 1692, "Ġsw": 1693, "ks": 1694, "ption": 1695, "ma": 1696, "Ġbelieve": 1697, "¬ë": 1698, "Ġwait": 1699, "ой": 1700, "Ġunt": 1701, "Ġquick": 1702, "hr": 1703, "ĠÑį": 1704, "ĠPro": 1705, "Ġmen": 1706, "à¹": 1707, "Ġdays": 1708, "Ġgoes": 1709, "Ġspeak": 1710, "ĠAt": 1711, "ement": 1712, "Ġmiss": 1713, "Ġaw": 1714, "Ġdesign": 1715, "Ġproject": 1716, "оÑĢ": 1717, "ij": 1718, "ants": 1719, "ats": 1720, "ĠChr": 1721, "Ġ9": 1722, "Ġcut": 1723, "Ġrequ": 1724, "Ġне": 1725, "ĠNot": 1726, "aster": 1727, "Ġmill": 1728, "Ġparticular": 1729, "Ġpie": 1730, "Ġstudents": 1731, "Ġfive": 1732, "oun": 1733, "ĠNe": 1734, "Ġgi": 1735, "Ġpas": 1736, "Ġfree": 1737, "ĠSp": 1738, "lich": 1739, "Ġprof": 1740, "Ġeng": 1741, "Ġprot": 1742, "ĠLike": 1743, "osed": 1744, "Ġconnect": 1745, "app": 1746, "Ġë§": 1747, "iting": 1748, "Ġblo": 1749, "Ġlos": 1750, "ists": 1751, "Ġexperience": 1752, "rent": 1753, "Ġstay": 1754, "Ġfood": 1755, "ton": 1756, "ruct": 1757, "Ġhist": 1758, "view": 1759, "ining": 1760, "most": 1761, "ivers": 1762, "bo": 1763, "ãģĦ": 1764, "ĠTr": 1765, "gen": 1766, "Ġplease": 1767, "Ġcommunity": 1768, "Ġce": 1769, "AN": 1770, "no": 1771, "Ġbody": 1772, "Ġhour": 1773, "Ġvers": 1774, "áº": 1775, "cer": 1776, "Ġê°": 1777, "Ġreason": 1778, "ĠRight": 1779, "Ġlater": 1780, "ÏĦ": 1781, "Ġhouse": 1782, "ĠX": 1783, "он": 1784, "Ġstate": 1785, "fic": 1786, "å¤": 1787, "ÅĽ": 1788, "ield": 1789, "Ġpri": 1790, "Ġpast": 1791, "Ġwalk": 1792, "ology": 1793, "ering": 1794, "anna": 1795, "Ġter": 1796, "Ġhold": 1797, "Ġorgan": 1798, "ben": 1799, "ο": 1800, "ón": 1801, "Ġeffect": 1802, "Ġyourself": 1803, "Ġplus": 1804, "aj": 1805, "ando": 1806, "ural": 1807, "Ġroom": 1808, "lect": 1809, "ê²Į": 1810, "?\"": 1811, "side": 1812, "Ġbecome": 1813, "ÑĨ": 1814, "ĠÂ": 1815, "ood": 1816, "Ġconst": 1817, "Ġnight": 1818, "utes": 1819, "ж": 1820, "Ġbreak": 1821, "Ġpain": 1822, "Ġstep": 1823, "ired": 1824, "Ġnothing": 1825, "Ġuntil": 1826, "Ñĸ": 1827, "ав": 1828, "ÙĬ": 1829, "Ġduring": 1830, "ì§Ģ": 1831, "less": 1832, "oll": 1833, "нÑĭ": 1834, "ι": 1835, "fect": 1836, "iver": 1837, "ıĦ": 1838, "ither": 1839, "ying": 1840, "Ġbegin": 1841, "×Ļ×": 1842, "ivid": 1843, "Ġç": 1844, "Ġsal": 1845, "Ġta": 1846, "Ġpot": 1847, "Ġ$": 1848, "Ġmar": 1849, "Ġclear": 1850, "Ġface": 1851, "Ġgrow": 1852, "Ġ*": 1853, "Ġinside": 1854, "Ġfriends": 1855, "Ġleave": 1856, "enn": 1857, "Ġeasy": 1858, "Ġarea": 1859, "ality": 1860, "oud": 1861, "Ġeat": 1862, "ÙĨ": 1863, "Ġpur": 1864, "orn": 1865, "Ġsaw": 1866, "Ġanswer": 1867, "Ġfront": 1868, "Ġbeaut": 1869, "¼ë": 1870, "Ġmatter": 1871, "Ġson": 1872, "ĠNew": 1873, "Ġresult": 1874, "ides": 1875, "che": 1876, "Ġfut": 1877, "ps": 1878, "Ġfocus": 1879, "Ġinteresting": 1880, "å¥": 1881, "Ġap": 1882, "\".": 1883, "Ġcreate": 1884, "оÑģ": 1885, "Ġpress": 1886, "ross": 1887, "Ġpick": 1888, "line": 1889, "Ġtook": 1890, "ĠMay": 1891, "row": 1892, "Ġich": 1893, "ĺë": 1894, "Ġref": 1895, "Ġmor": 1896, "ract": 1897, "arent": 1898, "AR": 1899, "Ġexact": 1900, "Ġspace": 1901, "work": 1902, "ни": 1903, "Ġbir": 1904, "Ġdev": 1905, "г": 1906, "Ġtold": 1907, "Ġpublic": 1908, "cially": 1909, "Ġview": 1910, "ĠHey": 1911, "med": 1912, "llo": 1913, "cc": 1914, "Ġfac": 1915, "Ġcouple": 1916, "Ġheart": 1917, "ler": 1918, "Ġready": 1919, "Ġalmost": 1920, "aring": 1921, "Ġhalf": 1922, "ĠMe": 1923, "avor": 1924, "ique": 1925, "Ġcharac": 1926, "Ġpract": 1927, "ON": 1928, "ane": 1929, "Ġil": 1930, "на": 1931, "Ġvi": 1932, "lish": 1933, "head": 1934, "Ġleast": 1935, "Ġbasically": 1936, "ased": 1937, "right": 1938, "Ġyet": 1939, "Ġtaking": 1940, "Ġcountry": 1941, "Ġwin": 1942, "Ġisn": 1943, "Ġpossible": 1944, "Ġcam": 1945, "Ġincre": 1946, "Ġpat": 1947, "Ġwanna": 1948, "Ġconsider": 1949, "Ġabs": 1950, "Ġwithin": 1951, "Ġhuman": 1952, "Ġthinking": 1953, "Ġoh": 1954, "¡ľ": 1955, "Ġqui": 1956, "ases": 1957, "Ġ0": 1958, "itely": 1959, "ä¸į": 1960, "Ġkill": 1961, "Ġmil": 1962, "Ġinvest": 1963, "ister": 1964, "Ġsuc": 1965, "ional": 1966, "elf": 1967, "Ġwhether": 1968, "Ġcontrol": 1969, "Ġagainst": 1970, "ots": 1971, "ëĭĪëĭ¤": 1972, "ior": 1973, "Ġpresent": 1974, "Ġا": 1975, "Ġwatching": 1976, "ube": 1977, "erv": 1978, "Ġnicht": 1979, "Ġgovern": 1980, "ĠThese": 1981, "Ġ:": 1982, "uit": 1983, "ugh": 1984, "Ġworks": 1985, "oo": 1986, "Ġwir": 1987, "Ġair": 1988, "ĠTe": 1989, "аз": 1990, "ision": 1991, "where": 1992, "Ġtot": 1993, "joy": 1994, "ìĭ": 1995, "Ġvol": 1996, "Ġе": 1997, "Ġclose": 1998, "ĠAd": 1999, "Ñī": 2000, "ined": 2001, "Ġuna": 2002, "Ġê·¸ë": 2003, "°ë": 2004, "orry": 2005, "Ġbro": 2006, "Ġfilm": 2007, "ift": 2008, "20": 2009, "Ġtype": 2010, "Ġhappened": 2011, "ĠAm": 2012, "Ġgirl": 2013, "ĠAre": 2014, "wards": 2015, "Ġpour": 2016, "Ġcolor": 2017, "elt": 2018, "аÑģ": 2019, "Ġsense": 2020, "lex": 2021, "ĠWith": 2022, "uss": 2023, "rib": 2024, "Ġrese": 2025, "Ġnorm": 2026, "Ġfuture": 2027, "Ġdeal": 2028, "ending": 2029, "ey": 2030, "Ġx": 2031, "ero": 2032, "ĠCl": 2033, "uk": 2034, "Ġwhatever": 2035, "selves": 2036, "Ġyoung": 2037, "ìĬ": 2038, "ĠMar": 2039, "ĠChrist": 2040, "Ġguess": 2041, "Ġperform": 2042, "Ġener": 2043, "ron": 2044, "Ġhit": 2045, "Ġwond": 2046, "Ġdirect": 2047, "ĠEvery": 2048, "Ġoften": 2049, "Ġfa": 2050, "Ġalong": 2051, "Ġclick": 2052, "ĠLook": 2053, "Ġsitu": 2054, "Ġhappy": 2055, "ead": 2056, "Ġago": 2057, "Ġenc": 2058, "Ġmyself": 2059, "Ġcover": 2060, "об": 2061, "Ġmid": 2062, "Ġcost": 2063, "Ġten": 2064, "ĠSch": 2065, "Ġexpect": 2066, "Ġwasn": 2067, "Ġstrong": 2068, "iful": 2069, "Ġopportun": 2070, "inal": 2071, "yle": 2072, "Ġshare": 2073, "Ġtrue": 2074, "Ġappro": 2075, "Ġchall": 2076, "Ġminutes": 2077, "Ġchann": 2078, "ĠëĤ": 2079, "ε": 2080, "li": 2081, "Ġmess": 2082, "ories": 2083, "pecially": 2084, "Ġwrong": 2085, "Ġyes": 2086, "ĠìĹ": 2087, "iron": 2088, "Ġallow": 2089, "Ġsubs": 2090, "Ġfore": 2091, "Ġfight": 2092, "Ġsocial": 2093, "Ġcra": 2094, "ana": 2095, "Ġaff": 2096, "Ġess": 2097, "Ġways": 2098, "Ġshort": 2099, "Ġfall": 2100, "Ġlaw": 2101, "ĠWho": 2102, "Ġenjoy": 2103, "Ġcal": 2104, "Ġaccess": 2105, "fe": 2106, "Ġnon": 2107, "Ġacross": 2108, "ery": 2109, "viously": 2110, "ĠEx": 2111, "ided": 2112, "Ġlink": 2113, "ĠPr": 2114, "Ġterms": 2115, "aces": 2116, "Ġland": 2117, "azing": 2118, "Ġ15": 2119, "Ġmult": 2120, "Ġspecial": 2121, "åĢ": 2122, "iving": 2123, "ìĿĢ": 2124, "Ġtyp": 2125, "Ġste": 2126, "ĠÄ": 2127, "Ġforward": 2128, "åı": 2129, "Ġfre": 2130, "好": 2131, "Ġresearch": 2132, "à¯į": 2133, "аÑĤ": 2134, "Ġmain": 2135, "Ġrecord": 2136, "Ġhu": 2137, "Ġdefinitely": 2138, "Ġeither": 2139, "Ġlisten": 2140, "Ġkey": 2141, "Ġmarket": 2142, "ĠÑĩÑĤо": 2143, "ization": 2144, "Ġvideos": 2145, "Ġguy": 2146, "Ġfig": 2147, "Ġstra": 2148, "ĠPl": 2149, "ully": 2150, "amos": 2151, "Ġmention": 2152, "Ġsong": 2153, "Ġintern": 2154, "ral": 2155, "urs": 2156, "Ġhon": 2157, "Ġvalue": 2158, "Ġbar": 2159, "cle": 2160, "ож": 2161, "Äĩ": 2162, "ľë": 2163, "Ġzu": 2164, "им": 2165, "ä½ł": 2166, "Ġsingle": 2167, "Ġauch": 2168, "cuss": 2169, "Ġgets": 2170, "Ġsometimes": 2171, "å¾": 2172, "amb": 2173, "mm": 2174, "cing": 2175, "Ġperfect": 2176, "ĠBl": 2177, "outh": 2178, "ìł": 2179, "Ġsci": 2180, "par": 2181, "Ġred": 2182, "Ġpost": 2183, "Ġmot": 2184, "Ġelect": 2185, "ĠEu": 2186, "itive": 2187, "ĠSome": 2188, "Ġdescri": 2189, "Ġcurrent": 2190, "és": 2191, "Ġtre": 2192, "ĠEn": 2193, "Ġmit": 2194, "EN": 2195, "Īë": 2196, "ium": 2197, "Ġheard": 2198, "Ġsimple": 2199, "lar": 2200, "Ġeverybody": 2201, "ilar": 2202, "Ġneeds": 2203, "Ġdiffic": 2204, "ĠGood": 2205, "ument": 2206, "cent": 2207, "Ġoper": 2208, "аÑĤÑĮ": 2209, "ety": 2210, "Ġblack": 2211, "Ġgiven": 2212, "ones": 2213, "Ġwel": 2214, "éĢ": 2215, "ĠìķĦ": 2216, "Ġ30": 2217, "AT": 2218, "Ġstat": 2219, "ouch": 2220, "ĠMr": 2221, "аÑĢ": 2222, "Ġsho": 2223, "Ġcond": 2224, "×Ķ": 2225, "my": 2226, "Ġchildren": 2227, "Ġeu": 2228, "ед": 2229, "ìķĦ": 2230, "tern": 2231, "Ġuh": 2232, "Ġhar": 2233, "Ġprom": 2234, "Ġpull": 2235, "rew": 2236, "Ġcompany": 2237, "Ġbeautiful": 2238, "ustom": 2239, "íķĺ": 2240, "ки": 2241, "Ġstre": 2242, "Ġamazing": 2243, "ries": 2244, "Ġsuccess": 2245, "Ġmach": 2246, "not": 2247, "Ġdiscuss": 2248, "Ġnat": 2249, "¦¬": 2250, "Ġune": 2251, "Ġdifficult": 2252, "Ġris": 2253, "ν": 2254, "Ġcamp": 2255, "Ġbuy": 2256, "ä¸Ģ": 2257, "Ġmag": 2258, "po": 2259, "ĠYour": 2260, "Ġbehind": 2261, "ica": 2262, "ın": 2263, "ĠOK": 2264, "Ġlang": 2265, "Ġwomen": 2266, "Ġenv": 2267, "Ġrece": 2268, "Ġchannel": 2269, "ially": 2270, "ule": 2271, "Ġ12": 2272, "thers": 2273, "Ġbott": 2274, "Ġreport": 2275, "ently": 2276, "fully": 2277, "The": 2278, "Ġsent": 2279, "Ġevent": 2280, "Ġenergy": 2281, "lt": 2282, "Ġwords": 2283, "arr": 2284, "dle": 2285, "Ġahead": 2286, "ards": 2287, "ر": 2288, "äºĨ": 2289, "Ġtool": 2290, "conom": 2291, "еÑģ": 2292, "Ġexactly": 2293, "Ġfavor": 2294, "Ġlow": 2295, "Ġproper": 2296, "ĠìŀĪ": 2297, "Ġ!": 2298, "Ġrelations": 2299, "Ġmas": 2300, "Ġkids": 2301, "Ġentire": 2302, "ude": 2303, "Ùħ": 2304, "ĠWhere": 2305, "Ġones": 2306, "Ġcity": 2307, "olut": 2308, "Ġsix": 2309, "ability": 2310, "ör": 2311, "ili": 2312, "ĠEs": 2313, "Ġhappens": 2314, "ains": 2315, "Ġmodel": 2316, "Ġpict": 2317, "Ġespecially": 2318, "Ġ100": 2319, "kt": 2320, "Ġsoon": 2321, "by": 2322, "rodu": 2323, "Ġann": 2324, "Ġsubscri": 2325, "ĠQu": 2326, "Ġavail": 2327, "iment": 2328, "Ġvoc": 2329, "ka": 2330, "Ġ200": 2331, "aper": 2332, "ĠInd": 2333, "Ġì§": 2334, "hor": 2335, "į°": 2336, "jor": 2337, "ил": 2338, "Ġsqu": 2339, "AU": 2340, "arning": 2341, "Ġг": 2342, "IS": 2343, "Ġл": 2344, "ей": 2345, "yes": 2346, "åħ": 2347, "ĠÐĴ": 2348, "Ġorig": 2349, "ого": 2350, "Ġasked": 2351, "ilt": 2352, "ог": 2353, "Ġcontinue": 2354, "Ġìĺ": 2355, "ram": 2356, "Ġothers": 2357, "ES": 2358, "ohn": 2359, "Ġlay": 2360, "Ġbased": 2361, "Ġpu": 2362, "Ġappe": 2363, "Ġlim": 2364, "Ġprop": 2365, "Ģë": 2366, "min": 2367, "Ġhot": 2368, "ĠLa": 2369, "Ġfast": 2370, "Ġprotect": 2371, "Ġamount": 2372, "Ġaqu": 2373, "Ġfund": 2374, "Ġcustom": 2375, "Ġcult": 2376, "Ġhands": 2377, "Ġhaven": 2378, "Ġaud": 2379, "Ġoutside": 2380, "ĠAfter": 2381, "aps": 2382, "Ġanim": 2383, "ploy": 2384, "Ġhat": 2385, "ĠFirst": 2386, "Ġtreat": 2387, "Ġep": 2388, "Ġmater": 2389, "Ġbuilding": 2390, "Ġë°": 2391, "åIJ": 2392, "ìĦľ": 2393, "za": 2394, "ughter": 2395, "ĠPe": 2396, "ney": 2397, "eter": 2398, "atic": 2399, "Ġeduc": 2400, "기": 2401, "Ġmov": 2402, "ĵ¤": 2403, "ama": 2404, "ration": 2405, "Ġsn": 2406, "ÙĪ": 2407, "Ġsum": 2408, "Ġphot": 2409, "ĠÐĿ": 2410, "Ġ.": 2411, "æľī": 2412, "Ġfinish": 2413, "itting": 2414, "å®": 2415, "Ġlarge": 2416, "Ġìĸ": 2417, "Ġwhite": 2418, "ara": 2419, "Ġmais": 2420, "ĠHi": 2421, "Ġdam": 2422, "ĠاÙĦ": 2423, "Ġbox": 2424, "ĠHello": 2425, "Ġsle": 2426, "Ġopt": 2427, "ried": 2428, "¥¼": 2429, "Ġactiv": 2430, "Ġnão": 2431, "ĠCom": 2432, "Ġplaying": 2433, "Th": 2434, "Ġavailable": 2435, "Ġport": 2436, "åĪ": 2437, "ĠAh": 2438, "Ġlas": 2439, "Ġearly": 2440, "Ġwonder": 2441, "±°": 2442, "Ġ18": 2443, "cul": 2444, "Ġfunction": 2445, "Ġmorning": 2446, "lle": 2447, "ients": 2448, "ux": 2449, "Ġcir": 2450, "itions": 2451, "Ġdeep": 2452, "Ġpolit": 2453, "yor": 2454, "mp": 2455, "aking": 2456, "Įë": 2457, "ĠMan": 2458, "Ġmillion": 2459, "Ġ/": 2460, "Ġindivid": 2461, "Ġpan": 2462, "Ġgovernment": 2463, "Ġwrite": 2464, "ĠTod": 2465, "ament": 2466, "ĠÏ": 2467, "Ġwind": 2468, "ĠEng": 2469, "chen": 2470, "Wh": 2471, "ìľ": 2472, "Ġident": 2473, "ãģ§": 2474, "vent": 2475, "urch": 2476, "Ġhy": 2477, "Ġya": 2478, "Ġtrad": 2479, "Ġrelationship": 2480, "ú": 2481, "Ġdou": 2482, "OR": 2483, "Ġswe": 2484, "Ġneg": 2485, "ination": 2486, "Ġtext": 2487, "ipp": 2488, "Ġfine": 2489, "ás": 2490, "ĠDr": 2491, "ĠCome": 2492, "Ġmonths": 2493, ",\"": 2494, "ени": 2495, "Ġhours": 2496, "Ġpod": 2497, "irt": 2498, "Ġinvol": 2499, "Ġcollect": 2500, "Ġauf": 2501, "Ġpa": 2502, "Ġhistory": 2503, "mb": 2504, "ify": 2505, "Ġ?": 2506, "Ġbelow": 2507, "asure": 2508, "aby": 2509, "Ġlangu": 2510, "Ġant": 2511, "Ġcomb": 2512, "ato": 2513, "Ġexist": 2514, "Ġëĭ": 2515, "Ġtakes": 2516, "Ġcharacter": 2517, "aff": 2518, "Ġfield": 2519, "Ġeconom": 2520, "ief": 2521, "Ġpiece": 2522, "åľ": 2523, "Ġreach": 2524, "Ġê²": 2525, "ony": 2526, "Ġmaterial": 2527, "Ġdig": 2528, "Ġphys": 2529, "Ġimpro": 2530, "Ġsimilar": 2531, "IC": 2532, "Ġnet": 2533, "yn": 2534, "Ġposition": 2535, "ÃŁ": 2536, "Ġbene": 2537, "read": 2538, "Ġlearning": 2539, "ume": 2540, "Ġclean": 2541, "ÑĤоÑĢ": 2542, "Ġcook": 2543, "Ġseems": 2544, "Ġol": 2545, "ĠUS": 2546, "ĠJes": 2547, "Ġà®": 2548, "ential": 2549, "iversity": 2550, "acy": 2551, "ĠÑı": 2552, "olutely": 2553, "rect": 2554, "ĠPlease": 2555, "Ġrepres": 2556, "Ġtouch": 2557, "men": 2558, "Ġа": 2559, "ión": 2560, "ĠThanks": 2561, "Ġang": 2562, "Ġmajor": 2563, "Ġitself": 2564, "ills": 2565, "\",": 2566, "ians": 2567, "Ġscreen": 2568, "Ġhor": 2569, "Ġknown": 2570, "Ġenviron": 2571, "Ġfinal": 2572, "Ġfigure": 2573, "ĠTw": 2574, "Ġeyes": 2575, "Ġimag": 2576, "Ġseeing": 2577, "Ġhair": 2578, "rem": 2579, "Ġapplic": 2580, "ends": 2581, "put": 2582, "Ġnews": 2583, "Ġcompletely": 2584, "ughs": 2585, "Ġknew": 2586, "ified": 2587, "ĠJe": 2588, "ĠDid": 2589, "Ġsituation": 2590, "Ġflo": 2591, "ms": 2592, "Ġphone": 2593, "Ġball": 2594, "do": 2595, "Ġparent": 2596, "Ġsorry": 2597, "ury": 2598, "ин": 2599, "ips": 2600, "ад": 2601, "Ġinstead": 2602, "Ġhuge": 2603, "Ġtu": 2604, "Ġãģ": 2605, "ĠGr": 2606, "Ġdetail": 2607, "ĠÐŁ": 2608, "Ġindividual": 2609, "Ġfire": 2610, "Ġclos": 2611, "Ġwer": 2612, "une": 2613, "Ġrunning": 2614, "Ġconvers": 2615, "Ġrecomm": 2616, "Ġcomo": 2617, "Ġsomebody": 2618, "ĠJohn": 2619, "ĠìĿ´": 2620, "ĠOur": 2621, "ples": 2622, "ĠPh": 2623, "Ġanal": 2624, "Ġ50": 2625, "Ġoffer": 2626, "Ġ<": 2627, "itional": 2628, "gest": 2629, "Ġvous": 2630, "let": 2631, "icy": 2632, "Ġfeeling": 2633, "LE": 2634, "ros": 2635, "Ġthird": 2636, "ок": 2637, "Ġseries": 2638, "ĠAny": 2639, "ised": 2640, "old": 2641, "Ġdraw": 2642, "Ġservice": 2643, "Ġcannot": 2644, "bal": 2645, "ãģĨ": 2646, "Ġliving": 2647, "ım": 2648, "Ġdifference": 2649, "Ġopportunity": 2650, "Ġnear": 2651, "orth": 2652, "ken": 2653, "Ġlocal": 2654, "ت": 2655, "ĠCon": 2656, "Ġobject": 2657, "Ġdass": 2658, "ãģĻ": 2659, "IJ×": 2660, "Ġquickly": 2661, "raph": 2662, "Ġissues": 2663, "éĢĻ": 2664, "ĠAmerican": 2665, "Ġprep": 2666, "ences": 2667, "Ġprofess": 2668, "lling": 2669, "of": 2670, "Ġfoot": 2671, "bre": 2672, "Ġusually": 2673, "Ġgeneral": 2674, "da": 2675, "ances": 2676, "Ġdest": 2677, "Ġocc": 2678, "Ġmembers": 2679, "Ġdans": 2680, "Ġequal": 2681, "zt": 2682, "Ġbecom": 2683, "Ġmoving": 2684, "Ġspecific": 2685, "ÃŃa": 2686, "Ġfur": 2687, "Ġnecess": 2688, "Ġcommon": 2689, "Ġattack": 2690, "ĠÑįÑĤо": 2691, "ĠToday": 2692, "Ġuns": 2693, "ĠGu": 2694, "iod": 2695, "Ġaccount": 2696, "Ġgrand": 2697, "Ġself": 2698, "ĠEl": 2699, "Ġtast": 2700, "Ġcontent": 2701, "Ġcu": 2702, "Ħë": 2703, "ĠMaybe": 2704, "ĠJesus": 2705, "ores": 2706, "port": 2707, "©´": 2708, "Ġgives": 2709, "Ġnormal": 2710, "ÑĢÑĥ": 2711, "Ġimpact": 2712, "är": 2713, "Ġdies": 2714, "Ġlab": 2715, "sh": 2716, "ios": 2717, "ĠPres": 2718, "ĠUnd": 2719, "ĠOf": 2720, "Ġfinally": 2721, "Ġdoll": 2722, "Ġvocê": 2723, "ply": 2724, "ĠAg": 2725, "Ġtaken": 2726, "Ġground": 2727, "fort": 2728, "Ġgave": 2729, "ĠInst": 2730, "Ġlost": 2731, "Ġworked": 2732, "Ġliter": 2733, "Ġissue": 2734, "Ġindust": 2735, "Ġreturn": 2736, "Ġhappening": 2737, "Ġwants": 2738, "ив": 2739, "Ġproblems": 2740, "ĠCar": 2741, "Ŀ¼": 2742, "ĠAlso": 2743, "Ġsize": 2744, "Ġobviously": 2745, "ĠSu": 2746, "ĠSc": 2747, "Ġrecommend": 2748, "ources": 2749, "astic": 2750, "....": 2751, "Ġmi": 2752, "lier": 2753, "ĠEven": 2754, "cia": 2755, "Ġhur": 2756, "va": 2757, "Ġmass": 2758, "Ġwouldn": 2759, "unt": 2760, "cks": 2761, "Ġfelt": 2762, "osp": 2763, "light": 2764, "олÑĮ": 2765, "nie": 2766, "Ġbottom": 2767, "ĠбÑĭ": 2768, "ored": 2769, "ison": 2770, "Ġgrad": 2771, "Ġuma": 2772, "Ġva": 2773, "ĠìĤ": 2774, "ression": 2775, "ulation": 2776, "ID": 2777, "idence": 2778, "Ġbur": 2779, "Ġgone": 2780, "lu": 2781, "ìĸ´ì": 2782, "Ġredu": 2783, "Ġja": 2784, "ìĿĺ": 2785, "ita": 2786, "Ġsoft": 2787, "Ġça": 2788, "ico": 2789, "eral": 2790, "ñ": 2791, "af": 2792, "Ġpoints": 2793, "gu": 2794, "Ġdé": 2795, "apt": 2796, "ax": 2797, "ĠAlright": 2798, "Ġcamera": 2799, "Ġach": 2800, "Ġпо": 2801, "Ġsever": 2802, "50": 2803, "Ġsie": 2804, "Ïģ": 2805, "Ġmal": 2806, "Ġcomput": 2807, "Ġmiddle": 2808, "Ġcouldn": 2809, "ming": 2810, "Ġìĭ": 2811, "ĠHis": 2812, "Ġgames": 2813, "Ġintrodu": 2814, "Ġcell": 2815, "por": 2816, "Ġsleep": 2817, "Ġë³": 2818, "iding": 2819, "Ġou": 2820, "Ġdeg": 2821, "Ġdrink": 2822, "Ġenvironment": 2823, "ĠUnited": 2824, "Ġtalked": 2825, "Ġchoose": 2826, "Ġjour": 2827, "ege": 2828, "ĠMin": 2829, "Ġinte": 2830, "Ġrather": 2831, "Ġoffic": 2832, "ка": 2833, "aching": 2834, "Ġmentioned": 2835, "Ġfill": 2836, "Ġtrack": 2837, "Ġnie": 2838, "Ġut": 2839, "ĠвÑĭ": 2840, "ibility": 2841, "Ġvac": 2842, "Ġrad": 2843, "Ġpack": 2844, "Ġsend": 2845, "ĠDas": 2846, "ĠAb": 2847, "Ġengine": 2848, "ãģĹ": 2849, "Ġcompet": 2850, "ô": 2851, "ĠвÑģ": 2852, "Ġdoor": 2853, "Ġlonger": 2854, "å°į": 2855, "Ġlanguage": 2856, "Ġextra": 2857, "play": 2858, "Ġwebs": 2859, "umb": 2860, "room": 2861, "çľ": 2862, "Ġbeginning": 2863, "Ġrefer": 2864, "AM": 2865, "nen": 2866, "igher": 2867, "face": 2868, "erc": 2869, "Ġforget": 2870, "Ġcomment": 2871, "ек": 2872, "лÑı": 2873, "ror": 2874, "że": 2875, "ĠGe": 2876, "Ġdark": 2877, "Ġanyone": 2878, "ante": 2879, "ges": 2880, "ìĬµ": 2881, "Ñij": 2882, "bed": 2883, "je": 2884, "ructure": 2885, "Ġprim": 2886, "ida": 2887, "è¦": 2888, "ãģ¾": 2889, "Ġmix": 2890, "Ġstarting": 2891, "ĠìĿ´ë": 2892, "Ġprovide": 2893, "action": 2894, "Ġmother": 2895, "Ġperiod": 2896, "Ġstick": 2897, "ĠYouT": 2898, "Ġtechnology": 2899, "ê¹": 2900, "Ġbed": 2901, "Ġgiving": 2902, "Ġexplain": 2903, "zen": 2904, "imate": 2905, "Ġrepresent": 2906, "load": 2907, "ĠHowever": 2908, "Ġlives": 2909, "uth": 2910, "irit": 2911, "ogn": 2912, "Ġlik": 2913, "Ġrespons": 2914, "Ġpriv": 2915, "Ġtom": 2916, "ção": 2917, "iam": 2918, "Ġexcited": 2919, "Ġcard": 2920, "ground": 2921, "Ġ×Ķ": 2922, "Ġsens": 2923, "Ġteach": 2924, "ido": 2925, "hod": 2926, "Ġepis": 2927, "Ġwelcome": 2928, "Ġwall": 2929, "ä¹": 2930, "Ġchance": 2931, "hen": 2932, "ĠС": 2933, "ĠÄij": 2934, "Ġsimply": 2935, "ĠÑĤак": 2936, "ring": 2937, "ja": 2938, "book": 2939, "Ġseveral": 2940, "ste": 2941, "Ġcreated": 2942, "ĠоÑĤ": 2943, "Ġpush": 2944, "==": 2945, "Ġhigher": 2946, "uf": 2947, "ource": 2948, "oke": 2949, "Ġonline": 2950, "Ġrele": 2951, "Ġton": 2952, "ensive": 2953, "Ġfavorite": 2954, "Ñĥд": 2955, "Ġlooked": 2956, "Ġvon": 2957, "âĢĶ": 2958, "Ġfür": 2959, "Ġbutton": 2960, "Ġbill": 2961, "Ġchanges": 2962, "!\"": 2963, "Ġslow": 2964, "ables": 2965, "Ġdeath": 2966, "ands": 2967, "ateg": 2968, "Ġthemselves": 2969, "ãģ£": 2970, "Ġcop": 2971, "ãģ®": 2972, "Ġpersonal": 2973, "ughing": 2974, "Ġ11": 2975, "gar": 2976, "ades": 2977, "Ġneeded": 2978, "Ġstudy": 2979, "aged": 2980, "ÑģÑĤв": 2981, "ino": 2982, "Ġdisc": 2983, "ki": 2984, "Ġaddress": 2985, "ר": 2986, "itten": 2987, "esome": 2988, "Ġж": 2989, "¤ë": 2990, "ura": 2991, "Ġmu": 2992, "Ġcontinu": 2993, "for": 2994, "Ġmatch": 2995, "ãģ¦": 2996, "Ġstraight": 2997, "IJë": 2998, "ners": 2999, "Ġdog": 3000, "Ġdeb": 3001, "ĠCO": 3002, "Ġos": 3003, "ged": 3004, "came": 3005, "Ġcorrect": 3006, "ette": 3007, "ĠSee": 3008, "Ġincluding": 3009, "ĠEuro": 3010, "ester": 3011, "Ġjump": 3012, "ĠWhich": 3013, "Ġкак": 3014, "son": 3015, "ya": 3016, "ING": 3017, "Ġeine": 3018, "osh": 3019, "ency": 3020, "Ġmedia": 3021, "Ġsubscribe": 3022, "éĤ": 3023, "Ġprin": 3024, "Ġhab": 3025, "ĠPer": 3026, "ĠWas": 3027, "Ġpage": 3028, "itor": 3029, "Ġtowards": 3030, "Ġtried": 3031, "enge": 3032, "artment": 3033, "Ġvari": 3034, "Ġpaper": 3035, "Ġpicture": 3036, "Ġversion": 3037, "Ġbrought": 3038, "ware": 3039, "ĠStates": 3040, "Ġsich": 3041, "ledge": 3042, "Ġpercent": 3043, "Ġgod": 3044, "ec": 3045, "ĠComm": 3046, "Ġdecided": 3047, "Ġselect": 3048, "íķľ": 3049, ").": 3050, "urity": 3051, "Ġfurther": 3052, "Ġcomments": 3053, "lement": 3054, "Ġdream": 3055, "Ġcenter": 3056, "mi": 3057, "Ġcas": 3058, "Ġwoman": 3059, "Ġroad": 3060, "Ġfail": 3061, "Ġbecame": 3062, "lus": 3063, "ilities": 3064, "ãģ¯": 3065, "ĠCo": 3066, "Ġmanage": 3067, "Ġrecogn": 3068, "Ġaction": 3069, "Ġbenef": 3070, "Ġearlier": 3071, "׾": 3072, "Ġspeed": 3073, "Ġment": 3074, "Ġsoci": 3075, "Ġshoot": 3076, "ui": 3077, "Ġä": 3078, "Ġapply": 3079, "vo": 3080, "xim": 3081, "Ġcause": 3082, "Ġsurpr": 3083, "Ġhaben": 3084, "DI": 3085, "Ġfather": 3086, "ĠNext": 3087, "ĠYouTube": 3088, "Ġcode": 3089, "Ġrole": 3090, "gress": 3091, "Ġgreen": 3092, "ett": 3093, "Ġbuilt": 3094, "Ġflow": 3095, "Ġbase": 3096, "Ġtraining": 3097, "Ġround": 3098, "ĠWill": 3099, "Ġpath": 3100, "ĠRo": 3101, "Ġinterested": 3102, "ìĸ´": 3103, "Ġrespect": 3104, "Ġchanged": 3105, "ission": 3106, "Ġstudent": 3107, "ograph": 3108, "Ġapproach": 3109, "Ġshows": 3110, "å°±": 3111, "Ġtar": 3112, "Ġcrit": 3113, "Ġglo": 3114, "ìĬµëĭĪëĭ¤": 3115, "Ġdead": 3116, "ĠPresident": 3117, "Ġthous": 3118, "Ġbal": 3119, "ster": 3120, "ex": 3121, "Ġabsolutely": 3122, "Ġmic": 3123, "Ġpractice": 3124, "Ġquality": 3125, "Ġlower": 3126, "ogle": 3127, "Ġsepar": 3128, "ball": 3129, "medi": 3130, "Ġreview": 3131, "ĠApp": 3132, "Ġok": 3133, "âĢĭ": 3134, "Ġexperien": 3135, "Ġconcern": 3136, "entially": 3137, "more": 3138, "ĠJo": 3139, "apan": 3140, "ĠIch": 3141, "istic": 3142, "Ġfair": 3143, "Ġwebsite": 3144, "ires": 3145, "ĠBy": 3146, "Ġtravel": 3147, "Ġrisk": 3148, "Ġmir": 3149, "Ġboard": 3150, "Ġsen": 3151, "Ġparents": 3152, "ĠWow": 3153, "Ġfeed": 3154, "Ġsave": 3155, "Ġserious": 3156, "Ġinit": 3157, "EL": 3158, "undred": 3159, "AS": 3160, "Ġvan": 3161, "orrow": 3162, "Ġworth": 3163, "Ġsearch": 3164, "Ġ16": 3165, "Ġparts": 3166, "ÑģÑĤÑĮ": 3167, "Ġcompan": 3168, "Ġmovie": 3169, "Ġmethod": 3170, "Ġill": 3171, "Ġwish": 3172, "dy": 3173, "Ġitem": 3174, "Ġminus": 3175, "anger": 3176, "Ġvoice": 3177, "Ġskin": 3178, "Ġareas": 3179, "Ġeight": 3180, "Ġobs": 3181, "Ġ,": 3182, "ай": 3183, "Ġoil": 3184, "Ġcy": 3185, "Ġbaby": 3186, "sy": 3187, "Ġemploy": 3188, "ĠKe": 3189, "Ġplaces": 3190, "Ġfix": 3191, "Ġestá": 3192, "ãģ¨": 3193, "ived": 3194, "Ġlots": 3195, "Ġseason": 3196, "unk": 3197, "alt": 3198, "Ġtable": 3199, "ĠТ": 3200, "â": 3201, "Ġattention": 3202, "ãģª": 3203, "ĠHer": 3204, "Ġage": 3205, "Ġpra": 3206, "back": 3207, "cil": 3208, "Ġnetwork": 3209, "rit": 3210, "Ġdoc": 3211, "Ġaren": 3212, "igen": 3213, "ĠëĦ": 3214, "د": 3215, "ender": 3216, "Ġtotal": 3217, "Ġprice": 3218, "Ġcrazy": 3219, "ìļ": 3220, "iqu": 3221, "though": 3222, "You": 3223, "Ùĩ": 3224, "ãĤĵ": 3225, "Ïħ": 3226, "Ġsat": 3227, "Ġbi": 3228, "ĠDie": 3229, "Ġsha": 3230, "Ġthanks": 3231, "uh": 3232, "Ġstage": 3233, "аж": 3234, "ĠFl": 3235, "Ġleav": 3236, "Ġboy": 3237, "Ġaf": 3238, "ön": 3239, "ĠGet": 3240, "Ġaccept": 3241, "Ġenter": 3242, "Ġtur": 3243, "ĠsiÄĻ": 3244, "Ġhonest": 3245, "ãĢĮ": 3246, "Ġsam": 3247, "Ġrepl": 3248, "ging": 3249, "Ġdevelopment": 3250, "ĠAct": 3251, "ora": 3252, "ãĢį": 3253, "ä¾": 3254, "Ġknows": 3255, "Ġimage": 3256, "ĠLord": 3257, "иÑĤÑĮ": 3258, "Ġweeks": 3259, "Ġsex": 3260, "Ķë": 3261, "Ġhundred": 3262, "Ġsounds": 3263, "Ġlearned": 3264, "Ġbud": 3265, "ĠÑģÑĤ": 3266, "Ġincred": 3267, "âĻ": 3268, "Ġnos": 3269, "Ġdrop": 3270, "Ġben": 3271, "ĠÐĺ": 3272, "Ġsafe": 3273, "ata": 3274, "Ġfuck": 3275, "soci": 3276, "Ġdan": 3277, "Ġcross": 3278, "10": 3279, "mo": 3280, "vert": 3281, "Ġ17": 3282, "zie": 3283, "åķ": 3284, "Ġdom": 3285, "ĠBo": 3286, "Ġsetting": 3287, "Ġinvolved": 3288, "arily": 3289, "Ġsind": 3290, "Ġsus": 3291, "Ġworry": 3292, "eth": 3293, "ê¹Į": 3294, "Ġsun": 3295, "Ġhier": 3296, "Ġcertainly": 3297, "oul": 3298, "orts": 3299, "ĠEr": 3300, "ĠUm": 3301, "Ġcaus": 3302, "Ġnatural": 3303, "Ġü": 3304, "Ġcry": 3305, "ĠSec": 3306, "Ġsom": 3307, "æ²": 3308, "Ġeducation": 3309, "аеÑĤ": 3310, "Ġmultip": 3311, "Ġalone": 3312, "Ġeye": 3313, "Ġrate": 3314, "ĠEurope": 3315, "è¿": 3316, "mon": 3317, "Ġfit": 3318, "izing": 3319, "pped": 3320, "Ġpressure": 3321, "the": 3322, "иÑģ": 3323, "ites": 3324, "ĠAf": 3325, "reci": 3326, "attle": 3327, "Ġservices": 3328, "ĠGoogle": 3329, "éģ": 3330, "Ġcases": 3331, "Ġdrive": 3332, "Ġchalleng": 3333, "uz": 3334, "ĠMo": 3335, "ìľ¼ë": 3336, "val": 3337, "åĢĭ": 3338, "Ġfol": 3339, "Ġì¢": 3340, "ffic": 3341, "Ġra": 3342, "Ġsin": 3343, "Ġblue": 3344, "Ġaffect": 3345, "Ġmis": 3346, "Ġshot": 3347, "Ġоб": 3348, "asing": 3349, "Ġsignific": 3350, "ĠChe": 3351, "Ġê³": 3352, "Ġpositive": 3353, "ì£": 3354, "Ġwie": 3355, "Ġ40": 3356, "ording": 3357, "ĠFrom": 3358, "êµ": 3359, "Ġbrand": 3360, "Ġtrust": 3361, "Ġple": 3362, "Ġcommunic": 3363, "Ġweight": 3364, "Ġasking": 3365, "Ġtax": 3366, "ĠJapan": 3367, "ãģŁ": 3368, "Ġíķĺ": 3369, "ops": 3370, "ÏĤ": 3371, "Ġputting": 3372, "Ġroll": 3373, "ĠAmerica": 3374, "reg": 3375, "ŀ×": 3376, "atures": 3377, "ension": 3378, "ĠSomet": 3379, "Ġoriginal": 3380, "ping": 3381, "ĠÅŁ": 3382, "Ġproducts": 3383, "ãĥ¼": 3384, "Ġcontact": 3385, "olution": 3386, "Ġgoal": 3387, "Ġpow": 3388, "Ġperformance": 3389, "Ġblood": 3390, "ators": 3391, "ĠMich": 3392, "Ġtemper": 3393, "ĠDan": 3394, "Ġsugg": 3395, "ÑĤи": 3396, "Ġimm": 3397, "Ġoffice": 3398, "Ġarri": 3399, "Ġcomfort": 3400, "ĠÐĶ": 3401, "Ġsuggest": 3402, "Ġplat": 3403, "Ĥĺ": 3404, "19": 3405, "Ġom": 3406, "Ġseven": 3407, "ĠCent": 3408, "ille": 3409, "Ġconcept": 3410, "Ġbag": 3411, "ün": 3412, "ively": 3413, "Ġdiv": 3414, "mos": 3415, "æī": 3416, "Ġfeels": 3417, "Ġir": 3418, "akes": 3419, "ley": 3420, "Ġparticip": 3421, "ĠÐļ": 3422, "fl": 3423, "just": 3424, "Ġsil": 3425, "ĠPa": 3426, "AL": 3427, "Ġgotta": 3428, "Ġfan": 3429, "Ġchallenge": 3430, "Ġcompanies": 3431, "ĠPeople": 3432, "": 12331, "Ġheroes": 12332, "ĠBoston": 12333, "Ġdependent": 12334, "Ġmotivation": 12335, "flix": 12336, "Ġseam": 12337, "кие": 12338, "Ġdrain": 12339, "oded": 12340, "Ġguilty": 12341, "ĠJenn": 12342, "ingen": 12343, "Ġgranted": 12344, "ĠKelly": 12345, "ĠSav": 12346, "ĠUncle": 12347, "ĠHonestly": 12348, "ELI": 12349, "Ġnavigate": 12350, "Ġblessed": 12351, "core": 12352, "Ġearning": 12353, "Ġsignals": 12354, "Ġdisk": 12355, "ials": 12356, "Ġages": 12357, "æħ": 12358, "Ġparticle": 12359, "ĠÑĩеÑĢ": 12360, "Ġcann": 12361, "Ġtier": 12362, "Ġstatements": 12363, "ê³łìļĶ": 12364, "ĠëķĮ문ìĹIJ": 12365, "ĠCho": 12366, "Ġpolar": 12367, "anç": 12368, "ĠKenn": 12369, "ĠNi": 12370, "ĠFight": 12371, "organ": 12372, "éķ": 12373, "ĠCha": 12374, "ĠSÃŃ": 12375, "ãĥª": 12376, "Ġslic": 12377, "Ġcertific": 12378, "Ġtemplate": 12379, "ĠFederal": 12380, "Ġconsideration": 12381, "Ġexplo": 12382, "ĠMain": 12383, "ĠNE": 12384, "Ġalongside": 12385, "Ġdressed": 12386, "ĠPoint": 12387, "Ġenvironments": 12388, "Ġpróxim": 12389, "Ġdaar": 12390, "Ġprompt": 12391, "Ġpursue": 12392, "Ġentertainment": 12393, "Ġthroat": 12394, "Ġproblema": 12395, "Ġmart": 12396, "ì¼": 12397, "Ġprovider": 12398, "ØĮ": 12399, "Ġ×Ĺ": 12400, "inte": 12401, "making": 12402, "Ġstroke": 12403, "Ġtissue": 12404, "Un": 12405, "Ġprecious": 12406, "ĠArts": 12407, "inking": 12408, "ĠÐŀн": 12409, "ĠиÑģ": 12410, "nah": 12411, "ĠÐķÑģли": 12412, "Ġcorners": 12413, "Ġtricky": 12414, "inch": 12415, "lijk": 12416, "Ġpressing": 12417, "level": 12418, "ANG": 12419, "Ġradiation": 12420, "ìĦł": 12421, "Ġconfront": 12422, "Ġvet": 12423, "Ġrepresentative": 12424, "Ġpropag": 12425, "Ġcrap": 12426, "ĠDec": 12427, "Ġramp": 12428, "епеÑĢÑĮ": 12429, "ués": 12430, "essen": 12431, "cription": 12432, "Ġbills": 12433, "ĠMatthew": 12434, "Ġanime": 12435, "ất": 12436, "Ġlowest": 12437, "has": 12438, "screen": 12439, "ograp": 12440, "ало": 12441, "inton": 12442, "ĠJah": 12443, "èĢħ": 12444, "itÃł": 12445, "Ġkay": 12446, "Ġrotation": 12447, "ĠWere": 12448, "abei": 12449, "Ġtrials": 12450, "Ġlever": 12451, "ighty": 12452, "Ġspoon": 12453, "Ġhunt": 12454, "cling": 12455, "Ġdism": 12456, "ĠболÑĮÑĪ": 12457, "Ġassault": 12458, "Ġíĺķ": 12459, "Ġweekly": 12460, "Ġmismo": 12461, "Ġgenetic": 12462, "ulpt": 12463, "ĠStudent": 12464, "Ġrealistic": 12465, "Ġauthentic": 12466, "æīĵ": 12467, "asta": 12468, "Ġarrested": 12469, "Ġguidelines": 12470, "Ġ׾×IJ": 12471, "Ġдав": 12472, "ĠComing": 12473, "für": 12474, "Ġrequests": 12475, "ĥIJ": 12476, "Ġanalyze": 12477, "Ġinteress": 12478, "Ġhalt": 12479, "ĠOper": 12480, "onom": 12481, "Ġduck": 12482, "Ġwithd": 12483, "ser": 12484, "ĠÏĮ": 12485, "ĠHistory": 12486, "Ġyoutube": 12487, "ãĤį": 12488, "Ġsaber": 12489, "walk": 12490, "font": 12491, "Ġoverview": 12492, "39": 12493, "üy": 12494, "etti": 12495, "Ġfrozen": 12496, "Ġflesh": 12497, "ÄŁi": 12498, "ĠPM": 12499, "ĠìĻĢ": 12500, "é¢": 12501, "ÑĨии": 12502, "Ġ기ë": 12503, "íģ¬": 12504, "Ġprose": 12505, "oooo": 12506, "rates": 12507, "WS": 12508, "Ġautomatic": 12509, "Ġcollecting": 12510, "Åij": 12511, "Ġneighbors": 12512, "».": 12513, "ĠExpl": 12514, "Ġcircul": 12515, "cover": 12516, "weg": 12517, "Ġsticks": 12518, "Ġeller": 12519, "Ġwww": 12520, "Ġdorm": 12521, "ĠExper": 12522, "Ġstatistics": 12523, "Ġemails": 12524, "Ġgrave": 12525, "imiz": 12526, "HS": 12527, "Ġuit": 12528, ",'": 12529, "Ġlaser": 12530, "èī": 12531, "ĠÑĤем": 12532, "ÑĭÑĪ": 12533, "ÑīÑij": 12534, "Ġgenau": 12535, "Ġtienen": 12536, "Ġmeditation": 12537, "ĠOrgan": 12538, "Ġestimate": 12539, "Ġ무ì": 12540, "lets": 12541, "ĠnÃły": 12542, "Ġmindset": 12543, "Ġreson": 12544, "Ġmés": 12545, "Ġnumerous": 12546, "Ġvielleicht": 12547, "ĠThird": 12548, "uous": 12549, "ĠDead": 12550, "анд": 12551, "HN": 12552, "Ġracing": 12553, "Ġagents": 12554, "ĠUt": 12555, "Ġtear": 12556, "ĠHP": 12557, "Ġchemistry": 12558, "Ġsurvival": 12559, "æĸ°": 12560, "Ġconvinced": 12561, "Ġ;": 12562, "Ġregulations": 12563, "ĠES": 12564, "åĴĮ": 12565, "300": 12566, "Ġense": 12567, "Ġìµ": 12568, "Ġdict": 12569, "GA": 12570, "ĠahÃŃ": 12571, "åĭķ": 12572, "Ġtej": 12573, "ĠоÑģÑĤ": 12574, "ĠElect": 12575, "Ġintellectual": 12576, "Ġbias": 12577, "Ġburden": 12578, "çĤ¹": 12579, "Ġìĸ´ëĸ»": 12580, "Ġcheer": 12581, "Ġsoph": 12582, "Ġportfolio": 12583, "uba": 12584, "Ġestos": 12585, "TV": 12586, "For": 12587, "Ġash": 12588, "Ġkommer": 12589, "Ġcollective": 12590, "Ġwrest": 12591, "ĠJetzt": 12592, "ĠWat": 12593, "reich": 12594, "Ġprimer": 12595, "active": 12596, "Ġmie": 12597, "icked": 12598, "Ġhunting": 12599, "Ġtestim": 12600, "Ġcompassion": 12601, "Ġر": 12602, "Ġbrut": 12603, "Ġsalad": 12604, "обÑīе": 12605, "Ġsolving": 12606, "Ġfloating": 12607, "ç·": 12608, "Ġattractive": 12609, "ÙĪÙĦ": 12610, "Ġperd": 12611, "iffer": 12612, "Ġsculpt": 12613, "hhh": 12614, "ĠWeek": 12615, "Ġenthus": 12616, "Ġnad": 12617, "Ġmerch": 12618, "ĠíĻķ": 12619, "Ġmile": 12620, "好äºĨ": 12621, "Ġθ": 12622, "ĠëĤĺë": 12623, "éĩį": 12624, "38": 12625, "Ġchains": 12626, "ĠAlmost": 12627, "Ġtickets": 12628, "rin": 12629, "ĠCC": 12630, "Ġdistributed": 12631, "abetes": 12632, "Ġtemperatures": 12633, "Ġgained": 12634, "Ġflexibility": 12635, "Ġscreaming": 12636, "Ġabroad": 12637, "uno": 12638, "Ġentrepreneurs": 12639, "ĠNetwork": 12640, "ĠCanadian": 12641, "Ġprev": 12642, "Ġsö": 12643, "ĠÑĤебÑı": 12644, "ĠPoke": 12645, "ĠPod": 12646, "ĠTurkey": 12647, "çı¾åľ¨": 12648, "Ġabstract": 12649, "Ġsnake": 12650, "ĠAmy": 12651, "ĠëĬIJëĤĮ": 12652, "Ġbrave": 12653, "ĠìŀĪìĸ´ìļĶ": 12654, "ĠKal": 12655, "Ġ2007": 12656, "ário": 12657, "Ġmarked": 12658, "gines": 12659, "Ġalloc": 12660, "ONG": 12661, "Ġscientist": 12662, "Ġesca": 12663, "Ġracism": 12664, "×ij×": 12665, "ĠSams": 12666, "ĠPenn": 12667, "Ġloads": 12668, "Ġந": 12669, "über": 12670, "Me": 12671, "ixò": 12672, "Ġperò": 12673, "anne": 12674, "Ġexpressed": 12675, "меÑĢ": 12676, "Ġmoet": 12677, "Ġreturning": 12678, "nia": 12679, "Ġexpon": 12680, "Pro": 12681, "Ġloyal": 12682, "ML": 12683, "Ġlamp": 12684, "Ġshy": 12685, "Ġcomposition": 12686, "ĠLy": 12687, "Ġmagnetic": 12688, "Ġpremier": 12689, "Ġmeasured": 12690, "Ġsummary": 12691, "Ġattacked": 12692, "Ġfinishing": 12693, "ÐĹ": 12694, "ç¥": 12695, "Ġsits": 12696, "Ġhydrogen": 12697, "Ġmai": 12698, "ĠDeutsch": 12699, "ası": 12700, "Ġobtain": 12701, "vie": 12702, "Ġsoit": 12703, "Ġë°Ķ": 12704, "Ġlane": 12705, "Ġconsegu": 12706, "во": 12707, "Ġease": 12708, "akin": 12709, "ĠFa": 12710, "Ġuntuk": 12711, "Ġburst": 12712, "Ġcum": 12713, "alım": 12714, "úblic": 12715, "idi": 12716, "ĠRoyal": 12717, "ĠKon": 12718, "Ġcommonly": 12719, "Ġremoving": 12720, "Ġjur": 12721, "ilib": 12722, "Ġanch": 12723, "íĸī": 12724, "ượ": 12725, "ĠÐľÑĭ": 12726, "ĠAnth": 12727, "ĠSÃ¥": 12728, "Ġinterrupt": 12729, "Ġstere": 12730, "ĠOS": 12731, "onym": 12732, "tery": 12733, "ĠMaria": 12734, "ê²ĥ": 12735, "Ġexploring": 12736, "Ġtransparent": 12737, "Ġfate": 12738, "ĠJung": 12739, "Ġgrup": 12740, "Ġdarker": 12741, "ĠDoug": 12742, "Ġmane": 12743, "æĶ¾": 12744, "ại": 12745, "dri": 12746, "look": 12747, "ĠDesign": 12748, "Ġtutaj": 12749, "Ġhorizontal": 12750, "reon": 12751, "orte": 12752, "ĠCorrect": 12753, "ĠSteven": 12754, "Ġvine": 12755, "02": 12756, "iÄĩ": 12757, "Ġsiempre": 12758, "ĠKey": 12759, "åĥı": 12760, "ĠGames": 12761, "Ġnaar": 12762, "Ġshocked": 12763, "elve": 12764, "ĠRose": 12765, "ìĭ¬": 12766, "Ġstopping": 12767, "ohl": 12768, "ĠMix": 12769, "Ġsuffered": 12770, "Ġsigma": 12771, "Ġweakness": 12772, "ĠOw": 12773, "ีà¹Ī": 12774, "IF": 12775, "Ġà®ħ": 12776, "aded": 12777, "ĠNetflix": 12778, "anes": 12779, "Ġremained": 12780, "iry": 12781, "Ġrip": 12782, "ellt": 12783, "Ġsilent": 12784, "Ġproven": 12785, "Ġtoxic": 12786, "Ġalumin": 12787, "Ġmultipl": 12788, "aland": 12789, "Ġ34": 12790, "06": 12791, "ĠBru": 12792, "Ġìłķë§IJ": 12793, "Just": 12794, "boy": 12795, "Ġshoe": 12796, "Ġcreature": 12797, "Ġheaded": 12798, "ĠоÑĤк": 12799, "æ±": 12800, "Ġessence": 12801, "Ġremarkable": 12802, "Ġnúmer": 12803, "Ġdrew": 12804, "Ġpuzzle": 12805, "ĠLibrary": 12806, "ĠFu": 12807, "ashes": 12808, "kk": 12809, "ĠIst": 12810, "¦°": 12811, "ĠBry": 12812, "Ġceremony": 12813, "Ġà®İ": 12814, "Ġcri": 12815, "equ": 12816, "ãĤ¢": 12817, "Ġprize": 12818, "Ġdimensions": 12819, "ogram": 12820, "Ġleather": 12821, "Ġpopulations": 12822, "uum": 12823, "Ġvegan": 12824, "Ñıд": 12825, "Ġcómo": 12826, "åĦ": 12827, "Ġstrip": 12828, "å£": 12829, "Ġvacation": 12830, "ħķ": 12831, "Ġmeals": 12832, "ilipp": 12833, "Ġents": 12834, "aram": 12835, "richt": 12836, "Ġgrain": 12837, "ĠSpain": 12838, "Ġcheek": 12839, "ĠAff": 12840, "ION": 12841, "ĠBring": 12842, "Ġ38": 12843, "ielen": 12844, "ulu": 12845, "ĠболÑĮÑĪе": 12846, "Ġannouncement": 12847, "ĠÑĤÑĥÑĤ": 12848, "ĠProphet": 12849, "ardo": 12850, "37": 12851, "Ġwoke": 12852, "Ġtranslation": 12853, "ĠNOT": 12854, "ĠCL": 12855, "ĠdÃ¼ÅŁ": 12856, "ÑĨÑĸ": 12857, "acer": 12858, "ĠLoc": 12859, "Ġperception": 12860, "NO": 12861, "Ġdiesen": 12862, "Look": 12863, "heart": 12864, "aved": 12865, "Ġboundary": 12866, "Ġflows": 12867, "Ñijм": 12868, "Ġarguments": 12869, "Ġelections": 12870, "ıs": 12871, "Ġheck": 12872, "Ġsuitable": 12873, "Ġfiber": 12874, "ĠStra": 12875, "xy": 12876, "ĠHum": 12877, "Ġmonthly": 12878, "uper": 12879, "Ġgolf": 12880, "Ġlately": 12881, "ĠGard": 12882, "ĠRen": 12883, "ĠAst": 12884, "ĠFant": 12885, "аÑģÑģ": 12886, "Ġobser": 12887, "ë¡ľ": 12888, "Ġeasiest": 12889, "įĶë": 12890, "Ġwebsites": 12891, "pol": 12892, "Ġcocon": 12893, "Ġà®ĩ": 12894, "ĠVeg": 12895, "Ġwalks": 12896, "Ġintro": 12897, "Ġdirected": 12898, "ĠAnna": 12899, "Ġëĵ¤ìĸ´": 12900, "ĠEastern": 12901, "ĠSaint": 12902, "ĠBow": 12903, "Ġroast": 12904, "ĠURL": 12905, "Ġjeden": 12906, "uras": 12907, "aja": 12908, "Ġsemi": 12909, "Ġrapidly": 12910, "Ġtargets": 12911, "ĠControl": 12912, "Ġbah": 12913, "Ġreflection": 12914, "Ġcreativity": 12915, "holders": 12916, "Ġìĺ¬ë": 12917, "Ġamongst": 12918, "Ġfeeding": 12919, "ÑįÑĤомÑĥ": 12920, "Ġвиде": 12921, "Ġë§Įëĵ¤": 12922, "ĠSmart": 12923, "Ġreliable": 12924, "Ġvezes": 12925, "Ġר": 12926, "chuckles": 12927, "azione": 12928, "ĠWilliams": 12929, "Ġaç": 12930, "Ġslee": 12931, "еÑī": 12932, "Ġtimeline": 12933, "Ġthorough": 12934, "á»į": 12935, "ĠOt": 12936, "ạn": 12937, "Ġimagination": 12938, "Ġmechanics": 12939, "rist": 12940, "Ġclaimed": 12941, "ÏĦη": 12942, "ête": 12943, "ĠHurry": 12944, "ĠiPad": 12945, "Ġconstru": 12946, "ĠCla": 12947, "ĠAls": 12948, "ä¼ļ": 12949, "utz": 12950, "Ġcultures": 12951, "Ġìĸ´ëĸ»ê²Į": 12952, "Ġbelongs": 12953, "Ġyer": 12954, "ĠDoesn": 12955, "Ġgeomet": 12956, "Ġbid": 12957, "Ġfoam": 12958, "Ġhob": 12959, "ĠBritain": 12960, "Ġsubstance": 12961, "Ġanniversary": 12962, "ĠëĦĪ": 12963, "Ġnoted": 12964, "Ġgovernor": 12965, "Ġstocks": 12966, "31": 12967, "Ġdiye": 12968, "ìĬ¤ë": 12969, "Ġreb": 12970, "zel": 12971, "Ġmultiply": 12972, "Ġoperator": 12973, "Ħ¤ìļĶ": 12974, "Ġwaters": 12975, "Ġdär": 12976, "Ġunser": 12977, "ĠElizabeth": 12978, "é«ĺ": 12979, "Ġincreasingly": 12980, "ĠGro": 12981, "Ġengines": 12982, "irs": 12983, "Ø«": 12984, "Ġtreasure": 12985, "PC": 12986, "inction": 12987, "iri": 12988, "Ġaccum": 12989, "Ġvariation": 12990, "Ġpom": 12991, "Ġtitles": 12992, "ĠFest": 12993, "ós": 12994, "Ġelder": 12995, "nym": 12996, "run": 12997, "Ñıв": 12998, "Ġinnovative": 12999, "Ġnombre": 13000, "Ġcoinc": 13001, "Ġfranch": 13002, "Ġentonces": 13003, "Ġnichts": 13004, "Ġexclusive": 13005, "ĠCheers": 13006, "ĠBi": 13007, "uje": 13008, "æŃ¡": 13009, "Ġpok": 13010, "ĠPrem": 13011, "Ġrocket": 13012, "ELIPE": 13013, "Ġhospitals": 13014, "rium": 13015, "Ġjuste": 13016, "Ġhammer": 13017, "Ġquantum": 13018, "Ġresponses": 13019, "lly": 13020, "endi": 13021, "Ġactively": 13022, "Ġfridge": 13023, "iate": 13024, "long": 13025, "Ġquem": 13026, "Ġdeaths": 13027, "Ġsuperior": 13028, "cken": 13029, "ìĿ´ìĹIJ": 13030, "ktop": 13031, "Ġgathered": 13032, "£¨": 13033, "Ġdazu": 13034, "Ġrecipes": 13035, "Ġbuzz": 13036, "cen": 13037, "Ġanytime": 13038, "onsense": 13039, "Ġcircles": 13040, "Ġsolved": 13041, "Ġìĭł": 13042, "Ġcoronavirus": 13043, "ĠLuke": 13044, "Ġbubb": 13045, "Ġcontempor": 13046, "rzy": 13047, "ĠJane": 13048, "Ġдом": 13049, "Ġscrews": 13050, "Ġhybrid": 13051, "Ġcasual": 13052, "Ġselbst": 13053, "being": 13054, "ĠÄIJ": 13055, "ĠColumb": 13056, "ĠÑħоÑĩ": 13057, "Ġbucket": 13058, "Ġevaluate": 13059, "Ġidol": 13060, "Ġreputation": 13061, "ĠìĨĮë": 13062, "ÙĪر": 13063, "Ġhecho": 13064, "Ġpoem": 13065, "Ġsubjects": 13066, "plant": 13067, "ĠBeh": 13068, "ĠSpeaking": 13069, "Ġbatteries": 13070, "Ġfollowers": 13071, "öl": 13072, "Ġgently": 13073, "Ġsixt": 13074, "Ġparameter": 13075, "Ġikke": 13076, "ĠTour": 13077, "ĠDJ": 13078, "otte": 13079, "ĠJahren": 13080, "Ġpreparation": 13081, "ĠдÑĥм": 13082, "Ġ800": 13083, "cop": 13084, "iking": 13085, "Ġ문": 13086, "ĠнÑĥ": 13087, "ĠлеÑĤ": 13088, "åIJĮ": 13089, "ĠIde": 13090, "Ġì¡°ê¸Ī": 13091, "Ġlaughter": 13092, "Ġmolecules": 13093, "ĠRest": 13094, "Ġobserved": 13095, "dzie": 13096, "Ġadvertising": 13097, "erto": 13098, "Ġmoins": 13099, "ĠMIT": 13100, "Ġexcit": 13101, "Ġtum": 13102, "Ġtyl": 13103, "Ġinvested": 13104, "Ġpharm": 13105, "Ġunexpected": 13106, "Ġphi": 13107, "otype": 13108, "weise": 13109, "Ġgeç": 13110, "jourd": 13111, "Ġhorses": 13112, "nÄħ": 13113, "=\"": 13114, "ĠSM": 13115, "Ġfib": 13116, "Ġclips": 13117, "çķ¶": 13118, "å¦Ĥæŀľ": 13119, "Ġregime": 13120, "Ġrotate": 13121, "rou": 13122, "nik": 13123, "Ġarmor": 13124, "ðŁĺ": 13125, "еÑĢа": 13126, "度": 13127, "ĠOch": 13128, "Ġrichtig": 13129, "üzel": 13130, "aneously": 13131, "mek": 13132, "éĮ¯": 13133, "ĠXiao": 13134, "Ġexisted": 13135, "worth": 13136, "ãģ£ãģ¨": 13137, "Ġnaught": 13138, "ĠheiÃŁt": 13139, "ĠBal": 13140, "Ġresid": 13141, "ivot": 13142, "omatic": 13143, "Ġhired": 13144, "Ġgradually": 13145, "Ġonions": 13146, "Ġcompat": 13147, "Ġintim": 13148, "Ġjew": 13149, "Ġcontribution": 13150, "ĠIre": 13151, "acji": 13152, "Ġslice": 13153, "Ġimmun": 13154, "ĠRus": 13155, "Ġgrows": 13156, "ĠSimilarly": 13157, "Ġhardest": 13158, "Ġstruck": 13159, "Ġmeasurement": 13160, "...]": 13161, "they": 13162, "ĠìłĢë": 13163, "Ġsneak": 13164, "Ġapplies": 13165, "Ġнем": 13166, "æĵ": 13167, "×ijר": 13168, "ĠЧÑĤо": 13169, "Ġoutro": 13170, "Ġinnocent": 13171, "Ġmog": 13172, "ĠSamsung": 13173, "Ġmercy": 13174, "Ġhandling": 13175, "Ġintervention": 13176, "idays": 13177, "got": 13178, "Ġcurric": 13179, "Ġboundaries": 13180, "Ġconfusing": 13181, "Ŀ¼ëĬĶ": 13182, "æĩ": 13183, "Ġstitches": 13184, "ÃŃvel": 13185, "Ġtunnel": 13186, "itä": 13187, "Ġgost": 13188, "imy": 13189, "Ġczas": 13190, "Ġmé": 13191, "Ġcatal": 13192, "ĠSimon": 13193, "ĠLIAM": 13194, "mic": 13195, "ĠФ": 13196, "Ġeyel": 13197, "isas": 13198, "ĠCPU": 13199, "ĠDou": 13200, "Ġnäch": 13201, "Ġinfinity": 13202, "Ġrif": 13203, "ĠPeace": 13204, "ĠCu": 13205, "Ġminimal": 13206, "Ġlistened": 13207, "Ġpole": 13208, "halb": 13209, "Ġloaded": 13210, "Ġsteady": 13211, "ĠBesides": 13212, "êm": 13213, "Ġlap": 13214, "Ġcoop": 13215, "Ġfriendship": 13216, "world": 13217, "Ġgeh": 13218, "Ġtylko": 13219, "ĠLaura": 13220, "Ġsurrounded": 13221, "ĠEvent": 13222, "Ġchap": 13223, "ĠWonder": 13224, "break": 13225, "Ġdrove": 13226, "Ġbroader": 13227, "Ġchi": 13228, "Fi": 13229, "Ġgehen": 13230, "Ġwestern": 13231, "Ġintelligent": 13232, "Ġpersist": 13233, "Ġfounded": 13234, "ãģĵãģ¨": 13235, "Ġhistoric": 13236, "ĠfrÃ¥": 13237, "cksÃ¥": 13238, "Ġhandy": 13239, "Ġsymp": 13240, "Ġrows": 13241, "Ġnutri": 13242, "bur": 13243, "ĠLeon": 13244, "Ġsistema": 13245, "Ġextensive": 13246, "ĠÑĥв": 13247, "íı": 13248, "Ġnights": 13249, "Ġcác": 13250, "Ġcounting": 13251, "ĠMust": 13252, "allow": 13253, "еÑģÑģ": 13254, "Mom": 13255, "Ġнадо": 13256, "Ġbarrel": 13257, "ãĥŀ": 13258, "ARD": 13259, "Ġinstallation": 13260, "Ġinsect": 13261, "Ġëħ¸ë": 13262, "ujÄħ": 13263, "ĠÄiji": 13264, "Ġpacked": 13265, "Ġfiction": 13266, "Now": 13267, "ĠYay": 13268, "Ġpert": 13269, "rons": 13270, "unde": 13271, "aches": 13272, "Ġstyles": 13273, "Ġaprès": 13274, "oku": 13275, "ĠVice": 13276, "ınız": 13277, "comm": 13278, "Ġassigned": 13279, "Ġinteractions": 13280, "Ġacab": 13281, "FELIPE": 13282, "Ġrescue": 13283, "Ġindustries": 13284, "ĠAndy": 13285, "Ġpraise": 13286, "Ġflame": 13287, "Ġsnack": 13288, "íĤ": 13289, "çģ": 13290, "Ġswo": 13291, "render": 13292, "Ġboards": 13293, "ĠÑĤом": 13294, "enne": 13295, "Ġpasta": 13296, "Ġdevil": 13297, "ĠFel": 13298, "Ġhatte": 13299, "Ġcolleg": 13300, "eh": 13301, "ì»": 13302, "ãģĵãģ®": 13303, "Ġproductive": 13304, "forward": 13305, "ип": 13306, "Ġsmartphone": 13307, "Ġinvis": 13308, "Ġbum": 13309, "Ġwhoa": 13310, "ìŀĦ": 13311, "ĠocksÃ¥": 13312, "ĠLang": 13313, "ĠSyria": 13314, "Ġsesi": 13315, "ία": 13316, "Ġapproval": 13317, "48": 13318, "Ġодин": 13319, "Ġëĸ": 13320, "ĠHarr": 13321, "ĠAdminist": 13322, "Ġפ": 13323, "ĠDean": 13324, "fi": 13325, "Ġcitizen": 13326, "Ġshark": 13327, "05": 13328, "Ġboil": 13329, "Ġindicate": 13330, "å¡": 13331, "Are": 13332, "Ġlayout": 13333, "Ġrefr": 13334, "ĠPacific": 13335, "AAAA": 13336, "ĠAustralian": 13337, "gression": 13338, "Voice": 13339, "алÑģÑı": 13340, "Ġshelter": 13341, "To": 13342, "aupt": 13343, "Ġevaluation": 13344, "apor": 13345, "Ġcurrency": 13346, "Ġмного": 13347, "igos": 13348, "ãģ°": 13349, "Ġoct": 13350, "Ġroyal": 13351, "è³": 13352, "asil": 13353, "ĠChildren": 13354, "Ġrien": 13355, "Ġëĵľë": 13356, "Ġbarrier": 13357, "Ġejemplo": 13358, "Ġek": 13359, "ND": 13360, "esp": 13361, "ена": 13362, "Ġpic": 13363, "Ġkiller": 13364, "Ġintegrate": 13365, "Ġfewer": 13366, "Ġdisabilities": 13367, "Ġ....": 13368, "Ġtriangle": 13369, "Ġfees": 13370, "Ġwidely": 13371, "emi": 13372, "Ġoverwhelming": 13373, "Ġzomb": 13374, "Ġbere": 13375, "Ġhood": 13376, "ĠAye": 13377, "ĠHarvard": 13378, "ev": 13379, "ĠÏĦοÏħ": 13380, "Ġcups": 13381, "ĠAuch": 13382, "zona": 13383, "Ġ1990": 13384, "ĠweiÃŁ": 13385, "Ġcrunch": 13386, "æ¥": 13387, "Ġзав": 13388, "Ġmeasuring": 13389, "Ġstations": 13390, "ĠStephen": 13391, "Ġshortly": 13392, "Ġsigning": 13393, "Ġcomedy": 13394, "omo": 13395, "Ġsuggestions": 13396, "Ġsignature": 13397, "ĠпÑĢив": 13398, "Ġdisorder": 13399, "aska": 13400, "Ġworlds": 13401, "Ġprecisely": 13402, "norm": 13403, "rav": 13404, "ĠCivil": 13405, "Inter": 13406, "ĠCertain": 13407, "Ġinjured": 13408, "Ġsuggests": 13409, "ĠGolden": 13410, "Ġcyber": 13411, "ĠØ´": 13412, "Ġtemporary": 13413, "Ġcooper": 13414, "Ġvoted": 13415, "Ġought": 13416, "ấy": 13417, "xual": 13418, "Ġpanels": 13419, "Ġ95": 13420, "Ġhandsome": 13421, "ĠпÑĢов": 13422, "Ġpermit": 13423, "Ġkein": 13424, "Ġbadly": 13425, "Ġnotifications": 13426, "iza": 13427, "ĠNotice": 13428, "Ġinclusive": 13429, "Ġanswering": 13430, "ĠíĹ": 13431, "uld": 13432, "íħĮ": 13433, "Ġnowadays": 13434, "Ġ37": 13435, "Ġbolt": 13436, "Ġstatic": 13437, "ĠHop": 13438, "Ġavant": 13439, "ajo": 13440, "Ġ맼ìŀĪ": 13441, "Ġfifty": 13442, "ĠFinal": 13443, "Ġscores": 13444, "ĠTap": 13445, "Ġcyl": 13446, "Ġconvince": 13447, "Ġanyways": 13448, "oda": 13449, "Ġìķ¼": 13450, "Ġserves": 13451, "ĠÑĤакой": 13452, "ĠZoom": 13453, "Ġsavings": 13454, "ulo": 13455, "Ġsouthern": 13456, "viewer": 13457, "Ġhoje": 13458, "Ġseja": 13459, "Ġrepresenting": 13460, "Īëįĺ": 13461, "lik": 13462, "ĠSomebody": 13463, "Ġbeast": 13464, "Ġsticking": 13465, "Ġinsist": 13466, "Ġtalented": 13467, "Ġexplaining": 13468, "Ġattorney": 13469, "éĥ¨": 13470, "Ġstairs": 13471, "ĠDog": 13472, "íĭ": 13473, "Ġcig": 13474, "Ġshaped": 13475, "Ġsons": 13476, "Ïģι": 13477, "utt": 13478, "ĠìĶ": 13479, "Ġparad": 13480, "ìĿ¸ëį°": 13481, "Ġhorn": 13482, "ĠJour": 13483, "anno": 13484, "Ġworldwide": 13485, "åĬĽ": 13486, "Ġparticipation": 13487, "¦Ħ": 13488, "Ġmów": 13489, "Ġburned": 13490, "Ġwriters": 13491, "allah": 13492, "ĠFund": 13493, "Ġclever": 13494, "ĠLeute": 13495, "bin": 13496, "Ġbeating": 13497, "foot": 13498, "ĠìĽIJ": 13499, "ĠStudio": 13500, "Ġvag": 13501, "bey": 13502, "rze": 13503, "Ġopposition": 13504, "Ġжиз": 13505, "who": 13506, "Ġê±´": 13507, "Ġtrace": 13508, "ĠденÑĮ": 13509, "Ġepid": 13510, "Ġgesch": 13511, "ĠNar": 13512, "ĠBE": 13513, "Ñĥй": 13514, "ĠSign": 13515, "edly": 13516, "Ġclay": 13517, "Ġinstantly": 13518, "Ġgathering": 13519, "ĠGalaxy": 13520, "Ġbored": 13521, "ĠBuddh": 13522, "cé": 13523, "Ġmam": 13524, "Ġslope": 13525, "Ġëĭ¤ìĿĮ": 13526, "Ġschön": 13527, "Ġpir": 13528, "gef": 13529, "amer": 13530, "Ġhö": 13531, "Ġcolleague": 13532, "Ġpresents": 13533, "adium": 13534, "Ġவ": 13535, "Ġfalar": 13536, "beep": 13537, "Ġdried": 13538, "isms": 13539, "Ġrope": 13540, "Ġworkshop": 13541, "Ġestud": 13542, "Ġbands": 13543, "Ġthemes": 13544, "åħ¬": 13545, "ÙĬر": 13546, "åIJİ": 13547, "Ġreminder": 13548, "ÑĤÑĥ": 13549, "ĠBh": 13550, "Ġcoconut": 13551, "ĠÑģÑĤо": 13552, "ĠChannel": 13553, "Ġimmigration": 13554, "äs": 13555, ".....": 13556, "主": 13557, "çĻ½": 13558, "stop": 13559, "ĠкаÑĢ": 13560, "Ġcoins": 13561, "ĠÑĩаÑģ": 13562, "Ġdestruction": 13563, "lined": 13564, "Ġbarriers": 13565, "antine": 13566, "Ġprinted": 13567, "Ġcongratulations": 13568, "ĠHeart": 13569, "Ġinqu": 13570, "tha": 13571, "Ġhardly": 13572, "ĠAven": 13573, "Ġtinha": 13574, "ĠSony": 13575, "ĠNF": 13576, "Ġgraduates": 13577, "Ġsqueeze": 13578, "eremy": 13579, "ÏĦι": 13580, "Ġepic": 13581, "ĠJu": 13582, "Ġolm": 13583, "ĠLaughter": 13584, "Ġbeliefs": 13585, "ĠCru": 13586, "ĠTrue": 13587, "ĠSoul": 13588, "oween": 13589, "Ġromantic": 13590, "Ġзв": 13591, "Ġanos": 13592, "ĠYup": 13593, "éĺ¿": 13594, "dim": 13595, "Ġinfer": 13596, "Ġзам": 13597, "Ġsoc": 13598, "uka": 13599, "Ġprecise": 13600, "Ġdropping": 13601, "Ġclue": 13602, "Ġerrors": 13603, "charge": 13604, "ĠPu": 13605, "ometer": 13606, "Ġlambda": 13607, "acional": 13608, "ĠDong": 13609, "Ġchamber": 13610, "Ġthankful": 13611, "ĠNu": 13612, "ĠHawai": 13613, "Ġinfo": 13614, "Ġactivate": 13615, "ĠQual": 13616, "Ġqued": 13617, "ÑĥлÑĮ": 13618, "Ġcloth": 13619, "åĸľ": 13620, "Ġwichtig": 13621, "55": 13622, "Ġotra": 13623, "ographer": 13624, "Ġcurios": 13625, "Ġ1980": 13626, "Ġempres": 13627, "dess": 13628, "eur": 13629, "Ġcluster": 13630, "arter": 13631, "obile": 13632, "ĠYan": 13633, "ĠAdv": 13634, "Ġdiscipline": 13635, "ĠìłķëıĦ": 13636, "ĠPlace": 13637, "ĠSelect": 13638, "TE": 13639, "ĠбÑĭла": 13640, "Ġwhis": 13641, "Ġbay": 13642, "ĠDor": 13643, "encing": 13644, "Ġrepet": 13645, "Ġficar": 13646, "pad": 13647, "Ġfog": 13648, "uyor": 13649, "Ġsnap": 13650, "ibt": 13651, "Ġsobie": 13652, "Ġappointment": 13653, "ĠRy": 13654, "Ġceiling": 13655, "ourse": 13656, "Ġwrites": 13657, "ĠAfghanistan": 13658, "Ġmos": 13659, "aze": 13660, "Ġpenal": 13661, "Ġcrystal": 13662, "ICE": 13663, "ê°IJ": 13664, "éŁ": 13665, "ĠTesla": 13666, "Ġtheories": 13667, "Ġappeal": 13668, "Ġnewspaper": 13669, "Ġcookies": 13670, "æ©": 13671, "ĠاÙĦÙĦ": 13672, "Ġmaj": 13673, "ĠGetting": 13674, "kommen": 13675, "ĠHeaven": 13676, "ells": 13677, "Ġdivine": 13678, "Ä«": 13679, "Ġakt": 13680, "Ġhopes": 13681, "ĠChen": 13682, "wegen": 13683, "***": 13684, "ĠFrage": 13685, "Ġни": 13686, "ู": 13687, "minister": 13688, "nesota": 13689, "which": 13690, "Ġexplicit": 13691, "Ġverdad": 13692, "Ġgraduated": 13693, "ĠPhilipp": 13694, "QL": 13695, "ĠMI": 13696, "Ġdevot": 13697, "Ġcure": 13698, "Ġclosest": 13699, "ĠÃĦ": 13700, "Ġsexy": 13701, "ãģĽ": 13702, "ĠDeath": 13703, "oko": 13704, "ugu": 13705, "ĠAnne": 13706, "itarian": 13707, "esa": 13708, "егод": 13709, "ĠDur": 13710, "Ġ000": 13711, "zeit": 13712, "Ġtournament": 13713, "Ġmelhor": 13714, "ส": 13715, "Ġindu": 13716, "Ġflaw": 13717, "Ġwars": 13718, "ĠMind": 13719, "ĠIron": 13720, "ÑĤак": 13721, "ĠVR": 13722, "Ġsiz": 13723, "ĠSouthern": 13724, "Ġê·¸ëŁ¬ë": 13725, "Ġawak": 13726, "Ġìķŀ": 13727, "Ġcube": 13728, "believable": 13729, "ifall": 13730, "dis": 13731, "Ġabandoned": 13732, "mind": 13733, "Ġparl": 13734, "Ġclassical": 13735, "èĭ": 13736, "á»Ļt": 13737, "ĠAuto": 13738, "ĠBor": 13739, "ç©": 13740, "400": 13741, "ĠSociety": 13742, "Ġsubtle": 13743, "Ġmissions": 13744, "Ġremembered": 13745, "ĠEither": 13746, "Ġdafür": 13747, "ORD": 13748, "Ġintensity": 13749, "ESIN": 13750, "ĠCup": 13751, "Ġrarely": 13752, "Ġtoys": 13753, "ĠCharlie": 13754, "ợ": 13755, "Ġglaube": 13756, "Ġrounds": 13757, "TIN": 13758, "Ġcapability": 13759, "Ġderivative": 13760, "Ġreferring": 13761, "ĠdÃ¥": 13762, "ĠTALI": 13763, "Ġcotton": 13764, "Ġconfer": 13765, "Ġcolumns": 13766, "Ġliberal": 13767, "Ġnunca": 13768, "Ġμε": 13769, "Ġindo": 13770, "iben": 13771, "ĠBeispiel": 13772, "Ġê·¸ëłĩ": 13773, "ĠÑĥÑĩ": 13774, "Ġhoy": 13775, "Ġfry": 13776, "ĠScottish": 13777, "èĬ": 13778, "Ġciv": 13779, "Ġconservative": 13780, "Ġairpl": 13781, "Ġsar": 13782, "rus": 13783, "Ġinvestments": 13784, "Ġinfinite": 13785, "Ġà®ķ": 13786, "ĠTALIESIN": 13787, "ĠGary": 13788, "uell": 13789, "Ġак": 13790, "ĠCir": 13791, "Ġritual": 13792, "Ġ>>>": 13793, "Ġtempt": 13794, "ĠTech": 13795, "ĠPokemon": 13796, "Ġimprovements": 13797, "Ġspare": 13798, "Ġtranslate": 13799, "Ġsonra": 13800, "ĠFilm": 13801, "wort": 13802, "Ġми": 13803, "Ġperiods": 13804, "Ġjealous": 13805, "ãģĦãģĦ": 13806, "Ġtir": 13807, "MI": 13808, "Ġconducted": 13809, "ĠìķĪëħķ": 13810, "09": 13811, "ĠPolit": 13812, "ĠWhereas": 13813, "Ġmoisture": 13814, "Ġsins": 13815, "Ġkap": 13816, "ĠÑįк": 13817, "Ġbenim": 13818, "Ġeliminate": 13819, "Ġathletes": 13820, "ĠManager": 13821, "Ġfeatured": 13822, "apore": 13823, "äºĽ": 13824, "Ġë°ľ": 13825, "Ġperf": 13826, "ĠThus": 13827, "Ġdebut": 13828, "обÑĢ": 13829, "Ġseñ": 13830, "Ġmysterious": 13831, "words": 13832, "Ķê°Ģ": 13833, "Ġchecks": 13834, "Ġvolunteer": 13835, "Ġwashing": 13836, "ĠMarvel": 13837, "ĠAB": 13838, "issors": 13839, "!'": 13840, "ĠFull": 13841, "yeon": 13842, "Ġweigh": 13843, "ĠJOHN": 13844, "Ġvos": 13845, "Ġprocedures": 13846, "Ġaddressed": 13847, "ĠBerlin": 13848, "puter": 13849, "ĠBan": 13850, "Ġmedication": 13851, "Ġdrone": 13852, "ĠÑĥб": 13853, "ĠJean": 13854, "Ġcaps": 13855, "Ġdisappointed": 13856, "Ġwore": 13857, "ĠêµŃ": 13858, "Ġorganize": 13859, "ĠHalloween": 13860, "Ġfantasy": 13861, "yard": 13862, "Ġnosotros": 13863, "Ġjumped": 13864, "Ġphotography": 13865, "ĠName": 13866, "rec": 13867, "AB": 13868, "Ġblessing": 13869, "ĠShut": 13870, "Ġbitter": 13871, "pop": 13872, "ãģĿãĤĮ": 13873, "Ġdei": 13874, "Ġfulfill": 13875, "çIJĨ": 13876, "Ġdengan": 13877, "Ġbelo": 13878, "ĠMeanwhile": 13879, "Ġdepois": 13880, "Ġdiabetes": 13881, "Ġbund": 13882, "ĠZealand": 13883, "Ġdigest": 13884, "Ġtires": 13885, "Ġdod": 13886, "agne": 13887, "ết": 13888, "Ġpeel": 13889, "Ġзаб": 13890, "Ġnodes": 13891, "Ġtrends": 13892, "ĠSwitch": 13893, "ĠAward": 13894, "ĠOrig": 13895, "ĠHal": 13896, "Ġestas": 13897, "Ġ360": 13898, "Ġsimult": 13899, "Ġcomic": 13900, "ĠmÃł": 13901, "Ġbalanced": 13902, "ĠPrincess": 13903, "Ġkilometers": 13904, "ứ": 13905, "Ġpartir": 13906, "ì¤ij": 13907, "soft": 13908, "ĠView": 13909, "Ġbiological": 13910, "inst": 13911, "44": 13912, "Ġmanera": 13913, "Ġcomprehensive": 13914, "ĠSab": 13915, "Ġcrimes": 13916, "yers": 13917, "ĠCompany": 13918, "ĠPhot": 13919, "Ġpouco": 13920, "iac": 13921, "Ġbeim": 13922, "inate": 13923, "Ġsubsequ": 13924, "ĠMayor": 13925, "Ġcenturies": 13926, "ères": 13927, "ìŀĸìķĦìļĶ": 13928, "Ġê·¸ëŁ¼": 13929, "ĠFrau": 13930, "ĠOH": 13931, "ĠëģĿ": 13932, "ĠNah": 13933, "ĠSeries": 13934, "Ġovernight": 13935, "íĴĪ": 13936, "ĠâĢ¢": 13937, "Ġtrave": 13938, "attered": 13939, "Ġwarri": 13940, "ĠGrund": 13941, "ĠIndones": 13942, "Ġscra": 13943, "oby": 13944, "ĠBrook": 13945, "Ġcurs": 13946, "Ġë¸": 13947, "Ġexplains": 13948, "ramatic": 13949, "Ġparticipating": 13950, "Ġminut": 13951, "Ġcontracts": 13952, "Ġgegen": 13953, "Ġdisappeared": 13954, "ĠSN": 13955, "Ġrobust": 13956, "aph": 13957, "Ġshrim": 13958, "Ġdevast": 13959, "cope": 13960, "Ġmeets": 13961, "Ġpeaceful": 13962, "mate": 13963, "Ġweld": 13964, "Ġת": 13965, "don": 13966, "ÑĥÑĤÑĮ": 13967, "Ġregistered": 13968, "ĠNik": 13969, "jin": 13970, "Ġcav": 13971, "Ġecht": 13972, "iox": 13973, "Ġflowing": 13974, "ноÑģÑĤи": 13975, "Ġtoe": 13976, "Ġentity": 13977, "ова": 13978, "fits": 13979, "ĠPatrick": 13980, "ÑĤÑĢ": 13981, "Ġleverage": 13982, "Ġcorrel": 13983, "iah": 13984, "Ġstrings": 13985, "istinct": 13986, "Ġgue": 13987, "archy": 13988, "Ġtengo": 13989, "ımız": 13990, "Ġorbit": 13991, "为": 13992, "ĠеÑīÑij": 13993, "cake": 13994, "Ġ׾×Ķ": 13995, "ĠMinnesota": 13996, "Ġbrake": 13997, "owie": 13998, "Ġcraw": 13999, "기를": 14000, "Ġprogramme": 14001, "ĠÑģлÑĥÑĩ": 14002, "åıª": 14003, "iences": 14004, "ĠOui": 14005, "ĠPers": 14006, "imiento": 14007, "ĠInvest": 14008, "Ġslower": 14009, "æĻĤåĢĻ": 14010, "ĠBeth": 14011, "Ġnurse": 14012, "ĠSpring": 14013, "Sp": 14014, "Ġunemploy": 14015, "ди": 14016, "Ġgenius": 14017, "ĠAaron": 14018, "Ġê·¸ëŁ¬": 14019, "Ġei": 14020, "ãģĹãĤĩ": 14021, "Ġtanks": 14022, "Ġaujourd": 14023, "Ġcomplexity": 14024, "ĠÑĢеÑĪ": 14025, "Ġoldest": 14026, "Ġletz": 14027, "åħ¥": 14028, "Ġphenomenon": 14029, "print": 14030, "ĠBundes": 14031, "itat": 14032, "ê»ĺ": 14033, "Ġ42": 14034, "ĠWi": 14035, "Ġincom": 14036, "Ġgek": 14037, "Ġembrace": 14038, "Ġties": 14039, "oute": 14040, "Ġdose": 14041, "ĠFriends": 14042, "ÑĭÑĤ": 14043, "егоднÑı": 14044, "Ġorg": 14045, "Ħë¡ľ": 14046, "óg": 14047, "Ġexceed": 14048, "Ġgods": 14049, "Ġê±°ìĺĪìļĶ": 14050, "Ġsociet": 14051, "ĠUnivers": 14052, "ität": 14053, "Ġworden": 14054, "Ġsmoking": 14055, "Ġintens": 14056, "abul": 14057, "emia": 14058, "èij": 14059, "47": 14060, "fly": 14061, "Ġ2006": 14062, "ĠSeriously": 14063, "Ġprzez": 14064, "æ¼": 14065, "cre": 14066, "Ġnan": 14067, "Ġmodes": 14068, "оваÑĤÑĮ": 14069, "ĠHang": 14070, "emen": 14071, "Ġbeneficial": 14072, "Ġvoters": 14073, "ĠBroad": 14074, "Ġbent": 14075, "Wow": 14076, "Ġmul": 14077, "åĵ¥": 14078, "ĠUC": 14079, "Ġdamaged": 14080, "ĠUkraine": 14081, "Ġwipe": 14082, "Ġstones": 14083, "Ġmanagers": 14084, "Ġrab": 14085, "ÑģÑĤÑĢо": 14086, "lat": 14087, "Ġdece": 14088, "Ġgraphic": 14089, "Ġfoss": 14090, "Ġdisagree": 14091, "ĠAmen": 14092, "Ġsecrets": 14093, "hole": 14094, "inkle": 14095, "Ġfortunate": 14096, "Ġì±": 14097, "ìľĦ": 14098, "èIJ¬": 14099, "Ġhabits": 14100, "Ġburied": 14101, "Ġhin": 14102, "Ġvirtually": 14103, "olas": 14104, "ĠRP": 14105, "ĠTab": 14106, "low": 14107, "Ġsacrific": 14108, "Ġestimated": 14109, "oln": 14110, "Ùĭ": 14111, "cur": 14112, "ĠFeel": 14113, "Ġcastle": 14114, "Ġuseless": 14115, "Ġdisg": 14116, "ĠJacob": 14117, "Ġgaan": 14118, "Ġupside": 14119, "Ġparece": 14120, "ãĥ³ãĥ": 14121, "Ġshipping": 14122, "ĠCR": 14123, "Ġdisrupt": 14124, "acter": 14125, "UND": 14126, "fu": 14127, "å®Į": 14128, "ĠPick": 14129, "ĠCharl": 14130, "ĠBull": 14131, "Ġenterprise": 14132, "Ġpunishment": 14133, "acking": 14134, "Ġfraction": 14135, "Ġtablet": 14136, "Ġchord": 14137, "Ġsimilarly": 14138, "åħ¶å¯¦": 14139, "ĠToronto": 14140, "Ġcourts": 14141, "ÄŁl": 14142, "eszcze": 14143, "Ġpronoun": 14144, "ĠSister": 14145, "ĠMP": 14146, "Ġgreatly": 14147, "ĠDank": 14148, "icop": 14149, "Ġgarbage": 14150, "Ġresolve": 14151, "ĠSaf": 14152, "ĠGun": 14153, "Ġcompound": 14154, "Ġë°°": 14155, "ĠMusik": 14156, "âĻ«": 14157, "Ġchaos": 14158, "ĠWhenever": 14159, "Ġeuros": 14160, "Ġorchest": 14161, "Ġrefriger": 14162, "alan": 14163, "ื": 14164, "ĠAmazing": 14165, "Ġpud": 14166, "agan": 14167, "Ġjeszcze": 14168, "isy": 14169, "Ġaccuracy": 14170, "ĠAma": 14171, "isode": 14172, "ëĮĢ": 14173, "Ġinterpretation": 14174, "ĠLiber": 14175, "æ·": 14176, "cam": 14177, "Ġevolved": 14178, "ĠKay": 14179, "ÑĨÑĭ": 14180, "Ġcreator": 14181, "itas": 14182, "Ġalarm": 14183, "Ġcelebration": 14184, "zent": 14185, "Ġfuncion": 14186, "Ġov": 14187, "umbling": 14188, "Ġ%": 14189, "à¸Ī": 14190, "Ġrestrictions": 14191, "Ġнав": 14192, "ĠKinder": 14193, "Ġbanana": 14194, "ÑĮÑı": 14195, "Ġdiameter": 14196, "Ġnorthern": 14197, "urers": 14198, "ĠPas": 14199, "æĪijçļĦ": 14200, "Ġworkforce": 14201, "Ġjung": 14202, "Ġguarante": 14203, "Ġequilib": 14204, "Ġsuite": 14205, "Ġeuro": 14206, "Ġdeliber": 14207, "Ste": 14208, "Ġdowntown": 14209, "Ġchin": 14210, "Ġcodes": 14211, "edia": 14212, "Ġsheep": 14213, "reshold": 14214, "wnie": 14215, "ób": 14216, "Ġunderlying": 14217, "lia": 14218, "jer": 14219, "ÏĢÏĮ": 14220, "çĿ": 14221, "throp": 14222, "Ġzap": 14223, "Ġvacuum": 14224, "ĠHab": 14225, "Ġwrapped": 14226, "ì¢": 14227, "Ġinventory": 14228, "ма": 14229, "Ġcoord": 14230, "Ġplates": 14231, "Ġsymm": 14232, "Te": 14233, "ĠwÅĤaÅĽnie": 14234, "Ġreaches": 14235, "Ġlonely": 14236, "Script": 14237, "lee": 14238, "esser": 14239, "Ġ걸": 14240, "ĠGesch": 14241, "ĠMoving": 14242, "Ġrép": 14243, "ĠVill": 14244, "åIJĪ": 14245, "ĠRachel": 14246, "Ġtemos": 14247, "ONE": 14248, "Ġstrain": 14249, "Ġangel": 14250, "ĠfÃ¥": 14251, "Tr": 14252, "Ġacho": 14253, "Ġhighlights": 14254, "ĠWer": 14255, "ĠCarl": 14256, "Ġblur": 14257, "Ġregards": 14258, "·": 14259, "илÑģÑı": 14260, "Ġrecre": 14261, "ĠYani": 14262, "UCK": 14263, "ł¸": 14264, "Ġelectrons": 14265, "ĠSpiel": 14266, "Ġved": 14267, "Ú¾": 14268, "Ġbeam": 14269, "Ġidiot": 14270, "ëĵ¤": 14271, "наÑĩ": 14272, "idd": 14273, "Ġski": 14274, "itative": 14275, "Ġhypothes": 14276, "ãģ§ãģĻãģŃ": 14277, "enter": 14278, "ĠìķĦëĭĪë": 14279, "Ġihre": 14280, "Ġpreview": 14281, "angel": 14282, "Ġdemon": 14283, "Ġdus": 14284, "Ġdic": 14285, "ĠKom": 14286, "LEY": 14287, "...!": 14288, "Ġsieht": 14289, "ĠSonic": 14290, "Ġtenho": 14291, "anas": 14292, "Ġdigit": 14293, "ĠMaar": 14294, "Ġundergrad": 14295, "ouncer": 14296, "uffy": 14297, "Ġconversion": 14298, "Ġdisconnect": 14299, "Ġecho": 14300, "omer": 14301, "Ġcurriculum": 14302, "Ġperché": 14303, "Ġwand": 14304, "..?": 14305, "Ġrolled": 14306, "Ġentrepreneur": 14307, "Ġtheoret": 14308, "ĠÑīо": 14309, "Ġinsights": 14310, "Ġzusammen": 14311, "oin": 14312, "rett": 14313, "produ": 14314, "Ġvisitors": 14315, "eous": 14316, "Ġgrandmother": 14317, "Ġhumor": 14318, "ĠниÑħ": 14319, "zenia": 14320, "inson": 14321, "Ġreset": 14322, "Ġbaseball": 14323, "Ġmatching": 14324, "ëĭ¤ê°Ģ": 14325, "Ġpunto": 14326, "ì¡": 14327, "Ġrede": 14328, "Ġaddressing": 14329, "Ġforecast": 14330, "ĠBol": 14331, "Ġcolored": 14332, "Ġdocumentation": 14333, "Ġexpectation": 14334, "ĠNorthern": 14335, "Ġcreo": 14336, "Ġà®ļ": 14337, "fon": 14338, "Ġunsere": 14339, "UM": 14340, "Ġcopies": 14341, "Ġexpanded": 14342, "Ġveterans": 14343, "ĠAlm": 14344, "ĠвообÑīе": 14345, "Ġpsychological": 14346, "Ġnosso": 14347, "Ġpayments": 14348, "imeters": 14349, "Ġ-->": 14350, "ĠJennifer": 14351, "Ġvolunteers": 14352, "osse": 14353, "orious": 14354, "ĠбÑĭли": 14355, "èĤ": 14356, "ĠEss": 14357, "ws": 14358, "ĠBC": 14359, "ĠIC": 14360, "Woman": 14361, "Ġvont": 14362, "Ġethnic": 14363, "ENN": 14364, "имо": 14365, "Ġlob": 14366, "Ġoui": 14367, "cs": 14368, "Ġrehe": 14369, "Ġìłģ": 14370, "Ġchick": 14371, "úsica": 14372, "Ġkont": 14373, "ĠDistrict": 14374, "Ġpile": 14375, "Ġав": 14376, "ейÑģÑĤв": 14377, "Ġ£": 14378, "Ġissued": 14379, "Ġкомп": 14380, "Ġprosper": 14381, "Ġprofound": 14382, "ĠDear": 14383, "Ġãģĵ": 14384, "Ġfunded": 14385, "Ġbisa": 14386, "ŀĺë": 14387, "ף": 14388, "ĠìĿĺ": 14389, "Ġtwelve": 14390, "ĠChampions": 14391, "éĿŀ常": 14392, "Ñģл": 14393, "Ġ2005": 14394, "pm": 14395, "Ġonde": 14396, "Ġdiffé": 14397, "ĠChall": 14398, "Ġdifficulties": 14399, "Ġgarage": 14400, "Ġdá": 14401, "ünk": 14402, "Ġ물": 14403, "Ġtran": 14404, "Ġsubmitted": 14405, "zw": 14406, "ÙĪا": 14407, "Ġark": 14408, "ĠìĦ±": 14409, "Ġgrocery": 14410, "она": 14411, "iere": 14412, "Ġaest": 14413, "Ġexhibition": 14414, "Ġrés": 14415, "Ġconsistency": 14416, "Ġcookie": 14417, "ней": 14418, "Ġreplacement": 14419, "æ²¹": 14420, "ĠSem": 14421, "ĠìĤ¬ìļ©": 14422, "800": 14423, "Ġgenes": 14424, "Ġtransaction": 14425, "ĠEL": 14426, "Ġdurante": 14427, "ibles": 14428, "ĠEat": 14429, "tail": 14430, "issance": 14431, "Ġtoss": 14432, "Ġsurvived": 14433, "Ġoffices": 14434, "Ġsupportive": 14435, "Where": 14436, "Ġtoutes": 14437, "Ġë§ī": 14438, "Ġjokes": 14439, "ieron": 14440, "apers": 14441, "Ġmature": 14442, "ĠMarsh": 14443, "Ġsido": 14444, "kind": 14445, "Ġrealmente": 14446, "ĠChef": 14447, "Ġquelque": 14448, "Ġjudges": 14449, "eft": 14450, "ERS": 14451, "Ġjet": 14452, "Ġpersons": 14453, "è»": 14454, "izations": 14455, "rik": 14456, "Ġshops": 14457, "ĠWy": 14458, "Ġeleg": 14459, "què": 14460, "quoi": 14461, "Ġjuga": 14462, "Ġíķľë²Ī": 14463, "ĠQuestion": 14464, "ĠGlobal": 14465, "Ġìķ½ê°Ħ": 14466, "ĠStation": 14467, "æİ¥": 14468, "ĠOhio": 14469, "Ġsticky": 14470, "Ġstressed": 14471, "Ġgün": 14472, "ĠíĿ": 14473, "ÑģÑĤÑĥп": 14474, "é¡Į": 14475, "ĠPhD": 14476, "immer": 14477, "Ġmentor": 14478, "Ġinvented": 14479, "Ġreun": 14480, "Ġinevit": 14481, "ĠpolÃŃt": 14482, "Ġexecute": 14483, "ĠStory": 14484, "Ġoutstanding": 14485, "Ġguer": 14486, "ĠRain": 14487, "Ġchoses": 14488, "ĠTit": 14489, "ĠÑģеÑĢ": 14490, "ĠSingapore": 14491, "ĠNone": 14492, "Ġchronic": 14493, "°ëį°": 14494, "Ġego": 14495, "æł·": 14496, "EST": 14497, "ãģĤãĤĬ": 14498, "ĠWang": 14499, "ĠNAT": 14500, "Ġaug": 14501, "Ġdesktop": 14502, "Ġeternal": 14503, "ĠìĤ¬ìĭ¤": 14504, "ĠConstitution": 14505, "ìĤ¬ë": 14506, "×Ļ׾": 14507, "pres": 14508, "ĠТÑĭ": 14509, "Ġinterf": 14510, "Ġlists": 14511, "Ġfights": 14512, "ften": 14513, "ĠIowa": 14514, "Ġmotivated": 14515, "ĠHosp": 14516, "Ġelsewhere": 14517, "Ġpaths": 14518, "Ġinstances": 14519, "Bl": 14520, "range": 14521, "á»±": 14522, "ĠSit": 14523, "mana": 14524, "Ġìĭľìŀij": 14525, "Ġmình": 14526, "ansas": 14527, "Ġsna": 14528, "Ġphilosoph": 14529, "Ġpasse": 14530, "Æ°á»Ŀi": 14531, "akh": 14532, "ental": 14533, "Ġihn": 14534, "ructor": 14535, "ĠваÑĪ": 14536, "Ġgenerous": 14537, "Ġpivot": 14538, "пол": 14539, "Ġjamais": 14540, "Ġcoment": 14541, "ĠLew": 14542, "odzi": 14543, "ĠXbox": 14544, "Ġвод": 14545, "Ġconsent": 14546, "īìŀ¥": 14547, "Ġdispar": 14548, "lass": 14549, "ĠGovernor": 14550, "Beifall": 14551, "Ġê°ľ": 14552, "Ġbeloved": 14553, "׳×ķ": 14554, "sell": 14555, "Ġhonored": 14556, "leh": 14557, "Ġwäre": 14558, "unting": 14559, "Ġfraud": 14560, "ĠRAM": 14561, "걸": 14562, "Ġkills": 14563, "Ġeconomics": 14564, "04": 14565, "пеÑĢ": 14566, "Ġcoisas": 14567, "ĠигÑĢ": 14568, "ÃŃm": 14569, "Ġmöchte": 14570, "Ġìµľ": 14571, "Ġstimul": 14572, "Ġfastest": 14573, "lv": 14574, "Ġgén": 14575, "ĠSounds": 14576, "Ġ1970": 14577, "Ġhomework": 14578, "speaking": 14579, "Ġencouraging": 14580, "Ġquery": 14581, "Ġrevers": 14582, "profit": 14583, "Ġdy": 14584, "Ġìŀij": 14585, "ëĬĶëį°ìļĶ": 14586, "Ġsoap": 14587, "ĠGall": 14588, "ĠCN": 14589, "ĠAns": 14590, "Ġfic": 14591, "anks": 14592, "Ġdessert": 14593, "ĠìłĢíĿ¬": 14594, "ĠMaking": 14595, "Ġcomeç": 14596, "ê³Ħ": 14597, "Ġassociation": 14598, "Dad": 14599, "hee": 14600, "Ġhogy": 14601, "Ġapro": 14602, "Ġinvisible": 14603, "American": 14604, "íİ": 14605, "Ġvibe": 14606, "Ġemissions": 14607, "Ġadvocate": 14608, "Ġkicked": 14609, "Ġvel": 14610, "Ġsummar": 14611, "Ġfreaking": 14612, "chron": 14613, "Ġpinch": 14614, "Ġwszystk": 14615, "iscal": 14616, "Ġproved": 14617, "Ġmindful": 14618, "Ġtä": 14619, "Ġnoises": 14620, "Ġisolated": 14621, "Ġcrossed": 14622, "Ġê°ķ": 14623, "ĠvoilÃł": 14624, "Ġchore": 14625, "ĠRA": 14626, "Com": 14627, "Ġrelaxed": 14628, "atro": 14629, "Ġprevention": 14630, "Voiceover": 14631, "OD": 14632, "ĠCovid": 14633, "Ġseparation": 14634, "Ġ-[": 14635, "иÑĩего": 14636, "çĻ¼": 14637, "ĠSD": 14638, "bleep": 14639, "Ġindependence": 14640, "Ġpartial": 14641, "Ġalgorithms": 14642, "ĠAnyone": 14643, "Ġassociate": 14644, "hum": 14645, "icular": 14646, "Ġbạn": 14647, "Ġbattles": 14648, "Good": 14649, "Applause": 14650, "Ġbastante": 14651, "Ġadvant": 14652, "ĠSweet": 14653, "Ġrefused": 14654, "ãĤ¸": 14655, "ĠÑĤебе": 14656, "plet": 14657, "Ġencouraged": 14658, "åĵ¦": 14659, "Ġmiracle": 14660, "ĠBun": 14661, "ĠVar": 14662, "rimination": 14663, "elect": 14664, "ĠMult": 14665, "Ġdelivering": 14666, "eing": 14667, "Ġcm": 14668, "nehmen": 14669, "ĠLine": 14670, "Ġë§Į": 14671, "enced": 14672, "ĠSound": 14673, "ĠContin": 14674, "ijd": 14675, "UNG": 14676, "kle": 14677, "Ġthreshold": 14678, "Ġcompact": 14679, "adt": 14680, "Ġtoes": 14681, "ĠPur": 14682, "owned": 14683, "mented": 14684, "Ġdesigning": 14685, "Ġvaccinated": 14686, "Ġexhaust": 14687, "Ġbasics": 14688, "Ġconsists": 14689, "ĠGuy": 14690, "aczy": 14691, "ĠmÃŃ": 14692, "won": 14693, "害": 14694, "Ġ85": 14695, "æĤ": 14696, "Ġmum": 14697, "Ġignor": 14698, "Ġprinting": 14699, "acular": 14700, "pow": 14701, "Ġexpanding": 14702, "Ġgir": 14703, "ĠCab": 14704, "íĺ¸": 14705, "ÑĤÑĮÑģÑı": 14706, "ĠìŬ룬ë¶Ħ": 14707, "Ġangles": 14708, "Ġterminal": 14709, "ĠWon": 14710, "ĠInteresting": 14711, "Ġcrossing": 14712, "Ġbonds": 14713, "Ġpueden": 14714, "Ġorb": 14715, "ların": 14716, "Ġcreepy": 14717, "Ġnutrition": 14718, "Ġallies": 14719, "Ġwireless": 14720, "Ġdesired": 14721, "Ġcompute": 14722, "ĠArizona": 14723, "ĠBeautiful": 14724, "Ġproduces": 14725, "Ġnuestro": 14726, "ted": 14727, "Ġeligible": 14728, "ĠÑģоз": 14729, "icial": 14730, "ĠHero": 14731, "Ġconsume": 14732, "Ġrobots": 14733, "Ġpurchased": 14734, "cción": 14735, "Ġiz": 14736, "ược": 14737, "ίναι": 14738, "ĠØ£ÙĨ": 14739, "Ġshadows": 14740, "ĠMedia": 14741, "Ġprincess": 14742, "Ġklar": 14743, "Ġwooden": 14744, "Ġusar": 14745, "Ġgüzel": 14746, "Ġslot": 14747, "rade": 14748, "ĠëĴ": 14749, "Ġharmon": 14750, "Ġingredient": 14751, "orship": 14752, "eki": 14753, "Ġgrandfather": 14754, "Ġexcitement": 14755, "Ġpoliticians": 14756, "..!": 14757, "Ġouts": 14758, "Ġseparately": 14759, "ĠÑıк": 14760, "ĠWelt": 14761, "ĠPow": 14762, "jan": 14763, "Ġorientation": 14764, "åıĭ": 14765, "LC": 14766, "agem": 14767, "ÛĮÚº": 14768, "åIJĹ": 14769, "Ġbranches": 14770, "aden": 14771, "rente": 14772, "ĠIhr": 14773, "asm": 14774, "Ġestão": 14775, "ĠNic": 14776, "Ġslave": 14777, "Ġcompress": 14778, "crowd": 14779, "Ġclimbing": 14780, "ĠManagement": 14781, "ĠBah": 14782, "Ġpanic": 14783, "Ġkor": 14784, "Ġcooling": 14785, "Ġbind": 14786, "Ġзад": 14787, "Ġrack": 14788, "Ġentit": 14789, "Ġsends": 14790, "Ġyourselves": 14791, "des": 14792, "ĠMuslims": 14793, "Ġíļ": 14794, "isma": 14795, "cycle": 14796, "unkt": 14797, "ĠCore": 14798, "Ġinjuries": 14799, "Ġidentical": 14800, "каÑı": 14801, "ĠDeutschland": 14802, "Ġее": 14803, "isan": 14804, "Ġtruc": 14805, "leton": 14806, "Ġbackup": 14807, "Ġultra": 14808, "Ġabund": 14809, "illeurs": 14810, "ĠbyÅĤo": 14811, "åħĥ": 14812, "orted": 14813, "Ġearthqu": 14814, "Ġкл": 14815, "Ġobservation": 14816, "Ġmaintenant": 14817, "elen": 14818, "Ġsettled": 14819, "Ġpela": 14820, "ĠEconom": 14821, "ĠÕ": 14822, "Ġsteering": 14823, "ĠALL": 14824, "ĠCher": 14825, "Ġpatience": 14826, "ĠSnow": 14827, "Ġbor": 14828, "Ġworthy": 14829, "Ġcái": 14830, "Ġק": 14831, "Ġκα": 14832, "dog": 14833, "ĠKaren": 14834, "illes": 14835, "β": 14836, "Ġagriculture": 14837, "×ķף": 14838, "ĠSean": 14839, "Ġsensors": 14840, "íķ´ë": 14841, "agh": 14842, "Ġpublicly": 14843, "Ġpeux": 14844, "ĠAlexander": 14845, "Ġpriorit": 14846, "Ġlazy": 14847, "ardon": 14848, "attering": 14849, "Ġcostume": 14850, "ست": 14851, "è¿ĺ": 14852, "Ġunw": 14853, "ÐĽ": 14854, "Ġthickness": 14855, "quito": 14856, "gunt": 14857, "istas": 14858, "neys": 14859, "ĠëIJĺê²Į": 14860, "ĠBrasil": 14861, "Ġtoken": 14862, "Ġaffili": 14863, "lon": 14864, "ĠfÃ¥r": 14865, "ĠBeach": 14866, "Ġwitch": 14867, "ĠSeven": 14868, "Ġpant": 14869, "λλ": 14870, "Ġcaptain": 14871, "åĿ": 14872, "Ġveut": 14873, "Ġpouvoir": 14874, "acz": 14875, "ĠBarb": 14876, "Ġutility": 14877, "Ġcontemporary": 14878, "Ġobtained": 14879, "Ġpaintings": 14880, "ear": 14881, "Ġpean": 14882, "ĠOg": 14883, "Ġcust": 14884, "лем": 14885, "Ĥĺë": 14886, "ĠIsso": 14887, "Ġaconte": 14888, "ĠTele": 14889, "ĠAssistant": 14890, "Ãī": 14891, "íĸĪìĬµëĭĪëĭ¤": 14892, "Ġcounts": 14893, "Ġbuck": 14894, "ĠDeep": 14895, "Ġtackle": 14896, "Ġharsh": 14897, "Ġdecides": 14898, "éĹľ": 14899, ".âĢĭ": 14900, "éĤĬ": 14901, "ĠAngel": 14902, "Ġlaying": 14903, "Ġcalories": 14904, "Ġcontrolling": 14905, "Ġadvantages": 14906, "ĠÑįÑĤой": 14907, "Ġapproaching": 14908, "Ġthreats": 14909, "akan": 14910, "ematic": 14911, "mann": 14912, "ê³µ": 14913, "mumbles": 14914, "ació": 14915, "Ġmaintaining": 14916, "Ġfounder": 14917, "lah": 14918, "fight": 14919, "Ġadmitted": 14920, "âĢ¦.": 14921, "ķĮ": 14922, "abol": 14923, "Ġusage": 14924, "Ġnonsense": 14925, "ĠPalest": 14926, "Ġcontre": 14927, "ĠDemocratic": 14928, "ĠER": 14929, "jekt": 14930, "Ġarbit": 14931, "Ġгол": 14932, "ĠMichelle": 14933, "icher": 14934, "esh": 14935, "ĠPho": 14936, "ком": 14937, "49": 14938, "ĠEnergy": 14939, "οÏį": 14940, "Ġcents": 14941, "Ġrefers": 14942, "Ġgospel": 14943, "ĠSha": 14944, "ĠShare": 14945, "×Ļ׳": 14946, "Ġclinic": 14947, "ĠëĦ£": 14948, "Ġequality": 14949, "ugs": 14950, "Ġshed": 14951, "Ġplanes": 14952, "Ġtoute": 14953, "reck": 14954, "Ġstrand": 14955, "Ġbiology": 14956, "Ġleague": 14957, "ĠPok": 14958, "Ġnúmero": 14959, "ĠCoast": 14960, "Ġconsistently": 14961, "Ġnucle": 14962, "OOOO": 14963, "Ġobjet": 14964, "Ġchor": 14965, "Ġginger": 14966, "Ġdabei": 14967, "Ġcooperation": 14968, "à¯į.": 14969, "nten": 14970, "ç¤": 14971, "lÃł": 14972, "ìĸij": 14973, "rado": 14974, "Ġpassive": 14975, "Ġgloves": 14976, "Ġunderground": 14977, "Ġlogical": 14978, "Ġket": 14979, "Ġfunctionality": 14980, "¸ë¦¬": 14981, "Ġportal": 14982, "eller": 14983, "×Ļר": 14984, "ĠTed": 14985, "ĠGre": 14986, "IJľ": 14987, "Ġpersonnel": 14988, "Ġemerging": 14989, "ĠFür": 14990, "Ġmeantime": 14991, "usalem": 14992, "ĠClear": 14993, "Ġtrapped": 14994, "Ġìļ°": 14995, "Ġdispl": 14996, "Ġmettre": 14997, "Ġmunicip": 14998, "Ġwithdraw": 14999, "Ġspat": 15000, "unes": 15001, "Ġaccessibility": 15002, "æĪij们": 15003, "Ġapare": 15004, "Ġprospect": 15005, "Ġназ": 15006, "Ġcopper": 15007, "ĠPRO": 15008, "ÏħÏĦ": 15009, "Ġattacking": 15010, "ĠVin": 15011, "ĠStone": 15012, "Ġinvestigate": 15013, "style": 15014, "Ġλ": 15015, "ë¡Ŀ": 15016, "ë§Ī": 15017, "Ġinspect": 15018, "Ġliver": 15019, "алиÑģÑĮ": 15020, "Ġsera": 15021, "halten": 15022, "eman": 15023, "Ġministry": 15024, "''": 15025, "Ġdots": 15026, "ãħĭãħĭãħĭãħĭ": 15027, "ÑĥÑģÑĤ": 15028, "ĠJak": 15029, "AKE": 15030, "Ġgaps": 15031, "ucker": 15032, "ĠинÑĤеÑĢеÑģ": 15033, "ĠEmily": 15034, "Ġinterval": 15035, "Ġtender": 15036, "ĠTechnology": 15037, "game": 15038, "Ġtrib": 15039, "ÙĦا": 15040, "ĠDevelopment": 15041, "Ùħا": 15042, "Ġwrist": 15043, "Ġfires": 15044, "Ġtargeted": 15045, "ìłIJ": 15046, "Ġsod": 15047, "íļĮ": 15048, "ĠolduÄŁ": 15049, "Ġseasons": 15050, "ventions": 15051, "Ġнего": 15052, "Ġsometime": 15053, "лив": 15054, "né": 15055, "Ġtú": 15056, "ĠDeus": 15057, "Ġexecution": 15058, "áp": 15059, "ĠChange": 15060, "ĠIndeed": 15061, "Ġregulation": 15062, "ĠHung": 15063, "éis": 15064, "Ġwishes": 15065, "Ġjazz": 15066, "Ġstructural": 15067, "Ġblowing": 15068, "ĠbyÄĩ": 15069, "Ġthermal": 15070, "phant": 15071, "ÑĢÑĥз": 15072, "анÑĤ": 15073, "ĠPull": 15074, "Ġconfusion": 15075, "нÑĭми": 15076, "Ġscenarios": 15077, "ìłģìľ¼ë¡ľ": 15078, "ĠдеÑĤ": 15079, "Ġtattoo": 15080, "Ġautre": 15081, "Ġheating": 15082, "Ġtreating": 15083, "Ġпоним": 15084, "Ġexclus": 15085, "ĠLOL": 15086, "wear": 15087, "agle": 15088, "Ġzurück": 15089, "Ġrational": 15090, "su": 15091, "Ġdeter": 15092, "ĠNative": 15093, "à®ķள": 15094, "ached": 15095, "Ġãĥ": 15096, "ĠEntonces": 15097, "Ġhora": 15098, "ìĿ´ìĹIJìļĶ": 15099, "Ġlite": 15100, "ë": 15101, "Ġsixth": 15102, "Ġболее": 15103, "actor": 15104, "Ġpsychology": 15105, "缸": 15106, "Ġdemands": 15107, "Ġpeer": 15108, "Ġnewly": 15109, "ĠWWE": 15110, "Donald": 15111, "ĠBox": 15112, "Ġpine": 15113, "Ġloading": 15114, "ĠNico": 15115, "ĠsÅĤ": 15116, "omme": 15117, "ART": 15118, "Ġrecruit": 15119, "Ġbugs": 15120, "arents": 15121, "ĠпÑĢоб": 15122, "ĠInside": 15123, "ipper": 15124, "dramatic": 15125, "Ġplanets": 15126, "orde": 15127, "Ġyoga": 15128, "child": 15129, "ĠMarie": 15130, "ĠãģĤ": 15131, "ĠBL": 15132, "Ġfilmed": 15133, "Ġrefresh": 15134, "Ġtomatoes": 15135, "Ġfet": 15136, "Qué": 15137, "Ġ!!": 15138, "ĠëĤ´ë": 15139, "rine": 15140, "Ġinteractive": 15141, "sal": 15142, "annah": 15143, "pez": 15144, "ç¶ĵ": 15145, "Ġunderstands": 15146, "ĠTokyo": 15147, "Ġlibraries": 15148, "Ġreader": 15149, "ijIJ": 15150, "oz": 15151, "ĠEnde": 15152, "ĠFlo": 15153, "Ġmild": 15154, "Ġpoetry": 15155, "Ġжив": 15156, "æĦĽ": 15157, "Ġbehave": 15158, "Ġdoen": 15159, "ĠSusan": 15160, "page": 15161, "raham": 15162, "Ġcommunications": 15163, "Ġtuning": 15164, "Ġpac": 15165, "Ġanxious": 15166, "IO": 15167, "Mark": 15168, "Ġhiç": 15169, "books": 15170, "Ġpiss": 15171, "Ġenabled": 15172, "achelor": 15173, "ĠFOR": 15174, "Ġéc": 15175, "ĠTR": 15176, "ilst": 15177, "hat": 15178, "ĠìĿĮ": 15179, "Ġtych": 15180, "Ġjar": 15181, "Ġbuilds": 15182, "ĠArgent": 15183, "Ġintermedi": 15184, "Ġlou": 15185, "Ġara": 15186, "Ġassignment": 15187, "Ġcabinet": 15188, "Ġretirement": 15189, "ãģ»": 15190, "Ġdisabled": 15191, "rica": 15192, "Ġawards": 15193, "Ġboots": 15194, "Ġacknowled": 15195, "Ġthy": 15196, "Ġ구": 15197, "Ġsynd": 15198, "ний": 15199, "ilton": 15200, "Ġprobl": 15201, "ĠFal": 15202, "Ġverdade": 15203, "Ġ700": 15204, "ĠLearning": 15205, "ocus": 15206, "Ġpalace": 15207, "Not": 15208, "tain": 15209, "cm": 15210, "Ġmagnet": 15211, "incoln": 15212, "Ġfiguring": 15213, "ĠLyn": 15214, "ĠBoss": 15215, "ĠVO": 15216, "Ġdiagnosis": 15217, "Ġequipped": 15218, "watch": 15219, "inos": 15220, "aders": 15221, "Ġshelf": 15222, "Ġorganis": 15223, "Ġnod": 15224, "Ġkız": 15225, "ppers": 15226, "Ġrestore": 15227, "Ġartic": 15228, "ĠVoice": 15229, "ıyorum": 15230, "격": 15231, "Ġspreading": 15232, "Ġhips": 15233, "Ġward": 15234, "ureau": 15235, "Ġintersection": 15236, "66": 15237, "Ġ39": 15238, "ç³": 15239, "Ġwaited": 15240, "ì´": 15241, "hhhh": 15242, "Ġdys": 15243, "ĠEN": 15244, "Ġbatch": 15245, "Ġcaf": 15246, "Ġmarker": 15247, "大家好": 15248, "orable": 15249, "ória": 15250, "Ġstepped": 15251, "Ġcelebrating": 15252, "ана": 15253, "Ġworn": 15254, "ĠFol": 15255, "Ġpla": 15256, "Ġattempts": 15257, "Ġtweet": 15258, "Ġrust": 15259, "gence": 15260, "íĨµ": 15261, "Ġrevel": 15262, "Ġrecept": 15263, "eness": 15264, "Ġ((": 15265, "ãĥ¼ãĥ": 15266, "!âĢĭ": 15267, "ĠìĨIJ": 15268, "Ġinfluenced": 15269, "иж": 15270, "ĠконеÑĩно": 15271, "Ġcolleges": 15272, "ioni": 15273, "Ġsag": 15274, "Ann": 15275, "olar": 15276, "Ġexpressions": 15277, "Ġsuits": 15278, "Ġownership": 15279, "eland": 15280, "piece": 15281, "æĢİä¹Ī": 15282, "Ġdespués": 15283, "Ġtel": 15284, "Ġinsult": 15285, "Ġêµīìŀ¥": 15286, "ĠSmall": 15287, "ĠFR": 15288, "oka": 15289, "berries": 15290, "ĠAnton": 15291, "елÑı": 15292, "ÑıÑģ": 15293, "Ġvalve": 15294, "acts": 15295, "Ġwoods": 15296, "ண": 15297, "Ġcultiv": 15298, "Ġfá": 15299, "ãģ¨ãģĦãģĨ": 15300, "Ġcheers": 15301, "Ġassumption": 15302, "Ġfitness": 15303, "ÃŃcul": 15304, "Ġpodr": 15305, "Ġweit": 15306, "ĠHind": 15307, "Ġdign": 15308, "Ġзн": 15309, "Ġsquad": 15310, "Ġdestro": 15311, "cere": 15312, "shirt": 15313, "immt": 15314, "engers": 15315, "Ġsä": 15316, "kÅĤad": 15317, "ĠÈĻ": 15318, "Ġoccas": 15319, "Ġì¤Ħ": 15320, "Ġprocessor": 15321, "ĠDM": 15322, "ĠDaddy": 15323, "Ġsooner": 15324, "Ġstraightforward": 15325, "Ġdepartments": 15326, "ĠChrome": 15327, "Ġworkplace": 15328, "ĠPython": 15329, "Ġmeng": 15330, "ĠDAN": 15331, "ĠIce": 15332, "ĠëĪĪ": 15333, "ĠGi": 15334, "Ġhiring": 15335, "Ġlanded": 15336, "Ġdemocratic": 15337, "iedz": 15338, "ãģĺãĤĥ": 15339, "Ġsev": 15340, "icia": 15341, "Ġespecial": 15342, "ĠNous": 15343, "Ġhät": 15344, "Ġbou": 15345, "pert": 15346, "iesz": 15347, "åijĢ": 15348, "Ġvil": 15349, "ÅĽli": 15350, "Ġîn": 15351, "Ġlosses": 15352, "éķ·": 15353, "Ġtoast": 15354, "Ġrealm": 15355, "ĠAustin": 15356, "ĠInformation": 15357, "Ġresume": 15358, "Ġchase": 15359, "Ġsalary": 15360, "Ġë¶Ħ": 15361, "лиÑĩ": 15362, "ĠÑģлед": 15363, "ĠFurther": 15364, "Ġcaring": 15365, "Ġvig": 15366, "Ġvalor": 15367, "è¿Ļ个": 15368, "ĠÑĩа": 15369, "Ġanalytics": 15370, "Ġglobe": 15371, "ĠMAN": 15372, "Ġnel": 15373, "ìĿ´ìķ¼": 15374, "Ł¼": 15375, "Ġoy": 15376, "íķĺìĦ¸ìļĶ": 15377, "jen": 15378, "Ġtroubles": 15379, "ahaha": 15380, "Ġchurches": 15381, "uet": 15382, "Ġmeasurements": 15383, "bil": 15384, "ì½": 15385, "ifully": 15386, "инÑĥ": 15387, "ĠWilson": 15388, "¦´": 15389, "ĠíĮĮ": 15390, "Ġì°¨": 15391, "Ġpúblic": 15392, "ĠJerusalem": 15393, "Ġnails": 15394, "Ġspine": 15395, "Ġhemos": 15396, "Ġzn": 15397, "quis": 15398, "ĠLeben": 15399, "Ġreferences": 15400, "ITH": 15401, "iper": 15402, "ĠÑģебÑı": 15403, "ìģ": 15404, "ĠWa": 15405, "state": 15406, "§Ŀ": 15407, "åħ±": 15408, "ĠGener": 15409, "Ġactress": 15410, "ĠEnjoy": 15411, "à¹ĥ": 15412, "Ġ×Ĵ": 15413, "Ġinfected": 15414, "Ġshaking": 15415, "Ġnick": 15416, "ุ": 15417, "Ġfot": 15418, "Ġaccomplished": 15419, "uke": 15420, "Ġsheets": 15421, "Ġfence": 15422, "Ġnursing": 15423, "Ġintroducing": 15424, "Ġfeat": 15425, "One": 15426, "TO": 15427, "Ġclubs": 15428, "ĠBruce": 15429, "onge": 15430, "change": 15431, "ĠBatman": 15432, "åı°": 15433, "ĠOfficer": 15434, "Ġhydro": 15435, "Ġsupplement": 15436, "Ġcela": 15437, "Ġlongest": 15438, "Ġcompeting": 15439, "Ġconhe": 15440, "giving": 15441, "Ġbrains": 15442, "Ġloans": 15443, "Ġwage": 15444, "ĠClinton": 15445, "ĠsÄĥ": 15446, "aneous": 15447, "Ġlord": 15448, "ÑĢÑĥж": 15449, "Ġquiz": 15450, "Ġstiff": 15451, "ĠLGB": 15452, "sz": 15453, "ME": 15454, "mare": 15455, "there": 15456, "Ġnär": 15457, "ĠMand": 15458, "last": 15459, "Ġdag": 15460, "Ġhalfway": 15461, "ĠBand": 15462, "Ġëĭ¤ìĭľ": 15463, "ĠAren": 15464, "Ġile": 15465, "PN": 15466, "ento": 15467, "Ġalgum": 15468, "Ġsoccer": 15469, "Ġblocked": 15470, "ĠJonathan": 15471, "Ġsew": 15472, "ĠTestament": 15473, "Ġvale": 15474, "Ġbehavi": 15475, "å§ĭ": 15476, "Ġconna": 15477, "ICH": 15478, "Ġaudiences": 15479, "ml": 15480, "ammad": 15481, "ĠìĤ´ì": 15482, "IGH": 15483, "Ġraces": 15484, "emed": 15485, "Ġmá»Ļt": 15486, "ï": 15487, "Ġovers": 15488, "Ġdeclared": 15489, "Ġsana": 15490, "ĠUna": 15491, "ĠÑĢе": 15492, "ucks": 15493, "Ġpairs": 15494, "Ġange": 15495, "Ne": 15496, "Ġups": 15497, "avy": 15498, "ør": 15499, "reek": 15500, "Ġbehaviors": 15501, "Ġreflected": 15502, "Ġpriorities": 15503, "Ġcondu": 15504, "Ġretreat": 15505, "Ġexpenses": 15506, "Ġë´IJ": 15507, "Ġtriple": 15508, "Ġêµīìŀ¥íŀĪ": 15509, "ält": 15510, "Ġindigenous": 15511, "Ġmining": 15512, "Ġacceptable": 15513, "Ġruin": 15514, "CA": 15515, "uine": 15516, "Ġpipeline": 15517, "ctic": 15518, "êt": 15519, "ĠвÑģего": 15520, "Ġboun": 15521, "ĠDigital": 15522, "ĠBoom": 15523, "ÑĨе": 15524, "ĠлÑĥÑĩ": 15525, "Ġasc": 15526, "ĮĢë¡ľ": 15527, "ĠGoodbye": 15528, "Ġrender": 15529, "enez": 15530, "arre": 15531, "ĠTHAT": 15532, "bour": 15533, "ición": 15534, "ãĤŃ": 15535, "Every": 15536, "Ġwires": 15537, "ĠParliament": 15538, "nung": 15539, "ateur": 15540, "ĠSave": 15541, "ĠPhys": 15542, "Ġamor": 15543, "ĠEve": 15544, "Ġfright": 15545, "Ġgamma": 15546, "Ġmicros": 15547, "mitt": 15548, "ĠCode": 15549, "ĠBey": 15550, "pled": 15551, "ĠиÑģполÑĮз": 15552, "çĹ": 15553, "ìĥī": 15554, "她": 15555, "Ġmonet": 15556, "ĠJahre": 15557, "Ġluxury": 15558, "Ġdeaf": 15559, "Ġbetray": 15560, "Ġê²°": 15561, "ики": 15562, "Ġdefeated": 15563, "Ġundert": 15564, "Ġweg": 15565, "Ġcooler": 15566, "ãģķãĤĵ": 15567, "iami": 15568, "éĤĦæľī": 15569, "ĠJessica": 15570, "ĠJoy": 15571, "Ġsophistic": 15572, "ении": 15573, "ðĿĺ": 15574, "Ġchili": 15575, "ĠType": 15576, "Ġproteins": 15577, "Ġpresenting": 15578, "alia": 15579, "ìļ¸": 15580, "ĠMajor": 15581, "Ġmolecule": 15582, "umer": 15583, "Ġcollapse": 15584, "ĠAnyways": 15585, "ĠMountain": 15586, "anted": 15587, "ãĢIJ": 15588, "Ġвидео": 15589, "æ°´": 15590, "Aud": 15591, "Ġconqu": 15592, "Ġvoll": 15593, "Ġknit": 15594, "Ġmembr": 15595, "ĠMarket": 15596, "Ġdari": 15597, "Ġcalculated": 15598, "ги": 15599, "Ġshrimp": 15600, "ĠMu": 15601, "ĠпÑĢоÑĤ": 15602, "Ġìĺģìĥģ": 15603, "Ġproductivity": 15604, "Ġcognitive": 15605, "ĠHeb": 15606, "ictions": 15607, "ê²½": 15608, "Ġcré": 15609, "för": 15610, "Ġpraying": 15611, "ashi": 15612, "ĠTik": 15613, "ór": 15614, "wen": 15615, "ÑĮÑİ": 15616, "ixo": 15617, "Ġ(\"": 15618, "ĠÑĤел": 15619, "Ġìĸ´ëĸ¤": 15620, "ĠпеÑĢед": 15621, "ĠDrive": 15622, "ãĢij": 15623, "ĠEqu": 15624, "Ġequilibrium": 15625, "Ġdescribes": 15626, "нее": 15627, "42": 15628, "ĠCurrent": 15629, "yy": 15630, "Ġabsorb": 15631, "Ġsoldier": 15632, "ders": 15633, "Ġtestimony": 15634, "Ġdecline": 15635, "ľë¡ľ": 15636, "gage": 15637, "Ġinspire": 15638, "lapping": 15639, "Ġspinning": 15640, "Ġslavery": 15641, "Ġfacial": 15642, "Ġtraditions": 15643, "ários": 15644, "ĠHospital": 15645, "Ġnest": 15646, "ĠëĪĦ": 15647, "Ġtoi": 15648, "Ġfears": 15649, "ìħ¨": 15650, "ĠMuh": 15651, "Ġgraduation": 15652, "Ġimpacted": 15653, "Ġaunt": 15654, "ĠLets": 15655, "Ġaluminum": 15656, "Ġdominant": 15657, "ĠDavis": 15658, "ĠNavy": 15659, "Ġcompt": 15660, "oples": 15661, "Ġestava": 15662, "è¥": 15663, "Ġscal": 15664, "Ġpreserve": 15665, "ĠOpp": 15666, "Ġpractically": 15667, "Ġmagnitude": 15668, "Ġfitting": 15669, "Ġcoordinate": 15670, "Ġfurniture": 15671, "ĠFamil": 15672, "Ġexplosion": 15673, "Ġdocumentary": 15674, "ĠScript": 15675, "Ġportray": 15676, "mat": 15677, "Ġscheduled": 15678, "Ġdynamics": 15679, "phy": 15680, "aky": 15681, "ĠUI": 15682, "Che": 15683, "Ġcontinuously": 15684, "ĠProv": 15685, "å°ij": 15686, "Ñĥз": 15687, "rah": 15688, "Ġgerne": 15689, "proof": 15690, "Ġsecretary": 15691, "ĠPatreon": 15692, "scream": 15693, "ĠKids": 15694, "á»ĵi": 15695, "Ġkg": 15696, "Ġuncertainty": 15697, "Ġкажд": 15698, "Ġmitig": 15699, "Ġreads": 15700, "å·²": 15701, "ĠRu": 15702, "Ġpriest": 15703, "Ġнед": 15704, "Ġlimitations": 15705, "Ġfloat": 15706, "600": 15707, "ĠToy": 15708, "ĠJimmy": 15709, "Ġoffensive": 15710, "eni": 15711, "ĠXi": 15712, "Ġeyebr": 15713, "ĠTurk": 15714, "Ġaccidentally": 15715, "Ġohne": 15716, "ĠSaud": 15717, "95": 15718, "ĠDutch": 15719, "анÑģ": 15720, "ĠSeattle": 15721, "Ġëĵ±": 15722, "check": 15723, "kÄĻ": 15724, "Ġcontributions": 15725, "Ġbeside": 15726, "Ġquindi": 15727, "Ġflew": 15728, "æŶ": 15729, "ذا": 15730, "ĠLO": 15731, "Ġwaist": 15732, "ĠEV": 15733, "Ġholidays": 15734, "jon": 15735, "Ġmisunder": 15736, "Ñıн": 15737, "Ġbout": 15738, "Ġdimin": 15739, "ẽ": 15740, "ól": 15741, "ĠGrace": 15742, "Ġinputs": 15743, "Ġdeny": 15744, "Ġforming": 15745, "ĠBild": 15746, "Ġadequ": 15747, "Ġfolk": 15748, "Ġrejected": 15749, "semb": 15750, "Ġfrustrated": 15751, "open": 15752, "ĠBetter": 15753, "ilon": 15754, "Ġtowel": 15755, "Ġdifferential": 15756, "Ġsacred": 15757, "Ġsail": 15758, "éĩĮ": 15759, "entimes": 15760, "Ġgentleman": 15761, "Ġiconic": 15762, "Ġcomparing": 15763, "Ġsagt": 15764, "Ġtexts": 15765, "Ġgrandma": 15766, "Ġrolls": 15767, "Ġcontents": 15768, "ä¸į好": 15769, "оÑģÑģ": 15770, "Ġsuspension": 15771, "roit": 15772, "¦¼": 15773, "Ġassez": 15774, "Ġdort": 15775, "ĠMath": 15776, "ĠVictor": 15777, "ĠJavaScript": 15778, "ä¸įå°į": 15779, "Ġenhan": 15780, "ÅĻ": 15781, "ĠBush": 15782, "Ġpromotion": 15783, "Ġkin": 15784, "Ġmonsters": 15785, "ĠColorado": 15786, "Ġβ": 15787, "íķ´ìļĶ": 15788, "æŃ£": 15789, "ifferent": 15790, "Ġnaked": 15791, "Ġprod": 15792, "etics": 15793, "ĠWoman": 15794, "Ġtreatments": 15795, "Ġestoy": 15796, "vé": 15797, "Ġlifting": 15798, "Ġyapt": 15799, "ĠRober": 15800, "Ġì¹ľ": 15801, "Ġsubstitute": 15802, "aku": 15803, "ridge": 15804, "Ġê±°ë": 15805, "Ġresponded": 15806, "Ġbé": 15807, "ĠEngineer": 15808, "Ġtransferred": 15809, "ë²": 15810, "Ġhaber": 15811, "oop": 15812, "ĠWE": 15813, "Ġvest": 15814, "Ġforty": 15815, "ĠDS": 15816, "Ġ2004": 15817, "Ġcoaching": 15818, "nom": 15819, "ĠBab": 15820, "Ġnossa": 15821, "ĠJake": 15822, "Ġgy": 15823, "Ġdeleg": 15824, "Ġìŀł": 15825, "ĠкÑĢаÑģ": 15826, "Ġstandpoint": 15827, "Ġdisad": 15828, "Ġartwork": 15829, "Ad": 15830, "illo": 15831, "ĠÄijược": 15832, "ĠProm": 15833, "ĠLib": 15834, "Ġcriticism": 15835, "Ġcontacts": 15836, "ÑĢам": 15837, "Ġachievement": 15838, "ÐĶа": 15839, "Ġdissol": 15840, "ĠVegas": 15841, "Ġstreams": 15842, "ĠKent": 15843, "ĠعÙĦÙī": 15844, "Ġradius": 15845, "Ġsucks": 15846, "ĠAch": 15847, "Ġfi": 15848, "oust": 15849, "ĠлÑİди": 15850, "Ġpalette": 15851, "ĠHaz": 15852, "ĠAnthony": 15853, "Ġtema": 15854, "ĠCos": 15855, "Ġsafer": 15856, "αÏĤ": 15857, "Ġcontrad": 15858, "Ġmaior": 15859, "Ġinflation": 15860, "ĠSilver": 15861, "Ġattending": 15862, "íķľíħĮ": 15863, "arto": 15864, "Ġapplauding": 15865, "Ġcomputing": 15866, "ĠHat": 15867, "æ»": 15868, "know": 15869, "makers": 15870, "Ġconoc": 15871, "Ġeducated": 15872, "Ġmodified": 15873, "Ġinclusion": 15874, "mental": 15875, "ŀIJ": 15876, "isia": 15877, "ĠÏĢοÏħ": 15878, "Ġaun": 15879, "ĠIreland": 15880, "Ġkö": 15881, "Ġcompliance": 15882, "Ġinspiring": 15883, "иÑĤелÑĮно": 15884, "Ġdispos": 15885, "ì°¨": 15886, "Ġwip": 15887, "rical": 15888, "rawd": 15889, "Ġtres": 15890, "Ġmobil": 15891, "olutions": 15892, "BO": 15893, "Ġbounce": 15894, "Ġassumed": 15895, "ĠMedical": 15896, "Ġfiscal": 15897, "ĠngÆ°á»Ŀi": 15898, "itionally": 15899, "Ġstolen": 15900, "ĠBM": 15901, "Ġmechanisms": 15902, "εί": 15903, "Ġqualified": 15904, "ĠìŀIJë": 15905, "ughters": 15906, "ĠHIV": 15907, "ĠLots": 15908, "Ġservers": 15909, "Ġcarr": 15910, "ĠTogether": 15911, "Ġattracted": 15912, "Ġkr": 15913, "æĪijæĺ¯": 15914, "thur": 15915, "inin": 15916, "ĠHalf": 15917, "ÈĽ": 15918, "ĠPap": 15919, "Ġreminded": 15920, "ALL": 15921, "Ġhelmet": 15922, "Ġbottles": 15923, "Ġprofessors": 15924, "Ġseine": 15925, "ÅĤÄħ": 15926, "ãĥı": 15927, "Ġê±°ìķ¼": 15928, "Ġ×¢×ľ": 15929, "fun": 15930, "ĠBird": 15931, "Ġfighter": 15932, "ĠëĶ°ë": 15933, "ĠTool": 15934, "Ġtin": 15935, "inois": 15936, "ë¶Ħ": 15937, "×Ļף": 15938, "ĠCAR": 15939, "åIJį": 15940, "irsty": 15941, "Ġoutdoor": 15942, "ĠNS": 15943, "ãħİ": 15944, "ffen": 15945, "Ġlud": 15946, "Hello": 15947, "Ġroller": 15948, "iele": 15949, "ĠPoland": 15950, "Ġapa": 15951, "exp": 15952, "Ġcertificate": 15953, "ĠTown": 15954, "аÑİÑĤÑģÑı": 15955, "ilde": 15956, "Ġdetermin": 15957, "PR": 15958, "Ġfreeze": 15959, "Ġmainstream": 15960, "Ġobjectives": 15961, "blo": 15962, "Ġtakie": 15963, "åĵĪåĵĪ": 15964, "Ġë°Ķë¡ľ": 15965, "elet": 15966, "ĠIV": 15967, "ĠFast": 15968, "Ġdere": 15969, "emp": 15970, "ĠDra": 15971, "ĠìŀĪìĹĪ": 15972, "Ġdiscrimination": 15973, "Ġείναι": 15974, "necess": 15975, "æ®": 15976, "ıģı": 15977, "Ġposting": 15978, "wiÅĽcie": 15979, "Ġlub": 15980, "Ġolive": 15981, "Ġrim": 15982, "Ġmodeling": 15983, "Ġaño": 15984, "ĠPakistan": 15985, "Ġoverl": 15986, "Ġinflam": 15987, "NE": 15988, "ìĹIJê²Į": 15989, "Ġattended": 15990, "Ġdealt": 15991, "ĠAlt": 15992, "ĠLincoln": 15993, "Ġawake": 15994, "Ġfilters": 15995, "ĠWithin": 15996, "czywiÅĽcie": 15997, "Ġsû": 15998, "ĠJohnny": 15999, "Ġintegrity": 16000, "Ġisolation": 16001, "ĠEasy": 16002, "ĠпÑĢин": 16003, "ĠAlice": 16004, "Ġsmiling": 16005, "enix": 16006, ",...": 16007, "ζ": 16008, "Ġbegun": 16009, "Ġjewel": 16010, "Ġconventional": 16011, "Ġstatist": 16012, "Ġhanded": 16013, "Ġirre": 16014, "Ġprohib": 16015, "Ġsatellite": 16016, "é¦Ļ": 16017, "ĠIndust": 16018, "Ġtraged": 16019, "Ġtrava": 16020, "Ġihm": 16021, "Ġcruel": 16022, "ĠAgora": 16023, "ĠDoc": 16024, "Ġzones": 16025, "Ġmall": 16026, "Ġtray": 16027, "×ķ׳": 16028, "Ġirrit": 16029, "Ġkans": 16030, "ĠBeat": 16031, "udge": 16032, "ielle": 16033, "Ġtrusted": 16034, "Ġbikes": 16035, "ĠÑĥп": 16036, "ĠMember": 16037, "wick": 16038, "Ġcreators": 16039, "Ġheritage": 16040, "indistinct": 16041, "Ġresur": 16042, "ennen": 16043, "Come": 16044, "Ġfiring": 16045, "ĠBueno": 16046, "ĠТо": 16047, "ikan": 16048, "ettes": 16049, "Ġkes": 16050, "Ġtrips": 16051, "Ġdivorce": 16052, "ĠKl": 16053, "Ġconsol": 16054, "keep": 16055, "기ê°Ģ": 16056, "ĠReport": 16057, "Ġhosting": 16058, "Ġdiamond": 16059, "Ġcomplic": 16060, "Ġhelicop": 16061, "Ġdepuis": 16062, "ds": 16063, "ĠChan": 16064, "Ñıл": 16065, "Ġscissors": 16066, "ilation": 16067, "Ġproportion": 16068, "ERE": 16069, "ĠÙĪاÙĦ": 16070, "inta": 16071, "Ġmuchas": 16072, "uation": 16073, "itis": 16074, "æĬĬ": 16075, "ÑıÑī": 16076, "Ġniin": 16077, "Ġemphasize": 16078, "uela": 16079, "Ġproducers": 16080, "Ġrze": 16081, "änder": 16082, "ETH": 16083, "æº": 16084, "Ġconstitu": 16085, "åĽ½": 16086, "Ġperformances": 16087, "istle": 16088, "gov": 16089, "ĠLiter": 16090, "Ġincorporate": 16091, "Ġeducate": 16092, "ĠNin": 16093, "쪽": 16094, "ÙĩÙħ": 16095, "eleration": 16096, "×ķ×ij": 16097, "ĠyaÅŁ": 16098, "orous": 16099, "ĠCas": 16100, "Ġgrants": 16101, "ëĬ¥": 16102, "amel": 16103, "Ġê·¸ëłĩê²Į": 16104, "ĠEste": 16105, "ÑħодиÑĤ": 16106, "ĠпоÑģле": 16107, "Ġgent": 16108, "Ġfocuses": 16109, "alities": 16110, "ĠRh": 16111, "ë³´": 16112, "æ°ij": 16113, "ĠDance": 16114, "rr": 16115, "Ġamer": 16116, "Ġutilize": 16117, "ĠlÃŃ": 16118, "ĠAmong": 16119, "Ġpregnancy": 16120, "Ġloops": 16121, "алоÑģÑĮ": 16122, "ĠMoh": 16123, "Ġcatching": 16124, "Ġglob": 16125, "Ġajud": 16126, "Ġ[?": 16127, "ĠAnal": 16128, "looking": 16129, "Ġsurfaces": 16130, "Ġprogressive": 16131, "Ġviral": 16132, "08": 16133, "ξ": 16134, "KA": 16135, "Ġży": 16136, "Ġpicks": 16137, "annon": 16138, "Ġbulk": 16139, "ĠRoss": 16140, "Ġdescribing": 16141, "ĠGel": 16142, "Ġlocally": 16143, "Ġendless": 16144, "Ġmassage": 16145, "Ġcleaned": 16146, "Ġtraveled": 16147, "енÑĭ": 16148, "Ġsentiment": 16149, "igma": 16150, "ĠNas": 16151, "Ġchemicals": 16152, "Ġrighteous": 16153, "ĠMagic": 16154, "Ġrelates": 16155, "Ġtrucks": 16156, "Ġ1960": 16157, "åĪ¥": 16158, "Ġappet": 16159, "Ġsnacks": 16160, "ĠSummer": 16161, "Ġyüz": 16162, "Ġpris": 16163, "ĠMexican": 16164, "Ġtransparen": 16165, "Ġminority": 16166, "Ġverte": 16167, "Ġlassen": 16168, "46": 16169, "лек": 16170, "ép": 16171, "ĠÑĦилÑĮ": 16172, "Ġiyi": 16173, "Ġspan": 16174, "íķĺì§Ģ": 16175, "Ġindicated": 16176, "quar": 16177, "Ġscholarship": 16178, "ĠLGBT": 16179, "Ġhistorically": 16180, "óÅĤ": 16181, "Ġminist": 16182, "Ġpenet": 16183, "ĠRap": 16184, "Ġconservation": 16185, "缴": 16186, "ĠHoney": 16187, "ĠBei": 16188, "idel": 16189, "Ġresponsibilities": 16190, "Ġmessy": 16191, "ĠExcept": 16192, "ORE": 16193, "Ġinitiatives": 16194, "Ġjunior": 16195, "Ġdesigners": 16196, "Ġexploration": 16197, "Ġsponsor": 16198, "Ġmobility": 16199, "Ġinteg": 16200, "lando": 16201, "Ġbark": 16202, "Ġindicates": 16203, "à¶": 16204, "Ġemployer": 16205, "å®ī": 16206, "Ġcousin": 16207, "Ġboiling": 16208, "Ġchrom": 16209, "Ġçal": 16210, "Ġperpet": 16211, "Ġcontained": 16212, "Ġparks": 16213, "Ы": 16214, "ĠEngineering": 16215, "Please": 16216, "ĠStarting": 16217, "hero": 16218, "Ġlawyers": 16219, "西": 16220, "Ġzd": 16221, "Ġfranchise": 16222, "rage": 16223, "Ġintuit": 16224, "ĠGL": 16225, "reach": 16226, "ĠElle": 16227, "ĠnhÆ°": 16228, "ĠNord": 16229, "Ġbean": 16230, "07": 16231, "Ġpleasant": 16232, "å½ĵ": 16233, "viron": 16234, "Ġgradient": 16235, "zus": 16236, "ĠEM": 16237, "Ġessay": 16238, "ìĹIJìļĶ": 16239, "ến": 16240, "nu": 16241, "ừ": 16242, "ĠÃīs": 16243, "Ġdenomin": 16244, "ĠGirls": 16245, "Ġpersonnes": 16246, "ĠاÙĦØ£": 16247, "bild": 16248, "ĠStat": 16249, "Ġcompliment": 16250, "ĠKate": 16251, "Ġoptimal": 16252, "Ġhid": 16253, "دÙĬ": 16254, "Ġquicker": 16255, "wall": 16256, "En": 16257, "INE": 16258, "???": 16259, "ì²´": 16260, "ĠAction": 16261, "åŁ": 16262, "Ġpenalty": 16263, "ĠKaz": 16264, "'?": 16265, "Ġcried": 16266, "Ġcanvas": 16267, "fte": 16268, "Ġexclud": 16269, "¸ë¡ľ": 16270, "Ġemphasis": 16271, "Ġenzy": 16272, "ĠHou": 16273, "Ġoverseas": 16274, "ÃŃamos": 16275, "師": 16276, "öglich": 16277, "Ġheadphones": 16278, "cn": 16279, "ĠAge": 16280, "Ġakan": 16281, "Ġcharacteristic": 16282, "íķĺë©´": 16283, "gets": 16284, "Ġë¶Ī": 16285, "Ġrival": 16286, "Ġborders": 16287, "emente": 16288, "emás": 16289, "Ġyol": 16290, "Ġcompe": 16291, "enders": 16292, "ından": 16293, "Ġmöglich": 16294, "Ġbubbles": 16295, "natural": 16296, "Ġarmed": 16297, "Ġelabor": 16298, "ĠìĿ´ë²Ī": 16299, "Ġwashed": 16300, "οÏħμε": 16301, "è«ĭ": 16302, "Ġflavors": 16303, "Ġexiste": 16304, "Ġprest": 16305, "ĠThema": 16306, "опÑĢоÑģ": 16307, "eron": 16308, "UE": 16309, "eri": 16310, "Ġconcer": 16311, "Ġaixò": 16312, "åħ©": 16313, "Ġprotective": 16314, "ĠзнаÑİ": 16315, "ĠëĤł": 16316, "ĠIII": 16317, "Ġmeer": 16318, "ĠShop": 16319, "lli": 16320, "ĠOrder": 16321, "ĠMY": 16322, "ĠGhost": 16323, "ãĤĤãģĨ": 16324, "adel": 16325, "Ġstole": 16326, "Ġreleasing": 16327, "ĠComment": 16328, "Ġtrains": 16329, "ëªħ": 16330, "Ġwissen": 16331, "ensed": 16332, "Ġdescend": 16333, "Ġfier": 16334, "Ġradi": 16335, "Ġpersu": 16336, "ç¢": 16337, "Ġмн": 16338, "ĠDest": 16339, "Ġworries": 16340, "itet": 16341, "bas": 16342, "Ġstab": 16343, "name": 16344, "oric": 16345, "ĠClose": 16346, "Ġalumni": 16347, "ĠSelf": 16348, "ffe": 16349, "itating": 16350, "atherine": 16351, "ĠRights": 16352, "Ġellos": 16353, "Ġwarrant": 16354, "Ġnerve": 16355, "Ġvegetable": 16356, "ĠTeil": 16357, "Ġê°ĻìĿ´": 16358, "RY": 16359, "Ġsustainability": 16360, "Ġsteht": 16361, "Ġbrid": 16362, "adaÅŁ": 16363, "Ġtv": 16364, "Ġduration": 16365, "Ġpessoa": 16366, "Ġmetrics": 16367, "Ġadam": 16368, "cas": 16369, "аÑĢи": 16370, "Ġevident": 16371, "Ġdisplayed": 16372, "ائ": 16373, "Ġreck": 16374, "ĠBuddha": 16375, "Ġdele": 16376, "ĠDiego": 16377, "osph": 16378, "Ġbla": 16379, "ĠMik": 16380, "ulator": 16381, "Ġ2001": 16382, "Ġpromoting": 16383, "ych": 16384, "ĠEX": 16385, "Ġlastly": 16386, "Ġoutline": 16387, "Ġspirits": 16388, "Ġveux": 16389, "Ġsubtract": 16390, "ĠÅŁimdi": 16391, "Ġpins": 16392, "Ġburger": 16393, "Ġmolto": 16394, "ĠhabÃŃa": 16395, "Ġë°ĺ": 16396, "igu": 16397, "erst": 16398, "Ġnen": 16399, "Ġbacon": 16400, "itious": 16401, "Ġcarries": 16402, "Ġpromises": 16403, "nde": 16404, "ĠLeft": 16405, "ĠLim": 16406, "æ£": 16407, "Ġ44": 16408, "Ġcareers": 16409, "Ġ주ë": 16410, "Ġspeeds": 16411, "qué": 16412, "mad": 16413, "market": 16414, "isme": 16415, "Ġ2003": 16416, "Ġrecess": 16417, "ĠJUD": 16418, "Ġracist": 16419, "ĠSchl": 16420, "Ġparler": 16421, "Ġotros": 16422, "ishes": 16423, "Ġconverted": 16424, "aaaa": 16425, "ании": 16426, "ĠArk": 16427, "ĠChance": 16428, "Ġelementary": 16429, "εν": 16430, "inks": 16431, "Interviewer": 16432, "Ġfreely": 16433, "alah": 16434, "Ġëĭ¤ë¥¸": 16435, "Ġrequested": 16436, "Ġtorque": 16437, "noÅĽci": 16438, "oured": 16439, "ĠStaff": 16440, "Ġstain": 16441, "ĠAlan": 16442, "Ġvere": 16443, "ĠWinter": 16444, "Ġdefect": 16445, "iedy": 16446, "Ġbeats": 16447, "Ġhá": 16448, "umn": 16449, "oons": 16450, "itudes": 16451, "Ġseit": 16452, "oly": 16453, "Ġreserv": 16454, "Ġextr": 16455, "Ġphysician": 16456, "visor": 16457, "Ġhandful": 16458, "ĠNations": 16459, "Ġì¢ĭìĿĢ": 16460, "uccess": 16461, "Ġupstairs": 16462, "ĠSquare": 16463, "Ġhein": 16464, "ĠSeason": 16465, "olis": 16466, "Ġprince": 16467, "Ġdefensive": 16468, "ç½": 16469, "ĠмеÑģÑĤ": 16470, "Ñĸй": 16471, "ĠاÙĨ": 16472, "umble": 16473, "ê¹ĮìļĶ": 16474, "Ġassass": 16475, "Ġcircular": 16476, "Ġqualities": 16477, "Ġhmm": 16478, "Ġblown": 16479, "ĠLiz": 16480, "ĠKur": 16481, "ĠSA": 16482, "Ġfindings": 16483, "Ġcolours": 16484, "Ġdelle": 16485, "ĠIR": 16486, "ĠAth": 16487, "ĠDub": 16488, "ĠOx": 16489, "ĠØ®": 16490, "Ġpockets": 16491, "Ġgrill": 16492, "Ġswitching": 16493, "Ġpreferred": 16494, "ĠWales": 16495, "Ġexemplo": 16496, "Ġchopped": 16497, "Ġvaccination": 16498, "Ġneuro": 16499, "Ġspecify": 16500, "ivos": 16501, "Ġserá": 16502, "Ġzie": 16503, "Ġà®®": 16504, "Ġresulting": 16505, "ĠUgh": 16506, "Ġmessed": 16507, "CD": 16508, "Ġpaar": 16509, "Ġcomer": 16510, "Ġcouch": 16511, "ĠFestival": 16512, "Ġ49": 16513, "vous": 16514, "zens": 16515, "種": 16516, "ĠKennedy": 16517, "ĠTs": 16518, "Ġë³´ìĹ": 16519, "Ġdemonstration": 16520, "Ġunto": 16521, "Ġfrustrating": 16522, "Ġlaboratory": 16523, "Ġegy": 16524, "Ġbeautifully": 16525, "Ġìŀ¬ë": 16526, "Ġalgu": 16527, "Ġöyle": 16528, "ä½łçľĭ": 16529, "ĠPH": 16530, "Ġfortune": 16531, "Ġcleaner": 16532, "ĠRobin": 16533, "Ġsaus": 16534, "ĠGeld": 16535, "Ġkat": 16536, "obs": 16537, "Ġolur": 16538, "Ġmatt": 16539, "Ġquesta": 16540, "Ġsuggestion": 16541, "encer": 16542, "оÑģÑĤ": 16543, "Ġradar": 16544, "Ġìŀ¡": 16545, "isha": 16546, "ந": 16547, "ãĤĵãģª": 16548, "jes": 16549, "Ġveel": 16550, "ìĤ°": 16551, "Ġauthors": 16552, "ãĢİ": 16553, "plan": 16554, "Ġcollaborative": 16555, "Ġinstinct": 16556, "Ġfarming": 16557, "auge": 16558, "Edu": 16559, "Ġmembership": 16560, "Ġsimultaneously": 16561, "Ġbake": 16562, "Ġkä": 16563, "Ġlectures": 16564, "ÑĩеÑģ": 16565, "Ġprendre": 16566, "Ġcollaps": 16567, "ĠSaya": 16568, "ĠFut": 16569, "Ġyog": 16570, "ĠRather": 16571, "رÙĬ": 16572, "Ġcamps": 16573, "олод": 16574, "Ġsimulation": 16575, "ĠMak": 16576, "Laughs": 16577, "Ġgrey": 16578, "Ġsentences": 16579, "yen": 16580, "ĠUnless": 16581, "Je": 16582, "ĠSatan": 16583, "ĠÑĤакже": 16584, "ĠNA": 16585, "Ġbron": 16586, "Ġ?]": 16587, "Ġsouls": 16588, "Ġlightning": 16589, "Ġimagined": 16590, "Ġczyli": 16591, "psilon": 16592, "etta": 16593, "Ġbelieving": 16594, "Ġstrongest": 16595, "ĠCON": 16596, "Ġquelques": 16597, "Ġimmigrants": 16598, "Ġwallet": 16599, "éĢĻæĺ¯": 16600, "ĠJersey": 16601, "Ġimplications": 16602, "Ġforb": 16603, "ãĢı": 16604, "Ġunbelievable": 16605, "اء": 16606, "Ġoperational": 16607, "üs": 16608, "ĠGM": 16609, "Ġê·¸ëŁ°ëį°": 16610, "Ġgracias": 16611, "Ġentend": 16612, "ĠRegard": 16613, "rob": 16614, "ĠÑĤеÑħ": 16615, "èı": 16616, "ĠRevolution": 16617, "Ġwaar": 16618, "ĠBiz": 16619, "theless": 16620, "Ġsponsored": 16621, "quier": 16622, "ĠìĿ¼ë": 16623, "Ġtek": 16624, "ĠëIJł": 16625, "igkeit": 16626, "ĠLuck": 16627, "ĠCertainly": 16628, "Ġtoll": 16629, "ĠниÑĩего": 16630, "ĠMoney": 16631, "ĠÑģÑĤоÑĢ": 16632, "ĠDouble": 16633, "ĠWolf": 16634, "Ġchunk": 16635, "άν": 16636, "ités": 16637, "oning": 16638, "Mar": 16639, "Ġgrandes": 16640, "Ġcollections": 16641, "ĠEuropa": 16642, "ĠаÑĢ": 16643, "ĠâĢĭâĢĭâĢĭ": 16644, "Ġê·¸ëŁ¬ë©´": 16645, "ĠобÑĬ": 16646, "Ġãģª": 16647, "Ġìĭľê°Ħ": 16648, "ĠCustom": 16649, "Ġì²ĺ": 16650, "ÑĸлÑĮ": 16651, "Ġindividually": 16652, "íĹ": 16653, "Ġdozen": 16654, "Ġowe": 16655, "ĠVictoria": 16656, "åı¯èĥ½": 16657, "Ġbeet": 16658, "urb": 16659, "Ġanalog": 16660, "ição": 16661, "Ĥľ": 16662, "soever": 16663, "Ġmodo": 16664, "Ġsubscribed": 16665, "ìŀ¬": 16666, "Ġentities": 16667, "çīĩ": 16668, "Ġcloset": 16669, "Ġresponding": 16670, "Ġprinter": 16671, "ĠStephan": 16672, "ĠbyÅĤ": 16673, "ĠDom": 16674, "ĠFern": 16675, "ĠPier": 16676, "ĠwiÄĻc": 16677, "Ġhence": 16678, "Ġmodules": 16679, "ãĥ¬": 16680, "ĠëĶ±": 16681, "ĠDanny": 16682, "ĠÑģебе": 16683, "Ġvad": 16684, "ĠìĹĦ": 16685, "Ġsous": 16686, "Ġsphere": 16687, "BY": 16688, "ĠPed": 16689, "igned": 16690, "Ġwheat": 16691, "Ġunders": 16692, "Ġevolve": 16693, "Ġdeclar": 16694, "Ġlightly": 16695, "Ġidentifying": 16696, "æĦıæĢĿ": 16697, "Ġlegendary": 16698, "Ġgenuine": 16699, "Ġgrind": 16700, "ĠUne": 16701, "geben": 16702, "Ġbicy": 16703, "Ġjumps": 16704, "Ġprovince": 16705, "ziÄĻ": 16706, "Ġ×IJ׳×Ļ": 16707, "Ġhoc": 16708, "Ġбл": 16709, "ĠGrad": 16710, "Ġrevenge": 16711, "ĠاÙĦت": 16712, "ooh": 16713, "æĭľ": 16714, "аÑĨии": 16715, "å¹³": 16716, "Ġelectro": 16717, "ĠëIJIJ": 16718, "ãģ§ãģ¯": 16719, "Ġfals": 16720, "riel": 16721, "oker": 16722, "ĠExcellent": 16723, "ĠMorgan": 16724, "Ġbrick": 16725, "Ġsubstantial": 16726, "Ġpollution": 16727, "ĠTür": 16728, "ĠEvet": 16729, "Ġlung": 16730, "ãģĸ": 16731, "×Ļש": 16732, "ommes": 16733, "Ġrealizing": 16734, "Ġhumble": 16735, "ĠLock": 16736, "Ġbod": 16737, "Ġìĸ¸": 16738, "Ġpeers": 16739, "uzz": 16740, "Ġembedded": 16741, "Ġclaro": 16742, "Ġaggreg": 16743, "Ġemployers": 16744, "ĠRaj": 16745, "Ġãģ¨": 16746, "ĠYi": 16747, "Ġjeu": 16748, "aters": 16749, "Ġstrikes": 16750, "nos": 16751, "autres": 16752, "dr": 16753, "opher": 16754, "ĠApparently": 16755, "íĺĦ": 16756, "Ġinfant": 16757, "اب": 16758, "ÑĤÑĭ": 16759, "íĽ": 16760, "Ú¯": 16761, "Ġredes": 16762, "acaģım": 16763, "ĠDAVID": 16764, "ĠChicken": 16765, "Ġperspectives": 16766, "Ġviewer": 16767, "Ġshar": 16768, "ĠпÑĢоиз": 16769, "ligt": 16770, "eros": 16771, "itable": 16772, "илоÑģÑĮ": 16773, "ĠdifÃŃ": 16774, "´ëį°": 16775, "Ġretired": 16776, "Ġthats": 16777, "zenie": 16778, "beiten": 16779, "Ġmycket": 16780, "ĠRab": 16781, "Ġinflamm": 16782, "ì°®": 16783, "Ġdum": 16784, "Ġdaddy": 16785, "æľŁ": 16786, "Ġimmers": 16787, "Ġplaylist": 16788, "à¯Ĩ": 16789, "Ġtraum": 16790, "Ġrefuse": 16791, "step": 16792, "à®ļ": 16793, "cup": 16794, "Ġpops": 16795, "rimin": 16796, "ayım": 16797, "Ġald": 16798, "Ġunnecess": 16799, "Ġdah": 16800, "ĠIrish": 16801, "Ġcompr": 16802, "laÅŁ": 16803, "TP": 16804, "Ġtranslated": 16805, "Sc": 16806, "ceÄŁim": 16807, "´IJ": 16808, "Ġdrei": 16809, "ĠлÑİдей": 16810, "Ġquiero": 16811, "Ġhele": 16812, "zlich": 16813, "Ġapples": 16814, "Ġdistricts": 16815, "Ġcredits": 16816, "Ġasp": 16817, "Ġëĭ¨": 16818, "oral": 16819, "å½±": 16820, "Ġstepping": 16821, "ĠVa": 16822, "Ġgains": 16823, "65": 16824, "Ġnuestra": 16825, "eday": 16826, "assador": 16827, "ĠLind": 16828, "Ġcrops": 16829, "ciendo": 16830, "igue": 16831, "Ġbana": 16832, "Am": 16833, "Ġpent": 16834, "Ġaddiction": 16835, "Ġpackaging": 16836, "äd": 16837, "ª¨": 16838, "Ġperquè": 16839, "Ġcampaigns": 16840, "Ġsteep": 16841, "Ġneue": 16842, "Ġembarrassed": 16843, "Ġdistinction": 16844, "itzer": 16845, "åijĬ": 16846, "Ġregistration": 16847, "Ġllam": 16848, "ĠAlmighty": 16849, "liest": 16850, "Ġuz": 16851, "nak": 16852, "çº": 16853, "Ġteraz": 16854, "iamente": 16855, "Ġtransactions": 16856, "Ġcôt": 16857, "Ġswitched": 16858, "Ġcombo": 16859, "Ġprayers": 16860, "Ġinternship": 16861, "Ġaddresses": 16862, "Ġcharity": 16863, "ĠWOO": 16864, "Ġbait": 16865, "è¿ĩ": 16866, "Ġ�": 16867, "Ġfica": 16868, "ĠTyler": 16869, "aru": 16870, "Ġatoms": 16871, "ĠLevel": 16872, "ĠпоÑĤом": 16873, "Ġfame": 16874, "ulk": 16875, "Ġteaches": 16876, "Ġrebuild": 16877, "едÑĮ": 16878, "ĠIndonesia": 16879, "ushi": 16880, "ĠShort": 16881, "Ġensuring": 16882, "fs": 16883, "ele": 16884, "Ġmarginal": 16885, "Ġconclude": 16886, "amt": 16887, "Ġverify": 16888, "ĠMcDonald": 16889, "Ġskal": 16890, "Ġreconst": 16891, "ĠMann": 16892, "Ġbasement": 16893, "Ġtransformed": 16894, "Ġoccasionally": 16895, "zone": 16896, "ĠDans": 16897, "Ġкакой": 16898, "Ġdiagnosed": 16899, "ĠÏĦα": 16900, "Ġcommands": 16901, "Ġpresidential": 16902, "Ġabb": 16903, "Ġbracket": 16904, "ĠLem": 16905, "Ã¥ng": 16906, "Ġfavorites": 16907, "Ġrevol": 16908, "ĠíĬ¹": 16909, "Ġharass": 16910, "éħ": 16911, "Ġcleans": 16912, "ständ": 16913, "Ġknocked": 16914, "Ġpeoples": 16915, "Ġmusicians": 16916, "Ġmutual": 16917, "ĠCold": 16918, "88": 16919, "zej": 16920, "atie": 16921, "ĠHonor": 16922, "Ġobsessed": 16923, "ĠMUSIC": 16924, "ĠBreak": 16925, "úng": 16926, "Ġmodify": 16927, "Ġsöyle": 16928, "Ġ×ŀ×Ķ": 16929, "ĠOnline": 16930, "fo": 16931, "ĠMiller": 16932, "Ġliking": 16933, "Ġinhab": 16934, "Ġgratitude": 16935, "ĠJournal": 16936, "arness": 16937, "John": 16938, "ĠGit": 16939, "åīĽ": 16940, "Ġsincere": 16941, "ĠSci": 16942, "ĠEli": 16943, "Ġsymbols": 16944, "Ġmanually": 16945, "εÏĤ": 16946, "ĠвÑĸд": 16947, "ĠFat": 16948, "Ġlabels": 16949, "Ġsophisticated": 16950, "umps": 16951, "Ġreleases": 16952, "Ġ47": 16953, "ĠOM": 16954, "ê°Ģë": 16955, "ĠBien": 16956, "ĠRef": 16957, "è¨ĺ": 16958, "ĠSta": 16959, "ĠEgg": 16960, "Ġindicator": 16961, "pson": 16962, "Ġnasıl": 16963, "Right": 16964, "Ġconvey": 16965, "Ġknot": 16966, "Ġconnects": 16967, "ulas": 16968, "Ġpreced": 16969, "Ġinequality": 16970, "amiento": 16971, "Ġreply": 16972, "OY": 16973, "Ġdismiss": 16974, "ĠëIJľ": 16975, "çĦ¡": 16976, "ĠÑħоÑĢоÑĪо": 16977, "Ġméd": 16978, "Ġrandomly": 16979, "ĠOnt": 16980, "uard": 16981, "Ġpulls": 16982, "ĠÑĤепеÑĢÑĮ": 16983, "ĠNeed": 16984, "ĠSoft": 16985, "Ġstrengths": 16986, "Ġgoed": 16987, "umen": 16988, "æŃ»": 16989, "Ġíݸ": 16990, "Ġдоб": 16991, "Ġclarity": 16992, "ĠAi": 16993, "Ġballoon": 16994, "ĠPand": 16995, "ĠìķĦëĭ": 16996, "Ġshiny": 16997, "Ġsmallest": 16998, "onia": 16999, "hill": 17000, "oting": 17001, "Ġeing": 17002, "Ġmerely": 17003, "Ġseus": 17004, "Ġнеп": 17005, "ĠíĨµ": 17006, "Ġguides": 17007, "Ġspecialist": 17008, "Ġsteak": 17009, "ãĤĪãģĨ": 17010, "Ġmigration": 17011, "quele": 17012, "Ġruined": 17013, "Ġpupp": 17014, "女": 17015, "Ġkend": 17016, "angan": 17017, "Ġpalm": 17018, "Ġunfair": 17019, "Ġzm": 17020, "ĠDV": 17021, "chester": 17022, "иÑİ": 17023, "Ġooh": 17024, "erg": 17025, "ATH": 17026, "°©": 17027, "åĵª": 17028, "rison": 17029, "Ġinvolving": 17030, "Ġpartly": 17031, "ançais": 17032, "Ġvow": 17033, "Ġprominent": 17034, "Ġcryst": 17035, "iba": 17036, "Ġdeserves": 17037, "Ġovert": 17038, "Ġsensit": 17039, "ĠWhe": 17040, "Ġtighten": 17041, "Ġintimid": 17042, "Ġaliment": 17043, "will": 17044, "Ġstrengthen": 17045, "ĠTan": 17046, "åıĪ": 17047, "ãģĹãģ¾ãģĻ": 17048, "oni": 17049, "ĠMun": 17050, "Ġproph": 17051, "Ġrehears": 17052, "ĠKle": 17053, "Ġveces": 17054, "Ġwondered": 17055, "oki": 17056, "Ġsenses": 17057, "´ìĭ": 17058, "Æ°á»Ľ": 17059, "ĠÈĻi": 17060, "Ġmuchos": 17061, "Ġwatches": 17062, "ortunate": 17063, "ĠJuan": 17064, "ìŀĸìķĦ": 17065, "ÑĢе": 17066, "ei": 17067, "ionen": 17068, "Ġexperimental": 17069, "Ġdaughters": 17070, "à¸Ľ": 17071, "Ġmentally": 17072, "becca": 17073, "aware": 17074, "ìĦĿ": 17075, "Ġwhatsoever": 17076, "Ġenables": 17077, "ĠLow": 17078, "oid": 17079, "à¸Ĭ": 17080, "ód": 17081, "غ": 17082, "Ġconstructed": 17083, "ĠLadies": 17084, "Ġaccused": 17085, "Ġан": 17086, "Dan": 17087, "Ġspawn": 17088, "Ġcontainers": 17089, "Ġartistic": 17090, "ıp": 17091, "Ġdiscl": 17092, "Ġautres": 17093, "inas": 17094, "ĠNation": 17095, "Ġnag": 17096, "bean": 17097, "whe": 17098, "ľëıĦ": 17099, "ĠSeoul": 17100, "Ġíı¬": 17101, "ĠNich": 17102, "Ġcomplement": 17103, "Ġinterven": 17104, "ĠModel": 17105, "ĠOrange": 17106, "namon": 17107, "Ġcalculation": 17108, "see": 17109, "Ġustedes": 17110, "Ġleb": 17111, "Ġdoct": 17112, "Ñĸн": 17113, "Ġfoster": 17114, "Ġelastic": 17115, "ĠAhh": 17116, "Ġace": 17117, "ĠPink": 17118, "ĠJeg": 17119, "Ġdeer": 17120, "ãģĹãģĦ": 17121, "sis": 17122, "Ġjako": 17123, "ĠEmma": 17124, "ÑģÑĤвенно": 17125, "Ġportrait": 17126, "Ġmaker": 17127, "Ġaument": 17128, "ÑĢоб": 17129, "Ġairplane": 17130, "Ġtransparency": 17131, "Ġadjustment": 17132, "ĠCDC": 17133, "çon": 17134, "Ġuploaded": 17135, "ĠдейÑģÑĤв": 17136, "ĠгоÑĤов": 17137, "Ġiter": 17138, "Ġcurse": 17139, "ôn": 17140, "merce": 17141, "aran": 17142, "Ġleak": 17143, "çµIJ": 17144, "Ġabsence": 17145, "Ñģкий": 17146, "Ġreaders": 17147, "aler": 17148, "Ġbeneath": 17149, "ango": 17150, "hetic": 17151, "Ġfinns": 17152, "Ġpoop": 17153, "Ġduplic": 17154, "Hi": 17155, "igs": 17156, "ologically": 17157, "opp": 17158, "Ġdizer": 17159, "ĠAllen": 17160, "Ġgli": 17161, "Ġacceleration": 17162, "Ġvitamin": 17163, "ãĥŃ": 17164, "vä": 17165, "ĠAccess": 17166, "à®Ļ": 17167, "rás": 17168, "Ġappreciated": 17169, "Ġnah": 17170, "Ġposter": 17171, "Ġtale": 17172, "Ġhighlighted": 17173, "æĸĩ": 17174, "żeli": 17175, "Ġblockchain": 17176, "Ġmicrow": 17177, "Ġcinema": 17178, "ĠChang": 17179, "ĠSearch": 17180, "usters": 17181, "ĠZero": 17182, "ĠDivision": 17183, "ÑĢаÑģ": 17184, "Ġscare": 17185, "Ġjelly": 17186, "ĠAdministration": 17187, "SO": 17188, "Ġlined": 17189, "Ġê°Ħ": 17190, "Ġgeben": 17191, "Ġsoda": 17192, "Ġwinners": 17193, "³¼": 17194, "ÙĴ": 17195, "ĠAmb": 17196, "åķıé¡Į": 17197, "åĶ": 17198, "Ġpeg": 17199, "å·±": 17200, "43": 17201, "Ġraus": 17202, "Ġrewards": 17203, "Ġinclus": 17204, "Ġhighway": 17205, "Ġhah": 17206, "Ġmultiplied": 17207, "Ġsẽ": 17208, "Ġdisciples": 17209, "Ġning": 17210, "Ġdressing": 17211, "Ġattributes": 17212, "ĠMosc": 17213, "ĠGreece": 17214, "Ġsek": 17215, "ĠLearn": 17216, "Ġjus": 17217, "rendre": 17218, "Ġpersonne": 17219, "plete": 17220, "Ġplacing": 17221, "Ġluego": 17222, "illance": 17223, "ĠобÑī": 17224, "Ġprovision": 17225, "Ġlion": 17226, "tra": 17227, "boards": 17228, "Ġbehaviour": 17229, "hey": 17230, "Ġsubscription": 17231, "Ġprotagon": 17232, "ãĥ£": 17233, "Ġvara": 17234, "ĠÅŁu": 17235, "Ġhaha": 17236, "Ġteaspoon": 17237, "æŁ": 17238, "avoir": 17239, "Ġcrypto": 17240, "ĠÑģÑĤаÑĢ": 17241, "ĠStore": 17242, "abs": 17243, "ĠStudents": 17244, "Ġlaund": 17245, "into": 17246, "Ġapproached": 17247, "°ľ": 17248, "ÑĥÑİÑī": 17249, "ĠLabor": 17250, "otes": 17251, "iatric": 17252, "ĠgroÃŁ": 17253, "utive": 17254, "Ġид": 17255, "ĠGib": 17256, "Ġplacement": 17257, "ĠdifÃŃcil": 17258, "Ġfrog": 17259, "ĠвÑģеÑħ": 17260, "ĠJr": 17261, "azed": 17262, "ÑĥÑī": 17263, "Ġê¼": 17264, "frame": 17265, "аеÑĪÑĮ": 17266, "Ġlockdown": 17267, "åij³": 17268, "Ġmedi": 17269, "Ġ×Ķ×ŀ×": 17270, "ений": 17271, "emale": 17272, "ì¢ħ": 17273, "ateral": 17274, "Ġdistant": 17275, "Ġbears": 17276, "Ġjournalist": 17277, "解": 17278, "ĠMarshall": 17279, "ĠIhnen": 17280, "uetooth": 17281, "bag": 17282, "ĠÄijã": 17283, "ĠHighness": 17284, "Ġì°į": 17285, "ика": 17286, "ĠWu": 17287, "ĠFran": 17288, "Ġpeng": 17289, "Ġfon": 17290, "Ġhypothesis": 17291, "ĠÑĢÑĥ": 17292, "Ġly": 17293, "×ļ": 17294, "ìĽĶ": 17295, "ĠRadio": 17296, "à¸ŀ": 17297, "Dav": 17298, "Ġembarrassing": 17299, "ĠìŀĪìĸ´": 17300, "Ġcasting": 17301, "Ġcage": 17302, "ĠPsych": 17303, "ĠìĿ¼ëĭ¨": 17304, "Ġž": 17305, "imb": 17306, "Ġdirectors": 17307, "SH": 17308, "ĠÏĦην": 17309, "á»ģu": 17310, "ĠkonuÅŁ": 17311, "Ġoptional": 17312, "quarters": 17313, "iker": 17314, "ĠSant": 17315, "Ġverses": 17316, "ë¶Ģ": 17317, "Ġolar": 17318, "ĠÏĩ": 17319, "ãĥķ": 17320, "Ġγια": 17321, "ĠImm": 17322, "Ġcontroversial": 17323, "Ġersten": 17324, "Ġrecip": 17325, "ĠChristianity": 17326, "Ġê´ľ": 17327, "ordon": 17328, "×ķש": 17329, "Ġslash": 17330, "ĠPf": 17331, "ÑĥдÑĮ": 17332, "×ķ×Ŀ": 17333, "ĠPerry": 17334, "Ġmamy": 17335, "Ġbackgrounds": 17336, "Ġà®İன": 17337, "Ġpendant": 17338, "ĠColumbia": 17339, "Ġinverse": 17340, "ĠÑĩеÑĢез": 17341, "Ġsv": 17342, "Ġdigging": 17343, "41": 17344, "chem": 17345, "Ġnavigation": 17346, "ĠShin": 17347, "ĠFront": 17348, "PD": 17349, "Ġbearing": 17350, "ĠWasser": 17351, "Ġwax": 17352, "ĠCHRIS": 17353, "ching": 17354, "Ġpressed": 17355, "El": 17356, "ĠDal": 17357, "onsin": 17358, "Ġbinding": 17359, "Ñģкой": 17360, "poons": 17361, "Ġmock": 17362, "arest": 17363, "кÑĢа": 17364, "MM": 17365, "Ġcorrupt": 17366, "storm": 17367, "Ġrefres": 17368, "ĠCoach": 17369, "llä": 17370, "ĠTHIS": 17371, "Ġparag": 17372, "Ġìĵ°": 17373, "pool": 17374, "Ġbillions": 17375, "Ġê¹Ģ": 17376, "group": 17377, "Ġwelcoming": 17378, "cellence": 17379, "ĠDuke": 17380, "긴": 17381, "Ġprimera": 17382, "ìł¸": 17383, "Ġpond": 17384, "Ġstatue": 17385, "Ġ구ë": 17386, "Ġhatch": 17387, "Ġinstrumental": 17388, "Ġresidential": 17389, "커": 17390, "Ġaccepting": 17391, "oshi": 17392, "date": 17393, "ĠìĶ¨": 17394, "Ġplanted": 17395, "Ġjoking": 17396, "ĠìĦľ": 17397, "Ġhated": 17398, "ĠÑĢаÑģÑģк": 17399, "Ġslept": 17400, "Ġpackages": 17401, "Ġislands": 17402, "esen": 17403, "ģı": 17404, "Ġdiagon": 17405, "ĠOsc": 17406, "Ġmesh": 17407, "Ġscales": 17408, "arity": 17409, "ĠDefense": 17410, "ãģ¡ãĤĩ": 17411, "ĠLewis": 17412, "ĠÑģегоднÑı": 17413, "Ġflies": 17414, "uinely": 17415, "ĠConsider": 17416, "Ġstark": 17417, "hew": 17418, "ĠAsÃŃ": 17419, "³´ë": 17420, "Ġpropose": 17421, "Ġíķĺë©´": 17422, "odo": 17423, "ĠNormally": 17424, "Ġheeft": 17425, "ĠHarris": 17426, "gro": 17427, "ĠBlood": 17428, "base": 17429, "ĠiOS": 17430, "Ġtouches": 17431, "Ġinspir": 17432, "Ġ×ĵ": 17433, "Ġbinary": 17434, "Ġì¶Ķ": 17435, "Ġserial": 17436, "Ġion": 17437, "Ġunemployment": 17438, "Ġodds": 17439, "ĠFab": 17440, "ĠFBI": 17441, "BRUN": 17442, "Ġweights": 17443, "νο": 17444, "atile": 17445, "Ġnurses": 17446, "Ġinvolvement": 17447, "ĠíĶ¼": 17448, "Ġgovernance": 17449, "ĠâĤ¬": 17450, "ÑĢÑĥп": 17451, "ierra": 17452, "íĺķ": 17453, "ĠJerry": 17454, "Ġbeard": 17455, "Ġsalvation": 17456, "ĠAlong": 17457, "gentle": 17458, "ĠKi": 17459, "bol": 17460, "ĠPlat": 17461, "Ġhasht": 17462, "è¿ij": 17463, "Ġware": 17464, "Ġpartie": 17465, "ycz": 17466, "Ġintr": 17467, "Fih": 17468, "nent": 17469, "Ġcheat": 17470, "ilen": 17471, "Ġë¯": 17472, "orie": 17473, "Ġfácil": 17474, "etric": 17475, "Ġaffecting": 17476, "unciation": 17477, "Ġaffairs": 17478, "Ġbee": 17479, "Ġviewing": 17480, "Ġorang": 17481, "ĠLan": 17482, "ĠСÑĤ": 17483, "ä¸ĸ": 17484, "ĠMes": 17485, "ĥģ": 17486, "erie": 17487, "Ġespa": 17488, "Ġinterpre": 17489, "Ġpossess": 17490, "Ġpurely": 17491, "rito": 17492, "found": 17493, "asma": 17494, "ìłģìĿ¸": 17495, "Ġexamine": 17496, "ĠÑĥм": 17497, "Ġbesch": 17498, "ĠTomorrow": 17499, "ĠBlock": 17500, "Ġvariant": 17501, "Ġpreference": 17502, "Ġcoaches": 17503, "Ġmedications": 17504, "ĠíĺĦ": 17505, "Ġempire": 17506, "ëĦ¤": 17507, "ĠIllinois": 17508, "Ġcrispy": 17509, "Ġthì": 17510, "Ġbees": 17511, "77": 17512, "Ġglow": 17513, "èº": 17514, "ĠStudies": 17515, "åIJĦ": 17516, "ĠChallenge": 17517, "Ġunlikely": 17518, "Ч": 17519, "ıyorsun": 17520, "DIE": 17521, "Ġminimize": 17522, "izard": 17523, "Ġún": 17524, "Ġencontrar": 17525, "ĠKill": 17526, "å»": 17527, "Ġvanilla": 17528, "ĠGrant": 17529, "ĠGT": 17530, "sea": 17531, "Ġsought": 17532, "вод": 17533, "Ġnäm": 17534, "ĠAunt": 17535, "OWN": 17536, "Ġpumpkin": 17537, "stellen": 17538, "Ġrag": 17539, "егда": 17540, "Ġstoryt": 17541, "Ġforum": 17542, "æ©Ł": 17543, "Ġestaba": 17544, "uche": 17545, "Ġcongress": 17546, "ĠRey": 17547, "Ġdramatically": 17548, "ĠSport": 17549, "ĠYellow": 17550, "Ġê³ĦìĨį": 17551, "Ġdisgusting": 17552, "ĠRecent": 17553, "Ġacquired": 17554, "Ġcables": 17555, "çĶļ": 17556, "din": 17557, "Ġvisto": 17558, "Ġcommunicating": 17559, "ÑģÑĤавлÑı": 17560, "еÑģÑĤо": 17561, "ãĥ»ãĥ»ãĥ»": 17562, "Ġrég": 17563, "Ġsocks": 17564, "Ġproces": 17565, "because": 17566, "Ġutter": 17567, "Ġcolocar": 17568, "Ġnewest": 17569, "Ġgramm": 17570, "表": 17571, "ä¸įçŁ¥éģĵ": 17572, "Ġshifting": 17573, "Ġcarrier": 17574, "ĠÑģкоÑĢ": 17575, "ĠSchw": 17576, "Ġexecuted": 17577, "Ġmaintained": 17578, "ĠÏĨ": 17579, "ĠMoses": 17580, "Ġdisse": 17581, "Ġhorr": 17582, "ãĢľ": 17583, "Ġrally": 17584, "Ġallem": 17585, "ĠEventually": 17586, "Ġdiyor": 17587, "lvania": 17588, "Ġschnell": 17589, "Ġê³¼": 17590, "Ġ매": 17591, "Ġstruggles": 17592, "late": 17593, "Ġclarify": 17594, "ément": 17595, "Ġmultiplic": 17596, "ибо": 17597, "Ġjourn": 17598, "Ġfragr": 17599, "Ġsurprisingly": 17600, "Ġdesperate": 17601, "52": 17602, "Ġsul": 17603, "ĠRead": 17604, "ĠFried": 17605, "Ġmond": 17606, "woo": 17607, "Ġorganizing": 17608, "ãģĹãĤĩãģĨ": 17609, "ĠSoon": 17610, "ĠвопÑĢоÑģ": 17611, "ĠNur": 17612, "ĠÐĹд": 17613, "Ġspider": 17614, "еÑģÑı": 17615, "Ġtutorials": 17616, "Ġnutrients": 17617, "orer": 17618, "Ġcoefficient": 17619, "Ġarrangement": 17620, "Ġpricing": 17621, "nan": 17622, "yu": 17623, "BL": 17624, "Ġtribe": 17625, "ĠHoward": 17626, "unks": 17627, "Ġnewer": 17628, "Ġprovin": 17629, "Ġprediction": 17630, "hos": 17631, "Ġolsun": 17632, "ĠAround": 17633, "Ġvier": 17634, "ĠÑģÑĤоÑĢон": 17635, "Ġvalley": 17636, "ĠEla": 17637, "ifi": 17638, "Ġgalaxy": 17639, "Ġtranqu": 17640, "Ġadvers": 17641, "ĠTemple": 17642, "iffs": 17643, "igence": 17644, "èĩªå·±": 17645, "Ġkönnte": 17646, "ĠÄijó": 17647, "Did": 17648, "Ġphotographs": 17649, "ĠAWS": 17650, "ÑĨиÑı": 17651, "Ġguards": 17652, "Ġappointed": 17653, "ĠGil": 17654, "Ġмом": 17655, "Ġcod": 17656, "ĠUnlike": 17657, "Ġevenly": 17658, "isconsin": 17659, "Ġestou": 17660, "Ġmnie": 17661, "ĠExec": 17662, "ĠMV": 17663, "ĠEine": 17664, "ä¿¡": 17665, "ĠRoger": 17666, "ĠFac": 17667, "ĠList": 17668, "Ġfuer": 17669, "аеÑĤе": 17670, "omed": 17671, "Ġattraction": 17672, "èī²": 17673, "Ġterrain": 17674, "ĠDrop": 17675, "Ġcorporations": 17676, "Ġsciences": 17677, "Ġthrone": 17678, "ãģĦãģŁ": 17679, "Ġaj": 17680, "ĠRot": 17681, "çī¹": 17682, "Ġsupporters": 17683, "ĠBere": 17684, "Here": 17685, "Ġdiferentes": 17686, "Ġsignificance": 17687, "Ïĥη": 17688, "æĪij覺å¾Ĺ": 17689, "Ġclamp": 17690, "ĠëĮĢë": 17691, "Ġfabulous": 17692, "rez": 17693, "æĮģ": 17694, "Ġassumptions": 17695, "uther": 17696, "wid": 17697, "pot": 17698, "è¿İ": 17699, "Ġyan": 17700, "ulin": 17701, "ÑĢÑĭв": 17702, "ĠSlow": 17703, "ĠPennsy": 17704, "Ġíķ´ìĦľ": 17705, "Ġmeio": 17706, "Ġwealthy": 17707, "ĠEight": 17708, "Ġpulse": 17709, "Ġfriction": 17710, "idity": 17711, "ĠHoll": 17712, "iyorum": 17713, "Ġsounded": 17714, "ĠCarr": 17715, "Ġfork": 17716, "âĺ": 17717, "ĠPA": 17718, "Ġconspir": 17719, "Ġcoding": 17720, "rt": 17721, "ĠTyp": 17722, "Ġìĸij": 17723, "Ġпог": 17724, "Ġmiser": 17725, "ĠÑģмоÑĤÑĢ": 17726, "ĠSweden": 17727, "Ġolarak": 17728, "ĠZhang": 17729, "ĠChi": 17730, "ĠTitan": 17731, "Ġscreening": 17732, "ĠSpider": 17733, "ĠÅŀimdi": 17734, "Ġobstacles": 17735, "lara": 17736, "Ġchallenged": 17737, "pse": 17738, "TON": 17739, "ụ": 17740, "ĠPi": 17741, "Ġlagi": 17742, "ieurs": 17743, "Ġhurting": 17744, "Ġneglect": 17745, "Ġgenerating": 17746, "Ġyoungest": 17747, "Ġaudit": 17748, "ĠÑĢез": 17749, "Ïģά": 17750, "Ġdonate": 17751, "ĠPDF": 17752, "Ġvisits": 17753, "Ġcruise": 17754, "PP": 17755, "aser": 17756, "Ġwsp": 17757, "backs": 17758, "ivals": 17759, "ãģĨãĤĵ": 17760, "Ġdeve": 17761, "Ġproport": 17762, "Ġcath": 17763, "ĠEffect": 17764, "Ġwinds": 17765, "ĠìĻĶ": 17766, "Ġcharts": 17767, "Ġsama": 17768, "Ġautomation": 17769, "Ġпока": 17770, "Ġolan": 17771, "Ġboats": 17772, "Ġcafe": 17773, "Ġdenied": 17774, "ĠMama": 17775, "Ġblocking": 17776, "ĠThor": 17777, "Ġphenomenal": 17778, "Ġstakeholders": 17779, "Ġunos": 17780, "ÑĥеÑĤ": 17781, "ĠAbraham": 17782, "ãģ§ãĤĤ": 17783, "Ġdetection": 17784, "Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, "ÑĤом": 17804, "Ġshore": 17805, "gunta": 17806, "Ġku": 17807, "Ġfaithful": 17808, "ĠJeremy": 17809, "aid": 17810, "à·": 17811, "ugal": 17812, "å°įåķĬ": 17813, "ĠVel": 17814, "Ġvrai": 17815, "stell": 17816, "¨¸": 17817, "Ġkol": 17818, "è½": 17819, "Ġquanto": 17820, "ĠзаÑĢ": 17821, "Ġ2002": 17822, "esy": 17823, "Ġreserve": 17824, "ĠмоменÑĤ": 17825, "Ġdeployed": 17826, "Ġdefining": 17827, "Ġsau": 17828, "Ġgaat": 17829, "\")": 17830, "Ġtransmit": 17831, "Ġpublishing": 17832, "Ġranking": 17833, "Ġoffense": 17834, "Ġ46": 17835, "pin": 17836, "ĠTaking": 17837, "Ġentitled": 17838, "Ġgenuinely": 17839, "Ġvariations": 17840, "Ġfinde": 17841, "Ġtau": 17842, "Ġunfortunate": 17843, "ĠRah": 17844, "ports": 17845, "ĠcÅ": 17846, "Ġmonkey": 17847, "Ġbrac": 17848, "wei": 17849, "lung": 17850, "Ġartif": 17851, "Ġsyrup": 17852, "ĠÐĶав": 17853, "Ġlifted": 17854, "Ġchez": 17855, "ĠAdvent": 17856, "ĠStock": 17857, "Ġdol": 17858, "мен": 17859, "иÑĪÑĮ": 17860, "Ġyn": 17861, "gio": 17862, "det": 17863, "Ġdesse": 17864, "Ġgri": 17865, "ĠChairman": 17866, "çħ": 17867, "Ġcuenta": 17868, "anim": 17869, "Ġcrab": 17870, "Ġescal": 17871, "Ġpremière": 17872, "ĠGef": 17873, "Ġdining": 17874, "Ġseventh": 17875, "Ġchasing": 17876, "ĠTower": 17877, "Ġbrutal": 17878, "Ġfundamentally": 17879, "ãģ¨ãģĨ": 17880, "лениÑı": 17881, "stage": 17882, "Ġacquis": 17883, "Ġcylinder": 17884, "Ġcommander": 17885, "mem": 17886, "ĠUV": 17887, "happy": 17888, "Ġepsilon": 17889, "Ġinvitation": 17890, "Ġfarmer": 17891, "chair": 17892, "Ġdestiny": 17893, "Ġsovere": 17894, "ĠHebrew": 17895, "Ġservant": 17896, "Ġbew": 17897, "Ġgast": 17898, "uties": 17899, "Ġadministrative": 17900, "ĠCommand": 17901, "éta": 17902, "Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, "ìĨĮë": 17921, "Ġting": 17922, "Ġnasty": 17923, "Ġgauge": 17924, "ĠÑĢод": 17925, "ĠHans": 17926, "Your": 17927, "αν": 17928, "Ġprojet": 17929, "ĠHawaii": 17930, "Ġsuspicious": 17931, "Ġschw": 17932, "Ġremoval": 17933, "Ġintrig": 17934, "ĠMU": 17935, "Ġponto": 17936, "ा": 17937, "ĠобÑĢаз": 17938, "Ġguessing": 17939, "pace": 17940, "Ġmothers": 17941, "Ġmillimeter": 17942, "ление": 17943, "没æľī": 17944, "Ġavailability": 17945, "icz": 17946, "æѤ": 17947, "Ġfract": 17948, "Ġbases": 17949, "km": 17950, "ĠBTS": 17951, "ĠField": 17952, "Ġdzie": 17953, "Ġsegundo": 17954, "ĠëĤĺëĬĶ": 17955, "Ġlegitimate": 17956, "imas": 17957, "Ġвн": 17958, "Ġcorruption": 17959, "Ġsmash": 17960, "ĠValent": 17961, "Ġaligned": 17962, "ĠPennsylvania": 17963, "Ġgab": 17964, "ĠEun": 17965, "enth": 17966, "ĠMorning": 17967, "Ġcandle": 17968, "Ġbackpack": 17969, "ĠIslamic": 17970, "ações": 17971, "Ġencry": 17972, "Ġmushrooms": 17973, "íĮĮ": 17974, "dit": 17975, "Ġtransit": 17976, "ĠWisconsin": 17977, "Ġparticipated": 17978, "ĠIls": 17979, "Ġunfold": 17980, "¶Ģë": 17981, "Ġprofits": 17982, "Ġwarming": 17983, "ĠGang": 17984, "Ġnetworking": 17985, "Ġmega": 17986, "Ġthoroughly": 17987, "lements": 17988, "ĠHm": 17989, "Ġdeciding": 17990, "Ġemotionally": 17991, "Ġexhausted": 17992, "ĠÐŁÐ¾ÑĤ": 17993, "cido": 17994, "ĠHTML": 17995, "Ġcopyright": 17996, "Ġmelody": 17997, "yim": 17998, "Ġanders": 17999, "oshop": 18000, "Ġë³¼": 18001, "Ġathlete": 18002, "ĠGE": 18003, "Ġfrequent": 18004, "Ġdesires": 18005, "Ġneeding": 18006, "ĠYun": 18007, "Ġrifle": 18008, "Ġlover": 18009, "'T": 18010, "Ġdense": 18011, "Ġtão": 18012, "Ġnotified": 18013, "Ġidi": 18014, "ìĹŃ": 18015, "íĨ": 18016, "Ġinteracting": 18017, "Ġrapport": 18018, "еÑĢи": 18019, "ski": 18020, "Ġbesser": 18021, "Ġmanufacturer": 18022, "ĠKyle": 18023, "Ġaccountable": 18024, "ĠSak": 18025, "ĠPil": 18026, "ĠDomin": 18027, "Ġpresum": 18028, "ĠÐĴÑģе": 18029, "Ġvinegar": 18030, "Ġguaranteed": 18031, "çľĭåĪ°": 18032, "Ġhandled": 18033, "éŁ³": 18034, "cat": 18035, "Ġcivilization": 18036, "Ġaccomp": 18037, "ĠVM": 18038, "émon": 18039, "Ġdeze": 18040, "Ġgrades": 18041, "Ġsollte": 18042, "Ġstaring": 18043, "×IJת": 18044, "arnt": 18045, "Ġhorizon": 18046, "Ġtravail": 18047, "hour": 18048, "第ä¸Ģ": 18049, "ĠED": 18050, "ĠDak": 18051, "Ġny": 18052, "Ġconve": 18053, "ĠCham": 18054, "Ġfirms": 18055, "ĠLiu": 18056, "ĠÑģÑĤÑĢан": 18057, "Ġlibert": 18058, "Ġlenses": 18059, "Ġintake": 18060, "ĠвÑĭб": 18061, "Ġmensen": 18062, "hel": 18063, "Ġpractition": 18064, "Ġ350": 18065, "ãĤ³": 18066, "FO": 18067, "Ġbeds": 18068, "Ġancestors": 18069, "ĠìĹĦì²Ń": 18070, "Ġdisturb": 18071, "ĠLastly": 18072, "ĠSupport": 18073, "ีà¹ī": 18074, "ĠCorona": 18075, "Ġenthusi": 18076, "Ġвозм": 18077, "ĠìĤ¬ëŀĮë": 18078, "Ġ52": 18079, "bird": 18080, "Ġreduces": 18081, "ĠìŀĪìĿĦ": 18082, "ĠGene": 18083, "êµIJ": 18084, "ÄĻp": 18085, "ĠÃľber": 18086, "Ġconcerning": 18087, "user": 18088, "Ġconcentrate": 18089, "ĠWHAT": 18090, "ishop": 18091, "onymous": 18092, "nold": 18093, "Ġsuggesting": 18094, "©°": 18095, "ĠFish": 18096, "........": 18097, "Ġvessel": 18098, "Ġtrabajo": 18099, "ãģµ": 18100, "ĠOcean": 18101, "å§IJ": 18102, "yg": 18103, "Ġtowns": 18104, "del": 18105, "Ġterrifying": 18106, "ĠçalÄ±ÅŁ": 18107, "Ġsino": 18108, "Ġeats": 18109, "Ġgez": 18110, "Ġgeme": 18111, "ĠìĻĦ": 18112, "Ġcompart": 18113, "Ġimplementing": 18114, "ĠPotter": 18115, "ĠGermans": 18116, "ĠgÅĤ": 18117, "Ġtennis": 18118, "Ġcarpet": 18119, "auer": 18120, "ĠSaudi": 18121, "yeong": 18122, "Ġcurry": 18123, "ĠForest": 18124, "Ñĭл": 18125, "Ġfifteen": 18126, "Ġbolts": 18127, "Ġ{\\": 18128, "¬´": 18129, "Ġsettlement": 18130, "Ġlange": 18131, "Ġbam": 18132, "Get": 18133, "íķĻ": 18134, "Ġswap": 18135, "ĠKhan": 18136, "Ġcommence": 18137, "Ġquarantine": 18138, "Ġscored": 18139, "çĸ": 18140, "Ġ1950": 18141, "Ġthicker": 18142, "Ġsûr": 18143, "åı£": 18144, "ĠLarry": 18145, "Ġallez": 18146, "ìĭľëĬĶ": 18147, "Ġgü": 18148, "Ġspectacular": 18149, "//": 18150, "both": 18151, "Ġstats": 18152, "妳": 18153, "ĠNancy": 18154, "Ġbunu": 18155, "Ġcrust": 18156, "Ġactivated": 18157, "Ġê·¸ëŀ": 18158, "outhe": 18159, "Ġports": 18160, "Ġneural": 18161, "Ġjaw": 18162, "Ġobservations": 18163, "Ġvoit": 18164, "aban": 18165, "ải": 18166, "¦¬ë¥¼": 18167, "omes": 18168, "à¯ĭ": 18169, "qui": 18170, "Ġkindness": 18171, "Ðij": 18172, "Ġ41": 18173, "Ġmoderate": 18174, "Ġangels": 18175, "ĠTamb": 18176, "èt": 18177, "Ġchlor": 18178, "ĠBilly": 18179, "ì²ĺë": 18180, "acon": 18181, "Ġselecting": 18182, "ĠDelta": 18183, "Ġnull": 18184, "denly": 18185, "Ġciud": 18186, "Ġtendency": 18187, "Ġbreakdown": 18188, "Ġmint": 18189, "ÑĦоÑĢм": 18190, "orph": 18191, "Ġdawn": 18192, "spr": 18193, "ĠWILL": 18194, "ächlich": 18195, "Ġpuppy": 18196, "700": 18197, "Ġத": 18198, "Ġfails": 18199, "ĠConc": 18200, "Ġrelatives": 18201, "Ġinviting": 18202, "Ġautonom": 18203, "Ġcomposed": 18204, "Ġunity": 18205, "Ġdecis": 18206, "Ġaccessories": 18207, "ĠCass": 18208, "Ġbist": 18209, "ĠTip": 18210, "째": 18211, "Ġpunt": 18212, "Ġráp": 18213, "éĢ²": 18214, "ANK": 18215, "ãģļ": 18216, "exist": 18217, "Ġcompatible": 18218, "Ġner": 18219, "ĠемÑĥ": 18220, "Ġaplic": 18221, "Ġbapt": 18222, "Ġfailing": 18223, "ĠTamam": 18224, "Ġoscill": 18225, "Ġletzten": 18226, "Ġrepeatedly": 18227, "Ġjungle": 18228, "ĠPush": 18229, "hai": 18230, "Ġη": 18231, "Ġdeadly": 18232, "Ñıж": 18233, "wiÄħ": 18234, "ĠCommon": 18235, "ĠÎķ": 18236, "Ġskate": 18237, "TC": 18238, "ĠMini": 18239, "Ġhobby": 18240, "ần": 18241, "Ġroutes": 18242, "Ġamigos": 18243, "Ġconjun": 18244, "Ġpartnerships": 18245, "Ġnovo": 18246, "Ġaver": 18247, "Ġpouvez": 18248, "bridge": 18249, "Ġpreoc": 18250, "him": 18251, "Ġturb": 18252, "Ġsob": 18253, "ĠSnap": 18254, "Ġì°¸": 18255, "minute": 18256, "Ġtraject": 18257, "ujÄĻ": 18258, "Ġeager": 18259, "Ġregulatory": 18260, "Ġbanking": 18261, "bling": 18262, "ÑĪÑĮ": 18263, "aż": 18264, "Ġbizarre": 18265, "itated": 18266, "dire": 18267, "Ġthreatened": 18268, "Ġshining": 18269, "Ġnesse": 18270, "Ġcorps": 18271, "ĠÑģÑĥ": 18272, "Ġteles": 18273, "Ġtemp": 18274, "tem": 18275, "Ġкан": 18276, "Ġfever": 18277, "New": 18278, "Ġheavier": 18279, "ĠSah": 18280, "bud": 18281, "Ġoutros": 18282, "Ġì°¾": 18283, "Ġëªħ": 18284, "arring": 18285, "Ġê´ľì°®": 18286, "ĠNap": 18287, "Ġsemin": 18288, "ĠThan": 18289, "ifs": 18290, "Ġdesen": 18291, "ĠÑĤакое": 18292, "Ġloses": 18293, "ĠBalt": 18294, "kon": 18295, "ĠнапÑĢ": 18296, "Ġvois": 18297, "ĠMoscow": 18298, "Ġchairs": 18299, "his": 18300, "Ġrefugees": 18301, "kg": 18302, "Ġkole": 18303, "į¨": 18304, "аÑģибо": 18305, "¦½": 18306, "ĠUniverse": 18307, "ĠDirect": 18308, "Ġcheating": 18309, "ĠCin": 18310, "Ġpatri": 18311, "Ġadvise": 18312, "ĠNether": 18313, "Ġprimeiro": 18314, "Ġmentioning": 18315, "nut": 18316, "56": 18317, "arı": 18318, "Ġpetite": 18319, "bled": 18320, "Ġpensar": 18321, "icio": 18322, "IND": 18323, "Ġveteran": 18324, "Ġladder": 18325, "Ġconsequence": 18326, "ожал": 18327, "ĠBurn": 18328, "Ġrug": 18329, "ĠMade": 18330, "Ġgit": 18331, "\"...": 18332, "Ġcompetitors": 18333, "Ġprzed": 18334, "Ġapparent": 18335, "ĠArgentina": 18336, "ĠWorking": 18337, "Ġcollaborate": 18338, "woman": 18339, "Ġretain": 18340, "Ġleurs": 18341, "Ġdashboard": 18342, "×Ļ×ĵ": 18343, "ĠEarly": 18344, "BM": 18345, "ĠеÑij": 18346, "олог": 18347, "Ġsatisfying": 18348, "Ġoftentimes": 18349, "Ġmapping": 18350, "ünkü": 18351, "arth": 18352, "fold": 18353, "Ġlaunching": 18354, "Ġaura": 18355, "Ġprecision": 18356, "works": 18357, "God": 18358, "Ġstrap": 18359, "ĠImper": 18360, "Ġrivers": 18361, "Ġ|": 18362, "Ġcuer": 18363, "regon": 18364, "Ġarrival": 18365, "каÑħ": 18366, "ĠMiami": 18367, "анÑĭ": 18368, "Ġsurvivors": 18369, "ĠSenior": 18370, "David": 18371, "Ġestado": 18372, "Ġsectors": 18373, "Ġpopping": 18374, "Ġchim": 18375, "ayı": 18376, "Ġkunnen": 18377, "Ġgallery": 18378, "Ġsunlight": 18379, "esehen": 18380, "Ġyelling": 18381, "ĠMein": 18382, "ĠPhoenix": 18383, "Ġmano": 18384, "Ġhistoria": 18385, "Ġoccurring": 18386, "欸": 18387, "ì¸": 18388, "ади": 18389, "å¾ħ": 18390, "Ġinstitutional": 18391, "ĠTut": 18392, "ç²": 18393, "Ġslaves": 18394, "ãģ©ãģĨ": 18395, "Ġforgiveness": 18396, "Ġtwin": 18397, "ĠHyun": 18398, "нÑĮ": 18399, "ĠKomm": 18400, "andra": 18401, "shot": 18402, "ssä": 18403, "ĠÑĨе": 18404, "atta": 18405, "Ġexpense": 18406, "ĠGPU": 18407, "ĠPast": 18408, "ribly": 18409, "ĠëŃIJìķ¼": 18410, "Ġгода": 18411, "Ġrespir": 18412, "æĿ±": 18413, "ĠQueens": 18414, "hops": 18415, "Ġsérie": 18416, "Ġpref": 18417, "Ġcomed": 18418, "Ġplut": 18419, "ĠOverall": 18420, "ĠãģĿ": 18421, "Ġcush": 18422, "Ġringing": 18423, "Ġincorrect": 18424, "ĠÑģÑĤÑĢ": 18425, "Ġgeometry": 18426, "Ġadvertis": 18427, "ĠШ": 18428, "Ġreviewed": 18429, "ãģĤãģĤ": 18430, "Ġdozens": 18431, "Ġdetermination": 18432, "ĠPhill": 18433, "Ġcontributed": 18434, "ĠCit": 18435, "Ġpassengers": 18436, "Ġcôté": 18437, "Ġrever": 18438, "Ġtechnological": 18439, "Ġallen": 18440, "Ġraining": 18441, "avi": 18442, "Ġsalty": 18443, "Ġtyping": 18444, "ĠÑĤе": 18445, "Ġtilt": 18446, "Ġì¹ĺ": 18447, "ĠоÑĢ": 18448, "ĠпÑĢÑıм": 18449, "Ġrou": 18450, "Ġarena": 18451, "arat": 18452, "åĪ«": 18453, "HHHH": 18454, "Ġmanufacturers": 18455, "ĠEdward": 18456, "Ġtuck": 18457, "Ġblows": 18458, "ingo": 18459, "ĠMarc": 18460, "ìķĦìĦľ": 18461, "Mich": 18462, "ĠClean": 18463, "è´": 18464, "esto": 18465, "ĠPack": 18466, "Ġshaft": 18467, "BRUNO": 18468, "Ġaven": 18469, "uur": 18470, "ÑģколÑĮко": 18471, "ê´Ģ": 18472, "Ġautomated": 18473, "Ġventure": 18474, "Ġsurveillance": 18475, "ĠGrow": 18476, "ĠEmer": 18477, "ĠдоÑĢ": 18478, "Ġinvestor": 18479, "ĠYok": 18480, "Ġlatter": 18481, "ĠNI": 18482, "Ġfunctioning": 18483, "ĠHamilton": 18484, "Ġ51": 18485, "Ġmurdered": 18486, "Ġanchor": 18487, "Ġcuc": 18488, "ĠSCP": 18489, "ĠMadam": 18490, "Ġconstraints": 18491, "Ġbarn": 18492, "anken": 18493, "Ġë§İìĿĢ": 18494, "ĠMotor": 18495, "ĠDoing": 18496, "Ġamen": 18497, "etts": 18498, "Ġinstructor": 18499, "egt": 18500, "ako": 18501, "Ġposture": 18502, "ivia": 18503, "ĠPolish": 18504, "Ġдва": 18505, "Ġcolorful": 18506, "Ġelbow": 18507, "Ġparle": 18508, "Ġpasser": 18509, "Ġcondem": 18510, "ortal": 18511, "Ġfertil": 18512, "اد": 18513, "ĠColomb": 18514, "Ġalignment": 18515, "Ġastronaut": 18516, "ĠMut": 18517, "Ġsalmon": 18518, "Ġstructured": 18519, "ŀר": 18520, "Ġclicks": 18521, "Ġmiej": 18522, "æĶ¿": 18523, "ãģĦãĤĦ": 18524, "ĠRound": 18525, "Ġrainbow": 18526, "ĠVA": 18527, "ãģĶãģĸ": 18528, "ì§Ī": 18529, "otz": 18530, ",": 21732, "Ġchords": 21733, "ĠSanders": 21734, "Ġë¶Ħë": 21735, "Ben": 21736, "Ġdarüber": 21737, "ilians": 21738, "Ġordering": 21739, "ĠManh": 21740, "Ġkilogram": 21741, "ĠkarÅŁ": 21742, "Ġgrasp": 21743, "Ġghosts": 21744, "alen": 21745, "ĠJedi": 21746, "Ġбли": 21747, "Ġdownloaded": 21748, "Ġconducting": 21749, "ĠHak": 21750, "Ġresearcher": 21751, "ilan": 21752, "good": 21753, "ĠHannah": 21754, "ĠdÃ¼ÅŁÃ¼n": 21755, "ĠMessiah": 21756, "uity": 21757, "iona": 21758, "Ġprobable": 21759, "ĠYE": 21760, "Ġindependently": 21761, "Ġbuffer": 21762, "burn": 21763, "ourd": 21764, "ĠMcK": 21765, "Ġlingu": 21766, "ujemy": 21767, "еÑĢÑĤ": 21768, "Ġintuitive": 21769, "Ġcracks": 21770, "appropri": 21771, "nty": 21772, "Ġgeen": 21773, "Ġlend": 21774, "Ġcertification": 21775, "IDS": 21776, "unter": 21777, "pees": 21778, "Ġtrump": 21779, "Ġbankrupt": 21780, "Ġfeas": 21781, "èĹ": 21782, "Ġduż": 21783, "æ¸ħ": 21784, "Ġviruses": 21785, "Ġ58": 21786, "god": 21787, "Ġжел": 21788, "Ġstalk": 21789, "Ind": 21790, "achi": 21791, "ĠCF": 21792, "ĠCond": 21793, "Ġsanct": 21794, "Ġconten": 21795, "Ġfreed": 21796, "ĠRT": 21797, "Ġmentors": 21798, "족": 21799, "Ġportable": 21800, "ĠPaulo": 21801, "rane": 21802, "HAHA": 21803, "ĠSection": 21804, "çĨ": 21805, "hyun": 21806, "ĠÎŃÏĩ": 21807, "ĠPub": 21808, "ĠIndepend": 21809, "Ġcompounds": 21810, "ĠÑģÑĭ": 21811, "Ġmessaging": 21812, "Ġdedication": 21813, "Ġnoticing": 21814, "Ġdevoted": 21815, "ÑİÑĤÑģÑı": 21816, "Ġsnakes": 21817, "Ġbattlefield": 21818, "pers": 21819, "Ġdela": 21820, "92": 21821, "Ġhai": 21822, "illä": 21823, "érer": 21824, "every": 21825, "Ġresponsive": 21826, "×Ļ×ķ": 21827, "opf": 21828, "éī": 21829, "Ĭ¸": 21830, "Because": 21831, "Ġtourism": 21832, "Ġê·¸ê²Į": 21833, "×ķצ": 21834, "Ġcans": 21835, "stüt": 21836, "Ġdonne": 21837, "ĠDios": 21838, "ĠUber": 21839, "actory": 21840, "Ġoriented": 21841, "ĠHerm": 21842, "Ġpatron": 21843, "urf": 21844, "bei": 21845, "Ġprograma": 21846, "ĠOhh": 21847, "gener": 21848, "Ġfist": 21849, "ĠWendy": 21850, "Ġanda": 21851, "Ġguessed": 21852, "Ġfreak": 21853, "ä¸Ńåľĭ": 21854, "ĠKings": 21855, "chool": 21856, "Ġoffline": 21857, "ĠIndiana": 21858, "ĠAlliance": 21859, "Ġ53": 21860, "Ġparticul": 21861, "ĠFocus": 21862, "Ġinhabit": 21863, "Ġê°ĻìĿĢëį°": 21864, "ĠMcG": 21865, "owski": 21866, "ĠìĿ´ê±´": 21867, "ĠpaÅĦst": 21868, "они": 21869, "itta": 21870, "Ġconfirmation": 21871, "ĠBrooklyn": 21872, "Ġnoodle": 21873, "fund": 21874, "itud": 21875, "Ġgrandparents": 21876, "Ġbarbecue": 21877, "ειÏĤ": 21878, "Ġá": 21879, "Ġballot": 21880, "ĠVeter": 21881, "Ġpipes": 21882, "igious": 21883, "ĠGraph": 21884, "ested": 21885, "Ġë¸Įë": 21886, "ĠKE": 21887, "ãģ¡ãĤĩãģ£ãģ¨": 21888, "Ġeins": 21889, "Ġhatred": 21890, "ãģijãģ©": 21891, "Ġdang": 21892, "eeee": 21893, "Ġarchae": 21894, "ĠJesse": 21895, "Ġdetected": 21896, "Ġseni": 21897, "burgh": 21898, "Ġdisplacement": 21899, "Ġdop": 21900, "Ġconditioning": 21901, "ĠнеÑģколÑĮко": 21902, "Ġdisturbing": 21903, "PH": 21904, "Ġthinner": 21905, "Ġwounded": 21906, "ĠCuando": 21907, "Ġcushion": 21908, "Ġwhites": 21909, "Ġpreferences": 21910, "Ġì¤Ģë¹Ħ": 21911, "Ġkaż": 21912, "ĠGate": 21913, "ĠPath": 21914, "dles": 21915, "à¸Ħร": 21916, "imore": 21917, "Ġë³´ìŬ": 21918, "Ġdisciplines": 21919, "á»ı": 21920, "Ġmesma": 21921, "ĠìĥĪë": 21922, "Ġìĭ¬": 21923, "Ġging": 21924, "Ġumbrella": 21925, "IGHT": 21926, "Ġpension": 21927, "Ġcombining": 21928, "SS": 21929, "Ġrectangle": 21930, "á»ĩt": 21931, "Ġproxim": 21932, "ĠCow": 21933, "¸Į": 21934, "Ġintentional": 21935, "æķĻ": 21936, "Ġdecid": 21937, "ĠÑģкаж": 21938, "ĠUma": 21939, "iasm": 21940, "buz": 21941, "Ġdebris": 21942, "Ġcass": 21943, "ĠProp": 21944, "iska": 21945, "ëł¥": 21946, "esterol": 21947, "ussian": 21948, "ìĿ´ëŀij": 21949, "Ġunlimited": 21950, "Ġadmire": 21951, "Ġtightly": 21952, "Ġgenome": 21953, "ĠJunior": 21954, "venir": 21955, "gus": 21956, "ĠcÄĥ": 21957, "ĠVlad": 21958, "ĠíĤ": 21959, "Ġrelativ": 21960, "inci": 21961, "Ġaunque": 21962, "ĠBoys": 21963, "ÑĨион": 21964, "ĠSwiss": 21965, "Ġphysicians": 21966, "Ġíıī": 21967, "ĠPET": 21968, "Ġwounds": 21969, "about": 21970, "Ãłi": 21971, "onz": 21972, "urities": 21973, "ĠÑĥвид": 21974, "å·¦": 21975, "Ġmentality": 21976, "Ġvariance": 21977, "Ġsegunda": 21978, "Ġvolcano": 21979, "alie": 21980, "à¥ĩ": 21981, "Ġtiles": 21982, "ĠTerry": 21983, "ĠاÙĦÙĦÙĩ": 21984, "Ġcanon": 21985, "Ġscattered": 21986, "pton": 21987, "Ġdefinitions": 21988, "Ġalgebra": 21989, "oten": 21990, "ablo": 21991, "ijuana": 21992, "Ġwrapping": 21993, "Ġsesame": 21994, "ĠнаÑĩина": 21995, "ĠAlf": 21996, "ĠÐłÐ¾ÑģÑģ": 21997, "orno": 21998, "Ġankle": 21999, "Ġspecialty": 22000, "Ġattempting": 22001, "iliation": 22002, "Ġ1920": 22003, "Ġphenomena": 22004, "ĠProduct": 22005, "ĠBuck": 22006, "ĠAww": 22007, "seen": 22008, "Ġvoid": 22009, "ĠFranklin": 22010, "Ġadvocacy": 22011, "ĠSep": 22012, "Ġcoolest": 22013, "ĠÑģÑĢазÑĥ": 22014, "ĠQuand": 22015, "Ġ900": 22016, "ĠTrad": 22017, "dies": 22018, "Ġhash": 22019, "æĪijå°±": 22020, "ä¹Łæĺ¯": 22021, "Ġpots": 22022, "Ġsadly": 22023, "Ġviable": 22024, "ĠTiger": 22025, "ĠONE": 22026, "Ġneurons": 22027, "owanie": 22028, "ÄĹ": 22029, "ĠShar": 22030, "ĠLandes": 22031, "Ġconferences": 22032, "該": 22033, "Ġcredential": 22034, "Ġlime": 22035, "inee": 22036, "xit": 22037, "pay": 22038, "Ġincons": 22039, "Ġ>>:": 22040, "èªį": 22041, "Ġíŀĺë": 22042, "Ġlesser": 22043, "Ġspill": 22044, "Ġpremise": 22045, "Ġ365": 22046, "ĠHost": 22047, "Ġtomar": 22048, "×IJ׾": 22049, "ë²Ī": 22050, "ĠWhats": 22051, "Ġlightweight": 22052, "ĠMap": 22053, "fia": 22054, "ellschaft": 22055, "Ġvendors": 22056, "uesto": 22057, "ĠMister": 22058, "ĠÐŁÑĢи": 22059, "åı³": 22060, "hma": 22061, "Ġintentionally": 22062, "ĠTang": 22063, "éĹ®": 22064, "Ġidentification": 22065, "Ġetcetera": 22066, "ĠNee": 22067, "ĠÑĤÑĢи": 22068, "ê·¸": 22069, "Ġcryptocur": 22070, "Ġinhale": 22071, "Ġaddict": 22072, "åIJĦä½į": 22073, "Ġmau": 22074, "ĠÑĤакаÑı": 22075, "Ġë²Ħ": 22076, "Ġcomprar": 22077, "iedzieÄĩ": 22078, "ĠоÑĤно": 22079, "Ġbeginner": 22080, "ĠмÑĥж": 22081, "Ġobsc": 22082, "Ġlimiting": 22083, "ascular": 22084, "Ġinspection": 22085, "aci": 22086, "Ġrejo": 22087, "Mus": 22088, "Ġzaten": 22089, "Ġszcz": 22090, "ĠMadrid": 22091, "Ġvarieties": 22092, "ĠestÃł": 22093, "ĠShakes": 22094, "Ġkits": 22095, "Ġadminister": 22096, "Ġlava": 22097, "ĠgÃ¥": 22098, "試": 22099, "ת×Ļ": 22100, "ĠWayne": 22101, "Ġinstagram": 22102, "Ġrated": 22103, "paper": 22104, "Ġbild": 22105, "Ġpretending": 22106, "Ġobserving": 22107, "ĠÑģамом": 22108, "Ġtror": 22109, "Ġorganisms": 22110, "Ġfalta": 22111, "Ġhometown": 22112, "ç±": 22113, "Ġíĭ": 22114, "Ġcheg": 22115, "Ġì¡": 22116, "Ġcomma": 22117, "isé": 22118, "Ġlikelihood": 22119, "avored": 22120, "Ġgeldi": 22121, "ников": 22122, "Ġmedio": 22123, "Ġjakie": 22124, "ĠJup": 22125, "Ġgreenhouse": 22126, "Ġspit": 22127, "кое": 22128, "Ġкаж": 22129, "ĠGram": 22130, "ĠConference": 22131, "Ġdeficit": 22132, "sın": 22133, "inse": 22134, "uÄŁ": 22135, "Ġricht": 22136, "Ġcoincidence": 22137, "åıį": 22138, "Ġeurop": 22139, "Ġbutterfly": 22140, "pread": 22141, "Ġìĸ¼": 22142, "èĢ¶": 22143, "Ġwavel": 22144, "ĠInfin": 22145, "ĠPlanet": 22146, "Ġselfie": 22147, "ientras": 22148, "Ġarrog": 22149, "oser": 22150, "idal": 22151, "ł×Ĺ׳×ķ": 22152, "ütün": 22153, "Ġfreshman": 22154, "ĠMachine": 22155, "ÏĥÏĦ": 22156, "ĠDia": 22157, "ìĿ´ëĭ¤": 22158, "ãģĵãģĨ": 22159, "nea": 22160, "Ġlisting": 22161, "Ġconfigure": 22162, "utor": 22163, "Up": 22164, "tschaft": 22165, "rière": 22166, "Ġupwards": 22167, "ĠÑħоÑĩÑĥ": 22168, "Ġsweep": 22169, "Br": 22170, "Ġexpressing": 22171, "Ġunhappy": 22172, "Ġmandatory": 22173, "gender": 22174, "ĠAÃŃ": 22175, "Ġindicators": 22176, "Ġoils": 22177, "note": 22178, "Ġsegur": 22179, "ожеÑĤ": 22180, "ynasty": 22181, "Ġdistances": 22182, "Ġmerge": 22183, "BERT": 22184, "Ġsurrender": 22185, "Ġbuat": 22186, "ĠAwards": 22187, "Ġseñor": 22188, "odox": 22189, "Ġflavour": 22190, "Ġabdom": 22191, "Ġconfigur": 22192, "86": 22193, "ĠDIY": 22194, "Ġrigid": 22195, "°ĺ": 22196, "Ġcorporation": 22197, "Ġgroom": 22198, "jaw": 22199, "ĠNear": 22200, "ило": 22201, "Ġopera": 22202, "ĠInnov": 22203, "иÑĢа": 22204, "ĵ±": 22205, "Ġspecified": 22206, "Ġcosm": 22207, "ĠFreedom": 22208, "Ġclown": 22209, "ĠNem": 22210, "Ġвол": 22211, "Ñijн": 22212, "Ġcharger": 22213, "à¹ģล": 22214, "Ġinfluential": 22215, "äsident": 22216, "é¤": 22217, "ĠìĦłë": 22218, "Ġvolumes": 22219, "æIJ": 22220, "Ġoutras": 22221, "ĠTwitch": 22222, "Ġfounding": 22223, "Ġawhile": 22224, "Ġcoil": 22225, "ê°Ļ": 22226, "Ġcả": 22227, "ĠThrow": 22228, "ĠHence": 22229, "ommt": 22230, "ĠBenjamin": 22231, "глÑıд": 22232, "Time": 22233, "obic": 22234, "Ġmour": 22235, "Ġdread": 22236, "ĠLÃł": 22237, "ĠChile": 22238, "Ġpreval": 22239, "Ġvain": 22240, "Ġartık": 22241, "Ġpreserved": 22242, "ĠоÑĤд": 22243, "Ġwarehouse": 22244, "Ġbeste": 22245, "ĠSeveral": 22246, "ĠSituation": 22247, "Ġcardboard": 22248, "Tod": 22249, "erna": 22250, "Ġgarant": 22251, "Ġgesture": 22252, "Ġhen": 22253, "Ġspelling": 22254, "osexual": 22255, "Ġanne": 22256, "Ġmice": 22257, "ĠMeine": 22258, "card": 22259, "Ġrebell": 22260, "Ġcerto": 22261, "Ġìľłë": 22262, "Ġverschied": 22263, "ĠBos": 22264, "Ġinvention": 22265, "Ġtrze": 22266, "Ġmanière": 22267, "ĠChad": 22268, "Ġspre": 22269, "Ġorganisations": 22270, "Ġpoorly": 22271, "Ġanterior": 22272, "Ġstair": 22273, "кÑĢ": 22274, "Ġatomic": 22275, "Ġsympath": 22276, "Ġcontinually": 22277, "Ġkleine": 22278, "ète": 22279, "иÑī": 22280, "οÏĤ": 22281, "peut": 22282, "Ġreposit": 22283, "Ġentra": 22284, "Em": 22285, "Ġfinancing": 22286, "Ġмног": 22287, "Ġthesis": 22288, "ĠComputer": 22289, "eau": 22290, "ĠTree": 22291, "Ġbride": 22292, "onsieur": 22293, "shire": 22294, "wic": 22295, "DE": 22296, "ĠìĪĺë": 22297, "Ġacom": 22298, "ĠPO": 22299, "ersch": 22300, "ĠпомоÑī": 22301, "ĠArmen": 22302, "Ġ죽": 22303, "Ġzor": 22304, "Ġprints": 22305, "ĠDass": 22306, "港": 22307, "Ġdurable": 22308, "ĠTransport": 22309, "ìŀIJê°Ģ": 22310, "Ġлег": 22311, "Ġdét": 22312, "ôle": 22313, "amous": 22314, "YN": 22315, "Ġcliff": 22316, "Ġgrammar": 22317, "ĠÐŁÐ¾ÑįÑĤомÑĥ": 22318, "ĠlÃłm": 22319, "esch": 22320, "Ġmiserable": 22321, "Ġvolts": 22322, "ĠCad": 22323, "ukan": 22324, "ÑĤив": 22325, "rust": 22326, "Ġìĺ¬ëĿ¼": 22327, "Ġverk": 22328, "Ġchickens": 22329, "ĠYoo": 22330, "Ġoutfits": 22331, "code": 22332, "Ġhierarchy": 22333, "netes": 22334, "Ġcounterpart": 22335, "Ġtôi": 22336, "Ġted": 22337, "ĠBart": 22338, "ĠëĿ¼": 22339, "ĠGenau": 22340, "Ġincoming": 22341, "ĠABC": 22342, "rique": 22343, "ĠоÑĤп": 22344, "qual": 22345, "Ġincentive": 22346, "Ġihren": 22347, "׳×Ļ": 22348, "loe": 22349, "Ġ1930": 22350, "Ġbarg": 22351, "Ġdiction": 22352, "Ġönce": 22353, "INS": 22354, "Ġreh": 22355, "isiaj": 22356, "mouth": 22357, "Ġscoring": 22358, "lık": 22359, "ĠìķĦ주": 22360, "ORIA": 22361, "ĠEstados": 22362, "Ġcompanion": 22363, "Ġassemble": 22364, "Ġpunished": 22365, "Ġital": 22366, "Ġprevents": 22367, "istes": 22368, "ĠKentucky": 22369, "Ġlocate": 22370, "Ġfasting": 22371, "ãģ¨æĢĿ": 22372, "ĥĢ": 22373, "ĠSeb": 22374, "ĠCrown": 22375, "opia": 22376, "Ġwhip": 22377, "usz": 22378, "ками": 22379, "Ġdatabases": 22380, "åŃĹ": 22381, "Ġprosec": 22382, "Ġ1997": 22383, "ĠìĤ´ì§Ŀ": 22384, "ĠSolar": 22385, "ĠPues": 22386, "ĠZen": 22387, "ollo": 22388, "ĠGuru": 22389, "Ġsqueez": 22390, "ĠÐĹа": 22391, "ĠÄį": 22392, "ceptions": 22393, "cca": 22394, "izable": 22395, "mand": 22396, "Ġbreakthrough": 22397, "Ġtablespoon": 22398, "ĠSEC": 22399, "ikh": 22400, "ĠSão": 22401, "Ġпло": 22402, "amen": 22403, "Ġprac": 22404, "Ġdarling": 22405, "Ġtaller": 22406, "Ġrendering": 22407, "Ġìļ°ë¦¬ê°Ģ": 22408, "ĠÏĦηÏĤ": 22409, "Ġmã": 22410, "Ġesos": 22411, "uerdo": 22412, "ĠÑģÑĩиÑĤ": 22413, "aller": 22414, "ìĹĪìĸ´ìļĶ": 22415, "Ġmillones": 22416, "lerin": 22417, "Ġpegar": 22418, "onne": 22419, "Ġenrollment": 22420, "Ġliegt": 22421, "Ġboa": 22422, "wiÄĻ": 22423, "bsp": 22424, "Ġcycling": 22425, "ĠBernie": 22426, "Ġ1989": 22427, "ĠдалÑĮ": 22428, "ĠDakota": 22429, "ĠÑģвÑıз": 22430, "ĠCP": 22431, "Ġstare": 22432, "íĤ¤": 22433, "Ġprosperity": 22434, "Ġarrangements": 22435, "Ġarriving": 22436, "mä": 22437, "Ġkayak": 22438, "ipt": 22439, "Ġpardon": 22440, "Ġrelat": 22441, "Ġverste": 22442, "ĠFig": 22443, "Ġfoil": 22444, "ĠTalking": 22445, "peare": 22446, "Ġnoi": 22447, "ĠпÑĢиÑĪ": 22448, "Ġhockey": 22449, "Ġado": 22450, "ĠOUT": 22451, "67": 22452, "Ġhormones": 22453, "ĠAvenue": 22454, "ĠSuperman": 22455, "Ġprescription": 22456, "ubernetes": 22457, "CL": 22458, "otive": 22459, "NIS": 22460, "ienen": 22461, "Ġsadness": 22462, "ĠVit": 22463, "Ty": 22464, "Ġstarter": 22465, "Ġbede": 22466, "Ġfoundations": 22467, "Ġsore": 22468, "åºĹ": 22469, "ÑīеÑģÑĤв": 22470, "ìļ°ë": 22471, "ĠÑĩÑĥв": 22472, "link": 22473, "Ġmaneu": 22474, "working": 22475, "Ãłn": 22476, "ĠAttack": 22477, "ĠCart": 22478, "veis": 22479, "ĠResp": 22480, "ensing": 22481, "Ġì¢ĭìķĦìļĶ": 22482, "Ġescuch": 22483, "ĠRNA": 22484, "Ĥ´": 22485, "Ġadop": 22486, "Ġbending": 22487, "عد": 22488, "Ġmanages": 22489, "usp": 22490, "Ġtart": 22491, "Ġrouter": 22492, "Bo": 22493, "Ġestablishing": 22494, "Ġbalancing": 22495, "Ġathletic": 22496, "ĠSlo": 22497, "Ġfills": 22498, "Ġнаб": 22499, "Ġдал": 22500, "Ġposso": 22501, "ĠVielen": 22502, "Ġcritics": 22503, "Ġlawsuit": 22504, "ĠIsaac": 22505, "ĠÑĦилÑĮм": 22506, "Ġtras": 22507, "Ġpraw": 22508, "ĠCrazy": 22509, "Ġneu": 22510, "Ġkull": 22511, "Ġtumor": 22512, "ĠAPP": 22513, "gate": 22514, "ĠARE": 22515, "98": 22516, "ĠSteam": 22517, "Ġfucked": 22518, "lage": 22519, "ĠâĻ¬": 22520, "ĠMD": 22521, "fy": 22522, "Ġshells": 22523, "ĠSeems": 22524, "izers": 22525, "Ġranges": 22526, "ĠAntonio": 22527, "ATION": 22528, "ĠBaba": 22529, "Ġìĥī": 22530, "kun": 22531, "Ġprayed": 22532, "ÑĢÑı": 22533, "ĠпÑĢоÑĤив": 22534, "Ġseas": 22535, "bury": 22536, "Ġ×Ķש": 22537, "Ġtrait": 22538, "ĠDepending": 22539, "Ġdre": 22540, "Ġkönnt": 22541, "ÑĨÑĥ": 22542, "Ġlipstick": 22543, "eez": 22544, "ĠпÑĢимеÑĢ": 22545, "Ġassignments": 22546, "Bob": 22547, "Ġmetals": 22548, "Ġspecially": 22549, "å°įä¸įå°į": 22550, "ĠìĺĪë": 22551, "ĠÅ¡": 22552, "Ġvista": 22553, "Ġά": 22554, "Ġtwins": 22555, "Ġnotable": 22556, "ĠSau": 22557, "Ġdévelop": 22558, "Ġçek": 22559, "Ġpolynom": 22560, "avam": 22561, "Ġtambé": 22562, "оном": 22563, "Ġplasma": 22564, "Ġefect": 22565, "Ġläng": 22566, "Ġcasi": 22567, "Ñģа": 22568, "ımı": 22569, "ãģĻãĤĭ": 22570, "ĵ¤ìĿĢ": 22571, "Ġlabour": 22572, "ossen": 22573, "ĠPun": 22574, "rif": 22575, "Ġdoses": 22576, "Ġoperates": 22577, "илли": 22578, "Ġjaar": 22579, "staw": 22580, "ĠìĤ¬ëŀij": 22581, "Ġatm": 22582, "Ġprotects": 22583, "Ġimped": 22584, "HO": 22585, "Ġcima": 22586, "Ġtoch": 22587, "abis": 22588, "Ġsendo": 22589, "laus": 22590, "Ġcurl": 22591, "ĠNum": 22592, "Ġsponsors": 22593, "Ġdébut": 22594, "ĠAlexa": 22595, "ĠBür": 22596, "ĠAmer": 22597, "Ġcope": 22598, "Ġизв": 22599, "jal": 22600, "Ġ1995": 22601, "apat": 22602, "resse": 22603, "ĠPrize": 22604, "ĠClaire": 22605, "ĠBrandon": 22606, "Ġwszystko": 22607, "Ġvalued": 22608, "à¸Ļะ": 22609, "Ġsect": 22610, "Ġsecretly": 22611, "Ġdiamonds": 22612, "ĠEvan": 22613, "ĠRPG": 22614, "ãģ«ãģª": 22615, "ĪëıĦ": 22616, "ĠUniversal": 22617, "Ġdoubts": 22618, "ĠPin": 22619, "wiÄħz": 22620, "ļ©": 22621, "Ġalbo": 22622, "Ġbraucht": 22623, "AUL": 22624, "ĠMobile": 22625, "grades": 22626, "Ġschem": 22627, "why": 22628, "ĠNicht": 22629, "pi": 22630, "gle": 22631, "Ġchorus": 22632, "Ġgly": 22633, "Ġreinforce": 22634, "Ġmuff": 22635, "ĠShen": 22636, "ĠHola": 22637, "Ñĥг": 22638, "videmment": 22639, "vial": 22640, "acious": 22641, "laimed": 22642, "ĠRico": 22643, "Ġvegg": 22644, "Ġillustration": 22645, "ĠButter": 22646, "owad": 22647, "Ġeux": 22648, "Ġenfants": 22649, "ĠLeader": 22650, "ĠVillage": 22651, "etically": 22652, "ÙĨÙĬ": 22653, "Ġstew": 22654, "Ġsurprises": 22655, "Ġcue": 22656, "ĠGrandma": 22657, "ĠCelsius": 22658, "ĠRicht": 22659, "enc": 22660, "Ġpetition": 22661, "Ġherb": 22662, "Ġwicked": 22663, "Ġschle": 22664, "ocaly": 22665, "Ġtransf": 22666, "Ġtokens": 22667, "ĠGray": 22668, "ĠBBC": 22669, "IK": 22670, "Ġ1500": 22671, "zn": 22672, "ĠNev": 22673, "Ġkoy": 22674, "Ġzar": 22675, "Ġbullshit": 22676, "ĠColombia": 22677, "ulative": 22678, "Ġwidespread": 22679, "yect": 22680, "kit": 22681, "Ġempresa": 22682, "Ġnour": 22683, "Ġburns": 22684, "atin": 22685, "aired": 22686, "Ġrevolutionary": 22687, "ĠгодÑĥ": 22688, "ĠLogan": 22689, "Ġ1996": 22690, "ĠGraham": 22691, "reb": 22692, "ĠNHS": 22693, "æľĽ": 22694, "Ġcostumes": 22695, "Ġnawet": 22696, "Ġlovers": 22697, "ĠLucy": 22698, "ĠIndigenous": 22699, "íķĺ기": 22700, "Ġimmunity": 22701, "¥´ë": 22702, "uito": 22703, "Ġexcessive": 22704, "Ġdonations": 22705, "Ġ×Ķר": 22706, "Ġ첫": 22707, "éīĦ": 22708, "Ġdrying": 22709, "melon": 22710, "Ġsurveys": 22711, "Ġ무ìĬ¨": 22712, "風": 22713, "aaa": 22714, "Ġprobe": 22715, "ancial": 22716, "Ġlouder": 22717, "Ġhotels": 22718, "Ã¼ÄŁ": 22719, "agner": 22720, "Ġorigins": 22721, "Ġë§Īì§Ģë§ī": 22722, "Ġ**": 22723, "Ġstrangers": 22724, "ĠHaus": 22725, "comed": 22726, "Ġanthrop": 22727, "Ġuso": 22728, "ĠìķĦì§ģ": 22729, "ĠYuan": 22730, "ĠíķĦìļĶ": 22731, "pler": 22732, "ressive": 22733, "Ġspraw": 22734, "ĠStew": 22735, "Ġ1994": 22736, "Ġelders": 22737, "Ġmeinen": 22738, "Ġjunt": 22739, "Ġacoust": 22740, "ĠWohn": 22741, "Ġbananas": 22742, "Ġprojection": 22743, "ĠStick": 22744, "legt": 22745, "speed": 22746, "ĠcÅ©ng": 22747, "ĠWort": 22748, "ĠBaltimore": 22749, "ĠÑĨел": 22750, "Ġdunno": 22751, "å¼·": 22752, "?,": 22753, "ãĥīãĥ³": 22754, "ĠLocal": 22755, "osto": 22756, "ÐŃ": 22757, "ода": 22758, "ĠPortuguese": 22759, "Ġtheirs": 22760, "Ġdém": 22761, "åı¦": 22762, "Ġdrauf": 22763, "ĠBuddhist": 22764, "erta": 22765, "Ge": 22766, "Ġcarrot": 22767, "ĠWonderful": 22768, "Ġsoak": 22769, "Ġchairman": 22770, "ggi": 22771, "ICA": 22772, "fried": 22773, "Ġflick": 22774, "ĠThroughout": 22775, "Ġìļ°ë": 22776, "Ġcough": 22777, "Ġfluffy": 22778, "school": 22779, "Ġripped": 22780, "--------": 22781, "ĠZukunft": 22782, "Ġнеб": 22783, "Ġsto": 22784, "ĠBO": 22785, "pent": 22786, "ĠLawrence": 22787, "ÏīÏĤ": 22788, "sticks": 22789, "ĠEins": 22790, "ĠÑĢÑĭ": 22791, "ĠStrong": 22792, "Ġcaramel": 22793, "Ġspite": 22794, "azar": 22795, "éĥ½æĺ¯": 22796, "Ġcritically": 22797, "Ġobra": 22798, "owitz": 22799, "ĠZone": 22800, "ĠÑĢек": 22801, "Ġsug": 22802, "arded": 22803, "Ġgì": 22804, "ffentlich": 22805, "anche": 22806, "ØŁ": 22807, "astically": 22808, "ìĿ¼ë": 22809, "лав": 22810, "Ġsimplest": 22811, "ĠFriend": 22812, "Ġquello": 22813, "Ġambition": 22814, "Ġabbiamo": 22815, "åºķ": 22816, "ĠÑĦоÑĢм": 22817, "ĠEssa": 22818, "Ġeducators": 22819, "Ġstatistical": 22820, "éĢĻéĤĬ": 22821, "Ġchanger": 22822, "Ġatau": 22823, "étais": 22824, "ĠShakespeare": 22825, "ëIJĺ": 22826, "Ġtriggers": 22827, "Ġrealiz": 22828, "Ġcelui": 22829, "wheel": 22830, "Ġloyalty": 22831, "Ġscreams": 22832, "kehr": 22833, "ĠMega": 22834, "east": 22835, "Ġtops": 22836, "ĠTotally": 22837, "ountain": 22838, "lord": 22839, "Ġviolation": 22840, "ĠGA": 22841, "Ġnicer": 22842, "ĠFresh": 22843, "ĠMelissa": 22844, "function": 22845, "Ġrape": 22846, "Ġexceptions": 22847, "Ġsilicon": 22848, "Ġliberty": 22849, "Ġhouseholds": 22850, "ãģįãģ¾ãģĻ": 22851, "ĠCA": 22852, "ĠÐŀб": 22853, "Ġlib": 22854, "ŀĮ": 22855, "cific": 22856, "Ġtropical": 22857, "Ġinvestigating": 22858, "HD": 22859, "Ġadapter": 22860, "ĠPitt": 22861, "ancia": 22862, "ĠShell": 22863, "friendly": 22864, "Ġconclusions": 22865, "Ġturtle": 22866, "Ġdecomp": 22867, "Ġanimations": 22868, "ĠÑģек": 22869, "insi": 22870, "Ġretention": 22871, "kie": 22872, "Ġinjection": 22873, "ĠMadison": 22874, "ì°°": 22875, "Ġvient": 22876, "Ġvaried": 22877, "Ġviolin": 22878, "ĠBil": 22879, "Ġluckily": 22880, "Ġhtt": 22881, "lä": 22882, "Ġranch": 22883, "çľĭçľĭ": 22884, "Ġsólo": 22885, "ìķħ": 22886, "ĠDerek": 22887, "ĠScripture": 22888, "оÑĢа": 22889, "Ġclassrooms": 22890, "avil": 22891, "formed": 22892, "Ġbeforehand": 22893, "ĠGem": 22894, "prech": 22895, "Ġlin": 22896, "Ġgreens": 22897, "ÑĨев": 22898, "ĠMercedes": 22899, "Ġdrought": 22900, "gasps": 22901, "Ġabortion": 22902, "Ġterribly": 22903, "Ġsposób": 22904, "Ġsecured": 22905, "Ġatrás": 22906, "Ġwavelength": 22907, "Ġgrains": 22908, "ective": 22909, "Ġspacecraft": 22910, "Ġtours": 22911, "Ġprofes": 22912, "Ġsurgeon": 22913, "ĠPie": 22914, "Ġideally": 22915, "arner": 22916, "UP": 22917, "opard": 22918, "sce": 22919, "Ġimmense": 22920, "ĠOrt": 22921, "roller": 22922, "ĠDallas": 22923, "ĠNicholas": 22924, "Ġsulf": 22925, "ĠToyota": 22926, "Ġquantities": 22927, "ceans": 22928, "Ġcui": 22929, "ança": 22930, "ĠCAN": 22931, "itzerland": 22932, "åĦ¿": 22933, "Ġzou": 22934, "ĠCyber": 22935, "legen": 22936, "ĠInit": 22937, "edu": 22938, "Ġapert": 22939, "Ġadjac": 22940, "ouv": 22941, "èĢĮä¸Ķ": 22942, "rs": 22943, "Ġcabbage": 22944, "Ġwheelchair": 22945, "inyl": 22946, "ĠDynam": 22947, "ĠìķĦëĭĪëĿ¼": 22948, "Ġling": 22949, "hl": 22950, "ĠмогÑĥ": 22951, "Ġcrisp": 22952, "Ġmij": 22953, "Ġdug": 22954, "nin": 22955, "Ġbloss": 22956, "Ġbelonging": 22957, "Ġloudly": 22958, "Ġminerals": 22959, "Ġconcluded": 22960, "Ġsearched": 22961, "96": 22962, "ĠMeet": 22963, "ĠSEO": 22964, "ĠСк": 22965, "ĠHob": 22966, "otta": 22967, "Ġpropaganda": 22968, "Ġcinnamon": 22969, "Ġhunter": 22970, "Ġgemeins": 22971, "Ġsculpture": 22972, "ulsion": 22973, "Ġväl": 22974, "Ġmagazines": 22975, "Ġcontroversy": 22976, "ä¸Ģ樣": 22977, "Ġsequences": 22978, "ãģĦãĤĭ": 22979, "ĠíļĮ": 22980, "Ġdeleted": 22981, "使": 22982, "IJëıĦ": 22983, "Ġvarying": 22984, "ãĥĨ": 22985, "Ġmounting": 22986, "Ġaffair": 22987, "Ġpathways": 22988, "æ¦": 22989, "Ġdigo": 22990, "亮": 22991, "Ġдок": 22992, "Alex": 22993, "Ġtobacco": 22994, "ĠCV": 22995, "Ġbothered": 22996, "Ġambient": 22997, "inky": 22998, "ĠSL": 22999, "Ġhates": 23000, "Ġjeżeli": 23001, "Ġcongreg": 23002, "Ġelas": 23003, "Ġdeuts": 23004, "ĠStudios": 23005, "chÄĻ": 23006, "Ġdocumented": 23007, "ĠCruz": 23008, "ĠLen": 23009, "ĠDouglas": 23010, "ĠPortugal": 23011, "enti": 23012, "Ġspouse": 23013, "Ġanalys": 23014, "avia": 23015, "Ġedited": 23016, "Ġlại": 23017, "built": 23018, "Ġville": 23019, "adora": 23020, "Ġbracelet": 23021, "Ġsushi": 23022, "Ġpm": 23023, "Ġtrails": 23024, "Ġlug": 23025, "Ġöver": 23026, "Ġsorrow": 23027, "Ġcolony": 23028, "adox": 23029, "Ġserie": 23030, "anyak": 23031, "ĠØ·": 23032, "ĠGulf": 23033, "æĺ¯ä¸įæĺ¯": 23034, "ĠPV": 23035, "ĠSamuel": 23036, "ĠKit": 23037, "ĠRal": 23038, "ontin": 23039, "expl": 23040, "Ġentries": 23041, "Ġactivists": 23042, "Ps": 23043, "Ġsant": 23044, "ĠÑĤоÑĩ": 23045, "ĠBruno": 23046, "keley": 23047, "Ġtutto": 23048, "éĶ": 23049, "Ġvintage": 23050, "Ġterrified": 23051, "ĠпоÑħ": 23052, "usive": 23053, "owers": 23054, "айÑĤ": 23055, "ëıĻ": 23056, "Ġtwisted": 23057, "ĠThought": 23058, "Ġtah": 23059, "Ġshrink": 23060, "Ġsheer": 23061, "lit": 23062, "Ġdalam": 23063, "Ġdib": 23064, "Ġvard": 23065, "owane": 23066, "Ġdobr": 23067, "ĠRena": 23068, "ĠÑģвоÑİ": 23069, "ĠpaÃŃses": 23070, "ĠEra": 23071, "ãģ®ãģ§": 23072, "ĠBUT": 23073, "sighs": 23074, "Ġ그거": 23075, "ĠgroÃŁen": 23076, "Ġ빨리": 23077, "Ġnerves": 23078, "Ġconstit": 23079, "Ġpreocup": 23080, "ĠGay": 23081, "ĠXu": 23082, "keeper": 23083, "heure": 23084, "..)": 23085, "ĠCalm": 23086, "ĠUnidos": 23087, "ĠìĿ´ê²ĥ": 23088, "ĠAqui": 23089, "ĠìłľìĿ¼": 23090, "dır": 23091, "ì¦ĺ": 23092, "your": 23093, "ĠÑįÑĤим": 23094, "2020": 23095, "Ġrund": 23096, "ĠHO": 23097, "ĠCatherine": 23098, "ieli": 23099, "Ġfusion": 23100, "Ġideology": 23101, "Ġforam": 23102, "shaped": 23103, "ĠíĽĦë": 23104, "Ġwt": 23105, "Ġretr": 23106, "Ġpréc": 23107, "Ġê°ij": 23108, "Ġopenly": 23109, "vity": 23110, "구ìļĶ": 23111, "Ġobstacle": 23112, "Ġboo": 23113, "Ġseiner": 23114, "icorn": 23115, "Ġeigenlijk": 23116, "Ġheader": 23117, "aremos": 23118, "Ġsofter": 23119, "ĠÐŁÐ¾Ð´": 23120, "Ġprejud": 23121, "Ġdefines": 23122, "ierte": 23123, "Ġblending": 23124, "Ġbelievers": 23125, "ĠWochen": 23126, "Ġникак": 23127, "ĠÐļогда": 23128, "ĠTypically": 23129, "Ġíģ¬": 23130, "管": 23131, "cios": 23132, "Ġmissiles": 23133, "Ġsponge": 23134, "ĠKitchen": 23135, "Ġtren": 23136, "ningen": 23137, "Ġscrap": 23138, "Ġserait": 23139, "´ìł": 23140, "ç¹": 23141, "Ġë°ĺë": 23142, "Ġrestored": 23143, "ĠprzykÅĤad": 23144, "ĠKubernetes": 23145, "Ġsait": 23146, "Ġuw": 23147, "Ġenabling": 23148, "Ġtravers": 23149, "amps": 23150, "åıĹ": 23151, "ĠOMG": 23152, "ensor": 23153, "Ġzosta": 23154, "Ġpronounced": 23155, "Ang": 23156, "normal": 23157, "Ġeconomies": 23158, "tin": 23159, "ĠChampion": 23160, "izen": 23161, "Ġarbeiten": 23162, "ĠGospel": 23163, "ĠZu": 23164, "nga": 23165, "Ġliteracy": 23166, "ĠMans": 23167, "Ġcirculation": 23168, "Ġadap": 23169, "ĠTotal": 23170, "Ġmereka": 23171, "Ġolacak": 23172, "ÑģÑĤаÑĤи": 23173, "Jack": 23174, "Ġmund": 23175, "Ġthief": 23176, "bies": 23177, "Ġê²ģ": 23178, "aque": 23179, "ĠÚ©ÛĮ": 23180, "ĠScar": 23181, "å²": 23182, "Ġabol": 23183, "Ġdevote": 23184, "Ġ01": 23185, "Ġsitten": 23186, "ĠVisual": 23187, "week": 23188, "some": 23189, "ingt": 23190, "Ġjournalism": 23191, "ĠHir": 23192, "ĠBachelor": 23193, "inery": 23194, "ÃľND": 23195, "ãĥŁ": 23196, "ç»Ļ": 23197, "Ġcoloring": 23198, "ĠCrist": 23199, "Ġcelebrities": 23200, "ĠÑĩиÑģ": 23201, "ĠCrit": 23202, "Ġdifferentiate": 23203, "ĠÐľÐ½Ðµ": 23204, "elim": 23205, "Ġseafood": 23206, "Ġalgumas": 23207, "otherapy": 23208, "æĪ°": 23209, "Ġglaub": 23210, "Ġarbitrary": 23211, "gens": 23212, "ĠбÑĥдем": 23213, "Ġtav": 23214, "Ġcreamy": 23215, "ĠCountry": 23216, "añ": 23217, "меÑĤ": 23218, "Ġhinter": 23219, "Ġmism": 23220, "Ġillustrate": 23221, "ÃľNDNIS": 23222, "Ġdecreasing": 23223, "Ġweniger": 23224, "AKI": 23225, "ixon": 23226, "Ġней": 23227, "Ġfatto": 23228, "Ġnerd": 23229, "çł": 23230, "Ġbitte": 23231, "Per": 23232, "Ġtane": 23233, "Ġgöz": 23234, "Ġforte": 23235, "ĠEy": 23236, "ĠнавеÑĢ": 23237, "被": 23238, "ĠWordPress": 23239, "ĠMis": 23240, "ů": 23241, "zäh": 23242, "Ġintéress": 23243, "osaurs": 23244, "ĠFalls": 23245, "Ġnessa": 23246, "97": 23247, "Ġmuseums": 23248, "Ġcorresponds": 23249, "Ġsings": 23250, "four": 23251, "Ġeder": 23252, "ĠCommunist": 23253, "oa": 23254, "nek": 23255, "ĠWHO": 23256, "Ġcorpo": 23257, "Ġmessing": 23258, "ÏĦαι": 23259, "Ġbrushes": 23260, "Ġbisc": 23261, "ĠArbeits": 23262, "ĠTax": 23263, "Ġsele": 23264, "Ġflags": 23265, "oupe": 23266, "Ġanticipated": 23267, "ãĥij": 23268, "ĠNad": 23269, "Ġpoured": 23270, "Ġml": 23271, "Ġllama": 23272, "Ġvisualize": 23273, "Ġlisteners": 23274, "ÙĦÙĥ": 23275, "alten": 23276, "Michael": 23277, "Ġcosì": 23278, "Õ¡Õ": 23279, "opus": 23280, "Ġíķ´ì£¼": 23281, "Ġhike": 23282, "ĠAttorney": 23283, "ĠHillary": 23284, "uded": 23285, "Ġíķĺì§Ģë§Į": 23286, "Ġdove": 23287, "Ġstorms": 23288, "акÑģ": 23289, "Ġdoctrine": 23290, "Ġhex": 23291, "iks": 23292, "noÅĽÄĩ": 23293, "Ġscripts": 23294, "Ġδεν": 23295, "ĠÑįÑĤиÑħ": 23296, "ĠÐĨ": 23297, "aber": 23298, "ĠVas": 23299, "Ġcentimeters": 23300, "×ŀ×Ķ": 23301, "ниб": 23302, "Ġriders": 23303, "ĠTrib": 23304, "åĮħ": 23305, "Ġtakże": 23306, "Ġnoun": 23307, "Ġicons": 23308, "Ġsolely": 23309, "minded": 23310, "Ġdispon": 23311, "ĠSwitzerland": 23312, "Ġclusters": 23313, "Ġqueda": 23314, "ailing": 23315, "Ġmanga": 23316, "Ġ68": 23317, "ĦĪ": 23318, "Ġtet": 23319, "gins": 23320, "haus": 23321, "空": 23322, "å·¥": 23323, "ĠOP": 23324, "oted": 23325, "Ġnouveau": 23326, "ALLY": 23327, "ÙĪد": 23328, "òn": 23329, "Ġmortality": 23330, "ĠGitHub": 23331, "drop": 23332, "Ġdisgu": 23333, "Ġrecom": 23334, "Ġlocals": 23335, "Ġhomemade": 23336, "amba": 23337, "Ġpronunciation": 23338, "Ġalphabet": 23339, "анÑĮ": 23340, "owany": 23341, "iras": 23342, "idency": 23343, "OME": 23344, "ĠÑĢаÑģÑģ": 23345, "arak": 23346, "viamente": 23347, "Ġnonprofit": 23348, "ĠYouTuber": 23349, "Ġparenth": 23350, "ĠBoo": 23351, "vat": 23352, "ĠStir": 23353, "Ġprecip": 23354, "Ġants": 23355, "Ġally": 23356, "ĠMaori": 23357, "ĠëĮĢíķľ": 23358, "åı¯æĺ¯": 23359, "ogene": 23360, "ĠLabour": 23361, "arette": 23362, "Ġrecycling": 23363, "ensa": 23364, "Ġpursuit": 23365, "Ġsak": 23366, "ĠÐĹдеÑģÑĮ": 23367, "Ġtolerance": 23368, "Ġsaat": 23369, "Ġclicked": 23370, "âĻ¥": 23371, "Ġfacebook": 23372, "ĠInto": 23373, "Ġincentives": 23374, "기ëĬĶ": 23375, "ĠDennis": 23376, "ĠWik": 23377, "gesch": 23378, "à¹Ģà¸Ľ": 23379, "ĠÏĢα": 23380, "ĠWhoo": 23381, "Ġrounded": 23382, "Ġdope": 23383, "Ġcapturing": 23384, "ĠWarri": 23385, "Ġcivilian": 23386, "Ġcharming": 23387, "Ġesas": 23388, "Ġsustained": 23389, "Ġleaning": 23390, "Ġabundance": 23391, "ÃŃlia": 23392, "алÑĮнÑĭй": 23393, "Ġphải": 23394, "acja": 23395, "Ġê°ĻìķĦ": 23396, "activ": 23397, "าย": 23398, "Ġ97": 23399, "Ġмой": 23400, "cro": 23401, "ĠJackie": 23402, "ittees": 23403, "bracht": 23404, "ulent": 23405, "Ġìłľë": 23406, "Ġplugin": 23407, "vantage": 23408, "party": 23409, "Ġsuas": 23410, "Ġante": 23411, "Ñĥл": 23412, "ÐĿÐIJ": 23413, "æĤ¨": 23414, "ĠÏĥÏħ": 23415, "Ġmeth": 23416, "Ġenthusiasm": 23417, "ÑıÑĤÑģÑı": 23418, "íĻĶë": 23419, "Ġsynthetic": 23420, "Ġseasoning": 23421, "ĠLost": 23422, "onomy": 23423, "ĠSpark": 23424, "Ġbure": 23425, "Ġassured": 23426, "Ġimagin": 23427, "Ġcarro": 23428, "Sha": 23429, "Äħt": 23430, "нÑĥÑĤÑĮ": 23431, "ática": 23432, "TY": 23433, "Ġkern": 23434, "ĠBrazilian": 23435, "ð": 23436, "Ġsuspended": 23437, "ĠCarib": 23438, "Ġbizim": 23439, "ĠOliver": 23440, "ãģ¶": 23441, "Tom": 23442, "Ġплан": 23443, "Ġnope": 23444, "omething": 23445, "Ġbeiden": 23446, "ÑĨен": 23447, "Ġfluct": 23448, "ĠμοÏħ": 23449, "Ġfathers": 23450, "ĠBlake": 23451, "Ġupward": 23452, "ĠDash": 23453, "ĠLil": 23454, "ĠìĪĺëıĦ": 23455, "Ġrevelation": 23456, "Ġelevated": 23457, "ĠJiang": 23458, "LED": 23459, "ĠThompson": 23460, "ĠмогÑĥÑĤ": 23461, "ÑģÑĤÑĢÑĥ": 23462, "ifiers": 23463, "Ġcomeback": 23464, "Ġbuyers": 23465, "ê²°": 23466, "ĠSales": 23467, "иÑĩе": 23468, "ciones": 23469, "Ġwhistle": 23470, "Ġdull": 23471, "LEX": 23472, "Ġíķĺê²łìĬµëĭĪëĭ¤": 23473, "Ġcriminals": 23474, "Ġdescent": 23475, "ipple": 23476, "ması": 23477, "Ġfoolish": 23478, "ĠдÑĥмаÑİ": 23479, "tar": 23480, "Ġmango": 23481, "Ġchoreography": 23482, "Matt": 23483, "Ġterritor": 23484, "Ġacaba": 23485, "ĠEinstein": 23486, "ĠIBM": 23487, "ĠMetal": 23488, "ĠCrystal": 23489, "Ġrah": 23490, "Ġfoul": 23491, "ĠIslands": 23492, "Ġintact": 23493, "ĠRail": 23494, ".:": 23495, "Ġacá": 23496, "ĠпÑĢоп": 23497, "еÑĢе": 23498, "ĠWrite": 23499, "hehe": 23500, "ĠFO": 23501, "ĠÏĥÏĦη": 23502, "Ġdoin": 23503, "held": 23504, "Ġappropriately": 23505, "Ġdeliberately": 23506, "Ġarchive": 23507, "Ġgiveaway": 23508, "ãģĵãģĵ": 23509, "Ġfinale": 23510, "лаÑģ": 23511, "ено": 23512, "Æ¡n": 23513, "æ£Ĵ": 23514, "ogo": 23515, "çī©": 23516, "ĠAudience": 23517, "ãħł": 23518, "Ġsubur": 23519, "Ġheadache": 23520, "аннÑı": 23521, "ĠWitch": 23522, "ĠSwedish": 23523, "ĠBI": 23524, "Ġerase": 23525, "Ġkhi": 23526, "Ġcommentary": 23527, "ĠSultan": 23528, "íĥĿ": 23529, "ĠLeban": 23530, "Ġë³´ìĭ": 23531, "ĠPam": 23532, "pekt": 23533, "month": 23534, "Ġgrounded": 23535, "ê¾": 23536, "ĠÅŁekilde": 23537, "250": 23538, "ĠSCH": 23539, "ioso": 23540, "Ġinaug": 23541, "heimer": 23542, "Ġreflecting": 23543, "ĠRuth": 23544, "ĠOil": 23545, "Ġtrouver": 23546, "uep": 23547, "..]": 23548, "ĠìŀĪë": 23549, "Ġolha": 23550, "Ġreasonably": 23551, "Ġglitch": 23552, "UB": 23553, "ĠGran": 23554, "Ġadalah": 23555, "Ġlent": 23556, "را": 23557, "Ġtraction": 23558, "Ġadjusting": 23559, "´¤": 23560, "нибÑĥдÑĮ": 23561, "Ġдоп": 23562, "Ġstretched": 23563, "Ġort": 23564, "Ġcosine": 23565, "viol": 23566, "Ġìħ": 23567, "cir": 23568, "Ġbastard": 23569, "ä¸ĩ": 23570, "ĠÑħод": 23571, "Ġquier": 23572, "Ġpressures": 23573, "ĠAnh": 23574, "å¹¾": 23575, "Ġelles": 23576, "ĠдÑĢÑĥз": 23577, "ĠможеÑĤе": 23578, "Ġchá»": 23579, "ĠMé": 23580, "ök": 23581, "ầu": 23582, "ìłĪ": 23583, "zin": 23584, "Ġcaution": 23585, "iban": 23586, "Ġjudging": 23587, "ÑĥÑİÑĤ": 23588, "Ġbaj": 23589, "ĠСейÑĩаÑģ": 23590, "ĠPoor": 23591, "ĠNazi": 23592, "Ġupbeat": 23593, "yang": 23594, "Ġweekends": 23595, "ĠEssentially": 23596, "Ġoluyor": 23597, "Ġspatial": 23598, "acker": 23599, "Ġseller": 23600, "Ġ×IJ×ķת": 23601, "ij׾": 23602, "Ġvivid": 23603, "ĠBond": 23604, "ê¶Į": 23605, "iskt": 23606, "ãĤµ": 23607, "Ġgoat": 23608, "driver": 23609, "Ġmug": 23610, "ictional": 23611, "Ġallt": 23612, "ĠIniti": 23613, "ĠRand": 23614, "Ġfinishes": 23615, "Ġê°Ī": 23616, "Ġvitam": 23617, "Ġteenagers": 23618, "ĠMorris": 23619, "ì¤Ħ": 23620, "ĠOri": 23621, "iya": 23622, "Ġmyös": 23623, "Step": 23624, "ĠKre": 23625, "辦": 23626, "Ġdinosaur": 23627, "Ġëªĩ": 23628, "affe": 23629, "ĠëIJ©ëĭĪëĭ¤": 23630, "Ġzeg": 23631, "åĪĩ": 23632, "ĠManhattan": 23633, "Ġsujet": 23634, "uelle": 23635, "stoff": 23636, "Ġdür": 23637, "Ġsubmar": 23638, "eses": 23639, "Ġaquele": 23640, "Ġnou": 23641, "ĠFaith": 23642, "tz": 23643, "ĠÑĤомÑĥ": 23644, "aceut": 23645, "liers": 23646, "Ġbandwidth": 23647, "Æ°á»Ŀ": 23648, "Ġrespective": 23649, "ĠAve": 23650, "Ġspreadshe": 23651, "ĠSent": 23652, "icamente": 23653, "Ġinfra": 23654, "Ġlearners": 23655, "Ġà®ī": 23656, "aiah": 23657, "renal": 23658, "Ġmustard": 23659, "Ġhabt": 23660, "çĥ": 23661, "ĠQué": 23662, "Ġanalyzing": 23663, "æ¯ı": 23664, "Ġsolic": 23665, "Ġ×Ķ×ķ×IJ": 23666, "Ġcausa": 23667, "Ġwelcomed": 23668, "ĠSuccess": 23669, "Ġfacile": 23670, "ĠÐŁÐ¾ÑĤомÑĥ": 23671, "schein": 23672, "Ġfetch": 23673, "Ġstrat": 23674, "ĠÑģÑĤоиÑĤ": 23675, "ìĹIJìĦľëĬĶ": 23676, "ĠÑģпоÑģоб": 23677, "mam": 23678, "ĠserÃŃa": 23679, "naments": 23680, "writer": 23681, "Ġconsulting": 23682, "íĺĢ": 23683, "ĠBerkeley": 23684, "eu": 23685, "asive": 23686, "UU": 23687, "ĠAnalyt": 23688, "Ġsubmission": 23689, "Ġmagnificent": 23690, "enza": 23691, "Ġecon": 23692, "Ġprofiles": 23693, "Ġincar": 23694, "Ab": 23695, "ĠNun": 23696, "Ġhic": 23697, "screaming": 23698, "Ġresilient": 23699, "åĪ©": 23700, "grund": 23701, "Ġconcur": 23702, "Ġbereits": 23703, "LD": 23704, "Ġnurt": 23705, "ìī": 23706, "Ġfeast": 23707, "Ġencuent": 23708, "ĠMichel": 23709, "Ġsuprem": 23710, "\"]": 23711, "Ġfeeds": 23712, "ĠKollegen": 23713, "isser": 23714, "ĠFeng": 23715, "ĠWen": 23716, "mun": 23717, "ĠtenÃŃa": 23718, "ĠWrest": 23719, "Ġìĺ¤ëĬĺìĿĢ": 23720, "Ġstead": 23721, "Ġrestoration": 23722, "Ġdonated": 23723, "Ġdels": 23724, "Ġcensus": 23725, "Ġdesperately": 23726, "worthy": 23727, "HE": 23728, "ĠSpa": 23729, "ĠBryan": 23730, "Ġhj": 23731, "ĠRaw": 23732, "ìķĦë": 23733, "ĠCamera": 23734, "Ġzien": 23735, "Ġstyl": 23736, "ĠTW": 23737, "ĠCheese": 23738, "borne": 23739, "Ġobl": 23740, "ĠAlready": 23741, "Ġunstable": 23742, "Ġflames": 23743, "post": 23744, "Ha": 23745, "romagn": 23746, "ĠìĹĦë§Ī": 23747, "dest": 23748, "Ġkolej": 23749, "Ġtemporarily": 23750, "Ġdetermining": 23751, "ĠGlass": 23752, "ÑĢон": 23753, "olan": 23754, "Ġdominated": 23755, "åĮĸ": 23756, "____": 23757, "ĠÙĩذا": 23758, "ĠDana": 23759, "Ġdinheiro": 23760, "aqu": 23761, "민": 23762, "ĠÃłs": 23763, "ĠJoey": 23764, "ĠGriff": 23765, "Ġattain": 23766, "Ġtransitions": 23767, "ĠLiterally": 23768, "енд": 23769, "ĠHaven": 23770, "Ġgrabbing": 23771, "Ġcrystals": 23772, "ĠFourth": 23773, "Ġcandles": 23774, "ĠÑģлÑĥÑĩа": 23775, "rico": 23776, "Ġ5000": 23777, "etto": 23778, "Ġundo": 23779, "Ġkto": 23780, "Ġdivert": 23781, "Ġchir": 23782, "Ġpersec": 23783, "Ġhiking": 23784, "Ġannouncements": 23785, "çĶ±": 23786, "зÑĭ": 23787, "Ġauc": 23788, "Ġsystemic": 23789, "ĠRM": 23790, "Ïĥα": 23791, "ĠÐĶж": 23792, "Ġyar": 23793, "ĠWard": 23794, "Ġpissed": 23795, "Ġcarn": 23796, "Ġautonomous": 23797, "ãħİãħİ": 23798, "sover": 23799, "æ²ĴéĮ¯": 23800, "å¾Ī好": 23801, "Ġreflex": 23802, "Ġgardens": 23803, "Ġdated": 23804, "ì±": 23805, "amiÄĻ": 23806, "Ġcontinuity": 23807, "Ġcitizenship": 23808, "Ġschwer": 23809, "Ġzak": 23810, "table": 23811, "ĠÑģÑĩ": 23812, "è§ģ": 23813, "ĠÏĥε": 23814, "Ġgenerates": 23815, "구ëĤĺ": 23816, "öh": 23817, "óm": 23818, "alam": 23819, "ĠJUDY": 23820, "ĠBug": 23821, "Ġãģ¦": 23822, "Ġdrones": 23823, "Ġágua": 23824, "acaks": 23825, "æļ": 23826, "ĠÐļон": 23827, "×ĸ×Ķ": 23828, "Ġstrive": 23829, "ĠAltern": 23830, "Ġnearest": 23831, "Ġproyect": 23832, "tera": 23833, "ĠASHLEY": 23834, "Ġworm": 23835, "Ġreplay": 23836, "Ġtara": 23837, "ĠIndians": 23838, "ãĤ°": 23839, "icaid": 23840, "ĠìĪľ": 23841, "Ġappealing": 23842, "ĠWes": 23843, "Ġmentions": 23844, "Ġделе": 23845, "Ġkw": 23846, "Ġfragile": 23847, "isz": 23848, "ków": 23849, "hang": 23850, "color": 23851, "Ġpresidente": 23852, "87": 23853, "еÑĦ": 23854, "çĪ¸": 23855, "Ġдобав": 23856, "ĠNelson": 23857, "áfic": 23858, "ĠMICHAEL": 23859, "Ġmechanic": 23860, "Ġmetres": 23861, "ĠoczywiÅĽcie": 23862, "ĠCind": 23863, "ĠogsÃ¥": 23864, "Ġlandsca": 23865, "ACE": 23866, "Ġheadlines": 23867, "Ġcatalyst": 23868, "ĠCatch": 23869, "inkles": 23870, "Ġpills": 23871, "ordo": 23872, "Ġimmigrant": 23873, "Ġexamination": 23874, "Ġaccidents": 23875, "zÄħd": 23876, "Ġquiere": 23877, "Ġnella": 23878, "Ġ67": 23879, "Ġpassa": 23880, "Ġsuperfic": 23881, "istor": 23882, "Ġnov": 23883, "ëĭµ": 23884, "Ġmandate": 23885, "isons": 23886, "ĠVirtual": 23887, "Ġselber": 23888, "Ġcounseling": 23889, "ĠNBA": 23890, "Ġsept": 23891, "Ġbeliever": 23892, "Ġmarvel": 23893, "ĠIntegr": 23894, "ĠмÑĸ": 23895, "Ġorph": 23896, "Ġbackward": 23897, "ĠGeneration": 23898, "ĠPict": 23899, "ĠÑĤоÑĤ": 23900, "Ġtapi": 23901, "prochen": 23902, "Ġhallway": 23903, "hte": 23904, "ĠÛģÛĴ": 23905, "ĠZum": 23906, "èĢģ師": 23907, "achment": 23908, "iquer": 23909, "folg": 23910, "ĠEddie": 23911, "ĠKil": 23912, "Ġwellness": 23913, "stock": 23914, "è¼ĥ": 23915, "Ġkaç": 23916, "Ġterrorism": 23917, "Ġpointer": 23918, "Of": 23919, "heric": 23920, "ĠUltimately": 23921, "Ġmeses": 23922, "ĠTrade": 23923, "Ġpint": 23924, "Ġtuition": 23925, "Ġdisagre": 23926, "Ġê²ĮìŀĦ": 23927, "Ġmanuscript": 23928, "Ġroomm": 23929, "Ġoutputs": 23930, "еÑĨи": 23931, "Ġries": 23932, "Ġsalud": 23933, "otzdem": 23934, "Ġmasses": 23935, "ĠbyÅĤa": 23936, "Ġclearing": 23937, "Ġdiscourse": 23938, "atson": 23939, "Ġfolded": 23940, "ĠJar": 23941, "ÙĦÙī": 23942, "900": 23943, "ĠÑĥÑģп": 23944, "Ġprophecy": 23945, "Ġinterfere": 23946, "иÑħод": 23947, "à¹Į": 23948, "Ġthri": 23949, "Ġ×ŀש": 23950, "Ġlazım": 23951, "Ġ1992": 23952, "Ġfuturo": 23953, "Ġlocking": 23954, "Ġembargo": 23955, "ĠNeither": 23956, "ivamente": 23957, "ĠmÃ¥ste": 23958, "Ġmik": 23959, "Ġcollector": 23960, "екоÑĤоÑĢ": 23961, "ĠGand": 23962, "Ġsentir": 23963, "ĠMight": 23964, "å¡Ķ": 23965, "Ġganzen": 23966, "UC": 23967, "Ġrelating": 23968, "SD": 23969, "Ġmosquito": 23970, "GR": 23971, "Ġhollow": 23972, "âĺħ": 23973, "ĠWalker": 23974, "Ġaffiliate": 23975, "Ġduplicate": 23976, "нем": 23977, "Ġgrape": 23978, "ĠOrganization": 23979, "Ġsynt": 23980, "Joe": 23981, "Ġgeg": 23982, "Ġrevealing": 23983, "ĠEthan": 23984, "outer": 23985, "Ġyay": 23986, "é«Ķ": 23987, "лаÑĢ": 23988, "Ġreportedly": 23989, "Ġihrer": 23990, "Ġrecognise": 23991, "Ġbumper": 23992, "ĠRandy": 23993, "ĠVenus": 23994, "tles": 23995, "Ġappetite": 23996, "Ġglucose": 23997, "Ġchodzi": 23998, "ĠFurthermore": 23999, "tir": 24000, "Ġconta": 24001, "Ġintuition": 24002, "Ġaltitude": 24003, "Ġchunks": 24004, "ĠJoshua": 24005, "ıģım": 24006, "rylic": 24007, "leans": 24008, "ĠíĶ¼ë": 24009, "LL": 24010, "Que": 24011, "Ġgor": 24012, "ĠзнаÑĩиÑĤ": 24013, "Ġpoems": 24014, "Ġexcel": 24015, "Ġexplored": 24016, "Ġpopul": 24017, "Ġincluso": 24018, "stä": 24019, "ĠGavin": 24020, "alling": 24021, "ĠÏĦον": 24022, "é©": 24023, "arbeit": 24024, "ĠGas": 24025, "Ġglorious": 24026, "rieben": 24027, "Ġspam": 24028, "Ġindoor": 24029, "Ġthrust": 24030, "ĠAld": 24031, "ĠPrior": 24032, "Ġonboard": 24033, "ãģłãģķãģĦ": 24034, "oca": 24035, "ASH": 24036, "£ł": 24037, "ĠChristine": 24038, "Ġdrawer": 24039, "Ġnoon": 24040, "Ġìŀĺë": 24041, "Ġpermanently": 24042, "æ·±": 24043, "ĠнапÑĢимеÑĢ": 24044, "Ġpodcasts": 24045, "erapeut": 24046, "prit": 24047, "Ġstainless": 24048, "ĠÚ©ÛĴ": 24049, "Ġfamilia": 24050, "ĠÑĢазÑĢ": 24051, "unto": 24052, "ĠÑģÑĤол": 24053, "Ġhä": 24054, "ĠHai": 24055, "ĠPB": 24056, "izon": 24057, "Ġkonnte": 24058, "Ġbüyük": 24059, "Ġutilizar": 24060, "ÚĨ": 24061, "Ġaquesta": 24062, "Ġmixer": 24063, "udent": 24064, "лекÑģ": 24065, "ÅĤu": 24066, "ĠÑģиÑģÑĤем": 24067, "ĠноÑĢм": 24068, "Ġfatal": 24069, "Ġconsiderations": 24070, "Ġvalidation": 24071, "Ġoli": 24072, "ĠkardeÅŁ": 24073, "ĠGLORIA": 24074, "Ġpall": 24075, "еÑģÑĤе": 24076, "Ġrectang": 24077, "Ġmedieval": 24078, "allahi": 24079, "asti": 24080, "ĠSyrian": 24081, "Ġshear": 24082, "Ġdebug": 24083, "ĠMai": 24084, "Ġknocking": 24085, "ĠLex": 24086, "ardan": 24087, "rov": 24088, "Ġmemorial": 24089, "æ°£": 24090, "ooky": 24091, "Ġstuffed": 24092, "Ġpassé": 24093, "Ġwig": 24094, "Ĥł": 24095, "Ġpróxima": 24096, "Ġ1991": 24097, "ĠмеждÑĥ": 24098, "Ġnuestros": 24099, "ĠBeast": 24100, "Ġsmo": 24101, "atched": 24102, "ologia": 24103, "Ġмод": 24104, "Ġgee": 24105, "Ġconceptual": 24106, "Ġô": 24107, "Ġdecreases": 24108, "Ġqueries": 24109, "олÑĮÑĪ": 24110, "ĠApart": 24111, "Ġexempl": 24112, "å±±": 24113, "Ġfled": 24114, "ĠOFF": 24115, "ggak": 24116, "Ġbead": 24117, "hir": 24118, "lies": 24119, "ĠClearly": 24120, "ılar": 24121, "Ġchess": 24122, "Ġwhichever": 24123, "Ġ96": 24124, "ằ": 24125, "Ġrespects": 24126, "ĠмоÑĢ": 24127, "Ġorganism": 24128, "Ġgrandpa": 24129, "ĠVie": 24130, "è·Łä½ł": 24131, "Ġflooding": 24132, "Ġupgraded": 24133, "ÑijÑĢ": 24134, "Ġcheeks": 24135, "Ġconquer": 24136, "Ġstubborn": 24137, "Ġpuzzles": 24138, "Ġauction": 24139, "Ġrelying": 24140, "ĠPROF": 24141, "ĠEsper": 24142, "ĠÐľÐ£": 24143, "Ġhype": 24144, "Ġpossibil": 24145, "Ġimprison": 24146, "ĠErn": 24147, "ìĹĪìĬµëĭĪëĭ¤": 24148, "Ġenvie": 24149, "Ġresurrection": 24150, "ä¸įè¡Į": 24151, "Ġsper": 24152, "ĠVenezuela": 24153, "som": 24154, "Ġìŀłê¹": 24155, "Ġnouvelle": 24156, "Ġcloses": 24157, "Ġ1940": 24158, "Ġqua": 24159, "ĠJared": 24160, "ĠPir": 24161, "Ġinde": 24162, "Ġscrub": 24163, "uku": 24164, "Ġrequiring": 24165, "Ġвами": 24166, "Ġconsiderable": 24167, "åIJĽ": 24168, "ilia": 24169, "Ġinne": 24170, "Ġmeinem": 24171, "Ġhardship": 24172, "Ġtraps": 24173, "roc": 24174, "ĠìĦ¤ë": 24175, "Ġresearching": 24176, "ĠMargaret": 24177, "Ġpenny": 24178, "Ġbırak": 24179, "Ñijл": 24180, "Ġwool": 24181, "Ġrhet": 24182, "Ġflatten": 24183, "çĩ": 24184, "à¹Ģร": 24185, "Ġpied": 24186, "ĠChap": 24187, "Ġunderm": 24188, "Ġfret": 24189, "Ġcrashed": 24190, "ĠFrauen": 24191, "Ø°Ùĩ": 24192, "ivan": 24193, "Ġliterary": 24194, "latego": 24195, "Ġspäter": 24196, "Ġsimilarities": 24197, "âĨ": 24198, "ĠCoron": 24199, "ĠCreek": 24200, "Ġbosses": 24201, "Ġaccompanied": 24202, "Ġdebates": 24203, "Ġassembled": 24204, "ĠÃģ": 24205, "ĠVai": 24206, "Ġtract": 24207, "Ġsimplement": 24208, "ĠArin": 24209, "Ġvulnerability": 24210, "Ġhormone": 24211, "IEL": 24212, "OOK": 24213, "Ġrelay": 24214, "ĠAndrea": 24215, "ril": 24216, "Ġnecessity": 24217, "aceutical": 24218, "ÑİÑī": 24219, "ousing": 24220, "nahmen": 24221, "Ġfootprint": 24222, "map": 24223, "ĠTier": 24224, "annya": 24225, "intend": 24226, "åĸ®": 24227, "å¢": 24228, "Ġdecorate": 24229, "Ġzombies": 24230, "ĠHyd": 24231, "ĠSuz": 24232, "Ġcampuses": 24233, "ĠEmb": 24234, "Ġthrottle": 24235, "Ġadmin": 24236, "Ġoportun": 24237, "Ġmirrors": 24238, "Ġidentities": 24239, "ĠClin": 24240, "Ġë¹Ħë": 24241, "á¹£": 24242, "ĠOtt": 24243, "Ġblues": 24244, "Ġimpressions": 24245, "-,": 24246, "Ġvague": 24247, "afe": 24248, "Ġinferior": 24249, "erald": 24250, "Ġmedicines": 24251, "Ġpregunta": 24252, "osely": 24253, "Ġtélé": 24254, "ĠMonth": 24255, "ĠLeaders": 24256, "ĠEgyptian": 24257, "Ġration": 24258, "kers": 24259, "heits": 24260, "Ġrecht": 24261, "Play": 24262, "Ġeg": 24263, "Ġpolls": 24264, "ĠWOODR": 24265, "Ġslots": 24266, "jam": 24267, "Both": 24268, "ĠRat": 24269, "ÑĢаж": 24270, "ĠBright": 24271, "ä¸Ģå®ļ": 24272, "á»iji": 24273, "urious": 24274, "Ġsingers": 24275, "Ġlogin": 24276, "Ġtêm": 24277, "lation": 24278, "ĠMum": 24279, "Æ°á»Ŀng": 24280, "ĠEditor": 24281, "åIJij": 24282, "Ġinnovations": 24283, "have": 24284, "ĠSek": 24285, "Ġweaker": 24286, "ĠGob": 24287, "After": 24288, "´ì§Ģ": 24289, "Ġë¬¸ìłľ": 24290, "ãĥ¼ãĥ¼": 24291, "Ġdisadvantage": 24292, "確": 24293, "Ġgaze": 24294, "ĠMack": 24295, "Ïģί": 24296, "ĠKiss": 24297, "ĠHolo": 24298, "ĠBirth": 24299, "izi": 24300, "bab": 24301, "ä¿Ŀ": 24302, "ìĭľê³ł": 24303, "деÑĢж": 24304, "Ġsquat": 24305, "кÑĥÑģ": 24306, "uni": 24307, "ĠComme": 24308, "ĠWOODRUFF": 24309, "ĠChampionship": 24310, "Ġwelche": 24311, "ĠYouth": 24312, "zem": 24313, "Ġodpow": 24314, "Ġpersistent": 24315, "rut": 24316, "ìĶ©": 24317, "íĸ¥": 24318, "lair": 24319, "iku": 24320, "Ġvendor": 24321, "Ġchúng": 24322, "Ġfinanci": 24323, "Ġoverly": 24324, "âu": 24325, "Ġgluten": 24326, "Ġ1800": 24327, "Ġdivisions": 24328, "Ġciudad": 24329, "Ġobed": 24330, "Ġwarum": 24331, "Ġeher": 24332, "Ġelim": 24333, "ĠÐĴо": 24334, "Ġpeuvent": 24335, "ĠWanna": 24336, "Ġattendance": 24337, "Ġassessments": 24338, "ĠBog": 24339, "Ġimagery": 24340, "Ġcollectively": 24341, "Ġinformal": 24342, "ĠSchwe": 24343, "Ġdeutlich": 24344, "ĠChel": 24345, "ĠPE": 24346, "owed": 24347, "Ġbanner": 24348, "Ġshelves": 24349, "ĠReturn": 24350, "æĭ¿": 24351, "LAUGHS": 24352, "Ġcongratulate": 24353, "ĠNorway": 24354, "Ġdwell": 24355, "ĠCaribbean": 24356, "Ġnorms": 24357, "ĠAnimal": 24358, "ĠValentine": 24359, "Ġextending": 24360, "ĠVou": 24361, "orr": 24362, "ĠCheng": 24363, "¡": 24364, "ĠдоÑĢог": 24365, "Ġveg": 24366, "ĠhÃ¥": 24367, "ĠXin": 24368, "Ġì¹´ë": 24369, "emet": 24370, "Ġhypoth": 24371, "Ġinteressante": 24372, "rices": 24373, "IZ": 24374, "ĠUSD": 24375, "Ġrunner": 24376, "ĠBag": 24377, "Ġê½": 24378, "Ġcomeçar": 24379, "Ġpigs": 24380, "Ġweaknesses": 24381, "Ph": 24382, "ĠViol": 24383, "ä¸įçĶ¨": 24384, "Ġdragging": 24385, "ĠAquÃŃ": 24386, "ĠCSS": 24387, "Ġmillimeters": 24388, "Ġestás": 24389, "Ġacute": 24390, "Ġdejar": 24391, "iÄŁ": 24392, "obra": 24393, "Love": 24394, "Ġsilk": 24395, "****": 24396, "Ġjoins": 24397, "Ġprol": 24398, "Ġê°IJìĤ¬íķ©ëĭĪëĭ¤": 24399, "æĶ¯": 24400, "ØŃد": 24401, "aghetti": 24402, "änner": 24403, "Ġstrang": 24404, "Ġdoubled": 24405, "Ġdescriptions": 24406, "Ġstellen": 24407, "Ġparti": 24408, "ç«ĭ": 24409, "²Ħë": 24410, "ĠÃ¶ÄŁ": 24411, "ighing": 24412, "Ġangular": 24413, "Ġnatuur": 24414, "ĠShel": 24415, "Æ°Æ¡": 24416, "Ġrays": 24417, "Ġseper": 24418, "start": 24419, "vised": 24420, "Ġrushed": 24421, "Ġinternationally": 24422, "Ġnivel": 24423, "Ġboxing": 24424, "fallen": 24425, "á»ijc": 24426, "Ġseinen": 24427, "plicity": 24428, "Ġcarboh": 24429, "ĠTravis": 24430, "uso": 24431, "ĠPhase": 24432, "Ġactivation": 24433, "Ġopio": 24434, "·¨": 24435, "Ġdecreased": 24436, "Car": 24437, "Ġbundle": 24438, "Ġexpend": 24439, "ormal": 24440, "Ġadjacent": 24441, "Ġmee": 24442, "ĠоÑĢг": 24443, "Ġtranscript": 24444, "ĠLanguage": 24445, "GS": 24446, "è§ī": 24447, "Ġseul": 24448, "Ãłnh": 24449, "Ġnya": 24450, "nings": 24451, "Ġìĭľë": 24452, "ĠëĶ°ëĿ¼": 24453, "ĠAgr": 24454, "ÃŃd": 24455, "çķĻ": 24456, "Ġaby": 24457, "ĠNeo": 24458, "ıyoruz": 24459, "ĠThinking": 24460, "aime": 24461, "Ġvite": 24462, "Ġtravés": 24463, "Ġ×ij×¢": 24464, "Ġмед": 24465, "Our": 24466, "hoot": 24467, "Ġliner": 24468, "ĠPizza": 24469, "Ġhyg": 24470, "flies": 24471, "ĠContinue": 24472, "Ġdental": 24473, "ĠTib": 24474, "Ġregulate": 24475, "lieÃŁ": 24476, "ALK": 24477, "ĠTae": 24478, "길": 24479, "ĠBrexit": 24480, "ĠGut": 24481, "Ġoccupation": 24482, "Ġzrobi": 24483, "âm": 24484, "Ġwhisk": 24485, "ä¸ĸçķĮ": 24486, "Ġkanske": 24487, "omon": 24488, "robe": 24489, "Ġwarfare": 24490, "Ġthá»ĥ": 24491, "Ġjaki": 24492, "Ġstrokes": 24493, "Ġpeas": 24494, "ĠDamit": 24495, "HAN": 24496, "Ġinterference": 24497, "ĠминÑĥÑĤ": 24498, "NER": 24499, "outing": 24500, "Ġtextures": 24501, "Łī": 24502, "owi": 24503, "ĠíķĻ": 24504, "Ġdens": 24505, "Ġprotagonist": 24506, "änn": 24507, "Ġgoddess": 24508, "Ġwollte": 24509, "ijo": 24510, "ĠWoche": 24511, "ĠVPN": 24512, "story": 24513, "Ġkinderg": 24514, "Ġfunnel": 24515, "Ġdistress": 24516, "ноÑģÑĤÑĮÑİ": 24517, "Ġnoisy": 24518, "ĠпÑĢодолж": 24519, "Ġdaran": 24520, "Ġenzyme": 24521, "лож": 24522, "Ġmute": 24523, "Ġdwar": 24524, "Ġاس": 24525, "Ġkompl": 24526, "Ġmerit": 24527, "Ġfosse": 24528, "ĠDrink": 24529, "Ġfora": 24530, "Ġwohl": 24531, "Ġbreeze": 24532, "Ġsanit": 24533, "Ġdrin": 24534, "ĠìĿ´ê±°ëĬĶ": 24535, "Ġ62": 24536, "Ġì°¨ë": 24537, "abytes": 24538, "Ġdeeds": 24539, "Ġй": 24540, "ième": 24541, "iggling": 24542, "Ġ\"'": 24543, "ĠÑĩаÑģÑĤÑĮ": 24544, "ĠAnswer": 24545, "Ġevangel": 24546, "Ġ1080": 24547, "ĠVisit": 24548, "icient": 24549, "Ġreliability": 24550, "ÑİÑģÑĮ": 24551, "ĠEarlier": 24552, "Ġfid": 24553, "çŃīä¸Ģä¸ĭ": 24554, "Ġsleeves": 24555, "iyorsun": 24556, "Ġbib": 24557, "ĠAccount": 24558, "Ñıли": 24559, "ciplinary": 24560, "zas": 24561, "ĠбеÑĢ": 24562, "Ġnecklace": 24563, "Ġblender": 24564, "ĠPhillips": 24565, "eti": 24566, "ĠJupiter": 24567, "Ġprovoc": 24568, "ĠYears": 24569, "entre": 24570, "acio": 24571, "Ġkü": 24572, "Ġantenna": 24573, "Ġnovels": 24574, "Ġfart": 24575, "ĠSugar": 24576, "ĠJudy": 24577, "Ġcollapsed": 24578, "ç°": 24579, "ritis": 24580, "ĠìĥģíĻ©": 24581, "ÐĹЫ": 24582, "ĠVerf": 24583, "ranean": 24584, "ereum": 24585, "ĠTarget": 24586, "Ġ88": 24587, "ĠÐĺз": 24588, "ideo": 24589, "Ġregression": 24590, "ì¶ľ": 24591, "Ġmówi": 24592, "Ġstudios": 24593, "iens": 24594, "iph": 24595, "Ġfrying": 24596, "Ġfascinated": 24597, "ĠWah": 24598, "bucks": 24599, "maya": 24600, "ĠSaturn": 24601, "ĠMommy": 24602, "Ġratings": 24603, "Ġautumn": 24604, "Æ°Æ¡ng": 24605, "Ġloser": 24606, "Ġcentro": 24607, "érieur": 24608, "ĠFold": 24609, "Ġsupervisor": 24610, "ĠNobel": 24611, "Ġunderest": 24612, "obia": 24613, "ĠвÑģÑı": 24614, "Ġverw": 24615, "Ġfuels": 24616, "Ġartifacts": 24617, "Ġë¶Ļ": 24618, "ĠAutom": 24619, "çļĦæĺ¯": 24620, "ÛĶ": 24621, "×ķס": 24622, "Ġihnen": 24623, "Ġ59": 24624, "ounding": 24625, "еÑĢÑĭ": 24626, "inars": 24627, "chant": 24628, "Ġaddicted": 24629, "Ġexplosive": 24630, "Ġdispers": 24631, "âĸĪ": 24632, "axis": 24633, "ARY": 24634, "Ġlum": 24635, "ĠÑĥÑģл": 24636, "ĠØĮ": 24637, "Ġrupees": 24638, "ĠPearl": 24639, "camp": 24640, "tv": 24641, "oya": 24642, "Ġconcludes": 24643, "Ġcollision": 24644, "Ġbuyer": 24645, "Ġplayground": 24646, "Ġsprings": 24647, "Ġfeminine": 24648, "ĠRas": 24649, "Ġincarcer": 24650, "íĹĺ": 24651, "Ġdialect": 24652, "Ġclosure": 24653, "Ġchatting": 24654, "Ġbabe": 24655, "Ġspotlight": 24656, "Ġnotation": 24657, "è·¯": 24658, "Star": 24659, "ião": 24660, "Ġtête": 24661, "Ġtide": 24662, "Ġjunto": 24663, "Ġsenator": 24664, "Ð¥": 24665, "Ġexcuses": 24666, "Ġblink": 24667, "Ġadmission": 24668, "ĠLily": 24669, "Ñĭми": 24670, "Ġamigo": 24671, "Ġlust": 24672, "ëĭ¬": 24673, "Ġamino": 24674, "äºĭæĥħ": 24675, "Ġconsultant": 24676, "ĠElectric": 24677, "Ġëħ¸ëŀĺ": 24678, "ujah": 24679, "Ġshooter": 24680, "ichten": 24681, "ĠUkrainian": 24682, "Ġaims": 24683, "ĠEntertain": 24684, "Ġmiracles": 24685, "èŃ°": 24686, "Ġzeigen": 24687, "Ġlam": 24688, "Ġress": 24689, "ĠJill": 24690, "ylan": 24691, "Ġrook": 24692, "Ġhaya": 24693, "Ġpassport": 24694, "adata": 24695, "Ġjuicy": 24696, "conf": 24697, "лей": 24698, "ĠSz": 24699, "Ġintercept": 24700, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸ": 24701, "ĠTeams": 24702, "Ġmaken": 24703, "irrel": 24704, "ĠLIKE": 24705, "áºŃy": 24706, "êµ°": 24707, "Ġshortage": 24708, "Ġparadigm": 24709, "Ġpapel": 24710, "Ġastero": 24711, "ãģ¾ãģŁ": 24712, "Ġsollen": 24713, "ĠMickey": 24714, "ĠOrleans": 24715, "Ġcholesterol": 24716, "Ġgoose": 24717, "ÑĨиÑİ": 24718, "ãģĤãĤĭ": 24719, "ĠFL": 24720, "Ġголов": 24721, "Ġtribute": 24722, "ĠGam": 24723, "Ġévidemment": 24724, "ÑıÑħ": 24725, "å®ŀ": 24726, "çĶ°": 24727, "Ġinappropri": 24728, "uhan": 24729, "Ġorganizational": 24730, "ailed": 24731, "Ġendure": 24732, "Ġ76": 24733, "Ġshotgun": 24734, "Ġlivre": 24735, "Ġsuited": 24736, "Ġwarmth": 24737, "ĠSIM": 24738, "Ġenvision": 24739, "Ġdegrad": 24740, "îne": 24741, "Laughing": 24742, "ĠWhoever": 24743, "ĠBuddhism": 24744, "Ġsprinkle": 24745, "ceÄŁiz": 24746, "Ġruins": 24747, "Ġstarch": 24748, "ĠHerz": 24749, "Ġinjustice": 24750, "Ġhumidity": 24751, "ожалÑĥй": 24752, "ĠObject": 24753, "ĠIgn": 24754, "ĠExam": 24755, "igers": 24756, "Ġthou": 24757, "ĠSoy": 24758, "ivas": 24759, "Ġpoles": 24760, "math": 24761, "Ġвним": 24762, "INGING": 24763, "edral": 24764, "Ġexplor": 24765, "Ġroasted": 24766, "Ġcrawl": 24767, "Ġcoff": 24768, "Ġanom": 24769, "Ġwij": 24770, "Ġimproves": 24771, "Ġtreaty": 24772, "Ġdiscovering": 24773, "Ġstatute": 24774, "Ġmercado": 24775, "ĠÑģил": 24776, "Ġintel": 24777, "ĠChancellor": 24778, "ĠMedicaid": 24779, "ugi": 24780, "Ġverbal": 24781, "Ġdön": 24782, "Ġscripture": 24783, "Ġiteration": 24784, "eks": 24785, "ĠOxford": 24786, "Ġwäh": 24787, "ĠVad": 24788, "ĠAK": 24789, "ĠìķĦìĿ´ë": 24790, "Ġiets": 24791, "Ġneedles": 24792, "ÙĥÙħ": 24793, "Ġpasado": 24794, "Ġalbums": 24795, "Ġyea": 24796, "etzen": 24797, "ĦëıĦ": 24798, "Ġdetermines": 24799, "Ġthee": 24800, "ĠPlaying": 24801, "ärt": 24802, "Ġצ": 24803, "cled": 24804, "Ġdownward": 24805, "alone": 24806, "Ġsolu": 24807, "Ġpartition": 24808, "Ġwz": 24809, "dd": 24810, "Ġpessoal": 24811, "媽": 24812, "Ġfactories": 24813, "Ġbleibt": 24814, "มา": 24815, "alsa": 24816, "ĠNFL": 24817, "Ġfuera": 24818, "Ġreserved": 24819, "ĠEarn": 24820, "Ġhelt": 24821, "Ġshortcut": 24822, "Ġconvincing": 24823, "space": 24824, "Ġenforce": 24825, "Ġcores": 24826, "Ġefter": 24827, "Ġrecession": 24828, "xico": 24829, "Ġproposition": 24830, "arians": 24831, "ropol": 24832, "Ġ몰ë": 24833, "ĠÎľ": 24834, "ĠìļĶì¦ĺ": 24835, "Ġactivist": 24836, "Ġconviction": 24837, "Ġzab": 24838, "Ġcanceled": 24839, "ÑĤоÑĩно": 24840, "Ġή": 24841, "éĢĻ樣åŃIJ": 24842, "nite": 24843, "Ġfundra": 24844, "buzzer": 24845, "ело": 24846, "ications": 24847, "Ġzona": 24848, "Ġteens": 24849, "Ġmethodology": 24850, "Ġì¤ijìļĶ": 24851, "than": 24852, "ĠUl": 24853, "ĠGrey": 24854, "Ġhog": 24855, "INK": 24856, "ĠSung": 24857, "ĠClaud": 24858, "ĠCNN": 24859, "Ġdelivers": 24860, "alin": 24861, "ĠAdobe": 24862, "othe": 24863, "ĠDeswegen": 24864, "ำ": 24865, "Ġwerde": 24866, "Ġgrease": 24867, "Ġupgrades": 24868, "ĠFinland": 24869, "accept": 24870, "Ġinterrog": 24871, "bee": 24872, "Ġãģ«": 24873, "Ġprede": 24874, "ĠNep": 24875, "ĠCambridge": 24876, "Ġgraphs": 24877, "Ġhaunted": 24878, "Ñģем": 24879, "æ§": 24880, "åħĭ": 24881, "Some": 24882, "ĠMall": 24883, "Ġrehearsal": 24884, "ĠUrban": 24885, "ĠLag": 24886, "Ġnim": 24887, "ê°ķ": 24888, "Ġpositioned": 24889, "Ġavoided": 24890, "EMA": 24891, "Ġllegar": 24892, "Ġrápido": 24893, "Ġgouvern": 24894, "Ġhing": 24895, "Ġdealer": 24896, "Ġreforms": 24897, "Ġfatty": 24898, "кол": 24899, "ĠAce": 24900, "Ġnep": 24901, "Ġì²Ń": 24902, "Ġcomputation": 24903, "ĠStream": 24904, "bourne": 24905, "tur": 24906, "Por": 24907, "Ġsleepy": 24908, "Ġbanget": 24909, "ãģĤãģ®": 24910, "Ġweighs": 24911, "Ġbleiben": 24912, "ĠGren": 24913, "Ġunions": 24914, "ĠêµIJ": 24915, "Ġaprender": 24916, "uitar": 24917, "ĠJest": 24918, "uming": 24919, "ĠPlayer": 24920, "ĠExtrem": 24921, "Ġinteger": 24922, "аÑĩе": 24923, "Ġconcerts": 24924, "×ķ׼": 24925, "ĠtrochÄĻ": 24926, "ĠRepe": 24927, "éĩįè¦ģ": 24928, "à¹Ĥ": 24929, "żen": 24930, "Ġsounding": 24931, "Ġanonymous": 24932, "Ġexca": 24933, "ĠIranian": 24934, "Ġenergetic": 24935, "Ġwives": 24936, "ĠÑĨвеÑĤ": 24937, "Ġais": 24938, "ãģĭãģª": 24939, "Ġsudah": 24940, "Ġunderwear": 24941, "Ġcrunchy": 24942, "ĠPain": 24943, "Ġgerçek": 24944, "redict": 24945, "Ġmisma": 24946, "ÑĸÑĤ": 24947, "Ġsurviving": 24948, "ÎŃÏĤ": 24949, "Ġparticipant": 24950, "ĠHessen": 24951, "árias": 24952, "Ġsubway": 24953, "istä": 24954, "Ġcoral": 24955, "Ġmarijuana": 24956, "ĠMemorial": 24957, "ÑĪий": 24958, "riz": 24959, "Ġsatellites": 24960, "Ġlease": 24961, "ĠCameron": 24962, "umph": 24963, "Ġclassmates": 24964, "ähän": 24965, "ÑģÑĤве": 24966, "Ġhue": 24967, "ĵ¤ìĿĦ": 24968, "Ġproportional": 24969, "Ġnoss": 24970, "Ġlaps": 24971, "rÃ¥": 24972, "Ġbitcoin": 24973, "ÐĹЫÐļÐIJ": 24974, "Ġ충": 24975, "ĠÙĦÙĦ": 24976, "ĠMort": 24977, "ĠEsp": 24978, "arnos": 24979, "ĠÑģказал": 24980, "Ġänd": 24981, "åħĦ": 24982, "×Ļ×Ļ×Ŀ": 24983, "ĠGeb": 24984, "gehen": 24985, "Inaudible": 24986, "borough": 24987, "ÑĦÑĦ": 24988, "Ġfellowship": 24989, "ĠPaper": 24990, "Ġcurved": 24991, "ĠGEOR": 24992, "Ġcalculator": 24993, "ĠCatal": 24994, "ĠvÃło": 24995, "Ġbypass": 24996, "леÑĤ": 24997, "à³": 24998, "trans": 24999, "rencies": 25000, "ì¡Į": 25001, "igent": 25002, "Ġtasted": 25003, "Ġoceans": 25004, "uft": 25005, "ervice": 25006, "ĠÐľÐ£ÐĹЫÐļÐIJ": 25007, "ĠClassic": 25008, "Ġrespectively": 25009, "~)": 25010, "ître": 25011, "ĠNash": 25012, "Ġzit": 25013, "ĠìĽĥ": 25014, "ĠëĨĴ": 25015, "quote": 25016, "ĠUns": 25017, "Ġtac": 25018, "Ġproves": 25019, "ĠPortland": 25020, "bly": 25021, "Ġere": 25022, "ì¶Ķ": 25023, "Ġépoca": 25024, "ĠÑĤÑĭÑģÑıÑĩ": 25025, "76": 25026, "Ġhade": 25027, "ĠFro": 25028, "ĠpolÃŃtica": 25029, "tag": 25030, "ĠíķŃ": 25031, "Ġschö": 25032, "arett": 25033, "Ġprovisions": 25034, "Ġmotors": 25035, "Ġimaging": 25036, "Ġdok": 25037, "ulously": 25038, "Ġmeille": 25039, "çİ°åľ¨": 25040, "ëIJ": 25041, "ĠISO": 25042, "ĠSTEM": 25043, "ĠBowl": 25044, "Ġtowers": 25045, "ĠEe": 25046, "ĠPerformance": 25047, "Ġloin": 25048, "cussion": 25049, "Ġcoastal": 25050, "iale": 25051, "compass": 25052, "Ġspells": 25053, "Ġdisappointing": 25054, "Ġë²Ī째": 25055, "EER": 25056, "Ġversatile": 25057, "asury": 25058, "Ġenfin": 25059, "Ġdownside": 25060, "Ġguiding": 25061, "ĠاÙĦÙĤ": 25062, "Ġninety": 25063, "charged": 25064, "ĠFans": 25065, "Ġphilosophical": 25066, "Ġgarn": 25067, "ĠmÃ¥nga": 25068, "Ġwillingness": 25069, "Ġportions": 25070, "aben": 25071, "Ġï": 25072, "¿": 25073, "raul": 25074, "Ġsprint": 25075, "ifen": 25076, "ıyla": 25077, "ĠкÑĥп": 25078, "ãģıãģłãģķãģĦ": 25079, "Ġensuite": 25080, "ĠCapitol": 25081, "Ġ63": 25082, "ĠговоÑĢиÑĤ": 25083, "Ġappointments": 25084, "æī¾": 25085, "omiast": 25086, "Ġcareg": 25087, "Ġpublisher": 25088, "Ġheraus": 25089, "Ġεί": 25090, "ĠVS": 25091, "ãģĿãģĹãģ¦": 25092, "ä¸Ńåħ±": 25093, "Ġsacrifices": 25094, "third": 25095, "Ġhumanitarian": 25096, "ĠëĤ´ì": 25097, "imon": 25098, "Ġinequ": 25099, "Ġzob": 25100, "Ġcomfortably": 25101, "ĠDinge": 25102, "Ġcancelled": 25103, "ĠPSAKI": 25104, "ĠRobinson": 25105, "Ġfins": 25106, ")?": 25107, "ĠHistor": 25108, "ĠÑĩеловека": 25109, "Ġtbsp": 25110, "text": 25111, "kim": 25112, "Ġupdating": 25113, "Ġgeld": 25114, "feld": 25115, "ı¼": 25116, "Ġmä": 25117, "Ġcafé": 25118, "ÖĢ": 25119, "ĠSri": 25120, "ĠRegion": 25121, "ĠHahaha": 25122, "Ġfinances": 25123, "ĠاÙĦØ´": 25124, "Ġbunk": 25125, "ruk": 25126, "haft": 25127, "Ġlateral": 25128, "Ġextensions": 25129, "ĠìķĦìĿ´": 25130, "Ġdefinite": 25131, "ĠZhao": 25132, "ĠLuis": 25133, "sty": 25134, "Ġcasos": 25135, "ĠKlim": 25136, "Ġ1993": 25137, "Ġrealization": 25138, "Ġhistorian": 25139, "Ġcracked": 25140, "ëĤ´": 25141, "Ġsystème": 25142, "ĠCIA": 25143, "ĠÑĤво": 25144, "ospheric": 25145, "Ġflee": 25146, "Ġrất": 25147, "ĠRegardless": 25148, "Ġreluct": 25149, "Ġtimely": 25150, "ĠJulian": 25151, "GM": 25152, "éĴ": 25153, "adura": 25154, "é£Ł": 25155, "Ġdresses": 25156, "çģ£": 25157, "ĠëĶĶ": 25158, "Ġnominated": 25159, "Ġadvocates": 25160, "ymph": 25161, "Ġrecordings": 25162, "Ġdeviation": 25163, "Ġprioritize": 25164, "Ġspiral": 25165, "ĠYOUR": 25166, "Ġtranspose": 25167, "ampoo": 25168, "ĠìĽIJëŀĺ": 25169, "ĠVision": 25170, "Ġpolite": 25171, "Ġhamb": 25172, "ĠPatient": 25173, "æ¯Ķè¼ĥ": 25174, "íģ¬ë": 25175, "Ġsia": 25176, "Ġê³³": 25177, "Ġže": 25178, "è§Ģ": 25179, "Ġsupermarket": 25180, "ë¹": 25181, "ĠSierra": 25182, "Ġgrilled": 25183, "ĠUpon": 25184, "Ġabsent": 25185, "Ġmec": 25186, "ĠApollo": 25187, "Ġpunk": 25188, "ĠPaÅĦst": 25189, "ĠÑģвой": 25190, "Ġ거기": 25191, "Girl": 25192, "Ġskinny": 25193, "ĠPremier": 25194, "Ġterritories": 25195, "Ġliability": 25196, "Ġjerk": 25197, "ratic": 25198, "Ġdancers": 25199, "ĠÑĥÑĢов": 25200, "Ġê´Ģë": 25201, "only": 25202, "ĠStu": 25203, "Ġskeleton": 25204, "ĠëŃIJë": 25205, "Ġзакон": 25206, "ıkt": 25207, "ĠMIKE": 25208, "Ġlö": 25209, "mie": 25210, "Ġreiter": 25211, "ãģĵãĤĮãģ¯": 25212, "ĠKolleg": 25213, "ĠAdams": 25214, "licher": 25215, "Ġçocuk": 25216, "Ñıг": 25217, "Ġblush": 25218, "Ġsunshine": 25219, "Ġez": 25220, "ĠDevil": 25221, "Ġ길": 25222, "ĠãģĬ": 25223, "add": 25224, "Ġlicensed": 25225, "Ġvinyl": 25226, "ĠCzech": 25227, "imag": 25228, "Ġcracking": 25229, "Ġìº": 25230, "Ġudah": 25231, "Ġsommes": 25232, "Ġìĸ¼êµ": 25233, "waÄĩ": 25234, "Ġfres": 25235, "åij½": 25236, "ĠWalmart": 25237, "ĠТепеÑĢÑĮ": 25238, "atisf": 25239, "CI": 25240, "lang": 25241, "Ġdiffusion": 25242, "çĶ·": 25243, "Ġsomos": 25244, "ĠMakes": 25245, "æĪijæĥ³": 25246, "ĠRicky": 25247, "Ġmucha": 25248, "íķ¨": 25249, "Ġhorsepower": 25250, "asia": 25251, "Ġfibers": 25252, "Ġerm": 25253, "Ñģкие": 25254, "Ġjeste": 25255, "Ġfirefight": 25256, "Ġcuisine": 25257, "Ġbesonders": 25258, "dig": 25259, "Ġì¢ħ": 25260, "ĠÑĥж": 25261, "Ġtracing": 25262, "Ġcertains": 25263, "ĠApply": 25264, "ÑĭваÑĤÑĮ": 25265, "çĮ": 25266, "Ġbru": 25267, "ĠYES": 25268, "ĠBai": 25269, "ĠDit": 25270, "ĠBis": 25271, "Ġunle": 25272, "ÑģÑĤаÑĤоÑĩно": 25273, "ĠAwak": 25274, "..\"": 25275, "Ġ125": 25276, "Ġrooted": 25277, "Ġcautious": 25278, "const": 25279, "Ġorchestra": 25280, "çľ¼": 25281, "ĠвнÑĥÑĤ": 25282, "Ġquelqu": 25283, "ĠоÑĤвеÑĤ": 25284, "ĠMethod": 25285, "ì¹ľ": 25286, "ĠμαÏĤ": 25287, "lü": 25288, "ĠìķĦê¹Į": 25289, "Ġnaming": 25290, "Char": 25291, "ĠSicher": 25292, "Ġprivileged": 25293, "ĠFly": 25294, "Ġãģĭ": 25295, "áºŃt": 25296, "Ġadvances": 25297, "ĠZelda": 25298, "Ġandra": 25299, "Ġgrinding": 25300, "ĠEdition": 25301, "pf": 25302, "Ġwarriors": 25303, "Ġhedge": 25304, "Ġunseren": 25305, "ĠÑģÑİда": 25306, "eliness": 25307, "Ġpersonalities": 25308, "Ġfö": 25309, "'M": 25310, "ĠÑĤоÑĩно": 25311, "Ġshipped": 25312, "Ġmeteor": 25313, "Ġsurroundings": 25314, "ĠFill": 25315, "uesta": 25316, "ĠPersonal": 25317, "ĠAlle": 25318, "ORT": 25319, "ä¹ħ": 25320, "ĠSche": 25321, "VI": 25322, "Ġcomparable": 25323, "damn": 25324, "Ġditch": 25325, "YAN": 25326, "ismus": 25327, "Ġpickup": 25328, "Ġdak": 25329, "ĠEP": 25330, "best": 25331, "ĠSue": 25332, "ällt": 25333, "Ġpopcorn": 25334, "Ġfolding": 25335, "home": 25336, "иваеÑĤ": 25337, "å·²ç¶ĵ": 25338, "Ġannot": 25339, "chuck": 25340, "Ġfierce": 25341, "Ġdamaging": 25342, "Ġflop": 25343, "Ġpasar": 25344, "Ġreef": 25345, "ĠÑģвоей": 25346, "Ġzoo": 25347, "overs": 25348, "jets": 25349, "Ġprès": 25350, "ĠSilicon": 25351, "teok": 25352, "ĠSeth": 25353, "atamente": 25354, "Ġtransmitted": 25355, "Ġreplicate": 25356, "Ġslim": 25357, "ĠCream": 25358, "æĦŁãģĺ": 25359, "Ġsidewalk": 25360, "ìĪĺë": 25361, "ĠжизнÑĮ": 25362, "ĠMonica": 25363, "ä¾ĨäºĨ": 25364, "Ġcopied": 25365, "ĠTerra": 25366, "istent": 25367, "ç³»": 25368, "Ġоно": 25369, "Ġwhale": 25370, "ĠWITH": 25371, "лÑĥÑĪ": 25372, "å½±çīĩ": 25373, "ĠEen": 25374, "ĠÑģвои": 25375, "Ġordin": 25376, "Ġplural": 25377, "Ġspokes": 25378, "Ġdispute": 25379, "Ġsensible": 25380, "Ġpreaching": 25381, "Ġktórzy": 25382, "pted": 25383, "avier": 25384, "Ġpistol": 25385, "ĠTapi": 25386, "ĠÅĤ": 25387, "ffff": 25388, "Ġacrylic": 25389, "Ġignorance": 25390, "ĠZiel": 25391, "rans": 25392, "Ġwelding": 25393, "mid": 25394, "æĪijä¸į": 25395, "Ġзаним": 25396, "Ġlanes": 25397, "Ġmines": 25398, "Ġmoms": 25399, "×ķ×Ĺ": 25400, "ĠChamber": 25401, "tier": 25402, "Ġmodest": 25403, "ĠìĹ¬ê¸°ìĦľ": 25404, "Ġunas": 25405, "Ġwrench": 25406, "handed": 25407, "Ġsaturated": 25408, "ĠFang": 25409, "ĠCommissioner": 25410, "र": 25411, "Ġ×ĸ": 25412, "ĠLouisiana": 25413, "ĠMask": 25414, "Ġcubes": 25415, "ìĶ¨": 25416, "Ġvidéos": 25417, "ĠnÃ¥gon": 25418, "Ġrider": 25419, "Ġì¶ľ": 25420, "Ġsón": 25421, "ĠLatino": 25422, "bank": 25423, "íķ´ì£¼": 25424, "ĠBrend": 25425, "Ġsexuality": 25426, "...,": 25427, "Ġforgetting": 25428, "ĠÛĮ": 25429, "ĠAvengers": 25430, "ĠBonjour": 25431, "cessor": 25432, "кÑĢаÑĹ": 25433, "cence": 25434, "Ġgeograph": 25435, "culo": 25436, "оÑģÑĤÑĮ": 25437, "Ġsweating": 25438, "íĥĢ": 25439, "Ġsymmetry": 25440, "tsÃ¥": 25441, "Ġjan": 25442, "ĠFerr": 25443, "é¦ĸ": 25444, "Ġambassador": 25445, "ziÄĻk": 25446, "Ġmusun": 25447, "ĠÑĥÑĤ": 25448, "ĠLG": 25449, "issent": 25450, "commun": 25451, "Ġcours": 25452, "Ġdevelops": 25453, "Ġbronze": 25454, "Ġsubstances": 25455, "driven": 25456, "주ìĦ¸ìļĶ": 25457, "Ġaos": 25458, "åĦĦ": 25459, "ĠPROFESS": 25460, "half": 25461, "Ġsorted": 25462, "ĠBomb": 25463, "лаг": 25464, "ĠMalaysia": 25465, "ĠChristina": 25466, "Ġteammate": 25467, "èģŀ": 25468, "FT": 25469, "Ġkı": 25470, "hearted": 25471, "++": 25472, "ogenic": 25473, "Ġbells": 25474, "ĠOuais": 25475, "Ġspecialists": 25476, "бÑĭ": 25477, "depth": 25478, "lasses": 25479, "gies": 25480, "ĠCoffee": 25481, "Ġmarking": 25482, "Ġfoll": 25483, "uli": 25484, "Ġadhesive": 25485, "ĠBot": 25486, "ĠPunkt": 25487, "eye": 25488, "ĠBub": 25489, "elong": 25490, "åĪ¶": 25491, "ĠпÑĢик": 25492, "Ġdonor": 25493, "84": 25494, "Ġenfor": 25495, "Ġcatches": 25496, "Ġbricks": 25497, "Ġknitting": 25498, "ĠKnowing": 25499, "oks": 25500, "HY": 25501, "ride": 25502, "ĠFantasy": 25503, "iman": 25504, "Ġpse": 25505, "Ġìĺ¨": 25506, "Ġвд": 25507, "Ġrestra": 25508, "Ġevaluated": 25509, "ÑĢев": 25510, "Ġfortunately": 25511, "Ġchegar": 25512, "رب": 25513, "Ġdomains": 25514, "ibi": 25515, "arry": 25516, "Ġshutter": 25517, "Ġficou": 25518, "Mike": 25519, "Ġinclu": 25520, "Ġdonors": 25521, "Ġapl": 25522, "ĠLower": 25523, "Ġimported": 25524, "Ġacademy": 25525, "Ġfinals": 25526, "Ġdisappears": 25527, "ÙĬا": 25528, "Ġadministrator": 25529, "js": 25530, "Ġcutter": 25531, "Ġranging": 25532, "örper": 25533, "Ġconstraint": 25534, "ĠTable": 25535, "ĠShan": 25536, "vic": 25537, "ĠFix": 25538, "ĠSwift": 25539, "ounces": 25540, "ĠWarum": 25541, "Ġlettuce": 25542, "appelle": 25543, "Ġshave": 25544, "Ġbás": 25545, "Ġ77": 25546, "ĠOoo": 25547, "ao": 25548, "ĠMcM": 25549, "ĠDrew": 25550, "Ġlump": 25551, "Ġlashes": 25552, "scheinlich": 25553, "Rep": 25554, "inis": 25555, "ĠCette": 25556, "Ġcomposite": 25557, "emetery": 25558, "Ġsorte": 25559, "ĠFinancial": 25560, "оне": 25561, "rones": 25562, "ĠVoy": 25563, "Ġtéc": 25564, "ł¹": 25565, "ĠNinja": 25566, "ĠCorin": 25567, "еннÑı": 25568, "ìĿ´ìĹĪ": 25569, "Ġnich": 25570, "Ġdetective": 25571, "âĢ¦\"": 25572, "Ïĥε": 25573, "Ŀ¼ëıĦ": 25574, "Ġë³Ģ": 25575, "Ġë¸Ķë": 25576, "Ġprope": 25577, "ĠWright": 25578, "Ġ×Ķת": 25579, "ĠShi": 25580, "ĠãģŁ": 25581, "Ġinvestigations": 25582, "éĤĦæĺ¯": 25583, "ĠPowerPoint": 25584, "ĠChu": 25585, "Ġìĺ¤í": 25586, "ĠìĻĦìłĦ": 25587, "ĠFragen": 25588, "unning": 25589, "Ġpourrait": 25590, "Ġtextbook": 25591, "мÑĭ": 25592, "Ġfahren": 25593, "ĠÑĤоÑĢ": 25594, "Ġlakes": 25595, "ünde": 25596, "Int": 25597, "ĠMetro": 25598, "Ġmansion": 25599, "Ġаб": 25600, "ĠZhou": 25601, "Ġcorridor": 25602, "Ġescol": 25603, "Ġindicating": 25604, "iaÅĤa": 25605, "Ġmommy": 25606, "Ġarchives": 25607, "Ġfounders": 25608, "engine": 25609, "ĠDieu": 25610, "Ġsickness": 25611, "Ġë³´ëĭĪê¹Į": 25612, "Ġarb": 25613, "Ġned": 25614, "ĠChop": 25615, "Ġcovid": 25616, "Ġslam": 25617, "Ġpublications": 25618, "DC": 25619, "Ġspends": 25620, "æ¾": 25621, "Ġrefugee": 25622, "Ġdile": 25623, "Ġ×IJ×ĸ": 25624, "ificar": 25625, "ĠSach": 25626, "Gu": 25627, "Ġreload": 25628, "????": 25629, "ĠjeÅĽli": 25630, "ĠÑģоÑģÑĤо": 25631, "Ġsimplicity": 25632, "Ġbullying": 25633, "Ġмол": 25634, "Ġrealidad": 25635, "Ġunclear": 25636, "appa": 25637, "levant": 25638, "ĠISIS": 25639, "ĠWatson": 25640, "Ġdein": 25641, "ĠMicro": 25642, "íķľë": 25643, "üg": 25644, "Ġdevam": 25645, "Ġtweeted": 25646, "å°İ": 25647, "Ġunderstandable": 25648, "atan": 25649, "Ġversa": 25650, "Ġpreca": 25651, "Ġvá»ģ": 25652, "ĠCopy": 25653, "ĠOracle": 25654, "Ġmindfulness": 25655, "Ġdiscret": 25656, "ernen": 25657, "ĠPle": 25658, "Have": 25659, "Ġisolate": 25660, "Ġdeu": 25661, "Ġseventy": 25662, "ĠHills": 25663, "Ġarcade": 25664, "ĠÑģпеÑĨи": 25665, "Ġsiguiente": 25666, "ĠBÃľNDNIS": 25667, "liga": 25668, "ĠвÑģÑĤÑĢеÑĩ": 25669, "ôm": 25670, "Ġtweets": 25671, "Ġschauen": 25672, "Ġcritique": 25673, "ĠðŁİµ": 25674, "Ġstatt": 25675, "ĠÑģамое": 25676, "ância": 25677, "Ġsupernatural": 25678, "Ġplugged": 25679, "Fl": 25680, "ynı": 25681, "ĠTambién": 25682, "Ġencouragement": 25683, "ĠServer": 25684, "ëĤľ": 25685, "upa": 25686, "Ġaston": 25687, "Ġhears": 25688, "ÑĢаÑħ": 25689, "Ġsche": 25690, "Ġrats": 25691, "Ġrecuper": 25692, "Ġunten": 25693, "ĠFighting": 25694, "Ġacademics": 25695, "示": 25696, "ĠSü": 25697, "ÑģкиÑħ": 25698, "Ġpaired": 25699, "ĢìĿĦ": 25700, "Ġárea": 25701, "Ġsweetness": 25702, "åıĬ": 25703, "Ġdefer": 25704, "Ġmuitas": 25705, "ĠAudio": 25706, "Ġlocker": 25707, "ÙĬد": 25708, "ĠÑģÑĤав": 25709, "Ġbuena": 25710, "ANS": 25711, "Ġdetector": 25712, "avo": 25713, "bek": 25714, "Ġαν": 25715, "íݸ": 25716, "Ġdragged": 25717, "Ġдолжен": 25718, "Ãĸ": 25719, "رة": 25720, "ìĿ´ì§Ģ": 25721, "Ġcelle": 25722, "cking": 25723, "ĠاÙĦج": 25724, "ĠCanvas": 25725, "Ġespañ": 25726, "Ġglimp": 25727, "Ġspreads": 25728, "ongo": 25729, "ĠMason": 25730, "ĠIng": 25731, "Ġê°ĢëĬ¥": 25732, "ÏĦικ": 25733, "Ġsecular": 25734, "Ġbater": 25735, "Ġinquiry": 25736, "Ġenergies": 25737, "Ġmanufactured": 25738, "Ġvegetarian": 25739, "Ġpineapple": 25740, "ÑıÑĤа": 25741, "Ġpractitioners": 25742, "2000": 25743, "Ġíķ´ìļĶ": 25744, "ĠìŬ룬ë¶Ħëĵ¤": 25745, "Ġë¶Īë": 25746, "ĠJefferson": 25747, "ĠJoan": 25748, "Ġtram": 25749, "容": 25750, "chmal": 25751, "ĠHait": 25752, "á¹ĩ": 25753, "Ġunreal": 25754, "Ġsymbolic": 25755, "Ġstealth": 25756, "Ġsplash": 25757, "ĠEntertainment": 25758, "Ġmetallic": 25759, "?\".": 25760, "è¶Ĭ": 25761, "around": 25762, "Ġdespair": 25763, "ĠNevada": 25764, "ĠFinance": 25765, "Ġkrie": 25766, "ĠLux": 25767, "ĠSmash": 25768, "keeping": 25769, "Ġзаг": 25770, "Ġnarciss": 25771, "Ġdzisiaj": 25772, "Ġtolerate": 25773, "oard": 25774, "Ġlinking": 25775, "ĠEconomic": 25776, "Ġì¼": 25777, "Ġmorph": 25778, "ĠNak": 25779, "ĠBaker": 25780, "aton": 25781, "rings": 25782, "ĠPeng": 25783, "ĠAirport": 25784, "ãģĭãģ£ãģŁ": 25785, "íķĺëĭ¤": 25786, "§ģ": 25787, "prints": 25788, "Ġhadi": 25789, "Ġempir": 25790, "ĠLives": 25791, "anners": 25792, "Ġним": 25793, "ĠPROFESSOR": 25794, "Ġpositively": 25795, "antom": 25796, "Ġbadge": 25797, "kelt": 25798, "Ġinterfer": 25799, "Ġfulfilling": 25800, "Ġvisualization": 25801, "éĹľä¿Ĥ": 25802, "ĠPrice": 25803, "��": 25804, "Ġscenery": 25805, "Ġprone": 25806, "Ġwizard": 25807, "Ġbanyak": 25808, "verb": 25809, "sky": 25810, "Ġwished": 25811, "Ġrailway": 25812, "Ġüzer": 25813, "Ġalguien": 25814, "ĠAW": 25815, "ĠколиÑĩе": 25816, "Ġreacting": 25817, "ĠBuch": 25818, "ึ": 25819, "Ġanth": 25820, "Ġsih": 25821, "Ġhust": 25822, "ĠScreen": 25823, "ilant": 25824, "aho": 25825, "Ġfragrance": 25826, "Ġelevation": 25827, "ĠMediter": 25828, "Ġë¿": 25829, "Ġéqu": 25830, "Ġwraps": 25831, "Ġinert": 25832, "Ġrecreate": 25833, "лаÑĤ": 25834, "Ġboleh": 25835, "Ġharassment": 25836, "unky": 25837, "Ġglimpse": 25838, "regierung": 25839, "Ġfutur": 25840, "Ġrepository": 25841, "Ġengra": 25842, "Ġtrafficking": 25843, "assis": 25844, "ĠTrek": 25845, "Ġë²Į": 25846, "Ġë§Īë": 25847, "ĠKab": 25848, "aniu": 25849, "give": 25850, "Ġdinosaurs": 25851, "Ġfeather": 25852, "Ġattitudes": 25853, "Ġplum": 25854, "ĠRS": 25855, "ĠAnfang": 25856, "illery": 25857, "ĠìĬ¤": 25858, "MY": 25859, "Ġtrzeba": 25860, "Ġskies": 25861, "ĠAj": 25862, "urable": 25863, "CU": 25864, "ĠShane": 25865, "Ġdeparture": 25866, "ĠTON": 25867, "ieten": 25868, "rats": 25869, "æ°Ĺ": 25870, "isu": 25871, "Ġbord": 25872, "Ġinterestingly": 25873, "çĻ»": 25874, "oughing": 25875, "Ġrushing": 25876, "Ġvolatility": 25877, "Ġpyt": 25878, "Ġformats": 25879, "ĠзаÑĤ": 25880, "Ġê¼Ń": 25881, "Ġwhatnot": 25882, "Ġcomport": 25883, "sw": 25884, "orean": 25885, "ĠRelax": 25886, "Ġclan": 25887, "ĠAH": 25888, "Ġpew": 25889, "Ġdictionary": 25890, "Take": 25891, "shirts": 25892, "ĠHugh": 25893, "ĠعÙĦÙĬ": 25894, "ĠPic": 25895, "Ġenrolled": 25896, "Ġjednak": 25897, "Ġofferings": 25898, "Ġcoraz": 25899, "Life": 25900, "Ġ!!!": 25901, "Ġcler": 25902, "ĠVideos": 25903, "ĠRodrig": 25904, "ĠIdent": 25905, "ĠPos": 25906, "ĠStage": 25907, "ĠRace": 25908, "Ġenact": 25909, "ãģĦãģ¾ãģĹãģŁ": 25910, "ĠGy": 25911, "ĠHispan": 25912, "Ġdefence": 25913, "ĠCampbell": 25914, "matic": 25915, "Ġrelev": 25916, "Ġpeach": 25917, "Ħ¸ìļĶ": 25918, "Ġparadise": 25919, "Ġceremon": 25920, "Ġannoyed": 25921, "æĮĩ": 25922, "lax": 25923, "Ġexploit": 25924, "Ġclause": 25925, "eker": 25926, "ĠBloom": 25927, "nant": 25928, "ateurs": 25929, "Ġheights": 25930, "Even": 25931, "Ñģон": 25932, "Ġoutrage": 25933, "ĠVietnamese": 25934, "ãģ¯ãģ¯": 25935, "TR": 25936, "Ġeer": 25937, "Ġcannon": 25938, "ĠComb": 25939, "IJë§Į": 25940, "è»Ĭ": 25941, "Ġê²ĥëıĦ": 25942, "Ġaccomplishments": 25943, "ĠAnalytics": 25944, "Ġshaping": 25945, "reiben": 25946, "Ġbachelor": 25947, "Ġfingert": 25948, "acked": 25949, "Ġpyramid": 25950, "ĠStewart": 25951, "ást": 25952, "Ġsurvivor": 25953, "Ġduct": 25954, "Ġdealers": 25955, "æ´»": 25956, "عÙħ": 25957, "лин": 25958, "Ġede": 25959, "×ķ×¢": 25960, "ĠÙĥاÙĨ": 25961, "ĠÏĦι": 25962, "Ġchooses": 25963, "ĠOwn": 25964, "гоÑĤов": 25965, "hire": 25966, "алÑĮнÑĭе": 25967, "ĠÐĽÑİ": 25968, "ĠоÑģÑĤав": 25969, "tech": 25970, "Ġdroit": 25971, "Ġsubjective": 25972, "enes": 25973, "Ġdivis": 25974, "avez": 25975, "Ġmaneuver": 25976, "à¹Ħà¸Ķ": 25977, "adece": 25978, "ĠEns": 25979, "acial": 25980, "ĠProtection": 25981, "ĸ´": 25982, "Ġformally": 25983, "Ġwyd": 25984, "inguém": 25985, "Ġziem": 25986, "Ġrecruiting": 25987, "×Ļ×ļ": 25988, "nem": 25989, "Ġforbidden": 25990, "ĠBapt": 25991, "×IJ׳×Ļ": 25992, "Ġsubset": 25993, "ĠMagaz": 25994, "nement": 25995, "Ġaquela": 25996, "ragon": 25997, "Ġcommittees": 25998, "Ġétaient": 25999, "udi": 26000, "ĠDawn": 26001, "Ġbore": 26002, "Ġcomposer": 26003, "ĠwiÄĻcej": 26004, "anga": 26005, "Ġdislike": 26006, "ĠDays": 26007, "åŁº": 26008, "Ġparal": 26009, "Ġmientras": 26010, "Ġheavens": 26011, "ãģĴ": 26012, "heid": 26013, "Ġtraders": 26014, "once": 26015, "Ġmascara": 26016, "ĠÏĢÏģο": 26017, "Ġwhisper": 26018, "ĠMusk": 26019, "éĽĨ": 26020, "ĠFamilie": 26021, "Allah": 26022, "ĠOlivia": 26023, "ĠPros": 26024, "Ġolika": 26025, "ilim": 26026, "Ġrépond": 26027, "ĠPeters": 26028, "Ġå¾Ī": 26029, "Ġbites": 26030, "Ġvic": 26031, "ĠNY": 26032, "emption": 26033, "Ġ450": 26034, "Ġvisuals": 26035, "Ġlieu": 26036, "ücken": 26037, "ĠSteel": 26038, "ĠGP": 26039, "wait": 26040, "Ġnoticeable": 26041, "ucha": 26042, "Ġrehabil": 26043, "Ġrejection": 26044, "ĠÑģледÑĥÑİÑī": 26045, "Ġslider": 26046, "Ġregarded": 26047, "Ġgravit": 26048, "ĠReserve": 26049, "count": 26050, "Ġbreeding": 26051, "Ġlonge": 26052, "aleb": 26053, "Ġknight": 26054, "Ġвой": 26055, "Ġprésent": 26056, "ĤĺìļĶ": 26057, "ĠSpecifically": 26058, "Ġposes": 26059, "Ġveure": 26060, "okay": 26061, "emas": 26062, "Ġãģ§ãģĻ": 26063, "ĠmajÄħ": 26064, "Ġwebinars": 26065, "Ġcannabis": 26066, "Ġdamals": 26067, "ĠNorthwest": 26068, "Ġpada": 26069, "Ġcrowds": 26070, "Ġfutures": 26071, "Ġän": 26072, "Ġcivilians": 26073, "ĠSachen": 26074, "æį": 26075, "Ġtraces": 26076, "Ġë¨¹ê³ł": 26077, "QU": 26078, "é¡ĺãģĦ": 26079, "ĠIF": 26080, "anın": 26081, "ìĤ´": 26082, "Ġbiblical": 26083, "ĠVed": 26084, "Ġstoring": 26085, "ÑĢавлÑı": 26086, "æĩī該": 26087, "Ġnast": 26088, "Ġdö": 26089, "ÑĢоп": 26090, "elia": 26091, "Ġsideways": 26092, "ĠUnderstand": 26093, "ĠQur": 26094, "Ġperpend": 26095, "ĠMillionen": 26096, "Ġwatermelon": 26097, "ĠDivine": 26098, "ultur": 26099, "abord": 26100, "Ġsuccesses": 26101, "Ġhombre": 26102, "Ġcarp": 26103, "Ġsuscept": 26104, "ungkin": 26105, "Ġkij": 26106, "ulus": 26107, "اج": 26108, "Ġnotch": 26109, "Ġpolynomial": 26110, "å¹²": 26111, "å©": 26112, "Ġúnico": 26113, "Ġtelescope": 26114, "Ġpolitique": 26115, "kiem": 26116, "ĠÎŃνα": 26117, "Ġaggregate": 26118, "ĠGeoff": 26119, "Ġtril": 26120, "ĠGRA": 26121, "Ġsubscriber": 26122, "imet": 26123, "ĠдоллаÑĢ": 26124, "oping": 26125, "Ġtherapeut": 26126, "ĠCancer": 26127, "Ġparade": 26128, "Ġirrig": 26129, "âĻªâĻª": 26130, "Ġclearer": 26131, "Ġbog": 26132, "ĠMaur": 26133, "าà¸ĩ": 26134, "ĠShanghai": 26135, "achte": 26136, "ĠKol": 26137, "elujah": 26138, "Ġhav": 26139, "ĠCrime": 26140, "sek": 26141, "Ġë¡ľ": 26142, "ienna": 26143, "ĠGor": 26144, "èĽ": 26145, "ĠпоÑĤÑĢ": 26146, "ĠкажеÑĤÑģÑı": 26147, "ĠLift": 26148, "ĠSort": 26149, "ĠPsal": 26150, "Ġping": 26151, "ĵĿ": 26152, "phis": 26153, "ĠFUCK": 26154, "ĠSyn": 26155, "Ġbamboo": 26156, "¬ìĺģ": 26157, "cuts": 26158, "Ġmmm": 26159, "Ġfunktioniert": 26160, "Ġ_": 26161, "ÃŃcio": 26162, "Stop": 26163, "Ġimaginary": 26164, "Ġnotamment": 26165, "ĠInitiative": 26166, "ãĥ¥": 26167, "ĠKurt": 26168, "Ġloosen": 26169, "Ġbuscar": 26170, "çģ«": 26171, "Ġzelf": 26172, "Ġprops": 26173, "åĽī": 26174, "Ġmoeten": 26175, "Ġmilli": 26176, "Ġhalls": 26177, "ĠMatch": 26178, "Ġbrackets": 26179, "ĠCou": 26180, "æ¦Ĥ": 26181, "ĠÐľÐ°ÑĢ": 26182, "ISA": 26183, "Ġcigarette": 26184, "Ġcompetitions": 26185, "ĠMIN": 26186, "Ġbehö": 26187, "voor": 26188, "Ġust": 26189, "ĠZi": 26190, "ĠOcc": 26191, "ulates": 26192, "Ġballoons": 26193, "Ġpronto": 26194, "ĠMiy": 26195, "ĠFile": 26196, "ĠклаÑģÑģ": 26197, "нÑĥл": 26198, "Ġcereal": 26199, "Ġincrement": 26200, "Ġrefined": 26201, "åı¦å¤ĸ": 26202, "prising": 26203, "ĠRF": 26204, "Ġrespectful": 26205, "Ġloot": 26206, "asket": 26207, "Ġdeixa": 26208, "ingle": 26209, "Ġfunciona": 26210, "ĠRevel": 26211, "Ġsober": 26212, "Ġperforms": 26213, "ĠGentle": 26214, "ãĤ¨": 26215, "Ġrecipient": 26216, "ĠHause": 26217, "Ġëĥ": 26218, "From": 26219, "Ġministers": 26220, "Ġparadox": 26221, "å°±æĺ¯èªª": 26222, "Ġtasting": 26223, "Ġ×Ķ×Ĺ": 26224, "Ġreuse": 26225, "ĠLane": 26226, "ĠÑģовеÑĢÑĪ": 26227, "Ġremembers": 26228, "Ġfeminist": 26229, "Ġcommitments": 26230, "Ġprojected": 26231, "Ġgaz": 26232, "iyoruz": 26233, "Ġobligations": 26234, "Ro": 26235, "zar": 26236, "Ġchw": 26237, "ĠJAM": 26238, "ĠbÄĻdÄħ": 26239, "aspberry": 26240, "ĠмеÑģÑĤо": 26241, "ë²ķ": 26242, "Ġregulated": 26243, "Ġwicht": 26244, "ĠTrevor": 26245, "Ġsecondly": 26246, "ĠIhre": 26247, "elsh": 26248, "Ġreporters": 26249, "ÑĤоÑĢа": 26250, "oyo": 26251, "GI": 26252, "Ġinterconnect": 26253, "éIJĺ": 26254, "OSH": 26255, "æŃ²": 26256, "Ġbrass": 26257, "Ġignoring": 26258, "ä»ĬæĹ¥": 26259, "infect": 26260, "Ġprojekt": 26261, "oret": 26262, "ÏĦαν": 26263, "ĠÑĤип": 26264, "Ġmutta": 26265, "Ġunboxing": 26266, "Ħ°": 26267, "å¡Ĭ": 26268, "Ġadvised": 26269, "ĠDenver": 26270, "Ġseverely": 26271, "ĠMhm": 26272, "Ġflipped": 26273, "Ġpien": 26274, "Ġkommun": 26275, "ĠFRE": 26276, "Ġà®ĩà®°": 26277, "ainted": 26278, "Ġknives": 26279, "Ġhabl": 26280, "Ġgeworden": 26281, "arettes": 26282, "CS": 26283, "ĠмаленÑĮ": 26284, "Ġgalax": 26285, "Ġninete": 26286, "ê±°ëĤĺ": 26287, "Ġsis": 26288, "Ġadvisory": 26289, "Ġdrilling": 26290, "ĠWouldn": 26291, "ünf": 26292, "gestellt": 26293, "ĠHelen": 26294, "Ġ×ŀ×IJ": 26295, "apolis": 26296, "Ġrzeczy": 26297, "Ġterra": 26298, "Ġhep": 26299, "Ġalgún": 26300, "ikk": 26301, "Ġastronom": 26302, "ĠStarbucks": 26303, "kÄħ": 26304, "Ġpatrol": 26305, "Ġì½Ķ": 26306, "Ġgon": 26307, "ĠãĢIJ": 26308, "Ġsonst": 26309, "Ġencounters": 26310, "Ġretrou": 26311, "Ġsharks": 26312, "Ġdor": 26313, "ĠRever": 26314, "Ġevapor": 26315, "Ġreservoir": 26316, "Ġalleged": 26317, "uler": 26318, "Ġverm": 26319, "Ġcommerce": 26320, "Ġfitted": 26321, "gem": 26322, "Ġtactical": 26323, "Ġlith": 26324, "éīĦå¡Ķ": 26325, "had": 26326, "è®Ĭ": 26327, "Ġcarbohyd": 26328, "Ġlengths": 26329, "ιο": 26330, "Ġdemographic": 26331, "Rob": 26332, "ĠSkin": 26333, "ccoli": 26334, "Ġsimplified": 26335, "Ġreadily": 26336, "ĠCum": 26337, "adesh": 26338, "ĠDÃ¥": 26339, "usst": 26340, "igne": 26341, "eton": 26342, "Ġmenor": 26343, "qi": 26344, "OOM": 26345, "à¸Ńà¸Ļ": 26346, "Ġpsychiat": 26347, "Ġeighty": 26348, "Ġмилли": 26349, "ĠTob": 26350, "edo": 26351, "網": 26352, "ĠÄijến": 26353, "Ġcircuits": 26354, "ĠLAUGH": 26355, "icism": 26356, "emor": 26357, "Ġregener": 26358, "egree": 26359, "Ġbureauc": 26360, "ĠAlber": 26361, "ä¹ĭå¾Į": 26362, "ĠWor": 26363, "夫": 26364, "Ġresin": 26365, "ĠbyÅĤy": 26366, "ĠIG": 26367, "à¯į,": 26368, "Ġ78": 26369, "Ġweeds": 26370, "ĠMyth": 26371, "93": 26372, "æ¿": 26373, "ĠëĤĺìĻĶ": 26374, "év": 26375, "á½": 26376, "ören": 26377, "çar": 26378, "ĠPAUL": 26379, "Ġdisadvant": 26380, "Ġpositioning": 26381, "Ġcocktail": 26382, "Ġagrees": 26383, "nn": 26384, "ĠSally": 26385, "Ms": 26386, "Ġinherent": 26387, "Ġmonetary": 26388, "Ġnatur": 26389, "ĠNh": 26390, "ĠImport": 26391, "Ġleben": 26392, "Ġwi": 26393, "ussy": 26394, "Ġobes": 26395, "Ġwandering": 26396, "Ġìĭłë": 26397, "Äħda": 26398, "etchup": 26399, "Ġdisposal": 26400, "ĠJA": 26401, "ĠCer": 26402, "zilla": 26403, "Ġvirgin": 26404, "ĠSlide": 26405, "andel": 26406, "Ġrighteousness": 26407, "ĠΣ": 26408, "Ġideia": 26409, "ä½łå¥½": 26410, "иÑĢоваÑĤÑĮ": 26411, "ר×IJ": 26412, "Comment": 26413, "Ġprelim": 26414, "ĠVale": 26415, "Ġì§ĢëĤľ": 26416, "ĠVanc": 26417, "OMAN": 26418, "ĠпÑĸд": 26419, "Ġyum": 26420, "stre": 26421, "cem": 26422, "Ġpocz": 26423, "Ġfragment": 26424, "ĠÑģлÑĥÑĩае": 26425, "Ġundergo": 26426, "ĠHank": 26427, "ceks": 26428, "ĠFPS": 26429, "Ġocur": 26430, "Ġdeterior": 26431, "注": 26432, "Ġempresas": 26433, "Paul": 26434, "Ġ)))": 26435, "ĠвÑĢемени": 26436, "Ġscold": 26437, "×Ļ×¢": 26438, "Ġsuspected": 26439, "Ġaccessing": 26440, "Ġsubstit": 26441, "Ġhistorians": 26442, "ä»»": 26443, "Ġдело": 26444, "Ġsocied": 26445, "rone": 26446, "Ġreden": 26447, "Ġextends": 26448, "epherd": 26449, "Ġbalcon": 26450, "ä¸įèµ·": 26451, "ĠSolo": 26452, "Ġpolitician": 26453, "олÑĮно": 26454, "Ġirgendw": 26455, "Ġtraumatic": 26456, "Ġrapper": 26457, "ĠROBERT": 26458, "Really": 26459, "æģ¯": 26460, "Ġlineup": 26461, "ASE": 26462, "Ġcontractor": 26463, "ĠCorporation": 26464, "gor": 26465, "ĠTodo": 26466, "ÑģÑĤÑĢой": 26467, "FBE": 26468, "Ġnewsletter": 26469, "ĠkoÅĦ": 26470, "alties": 26471, "ĠпÑĢиÑĩ": 26472, "ĠHeavy": 26473, "Ġswords": 26474, "Ġmanipulation": 26475, "Ġfunk": 26476, "ĠvÃ¥r": 26477, "ĠTaliban": 26478, "Ġë°¥": 26479, "Ġacne": 26480, "ürü": 26481, "Ġdeswegen": 26482, "ĠDust": 26483, "Ġsilic": 26484, "Ġhooks": 26485, "Ġblij": 26486, "Ġpetits": 26487, "Ġfilme": 26488, "ĠBereich": 26489, "ĠSaid": 26490, "Ġimposed": 26491, "Ġdiary": 26492, "ĠгоÑĢ": 26493, "ĠGates": 26494, "Ġalta": 26495, "å¸Į": 26496, "Ġchcia": 26497, "pleasant": 26498, "Ġë°Ŀ": 26499, "Ġmożemy": 26500, "ĠAustria": 26501, "Ġbroker": 26502, "Ġsucked": 26503, "èĢĥ": 26504, "Ġcompartment": 26505, "Ġclone": 26506, "Ġ×Ķ×¢": 26507, "ĠDanke": 26508, "Ġnochmal": 26509, "езд": 26510, "Ġadrenal": 26511, "Ġkleinen": 26512, "ãģ¾ãģĹãĤĩãģĨ": 26513, "Ġsubsequently": 26514, "Ġdecentral": 26515, "Ġgenetics": 26516, "Ġê´ij": 26517, "Ġmonitors": 26518, "ĠApplic": 26519, "ĠReporter": 26520, "wert": 26521, "Ġwiem": 26522, "ĠMovement": 26523, "Ġinterviewing": 26524, "Ġhairs": 26525, "Ġpuò": 26526, "ĠChelsea": 26527, "Ġcoher": 26528, "Ġcot": 26529, "Ġzas": 26530, "Ġpatches": 26531, "Ġlah": 26532, "Ñĥнк": 26533, "ĠReagan": 26534, "ĠMarco": 26535, "city": 26536, "Ġdefender": 26537, "Ġdecoration": 26538, "iji": 26539, "Ġlitter": 26540, "Ш": 26541, "Ġjego": 26542, "REW": 26543, "ĠPik": 26544, "ĠHee": 26545, "ĠIv": 26546, "Ġиде": 26547, "ĠTheater": 26548, "ĠÑĩаÑģÑĤо": 26549, "Ġsweater": 26550, "Ġhighlighting": 26551, "Ġainsi": 26552, "Ġdiplomatic": 26553, "ĠNevertheless": 26554, "å³": 26555, "ASON": 26556, "Ġpúblico": 26557, "Ġferm": 26558, "reated": 26559, "cod": 26560, "Ġ물ë": 26561, "Ġmister": 26562, "ĠVancouver": 26563, "Ġrecognizes": 26564, "ecd": 26565, "Ġcomplications": 26566, "encial": 26567, "ãģĹãģı": 26568, "Ġê°Ģì§Ģ": 26569, "ĠUltimate": 26570, "Ġvaig": 26571, "ĠMerry": 26572, "×ķ×Ĵ": 26573, "ĠMarcus": 26574, "總": 26575, "owego": 26576, "Ġmente": 26577, "Sm": 26578, "Ġaja": 26579, "ĠTao": 26580, "Ġjudicial": 26581, "Ġentrepreneurship": 26582, "Ġнемного": 26583, "Ġpis": 26584, "Ġerg": 26585, "Ġchrist": 26586, "ĠCurt": 26587, "ĠÑĢаÑģп": 26588, "λε": 26589, "ensch": 26590, "ÃŃre": 26591, "Ġfocal": 26592, "ĠDiamond": 26593, "avÃŃa": 26594, "Ġhanno": 26595, "ĠSquad": 26596, "Ġassociations": 26597, "ĠCreative": 26598, "Ġmessenger": 26599, "Ġbegging": 26600, "Ġdecimal": 26601, "ĠdÄ±ÅŁ": 26602, "Ġmetadata": 26603, "sels": 26604, "ĠÄ°ÅŁ": 26605, "ữa": 26606, "Ġdifficile": 26607, "dı": 26608, "Ġslaughter": 26609, "ĠVerg": 26610, "Ġ×Ĵ×Ŀ": 26611, "ç°¡": 26612, "æĮī": 26613, "ĠTea": 26614, "asses": 26615, "Ok": 26616, "Ġsynthes": 26617, "otiation": 26618, "Ġpainter": 26619, "Ġelbows": 26620, "Ġarchitectural": 26621, "ĠÑĢад": 26622, "Ġglor": 26623, "image": 26624, "ampa": 26625, "culiar": 26626, "ł¨": 26627, "Ġteve": 26628, "ĠStelle": 26629, "ĠBam": 26630, "Ġì´Ī": 26631, "asis": 26632, "ipedia": 26633, "ĠGI": 26634, "ĠActive": 26635, "çĦ¶åIJİ": 26636, "azi": 26637, "ãĤĮãģ¦": 26638, "ĠLucky": 26639, "íķ©": 26640, "ĠпÑĢиÑħод": 26641, "Ġrunway": 26642, "Ġauthentication": 26643, "Ġposible": 26644, "Ġsupplements": 26645, "Ġsurgical": 26646, "Gen": 26647, "Ġfeasible": 26648, "DO": 26649, "Ġoutlook": 26650, "Ġintervals": 26651, "Ġanecd": 26652, "Ãłng": 26653, "Ġstraps": 26654, "ĠShu": 26655, "udd": 26656, "issenschaft": 26657, "Ġporte": 26658, "Ġcommitting": 26659, "Ġalley": 26660, "Ġcovenant": 26661, "ĠPedro": 26662, "lessness": 26663, "ĠSolid": 26664, "ĠMolly": 26665, "ĠнекоÑĤоÑĢ": 26666, "Ġcooperate": 26667, "åĮĹ": 26668, "ollen": 26669, "Ġtuna": 26670, "Ġkindergarten": 26671, "ĠSiz": 26672, "Ġdużo": 26673, "ĠMBA": 26674, "ĠGEORGE": 26675, "ĠFisher": 26676, "å¿ĺ": 26677, "ĠCaesar": 26678, "ĠкÑĢаÑģив": 26679, "ĠDelhi": 26680, "zym": 26681, "Ġexplicar": 26682, "ê°Ģì§Ģ": 26683, "uns": 26684, "grow": 26685, "ĠпÑĢиÑģ": 26686, "Ġ86": 26687, "Ġstating": 26688, "Ġmassa": 26689, "chter": 26690, "Ġì»¬ëŁ¬": 26691, "Ġdeputy": 26692, "SM": 26693, "noc": 26694, "Ġgeography": 26695, "ĠEnterprise": 26696, "ĠCant": 26697, "öz": 26698, "Ġunpack": 26699, "ĠíĻĶë": 26700, "Ġsearches": 26701, "Ġpresidency": 26702, "Ġtrivial": 26703, "Ġpige": 26704, "oubt": 26705, "ãĤļ": 26706, "ì¼ĢìĿ´": 26707, "Ġbudgets": 26708, "Ġub": 26709, "Ġpne": 26710, "ĠYale": 26711, "ĠÅŁÃ¶yle": 26712, "regular": 26713, "Ġimperfect": 26714, "ARA": 26715, "ĠfamÃŃlia": 26716, "urm": 26717, "ĠAdventure": 26718, "ãĥĬ": 26719, "cis": 26720, "emark": 26721, "Ġnego": 26722, "Ġinappropriate": 26723, "ĠпÑĢиз": 26724, "ĠÑĢол": 26725, "Ġdreamed": 26726, "Bry": 26727, "Ġshuttle": 26728, "Ġpillars": 26729, "Ġbik": 26730, "inum": 26731, "ĠÑĥÑģ": 26732, "ĠNebr": 26733, "Ġperpendicular": 26734, "Ġbooked": 26735, "bery": 26736, "Ġvikt": 26737, "bear": 26738, "esus": 26739, "Ġвозможно": 26740, "¨¹": 26741, "Ġpresumably": 26742, "ĠMemphis": 26743, "Ġambulance": 26744, "×ķ×ŀר": 26745, "Ġthumbnail": 26746, "Ġmodification": 26747, "éĩı": 26748, "Ġinterpreted": 26749, "Ġpromo": 26750, "Ġκά": 26751, "ĠεÏĢ": 26752, "Ġacoustic": 26753, "ĠDB": 26754, "åĵİ": 26755, "Ġnonetheless": 26756, "oule": 26757, "Ġpequ": 26758, "Ġknob": 26759, "ãĤ£": 26760, "ĠëıĮìķĦ": 26761, "Ġpurchases": 26762, "ĠÃĩünkü": 26763, "Ġdividing": 26764, "perform": 26765, "raction": 26766, "healthy": 26767, "ĠTitle": 26768, "Ġuk": 26769, "Ġcerca": 26770, "Ġarguably": 26771, "Ġfale": 26772, "ë³µ": 26773, "Ġgamers": 26774, "Ġutilizing": 26775, "Ġoffended": 26776, "Ġtava": 26777, "alı": 26778, "Ġmedian": 26779, "Ġinfectious": 26780, "ĠAnnie": 26781, "Ġsmartphones": 26782, "Ġparole": 26783, "åĸĿ": 26784, "ĠEpic": 26785, "zza": 26786, "Ġunified": 26787, "Ġê·¸ëķĮ": 26788, "Ġcurtain": 26789, "ĠÄĥ": 26790, "Ġsexually": 26791, "Ġunserem": 26792, "ĠConvention": 26793, "Ġallegedly": 26794, "Ya": 26795, "ĠHoo": 26796, "enment": 26797, "æĢª": 26798, "íĽĦ": 26799, "Ġgigantic": 26800, "Ġnoting": 26801, "Ġrebo": 26802, "ĠJama": 26803, "ĠAlz": 26804, "Ġborrowed": 26805, "침": 26806, "Ġperipher": 26807, "оÑĤа": 26808, "ĠGB": 26809, "ĠGear": 26810, "Ġeconomically": 26811, "Ġtelefon": 26812, "Ġqueremos": 26813, "ĠдалÑĮÑĪе": 26814, "Ġras": 26815, "ĠTeach": 26816, "icios": 26817, "atos": 26818, "Ġpledge": 26819, "bau": 26820, "ĠHimself": 26821, "Link": 26822, "Ġespero": 26823, "Ġchromos": 26824, "ĠPER": 26825, "Ġerle": 26826, "Ġpodium": 26827, "ços": 26828, "Ġnieu": 26829, "Ġfen": 26830, "ĠGOD": 26831, "ĠChocolate": 26832, "werk": 26833, "Ġtừ": 26834, "Ġsuppress": 26835, "λη": 26836, "Ġ240": 26837, "Ġsitä": 26838, "Ġhonesty": 26839, "ĠBio": 26840, "ĠBard": 26841, "ĠобÑīем": 26842, "ĠмÑĥз": 26843, "Ġmarble": 26844, "ĠÑĨенÑĤ": 26845, "Ġprocure": 26846, "Ġrotor": 26847, "bern": 26848, "Ġtuh": 26849, "Ġheadset": 26850, "atem": 26851, "Ġwarranty": 26852, "à®´": 26853, "Ġfiling": 26854, "ιά": 26855, "Ġcomprendre": 26856, "Ġimpulse": 26857, "Ġsalv": 26858, "written": 26859, "Ġinstitute": 26860, "Kim": 26861, "ĠLGBTQ": 26862, "ficiente": 26863, "His": 26864, "ĠαÏħÏĦÏĮ": 26865, "Ġteenage": 26866, "orus": 26867, "ĠÑĢазб": 26868, "See": 26869, "ĠConserv": 26870, "á»ģn": 26871, "fulness": 26872, "Ġstrawberries": 26873, "ĠAbu": 26874, "ион": 26875, "Ġolla": 26876, "NOISE": 26877, "ĠEmploy": 26878, "Ġwiped": 26879, "urger": 26880, "Ġmodifications": 26881, "Ġíķĺì§Ģ": 26882, "Ġfootsteps": 26883, "Ġhonors": 26884, "Ġadul": 26885, "Ġflipping": 26886, "ĠHU": 26887, "ZY": 26888, "Ġintegrating": 26889, "بر": 26890, "ulla": 26891, "Ġnatuurlijk": 26892, "ĠíĹĪ": 26893, "ĠEthereum": 26894, "ÙĬÙĦ": 26895, "wed": 26896, "Ġpeaks": 26897, "ĠKes": 26898, "Ġbloom": 26899, "Ġcrashing": 26900, "Ġ911": 26901, "ĠоÑĤлиÑĩ": 26902, "Ġcontrollers": 26903, "ĠDod": 26904, "ĠвмеÑģÑĤе": 26905, "Ġsortir": 26906, "å¥ĩ": 26907, "ĠStraight": 26908, "ĠGracias": 26909, "Ġgroove": 26910, "Ġtogg": 26911, "Ġìĭ¶ìĿĢ": 26912, "éro": 26913, "Ġoutward": 26914, "ĠWA": 26915, "ĠRocky": 26916, "Ġscam": 26917, "Ġhayat": 26918, "ignty": 26919, "âĦ": 26920, "plings": 26921, "Ġantibiotics": 26922, "Ġä¸Ģ": 26923, "Ġnevertheless": 26924, "jang": 26925, "commerce": 26926, "Ġspoiler": 26927, "Ġglove": 26928, "Ġchatter": 26929, "ĠBY": 26930, "~?": 26931, "Ġíĺ¸": 26932, "Ġdemol": 26933, "wechsel": 26934, "imir": 26935, "Ġraid": 26936, "еÑĢÑħ": 26937, "ìŀIJ기": 26938, "enf": 26939, "Ġcommented": 26940, "Ġoptimized": 26941, "Ġconvicted": 26942, "Ġbats": 26943, "ĠSB": 26944, "ĠAur": 26945, "ĠTong": 26946, "Ġimplicit": 26947, "ĠJanet": 26948, "Ġreag": 26949, "ãģ²": 26950, "ĠAdvanced": 26951, "Ġimpose": 26952, "ש×Ķ": 26953, "Ġschemes": 26954, "ougher": 26955, "abolic": 26956, "Ġê±°ì£ł": 26957, "Ġslowing": 26958, "Ġwtedy": 26959, "Ġdestructive": 26960, "ĠопÑĢед": 26961, "Ġlandmark": 26962, "ĠëıĪ": 26963, "ĠWalking": 26964, "ẹ": 26965, "Ġtijd": 26966, "ĠKN": 26967, "ĠQuant": 26968, "ìĺ¤ë": 26969, "ĠкÑĢÑĥ": 26970, "Ġperder": 26971, "Ġnove": 26972, "ände": 26973, "ĠãģĹ": 26974, "bia": 26975, "Ġcustody": 26976, "Ġbiod": 26977, "æĿ±è¥¿": 26978, "Ġdirecting": 26979, "...âĢĭ": 26980, "Ġreloc": 26981, "Ġdemande": 26982, "ãĤĵãģł": 26983, "ĠoÄŁlum": 26984, "Ġодна": 26985, "ĠMilk": 26986, "åı·": 26987, "ĠKra": 26988, "ĠHonda": 26989, "Ġpue": 26990, "Ġelekt": 26991, "Ġbeginners": 26992, "Ġspear": 26993, "ÃŃnh": 26994, "ĠLuft": 26995, "Ġnig": 26996, "ĠSchools": 26997, "Ġforums": 26998, "ĠQin": 26999, "ppo": 27000, "Ġzag": 27001, "ĠЮ": 27002, "Ġtoothp": 27003, "ĠStyle": 27004, "ì´Ī": 27005, "Ġpunct": 27006, "Ġreps": 27007, "ĠAly": 27008, "Ġamendments": 27009, "Ġöz": 27010, "Ġdigits": 27011, "urai": 27012, "Ġchaotic": 27013, "ĠMasters": 27014, "eon": 27015, "ĠCash": 27016, "ĠCuz": 27017, "Ġbedeutet": 27018, "Ġscanning": 27019, "Ġжд": 27020, "неÑĤ": 27021, "Ġcertainty": 27022, "jek": 27023, "Ġdijo": 27024, "ĠClimate": 27025, "Ġrinse": 27026, "Ġkrij": 27027, "veland": 27028, "Ġsoundtrack": 27029, "ĠSafe": 27030, "ĠNova": 27031, "94": 27032, "Ġathe": 27033, "ĠVerb": 27034, "oler": 27035, "ìĿ´ì£ł": 27036, "Ġvin": 27037, "Ġrespiratory": 27038, "ĠStudy": 27039, "ĠCAM": 27040, "Ġavocado": 27041, "ĠZhen": 27042, "Ġlatency": 27043, "Ġfeathers": 27044, "Ġcontar": 27045, "ĠвеÑī": 27046, "Ġfark": 27047, "Ġblended": 27048, "Ġexploded": 27049, "ĠXX": 27050, "ĠBenim": 27051, "Ġalguém": 27052, "istoire": 27053, "Ġconfidential": 27054, "Ġmast": 27055, "Ġì¿": 27056, "geh": 27057, "Ġdisrespect": 27058, "ĠSystems": 27059, "Æ°a": 27060, "Ed": 27061, "Ġwys": 27062, "Ġexotic": 27063, "Ġglowing": 27064, "ùng": 27065, "ounge": 27066, "èĦ": 27067, "аниз": 27068, "Ġpalav": 27069, "ĠSword": 27070, "Ġgim": 27071, "ĠCrow": 27072, "Ġpotent": 27073, "bish": 27074, "Ġabused": 27075, "ĠJed": 27076, "Ġgambling": 27077, "ĠSpect": 27078, "Ġinvestigators": 27079, "æĻļ": 27080, "Ġratt": 27081, "Ġdob": 27082, "ĠDES": 27083, "hog": 27084, "ĠоÑĤкÑĢÑĭ": 27085, "íĮħ": 27086, "ĠденÑĮги": 27087, "Ġíĺ¹": 27088, "Ġ머리": 27089, "Ġsaturation": 27090, "Ġinherited": 27091, "ĠInnovation": 27092, "ìĹĪëįĺ": 27093, "Ġtangible": 27094, "Ġdepri": 27095, "hed": 27096, "Ġпомог": 27097, "Ġsliced": 27098, "à¥į": 27099, "Ġthế": 27100, "Å¥": 27101, "68": 27102, "Ġcorona": 27103, "Ġgifted": 27104, "Ġsoir": 27105, "Ġhumility": 27106, "ĠìĿ´ê±¸": 27107, "Ġflaws": 27108, "ĠпÑĢакÑĤи": 27109, "Ġkald": 27110, "waż": 27111, "yw": 27112, "ãĤĵãģ§ãģĻ": 27113, "irteen": 27114, "Ġcrochets": 27115, "¦¬ê°Ģ": 27116, "ĠìłĦìĹIJ": 27117, "Ġdese": 27118, "æ¥Ń": 27119, "Ġмаг": 27120, "ĠdziaÅĤ": 27121, "Ġlég": 27122, "changing": 27123, "Ġllev": 27124, "ÅĦsk": 27125, "çĶ»": 27126, "Ġ1984": 27127, "orns": 27128, "ĠWelsh": 27129, "Ġpharmaceutical": 27130, "Ġpumping": 27131, "ĠShaw": 27132, "punk": 27133, "Ġvault": 27134, "Ġkinetic": 27135, "Ġhurricane": 27136, "ĠIncluding": 27137, "ức": 27138, "ĠGrandpa": 27139, "anship": 27140, "é¦Ļ港": 27141, "ĠвÑĭÑħод": 27142, "нож": 27143, "ľł": 27144, "utta": 27145, "Ġê²ģëĭĪëĭ¤": 27146, "Ġbaz": 27147, "ĠпоÑĪ": 27148, "Ġpeculiar": 27149, "zyÄĩ": 27150, "ĠEllie": 27151, "Ġlearns": 27152, "ĠKrishna": 27153, "Ġconsecut": 27154, "Ġempath": 27155, "ĠDin": 27156, "Ġtraded": 27157, "ĠBoris": 27158, "uggage": 27159, "olla": 27160, "Ġназв": 27161, "Ġeternity": 27162, "Ġвп": 27163, "èmes": 27164, "Ġgrapp": 27165, "bé": 27166, "ĠпÑĢедÑģÑĤав": 27167, "ĠFC": 27168, "įëĭĪëĭ¤": 27169, "even": 27170, "ĠNebraska": 27171, "ortune": 27172, "Ġkarena": 27173, "ĠAgent": 27174, "Ġsting": 27175, "ĠPI": 27176, "Ġmunicipal": 27177, "powered": 27178, "Ġconsegue": 27179, "ĠManchester": 27180, "Ġrainy": 27181, "Ġbli": 27182, "Ġkost": 27183, "Ġhalten": 27184, "ĠAhhh": 27185, "insula": 27186, "erting": 27187, "ĠاÙĦÙģ": 27188, "Ġrelacion": 27189, "Ġkomen": 27190, "Ġdome": 27191, "Ġpriests": 27192, "ĠIntrodu": 27193, "rophe": 27194, "shore": 27195, "velt": 27196, "clipse": 27197, "ĠÑĢÑĥÑģ": 27198, "×Ļס": 27199, "Ġsabemos": 27200, "ĠHolland": 27201, "ogi": 27202, "anki": 27203, "ĠMats": 27204, "Ġsmoked": 27205, "ullie": 27206, "Ġeurope": 27207, "ĠдейÑģÑĤвиÑĤелÑĮно": 27208, "Ġbardziej": 27209, "Ġtransforming": 27210, "ĠEz": 27211, "opath": 27212, "Ġìĸ¸ëĭĪ": 27213, "ĠÑģÑĤан": 27214, "ằng": 27215, "ัà¹ī": 27216, "ĠOuch": 27217, "Ġclearance": 27218, "ustain": 27219, "Ġsolidarity": 27220, "Ġproving": 27221, "ĠÐĺн": 27222, "ĠÑģÑĬ": 27223, "Ġprolong": 27224, "адно": 27225, "Ġsos": 27226, "ĠDeal": 27227, "Ġ170": 27228, "mons": 27229, "Ġзем": 27230, "Ġlogged": 27231, "Ġlifelong": 27232, "Ġsensory": 27233, "Ġbehold": 27234, "ĠFAR": 27235, "ètement": 27236, "ĠFederation": 27237, "Ġdodge": 27238, "ĠShir": 27239, "Ġdragons": 27240, "ĠArctic": 27241, "Äħż": 27242, "Åį": 27243, "º": 27244, "Ġdenke": 27245, "ĠpodrÃŃa": 27246, "cole": 27247, "ÑĥлÑĮÑĤаÑĤ": 27248, "Ġsystematic": 27249, "ама": 27250, "chos": 27251, "Ġclinics": 27252, "ĠBS": 27253, "Ġtales": 27254, "usions": 27255, "ĠíĪ¬": 27256, "Ġpreservation": 27257, "Ġlore": 27258, "ĠProtest": 27259, "Ỽ": 27260, "å¸Ĥ": 27261, "Ġacknowledged": 27262, "ĠIsaiah": 27263, "ĠëķĮëĬĶ": 27264, "Ġ×ĺ": 27265, "Ġcompetitor": 27266, "Ġadvancing": 27267, "zip": 27268, "Ġtenth": 27269, "ĠLaure": 27270, "Ġhints": 27271, "Ġexercising": 27272, "ŀľë": 27273, "ĠIntelligence": 27274, "uated": 27275, "OUT": 27276, "oped": 27277, "Ġautonomy": 27278, "Ġbranding": 27279, "ĠMediterranean": 27280, "Ñĸк": 27281, "Ġscrewdriver": 27282, "Ġsupre": 27283, "Ġstap": 27284, "Ġjurisdiction": 27285, "ĠSettings": 27286, "Ġforefront": 27287, "ĠFemale": 27288, "comfort": 27289, "Ġmultiplication": 27290, "ĠMurray": 27291, "Ġbob": 27292, "ĠTas": 27293, "Ġtahu": 27294, "Ġonun": 27295, "etter": 27296, "Ġprophets": 27297, "lag": 27298, "Ġrevenues": 27299, "Ġprá": 27300, "Ġuploading": 27301, "Ġmachinery": 27302, "ascal": 27303, "ĠEstá": 27304, "ĠGoth": 27305, "ĠBald": 27306, "ĠSaw": 27307, "Ġstripes": 27308, "ìłij": 27309, "Ġpowin": 27310, "æĹ¥æľ¬": 27311, "Ġhostile": 27312, "Ġdarum": 27313, "Ġprevented": 27314, "ожалÑĥйÑģÑĤа": 27315, "Ġalgunas": 27316, "Ġhopeless": 27317, "Ġznaj": 27318, "Ġreadings": 27319, "Ġcraving": 27320, "tat": 27321, "ĠPig": 27322, "Ġliar": 27323, "çĪ±": 27324, "Ġmultiplayer": 27325, "Ġdale": 27326, "ĠCourse": 27327, "íģ¼": 27328, "ĠKita": 27329, "Ġcustoms": 27330, "Ġresponds": 27331, "endra": 27332, "è¦ĸ": 27333, "Ġmetro": 27334, "Ñģол": 27335, "Ġmitigate": 27336, "Ġoppression": 27337, "ĠæĪijåĢij": 27338, "quinho": 27339, "Ġammo": 27340, "Ġenfer": 27341, "Ġpony": 27342, "Ġounces": 27343, "°Ķ": 27344, "ĠìĪĺê°Ģ": 27345, "Ġdicho": 27346, "ĠDeb": 27347, "Ġwonders": 27348, "ĠRoose": 27349, "Ġprizes": 27350, "ĠALEX": 27351, "Ġthankfully": 27352, "Ġtissues": 27353, "ĠÑĢавно": 27354, "ĠLuna": 27355, "intelligible": 27356, "ĠìĻ¸": 27357, "ê°ij": 27358, "ĠHeat": 27359, "ĠÑģид": 27360, "ĠQui": 27361, "Ġions": 27362, "Ġaccommodation": 27363, "便": 27364, "ĠKart": 27365, "ienst": 27366, "Ġtarde": 27367, "Ġsoaked": 27368, "ĠCasey": 27369, "Ġì´Ŀ": 27370, "ĠÑĢÑĥб": 27371, "Ġdifferenti": 27372, "Ġleftover": 27373, "Ġexchanges": 27374, "second": 27375, "Ġfirstly": 27376, "Ġbuilder": 27377, "rien": 27378, "Ġdw": 27379, "Ġbouncing": 27380, "?<": 29986, "ologÃŃa": 29987, "wealth": 29988, "Ġmeditate": 29989, "ĵ¤ìĿĺ": 29990, "ĠCraft": 29991, "è§īå¾Ĺ": 29992, "æĻ®": 29993, "riv": 29994, "ĠAgainst": 29995, "Ġceramic": 29996, "espère": 29997, "Ġcompetent": 29998, "ĠHopkins": 29999, "Ġkilos": 30000, "Ġgravel": 30001, "Ġpiston": 30002, "Ġfriendships": 30003, "Ġescre": 30004, "Ġvoz": 30005, "ĠGesellschaft": 30006, "Ġunterstüt": 30007, "Ġmuj": 30008, "Ġwarnings": 30009, "pos": 30010, "ĠProfessional": 30011, "wszy": 30012, "odle": 30013, "bands": 30014, "Ġteamwork": 30015, "stellung": 30016, "Ġdx": 30017, "åįĬ": 30018, "Ġattorneys": 30019, "Ġweitere": 30020, "ãħĭãħĭãħĭ": 30021, "ĠOriginal": 30022, "×Ļ×Ĺ": 30023, "Ġbroadcasting": 30024, "ĠпеÑĢвÑĭй": 30025, "uchi": 30026, "Ġheure": 30027, "Ġgrabs": 30028, "ĠWOR": 30029, "ĠPlaid": 30030, "Min": 30031, "Ġpaz": 30032, "ĠPuis": 30033, "umu": 30034, "itates": 30035, "Ġcoats": 30036, "Ġbuen": 30037, "Ġheir": 30038, "Ġpneum": 30039, "שר": 30040, "enser": 30041, "ĠJUDGE": 30042, "Ġblonde": 30043, "á¹Ľ": 30044, "Ġgak": 30045, "Ġsık": 30046, "Ġquoted": 30047, "Ġequipo": 30048, "Ġwishing": 30049, "ÃŃcia": 30050, "Ġverbs": 30051, "çµĦ": 30052, "ĠCanadians": 30053, "Ġgoverning": 30054, "ĠEvans": 30055, "Euro": 30056, "Ġgenres": 30057, "Ġunterschied": 30058, "ĠBecky": 30059, "³¼ê²ĮìļĶ": 30060, "Ġeinge": 30061, "ĠRaise": 30062, "oland": 30063, "ĠStrateg": 30064, "Ġeres": 30065, "ĠVeterans": 30066, "Ġbreakout": 30067, "Ġsanté": 30068, "Ġadel": 30069, "Ġinvestigated": 30070, "Ġpeur": 30071, "Ġagile": 30072, "Ġrailroad": 30073, "anska": 30074, "Ġей": 30075, "Ġexpos": 30076, "atories": 30077, "ĠContent": 30078, "Ġtruths": 30079, "ĠTrail": 30080, "Ġgua": 30081, "Ġpores": 30082, "Ġwritings": 30083, "ĠUhr": 30084, "ĠThats": 30085, "Ġicing": 30086, "OC": 30087, "ĠProduction": 30088, "Ġcarne": 30089, "ISS": 30090, "Ġninguém": 30091, "non": 30092, "Ġvicious": 30093, "×ķ×Ķ": 30094, "Ġreconnect": 30095, "Ġcentres": 30096, "ĠKem": 30097, "Ġcrease": 30098, "ĠìĿ´ë¯¸": 30099, "айÑĤеÑģÑĮ": 30100, "ĠбоÑĢ": 30101, "ĠHayır": 30102, "ĠÑģÑĥд": 30103, "Ġúnica": 30104, "owaÅĤ": 30105, "Ġadher": 30106, "hua": 30107, "ZZ": 30108, "Ġpreciso": 30109, "Ġcurrents": 30110, "Ġseasoned": 30111, "ĠIoT": 30112, "ĠBishop": 30113, "è¨Ī": 30114, "sted": 30115, "ĠBernard": 30116, "ì¤ĺ": 30117, "æ²»": 30118, "ĠGlenn": 30119, "Ġktórym": 30120, "ืà¹Ī": 30121, "Ġastrolog": 30122, "ĠKot": 30123, "å¤ľ": 30124, "Ġparfois": 30125, "Ġforwards": 30126, "ĠWiÄĻ": 30127, "ĠÎĺ": 30128, "Ġnano": 30129, "è»į": 30130, "sub": 30131, "ĠBrill": 30132, "Ġgrit": 30133, "Ġcited": 30134, "gado": 30135, "Ġmelts": 30136, "Ġforcé": 30137, "âĸĪâĸĪ": 30138, "Ġbajo": 30139, "Ġdiscretion": 30140, "°°": 30141, "ativity": 30142, "Ġsituated": 30143, "ãĥ«ãĤ¯": 30144, "Ñīее": 30145, "åľ°æĸ¹": 30146, "ĠпÑĢинÑĨип": 30147, "amaz": 30148, "Ġaquarium": 30149, "Ġdissolve": 30150, "ĠGods": 30151, "Super": 30152, "Ġamid": 30153, "zk": 30154, "ĠãģĦ": 30155, "éłIJ": 30156, "ampf": 30157, "Ġhela": 30158, "'!": 30159, "Ġdevelopmental": 30160, "ĠDise": 30161, "ĠÑĢабоÑĤаеÑĤ": 30162, "Ġsnapshot": 30163, "好好": 30164, "Õ¸": 30165, "ĠYue": 30166, "ĠHulk": 30167, "ĠDoom": 30168, "ĠFelix": 30169, "Ġréf": 30170, "Male": 30171, "ç·Ĭ": 30172, "phants": 30173, "ENS": 30174, "ĠMechan": 30175, "ĠGolf": 30176, "åĨįè¦ĭ": 30177, "Ġgenerosity": 30178, "ätze": 30179, "Ġunlocked": 30180, "ĠãĤĴ": 30181, "íĥģ": 30182, "ocalypse": 30183, "Alright": 30184, "Ġê°ľë": 30185, "Ġ×IJ×ij׾": 30186, "ĠKeeping": 30187, "Ġcollaborating": 30188, "chief": 30189, "ĠFernando": 30190, "Ġchefs": 30191, "ĠíĶ¼ë¶Ģ": 30192, "Ġskipped": 30193, "Ġpersonn": 30194, "Ġaxe": 30195, "chez": 30196, "Ġextraction": 30197, "ĠAV": 30198, "ĠGibbs": 30199, "Ġíľ": 30200, "Ġsı": 30201, "IAM": 30202, "View": 30203, "ĠGRANT": 30204, "Ġ몸": 30205, "Ġverification": 30206, "Ġdepicted": 30207, "ĠMoz": 30208, "oux": 30209, "Ġtul": 30210, "Ġscanner": 30211, "Ġcomedian": 30212, "ĠVolks": 30213, "ĠJEFF": 30214, "è¨Ĥéĸ±": 30215, "§Ħ": 30216, "Ġdistraction": 30217, "rá": 30218, "ĠINTER": 30219, "Ġsincer": 30220, "Ġ×ŀת": 30221, "Ġש׳": 30222, "Ġconstructive": 30223, "arf": 30224, "ĠëĪĦë": 30225, "Ġeco": 30226, "ramos": 30227, "Ġrenewed": 30228, "inement": 30229, "ĠUb": 30230, "ĠPepper": 30231, "ì§Ģê°Ģ": 30232, "ĠDarwin": 30233, "Ġmerchand": 30234, "Ġvárias": 30235, "èce": 30236, "NG": 30237, "ĠìľĦíķ´ìĦľ": 30238, "ĠакÑĤив": 30239, "ĠUnters": 30240, "عÙĦ": 30241, "Ġintric": 30242, "omma": 30243, "ieving": 30244, "ĠCaroline": 30245, "åĵģ": 30246, "ĠPRES": 30247, "Ġperformer": 30248, "Ġautour": 30249, "ãģ¾ãģĽãĤĵ": 30250, "Ġutterly": 30251, "Ġsynthesis": 30252, "Ġlesbian": 30253, "Ġretrieve": 30254, "Ġmaneira": 30255, "Ġimpair": 30256, "Ġmentoring": 30257, "ĠSouls": 30258, "ĠGoPro": 30259, "ÑĢаÑĤÑĮ": 30260, "Ġcose": 30261, "ĠSSD": 30262, "IRE": 30263, "Ġupfront": 30264, "ĠAun": 30265, "Ġgamer": 30266, "Ġlitt": 30267, "Ġaggression": 30268, "ĠLikewise": 30269, "ĠBetty": 30270, "ĠDart": 30271, "ĠDLC": 30272, "ishment": 30273, "ìŀ¥ìĿĦ": 30274, "Ġ对": 30275, "ç»ı": 30276, "cream": 30277, "ĠBabylon": 30278, "Ġnug": 30279, "brar": 30280, "Ġaynı": 30281, "amily": 30282, "bike": 30283, "ahahaha": 30284, "loyd": 30285, "Ġmira": 30286, "Ġperme": 30287, "ĠGaming": 30288, "Ġfirmware": 30289, "Ma": 30290, "Ġassisted": 30291, "atics": 30292, "Ġìķŀìľ¼ë¡ľ": 30293, "ĠMental": 30294, "niejs": 30295, "ĠIz": 30296, "owÄħ": 30297, "Ġtougher": 30298, "Ġdeed": 30299, "èĭ¦": 30300, "Ġstylish": 30301, "ĠTools": 30302, "ĠHamp": 30303, "Ġsunscreen": 30304, "Ġarticulate": 30305, "iye": 30306, "иÑĦ": 30307, "ĠSpread": 30308, "ĠHAVE": 30309, "Ġswirl": 30310, "Ġsponsoring": 30311, "ä»ĭ": 30312, "iovascular": 30313, "mesi": 30314, "Ġrelaxation": 30315, "ĠÑģвоиÑħ": 30316, "Ġmargins": 30317, "ĠsaÄŁ": 30318, "ĠPride": 30319, "ĠÏĦοÏħÏĤ": 30320, "иÑĨи": 30321, "enci": 30322, "Does": 30323, "Ġcorpse": 30324, "Ġendurance": 30325, "Ġíŀĺ": 30326, "ì¹´": 30327, "Ġhaircut": 30328, "Ġinterrupted": 30329, "Ġwindy": 30330, "ĠCaleb": 30331, "ÏģÏĩ": 30332, "ĠPourquoi": 30333, "Ġholistic": 30334, "uclear": 30335, "ĠWhole": 30336, "士": 30337, "Act": 30338, "Ġgallon": 30339, "cade": 30340, "ĠRegional": 30341, "roads": 30342, "ĠSchne": 30343, "áng": 30344, "Ġизмен": 30345, "ãĤĪãģŃ": 30346, "Ġmenus": 30347, "Ġsplitting": 30348, "Ġpriced": 30349, "ĠÎĵ": 30350, "Ġusername": 30351, "ĠÐŀÑĩ": 30352, "Ġcompressed": 30353, "yin": 30354, "Ġguardian": 30355, "Ġgoof": 30356, "Ġchecklist": 30357, "Ġinterchange": 30358, "Ġexpedition": 30359, "Ġextern": 30360, "Ġinfrared": 30361, "engo": 30362, "Ġdenying": 30363, "Ġpackets": 30364, "onent": 30365, "BB": 30366, "ĠIncre": 30367, "Ġsini": 30368, "ÃŁer": 30369, "èg": 30370, "maal": 30371, "generation": 30372, "Ġminorities": 30373, "Ġllevar": 30374, "Ġnomination": 30375, "Ġconsid": 30376, "Ġ×ľ×¢": 30377, "muÅŁ": 30378, "ĠEsc": 30379, "Ġnumerator": 30380, "Ġkaik": 30381, "Ġktórych": 30382, "iesen": 30383, "Ġvê": 30384, "ĠUSS": 30385, "ĠPrivate": 30386, "Ġодно": 30387, "Ġalém": 30388, "ÃŃtulo": 30389, "Ġlimb": 30390, "Ġforgiven": 30391, "Ġdisclosure": 30392, "ÏĦί": 30393, "Ġningún": 30394, "Ġtherapeutic": 30395, "Ġnegotiating": 30396, "ĠNike": 30397, "enseful": 30398, "Ġincap": 30399, "Ġflagship": 30400, "town": 30401, "âĪ": 30402, "ĠÏĢολ": 30403, "Ġwolves": 30404, "Ġviolations": 30405, "ĠArnold": 30406, "Ġintervene": 30407, "Ġheater": 30408, "Ġrecursos": 30409, "Ġmaid": 30410, "ê²¼": 30411, "ĠдавайÑĤе": 30412, "ĠCelebr": 30413, "Ġcape": 30414, "ĠSty": 30415, "ainen": 30416, "site": 30417, "bij": 30418, "ĠполÑĮз": 30419, "Ġframed": 30420, "Ġpublishers": 30421, "ĠÑĩÑĥÑĤÑĮ": 30422, "Ġtemptation": 30423, "Ġcerteza": 30424, "Ġexempt": 30425, "ìĬ¹": 30426, "selling": 30427, "ĠTask": 30428, "hoon": 30429, "ĠCoc": 30430, "ĠParks": 30431, "Ġrepetition": 30432, "ĠÑĤÑĥда": 30433, "Ġensl": 30434, "ĠdeÄŁiÅŁ": 30435, "ĠOrlando": 30436, "ĠMainten": 30437, "æŃ¢": 30438, "ocument": 30439, "ĠHC": 30440, "Ġscooter": 30441, "ĠнапиÑģ": 30442, "Ġtighter": 30443, "Ġtease": 30444, "Ġremoves": 30445, "Ġkijken": 30446, "ĠÑģÑĥÑīеÑģÑĤв": 30447, "Ġthé": 30448, "ĠвÑĭглÑıд": 30449, "Ġrelieve": 30450, "Ġmitä": 30451, "Ġstationary": 30452, "öff": 30453, "pable": 30454, "Ġarter": 30455, "Ġdéf": 30456, "rative": 30457, "Ġconect": 30458, "Ġsaddle": 30459, "ĠDiane": 30460, "Ġcommemor": 30461, "fendim": 30462, "SÃŃ": 30463, "Ġíģ´ë": 30464, "Ġmange": 30465, "atte": 30466, "Ġarrogant": 30467, "Ġrobotic": 30468, "ĠgiÃł": 30469, "æĺ¯çļĦ": 30470, "Ġneighbourhood": 30471, "isson": 30472, "Ġдвиж": 30473, "ĠRI": 30474, "ĠNorman": 30475, "brand": 30476, "amation": 30477, "Ġrazor": 30478, "Ġmurders": 30479, "ĠÑĤÑĥ": 30480, "Ġwszystkim": 30481, "Ġutilities": 30482, "Ġmicroscop": 30483, "ê¿": 30484, "Ġdaqui": 30485, "ollar": 30486, "ĠÐĶавайÑĤе": 30487, "Ġannée": 30488, "Ġkilometres": 30489, "Ġhomosexual": 30490, "Ġarchitects": 30491, "ãģ¡ãģ¯": 30492, "Ġniye": 30493, "LER": 30494, "Ġmicrophones": 30495, "ĠStunden": 30496, "Ġconsecutive": 30497, "ienda": 30498, "vänd": 30499, "DER": 30500, "Ġlifts": 30501, "ĠMeat": 30502, "Ġsavez": 30503, "íĸĪëįĺ": 30504, "Men": 30505, "Ġdismant": 30506, "거를": 30507, "Ġinsulation": 30508, "Ġscall": 30509, "Ġspooky": 30510, "Ġparc": 30511, "Ġballet": 30512, "ĠWhatsApp": 30513, "Ġfranc": 30514, "Ġdeliberate": 30515, "ĠíħĮ": 30516, "Ġmars": 30517, "ĠZur": 30518, "Pr": 30519, "disciplinary": 30520, "Ġobsession": 30521, "ме": 30522, "Ġmarching": 30523, "ĠEmergency": 30524, "iguous": 30525, "Ġszy": 30526, "ĠLands": 30527, "Ġboarding": 30528, "ĠпоÑĩÑĤи": 30529, "Ġenvy": 30530, "Ġcompassionate": 30531, "Ġmerci": 30532, "Ġdesirable": 30533, "dale": 30534, "Ġcanım": 30535, "ĠAntar": 30536, "temps": 30537, "Ġconfigured": 30538, "ĠCompared": 30539, "neh": 30540, "icating": 30541, "Ġnickel": 30542, "ÙĪÙĤ": 30543, "ÙĥÙĪÙĨ": 30544, "opes": 30545, "Ġformulas": 30546, "ĠÐķÑģÑĤÑĮ": 30547, "Ġpobl": 30548, "ĠPJ": 30549, "ĠLud": 30550, "ä»ĬåĽŀ": 30551, "ĠBrid": 30552, "ĠHog": 30553, "ĠBris": 30554, "Jen": 30555, "Ġshading": 30556, "ĠYas": 30557, "Ġdisturbed": 30558, "Ġrecommending": 30559, "Ġcé": 30560, "ĠHOW": 30561, "ìĹĪìĸ´": 30562, "Ġreversed": 30563, "ĠInterestingly": 30564, "ioxid": 30565, "åħŃ": 30566, "Ġìĺ¤ì¼ĢìĿ´": 30567, "ếu": 30568, "xx": 30569, "Ġouais": 30570, "ĠYouTubers": 30571, "ĠRosa": 30572, "ĠHaupt": 30573, "jadi": 30574, "Ġvlogs": 30575, "Ġcultura": 30576, "ĠLeadership": 30577, "ĠHep": 30578, "Ġillum": 30579, "´ëıĻ": 30580, "Ġcustomized": 30581, "Ġmarca": 30582, "Ġquatro": 30583, "Ġнаг": 30584, "ĠSpaceX": 30585, "ĠEigen": 30586, "asting": 30587, "ĠolduÄŁu": 30588, "Ġforts": 30589, "ãģī": 30590, "riment": 30591, "iencia": 30592, "Ġtenir": 30593, "roffen": 30594, "Ġ1979": 30595, "Ġcie": 30596, "ĠëIJĺê³ł": 30597, "Ġescri": 30598, "ÏĮÏĤ": 30599, "íı¬": 30600, "uzzy": 30601, "Cong": 30602, "ìĿ¸ìĿ´": 30603, "Great": 30604, "sil": 30605, "éch": 30606, "ãģ¨ãģĭ": 30607, "Ġmultic": 30608, "ĠDisk": 30609, "²ķ": 30610, "Ġfazla": 30611, "Ġlevant": 30612, "Ġabajo": 30613, "urry": 30614, "stru": 30615, "Ġ먹ëĬĶ": 30616, "Ġaccessory": 30617, "Ġдвиг": 30618, "ĠRid": 30619, "2019": 30620, "Ġdownstream": 30621, "æķ¸": 30622, "Ġkaz": 30623, "utan": 30624, "Ġcharcoal": 30625, "Ġafect": 30626, "wu": 30627, "Ġcontexts": 30628, "Ġfeared": 30629, "ĠìĦ¤": 30630, "Ġhistories": 30631, "Ġfas": 30632, "ensible": 30633, "Ġcocoa": 30634, "illar": 30635, "geons": 30636, "Ġspirituality": 30637, "ĠPew": 30638, "Ġpharmacy": 30639, "Ġpassions": 30640, "Ġbos": 30641, "Ġallá": 30642, "Ġthriving": 30643, "ĠReact": 30644, "Ġoccupy": 30645, "Ġwithdrawal": 30646, "Ġallowance": 30647, "ĠFraktion": 30648, "Ġbuddies": 30649, "Ġidle": 30650, "Ġdissolved": 30651, "Ġprevalent": 30652, "Ġmilitar": 30653, "Ġsensing": 30654, "Ġpojaw": 30655, "Ġancora": 30656, "Ġabundant": 30657, "Ġhairst": 30658, "ãģĤãĤĮ": 30659, "Ġtwee": 30660, "Ġnächste": 30661, "ĠMöglichkeit": 30662, "Ġhoo": 30663, "ufficient": 30664, "Ġfantast": 30665, "Ġedible": 30666, "Ġëĸ¨ìĸ´ì": 30667, "ìĽĥ": 30668, "Ġvein": 30669, "ucci": 30670, "Ġdevotion": 30671, "Ġconcealer": 30672, "income": 30673, "Ġrecycled": 30674, "ĠìĬ¤íĥĢ": 30675, "Ġpontos": 30676, "Ġdessus": 30677, "Ġvérit": 30678, "Ġreflections": 30679, "ĠAA": 30680, "Ġtakeaway": 30681, "bare": 30682, "ĠContact": 30683, "eil": 30684, "ĠHear": 30685, "Ġmirac": 30686, "ĠGerilim": 30687, "ĠÑģамÑĭй": 30688, "Ġvivo": 30689, "Ġkilograms": 30690, "ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 32340, "ĠвÑģÑİ": 32341, "ĠQuem": 32342, "Ġübrig": 32343, "ĠSke": 32344, "ĠLynch": 32345, "rooms": 32346, "artet": 32347, "fest": 32348, "Ġfrüher": 32349, "Ġlure": 32350, "ä¸į好æĦıæĢĿ": 32351, "ĠìķĮìķĦ": 32352, "ĠWIN": 32353, "ĠRYAN": 32354, "ĠкоÑĤоÑĢÑĥÑİ": 32355, "ĠKash": 32356, "Ġ×Ķ×ŀ": 32357, "Ġsafeg": 32358, "ĠHallelujah": 32359, "ĠдвÑĥÑħ": 32360, "Ġstaple": 32361, "Ġsediment": 32362, "ĠActs": 32363, "Ġblaming": 32364, "Ġmainland": 32365, "Ġsporting": 32366, "Ġdecorations": 32367, "Ġexecuting": 32368, "Ġparan": 32369, "ĠDollar": 32370, "Ġprojections": 32371, "Ġcommissioned": 32372, "Ġbour": 32373, "öm": 32374, "Ġsteamed": 32375, "ĠëŃĺ": 32376, "Ġpetrol": 32377, "Ġcelular": 32378, "帶": 32379, "ĠHungary": 32380, "Ġrented": 32381, "ĠваÑĢи": 32382, "bbie": 32383, "Ġsécur": 32384, "üll": 32385, "Ġswings": 32386, "between": 32387, "ĠиÑĤ": 32388, "estro": 32389, "Ġniemand": 32390, "ĠìĤ¼": 32391, "ĠPardon": 32392, "esses": 32393, "ĠMID": 32394, "Ġcentralized": 32395, "ĠAlien": 32396, "culos": 32397, "Ġcrise": 32398, "裡éĿ¢": 32399, "Ġclasse": 32400, "beitet": 32401, "iÄŁi": 32402, "Ġwhales": 32403, "Ġperimeter": 32404, "Ġtying": 32405, "Ġstrony": 32406, "Ġlikewise": 32407, "ĠPunch": 32408, "Da": 32409, "ĠBaptist": 32410, "Ġsorting": 32411, "Ġiv": 32412, "Ġíķ©": 32413, "Ġrehab": 32414, "Ġeta": 32415, "river": 32416, "Ġsai": 32417, "ãģĦãģŁãģł": 32418, "odus": 32419, "ãģĬé¡ĺãģĦãģĹãģ¾ãģĻ": 32420, "Ġessayer": 32421, "Ġturtles": 32422, "ĠHazrat": 32423, "Ġfabrics": 32424, "Ġcavity": 32425, "Ġponieważ": 32426, "Ġschlecht": 32427, "Ġsalsa": 32428, "ÅŁekkür": 32429, "Ġseating": 32430, "Ġeconomists": 32431, "Ġmang": 32432, "Ġseguinte": 32433, "Ġrang": 32434, "Ġratios": 32435, "Ġconstell": 32436, "Ġlongtemps": 32437, "uating": 32438, "Ġspoiled": 32439, "Ġrecipients": 32440, "Ġsniper": 32441, "ä¹ĭåīį": 32442, "ìĬµëĭĪê¹Į": 32443, "Ġwp": 32444, "ĠLINKE": 32445, "Ġflare": 32446, "ĠAdri": 32447, "ñas": 32448, "Ġbackl": 32449, "mÃ¤ÃŁ": 32450, "ĠBend": 32451, "Ġworkloads": 32452, "ĠÑģÑĥп": 32453, "Ġ1975": 32454, "имÑģÑı": 32455, "ане": 32456, "Ġмон": 32457, "Ġaspirations": 32458, "ĠAer": 32459, "ĠговоÑĢиÑĤÑĮ": 32460, "ĠQian": 32461, "å¦Ī": 32462, "Ġcompromised": 32463, "Ġyolk": 32464, "лаÑģÑĤ": 32465, "Ġhemen": 32466, "rove": 32467, "dens": 32468, "ĠкомменÑĤ": 32469, "Ġ---": 32470, "Ġfluores": 32471, "ноÑģ": 32472, "ĠLiverpool": 32473, "ĠÑģобой": 32474, "ĠZwe": 32475, "Ġlumin": 32476, "ĠOG": 32477, "á¸": 32478, "holm": 32479, "profits": 32480, "SN": 32481, "Ġproportions": 32482, "Ġmica": 32483, "ĠBoh": 32484, "ĠAtlas": 32485, "Ġunsure": 32486, "Ġtouring": 32487, "Ġnied": 32488, "ĠtÄĻ": 32489, "Ġimperative": 32490, "Ġdemek": 32491, "ĠSheriff": 32492, "rance": 32493, "Ġhomeland": 32494, "ĠHail": 32495, "ĠGanz": 32496, "ymm": 32497, "Mon": 32498, "åĨ·": 32499, "vida": 32500, "Ġdesarroll": 32501, "æĬĢ": 32502, "Ġintriguing": 32503, "ĠHugo": 32504, "ĠãĤĤ": 32505, "é¬": 32506, "аÑĨ": 32507, "ĠWiÄĻc": 32508, "atted": 32509, "ĠìķĦëĭĪê³ł": 32510, "ĠVari": 32511, "ád": 32512, "Ġsurreal": 32513, "Ġdisparities": 32514, "Ġmó": 32515, "ullen": 32516, "ĠìŀĪëĭ¤ê³ł": 32517, "ĠпожалÑĥйÑģÑĤа": 32518, "Ġmains": 32519, "Ġeject": 32520, "Ġmethane": 32521, "Ġmarginalized": 32522, "Ġchilli": 32523, "rès": 32524, "Ġyem": 32525, "ä½łæĺ¯": 32526, "ĠChun": 32527, "Ġdebts": 32528, "Ġdownloading": 32529, "ĠAthens": 32530, "isierung": 32531, "ryn": 32532, "Ġtekn": 32533, "ĠQuindi": 32534, "éľĢ": 32535, "Ġtaraf": 32536, "Ġhé": 32537, "Ġconsciously": 32538, "Ġfixes": 32539, "uckle": 32540, "mayın": 32541, "Ġfrei": 32542, "Ġspa": 32543, "Ġì§Ħíĸī": 32544, "ĠاÙĦØ°": 32545, "ĠÑĥк": 32546, "lett": 32547, "ĠolmuÅŁ": 32548, "Ġcheesy": 32549, "าà¸ģ": 32550, "naire": 32551, "Ġwiden": 32552, "Ġlien": 32553, "Ġescaping": 32554, "iggs": 32555, "ĠBlick": 32556, "cÄħ": 32557, "ĠìĦľë": 32558, "Ġ×Ķס": 32559, "ĠвпеÑĢ": 32560, "ophone": 32561, "iell": 32562, "ĠSUBSCRI": 32563, "Ġlions": 32564, "Ġê·¸ê²ĥ": 32565, "Ġinspires": 32566, "Ġguarantees": 32567, "Ġcomeça": 32568, "ĠGrowing": 32569, "Ġneglig": 32570, "ĠFrankf": 32571, "Ġgegeben": 32572, "ĠÄijầu": 32573, "Ġendlich": 32574, "Ġìį¨": 32575, "ĠTT": 32576, "ĠLith": 32577, "ÏĢα": 32578, "astern": 32579, "ĠAzer": 32580, "Ġlunar": 32581, "hic": 32582, "ĠнаÑĢод": 32583, "Ġnenhum": 32584, "è·ij": 32585, "ĠSalvador": 32586, "ĠProgress": 32587, "Ġprivileges": 32588, "ĠëıĻìķĪ": 32589, "Ġantagon": 32590, "ĠImpf": 32591, "Ġdescub": 32592, "ĠLei": 32593, "ĠìĥĪë¡ľ": 32594, "Ñĩе": 32595, "Ġdólares": 32596, "ĠMeghan": 32597, "ĠWire": 32598, "too": 32599, "aying": 32600, "usc": 32601, "Ġtud": 32602, "Ġappeals": 32603, "educ": 32604, "Ġpane": 32605, "Ġji": 32606, "Ġdecks": 32607, "ĠAlter": 32608, "Ġå°±": 32609, "ìĦ¤": 32610, "åĪĨéIJĺ": 32611, "Ġproductions": 32612, "ĠWILLIAM": 32613, "Ġimplied": 32614, "Ġfulfillment": 32615, "ĠAah": 32616, "Ġsaja": 32617, "xus": 32618, "ĠÎļαι": 32619, "Ãłs": 32620, "ucch": 32621, "око": 32622, "ĠDiscord": 32623, "ĠSY": 32624, "jsk": 32625, "ĠWallace": 32626, "unction": 32627, "Daniel": 32628, "Ġköt": 32629, "ijah": 32630, "Ġmarche": 32631, "Ġdisgr": 32632, "Ġmungkin": 32633, "Ġalma": 32634, "³µ": 32635, "Ġextensively": 32636, "ĠFloren": 32637, "ĠAllison": 32638, "ãĤ±": 32639, "ÙĬÙħ": 32640, "Ġjuven": 32641, "ĠRenaissance": 32642, "Ġfundraising": 32643, "ĠChaos": 32644, "Ġparaly": 32645, "Ġnarrator": 32646, "Ġecosystems": 32647, "Ash": 32648, "Ġmitigation": 32649, "ĠAujourd": 32650, "ĠIdee": 32651, "!,": 32652, "Ġ½": 32653, "Ġlandlord": 32654, "Ġdefects": 32655, "Ġacre": 32656, "ulsive": 32657, "Ġalgae": 32658, "pek": 32659, "Ġemba": 32660, "ĠRoc": 32661, "éĽ¢": 32662, "ksom": 32663, "äche": 32664, "Ġleuk": 32665, "Ġleveraging": 32666, "Ġê·¸ëłĩì§Ģ": 32667, "ĠPalm": 32668, "Ġäven": 32669, "Ġlis": 32670, "ĠInsp": 32671, "ĠRita": 32672, "ĠAbb": 32673, "ithm": 32674, "Ġsupervision": 32675, "Ġrevisit": 32676, "ĠpiÄĻ": 32677, "Ġeuh": 32678, "Ġfades": 32679, "Ġmotto": 32680, "åį¡": 32681, "езж": 32682, "ĠShim": 32683, "Ġrelevance": 32684, "Ġoo": 32685, "Ġostat": 32686, "nica": 32687, "Ġchoix": 32688, "ĠFaculty": 32689, "Ġì¤ijìĹIJ": 32690, "ĠAbove": 32691, "ĠнеболÑĮÑĪ": 32692, "Ġsequencing": 32693, "Ġnutrient": 32694, "Ġconquered": 32695, "Ġdigestive": 32696, "Ġbackdrop": 32697, "ĠLori": 32698, "ailable": 32699, "Game": 32700, "Ġneglected": 32701, "omorph": 32702, "illah": 32703, "Ġkne": 32704, "Ġsiitä": 32705, "Ġworkspace": 32706, "ĠVenice": 32707, "ĠKne": 32708, "Ñīо": 32709, "ħĢ": 32710, "ĠHass": 32711, "Ġvita": 32712, "Ŀ¼ë©´": 32713, "Ġlays": 32714, "ências": 32715, "érica": 32716, "ĠLl": 32717, "æ±Ĥ": 32718, "ĠCoca": 32719, "ĠWHY": 32720, "èĪŀ": 32721, "Ġrouting": 32722, "Ġpermissions": 32723, "Ġdings": 32724, "prend": 32725, "program": 32726, "Ġcrocod": 32727, "bral": 32728, "AAAAAAAA": 32729, "agit": 32730, "ĠNä": 32731, "Ġgekommen": 32732, "atten": 32733, "Ġreferenced": 32734, "Ġpairing": 32735, "ĠPartner": 32736, "ĠCoronavirus": 32737, "ÑĸÑģ": 32738, "è½ī": 32739, "Ġ×Ķ×ĵ": 32740, "ĠespecÃŃfic": 32741, "arsi": 32742, "quelle": 32743, "Ġspontaneous": 32744, "çĨ±": 32745, "Ġê²ĥìĿĦ": 32746, "ĠÐŁÐ¾Ñģле": 32747, "ĠاÙĦد": 32748, "ĠShout": 32749, "Ġнал": 32750, "Ġdisguise": 32751, "ĠJord": 32752, "Ġwee": 32753, "Ġmiejsc": 32754, "Ġserum": 32755, "Ġplaisir": 32756, "Ġcredible": 32757, "ĠbÃ¥": 32758, "ĠAJ": 32759, "mares": 32760, "Ġrods": 32761, "Ġeran": 32762, "ãģ¾ãģĤ": 32763, "Ġpää": 32764, "ĠUA": 32765, "ĠUnknown": 32766, "ĠÙĦÙħ": 32767, "ĠRabbi": 32768, "Ġlaat": 32769, "Ġhairstyle": 32770, "Ġغ": 32771, "éģĭ": 32772, "Ġcach": 32773, "ĠWriting": 32774, "оÑĩки": 32775, "abad": 32776, "Ġstraighten": 32777, "--\"": 32778, "wife": 32779, "Ġhottest": 32780, "Ġpunya": 32781, "ĠFashion": 32782, "griff": 32783, "ĠQR": 32784, "otch": 32785, "ĠÐľÐ¾Ð¶ÐµÑĤ": 32786, "Cloud": 32787, "ĠStrike": 32788, "ĠHein": 32789, "Ġ羣çļĦ": 32790, "Ġlei": 32791, "ĠFlow": 32792, "wegs": 32793, "Ġhabr": 32794, "åīĽåīĽ": 32795, "nahme": 32796, "Ìģ": 32797, "Ġpleasing": 32798, "opping": 32799, "Ġ구ëıħ": 32800, "Ġdran": 32801, "Ġbangs": 32802, "Ġ79": 32803, "Ġsket": 32804, "Ġcaval": 32805, "ĠMacron": 32806, "Ġweighted": 32807, "Ġmuted": 32808, "Ġnuestras": 32809, "EEP": 32810, "Ġmathematic": 32811, "ĠMRI": 32812, "agus": 32813, "Ġtherapies": 32814, "θε": 32815, "Ġunpl": 32816, "Ġcommencer": 32817, "full": 32818, "Ġtowels": 32819, "Ġprue": 32820, "Ġlicenses": 32821, "׼×ķ׾": 32822, "ĠÐŁÐ¾ÑĩемÑĥ": 32823, "Ġpointless": 32824, "Bye": 32825, "Ġeligibility": 32826, "Ġscrape": 32827, "Ġabusive": 32828, "ĠMant": 32829, "Ġjeunes": 32830, "tal": 32831, "ĠPrincip": 32832, "ĠOrthodox": 32833, "Ġmelod": 32834, "ĠмаÑĤеÑĢи": 32835, "Ġprosecutor": 32836, "Ġopioid": 32837, "ĠÑĥвеÑĢ": 32838, "ĠBeen": 32839, "Ġìłijì¢ħ": 32840, "Ġdynasty": 32841, "Ġajuda": 32842, "Ġentreg": 32843, "Ġweighed": 32844, "Ġeure": 32845, "ĠBem": 32846, "Ġabnormal": 32847, "82": 32848, "ĠJR": 32849, "ĠAkt": 32850, "ĠBri": 32851, "út": 32852, "Ġstagn": 32853, "!*": 32854, "Ġwegen": 32855, "Ġleaking": 32856, "ĠWords": 32857, "ĠMau": 32858, "Ġvue": 32859, "ĠLiam": 32860, "анием": 32861, "Ġclinicians": 32862, "ĠPump": 32863, "Ġförst": 32864, "?...": 32865, "Ġautomotive": 32866, "ĠOwen": 32867, "zusagen": 32868, "ĠHundred": 32869, "Ġdecentralized": 32870, "Ġbulbs": 32871, "Ġ׾׼": 32872, "Ġprovinces": 32873, "ĠMilan": 32874, "81": 32875, "kas": 32876, "Ġëĵ£": 32877, "Ġforça": 32878, "Ġrightly": 32879, "島": 32880, "rÄħ": 32881, "Ġvenues": 32882, "Ġwai": 32883, "Ġpredicting": 32884, "ĠWiFi": 32885, "Ġê¶ģê¸Ī": 32886, "رÙĪ": 32887, "Ġ×Ķ×ĸ": 32888, "century": 32889, "Ġgradual": 32890, "ĠProbleme": 32891, "ĠìĹħ": 32892, "Ġcoping": 32893, "ĠBrus": 32894, "Ġpeanuts": 32895, "irtschaft": 32896, "Ġзал": 32897, "ĠTroy": 32898, "Ġsperm": 32899, "ĠMitar": 32900, "ĠTürkiye": 32901, "grand": 32902, "¦Ń": 32903, "Ġ×ŀס": 32904, "Ġpans": 32905, "ĠKnowledge": 32906, "berly": 32907, "ĠÐķго": 32908, "Ġdanced": 32909, "ĠFrost": 32910, "ĠBurg": 32911, "Ġbiting": 32912, "ìłķìĿĦ": 32913, "meal": 32914, "Ġheroic": 32915, "Ġmotherboard": 32916, "ĠLicht": 32917, "ãģ£ãģ": 32918, "llan": 32919, "айн": 32920, "ĠÑĢÑıд": 32921, "Ġà¹Ģà¸": 32922, "onen": 32923, "irie": 32924, "Art": 32925, "rang": 32926, "νη": 32927, "Ġnewborn": 32928, "Ġamis": 32929, "ĠاÙĪر": 32930, "Ġsophom": 32931, "ĠCareful": 32932, "Ġprospects": 32933, "ensen": 32934, "Ġthrill": 32935, "ĠViá»ĩt": 32936, "Adam": 32937, "rition": 32938, "entric": 32939, "uden": 32940, "Ġcertificates": 32941, "Ġashes": 32942, "調": 32943, "playing": 32944, "Ġsadece": 32945, "Ġost": 32946, "Ġairplanes": 32947, "ÑĢок": 32948, "oner": 32949, "Ġmagnesium": 32950, "Ġgoddamn": 32951, "Ġ1972": 32952, "ĠSchule": 32953, "Ġtemat": 32954, "Ġpartout": 32955, "à¯Ĥ": 32956, "Ġinve": 32957, "ĠScientists": 32958, "ĠHudson": 32959, "winning": 32960, "ceksin": 32961, "Ġcongressional": 32962, "oru": 32963, "Ġropes": 32964, "вед": 32965, "Ġmadre": 32966, "Ġferry": 32967, "ĠCohen": 32968, "ĠPred": 32969, "Ġvagy": 32970, "ĠбеÑģп": 32971, "Ġmultim": 32972, "Ġdrainage": 32973, "Ġsimulator": 32974, "giggles": 32975, "ĠStadium": 32976, "обÑī": 32977, "Ġnotices": 32978, "Ġcrawling": 32979, "Ġgroupe": 32980, "åı¸": 32981, "ĠktoÅĽ": 32982, "ĠYoga": 32983, "Ġmedida": 32984, "ĠÑħваÑĤ": 32985, "ĠLite": 32986, "Ġrav": 32987, "orama": 32988, "Ġdiscord": 32989, "ĠDIRE": 32990, "Ġteh": 32991, "ĠNurs": 32992, "ç²ī": 32993, "Ġpitched": 32994, "Ġbarking": 32995, "ĠCoke": 32996, "wiad": 32997, "Ġpopulated": 32998, "éĻ¤": 32999, "pelled": 33000, "Ġбог": 33001, "Ġpewno": 33002, "ĠCube": 33003, "Ġrecruited": 33004, "éĢĻ種": 33005, "ĠCara": 33006, "ıģını": 33007, "imated": 33008, "ĠÑĪкол": 33009, "icional": 33010, "ĠпÑĢоÑĦ": 33011, "Ġcontamination": 33012, "Ġúltimos": 33013, "Ġfearful": 33014, "Ġelephants": 33015, "usi": 33016, "ĠiTunes": 33017, "ĠSwami": 33018, "ê¼": 33019, "ĠìĦ¤ëªħ": 33020, "ĠRichards": 33021, "Ġmagnets": 33022, "ĠRichtung": 33023, "ĠLegion": 33024, "èıľ": 33025, "Ġkitty": 33026, "Ġkissed": 33027, "Ġwatering": 33028, "Ġcono": 33029, "ĠPalestine": 33030, "idir": 33031, "Ġmaze": 33032, "Ġfluids": 33033, "ĠProducer": 33034, "ĠKrsna": 33035, "好åķ¦": 33036, "laf": 33037, "Ġ×IJ×ķ": 33038, "Ġmiesz": 33039, "ĠXing": 33040, "ointed": 33041, "sein": 33042, "ĠFuk": 33043, "ĠDepression": 33044, "ĠDuty": 33045, "ĠPanther": 33046, "Ġsund": 33047, "Ġrefere": 33048, "Ġexclusion": 33049, "Ġnaval": 33050, "ĠWinston": 33051, "Ġslogan": 33052, "Ġhypothetical": 33053, "Ġelevate": 33054, "ëł¹": 33055, "Ġcabeça": 33056, "ĠGesund": 33057, "meter": 33058, "ĠìķĦëĭĪë©´": 33059, "Ġcloudy": 33060, "âĢ¦?": 33061, "ĠSchritt": 33062, "ĠJS": 33063, "ìį": 33064, "ĠSprings": 33065, "ĠBatter": 33066, "·°": 33067, "Ġtailor": 33068, "ĠPTSD": 33069, "ĠGent": 33070, "ĠbaÄŁ": 33071, "Ġspatula": 33072, "Ġcray": 33073, "ĠLegisl": 33074, "Ġsú": 33075, "Ġleve": 33076, "าม": 33077, "Ġerad": 33078, "Ġdong": 33079, "Ġderm": 33080, "ĠBanks": 33081, "icho": 33082, "åħĪçĶŁ": 33083, "ĠFranz": 33084, "ravel": 33085, "éģĶ": 33086, "оло": 33087, "Ġflute": 33088, "ĠEk": 33089, "Ġjoyful": 33090, "Ġchased": 33091, "ĠLarge": 33092, "Over": 33093, "Ġentrepreneurial": 33094, "Ġconsiders": 33095, "Ñĥем": 33096, "opa": 33097, "Ġdormir": 33098, "ĠElementary": 33099, "Ġprzypad": 33100, "ÑĥÑģка": 33101, "ĠоÑĩеÑĢ": 33102, "ugene": 33103, "Ġtenido": 33104, "Ġlugares": 33105, "ë¥": 33106, "ĠÑĩаÑģÑĤ": 33107, "Ġsao": 33108, "Ġbraid": 33109, "ĠVere": 33110, "ĠReich": 33111, "ĠPoss": 33112, "Ġinan": 33113, "wand": 33114, "ref": 33115, "Ġmontrer": 33116, "Ġ1981": 33117, "çķª": 33118, "asında": 33119, "Ġchrome": 33120, "ĠTrinity": 33121, "Ġexploitation": 33122, "ĠSense": 33123, "ĠCMS": 33124, "ĠNoble": 33125, "ĠìĦłíĥĿ": 33126, "Ġswelling": 33127, "electronic": 33128, "]?": 33129, "Ġbrushing": 33130, "Ġliquidity": 33131, "ĠHook": 33132, "ĠConnor": 33133, "ĠAlum": 33134, "Ġgucken": 33135, "suite": 33136, "Ġwiele": 33137, "Ġbarrels": 33138, "ĠRegel": 33139, "ĠMent": 33140, "ĠTrip": 33141, "ĠBrush": 33142, "ĠErik": 33143, "urate": 33144, "ÉĻr": 33145, "ĠCyr": 33146, "ouble": 33147, "ĠBecca": 33148, "Ġpasswords": 33149, "ű": 33150, "borg": 33151, "Ġvendo": 33152, "ĠClaus": 33153, "ĠFaz": 33154, "indest": 33155, "Ġdeceased": 33156, "Ġcomparisons": 33157, "ĠLCD": 33158, "ĠPork": 33159, "Ġeventual": 33160, "Ġpatreon": 33161, "Ġinability": 33162, "Ġextinction": 33163, "Ġì¢ĭìķĦíķĺëĬĶ": 33164, "ĠÑģоÑģ": 33165, "aju": 33166, "Ġ×ij×IJ×": 33167, "Ġsofort": 33168, "Ġdestined": 33169, "ĠRin": 33170, "Ġmouths": 33171, "ĠNatürlich": 33172, "Ġpreserving": 33173, "Ġlimp": 33174, "黨": 33175, "ocused": 33176, "инг": 33177, "Ġexposing": 33178, "Ġξ": 33179, "ëį": 33180, "laugh": 33181, "Ġhiss": 33182, "ãģłãģĭãĤī": 33183, "Ġindie": 33184, "Ġdetal": 33185, "ÑĢавÑģÑĤв": 33186, "Ġtrên": 33187, "æķ°": 33188, "Ġogni": 33189, "Ġsimplemente": 33190, "Ġ1978": 33191, "Ġgoo": 33192, "Ġ1967": 33193, "Ġgenug": 33194, "hö": 33195, "Ġhistó": 33196, "å®Ł": 33197, "Ġlobster": 33198, "cendo": 33199, "Ġteil": 33200, "Ġallevi": 33201, "0000": 33202, "OLD": 33203, "Ġpesos": 33204, "Ġbonuses": 33205, "Ġami": 33206, "Ġrevival": 33207, "ĠHorse": 33208, "Ġsack": 33209, "Talk": 33210, "Ġmulher": 33211, "ĠпоÑģÑĤоÑıн": 33212, "ĠHood": 33213, "Huh": 33214, "Ġë¶ģ": 33215, "Ġhyung": 33216, "ĠMeeting": 33217, "Ġimporta": 33218, "Ġì°¾ìķĦ": 33219, "ĠVern": 33220, "Ġstripped": 33221, "Ġrefuses": 33222, "Ġqualifications": 33223, "opl": 33224, "ĢëıĦ": 33225, "ixÃŃ": 33226, "Ġdiab": 33227, "itime": 33228, "flows": 33229, "Ġinac": 33230, "ĠGong": 33231, "Ġmeaningless": 33232, "Ġcourageous": 33233, "Ġmicrobi": 33234, "azy": 33235, "hist": 33236, "Ġvolunteering": 33237, "VIE": 33238, "Ġviolated": 33239, "Ġsympathy": 33240, "ĠEdit": 33241, "好åĥı": 33242, "electric": 33243, "product": 33244, "Ġpandemia": 33245, "Ġgeometric": 33246, "ĠConvers": 33247, "gre": 33248, "Ġglut": 33249, "isted": 33250, "ĠاÙĦÙĥ": 33251, "ĠChain": 33252, "ĠPresent": 33253, "ĠYin": 33254, "ĠÑģог": 33255, "ĠVlog": 33256, "Ġìĸ´ë¨¸": 33257, "Ġdonn": 33258, "Ġhitch": 33259, "ucking": 33260, "ãģĬãģĦ": 33261, "wald": 33262, "risk": 33263, "Ġhari": 33264, "ĠKens": 33265, "ĠIdol": 33266, "Ġвнимание": 33267, "Ġtodd": 33268, "Ġsmashed": 33269, "Ġinvari": 33270, "ĠконÑĤÑĢ": 33271, "Ġautistic": 33272, "ìŀ¥ëĭĺ": 33273, "Res": 33274, "дÑĭ": 33275, "chau": 33276, "Ġselv": 33277, "Ġhätten": 33278, "ि": 33279, "Ġexpects": 33280, "Ïģη": 33281, "Ġaçık": 33282, "ĠHTTP": 33283, "leÅŁ": 33284, "Ġsweeping": 33285, "ĠBeta": 33286, "Ġcounterparts": 33287, "abile": 33288, "ĠSims": 33289, "Cs": 33290, "Ġrepar": 33291, "squ": 33292, "Ġprovincial": 33293, "Ġshareholders": 33294, "Ġrunter": 33295, "Ġgedacht": 33296, "ĠTeen": 33297, "Ġgrands": 33298, "çĶ¢": 33299, "agles": 33300, "Ġrocky": 33301, "vens": 33302, "Ġrivals": 33303, "unal": 33304, "Ġreacts": 33305, "ë©": 33306, "Ġmercury": 33307, "ĠLuigi": 33308, "Ġог": 33309, "ĠJUST": 33310, "Ġlod": 33311, "Ġcortex": 33312, "wig": 33313, "Ġlakh": 33314, "ì¤ijìĹIJ": 33315, "ĠVic": 33316, "ĠMund": 33317, "Ġmapped": 33318, "ĠDell": 33319, "ĠDruck": 33320, "Ġlifes": 33321, "алÑĮное": 33322, "ividual": 33323, "adım": 33324, "Ġatrav": 33325, "ĠFlug": 33326, "ĠKlein": 33327, "ê±°ìķ¼": 33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "ĠìĪ¨": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æݨ": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪت": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 34712, "ĠExtremadura": 34713, "Ġdemonstrations": 34714, "ĠMontgomery": 34715, "íķ´ìĦ¤": 34716, "ĠGandhi": 34717, "ãĥĿ": 34718, "ç½®": 34719, "Ġreunion": 34720, "ĠjakiÅĽ": 34721, "ĠZug": 34722, "OUGH": 34723, "lifting": 34724, "Ġà²": 34725, "á¹Ľá¹£": 34726, "eb": 34727, "ĠWOW": 34728, "ĠShiva": 34729, "ometry": 34730, "Ġwildly": 34731, "Ġtended": 34732, "Ġmegap": 34733, "ì²ĺ": 34734, "Ġnause": 34735, "Ġgerek": 34736, "ãĥĭ": 34737, "ĠMarcel": 34738, "Ġneste": 34739, "خر": 34740, "Ġfeh": 34741, "åĨħ": 34742, "suspenseful": 34743, "ĠWrestle": 34744, "ĠPalestinians": 34745, "ĠGORD": 34746, "iyet": 34747, "ĠÑĢади": 34748, "Ġversuchen": 34749, "Ġtransistor": 34750, "ĠÐŁÑĢоÑģÑĤо": 34751, "ĠпонÑĢав": 34752, "Ġrhyme": 34753, "ĠVermont": 34754, "platz": 34755, "è®°": 34756, "ĠÄ°ÅŁte": 34757, "ĠHag": 34758, "ĠÐĺм": 34759, "ĠÑĢаÑģÑģказ": 34760, "Ġmetros": 34761, "ĠInfinity": 34762, "wolf": 34763, "ibal": 34764, "ftig": 34765, "ĠÚĨ": 34766, "Ġíĺ¹ìĭľ": 34767, "Ġoggi": 34768, "Ġdisposit": 34769, "ĠпÑĢил": 34770, "ĠвÑĭпол": 34771, "Ġthôi": 34772, "ĠKENN": 34773, "Ġhanding": 34774, "actus": 34775, "Ġtacos": 34776, "Ġformerly": 34777, "ĠCorinthians": 34778, "ãģ«ãģ¯": 34779, "ÑĨÑĸÑĹ": 34780, "Ġpadre": 34781, "Ġcongregation": 34782, "æij": 34783, "fert": 34784, "Ġsubir": 34785, "aiser": 34786, "qua": 34787, "araoh": 34788, "ĠCurry": 34789, "ĠìķĬëĬĶ": 34790, "елÑİ": 34791, "Ġfuss": 34792, "Ġbooty": 34793, "Ġlows": 34794, "Ġhommes": 34795, "ĠMH": 34796, "ĠDisneyland": 34797, "went": 34798, "Ġresidue": 34799, "Ġbeeping": 34800, "è¼ķ": 34801, "ätta": 34802, "Ġmould": 34803, "ĠProjekt": 34804, "stalk": 34805, "Ġartifact": 34806, "ĠAntrag": 34807, "ĠAMD": 34808, "ĠCrypt": 34809, "Ġë©Ķ": 34810, "ĠFelipe": 34811, "ĠCOB": 34812, "elu": 34813, "Ġselfies": 34814, "ĠSanti": 34815, "chutz": 34816, "ĠУкÑĢаÑĹ": 34817, "gesamt": 34818, "Ġflock": 34819, "jaz": 34820, "plain": 34821, "Ġwrinkles": 34822, "Ġreais": 34823, "Ġpaljon": 34824, "Ġempowerment": 34825, "Ġattendees": 34826, "ppa": 34827, "Ġneden": 34828, "онÑĭ": 34829, "Ġtimeframe": 34830, "ĠCherry": 34831, "Ġidée": 34832, "Ġgag": 34833, "Ġdonkey": 34834, "Ġông": 34835, "ĠHare": 34836, "éļĽ": 34837, "ĠKara": 34838, "Ġacompan": 34839, "places": 34840, "imientos": 34841, "ĠHamm": 34842, "би": 34843, "uben": 34844, "iliyor": 34845, "Ġthirst": 34846, "Ġkry": 34847, "ĠGeorgetown": 34848, "׳×Ķ": 34849, "Ġorch": 34850, "Ġheartbeat": 34851, "Ġtransformations": 34852, "estones": 34853, "ĠKH": 34854, "Ġcartoons": 34855, "Ġanci": 34856, "Ġworthless": 34857, "Ġtailored": 34858, "pu": 34859, "Americans": 34860, "Ġpiles": 34861, "ĠMonkey": 34862, "Ġbasin": 34863, "ĠTemper": 34864, "ĠPaint": 34865, "Ġpunching": 34866, "Ġbaik": 34867, "ĠOakland": 34868, "vre": 34869, "ÅŁallah": 34870, "ydd": 34871, "Ġcasually": 34872, "odu": 34873, "Ġcoded": 34874, "ĠNorwegian": 34875, "ĠVince": 34876, "Ġpremature": 34877, "ĠPromise": 34878, "екÑģÑĤ": 34879, "Ġdevastated": 34880, "ĠPremium": 34881, "ĠParam": 34882, "ĠÃĸyle": 34883, "umuz": 34884, "PO": 34885, "rators": 34886, "Ġlamps": 34887, "Ġterritorial": 34888, "Ġbackbone": 34889, "listed": 34890, "DY": 34891, "ĠاÙĦر": 34892, "Ġpursued": 34893, "ĠCommons": 34894, "Ġ곡": 34895, "locks": 34896, "edor": 34897, "Ġconceived": 34898, "gere": 34899, "Ġdisappearing": 34900, "ĠSull": 34901, "ĠìĹ°ë": 34902, "Ġhoffe": 34903, "Ġdetox": 34904, "íĶĮ": 34905, "Ġretir": 34906, "ĠëģĿëĤ": 34907, "Ġpergunta": 34908, "ĠBOY": 34909, "ç²¾": 34910, "Ġpenn": 34911, "æĿ¥äºĨ": 34912, "hés": 34913, "hon": 34914, "Ġcatastrophic": 34915, "Ġaust": 34916, "Ġtorso": 34917, "Ġìĸ´ëĬIJ": 34918, "ĠìĤ¬ëŀĮëĵ¤ìĿ´": 34919, "Ġmarvelous": 34920, "ĠHarley": 34921, "achine": 34922, "Ġtiế": 34923, "itto": 34924, "ĠIÃŃm": 34925, "ylon": 34926, "Ġshutdown": 34927, ".''": 34928, "Ġapologies": 34929, "ĠCommunication": 34930, "ĠговоÑĢÑİ": 34931, "ãģĤãĥ¼": 34932, "âĦ¢": 34933, "ÃŃveis": 34934, "acun": 34935, "Ġretaining": 34936, "Ġcontradiction": 34937, "ĠADAM": 34938, "COM": 34939, "Bryan": 34940, "ĠMonsieur": 34941, "Ġadapting": 34942, "ШÐIJ": 34943, "ĠScr": 34944, "ändert": 34945, "Ġplaus": 34946, "ä»Ĭ天çļĦ": 34947, "Ġonset": 34948, "Ġassistants": 34949, "Ġvalves": 34950, "Ġscatter": 34951, "ĠRust": 34952, "awia": 34953, "Ġreadiness": 34954, "Ġpais": 34955, "Ġbible": 34956, "Ġambiente": 34957, "ĠамеÑĢик": 34958, "Ġuncond": 34959, "Ġkalk": 34960, "åĬ¨": 34961, "Ġmoc": 34962, "unn": 34963, "Ġactu": 34964, "Ġhumming": 34965, "issimo": 34966, "ĠPatrol": 34967, "gow": 34968, "ãĥ¤": 34969, "ĠTHEY": 34970, "ĠBoden": 34971, "ĠBie": 34972, "Ġreel": 34973, "ĠÑĥÑģлов": 34974, "Ġendeavor": 34975, "ĠPeriod": 34976, "ustomed": 34977, "mals": 34978, "alon": 34979, "Box": 34980, "ĠÏĥαÏĤ": 34981, "Ġomdat": 34982, "Ġaltre": 34983, "ĠHeh": 34984, "kad": 34985, "Ġprotector": 34986, "Ġdominance": 34987, "odynamic": 34988, "Ġcommunicated": 34989, "kö": 34990, "Ġpredecessor": 34991, "ĠLuk": 34992, "ĠFlower": 34993, "Ġãģ©": 34994, "poque": 34995, "ÑĤиÑĢов": 34996, "Ġretrospect": 34997, "Ġdecisive": 34998, "Ġexempel": 34999, "{\\": 35000, "ĠRück": 35001, "rite": 35002, "ĠZeus": 35003, "Ġcalorie": 35004, "Ġattractions": 35005, "ĠHinter": 35006, "Ġuhm": 35007, "ĠíĮIJ": 35008, "Ġrulers": 35009, "Ġdiscouraged": 35010, "Ġacontecer": 35011, "Ġaccents": 35012, "ĠOptim": 35013, "ĠAlg": 35014, "kids": 35015, "2021": 35016, "ĠLindsay": 35017, "Ġfilmmakers": 35018, "prowad": 35019, "Ġterug": 35020, "ëĭ´": 35021, "ĠSommer": 35022, "2018": 35023, "Ġborrowing": 35024, "ĠTransfer": 35025, "ноп": 35026, "arias": 35027, "Ġheadphone": 35028, "ì¼ľ": 35029, "Ġtranslating": 35030, "Ġaufge": 35031, "à®ªà®Ł": 35032, "weis": 35033, "avant": 35034, "paid": 35035, "baby": 35036, "Ġtoughest": 35037, "Ġrepeats": 35038, "ĠTeresa": 35039, "Lord": 35040, "Ġacabar": 35041, "ĠRide": 35042, "dir": 35043, "Ġleng": 35044, "Ġdwa": 35045, "Ġheadaches": 35046, "Ġnữa": 35047, "ĠнаÑģÑĤоÑıÑī": 35048, "Ġboils": 35049, "Ġlonging": 35050, "rias": 35051, "ório": 35052, "ĠParadise": 35053, "ĠSeñor": 35054, "erdem": 35055, "Ġreinst": 35056, "Ġsalaries": 35057, "Ġinsecurity": 35058, "ÅĤoÅĽci": 35059, "ĠабÑģолÑİÑĤно": 35060, "inken": 35061, "ĠEddy": 35062, "udos": 35063, "Ġdummy": 35064, "Ðļак": 35065, "six": 35066, "Ġinbox": 35067, "ẩ": 35068, "People": 35069, "á»ĵng": 35070, "Ġorganizers": 35071, "find": 35072, "Ġül": 35073, "ĠCOM": 35074, "ża": 35075, "weile": 35076, "Commentary": 35077, "íĬ¸ë¥¼": 35078, "ĠMittel": 35079, "kus": 35080, "èĽĭ": 35081, "न": 35082, "iral": 35083, "Ġgarment": 35084, "ικά": 35085, "Ġstool": 35086, "payers": 35087, "Ġshimmer": 35088, "ĠOllie": 35089, "ĠJeżeli": 35090, "è¿ĺæľī": 35091, "Ġ1977": 35092, "Ġjeux": 35093, "Ġextinct": 35094, "ĠTransportation": 35095, "ĠMaker": 35096, "Ġjohn": 35097, "Ġrichest": 35098, "Ġtraumat": 35099, "Ġliegen": 35100, "´ë¥¼": 35101, "è¿ĻéĩĮ": 35102, "Ġunrest": 35103, "ĠStraw": 35104, "æĭľæĭľ": 35105, "Ġcoma": 35106, "ĠKristen": 35107, "ĠÐļонеÑĩно": 35108, "ĠBryce": 35109, "ĠÑıкÑĸ": 35110, "Ġpearls": 35111, "ĠпонимаÑİ": 35112, "Ġadditions": 35113, "Ġasympt": 35114, "ĠменÑĮÑĪе": 35115, "Ġscans": 35116, "Child": 35117, "ĠHide": 35118, "кÑĥÑİ": 35119, "etas": 35120, "Ġdank": 35121, "Ġpleas": 35122, "Ġessays": 35123, "Ġjets": 35124, "åħĴ": 35125, "Ġвед": 35126, "Ġpositives": 35127, "hof": 35128, "-)": 35129, "zzo": 35130, "Ġstarters": 35131, "Ġsmiled": 35132, "Ġ1944": 35133, "quiera": 35134, "Ġrok": 35135, "Ġpuesto": 35136, "Nico": 35137, "Ġsimulations": 35138, "Ġà¶": 35139, "Ġintrigued": 35140, "ĠOverwatch": 35141, "åĸĤ": 35142, "sigh": 35143, "bai": 35144, "Ġë§IJê³ł": 35145, "idé": 35146, "Ġcrabs": 35147, "áºŃp": 35148, "ĠIraqi": 35149, "ìĿ´ë¥¼": 35150, "ÑĤÑı": 35151, "ĠSophia": 35152, "ĠDNS": 35153, "Ġönemli": 35154, "ĠLuo": 35155, "Ŀ¤": 35156, "ĠCounsel": 35157, "ligen": 35158, "анÑĮÑĪе": 35159, "Ġtrumpet": 35160, "Ġdapat": 35161, "ĠJM": 35162, "ĠEVERY": 35163, "Ġå°įä¸įå°į": 35164, "夢": 35165, "ĠLayer": 35166, "Ġcô": 35167, "нал": 35168, "ĠJoo": 35169, "ĠHack": 35170, "Ġsunt": 35171, "ĠLeonard": 35172, "ĠFirebase": 35173, "änger": 35174, "Ġexploding": 35175, "voy": 35176, "Ġì¦IJ": 35177, "ĠÑģеÑĢÑĮ": 35178, "Ġseverity": 35179, "Ġbestimm": 35180, "çµIJæŀľ": 35181, "Ġtiring": 35182, "Ġprocurement": 35183, "Ġdiplomacy": 35184, "Ġdecorative": 35185, "ĠÙĬا": 35186, "Ġpenetration": 35187, "Õ«": 35188, "Ġoutright": 35189, "ENE": 35190, "ĠUni": 35191, "odles": 35192, "Ġzeros": 35193, "Ġdelightful": 35194, "jm": 35195, "Ġdopo": 35196, "没äºĭ": 35197, "Ġpositivity": 35198, "ĠVISTA": 35199, "ĠResource": 35200, "íĥĢë": 35201, "ÑĪие": 35202, "Carl": 35203, "Ġpiping": 35204, "Ġchopping": 35205, "ĠGanze": 35206, "üss": 35207, "ĠAo": 35208, "Ġshattered": 35209, "ĠDetective": 35210, "Ġundoubtedly": 35211, "Ġhalluc": 35212, "Ġench": 35213, "ÑĭÑĩно": 35214, "ÑĥлÑıÑĢ": 35215, "isesti": 35216, "Ġpedals": 35217, "Ġdurum": 35218, "¤íĶ": 35219, "laimer": 35220, "Ġpropre": 35221, "Cu": 35222, "Ġtranslator": 35223, "ĠcaÅĤ": 35224, "Ġ그걸": 35225, "ĠcaÅĤy": 35226, "UA": 35227, "Ġrevised": 35228, "Ġподоб": 35229, "ĠArticle": 35230, "ĠHaiti": 35231, "ĠÃĵ": 35232, "ĠCtrl": 35233, "Ġrozm": 35234, "lait": 35235, "Ġletzte": 35236, "ispering": 35237, "display": 35238, "Ġaluminium": 35239, "Ġpalabras": 35240, "Ġconocer": 35241, "Ġzitten": 35242, "Ġdirig": 35243, "åıªæľī": 35244, "Ġbrainstorm": 35245, "Ġwifi": 35246, "ĠParticip": 35247, "Ġviewpoint": 35248, "ĠQuan": 35249, "Ġhierarch": 35250, "Welcome": 35251, "対": 35252, "Ġoffen": 35253, "ĠRecovery": 35254, "gano": 35255, "Would": 35256, "Ġrepro": 35257, "Ġperceptions": 35258, "Ġdemasi": 35259, "ĠBangladesh": 35260, "ĠIncredible": 35261, "Ġletzt": 35262, "Ġbehaving": 35263, "Ġastonishing": 35264, "ĠâĨ": 35265, "ĠëĤ¨ìŀIJ": 35266, "èµ°äºĨ": 35267, "ãĥĶ": 35268, "ĠGORDON": 35269, "CAR": 35270, "?!\"": 35271, "ĠPrest": 35272, "Ġë§ŀìķĦìļĶ": 35273, "Ġtand": 35274, "Ġlash": 35275, "çĬ": 35276, "ificant": 35277, "Ġintoler": 35278, "ĠгеÑĢо": 35279, "Ġteu": 35280, "aso": 35281, "ĠÑģовеÑĤ": 35282, "Ġtravelers": 35283, "ĠSynd": 35284, "ĠвеÑĢÑģ": 35285, "Fonda": 35286, "adı": 35287, "Ġtranscription": 35288, "Ġtitanium": 35289, "Ġtwists": 35290, "Ġgearbox": 35291, "ensation": 35292, "fat": 35293, "Coll": 35294, "ĠCommonwealth": 35295, "zon": 35296, "ĠPolizei": 35297, "ĠAPPLAUSE": 35298, "fry": 35299, "ĠJuda": 35300, "esteem": 35301, "Ġsock": 35302, "ĠJugend": 35303, "ĠкÑģÑĤаÑĤи": 35304, "ĠDro": 35305, "Ġprochaine": 35306, "ãĥ¼ãĥ«": 35307, "Ġliksom": 35308, "ĠEnergie": 35309, "ĠMarina": 35310, "Ġ230": 35311, "Ġê°ĢìĦľ": 35312, "umping": 35313, "Ġlone": 35314, "ç´ļ": 35315, "Ġfonts": 35316, "Ġbusinessman": 35317, "Ġply": 35318, "Ġdoe": 35319, "grid": 35320, "ĠMilwaukee": 35321, "ĠEden": 35322, "!\".": 35323, "ĠÛĮÛģ": 35324, "ogens": 35325, "Ġteaser": 35326, "Ġquién": 35327, "Ġincentiv": 35328, "govern": 35329, "Ġchildcare": 35330, "Ġsneakers": 35331, "Ġimprisoned": 35332, "®": 35333, "иÑĤеÑģÑĮ": 35334, "anbul": 35335, "Ġregain": 35336, "Ġtranquil": 35337, "Redner": 35338, "鼨": 35339, "IFA": 35340, "Ġideological": 35341, "ĠmayorÃŃa": 35342, "Ġbureau": 35343, "eterm": 35344, "ĠDID": 35345, "ìĬ·": 35346, "Ġwaving": 35347, "Ġbeb": 35348, "Ġár": 35349, "Ġкв": 35350, "Ġenvoy": 35351, "anut": 35352, "икÑĥ": 35353, "ĠEnvironment": 35354, "ĠAssass": 35355, "ãĤĵãģ§": 35356, "ĠBread": 35357, "ĠТÑĥÑĤ": 35358, "Ġstaircase": 35359, "ĠDisease": 35360, "Ġaucun": 35361, "ĠëĭĪ": 35362, "Ġconfrontation": 35363, "Ġ1941": 35364, "Ġirony": 35365, "Ġworsh": 35366, "ãĤĮãĤĭ": 35367, "Ġfick": 35368, "ĠNaomi": 35369, "Ġbackside": 35370, "ieux": 35371, "Kap": 35372, "Ġvedere": 35373, "Ġlengthy": 35374, "Ġbreaker": 35375, "ĠRolle": 35376, "Ġpredator": 35377, "Ġnossos": 35378, "Ġadvertise": 35379, "è³ĩ": 35380, "ÑĢоде": 35381, "Rednerwechsel": 35382, "reten": 35383, "Ġcollectors": 35384, "ıģımız": 35385, "Ġtrig": 35386, "Ġaxes": 35387, "inters": 35388, "Ġpenalties": 35389, "ĠOsman": 35390, "ĠJenna": 35391, "Ġflakes": 35392, "Ġtrainers": 35393, "Ġstunned": 35394, "ĠScroll": 35395, "ĠPip": 35396, "ĠнаÑģÑĤ": 35397, "ĠnhÃł": 35398, "ĠSmack": 35399, "ẫn": 35400, "ratos": 35401, "ĠÑĢабоÑĤÑĭ": 35402, "Ġucz": 35403, "ĠLemon": 35404, "ĠSind": 35405, "Ġpsychic": 35406, "ĠAbg": 35407, "Ġmammals": 35408, "Ġimmersive": 35409, "Ġbots": 35410, "Ġverschiedene": 35411, "Ġgeral": 35412, "Ġfollower": 35413, "Ġä»ĸ": 35414, "Ġseguridad": 35415, "Ġimmersed": 35416, "feito": 35417, "cross": 35418, "Ġöld": 35419, "íĥĦ": 35420, "Ġãģĵãģ®": 35421, "Ġ×Ķ×Ļ×IJ": 35422, "ĠJian": 35423, "Ġbiliyor": 35424, "area": 35425, "Ġkaf": 35426, "Ġgodt": 35427, "çĽ¸ä¿¡": 35428, "Ġë°©ìĨ¡": 35429, "Ġdetriment": 35430, "æ¥ļ": 35431, "Ñĸл": 35432, "ĠÄijâu": 35433, "Ġchloride": 35434, "øre": 35435, "lei": 35436, "Ġmonte": 35437, "Ġdifférentes": 35438, "à¯ģ.": 35439, "Ġcaregivers": 35440, "Ġinadequ": 35441, "Ġfarewell": 35442, "ĠÑĤипа": 35443, "ontec": 35444, "ĠEph": 35445, "HHH": 35446, "ĠTodos": 35447, "ĠСШÐIJ": 35448, "Ġtrov": 35449, "Ġlige": 35450, "Ġcông": 35451, "ĠCiv": 35452, "Ġcapaz": 35453, "ĠVallahi": 35454, "Ġqueste": 35455, "Ġreplica": 35456, "سب": 35457, "zna": 35458, "ĠÑģлÑĥж": 35459, "ĠPT": 35460, "wave": 35461, "ieni": 35462, "Ġrelied": 35463, "develop": 35464, "Ġdeme": 35465, "ĠAman": 35466, "Ġ[...]": 35467, "Ġcompliments": 35468, "uais": 35469, "ĠíĮ¨": 35470, "Ġsmelling": 35471, "Ġdadurch": 35472, "ÙĪت": 35473, "Ġoranges": 35474, "Ġлай": 35475, "Ġstabilization": 35476, "åĢį": 35477, "ãĤĮãģŁ": 35478, "楽": 35479, "Ġappliances": 35480, "Ġhm": 35481, "ĥIJë©´": 35482, "odynamics": 35483, "ĠciÄĻ": 35484, "ĠCott": 35485, "MON": 35486, "ĠMang": 35487, "æĶ¯æĮģ": 35488, "Ġallerdings": 35489, "ική": 35490, "shots": 35491, "Ġts": 35492, "ĠGör": 35493, "ĠCHAR": 35494, "Ġ:(": 35495, "Ġwrath": 35496, "Ġfique": 35497, "Ġführen": 35498, "Ġtestament": 35499, "Ġ^^": 35500, "á¹Ľá¹£á¹ĩa": 35501, "ALD": 35502, "Ġtexto": 35503, "ĠDogs": 35504, "Ġsib": 35505, "Ġpathetic": 35506, "ocks": 35507, "Ġradically": 35508, "ĠMORE": 35509, "ĠJAMES": 35510, "Ġingl": 35511, "ĠTechnical": 35512, "Ġporch": 35513, "ĠUT": 35514, "ĠобÑıзаÑĤелÑĮно": 35515, "Ġrenewal": 35516, "Ġaesthetics": 35517, "ikum": 35518, "Ġbeverage": 35519, "dern": 35520, "Ġpredictive": 35521, "Ġchuy": 35522, "ĠRegarding": 35523, "ĠForward": 35524, "ĠÙĪÙĦ": 35525, "Ġcontextual": 35526, "Ġdwarf": 35527, "Ġprehe": 35528, "Ġgoverned": 35529, "ħĦ": 35530, "Ġtrabalhar": 35531, "Ġnegócio": 35532, "ĠболÑĮÑĪой": 35533, "еÑĩаÑĤ": 35534, "ĠдÑĥÑħ": 35535, "Ġfloods": 35536, "Ġbowling": 35537, "ĠOB": 35538, "ĠHär": 35539, "Ġgrading": 35540, "주ëĬĶ": 35541, "Ġgars": 35542, "dling": 35543, "Ġrak": 35544, "ëĪ": 35545, "creat": 35546, "ĠÑīе": 35547, "Ġneighbours": 35548, "food": 35549, "Query": 35550, "Ġheroin": 35551, "iceps": 35552, "ĠKinda": 35553, "NET": 35554, "Ġmari": 35555, "Ġimitate": 35556, "Ġachter": 35557, "Ġsettlements": 35558, "rare": 35559, "cciones": 35560, "Ġëĵľ": 35561, "Ġfik": 35562, "itung": 35563, "ĠмакÑģим": 35564, "Ġelf": 35565, "Ġdalla": 35566, "ĠPolsce": 35567, "ĠPul": 35568, "ЧÑĤо": 35569, "ĠMorgen": 35570, "ØŃÙħ": 35571, "Ġsupremacy": 35572, "Ġkys": 35573, "ĠHurricane": 35574, "ĠGTA": 35575, "ĠFeh": 35576, "Ġfinalmente": 35577, "mund": 35578, "ĠKrie": 35579, "époque": 35580, "ĠTucker": 35581, "ITT": 35582, "Ġlur": 35583, "Ġdipping": 35584, "äv": 35585, "Ġeerste": 35586, "ĠFlint": 35587, "bildung": 35588, "ูà¹ī": 35589, "Ġtoim": 35590, "Ġpracy": 35591, "Ġtransforms": 35592, "Ġspeeding": 35593, "Ġpresenter": 35594, "Ġfellows": 35595, "filled": 35596, "ieza": 35597, "Ġadvising": 35598, "ĠInterview": 35599, "игÑĢ": 35600, "wehr": 35601, "ĠDante": 35602, "pture": 35603, "Ī문": 35604, "¯¸ë": 35605, "IJIJ": 35606, "ĠCounter": 35607, "Ġcrist": 35608, "Ġì§ľ": 35609, "Ġjeune": 35610, "ĠÑģÑĤÑĢаÑĪ": 35611, "ĠmieÄĩ": 35612, "Ġtutor": 35613, "Ġmasala": 35614, "Ġpowdered": 35615, "Ġnau": 35616, "ĠFrederick": 35617, "Ġbilling": 35618, "ĠEisen": 35619, "ĠдобÑĢ": 35620, "Ġmest": 35621, "æ½": 35622, "Ġsnipp": 35623, "Ġmono": 35624, "ĠAlo": 35625, "ĠMercy": 35626, "érience": 35627, "Ġcasualties": 35628, "ĠANNOUNCER": 35629, "ä»İ": 35630, "Ġtocar": 35631, "Ġbacterial": 35632, "Ho": 35633, "Ġstreak": 35634, "ĠJENN": 35635, "Ġplast": 35636, "Ñģлед": 35637, "Ġreapp": 35638, "Ġpaycheck": 35639, "Ġminers": 35640, "habt": 35641, "ĠJap": 35642, "нÑĥÑĤ": 35643, "Ġredemption": 35644, "Ġquir": 35645, "hnlich": 35646, "Ġaccumulation": 35647, "Ġshove": 35648, "Ġadrenaline": 35649, "Make": 35650, "ĠHern": 35651, "ossing": 35652, "ĠVil": 35653, "ubby": 35654, "hertz": 35655, "breaks": 35656, "Ġspur": 35657, "ĠDaha": 35658, "USTIN": 35659, "Ġcontinuer": 35660, "ĠSaul": 35661, "ãģ®ãģ¯": 35662, "ĠíıŃ": 35663, "ĠëIJĺë©´": 35664, "Ġë§IJìĶĢ": 35665, "Ġож": 35666, "Ġsuspects": 35667, "Ġlaquelle": 35668, "ĠMuchas": 35669, "Ġvöllig": 35670, "ulen": 35671, "Ġimpres": 35672, "Ġlobb": 35673, "enee": 35674, "Ġнаж": 35675, "Ta": 35676, "Ġréalité": 35677, "ĠRex": 35678, "Ġharvesting": 35679, "Ġestr": 35680, "æ¶": 35681, "ospace": 35682, "OSS": 35683, "Ġdisturbance": 35684, "assic": 35685, "ĠIsab": 35686, "Ġdécouv": 35687, "ĠHampshire": 35688, "Ġornament": 35689, "Ġluôn": 35690, "ĠUW": 35691, "ĠjÄħ": 35692, "éĤ£ä¹Ī": 35693, "Ġrespecto": 35694, "Ġcomunidad": 35695, "Ġcomigo": 35696, "agna": 35697, "Ġintrinsic": 35698, "ĠAlumni": 35699, "Ġsesleri": 35700, "Ġestimation": 35701, "âĢĶâĢĶ": 35702, "Ġproduit": 35703, "ãĢĤãĢį": 35704, "ĠвÑĢ": 35705, "Ġwhirl": 35706, "Ġacces": 35707, "çu": 35708, "Ġvariability": 35709, "Ġvodka": 35710, "itsu": 35711, "Ġinternships": 35712, "Ġallocate": 35713, "RR": 35714, "íĽĪ": 35715, "Ġinstructional": 35716, "tant": 35717, "Ġà®ħத": 35718, "Ġinvites": 35719, "Ġhak": 35720, "Ġscares": 35721, "Ġeclipse": 35722, "пов": 35723, "колÑĮ": 35724, "ativas": 35725, "Ġstabbed": 35726, "ĠDOM": 35727, "ä¸įåĪ°": 35728, "roots": 35729, "ĠPicture": 35730, "íĺ¼": 35731, "ĠCHA": 35732, "iec": 35733, "ıı": 35734, "hanol": 35735, "Ġmisunderstand": 35736, "Ray": 35737, "Ġroadmap": 35738, "ocumented": 35739, "izione": 35740, "ĠOlive": 35741, "rift": 35742, "Ġ×Ķ׳": 35743, "æ¯į": 35744, "lest": 35745, ";;": 35746, "ĠEA": 35747, "éľĢè¦ģ": 35748, "одÑĥ": 35749, "Ġhobbies": 35750, "Ġburial": 35751, "ãģ«ãģ¡ãģ¯": 35752, "Ф": 35753, "lege": 35754, "ĠHJ": 35755, "Ġobjection": 35756, "ĠãģŃ": 35757, "ctory": 35758, "Ġincremental": 35759, "Ġgymn": 35760, "Ġepidemi": 35761, "ÑģÑĭл": 35762, "Ãij": 35763, "Ġadvancement": 35764, "Ġparch": 35765, "News": 35766, "Ġayr": 35767, "лам": 35768, "Ġ׾ש": 35769, "Ġdiploma": 35770, "ãģ¡ãĤĥãĤĵ": 35771, "Ġrobbed": 35772, "Only": 35773, "Ġincur": 35774, "Ġchanting": 35775, "Ġíķ´ëıĦ": 35776, "Ġriches": 35777, "ĠCarmen": 35778, "Ġnostro": 35779, "λÎŃ": 35780, "ĠPowder": 35781, "à¹Ģห": 35782, "ĠìŀĪìľ¼ë©´": 35783, "Ġgerçekten": 35784, "ĠPikachu": 35785, "емон": 35786, "OLL": 35787, "Ġplanetary": 35788, "Ġslows": 35789, "Ġclockwise": 35790, "alion": 35791, "ĠìĮ": 35792, "Ġvern": 35793, "Ġhomme": 35794, "Ġendpoint": 35795, "Ġinnocence": 35796, "Ġelementos": 35797, "Ġsophomore": 35798, "Ġnotions": 35799, "ĠCouldn": 35800, "pur": 35801, "Ġzat": 35802, "Ġobsess": 35803, "Ġmotivo": 35804, "ĠKub": 35805, "ĠDrug": 35806, "Ant": 35807, "ĠPlayers": 35808, "ĠHumans": 35809, "Ġmelee": 35810, "ĠWildlife": 35811, "ĠVP": 35812, "Ġvolcanic": 35813, "Ġcomin": 35814, "ĠGuang": 35815, "ĠÏĦιÏĤ": 35816, "ĠоÑģобенно": 35817, "ĠSize": 35818, "Listen": 35819, "ĠAaa": 35820, "appro": 35821, "Ġbarbar": 35822, "ĠParkinson": 35823, "нÑıÑĤÑĮ": 35824, "åį°": 35825, "Ġunderestimate": 35826, "Ġsubstitution": 35827, "Ġcosmetic": 35828, "ä¸ĭ次": 35829, "Ġwillen": 35830, "Ġbeide": 35831, "anni": 35832, "Ġconditioned": 35833, "ĠDebbie": 35834, "Ġisto": 35835, "ĠEdwards": 35836, "ìĽĮìļĶ": 35837, "ĠÑĤов": 35838, "Ġabbrevi": 35839, "ĠMün": 35840, "ĠPrinc": 35841, "ĠLiang": 35842, "Ġstink": 35843, "Ġradioactive": 35844, "ãģĨãĤı": 35845, "Ġacontec": 35846, "Ġuncon": 35847, "ĠTurbo": 35848, "ãģIJ": 35849, "Ġkisses": 35850, "æĺ¯ä»Ģ麼": 35851, "еÑĤÑĢов": 35852, "Ġfrontier": 35853, "ĠSpy": 35854, "ĠBelarus": 35855, "ĠCBS": 35856, "á»Ĺ": 35857, "amoto": 35858, "íķľëį°": 35859, "ĠÑģÑĤÑĢо": 35860, "ĠEnfin": 35861, "Ġbreadth": 35862, "éĺ²": 35863, "ĠCafe": 35864, "ĠDafür": 35865, "ĠBour": 35866, "aras": 35867, "Ġblueprint": 35868, "anı": 35869, "Ġconstants": 35870, "Ġattacker": 35871, "ĠFormula": 35872, "zaÄĩ": 35873, "Ġsowie": 35874, "Ġeyebrow": 35875, "obook": 35876, "Ġsetzen": 35877, "第ä¸ī": 35878, "onsider": 35879, "awning": 35880, "Ġsöyleye": 35881, "Ġinvaded": 35882, "Ġpronouns": 35883, "Ġdobry": 35884, "Si": 35885, "ĠХоÑĤ": 35886, "Ġvolleyball": 35887, "Ġlament": 35888, "isches": 35889, "arme": 35890, "api": 35891, "ĠWiki": 35892, "лиÑĪ": 35893, "Ġkasih": 35894, "Ġpess": 35895, "ĠÑĦоÑĤ": 35896, "ĠSul": 35897, "å¾·": 35898, "Ġpseudo": 35899, "Ġmemo": 35900, "ĠìĹ°ìĬµ": 35901, "ĠдоллаÑĢов": 35902, "ĠпеÑĢем": 35903, "ĠReach": 35904, "miral": 35905, "alted": 35906, "Ġstatut": 35907, "reading": 35908, "Ġsöyled": 35909, "ĠLindsey": 35910, "ĠAhmad": 35911, "ë¶Ģë": 35912, "ĠСегоднÑı": 35913, "Ġprzygot": 35914, "Ġhyster": 35915, "URE": 35916, "ĠNeigh": 35917, "Reporter": 35918, "ĠBunu": 35919, "ĠTreaty": 35920, "ĠRank": 35921, "ĠFame": 35922, "inished": 35923, "Ġgeared": 35924, "Ġcompose": 35925, "odia": 35926, "ĠLon": 35927, "ĠjesteÅĽmy": 35928, "ĠDIRECTOR": 35929, "Ġelkaar": 35930, "ĠViel": 35931, "×IJש": 35932, "ynthia": 35933, "並": 35934, "Ġmère": 35935, "ĠTomato": 35936, "Ġexatamente": 35937, "niÄĻ": 35938, "ĠFrei": 35939, "ĠDif": 35940, "Ġopenings": 35941, "Ġgraphical": 35942, "ĠÑĥдоб": 35943, "ĠвÑģп": 35944, "ĠWeekly": 35945, "ева": 35946, "Ġhangs": 35947, "Ġunsafe": 35948, "Ġemblem": 35949, "ĠKolleginnen": 35950, "alay": 35951, "Ġksi": 35952, "Ġhides": 35953, "Ġolmay": 35954, "Ġentste": 35955, "Ġarthritis": 35956, "ÃŁerdem": 35957, "Ġbinnen": 35958, "Ġlistens": 35959, "ĠHess": 35960, "åĨįä¾Ĩ": 35961, "ĠLouise": 35962, "lden": 35963, "енÑģ": 35964, "ĠVersion": 35965, "ĠAgriculture": 35966, "ìĬ¤ë¥¼": 35967, "ман": 35968, "ëĦ¤ìļĶ": 35969, "Ġwines": 35970, "ĠINF": 35971, "rul": 35972, "ĠJK": 35973, "ıyorlar": 35974, "shield": 35975, "reath": 35976, "Ġterus": 35977, "ĠLum": 35978, "Ġanticipation": 35979, "Ġaccustomed": 35980, "ĠMina": 35981, "Ġwield": 35982, "ioè": 35983, "mera": 35984, "Ġcountdown": 35985, "Ġcling": 35986, "Ġcommend": 35987, "Ġfaktiskt": 35988, "Ġdefenses": 35989, "Ġcockpit": 35990, "Ġкоманд": 35991, "Ġdishwas": 35992, "ĠThanos": 35993, "Ġkidneys": 35994, "Ġsehe": 35995, "Ġmicrobes": 35996, "Ġcuff": 35997, "ĠвÑĭÑģок": 35998, "ĠSpicy": 35999, "çŃīçŃī": 36000, "வர": 36001, "culus": 36002, "orc": 36003, "ç¾ħ": 36004, "ixes": 36005, "ĠCredit": 36006, "Ġraj": 36007, "Ġbringt": 36008, "ĠNiss": 36009, "Ġgrim": 36010, "ĠSOL": 36011, "Ġtenim": 36012, "ĠSudan": 36013, "ĠSpart": 36014, "Ġpromotes": 36015, "ĠNossa": 36016, "ĠÑģоÑģÑĤоÑıни": 36017, "Ġì°©": 36018, "Ġuncont": 36019, "ĠLiberal": 36020, "ĠТолÑĮко": 36021, "ĠViele": 36022, "Ġktórej": 36023, "Ġ****": 36024, "Max": 36025, "ĠЧÑĤобÑĭ": 36026, "350": 36027, "Ġíĺ¼ìŀIJ": 36028, "Ġë¶Ħëĵ¤ìĿ´": 36029, "Ġwarp": 36030, "Ġtenga": 36031, "Ġsympathetic": 36032, "Ġbizi": 36033, "ĠZack": 36034, "iedo": 36035, "Ġëī´ì": 36036, "piel": 36037, "ĠÑĤол": 36038, "Ġscaled": 36039, "ĠPETER": 36040, "ĠCOMM": 36041, "ĠCame": 36042, "Ġcatastrophe": 36043, "Ġsweaty": 36044, "igration": 36045, "Ġstuffing": 36046, "ĠÏĢολÏį": 36047, "ĠDriver": 36048, "zyst": 36049, "Tech": 36050, "Ġassessed": 36051, "ĠSurface": 36052, "ırım": 36053, "sur": 36054, "lerweile": 36055, "Ġдог": 36056, "Ġshutting": 36057, "Ġfractions": 36058, "ĠÑģол": 36059, "everyone": 36060, "Ġern": 36061, "ĠÐĿов": 36062, "Ġdefenders": 36063, "Ġversucht": 36064, "ãĥ³ãĥĢ": 36065, "Ġpolity": 36066, "ĠÐŁÐ¾Ð½": 36067, "verständ": 36068, "Ġbrowsers": 36069, "Ġtransformative": 36070, "Ġdictate": 36071, "ĠLEGO": 36072, "Ġninguna": 36073, "ê´ij": 36074, "Ġpizz": 36075, "ĠHarold": 36076, "ĠLopez": 36077, "Ú¾ÛĮ": 36078, "anız": 36079, "atchet": 36080, "ÙĬت": 36081, "Ġlernen": 36082, "Ġê·ĢìŬ": 36083, "Ġhoused": 36084, "Ġcleanse": 36085, "ĠWAT": 36086, "laration": 36087, "Ġbytes": 36088, "Ġtucked": 36089, "Ġfaults": 36090, "до": 36091, "FX": 36092, "Ġìĸ¼ë§ĪëĤĺ": 36093, "Ġdeform": 36094, "Ġcontracting": 36095, "ĠTIME": 36096, "irse": 36097, "Ġneben": 36098, "Ġcerc": 36099, "ĠArmstrong": 36100, "Ġtester": 36101, "Ġparfait": 36102, "Ġjealousy": 36103, "Ġtoxins": 36104, "Ġdisbel": 36105, "ÑĥÑĢÑĭ": 36106, "impression": 36107, "Ġprostate": 36108, "Ġfirewall": 36109, "Ġclassics": 36110, "еÑĩÑĮ": 36111, "Ġsocialism": 36112, "Ġgracious": 36113, "ĠÑģнова": 36114, "ĠднÑı": 36115, "Ġburner": 36116, "ĠMinor": 36117, "Ġìļ°ë¦¬ë": 36118, "Ġjedes": 36119, "Ġcontinuum": 36120, "Ġhots": 36121, "Ġoccurrence": 36122, "Ġadministered": 36123, "ĠзамеÑĤ": 36124, "Ġhesitation": 36125, "Ġdrills": 36126, "erca": 36127, "ĠвÑĤоÑĢой": 36128, "Ġsteadily": 36129, "Ġinsanlar": 36130, "Ġihan": 36131, "íij": 36132, "Ġhelper": 36133, "ĠSenin": 36134, "åģľ": 36135, "ование": 36136, "ĠERIC": 36137, "bla": 36138, "ĠAcademic": 36139, "Ġhumanities": 36140, "black": 36141, "umpy": 36142, "ortex": 36143, "ĠìłĪë": 36144, "ĠØ¥ÙĨ": 36145, "Ġdisclose": 36146, "ĠElijah": 36147, "ĠλÎŃ": 36148, "ĠQuer": 36149, "بÙĦ": 36150, "ãĤ¡": 36151, "Tell": 36152, "arle": 36153, "ÑĸÑĢ": 36154, "Ġaugmented": 36155, "Ġë¹ĦìĬ·": 36156, "Ġandroid": 36157, "त": 36158, "arma": 36159, "Ġszer": 36160, "geord": 36161, "Ġgeek": 36162, "Ġyeux": 36163, "Ġpong": 36164, "ĠãģĿãģĨ": 36165, "Ġtortured": 36166, "ĠBath": 36167, "zig": 36168, "asonable": 36169, "Ġnets": 36170, "Ġbaru": 36171, "ĠFlat": 36172, "ĠVater": 36173, "ĠTerror": 36174, "ĠAvo": 36175, "Ġceremonies": 36176, "roe": 36177, "Ùģس": 36178, "Ops": 36179, "Ġhyvin": 36180, "Ġapresent": 36181, "olor": 36182, "ĠигÑĢÑĭ": 36183, "orton": 36184, "Ġê·¸ëŀ¬": 36185, "Ġlookin": 36186, "ĠTY": 36187, "ĠMint": 36188, "Add": 36189, "Ġmite": 36190, "ĠSmoke": 36191, "Ġnota": 36192, "Ġmoss": 36193, "ĠAbend": 36194, "Ġ컨": 36195, "Ġexaggerated": 36196, "fires": 36197, "Ġredist": 36198, "ffiti": 36199, "Ġopenness": 36200, "ê°IJìĿ´": 36201, "endeu": 36202, "енной": 36203, "Watch": 36204, "Ġavatar": 36205, "ĠPey": 36206, "urun": 36207, "Ġsenza": 36208, "Ġì§ĢìĹŃ": 36209, "ĠNatomiast": 36210, "Ġemergence": 36211, "rays": 36212, "Ġcrafted": 36213, "gary": 36214, "ãģłãģij": 36215, "üng": 36216, "-\"": 36217, "Ġhacked": 36218, "Ġstray": 36219, "encie": 36220, "emo": 36221, "Ġcomen": 36222, "ĠKız": 36223, "ĠJasmine": 36224, "ĠHindi": 36225, "manas": 36226, "Ġinfinitely": 36227, "emon": 36228, "ìĿ¸ëį°ìļĶ": 36229, "jak": 36230, "Ġroaring": 36231, "érique": 36232, "sweise": 36233, "ĠRolex": 36234, "åł±å°İ": 36235, "ĠStuart": 36236, "bnb": 36237, "Ġdiagnose": 36238, "Ġcoherent": 36239, "ĠMJ": 36240, "æºĸåĤĻ": 36241, "Ġpike": 36242, "lav": 36243, "Ġorchestral": 36244, "аÑģÑĤи": 36245, "Ġterminar": 36246, "Ġgatherings": 36247, "Ġcompliant": 36248, "Ġupgrading": 36249, "Ġregulator": 36250, "Ġlanç": 36251, "éĢ£": 36252, "Ġmerchants": 36253, "tawa": 36254, "Ġmonitored": 36255, "Ġrendre": 36256, "两": 36257, "Ġunterwegs": 36258, "anguard": 36259, "gard": 36260, "ĠBelow": 36261, "duino": 36262, "ĠЦе": 36263, "Ġimpedance": 36264, "ìľ¡": 36265, "份": 36266, "Ġaktuell": 36267, "ĠVatic": 36268, "åŃ©": 36269, "Ġstewards": 36270, "Ġbrightest": 36271, "Ġkenn": 36272, "Ġkau": 36273, "ĠMatrix": 36274, "ĠBark": 36275, "ĠðŁij": 36276, "Ġtaper": 36277, "Ġcasino": 36278, "ר×Ķ": 36279, "ysical": 36280, "Ġbuilders": 36281, "ĠczÅĤowie": 36282, "ĠNepal": 36283, "Ġ!\"": 36284, "Ġterme": 36285, "Ġinnych": 36286, "Ġmaths": 36287, "Ġdrafted": 36288, "ĠBalk": 36289, "Ġhesitant": 36290, "Ġvoltar": 36291, "Ġrevive": 36292, "ĠÑĦилÑĮма": 36293, "Ġassassin": 36294, "ĠSolutions": 36295, "Ġduel": 36296, "Ġbearings": 36297, "à¸Ħะ": 36298, "Ġrookie": 36299, "ikat": 36300, "Ġbiscuits": 36301, "Ġcords": 36302, "ÑĥваÑĤи": 36303, "ARIN": 36304, "Ġprogressing": 36305, "ĠGir": 36306, "Ġpenetrate": 36307, "ĠStorage": 36308, "eight": 36309, "ĠÑĤÑĢÑĥ": 36310, "ĠdonÃŃt": 36311, "Ġsizin": 36312, "Ġoutdated": 36313, "ĠнаÑĪи": 36314, "Ġaffir": 36315, "Ġspoons": 36316, "Ġoni": 36317, "Ġflank": 36318, "ĠGol": 36319, "hã": 36320, "Ġpéri": 36321, "Ġhonorable": 36322, "ĠBreathe": 36323, "scenes": 36324, "Ġobviamente": 36325, "икÑģ": 36326, "Ġש×ŀ×": 36327, "Ġsmoothie": 36328, "ŀĪë": 36329, "Ġdime": 36330, "ĠíĸĪìĸ´ìļĶ": 36331, "Ġappel": 36332, "ĠCatholics": 36333, "Ġsingles": 36334, "Ġlaten": 36335, "Ġçünkü": 36336, "ĠVader": 36337, "æıĽ": 36338, "Ġvardı": 36339, "ĠIstanbul": 36340, "gré": 36341, "ĠElsa": 36342, "ël": 36343, "Ġinvece": 36344, "Ġcrane": 36345, "Ġobe": 36346, "ĠShark": 36347, "Ġsmack": 36348, "Ġrestoring": 36349, ".\\": 36350, "Ġë¹łë": 36351, "Ġfaded": 36352, "umbers": 36353, "Singing": 36354, "Ġdepressing": 36355, "thest": 36356, "ĠWahr": 36357, "Ġmultitude": 36358, "ÑĢавÑģÑĤвÑĥйÑĤе": 36359, "rijk": 36360, "eka": 36361, "Ġcompletes": 36362, "ĠWells": 36363, "Ġroy": 36364, "ĠPray": 36365, "ĠKalau": 36366, "izin": 36367, "iaÅĤem": 36368, "Ġlocom": 36369, "ĠNashville": 36370, "ĠPentagon": 36371, "미": 36372, "ĠNEW": 36373, "ÄħÄĩ": 36374, "ÃŃss": 36375, "Ġmarrying": 36376, "Ġfeud": 36377, "íĻķ": 36378, "æĢ¥": 36379, ")!": 36380, "ĠOperations": 36381, "ÑĥÑĶ": 36382, "Ġmoje": 36383, "Ġinstructed": 36384, "ĠëĪĦ구": 36385, "Ġ×Ķ×Ĵ": 36386, "ĠпомоÑīÑĮÑİ": 36387, "Ġsabia": 36388, "ìķĺìĸ´ìļĶ": 36389, "plane": 36390, "pri": 36391, "ĠполноÑģÑĤÑĮÑİ": 36392, "ĠKitty": 36393, "Ġpróprio": 36394, "edere": 36395, "Ġinteresante": 36396, "Ġде": 36397, "Ġcondensed": 36398, "Ġavent": 36399, "TOR": 36400, "Ġgreasy": 36401, "ARK": 36402, "orta": 36403, "AJ": 36404, "Ġdisreg": 36405, "Ġcorrections": 36406, "Ġstero": 36407, "Ġinfluenza": 36408, "Ġdesses": 36409, "Ġballots": 36410, "Ġmeget": 36411, "Ġmafia": 36412, "Ġböl": 36413, "nost": 36414, "ĠÑģÑĤаÑĤÑĮ": 36415, "Ġresponder": 36416, "Ġhinten": 36417, "grav": 36418, "à¸Ńะ": 36419, "ynchron": 36420, "Ġviens": 36421, "Ġsamo": 36422, "Ġdt": 36423, "pannt": 36424, "ĠÅĽwiat": 36425, "ĠзапиÑģ": 36426, "Ġmerged": 36427, "Ġkep": 36428, "Ġmisleading": 36429, "Ġdigamos": 36430, "Ġammon": 36431, "è¾Ľ": 36432, "chet": 36433, "Ġê°Ģìł¸": 36434, "Ġuni": 36435, "ĠëIJĺëĬĶëį°": 36436, "ĠнапÑĢав": 36437, "ĠкоÑĤоÑĢого": 36438, "Ġanimate": 36439, "×ķ×IJ×": 36440, "еÑĢв": 36441, "Ġminced": 36442, "Ġkaum": 36443, "ãģĤãģģ": 36444, "ÏĢε": 36445, "лег": 36446, "existing": 36447, "Ġplataform": 36448, "ĠKRIS": 36449, "ìĽł": 36450, "ĠFamilien": 36451, "ĠLibya": 36452, "Ġbiodiversity": 36453, "Ġidiots": 36454, "irdi": 36455, "Ġszyb": 36456, "ĠRolling": 36457, "ücht": 36458, "ĠÑĥдив": 36459, "ÑģÑĥд": 36460, "Ġrealizar": 36461, "Ġcanned": 36462, "ĠÑĢан": 36463, "Ġmetabolic": 36464, "ĠBeef": 36465, "Ġkilka": 36466, "лÑİÑģ": 36467, "Ġregistry": 36468, "моÑĤÑĢиÑĤе": 36469, "Ġvielä": 36470, "Ġodc": 36471, "Ġcondemned": 36472, "æ©ĭ": 36473, "fal": 36474, "ĠDil": 36475, "woÅĽci": 36476, "Aw": 36477, "Ġstatistically": 36478, "Ġsogen": 36479, "ĠBETH": 36480, "Ġshaving": 36481, "幸": 36482, "ocal": 36483, "ĠFunny": 36484, "Ġpeacefully": 36485, "Ġaddictive": 36486, "ĠInsert": 36487, "lauf": 36488, "Ġexperiencia": 36489, "é¦ĸåħĪ": 36490, "иÑĤелÑı": 36491, "ÃŃgen": 36492, "ágina": 36493, "Ġabdomen": 36494, "íķľëĭ¤": 36495, "icus": 36496, "imana": 36497, "ìį¨": 36498, "arching": 36499, "Ġkonkret": 36500, "ìķĺë": 36501, "ека": 36502, "oufl": 36503, "ivel": 36504, "Ġnude": 36505, "ètres": 36506, "Ġmonsieur": 36507, "Ġclash": 36508, "Ġtherapists": 36509, "Ġcubed": 36510, "Ġretrouver": 36511, "Ġwaveform": 36512, "Ġpotem": 36513, "ĠFormer": 36514, "isión": 36515, "åºľ": 36516, "Ġ×IJ×Ŀ": 36517, "undos": 36518, "ĠMeinung": 36519, "صÙĦ": 36520, "ĠJude": 36521, "ĠnÃ¥r": 36522, "ĠLeonardo": 36523, "ĠCristo": 36524, "ĠGOT": 36525, "ÑģÑĤÑĢÑĥк": 36526, "LAN": 36527, "ĠgÃ¥ng": 36528, "Ġdéb": 36529, "ĠFrankfurt": 36530, "Ġcrappy": 36531, "Ġlil": 36532, "année": 36533, "ĠмеÑģÑĤе": 36534, "RET": 36535, "ĠNer": 36536, "ĠCOSTA": 36537, "Ġjedem": 36538, "Ġcurtains": 36539, "Ġiterations": 36540, "Ġunav": 36541, "Ġplaque": 36542, "orum": 36543, "Ġζ": 36544, "Ġnúmeros": 36545, "Ġdesap": 36546, "²½": 36547, "Ġcompiled": 36548, "Ġrefle": 36549, "Ġrankings": 36550, "Ġrepaired": 36551, "ĠÐĿапÑĢ": 36552, "Ġdownloads": 36553, "Ġarmour": 36554, "Ġ×Ļ×ķתר": 36555, "Ġlongevity": 36556, "ĠTONER": 36557, "ĠкомменÑĤаÑĢ": 36558, "Ġczego": 36559, "Ġnotify": 36560, "Ġairports": 36561, "Ġenduring": 36562, "lette": 36563, "Ġapparat": 36564, "Ġhabil": 36565, "á»ĩc": 36566, "nad": 36567, "ICO": 36568, "ĠBrah": 36569, "Ġsegún": 36570, "Ġgovernors": 36571, "kaha": 36572, "ĠSchluss": 36573, "Ġodpowied": 36574, "irting": 36575, "Ġrempl": 36576, "ĠAboriginal": 36577, "identally": 36578, "Ġenhancing": 36579, "licting": 36580, "ĠHawaiian": 36581, "Ġstriving": 36582, "ĠNiet": 36583, "Ġznaczy": 36584, "Ġobedience": 36585, "ĠnÃ¥got": 36586, "Ġexpired": 36587, "Ġ1918": 36588, "presented": 36589, "Ġprowad": 36590, "ĠTerr": 36591, "ĠPrinceton": 36592, "Ġmorgen": 36593, "Ġattracting": 36594, "ĠSigma": 36595, "igner": 36596, "ĠRechts": 36597, "ĠPeki": 36598, "Ġmethy": 36599, "Ġhamm": 36600, "Ġdireito": 36601, "Ġdelegation": 36602, "иваÑİÑĤ": 36603, "Ġgin": 36604, "Young": 36605, "Ġdependencies": 36606, "ĠBradley": 36607, "buds": 36608, "Ġfis": 36609, "Ġpytanie": 36610, "Ġinterconnected": 36611, "Ġembaixo": 36612, "ĠSas": 36613, "Ġruh": 36614, "ĠSicht": 36615, "Sur": 36616, "Ġsuperb": 36617, "ĠSabbath": 36618, "ĠDanger": 36619, "kol": 36620, "Ġhou": 36621, "supp": 36622, "ĠNacional": 36623, "Ġsuccession": 36624, "Ġvá": 36625, "ĠMaÃŁnahmen": 36626, "ĠJessie": 36627, "ĠIdaho": 36628, "forest": 36629, "ħĺ": 36630, "Ġ×ŀ×ĵ": 36631, "ĠØ£ÙĬ": 36632, "Ġsweetheart": 36633, "Ġneatly": 36634, "ĠEvangel": 36635, "곡": 36636, "ĠSuite": 36637, "ública": 36638, "ĠÑĥли": 36639, "ĠAnnouncer": 36640, "ligh": 36641, "Ġsensations": 36642, "Ġshelters": 36643, "Ġhart": 36644, "Ġsqueezing": 36645, "ĠRivers": 36646, "ĠCooking": 36647, "ì±ħ": 36648, "personal": 36649, "Ġmanos": 36650, "ÑijÑĤÑģÑı": 36651, "wij": 36652, "Ġgogg": 36653, "ĠMilli": 36654, "ĠFP": 36655, "ünst": 36656, "ĠLS": 36657, "Ġspraying": 36658, "Ġfaux": 36659, "Ġautograph": 36660, "ologic": 36661, "Ġtorment": 36662, "Ġencrypted": 36663, "á»ħ": 36664, "Ġestre": 36665, "ç¹¼": 36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, "ĠQuinn": 36723, "Ġgauche": 36724, "ĠÙĪØ£": 36725, "ĠÈ": 36726, "çĵľ": 36727, "Ġtechno": 36728, "Ġdispatch": 36729, "ĠaÅŁk": 36730, "Ġeinzel": 36731, "ĠGmail": 36732, "çŀ": 36733, "Ġê°ľìĿ¸": 36734, "ĠÑģемÑĮ": 36735, "Ġjourneys": 36736, "Ġiht": 36737, "Ġfibre": 36738, "Ġdramas": 36739, "ouched": 36740, "Ġrename": 36741, "ĠопеÑĢ": 36742, "Ġpoo": 36743, "ĠDru": 36744, "ĠиÑĤог": 36745, "Ġzast": 36746, "Ġcoz": 36747, "Ġzucch": 36748, "Ġobtaining": 36749, "Ġcommute": 36750, "Ġsubmer": 36751, "ĠVish": 36752, "ĠRabb": 36753, "ogg": 36754, "Ġhut": 36755, "íĸĪìĸ´": 36756, "æ¯Ķå¦Ĥ": 36757, "eremi": 36758, "Ġμα": 36759, "Ġdiskut": 36760, "ĠбÑĥк": 36761, "Ġimpaired": 36762, "depend": 36763, "ĠÙĪا": 36764, "ĠÑĢÑĥк": 36765, "ĠбаÑĢ": 36766, "Ġoxidation": 36767, "Ġsituação": 36768, "ÉĻn": 36769, "ução": 36770, "Ġsagte": 36771, "ĠSER": 36772, "ĠCake": 36773, "Ġturmeric": 36774, "ĠKak": 36775, "bung": 36776, "ĠKá¹Ľá¹£á¹ĩa": 36777, "Ġpoisoning": 36778, "Ġslipping": 36779, "ĠSays": 36780, "å°±åı¯ä»¥": 36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, "Ġtampoco": 36838, "Ġphilosophers": 36839, "ĠRecht": 36840, "æĵļ": 36841, "Ġcomentarios": 36842, "yse": 36843, "Ġìľ¤": 36844, "Ġmise": 36845, "ĠGin": 36846, "Ġном": 36847, "ĠFROM": 36848, "liner": 36849, "atif": 36850, "ĠspoÅĤec": 36851, "xa": 36852, "ĠÑĤÑĢÑĥд": 36853, "Ġwag": 36854, "기ìĹIJ": 36855, "ĠMG": 36856, "Ġoffspring": 36857, "ĠUnderstanding": 36858, "åıªæĺ¯": 36859, "ORA": 36860, "Ġwhirring": 36861, "Ġsurrend": 36862, "Ġpoker": 36863, "Ġmonuments": 36864, "ĠâĻ©": 36865, "Ġorganised": 36866, "ĠSozial": 36867, "ĠFactory": 36868, "Ñħа": 36869, "Ġresemble": 36870, "зд": 36871, "Ġexplosions": 36872, "Ġpayroll": 36873, "Ġomn": 36874, "ĠJorge": 36875, "ιÏĥ": 36876, "Ġfracture": 36877, "Ġpersecution": 36878, "Ġdemais": 36879, "ECH": 36880, ",)": 36881, "Ġcriar": 36882, "ĠJOSH": 36883, "Ġdemographics": 36884, "Ġ1600": 36885, "Ġcurrencies": 36886, "ĠTips": 36887, "ĠéĢĻåĢĭ": 36888, "ĠRefer": 36889, "ĠDancing": 36890, "Ġinconsistent": 36891, "Ġdeh": 36892, "Ġimmens": 36893, "Ġmeist": 36894, "Ġimpatient": 36895, "Ġbehaves": 36896, "æĿ¾": 36897, "ĠëĤ´ìļ©": 36898, "Ġbackstory": 36899, "Ġagreeing": 36900, "ĠÅģ": 36901, "ihin": 36902, "Ġtemperatura": 36903, "ĠBackground": 36904, "Ġnutzen": 36905, "Ġëħ¹": 36906, "ĠMänner": 36907, "Ġcollaborations": 36908, "ĠKos": 36909, "éģİåİ»": 36910, "Ġnightmares": 36911, "ëĵ±": 36912, "ĠQueensland": 36913, "Ġassociates": 36914, "ĠKok": 36915, "Ġfactorial": 36916, "ĠHyung": 36917, "Ġê·¸ëĭ¤ìĿĮ": 36918, "Ġfilho": 36919, "Ġelét": 36920, "Ġíĸīë³µ": 36921, "°±": 36922, "Ġgefunden": 36923, "Ġsemicondu": 36924, "Ġcounselors": 36925, "ĠUpper": 36926, "ĠAub": 36927, "ickers": 36928, "Ver": 36929, "Ġnorthwest": 36930, "ĠMaintenant": 36931, "ĠLakes": 36932, "аÑıв": 36933, "inté": 36934, "ì°½": 36935, "Ġгаз": 36936, "Ġgiorn": 36937, "Ġdigitally": 36938, "ĠCircuit": 36939, "ì¼Ģ": 36940, "ãĤĬãģ¾ãģĹãģŁ": 36941, "Ġcheerful": 36942, "ĠPeterson": 36943, "ĠDanish": 36944, "ativos": 36945, "Ġliken": 36946, "Ġharbor": 36947, "алиÑģÑĤ": 36948, "xe": 36949, "Ġcurls": 36950, "ĠRhod": 36951, "End": 36952, "ĠET": 36953, "Ġacquaint": 36954, "ĠKelvin": 36955, "Ġtrif": 36956, "ĠAway": 36957, "ìŀIJëĬĶ": 36958, "vs": 36959, "Ġpágina": 36960, "Ġinlet": 36961, "ĠSantos": 36962, "Ġìļ°ìĻĢ": 36963, "Ġyapıyorsun": 36964, "theme": 36965, "Ġsouff": 36966, "Ġinjected": 36967, "Ġpóźniej": 36968, "iverso": 36969, "amped": 36970, "Ġdaher": 36971, "Ġdagger": 36972, "ĠлÑİбим": 36973, "Ġtummy": 36974, "Ġenlightened": 36975, "cents": 36976, "ĠDah": 36977, "Ġcuest": 36978, "ä¾Ĩ說": 36979, "ILY": 36980, "Ġ×ijר": 36981, "Ġbanging": 36982, "ĠEmil": 36983, "ĠCler": 36984, "ĠBorder": 36985, "ижÑĥ": 36986, "Ġpresenters": 36987, "ĠSTUD": 36988, "coins": 36989, "ĠíĻį": 36990, "Ġperks": 36991, "Ġparap": 36992, "Ġcertaines": 36993, "ĠLore": 36994, "öst": 36995, "ĠMARTIN": 36996, "Ġbios": 36997, "Ġwhereby": 36998, "verts": 36999, "ĠMiranda": 37000, "Ġstip": 37001, "澤": 37002, "andez": 37003, "׼׾": 37004, "ujin": 37005, "Ġê¾": 37006, "Ġallergies": 37007, "plate": 37008, "Ġyapıl": 37009, "Ġundertake": 37010, "ĠëĤĺê°Ģ": 37011, "Part": 37012, "Ġkızım": 37013, "hguru": 37014, "ãģĤãģ¨": 37015, "ĠJohns": 37016, "Ġeyelashes": 37017, "Ġdrained": 37018, "ĠstÃ¥r": 37019, "ãģĤãĤĬãģ¾ãģĻ": 37020, "ĠJade": 37021, "Ġcalend": 37022, "film": 37023, "Ġmesa": 37024, "Ġludzie": 37025, "Ġattracts": 37026, "Ġjuices": 37027, "Ġкил": 37028, "Ġnieuwe": 37029, "Ġmencion": 37030, "Ġignition": 37031, "Ġbladder": 37032, "andaag": 37033, "ĠExtension": 37034, "íĤ¨": 37035, "feed": 37036, "ĠÙĪÙĩ": 37037, "Ġspun": 37038, "Ġtät": 37039, "оÑĢоÑĤ": 37040, "tyard": 37041, "ronics": 37042, "ĠHuge": 37043, "Ñĥжд": 37044, "string": 37045, "Ġunjust": 37046, "Ġprawn": 37047, "Ġfrosting": 37048, "Ġdisappearance": 37049, "iosa": 37050, "Ġcardi": 37051, "ĠPriest": 37052, "ĠcientÃŃfic": 37053, "åĵªè£¡": 37054, "ĠÐĴаÑģ": 37055, "Ġë¶Ģíĥģ": 37056, "Ġthieves": 37057, "Ġphysique": 37058, "ĠEugene": 37059, "Ġблиз": 37060, "Ġmonopoly": 37061, "Ġbiography": 37062, "ĠhoÅŁ": 37063, "Ġtö": 37064, "mac": 37065, "Ġshocks": 37066, "ìĦ¸ë": 37067, "hit": 37068, "Ġsnug": 37069, "Ġincl": 37070, "Ġdedic": 37071, "Ġultras": 37072, "ĠизвеÑģÑĤ": 37073, "Ġutilization": 37074, "ĠÑģовеÑĢÑĪенно": 37075, "Ġservi": 37076, "stag": 37077, "180": 37078, "Ġsewer": 37079, "ĠChoice": 37080, "Ġdischarged": 37081, "ĠJD": 37082, "олеÑĤ": 37083, "ĠкваÑĢÑĤи": 37084, "Ġtelescop": 37085, "ĠJeÅĽli": 37086, "ĠNana": 37087, "cale": 37088, "ĠÑĤон": 37089, "mmm": 37090, "äºĨåIJ§": 37091, "Ġgehabt": 37092, "ëĤł": 37093, "æĬķ": 37094, "à¸Ļà¸Ļ": 37095, "Ġether": 37096, "Ġzen": 37097, "Ġresearched": 37098, "ĠCzyli": 37099, "å®Įåħ¨": 37100, "workers": 37101, "Ġ경찰": 37102, "Ġsheriff": 37103, "allo": 37104, "Ġtipos": 37105, "Ġprosecution": 37106, "Ġfrogs": 37107, "Ġfalt": 37108, "jd": 37109, "ĠíĮĶ": 37110, "Ġfiltered": 37111, "ĠOft": 37112, "Ġìį": 37113, "Ġdisfr": 37114, "ĠMustang": 37115, "Ġwoah": 37116, "ĠREALLY": 37117, "Ġмогли": 37118, "Ġentrada": 37119, "ĠигÑĢа": 37120, "Ġmixes": 37121, "ĠавÑĤомоб": 37122, "ÐĻ": 37123, "Ġshin": 37124, "Ġparanormal": 37125, "Ġsomeplace": 37126, "Ġdishon": 37127, "etaan": 37128, "Ġfuerte": 37129, "Ù¹": 37130, "Ġdoom": 37131, "ìĪľ": 37132, "Ġexistential": 37133, "Ġbuld": 37134, "ĠSDK": 37135, "ĠпÑĢавда": 37136, "Ġturnover": 37137, "ĠìĹ¬ê¸°ìĹIJ": 37138, "Ġह": 37139, "Ġmodeled": 37140, "Ġbugün": 37141, "Ġexperimentation": 37142, "Ġmornings": 37143, "Ġmedo": 37144, "Stevie": 37145, "Ġplayable": 37146, "Ġairlines": 37147, "gments": 37148, "Ġ기ë¶Ħ": 37149, "ĠTomb": 37150, "ĠMVP": 37151, "AUDIENCE": 37152, "Ġcheckout": 37153, "Ġpasst": 37154, "Ġbeispiel": 37155, "ĠLinks": 37156, "heavy": 37157, "Ġquestionable": 37158, "Ġìĵ°ë": 37159, "Ġsill": 37160, "Ġmanipulated": 37161, "ĠLoren": 37162, "Ġìľ¼": 37163, "Ġverge": 37164, "ák": 37165, "IES": 37166, "Ġsabot": 37167, "ĠCustomer": 37168, "ależy": 37169, "Ġnominee": 37170, "ĠGad": 37171, "Ġnouvelles": 37172, "ĠSPE": 37173, "istling": 37174, "Ġoval": 37175, "обÑĢаж": 37176, "ifty": 37177, "éĩİ": 37178, "Ġbezel": 37179, "yet": 37180, "Ġfreight": 37181, "ĠHanım": 37182, "rÃŃa": 37183, "Ġzoning": 37184, "Ġindem": 37185, "ĠBü": 37186, "Ġfeminism": 37187, "Ġvoix": 37188, "Ġoficial": 37189, "Ġdiyorum": 37190, "»IJ": 37191, "Ġarose": 37192, "Ġparar": 37193, "ìĿ¸ì§Ģ": 37194, "ĠMartine": 37195, "ĠLect": 37196, "Ġrester": 37197, "Ġdrowning": 37198, "uya": 37199, "cida": 37200, "ĠAriel": 37201, "Ġ02": 37202, "Ġ×Ķ×Ķ": 37203, "ç´ł": 37204, "ĠWert": 37205, "ТÑĭ": 37206, "Ġwidow": 37207, "Ġparchment": 37208, "Ġcottage": 37209, "ĠXL": 37210, "ĠSlack": 37211, "ĠNES": 37212, "Ġrobe": 37213, "Ġgimm": 37214, "Ġcaminho": 37215, "ĠHarper": 37216, "Ġcitrus": 37217, "Ġfirefighters": 37218, "Ġdopamine": 37219, "elets": 37220, "Ġdemocrat": 37221, "ìłľë¡ľ": 37222, "Ġplayback": 37223, "oj": 37224, "ĠпÑĢок": 37225, "ĠSullivan": 37226, "semble": 37227, "ĠWorth": 37228, "ĠMustafa": 37229, "าร": 37230, "Ġmets": 37231, "éĸĢ": 37232, "лоÑģÑĮ": 37233, "Ġinertia": 37234, "Ġuniforms": 37235, "足": 37236, "ério": 37237, "×ķר×Ķ": 37238, "ént": 37239, "Ġà®Ĵ": 37240, "ĠÑģамÑĭÑħ": 37241, "Ġvoulais": 37242, "ĠZimmer": 37243, "ê²łë": 37244, "ĠноÑģ": 37245, "encias": 37246, "Ġrelación": 37247, "Ġ걸ë": 37248, "Ġfaction": 37249, "Ġgosp": 37250, "полож": 37251, "nap": 37252, "hak": 37253, "Ġproceedings": 37254, "ĠìĨĶ": 37255, "ìķĦëĭĪ": 37256, "ĠìŀIJ기": 37257, "Ġwerd": 37258, "Ġsof": 37259, "Ġschlim": 37260, "Ġflavored": 37261, "Ġquadratic": 37262, "ĠBoot": 37263, "Ġpublicity": 37264, "ĠCaro": 37265, "Ġ?\"": 37266, "ниÑĨа": 37267, "mania": 37268, "ĠSUR": 37269, "ĠBUR": 37270, "lance": 37271, "ética": 37272, "Ġzobaczy": 37273, "Ġtrio": 37274, "sama": 37275, "ĠtaÅŁ": 37276, "Ġasymm": 37277, "resser": 37278, "Ġتع": 37279, "ĠпеÑģ": 37280, "Ġbeginnings": 37281, "ladım": 37282, "ĠбÑĭÑģÑĤÑĢ": 37283, "Ġmoo": 37284, "ĠGeneva": 37285, "Ġåľ¨": 37286, "erus": 37287, "borah": 37288, "Ġrefusing": 37289, "bull": 37290, "ĠWaiting": 37291, "ĠIndividual": 37292, "Ġanonym": 37293, "imens": 37294, "Ġmedidas": 37295, "Ġfragrant": 37296, "Ġdirectement": 37297, "ĠìķĦë§Ī": 37298, "uria": 37299, "Ġspherical": 37300, "Ġabge": 37301, "ĠVictorian": 37302, "Ġspectacle": 37303, "ĠRodriguez": 37304, "Ġocup": 37305, "ĠNär": 37306, "marks": 37307, "ngulo": 37308, "ĠLuci": 37309, "Ġshouted": 37310, "Ġregulators": 37311, "ÄŁini": 37312, "Ġdisent": 37313, "ĠÑĢÑĭн": 37314, "ëĤ¨": 37315, "ĠìĤ´ë": 37316, "Ġproblèmes": 37317, "ĠFinger": 37318, "assemble": 37319, "Ġpear": 37320, "Ġdroite": 37321, "ĠEverywhere": 37322, "tam": 37323, "оÑĤив": 37324, "вой": 37325, "ordinate": 37326, "ĠLak": 37327, "ĠmỼi": 37328, "ĠTelevision": 37329, "Ġexponentially": 37330, "avas": 37331, "Ġblev": 37332, "ĠMT": 37333, "俺": 37334, "Connell": 37335, "ĠêµŃ민": 37336, "ĠÑģвоим": 37337, "Ġacha": 37338, "ĠDynasty": 37339, "Jin": 37340, "Ġtore": 37341, "Ġflor": 37342, "Ġмногие": 37343, "æ²Ĵäºĭ": 37344, "owan": 37345, "bah": 37346, "Ġì£Ħ": 37347, "ĠCela": 37348, "Ġìµľê·¼": 37349, "Ġpermettre": 37350, "Ġabras": 37351, "Ġverstehen": 37352, "Ġescort": 37353, "ĠThem": 37354, "ärke": 37355, "porter": 37356, "Ġkahkaha": 37357, "Ġhect": 37358, "Ġdau": 37359, "wah": 37360, "olve": 37361, "ĠAges": 37362, "schaft": 37363, "ĠStell": 37364, "nelle": 37365, "ĠEnsuite": 37366, "ĠÐĴÑģем": 37367, "Ġcréd": 37368, "ĠPP": 37369, "lords": 37370, "grunting": 37371, "Ġcontraction": 37372, "Got": 37373, "Ġacquiring": 37374, "Ġsopr": 37375, "Ġpoisonous": 37376, "RNA": 37377, "Ġanar": 37378, "ĠHof": 37379, "')": 37380, "Ġremarkably": 37381, "Ġinternacional": 37382, "ücke": 37383, "inqu": 37384, "Ġduy": 37385, "Ġbeasts": 37386, "ĠLAN": 37387, "Ġprecedent": 37388, "ĠRPM": 37389, "åij¨": 37390, "Ġselon": 37391, "Ġmorte": 37392, "Ġcomeçou": 37393, "Ñıла": 37394, "Ġinterpreting": 37395, "ĠBurke": 37396, "ÑĤÑĢа": 37397, "ĠìĿ´ëŁ¬": 37398, "Ġpessim": 37399, "ĠNok": 37400, "íĮĿ": 37401, "Female": 37402, "Ġìĭ¤í": 37403, "ĻĢ": 37404, "Ġstimulation": 37405, "Ġslick": 37406, "Ġê°ĢëĬĶ": 37407, "Ġказ": 37408, "ĠHBO": 37409, "Ġpapier": 37410, "Ġkönnten": 37411, "Ñĥбли": 37412, "ĠConstant": 37413, "SPEAKING": 37414, "ĠktórÄħ": 37415, "Ġcosmetics": 37416, "ĠTrend": 37417, "Ġrobbery": 37418, "Ġtitt": 37419, "Ġgjort": 37420, "Ġdietary": 37421, "łĮ": 37422, "ĠKirby": 37423, "ĠпÑĢимеÑĢно": 37424, "Ġqualification": 37425, "Ġìķī": 37426, "Ġcabinets": 37427, "Ġhttp": 37428, "ĠErica": 37429, "義": 37430, "Ġdisadvantages": 37431, "Ġchattering": 37432, "yz": 37433, "feit": 37434, "Ġguild": 37435, "ĠETF": 37436, "ĠDragons": 37437, "ĠHERE": 37438, "venth": 37439, "ÙĦاÙħ": 37440, "Ġmarché": 37441, "Dam": 37442, "Ġphoton": 37443, "Ġestable": 37444, "Mag": 37445, "Ġolhar": 37446, "Ġcoupling": 37447, "ĠHilfe": 37448, "ĠWizard": 37449, "Ġмало": 37450, "help": 37451, "ĠlÃŃnea": 37452, "Ġì«": 37453, "Ġstandalone": 37454, "Ġmorale": 37455, "Ġzweite": 37456, "ãĤĪãĤįãģĹãģı": 37457, "ährt": 37458, "Ġdotted": 37459, "Ġdripping": 37460, "ĠFlag": 37461, "éĿĴ": 37462, "rocket": 37463, "rategy": 37464, "irim": 37465, "Ġíķĺë©´ìĦľ": 37466, "Ġsogenan": 37467, "ĠUno": 37468, "ĠSchutz": 37469, "Ġestilo": 37470, "ĠSubs": 37471, "ĠDaisy": 37472, "ÐĿеÑĤ": 37473, "'...": 37474, "Ġplatinum": 37475, "Ġbirl": 37476, "ĠSovi": 37477, "Ġviolate": 37478, "ÑĥеÑĤÑģÑı": 37479, "rill": 37480, "Ġtraz": 37481, "Ġsnip": 37482, "Ġcumpl": 37483, "à¸Ńà¸ģ": 37484, "Ġcuk": 37485, "éħĴ": 37486, "ĠParlament": 37487, "Ġhypert": 37488, "Ġpulp": 37489, "Ġtongues": 37490, "atto": 37491, "Ġbusca": 37492, "ihn": 37493, "ERO": 37494, "ĠÙĬع": 37495, "Ġvarias": 37496, "ĠMarian": 37497, "Ġbounded": 37498, "Ġpitching": 37499, "Ġdeficiency": 37500, "ĠBlessed": 37501, "ĠExerc": 37502, "uchs": 37503, "ĠnhÆ°ng": 37504, "æľ¬å½ĵ": 37505, "Ġraped": 37506, "hales": 37507, "Ġmala": 37508, "pic": 37509, "Ġ401": 37510, "ÅĽniej": 37511, "arina": 37512, "ëĵ¤ìĿĦ": 37513, "otti": 37514, "Ġдолго": 37515, "Ġtracker": 37516, "ĠShelby": 37517, "Ġvanished": 37518, "Ġbakery": 37519, "Kapı": 37520, "Jesus": 37521, "ĠKR": 37522, "JO": 37523, "ħ¸": 37524, "Ġdiscs": 37525, "ìĦ¯": 37526, "ì§Ģë": 37527, "×Ļצ": 37528, "emary": 37529, "Kendra": 37530, "Ġyük": 37531, "ückt": 37532, "Ġvaz": 37533, "Ġkup": 37534, "aktu": 37535, "ĠÑģпаÑģибо": 37536, "Ġaik": 37537, "Ġnursery": 37538, "Ġendangered": 37539, "êmement": 37540, "ematics": 37541, "Ġresponders": 37542, "ĠRepresentatives": 37543, "Ġsculptures": 37544, "igkeiten": 37545, "Ġdepl": 37546, "Ġinterpretations": 37547, "Ġdeadlines": 37548, "Ġ1942": 37549, "ÃĹ": 37550, "Ġsugars": 37551, "emu": 37552, "lively": 37553, "Ġrecreational": 37554, "Ġdistort": 37555, "Ġunderscore": 37556, "Ġunquote": 37557, "Ġsafest": 37558, "Ġswollen": 37559, "Ġanalyses": 37560, "Ġcommencé": 37561, "妹": 37562, "andin": 37563, "ĠХоÑĢоÑĪо": 37564, "Ġdiarr": 37565, "ãģ¾ãģģ": 37566, "ziest": 37567, "Ġtoothbrush": 37568, "éł»éģĵ": 37569, "uations": 37570, "Ġcade": 37571, "Ġbacklash": 37572, "hind": 37573, "Ġrisque": 37574, "zess": 37575, "ĠìĿ´ìķ¼ê¸°": 37576, "Ġesperar": 37577, "Ġtranslations": 37578, "ioned": 37579, "groans": 37580, "ĠпÑĥÑĤ": 37581, "Ġgenetically": 37582, "éĢł": 37583, "Ġhappiest": 37584, "Ġwerk": 37585, "atoon": 37586, "Ġmusi": 37587, "Ġfunção": 37588, "ĠìŀħëĭĪëĭ¤": 37589, "ĠÑĢай": 37590, "Ġbevor": 37591, "BLANK": 37592, "Ġrepentance": 37593, "Put": 37594, "Ġpotrzeb": 37595, "Ġsala": 37596, "Ġcampa": 37597, "WER": 37598, "ĠdecÃŃa": 37599, "Ġsécurité": 37600, "ĠAppreciate": 37601, "Ñĩи": 37602, "ĠRandom": 37603, "ë³Ħ": 37604, "kah": 37605, "Ġmöj": 37606, "Ġsäger": 37607, "Ġ×Ļ׼×ķ׾": 37608, "Ġ190": 37609, "xtures": 37610, "Eu": 37611, "Ġgä": 37612, "Ġ×ijת": 37613, "ĠCroat": 37614, "apo": 37615, "PLE": 37616, "Ġpersistence": 37617, "åĬ©": 37618, "Ġblends": 37619, "Ġtreffen": 37620, "ĠSantiago": 37621, "ydia": 37622, "aldo": 37623, "ĠTensorFlow": 37624, "ĠDual": 37625, "ãĥľ": 37626, "Ġchiff": 37627, "ìĹ´": 37628, "Ġcontracted": 37629, "Ġsegreg": 37630, "ĠFairy": 37631, "Ġwisely": 37632, "Ġvulnerabilities": 37633, "Ġhandheld": 37634, "Ġgadgets": 37635, "ĠboÅŁ": 37636, "ĠPopular": 37637, "Ġcurvature": 37638, "문": 37639, "ĠMARY": 37640, "ìĿ´ìĬ": 37641, "Ġformulation": 37642, "Ġcelery": 37643, "Ġblurry": 37644, "ĠTS": 37645, "alez": 37646, "Ġws": 37647, "Ġprogramm": 37648, "ĠStack": 37649, "ĠJIM": 37650, "овали": 37651, "ıll": 37652, "Ġpère": 37653, "ĠKanye": 37654, "ĠDelaware": 37655, "Ġãģł": 37656, "Ġdaunting": 37657, "ĠбеÑģ": 37658, "ĠStupid": 37659, "big": 37660, "fficial": 37661, "Ġprecipitation": 37662, "Ġplung": 37663, "ục": 37664, "burse": 37665, "Ġdarle": 37666, "Ġcripp": 37667, "Ġpioneer": 37668, "Ġdisput": 37669, "Ġsean": 37670, "ãģĵãĤĵãģª": 37671, "Ġresistor": 37672, "Ġallein": 37673, "ipples": 37674, "arel": 37675, "Ġendors": 37676, "zust": 37677, "ĠÑĢебÑıÑĤа": 37678, "eded": 37679, "Ġì¹´ë©Ķë": 37680, "Ġlleva": 37681, "Ġkennt": 37682, "Ġбал": 37683, "ĠDocument": 37684, "ĠKnights": 37685, "Ġbuckle": 37686, "Ġìī¬": 37687, "Ġalk": 37688, "ĠEveryday": 37689, "atters": 37690, "Ġtoilets": 37691, "Ġjugar": 37692, "ĠìŀĪì§Ģ": 37693, "Ġgenauso": 37694, "ĠLandesregierung": 37695, "ãģ£ãģ±": 37696, "ije": 37697, "Ġtrailers": 37698, "ĠTigers": 37699, "Ġgitti": 37700, "Ġforgiving": 37701, "Ġconcurrent": 37702, "ĠVu": 37703, "ĠíĬ¹íŀĪ": 37704, "ĠBROWN": 37705, "ounded": 37706, "\";": 37707, "Ġtremb": 37708, "Ġtiet": 37709, "ĠÑĢежим": 37710, "Ġnutshell": 37711, "елиÑĩ": 37712, "Ġlosers": 37713, "ricting": 37714, "Ġredeem": 37715, "defined": 37716, "Nice": 37717, "Ġbroadband": 37718, "KO": 37719, "Ġteasing": 37720, "Ġpartisan": 37721, "ıma": 37722, "Ġìŀ¬ë¯¸": 37723, "ĠJourney": 37724, "Ġslopes": 37725, "uning": 37726, "grunts": 37727, "Ġtäll": 37728, "Ġuncovered": 37729, "ĠmyÅĽlÄĻ": 37730, "ĠEsther": 37731, "äºİ": 37732, "ĠHealthy": 37733, "Ġë°ij": 37734, "rée": 37735, "Ġpolarization": 37736, "Ġflav": 37737, "Ġcambiar": 37738, "Ġyr": 37739, "ĠRanch": 37740, "Ġsplits": 37741, "Ġtrouvé": 37742, "åľĭ家": 37743, "Ġrecorder": 37744, "Ġdépart": 37745, "ÙĪب": 37746, "ĠKry": 37747, "Ġinteressant": 37748, "Ġederim": 37749, "ÅĽwiad": 37750, "ilateral": 37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 38618, "iries": 38619, "orest": 38620, "Ġtasked": 38621, "Ġmorally": 38622, "ìķ¼ì§Ģ": 38623, "ustered": 38624, "Ġfools": 38625, "Ġirrespons": 38626, "Ġeinf": 38627, "Ġviá»ĩc": 38628, "Ġscor": 38629, "Ġpillows": 38630, "ĠGegen": 38631, "Ġtutte": 38632, "Ġquarterly": 38633, "Ġdidnt": 38634, "ĠGym": 38635, "ĠEther": 38636, "ĠØ«": 38637, "лиÑĪком": 38638, "Ġsignaling": 38639, "ĠNode": 38640, "ĠDoncs": 38641, "Ġyah": 38642, "ĠKanal": 38643, "Ġfading": 38644, "etin": 38645, "Ġinfluencers": 38646, "Ġmedals": 38647, "Ġengineered": 38648, "Ġfermented": 38649, "ê²łì§Ģë§Į": 38650, "ĠBeethoven": 38651, "×ŀש": 38652, "inental": 38653, "ĠìķĮ볤": 38654, "ütfen": 38655, "alnya": 38656, "Ġovere": 38657, "Ġdenkt": 38658, "акÑĤеÑĢ": 38659, "Ġâĺ": 38660, "Ġnecesit": 38661, "Ġgenerators": 38662, "grass": 38663, "ĠподÑĥм": 38664, "lieÃŁen": 38665, "Bar": 38666, "ľëıĻ": 38667, "ĠдеÑĤей": 38668, "Ġsucking": 38669, "Ġstencil": 38670, "Ġprimo": 38671, "ĠBreath": 38672, "strom": 38673, "Ġimmensely": 38674, "Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, "Ġvalores": 38790, "ê·ľ": 38791, "Hy": 38792, "ĠзадаÑĩ": 38793, "ĠKendall": 38794, "ĠÑħаÑĢ": 38795, "ĠVamp": 38796, "Ġpython": 38797, "Ġmanageable": 38798, "ĠGente": 38799, "oise": 38800, "iciary": 38801, "Ġimposs": 38802, "ĠBunny": 38803, "iesta": 38804, "Andrew": 38805, "Ġsert": 38806, "ĠCec": 38807, "zzarella": 38808, "Ġautomobile": 38809, "ĠTiere": 38810, "allows": 38811, "åĨĨ": 38812, "Ġë°Ģ": 38813, "ĠScorp": 38814, "ĠJelly": 38815, "agara": 38816, "ĠStretch": 38817, "Ġredef": 38818, "Ġexacerb": 38819, "ĠSHA": 38820, "éf": 38821, "orsa": 38822, "Ġflawed": 38823, "ĠNoel": 38824, "?!?": 38825, "Ġprocent": 38826, "Ġmenstru": 38827, "ĠпÑĢоÑĩ": 38828, "Ġinfants": 38829, "ðŁİµ": 38830, "pause": 38831, "ĠRacing": 38832, "Ġ1948": 38833, "Ġsuperintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "Ġunlucky": 38838, "Ġperk": 38839, "anci": 38840, "Ġë§ĮëĤĺ": 38841, "ĠÐľÐ¾Ñģкв": 38842, "Ġfinans": 38843, "Ġdiferencia": 38844, "łĪìĿ´": 38845, "éħį": 38846, "ORY": 38847, "ĠTac": 38848, "ÛĮا": 38849, "Ġdesem": 38850, "Ġважно": 38851, "ĠJU": 38852, "ĠìŀĪìŀĸìķĦìļĶ": 38853, "ĠÎĿ": 38854, "Ġinformations": 38855, "ĠHEL": 38856, "hst": 38857, "ĠпоговоÑĢ": 38858, "Ġvoiture": 38859, "Ġreus": 38860, "ändig": 38861, "ĠпоÑħож": 38862, "jing": 38863, "Ġdru": 38864, "altra": 38865, "Ġproduits": 38866, "Ġkite": 38867, "Ġeyeball": 38868, "ĠBelt": 38869, "ĠRestaurant": 38870, "Ġgamb": 38871, "Ġporridge": 38872, "itters": 38873, "Ġconverts": 38874, "Ġyardım": 38875, "Ġmáximo": 38876, "wirtschaft": 38877, "ĠíķĺëĤĺë": 38878, "Ġì¤Ģ": 38879, "Ġiceberg": 38880, "Ġvorbei": 38881, "Ġ256": 38882, "ocratic": 38883, "Ġreckless": 38884, "onner": 38885, "Ġmús": 38886, "Ġlogically": 38887, "ĠPrison": 38888, "ĠNetz": 38889, "Ġvacant": 38890, "Ġnimmt": 38891, "ĠHARR": 38892, "Ġзов": 38893, "ĠDee": 38894, "ringe": 38895, "niest": 38896, "ĠRules": 38897, "ìĬ¤ëŁ½": 38898, "cussions": 38899, "Ġfloral": 38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, "Ġpública": 38905, "itel": 38906, "Ġaccommodations": 38907, "ĠGrü": 38908, "íľ": 38909, "Ġpickles": 38910, "иÑĩеÑģкиÑħ": 38911, "Ġcommissions": 38912, "ĠBaek": 38913, "ĠçocuÄŁ": 38914, "ĠMedium": 38915, "Ġperiodically": 38916, "Ġwonderfully": 38917, "Ġstaffing": 38918, "ìĽIJë": 38919, "rire": 38920, "fle": 38921, "ĠMcL": 38922, "ĠÑĤеп": 38923, "ĠпеÑĢек": 38924, "нолог": 38925, "Ġíģ¬ê²Į": 38926, "çĻ¼çı¾": 38927, "Ġprosperous": 38928, "ĠSpiritual": 38929, "ĠChick": 38930, "DIA": 38931, "ĠÐŁÑĢивеÑĤ": 38932, "ĠperÃŃ": 38933, "ÑĮÑİÑĤ": 38934, "Ġconsultants": 38935, "ĠEarl": 38936, "ä»Ĭå¹´": 38937, "Ġruining": 38938, "оÑĢе": 38939, "Ġpenser": 38940, "Ġtakiej": 38941, "Ġstrengthened": 38942, "ĠLiquid": 38943, "онеÑĨ": 38944, "аваÑĤÑĮ": 38945, "Ġcamer": 38946, "Ġdisagreement": 38947, "Ġbathing": 38948, "ĠYosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "Ġsuperstar": 38953, "æģŃ": 38954, "лÑıÑĤÑĮ": 38955, "Ġnib": 38956, "ĠTherm": 38957, "ĠDANIEL": 38958, "Ġpaw": 38959, "Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, "Ġendured": 39017, "iada": 39018, "Ġbrewing": 39019, "ìŬë": 39020, "ĠÑģвобод": 39021, "ĠSaints": 39022, "ĠFrançais": 39023, "ĠEconomics": 39024, "Ġdisloc": 39025, "ophobia": 39026, "Camer": 39027, "Ġnegotiated": 39028, "ĠÑģÑĤали": 39029, "ìĬ¤íģ": 39030, "ogie": 39031, "Ġtsunami": 39032, "Ġpeeled": 39033, "Ġmotivations": 39034, "è¨Ń": 39035, "ostat": 39036, "flan": 39037, "ĠDAC": 39038, "Ġkav": 39039, "'RE": 39040, "ĠPearson": 39041, "bbe": 39042, "czenie": 39043, "Ġatenção": 39044, "íĨµëł¹": 39045, "ãģ£ãģ¡": 39046, "ĠÑĥдаÑĢ": 39047, "Ġintroductory": 39048, "ĠIci": 39049, "ëĮĢë": 39050, "akat": 39051, "Ġtrench": 39052, "Ġproceeded": 39053, "ĠCoin": 39054, "Ġderecho": 39055, "ĠRede": 39056, "æ¯Ľ": 39057, "аннÑĭй": 39058, "Ġincarcerated": 39059, "ĠRichmond": 39060, "Rock": 39061, "ĠPav": 39062, "ĠKarma": 39063, "uges": 39064, "Ġconteú": 39065, "ë¹Ħ": 39066, "Ġê·¸ë§Į": 39067, "ĠGone": 39068, "ĠwspóÅĤ": 39069, "ĠRahmen": 39070, "unken": 39071, "Ġì¤ijìļĶíķľ": 39072, "Ġib": 39073, "Ġattaching": 39074, "Hay": 39075, "Ġsuka": 39076, "ìį¹": 39077, "Ġpivotal": 39078, "ĠRespect": 39079, "ÃŃda": 39080, "IB": 39081, "ĠVerantwort": 39082, "wiet": 39083, "Ġforensic": 39084, "ÑĢиÑģÑĤ": 39085, "ĠпÑĢинÑĨипе": 39086, "Ġmarkings": 39087, "Ġkettle": 39088, "ĠOpera": 39089, "ĠDoctors": 39090, "Ġshredded": 39091, "Ġrecuer": 39092, "Ġvigil": 39093, "ĠFail": 39094, "Ġentrev": 39095, "ĠдÑĥÑĪ": 39096, "Ġoutbreaks": 39097, "èµ°åIJ§": 39098, "ĠÏĢο": 39099, "Ġrogue": 39100, "angled": 39101, "Ġyearly": 39102, "ĠCreed": 39103, "Ġwam": 39104, "Ġlotus": 39105, "ê³¼ë": 39106, "ãĢģãĢģ": 39107, "ĠSpit": 39108, "ĠItu": 39109, "Ġstrains": 39110, "Ġstamped": 39111, "Ġplaint": 39112, "Ġpotion": 39113, "Ġconsolidation": 39114, "è©ķ": 39115, "оÑĩкÑĥ": 39116, "Ġvlogging": 39117, "Ġslate": 39118, "ĠAuft": 39119, "ĠIncor": 39120, "ừng": 39121, "§IJ": 39122, "enh": 39123, "ĠheiÃŁ": 39124, "Ġdomest": 39125, "ĠStrom": 39126, "åį³": 39127, "akis": 39128, "Ġfragen": 39129, "Ġfiner": 39130, "ĠSug": 39131, "Ġuphill": 39132, "Ġéén": 39133, "âĢ¦)": 39134, "ĠÑģоп": 39135, "ĠCorey": 39136, "Ġsiebie": 39137, "Ġmuse": 39138, "Ġcloves": 39139, "Ġpous": 39140, "ĠFinanz": 39141, "ĠRoute": 39142, "amat": 39143, "Ġmutually": 39144, "ĠвнÑĥÑĤÑĢи": 39145, "ĠSelena": 39146, "ëĶ": 39147, "ĠGaussian": 39148, "ë¶ĢíĦ°": 39149, "Ġ×ij׼": 39150, "Ġejerc": 39151, "å¾®": 39152, "kea": 39153, "ĠGerry": 39154, "ĠSic": 39155, "大çļĦ": 39156, "Ġ1966": 39157, "iese": 39158, "Ġfossils": 39159, "Ġestad": 39160, "ĠKane": 39161, "ciÄĩ": 39162, "ĠìľłíĬľë": 39163, "Ġпам": 39164, "ĠCruise": 39165, "intérieur": 39166, "Ġbekannt": 39167, "ĠPode": 39168, "Ġdemander": 39169, "Rem": 39170, "Ġinvade": 39171, "Ġdecorating": 39172, "ropic": 39173, "Ġcowboy": 39174, "ĠPhoto": 39175, "opolit": 39176, "Ġì»¬ëŁ¬ë": 39177, "Ġreap": 39178, "Ġhandwriting": 39179, "à¹Ħร": 39180, "Ġëļ": 39181, "Ġبعد": 39182, "ĠMt": 39183, "ÙĢ": 39184, "Ġspaceship": 39185, "Ġnationalism": 39186, "Ġcouncils": 39187, "ĠGriffin": 39188, "ĠAhmed": 39189, "Ġclich": 39190, "ĠOL": 39191, "wl": 39192, "ĠPilot": 39193, "å®®": 39194, "Ġacronym": 39195, "Ġgels": 39196, "Ġelectroly": 39197, "èĵ": 39198, "Ġмной": 39199, "Ġepisod": 39200, "ĠDieses": 39201, "ĠATP": 39202, "Ġediyorum": 39203, "Ġexpresses": 39204, "Ġexhibits": 39205, "Comm": 39206, "ĠкÑĢÑĥп": 39207, "Ġmatar": 39208, "Ġ2025": 39209, "ĠArtem": 39210, "vasive": 39211, "rÃł": 39212, "ĠbeÅŁ": 39213, "é»ĥ": 39214, "Ġlizard": 39215, "Ġfille": 39216, "Ġì§Ī문": 39217, "ĠмоÑī": 39218, "Ġtür": 39219, "Ġculprit": 39220, "Ġwoven": 39221, "ĠANY": 39222, "nim": 39223, "Ġtay": 39224, "Ġpromin": 39225, "Ġacompa": 39226, "Ġidé": 39227, "Ġboiler": 39228, "ĠThemen": 39229, "Ġavenue": 39230, "ĠMud": 39231, "ĠновÑĭе": 39232, "Ġwitnessing": 39233, "Ġlance": 39234, "ĠCHAN": 39235, "ĠBever": 39236, "تÙħ": 39237, "Ġchemotherapy": 39238, "King": 39239, "ĠbÄĻdÄĻ": 39240, "Ġatual": 39241, "Ġtive": 39242, "Ġtalkin": 39243, "Ġquedar": 39244, "ieÃŁ": 39245, "edel": 39246, "Ġìĸ´ìłľ": 39247, "Ġjogar": 39248, "Ġör": 39249, "Ġundertaking": 39250, "ĠStrength": 39251, "Ġmilhões": 39252, "ĠWine": 39253, "ĠMolt": 39254, "讲": 39255, "ãģijãĤĮ": 39256, "Ġundermine": 39257, "ĠArchives": 39258, "vana": 39259, "mercial": 39260, "MC": 39261, "Ġcaste": 39262, "пÑĢ": 39263, "Ġlegislators": 39264, "ulators": 39265, "ênio": 39266, "Ġëį°ë": 39267, "ĠÑħоÑĤиÑĤе": 39268, "Ġнек": 39269, "Ġsurn": 39270, "Ġconsci": 39271, "ĠPOW": 39272, "Ġculinary": 39273, "ĠKAT": 39274, "ĠFolks": 39275, "Ñĭваем": 39276, "Ġвок": 39277, "ãģijãĤĭ": 39278, "service": 39279, "pts": 39280, "Ġпобед": 39281, "æĺ¯åķĬ": 39282, "Ġtents": 39283, "Ġnord": 39284, "STE": 39285, "Ġrepublican": 39286, "Ġwyk": 39287, "Ġminions": 39288, "èĻķ": 39289, "Ġmemang": 39290, "jest": 39291, "Ġcomparative": 39292, "Ġtyle": 39293, "carbon": 39294, "bedingt": 39295, "ksen": 39296, "Ġnegativity": 39297, "Ġsjälv": 39298, "Ġdú": 39299, "æīĢæľī": 39300, "Ġrecalled": 39301, "cra": 39302, "ĠTada": 39303, "ĠÑĢÑĥки": 39304, "ĠопÑĢедел": 39305, "Ġprocrast": 39306, "Ġjogos": 39307, "ĠOo": 39308, "ĠHearts": 39309, "Ġéch": 39310, "ĠksiÄħż": 39311, "Ġcoarse": 39312, "ĠTube": 39313, "ĠGreens": 39314, "Ġén": 39315, "Ġdumbbell": 39316, "ĠÑĤи": 39317, "Ġquerer": 39318, "اØŃ": 39319, "Ïĥει": 39320, "ĠпÑĢавилÑĮно": 39321, "Ġпап": 39322, "Ġcompra": 39323, "Ġtér": 39324, "ĠAntes": 39325, "Ġoptimum": 39326, "Ġbiscuit": 39327, "κι": 39328, "aczego": 39329, "Ġìĭľê°ĦìĿ´": 39330, "ĠMarines": 39331, "vero": 39332, "Ġvaccinations": 39333, "Ġpetty": 39334, "riters": 39335, "Ġал": 39336, "country": 39337, "Ġcounters": 39338, "Ġattendant": 39339, "ĠHui": 39340, "ãģ¨ãģĦãģĨãģĵãģ¨ãģ§": 39341, "cka": 39342, "ÑģÑĤвеннÑĭй": 39343, "guy": 39344, "Ġtricked": 39345, "ĠRED": 39346, "Ġthrilling": 39347, "ÏĢοι": 39348, "Ġpiggy": 39349, "Ġanunci": 39350, "ORTER": 39351, "ĠValue": 39352, "Ġrond": 39353, "ĠADA": 39354, "Ġposer": 39355, "hores": 39356, "ĠRoland": 39357, "ĵ¯": 39358, "Ġnoir": 39359, "Ġש×IJ×": 39360, "ë°ľ": 39361, "iemand": 39362, "ĠпоÑĤеÑĢ": 39363, "ê³³": 39364, "Ġê±±": 39365, "Ġformatting": 39366, "ĠLed": 39367, "è§Ģçľ¾": 39368, "Ġkillers": 39369, "ĠÄijấy": 39370, "Ġhaar": 39371, "again": 39372, "!>[": 45687, "minster": 45688, "Ġвли": 45689, "Ġidentifier": 45690, "ĠLambda": 45691, "Ġtros": 45692, "Ġflawless": 45693, "Ġdetrimental": 45694, "Ġbunları": 45695, "War": 45696, "Ġregião": 45697, "羣çļĦæĺ¯": 45698, "ĠBike": 45699, "cessors": 45700, "Ġcùng": 45701, "ĠRN": 45702, "Ġê½ĥ": 45703, "Ġküçük": 45704, "ĠBeginning": 45705, "íĺ¸ë": 45706, "Ġgewe": 45707, "Ġdenote": 45708, "ĠAlberto": 45709, "Ġprobiot": 45710, "Ġode": 45711, "Ġmolar": 45712, "Ġbursting": 45713, "assumed": 45714, "Ġfootprints": 45715, "veda": 45716, "Ġsteroids": 45717, "Ġflaming": 45718, "ĠEller": 45719, "Ġerkennen": 45720, "ätzen": 45721, "Ġlifecycle": 45722, "ĠDOU": 45723, "ĠKarena": 45724, "ĠGuerra": 45725, "è¿ĺæĺ¯": 45726, "Ġsinister": 45727, "Ġpodéis": 45728, "Ġparab": 45729, "Ġoko": 45730, "Ġmatéri": 45731, "Ġcaric": 45732, "sonaro": 45733, "Ġpraticamente": 45734, "ÑĥÑģа": 45735, "Ġcomunque": 45736, "Ġvigilant": 45737, "Ġregimes": 45738, "ĠShooting": 45739, "Ġraids": 45740, "ĠNora": 45741, "ĠWieder": 45742, "mens": 45743, "ĠÑģод": 45744, "Ġê²½ìļ°ìĹIJëĬĶ": 45745, "ĠвÑħод": 45746, "Ġautobi": 45747, "ĠSchn": 45748, "ĠRobbie": 45749, "ĠFitness": 45750, "ĠконÑĦ": 45751, "Ġpenguin": 45752, "моÑĤÑĢÑı": 45753, "Ġминим": 45754, "plays": 45755, "Ġdelegates": 45756, "Mer": 45757, "Ġsistem": 45758, "ĠMichaels": 45759, "male": 45760, "اع": 45761, "Ġcách": 45762, "ĠHä": 45763, "Ġ×Ļ×ķ×ĵ×¢": 45764, "Ġsuperpower": 45765, "Ġstron": 45766, "Ġrover": 45767, "Ġdépend": 45768, "éĻ³": 45769, "Ġretiring": 45770, "Ġvampires": 45771, "Ġmerde": 45772, "ĠChanging": 45773, "Ġtame": 45774, "Ġspokesperson": 45775, "Ġcay": 45776, "Ġflirting": 45777, "ĠGrö": 45778, "Ġwär": 45779, "Ġwyb": 45780, "Ġcoeur": 45781, "ạnh": 45782, "ĠìĻĢìĦľ": 45783, "Ġconnais": 45784, "ĠHundreds": 45785, "ĠBea": 45786, "ĠαÏĢ": 45787, "pruch": 45788, "Ġsociedade": 45789, "ĠWhilst": 45790, "ĠKait": 45791, "espace": 45792, "Ġchia": 45793, "ĠErm": 45794, "Ġë°Ķê¿": 45795, "Ġfences": 45796, "ĠMortal": 45797, "ê²ģ": 45798, "ĠгÑĢаÑĦ": 45799, "ĠHomeland": 45800, "ĠJUN": 45801, "isst": 45802, "Ġparlar": 45803, "Ġsporty": 45804, "éo": 45805, "Ġdeepen": 45806, "ĠBehavior": 45807, "éĢı": 45808, "åĵĪåĵĪåĵĪ": 45809, "Ġerrand": 45810, "Ġrotary": 45811, "ĠWellington": 45812, "Wind": 45813, "Ġmesela": 45814, "ảng": 45815, "iende": 45816, "Ġexcell": 45817, "ĠGenius": 45818, "ĠEduardo": 45819, "æľī人": 45820, "ĠÅŁunu": 45821, "ĠÄ°stanbul": 45822, "Ġproduto": 45823, "Ġãħİãħİ": 45824, "OFF": 45825, "Ġwollt": 45826, "çĪĨ": 45827, "Ġëī´ìĬ¤": 45828, "Ġlass": 45829, "Ġhertz": 45830, "Ġaromatic": 45831, "Ġзвон": 45832, "Ġautoc": 45833, "ĠLust": 45834, "Ġ112": 45835, "ĠÎĹ": 45836, "Ġreviewers": 45837, "Ġreceptive": 45838, "å°įäºĨ": 45839, "ând": 45840, "oglo": 45841, "ĠìķĦëĭĻ": 45842, "Ġngo": 45843, "ÑĸÑĤи": 45844, "Ã¥t": 45845, "cono": 45846, "Ġtekrar": 45847, "Ġì£¼ê³ł": 45848, "ĠgelmiÅŁ": 45849, "Ġbedtime": 45850, "ĠArgh": 45851, "ADA": 45852, "ĠгоÑĢода": 45853, "ĠÄĩ": 45854, "Ġalliances": 45855, "giggling": 45856, "Ġyerde": 45857, "Ġspies": 45858, "Ġgutes": 45859, "çi": 45860, "Ġalltid": 45861, "ĠLah": 45862, "ŀIJë": 45863, "ĠdokÅĤad": 45864, "ÙĪÙĬ": 45865, "Ġtoxicity": 45866, "Ġcancellation": 45867, "Ġ1958": 45868, "dro": 45869, "ĠìŀijìĿĢ": 45870, "ĠMotorola": 45871, "Ġmultin": 45872, "Ġenthusiasts": 45873, "ĠMighty": 45874, "ĠCoconut": 45875, ":ãĢĮ": 45876, "ĠPictures": 45877, "Ġsangre": 45878, "Ġblinking": 45879, "olesome": 45880, "ĠìĬ¤íĥĢìĿ¼": 45881, "FP": 45882, "Ġbooming": 45883, "ĠдеÑģÑıÑĤ": 45884, "Ġratchet": 45885, "Ġtimelines": 45886, "leness": 45887, "Ġcages": 45888, "ĠGoodnight": 45889, "ometimes": 45890, "Ġcunning": 45891, "ĠRisk": 45892, "uled": 45893, "dade": 45894, "Ġprata": 45895, "ĠgustarÃŃa": 45896, "amus": 45897, "ĠJinping": 45898, "Ġestrut": 45899, "Ġdescobrir": 45900, "ĠMÄģ": 45901, "ĠAllan": 45902, "ĠåĪĨ": 45903, "Ġ׾ק": 45904, "Ġpreserv": 45905, "ĠStrawberry": 45906, "Äı": 45907, "Lu": 45908, "Ġkro": 45909, "ĠReports": 45910, "ìħĶìķ¼": 45911, "Ġvalt": 45912, "Ġpouvait": 45913, "Ġappar": 45914, "ĠBone": 45915, "Ġpreferably": 45916, "ĠRepública": 45917, "å°±åĪ°": 45918, "Ġherzlich": 45919, "Ġchimney": 45920, "Ġçev": 45921, "Ġvisas": 45922, "Ġverr": 45923, "Ġcultivation": 45924, "ĠArmenia": 45925, "ĠвдÑĢÑĥг": 45926, "Ġcockro": 45927, "retched": 45928, "artz": 45929, "ĠлÑİдÑıм": 45930, "ĠpolÃŃticas": 45931, "ĠPanz": 45932, "ĠAKA": 45933, "ĠëĪĮ룬": 45934, "Ġerro": 45935, "Ġcamper": 45936, "Ġ102": 45937, "स": 45938, "done": 45939, "Ġhoard": 45940, "ĠÐŁÐ¾ÑĤом": 45941, "jeong": 45942, "Ġdesta": 45943, "pak": 45944, "Ġinim": 45945, "Ġgrowers": 45946, "ĠMessage": 45947, "Ġelector": 45948, "engage": 45949, "ĠForbes": 45950, "ĠCincinnati": 45951, "Ġdifférence": 45952, "df": 45953, "Ġspar": 45954, "Ġawaits": 45955, "ĠUSSR": 45956, "ĠRising": 45957, "ĠHoÅŁ": 45958, "Ġfooting": 45959, "Ġcondiciones": 45960, "ÑĤоÑĢов": 45961, "Ġclinician": 45962, "ĠDiskuss": 45963, "å£ĵ": 45964, "ר×Ĵ": 45965, "×¥": 45966, "iteit": 45967, "gren": 45968, "Ġcharisma": 45969, "Ġleuke": 45970, "Ġirritating": 45971, "Ġcirca": 45972, "ĠRhodes": 45973, "Ġpior": 45974, "Ġhandicap": 45975, "royable": 45976, "Ġvull": 45977, "OG": 45978, "ĠinÃŃcio": 45979, "ieri": 45980, "Ġsplashing": 45981, "Ġdemise": 45982, "Ġassistir": 45983, "ÑĩÑĤо": 45984, "Ġcovert": 45985, "ĠGud": 45986, "à¸ī": 45987, "klär": 45988, "ĠìŀIJ꾸": 45989, "Ġverändert": 45990, "ĠREM": 45991, "ĠConven": 45992, "atge": 45993, "Ġpierwsze": 45994, "Ġclergy": 45995, "lington": 45996, "liv": 45997, "VPN": 45998, "ĠÑģожал": 45999, "ĠHate": 46000, "ãģ¨ãģĵãĤį": 46001, "ÏĨο": 46002, "ĠRespons": 46003, "озд": 46004, "Ġetmek": 46005, "Ġchemin": 46006, "ÙħØ©": 46007, "Ġê°Ģ족": 46008, "Tre": 46009, "Ġumas": 46010, "ĠBurton": 46011, "Ġpatriarch": 46012, "ĠSmithsonian": 46013, "¥ĺ": 46014, "Moon": 46015, "Air": 46016, "Ġmedios": 46017, "Ġeraser": 46018, "Ġwollten": 46019, "Ġpareil": 46020, "ĠBillie": 46021, "æĬ½": 46022, "еÑĢÑĤв": 46023, "Ġparlament": 46024, "Ġagony": 46025, "ĠQUE": 46026, "sequently": 46027, "Another": 46028, "ĠWhew": 46029, "ĠAnnual": 46030, "Ġseben": 46031, "ìĥģìĿĦ": 46032, "values": 46033, "ŀľë§Į": 46034, "Ġsinon": 46035, "ereal": 46036, "ĠEnlight": 46037, "ĠChemistry": 46038, "ĠCatalunya": 46039, "Ġdoctr": 46040, "anton": 46041, "Ġstuk": 46042, "ĠPlate": 46043, "ĠKardashian": 46044, "Ġfilos": 46045, "ĠWet": 46046, "ĠпопÑĭÑĤ": 46047, "Ġunknowns": 46048, "ĠSchon": 46049, "ĠBaldwin": 46050, "Ġtelescopes": 46051, "ĠGucci": 46052, "oxide": 46053, "ĠConservative": 46054, "ìĦ±ìĿĦ": 46055, "Ġhinaus": 46056, "Power": 46057, "Ġê±´ê°ķ": 46058, "Ġprevail": 46059, "orman": 46060, "machine": 46061, "Ġ1946": 46062, "Ġunbel": 46063, "Ġschaut": 46064, "Ġpiel": 46065, "eenth": 46066, "Ġobjectively": 46067, "Ġchakra": 46068, "audio": 46069, "Ġchicos": 46070, "ĠVault": 46071, "å°Ī": 46072, "Ġmedicinal": 46073, "ĠTail": 46074, "While": 46075, "Ġasphalt": 46076, "Ġfroze": 46077, "ĠEK": 46078, "unching": 46079, "nosis": 46080, "2015": 46081, "ĠGri": 46082, "Ġoddly": 46083, "ĠMär": 46084, "ĠAeg": 46085, "colo": 46086, "Par": 46087, "Ġëĵ¤ìĸ´ë": 46088, "Ġvinden": 46089, "ĠOVER": 46090, "Ġiced": 46091, "Ġscorp": 46092, "Ġhac": 46093, "qualified": 46094, "ĠÑĥвидеÑĤÑĮ": 46095, "ermo": 46096, "HEN": 46097, "Ġsoi": 46098, "Ġmultiples": 46099, "Ġlayouts": 46100, "Ġblindness": 46101, "ĠBowser": 46102, "ĠподÑĤ": 46103, "ĠÃİ": 46104, "ventional": 46105, "Ġmata": 46106, "madı": 46107, "Ġgeez": 46108, "Ġcadence": 46109, "Ġważne": 46110, "ĠChristie": 46111, "venge": 46112, "Call": 46113, "Ġturnaround": 46114, "Ġblob": 46115, "ĠЯк": 46116, "ĠVoiceover": 46117, "Ġperil": 46118, "ĠJaime": 46119, "ĠHOY": 46120, "lane": 46121, "Ġsebel": 46122, "ĠDuo": 46123, "ĠHistorical": 46124, "Ġdni": 46125, "Ġgema": 46126, "yk": 46127, "Ġsabem": 46128, "ắng": 46129, "Ġvars": 46130, "ĠRonnie": 46131, "ĠRonaldo": 46132, "ĠPerquè": 46133, "nsinn": 46134, "hair": 46135, "Ġrelentless": 46136, "Ġlyn": 46137, "Ġtraveler": 46138, "æĢİ麼äºĨ": 46139, "nine": 46140, "Ġantim": 46141, "Ġì¼Ģ": 46142, "Ġsnowball": 46143, "ĠÑħаÑĢакÑĤеÑĢ": 46144, "Ġinterns": 46145, "Ġconstituency": 46146, "ĠÐĿам": 46147, "׾׾": 46148, "VEL": 46149, "Ġviktigt": 46150, "Ġapoyo": 46151, "ÙĦب": 46152, "Ġjard": 46153, "Ġheightened": 46154, "ÑĢоÑģÑĤ": 46155, "ĠSMITH": 46156, "Ġдела": 46157, "Ġrepairing": 46158, "Ġrigt": 46159, "ĠSheikh": 46160, "ĠBritney": 46161, "Ġeverytime": 46162, "Ġadventurous": 46163, "ockey": 46164, "ernt": 46165, "Ġataque": 46166, "ĠAlternatively": 46167, "effect": 46168, "Ġpalavras": 46169, "ĠElliott": 46170, "Ġréussi": 46171, "Ġhypertension": 46172, "ĠManual": 46173, "Ġprophetic": 46174, "Ġhandc": 46175, "ÑĮе": 46176, "Ġrefrain": 46177, "ĠSquid": 46178, "ìŀ¡": 46179, "Ġкоман": 46180, "ällen": 46181, "Ġllegó": 46182, "Ġbash": 46183, "iony": 46184, "ĠÑģклад": 46185, "Ġкаб": 46186, "Ġcareless": 46187, "ĠPool": 46188, "Ġtrás": 46189, "Ġfils": 46190, "ĠSchr": 46191, "Ġsprawd": 46192, "ĠMonaten": 46193, "Ġunforgettable": 46194, "ĠCotton": 46195, "Ġinconvenient": 46196, "ĠRX": 46197, "oris": 46198, "Ġhumbled": 46199, "ת×Ĺ": 46200, "Ġآپ": 46201, "ĠincreÃŃ": 46202, "ĠKommentare": 46203, "èĪĴ": 46204, "ración": 46205, "Ġvantage": 46206, "ĠSeal": 46207, "ĠìĿ´ê±°ë¥¼": 46208, "Ġjoue": 46209, "ãģĿãģĨãģ§ãģĻãģŃ": 46210, "Ġìĺ¤ëŀĺ": 46211, "ĠиÑģпÑĭÑĤ": 46212, "oben": 46213, "Ġgrate": 46214, "Ġcontrole": 46215, "ĠPercy": 46216, "ÅĤada": 46217, "Ġsimultaneous": 46218, "Ġprototy": 46219, "ĠgroÃŁer": 46220, "Ġbewusst": 46221, "inizi": 46222, "Ġpassieren": 46223, "ĠHappiness": 46224, "åīĩ": 46225, "shi": 46226, "geht": 46227, "Ġstationed": 46228, "ĠErgebnis": 46229, "Ġdirectamente": 46230, "Ġsurvives": 46231, "Ġpersones": 46232, "BERG": 46233, "Ġvomiting": 46234, "Ġconhecer": 46235, "Ġadjour": 46236, "ĠCivic": 46237, "pei": 46238, "burst": 46239, "Ġëĭ¤ëĭĪ": 46240, "éı": 46241, "Ġsled": 46242, "Ġplataforma": 46243, "ĠSect": 46244, "ĠDefin": 46245, "çĻ»éĮ²": 46246, "énom": 46247, "chnet": 46248, "Ġprofitability": 46249, "Ġerreicht": 46250, "á»ıi": 46251, "cation": 46252, "Ġì§Ģê¸": 46253, "Ġperdre": 46254, "Ġfelony": 46255, "Ġ1957": 46256, "æĪijå¾Ī": 46257, "Ġunsuccessful": 46258, "Ġnagyon": 46259, "Ġelasticity": 46260, "Ġfacade": 46261, "Ġearthly": 46262, "ĠамеÑĢикан": 46263, "Ġconn": 46264, "cla": 46265, "Du": 46266, "Ġpolitiques": 46267, "Ġhalo": 46268, "iantes": 46269, "Ġмоей": 46270, "ãĥ³ãĥī": 46271, "tones": 46272, "elier": 46273, "è®ļ": 46274, "htaking": 46275, "Ġwichtige": 46276, "Ġanno": 46277, "ĠLok": 46278, "illions": 46279, "Ġviver": 46280, "Ġsolchen": 46281, "Ġsuf": 46282, "ĠSalz": 46283, "ĠNvidia": 46284, "zuge": 46285, "ĠSpike": 46286, "Video": 46287, "Ġtwor": 46288, "ĠAla": 46289, "èijī": 46290, "Ġhanya": 46291, "ĠAdm": 46292, "ìĿµ": 46293, "ĠPatienten": 46294, "ĠOnion": 46295, "ĠKobe": 46296, "ĠScene": 46297, "ĠRash": 46298, "æ¨Ļ": 46299, "ÑĢаÑģÑĤ": 46300, "istani": 46301, "General": 46302, "leye": 46303, "imbap": 46304, "Ġconcealed": 46305, "ĠFridays": 46306, "ĠWool": 46307, "ĠновÑĭÑħ": 46308, "شر": 46309, "Ġê²°ê³¼": 46310, "Ġjedoch": 46311, "´ìĭľ": 46312, "ĵ¤ëıĦ": 46313, "Ġìŀ¥ëĤľ": 46314, "ukt": 46315, "Lou": 46316, "Ġ먹ìĸ´": 46317, "ĠExpect": 46318, "Ġдомой": 46319, "Ġirresponsible": 46320, "Ġacerca": 46321, "ĠZust": 46322, "ר×ĺ": 46323, "UI": 46324, "Ġyoutubers": 46325, "ĠPositive": 46326, "Ġsocioe": 46327, "Ġsnatch": 46328, "èĥĮ": 46329, "Ġrefreshed": 46330, "Ġnominations": 46331, "ĠPatt": 46332, "Ġobsolete": 46333, "ĠdemiÅŁ": 46334, "åı¤": 46335, "ormuÅŁ": 46336, "ĠìĨĶì§ģíŀĪ": 46337, "Ġfla": 46338, "Ġcraziest": 46339, "ĠZie": 46340, "ĠTú": 46341, "zep": 46342, "icem": 46343, "Ġë©ĭìŀĪ": 46344, "Ġcynical": 46345, "ãģĿãĤĵãģª": 46346, "Ġtresp": 46347, "Ġcraz": 46348, "Õ¥Õ": 46349, "Ġnelle": 46350, "Ġmph": 46351, "ĠNered": 46352, "ĠKob": 46353, "ĠEck": 46354, "¨¸ëĭĪ": 46355, "Jan": 46356, "ĠТогда": 46357, "Ġdeci": 46358, "ĠVog": 46359, "Ġbubbling": 46360, "éĢĢ": 46361, "úa": 46362, "Ġproductos": 46363, "iberal": 46364, "Ġreplicated": 46365, "ĠImprove": 46366, "illary": 46367, "Cha": 46368, "Ġrédu": 46369, "ĥIJíķĺë©´": 46370, "Ġconnot": 46371, "ĠKrit": 46372, "ĠдÑĥÑħов": 46373, "Ġtreadmill": 46374, "ĠPW": 46375, "ĠзовÑĥÑĤ": 46376, "Ġclams": 46377, "Ġdrafting": 46378, "Ġ1956": 46379, "unta": 46380, "Ġexpenditures": 46381, "ĠHoover": 46382, "WOO": 46383, "ÑĪее": 46384, "Ġdeduction": 46385, "monary": 46386, "Ġrecib": 46387, "Ġpovo": 46388, "ĠëįĶë": 46389, "ĠPAL": 46390, "ĠBlow": 46391, "Ġwyp": 46392, "Ġdestac": 46393, "deal": 46394, "Graeme": 46395, "Ġnécessaire": 46396, "Ġdamned": 46397, "Ġ1938": 46398, "Ġìĭ¤ìłľë¡ľ": 46399, "Ġtroop": 46400, "Ġinsightful": 46401, "ĠTJ": 46402, "ĠоÑģв": 46403, "Ġfidelity": 46404, "ĠSkip": 46405, "ĠMayo": 46406, "ë§Ŀ": 46407, "appe": 46408, "Ġblas": 46409, "ĠWY": 46410, "ĠGN": 46411, "ctar": 46412, "Su": 46413, "Ġcuent": 46414, "hews": 46415, "Ġcorpses": 46416, "Abs": 46417, "Ġwastewater": 46418, "Ġciek": 46419, "ĠOnu": 46420, "Ġexplosives": 46421, "Ġarma": 46422, "ĠSTEPHAN": 46423, "politik": 46424, "ĠOsaka": 46425, "taÅĤ": 46426, "Ġyapıyor": 46427, "Ġizquier": 46428, "Ġbeleza": 46429, "ĠWyatt": 46430, "åIJ¸": 46431, "Ġsuk": 46432, "Ġspecjal": 46433, "Ġdanke": 46434, "whistle": 46435, "ĠfÃŃsica": 46436, "ĠHarriet": 46437, "ĠìķĦíĮĮ": 46438, "Ġwillkommen": 46439, "iping": 46440, "ĠÑģмоÑĤÑĢиÑĤе": 46441, "ĠможеÑĪÑĮ": 46442, "Ġinaccurate": 46443, "Ġarrogance": 46444, "ĠRemo": 46445, "γά": 46446, "assed": 46447, "Ġdeliveries": 46448, "Ġstinky": 46449, "ĠпеÑĢеж": 46450, "jay": 46451, "Ġtransitional": 46452, "Ġrere": 46453, "ĠNGOs": 46454, "ĠATM": 46455, "خت": 46456, "iology": 46457, "Ġвлад": 46458, "Ġschme": 46459, "ĠShine": 46460, "ìķ¡": 46461, "pants": 46462, "Ġserge": 46463, "Ġsenhor": 46464, "Ġabduct": 46465, "ĠBryant": 46466, "VES": 46467, "Ġawakened": 46468, "ĠLaz": 46469, "ropolis": 46470, "ĠLao": 46471, "è¾Ľèĭ¦": 46472, "Ġvilla": 46473, "Ġsummers": 46474, "Ġenthal": 46475, "Ġ1949": 46476, "Via": 46477, "Ġìĸ´ì¨": 46478, "Ġtendon": 46479, "Ġviolet": 46480, "Ġintellectually": 46481, "Ġbounced": 46482, "araus": 46483, "Ġ1919": 46484, "Ġvraag": 46485, "Ġspel": 46486, "ĠSchwar": 46487, "Scott": 46488, "ĠIndo": 46489, "Ġë§Ŀ": 46490, "Ġcanonical": 46491, "ĠIKE": 46492, "ĠthatÃŃs": 46493, "Ġmellan": 46494, "æ¯Ĵ": 46495, "igmat": 46496, "Could": 46497, "...?)": 46498, "Ġfoarte": 46499, "ĠKumar": 46500, "rendo": 46501, "Ġélé": 46502, "à´": 46503, "valuation": 46504, "cases": 46505, "Ġintuitively": 46506, "hong": 46507, "etted": 46508, "Ġsouven": 46509, "Ġmorb": 46510, "Ġcors": 46511, "ĠNV": 46512, "ĠHasan": 46513, "æĥħåĨµ": 46514, "ieved": 46515, "Ġì§Ģê¸ĪìĿĢ": 46516, "Ġdumpling": 46517, "Ġcontrôle": 46518, "Ġambiguity": 46519, "æ©Łæľĥ": 46520, "Ġcog": 46521, "ĠScriptures": 46522, "Ġcai": 46523, "Ġbever": 46524, "大家éĥ½": 46525, "Ġhuis": 46526, "Ġaime": 46527, "Ġerklären": 46528, "ĠLM": 46529, "ĠFey": 46530, "éļ¾": 46531, "றத": 46532, "Ġsupervised": 46533, "Ġjewe": 46534, "spl": 46535, "ĠÑĨенÑĤÑĢ": 46536, "Ġcollisions": 46537, "ÙĦÙģ": 46538, "ĠHogwarts": 46539, "ĠDurham": 46540, "×ķ×£": 46541, "Ġphosphate": 46542, "Ġoversee": 46543, "Ġinspections": 46544, "Ġbrinc": 46545, "ĠZak": 46546, "Ġpayoff": 46547, "Ġchaud": 46548, "ĠHunger": 46549, "ãos": 46550, "vir": 46551, "Ġfiance": 46552, "Ġboug": 46553, "lived": 46554, "cry": 46555, "åĽŀä¾Ĩ": 46556, "Ġjointly": 46557, "Ġgirlfriends": 46558, "ĠNexus": 46559, "¦¬ê²łìĬµëĭĪëĭ¤": 46560, "ĠKwang": 46561, "åĵĪåĽī": 46562, "å§ij": 46563, "ÅĤÄĻ": 46564, "ĠNeden": 46565, "iece": 46566, "Ġinserting": 46567, "æŁĵ": 46568, "ĠMummy": 46569, "ĠGlobe": 46570, "Ġlee": 46571, "Ġgerman": 46572, "Ġcreams": 46573, "acho": 46574, "ĠchÆ°a": 46575, "ĠGalile": 46576, "Ġfürs": 46577, "Ġestiver": 46578, "cidos": 46579, "Christian": 46580, "Ġlorsqu": 46581, "Ġcutest": 46582, "vale": 46583, "ĠкÑĢеп": 46584, "Ġwary": 46585, "Ġslicing": 46586, "Ġesperando": 46587, "ĠVander": 46588, "ĠDeixa": 46589, "Ġ1954": 46590, "ĠmówiÄħ": 46591, "ÑĸÑĶ": 46592, "Ġtooling": 46593, "Ġrestor": 46594, "Ġposición": 46595, "Ġintentar": 46596, "ĠApache": 46597, "OUL": 46598, "ĠÙĪب": 46599, "Ġmatière": 46600, "ãĥ¼ãĤĵ": 46601, "Ġlinen": 46602, "Ġestratég": 46603, "ĠMutta": 46604, "顯": 46605, "è¡ĮäºĨ": 46606, "Ġparting": 46607, "Ġminimizing": 46608, "Ġapprendre": 46609, "æľĿ": 46610, "Ġанглий": 46611, "ĠDoo": 46612, "ĠFirefox": 46613, "cómo": 46614, "Ġgeopolit": 46615, "Ġmakan": 46616, "Ġmogelijk": 46617, "ĠÏĢεÏģι": 46618, "Ġcứ": 46619, "Ġinstaller": 46620, "Ġdibuj": 46621, "ĠHeath": 46622, "loop": 46623, "ĠBroken": 46624, "HYUN": 46625, "shelf": 46626, "Ġfizer": 46627, "Ġenhances": 46628, "ä¾ĭãģĪãģ°": 46629, "ĠдоÑģÑĤи": 46630, "ĠPUB": 46631, "ĠKollegin": 46632, "Ġattained": 46633, "ľ": 46634, "Ġmistress": 46635, "ĠOftentimes": 46636, "×ŀ×Ļ×Ŀ": 46637, "Ġbewe": 46638, "ĠSora": 46639, "rauen": 46640, "baum": 46641, "Ġrollers": 46642, "Ġmering": 46643, "ĠPAC": 46644, "ĠнÑĸ": 46645, "ĠRépublique": 46646, "ĠÑĤÑĢав": 46647, "ĠVanguard": 46648, "uciones": 46649, "Ġ무ëĮĢ": 46650, "Ġgour": 46651, "¯¤": 46652, "ĠÏī": 46653, "Ġsauna": 46654, "Ġpeine": 46655, "ĠValerie": 46656, "ĠSikh": 46657, "fendimiz": 46658, "bero": 46659, "ĠÑĩи": 46660, "ĠdoÅĽwiad": 46661, "ĠEuros": 46662, "Ġcommentaires": 46663, "Ġtweaks": 46664, "ĠFaster": 46665, "ĠÑĢаÑģк": 46666, "Ġprogressively": 46667, "ĠEuch": 46668, "boro": 46669, "ĠIngred": 46670, "Cap": 46671, "Ġuncheck": 46672, "Ġìĺ¤ë¥¸": 46673, "Ġwre": 46674, "ĠFT": 46675, "örung": 46676, "Ġmemorized": 46677, "ĠDinner": 46678, "ĠPhew": 46679, "oubl": 46680, "Ġputa": 46681, "Ġadmits": 46682, "езде": 46683, "opod": 46684, "Ġpanda": 46685, "Ġhinges": 46686, "cipe": 46687, "Ġtransact": 46688, "Ġpodia": 46689, "Ġpics": 46690, "Ġcriterion": 46691, "ĠOrchestra": 46692, "ĠBlog": 46693, "Ġsolemn": 46694, "ĠPixar": 46695, "Three": 46696, "Ġвниз": 46697, "ĠVolunte": 46698, "ĠSavage": 46699, "ĠPVC": 46700, "ĠCaf": 46701, "Ġwykon": 46702, "Ġgraders": 46703, "Ġcrouch": 46704, "Ġcliche": 46705, "Ġsoybeans": 46706, "ĠMUR": 46707, "ĠGonzalez": 46708, "ĠMimi": 46709, "ĠBolsonaro": 46710, "Ġdiaphrag": 46711, "Ġbilang": 46712, "ëIJĺëĬĶ": 46713, "éĤ£æĪijåĢij": 46714, "Ġregulating": 46715, "Mc": 46716, "Judge": 46717, "Ġнож": 46718, "ĠjakÄħ": 46719, "itesse": 46720, "ĠWij": 46721, "Ġlata": 46722, "groaning": 46723, "POSING": 46724, "Ġ×IJ×ķת×ķ": 46725, "Ġhaga": 46726, "Ġgrounding": 46727, "Ġviolently": 46728, "Ġtills": 46729, "Ġengag": 46730, "ĠHollow": 46731, "ĠпопÑĥлÑıÑĢ": 46732, "Ġwprowad": 46733, "Ġreplaces": 46734, "Ġfluorescent": 46735, "urgical": 46736, "iggly": 46737, "ĠTraditional": 46738, "tte": 46739, "ĠÙĦÙĩ": 46740, "Ġphosphorus": 46741, "Ġapron": 46742, "ĠWaters": 46743, "ĠKultur": 46744, "авай": 46745, "Ġolives": 46746, "Ġ×Ķ×IJ׾": 46747, "Ġteilweise": 46748, "Ġsencill": 46749, "Ġprends": 46750, "Ġnarrower": 46751, "Ġjätte": 46752, "ĠInformationen": 46753, "ìĥģìĿ´": 46754, "Ġstarve": 46755, "Ġfrick": 46756, "ĠBeweg": 46757, "ल": 46758, "Ġdolphin": 46759, "ĠLAUGHTER": 46760, "ĠINTERVIE": 46761, "åĶī": 46762, "ĠyanlÄ±ÅŁ": 46763, "Ġtorpedo": 46764, "Ġshortages": 46765, "ìĿ´ëĵľ": 46766, "ıldı": 46767, "Ġpaws": 46768, "Ġozone": 46769, "Ġcultivated": 46770, "ĠFot": 46771, "Ġnotor": 46772, "ноз": 46773, "ĠкоÑĪ": 46774, "Ġtouchscreen": 46775, "ĠAlly": 46776, "æľĢè¿ij": 46777, "Ġ맼ìŀĪìĸ´ìļĶ": 46778, "ĠСеÑĢ": 46779, "Ġвполне": 46780, "Ġpaprika": 46781, "ĠDustin": 46782, "Ġefecto": 46783, "Ġopini": 46784, "Ġmuut": 46785, "Ġhá»įc": 46786, "Ġinterject": 46787, "ÄĻt": 46788, "Ġbutts": 46789, "urez": 46790, "ĠPike": 46791, "ĠHok": 46792, "ĠGuinea": 46793, "ĠCathedral": 46794, "Ġ1400": 46795, "Cra": 46796, "+,": 46797, "맼": 46798, "³´ëıĦë¡Ŀ": 46799, "abyrin": 46800, "Ġvideog": 46801, "ĠоÑĢÑĥж": 46802, "Ġuž": 46803, "Ġbuscando": 46804, "ĠAssistance": 46805, "éĻ½": 46806, "Ġmelhores": 46807, "ì¡´": 46808, "Ġëģ¼": 46809, "ĠRJ": 46810, "ĠتÙħ": 46811, "Ġomin": 46812, "Ġmotorcycles": 46813, "ĠSapp": 46814, "Ġsupplying": 46815, "ĠAlgun": 46816, "Ġaerospace": 46817, "×¢×ľ": 46818, "occup": 46819, "leist": 46820, "Ġê±°ëĬĶ": 46821, "Ġcompleta": 46822, "bres": 46823, "!(": 46824, "ĠÐŁÑĢед": 46825, "Ġdisadvantaged": 46826, "ĠAttend": 46827, "ĠJudah": 46828, "á»ĭch": 46829, "ylene": 46830, "actly": 46831, "Ġsetups": 46832, "Ġammonia": 46833, "ĠSchweiz": 46834, "ĠShame": 46835, "Ġbande": 46836, "ĠFuel": 46837, "Ġtroublesome": 46838, "Ġnumero": 46839, "ĠMOM": 46840, "ĠпÑĢедлаг": 46841, "mentioned": 46842, "ĠболÑĮÑĪое": 46843, "ĠViktor": 46844, "ĠStyles": 46845, "Ġcrucified": 46846, "ructured": 46847, "environ": 46848, "Ġmorals": 46849, "Ġmeditating": 46850, "Ġaxial": 46851, "isance": 46852, "ĠAbst": 46853, "Green": 46854, "Ġê±´ì": 46855, "Ġquadrant": 46856, "Ġpergi": 46857, "Ġcameraman": 46858, "ĠSequ": 46859, "Ġpaused": 46860, "ĠLaughing": 46861, "ê·Ģ": 46862, "?..": 46863, "ĠÅ»e": 46864, "Ġpermitir": 46865, "Ġdetectors": 46866, "ĠHUD": 46867, "aval": 46868, "ĠìĹ¬ê¸°ê¹Įì§Ģ": 46869, "Ġhubs": 46870, "Ġbestimmt": 46871, "ĠбÑĥдеÑĤе": 46872, "INTERPOSING": 46873, "Ġtengan": 46874, "Ġcrave": 46875, "ĠBundesregierung": 46876, "ĠBloody": 46877, "Ġusability": 46878, "ĠEas": 46879, "ĠÄijá»Ļng": 46880, "Ġ1955": 46881, "Ġkriegen": 46882, "Ġhabitual": 46883, "Ġessentials": 46884, "riminal": 46885, "Ġroommates": 46886, "éĤ£å°±": 46887, "ĠпеÑĢеÑħод": 46888, "Ġnghi": 46889, "Ġmening": 46890, "ĠSymphony": 46891, "ĠHug": 46892, "aggi": 46893, "Ġwied": 46894, "Ġmitad": 46895, "ãģ£ãģ¦ãģĦãģĨ": 46896, "teenth": 46897, "idaÄĩ": 46898, "Save": 46899, "ĠrobiÄĩ": 46900, "Ġbounces": 46901, "°ĸìĹIJ": 46902, "stars": 46903, "Ġpragmatic": 46904, "Ġcognition": 46905, "Ġwrapper": 46906, "Ġwarten": 46907, "adh": 46908, "Ġpensa": 46909, "ĠHertz": 46910, "ĠnÄĽ": 46911, "ĠReid": 46912, "ĠPCs": 46913, "ĠMole": 46914, "Ġ.....": 46915, "Ġprecio": 46916, "ĠChampionships": 46917, "ê°ĢëĿ½": 46918, "Ġvér": 46919, "Ġcorridors": 46920, "ĠElectronic": 46921, "Sl": 46922, "Ġале": 46923, "Ġoverthrow": 46924, "Ġkabul": 46925, "ĠRES": 46926, "ĠCyberpunk": 46927, "огод": 46928, "ĠÐĿав": 46929, "Ġwan": 46930, "Ġmanifestations": 46931, "Ġcuales": 46932, "ĠWise": 46933, "ĠLösung": 46934, "Ġexfol": 46935, "Ġearns": 46936, "ÑĥÑģÑĤиÑĤÑĮ": 46937, "Ġsapp": 46938, "ĠBraun": 46939, "ĠBRANDON": 46940, "ì¹Ļ": 46941, "Ġsano": 46942, "ĠFEL": 46943, "ÑĭвайÑĤеÑģÑĮ": 46944, "ождениÑı": 46945, "Ġsewn": 46946, "Fun": 46947, "Ġreciprocal": 46948, "Ġexpansive": 46949, "ĠTraffic": 46950, "Ġktórego": 46951, "ĠÙĪس": 46952, "æĺ¥": 46953, "Ġ빨": 46954, "prove": 46955, "igare": 46956, "Ġloh": 46957, "اض": 46958, "Hope": 46959, "Ġdevotees": 46960, "ĠGom": 46961, "Ġsteals": 46962, "ĠUms": 46963, "ĠTwice": 46964, "ãĤ²": 46965, "iyim": 46966, "Ġrhythmic": 46967, "ĠVorte": 46968, "Ġprefix": 46969, "omination": 46970, "Ġdato": 46971, "Ġcustard": 46972, "ĠVOICE": 46973, "å·ŀ": 46974, "Ġmeny": 46975, "istors": 46976, "Ġíĺij": 46977, "ĠìĤ´ìķĦ": 46978, "ĠíĥĦ": 46979, "Ġkort": 46980, "Ġaba": 46981, "ĠVera": 46982, "epy": 46983, "Ġì¹´ë©ĶëĿ¼": 46984, "Ġsubmerged": 46985, "ĠClock": 46986, "Ġthumbnails": 46987, "Ġboast": 46988, "ĠFare": 46989, "!!]": 46990, "ĠÅĽm": 46991, "Ġkaikki": 46992, "ĠTechnologies": 46993, "ìĻ¸": 46994, "ãĥĴ": 46995, "иÑĤай": 46996, "å°ıæĻĤ": 46997, "ĠаÑĤ": 46998, "Ġknobs": 46999, "Ġreicht": 47000, "ượng": 47001, "glio": 47002, "Ġ맼ìĿ´": 47003, "ê°IJìĿĦ": 47004, "Ġjotka": 47005, "ĠHandy": 47006, "ĠHaben": 47007, "nous": 47008, "Ġinland": 47009, "Ġamazon": 47010, "hooting": 47011, "SL": 47012, "Ġleisten": 47013, "~\"": 47014, "Ġprovoke": 47015, "ĠTwist": 47016, "Ġ×ij×Ĺ": 47017, "Ġdeparted": 47018, "ê°ľë¥¼": 47019, "Ġkonse": 47020, "ĠCarwyn": 47021, "íķĺìĭł": 47022, "idental": 47023, "ESCO": 47024, "Ġtteokbokki": 47025, "Ġdizendo": 47026, "ç·´": 47027, "ındaki": 47028, "imasu": 47029, "afar": 47030, "Ġlandfill": 47031, "Ġcorrecting": 47032, "Ġclears": 47033, "ĠNummer": 47034, "HAM": 47035, "Ġcartridges": 47036, "ĠDiesel": 47037, "paced": 47038, "Ġobliv": 47039, "Ġmoyens": 47040, "ĠSinne": 47041, "ĠPreis": 47042, "iliz": 47043, "ĠÑģмож": 47044, "Ġbroaden": 47045, "ä»ĸæĺ¯": 47046, "xes": 47047, "Ġcarbohydrate": 47048, "íĺ¹": 47049, "seok": 47050, "Ġechoes": 47051, "Ġcess": 47052, "ë°Ķ": 47053, "ĠбизнеÑģ": 47054, "Ġllamado": 47055, "Ġessent": 47056, "ĠìĿ¼ë°ĺ": 47057, "ĠAires": 47058, "phen": 47059, "Ġzebra": 47060, "Ġsymbolism": 47061, "Once": 47062, "Ġracks": 47063, "ĠKafka": 47064, "ĠÑģеÑĢÑĮез": 47065, "Ġsinn": 47066, "picious": 47067, "kaa": 47068, "Ġmotherfucker": 47069, "Ġapprenticeship": 47070, "Ġrpm": 47071, "Ġtaxation": 47072, "Ġfurry": 47073, "ĠSacred": 47074, "ĠÑĢазм": 47075, "pora": 47076, "enges": 47077, "ĠíĹĪë": 47078, "ĠÑģин": 47079, "Ġsanitizer": 47080, "Ġcringe": 47081, "ĠSca": 47082, "оÑĩно": 47083, "Ġofere": 47084, "Ġmelodies": 47085, "ĠVelvet": 47086, "ĠIhrer": 47087, "ĠHybrid": 47088, "ĠGiov": 47089, "Ġirgendwas": 47090, "Ġdepende": 47091, "ĠUsers": 47092, "Ġhump": 47093, "driving": 47094, "Ġsf": 47095, "Ġruthless": 47096, "à¹Ģà¸Ħ": 47097, "Ġlemons": 47098, "Ġföret": 47099, "ĠOj": 47100, "Ġмама": 47101, "Ġinterpersonal": 47102, "Ġgev": 47103, "Ġabnorm": 47104, "иÑģл": 47105, "Ġинд": 47106, "Ġkontroll": 47107, "Ġregres": 47108, "Ġledge": 47109, "Ġerzählt": 47110, "ĠTact": 47111, "Ġarrivé": 47112, "Ġsubstantive": 47113, "Ġspoonful": 47114, "zwischen": 47115, "ooooo": 47116, "Ġcontenido": 47117, "Ġbesl": 47118, "á»ĥm": 47119, "kten": 47120, "Jamie": 47121, "Ġsandy": 47122, "ä¸įåIJĮ": 47123, "âĭ": 47124, "Ġpase": 47125, "Ġdette": 47126, "ĠBelgian": 47127, "ê°ľë": 47128, "ulares": 47129, "rud": 47130, "igor": 47131, "ĠíĮ¬ë": 47132, "Ġremedies": 47133, "Ġblasting": 47134, "ĠSich": 47135, "Ġожид": 47136, "Ġmonstr": 47137, "Ġmanifold": 47138, "Ġglauben": 47139, "ĠEST": 47140, "Ġstreamline": 47141, "Ġlobbying": 47142, "ĠGothic": 47143, "toire": 47144, "..'": 47145, "Ġdémocr": 47146, "ĠнаблÑİд": 47147, "Ġwspól": 47148, "ĠczÄĻÅĽÄĩ": 47149, "ä¸ĭéĿ¢": 47150, "isés": 47151, "gangen": 47152, "Ġbezpie": 47153, "remlin": 47154, "ê°Ŀ": 47155, "Still": 47156, "Ġresides": 47157, "Ġgelecek": 47158, "Ġtéléphone": 47159, "Ġpewn": 47160, "Ġleopard": 47161, "Ġcomplimentary": 47162, "Ġcrib": 47163, "ĠAnimals": 47164, "Ġgeil": 47165, "essel": 47166, "Ġgarder": 47167, "Ġcatchy": 47168, "樹": 47169, "ĠEts": 47170, "ĠCommercial": 47171, "ĠDENNIS": 47172, "ĠCoordinator": 47173, "ĠAbigail": 47174, "ffffff": 47175, "ấp": 47176, "Ġpequeña": 47177, "Ġinjections": 47178, "cekt": 47179, "Ġphilanthropy": 47180, "Ġpuck": 47181, "Ġcelebrates": 47182, "ĠDunk": 47183, "ĠDlatego": 47184, "ãģ¾ãģł": 47185, "δή": 47186, "graduate": 47187, "ĠMobil": 47188, "till": 47189, "acam": 47190, "Ġyolks": 47191, "Ġtangled": 47192, "Ġmaniac": 47193, "Ġobliged": 47194, "ĠLaink": 47195, "Ġverder": 47196, "ĠDamon": 47197, "Ġmutant": 47198, "Ġhopping": 47199, "Ġreins": 47200, "Ġinverter": 47201, "Ġcontempt": 47202, "×ł×¡": 47203, "learning": 47204, "Miss": 47205, "ĠÐĵоÑģ": 47206, "ĠMeyer": 47207, "ê»ĺìĦľ": 47208, "é£İ": 47209, "×ķ׳×Ļ×Ŀ": 47210, "asking": 47211, "Ġtrimming": 47212, "Ġtreasury": 47213, "Ġsente": 47214, "Aust": 47215, "ĠUnterstützung": 47216, "ĠComedy": 47217, "ĠAnakin": 47218, "é¹": 47219, "ÑĢÑĥÑĤ": 47220, "ĠHari": 47221, "ographers": 47222, "Ġoatmeal": 47223, "ĠBots": 47224, "ä¸įäºĨ": 47225, "ĠпалÑĮ": 47226, "Ġacknowledgement": 47227, "xic": 47228, "Ġê´Ģìĭ¬": 47229, "gasping": 47230, "Ġãģķ": 47231, "Ġterrace": 47232, "Ġornaments": 47233, "ĠMER": 47234, "committee": 47235, "ĠìĹĨìĬµëĭĪëĭ¤": 47236, "Ġrij": 47237, "é³": 47238, "צ×Ŀ": 47239, "leme": 47240, "Ġliberties": 47241, "Ġfellas": 47242, "ĠCopper": 47243, "bench": 47244, "ĠIdea": 47245, "á»įn": 47246, "ÑĪа": 47247, "Ġversión": 47248, "ÏĦοÏį": 47249, "ĠÐľÐ¸": 47250, "ĠпÑĢилож": 47251, "Ġboxer": 47252, "ĠTanner": 47253, "ĠMoy": 47254, "ì¹ĺëĬĶ": 47255, "Thr": 47256, "Ġtinham": 47257, "Ġpolishing": 47258, "Ġconsequently": 47259, "Ġamenities": 47260, "ĠKI": 47261, "ĠGREEN": 47262, "ĠFrankie": 47263, "ниÑĤ": 47264, "ittel": 47265, "Ñģкое": 47266, "ursed": 47267, "Ġupbringing": 47268, "Ġthứ": 47269, "ĠìĭĿìľ¼ë¡ľ": 47270, "Ġwhim": 47271, "Ġchinese": 47272, "confidence": 47273, "ĠJeder": 47274, "ãģªãģ®ãģ§": 47275, "ajcie": 47276, "ĠTous": 47277, "ĠPowers": 47278, "ừa": 47279, "othermal": 47280, "ĠвÑĭÑĪе": 47281, "rale": 47282, "اخ": 47283, "Ġì§ĢìĽIJ": 47284, "Ġépisode": 47285, "Ġsulph": 47286, "Ġencara": 47287, "kraft": 47288, "aları": 47289, "ĠComes": 47290, "Ġdivul": 47291, "ĠRudolph": 47292, "ĠMuse": 47293, "Ġutens": 47294, "ĠìŀIJ주": 47295, "Ġpana": 47296, "ĠVegeta": 47297, "ĠPHP": 47298, "ĠNSA": 47299, "entin": 47300, "ĠCarnegie": 47301, "اÙĬ": 47302, "iÄĻcy": 47303, "Harry": 47304, "Ġfır": 47305, "Сп": 47306, "Ġgladly": 47307, "Ġaveraging": 47308, "íķĺê²łìĬµëĭĪëĭ¤": 47309, "лÑıÑİÑĤÑģÑı": 47310, "ĠÐľÐµÐ½Ñı": 47311, "Ġquotation": 47312, "rires": 47313, "itchens": 47314, "ayed": 47315, "Ġunatt": 47316, "ĠPerez": 47317, "ĠоÑĤмеÑĤ": 47318, "Ġtactile": 47319, "ĠEuh": 47320, "isini": 47321, "buh": 47322, "Ġhatır": 47323, "ĠìŀĪìľ¼": 47324, "Ġpolicymakers": 47325, "³´ìĦ¸ìļĶ": 47326, "acı": 47327, "Ġκι": 47328, "Ġregistering": 47329, "reto": 47330, "ĠSprinkle": 47331, "ĠGrammy": 47332, "axter": 47333, "Ġби": 47334, "Ġsitter": 47335, "Ġpredic": 47336, "Ġthinly": 47337, "Ġstrum": 47338, "Ġaggrav": 47339, "Ġaha": 47340, "رج": 47341, "mellow": 47342, "Ġconstante": 47343, "ĠLaut": 47344, "iston": 47345, "Ġtransitioned": 47346, "ĠCambodia": 47347, "ãģĦãģįãģ¾ãģĻ": 47348, "è·Łå¤§å®¶": 47349, "arted": 47350, "Ġmisf": 47351, "ĠPunkte": 47352, "Įëĵł": 47353, "Ġtrembling": 47354, "Ġgespannt": 47355, "ĠعÙĦÙĬÙĩ": 47356, "ĠникакиÑħ": 47357, "Ġë¶Ģëĵľë": 47358, "ĠÑĢазвиÑĤ": 47359, "Ġitchy": 47360, "Ġciento": 47361, "Ġplains": 47362, "Ġkittens": 47363, "Ġbacklog": 47364, "ĠPresiding": 47365, "pta": 47366, "Ġhavoc": 47367, "ĠDarrin": 47368, "ĠÐĽÑİб": 47369, "Ġsegregated": 47370, "Ġghetto": 47371, "Ġerlebt": 47372, "Ġdrugiej": 47373, "ĠSixt": 47374, "åıĥ": 47375, "ระ": 47376, "uencia": 47377, "Ġíķĺ기": 47378, "ĠëĨį": 47379, "Ġrobi": 47380, "Ġpioneers": 47381, "Ġmilliards": 47382, "ĠWitcher": 47383, "Ġ무ìĹĩ": 47384, "orro": 47385, "mass": 47386, "Ġdivergence": 47387, "ĠRivera": 47388, "ĠNoodles": 47389, "Ġendroit": 47390, "ĠKosten": 47391, "ĠдÑĢÑĥга": 47392, "ĠmÃŃnimo": 47393, "ĠKazakhstan": 47394, "تÙĩ": 47395, "ĠвоздÑĥ": 47396, "Ġgeschrieben": 47397, "ĠNil": 47398, "Ñģки": 47399, "ĠFrüh": 47400, "Ġbeverages": 47401, "æºIJ": 47402, "ĠGon": 47403, "æĺ¨": 47404, "Arin": 47405, "ĠIntro": 47406, "ocalyptic": 47407, "Ġexhaustion": 47408, "ĠStatus": 47409, "ĠBattery": 47410, "ész": 47411, "£¼ë": 47412, "airy": 47413, "Ġë³´ìŬëĵľë": 47414, "Ġdisparity": 47415, "ÙĮ": 47416, "ĠTucson": 47417, "Ġbrightly": 47418, "problem": 47419, "Ġbiomass": 47420, "éĻį": 47421, "§ī": 47422, "Ġhurdle": 47423, "Ġwavelengths": 47424, "Ġ<<": 47425, "Ġteamed": 47426, "FFFF": 47427, "ĠSlim": 47428, "omial": 47429, "Ġunveiled": 47430, "ĠVerein": 47431, "ÙĤØ·": 47432, "estry": 47433, "Ġclás": 47434, "Ġcheddar": 47435, "Ġaccusing": 47436, "ĠScientific": 47437, "ĠбÑĥде": 47438, "ĠCyrus": 47439, "εÏĦε": 47440, "Ĩĵê³ł": 47441, "Ġë³Ħ": 47442, "Ġcurd": 47443, "Ġreferrals": 47444, "shift": 47445, "åįķ": 47446, "ników": 47447, "Ġmier": 47448, "Ġconfronting": 47449, "ê²ĥëıĦ": 47450, "awl": 47451, "Ġtryin": 47452, "Ġê·¸ëŀĺìļĶ": 47453, "Ġchiar": 47454, "Ġìĺ¤ëĬĺëıĦ": 47455, "æĶ¿æ²»": 47456, "esque": 47457, "Ġmismos": 47458, "ĠShak": 47459, "Ġsociaux": 47460, "ĠpiÅŁ": 47461, "ĠkiÅŁi": 47462, "Ġcyan": 47463, "hay": 47464, "bew": 47465, "bod": 47466, "Ġι": 47467, "ĠMainly": 47468, "ÑİÑĤÑĮ": 47469, "habitude": 47470, "ĠÑģпокой": 47471, "è·ŁæĪij": 47472, "Ġprecon": 47473, "ĠMandy": 47474, "ðŁ¤£": 47475, "illos": 47476, "Ġgrupp": 47477, "Ġcrumble": 47478, "Ġconstructor": 47479, "ervices": 47480, "Ġlighthouse": 47481, "ĠConcept": 47482, "анÑĤи": 47483, "altro": 47484, "hope": 47485, "ĠAlleg": 47486, "ìĸ´ë¥¼": 47487, "pieces": 47488, "ounter": 47489, "ĠíķĺëĭĪê¹Į": 47490, "ĠìĿ¸íĦ°ë": 47491, "Ġvéritable": 47492, "Ġthreaded": 47493, "blind": 47494, "ĤĺëĿ¼": 47495, "Ġtrays": 47496, "ĠEdison": 47497, "ĠÃĸz": 47498, "ĠStevie": 47499, "Ġlender": 47500, "Ġbrigade": 47501, "Ġdeutsche": 47502, "muffled": 47503, "bart": 47504, "Ġinsanity": 47505, "Ġsavvy": 47506, "Ġsensational": 47507, "Ġderechos": 47508, "ĠMX": 47509, "ĠпÑĢеп": 47510, "Ġthreatens": 47511, "ĠrealtÃł": 47512, "Ġindicative": 47513, "Ġchops": 47514, "Ġbenefiting": 47515, "ĠVernon": 47516, "ĠStrand": 47517, "nun": 47518, "quently": 47519, "101": 47520, "Ġeel": 47521, "ìĪĻ": 47522, "rints": 47523, "ĠÙħس": 47524, "Ġبد": 47525, "ĠпоÑģÑĤÑĢо": 47526, "ĠyapmÄ±ÅŁ": 47527, "Ġolması": 47528, "Ġiedereen": 47529, "olé": 47530, "kef": 47531, "Ġë°ľìĥĿ": 47532, "Ġrained": 47533, "Ġalmighty": 47534, "ĠвÑĭд": 47535, "ĠCPR": 47536, "Fre": 47537, "Ġinhabited": 47538, "Ġarbets": 47539, "Ġakin": 47540, "аÑģÑĤв": 47541, "vania": 47542, "Ġhäufig": 47543, "ĠMatte": 47544, "sorry": 47545, "Jenny": 47546, "ĠгÑĢад": 47547, "Ġwhit": 47548, "Ġbrokers": 47549, "å¯Ł": 47550, "Ġhine": 47551, "asten": 47552, "ĠгÑĢÑĥ": 47553, "MB": 47554, "ĠPRI": 47555, "Sab": 47556, "Ġwrestler": 47557, "Ġfacilitating": 47558, "Ġehkä": 47559, "ĠCred": 47560, "Ġ127": 47561, "Ġnothin": 47562, "Ġmandated": 47563, "å¯Į": 47564, "ÑĥÑĤÑģÑĤв": 47565, "Frank": 47566, "Ġwors": 47567, "ĠdzieÅĦ": 47568, "ĠUnderground": 47569, "Ġznajdu": 47570, "ĠBä": 47571, "ĠPrinzip": 47572, "аÑĤелей": 47573, "Ġveterinar": 47574, "Ġsplendid": 47575, "Ġrozp": 47576, "Ġpsychopath": 47577, "igon": 47578, "Ġhops": 47579, "Ġcần": 47580, "ĠXian": 47581, "Ġtroisième": 47582, "Ġproducto": 47583, "ĠdeÄŁer": 47584, "ĠContinuing": 47585, "ивал": 47586, "cık": 47587, "Ġmoisturizer": 47588, "White": 47589, "Ġsiis": 47590, "ĠEverest": 47591, "ienced": 47592, "Ġcảm": 47593, "ĠJapon": 47594, "´ìłĦ": 47595, "ĠtenÃŃan": 47596, "Ġencanta": 47597, "Mm": 47598, "Ġdropdown": 47599, "ĠIya": 47600, "³´ë©´": 47601, "Ġwording": 47602, "ĠSqueeze": 47603, "ĠMaple": 47604, "Ġclarified": 47605, "ĠMunicip": 47606, "ĠRouge": 47607, "ĠNicki": 47608, "ĠGoo": 47609, "volt": 47610, "tek": 47611, "fecture": 47612, "fred": 47613, "arrive": 47614, "ãĥ¼ãģĦ": 47615, "tez": 47616, "Ep": 47617, "Ġobras": 47618, "ĠVID": 47619, "ĠRiv": 47620, "ĠModi": 47621, "ibe": 47622, "Ġacontecendo": 47623, "Ġimitation": 47624, "Ġcamouflage": 47625, "Ġspanning": 47626, "ĠSECRET": 47627, "ĠOreo": 47628, "ìĨĮ리": 47629, "Ġhunch": 47630, "ĠcaÅĤe": 47631, "Ġspontaneously": 47632, "ĠPerd": 47633, "Ġetap": 47634, "ĠHole": 47635, "ĠDisability": 47636, "Ġafterlife": 47637, "æģ©": 47638, "Ġtestified": 47639, "Ġpresup": 47640, "Ġpetroleum": 47641, "Ġcontrario": 47642, "ĠAssessment": 47643, "ÄŁlu": 47644, "Ġpests": 47645, "Ġdilig": 47646, "ĠвÑģÑĤÑĢеÑĤ": 47647, "Ġconséqu": 47648, "Ġcannons": 47649, "Ġcanoe": 47650, "ĠMile": 47651, "Ġcitoy": 47652, "Ġbegged": 47653, "ĠMinnie": 47654, "ÅĤych": 47655, "Ġprincipe": 47656, "ÏĢÏĮν": 47657, "mniej": 47658, "Ġwert": 47659, "Ġëĭ¤ëĵ¤": 47660, "anse": 47661, "Ġuncles": 47662, "Ġprovocative": 47663, "Ġintersections": 47664, "Ġdemocrats": 47665, "ĠJulius": 47666, "инки": 47667, "ygusal": 47668, "Ġ׾×ķ": 47669, "Ġgjorde": 47670, "Ġgasket": 47671, "ĠBock": 47672, "ĠÄ°n": 47673, "breat": 47674, "ĠEquity": 47675, "ardı": 47676, "Ġканале": 47677, "Ġдней": 47678, "ĠtỼi": 47679, "Ġfixture": 47680, "Ġabuses": 47681, "Ġvaya": 47682, "Ġouvert": 47683, "Ġmulticultural": 47684, "Ġcontexto": 47685, "ĠSesame": 47686, "Ġdépl": 47687, "Ġconsomm": 47688, "ĠParte": 47689, "Ġpem": 47690, "ĠConan": 47691, "ĠбÑĸлÑĮ": 47692, "Ġpersuaded": 47693, "Ġdrains": 47694, "Moo": 47695, "FORE": 47696, "ĠбаÑĤ": 47697, "Ġfod": 47698, "ĠProducts": 47699, "ì§Ħì§ľ": 47700, "Ġ\"[": 47701, "ĠWick": 47702, "ĠNaruto": 47703, "нали": 47704, "ryw": 47705, "Ġlodge": 47706, "Ġinh": 47707, "Ġvontade": 47708, "Ġdij": 47709, "ĠJesús": 47710, "Looking": 47711, "Ġforearm": 47712, "ĠIntegration": 47713, "ĠHARRIS": 47714, "Ġtoolbar": 47715, "leader": 47716, "Ġseldom": 47717, "ĠбÑĢоÑģ": 47718, "ĠKook": 47719, "онд": 47720, "Ġmonopol": 47721, "Ġmillet": 47722, "Ġlira": 47723, "ĠAsians": 47724, "Ġ1890": 47725, "ciÄŁim": 47726, "Ġeden": 47727, "ĠIKEA": 47728, "ĠNeighbor": 47729, "ĠKazuya": 47730, "üd": 47731, "Ġpsychedel": 47732, "Ġenvisioned": 47733, "åĿĹ": 47734, "Ġï·»": 47735, "Ġwunder": 47736, "ĠBulgaria": 47737, "Brid": 47738, "Ġmarrow": 47739, "Ġdepiction": 47740, "ĠTin": 47741, "ĠPharise": 47742, "Ġeinzige": 47743, "Ġblindly": 47744, "ãģĽãģ¦": 47745, "Ġdefens": 47746, "Dire": 47747, "Ġvibrating": 47748, "Ġtrolls": 47749, "Ġdisrespectful": 47750, "Ġwod": 47751, "Ġstimuli": 47752, "Ġcreeping": 47753, "Ġclairement": 47754, "Ġscariest": 47755, "Ġdécouvrir": 47756, "Ġ104": 47757, "ĠвеÑĢÑħ": 47758, "ĠÅĤat": 47759, "Ġróżne": 47760, "Ġbarley": 47761, "ĠRepl": 47762, "ĠTwe": 47763, "kke": 47764, "ĠãģĿãĤĮ": 47765, "ĠRedmi": 47766, "ĠMetroid": 47767, "ĠήÏĦαν": 47768, "Check": 47769, "ĠSEN": 47770, "Ġido": 47771, "ÑĤоÑĢии": 47772, "óp": 47773, "UNKNOWN": 47774, "Ġändern": 47775, "ĠJuice": 47776, "ĠGesicht": 47777, "å°±æľĥ": 47778, "ĠнаÑģÑĤолÑĮко": 47779, "íĥķ": 47780, "ÂŃ": 47781, "exhales": 47782, "Ġì´ī": 47783, "Ġjsem": 47784, "ÏĢÏīÏĤ": 47785, "Ġitt": 47786, "ëªħìĿ´": 47787, "Ġremix": 47788, "Ġblossoms": 47789, "ĠRenee": 47790, "isations": 47791, "ìĬ¤íĦ°": 47792, "Ġë³´ìĿ´ëĬĶ": 47793, "uestas": 47794, "opedia": 47795, "ĠAim": 47796, "ìĿ´ì¦Ī": 47797, "scene": 47798, "Ġleakage": 47799, "uckt": 47800, "Sad": 47801, "Ask": 47802, "Ġsuspense": 47803, "Ġimpost": 47804, "ĠStrategic": 47805, "ĠItÃŃs": 47806, "âĢĮ": 47807, "Ġkeyboards": 47808, "Ġamusing": 47809, "ogr": 47810, "iderman": 47811, "ŀĸ": 47812, "ĠвижÑĥ": 47813, "Ġdips": 47814, "Ġapologized": 47815, "ĠSTAR": 47816, "Ġescuela": 47817, "ĠChing": 47818, "нениÑı": 47819, "Ġë¶Ģë¶ĦìĿ´": 47820, "ĠFleet": 47821, "Ġsamb": 47822, "Ġentsprechend": 47823, "Ġelectrodes": 47824, "ĠFreiheit": 47825, "æĪijä¸įçŁ¥éģĵ": 47826, "ĠShrim": 47827, "iÃŁe": 47828, "Ġselections": 47829, "Ġfordi": 47830, "Ġdoss": 47831, "ÑıÑĩ": 47832, "Ġdiscriminate": 47833, "ĠAuÃŁerdem": 47834, "Ġdesenvolv": 47835, "ĠInternal": 47836, "ĠBenedict": 47837, "å¯Ĩ": 47838, "ĠShiv": 47839, "Missy": 47840, "ĠобнаÑĢÑĥж": 47841, "ĠнаÑģÑĤÑĢо": 47842, "Ġcontrolar": 47843, "ĠLia": 47844, "Ġopioids": 47845, "antu": 47846, "Ġcupboard": 47847, "æģIJ": 47848, "ге": 47849, "achts": 47850, "Ġcurated": 47851, "Ġxem": 47852, "Ġweary": 47853, "Ġbrethren": 47854, "Ġbudgeting": 47855, "Ġpourtant": 47856, "éļ»": 47857, "aisia": 47858, "ĠоÑĤвеÑĩ": 47859, "ĠGIS": 47860, "μαι": 47861, "Ġש×Ķ×ķ×IJ": 47862, "Ġsaud": 47863, "ĠlỼ": 47864, "ÐķТ": 47865, "ubine": 47866, "ĠнÑĥжен": 47867, "Ġkidnapping": 47868, "Ġbrat": 47869, "ĠTerre": 47870, "ĠMonet": 47871, "Ġë§ĪìĬ¤íģ": 47872, "Ġflashy": 47873, "ĠISBN": 47874, "Ġfreelance": 47875, "iage": 47876, "Ġjunge": 47877, "충": 47878, "ceral": 47879, "ĠÑĤоÑĩки": 47880, "Ġformulate": 47881, "ĠFER": 47882, "ĠDartmouth": 47883, "ìľ¼ë©´ìĦľ": 47884, "å¢ĥ": 47885, "owiÄħ": 47886, "ĠëĶĶìŀIJ": 47887, "Ġregiment": 47888, "Ġmetabolismo": 47889, "ĠParr": 47890, "Ġ충ë¶Ħ": 47891, "Ġsanity": 47892, "ĠLal": 47893, "ĠGö": 47894, "ĠGla": 47895, "Ġproto": 47896, "Ġmicroscopic": 47897, "Ġkang": 47898, "ĠScalia": 47899, "Ġpug": 47900, "ĠScore": 47901, "ĠSavannah": 47902, "Ġgarde": 47903, "ĠNOR": 47904, "å°įåIJ§": 47905, "Ġscheint": 47906, "ĠpóÅĤ": 47907, "Ġcorri": 47908, "Ġbrute": 47909, "ĠÅĤad": 47910, "ä»ĸ们": 47911, "Ġsucceeding": 47912, "Ġbicycles": 47913, "Non": 47914, "Ġseekers": 47915, "Ġunconditional": 47916, "Ġrhymes": 47917, "ĠGarage": 47918, "Ġinvoice": 47919, "Ġcanvi": 47920, "neck": 47921, "Ġcustomizable": 47922, "iritual": 47923, "Queen": 47924, "íķĺìĭľëĬĶ": 47925, "Ġpowerless": 47926, "Ġcsak": 47927, "ä¸įä¼ļ": 47928, "isoft": 47929, "ĠìłķíĻķ": 47930, "Ġnhân": 47931, "ĠMAND": 47932, "ĠHaf": 47933, "Ġrevolves": 47934, "ä¹Łåı¯ä»¥": 47935, "ovan": 47936, "aroo": 47937, "ĠGrind": 47938, "éĽª": 47939, "Ġindispensable": 47940, "Ġconsulted": 47941, "ĠClinical": 47942, "Acc": 47943, "Ġolhos": 47944, "Ġmonter": 47945, "ĠHana": 47946, "etah": 47947, "Ġvaan": 47948, "Ġtigers": 47949, "Ġcaucus": 47950, "ðŁĺĤ": 47951, "³´ìŀIJ": 47952, "powers": 47953, "iums": 47954, "ĠíĨłë": 47955, "Ġtradicional": 47956, "Ġresonated": 47957, "Ġìĭłê¸°": 47958, "them": 47959, "Robert": 47960, "Ġelemento": 47961, "Ġantid": 47962, "ĠобÑģ": 47963, "Ġnatives": 47964, "Ġloca": 47965, "owment": 47966, "ĠTight": 47967, "ĠæĢĿ": 47968, "Ġmelan": 47969, "ĠNue": 47970, "amis": 47971, "Ġsorgen": 47972, "asına": 47973, "Home": 47974, "ĠPUBG": 47975, "Ġawfully": 47976, "ĠShore": 47977, "ĠPerché": 47978, "ĠLau": 47979, "ĠCinderella": 47980, "ĠChest": 47981, "Ġsemantic": 47982, "Ġdeserted": 47983, "ĠMomo": 47984, "ĠHernandez": 47985, "genes": 47986, "ĠAdult": 47987, "иÑĩеÑģкого": 47988, "oshima": 47989, "ĠcaracterÃŃsticas": 47990, "ĠKL": 47991, "´ìŀ¥": 47992, "ocar": 47993, "Ġfehlt": 47994, "Ġdruk": 47995, "ĠPoppy": 47996, "ENGLISH": 47997, "ĠVergleich": 47998, "Brien": 47999, "Ġrecomp": 48000, "ĠÑģд": 48001, "Ġmerger": 48002, "Ġmarketers": 48003, "Ġhoneymoon": 48004, "Ġpenso": 48005, "Ġbelli": 48006, "еÑĤÑĥ": 48007, "Ġbanker": 48008, "Camera": 48009, "ĠStall": 48010, "ĠStamp": 48011, "ĠBite": 48012, "ежде": 48013, "Ġsür": 48014, "Ġgüç": 48015, "ĠPassover": 48016, "ĠBugün": 48017, "ĠÑģожалениÑİ": 48018, "Ġниз": 48019, "Ġmanure": 48020, "Ġglacier": 48021, "è«ĩ": 48022, "RAY": 48023, "terror": 48024, "Ġsalads": 48025, "Ġhurricanes": 48026, "ĠDesigner": 48027, "atorio": 48028, "Ġfactual": 48029, "ĠTammy": 48030, "ĠзвÑĥÑĩ": 48031, "Ġintroductions": 48032, "Ġhousekeeping": 48033, "Ġhanger": 48034, "ëĭĺë": 48035, "akte": 48036, "ĠCola": 48037, "']": 48038, "ĠGender": 48039, "оÑĢон": 48040, "ipse": 48041, "icias": 48042, "Ġsuccessive": 48043, "Ġpolitic": 48044, "Ġhöher": 48045, "ĠQiao": 48046, "ĠGimme": 48047, "Ġлож": 48048, "Ġseb": 48049, "ĠWeiter": 48050, "ĠSakura": 48051, "ĠBoulder": 48052, "ĠAmérica": 48053, "peÅĤnie": 48054, "ĠtecnologÃŃa": 48055, "ishops": 48056, "fur": 48057, "Ġmoonlight": 48058, "Ġdispersed": 48059, "Ġrez": 48060, "енное": 48061, "алÑĮнÑĥÑİ": 48062, "ĠTwelve": 48063, "ĠHOR": 48064, "ìĭ¤íŀĪ": 48065, "ilage": 48066, "Ġshaded": 48067, "Ġresumes": 48068, "ĠPeanut": 48069, "ĠMILL": 48070, "apons": 48071, "ĠUFC": 48072, "ĠSole": 48073, "Ġjoystick": 48074, "ĠOlivier": 48075, "warming": 48076, "Ġsyllabus": 48077, "ĠобÑīе": 48078, "Ġhiá»ĩn": 48079, "Ġfesta": 48080, "Ġcradle": 48081, "ĠZac": 48082, "Ġremembrance": 48083, "Ġê°ĻìķĦìĦľ": 48084, "ĠpiÄĻk": 48085, "Ġcoexist": 48086, "ĠVII": 48087, "Ġáreas": 48088, "Ġuważ": 48089, "Ġobservers": 48090, "Ġmänniskor": 48091, "coon": 48092, "ĠDAM": 48093, "Ġnaszym": 48094, "Ġalligator": 48095, "ĠFreeze": 48096, "ĠEstate": 48097, "ĠÑĤÑĢади": 48098, "Ġundercover": 48099, "Ġnies": 48100, "ĠFehler": 48101, "plin": 48102, "ĠKabul": 48103, "ilate": 48104, "Ġê³łìĸij": 48105, "Ġmop": 48106, "ìĦ¼": 48107, "Ġanderer": 48108, "ĠKELL": 48109, "оки": 48110, "ĠжеÑģÑĤ": 48111, "Ġgrazing": 48112, "ĠdaÃŃ": 48113, "Ġcapitalize": 48114, "Ġapex": 48115, "Ġnurturing": 48116, "Ġcortar": 48117, "Ġcontrac": 48118, "ımızı": 48119, "Ġtandem": 48120, "éĥ½æľī": 48121, "gement": 48122, "ĠÑģиÑģÑĤема": 48123, "Ġmanque": 48124, "iajÄħ": 48125, "WOR": 48126, "Ġاب": 48127, "Ġcarts": 48128, "ANO": 48129, "Ġë°Ľê³ł": 48130, "ĠCena": 48131, "ĠBiology": 48132, "idar": 48133, "Ġaż": 48134, "erne": 48135, "anu": 48136, "Ġthanked": 48137, "Ġsubmarines": 48138, "Ġmanic": 48139, "Ġмоз": 48140, "ä¼Ĭ": 48141, "instant": 48142, "essential": 48143, "Ġsamurai": 48144, "Ġpasti": 48145, "Ġalan": 48146, "Ġbroch": 48147, "Ġbaker": 48148, "ĠGuill": 48149, "¨¼": 48150, "Ġwithdrawn": 48151, "ëĭĿ": 48152, "Perfect": 48153, "quency": 48154, "Ġstreamlined": 48155, "Ġ1300": 48156, "´ëıĦ": 48157, "Ġëĸłë": 48158, "Ġãģ¯ãģĦ": 48159, "Ġhvad": 48160, "ä¸Ģå®ļè¦ģ": 48161, "Ġverbally": 48162, "ĠKons": 48163, "Ġì¡°ìĭ¬": 48164, "Ġdiez": 48165, "æİ°æİ°": 48166, "Ġchuckling": 48167, "ĠMih": 48168, "Ġrallies": 48169, "Ġmanter": 48170, "Ġearnest": 48171, "super": 48172, "Ġgece": 48173, "ĠRend": 48174, "ĠGerade": 48175, "jenigen": 48176, "ĠVall": 48177, "ĠìŀĪëĤĺ": 48178, "ĠÑģказала": 48179, "Ġtrabalh": 48180, "ĠнаÑĪем": 48181, "ĠмеÑħ": 48182, "ikit": 48183, "Ġnouns": 48184, "Ġneurological": 48185, "Ġmotivational": 48186, "ĠMcMahon": 48187, "ĠFinished": 48188, "Ġë³´ìĿ´": 48189, "ĠFields": 48190, "Ġadolescents": 48191, "ĠTisch": 48192, "ĠNeben": 48193, "ĠFlowers": 48194, "ĠEnerg": 48195, "Ġdiret": 48196, "ĠThi": 48197, "ĠPicas": 48198, "æĥľ": 48199, "æĢİä¹Īæł·": 48200, "Ġavete": 48201, "ĠFors": 48202, "ĠChapel": 48203, "Não": 48204, "Et": 48205, "ĠÑģодеÑĢж": 48206, "reno": 48207, "Ġsven": 48208, "ĠdostÄĻp": 48209, "nee": 48210, "ĠSnapdragon": 48211, "ĠIDs": 48212, "ìķĺëĬĶëį°": 48213, "ר×ļ": 48214, "Ġsunflower": 48215, "Ġperpetual": 48216, "ç³ĸ": 48217, "Ġknights": 48218, "Ġgird": 48219, "ĠTold": 48220, "Ġvolcanoes": 48221, "Ġadversary": 48222, "ĠEconomy": 48223, "Ġextrapol": 48224, "Ġbluetooth": 48225, "Ġzooming": 48226, "Ġskys": 48227, "Ġgenial": 48228, "ÃŃculos": 48229, "ambre": 48230, "ĠмеÑĢ": 48231, "Ġteeny": 48232, "Ġstressing": 48233, "ìķĮ": 48234, "ONY": 48235, "Ġtranslucent": 48236, "Ġrounding": 48237, "Ġgrues": 48238, "×Ļ׳×Ķ": 48239, "après": 48240, "Ġprueba": 48241, "Ġpolygon": 48242, "Ġblueberry": 48243, "ĠProgramm": 48244, "Ġtrenches": 48245, "Ġsebagai": 48246, "Ġpalate": 48247, "Ġlaude": 48248, "Ġbehaved": 48249, "Ġlongitudinal": 48250, "ĠModule": 48251, "Ġadmir": 48252, "λι": 48253, "Greg": 48254, "Ġwyst": 48255, "Ġpropagate": 48256, "Ġmolds": 48257, "ĠTub": 48258, "ĠLoud": 48259, "usto": 48260, "Ġunstoppable": 48261, "Ġreinforcing": 48262, "éĿŀ常çļĦ": 48263, "ĠпÑĢоблема": 48264, "Ġpotencial": 48265, "Ġhemp": 48266, "ìŀĶ": 48267, "य": 48268, "Ġoptic": 48269, "Ġerfolgreich": 48270, "ÑģÑĭ": 48271, "олÑĮÑĪе": 48272, "urst": 48273, "ĠPois": 48274, "Ġrespondents": 48275, "Ġnehme": 48276, "ĠExternal": 48277, "olate": 48278, "Hyun": 48279, "Ġquartz": 48280, "Ġmathematician": 48281, "Ġbásicamente": 48282, "Ġail": 48283, "ìłľë¥¼": 48284, "attutto": 48285, "Ġnooit": 48286, "Ġafflict": 48287, "ĠOlga": 48288, "èŃ·": 48289, "ĠнаÑĤ": 48290, "Ġdites": 48291, "Ġrealidade": 48292, "Ġkän": 48293, "Ġuniqueness": 48294, "Ġpadres": 48295, "Ġsubsidi": 48296, "Ġpigeons": 48297, "βα": 48298, "stad": 48299, "Ġderen": 48300, "ĠСлед": 48301, "doo": 48302, "ĠопиÑģании": 48303, "Ġamber": 48304, "Ġgoosebumps": 48305, "ĠfrÃ¥gor": 48306, "ĠVital": 48307, "ĠIsraelites": 48308, "wasser": 48309, "Isn": 48310, "Ġcommits": 48311, "ĠSTEVEN": 48312, "ĠBevölker": 48313, "uitive": 48314, "Ġlegen": 48315, "Ġbruk": 48316, "иÑĢован": 48317, "ynen": 48318, "helm": 48319, "Ġgenerational": 48320, "ĠLändern": 48321, "οιÏĢÏĮν": 48322, "uzu": 48323, "Ġcaller": 48324, "онÑĮ": 48325, "ümü": 48326, "Ġbesar": 48327, "Ġplats": 48328, "Ġmigrated": 48329, "Ġjap": 48330, "ĠWAR": 48331, "Ġdissect": 48332, "ĠZusch": 48333, "ĠZeiten": 48334, "ĠLions": 48335, "ĠDF": 48336, "âĶ": 48337, "кив": 48338, "Ġpedestrians": 48339, "ĠMarilyn": 48340, "dock": 48341, "Ġyht": 48342, "Ġreincarn": 48343, "ĠSono": 48344, "ĠGrowth": 48345, "ÑĥÑģов": 48346, "Ġdungeons": 48347, "Ġbagus": 48348, "kich": 48349, "ĠÑĥкÑĢаÑĹ": 48350, "éĨ«": 48351, "ĠKeller": 48352, "chemistry": 48353, "Japanese": 48354, "Ġwillst": 48355, "Ġdecomposition": 48356, "ĠÑģÑĤен": 48357, "Ġrevived": 48358, "íķĻêµIJ": 48359, "ĠÅĵ": 48360, "ä½IJ": 48361, "ìĭ¸": 48362, "ippy": 48363, "Ġhourly": 48364, "jän": 48365, "ĠWorkshop": 48366, "Ŀ¼ìĦľ": 48367, "Ġcuarto": 48368, "Ġpatrim": 48369, "ĠBurch": 48370, "ĠìŀĪ기": 48371, "Ġhepat": 48372, "ĠhÃłng": 48373, "ĠëĮĢíķ´": 48374, "ĠваÑĪи": 48375, "Ġrework": 48376, "Ġparse": 48377, "Ġçıktı": 48378, "ĠSax": 48379, "ĠMongo": 48380, "ĠAaah": 48381, "ramble": 48382, "DJ": 48383, "Ġstabilized": 48384, "ĠSpeech": 48385, "Books": 48386, "Ġhurdles": 48387, "ĠWO": 48388, "ĠLamborg": 48389, "Ġ1933": 48390, "Ġvorbere": 48391, "Ġclinically": 48392, "Ġbreathtaking": 48393, "ĠGateway": 48394, "пеÑĢвÑĭÑħ": 48395, "uters": 48396, "Ġë¹µ": 48397, "Ġyeter": 48398, "Ġpulley": 48399, "Ġmuffin": 48400, "ĠPrefer": 48401, "ĠPence": 48402, "Ġinformação": 48403, "ìĬ¤íĬ¸ë": 48404, "ãĤ¸ãĥ£": 48405, "ĠTurtle": 48406, "ĠRegina": 48407, "ĠLoad": 48408, "does": 48409, "panze": 48410, "¸Ķ": 48411, "Ġmina": 48412, "ĠLatinos": 48413, "ammers": 48414, "ĠTort": 48415, "ĠBeyonce": 48416, "имоÑģÑĤи": 48417, "ĠвопÑĢоÑģÑĭ": 48418, "Ġbulun": 48419, "èĢĮå·²": 48420, "inek": 48421, "bereich": 48422, "Ġpasture": 48423, "ĠOA": 48424, "ĠMelt": 48425, "ĠEtt": 48426, "ĠDY": 48427, "Ġobwohl": 48428, "Ġleagues": 48429, "ÑĤеÑģÑĮ": 48430, "ĠкÑĥÑģ": 48431, "Ġvors": 48432, "Ġtopp": 48433, "ographical": 48434, "asst": 48435, "Ġlindo": 48436, "Ġë°ĿíĺĶ": 48437, "Ġréfl": 48438, "Ġclimbs": 48439, "Ġvarsa": 48440, "Ġmethyl": 48441, "ĠKarere": 48442, "Æ°á»Ł": 48443, "Rad": 48444, "Ġpreparedness": 48445, "онÑĩ": 48446, "ĠOD": 48447, "ĠCGI": 48448, "Ġम": 48449, "Ġspeechless": 48450, "Ġlasci": 48451, "Ġbolag": 48452, "ĠÑħоÑĩеÑĤÑģÑı": 48453, "Ġgrieving": 48454, "ĠJohannes": 48455, "ĠCarroll": 48456, "adaki": 48457, "Ī¬ë": 48458, "ĠsÅĤu": 48459, "Ġinnerhalb": 48460, "Ġgymnastics": 48461, "пÑĢи": 48462, "ifiques": 48463, "Ġkarate": 48464, "Ġdomu": 48465, "ãģĿãĤĮãģ§": 48466, "OTHER": 48467, "Ġdemandé": 48468, "Ġbooklet": 48469, "ĠKyoto": 48470, "Ġwoh": 48471, "ĠMarÃŃa": 48472, "violent": 48473, "JE": 48474, "Ġlóg": 48475, "Ġbrutally": 48476, "cot": 48477, "ĠÙħÛĮ": 48478, "ĠWarsz": 48479, "å®Ī": 48480, "wol": 48481, "Ġmikä": 48482, "ĠPronounce": 48483, "ĠBrendan": 48484, "Ġroup": 48485, "Ġitaliano": 48486, "å¦ĤæѤ": 48487, "ĠкомпÑĮÑİÑĤ": 48488, "Ġurging": 48489, "edes": 48490, "Ġcarbono": 48491, "ĠRichardson": 48492, "ĠÐĿаÑĩ": 48493, "ĠTrainer": 48494, "ĠCrimea": 48495, "Ġdiapers": 48496, "Ġcovet": 48497, "ĠMahar": 48498, "ĠHutch": 48499, "ĠAusw": 48500, "berty": 48501, "Ġindifferent": 48502, "кÑĢеÑĤ": 48503, "uldade": 48504, "Ġharms": 48505, "¢ÙĨ": 48506, "lesia": 48507, "Ġgio": 48508, "ĠMistress": 48509, "ĠKnox": 48510, "ĠFREE": 48511, "Ġ루ë": 48512, "ĠнаÑĪа": 48513, "Ġinvincible": 48514, "Ġmaiden": 48515, "ĠJeez": 48516, "Ġbreve": 48517, "pole": 48518, "Ġcriticisms": 48519, "ĠRusia": 48520, "म": 48521, "phin": 48522, "ĠCompare": 48523, "ĠBON": 48524, "Ġsneaking": 48525, "ĠRails": 48526, "ĠGeral": 48527, "Ġ1953": 48528, "Hola": 48529, "ĠопÑĭÑĤ": 48530, "Ġrainforest": 48531, "Ġbelum": 48532, "ĠObi": 48533, "ĠISS": 48534, "ãĤĮãģªãģĦ": 48535, "ĠСв": 48536, "Ġblond": 48537, "Ġwzgl": 48538, "ĠpowiedziaÅĤ": 48539, "Ġchoking": 48540, "ĠSongs": 48541, "ĠBiraz": 48542, "Ġyells": 48543, "Ġstylist": 48544, "ÏĮÏĦε": 48545, "Ġschreiben": 48546, "ĠJaw": 48547, "ĠEleven": 48548, "ĠRif": 48549, "/.": 48550, "Ġìĺ¤ëŀľë§Į": 48551, "Ġtreaties": 48552, "uffed": 48553, "ĠâĪĴ": 48554, "Ġroofs": 48555, "à¹Ģส": 48556, "Ġë»": 48557, "Ġsparkle": 48558, "ĠKiev": 48559, "ĠArgu": 48560, "erecht": 48561, "ĠÐĿадо": 48562, "ĠFIL": 48563, "Ġmolta": 48564, "ĠDevi": 48565, "Ġcampe": 48566, "Ġbenevol": 48567, "ĠTough": 48568, "Ġmoim": 48569, "Ġevacuate": 48570, "Ġerrado": 48571, "å©Ĩ": 48572, "ÑĢÑĥго": 48573, "Ġíİĺ": 48574, "ĠÎĵια": 48575, "Ġweaken": 48576, "Ġilluminated": 48577, "Ġsiglo": 48578, "ĠVacc": 48579, "ией": 48580, "alis": 48581, "ĠÑĥÑģÑĤÑĢой": 48582, "Ġdona": 48583, "ÅĤos": 48584, "üman": 48585, "Ġproducción": 48586, "Ġclot": 48587, "ĠMango": 48588, "Ġuneasy": 48589, "Ġshuts": 48590, "ĠExamples": 48591, "vell": 48592, "ebe": 48593, "Ġpromptly": 48594, "ĠTeles": 48595, "ĠпÑĢоÑĪл": 48596, "Ġpuerta": 48597, "Ġüberzeug": 48598, "Ġcoch": 48599, "social": 48600, "ĠBenson": 48601, "ĠMeth": 48602, "ĠExped": 48603, "Ġsupplemental": 48604, "Ġconceive": 48605, "Ġ×ĺ×ķ×ij": 48606, "Ġcaptivity": 48607, "ıĻìķĪ": 48608, "ĠÑħÑĥд": 48609, "forming": 48610, "Ġuploads": 48611, "Ġturbulence": 48612, "joint": 48613, "Ġsatisfactory": 48614, "ĠAnime": 48615, "Ġwashes": 48616, "Ġliberals": 48617, "ĠSunshine": 48618, "ĠREAL": 48619, "ublik": 48620, "binary": 48621, "Tony": 48622, "Ġpolarized": 48623, "Ġenriched": 48624, "taking": 48625, "ĠëģĿëĤĺ": 48626, "Ġpleasures": 48627, "Ġextermin": 48628, "inese": 48629, "atl": 48630, "vär": 48631, "аÑĢÑĭ": 48632, "ĠmyÅĽ": 48633, "narrator": 48634, "Ġодном": 48635, "ĠnajwiÄĻ": 48636, "Ġmobilize": 48637, "Ġmillor": 48638, "Ġata": 48639, "æ··": 48640, "ĠpolÃŃtico": 48641, "Ġplead": 48642, "Ġpainters": 48643, "ĠSow": 48644, "оÑĦ": 48645, "ĠìĺĽëĤł": 48646, "ĠÑĩÑĤоб": 48647, "Ġsabor": 48648, "ĠUndert": 48649, "ĠJERRY": 48650, "Å¡ÃŃ": 48651, "Ġë°ĸìĹIJ": 48652, "Ġprécéd": 48653, "Ġannotation": 48654, "ĠInaudible": 48655, "Ġtextured": 48656, "Ġfisherman": 48657, "vordan": 48658, "icherung": 48659, "ĠìłģìĿ´": 48660, "Ġgezeigt": 48661, "Ġmandates": 48662, "Ġbeak": 48663, "ĠTWO": 48664, "ĠAkbar": 48665, "ilian": 48666, "Ġtiếp": 48667, "Ġsuperiority": 48668, "inku": 48669, "Ġlys": 48670, "ĠFCC": 48671, "ĠCPA": 48672, "ustering": 48673, "nicos": 48674, "anja": 48675, "Ġchills": 48676, "ĠCage": 48677, "Ġsealing": 48678, "Ġsaç": 48679, "Ġdedans": 48680, "ĠAlger": 48681, "Ġspezie": 48682, "Ġcoloss": 48683, "ıyı": 48684, "clockwise": 48685, "Ġexactamente": 48686, "Ġiemand": 48687, "amı": 48688, "Ġmandar": 48689, "raj": 48690, "faced": 48691, "agua": 48692, "Ġê¹Ķë": 48693, "Ġinsbesondere": 48694, "Ġdrizzle": 48695, "Ġdiminish": 48696, "ĠYoda": 48697, "AI": 48698, "Ġbilmiyorum": 48699, "ĠMMA": 48700, "ategory": 48701, "ĠпеÑĢеп": 48702, "Ġparticipar": 48703, "Ġnormalized": 48704, "Ġcomplexities": 48705, "æ´²": 48706, "æݧ": 48707, "аÑĢов": 48708, "mist": 48709, "icha": 48710, "Group": 48711, "Ġresiliency": 48712, "Ġnogle": 48713, "ĠCNC": 48714, "prü": 48715, "Ġphysicists": 48716, "нок": 48717, "LI": 48718, "Ġstuffs": 48719, "Ġsistemas": 48720, "Ġinterfering": 48721, "ĠMarvin": 48722, "ército": 48723, "ĠìĹĨê³ł": 48724, "Ġsonic": 48725, "Ġequiv": 48726, "Ġabord": 48727, "ĠRamen": 48728, "Ġ09": 48729, "medim": 48730, "atiques": 48731, "ĠделаÑİÑĤ": 48732, "Ġunanimously": 48733, "Ġskirts": 48734, "ĠíĬ¹ë³Ħ": 48735, "ĠPrix": 48736, "kami": 48737, "Ġfruition": 48738, "Ġbirthdays": 48739, "иком": 48740, "Ġinaugural": 48741, "Ġcorrelate": 48742, "ĠTory": 48743, "ĠëĤĺìģ": 48744, "Ġdew": 48745, "ĠPrecis": 48746, "ihi": 48747, "Ġë¬¸ìłľê°Ģ": 48748, "Ġciting": 48749, "ĠLana": 48750, "ĠKag": 48751, "Ġplaythrough": 48752, "ĠProtocol": 48753, "frist": 48754, "hovah": 48755, "Ġmerciful": 48756, "Ġbilingual": 48757, "ĠGuitar": 48758, "rh": 48759, "Ġglamorous": 48760, "ĠVikings": 48761, "ĠOoooh": 48762, "íķĺëĬĶëį°": 48763, "ĠUganda": 48764, "Ġcollapses": 48765, "entry": 48766, "Ġantioxidants": 48767, "ëĤĺë": 48768, "ÑĪаÑı": 48769, "Ġtrivia": 48770, "Ġgäller": 48771, "Ġfungi": 48772, "Ġmilks": 48773, "Ġdicht": 48774, "μη": 48775, "poke": 48776, "ĠвÑĭпÑĥÑģк": 48777, "Ġfeeder": 48778, "ĠAlcohol": 48779, "hower": 48780, "Ġdeserving": 48781, "ĠRebel": 48782, "iosis": 48783, "Ġ103": 48784, "Ġhandout": 48785, "Ġenm": 48786, "Ġlandlords": 48787, "Ġgeology": 48788, "rils": 48789, "Ġcobra": 48790, "ĠVold": 48791, "ĠPanch": 48792, "ĠGREG": 48793, "Ġpross": 48794, "Ġbracelets": 48795, "ĠVega": 48796, "Ġrozum": 48797, "款": 48798, "азд": 48799, "ĠLynd": 48800, "ĠHonors": 48801, "Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "> length: + array = array.index_select( + dim=axis, index=torch.arange(length, device=array.device) + ) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) + else: + if array.shape[axis] > length: + array = array.take(indices=range(length), axis=axis) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = np.pad(array, pad_widths) + + return array + + +@lru_cache(maxsize=None) +def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: + """ + load the mel filterbank matrix for projecting STFT into a Mel spectrogram. + Allows decoupling librosa dependency; saved using: + + np.savez_compressed( + "mel_filters.npz", + mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), + ) + """ + assert n_mels == 80, f"Unsupported n_mels: {n_mels}" + with np.load( + os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") + ) as f: + return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) + + +def log_mel_spectrogram( + audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS +): + """ + Compute the log-Mel spectrogram of + + Parameters + ---------- + audio: Union[str, np.ndarray, torch.Tensor], shape = (*) + The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz + + n_mels: int + The number of Mel-frequency filters, only 80 is supported + + Returns + ------- + torch.Tensor, shape = (80, n_frames) + A Tensor that contains the Mel spectrogram + """ + if not torch.is_tensor(audio): + if isinstance(audio, str): + audio = load_audio(audio) + audio = torch.from_numpy(audio) + + window = torch.hann_window(N_FFT).to(audio.device) + stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) + magnitudes = stft[..., :-1].abs() ** 2 + + filters = mel_filters(audio.device, n_mels) + mel_spec = filters @ magnitudes + + log_spec = torch.clamp(mel_spec, min=1e-10).log10() + log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + return log_spec diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/decoding.py b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/decoding.py new file mode 100644 index 0000000000000000000000000000000000000000..5cb8353d795b2fba48dba5075e229ee783f3b388 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/decoding.py @@ -0,0 +1,811 @@ +from dataclasses import dataclass, field +from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.distributions import Categorical + +from funasr_detach.models.whisper.utils.audio import CHUNK_LENGTH +from funasr_detach.models.whisper.utils.tokenizer import Tokenizer, get_tokenizer +from funasr_detach.models.whisper.utils.utils import compression_ratio + +if TYPE_CHECKING: + from funasr_detach.models.whisper_models.model import Whisper + + +@torch.no_grad() +def detect_language( + model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None +) -> Tuple[Tensor, List[dict]]: + """ + Detect the spoken language in the audio, and return them as list of strings, along with the ids + of the most probable language tokens and the probability distribution over all language tokens. + This is performed outside the main decode loop in order to not interfere with kv-caching. + + Returns + ------- + language_tokens : Tensor, shape = (n_audio,) + ids of the most probable language tokens, which appears after the startoftranscript token. + language_probs : List[Dict[str, float]], length = n_audio + list of dictionaries containing the probability distribution over all languages. + """ + if tokenizer is None: + tokenizer = get_tokenizer(model.is_multilingual) + if ( + tokenizer.language is None + or tokenizer.language_token not in tokenizer.sot_sequence + ): + raise ValueError( + f"This model doesn't have language tokens so it can't perform lang id" + ) + + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + # skip encoder forward pass if already-encoded audio features were given + if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): + mel = model.encoder(mel) + + # forward pass using a single token, startoftranscript + n_audio = mel.shape[0] + x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] + logits = model.logits(x, mel)[:, 0] + + # collect detected languages; suppress all non-language tokens + mask = torch.ones(logits.shape[-1], dtype=torch.bool) + mask[list(tokenizer.all_language_tokens)] = False + logits[:, mask] = -np.inf + language_tokens = logits.argmax(dim=-1) + language_token_probs = logits.softmax(dim=-1).cpu() + language_probs = [ + { + c: language_token_probs[i, j].item() + for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) + } + for i in range(n_audio) + ] + + if single: + language_tokens = language_tokens[0] + language_probs = language_probs[0] + + return language_tokens, language_probs + + +@dataclass(frozen=True) +class DecodingOptions: + task: str = ( + "transcribe" # whether to perform X->X "transcribe" or X->English "translate" + ) + language: Optional[str] = ( + None # language that the audio is in; uses detected language if None + ) + + # sampling-related options + temperature: float = 0.0 + sample_len: Optional[int] = None # maximum number of tokens to sample + best_of: Optional[int] = ( + None # number of independent samples to collect, when t > 0 + ) + beam_size: Optional[int] = None # number of beams in beam search, when t == 0 + patience: Optional[float] = ( + None # patience in beam search (https://arxiv.org/abs/2204.05424) + ) + + # options for ranking generations (either beams or best-of-N samples) + length_penalty: Optional[float] = ( + None # "alpha" in Google NMT, None defaults to length norm + ) + + # prompt, prefix, and token suppression + prompt: Optional[Union[str, List[int]]] = ( + None # text or tokens for the previous context + ) + prefix: Optional[Union[str, List[int]]] = ( + None # text or tokens to prefix the current context + ) + suppress_blank: bool = True # this will suppress blank outputs + + # list of tokens ids (or comma-separated token ids) to suppress + # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` + suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" + + # timestamp sampling options + without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only + max_initial_timestamp: Optional[float] = ( + 1.0 # the initial timestamp cannot be later than this + ) + + # implementation details + fp16: bool = True # use fp16 for most of the calculation + + +@dataclass(frozen=True) +class DecodingResult: + audio_features: Tensor + language: str + language_probs: Optional[Dict[str, float]] = None + tokens: List[int] = field(default_factory=list) + text: str = "" + avg_logprob: float = np.nan + no_speech_prob: float = np.nan + temperature: float = np.nan + compression_ratio: float = np.nan + + +class Inference: + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + """Perform a forward pass on the decoder and return per-token logits""" + raise NotImplementedError + + def rearrange_kv_cache(self, source_indices) -> None: + """Update the key-value cache according to the updated beams""" + raise NotImplementedError + + def cleanup_caching(self) -> None: + """Clean up any resources or hooks after decoding is finished""" + pass + + +class PyTorchInference(Inference): + def __init__(self, model: "Whisper", initial_token_length: int): + self.model: "Whisper" = model + self.initial_token_length = initial_token_length + self.kv_cache = {} + self.hooks = [] + + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + if not self.kv_cache: + self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() + + if tokens.shape[-1] > self.initial_token_length: + # only need to use the last token except in the first forward pass + tokens = tokens[:, -1:] + + return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache) + + def cleanup_caching(self): + for hook in self.hooks: + hook.remove() + + self.kv_cache = {} + self.hooks = [] + + def rearrange_kv_cache(self, source_indices): + for module, tensor in self.kv_cache.items(): + # update the key/value cache to contain the selected sequences + self.kv_cache[module] = tensor[source_indices].detach() + + +class SequenceRanker: + def rank( + self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]] + ) -> List[int]: + """ + Given a list of groups of samples and their cumulative log probabilities, + return the indices of the samples in each group to select as the final result + """ + raise NotImplementedError + + +class MaximumLikelihoodRanker(SequenceRanker): + """ + Select the sample with the highest log probabilities, penalized using either + a simple length normalization or Google NMT paper's length penalty + """ + + def __init__(self, length_penalty: Optional[float]): + self.length_penalty = length_penalty + + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): + def scores(logprobs, lengths): + result = [] + for logprob, length in zip(logprobs, lengths): + if self.length_penalty is None: + penalty = length + else: + # from the Google NMT paper + penalty = ((5 + length) / 6) ** self.length_penalty + result.append(logprob / penalty) + return result + + # get the sequence with the highest score + lengths = [[len(t) for t in s] for s in tokens] + return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] + + +class TokenDecoder: + def reset(self): + """Initialize any stateful variables for decoding a new sequence""" + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + """Specify how to select the next token, based on the current trace and logits + + Parameters + ---------- + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + sum_logprobs : Tensor, shape = (n_batch) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Tensor, shape = (n_batch, current_sequence_length + 1) + the tokens, appended with the selected next token + + completed : bool + True if all sequences has reached the end of text + + """ + raise NotImplementedError + + def finalize( + self, tokens: Tensor, sum_logprobs: Tensor + ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: + """Finalize search and return the final candidate sequences + + Parameters + ---------- + tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence + + sum_logprobs : Tensor, shape = (n_audio, n_group) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Sequence[Sequence[Tensor]], length = n_audio + sequence of Tensors containing candidate token sequences, for each audio input + + sum_logprobs : List[List[float]], length = n_audio + sequence of cumulative log probabilities corresponding to the above + + """ + raise NotImplementedError + + +class GreedyDecoder(TokenDecoder): + def __init__(self, temperature: float, eot: int): + self.temperature = temperature + self.eot = eot + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + temperature = self.temperature + if temperature == 0: + next_tokens = logits.argmax(dim=-1) + else: + next_tokens = Categorical(logits=logits / temperature).sample() + + logprobs = F.log_softmax(logits.float(), dim=-1) + current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] + sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) + + next_tokens[tokens[:, -1] == self.eot] = self.eot + tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) + + completed = (tokens[:, -1] == self.eot).all() + return tokens, completed + + def finalize(self, tokens: Tensor, sum_logprobs: Tensor): + # make sure each sequence has at least one EOT token at the end + tokens = F.pad(tokens, (0, 1), value=self.eot) + return tokens, sum_logprobs.tolist() + + +class BeamSearchDecoder(TokenDecoder): + def __init__( + self, + beam_size: int, + eot: int, + inference: Inference, + patience: Optional[float] = None, + ): + self.beam_size = beam_size + self.eot = eot + self.inference = inference + self.patience = patience or 1.0 + self.max_candidates: int = round(beam_size * self.patience) + self.finished_sequences = None + + assert ( + self.max_candidates > 0 + ), f"Invalid beam size ({beam_size}) or patience ({patience})" + + def reset(self): + self.finished_sequences = None + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + if tokens.shape[0] % self.beam_size != 0: + raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") + + n_audio = tokens.shape[0] // self.beam_size + if self.finished_sequences is None: # for the first update + self.finished_sequences = [{} for _ in range(n_audio)] + + logprobs = F.log_softmax(logits.float(), dim=-1) + next_tokens, source_indices, finished_sequences = [], [], [] + for i in range(n_audio): + scores, sources, finished = {}, {}, {} + + # STEP 1: calculate the cumulative log probabilities for possible candidates + for j in range(self.beam_size): + idx = i * self.beam_size + j + prefix = tokens[idx].tolist() + for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): + new_logprob = (sum_logprobs[idx] + logprob).item() + sequence = tuple(prefix + [token.item()]) + scores[sequence] = new_logprob + sources[sequence] = idx + + # STEP 2: rank the candidates and keep the top beam_size sequences for each audio + saved = 0 + for sequence in sorted(scores, key=scores.get, reverse=True): + if sequence[-1] == self.eot: + finished[sequence] = scores[sequence] + else: + sum_logprobs[len(next_tokens)] = scores[sequence] + next_tokens.append(sequence) + source_indices.append(sources[sequence]) + + saved += 1 + if saved == self.beam_size: + break + + finished_sequences.append(finished) + + tokens = torch.tensor(next_tokens, device=tokens.device) + self.inference.rearrange_kv_cache(source_indices) + + # add newly finished sequences to self.finished_sequences + assert len(self.finished_sequences) == len(finished_sequences) + for previously_finished, newly_finished in zip( + self.finished_sequences, finished_sequences + ): + for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): + if len(previously_finished) >= self.max_candidates: + break # the candidate list is full + previously_finished[seq] = newly_finished[seq] + + # mark as completed if all audio has enough number of samples + completed = all( + len(sequences) >= self.max_candidates + for sequences in self.finished_sequences + ) + return tokens, completed + + def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): + # collect all finished sequences, including patience, and add unfinished ones if not enough + sum_logprobs = sum_logprobs.cpu() + for i, sequences in enumerate(self.finished_sequences): + if ( + len(sequences) < self.beam_size + ): # when not enough sequences are finished + for j in list(np.argsort(sum_logprobs[i]))[::-1]: + sequence = preceding_tokens[i, j].tolist() + [self.eot] + sequences[tuple(sequence)] = sum_logprobs[i][j].item() + if len(sequences) >= self.beam_size: + break + + tokens: List[List[Tensor]] = [ + [torch.tensor(seq) for seq in sequences.keys()] + for sequences in self.finished_sequences + ] + sum_logprobs: List[List[float]] = [ + list(sequences.values()) for sequences in self.finished_sequences + ] + return tokens, sum_logprobs + + +class LogitFilter: + def apply(self, logits: Tensor, tokens: Tensor) -> None: + """Apply any filtering or masking to logits in-place + + Parameters + ---------- + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + """ + raise NotImplementedError + + +class SuppressBlank(LogitFilter): + def __init__(self, tokenizer: Tokenizer, sample_begin: int): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + + def apply(self, logits: Tensor, tokens: Tensor): + if tokens.shape[1] == self.sample_begin: + logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf + + +class SuppressTokens(LogitFilter): + def __init__(self, suppress_tokens: Sequence[int]): + self.suppress_tokens = list(suppress_tokens) + + def apply(self, logits: Tensor, tokens: Tensor): + logits[:, self.suppress_tokens] = -np.inf + + +class ApplyTimestampRules(LogitFilter): + def __init__( + self, + tokenizer: Tokenizer, + sample_begin: int, + max_initial_timestamp_index: Optional[int], + ): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + self.max_initial_timestamp_index = max_initial_timestamp_index + + def apply(self, logits: Tensor, tokens: Tensor): + # suppress <|notimestamps|> which is handled by without_timestamps + if self.tokenizer.no_timestamps is not None: + logits[:, self.tokenizer.no_timestamps] = -np.inf + + # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly + for k in range(tokens.shape[0]): + seq = [t for t in tokens[k, self.sample_begin :].tolist()] + last_was_timestamp = ( + len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin + ) + penultimate_was_timestamp = ( + len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin + ) + + if last_was_timestamp: + if penultimate_was_timestamp: # has to be non-timestamp + logits[k, self.tokenizer.timestamp_begin :] = -np.inf + else: # cannot be normal text tokens + logits[k, : self.tokenizer.eot] = -np.inf + + if tokens.shape[1] == self.sample_begin: + # suppress generating non-timestamp tokens at the beginning + logits[:, : self.tokenizer.timestamp_begin] = -np.inf + + # apply the `max_initial_timestamp` option + if self.max_initial_timestamp_index is not None: + last_allowed = ( + self.tokenizer.timestamp_begin + self.max_initial_timestamp_index + ) + logits[:, last_allowed + 1 :] = -np.inf + + # if sum of probability over timestamps is above any other token, sample timestamp + logprobs = F.log_softmax(logits.float(), dim=-1) + for k in range(tokens.shape[0]): + timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp( + dim=-1 + ) + max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() + if timestamp_logprob > max_text_token_logprob: + logits[k, : self.tokenizer.timestamp_begin] = -np.inf + + +class DecodingTask: + inference: Inference + sequence_ranker: SequenceRanker + decoder: TokenDecoder + logit_filters: List[LogitFilter] + + def __init__(self, model: "Whisper", options: DecodingOptions): + self.model = model + + language = options.language or "en" + tokenizer = get_tokenizer( + model.is_multilingual, language=language, task=options.task + ) + self.tokenizer: Tokenizer = tokenizer + self.options: DecodingOptions = self._verify_options(options) + + self.n_group: int = options.beam_size or options.best_of or 1 + self.n_ctx: int = model.dims.n_text_ctx + self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 + + self.sot_sequence: Tuple[int] = tokenizer.sot_sequence + if self.options.without_timestamps: + self.sot_sequence = tokenizer.sot_sequence_including_notimestamps + + self.initial_tokens: Tuple[int] = self._get_initial_tokens() + self.sample_begin: int = len(self.initial_tokens) + self.sot_index: int = self.initial_tokens.index(tokenizer.sot) + + # inference: implements the forward pass through the decoder, including kv caching + self.inference = PyTorchInference(model, len(self.initial_tokens)) + + # sequence ranker: implements how to rank a group of sampled sequences + self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) + + # decoder: implements how to select the next tokens, given the autoregressive distribution + if options.beam_size is not None: + self.decoder = BeamSearchDecoder( + options.beam_size, tokenizer.eot, self.inference, options.patience + ) + else: + self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) + + # logit filters: applies various rules to suppress or penalize certain tokens + self.logit_filters = [] + if self.options.suppress_blank: + self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) + if self.options.suppress_tokens: + self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) + if not options.without_timestamps: + precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds + max_initial_timestamp_index = None + if options.max_initial_timestamp: + max_initial_timestamp_index = round( + self.options.max_initial_timestamp / precision + ) + self.logit_filters.append( + ApplyTimestampRules( + tokenizer, self.sample_begin, max_initial_timestamp_index + ) + ) + + def _verify_options(self, options: DecodingOptions) -> DecodingOptions: + if options.beam_size is not None and options.best_of is not None: + raise ValueError("beam_size and best_of can't be given together") + if options.temperature == 0: + if options.best_of is not None: + raise ValueError("best_of with greedy sampling (T=0) is not compatible") + if options.patience is not None and options.beam_size is None: + raise ValueError("patience requires beam_size to be given") + if options.length_penalty is not None and not ( + 0 <= options.length_penalty <= 1 + ): + raise ValueError("length_penalty (alpha) should be a value between 0 and 1") + + return options + + def _get_initial_tokens(self) -> Tuple[int]: + tokens = list(self.sot_sequence) + prefix = self.options.prefix + prompt = self.options.prompt + + if prefix: + prefix_tokens = ( + self.tokenizer.encode(" " + prefix.strip()) + if isinstance(prefix, str) + else prefix + ) + if self.sample_len is not None: + max_prefix_len = self.n_ctx // 2 - self.sample_len + prefix_tokens = prefix_tokens[-max_prefix_len:] + tokens = tokens + prefix_tokens + + if prompt: + prompt_tokens = ( + self.tokenizer.encode(" " + prompt.strip()) + if isinstance(prompt, str) + else prompt + ) + tokens = ( + [self.tokenizer.sot_prev] + + prompt_tokens[-(self.n_ctx // 2 - 1) :] + + tokens + ) + + return tuple(tokens) + + def _get_suppress_tokens(self) -> Tuple[int]: + suppress_tokens = self.options.suppress_tokens + + if isinstance(suppress_tokens, str): + suppress_tokens = [int(t) for t in suppress_tokens.split(",")] + + if -1 in suppress_tokens: + suppress_tokens = [t for t in suppress_tokens if t >= 0] + suppress_tokens.extend(self.tokenizer.non_speech_tokens) + elif suppress_tokens is None or len(suppress_tokens) == 0: + suppress_tokens = [] # interpret empty string as an empty list + else: + assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" + + suppress_tokens.extend( + [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] + ) + if self.tokenizer.no_speech is not None: + # no-speech probability is collected separately + suppress_tokens.append(self.tokenizer.no_speech) + + return tuple(sorted(set(suppress_tokens))) + + def _get_audio_features(self, mel: Tensor): + if self.options.fp16: + mel = mel.half() + + if mel.shape[-2:] == ( + self.model.dims.n_audio_ctx, + self.model.dims.n_audio_state, + ): + # encoded audio features are given; skip audio encoding + audio_features = mel + else: + audio_features = self.model.encoder(mel) + + if audio_features.dtype != ( + torch.float16 if self.options.fp16 else torch.float32 + ): + return TypeError( + f"audio_features has an incorrect dtype: {audio_features.dtype}" + ) + + return audio_features + + def _detect_language(self, audio_features: Tensor, tokens: Tensor): + languages = [self.options.language] * audio_features.shape[0] + lang_probs = None + + if self.options.language is None or self.options.task == "lang_id": + lang_tokens, lang_probs = self.model.detect_language( + audio_features, self.tokenizer + ) + languages = [max(probs, key=probs.get) for probs in lang_probs] + if self.options.language is None: + tokens[:, self.sot_index + 1] = lang_tokens # write language tokens + + return languages, lang_probs + + def _main_loop(self, audio_features: Tensor, tokens: Tensor): + assert audio_features.shape[0] == tokens.shape[0] + n_batch = tokens.shape[0] + sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) + no_speech_probs = [np.nan] * n_batch + + try: + for i in range(self.sample_len): + logits = self.inference.logits(tokens, audio_features) + + if ( + i == 0 and self.tokenizer.no_speech is not None + ): # save no_speech_probs + probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) + no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() + + # now we need to consider the logits at the last token only + logits = logits[:, -1] + + # apply the logit filters, e.g. for suppressing or applying penalty to + for logit_filter in self.logit_filters: + logit_filter.apply(logits, tokens) + + # expand the tokens tensor with the selected next tokens + tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) + + if completed or tokens.shape[-1] > self.n_ctx: + break + finally: + self.inference.cleanup_caching() + + return tokens, sum_logprobs, no_speech_probs + + @torch.no_grad() + def run(self, mel: Tensor) -> List[DecodingResult]: + self.decoder.reset() + tokenizer: Tokenizer = self.tokenizer + n_audio: int = mel.shape[0] + + audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass + tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) + + # detect language if requested, overwriting the language token + languages, language_probs = self._detect_language(audio_features, tokens) + if self.options.task == "lang_id": + return [ + DecodingResult( + audio_features=features, language=language, language_probs=probs + ) + for features, language, probs in zip( + audio_features, languages, language_probs + ) + ] + + # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling + audio_features = audio_features.repeat_interleave(self.n_group, dim=0) + tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) + + # call the main sampling loop + tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) + + # reshape the tensors to have (n_audio, n_group) as the first two dimensions + audio_features = audio_features[:: self.n_group] + no_speech_probs = no_speech_probs[:: self.n_group] + assert audio_features.shape[0] == len(no_speech_probs) == n_audio + + tokens = tokens.reshape(n_audio, self.n_group, -1) + sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) + + # get the final candidates for each group, and slice between the first sampled token and EOT + tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) + tokens: List[List[Tensor]] = [ + [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] + for s in tokens + ] + + # select the top-ranked sample in each group + selected = self.sequence_ranker.rank(tokens, sum_logprobs) + tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] + texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] + + sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] + avg_logprobs: List[float] = [ + lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs) + ] + + fields = ( + texts, + languages, + tokens, + audio_features, + avg_logprobs, + no_speech_probs, + ) + if len(set(map(len, fields))) != 1: + raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") + + return [ + DecodingResult( + audio_features=features, + language=language, + tokens=tokens, + text=text, + avg_logprob=avg_logprob, + no_speech_prob=no_speech_prob, + temperature=self.options.temperature, + compression_ratio=compression_ratio(text), + ) + for text, language, tokens, features, avg_logprob, no_speech_prob in zip( + *fields + ) + ] + + +@torch.no_grad() +def decode( + model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions() +) -> Union[DecodingResult, List[DecodingResult]]: + """ + Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). + + Parameters + ---------- + model: Whisper + the Whisper model instance + + mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) + A tensor containing the Mel spectrogram(s) + + options: DecodingOptions + A dataclass that contains all necessary options for decoding 30-second segments + + Returns + ------- + result: Union[DecodingResult, List[DecodingResult]] + The result(s) of decoding contained in `DecodingResult` dataclass instance(s) + """ + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + result = DecodingTask(model, options).run(mel) + + if single: + result = result[0] + + return result diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/tokenizer.py b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..576328e52f325fcdb0afa4e35dd1fc549974432f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/tokenizer.py @@ -0,0 +1,351 @@ +import os +from dataclasses import dataclass +from functools import lru_cache +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch + +try: + from transformers import GPT2TokenizerFast +except ImportError: + raise ImportError( + "transformers was not installed. Please install transformers first." + ) + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "he": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", +} + + +@dataclass(frozen=True) +class Tokenizer: + """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens""" + + tokenizer: "GPT2TokenizerFast" + language: Optional[str] + sot_sequence: Tuple[int] + + def encode(self, text, **kwargs): + return self.tokenizer.encode(text, **kwargs) + + def decode( + self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs + ): + return self.tokenizer.decode(token_ids, **kwargs) + + def decode_with_timestamps(self, tokens) -> str: + """ + Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. + This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + outputs = [[]] + for token in tokens: + if token >= self.timestamp_begin: + timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" + outputs.append(timestamp) + outputs.append([]) + else: + outputs[-1].append(token) + outputs = [ + s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs + ] + return "".join(outputs) + + @property + @lru_cache() + def eot(self) -> int: + return self.tokenizer.eos_token_id + + @property + @lru_cache() + def sot(self) -> int: + return self._get_single_token_id("<|startoftranscript|>") + + @property + @lru_cache() + def sot_lm(self) -> int: + return self._get_single_token_id("<|startoflm|>") + + @property + @lru_cache() + def sot_prev(self) -> int: + return self._get_single_token_id("<|startofprev|>") + + @property + @lru_cache() + def no_speech(self) -> int: + return self._get_single_token_id("<|nospeech|>") + + @property + @lru_cache() + def no_timestamps(self) -> int: + return self._get_single_token_id("<|notimestamps|>") + + @property + @lru_cache() + def timestamp_begin(self) -> int: + return self.tokenizer.all_special_ids[-1] + 1 + + @property + @lru_cache() + def language_token(self) -> int: + """Returns the token id corresponding to the value of the `language` field""" + if self.language is None: + raise ValueError(f"This tokenizer does not have language token configured") + + additional_tokens = dict( + zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ) + ) + candidate = f"<|{self.language}|>" + if candidate in additional_tokens: + return additional_tokens[candidate] + + raise KeyError(f"Language {self.language} not found in tokenizer.") + + @property + @lru_cache() + def all_language_tokens(self) -> Tuple[int]: + result = [] + for token, token_id in zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ): + if token.strip("<|>") in LANGUAGES: + result.append(token_id) + return tuple(result) + + @property + @lru_cache() + def all_language_codes(self) -> Tuple[str]: + return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) + + @property + @lru_cache() + def sot_sequence_including_notimestamps(self) -> Tuple[int]: + return tuple(list(self.sot_sequence) + [self.no_timestamps]) + + @property + @lru_cache() + def non_speech_tokens(self) -> Tuple[int]: + """ + Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech + annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. + + - ♪♪♪ + - ( SPEAKING FOREIGN LANGUAGE ) + - [DAVID] Hey there, + + keeping basic punctuations like commas, periods, question marks, exclamation points, etc. + """ + symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』') + symbols += ( + "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + ) + + # symbols that may be a single token or multiple tokens depending on the tokenizer. + # In case they're multiple tokens, suppress the first token, which is safe because: + # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress + # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. + miscellaneous = set("♩♪♫♬♭♮♯") + assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) + + # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word + result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} + for symbol in symbols + list(miscellaneous): + for tokens in [ + self.tokenizer.encode(symbol), + self.tokenizer.encode(" " + symbol), + ]: + if len(tokens) == 1 or symbol in miscellaneous: + result.add(tokens[0]) + + return tuple(sorted(result)) + + def _get_single_token_id(self, text) -> int: + tokens = self.tokenizer.encode(text) + assert len(tokens) == 1, f"{text} is not encoded as a single token" + return tokens[0] + + +@lru_cache(maxsize=None) +def build_tokenizer(name: str = "gpt2", resource_path: str = None): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + if resource_path is not None: + path = os.path.join(resource_path, name) + else: + path = os.path.join(os.path.dirname(__file__), "assets", name) + tokenizer = GPT2TokenizerFast.from_pretrained(path) + + specials = [ + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in LANGUAGES.keys()], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + ] + + tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) + return tokenizer + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + task: Optional[str] = None, # Literal["transcribe", "translate", None] + language: Optional[str] = None, +) -> Tokenizer: + if language is not None: + language = language.lower() + if language not in LANGUAGES: + if language in TO_LANGUAGE_CODE: + language = TO_LANGUAGE_CODE[language] + else: + raise ValueError(f"Unsupported language: {language}") + + if multilingual: + tokenizer_name = "multilingual" + task = task or "transcribe" + language = language or "en" + else: + tokenizer_name = "gpt2" + task = None + language = None + + tokenizer = build_tokenizer(name=tokenizer_name) + all_special_ids: List[int] = tokenizer.all_special_ids + sot: int = all_special_ids[1] + translate: int = all_special_ids[-6] + transcribe: int = all_special_ids[-5] + + langs = tuple(LANGUAGES.keys()) + sot_sequence = [sot] + if language is not None: + sot_sequence.append(sot + 1 + langs.index(language)) + if task is not None: + sot_sequence.append(transcribe if task == "transcribe" else translate) + + return Tokenizer( + tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence) + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/transcribe.py b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/transcribe.py new file mode 100644 index 0000000000000000000000000000000000000000..f2ed113eb83bf2ed5e4ab938f8b38e8c10da34d0 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/transcribe.py @@ -0,0 +1,494 @@ +import argparse +import os +import warnings +from typing import Optional, Tuple, Union, TYPE_CHECKING + +import numpy as np +import torch +import tqdm + +from funasr_detach.models.whisper.utils.audio import ( + SAMPLE_RATE, + N_FRAMES, + HOP_LENGTH, + pad_or_trim, + log_mel_spectrogram, +) +from funasr_detach.models.whisper.utils.decoding import DecodingOptions, DecodingResult +from funasr_detach.models.whisper.utils.tokenizer import ( + LANGUAGES, + TO_LANGUAGE_CODE, + get_tokenizer, +) +from funasr_detach.models.whisper.utils.utils import ( + exact_div, + format_timestamp, + make_safe, + optional_int, + optional_float, + str2bool, + get_writer, +) + +if TYPE_CHECKING: + from .model import Whisper + + +def transcribe( + model: "Whisper", + audio: Union[str, np.ndarray, torch.Tensor], + *, + verbose: Optional[bool] = None, + temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), + compression_ratio_threshold: Optional[float] = 2.4, + logprob_threshold: Optional[float] = -1.0, + no_speech_threshold: Optional[float] = 0.6, + condition_on_previous_text: bool = True, + **decode_options, +): + """ + Transcribe an audio file using Whisper + + Parameters + ---------- + model: Whisper + The Whisper model instance + + audio: Union[str, np.ndarray, torch.Tensor] + The path to the audio file to open, or the audio waveform + + verbose: bool + Whether to display the text being decoded to the console. If True, displays all the details, + If False, displays minimal details. If None, does not display anything + + temperature: Union[float, Tuple[float, ...]] + Temperature for sampling. It can be a tuple of temperatures, which will be successively used + upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. + + compression_ratio_threshold: float + If the gzip compression ratio is above this value, treat as failed + + logprob_threshold: float + If the average log probability over sampled tokens is below this value, treat as failed + + no_speech_threshold: float + If the no_speech probability is higher than this value AND the average log probability + over sampled tokens is below `logprob_threshold`, consider the segment as silent + + condition_on_previous_text: bool + if True, the previous output of the model is provided as a prompt for the next window; + disabling may make the text inconsistent across windows, but the model becomes less prone to + getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. + + decode_options: dict + Keyword arguments to construct `DecodingOptions` instances + + Returns + ------- + A dictionary containing the resulting text ("text") and segment-level details ("segments"), and + the spoken language ("language"), which is detected when `decode_options["language"]` is None. + """ + dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32 + if model.device == torch.device("cpu"): + if torch.cuda.is_available(): + warnings.warn("Performing inference on CPU when CUDA is available") + if dtype == torch.float16: + warnings.warn("FP16 is not supported on CPU; using FP32 instead") + dtype = torch.float32 + + if dtype == torch.float32: + decode_options["fp16"] = False + + mel = log_mel_spectrogram(audio) + + if decode_options.get("language", None) is None: + if not model.is_multilingual: + decode_options["language"] = "en" + else: + if verbose: + print( + "Detecting language using up to the first 30 seconds. Use `--language` to specify the language" + ) + segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) + _, probs = model.detect_language(segment) + decode_options["language"] = max(probs, key=probs.get) + if verbose is not None: + print( + f"Detected language: {LANGUAGES[decode_options['language']].title()}" + ) + + language = decode_options["language"] + task = decode_options.get("task", "transcribe") + tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task) + + def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: + temperatures = ( + [temperature] if isinstance(temperature, (int, float)) else temperature + ) + decode_result = None + + for t in temperatures: + kwargs = {**decode_options} + if t > 0: + # disable beam_size and patience when t > 0 + kwargs.pop("beam_size", None) + kwargs.pop("patience", None) + else: + # disable best_of when t == 0 + kwargs.pop("best_of", None) + + options = DecodingOptions(**kwargs, temperature=t) + decode_result = model.decode(segment, options) + + needs_fallback = False + if ( + compression_ratio_threshold is not None + and decode_result.compression_ratio > compression_ratio_threshold + ): + needs_fallback = True # too repetitive + if ( + logprob_threshold is not None + and decode_result.avg_logprob < logprob_threshold + ): + needs_fallback = True # average log probability is too low + + if not needs_fallback: + break + + return decode_result + + seek = 0 + input_stride = exact_div( + N_FRAMES, model.dims.n_audio_ctx + ) # mel frames per output token: 2 + time_precision = ( + input_stride * HOP_LENGTH / SAMPLE_RATE + ) # time per output token: 0.02 (seconds) + all_tokens = [] + all_segments = [] + prompt_reset_since = 0 + + initial_prompt = decode_options.pop("initial_prompt", None) or [] + if initial_prompt: + initial_prompt = tokenizer.encode(" " + initial_prompt.strip()) + all_tokens.extend(initial_prompt) + + def add_segment( + *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult + ): + text = tokenizer.decode( + [token for token in text_tokens if token < tokenizer.eot] + ) + if len(text.strip()) == 0: # skip empty text output + return + + all_segments.append( + { + "id": len(all_segments), + "seek": seek, + "start": start, + "end": end, + "text": text, + "tokens": text_tokens.tolist(), + "temperature": result.temperature, + "avg_logprob": result.avg_logprob, + "compression_ratio": result.compression_ratio, + "no_speech_prob": result.no_speech_prob, + } + ) + if verbose: + print( + make_safe( + f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}" + ) + ) + + # show the progress bar when verbose is False (otherwise the transcribed text will be printed) + num_frames = mel.shape[-1] + previous_seek_value = seek + + with tqdm.tqdm( + total=num_frames, unit="frames", disable=verbose is not False + ) as pbar: + while seek < num_frames: + timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) + segment = pad_or_trim(mel[:, seek:], N_FRAMES).to(model.device).to(dtype) + segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE + + decode_options["prompt"] = all_tokens[prompt_reset_since:] + result: DecodingResult = decode_with_fallback(segment) + tokens = torch.tensor(result.tokens) + + if no_speech_threshold is not None: + # no voice activity check + should_skip = result.no_speech_prob > no_speech_threshold + if ( + logprob_threshold is not None + and result.avg_logprob > logprob_threshold + ): + # don't skip if the logprob is high enough, despite the no_speech_prob + should_skip = False + + if should_skip: + seek += segment.shape[ + -1 + ] # fast-forward to the next segment boundary + continue + + timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) + consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[ + 0 + ].add_(1) + if ( + len(consecutive) > 0 + ): # if the output contains two consecutive timestamp tokens + last_slice = 0 + for current_slice in consecutive: + sliced_tokens = tokens[last_slice:current_slice] + start_timestamp_position = ( + sliced_tokens[0].item() - tokenizer.timestamp_begin + ) + end_timestamp_position = ( + sliced_tokens[-1].item() - tokenizer.timestamp_begin + ) + add_segment( + start=timestamp_offset + + start_timestamp_position * time_precision, + end=timestamp_offset + end_timestamp_position * time_precision, + text_tokens=sliced_tokens[1:-1], + result=result, + ) + last_slice = current_slice + last_timestamp_position = ( + tokens[last_slice - 1].item() - tokenizer.timestamp_begin + ) + seek += last_timestamp_position * input_stride + all_tokens.extend(tokens[: last_slice + 1].tolist()) + else: + duration = segment_duration + timestamps = tokens[timestamp_tokens.nonzero().flatten()] + if ( + len(timestamps) > 0 + and timestamps[-1].item() != tokenizer.timestamp_begin + ): + # no consecutive timestamps but it has a timestamp; use the last one. + # single timestamp at the end means no speech after the last timestamp. + last_timestamp_position = ( + timestamps[-1].item() - tokenizer.timestamp_begin + ) + duration = last_timestamp_position * time_precision + + add_segment( + start=timestamp_offset, + end=timestamp_offset + duration, + text_tokens=tokens, + result=result, + ) + + seek += segment.shape[-1] + all_tokens.extend(tokens.tolist()) + + if not condition_on_previous_text or result.temperature > 0.5: + # do not feed the prompt tokens if a high temperature was used + prompt_reset_since = len(all_tokens) + + # update progress bar + pbar.update(min(num_frames, seek) - previous_seek_value) + previous_seek_value = seek + + return dict( + text=tokenizer.decode(all_tokens[len(initial_prompt) :]), + segments=all_segments, + language=language, + ) + + +def cli(): + from . import available_models + + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + parser.add_argument( + "audio", nargs="+", type=str, help="audio file(s) to transcribe" + ) + parser.add_argument( + "--model", + default="small", + choices=available_models(), + help="name of the Whisper model to use", + ) + parser.add_argument( + "--model_dir", + type=str, + default=None, + help="the path to save model files; uses ~/.cache/whisper by default", + ) + parser.add_argument( + "--device", + default="cuda" if torch.cuda.is_available() else "cpu", + help="device to use for PyTorch inference", + ) + parser.add_argument( + "--output_dir", + "-o", + type=str, + default=".", + help="directory to save the outputs", + ) + parser.add_argument( + "--output_format", + "-f", + type=str, + default="all", + choices=["txt", "vtt", "srt", "tsv", "json", "all"], + help="format of the output file; if not specified, all available formats will be produced", + ) + parser.add_argument( + "--verbose", + type=str2bool, + default=True, + help="whether to print out the progress and debug messages", + ) + + parser.add_argument( + "--task", + type=str, + default="transcribe", + choices=["transcribe", "translate"], + help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')", + ) + parser.add_argument( + "--language", + type=str, + default=None, + choices=sorted(LANGUAGES.keys()) + + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), + help="language spoken in the audio, specify None to perform language detection", + ) + + parser.add_argument( + "--temperature", type=float, default=0, help="temperature to use for sampling" + ) + parser.add_argument( + "--best_of", + type=optional_int, + default=5, + help="number of candidates when sampling with non-zero temperature", + ) + parser.add_argument( + "--beam_size", + type=optional_int, + default=5, + help="number of beams in beam search, only applicable when temperature is zero", + ) + parser.add_argument( + "--patience", + type=float, + default=None, + help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search", + ) + parser.add_argument( + "--length_penalty", + type=float, + default=None, + help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default", + ) + + parser.add_argument( + "--suppress_tokens", + type=str, + default="-1", + help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations", + ) + parser.add_argument( + "--initial_prompt", + type=str, + default=None, + help="optional text to provide as a prompt for the first window.", + ) + parser.add_argument( + "--condition_on_previous_text", + type=str2bool, + default=True, + help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop", + ) + parser.add_argument( + "--fp16", + type=str2bool, + default=True, + help="whether to perform inference in fp16; True by default", + ) + + parser.add_argument( + "--temperature_increment_on_fallback", + type=optional_float, + default=0.2, + help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below", + ) + parser.add_argument( + "--compression_ratio_threshold", + type=optional_float, + default=2.4, + help="if the gzip compression ratio is higher than this value, treat the decoding as failed", + ) + parser.add_argument( + "--logprob_threshold", + type=optional_float, + default=-1.0, + help="if the average log probability is lower than this value, treat the decoding as failed", + ) + parser.add_argument( + "--no_speech_threshold", + type=optional_float, + default=0.6, + help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence", + ) + parser.add_argument( + "--threads", + type=optional_int, + default=0, + help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS", + ) + + args = parser.parse_args().__dict__ + model_name: str = args.pop("model") + model_dir: str = args.pop("model_dir") + output_dir: str = args.pop("output_dir") + output_format: str = args.pop("output_format") + device: str = args.pop("device") + os.makedirs(output_dir, exist_ok=True) + + if model_name.endswith(".en") and args["language"] not in {"en", "English"}: + if args["language"] is not None: + warnings.warn( + f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead." + ) + args["language"] = "en" + + temperature = args.pop("temperature") + temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") + if temperature_increment_on_fallback is not None: + temperature = tuple( + np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback) + ) + else: + temperature = [temperature] + + threads = args.pop("threads") + if threads > 0: + torch.set_num_threads(threads) + + from . import load_model + + model = load_model(model_name, device=device, download_root=model_dir) + + writer = get_writer(output_format, output_dir) + + for audio_path in args.pop("audio"): + result = transcribe(model, audio_path, temperature=temperature, **args) + writer(result, audio_path) + + +if __name__ == "__main__": + cli() diff --git a/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/utils.py b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fadf21fe1fa1606d0fc34f5e0b6ab9ae9b6cdca5 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/whisper/utils/utils.py @@ -0,0 +1,172 @@ +import json +import os +import sys +import zlib +from typing import Callable, TextIO + +system_encoding = sys.getdefaultencoding() + +if system_encoding != "utf-8": + + def make_safe(string): + # replaces any character not representable using the system default encoding with an '?', + # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729). + return string.encode(system_encoding, errors="replace").decode(system_encoding) + +else: + + def make_safe(string): + # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding + return string + + +def exact_div(x, y): + assert x % y == 0 + return x // y + + +def str2bool(string): + str2val = {"True": True, "False": False} + if string in str2val: + return str2val[string] + else: + raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") + + +def optional_int(string): + return None if string == "None" else int(string) + + +def optional_float(string): + return None if string == "None" else float(string) + + +def compression_ratio(text) -> float: + text_bytes = text.encode("utf-8") + return len(text_bytes) / len(zlib.compress(text_bytes)) + + +def format_timestamp( + seconds: float, always_include_hours: bool = False, decimal_marker: str = "." +): + assert seconds >= 0, "non-negative timestamp expected" + milliseconds = round(seconds * 1000.0) + + hours = milliseconds // 3_600_000 + milliseconds -= hours * 3_600_000 + + minutes = milliseconds // 60_000 + milliseconds -= minutes * 60_000 + + seconds = milliseconds // 1_000 + milliseconds -= seconds * 1_000 + + hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" + return ( + f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + ) + + +class ResultWriter: + extension: str + + def __init__(self, output_dir: str): + self.output_dir = output_dir + + def __call__(self, result: dict, audio_path: str): + audio_basename = os.path.basename(audio_path) + output_path = os.path.join( + self.output_dir, audio_basename + "." + self.extension + ) + + with open(output_path, "w", encoding="utf-8") as f: + self.write_result(result, file=f) + + def write_result(self, result: dict, file: TextIO): + raise NotImplementedError + + +class WriteTXT(ResultWriter): + extension: str = "txt" + + def write_result(self, result: dict, file: TextIO): + for segment in result["segments"]: + print(segment["text"].strip(), file=file, flush=True) + + +class WriteVTT(ResultWriter): + extension: str = "vtt" + + def write_result(self, result: dict, file: TextIO): + print("WEBVTT\n", file=file) + for segment in result["segments"]: + print( + f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +class WriteSRT(ResultWriter): + extension: str = "srt" + + def write_result(self, result: dict, file: TextIO): + for i, segment in enumerate(result["segments"], start=1): + # write srt lines + print( + f"{i}\n" + f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " + f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +class WriteTSV(ResultWriter): + """ + Write a transcript to a file in TSV (tab-separated values) format containing lines like: + \t\t + + Using integer milliseconds as start and end times means there's no chance of interference from + an environment setting a language encoding that causes the decimal in a floating point number + to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++. + """ + + extension: str = "tsv" + + def write_result(self, result: dict, file: TextIO): + print("start", "end", "text", sep="\t", file=file) + for segment in result["segments"]: + print(round(1000 * segment["start"]), file=file, end="\t") + print(round(1000 * segment["end"]), file=file, end="\t") + print(segment["text"].strip().replace("\t", " "), file=file, flush=True) + + +class WriteJSON(ResultWriter): + extension: str = "json" + + def write_result(self, result: dict, file: TextIO): + json.dump(result, file) + + +def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]: + writers = { + "txt": WriteTXT, + "vtt": WriteVTT, + "srt": WriteSRT, + "tsv": WriteTSV, + "json": WriteJSON, + } + + if output_format == "all": + all_writers = [writer(output_dir) for writer in writers.values()] + + def write_all(result: dict, file: TextIO): + for writer in all_writers: + writer(result, file) + + return write_all + + return writers[output_format](output_dir) diff --git a/demo/Step-Audio-EditX/funasr_detach/models/xvector/__init__.py b/demo/Step-Audio-EditX/funasr_detach/models/xvector/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/models/xvector/e2e_sv.py b/demo/Step-Audio-EditX/funasr_detach/models/xvector/e2e_sv.py new file mode 100644 index 0000000000000000000000000000000000000000..613a848cb3a9f572d480d950adb0586b76c4bce8 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/models/xvector/e2e_sv.py @@ -0,0 +1,270 @@ +""" +Author: Speech Lab, Alibaba Group, China +""" + +import logging +from contextlib import contextmanager +from distutils.version import LooseVersion +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple +from typing import Union + +import torch + +from funasr_detach.layers.abs_normalize import AbsNormalize +from funasr_detach.losses.label_smoothing_loss import ( + LabelSmoothingLoss, # noqa: H301 +) +from funasr_detach.models.ctc import CTC +from funasr_detach.models.decoder.abs_decoder import AbsDecoder +from funasr_detach.models.encoder.abs_encoder import AbsEncoder +from funasr_detach.frontends.abs_frontend import AbsFrontend +from funasr_detach.models.postencoder.abs_postencoder import AbsPostEncoder +from funasr_detach.models.preencoder.abs_preencoder import AbsPreEncoder +from funasr_detach.models.specaug.abs_specaug import AbsSpecAug +from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos +from funasr_detach.metrics import ErrorCalculator +from funasr_detach.metrics.compute_acc import th_accuracy +from funasr_detach.train_utils.device_funcs import force_gatherable +from funasr_detach.models.base_model import FunASRModel + +if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): + from torch.cuda.amp import autocast +else: + # Nothing to do if torch<1.6.0 + @contextmanager + def autocast(enabled=True): + yield + + +class ESPnetSVModel(FunASRModel): + """CTC-attention hybrid Encoder-Decoder model""" + + def __init__( + self, + vocab_size: int, + token_list: Union[Tuple[str, ...], List[str]], + frontend: Optional[AbsFrontend], + specaug: Optional[AbsSpecAug], + normalize: Optional[AbsNormalize], + preencoder: Optional[AbsPreEncoder], + encoder: AbsEncoder, + postencoder: Optional[AbsPostEncoder], + pooling_layer: torch.nn.Module, + decoder: AbsDecoder, + ): + + super().__init__() + # note that eos is the same as sos (equivalent ID) + self.vocab_size = vocab_size + self.token_list = token_list.copy() + + self.frontend = frontend + self.specaug = specaug + self.normalize = normalize + self.preencoder = preencoder + self.postencoder = postencoder + self.encoder = encoder + self.pooling_layer = pooling_layer + self.decoder = decoder + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: + """Frontend + Encoder + Decoder + Calc loss + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + assert text_lengths.dim() == 1, text_lengths.shape + # Check that batch_size is unified + assert ( + speech.shape[0] + == speech_lengths.shape[0] + == text.shape[0] + == text_lengths.shape[0] + ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) + batch_size = speech.shape[0] + + # for data-parallel + text = text[:, : text_lengths.max()] + + # 1. Encoder + encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) + intermediate_outs = None + if isinstance(encoder_out, tuple): + intermediate_outs = encoder_out[1] + encoder_out = encoder_out[0] + + loss_att, acc_att, cer_att, wer_att = None, None, None, None + loss_ctc, cer_ctc = None, None + loss_transducer, cer_transducer, wer_transducer = None, None, None + stats = dict() + + # 1. CTC branch + if self.ctc_weight != 0.0: + loss_ctc, cer_ctc = self._calc_ctc_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # Collect CTC branch stats + stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None + stats["cer_ctc"] = cer_ctc + + # Intermediate CTC (optional) + loss_interctc = 0.0 + if self.interctc_weight != 0.0 and intermediate_outs is not None: + for layer_idx, intermediate_out in intermediate_outs: + # we assume intermediate_out has the same length & padding + # as those of encoder_out + loss_ic, cer_ic = self._calc_ctc_loss( + intermediate_out, encoder_out_lens, text, text_lengths + ) + loss_interctc = loss_interctc + loss_ic + + # Collect Intermedaite CTC stats + stats["loss_interctc_layer{}".format(layer_idx)] = ( + loss_ic.detach() if loss_ic is not None else None + ) + stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic + + loss_interctc = loss_interctc / len(intermediate_outs) + + # calculate whole encoder loss + loss_ctc = ( + 1 - self.interctc_weight + ) * loss_ctc + self.interctc_weight * loss_interctc + + if self.use_transducer_decoder: + # 2a. Transducer decoder branch + ( + loss_transducer, + cer_transducer, + wer_transducer, + ) = self._calc_transducer_loss( + encoder_out, + encoder_out_lens, + text, + ) + + if loss_ctc is not None: + loss = loss_transducer + (self.ctc_weight * loss_ctc) + else: + loss = loss_transducer + + # Collect Transducer branch stats + stats["loss_transducer"] = ( + loss_transducer.detach() if loss_transducer is not None else None + ) + stats["cer_transducer"] = cer_transducer + stats["wer_transducer"] = wer_transducer + + else: + # 2b. Attention decoder branch + if self.ctc_weight != 1.0: + loss_att, acc_att, cer_att, wer_att = self._calc_att_loss( + encoder_out, encoder_out_lens, text, text_lengths + ) + + # 3. CTC-Att loss definition + if self.ctc_weight == 0.0: + loss = loss_att + elif self.ctc_weight == 1.0: + loss = loss_ctc + else: + loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + + # Collect Attn branch stats + stats["loss_att"] = loss_att.detach() if loss_att is not None else None + stats["acc"] = acc_att + stats["cer"] = cer_att + stats["wer"] = wer_att + + # Collect total loss stats + stats["loss"] = torch.clone(loss.detach()) + + # force_gatherable: to-device and to-tensor if scalar for DataParallel + loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) + return loss, stats, weight + + def collect_feats( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Dict[str, torch.Tensor]: + if self.extract_feats_in_collect_stats: + feats, feats_lengths = self._extract_feats(speech, speech_lengths) + else: + # Generate dummy stats if extract_feats_in_collect_stats is False + logging.warning( + "Generating dummy stats for feats and feats_lengths, " + "because encoder_conf.extract_feats_in_collect_stats is " + f"{self.extract_feats_in_collect_stats}" + ) + feats, feats_lengths = speech, speech_lengths + return {"feats": feats, "feats_lengths": feats_lengths} + + def encode( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Frontend + Encoder. Note that this method is used by asr_inference.py + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + """ + with autocast(False): + # 1. Extract feats + feats, feats_lengths = self._extract_feats(speech, speech_lengths) + + # 2. Data augmentation + if self.specaug is not None and self.training: + feats, feats_lengths = self.specaug(feats, feats_lengths) + + # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN + if self.normalize is not None: + feats, feats_lengths = self.normalize(feats, feats_lengths) + + # Pre-encoder, e.g. used for raw input data + if self.preencoder is not None: + feats, feats_lengths = self.preencoder(feats, feats_lengths) + + # 4. Forward encoder + # feats: (Batch, Length, Dim) -> (Batch, Channel, Length2, Dim2) + encoder_out, encoder_out_lens = self.encoder(feats, feats_lengths) + + # Post-encoder, e.g. NLU + if self.postencoder is not None: + encoder_out, encoder_out_lens = self.postencoder( + encoder_out, encoder_out_lens + ) + + return encoder_out, encoder_out_lens + + def _extract_feats( + self, speech: torch.Tensor, speech_lengths: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + assert speech_lengths.dim() == 1, speech_lengths.shape + + # for data-parallel + speech = speech[:, : speech_lengths.max()] + + if self.frontend is not None: + # Frontend + # e.g. STFT and Feature extract + # data_loader may send time-domain signal in this case + # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim) + feats, feats_lengths = self.frontend(speech, speech_lengths) + else: + # No frontend and no feature extract + feats, feats_lengths = speech, speech_lengths + return feats, feats_lengths diff --git a/demo/Step-Audio-EditX/funasr_detach/optimizers/__init__.py b/demo/Step-Audio-EditX/funasr_detach/optimizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aa220473d7358550c3e360b3bc34ee2e77fe15ff --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/optimizers/__init__.py @@ -0,0 +1,17 @@ +import torch +from funasr_detach.optimizers.fairseq_adam import FairseqAdam +from funasr_detach.optimizers.sgd import SGD + +optim_classes = dict( + adam=torch.optim.Adam, + fairseq_adam=FairseqAdam, + adamw=torch.optim.AdamW, + sgd=SGD, + adadelta=torch.optim.Adadelta, + adagrad=torch.optim.Adagrad, + adamax=torch.optim.Adamax, + asgd=torch.optim.ASGD, + lbfgs=torch.optim.LBFGS, + rmsprop=torch.optim.RMSprop, + rprop=torch.optim.Rprop, +) diff --git a/demo/Step-Audio-EditX/funasr_detach/optimizers/fairseq_adam.py b/demo/Step-Audio-EditX/funasr_detach/optimizers/fairseq_adam.py new file mode 100644 index 0000000000000000000000000000000000000000..92d31d71e9889225c35298ff3de77edca8a77eea --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/optimizers/fairseq_adam.py @@ -0,0 +1,152 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import math + +import torch +import torch.optim + + +class FairseqAdam(torch.optim.Optimizer): + r"""Implements Adam algorithm. + + This implementation is modified from torch.optim.Adam based on: + `Fixed Weight Decay Regularization in Adam` + (see https://arxiv.org/abs/1711.05101) + + It has been proposed in `Adam: A Method for Stochastic Optimization`_. + + Args: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups + lr (float, optional): learning rate (default: 1e-3) + betas (Tuple[float, float], optional): coefficients used for computing + running averages of gradient and its square (default: (0.9, 0.999)) + eps (float, optional): term added to the denominator to improve + numerical stability (default: 1e-8) + weight_decay (float, optional): weight decay (L2 penalty) (default: 0) + amsgrad (boolean, optional): whether to use the AMSGrad variant of this + algorithm from the paper `On the Convergence of Adam and Beyond`_ + + .. _Adam\: A Method for Stochastic Optimization: + https://arxiv.org/abs/1412.6980 + .. _On the Convergence of Adam and Beyond: + https://openreview.net/forum?id=ryQu7f-RZ + """ + + def __init__( + self, + params, + lr=1e-3, + adam_betas=(0.9, 0.999), + adam_eps=1e-8, + weight_decay=0, + amsgrad=False, + ): + defaults = dict( + lr=lr, + betas=adam_betas, + eps=adam_eps, + weight_decay=weight_decay, + amsgrad=amsgrad, + ) + super(FairseqAdam, self).__init__(params, defaults) + self.optimizer_lr = lr + + @property + def supports_memory_efficient_fp16(self): + return True + + @property + def supports_flat_params(self): + return True + + def step(self, closure=None): + """Performs a single optimization step. + + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad.data + if grad.dtype in {torch.float16, torch.bfloat16}: + grad = grad.float() + if grad.is_sparse: + raise RuntimeError( + "Adam does not support sparse gradients, please consider SparseAdam instead" + ) + amsgrad = group.get("amsgrad", False) + + p_data_fp32 = p.data + if p.data.dtype in {torch.float16, torch.bfloat16}: + p_data_fp32 = p_data_fp32.float() + + state = self.state[p] + + # State initialization + if len(state) == 0: + state["step"] = 0 + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(p_data_fp32) + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) + if amsgrad: + # Maintains max of all exp. moving avg. of sq. grad. values + state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32) + else: + state["exp_avg"] = state["exp_avg"].to(p_data_fp32) + state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32) + if amsgrad: + state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to( + p_data_fp32 + ) + + exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] + if amsgrad: + max_exp_avg_sq = state["max_exp_avg_sq"] + beta1, beta2 = group["betas"] + + state["step"] += 1 + + # Decay the first and second moment running average coefficient + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) + exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) + if amsgrad: + # Maintains the maximum of all 2nd moment running avg. till now + torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) + # Use the max. for normalizing running avg. of gradient + denom = max_exp_avg_sq.sqrt().add_(group["eps"]) + else: + denom = exp_avg_sq.sqrt().add_(group["eps"]) + + bias_correction1 = 1 - beta1 ** state["step"] + bias_correction2 = 1 - beta2 ** state["step"] + step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1 + + if group["weight_decay"] != 0: + p_data_fp32.add_( + p_data_fp32, alpha=-group["weight_decay"] * group["lr"] + ) + + p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size) + + if p.data.dtype in {torch.float16, torch.bfloat16}: + p.data.copy_(p_data_fp32) + + return loss + + def set_lr(self, lr): + """Set the learning rate.""" + for param_group in self.param_groups: + param_group["lr"] = lr diff --git a/demo/Step-Audio-EditX/funasr_detach/optimizers/sgd.py b/demo/Step-Audio-EditX/funasr_detach/optimizers/sgd.py new file mode 100644 index 0000000000000000000000000000000000000000..84322174702b9954b427ef3861ca60639ef35048 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/optimizers/sgd.py @@ -0,0 +1,30 @@ +import torch + + +class SGD(torch.optim.SGD): + """Thin inheritance of torch.optim.SGD to bind the required arguments, 'lr' + + Note that + the arguments of the optimizer invoked by AbsTask.main() + must have default value except for 'param'. + + I can't understand why only SGD.lr doesn't have the default value. + """ + + def __init__( + self, + params, + lr: float = 0.1, + momentum: float = 0.0, + dampening: float = 0.0, + weight_decay: float = 0.0, + nesterov: bool = False, + ): + super().__init__( + params, + lr=lr, + momentum=momentum, + dampening=dampening, + weight_decay=weight_decay, + nesterov=nesterov, + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/register.py b/demo/Step-Audio-EditX/funasr_detach/register.py new file mode 100644 index 0000000000000000000000000000000000000000..cf9b175cce0f97027f2c110dc8d4a3c7ab4d6c61 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/register.py @@ -0,0 +1,93 @@ +import logging +import inspect +from dataclasses import dataclass +import re + + +@dataclass +class RegisterTables: + model_classes = {} + frontend_classes = {} + specaug_classes = {} + normalize_classes = {} + encoder_classes = {} + decoder_classes = {} + joint_network_classes = {} + predictor_classes = {} + stride_conv_classes = {} + tokenizer_classes = {} + batch_sampler_classes = {} + dataset_classes = {} + index_ds_classes = {} + + def print(self, key=None): + print("\ntables: \n") + fields = vars(self) + for classes_key, classes_dict in fields.items(): + + flag = True + if key is not None: + flag = key in classes_key + if classes_key.endswith("_meta") and flag: + print( + f"----------- ** {classes_key.replace('_meta', '')} ** --------------" + ) + headers = ["class name", "class location"] + metas = [] + for register_key, meta in classes_dict.items(): + metas.append(meta) + metas.sort(key=lambda x: x[0]) + data = [headers] + metas + col_widths = [max(len(str(item)) for item in col) for col in zip(*data)] + + for row in data: + print( + "| " + + " | ".join( + str(item).ljust(width) + for item, width in zip(row, col_widths) + ) + + " |" + ) + print("\n") + + def register(self, register_tables_key: str, key=None): + def decorator(target_class): + + if not hasattr(self, register_tables_key): + setattr(self, register_tables_key, {}) + logging.info( + "new registry table has been added: {}".format(register_tables_key) + ) + + registry = getattr(self, register_tables_key) + registry_key = key if key is not None else target_class.__name__ + + # assert not registry_key in registry, "(key: {} / class: {}) has been registered already，in {}".format( + # registry_key, target_class, register_tables_key) + + registry[registry_key] = target_class + + # meta， headers = ["class name", "register name", "class location"] + register_tables_key_meta = register_tables_key + "_meta" + if not hasattr(self, register_tables_key_meta): + setattr(self, register_tables_key_meta, {}) + registry_meta = getattr(self, register_tables_key_meta) + # doc = target_class.__doc__ + class_file = inspect.getfile(target_class) + class_line = inspect.getsourcelines(target_class)[1] + pattern = r"^.+/funasr/" + class_file = re.sub(pattern, "funasr/", class_file) + meata_data = [f"{target_class.__name__}", f"{class_file}:{class_line}"] + # meata_data = [f"{target_class.__name__}", f"{registry_key}", f"{class_file}:{class_line}"] + registry_meta[registry_key] = meata_data + # print(f"Registering class: {class_file}:{class_line} - {target_class.__name__} as {registry_key}") + return target_class + + return decorator + + +tables = RegisterTables() + +# from funasr_detach.models.paraformer_streaming.model import ParaformerStreaming +import funasr diff --git a/demo/Step-Audio-EditX/funasr_detach/schedulers/__init__.py b/demo/Step-Audio-EditX/funasr_detach/schedulers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aad5b87790989c82111c9d4f342a798c5fa05694 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/schedulers/__init__.py @@ -0,0 +1,23 @@ +import torch +import torch.multiprocessing +import torch.nn +import torch.optim + +from funasr_detach.schedulers.noam_lr import NoamLR +from funasr_detach.schedulers.tri_stage_scheduler import TriStageLR +from funasr_detach.schedulers.warmup_lr import WarmupLR + +scheduler_classes = dict( + ReduceLROnPlateau=torch.optim.lr_scheduler.ReduceLROnPlateau, + lambdalr=torch.optim.lr_scheduler.LambdaLR, + steplr=torch.optim.lr_scheduler.StepLR, + multisteplr=torch.optim.lr_scheduler.MultiStepLR, + exponentiallr=torch.optim.lr_scheduler.ExponentialLR, + CosineAnnealingLR=torch.optim.lr_scheduler.CosineAnnealingLR, + noamlr=NoamLR, + warmuplr=WarmupLR, + tri_stage=TriStageLR, + cycliclr=torch.optim.lr_scheduler.CyclicLR, + onecyclelr=torch.optim.lr_scheduler.OneCycleLR, + CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts, +) diff --git a/demo/Step-Audio-EditX/funasr_detach/schedulers/abs_scheduler.py b/demo/Step-Audio-EditX/funasr_detach/schedulers/abs_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..eb865ae521afeafa1e7ea819999dfd768e2284bc --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/schedulers/abs_scheduler.py @@ -0,0 +1,84 @@ +from abc import ABC +from abc import abstractmethod + +import torch.optim.lr_scheduler as L + + +class AbsScheduler(ABC): + @abstractmethod + def step(self, epoch: int = None): + pass + + @abstractmethod + def state_dict(self): + pass + + @abstractmethod + def load_state_dict(self, state): + pass + + +# If you need to define custom scheduler, please inherit these classes +class AbsBatchStepScheduler(AbsScheduler): + @abstractmethod + def step(self, epoch: int = None): + pass + + @abstractmethod + def state_dict(self): + pass + + @abstractmethod + def load_state_dict(self, state): + pass + + +class AbsEpochStepScheduler(AbsScheduler): + @abstractmethod + def step(self, epoch: int = None): + pass + + @abstractmethod + def state_dict(self): + pass + + @abstractmethod + def load_state_dict(self, state): + pass + + +class AbsValEpochStepScheduler(AbsEpochStepScheduler): + @abstractmethod + def step(self, val, epoch: int = None): + pass + + @abstractmethod + def state_dict(self): + pass + + @abstractmethod + def load_state_dict(self, state): + pass + + +# Create alias type to check the type +# Note(kamo): Currently PyTorch doesn't provide the base class +# to judge these classes. +AbsValEpochStepScheduler.register(L.ReduceLROnPlateau) +for s in [ + L.ReduceLROnPlateau, + L.LambdaLR, + L.StepLR, + L.MultiStepLR, + L.MultiStepLR, + L.ExponentialLR, + L.CosineAnnealingLR, +]: + AbsEpochStepScheduler.register(s) + +AbsBatchStepScheduler.register(L.CyclicLR) +for s in [ + L.OneCycleLR, + L.CosineAnnealingWarmRestarts, +]: + AbsBatchStepScheduler.register(s) diff --git a/demo/Step-Audio-EditX/funasr_detach/schedulers/noam_lr.py b/demo/Step-Audio-EditX/funasr_detach/schedulers/noam_lr.py new file mode 100644 index 0000000000000000000000000000000000000000..8899cc6c927c712934e624c4162ac78bebae69e3 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/schedulers/noam_lr.py @@ -0,0 +1,64 @@ +"""Noam learning rate scheduler module.""" + +from typing import Union +import warnings + +import torch +from torch.optim.lr_scheduler import _LRScheduler + +from funasr_detach.schedulers.abs_scheduler import AbsBatchStepScheduler + + +class NoamLR(_LRScheduler, AbsBatchStepScheduler): + """The LR scheduler proposed by Noam + + Ref: + "Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf + + FIXME(kamo): PyTorch doesn't provide _LRScheduler as public class, + thus the behaviour isn't guaranteed at forward PyTorch version. + + NOTE(kamo): The "model_size" in original implementation is derived from + the model, but in this implementation, this parameter is a constant value. + You need to change it if the model is changed. + + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + model_size: Union[int, float] = 320, + warmup_steps: Union[int, float] = 25000, + last_epoch: int = -1, + ): + self.model_size = model_size + self.warmup_steps = warmup_steps + + lr = list(optimizer.param_groups)[0]["lr"] + new_lr = self.lr_for_WarmupLR(lr) + warnings.warn( + f"NoamLR is deprecated. " + f"Use WarmupLR(warmup_steps={warmup_steps}) with Optimizer(lr={new_lr})", + ) + + # __init__() must be invoked before setting field + # because step() is also invoked in __init__() + super().__init__(optimizer, last_epoch) + + def lr_for_WarmupLR(self, lr: float) -> float: + return lr / self.model_size**0.5 / self.warmup_steps**0.5 + + def __repr__(self): + return ( + f"{self.__class__.__name__}(model_size={self.model_size}, " + f"warmup_steps={self.warmup_steps})" + ) + + def get_lr(self): + step_num = self.last_epoch + 1 + return [ + lr + * self.model_size**-0.5 + * min(step_num**-0.5, step_num * self.warmup_steps**-1.5) + for lr in self.base_lrs + ] diff --git a/demo/Step-Audio-EditX/funasr_detach/schedulers/tri_stage_scheduler.py b/demo/Step-Audio-EditX/funasr_detach/schedulers/tri_stage_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..06733a5cadd5cc8682fa1621fc03c57463a12879 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/schedulers/tri_stage_scheduler.py @@ -0,0 +1,106 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Optional, List + +import torch +from torch.optim.lr_scheduler import _LRScheduler + +from funasr_detach.schedulers.abs_scheduler import AbsBatchStepScheduler + + +class TriStageLR(_LRScheduler, AbsBatchStepScheduler): + def __init__( + self, + optimizer: torch.optim.Optimizer, + last_epoch: int = -1, + phase_ratio: Optional[List[float]] = None, + init_lr_scale: float = 0.01, + final_lr_scale: float = 0.01, + ): + self.optimizer = optimizer + self.last_epoch = last_epoch + self.phase_ratio = phase_ratio + self.init_lr_scale = init_lr_scale + self.final_lr_scale = final_lr_scale + self.optimizer_lr = self.optimizer.defaults["lr"] + + def init_tri_stage_scheudler(self, max_update): + self.max_update = max_update + self.peak_lr = self.optimizer_lr + self.init_lr = self.init_lr_scale * self.optimizer_lr + self.final_lr = self.final_lr_scale * self.optimizer_lr + + assert self.max_update > 0 + assert sum(self.phase_ratio) == 1, "phase ratios must add up to 1" + assert len(self.phase_ratio) == 3 + self.warmup_steps = int(self.max_update * self.phase_ratio[0]) + self.hold_steps = int(self.max_update * self.phase_ratio[1]) + self.decay_steps = int(self.max_update * self.phase_ratio[2]) + + self.warmup_rate = ( + (self.peak_lr - self.init_lr) / self.warmup_steps + if self.warmup_steps != 0 + else 0 + ) + self.decay_factor = -math.log(self.final_lr_scale) / self.decay_steps + + # initial learning rate + self.lr = self.init_lr + + # __init__() must be invoked before setting field + # because step() is also invoked in __init__() + self.set_optimizer_lr(self.lr) + super().__init__(self.optimizer, self.last_epoch) + + def _decide_stage(self, update_step): + """ + return stage, and the corresponding steps within the current stage + """ + if update_step < self.warmup_steps: + # warmup state + return 0, update_step + + offset = self.warmup_steps + + if update_step < offset + self.hold_steps: + # hold stage + return 1, update_step - offset + + offset += self.hold_steps + + if update_step <= offset + self.decay_steps: + # decay stage + return 2, update_step - offset + + offset += self.decay_steps + + # still here ? constant lr stage + return 3, update_step - offset + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + stage, steps_in_stage = self._decide_stage(num_updates) + if stage == 0: + self.lr = self.init_lr + self.warmup_rate * steps_in_stage + elif stage == 1: + self.lr = self.peak_lr + elif stage == 2: + self.lr = self.peak_lr * math.exp(-self.decay_factor * steps_in_stage) + elif stage == 3: + self.lr = self.final_lr + else: + raise ValueError("Undefined stage") + self.set_optimizer_lr(self.lr) + + def set_optimizer_lr(self, lr): + for param_group in self.optimizer.param_groups: + param_group["lr"] = lr + + def get_lr(self): + step_num = self.last_epoch + 1 + self.step_update(step_num) + return [self.lr] diff --git a/demo/Step-Audio-EditX/funasr_detach/schedulers/warmup_lr.py b/demo/Step-Audio-EditX/funasr_detach/schedulers/warmup_lr.py new file mode 100644 index 0000000000000000000000000000000000000000..d66e988c4526b8581153a382024ce9944ec0b545 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/schedulers/warmup_lr.py @@ -0,0 +1,49 @@ +"""Warm up learning rate scheduler module.""" + +from typing import Union + +import torch +from torch.optim.lr_scheduler import _LRScheduler + +from funasr_detach.schedulers.abs_scheduler import AbsBatchStepScheduler + + +class WarmupLR(_LRScheduler, AbsBatchStepScheduler): + """The WarmupLR scheduler + + This scheduler is almost same as NoamLR Scheduler except for following difference: + + NoamLR: + lr = optimizer.lr * model_size ** -0.5 + * min(step ** -0.5, step * warmup_step ** -1.5) + WarmupLR: + lr = optimizer.lr * warmup_step ** 0.5 + * min(step ** -0.5, step * warmup_step ** -1.5) + + Note that the maximum lr equals to optimizer.lr in this scheduler. + + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + warmup_steps: Union[int, float] = 25000, + last_epoch: int = -1, + ): + self.warmup_steps = warmup_steps + + # __init__() must be invoked before setting field + # because step() is also invoked in __init__() + super().__init__(optimizer, last_epoch) + + def __repr__(self): + return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})" + + def get_lr(self): + step_num = self.last_epoch + 1 + return [ + lr + * self.warmup_steps**0.5 + * min(step_num**-0.5, step_num * self.warmup_steps**-1.5) + for lr in self.base_lrs + ] diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/__init__.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/abs_tokenizer.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/abs_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..4923cf9e4aff1ad506203e2e7b4037f22aa9b14d --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/tokenizer/abs_tokenizer.py @@ -0,0 +1,95 @@ +import json +import numpy as np +from abc import ABC +from pathlib import Path +from abc import abstractmethod +from typing import Union, Iterable, List, Dict + + +class AbsTokenizer(ABC): + @abstractmethod + def text2tokens(self, line: str) -> List[str]: + raise NotImplementedError + + @abstractmethod + def tokens2text(self, tokens: Iterable[str]) -> str: + raise NotImplementedError + + +class BaseTokenizer(ABC): + def __init__( + self, + token_list: Union[Path, str, Iterable[str]] = None, + unk_symbol: str = "", + **kwargs, + ): + + if token_list is not None: + if isinstance(token_list, (Path, str)) and token_list.endswith(".txt"): + token_list = Path(token_list) + self.token_list_repr = str(token_list) + self.token_list: List[str] = [] + + with token_list.open("r", encoding="utf-8") as f: + for idx, line in enumerate(f): + line = line.rstrip() + self.token_list.append(line) + elif isinstance(token_list, (Path, str)) and token_list.endswith(".json"): + token_list = Path(token_list) + self.token_list_repr = str(token_list) + self.token_list: List[str] = [] + + with open(token_list, "r", encoding="utf-8") as f: + self.token_list = json.load(f) + + else: + self.token_list: List[str] = list(token_list) + self.token_list_repr = "" + for i, t in enumerate(self.token_list): + if i == 3: + break + self.token_list_repr += f"{t}, " + self.token_list_repr += f"... (NVocab={(len(self.token_list))})" + + self.token2id: Dict[str, int] = {} + for i, t in enumerate(self.token_list): + if t in self.token2id: + raise RuntimeError(f'Symbol "{t}" is duplicated') + self.token2id[t] = i + + self.unk_symbol = unk_symbol + if self.unk_symbol not in self.token2id: + raise RuntimeError( + f"Unknown symbol '{unk_symbol}' doesn't exist in the token_list" + ) + self.unk_id = self.token2id[self.unk_symbol] + + def encode(self, text): + tokens = self.text2tokens(text) + text_ints = self.tokens2ids(tokens) + + return text_ints + + def decode(self, text_ints): + token = self.ids2tokens(text_ints) + text = self.tokens2text(token) + return text + + def get_num_vocabulary_size(self) -> int: + return len(self.token_list) + + def ids2tokens(self, integers: Union[np.ndarray, Iterable[int]]) -> List[str]: + if isinstance(integers, np.ndarray) and integers.ndim != 1: + raise ValueError(f"Must be 1 dim ndarray, but got {integers.ndim}") + return [self.token_list[i] for i in integers] + + def tokens2ids(self, tokens: Iterable[str]) -> List[int]: + return [self.token2id.get(i, self.unk_id) for i in tokens] + + @abstractmethod + def text2tokens(self, line: str) -> List[str]: + raise NotImplementedError + + @abstractmethod + def tokens2text(self, tokens: Iterable[str]) -> str: + raise NotImplementedError diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/build_tokenizer.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/build_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..f1239e254188061081625564b30fcb39c9050645 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/tokenizer/build_tokenizer.py @@ -0,0 +1,61 @@ +from pathlib import Path +from typing import Iterable +from typing import Union + + +from funasr_detach.tokenizer.abs_tokenizer import AbsTokenizer +from funasr_detach.tokenizer.char_tokenizer import CharTokenizer +from funasr_detach.tokenizer.phoneme_tokenizer import PhonemeTokenizer +from funasr_detach.tokenizer.sentencepiece_tokenizer import SentencepiecesTokenizer +from funasr_detach.tokenizer.word_tokenizer import WordTokenizer + + +def build_tokenizer( + token_type: str, + bpemodel: Union[Path, str, Iterable[str]] = None, + non_linguistic_symbols: Union[Path, str, Iterable[str]] = None, + remove_non_linguistic_symbols: bool = False, + space_symbol: str = "", + delimiter: str = None, + g2p_type: str = None, +) -> AbsTokenizer: + """A helper function to instantiate Tokenizer""" + if token_type == "bpe": + if bpemodel is None: + raise ValueError('bpemodel is required if token_type = "bpe"') + + if remove_non_linguistic_symbols: + raise RuntimeError( + "remove_non_linguistic_symbols is not implemented for token_type=bpe" + ) + return SentencepiecesTokenizer(bpemodel) + + elif token_type == "word": + if remove_non_linguistic_symbols and non_linguistic_symbols is not None: + return WordTokenizer( + delimiter=delimiter, + non_linguistic_symbols=non_linguistic_symbols, + remove_non_linguistic_symbols=True, + ) + else: + return WordTokenizer(delimiter=delimiter) + + elif token_type == "char": + return CharTokenizer( + non_linguistic_symbols=non_linguistic_symbols, + space_symbol=space_symbol, + remove_non_linguistic_symbols=remove_non_linguistic_symbols, + ) + + elif token_type == "phn": + return PhonemeTokenizer( + g2p_type=g2p_type, + non_linguistic_symbols=non_linguistic_symbols, + space_symbol=space_symbol, + remove_non_linguistic_symbols=remove_non_linguistic_symbols, + ) + + else: + raise ValueError( + f"token_mode must be one of bpe, word, char or phn: " f"{token_type}" + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/char_tokenizer.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/char_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..a9e589633f22176d60971ac5490d5069d3dd63b5 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/tokenizer/char_tokenizer.py @@ -0,0 +1,111 @@ +from pathlib import Path +from typing import Iterable +from typing import List +from typing import Union +import warnings +import re + +from funasr_detach.tokenizer.abs_tokenizer import BaseTokenizer +from funasr_detach.register import tables + + +@tables.register("tokenizer_classes", "CharTokenizer") +class CharTokenizer(BaseTokenizer): + def __init__( + self, + non_linguistic_symbols: Union[Path, str, Iterable[str]] = None, + space_symbol: str = "", + remove_non_linguistic_symbols: bool = False, + split_with_space: bool = False, + seg_dict: str = None, + **kwargs, + ): + super().__init__(**kwargs) + self.space_symbol = space_symbol + if non_linguistic_symbols is None: + self.non_linguistic_symbols = set() + elif isinstance(non_linguistic_symbols, (Path, str)): + non_linguistic_symbols = Path(non_linguistic_symbols) + try: + with non_linguistic_symbols.open("r", encoding="utf-8") as f: + self.non_linguistic_symbols = set(line.rstrip() for line in f) + except FileNotFoundError: + warnings.warn(f"{non_linguistic_symbols} doesn't exist.") + self.non_linguistic_symbols = set() + else: + self.non_linguistic_symbols = set(non_linguistic_symbols) + self.remove_non_linguistic_symbols = remove_non_linguistic_symbols + self.split_with_space = split_with_space + self.seg_dict = None + if seg_dict is not None: + self.seg_dict = load_seg_dict(seg_dict) + + def __repr__(self): + return ( + f"{self.__class__.__name__}(" + f'space_symbol="{self.space_symbol}"' + f'non_linguistic_symbols="{self.non_linguistic_symbols}"' + f")" + ) + + def text2tokens(self, line: Union[str, list]) -> List[str]: + + # if self.split_with_space: + + if self.seg_dict is not None: + tokens = line.strip().split(" ") + tokens = seg_tokenize(tokens, self.seg_dict) + else: + tokens = [] + while len(line) != 0: + for w in self.non_linguistic_symbols: + if line.startswith(w): + if not self.remove_non_linguistic_symbols: + tokens.append(line[: len(w)]) + line = line[len(w) :] + break + else: + t = line[0] + if t == " ": + # t = "" + line = line[1:] + continue + tokens.append(t) + line = line[1:] + return tokens + + def tokens2text(self, tokens: Iterable[str]) -> str: + tokens = [t if t != self.space_symbol else " " for t in tokens] + return "".join(tokens) + + +def load_seg_dict(seg_dict_file): + seg_dict = {} + assert isinstance(seg_dict_file, str) + with open(seg_dict_file, "r", encoding="utf8") as f: + lines = f.readlines() + for line in lines: + s = line.strip().split() + key = s[0] + value = s[1:] + seg_dict[key] = " ".join(value) + return seg_dict + + +def seg_tokenize(txt, seg_dict): + pattern = re.compile(r"^[\u4E00-\u9FA50-9]+$") + out_txt = "" + for word in txt: + word = word.lower() + if word in seg_dict: + out_txt += seg_dict[word] + " " + else: + if pattern.match(word): + for char in word: + if char in seg_dict: + out_txt += seg_dict[char] + " " + else: + out_txt += "" + " " + else: + out_txt += "" + " " + return out_txt.strip().split() diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/cleaner.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/cleaner.py new file mode 100644 index 0000000000000000000000000000000000000000..d71ac768d690f2490c47823b3cf7d98f74778955 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/tokenizer/cleaner.py @@ -0,0 +1,48 @@ +from typing import Collection + +from jaconv import jaconv + +# import tacotron_cleaner.cleaners + +try: + from vietnamese_cleaner import vietnamese_cleaners +except ImportError: + vietnamese_cleaners = None + + +class TextCleaner: + """Text cleaner. + + Examples: + >>> cleaner = TextCleaner("tacotron") + >>> cleaner("(Hello-World); & jr. & dr.") + 'HELLO WORLD, AND JUNIOR AND DOCTOR' + + """ + + def __init__(self, cleaner_types: Collection[str] = None): + + if cleaner_types is None: + self.cleaner_types = [] + elif isinstance(cleaner_types, str): + self.cleaner_types = [cleaner_types] + else: + self.cleaner_types = list(cleaner_types) + + def __call__(self, text: str) -> str: + for t in self.cleaner_types: + if t == "tacotron": + # text = tacotron_cleaner.cleaners.custom_english_cleaners(text) + pass + elif t == "jaconv": + text = jaconv.normalize(text) + elif t == "vietnamese": + if vietnamese_cleaners is None: + raise RuntimeError("Please install underthesea") + text = vietnamese_cleaners.vietnamese_cleaner(text) + elif t == "korean_cleaner": + text = KoreanCleaner.normalize_text(text) + else: + raise RuntimeError(f"Not supported: type={t}") + + return text diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/korean_cleaner.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/korean_cleaner.py new file mode 100644 index 0000000000000000000000000000000000000000..6c1253903ad7f8110f20e5d15d1113ab65e943c9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/tokenizer/korean_cleaner.py @@ -0,0 +1,79 @@ +# Referenced from https://github.com/hccho2/Tacotron-Wavenet-Vocoder-Korean + +import re + + +class KoreanCleaner: + @classmethod + def _normalize_numbers(cls, text): + number_to_kor = { + "0": "영", + "1": "일", + "2": "이", + "3": "삼", + "4": "사", + "5": "오", + "6": "육", + "7": "칠", + "8": "팔", + "9": "구", + } + new_text = "".join( + number_to_kor[char] if char in number_to_kor.keys() else char + for char in text + ) + return new_text + + @classmethod + def _normalize_english_text(cls, text): + upper_alphabet_to_kor = { + "A": "에이", + "B": "비", + "C": "씨", + "D": "디", + "E": "이", + "F": "에프", + "G": "지", + "H": "에이치", + "I": "아이", + "J": "제이", + "K": "케이", + "L": "엘", + "M": "엠", + "N": "엔", + "O": "오", + "P": "피", + "Q": "큐", + "R": "알", + "S": "에스", + "T": "티", + "U": "유", + "V": "브이", + "W": "더블유", + "X": "엑스", + "Y": "와이", + "Z": "지", + } + new_text = re.sub("[a-z]+", lambda x: str.upper(x.group()), text) + new_text = "".join( + ( + upper_alphabet_to_kor[char] + if char in upper_alphabet_to_kor.keys() + else char + ) + for char in new_text + ) + + return new_text + + @classmethod + def normalize_text(cls, text): + # stage 0 : text strip + text = text.strip() + + # stage 1 : normalize numbers + text = cls._normalize_numbers(text) + + # stage 2 : normalize english text + text = cls._normalize_english_text(text) + return text diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/phoneme_tokenizer.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/phoneme_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..a6850c5b05ddc6eced796145797b442ad98ecbe9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/tokenizer/phoneme_tokenizer.py @@ -0,0 +1,526 @@ +import logging +from pathlib import Path +import re +from typing import Iterable +from typing import List +from typing import Optional +from typing import Union +import warnings + +# import g2p_en +import jamo + +from funasr_detach.tokenizer.abs_tokenizer import AbsTokenizer + + +g2p_classes = [ + None, + "g2p_en", + "g2p_en_no_space", + "pyopenjtalk", + "pyopenjtalk_kana", + "pyopenjtalk_accent", + "pyopenjtalk_accent_with_pause", + "pyopenjtalk_prosody", + "pypinyin_g2p", + "pypinyin_g2p_phone", + "espeak_ng_arabic", + "espeak_ng_german", + "espeak_ng_french", + "espeak_ng_spanish", + "espeak_ng_russian", + "espeak_ng_greek", + "espeak_ng_finnish", + "espeak_ng_hungarian", + "espeak_ng_dutch", + "espeak_ng_english_us_vits", + "espeak_ng_hindi", + "g2pk", + "g2pk_no_space", + "korean_jaso", + "korean_jaso_no_space", +] + + +def split_by_space(text) -> List[str]: + if " " in text: + text = text.replace(" ", " ") + return [c.replace("", " ") for c in text.split(" ")] + else: + return text.split(" ") + + +def pyopenjtalk_g2p(text) -> List[str]: + import pyopenjtalk + + # phones is a str object separated by space + phones = pyopenjtalk.g2p(text, kana=False) + phones = phones.split(" ") + return phones + + +def pyopenjtalk_g2p_accent(text) -> List[str]: + import pyopenjtalk + import re + + phones = [] + for labels in pyopenjtalk.run_frontend(text)[1]: + p = re.findall(r"\-(.*?)\+.*?\/A:([0-9\-]+).*?\/F:.*?_([0-9]+)", labels) + if len(p) == 1: + phones += [p[0][0], p[0][2], p[0][1]] + return phones + + +def pyopenjtalk_g2p_accent_with_pause(text) -> List[str]: + import pyopenjtalk + import re + + phones = [] + for labels in pyopenjtalk.run_frontend(text)[1]: + if labels.split("-")[1].split("+")[0] == "pau": + phones += ["pau"] + continue + p = re.findall(r"\-(.*?)\+.*?\/A:([0-9\-]+).*?\/F:.*?_([0-9]+)", labels) + if len(p) == 1: + phones += [p[0][0], p[0][2], p[0][1]] + return phones + + +def pyopenjtalk_g2p_kana(text) -> List[str]: + import pyopenjtalk + + kanas = pyopenjtalk.g2p(text, kana=True) + return list(kanas) + + +def pyopenjtalk_g2p_prosody(text: str, drop_unvoiced_vowels: bool = True) -> List[str]: + """Extract phoneme + prosoody symbol sequence from input full-context labels. + + The algorithm is based on `Prosodic features control by symbols as input of + sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks. + + Args: + text (str): Input text. + drop_unvoiced_vowels (bool): whether to drop unvoiced vowels. + + Returns: + List[str]: List of phoneme + prosody symbols. + + Examples: + >>> from funasr_detach.tokenizer.phoneme_tokenizer import pyopenjtalk_g2p_prosody + >>> pyopenjtalk_g2p_prosody("こんにちは。") + ['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$'] + + .. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic + modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104 + + """ + import pyopenjtalk + + labels = pyopenjtalk.run_frontend(text)[1] + N = len(labels) + + phones = [] + for n in range(N): + lab_curr = labels[n] + + # current phoneme + p3 = re.search(r"\-(.*?)\+", lab_curr).group(1) + + # deal unvoiced vowels as normal vowels + if drop_unvoiced_vowels and p3 in "AEIOU": + p3 = p3.lower() + + # deal with sil at the beginning and the end of text + if p3 == "sil": + assert n == 0 or n == N - 1 + if n == 0: + phones.append("^") + elif n == N - 1: + # check question form or not + e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr) + if e3 == 0: + phones.append("$") + elif e3 == 1: + phones.append("?") + continue + elif p3 == "pau": + phones.append("_") + continue + else: + phones.append(p3) + + # accent type and position info (forward or backward) + a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr) + a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr) + a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr) + + # number of mora in accent phrase + f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr) + + a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1]) + # accent phrase border + if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl": + phones.append("#") + # pitch falling + elif a1 == 0 and a2_next == a2 + 1 and a2 != f1: + phones.append("]") + # pitch rising + elif a2 == 1 and a2_next == 2: + phones.append("[") + + return phones + + +def _numeric_feature_by_regex(regex, s): + match = re.search(regex, s) + if match is None: + return -50 + return int(match.group(1)) + + +def pypinyin_g2p(text) -> List[str]: + from pypinyin import pinyin + from pypinyin import Style + + phones = [phone[0] for phone in pinyin(text, style=Style.TONE3)] + return phones + + +def pypinyin_g2p_phone(text) -> List[str]: + from pypinyin import pinyin + from pypinyin import Style + from pypinyin.style._utils import get_finals + from pypinyin.style._utils import get_initials + + phones = [ + p + for phone in pinyin(text, style=Style.TONE3) + for p in [ + get_initials(phone[0], strict=True), + get_finals(phone[0], strict=True), + ] + if len(p) != 0 + ] + return phones + + +class G2p_en: + """On behalf of g2p_en.G2p. + + g2p_en.G2p isn't pickalable and it can't be copied to the other processes + via multiprocessing module. + As a workaround, g2p_en.G2p is instantiated upon calling this class. + + """ + + def __init__(self, no_space: bool = False): + self.no_space = no_space + self.g2p = None + + def __call__(self, text) -> List[str]: + if self.g2p is None: + self.g2p = g2p_en.G2p() + + phones = self.g2p(text) + if self.no_space: + # remove space which represents word serapater + phones = list(filter(lambda s: s != " ", phones)) + return phones + + +class G2pk: + """On behalf of g2pk.G2p. + + g2pk.G2p isn't pickalable and it can't be copied to the other processes + via multiprocessing module. + As a workaround, g2pk.G2p is instantiated upon calling this class. + + """ + + def __init__( + self, descritive=False, group_vowels=False, to_syl=False, no_space=False + ): + self.descritive = descritive + self.group_vowels = group_vowels + self.to_syl = to_syl + self.no_space = no_space + self.g2p = None + + def __call__(self, text) -> List[str]: + if self.g2p is None: + import g2pk + + self.g2p = g2pk.G2p() + + phones = list( + self.g2p( + text, + descriptive=self.descritive, + group_vowels=self.group_vowels, + to_syl=self.to_syl, + ) + ) + if self.no_space: + # remove space which represents word serapater + phones = list(filter(lambda s: s != " ", phones)) + return phones + + +class Jaso: + PUNC = "!'(),-.:;?" + SPACE = " " + + JAMO_LEADS = "".join([chr(_) for _ in range(0x1100, 0x1113)]) + JAMO_VOWELS = "".join([chr(_) for _ in range(0x1161, 0x1176)]) + JAMO_TAILS = "".join([chr(_) for _ in range(0x11A8, 0x11C3)]) + + VALID_CHARS = JAMO_LEADS + JAMO_VOWELS + JAMO_TAILS + PUNC + SPACE + + def __init__(self, space_symbol=" ", no_space=False): + self.space_symbol = space_symbol + self.no_space = no_space + + def _text_to_jaso(self, line: str) -> List[str]: + jasos = list(jamo.hangul_to_jamo(line)) + return jasos + + def _remove_non_korean_characters(self, tokens): + new_tokens = [token for token in tokens if token in self.VALID_CHARS] + return new_tokens + + def __call__(self, text) -> List[str]: + graphemes = [x for x in self._text_to_jaso(text)] + graphemes = self._remove_non_korean_characters(graphemes) + + if self.no_space: + graphemes = list(filter(lambda s: s != " ", graphemes)) + else: + graphemes = [x if x != " " else self.space_symbol for x in graphemes] + return graphemes + + +class Phonemizer: + """Phonemizer module for various languages. + + This is wrapper module of https://github.com/bootphon/phonemizer. + You can define various g2p modules by specifying options for phonemizer. + + See available options: + https://github.com/bootphon/phonemizer/blob/master/phonemizer/phonemize.py#L32 + + """ + + def __init__( + self, + backend, + word_separator: Optional[str] = None, + syllable_separator: Optional[str] = None, + phone_separator: Optional[str] = " ", + strip=False, + split_by_single_token: bool = False, + **phonemizer_kwargs, + ): + # delayed import + from phonemizer.backend import BACKENDS + from phonemizer.separator import Separator + + self.separator = Separator( + word=word_separator, + syllable=syllable_separator, + phone=phone_separator, + ) + + # define logger to suppress the warning in phonemizer + logger = logging.getLogger("phonemizer") + logger.setLevel(logging.ERROR) + self.phonemizer = BACKENDS[backend]( + **phonemizer_kwargs, + logger=logger, + ) + self.strip = strip + self.split_by_single_token = split_by_single_token + + def __call__(self, text) -> List[str]: + tokens = self.phonemizer.phonemize( + [text], + separator=self.separator, + strip=self.strip, + njobs=1, + )[0] + if not self.split_by_single_token: + return tokens.split() + else: + # "a: ab" -> ["a", ":", "", "a", "b"] + # TODO(kan-bayashi): space replacement should be dealt in PhonemeTokenizer + return [c.replace(" ", "") for c in tokens] + + +class PhonemeTokenizer(AbsTokenizer): + def __init__( + self, + g2p_type: Union[None, str], + non_linguistic_symbols: Union[Path, str, Iterable[str]] = None, + space_symbol: str = "", + remove_non_linguistic_symbols: bool = False, + ): + if g2p_type is None: + self.g2p = split_by_space + elif g2p_type == "g2p_en": + self.g2p = G2p_en(no_space=False) + elif g2p_type == "g2p_en_no_space": + self.g2p = G2p_en(no_space=True) + elif g2p_type == "pyopenjtalk": + self.g2p = pyopenjtalk_g2p + elif g2p_type == "pyopenjtalk_kana": + self.g2p = pyopenjtalk_g2p_kana + elif g2p_type == "pyopenjtalk_accent": + self.g2p = pyopenjtalk_g2p_accent + elif g2p_type == "pyopenjtalk_accent_with_pause": + self.g2p = pyopenjtalk_g2p_accent_with_pause + elif g2p_type == "pyopenjtalk_prosody": + self.g2p = pyopenjtalk_g2p_prosody + elif g2p_type == "pypinyin_g2p": + self.g2p = pypinyin_g2p + elif g2p_type == "pypinyin_g2p_phone": + self.g2p = pypinyin_g2p_phone + elif g2p_type == "espeak_ng_arabic": + self.g2p = Phonemizer( + language="ar", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "espeak_ng_german": + self.g2p = Phonemizer( + language="de", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "espeak_ng_french": + self.g2p = Phonemizer( + language="fr-fr", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "espeak_ng_spanish": + self.g2p = Phonemizer( + language="es", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "espeak_ng_russian": + self.g2p = Phonemizer( + language="ru", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "espeak_ng_greek": + self.g2p = Phonemizer( + language="el", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "espeak_ng_finnish": + self.g2p = Phonemizer( + language="fi", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "espeak_ng_hungarian": + self.g2p = Phonemizer( + language="hu", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "espeak_ng_dutch": + self.g2p = Phonemizer( + language="nl", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "espeak_ng_hindi": + self.g2p = Phonemizer( + language="hi", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + ) + elif g2p_type == "g2pk": + self.g2p = G2pk(no_space=False) + elif g2p_type == "g2pk_no_space": + self.g2p = G2pk(no_space=True) + elif g2p_type == "espeak_ng_english_us_vits": + # VITS official implementation-like processing + # Reference: https://github.com/jaywalnut310/vits + self.g2p = Phonemizer( + language="en-us", + backend="espeak", + with_stress=True, + preserve_punctuation=True, + strip=True, + word_separator=" ", + phone_separator="", + split_by_single_token=True, + ) + elif g2p_type == "korean_jaso": + self.g2p = Jaso(space_symbol=space_symbol, no_space=False) + elif g2p_type == "korean_jaso_no_space": + self.g2p = Jaso(no_space=True) + else: + raise NotImplementedError(f"Not supported: g2p_type={g2p_type}") + + self.g2p_type = g2p_type + self.space_symbol = space_symbol + if non_linguistic_symbols is None: + self.non_linguistic_symbols = set() + elif isinstance(non_linguistic_symbols, (Path, str)): + non_linguistic_symbols = Path(non_linguistic_symbols) + try: + with non_linguistic_symbols.open("r", encoding="utf-8") as f: + self.non_linguistic_symbols = set(line.rstrip() for line in f) + except FileNotFoundError: + warnings.warn(f"{non_linguistic_symbols} doesn't exist.") + self.non_linguistic_symbols = set() + else: + self.non_linguistic_symbols = set(non_linguistic_symbols) + self.remove_non_linguistic_symbols = remove_non_linguistic_symbols + + def __repr__(self): + return ( + f"{self.__class__.__name__}(" + f'g2p_type="{self.g2p_type}", ' + f'space_symbol="{self.space_symbol}", ' + f'non_linguistic_symbols="{self.non_linguistic_symbols}"' + ")" + ) + + def text2tokens(self, line: str) -> List[str]: + tokens = [] + while len(line) != 0: + for w in self.non_linguistic_symbols: + if line.startswith(w): + if not self.remove_non_linguistic_symbols: + tokens.append(line[: len(w)]) + line = line[len(w) :] + break + else: + t = line[0] + tokens.append(t) + line = line[1:] + + line = "".join(tokens) + tokens = self.g2p(line) + return tokens + + def tokens2text(self, tokens: Iterable[str]) -> str: + # phoneme type is not invertible + return "".join(tokens) diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/sentencepiece_tokenizer.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/sentencepiece_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..e59eda629752367b9f92ffc55fe894be77bf6fef --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/tokenizer/sentencepiece_tokenizer.py @@ -0,0 +1,47 @@ +from pathlib import Path +from typing import Iterable +from typing import List +from typing import Union + +import sentencepiece as spm + +from funasr_detach.tokenizer.abs_tokenizer import BaseTokenizer +from funasr_detach.register import tables + + +@tables.register("tokenizer_classes", "SentencepiecesTokenizer") +class SentencepiecesTokenizer(BaseTokenizer): + def __init__(self, bpemodel: Union[Path, str], **kwargs): + super().__init__(**kwargs) + self.bpemodel = str(bpemodel) + # NOTE(kamo): + # Don't build SentencePieceProcessor in __init__() + # because it's not picklable and it may cause following error, + # "TypeError: can't pickle SwigPyObject objects", + # when giving it as argument of "multiprocessing.Process()". + self.sp = None + + def __repr__(self): + return f'{self.__class__.__name__}(model="{self.bpemodel}")' + + def _build_sentence_piece_processor(self): + # Build SentencePieceProcessor lazily. + if self.sp is None: + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.bpemodel) + + def text2tokens(self, line: str) -> List[str]: + self._build_sentence_piece_processor() + return self.sp.EncodeAsPieces(line) + + def tokens2text(self, tokens: Iterable[str]) -> str: + self._build_sentence_piece_processor() + return self.sp.DecodePieces(list(tokens)) + + def encode(self, line: str) -> List[int]: + self._build_sentence_piece_processor() + return self.sp.EncodeAsIds(line) + + def decode(self, line: List[int]): + self._build_sentence_piece_processor() + return self.sp.DecodeIds(line) diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/token_id_converter.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/token_id_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..2b9c78365099461a0f59eae86a7c49c38f3a5348 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/tokenizer/token_id_converter.py @@ -0,0 +1,58 @@ +from pathlib import Path +from typing import Dict +from typing import Iterable +from typing import List +from typing import Union + +import numpy as np + + +class TokenIDConverter: + def __init__( + self, + token_list: Union[Path, str, Iterable[str]], + unk_symbol: str = "", + ): + + if isinstance(token_list, (Path, str)): + token_list = Path(token_list) + self.token_list_repr = str(token_list) + self.token_list: List[str] = [] + + with token_list.open("r", encoding="utf-8") as f: + for idx, line in enumerate(f): + line = line.rstrip() + self.token_list.append(line) + + else: + self.token_list: List[str] = list(token_list) + self.token_list_repr = "" + for i, t in enumerate(self.token_list): + if i == 3: + break + self.token_list_repr += f"{t}, " + self.token_list_repr += f"... (NVocab={(len(self.token_list))})" + + self.token2id: Dict[str, int] = {} + for i, t in enumerate(self.token_list): + if t in self.token2id: + raise RuntimeError(f'Symbol "{t}" is duplicated') + self.token2id[t] = i + + self.unk_symbol = unk_symbol + if self.unk_symbol not in self.token2id: + raise RuntimeError( + f"Unknown symbol '{unk_symbol}' doesn't exist in the token_list" + ) + self.unk_id = self.token2id[self.unk_symbol] + + def get_num_vocabulary_size(self) -> int: + return len(self.token_list) + + def ids2tokens(self, integers: Union[np.ndarray, Iterable[int]]) -> List[str]: + if isinstance(integers, np.ndarray) and integers.ndim != 1: + raise ValueError(f"Must be 1 dim ndarray, but got {integers.ndim}") + return [self.token_list[i] for i in integers] + + def tokens2ids(self, tokens: Iterable[str]) -> List[int]: + return [self.token2id.get(i, self.unk_id) for i in tokens] diff --git a/demo/Step-Audio-EditX/funasr_detach/tokenizer/word_tokenizer.py b/demo/Step-Audio-EditX/funasr_detach/tokenizer/word_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..a65642e9c220f61379de1591478520480016c15c --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/tokenizer/word_tokenizer.py @@ -0,0 +1,56 @@ +from pathlib import Path +from typing import Iterable +from typing import List +from typing import Union +import warnings + + +from funasr_detach.tokenizer.abs_tokenizer import AbsTokenizer + + +class WordTokenizer(AbsTokenizer): + def __init__( + self, + delimiter: str = None, + non_linguistic_symbols: Union[Path, str, Iterable[str]] = None, + remove_non_linguistic_symbols: bool = False, + ): + self.delimiter = delimiter + + if not remove_non_linguistic_symbols and non_linguistic_symbols is not None: + warnings.warn( + "non_linguistic_symbols is only used " + "when remove_non_linguistic_symbols = True" + ) + + if non_linguistic_symbols is None: + self.non_linguistic_symbols = set() + elif isinstance(non_linguistic_symbols, (Path, str)): + non_linguistic_symbols = Path(non_linguistic_symbols) + try: + with non_linguistic_symbols.open("r", encoding="utf-8") as f: + self.non_linguistic_symbols = set(line.rstrip() for line in f) + except FileNotFoundError: + warnings.warn(f"{non_linguistic_symbols} doesn't exist.") + self.non_linguistic_symbols = set() + else: + self.non_linguistic_symbols = set(non_linguistic_symbols) + self.remove_non_linguistic_symbols = remove_non_linguistic_symbols + + def __repr__(self): + return f'{self.__class__.__name__}(delimiter="{self.delimiter}")' + + def text2tokens(self, line: str) -> List[str]: + tokens = [] + for t in line.split(self.delimiter): + if self.remove_non_linguistic_symbols and t in self.non_linguistic_symbols: + continue + tokens.append(t) + return tokens + + def tokens2text(self, tokens: Iterable[str]) -> str: + if self.delimiter is None: + delimiter = " " + else: + delimiter = self.delimiter + return delimiter.join(tokens) diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/__init__.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/add_gradient_noise.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/add_gradient_noise.py new file mode 100644 index 0000000000000000000000000000000000000000..744e35891a28176c8225de5d4e9366a6322b507a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/add_gradient_noise.py @@ -0,0 +1,31 @@ +import torch + + +def add_gradient_noise( + model: torch.nn.Module, + iteration: int, + duration: float = 100, + eta: float = 1.0, + scale_factor: float = 0.55, +): + """Adds noise from a standard normal distribution to the gradients. + + The standard deviation (`sigma`) is controlled + by the three hyper-parameters below. + `sigma` goes to zero (no noise) with more iterations. + + Args: + model: Model. + iteration: Number of iterations. + duration: {100, 1000}: Number of durations to control + the interval of the `sigma` change. + eta: {0.01, 0.3, 1.0}: The magnitude of `sigma`. + scale_factor: {0.55}: The scale of `sigma`. + """ + interval = (iteration // duration) + 1 + sigma = eta / interval**scale_factor + for param in model.parameters(): + if param.grad is not None: + _shape = param.grad.size() + noise = sigma * torch.randn(_shape).to(param.device) + param.grad += noise diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/average_nbest_models.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/average_nbest_models.py new file mode 100644 index 0000000000000000000000000000000000000000..161fc31bfadd5763f71439956899dd0869a60a2e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/average_nbest_models.py @@ -0,0 +1,187 @@ +import logging +from pathlib import Path +from typing import Optional +from typing import Sequence +from typing import Union +import warnings +import os +from io import BytesIO + +import torch +from typing import Collection +import os +import torch +import re +from collections import OrderedDict +from functools import cmp_to_key + + +# @torch.no_grad() +# def average_nbest_models( +# output_dir: Path, +# best_model_criterion: Sequence[Sequence[str]], +# nbest: Union[Collection[int], int], +# suffix: Optional[str] = None, +# oss_bucket=None, +# pai_output_dir=None, +# ) -> None: +# """Generate averaged model from n-best models +# +# Args: +# output_dir: The directory contains the model file for each epoch +# reporter: Reporter instance +# best_model_criterion: Give criterions to decide the best model. +# e.g. [("valid", "loss", "min"), ("train", "acc", "max")] +# nbest: Number of best model files to be averaged +# suffix: A suffix added to the averaged model file name +# """ +# if isinstance(nbest, int): +# nbests = [nbest] +# else: +# nbests = list(nbest) +# if len(nbests) == 0: +# warnings.warn("At least 1 nbest values are required") +# nbests = [1] +# if suffix is not None: +# suffix = suffix + "." +# else: +# suffix = "" +# +# # 1. Get nbests: List[Tuple[str, str, List[Tuple[epoch, value]]]] +# nbest_epochs = [ +# (ph, k, reporter.sort_epochs_and_values(ph, k, m)[: max(nbests)]) +# for ph, k, m in best_model_criterion +# if reporter.has(ph, k) +# ] +# +# _loaded = {} +# for ph, cr, epoch_and_values in nbest_epochs: +# _nbests = [i for i in nbests if i <= len(epoch_and_values)] +# if len(_nbests) == 0: +# _nbests = [1] +# +# for n in _nbests: +# if n == 0: +# continue +# elif n == 1: +# # The averaged model is same as the best model +# e, _ = epoch_and_values[0] +# op = output_dir / f"{e}epoch.pb" +# sym_op = output_dir / f"{ph}.{cr}.ave_1best.{suffix}pb" +# if sym_op.is_symlink() or sym_op.exists(): +# sym_op.unlink() +# sym_op.symlink_to(op.name) +# else: +# op = output_dir / f"{ph}.{cr}.ave_{n}best.{suffix}pb" +# logging.info( +# f"Averaging {n}best models: " f'criterion="{ph}.{cr}": {op}' +# ) +# +# avg = None +# # 2.a. Averaging model +# for e, _ in epoch_and_values[:n]: +# if e not in _loaded: +# if oss_bucket is None: +# _loaded[e] = torch.load( +# output_dir / f"{e}epoch.pb", +# map_location="cpu", +# ) +# else: +# buffer = BytesIO( +# oss_bucket.get_object(os.path.join(pai_output_dir, f"{e}epoch.pb")).read()) +# _loaded[e] = torch.load(buffer) +# states = _loaded[e] +# +# if avg is None: +# avg = states +# else: +# # Accumulated +# for k in avg: +# avg[k] = avg[k] + states[k] +# for k in avg: +# if str(avg[k].dtype).startswith("torch.int"): +# # For int type, not averaged, but only accumulated. +# # e.g. BatchNorm.num_batches_tracked +# # (If there are any cases that requires averaging +# # or the other reducing method, e.g. max/min, for integer type, +# # please report.) +# pass +# else: +# avg[k] = avg[k] / n +# +# # 2.b. Save the ave model and create a symlink +# if oss_bucket is None: +# torch.save(avg, op) +# else: +# buffer = BytesIO() +# torch.save(avg, buffer) +# oss_bucket.put_object(os.path.join(pai_output_dir, f"{ph}.{cr}.ave_{n}best.{suffix}pb"), +# buffer.getvalue()) +# +# # 3. *.*.ave.pb is a symlink to the max ave model +# if oss_bucket is None: +# op = output_dir / f"{ph}.{cr}.ave_{max(_nbests)}best.{suffix}pb" +# sym_op = output_dir / f"{ph}.{cr}.ave.{suffix}pb" +# if sym_op.is_symlink() or sym_op.exists(): +# sym_op.unlink() +# sym_op.symlink_to(op.name) + + +def _get_checkpoint_paths(output_dir: str, last_n: int = 5): + """ + Get the paths of the last 'last_n' checkpoints by parsing filenames + in the output directory. + """ + # List all files in the output directory + files = os.listdir(output_dir) + # Filter out checkpoint files and extract epoch numbers + checkpoint_files = [f for f in files if f.startswith("model.pt.e")] + # Sort files by epoch number in descending order + checkpoint_files.sort( + key=lambda x: int(re.search(r"(\d+)", x).group()), reverse=True + ) + # Get the last 'last_n' checkpoint paths + checkpoint_paths = [os.path.join(output_dir, f) for f in checkpoint_files[:last_n]] + return checkpoint_paths + + +@torch.no_grad() +def average_checkpoints(output_dir: str, last_n: int = 5): + """ + Average the last 'last_n' checkpoints' model state_dicts. + If a tensor is of type torch.int, perform sum instead of average. + """ + checkpoint_paths = _get_checkpoint_paths(output_dir, last_n) + state_dicts = [] + + # Load state_dicts from checkpoints + for path in checkpoint_paths: + if os.path.isfile(path): + state_dicts.append(torch.load(path, map_location="cpu")["state_dict"]) + else: + print(f"Checkpoint file {path} not found.") + continue + + # Check if we have any state_dicts to average + if not state_dicts: + raise RuntimeError("No checkpoints found for averaging.") + + # Average or sum weights + avg_state_dict = OrderedDict() + for key in state_dicts[0].keys(): + tensors = [state_dict[key].cpu() for state_dict in state_dicts] + # Check the type of the tensor + if str(tensors[0].dtype).startswith("torch.int"): + # Perform sum for integer tensors + summed_tensor = sum(tensors) + avg_state_dict[key] = summed_tensor + else: + # Perform average for other types of tensors + stacked_tensors = torch.stack(tensors) + avg_state_dict[key] = torch.mean(stacked_tensors, dim=0) + + torch.save( + {"state_dict": avg_state_dict}, + os.path.join(output_dir, f"model.pt.avg{last_n}"), + ) + return avg_state_dict diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/device_funcs.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/device_funcs.py new file mode 100644 index 0000000000000000000000000000000000000000..e6b2dd754442aba471026ad42ff4b489fa86eb52 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/device_funcs.py @@ -0,0 +1,71 @@ +import dataclasses +import warnings + +import numpy as np +import torch + + +def to_device(data, device=None, dtype=None, non_blocking=False, copy=False): + """Change the device of object recursively""" + if isinstance(data, dict): + return { + k: to_device(v, device, dtype, non_blocking, copy) for k, v in data.items() + } + elif dataclasses.is_dataclass(data) and not isinstance(data, type): + return type(data)( + *[ + to_device(v, device, dtype, non_blocking, copy) + for v in dataclasses.astuple(data) + ] + ) + # maybe namedtuple. I don't know the correct way to judge namedtuple. + elif isinstance(data, tuple) and type(data) is not tuple: + return type(data)( + *[to_device(o, device, dtype, non_blocking, copy) for o in data] + ) + elif isinstance(data, (list, tuple)): + return type(data)(to_device(v, device, dtype, non_blocking, copy) for v in data) + elif isinstance(data, np.ndarray): + return to_device(torch.from_numpy(data), device, dtype, non_blocking, copy) + elif isinstance(data, torch.Tensor): + return data.to(device, dtype, non_blocking, copy) + else: + return data + + +def force_gatherable(data, device): + """Change object to gatherable in torch.nn.DataParallel recursively + + The difference from to_device() is changing to torch.Tensor if float or int + value is found. + + The restriction to the returned value in DataParallel: + The object must be + - torch.cuda.Tensor + - 1 or more dimension. 0-dimension-tensor sends warning. + or a list, tuple, dict. + + """ + if isinstance(data, dict): + return {k: force_gatherable(v, device) for k, v in data.items()} + # DataParallel can't handle NamedTuple well + elif isinstance(data, tuple) and type(data) is not tuple: + return type(data)(*[force_gatherable(o, device) for o in data]) + elif isinstance(data, (list, tuple, set)): + return type(data)(force_gatherable(v, device) for v in data) + elif isinstance(data, np.ndarray): + return force_gatherable(torch.from_numpy(data), device) + elif isinstance(data, torch.Tensor): + if data.dim() == 0: + # To 1-dim array + data = data[None] + return data.to(device) + elif isinstance(data, float): + return torch.tensor([data], dtype=torch.float, device=device) + elif isinstance(data, int): + return torch.tensor([data], dtype=torch.long, device=device) + elif data is None: + return None + else: + warnings.warn(f"{type(data)} may not be gatherable by DataParallel") + return data diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/forward_adaptor.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/forward_adaptor.py new file mode 100644 index 0000000000000000000000000000000000000000..72093d9fb32df090b990a16edc2ba45b3ffc703b --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/forward_adaptor.py @@ -0,0 +1,31 @@ +import torch + + +class ForwardAdaptor(torch.nn.Module): + """Wrapped module to parallelize specified method + + torch.nn.DataParallel parallelizes only "forward()" + and, maybe, the method having the other name can't be applied + except for wrapping the module just like this class. + + Examples: + >>> class A(torch.nn.Module): + ... def foo(self, x): + ... ... + >>> model = A() + >>> model = ForwardAdaptor(model, "foo") + >>> model = torch.nn.DataParallel(model, device_ids=[0, 1]) + >>> x = torch.randn(2, 10) + >>> model(x) + """ + + def __init__(self, module: torch.nn.Module, name: str): + super().__init__() + self.module = module + self.name = name + if not hasattr(module, name): + raise ValueError(f"{module} doesn't have {name}") + + def forward(self, *args, **kwargs): + func = getattr(self.module, self.name) + return func(*args, **kwargs) diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/initialize.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/initialize.py new file mode 100644 index 0000000000000000000000000000000000000000..57adb1b97308508cc6d4655ed1bfabc3f90cbaee --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/initialize.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 + +"""Initialize modules for espnet2 neural networks.""" + +import math +import torch + + +def initialize(model: torch.nn.Module, init: str): + """Initialize weights of a neural network module. + + Parameters are initialized using the given method or distribution. + + Custom initialization routines can be implemented into submodules + as function `espnet_initialization_fn` within the custom module. + + Args: + model: Target. + init: Method of initialization. + """ + + # weight init + for p in model.parameters(): + if p.dim() > 1: + if init == "xavier_uniform": + torch.nn.init.xavier_uniform_(p.data) + elif init == "xavier_normal": + torch.nn.init.xavier_normal_(p.data) + elif init == "kaiming_uniform": + torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu") + elif init == "kaiming_normal": + torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu") + else: + raise ValueError("Unknown initialization: " + init) + # bias init + for p in model.parameters(): + if p.dim() == 1: + p.data.zero_() + + # reset some modules with default init + for m in model.modules(): + if isinstance(m, (torch.nn.Embedding, torch.nn.LayerNorm, torch.nn.GroupNorm)): + m.reset_parameters() + if hasattr(m, "espnet_initialization_fn"): + m.espnet_initialization_fn() + + # TODO(xkc): Hacking s3prl_frontend and wav2vec2encoder initialization + if getattr(model, "encoder", None) and getattr( + model.encoder, "reload_pretrained_parameters", None + ): + model.encoder.reload_pretrained_parameters() + if getattr(model, "frontend", None) and getattr( + model.frontend, "reload_pretrained_parameters", None + ): + model.frontend.reload_pretrained_parameters() diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/load_pretrained_model.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/load_pretrained_model.py new file mode 100644 index 0000000000000000000000000000000000000000..964b997df138e298ad5449d6a45705f4ac7ba09c --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/load_pretrained_model.py @@ -0,0 +1,172 @@ +from typing import Any +from typing import Dict +from typing import Union +from io import BytesIO + +import logging +import torch +import torch.nn +import torch.optim + + +def filter_state_dict( + dst_state: Dict[str, Union[float, torch.Tensor]], + src_state: Dict[str, Union[float, torch.Tensor]], +): + """Filter name, size mismatch instances between dicts. + + Args: + dst_state: reference state dict for filtering + src_state: target state dict for filtering + + """ + match_state = {} + for key, value in src_state.items(): + if key in dst_state and (dst_state[key].size() == src_state[key].size()): + match_state[key] = value + else: + if key not in dst_state: + logging.warning( + f"Filter out {key} from pretrained dict" + + " because of name not found in target dict" + ) + else: + logging.warning( + f"Filter out {key} from pretrained dict" + + " because of size mismatch" + + f"({dst_state[key].size()}-{src_state[key].size()})" + ) + return match_state + + +def assigment_scope_map(dst_state: dict, src_state: dict, scope_map: str = None): + """Compute the union of the current variables and checkpoint variables.""" + import collections + import re + + # current model variables + name_to_variable = collections.OrderedDict() + for name, var in dst_state.items(): + name_to_variable[name] = var + + scope_map_num = 0 + if scope_map is not None: + scope_map = scope_map.split(",") + scope_map_num = len(scope_map) // 2 + for scope_map_idx in range(scope_map_num): + scope_map_id = scope_map_idx * 2 + logging.info( + "assignment_map from scope {} to {}".format( + scope_map[scope_map_id], scope_map[scope_map_id + 1] + ) + ) + + assignment_map = {} + for name, var in src_state.items(): + + if scope_map: + for scope_map_idx in range(scope_map_num): + scope_map_id = scope_map_idx * 2 + try: + idx = name.index(scope_map[scope_map_id]) + new_name = ( + scope_map[scope_map_id + 1] + + name[idx + len(scope_map[scope_map_id]) :] + ) + if new_name in name_to_variable: + assignment_map[name] = var + except: + continue + else: + if name in name_to_variable: + assignment_map[name] = var + + return assignment_map + + +def load_pretrained_model( + path: str, + model: torch.nn.Module, + ignore_init_mismatch: bool, + map_location: str = "cpu", + oss_bucket=None, + scope_map=None, + excludes=None, +): + """Load a model state and set it to the model. + + Args: + init_param: ::: + + Examples: + + """ + + obj = model + dst_state = obj.state_dict() + # import pdb; + # pdb.set_trace() + print(f"ckpt: {path}") + if oss_bucket is None: + src_state = torch.load(path, map_location=map_location) + else: + buffer = BytesIO(oss_bucket.get_object(path).read()) + src_state = torch.load(buffer, map_location=map_location) + if "state_dict" in src_state: + src_state = src_state["state_dict"] + + for k in dst_state.keys(): + if not k.startswith("module.") and "module." + k in src_state.keys(): + k_ddp = "module." + k + else: + k_ddp = k + if k_ddp in src_state: + dst_state[k] = src_state[k_ddp] + else: + print(f"Miss key in ckpt: model: {k}, ckpt: {k_ddp}") + + flag = obj.load_state_dict(dst_state, strict=True) + # print(flag) + + +# def load_pretrained_model( +# path: str, +# model: torch.nn.Module, +# ignore_init_mismatch: bool, +# map_location: str = "cpu", +# oss_bucket=None, +# scope_map=None, +# excludes=None, +# ): +# """Load a model state and set it to the model. +# +# Args: +# init_param: ::: +# +# Examples: +# +# """ +# +# obj = model +# +# if oss_bucket is None: +# src_state = torch.load(path, map_location=map_location) +# else: +# buffer = BytesIO(oss_bucket.get_object(path).read()) +# src_state = torch.load(buffer, map_location=map_location) +# src_state = src_state["model"] if "model" in src_state else src_state +# +# if excludes is not None: +# for e in excludes.split(","): +# src_state = {k: v for k, v in src_state.items() if not k.startswith(e)} +# +# dst_state = obj.state_dict() +# src_state = assigment_scope_map(dst_state, src_state, scope_map) +# +# if ignore_init_mismatch: +# src_state = filter_state_dict(dst_state, src_state) +# +# logging.debug("Loaded src_state keys: {}".format(src_state.keys())) +# logging.debug("Loaded dst_state keys: {}".format(dst_state.keys())) +# dst_state.update(src_state) +# obj.load_state_dict(dst_state, strict=True) diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/model_summary.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/model_summary.py new file mode 100644 index 0000000000000000000000000000000000000000..031ad3adb286320f823e763f3a32a9c41ebb781e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/model_summary.py @@ -0,0 +1,64 @@ +import numpy as np +import torch + + +def get_human_readable_count(number: int) -> str: + """Return human_readable_count + + Originated from: + https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/core/memory.py + + Abbreviates an integer number with K, M, B, T for thousands, millions, + billions and trillions, respectively. + Examples: + >>> get_human_readable_count(123) + '123 ' + >>> get_human_readable_count(1234) # (one thousand) + '1 K' + >>> get_human_readable_count(2e6) # (two million) + '2 M' + >>> get_human_readable_count(3e9) # (three billion) + '3 B' + >>> get_human_readable_count(4e12) # (four trillion) + '4 T' + >>> get_human_readable_count(5e15) # (more than trillion) + '5,000 T' + Args: + number: a positive integer number + Return: + A string formatted according to the pattern described above. + """ + assert number >= 0 + labels = [" ", "K", "M", "B", "T"] + num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1) + num_groups = int(np.ceil(num_digits / 3)) + num_groups = min(num_groups, len(labels)) # don't abbreviate beyond trillions + shift = -3 * (num_groups - 1) + number = number * (10**shift) + index = num_groups - 1 + return f"{number:.2f} {labels[index]}" + + +def to_bytes(dtype) -> int: + # torch.float16 -> 16 + return int(str(dtype)[-2:]) // 8 + + +def model_summary(model: torch.nn.Module) -> str: + message = "Model structure:\n" + message += str(model) + tot_params = sum(p.numel() for p in model.parameters()) + num_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + percent_trainable = "{:.1f}".format(num_params * 100.0 / tot_params) + tot_params = get_human_readable_count(tot_params) + num_params = get_human_readable_count(num_params) + message += "\n\nModel summary:\n" + message += f" Class Name: {model.__class__.__name__}\n" + message += f" Total Number of model parameters: {tot_params}\n" + message += ( + f" Number of trainable parameters: {num_params} ({percent_trainable}%)\n" + ) + + dtype = next(iter(model.parameters())).dtype + message += f" Type: {dtype}" + return message diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/recursive_op.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/recursive_op.py new file mode 100644 index 0000000000000000000000000000000000000000..512d0a5c97e97598bba8b8c84ba65e8d6b411658 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/recursive_op.py @@ -0,0 +1,48 @@ +"""Torch utility module.""" + +import torch + +if torch.distributed.is_available(): + from torch.distributed import ReduceOp + + +def recursive_sum(obj, weight: torch.Tensor, distributed: bool = False): + assert weight.dim() == 1, weight.size() + if isinstance(obj, (tuple, list)): + return type(obj)(recursive_sum(v, weight, distributed) for v in obj) + elif isinstance(obj, dict): + return {k: recursive_sum(v, weight, distributed) for k, v in obj.items()} + elif isinstance(obj, torch.Tensor): + assert obj.size() == weight.size(), (obj.size(), weight.size()) + obj = (obj * weight.type(obj.dtype)).sum() + if distributed: + torch.distributed.all_reduce(obj, op=ReduceOp.SUM) + return obj + elif obj is None: + return None + else: + raise ValueError(type(obj)) + + +def recursive_divide(a, b: torch.Tensor): + if isinstance(a, (tuple, list)): + return type(a)(recursive_divide(v, b) for v in a) + elif isinstance(a, dict): + return {k: recursive_divide(v, b) for k, v in a.items()} + elif isinstance(a, torch.Tensor): + assert a.size() == b.size(), (a.size(), b.size()) + return a / b.type(a.dtype) + elif a is None: + return None + else: + raise ValueError(type(a)) + + +def recursive_average(obj, weight: torch.Tensor, distributed: bool = False): + obj = recursive_sum(obj, weight, distributed) + weight = weight.sum() + if distributed: + torch.distributed.all_reduce(weight, op=ReduceOp.SUM) + # Normalize weight to be sum-to-1 + obj = recursive_divide(obj, weight) + return obj, weight diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/set_all_random_seed.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/set_all_random_seed.py new file mode 100644 index 0000000000000000000000000000000000000000..9988c13b06a6cb19b7a155d7e2aa6173f73ed84d --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/set_all_random_seed.py @@ -0,0 +1,10 @@ +import random + +import numpy as np +import torch + + +def set_all_random_seed(seed: int): + random.seed(seed) + np.random.seed(seed) + torch.random.manual_seed(seed) diff --git a/demo/Step-Audio-EditX/funasr_detach/train_utils/trainer.py b/demo/Step-Audio-EditX/funasr_detach/train_utils/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..eff48a343facae71e69c7f259ee6ea8ab8f19585 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/train_utils/trainer.py @@ -0,0 +1,417 @@ +import os +import time +import torch +import logging +from tqdm import tqdm +from datetime import datetime +import torch.distributed as dist +from contextlib import nullcontext + +# from torch.utils.tensorboard import SummaryWriter +from tensorboardX import SummaryWriter +from pathlib import Path + +from funasr_detach.train_utils.device_funcs import to_device +from funasr_detach.train_utils.recursive_op import recursive_average +from funasr_detach.train_utils.average_nbest_models import average_checkpoints + + +class Trainer: + """ + A simple trainer class for training a PyTorch model, saving checkpoints at the end of each epoch, + and optionally resuming from a saved checkpoint. + + Attributes: + max_epoch (int): Maximum number of epochs for training. + model (torch.nn.Module): The model to be trained. + optim (torch.optim.Optimizer): The optimizer to use for training. + scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler. + dataloader_train (torch.utils.data.DataLoader): DataLoader for the training dataset. + dataloader_val (torch.utils.data.DataLoader): DataLoader for the validation dataset. + output_dir (str): Directory where model checkpoints will be saved. + resume (str, optional): Path to a checkpoint to resume training from. + """ + + def __init__( + self, + model, + optim, + scheduler, + dataloader_train, + dataloader_val, + local_rank, + use_ddp=False, + use_fsdp=False, + output_dir: str = "./", + **kwargs, + ): + """ + Initializes the Trainer class with the model, optimizer, scheduler, dataloaders, and other settings. + + Args: + model (torch.nn.Module): The model to be trained. + optim (torch.optim.Optimizer): The optimizer to use for training. + scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler. + dataloader_train (torch.utils.data.DataLoader): The DataLoader for the training dataset. + dataloader_val (torch.utils.data.DataLoader): The DataLoader for the validation dataset. + **kwargs: Additional keyword arguments: + max_epoch (int): The maximum number of epochs for training. + output_dir (str): The directory where model checkpoints will be saved. Default is './'. + resume (str, optional): The file path to a checkpoint to resume training from. + """ + + self.model = model + self.optim = optim + self.scheduler = scheduler + self.dataloader_train = dataloader_train + self.dataloader_val = dataloader_val + self.output_dir = output_dir + self.resume = kwargs.get("resume", True) + self.start_epoch = 0 + self.max_epoch = kwargs.get("max_epoch", 100) + self.local_rank = local_rank + self.use_ddp = use_ddp + self.use_fsdp = use_fsdp + self.device = next(model.parameters()).device + self.avg_nbest_model = kwargs.get("avg_nbest_model", 5) + self.kwargs = kwargs + self.log_interval = kwargs.get("log_interval", 50) + self.batch_total = 0 + + try: + rank = dist.get_rank() + world_size = dist.get_world_size() + except: + rank = 0 + world_size = 1 + logging.warning("distributed is not initialized, only single shard") + self.rank = rank + self.world_size = world_size + + os.makedirs(os.path.join(self.output_dir, "tensorboard"), exist_ok=True) + self.writer = ( + SummaryWriter(os.path.join(self.output_dir, "tensorboard")) + if rank == 0 + else None + ) + + def _save_checkpoint(self, epoch): + """ + Saves a checkpoint containing the model's state, the optimizer's state, + and the scheduler's state at the end of the given epoch. This method is + intended to be called at the end of each epoch to save the training progress. + + Args: + epoch (int): The epoch number at which the checkpoint is being saved. + """ + state = { + "epoch": epoch, + "state_dict": self.model.state_dict(), + "optimizer": self.optim.state_dict(), + "scheduler": self.scheduler.state_dict(), + } + # Create output directory if it does not exist + os.makedirs(self.output_dir, exist_ok=True) + filename = os.path.join(self.output_dir, f"model.pt.ep{epoch}") + torch.save(state, filename) + + print(f"\nCheckpoint saved to {filename}\n") + latest = Path(os.path.join(self.output_dir, f"model.pt")) + torch.save(state, latest) + + def _resume_checkpoint(self, resume_path): + """ + Resumes training from a checkpoint at the given file path. + Loads the model's state, the optimizer's state, and the scheduler's state. + + Args: + resume_path (str): The file path to the checkpoint to resume from. + """ + ckpt = os.path.join(resume_path, "model.pt") + if os.path.isfile(ckpt): + checkpoint = torch.load(ckpt) + self.start_epoch = checkpoint["epoch"] + 1 + # self.model.load_state_dict(checkpoint['state_dict']) + src_state = checkpoint["state_dict"] + dst_state = self.model.state_dict() + for k in dst_state.keys(): + if not k.startswith("module.") and "module." + k in src_state.keys(): + k_ddp = "module." + k + else: + k_ddp = k + if k_ddp in src_state.keys(): + dst_state[k] = src_state[k_ddp] + else: + print(f"Miss key in ckpt: model: {k}, ckpt: {k_ddp}") + + self.model.load_state_dict(dst_state) + self.optim.load_state_dict(checkpoint["optimizer"]) + self.scheduler.load_state_dict(checkpoint["scheduler"]) + print(f"Checkpoint loaded successfully from '{ckpt}'") + else: + print(f"No checkpoint found at '{ckpt}', starting from scratch") + + if self.use_ddp or self.use_fsdp: + dist.barrier() + + def run(self): + """ + Starts the training process, iterating over epochs, training the model, + and saving checkpoints at the end of each epoch. + """ + if self.resume: + self._resume_checkpoint(self.output_dir) + + for epoch in range(self.start_epoch, self.max_epoch + 1): + time1 = time.perf_counter() + self._train_epoch(epoch) + + if self.use_ddp or self.use_fsdp: + dist.barrier() + + self._validate_epoch(epoch) + + if self.use_ddp or self.use_fsdp: + dist.barrier() + + if self.rank == 0: + self._save_checkpoint(epoch) + + if self.use_ddp or self.use_fsdp: + dist.barrier() + + self.scheduler.step() + + time2 = time.perf_counter() + time_escaped = (time2 - time1) / 3600.0 + print( + f"\nrank: {self.local_rank}, time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f}\n" + ) + + if self.rank == 0: + average_checkpoints(self.output_dir, self.avg_nbest_model) + + if self.use_ddp or self.use_fsdp: + dist.barrier() + + if self.writer: + self.writer.close() + + def _train_epoch(self, epoch): + """ + Defines the training process for a single epoch with gradient accumulation. + Args: + epoch (int): The current epoch number. + """ + self.model.train() + pbar = tqdm( + colour="blue", + desc=f"rank: {self.local_rank}, Training Epoch: {epoch + 1}", + total=len(self.dataloader_train), + dynamic_ncols=True, + ) + + # Set the number of steps for gradient accumulation + accum_grad = self.kwargs.get("accum_grad", 1) + # Initialize the gradient accumulation + self.optim.zero_grad() + speed_stats = {} + time5 = time.perf_counter() + + for batch_idx, batch in enumerate(self.dataloader_train): + self.batch_total += 1 + time1 = time.perf_counter() + speed_stats["data_load"] = f"{time1-time5:0.3f}" + + batch = to_device(batch, self.device) + + my_context = ( + self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext + ) + with my_context(): + time2 = time.perf_counter() + + retval = self.model(**batch) + torch.cuda.empty_cache() + + time3 = time.perf_counter() + speed_stats["forward_time"] = f"{time3 - time2:0.3f}" + loss, stats, weight = retval + stats = {k: v for k, v in stats.items() if v is not None} + if self.use_ddp or self.use_fsdp: + # Apply weighted averaging for loss and stats + loss = (loss * weight.type(loss.dtype)).sum() + # if distributed, this method can also apply all_reduce() + stats, weight = recursive_average(stats, weight, distributed=True) + # Now weight is summation over all workers + loss /= weight + # Multiply world_size because DistributedDataParallel + # automatically normalizes the gradient by world_size. + loss *= self.world_size + # Scale the loss since we're not updating for every mini-batch + loss = loss / accum_grad + loss.backward() + time4 = time.perf_counter() + speed_stats["backward_time"] = f"{time4 - time3:0.3f}" + + # Perform an optimizer step only after accumulating enough gradients + if (batch_idx + 1) % accum_grad == 0 or (batch_idx + 1) == len( + self.dataloader_train + ): + # Perform gradient clipping if it is set + if self.kwargs.get("grad_clip", None) is not None: + grad_norm = torch.nn.utils.clip_grad_norm_( + self.model.parameters(), + max_norm=self.kwargs.get("grad_clip", 10.0), + norm_type=self.kwargs.get("grad_clip_type", 2.0), + ) + if not torch.isfinite(grad_norm): + logging.warning( + f"The grad norm is {grad_norm}. Skipping updating the model." + ) + self.optim.zero_grad() # Reset gradients + continue + + # Execute an optimization step (update model parameters) + if self.use_ddp or self.use_fsdp: + dist.barrier() + self.optim.step() + self.scheduler.step() + # Clear gradients for the next accumulation stage + self.optim.zero_grad() + total_time = f"{time.perf_counter() - time5:0.3f}" + time5 = time.perf_counter() + speed_stats["optim_time"] = f"{time5 - time4:0.3f}" + + speed_stats["total_time"] = total_time + + if (batch_idx + 1) % self.log_interval == 0 or (batch_idx + 1) == len( + self.dataloader_train + ): + pbar.update(self.log_interval) + gpu_info = ( + "GPU, memory: {:.3f} GB, " + "{:.3f} GB, " + "{:.3f} GB, " + "{:.3f} GB".format( + torch.cuda.memory_allocated() / 1024 / 1024 / 1024, + torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024, + torch.cuda.memory_reserved() / 1024 / 1024 / 1024, + torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, + ) + ) + lr = self.scheduler.get_last_lr()[0] + time_now = datetime.now() + time_now = time_now.strftime("%Y-%m-%d %H:%M:%S") + description = ( + f"{time_now}, " + f"rank: {self.local_rank}, " + f"epoch: {epoch}/{self.max_epoch}, " + f"step: {batch_idx+1}/{len(self.dataloader_train)}, total: {self.batch_total}, " + f"(loss: {loss.detach().cpu().item():.3f}), " + f"(lr: {lr:.3e}), " + f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, " + f"{speed_stats}, " + f"{gpu_info}" + ) + pbar.set_description(description) + if self.writer: + self.writer.add_scalar( + f"rank{self.local_rank}_Loss/train", + loss.item(), + self.batch_total, + ) + self.writer.add_scalar( + f"rank{self.local_rank}_lr/train", lr, self.batch_total + ) + for key, var in stats.items(): + self.writer.add_scalar( + f"rank{self.local_rank}_{key}/train", + var.item(), + self.batch_total, + ) + for key, var in speed_stats.items(): + self.writer.add_scalar( + f"rank{self.local_rank}_{key}/train", + eval(var), + self.batch_total, + ) + + pbar.close() + + def _validate_epoch(self, epoch): + """ + Defines the validation process for a single epoch. + Should be implemented with the actual model validation steps. + + Args: + epoch (int): The current epoch number. + """ + self.model.eval() + with torch.no_grad(): + pbar = tqdm( + colour="red", + desc=f"rank: {self.local_rank}, Validation Epoch: {epoch + 1}", + total=len(self.dataloader_val), + dynamic_ncols=True, + ) + speed_stats = {} + time5 = time.perf_counter() + for batch_idx, batch in enumerate(self.dataloader_val): + time1 = time.perf_counter() + speed_stats["data_load"] = f"{time1 - time5:0.3f}" + batch = to_device(batch, self.device) + time2 = time.perf_counter() + retval = self.model(**batch) + time3 = time.perf_counter() + speed_stats["forward_time"] = f"{time3 - time2:0.3f}" + loss, stats, weight = retval + stats = {k: v for k, v in stats.items() if v is not None} + if self.use_ddp or self.use_fsdp: + # Apply weighted averaging for loss and stats + loss = (loss * weight.type(loss.dtype)).sum() + # if distributed, this method can also apply all_reduce() + stats, weight = recursive_average(stats, weight, distributed=True) + # Now weight is summation over all workers + loss /= weight + # Multiply world_size because DistributedDataParallel + # automatically normalizes the gradient by world_size. + loss *= self.world_size + # Scale the loss since we're not updating for every mini-batch + loss = loss + time4 = time.perf_counter() + + if (batch_idx + 1) % self.log_interval == 0 or (batch_idx + 1) == len( + self.dataloader_val + ): + pbar.update(self.log_interval) + time_now = datetime.now() + time_now = time_now.strftime("%Y-%m-%d %H:%M:%S") + description = ( + f"{time_now}, " + f"rank: {self.local_rank}, " + f"validation epoch: {epoch}/{self.max_epoch}, " + f"step: {batch_idx+1}/{len(self.dataloader_val)}, " + f"(loss: {loss.detach().cpu().item():.3f}), " + f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, " + f"{speed_stats}, " + ) + pbar.set_description(description) + if self.writer: + self.writer.add_scalar( + f"rank{self.local_rank}_Loss/val", + loss.item(), + epoch * len(self.dataloader_val) + batch_idx, + ) + for key, var in stats.items(): + self.writer.add_scalar( + f"rank{self.local_rank}_{key}/val", + var.item(), + epoch * len(self.dataloader_val) + batch_idx, + ) + for key, var in speed_stats.items(): + self.writer.add_scalar( + f"rank{self.local_rank}_{key}/val", + eval(var), + epoch * len(self.dataloader_val) + batch_idx, + ) diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/__init__.py b/demo/Step-Audio-EditX/funasr_detach/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/datadir_writer.py b/demo/Step-Audio-EditX/funasr_detach/utils/datadir_writer.py new file mode 100644 index 0000000000000000000000000000000000000000..6a3b9a00e7bcff136f750d4a3441bf54e93d8a32 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/utils/datadir_writer.py @@ -0,0 +1,71 @@ +from pathlib import Path +from typing import Union +import warnings + + +class DatadirWriter: + """Writer class to create kaldi like data directory. + + Examples: + >>> with DatadirWriter("output") as writer: + ... # output/sub.txt is created here + ... subwriter = writer["sub.txt"] + ... # Write "uttidA some/where/a.wav" + ... subwriter["uttidA"] = "some/where/a.wav" + ... subwriter["uttidB"] = "some/where/b.wav" + + """ + + def __init__(self, p: Union[Path, str]): + self.path = Path(p) + self.chilidren = {} + self.fd = None + self.has_children = False + self.keys = set() + + def __enter__(self): + return self + + def __getitem__(self, key: str) -> "DatadirWriter": + if self.fd is not None: + raise RuntimeError("This writer points out a file") + + if key not in self.chilidren: + w = DatadirWriter((self.path / key)) + self.chilidren[key] = w + self.has_children = True + + retval = self.chilidren[key] + return retval + + def __setitem__(self, key: str, value: str): + if self.has_children: + raise RuntimeError("This writer points out a directory") + if key in self.keys: + warnings.warn(f"Duplicated: {key}") + + if self.fd is None: + self.path.parent.mkdir(parents=True, exist_ok=True) + self.fd = self.path.open("w", encoding="utf-8") + + self.keys.add(key) + self.fd.write(f"{key} {value}\n") + self.fd.flush() + + def __exit__(self, exc_type, exc_val, exc_tb): + self.close() + + def close(self): + if self.has_children: + prev_child = None + for child in self.chilidren.values(): + child.close() + if prev_child is not None and prev_child.keys != child.keys: + warnings.warn( + f"Ids are mismatching between " + f"{prev_child.path} and {child.path}" + ) + prev_child = child + + elif self.fd is not None: + self.fd.close() diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/load_utils.py b/demo/Step-Audio-EditX/funasr_detach/utils/load_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..01c2a981009ee95f984be8919fb7cf6f1765dfe7 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/utils/load_utils.py @@ -0,0 +1,148 @@ +import os +import io +import torch +import numpy as np +import torchaudio +from torch.nn.utils.rnn import pad_sequence + +try: + from funasr_detach.download.file import download_from_url +except: + print("urllib is not installed, if you infer from url, please install it first.") + + +def load_audio_text_image_video( + data_or_path_or_list, + fs: int = 16000, + audio_fs: int = 16000, + data_type="sound", + tokenizer=None, + **kwargs +): + if isinstance(data_or_path_or_list, (list, tuple)): + if data_type is not None and isinstance(data_type, (list, tuple)): + + data_types = [data_type] * len(data_or_path_or_list) + data_or_path_or_list_ret = [[] for d in data_type] + for i, (data_type_i, data_or_path_or_list_i) in enumerate( + zip(data_types, data_or_path_or_list) + ): + + for j, (data_type_j, data_or_path_or_list_j) in enumerate( + zip(data_type_i, data_or_path_or_list_i) + ): + + data_or_path_or_list_j = load_audio_text_image_video( + data_or_path_or_list_j, + fs=fs, + audio_fs=audio_fs, + data_type=data_type_j, + tokenizer=tokenizer, + **kwargs + ) + data_or_path_or_list_ret[j].append(data_or_path_or_list_j) + + return data_or_path_or_list_ret + else: + return [ + load_audio_text_image_video( + audio, fs=fs, audio_fs=audio_fs, data_type=data_type, **kwargs + ) + for audio in data_or_path_or_list + ] + + if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith( + "http" + ): # download url to local file + data_or_path_or_list = download_from_url(data_or_path_or_list) + + if isinstance(data_or_path_or_list, io.BytesIO): + data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list) + if kwargs.get("reduce_channels", True): + data_or_path_or_list = data_or_path_or_list.mean(0) + elif isinstance(data_or_path_or_list, str) and os.path.exists( + data_or_path_or_list + ): # local file + if data_type is None or data_type == "sound": + data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list) + if kwargs.get("reduce_channels", True): + data_or_path_or_list = data_or_path_or_list.mean(0) + elif data_type == "text" and tokenizer is not None: + data_or_path_or_list = tokenizer.encode(data_or_path_or_list) + elif data_type == "image": # undo + pass + elif data_type == "video": # undo + pass + + # if data_in is a file or url, set is_final=True + if "cache" in kwargs: + kwargs["cache"]["is_final"] = True + kwargs["cache"]["is_streaming_input"] = False + elif ( + isinstance(data_or_path_or_list, str) + and data_type == "text" + and tokenizer is not None + ): + data_or_path_or_list = tokenizer.encode(data_or_path_or_list) + elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point + data_or_path_or_list = torch.from_numpy( + data_or_path_or_list + ).squeeze() # [n_samples,] + else: + pass + # print(f"unsupport data type: {data_or_path_or_list}, return raw data") + + if audio_fs != fs and data_type != "text": + resampler = torchaudio.transforms.Resample(audio_fs, fs) + data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :] + return data_or_path_or_list + + +def load_bytes(input): + middle_data = np.frombuffer(input, dtype=np.int16) + middle_data = np.asarray(middle_data) + if middle_data.dtype.kind not in "iu": + raise TypeError("'middle_data' must be an array of integers") + dtype = np.dtype("float32") + if dtype.kind != "f": + raise TypeError("'dtype' must be a floating point type") + + i = np.iinfo(middle_data.dtype) + abs_max = 2 ** (i.bits - 1) + offset = i.min + abs_max + array = np.frombuffer( + (middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32 + ) + return array + + +def extract_fbank( + data, data_len=None, data_type: str = "sound", frontend=None, **kwargs +): + # import pdb; + # pdb.set_trace() + if isinstance(data, np.ndarray): + data = torch.from_numpy(data) + if len(data.shape) < 2: + data = data[None, :] # data: [batch, N] + data_len = [data.shape[1]] if data_len is None else data_len + elif isinstance(data, torch.Tensor): + if len(data.shape) < 2: + data = data[None, :] # data: [batch, N] + data_len = [data.shape[1]] if data_len is None else data_len + elif isinstance(data, (list, tuple)): + data_list, data_len = [], [] + for data_i in data: + if isinstance(data_i, np.ndarray): + data_i = torch.from_numpy(data_i) + data_list.append(data_i) + data_len.append(data_i.shape[0]) + data = pad_sequence(data_list, batch_first=True) # data: [batch, N] + # import pdb; + # pdb.set_trace() + # if data_type == "sound": + data, data_len = frontend(data, data_len, **kwargs) + + if isinstance(data_len, (list, tuple)): + data_len = torch.tensor([data_len]) + return data.to(torch.float32), data_len.to(torch.int32) diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/misc.py b/demo/Step-Audio-EditX/funasr_detach/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..467c1aa9f7c31321ad4c2dafe0506f5ab49d5fa9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/utils/misc.py @@ -0,0 +1,50 @@ +import io +from collections import OrderedDict +import numpy as np + + +def statistic_model_parameters(model, prefix=None): + var_dict = model.state_dict() + numel = 0 + for i, key in enumerate( + sorted(list([x for x in var_dict.keys() if "num_batches_tracked" not in x])) + ): + if prefix is None or key.startswith(prefix): + numel += var_dict[key].numel() + return numel + + +def int2vec(x, vec_dim=8, dtype=np.int32): + b = ("{:0" + str(vec_dim) + "b}").format(x) + # little-endian order: lower bit first + return (np.array(list(b)[::-1]) == "1").astype(dtype) + + +def seq2arr(seq, vec_dim=8): + return np.row_stack([int2vec(int(x), vec_dim) for x in seq]) + + +def load_scp_as_dict(scp_path, value_type="str", kv_sep=" "): + with io.open(scp_path, "r", encoding="utf-8") as f: + ret_dict = OrderedDict() + for one_line in f.readlines(): + one_line = one_line.strip() + pos = one_line.find(kv_sep) + key, value = one_line[:pos], one_line[pos + 1 :] + if value_type == "list": + value = value.split(" ") + ret_dict[key] = value + return ret_dict + + +def load_scp_as_list(scp_path, value_type="str", kv_sep=" "): + with io.open(scp_path, "r", encoding="utf8") as f: + ret_dict = [] + for one_line in f.readlines(): + one_line = one_line.strip() + pos = one_line.find(kv_sep) + key, value = one_line[:pos], one_line[pos + 1 :] + if value_type == "list": + value = value.split(" ") + ret_dict.append((key, value)) + return ret_dict diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/postprocess_utils.py b/demo/Step-Audio-EditX/funasr_detach/utils/postprocess_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7db9a8df80787924a990b773ed1a233a3445ec07 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/utils/postprocess_utils.py @@ -0,0 +1,301 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. + +import string +import logging +from typing import Any, List, Union + + +def isChinese(ch: str): + if "\u4e00" <= ch <= "\u9fff" or "\u0030" <= ch <= "\u0039" or ch == "@": + return True + return False + + +def isAllChinese(word: Union[List[Any], str]): + word_lists = [] + for i in word: + cur = i.replace(" ", "") + cur = cur.replace("", "") + cur = cur.replace("", "") + cur = cur.replace("", "") + cur = cur.replace("", "") + word_lists.append(cur) + + if len(word_lists) == 0: + return False + + for ch in word_lists: + if isChinese(ch) is False: + return False + return True + + +def isAllAlpha(word: Union[List[Any], str]): + word_lists = [] + for i in word: + cur = i.replace(" ", "") + cur = cur.replace("", "") + cur = cur.replace("", "") + cur = cur.replace("", "") + cur = cur.replace("", "") + word_lists.append(cur) + + if len(word_lists) == 0: + return False + + for ch in word_lists: + if ch.isalpha() is False and ch != "'": + return False + elif ch.isalpha() is True and isChinese(ch) is True: + return False + + return True + + +# def abbr_dispose(words: List[Any]) -> List[Any]: +def abbr_dispose(words: List[Any], time_stamp: List[List] = None) -> List[Any]: + words_size = len(words) + word_lists = [] + abbr_begin = [] + abbr_end = [] + last_num = -1 + ts_lists = [] + ts_nums = [] + ts_index = 0 + for num in range(words_size): + if num <= last_num: + continue + + if len(words[num]) == 1 and words[num].encode("utf-8").isalpha(): + if ( + num + 1 < words_size + and words[num + 1] == " " + and num + 2 < words_size + and len(words[num + 2]) == 1 + and words[num + 2].encode("utf-8").isalpha() + ): + # found the begin of abbr + abbr_begin.append(num) + num += 2 + abbr_end.append(num) + # to find the end of abbr + while True: + num += 1 + if num < words_size and words[num] == " ": + num += 1 + if ( + num < words_size + and len(words[num]) == 1 + and words[num].encode("utf-8").isalpha() + ): + abbr_end.pop() + abbr_end.append(num) + last_num = num + else: + break + else: + break + + for num in range(words_size): + if words[num] == " ": + ts_nums.append(ts_index) + else: + ts_nums.append(ts_index) + ts_index += 1 + last_num = -1 + for num in range(words_size): + if num <= last_num: + continue + + if num in abbr_begin: + if time_stamp is not None: + begin = time_stamp[ts_nums[num]][0] + abbr_word = words[num].upper() + num += 1 + while num < words_size: + if num in abbr_end: + abbr_word += words[num].upper() + last_num = num + break + else: + if words[num].encode("utf-8").isalpha(): + abbr_word += words[num].upper() + num += 1 + word_lists.append(abbr_word) + if time_stamp is not None: + end = time_stamp[ts_nums[num]][1] + ts_lists.append([begin, end]) + else: + word_lists.append(words[num]) + if time_stamp is not None and words[num] != " ": + begin = time_stamp[ts_nums[num]][0] + end = time_stamp[ts_nums[num]][1] + ts_lists.append([begin, end]) + begin = end + + if time_stamp is not None: + return word_lists, ts_lists + else: + return word_lists + + +def sentence_postprocess(words: List[Any], time_stamp: List[List] = None): + middle_lists = [] + word_lists = [] + word_item = "" + ts_lists = [] + + # wash words lists + for i in words: + word = "" + if isinstance(i, str): + word = i + else: + word = i.decode("utf-8") + + if word in ["", "", "", ""]: + continue + else: + middle_lists.append(word) + + # all chinese characters + if isAllChinese(middle_lists): + for i, ch in enumerate(middle_lists): + word_lists.append(ch.replace(" ", "")) + if time_stamp is not None: + ts_lists = time_stamp + + # all alpha characters + elif isAllAlpha(middle_lists): + ts_flag = True + for i, ch in enumerate(middle_lists): + if ts_flag and time_stamp is not None: + begin = time_stamp[i][0] + end = time_stamp[i][1] + word = "" + if "@@" in ch: + word = ch.replace("@@", "") + word_item += word + if time_stamp is not None: + ts_flag = False + end = time_stamp[i][1] + else: + word_item += ch + word_lists.append(word_item) + word_lists.append(" ") + word_item = "" + if time_stamp is not None: + ts_flag = True + end = time_stamp[i][1] + ts_lists.append([begin, end]) + begin = end + + # mix characters + else: + alpha_blank = False + ts_flag = True + begin = -1 + end = -1 + for i, ch in enumerate(middle_lists): + if ts_flag and time_stamp is not None: + begin = time_stamp[i][0] + end = time_stamp[i][1] + word = "" + if isAllChinese(ch): + if alpha_blank is True: + word_lists.pop() + word_lists.append(ch) + alpha_blank = False + if time_stamp is not None: + ts_flag = True + ts_lists.append([begin, end]) + begin = end + elif "@@" in ch: + word = ch.replace("@@", "") + word_item += word + alpha_blank = False + if time_stamp is not None: + ts_flag = False + end = time_stamp[i][1] + elif isAllAlpha(ch): + word_item += ch + word_lists.append(word_item) + word_lists.append(" ") + word_item = "" + alpha_blank = True + if time_stamp is not None: + ts_flag = True + end = time_stamp[i][1] + ts_lists.append([begin, end]) + begin = end + else: + word_lists.append(ch) + + if time_stamp is not None: + word_lists, ts_lists = abbr_dispose(word_lists, ts_lists) + real_word_lists = [] + for ch in word_lists: + if ch != " ": + real_word_lists.append(ch) + sentence = " ".join(real_word_lists).strip() + return sentence, ts_lists, real_word_lists + else: + word_lists = abbr_dispose(word_lists) + real_word_lists = [] + for ch in word_lists: + if ch != " ": + real_word_lists.append(ch) + sentence = "".join(word_lists).strip() + return sentence, real_word_lists + + +def sentence_postprocess_sentencepiece(words): + middle_lists = [] + word_lists = [] + word_item = "" + + # wash words lists + for i in words: + word = "" + if isinstance(i, str): + word = i + else: + word = i.decode("utf-8") + + if word in ["", "", "", ""]: + continue + else: + middle_lists.append(word) + + # all alpha characters + for i, ch in enumerate(middle_lists): + word = "" + if "\u2581" in ch and i == 0: + word_item = "" + word = ch.replace("\u2581", "") + word_item += word + elif "\u2581" in ch and i != 0: + word_lists.append(word_item) + word_lists.append(" ") + word_item = "" + word = ch.replace("\u2581", "") + word_item += word + else: + word_item += ch + if word_item is not None: + word_lists.append(word_item) + # word_lists = abbr_dispose(word_lists) + real_word_lists = [] + for ch in word_lists: + if ch != " ": + if ch == "i": + ch = ch.replace("i", "I") + elif ch == "i'm": + ch = ch.replace("i'm", "I'm") + elif ch == "i've": + ch = ch.replace("i've", "I've") + elif ch == "i'll": + ch = ch.replace("i'll", "I'll") + real_word_lists.append(ch) + sentence = "".join(word_lists) + return sentence, real_word_lists diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/prepare_data.py b/demo/Step-Audio-EditX/funasr_detach/utils/prepare_data.py new file mode 100644 index 0000000000000000000000000000000000000000..04ede3a6c31920219dea75d9aed93fd6419e0f2f --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/utils/prepare_data.py @@ -0,0 +1,308 @@ +import logging +import os +import shutil +from multiprocessing import Pool + +import kaldiio +import numpy as np +import librosa +import torch.distributed as dist +import torchaudio + + +def filter_wav_text(data_dir, dataset): + wav_file = os.path.join(data_dir, dataset, "wav.scp") + text_file = os.path.join(data_dir, dataset, "text") + with open(wav_file) as f_wav, open(text_file) as f_text: + wav_lines = f_wav.readlines() + text_lines = f_text.readlines() + os.rename(wav_file, "{}.bak".format(wav_file)) + os.rename(text_file, "{}.bak".format(text_file)) + wav_dict = {} + for line in wav_lines: + parts = line.strip().split() + if len(parts) < 2: + continue + wav_dict[parts[0]] = parts[1] + text_dict = {} + for line in text_lines: + parts = line.strip().split() + if len(parts) < 2: + continue + text_dict[parts[0]] = " ".join(parts[1:]) + filter_count = 0 + with open(wav_file, "w") as f_wav, open(text_file, "w") as f_text: + for sample_name, wav_path in wav_dict.items(): + if sample_name in text_dict.keys(): + f_wav.write(sample_name + " " + wav_path + "\n") + f_text.write(sample_name + " " + text_dict[sample_name] + "\n") + else: + filter_count += 1 + logging.info( + "{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".format( + filter_count, len(wav_lines), dataset + ) + ) + + +def wav2num_frame(wav_path, frontend_conf): + try: + waveform, sampling_rate = torchaudio.load(wav_path) + except: + waveform, sampling_rate = librosa.load(wav_path) + waveform = np.expand_dims(waveform, axis=0) + n_frames = (waveform.shape[1] * 1000.0) / ( + sampling_rate * frontend_conf["frame_shift"] * frontend_conf["lfr_n"] + ) + feature_dim = frontend_conf["n_mels"] * frontend_conf["lfr_m"] + return n_frames, feature_dim + + +def calc_shape_core(root_path, args, idx): + file_name = args.data_file_names.split(",")[0] + data_name = args.dataset_conf.get("data_names", "speech,text").split(",")[0] + scp_file = os.path.join(root_path, "{}.{}".format(file_name, idx)) + shape_file = os.path.join(root_path, "{}_shape.{}".format(data_name, idx)) + with open(scp_file) as f: + lines = f.readlines() + data_type = args.dataset_conf.get("data_types", "sound,text").split(",")[0] + if data_type == "sound": + frontend_conf = args.frontend_conf + dataset_conf = args.dataset_conf + length_min = ( + dataset_conf.speech_length_min + if hasattr(dataset_conf, "{}_length_min".format(data_name)) + else -1 + ) + length_max = ( + dataset_conf.speech_length_max + if hasattr(dataset_conf, "{}_length_max".format(data_name)) + else -1 + ) + with open(shape_file, "w") as f: + for line in lines: + sample_name, wav_path = line.strip().split() + n_frames, feature_dim = wav2num_frame(wav_path, frontend_conf) + write_flag = True + if n_frames > 0 and length_min > 0: + write_flag = n_frames >= length_min + if n_frames > 0 and length_max > 0: + write_flag = n_frames <= length_max + if write_flag: + f.write( + "{} {},{}\n".format( + sample_name, + str(int(np.ceil(n_frames))), + str(int(feature_dim)), + ) + ) + f.flush() + elif data_type == "kaldi_ark": + dataset_conf = args.dataset_conf + length_min = ( + dataset_conf.speech_length_min + if hasattr(dataset_conf, "{}_length_min".format(data_name)) + else -1 + ) + length_max = ( + dataset_conf.speech_length_max + if hasattr(dataset_conf, "{}_length_max".format(data_name)) + else -1 + ) + with open(shape_file, "w") as f: + for line in lines: + sample_name, feature_path = line.strip().split() + feature = kaldiio.load_mat(feature_path) + n_frames, feature_dim = feature.shape + write_flag = True + if n_frames > 0 and length_min > 0: + write_flag = n_frames >= length_min + if n_frames > 0 and length_max > 0: + write_flag = n_frames <= length_max + if write_flag: + f.write( + "{} {},{}\n".format( + sample_name, + str(int(np.ceil(n_frames))), + str(int(feature_dim)), + ) + ) + f.flush() + elif data_type == "text": + with open(shape_file, "w") as f: + for line in lines: + sample_name, text = line.strip().split(maxsplit=1) + n_tokens = len(text.split()) + f.write("{} {}\n".format(sample_name, str(int(np.ceil(n_tokens))))) + f.flush() + else: + raise RuntimeError("Unsupported data_type: {}".format(data_type)) + + +def calc_shape(args, dataset, nj=64): + data_name = args.dataset_conf.get("data_names", "speech,text").split(",")[0] + shape_path = os.path.join(args.data_dir, dataset, "{}_shape".format(data_name)) + if os.path.exists(shape_path): + logging.info("Shape file for small dataset already exists.") + return + + split_shape_path = os.path.join( + args.data_dir, dataset, "{}_shape_files".format(data_name) + ) + if os.path.exists(split_shape_path): + shutil.rmtree(split_shape_path) + os.mkdir(split_shape_path) + + # split + file_name = args.data_file_names.split(",")[0] + scp_file = os.path.join(args.data_dir, dataset, file_name) + with open(scp_file) as f: + lines = f.readlines() + num_lines = len(lines) + num_job_lines = num_lines // nj + start = 0 + for i in range(nj): + end = start + num_job_lines + file = os.path.join(split_shape_path, "{}.{}".format(file_name, str(i + 1))) + with open(file, "w") as f: + if i == nj - 1: + f.writelines(lines[start:]) + else: + f.writelines(lines[start:end]) + start = end + + p = Pool(nj) + for i in range(nj): + p.apply_async(calc_shape_core, args=(split_shape_path, args, str(i + 1))) + logging.info("Generating shape files, please wait a few minutes...") + p.close() + p.join() + + # combine + with open(shape_path, "w") as f: + for i in range(nj): + job_file = os.path.join( + split_shape_path, "{}_shape.{}".format(data_name, str(i + 1)) + ) + with open(job_file) as job_f: + lines = job_f.readlines() + f.writelines(lines) + logging.info("Generating shape files done.") + + +def generate_data_list(args, data_dir, dataset, nj=64): + data_names = args.dataset_conf.get("data_names", "speech,text").split(",") + file_names = args.data_file_names.split(",") + concat_data_name = "_".join(data_names) + list_file = os.path.join(data_dir, dataset, "{}_data.list".format(concat_data_name)) + if os.path.exists(list_file): + logging.info("Data list for large dataset already exists.") + return + split_path = os.path.join(data_dir, dataset, "split") + if os.path.exists(split_path): + shutil.rmtree(split_path) + os.mkdir(split_path) + + data_lines_list = [] + for file_name in file_names: + with open(os.path.join(data_dir, dataset, file_name)) as f: + lines = f.readlines() + data_lines_list.append(lines) + num_lines = len(data_lines_list[0]) + num_job_lines = num_lines // nj + start = 0 + for i in range(nj): + end = start + num_job_lines + split_path_nj = os.path.join(split_path, str(i + 1)) + os.mkdir(split_path_nj) + for file_id, file_name in enumerate(file_names): + file = os.path.join(split_path_nj, file_name) + with open(file, "w") as f: + if i == nj - 1: + f.writelines(data_lines_list[file_id][start:]) + else: + f.writelines(data_lines_list[file_id][start:end]) + start = end + + with open(list_file, "w") as f_data: + for i in range(nj): + path = "" + for file_name in file_names: + path = path + " " + os.path.join(split_path, str(i + 1), file_name) + f_data.write(path + "\n") + + +def prepare_data(args, distributed_option): + data_names = args.dataset_conf.get("data_names", "speech,text").split(",") + data_types = args.dataset_conf.get("data_types", "sound,text").split(",") + file_names = args.data_file_names.split(",") + batch_type = args.dataset_conf["batch_conf"]["batch_type"] + print( + "data_names: {}, data_types: {}, file_names: {}".format( + data_names, data_types, file_names + ) + ) + assert len(data_names) == len(data_types) == len(file_names) + if args.dataset_type == "small": + args.train_shape_file = [ + os.path.join( + args.data_dir, args.train_set, "{}_shape".format(data_names[0]) + ) + ] + args.valid_shape_file = [ + os.path.join( + args.data_dir, args.valid_set, "{}_shape".format(data_names[0]) + ) + ] + ( + args.train_data_path_and_name_and_type, + args.valid_data_path_and_name_and_type, + ) = ([], []) + for file_name, data_name, data_type in zip(file_names, data_names, data_types): + args.train_data_path_and_name_and_type.append( + [ + "{}/{}/{}".format(args.data_dir, args.train_set, file_name), + data_name, + data_type, + ] + ) + args.valid_data_path_and_name_and_type.append( + [ + "{}/{}/{}".format(args.data_dir, args.valid_set, file_name), + data_name, + data_type, + ] + ) + if os.path.exists(args.train_shape_file[0]): + assert os.path.exists(args.valid_shape_file[0]) + print("shape file for small dataset already exists.") + return + else: + concat_data_name = "_".join(data_names) + args.train_data_file = os.path.join( + args.data_dir, args.train_set, "{}_data.list".format(concat_data_name) + ) + args.valid_data_file = os.path.join( + args.data_dir, args.valid_set, "{}_data.list".format(concat_data_name) + ) + if os.path.exists(args.train_data_file): + assert os.path.exists(args.valid_data_file) + print("data list for large dataset already exists.") + return + + distributed = distributed_option.distributed + if not distributed or distributed_option.dist_rank == 0: + if hasattr(args, "filter_input") and args.filter_input: + filter_wav_text(args.data_dir, args.train_set) + filter_wav_text(args.data_dir, args.valid_set) + + if args.dataset_type == "small" and batch_type != "unsorted": + calc_shape(args, args.train_set) + calc_shape(args, args.valid_set) + + if args.dataset_type == "large": + generate_data_list(args, args.data_dir, args.train_set) + generate_data_list(args, args.data_dir, args.valid_set) + + if distributed: + dist.barrier() diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/speaker_utils.py b/demo/Step-Audio-EditX/funasr_detach/utils/speaker_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4d8c4948ab94f2809df8bdb22742688747adffd9 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/utils/speaker_utils.py @@ -0,0 +1,200 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. +"""Some implementations are adapted from https://github.com/yuyq96/D-TDNN""" + +import io +from typing import Union + +import librosa as sf +import numpy as np +import torch +import torch.nn.functional as F +import torchaudio.compliance.kaldi as Kaldi +from torch import nn + +from funasr_detach.utils.modelscope_file import File + + +def check_audio_list(audio: list): + audio_dur = 0 + for i in range(len(audio)): + seg = audio[i] + assert seg[1] >= seg[0], "modelscope error: Wrong time stamps." + assert isinstance(seg[2], np.ndarray), "modelscope error: Wrong data type." + assert ( + int(seg[1] * 16000) - int(seg[0] * 16000) == seg[2].shape[0] + ), "modelscope error: audio data in list is inconsistent with time length." + if i > 0: + assert seg[0] >= audio[i - 1][1], "modelscope error: Wrong time stamps." + audio_dur += seg[1] - seg[0] + return audio_dur + # assert audio_dur > 5, 'modelscope error: The effective audio duration is too short.' + + +def sv_preprocess(inputs: Union[np.ndarray, list]): + output = [] + for i in range(len(inputs)): + if isinstance(inputs[i], str): + file_bytes = File.read(inputs[i]) + data, fs = sf.load(io.BytesIO(file_bytes), dtype="float32") + if len(data.shape) == 2: + data = data[:, 0] + data = torch.from_numpy(data).unsqueeze(0) + data = data.squeeze(0) + elif isinstance(inputs[i], np.ndarray): + assert ( + len(inputs[i].shape) == 1 + ), "modelscope error: Input array should be [N, T]" + data = inputs[i] + if data.dtype in ["int16", "int32", "int64"]: + data = (data / (1 << 15)).astype("float32") + else: + data = data.astype("float32") + data = torch.from_numpy(data) + else: + raise ValueError( + "modelscope error: The input type is restricted to audio address and nump array." + ) + output.append(data) + return output + + +def sv_chunk(vad_segments: list, fs=16000) -> list: + config = { + "seg_dur": 1.5, + "seg_shift": 0.75, + } + + def seg_chunk(seg_data): + seg_st = seg_data[0] + data = seg_data[2] + chunk_len = int(config["seg_dur"] * fs) + chunk_shift = int(config["seg_shift"] * fs) + last_chunk_ed = 0 + seg_res = [] + for chunk_st in range(0, data.shape[0], chunk_shift): + chunk_ed = min(chunk_st + chunk_len, data.shape[0]) + if chunk_ed <= last_chunk_ed: + break + last_chunk_ed = chunk_ed + chunk_st = max(0, chunk_ed - chunk_len) + chunk_data = data[chunk_st:chunk_ed] + if chunk_data.shape[0] < chunk_len: + chunk_data = np.pad( + chunk_data, (0, chunk_len - chunk_data.shape[0]), "constant" + ) + seg_res.append([chunk_st / fs + seg_st, chunk_ed / fs + seg_st, chunk_data]) + return seg_res + + segs = [] + for i, s in enumerate(vad_segments): + segs.extend(seg_chunk(s)) + + return segs + + +def extract_feature(audio): + features = [] + for au in audio: + feature = Kaldi.fbank(au.unsqueeze(0), num_mel_bins=80) + feature = feature - feature.mean(dim=0, keepdim=True) + features.append(feature.unsqueeze(0)) + features = torch.cat(features) + return features + + +def postprocess( + segments: list, vad_segments: list, labels: np.ndarray, embeddings: np.ndarray +) -> list: + assert len(segments) == len(labels) + labels = correct_labels(labels) + distribute_res = [] + for i in range(len(segments)): + distribute_res.append([segments[i][0], segments[i][1], labels[i]]) + # merge the same speakers chronologically + distribute_res = merge_seque(distribute_res) + + # accquire speaker center + spk_embs = [] + for i in range(labels.max() + 1): + spk_emb = embeddings[labels == i].mean(0) + spk_embs.append(spk_emb) + spk_embs = np.stack(spk_embs) + + def is_overlapped(t1, t2): + if t1 > t2 + 1e-4: + return True + return False + + # distribute the overlap region + for i in range(1, len(distribute_res)): + if is_overlapped(distribute_res[i - 1][1], distribute_res[i][0]): + p = (distribute_res[i][0] + distribute_res[i - 1][1]) / 2 + distribute_res[i][0] = p + distribute_res[i - 1][1] = p + + # smooth the result + distribute_res = smooth(distribute_res) + + return distribute_res + + +def correct_labels(labels): + labels_id = 0 + id2id = {} + new_labels = [] + for i in labels: + if i not in id2id: + id2id[i] = labels_id + labels_id += 1 + new_labels.append(id2id[i]) + return np.array(new_labels) + + +def merge_seque(distribute_res): + res = [distribute_res[0]] + for i in range(1, len(distribute_res)): + if distribute_res[i][2] != res[-1][2] or distribute_res[i][0] > res[-1][1]: + res.append(distribute_res[i]) + else: + res[-1][1] = distribute_res[i][1] + return res + + +def smooth(res, mindur=1): + # short segments are assigned to nearest speakers. + for i in range(len(res)): + res[i][0] = round(res[i][0], 2) + res[i][1] = round(res[i][1], 2) + if res[i][1] - res[i][0] < mindur: + if i == 0: + res[i][2] = res[i + 1][2] + elif i == len(res) - 1: + res[i][2] = res[i - 1][2] + elif res[i][0] - res[i - 1][1] <= res[i + 1][0] - res[i][1]: + res[i][2] = res[i - 1][2] + else: + res[i][2] = res[i + 1][2] + # merge the speakers + res = merge_seque(res) + + return res + + +def distribute_spk(sentence_list, sd_time_list): + sd_sentence_list = [] + for d in sentence_list: + sentence_start = d["ts_list"][0][0] + sentence_end = d["ts_list"][-1][1] + sentence_spk = 0 + max_overlap = 0 + for sd_time in sd_time_list: + spk_st, spk_ed, spk = sd_time + spk_st = spk_st * 1000 + spk_ed = spk_ed * 1000 + overlap = max(min(sentence_end, spk_ed) - max(sentence_start, spk_st), 0) + if overlap > max_overlap: + max_overlap = overlap + sentence_spk = spk + d["spk"] = sentence_spk + sd_sentence_list.append(d) + return sd_sentence_list diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/timestamp_tools.py b/demo/Step-Audio-EditX/funasr_detach/utils/timestamp_tools.py new file mode 100644 index 0000000000000000000000000000000000000000..c201e73eb84135f9442c121c68100540deaadcd1 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/utils/timestamp_tools.py @@ -0,0 +1,201 @@ +import torch +import codecs +import logging +import argparse +import numpy as np + +# import edit_distance +from itertools import zip_longest + + +def cif_wo_hidden(alphas, threshold): + batch_size, len_time = alphas.size() + # loop varss + integrate = torch.zeros([batch_size], device=alphas.device) + # intermediate vars along time + list_fires = [] + for t in range(len_time): + alpha = alphas[:, t] + integrate += alpha + list_fires.append(integrate) + fire_place = integrate >= threshold + integrate = torch.where( + fire_place, + integrate - torch.ones([batch_size], device=alphas.device) * threshold, + integrate, + ) + fires = torch.stack(list_fires, 1) + return fires + + +def ts_prediction_lfr6_standard( + us_alphas, + us_peaks, + char_list, + vad_offset=0.0, + force_time_shift=-1.5, + sil_in_str=True, +): + if not len(char_list): + return "", [] + START_END_THRESHOLD = 5 + MAX_TOKEN_DURATION = 12 + TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled + if len(us_alphas.shape) == 2: + alphas, peaks = us_alphas[0], us_peaks[0] # support inference batch_size=1 only + else: + alphas, peaks = us_alphas, us_peaks + if char_list[-1] == "": + char_list = char_list[:-1] + fire_place = ( + torch.where(peaks > 1.0 - 1e-4)[0].cpu().numpy() + force_time_shift + ) # total offset + if len(fire_place) != len(char_list) + 1: + alphas /= alphas.sum() / (len(char_list) + 1) + alphas = alphas.unsqueeze(0) + peaks = cif_wo_hidden(alphas, threshold=1.0 - 1e-4)[0] + fire_place = ( + torch.where(peaks > 1.0 - 1e-4)[0].cpu().numpy() + force_time_shift + ) # total offset + num_frames = peaks.shape[0] + timestamp_list = [] + new_char_list = [] + # for bicif model trained with large data, cif2 actually fires when a character starts + # so treat the frames between two peaks as the duration of the former token + fire_place = ( + torch.where(peaks > 1.0 - 1e-4)[0].cpu().numpy() + force_time_shift + ) # total offset + # assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1 + # begin silence + if fire_place[0] > START_END_THRESHOLD: + # char_list.insert(0, '') + timestamp_list.append([0.0, fire_place[0] * TIME_RATE]) + new_char_list.append("") + # tokens timestamp + for i in range(len(fire_place) - 1): + new_char_list.append(char_list[i]) + if ( + MAX_TOKEN_DURATION < 0 + or fire_place[i + 1] - fire_place[i] <= MAX_TOKEN_DURATION + ): + timestamp_list.append( + [fire_place[i] * TIME_RATE, fire_place[i + 1] * TIME_RATE] + ) + else: + # cut the duration to token and sil of the 0-weight frames last long + _split = fire_place[i] + MAX_TOKEN_DURATION + timestamp_list.append([fire_place[i] * TIME_RATE, _split * TIME_RATE]) + timestamp_list.append([_split * TIME_RATE, fire_place[i + 1] * TIME_RATE]) + new_char_list.append("") + # tail token and end silence + # new_char_list.append(char_list[-1]) + if num_frames - fire_place[-1] > START_END_THRESHOLD: + _end = (num_frames + fire_place[-1]) * 0.5 + # _end = fire_place[-1] + timestamp_list[-1][1] = _end * TIME_RATE + timestamp_list.append([_end * TIME_RATE, num_frames * TIME_RATE]) + new_char_list.append("") + else: + timestamp_list[-1][1] = num_frames * TIME_RATE + if vad_offset: # add offset time in model with vad + for i in range(len(timestamp_list)): + timestamp_list[i][0] = timestamp_list[i][0] + vad_offset / 1000.0 + timestamp_list[i][1] = timestamp_list[i][1] + vad_offset / 1000.0 + res_txt = "" + for char, timestamp in zip(new_char_list, timestamp_list): + # if char != '': + if not sil_in_str and char == "": + continue + res_txt += "{} {} {};".format( + char, str(timestamp[0] + 0.0005)[:5], str(timestamp[1] + 0.0005)[:5] + ) + res = [] + for char, timestamp in zip(new_char_list, timestamp_list): + if char != "": + res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)]) + return res_txt, res + + +def timestamp_sentence( + punc_id_list, timestamp_postprocessed, text_postprocessed, return_raw_text=False +): + punc_list = ["，", "。", "？", "、"] + res = [] + if text_postprocessed is None: + return res + if timestamp_postprocessed is None: + return res + if len(timestamp_postprocessed) == 0: + return res + if len(text_postprocessed) == 0: + return res + + if punc_id_list is None or len(punc_id_list) == 0: + res.append( + { + "text": text_postprocessed.split(), + "start": timestamp_postprocessed[0][0], + "end": timestamp_postprocessed[-1][1], + "timestamp": timestamp_postprocessed, + } + ) + return res + if len(punc_id_list) != len(timestamp_postprocessed): + logging.warning("length mismatch between punc and timestamp") + sentence_text = "" + sentence_text_seg = "" + ts_list = [] + sentence_start = timestamp_postprocessed[0][0] + sentence_end = timestamp_postprocessed[0][1] + texts = text_postprocessed.split() + punc_stamp_text_list = list( + zip_longest(punc_id_list, timestamp_postprocessed, texts, fillvalue=None) + ) + for punc_stamp_text in punc_stamp_text_list: + punc_id, timestamp, text = punc_stamp_text + # sentence_text += text if text is not None else '' + if text is not None: + if "a" <= text[0] <= "z" or "A" <= text[0] <= "Z": + sentence_text += " " + text + elif len(sentence_text) and ( + "a" <= sentence_text[-1] <= "z" or "A" <= sentence_text[-1] <= "Z" + ): + sentence_text += " " + text + else: + sentence_text += text + sentence_text_seg += text + " " + ts_list.append(timestamp) + + punc_id = int(punc_id) if punc_id is not None else 1 + sentence_end = timestamp[1] if timestamp is not None else sentence_end + sentence_text_seg = ( + sentence_text_seg[:-1] + if sentence_text_seg[-1] == " " + else sentence_text_seg + ) + if punc_id > 1: + sentence_text += punc_list[punc_id - 2] + if return_raw_text: + res.append( + { + "text": sentence_text, + "start": sentence_start, + "end": sentence_end, + "timestamp": ts_list, + "raw_text": sentence_text_seg, + } + ) + else: + res.append( + { + "text": sentence_text, + "start": sentence_start, + "end": sentence_end, + "timestamp": ts_list, + } + ) + sentence_text = "" + sentence_text_seg = "" + ts_list = [] + sentence_start = sentence_end + return res diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/types.py b/demo/Step-Audio-EditX/funasr_detach/utils/types.py new file mode 100644 index 0000000000000000000000000000000000000000..fc53d0e9cc6d62961c25d9e7a4b74f0337d5d78a --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/utils/types.py @@ -0,0 +1,149 @@ +from distutils.util import strtobool +from typing import Optional +from typing import Tuple +from typing import Union + +import humanfriendly + + +def str2bool(value: str) -> bool: + return bool(strtobool(value)) + + +def remove_parenthesis(value: str): + value = value.strip() + if value.startswith("(") and value.endswith(")"): + value = value[1:-1] + elif value.startswith("[") and value.endswith("]"): + value = value[1:-1] + return value + + +def remove_quotes(value: str): + value = value.strip() + if value.startswith('"') and value.endswith('"'): + value = value[1:-1] + elif value.startswith("'") and value.endswith("'"): + value = value[1:-1] + return value + + +def int_or_none(value: str) -> Optional[int]: + """int_or_none. + + Examples: + >>> import argparse + >>> parser = argparse.ArgumentParser() + >>> _ = parser.add_argument('--foo', type=int_or_none) + >>> parser.parse_args(['--foo', '456']) + Namespace(foo=456) + >>> parser.parse_args(['--foo', 'none']) + Namespace(foo=None) + >>> parser.parse_args(['--foo', 'null']) + Namespace(foo=None) + >>> parser.parse_args(['--foo', 'nil']) + Namespace(foo=None) + + """ + if value.strip().lower() in ("none", "null", "nil"): + return None + return int(value) + + +def float_or_none(value: str) -> Optional[float]: + """float_or_none. + + Examples: + >>> import argparse + >>> parser = argparse.ArgumentParser() + >>> _ = parser.add_argument('--foo', type=float_or_none) + >>> parser.parse_args(['--foo', '4.5']) + Namespace(foo=4.5) + >>> parser.parse_args(['--foo', 'none']) + Namespace(foo=None) + >>> parser.parse_args(['--foo', 'null']) + Namespace(foo=None) + >>> parser.parse_args(['--foo', 'nil']) + Namespace(foo=None) + + """ + if value.strip().lower() in ("none", "null", "nil"): + return None + return float(value) + + +def humanfriendly_parse_size_or_none(value) -> Optional[float]: + if value.strip().lower() in ("none", "null", "nil"): + return None + return humanfriendly.parse_size(value) + + +def str_or_int(value: str) -> Union[str, int]: + try: + return int(value) + except ValueError: + return value + + +def str_or_none(value: str) -> Optional[str]: + """str_or_none. + + Examples: + >>> import argparse + >>> parser = argparse.ArgumentParser() + >>> _ = parser.add_argument('--foo', type=str_or_none) + >>> parser.parse_args(['--foo', 'aaa']) + Namespace(foo='aaa') + >>> parser.parse_args(['--foo', 'none']) + Namespace(foo=None) + >>> parser.parse_args(['--foo', 'null']) + Namespace(foo=None) + >>> parser.parse_args(['--foo', 'nil']) + Namespace(foo=None) + + """ + if value.strip().lower() in ("none", "null", "nil"): + return None + return value + + +def str2pair_str(value: str) -> Tuple[str, str]: + """str2pair_str. + + Examples: + >>> import argparse + >>> str2pair_str('abc,def ') + ('abc', 'def') + >>> parser = argparse.ArgumentParser() + >>> _ = parser.add_argument('--foo', type=str2pair_str) + >>> parser.parse_args(['--foo', 'abc,def']) + Namespace(foo=('abc', 'def')) + + """ + value = remove_parenthesis(value) + a, b = value.split(",") + + # Workaround for configargparse issues: + # If the list values are given from yaml file, + # the value givent to type() is shaped as python-list, + # e.g. ['a', 'b', 'c'], + # so we need to remove double quotes from it. + return remove_quotes(a), remove_quotes(b) + + +def str2triple_str(value: str) -> Tuple[str, str, str]: + """str2triple_str. + + Examples: + >>> str2triple_str('abc,def ,ghi') + ('abc', 'def', 'ghi') + """ + value = remove_parenthesis(value) + a, b, c = value.split(",") + + # Workaround for configargparse issues: + # If the list values are given from yaml file, + # the value givent to type() is shaped as python-list, + # e.g. ['a', 'b', 'c'], + # so we need to remove quotes from it. + return remove_quotes(a), remove_quotes(b), remove_quotes(c) diff --git a/demo/Step-Audio-EditX/funasr_detach/utils/vad_utils.py b/demo/Step-Audio-EditX/funasr_detach/utils/vad_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1c7296296aebaa6c968a2d7582951b1eb620493e --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/utils/vad_utils.py @@ -0,0 +1,32 @@ +import torch +from torch.nn.utils.rnn import pad_sequence + + +def slice_padding_fbank(speech, speech_lengths, vad_segments): + speech_list = [] + speech_lengths_list = [] + for i, segment in enumerate(vad_segments): + + bed_idx = int(segment[0][0] * 16) + end_idx = min(int(segment[0][1] * 16), speech_lengths[0]) + speech_i = speech[0, bed_idx:end_idx] + speech_lengths_i = end_idx - bed_idx + speech_list.append(speech_i) + speech_lengths_list.append(speech_lengths_i) + feats_pad = pad_sequence(speech_list, batch_first=True, padding_value=0.0) + speech_lengths_pad = torch.Tensor(speech_lengths_list).int() + return feats_pad, speech_lengths_pad + + +def slice_padding_audio_samples(speech, speech_lengths, vad_segments): + speech_list = [] + speech_lengths_list = [] + for i, segment in enumerate(vad_segments): + bed_idx = int(segment[0][0] * 16) + end_idx = min(int(segment[0][1] * 16), speech_lengths) + speech_i = speech[bed_idx:end_idx] + speech_lengths_i = end_idx - bed_idx + speech_list.append(speech_i) + speech_lengths_list.append(speech_lengths_i) + + return speech_list, speech_lengths_list diff --git a/demo/Step-Audio-EditX/funasr_detach/version.txt b/demo/Step-Audio-EditX/funasr_detach/version.txt new file mode 100644 index 0000000000000000000000000000000000000000..c7981f4d1e719f3ad2facb3bde2b2f3abbbf0f54 --- /dev/null +++ b/demo/Step-Audio-EditX/funasr_detach/version.txt @@ -0,0 +1 @@ +1.0.8 diff --git a/demo/Step-Audio-EditX/model_loader.py b/demo/Step-Audio-EditX/model_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..d09b378be650ab1252d50d05d35a38691a1fbf6d --- /dev/null +++ b/demo/Step-Audio-EditX/model_loader.py @@ -0,0 +1,281 @@ +""" +Unified model loading utility supporting ModelScope, HuggingFace and local path loading +""" +import importlib +import os +import logging +from pathlib import Path +import sys +import threading +from typing import Union, Optional, Dict, Any +import spaces +from transformers import AutoModelForCausalLM, AutoTokenizer +from funasr_detach import AutoModel +from transformers.models.auto import tokenization_auto, configuration_auto + +# Global cache for downloaded models to avoid repeated downloads +# Key: (model_path, source) +# Value: local_model_path +_model_download_cache = {} +_download_cache_lock = threading.Lock() + + +class ModelSource: + """Model source enumeration""" + MODELSCOPE = "modelscope" + HUGGINGFACE = "huggingface" + LOCAL = "local" + AUTO = "auto" # Auto-detect + + +class UnifiedModelLoader: + """Unified model loader""" + + def __init__(self): + self.logger = logging.getLogger(__name__) + + def _cached_snapshot_download(self, model_path: str, source: str, **kwargs) -> str: + """ + Cached version of snapshot_download to avoid repeated downloads + + Args: + model_path: Model path or ID to download + source: Model source ('modelscope' or 'huggingface') + **kwargs: Additional arguments for snapshot_download + + Returns: + Local path to downloaded model + """ + cache_key = (model_path, source, str(sorted(kwargs.items()))) + + # Check cache first + with _download_cache_lock: + if cache_key in _model_download_cache: + cached_path = _model_download_cache[cache_key] + self.logger.info(f"Using cached download for {model_path} from {source}: {cached_path}") + return cached_path + + # Cache miss, need to download + if source == ModelSource.MODELSCOPE: + from modelscope.hub.snapshot_download import snapshot_download + local_path = snapshot_download(model_path, **kwargs) + elif source == ModelSource.HUGGINGFACE: + from huggingface_hub import snapshot_download + local_path = snapshot_download(model_path, **kwargs) + else: + raise ValueError(f"Unsupported source for cached download: {source}") + + # Cache the result + with _download_cache_lock: + _model_download_cache[cache_key] = local_path + + self.logger.info(f"Downloaded and cached {model_path} from {source}: {local_path}") + return local_path + + def detect_model_source(self, model_path: str) -> str: + """ + Automatically detect model source + + Args: + model_path: Model path or ID + + Returns: + Model source type + """ + # Local path detection + if os.path.exists(model_path) or os.path.isabs(model_path): + return ModelSource.LOCAL + + # ModelScope format detection (usually includes username/model_name) + if "/" in model_path and not model_path.startswith("http"): + # If contains modelscope keyword or is known modelscope format + if "modelscope" in model_path.lower() or self._is_modelscope_format(model_path): + return ModelSource.MODELSCOPE + else: + # Default to HuggingFace + return ModelSource.HUGGINGFACE + + return ModelSource.LOCAL + + def _is_modelscope_format(self, model_path: str) -> bool: + """Detect if it's ModelScope format model ID""" + # Can be judged according to known ModelScope model ID formats + # For example: iic/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online + modelscope_patterns = [] + return any(pattern in model_path for pattern in modelscope_patterns) + + @spaces.GPU + def load_transformers_model( + self, + model_path: str, + source: str = ModelSource.AUTO, + **kwargs + ) -> tuple: + """ + Load Transformers model (for StepAudioTTS) + + Args: + model_path: Model path or ID + source: Model source, auto means auto-detect + **kwargs: Other parameters + + Returns: + (model, tokenizer) tuple + """ + if source == ModelSource.AUTO: + source = self.detect_model_source(model_path) + + self.logger.info(f"Loading Transformers model from {source}: {model_path}") + + try: + if source == ModelSource.LOCAL: + # Local loading + model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=kwargs.get("torch_dtype"), + device_map=kwargs.get("device_map", "auto"), + trust_remote_code=True, + local_files_only=True + ) + tokenizer = AutoTokenizer.from_pretrained( + model_path, + trust_remote_code=True, + local_files_only=True + ) + + elif source == ModelSource.MODELSCOPE: + # Load from ModelScope + from modelscope import AutoModelForCausalLM as MSAutoModelForCausalLM + from modelscope import AutoTokenizer as MSAutoTokenizer + model_path = self._cached_snapshot_download(model_path, ModelSource.MODELSCOPE) + + model = MSAutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=kwargs.get("torch_dtype"), + device_map=kwargs.get("device_map", "auto"), + trust_remote_code=True, + local_files_only=True + ) + tokenizer = MSAutoTokenizer.from_pretrained( + model_path, + trust_remote_code=True, + local_files_only=True + ) + + elif source == ModelSource.HUGGINGFACE: + model_path = self._cached_snapshot_download(model_path, ModelSource.HUGGINGFACE) + + # Load from HuggingFace + model = AutoModelForCausalLM.from_pretrained( + model_path, + torch_dtype=kwargs.get("torch_dtype"), + device_map=kwargs.get("device_map", "auto"), + trust_remote_code=True, + local_files_only=True + ) + tokenizer = AutoTokenizer.from_pretrained( + model_path, + trust_remote_code=True, + local_files_only=True + ) + + else: + raise ValueError(f"Unsupported model source: {source}") + + self.logger.info(f"Successfully loaded model from {source}") + return model, tokenizer, model_path + + except Exception as e: + self.logger.error(f"Failed to load model from {source}: {e}") + raise + + @spaces.GPU + def load_funasr_model( + self, + repo_path: str, + model_path: str, + source: str = ModelSource.AUTO, + **kwargs + ) -> AutoModel: + """ + Load FunASR model (for StepAudioTokenizer) + + Args: + model_path: Model path or ID + source: Model source, auto means auto-detect + **kwargs: Other parameters + + Returns: + FunASR AutoModel instance + """ + if source == ModelSource.AUTO: + source = self.detect_model_source(model_path) + + self.logger.info(f"Loading FunASR model from {source}: {model_path}") + + try: + # Extract model_revision to avoid duplicate passing + model_revision = kwargs.pop("model_revision", "main") + + # Map ModelSource to model_hub parameter + if source == ModelSource.LOCAL: + model_hub = "local" + elif source == ModelSource.MODELSCOPE: + model_hub = "ms" + elif source == ModelSource.HUGGINGFACE: + model_hub = "hf" + else: + raise ValueError(f"Unsupported model source: {source}") + + # Use unified download_model for all cases + model = AutoModel( + repo_path=repo_path, + model=model_path, + model_hub=model_hub, + model_revision=model_revision, + **kwargs + ) + + self.logger.info(f"Successfully loaded FunASR model from {source}") + return model + + except Exception as e: + self.logger.error(f"Failed to load FunASR model from {source}: {e}") + raise + + def resolve_model_path( + self, + base_path: str, + model_name: str, + source: str = ModelSource.AUTO + ) -> str: + """ + Resolve model path + + Args: + base_path: Base path + model_name: Model name + source: Model source + + Returns: + Resolved model path + """ + if source == ModelSource.AUTO: + # First check local path + local_path = os.path.join(base_path, model_name) + if os.path.exists(local_path): + return local_path + + # If local doesn't exist, return model name for online download + return model_name + + elif source == ModelSource.LOCAL: + return os.path.join(base_path, model_name) + + else: + # For online sources, directly return model name/ID + return model_name + + +# Global instance +model_loader = UnifiedModelLoader() \ No newline at end of file diff --git a/demo/Step-Audio-EditX/requirements.txt b/demo/Step-Audio-EditX/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..dba8bfe97a4970a4e4039fd2697958f18a00a026 --- /dev/null +++ b/demo/Step-Audio-EditX/requirements.txt @@ -0,0 +1,24 @@ +torch==2.8.0 +torchaudio==2.8.0 +torchvision==0.23.0 +transformers==4.48.3 +accelerate==1.3.0 +openai-whisper==20240930 +onnxruntime-gpu==1.17.0 +omegaconf==2.3.0 +librosa==0.10.2.post1 +sox==1.5.0 +modelscope +numpy==1.26.4 +six==1.16.0 +hyperpyyaml +conformer==0.3.2 +diffusers +pillow +sentencepiece +funasr>=1.1.3 +protobuf==5.29.3 +gradio>=5.16.0 +nvidia-cuda-nvrtc-cu12==12.8.93 +spaces==0.42.1 +matplotlib==3.10.7 diff --git a/demo/Step-Audio-EditX/source.txt b/demo/Step-Audio-EditX/source.txt new file mode 100644 index 0000000000000000000000000000000000000000..7cacb11ed22636ac7f9ae7bd3768a0413f24d900 --- /dev/null +++ b/demo/Step-Audio-EditX/source.txt @@ -0,0 +1 @@ +https://huggingface.co/spaces/stepfun-ai/Step-Audio-EditX \ No newline at end of file diff --git a/demo/Step-Audio-EditX/stepvocoder/__init__.py b/demo/Step-Audio-EditX/stepvocoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/activations.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/activations.py new file mode 100644 index 0000000000000000000000000000000000000000..b1e83fcdef46300f5d5bff1f0dbf71f58f3b1186 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/activations.py @@ -0,0 +1,126 @@ +# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license. +# LICENSE is in incl_licenses directory. + +import torch +from torch import nn, sin, pow +from torch.nn import Parameter + + +class Snake(nn.Module): + """ + Implementation of a sine-based periodic activation function + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter + References: + - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snake(256) + >>> x = torch.randn(256) + >>> x = a1(x) + """ + + def __init__( + self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False + ): + """ + Initialization. + INPUT: + - in_features: shape of the input + - alpha: trainable parameter + alpha is initialized to 1 by default, higher values = higher-frequency. + alpha will be trained along with the rest of your model. + """ + super(Snake, self).__init__() + self.in_features = in_features + + # Initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # Log scale alphas initialized to zeros + self.alpha = Parameter(torch.zeros(in_features) * alpha) + else: # Linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + """ + Forward pass of the function. + Applies the function to the input elementwise. + Snake ∶= x + 1/a * sin^2 (xa) + """ + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # Line up with x to [B, C, T] + if self.alpha_logscale: + alpha = torch.exp(alpha) + x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x + + +class SnakeBeta(nn.Module): + """ + A modified Snake function which uses separate parameters for the magnitude of the periodic components + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + References: + - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snakebeta(256) + >>> x = torch.randn(256) + >>> x = a1(x) + """ + + def __init__( + self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False + ): + """ + Initialization. + INPUT: + - in_features: shape of the input + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + alpha is initialized to 1 by default, higher values = higher-frequency. + beta is initialized to 1 by default, higher values = higher-magnitude. + alpha will be trained along with the rest of your model. + """ + super(SnakeBeta, self).__init__() + self.in_features = in_features + + # Initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # Log scale alphas initialized to zeros + self.alpha = Parameter(torch.zeros(in_features) * alpha) + self.beta = Parameter(torch.zeros(in_features) * alpha) + else: # Linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + self.beta = Parameter(torch.ones(in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + self.beta.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + """ + Forward pass of the function. + Applies the function to the input elementwise. + SnakeBeta ∶= x + 1/b * sin^2 (xa) + """ + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # Line up with x to [B, C, T] + beta = self.beta.unsqueeze(0).unsqueeze(-1) + if self.alpha_logscale: + alpha = torch.exp(alpha) + beta = torch.exp(beta) + x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/__init__.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/activation1d.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/activation1d.py new file mode 100644 index 0000000000000000000000000000000000000000..fc0d313cb265170943fb7cb16742b031038f7859 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/activation1d.py @@ -0,0 +1,77 @@ +# Copyright (c) 2024 NVIDIA CORPORATION. +# Licensed under the MIT license. + +import torch +import torch.nn as nn +from alias_free_activation.torch.resample import UpSample1d, DownSample1d + +# load fused CUDA kernel: this enables importing anti_alias_activation_cuda +from alias_free_activation.cuda import load + +anti_alias_activation_cuda = load.load() + + +class FusedAntiAliasActivation(torch.autograd.Function): + """ + Assumes filter size 12, replication padding on upsampling/downsampling, and logscale alpha/beta parameters as inputs. + The hyperparameters are hard-coded in the kernel to maximize speed. + NOTE: The fused kenrel is incorrect for Activation1d with different hyperparameters. + """ + + @staticmethod + def forward(ctx, inputs, up_ftr, down_ftr, alpha, beta): + activation_results = anti_alias_activation_cuda.forward( + inputs, up_ftr, down_ftr, alpha, beta + ) + + return activation_results + + @staticmethod + def backward(ctx, output_grads): + raise NotImplementedError + return output_grads, None, None + + +class Activation1d(nn.Module): + def __init__( + self, + activation, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12, + fused: bool = True, + ): + super().__init__() + self.up_ratio = up_ratio + self.down_ratio = down_ratio + self.act = activation + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + self.fused = fused # Whether to use fused CUDA kernel or not + + def forward(self, x): + if not self.fused: + x = self.upsample(x) + x = self.act(x) + x = self.downsample(x) + return x + else: + if self.act.__class__.__name__ == "Snake": + beta = self.act.alpha.data # Snake uses same params for alpha and beta + else: + beta = ( + self.act.beta.data + ) # Snakebeta uses different params for alpha and beta + alpha = self.act.alpha.data + if ( + not self.act.alpha_logscale + ): # Exp baked into cuda kernel, cancel it out with a log + alpha = torch.log(alpha) + beta = torch.log(beta) + + x = FusedAntiAliasActivation.apply( + x, self.upsample.filter, self.downsample.lowpass.filter, alpha, beta + ) + return x diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/anti_alias_activation.cpp b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/anti_alias_activation.cpp new file mode 100644 index 0000000000000000000000000000000000000000..94fd90da386e66ce12a64ef243e4d125926dfd2a --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/anti_alias_activation.cpp @@ -0,0 +1,23 @@ +/* coding=utf-8 + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + #include + +extern "C" torch::Tensor fwd_cuda(torch::Tensor const &input, torch::Tensor const &up_filter, torch::Tensor const &down_filter, torch::Tensor const &alpha, torch::Tensor const &beta); + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("forward", &fwd_cuda, "Anti-Alias Activation forward (CUDA)"); +} \ No newline at end of file diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/anti_alias_activation_cuda.cu b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/anti_alias_activation_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..7ee97492984a92c753f2357f03e7c04252060824 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/anti_alias_activation_cuda.cu @@ -0,0 +1,246 @@ +/* coding=utf-8 + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include +#include +#include +#include +#include +#include +#include "type_shim.h" +#include +#include +#include +#include +#include + +namespace +{ + // Hard-coded hyperparameters + // WARP_SIZE and WARP_BATCH must match the return values batches_per_warp and + constexpr int ELEMENTS_PER_LDG_STG = 1; //(WARP_ITERATIONS < 4) ? 1 : 4; + constexpr int BUFFER_SIZE = 32; + constexpr int FILTER_SIZE = 12; + constexpr int HALF_FILTER_SIZE = 6; + constexpr int UPSAMPLE_REPLICATION_PAD = 5; // 5 on each side, matching torch impl + constexpr int DOWNSAMPLE_REPLICATION_PAD_LEFT = 5; // matching torch impl + constexpr int DOWNSAMPLE_REPLICATION_PAD_RIGHT = 6; // matching torch impl + + template + __global__ void anti_alias_activation_forward( + output_t *dst, + const input_t *src, + const input_t *up_ftr, + const input_t *down_ftr, + const input_t *alpha, + const input_t *beta, + int batch_size, + int channels, + int seq_len) + { + // Up and downsample filters + input_t up_filter[FILTER_SIZE]; + input_t down_filter[FILTER_SIZE]; + + // Load data from global memory including extra indices reserved for replication paddings + input_t elements[2 * FILTER_SIZE + 2 * BUFFER_SIZE + 2 * UPSAMPLE_REPLICATION_PAD] = {0}; + input_t intermediates[2 * FILTER_SIZE + 2 * BUFFER_SIZE + DOWNSAMPLE_REPLICATION_PAD_LEFT + DOWNSAMPLE_REPLICATION_PAD_RIGHT] = {0}; + + // Output stores downsampled output before writing to dst + output_t output[BUFFER_SIZE]; + + // blockDim/threadIdx = (128, 1, 1) + // gridDim/blockIdx = (seq_blocks, channels, batches) + int block_offset = (blockIdx.x * 128 * BUFFER_SIZE + seq_len * (blockIdx.y + gridDim.y * blockIdx.z)); + int local_offset = threadIdx.x * BUFFER_SIZE; + int seq_offset = blockIdx.x * 128 * BUFFER_SIZE + local_offset; + + // intermediate have double the seq_len + int intermediate_local_offset = threadIdx.x * BUFFER_SIZE * 2; + int intermediate_seq_offset = blockIdx.x * 128 * BUFFER_SIZE * 2 + intermediate_local_offset; + + // Get values needed for replication padding before moving pointer + const input_t *right_most_pntr = src + (seq_len * (blockIdx.y + gridDim.y * blockIdx.z)); + input_t seq_left_most_value = right_most_pntr[0]; + input_t seq_right_most_value = right_most_pntr[seq_len - 1]; + + // Move src and dst pointers + src += block_offset + local_offset; + dst += block_offset + local_offset; + + // Alpha and beta values for snake activatons. Applies exp by default + alpha = alpha + blockIdx.y; + input_t alpha_val = expf(alpha[0]); + beta = beta + blockIdx.y; + input_t beta_val = expf(beta[0]); + + #pragma unroll + for (int it = 0; it < FILTER_SIZE; it += 1) + { + up_filter[it] = up_ftr[it]; + down_filter[it] = down_ftr[it]; + } + + // Apply replication padding for upsampling, matching torch impl + #pragma unroll + for (int it = -HALF_FILTER_SIZE; it < BUFFER_SIZE + HALF_FILTER_SIZE; it += 1) + { + int element_index = seq_offset + it; // index for element + if ((element_index < 0) && (element_index >= -UPSAMPLE_REPLICATION_PAD)) + { + elements[2 * (HALF_FILTER_SIZE + it)] = 2 * seq_left_most_value; + } + if ((element_index >= seq_len) && (element_index < seq_len + UPSAMPLE_REPLICATION_PAD)) + { + elements[2 * (HALF_FILTER_SIZE + it)] = 2 * seq_right_most_value; + } + if ((element_index >= 0) && (element_index < seq_len)) + { + elements[2 * (HALF_FILTER_SIZE + it)] = 2 * src[it]; + } + } + + // Apply upsampling strided convolution and write to intermediates. It reserves DOWNSAMPLE_REPLICATION_PAD_LEFT for replication padding of the downsampilng conv later + #pragma unroll + for (int it = 0; it < (2 * BUFFER_SIZE + 2 * FILTER_SIZE); it += 1) + { + input_t acc = 0.0; + int element_index = intermediate_seq_offset + it; // index for intermediate + #pragma unroll + for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx += 1) + { + if ((element_index + f_idx) >= 0) + { + acc += up_filter[f_idx] * elements[it + f_idx]; + } + } + intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] = acc; + } + + // Apply activation function. It reserves DOWNSAMPLE_REPLICATION_PAD_LEFT and DOWNSAMPLE_REPLICATION_PAD_RIGHT for replication padding of the downsampilng conv later + double no_div_by_zero = 0.000000001; + #pragma unroll + for (int it = 0; it < 2 * BUFFER_SIZE + 2 * FILTER_SIZE; it += 1) + { + intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] += (1.0 / (beta_val + no_div_by_zero)) * sinf(intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] * alpha_val) * sinf(intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] * alpha_val); + } + + // Apply replication padding before downsampling conv from intermediates + #pragma unroll + for (int it = 0; it < DOWNSAMPLE_REPLICATION_PAD_LEFT; it += 1) + { + intermediates[it] = intermediates[DOWNSAMPLE_REPLICATION_PAD_LEFT]; + } + #pragma unroll + for (int it = DOWNSAMPLE_REPLICATION_PAD_LEFT + 2 * BUFFER_SIZE + 2 * FILTER_SIZE; it < DOWNSAMPLE_REPLICATION_PAD_LEFT + 2 * BUFFER_SIZE + 2 * FILTER_SIZE + DOWNSAMPLE_REPLICATION_PAD_RIGHT; it += 1) + { + intermediates[it] = intermediates[DOWNSAMPLE_REPLICATION_PAD_LEFT + 2 * BUFFER_SIZE + 2 * FILTER_SIZE - 1]; + } + + // Apply downsample strided convolution (assuming stride=2) from intermediates + #pragma unroll + for (int it = 0; it < BUFFER_SIZE; it += 1) + { + input_t acc = 0.0; + #pragma unroll + for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx += 1) + { + // Add constant DOWNSAMPLE_REPLICATION_PAD_RIGHT to match torch implementation + acc += down_filter[f_idx] * intermediates[it * 2 + f_idx + DOWNSAMPLE_REPLICATION_PAD_RIGHT]; + } + output[it] = acc; + } + + // Write output to dst + #pragma unroll + for (int it = 0; it < BUFFER_SIZE; it += ELEMENTS_PER_LDG_STG) + { + int element_index = seq_offset + it; + if (element_index < seq_len) + { + dst[it] = output[it]; + } + } + + } + + template + void dispatch_anti_alias_activation_forward( + output_t *dst, + const input_t *src, + const input_t *up_ftr, + const input_t *down_ftr, + const input_t *alpha, + const input_t *beta, + int batch_size, + int channels, + int seq_len) + { + if (seq_len == 0) + { + return; + } + else + { + // Use 128 threads per block to maximimize gpu utilization + constexpr int threads_per_block = 128; + constexpr int seq_len_per_block = 4096; + int blocks_per_seq_len = (seq_len + seq_len_per_block - 1) / seq_len_per_block; + dim3 blocks(blocks_per_seq_len, channels, batch_size); + dim3 threads(threads_per_block, 1, 1); + + anti_alias_activation_forward + <<>>(dst, src, up_ftr, down_ftr, alpha, beta, batch_size, channels, seq_len); + } + } +} + +extern "C" torch::Tensor fwd_cuda(torch::Tensor const &input, torch::Tensor const &up_filter, torch::Tensor const &down_filter, torch::Tensor const &alpha, torch::Tensor const &beta) +{ + // Input is a 3d tensor with dimensions [batches, channels, seq_len] + const int batches = input.size(0); + const int channels = input.size(1); + const int seq_len = input.size(2); + + // Output + auto act_options = input.options().requires_grad(false); + + torch::Tensor anti_alias_activation_results = + torch::empty({batches, channels, seq_len}, act_options); + + void *input_ptr = static_cast(input.data_ptr()); + void *up_filter_ptr = static_cast(up_filter.data_ptr()); + void *down_filter_ptr = static_cast(down_filter.data_ptr()); + void *alpha_ptr = static_cast(alpha.data_ptr()); + void *beta_ptr = static_cast(beta.data_ptr()); + void *anti_alias_activation_results_ptr = static_cast(anti_alias_activation_results.data_ptr()); + + DISPATCH_FLOAT_HALF_AND_BFLOAT( + input.scalar_type(), + "dispatch anti alias activation_forward", + dispatch_anti_alias_activation_forward( + reinterpret_cast(anti_alias_activation_results_ptr), + reinterpret_cast(input_ptr), + reinterpret_cast(up_filter_ptr), + reinterpret_cast(down_filter_ptr), + reinterpret_cast(alpha_ptr), + reinterpret_cast(beta_ptr), + batches, + channels, + seq_len);); + return anti_alias_activation_results; +} \ No newline at end of file diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/compat.h b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/compat.h new file mode 100644 index 0000000000000000000000000000000000000000..0f93af5700470e7f6066af7dbe56aced98ea32d9 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/compat.h @@ -0,0 +1,29 @@ +/* coding=utf-8 + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/*This code is copied fron NVIDIA apex: + * https://github.com/NVIDIA/apex + * with minor changes. */ + +#ifndef TORCH_CHECK +#define TORCH_CHECK AT_CHECK +#endif + +#ifdef VERSION_GE_1_3 +#define DATA_PTR data_ptr +#else +#define DATA_PTR data +#endif diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/load.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/load.py new file mode 100644 index 0000000000000000000000000000000000000000..82afde3d73dda72b06af28a622fdab1954825a28 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/load.py @@ -0,0 +1,86 @@ +# Copyright (c) 2024 NVIDIA CORPORATION. +# Licensed under the MIT license. + +import os +import pathlib +import subprocess + +from torch.utils import cpp_extension + +""" +Setting this param to a list has a problem of generating different compilation commands (with diferent order of architectures) and leading to recompilation of fused kernels. +Set it to empty stringo avoid recompilation and assign arch flags explicity in extra_cuda_cflags below +""" +os.environ["TORCH_CUDA_ARCH_LIST"] = "" + + +def load(): + # Check if cuda 11 is installed for compute capability 8.0 + cc_flag = [] + _, bare_metal_major, _ = _get_cuda_bare_metal_version(cpp_extension.CUDA_HOME) + if int(bare_metal_major) >= 11: + cc_flag.append("-gencode") + cc_flag.append("arch=compute_80,code=sm_80") + + # Build path + srcpath = pathlib.Path(__file__).parent.absolute() + buildpath = srcpath / "build" + _create_build_dir(buildpath) + + # Helper function to build the kernels. + def _cpp_extention_load_helper(name, sources, extra_cuda_flags): + return cpp_extension.load( + name=name, + sources=sources, + build_directory=buildpath, + extra_cflags=[ + "-O3", + ], + extra_cuda_cflags=[ + "-O3", + "-gencode", + "arch=compute_70,code=sm_70", + "--use_fast_math", + ] + + extra_cuda_flags + + cc_flag, + verbose=True, + ) + + extra_cuda_flags = [ + "-U__CUDA_NO_HALF_OPERATORS__", + "-U__CUDA_NO_HALF_CONVERSIONS__", + "--expt-relaxed-constexpr", + "--expt-extended-lambda", + ] + + sources = [ + srcpath / "anti_alias_activation.cpp", + srcpath / "anti_alias_activation_cuda.cu", + ] + anti_alias_activation_cuda = _cpp_extention_load_helper( + "anti_alias_activation_cuda", sources, extra_cuda_flags + ) + + return anti_alias_activation_cuda + + +def _get_cuda_bare_metal_version(cuda_dir): + raw_output = subprocess.check_output( + [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True + ) + output = raw_output.split() + release_idx = output.index("release") + 1 + release = output[release_idx].split(".") + bare_metal_major = release[0] + bare_metal_minor = release[1][0] + + return raw_output, bare_metal_major, bare_metal_minor + + +def _create_build_dir(buildpath): + try: + os.mkdir(buildpath) + except OSError: + if not os.path.isdir(buildpath): + print(f"Creation of the build directory {buildpath} failed") diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/type_shim.h b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/type_shim.h new file mode 100644 index 0000000000000000000000000000000000000000..4328d0369a5fb8730cdf236d9f267453f4201d84 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/cuda/type_shim.h @@ -0,0 +1,92 @@ +/* coding=utf-8 + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include "compat.h" + +#define DISPATCH_FLOAT_HALF_AND_BFLOAT(TYPE, NAME, ...) \ + switch (TYPE) \ + { \ + case at::ScalarType::Float: \ + { \ + using scalar_t = float; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Half: \ + { \ + using scalar_t = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: \ + { \ + using scalar_t = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + +#define DISPATCH_FLOAT_HALF_AND_BFLOAT_INOUT_TYPES(TYPEIN, TYPEOUT, NAME, ...) \ + switch (TYPEIN) \ + { \ + case at::ScalarType::Float: \ + { \ + using scalar_t_in = float; \ + switch (TYPEOUT) \ + { \ + case at::ScalarType::Float: \ + { \ + using scalar_t_out = float; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Half: \ + { \ + using scalar_t_out = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: \ + { \ + using scalar_t_out = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPEOUT), "'"); \ + } \ + break; \ + } \ + case at::ScalarType::Half: \ + { \ + using scalar_t_in = at::Half; \ + using scalar_t_out = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: \ + { \ + using scalar_t_in = at::BFloat16; \ + using scalar_t_out = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPEIN), "'"); \ + } diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/__init__.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7bb0ad84ef184dcb15464c8ca827ae1c284f8990 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/__init__.py @@ -0,0 +1,6 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +from .filter import * +from .resample import * +from .act import * diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/act.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/act.py new file mode 100644 index 0000000000000000000000000000000000000000..8397cce65d6f1bd99846dc357810cd4cac384c70 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/act.py @@ -0,0 +1,30 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +import torch.nn as nn +from stepvocoder.cosyvoice2.bigvgan.alias_free_activation.torch.resample import UpSample1d, DownSample1d + + +class Activation1d(nn.Module): + def __init__( + self, + activation, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12, + ): + super().__init__() + self.up_ratio = up_ratio + self.down_ratio = down_ratio + self.act = activation + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + # x: [B,C,T] + def forward(self, x): + x = self.upsample(x) + x = self.act(x) + x = self.downsample(x) + + return x diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/filter.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/filter.py new file mode 100644 index 0000000000000000000000000000000000000000..81a4a9a7cefb457f8880f54385335180fbd43f1b --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/filter.py @@ -0,0 +1,101 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +if "sinc" in dir(torch): + sinc = torch.sinc +else: + # This code is adopted from adefossez's julius.core.sinc under the MIT License + # https://adefossez.github.io/julius/julius/core.html + # LICENSE is in incl_licenses directory. + def sinc(x: torch.Tensor): + """ + Implementation of sinc, i.e. sin(pi * x) / (pi * x) + __Warning__: Different to julius.sinc, the input is multiplied by `pi`! + """ + return torch.where( + x == 0, + torch.tensor(1.0, device=x.device, dtype=x.dtype), + torch.sin(math.pi * x) / math.pi / x, + ) + + +# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License +# https://adefossez.github.io/julius/julius/lowpass.html +# LICENSE is in incl_licenses directory. +def kaiser_sinc_filter1d( + cutoff, half_width, kernel_size +): # return filter [1,1,kernel_size] + even = kernel_size % 2 == 0 + half_size = kernel_size // 2 + + # For kaiser window + delta_f = 4 * half_width + A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 + if A > 50.0: + beta = 0.1102 * (A - 8.7) + elif A >= 21.0: + beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0) + else: + beta = 0.0 + window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) + + # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio + if even: + time = torch.arange(-half_size, half_size) + 0.5 + else: + time = torch.arange(kernel_size) - half_size + if cutoff == 0: + filter_ = torch.zeros_like(time) + else: + filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) + """ + Normalize filter to have sum = 1, otherwise we will have a small leakage of the constant component in the input signal. + """ + filter_ /= filter_.sum() + filter = filter_.view(1, 1, kernel_size) + + return filter + + +class LowPassFilter1d(nn.Module): + def __init__( + self, + cutoff=0.5, + half_width=0.6, + stride: int = 1, + padding: bool = True, + padding_mode: str = "replicate", + kernel_size: int = 12, + ): + """ + kernel_size should be even number for stylegan3 setup, in this implementation, odd number is also possible. + """ + super().__init__() + if cutoff < -0.0: + raise ValueError("Minimum cutoff must be larger than zero.") + if cutoff > 0.5: + raise ValueError("A cutoff above 0.5 does not make sense.") + self.kernel_size = kernel_size + self.even = kernel_size % 2 == 0 + self.pad_left = kernel_size // 2 - int(self.even) + self.pad_right = kernel_size // 2 + self.stride = stride + self.padding = padding + self.padding_mode = padding_mode + filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) + self.register_buffer("filter", filter) + + # Input [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + if self.padding: + x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode) + out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C) + + return out diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/resample.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/resample.py new file mode 100644 index 0000000000000000000000000000000000000000..e4d4229678bc6a58227ed45499ee4e66d14cd602 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/alias_free_activation/torch/resample.py @@ -0,0 +1,58 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +import torch.nn as nn +from torch.nn import functional as F +from stepvocoder.cosyvoice2.bigvgan.alias_free_activation.torch.filter import LowPassFilter1d +from stepvocoder.cosyvoice2.bigvgan.alias_free_activation.torch.filter import kaiser_sinc_filter1d + + +class UpSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = ( + int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + ) + self.stride = ratio + self.pad = self.kernel_size // ratio - 1 + self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 + self.pad_right = ( + self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 + ) + filter = kaiser_sinc_filter1d( + cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size + ) + self.register_buffer("filter", filter) + + # x: [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + x = F.pad(x, (self.pad, self.pad), mode="replicate") + x = self.ratio * F.conv_transpose1d( + x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C + ) + x = x[..., self.pad_left : -self.pad_right] + + return x + + +class DownSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = ( + int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + ) + self.lowpass = LowPassFilter1d( + cutoff=0.5 / ratio, + half_width=0.6 / ratio, + stride=ratio, + kernel_size=self.kernel_size, + ) + + def forward(self, x): + xx = self.lowpass(x) + + return xx diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/bigvgan.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/bigvgan.py new file mode 100644 index 0000000000000000000000000000000000000000..96aaddfeb28ffbb30622a7e7f09b5ff7be654d30 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/bigvgan.py @@ -0,0 +1,402 @@ +# Copyright (c) 2024 NVIDIA CORPORATION. +# Licensed under the MIT license. + +# Adapted from https://github.com/jik876/hifi-gan under the MIT license. +# LICENSE is in incl_licenses directory. + +import os +import json +from pathlib import Path +from typing import Optional, Union, Dict + +import torch +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d +from torch.nn.utils import weight_norm, remove_weight_norm + +from stepvocoder.cosyvoice2.bigvgan import activations +from stepvocoder.cosyvoice2.bigvgan.bigvgan_utils import init_weights, get_padding +from stepvocoder.cosyvoice2.bigvgan.alias_free_activation.torch.act import Activation1d as TorchActivation1d + + +class AMPBlock1(torch.nn.Module): + """ + AMPBlock applies Snake / SnakeBeta activation functions with trainable parameters that control periodicity, defined for each layer. + AMPBlock1 has additional self.convs2 that contains additional Conv1d layers with a fixed dilation=1 followed by each layer in self.convs1 + + Args: + h (AttrDict): Hyperparameters. + channels (int): Number of convolution channels. + kernel_size (int): Size of the convolution kernel. Default is 3. + dilation (tuple): Dilation rates for the convolutions. Each dilation layer has two convolutions. Default is (1, 3, 5). + activation (str): Activation function type. Should be either 'snake' or 'snakebeta'. Default is None. + """ + + def __init__( + self, + channels: int, + kernel_size: int = 3, + dilation: tuple = (1, 3, 5), + activation: str = None, + use_cuda_kernel: bool = False, + snake_logscale: bool = True + ): + super().__init__() + + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + stride=1, + dilation=d, + padding=get_padding(kernel_size, d), + ) + ) + for d in dilation + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + stride=1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ) + for _ in range(len(dilation)) + ] + ) + self.convs2.apply(init_weights) + + self.num_layers = len(self.convs1) + len( + self.convs2 + ) # Total number of conv layers + + # Select which Activation1d, lazy-load cuda version to ensure backward compatibility + if use_cuda_kernel: + from alias_free_activation.cuda.activation1d import ( + Activation1d as CudaActivation1d, + ) + + Activation1d = CudaActivation1d + else: + Activation1d = TorchActivation1d + + # Activation functions + if activation == "snake": + self.activations = nn.ModuleList( + [ + Activation1d( + activation=activations.Snake( + channels, alpha_logscale=snake_logscale + ) + ) + for _ in range(self.num_layers) + ] + ) + elif activation == "snakebeta": + self.activations = nn.ModuleList( + [ + Activation1d( + activation=activations.SnakeBeta( + channels, alpha_logscale=snake_logscale + ) + ) + for _ in range(self.num_layers) + ] + ) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + def forward(self, x): + acts1, acts2 = self.activations[::2], self.activations[1::2] + for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): + xt = a1(x) + xt = c1(xt) + xt = a2(xt) + xt = c2(xt) + x = xt + x + + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class AMPBlock2(torch.nn.Module): + """ + AMPBlock applies Snake / SnakeBeta activation functions with trainable parameters that control periodicity, defined for each layer. + Unlike AMPBlock1, AMPBlock2 does not contain extra Conv1d layers with fixed dilation=1 + + Args: + h (AttrDict): Hyperparameters. + channels (int): Number of convolution channels. + kernel_size (int): Size of the convolution kernel. Default is 3. + dilation (tuple): Dilation rates for the convolutions. Each dilation layer has two convolutions. Default is (1, 3, 5). + activation (str): Activation function type. Should be either 'snake' or 'snakebeta'. Default is None. + """ + + def __init__( + self, + channels: int, + kernel_size: int = 3, + dilation: tuple = (1, 3, 5), + activation: str = None, + use_cuda_kernel: bool = False, + snake_logscale: bool = True + ): + super().__init__() + + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + stride=1, + dilation=d, + padding=get_padding(kernel_size, d), + ) + ) + for d in dilation + ] + ) + self.convs.apply(init_weights) + + self.num_layers = len(self.convs) # Total number of conv layers + + # Select which Activation1d, lazy-load cuda version to ensure backward compatibility + if use_cuda_kernel: + from alias_free_activation.cuda.activation1d import ( + Activation1d as CudaActivation1d, + ) + + Activation1d = CudaActivation1d + else: + Activation1d = TorchActivation1d + + # Activation functions + if activation == "snake": + self.activations = nn.ModuleList( + [ + Activation1d( + activation=activations.Snake( + channels, alpha_logscale=snake_logscale + ) + ) + for _ in range(self.num_layers) + ] + ) + elif activation == "snakebeta": + self.activations = nn.ModuleList( + [ + Activation1d( + activation=activations.SnakeBeta( + channels, alpha_logscale=snake_logscale + ) + ) + for _ in range(self.num_layers) + ] + ) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + def forward(self, x): + for c, a in zip(self.convs, self.activations): + xt = a(x) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class BigVGAN(torch.nn.Module): + """ + BigVGAN is a neural vocoder model that applies anti-aliased periodic activation for residual blocks (resblocks). + New in BigVGAN-v2: it can optionally use optimized CUDA kernels for AMP (anti-aliased multi-periodicity) blocks. + + Args: + use_cuda_kernel (bool): If set to True, loads optimized CUDA kernels for AMP. This should be used for inference only, as training is not supported with CUDA kernels. + + Note: + - The `use_cuda_kernel` parameter should be used for inference only, as training with CUDA kernels is not supported. + - Ensure that the activation function is correctly specified in the hyperparameters (h.activation). + """ + + def __init__( + self, + use_cuda_kernel: bool = False, + num_mels: int = 80, + upsample_initial_channel: int = 512, + upsample_rates: list[int] = [5, 4, 3, 2, 2, 2], + upsample_kernel_sizes: list[int] = [11, 8, 7, 4, 4, 4], + resblock: str = "1", + resblock_kernel_sizes: list[int] = [3, 7, 11], + resblock_dilation_sizes: list[tuple] = [(1, 3, 5), (1, 3, 5), (1, 3, 5)], + activation: str = "snakebeta", + snake_logscale: bool = True, + use_bias_at_final: bool = False, + use_tanh_at_final: bool = False, + ): + super().__init__() + self.use_cuda_kernel = use_cuda_kernel + + # Select which Activation1d, lazy-load cuda version to ensure backward compatibility + if self.use_cuda_kernel: + from alias_free_activation.cuda.activation1d import ( + Activation1d as CudaActivation1d, + ) + + Activation1d = CudaActivation1d + else: + Activation1d = TorchActivation1d + + self.num_kernels = len(resblock_kernel_sizes) + self.num_upsamples = len(upsample_rates) + + # Pre-conv + # for context smoothing, the padding=3 in the first layer conv_pre is removed + self.conv_pre = weight_norm( + Conv1d(num_mels, upsample_initial_channel, 7, 1, padding=0) + ) + + # Define which AMPBlock to use. BigVGAN uses AMPBlock1 as default + if resblock == "1": + resblock_class = AMPBlock1 + elif resblock == "2": + resblock_class = AMPBlock2 + else: + raise ValueError( + f"Incorrect resblock class specified in hyperparameters. Got {resblock}" + ) + + # Transposed conv-based upsamplers. does not apply anti-aliasing + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + self.ups.append( + nn.ModuleList( + [ + weight_norm( + ConvTranspose1d( + upsample_initial_channel // (2**i), + upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ] + ) + ) + + # Residual blocks using anti-aliased multi-periodicity composition modules (AMP) + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate( + zip(resblock_kernel_sizes, resblock_dilation_sizes) + ): + self.resblocks.append( + resblock_class(ch, k, d, activation=activation, use_cuda_kernel=self.use_cuda_kernel, snake_logscale=snake_logscale) + ) + + # Post-conv + activation_post = ( + activations.Snake(ch, alpha_logscale=snake_logscale) + if activation == "snake" + else ( + activations.SnakeBeta(ch, alpha_logscale=snake_logscale) + if activation == "snakebeta" + else None + ) + ) + if activation_post is None: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + self.activation_post = Activation1d(activation=activation_post) + + # Whether to use bias for the final conv_post. Default to True for backward compatibility + self.use_bias_at_final = use_bias_at_final + self.conv_post = weight_norm( + Conv1d(ch, 1, 7, 1, padding=3, bias=self.use_bias_at_final) + ) + + # Weight initialization + for i in range(len(self.ups)): + self.ups[i].apply(init_weights) + self.conv_post.apply(init_weights) + + # Final tanh activation. Defaults to True for backward compatibility + self.use_tanh_at_final = use_tanh_at_final + + def forward(self, x): + # Pre-conv + x = self.conv_pre(x) + + for i in range(self.num_upsamples): + # Upsampling + for i_up in range(len(self.ups[i])): + x = self.ups[i][i_up](x) + # AMP blocks + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + + # Post-conv + x = self.activation_post(x) + x = self.conv_post(x) + # Final tanh activation + if self.use_tanh_at_final: + x = torch.tanh(x) + else: + x = torch.clamp(x, min=-1.0, max=1.0) # Bound the output to [-1, 1] + + return x + + def remove_weight_norm(self): + try: + print("Removing weight norm...") + for l in self.ups: + for l_i in l: + remove_weight_norm(l_i) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + except ValueError: + print("[INFO] Model already removed weight norm. Skipping!") + pass + + def _init_cuda_graph(self): + pass + + @torch.inference_mode() + def inference(self, x): + x = self.forward(x) + return x diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/bigvgan_utils.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/bigvgan_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5de3bdbbaccd8052a1b84d507626530222661359 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/bigvgan/bigvgan_utils.py @@ -0,0 +1,91 @@ +# Adapted from https://github.com/jik876/hifi-gan under the MIT license. +# LICENSE is in incl_licenses directory. + +import glob +import os +import matplotlib +import torch +from torch.nn.utils import weight_norm + +matplotlib.use("Agg") +import matplotlib.pylab as plt +from scipy.io.wavfile import write + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def plot_spectrogram_clipped(spectrogram, clip_max=2.0): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow( + spectrogram, + aspect="auto", + origin="lower", + interpolation="none", + vmin=1e-6, + vmax=clip_max, + ) + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print(f"Loading '{filepath}'") + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print(f"Saving checkpoint to {filepath}") + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix, renamed_file=None): + # Fallback to original scanning logic first + pattern = os.path.join(cp_dir, prefix + "????????") + cp_list = glob.glob(pattern) + + if len(cp_list) > 0: + last_checkpoint_path = sorted(cp_list)[-1] + print(f"[INFO] Resuming from checkpoint: '{last_checkpoint_path}'") + return last_checkpoint_path + + # If no pattern-based checkpoints are found, check for renamed file + if renamed_file: + renamed_path = os.path.join(cp_dir, renamed_file) + if os.path.isfile(renamed_path): + print(f"[INFO] Resuming from renamed checkpoint: '{renamed_file}'") + return renamed_path + + return None diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/cli/cosyvoice.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/cli/cosyvoice.py new file mode 100644 index 0000000000000000000000000000000000000000..d021885121d9600a8fe1697e97061a629b7ed4b7 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/cli/cosyvoice.py @@ -0,0 +1,411 @@ +from functools import cached_property, reduce +from typing import List, Optional, Union +from copy import deepcopy +from collections import defaultdict +import numpy as np +import torch +import torchaudio +import torch.nn.functional as F +from hyperpyyaml import load_hyperpyyaml +from stepvocoder.cosyvoice2.cli.frontend import CosyVoiceFrontEnd +from stepvocoder.cosyvoice2.flow.flow import CausalMaskedDiffWithXvec +from stepvocoder.cosyvoice2.hifigan.generator import HiFTGenerator +from stepvocoder.cosyvoice2.bigvgan.bigvgan import BigVGAN +# from stepvocoder.cosyvoice2.utils.common import fade_in_out +import threading + +"""perform fade_in_out in tensor style +""" +def fade_in_out(fade_in_mel:torch.Tensor, fade_out_mel:torch.Tensor, window:torch.Tensor): + mel_overlap_len = int(window.shape[0] / 2) + fade_in_mel = fade_in_mel.clone() + fade_in_mel[..., :mel_overlap_len] = \ + fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \ + fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:] + return fade_in_mel + + +# torch._dynamo.config.cache_size_limit = 128 +# torch._dynamo.config.accumulated_cache_size_limit = 128 + + +""" +A wrapper for managing stream caches. +""" +class CosyVoice_stream_impl_(torch.nn.Module): + def __init__(self, + flow: CausalMaskedDiffWithXvec, + hift: Union[HiFTGenerator, BigVGAN], + chunk_size_list: List = [15, 24, 48], # (0.6s, 0.96s, 1.92s) + mel_cache_len: int = 8, + n_timesteps: int = 10, # for both stream/non-stream + ): + super().__init__() + self.flow = flow + self.hift = hift + self.n_timesteps = n_timesteps + # hard coded! + # self.sample_rate = hift.sampling_rate + self.token_lookahead = flow.pre_lookahead_len + # stream conf + self.mel_cache_len = mel_cache_len + + if isinstance(self.hift, BigVGAN): + # bigvgan use left 3 frames and right 3 frames as context + self.source_cache_len = int((mel_cache_len - 6)* 480) # 50hz mel -> 24k wave + elif isinstance(self.hift, HiFTGenerator): + self.source_cache_len = int(mel_cache_len * 480) # 50hz mel -> 24k wave + else: + raise ValueError(f'unsupported vocoder type {type(self.hift)}') + + self.register_buffer('speech_window', torch.from_numpy(np.hamming(2 * self.source_cache_len)), persistent=False) + # session management + self.speech_token_dict = defaultdict(list) + self.chunk_size_list = chunk_size_list + self.chunk_size_dict = {} + self.b_first_chunk_dict = {} # indicate if it's the first chunk of this session + # hifigan cache + self.hift_cache_dict = {} + # model att/cnn cache + self.chunk_cache_dict = {} + self.estimator_prompt_length_dict = {} + # speaker embedding cache + self.spk_embedding_cache_dict = {} + # setup lock + self.setup_lock = threading.Lock() + + @cached_property + def device(self): + return next(self.hift.parameters()).device + + @cached_property + def dtype(self): + return next(self.hift.parameters()).dtype + + """NOTE Non-stream interface. + """ + def token2wav_nonstream(self, + token: torch.Tensor, + prompt_token: torch.Tensor, + prompt_feat: torch.Tensor, + embedding: torch.Tensor, + ): + def _make_len(ts:torch.Tensor): + return torch.tensor([ts.shape[1]], dtype=torch.long, device=ts.device) + # [02, 02, 06, 06, 06] -> [[02, 02, PAD], [06, 06, 06]] + + token = self._reshape( + token.squeeze().tolist() + ).unsqueeze(0) + prompt_token = self._reshape( + prompt_token.squeeze().tolist() + ).unsqueeze(0) + # align prompt mel + prompt_feat = F.interpolate( + prompt_feat.transpose(1, 2), + size=prompt_token.shape[1]*2, + mode='nearest' + ).transpose(1, 2) + + token, prompt_token, prompt_feat, embedding = map( + lambda ts: ts.to(self.device), + (token, prompt_token, prompt_feat, embedding), + ) + # inference flow + mel = self.flow.inference( + token, + _make_len(token), + prompt_token, + _make_len(prompt_token), + prompt_feat.to(self.dtype), + _make_len(prompt_feat), + embedding.to(self.dtype), + self.n_timesteps, + ) + # inference vocoder + with torch.no_grad(): + if isinstance(self.hift, BigVGAN): + mel = torch.nn.functional.pad(mel, (3,3), mode='reflect') + speech = self.hift.inference(mel).squeeze(0) # [1,1,T] -> [1,T] + elif isinstance(self.hift, HiFTGenerator): + speech, _ = self.hift.inference(mel) + else: + raise ValueError(f'unsupported vocoder type {type(self.hift)}') + speech = speech.cpu().to(torch.float32) + return speech + + """NOTE Internal method, do not call this method! + Handle device & dtype transfer. + """ + def _setup_cache(self, + token: torch.Tensor, + mel: torch.Tensor, + spk: torch.Tensor, + session_id: str, + ): + # att/cnn-cache + with self.setup_lock: + cache = self.flow.setup_cache( + token.to(self.device), + mel.to(self.device, self.dtype), + spk.to(self.device, self.dtype), + self.n_timesteps, + ) + # 对 cache dict 里的每个 tensor 做 clone().detach() + cache = {k: (v.clone().detach() if isinstance(v, torch.Tensor) else v) for k, v in cache.items()} + self.chunk_cache_dict[session_id] = cache + self.estimator_prompt_length_dict[session_id] = mel.shape[1] + self.b_first_chunk_dict[session_id] = True + # spk embedding + self.spk_embedding_cache_dict[session_id] = spk.to(self.device, self.dtype).clone() + # hift cache + self.hift_cache_dict[session_id] = dict( + mel = torch.zeros(1, mel.shape[2], 0, device=self.device, dtype=self.dtype), + source = torch.zeros(1, 1, 0, device=self.device, dtype=self.dtype), + speech = torch.zeros(1, 0, device=self.device, dtype=self.dtype), + ) + return + + """NOTE Internal method, do not call this method! + Handle device transfer. + """ + def _token2wav_stream(self, + token: torch.Tensor, + session_id: str, + last_chunk: bool, + ): + + assert session_id in self.chunk_cache_dict, 'call setup_cache first to obtain cache' + # fetch cache & speaker embedding + cache = self.chunk_cache_dict[session_id] + embedding = self.spk_embedding_cache_dict[session_id] + # inference this chunk + mel, new_cache = self.flow.inference_chunk( + token.to(self.device), # int64 + embedding, + cache, + last_chunk, + self.n_timesteps, + ) + # NOTE(sfy) truncate attention cache (prompt_length + 2s left context) + left_context_length = int(2 * 48) + estimator_att_cache = new_cache['estimator_att_cache'] + prompt_length = self.estimator_prompt_length_dict[session_id] + if estimator_att_cache.shape[4] > (prompt_length + left_context_length): + new_cache['estimator_att_cache'] = torch.cat([ + estimator_att_cache[:, :, :, :, :left_context_length], + estimator_att_cache[:, :, :, :, -prompt_length:], + ], dim=4) + + self.chunk_cache_dict[session_id] = {k: v.clone().detach() for k, v in new_cache.items()} + # vocoder cache + hift_cache_mel = self.hift_cache_dict[session_id]['mel'] + hift_cache_source = self.hift_cache_dict[session_id]['source'] + hift_cache_speech = self.hift_cache_dict[session_id]['speech'] + mel = torch.concat([hift_cache_mel, mel], dim=2) + # inference vocoder + with torch.no_grad(): + if isinstance(self.hift, BigVGAN): + if self.b_first_chunk_dict[session_id] and mel.shape[2] > 0: + print(f'[INFO] first chunk mel len: {mel.shape[2]}') + self.b_first_chunk_dict[session_id] = False + mel = F.pad(mel, (3,0), mode='reflect') + if last_chunk: + mel = F.pad(mel, (0,3), mode='reflect') + speech = self.hift.inference(mel).squeeze(0) # [1,1,T] -> [1,T] + source = torch.zeros(1, 1, 0, device=self.device, dtype=self.dtype) # dummy source + elif isinstance(self.hift, HiFTGenerator): + speech, source = self.hift.inference(mel, hift_cache_source) + # overlap speech smooth + if hift_cache_speech.shape[-1] > 0: + speech = fade_in_out(speech, hift_cache_speech, self.speech_window) + # update vocoder cache + self.hift_cache_dict[session_id] = dict( + mel = mel[..., -self.mel_cache_len:].clone().detach(), + source = source[:, :, -self.source_cache_len:].clone().detach(), + speech = speech[:, -self.source_cache_len:].clone().detach(), + ) + if not last_chunk: + speech = speech[:, :-self.source_cache_len] + return speech.cpu().to(torch.float32) + + @staticmethod + def _reshape(mix_seq: List[int])->torch.Tensor: + # assert len(mix_seq)%5 == 0, len(mix_seq) + # NOTE add padding to avoid assert error + # (don't care the final speech as it's wrong anyway) + if len(mix_seq)%5 > 0: + pad_len = 5-(len(mix_seq)%5) + mix_seq += [0, 0, 0, 1024, 1024, 1024][-pad_len:] + + num_groups = len(mix_seq) // 5 + vq02 = reduce( + lambda x, y: x+y, + [mix_seq[i*5: i*5+2] + [1024] for i in range(num_groups)] + ) + vq06 = reduce( + lambda x, y: x+y, + [mix_seq[i*5+2: i*5+5] for i in range(num_groups)] + ) + vq0206 = torch.stack([ + torch.tensor(vq02, dtype=torch.long), + torch.tensor(vq06, dtype=torch.long)-1024+1025, + ], dim=1) + return vq0206 + + """NOTE Stream interface. Called whenever one token is generated. + NOTE(sfy) not need to transfer device or dtype + + This is a specialized version for vq0206, we change the mixed sequence to time-aligned sequence. + eg.: [02, 02, 06, 06, 06] -> [[02, 02, PAD], [06, 06, 06]] + """ + def token2wav_stream(self, + token: List[int], # vq0206 mixed seq tokens + prompt_token: torch.Tensor, + prompt_feat: torch.Tensor, + embedding: torch.Tensor, + session_id: str, + last_chunk: bool, + )->Optional[torch.Tensor]: + # FIXME hard coded + def _mixed_len(l:int): + return (l // 3) * 5 + + # init chunk size tracking + if session_id not in self.chunk_size_dict: + self.chunk_size_dict[session_id] = deepcopy(self.chunk_size_list) + # add token + self.speech_token_dict[session_id].extend(token) + # waiting to setup cache + mix_token_lookahead_len = _mixed_len(self.token_lookahead) + if session_id not in self.chunk_cache_dict: + if len(self.speech_token_dict[session_id]) >= mix_token_lookahead_len: + # [02, 02, 06, 06, 06] -> [[02, 02, PAD], [06, 06, 06]] + lookahead_token = self._reshape( + self.speech_token_dict[session_id][:mix_token_lookahead_len] + ).unsqueeze(0) # (1, t, 2) + prompt_token = self._reshape( + prompt_token.squeeze().tolist() + ).unsqueeze(0) + # align prompt mel + prompt_feat = F.interpolate( + prompt_feat.transpose(1, 2), + size=prompt_token.shape[1]*2, + mode='nearest' + ).transpose(1, 2) + self._setup_cache( + torch.cat([prompt_token, lookahead_token], dim=1), + prompt_feat, + embedding, + session_id, + ) + return None + + # deal with remaining tokens + if last_chunk: + this_token = self.speech_token_dict[session_id] + else: + # cut to one chunk + this_token = None + mix_token_chunk_len = _mixed_len(self.chunk_size_dict[session_id][0]) + if len(self.speech_token_dict[session_id]) >= (mix_token_chunk_len+mix_token_lookahead_len): + this_token = self.speech_token_dict[session_id][:(mix_token_chunk_len+mix_token_lookahead_len)] + self.speech_token_dict[session_id] = self.speech_token_dict[session_id][mix_token_chunk_len:] + # go synthesis + if this_token is not None: + # [02, 02, 06, 06, 06] -> [[02, 02, PAD], [06, 06, 06]] + this_token = self._reshape(this_token).unsqueeze(0) + this_speech = self._token2wav_stream( + this_token, + session_id, + last_chunk, + ) + # update chunk size + if len(self.chunk_size_dict[session_id]) > 1: + self.chunk_size_dict[session_id].pop(0) + else: + this_speech = None + # clear all caches + if last_chunk: + self.clean_up(session_id) + return this_speech + + def clean_up(self, session_id: str): + self.chunk_size_dict.pop(session_id, None) + self.hift_cache_dict.pop(session_id, None) + self.chunk_cache_dict.pop(session_id, None) + self.estimator_prompt_length_dict.pop(session_id, None) + self.spk_embedding_cache_dict.pop(session_id, None) + self.speech_token_dict.pop(session_id, None) + torch.cuda.empty_cache() + + +"""Keep compatible with cosyvoice1 +""" +class CosyVoice: + def __init__(self, + model_dir:str, + chunk_size_list: List = [15, 24, 48], # (0.6s, 0.96s, 1.92s) + mel_cache_len: int = 8, + n_timesteps: int = 10, + enable_cuda_graph: bool = True, + dtype=torch.float32, + ): + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.dtype = dtype + # initiate streaming wrapper + self.model_dir = model_dir + with open("{}/cosyvoice.yaml".format(model_dir), "r") as f: + configs = load_hyperpyyaml(f) + flow, hift = configs['flow'], configs['hift'] + mel_conf = configs['mel_conf'] + flow.load_state_dict(torch.load(f"{model_dir}/flow.pt", map_location='cpu')) + flow = flow.eval() + hift.load_state_dict(torch.load(f"{model_dir}/hift.pt", map_location='cpu')) + hift = hift.eval() + cosy_impl = CosyVoice_stream_impl_(flow, hift, chunk_size_list, mel_cache_len, n_timesteps) + self.cosy_impl = cosy_impl.to(self.device, self.dtype) + if enable_cuda_graph: + self.cosy_impl.flow.scatter_cuda_graph(enable_cuda_graph) + self.cosy_impl.hift._init_cuda_graph() + # feature frontend + self.frontend = CosyVoiceFrontEnd( + mel_conf, + campplus_model='{}/campplus.onnx'.format(model_dir), + speech_tokenizer_model='{}/speech_tokenizer_v1.onnx'.format(model_dir), + ) + + # Just proxy + def token2wav_nonstream(self, + token: torch.Tensor, # vq0206 mixed seq + prompt_token: torch.Tensor, + prompt_feat: torch.Tensor, + embedding: torch.Tensor, + )->torch.Tensor: + return self.cosy_impl.token2wav_nonstream( + token, + prompt_token, + prompt_feat, + embedding, + ) + + # Just proxy + def token2wav_stream(self, + token: List[int], # vq0206 mixed seq tokens + prompt_token: torch.Tensor, + prompt_feat: torch.Tensor, + embedding: torch.Tensor, + session_id: str, + last_chunk: bool, + )->Optional[torch.Tensor]: + return self.cosy_impl.token2wav_stream( + token, + prompt_token, + prompt_feat, + embedding, + session_id, + last_chunk, + ) + + def clean_up(self, session_id: str): + self.cosy_impl.clean_up(session_id) diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/cli/frontend.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/cli/frontend.py new file mode 100644 index 0000000000000000000000000000000000000000..01cb9214871deda72ac6ea3c2137b478fd226f3b --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/cli/frontend.py @@ -0,0 +1,72 @@ +import onnxruntime +import torch +import numpy as np +import whisper +from typing import Callable, Dict, Literal +import torch +import torchaudio +import torchaudio.compliance.kaldi as kaldi + +from stepvocoder.cosyvoice2.matcha.audio import mel_spectrogram + + +class CosyVoiceFrontEnd(object): + def __init__(self, + mel_conf:Dict, + campplus_model:str, + speech_tokenizer_model:str, + onnx_provider:str='CUDAExecutionProvider', + ): + super().__init__() + assert onnx_provider in ['CUDAExecutionProvider', 'CPUExecutionProvider'], 'invalid onnx provider' + self.mel_conf = mel_conf + self.sample_rate = mel_conf['sampling_rate'] + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + self.campplus_session = onnxruntime.InferenceSession( + campplus_model, sess_options=option, + providers=["CPUExecutionProvider"] + ) + self.speech_tokenizer_session = onnxruntime.InferenceSession( + speech_tokenizer_model, sess_options=option, providers=["CUDAExecutionProvider" if torch.cuda.is_available() else "CPUExecutionProvider"], + ) + + def extract_speech_feat(self, audio:torch.Tensor, audio_sr:int): + if audio_sr != self.sample_rate: + audio = torchaudio.functional.resample(audio, orig_freq=audio_sr, new_freq=self.sample_rate) + audio_sr = self.sample_rate + speech_feat = mel_spectrogram(y=audio, **self.mel_conf).transpose(1, 2) # (b=1, t, num_mels) + speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.long) + return speech_feat, speech_feat_len + + def extract_spk_embedding(self, audio:torch.Tensor, audio_sr:int): + if audio_sr != 16000: + audio = torchaudio.functional.resample(audio, orig_freq=audio_sr, new_freq=16000) + audio_sr = 16000 + feat = kaldi.fbank(audio, num_mel_bins=80, dither=0, sample_frequency=16000) + feat = feat - feat.mean(dim=0, keepdim=True) + onnx_in = { + self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy() + } + embedding = self.campplus_session.run(None, onnx_in)[0].flatten().tolist() + embedding = torch.tensor([embedding]) + return embedding + + def extract_speech_token(self, audio:torch.Tensor, audio_sr:int): + if audio_sr != 16000: + audio = torchaudio.functional.resample(audio, orig_freq=audio_sr, new_freq=16000) + audio_sr = 16000 + assert ( + audio.shape[1] / 16000 <= 30 + ), "do not support extract speech token for audio longer than 30s" + feat = whisper.log_mel_spectrogram(audio, n_mels=128) + + onnx_in = { + self.speech_tokenizer_session.get_inputs()[0].name: feat.detach().cpu().numpy(), + self.speech_tokenizer_session.get_inputs()[1].name: np.array([feat.shape[2]], dtype=np.int32), + } + speech_token = self.speech_tokenizer_session.run(None, onnx_in)[0].flatten().tolist() + speech_token = torch.tensor([speech_token], dtype=torch.int32) + speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32) + return speech_token, speech_token_len diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/embedding/__init__.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/embedding/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/embedding/dual_codebook.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/embedding/dual_codebook.py new file mode 100644 index 0000000000000000000000000000000000000000..eea8887c67c9f0618c7d62a21aaf28db94568445 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/embedding/dual_codebook.py @@ -0,0 +1,22 @@ +import torch + + +class DualCodebookEmbedding(torch.nn.Module): + def __init__(self, + vocab_size: int, + input_size: int, + ): + super().__init__() + self.embedding = torch.nn.Embedding(vocab_size, input_size // 2) + + def forward(self, token: torch.Tensor): + """ + Args: + token (torch.Tensor): shape (b, t, 2) + Returns: + xs: shape (b, t, c) + """ + embed1 = self.embedding(token[..., 0]) + embed2 = self.embedding(token[..., 1]) + return torch.cat([embed1, embed2], dim=-1) + diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/__init__.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/decoder_dit.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/decoder_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..eb6a99ab658ebdd716d0d0a2cb64a506d41e7697 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/decoder_dit.py @@ -0,0 +1,585 @@ +import math +import torch +import numpy as np +from typing import Optional +from einops import pack, rearrange, repeat +import torch.nn as nn +import torch.nn.functional as F + + + +""" +DiT-v5 +- Add convolution in DiTBlock to increase high-freq component +""" + + +class MLP(torch.nn.Module): + def __init__( + self, + in_features:int, + hidden_features:Optional[int]=None, + out_features:Optional[int]=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0., + ): + super().__init__() + hidden_features = hidden_features or in_features + out_features = out_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) + self.act = act_layer() + self.drop1 = nn.Dropout(drop) + self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() + self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) + self.drop2 = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +class Attention(torch.nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + head_dim: int = 64, + qkv_bias: bool = False, + qk_norm: bool = False, + attn_drop: float = 0., + proj_drop: float = 0., + norm_layer: nn.Module = nn.LayerNorm, + ) -> None: + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + self.inner_dim = num_heads * head_dim + self.scale = head_dim ** -0.5 + + self.to_q = nn.Linear(dim, self.inner_dim, bias=qkv_bias) + self.to_k = nn.Linear(dim, self.inner_dim, bias=qkv_bias) + self.to_v = nn.Linear(dim, self.inner_dim, bias=qkv_bias) + + self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity() + + self.attn_drop = nn.Dropout(attn_drop) + self.proj_drop = nn.Dropout(proj_drop) + + self.proj = nn.Linear(self.inner_dim, dim) + + def to_heads(self, ts:torch.Tensor): + b, t, c = ts.shape + # (b, t, nh, c) + ts = ts.reshape(b, t, self.num_heads, c // self.num_heads) + ts = ts.transpose(1, 2) + return ts + + def forward(self, x: torch.Tensor, attn_mask: torch.Tensor) -> torch.Tensor: + """Args: + x(torch.Tensor): shape (b, t, c) + attn_mask(torch.Tensor): shape (b, t, t) + """ + b, t, c = x.shape + + q = self.to_q(x) + k = self.to_k(x) + v = self.to_v(x) + + q = self.to_heads(q) # (b, nh, t, c) + k = self.to_heads(k) + v = self.to_heads(v) + + q = self.q_norm(q) + k = self.k_norm(k) + + attn_mask = attn_mask.unsqueeze(1) + x = F.scaled_dot_product_attention( + q, k, v, + attn_mask=attn_mask, + dropout_p=self.attn_drop.p if self.training else 0., + ) # (b, nh, t, c) + x = x.transpose(1, 2).reshape(b, t, -1) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def forward_chunk(self, x: torch.Tensor, att_cache: torch.Tensor=None, attn_mask: torch.Tensor=None): + """ + Args: + x: shape (b, dt, c) + att_cache: shape (b, nh, t, c*2) + """ + b, t, c = x.shape + + q = self.to_q(x) + k = self.to_k(x) + v = self.to_v(x) + + q = self.to_heads(q) # (b, nh, t, c) + k = self.to_heads(k) + v = self.to_heads(v) + + q = self.q_norm(q) + k = self.k_norm(k) + + # unpack {k,v}_cache + if att_cache is not None: + if attn_mask is not None: + k_cache, v_cache = att_cache.chunk(2, dim=3) + k = torch.cat([k, k_cache], dim=2) + v = torch.cat([v, v_cache], dim=2) + + else: + k_cache, v_cache = att_cache.chunk(2, dim=3) + k = torch.cat([k, k_cache], dim=2) + v = torch.cat([v, v_cache], dim=2) + + # new {k,v}_cache + new_att_cache = torch.cat([k, v], dim=3) + # attn_mask = torch.ones((b, 1, t, t1), dtype=torch.bool, device=x.device) + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(1) + x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) # (b, nh, t, c) + x = x.transpose(1, 2).reshape(b, t, -1) + x = self.proj(x) + x = self.proj_drop(x) + return x, new_att_cache + + +def modulate(x, shift, scale): + return x * (1 + scale) + shift + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + # from SinusoidalPosEmb + self.scale = 1000 + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half) / half + ).to(t) + args = t[:, None] * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t * self.scale, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb + + +# Convolution related +class Transpose(torch.nn.Module): + def __init__(self, dim0: int, dim1: int): + super().__init__() + self.dim0 = dim0 + self.dim1 = dim1 + + def forward(self, x: torch.Tensor): + x = torch.transpose(x, self.dim0, self.dim1) + return x + + +class CausalConv1d(torch.nn.Conv1d): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + ) -> None: + super(CausalConv1d, self).__init__(in_channels, out_channels, kernel_size) + self.causal_padding = (kernel_size - 1, 0) + + def forward(self, x: torch.Tensor): + x = F.pad(x, self.causal_padding) + x = super(CausalConv1d, self).forward(x) + return x + + def forward_chunk(self, x: torch.Tensor, cnn_cache: torch.Tensor=None): + if cnn_cache is None: + cnn_cache = x.new_zeros((x.shape[0], self.in_channels, self.causal_padding[0])) + x = torch.cat([cnn_cache, x], dim=2) + new_cnn_cache = x[..., -self.causal_padding[0]:] + x = super(CausalConv1d, self).forward(x) + return x, new_cnn_cache + + +class CausalConvBlock(nn.Module): + def __init__(self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + + self.block = torch.nn.Sequential( + # norm + # conv1 + Transpose(1, 2), + CausalConv1d(in_channels, out_channels, kernel_size), + Transpose(1, 2), + # norm & act + nn.LayerNorm(out_channels), + nn.Mish(), + # conv2 + Transpose(1, 2), + CausalConv1d(out_channels, out_channels, kernel_size), + Transpose(1, 2), + ) + + def forward(self, x: torch.Tensor, mask: torch.Tensor = None): + """ + Args: + x: shape (b, t, c) + mask: shape (b, t, 1) + """ + if mask is not None: x = x * mask + x = self.block(x) + if mask is not None: x = x * mask + return x + + def forward_chunk(self, x: torch.Tensor, cnn_cache: torch.Tensor=None): + """ + Args: + x: shape (b, dt, c) + cnn_cache: shape (b, c1+c2, 2) + """ + if cnn_cache is not None: + cnn_cache1, cnn_cache2 = cnn_cache.split((self.in_channels, self.out_channels), dim=1) + else: + cnn_cache1, cnn_cache2 = None, None + x = self.block[0](x) + x, new_cnn_cache1 = self.block[1].forward_chunk(x, cnn_cache1) + x = self.block[2:6](x) + x, new_cnn_cache2 = self.block[6].forward_chunk(x, cnn_cache2) + x = self.block[7](x) + new_cnn_cache = torch.cat((new_cnn_cache1, new_cnn_cache2), dim=1) + return x, new_cnn_cache + + +class DiTBlock(nn.Module): + """ + A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, hidden_size, num_heads, head_dim, mlp_ratio=4.0, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = Attention(hidden_size, num_heads=num_heads, head_dim=head_dim, qkv_bias=True, qk_norm=True, **block_kwargs) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = MLP(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) + self.norm3 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.conv = CausalConvBlock(in_channels=hidden_size, out_channels=hidden_size, kernel_size=3) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 9 * hidden_size, bias=True) + ) + + def forward(self, x:torch.Tensor, c:torch.Tensor, attn_mask:torch.Tensor): + """Args + x: shape (b, t, c) + c: shape (b, 1, c) + attn_mask: shape (b, t, t), bool type attention mask + """ + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_conv, scale_conv, gate_conv \ + = self.adaLN_modulation(c).chunk(9, dim=-1) + # attention + x = x + gate_msa * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), attn_mask) + # conv + x = x + gate_conv * self.conv(modulate(self.norm3(x), shift_conv, scale_conv)) + # mlp + x = x + gate_mlp * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + + def forward_chunk(self, x: torch.Tensor, c: torch.Tensor, cnn_cache: torch.Tensor=None, att_cache: torch.Tensor=None, mask: torch.Tensor=None): + """ + Args: + x: shape (b, dt, c) + c: shape (b, 1, c) + cnn_cache: shape (b, c1+c2, 2) + att_cache: shape (b, nh, t, c * 2) + """ + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_conv, scale_conv, gate_conv \ + = self.adaLN_modulation(c).chunk(9, dim=-1) + # attention + x_att, new_att_cache = self.attn.forward_chunk(modulate(self.norm1(x), shift_msa, scale_msa), att_cache, mask) + x = x + gate_msa * x_att + # conv + x_conv, new_cnn_cache = self.conv.forward_chunk(modulate(self.norm3(x), shift_conv, scale_conv), cnn_cache) + x = x + gate_conv * x_conv + # mlp + x = x + gate_mlp * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x, new_cnn_cache, new_att_cache + + +class FinalLayer(nn.Module): + """ + The final layer of DiT. + """ + def __init__(self, hidden_size, out_channels): + super().__init__() + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True) + ) + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, out_channels, bias=True) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class DiT(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + def __init__( + self, + in_channels: int, + out_channels: int, + mlp_ratio: float = 4.0, + depth: int = 28, + num_heads: int = 8, + head_dim: int = 64, + hidden_size: int = 256, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.t_embedder = TimestepEmbedder(hidden_size) + + self.in_proj = nn.Linear(in_channels, hidden_size) + + self.blocks = nn.ModuleList([ + DiTBlock(hidden_size, num_heads, head_dim, mlp_ratio=mlp_ratio) for _ in range(depth) + ]) + self.final_layer = FinalLayer(hidden_size, self.out_channels) + + self.initialize_weights() + + self.enable_cuda_graph = False + self.use_cuda_graph = False + + self.graph_chunk = {} + self.inference_buffers_chunk = {} + self.max_size_chunk = {} + + self.register_buffer('att_cache_buffer', torch.zeros((16, 2, 8, 1000, 128)), persistent=False) + self.register_buffer('cnn_cache_buffer', torch.zeros((16, 2, 1024, 2)), persistent=False) + + def initialize_weights(self): + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + # Initialize timestep embedding MLP: + nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) + + # Zero-out adaLN modulation layers in DiT blocks: + for block in self.blocks: + nn.init.constant_(block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.adaLN_modulation[-1].bias, 0) + + # Zero-out output layers: + nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) + nn.init.constant_(self.final_layer.linear.weight, 0) + nn.init.constant_(self.final_layer.linear.bias, 0) + + def _init_cuda_graph_chunk(self): + # get dtype, device from registered buffer + dtype, device = self.cnn_cache_buffer.dtype, self.cnn_cache_buffer.device + # init cuda graph for streaming forward + with torch.no_grad(): + for chunk_size in [30, 48, 96]: + if chunk_size == 30 or chunk_size == 48: + max_size = 500 + self.max_size_chunk[chunk_size] = max_size + else: + max_size = 1000 + self.max_size_chunk[chunk_size] = max_size + static_x1 = torch.zeros((2, 320, chunk_size), dtype=dtype, device=device) + static_t1 = torch.zeros((2, 1, 512), dtype=dtype, device=device) + static_mask1 = torch.ones((2, chunk_size, max_size+chunk_size), dtype=torch.bool, device=device) + static_att_cache = torch.zeros((16, 2, 8, max_size, 128), dtype=dtype, device=device) + static_cnn_cache = torch.zeros((16, 2, 1024, 2), dtype=dtype, device=device) + static_inputs1 = [ + static_x1, + static_t1, + static_mask1, + static_cnn_cache, + static_att_cache, + ] + static_new_cnn_cache = torch.zeros((16, 2, 1024, 2), dtype=dtype, device=device) + static_new_att_cache = torch.zeros((16, 2, 8, max_size+chunk_size, 128), dtype=dtype, device=device) + self.blocks_forward_chunk( + static_inputs1[0], + static_inputs1[1], + static_inputs1[2], + static_inputs1[3], + static_inputs1[4], + static_new_cnn_cache, + static_new_att_cache) + graph_chunk = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph_chunk): + static_out1 = self.blocks_forward_chunk(static_x1, static_t1, static_mask1, static_cnn_cache, static_att_cache, static_new_cnn_cache, static_new_att_cache) + static_outputs1 = [static_out1, static_new_cnn_cache, static_new_att_cache] + self.inference_buffers_chunk[chunk_size] = { + 'static_inputs': static_inputs1, + 'static_outputs': static_outputs1 + } + self.graph_chunk[chunk_size] = graph_chunk + + def _init_cuda_graph_all(self): + self._init_cuda_graph_chunk() + self.use_cuda_graph = True + print(f"CUDA Graph initialized successfully for chunk decoder") + + def forward(self, x, mask, mu, t, spks=None, cond=None): + """Args: + x: shape (b, c, t) + mask: shape (b, 1, t) + t: shape (b,) + spks: shape (b, c) + cond: shape (b, c, t) + """ + # (sfy) chunk training strategy should not be open-sourced + + # time + t = self.t_embedder(t).unsqueeze(1) # (b, 1, c) + x = pack([x, mu], "b * t")[0] + if spks is not None: + spks = repeat(spks, "b c -> b c t", t=x.shape[-1]) + x = pack([x, spks], "b * t")[0] + if cond is not None: + x = pack([x, cond], "b * t")[0] + + return self.blocks_forward(x, t, mask) + + def blocks_forward(self, x, t, mask): + x = x.transpose(1, 2) + attn_mask = mask.bool() + x = self.in_proj(x) + for block in self.blocks: + x = block(x, t, attn_mask) + x = self.final_layer(x, t) + x = x.transpose(1, 2) + return x + + def forward_chunk(self, + x: torch.Tensor, + mu: torch.Tensor, + t: torch.Tensor, + spks: torch.Tensor, + cond: torch.Tensor, + cnn_cache: torch.Tensor = None, + att_cache: torch.Tensor = None, + ): + """ + Args: + x: shape (b, dt, c) + mu: shape (b, dt, c) + t: shape (b,) + spks: shape (b, c) + cond: shape (b, dt, c) + cnn_cache: shape (depth, b, c1+c2, 2) + att_cache: shape (depth, b, nh, t, c * 2) + """ + + # time + t = self.t_embedder(t).unsqueeze(1) # (b, 1, c) + x = pack([x, mu], "b * t")[0] + if spks is not None: + spks = repeat(spks, "b c -> b c t", t=x.shape[-1]) + x = pack([x, spks], "b * t")[0] + if cond is not None: + x = pack([x, cond], "b * t")[0] + + # create fake cache + if cnn_cache is None: + cnn_cache = [None] * len(self.blocks) + if att_cache is None: + att_cache = [None] * len(self.blocks) + if att_cache[0] is not None: + last_att_len = att_cache.shape[3] + else: + last_att_len = 0 + chunk_size = x.shape[2] + mask = torch.ones(x.shape[0], chunk_size, last_att_len+chunk_size, dtype=torch.bool, device=x.device) + if self.use_cuda_graph and att_cache[0] is not None and chunk_size in self.graph_chunk and last_att_len <= self.max_size_chunk[chunk_size]: + padded_mask = torch.zeros((2, chunk_size, self.max_size_chunk[chunk_size]+chunk_size), dtype=mask.dtype, device=mask.device) + padded_mask[:, :, :mask.shape[-1]] = mask + padded_att_cache = torch.zeros((16, 2, 8, self.max_size_chunk[chunk_size], 128), dtype=att_cache.dtype, device=att_cache.device) + padded_att_cache[:, :, :, :last_att_len, :] = att_cache + self.inference_buffers_chunk[chunk_size]['static_inputs'][0].copy_(x) + self.inference_buffers_chunk[chunk_size]['static_inputs'][1].copy_(t) + self.inference_buffers_chunk[chunk_size]['static_inputs'][2].copy_(padded_mask) + self.inference_buffers_chunk[chunk_size]['static_inputs'][3].copy_(cnn_cache) + self.inference_buffers_chunk[chunk_size]['static_inputs'][4].copy_(padded_att_cache) + self.graph_chunk[chunk_size].replay() + x = self.inference_buffers_chunk[chunk_size]['static_outputs'][0][:, :, :chunk_size] + new_cnn_cache = self.inference_buffers_chunk[chunk_size]['static_outputs'][1] + new_att_cache = self.inference_buffers_chunk[chunk_size]['static_outputs'][2][:, :, :, :chunk_size+last_att_len, :] + else: + mask = None + x = self.blocks_forward_chunk(x, t, mask, cnn_cache, att_cache, self.cnn_cache_buffer, self.att_cache_buffer) + new_cnn_cache = self.cnn_cache_buffer + new_att_cache = self.att_cache_buffer[:, :, :, :last_att_len+chunk_size, :] + + return x, new_cnn_cache, new_att_cache + + def blocks_forward_chunk(self, x, t, mask, cnn_cache=None, att_cache=None, cnn_cache_buffer=None, att_cache_buffer=None): + x = x.transpose(1, 2) + x = self.in_proj(x) + for b_idx, block in enumerate(self.blocks): + x, this_new_cnn_cache, this_new_att_cache \ + = block.forward_chunk(x, t, cnn_cache[b_idx], att_cache[b_idx], mask) + cnn_cache_buffer[b_idx] = this_new_cnn_cache + att_cache_buffer[b_idx][:, :, :this_new_att_cache.shape[2], :] = this_new_att_cache + x = self.final_layer(x, t) + x = x.transpose(1, 2) + return x diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/flow.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/flow.py new file mode 100644 index 0000000000000000000000000000000000000000..c107f1bc4337ab5d75e8ac9ea06340a05622731b --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/flow.py @@ -0,0 +1,225 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn +from torch.nn import functional as F + +from stepvocoder.cosyvoice2.utils.mask import make_pad_mask +from stepvocoder.cosyvoice2.flow.flow_matching import CausalConditionalCFM +from stepvocoder.cosyvoice2.transformer.upsample_encoder_v2 import UpsampleConformerEncoderV2 + + +class CausalMaskedDiffWithXvec(torch.nn.Module): + def __init__(self, + input_size: int = 512, + output_size: int = 80, + spk_embed_dim: int = 192, + output_type: str = "mel", + vocab_size: int = 5121, + encoder: UpsampleConformerEncoderV2 = None, + decoder: CausalConditionalCFM = None, + input_embedding: torch.nn.Module = None, + ): + super().__init__() + self.input_size = input_size + self.output_size = output_size + self.vocab_size = vocab_size + self.output_type = output_type + self.pre_lookahead_len = int(encoder.pre_lookahead_layer.pre_lookahead_len) + self.up_rate = int(encoder.up_layer.stride) + if input_embedding is None: + self.input_embedding = nn.Embedding(vocab_size, input_size) + else: + self.input_embedding = input_embedding + self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size) + self.encoder = encoder + self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size) + self.decoder = decoder + + # xvec projection with CUDA Graph optimization + # 初始化 CUDA Graph 相关变量 + self.enable_cuda_graph = False + self.static_embedding = None + self.static_output = None + self.graph = None + self.embedding_shape = None + + def scatter_cuda_graph(self, enable_cuda_graph: bool): + self.enable_cuda_graph = enable_cuda_graph + if self.enable_cuda_graph: + # self.encoder.scatter_cuda_graph(enable_cuda_graph) + self.decoder.scatter_cuda_graph(enable_cuda_graph) + + @torch.inference_mode() + def inference(self, + token, + token_len, + prompt_token, + prompt_token_len, + prompt_feat, + prompt_feat_len, + embedding, + n_timesteps: int = 10, + ): + assert token.shape[0] == 1 + + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + token_len = prompt_token_len + token_len + token = torch.concat([prompt_token, token], dim=1) + + mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # token encode + h, _ = self.encoder.forward(token, token_len) + h = self.encoder_proj(h) + + # condition + mel_len1 = prompt_feat.shape[1] + mel_len2 = h.shape[1] - prompt_feat.shape[1] + + conds = torch.zeros_like(h) + conds[:, :mel_len1] = prompt_feat + conds = conds.transpose(1, 2).contiguous() + + mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h) + + feat = self.decoder.forward( + mu=h.transpose(1, 2).contiguous(), + mask=mask.unsqueeze(1), + spks=embedding, + cond=conds, + n_timesteps=n_timesteps, + ) + + feat = feat[:, :, mel_len1:] + assert feat.shape[2] == mel_len2 + return feat + + @torch.inference_mode() + def setup_cache(self, + token: torch.Tensor, + mel: torch.Tensor, + spk: torch.Tensor, + n_timesteps: int = 10, + ): + """ + Args: + token: shape (b, t), with look ahead tokens + mel: shape (b, t, c), groundtruth mel + spk: shape (b, 192), speaker embedding + Returns: + cache: dict { + 'conformer': {'cnn_cache': xxx, 'att_cache': xxx}, + 'estimator': {'cnn_cache': xxx, 'att_cache': xxx} + } + """ + # check if look ahead token included + assert (token.shape[1] - self.pre_lookahead_len) * self.up_rate == mel.shape[1], (token.shape, mel.shape) + + # xvec projection + spk = F.normalize(spk, dim=1) + spk = self.spk_embed_affine_layer(spk) + + token = self.input_embedding(token) + # NOTE encoder.forward_chunk will strip the look ahead part + h, conformer_cnn_cache, conformer_att_cache = self.encoder.forward_chunk( + xs = token, + last_chunk = False, + cnn_cache = None, + att_cache = None, + ) + h = self.encoder_proj(h) + + feat, estimator_cnn_cache, estimator_att_cache = self.decoder.forward_chunk( + mu = h.transpose(1, 2).contiguous(), + spks = spk, + cond = mel.transpose(1, 2).contiguous(), + n_timesteps = n_timesteps, + temperature = 1.0, + cnn_cache = None, + att_cache = None, + ) + + cache = { + 'conformer_cnn_cache': conformer_cnn_cache, + 'conformer_att_cache': conformer_att_cache, + 'estimator_cnn_cache': estimator_cnn_cache, + 'estimator_att_cache': estimator_att_cache, + } + return cache + + @torch.inference_mode() + def inference_chunk(self, + token: torch.Tensor, + spk: torch.Tensor, + cache: dict, + last_chunk: bool = False, + n_timesteps: int = 10, + ): + """ + Args: + token: shape (b, t), with look ahead tokens + spk: shape (b, 192), speaker embedding + cache: dict { + 'conformer_cnn_cache': xxx, + ... + } + """ + # unpack cache + conformer_cnn_cache = cache['conformer_cnn_cache'] + conformer_att_cache = cache['conformer_att_cache'] + estimator_cnn_cache = cache['estimator_cnn_cache'] + estimator_att_cache = cache['estimator_att_cache'] + + # xvec projection + spk = F.normalize(spk, dim=1) + spk = self.spk_embed_affine_layer(spk) + + token = self.input_embedding(token) + # if not the last chunk, h is shorter than xs for a length of lookahead_length * stride (6) + h, conformer_cnn_cache, conformer_att_cache = self.encoder.forward_chunk( + xs = token, + last_chunk = last_chunk, + cnn_cache = conformer_cnn_cache, + att_cache = conformer_att_cache, + ) + h = self.encoder_proj(h) + + cond = torch.zeros_like(h) + # forward estimator + feat, estimator_cnn_cache, estimator_att_cache = self.decoder.forward_chunk( + mu = h.transpose(1, 2).contiguous(), + spks = spk, + cond = cond.transpose(1, 2).contiguous(), + n_timesteps = n_timesteps, + temperature = 1.0, + cnn_cache = estimator_cnn_cache, + att_cache = estimator_att_cache, + ) + + + new_cache = { + 'conformer_cnn_cache': conformer_cnn_cache, + 'conformer_att_cache': conformer_att_cache, + 'estimator_cnn_cache': estimator_cnn_cache, + 'estimator_att_cache': estimator_att_cache, + } + + return feat, new_cache + diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/flow_matching.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/flow_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..7ddd429ff2b26a48f08b0855898c1d2c1a7118a2 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/flow/flow_matching.py @@ -0,0 +1,205 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import List +import onnxruntime +import torch +import torch.nn.functional as F + +from stepvocoder.cosyvoice2.flow.decoder_dit import DiT +from stepvocoder.cosyvoice2.utils.mask import make_pad_mask + + +""" +Inference wrapper +""" +class CausalConditionalCFM(torch.nn.Module): + def __init__(self, estimator: DiT, inference_cfg_rate:float=0.7): + super().__init__() + self.estimator = estimator + self.inference_cfg_rate = inference_cfg_rate + self.out_channels = estimator.out_channels + # a maximum of 600s + self.register_buffer('rand_noise', torch.randn([1, self.out_channels, 50 * 600]), persistent=False) + + self.register_buffer('cnn_cache_buffer', torch.zeros(16, 16, 2, 1024, 2), persistent=False) + self.register_buffer('att_cache_buffer', torch.zeros(16, 16, 2, 8, 1000, 128), persistent=False) + + def scatter_cuda_graph(self, enable_cuda_graph: bool): + if enable_cuda_graph: + self.estimator._init_cuda_graph_all() + + def solve_euler(self, x, t_span, mu, mask, spks, cond): + """ + Fixed euler solver for ODEs. + Args: + x (torch.Tensor): random noise + t_span (torch.Tensor): n_timesteps interpolated + shape: (n_timesteps + 1,) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + """ + t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0] + t = t.unsqueeze(dim=0) + assert self.inference_cfg_rate > 0, 'inference_cfg_rate better > 0' + + # constant during denoising + mask_in = torch.cat([mask, mask], dim=0) + mu_in = torch.cat([mu, torch.zeros_like(mu)], dim=0) + spks_in = torch.cat([spks, torch.zeros_like(spks)], dim=0) + cond_in = torch.cat([cond, torch.zeros_like(cond)], dim=0) + + for step in range(1, len(t_span)): + + x_in = torch.cat([x, x], dim=0) + t_in = torch.cat([t, t], dim=0) + + dphi_dt = self.estimator.forward( + x_in, + mask_in, + mu_in, + t_in, + spks_in, + cond_in, + ) + dphi_dt, cfg_dphi_dt = torch.split(dphi_dt, [x.size(0), x.size(0)], dim=0) + dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt) + x = x + dt * dphi_dt + t = t + dt + if step < len(t_span) - 1: + dt = t_span[step + 1] - t + + return x + + @torch.inference_mode() + def forward(self, mu, mask, spks, cond, n_timesteps=10, temperature=1.0): + z = self.rand_noise[:, :, :mu.size(2)] * temperature + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype) + # cosine scheduling + t_span = 1 - torch.cos(t_span * 0.5 * torch.pi) + return self.solve_euler(z, t_span, mu, mask, spks, cond) + + def solve_euler_chunk(self, + x:torch.Tensor, + t_span:torch.Tensor, + mu:torch.Tensor, + spks:torch.Tensor, + cond:torch.Tensor, + cnn_cache:torch.Tensor=None, + att_cache:torch.Tensor=None, + ): + """ + Fixed euler solver for ODEs. + Args: + x (torch.Tensor): random noise + t_span (torch.Tensor): n_timesteps interpolated + shape: (n_timesteps + 1,) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + cnn_cache: shape (n_time, depth, b, c1+c2, 2) + att_cache: shape (n_time, depth, b, nh, t, c * 2) + """ + assert self.inference_cfg_rate > 0, 'cfg rate should be > 0' + + t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0] + t = t.unsqueeze(dim=0) # (b,) + + # setup initial cache + if cnn_cache is None: + cnn_cache = [None for _ in range(len(t_span)-1)] + if att_cache is None: + att_cache = [None for _ in range(len(t_span)-1)] + # next chunk's cache at each timestep + + if att_cache[0] is not None: + last_att_len = att_cache.shape[4] + else: + last_att_len = 0 + + # constant during denoising + mu_in = torch.cat([mu, torch.zeros_like(mu)], dim=0) + spks_in = torch.cat([spks, torch.zeros_like(spks)], dim=0) + cond_in = torch.cat([cond, torch.zeros_like(cond)], dim=0) + for step in range(1, len(t_span)): + # torch.cuda.memory._record_memory_history(max_entries=100000) + # torch.cuda.memory._record_memory_history(max_entries=100000) + this_att_cache = att_cache[step-1] + this_cnn_cache = cnn_cache[step-1] + + dphi_dt, this_new_cnn_cache, this_new_att_cache = self.estimator.forward_chunk( + x = x.repeat(2, 1, 1), + mu = mu_in, + t = t.repeat(2), + spks = spks_in, + cond = cond_in, + cnn_cache = this_cnn_cache, + att_cache = this_att_cache, + ) + dphi_dt, cfg_dphi_dt = dphi_dt.chunk(2, dim=0) + dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt) + x = x + dt * dphi_dt + t = t + dt + if step < len(t_span) - 1: + dt = t_span[step + 1] - t + + self.cnn_cache_buffer[step-1] = this_new_cnn_cache + self.att_cache_buffer[step-1][:, :, :, :x.shape[2]+last_att_len, :] = this_new_att_cache + + cnn_cache = self.cnn_cache_buffer + att_cache = self.att_cache_buffer[:, :, :, :, :x.shape[2]+last_att_len, :] + return x, cnn_cache, att_cache + + @torch.inference_mode() + def forward_chunk(self, + mu:torch.Tensor, + spks:torch.Tensor, + cond:torch.Tensor, + n_timesteps:int=10, + temperature:float=1.0, + cnn_cache:torch.Tensor=None, + att_cache:torch.Tensor=None, + ): + """ + Args: + mu(torch.Tensor): shape (b, c, t) + spks(torch.Tensor): shape (b, 192) + cond(torch.Tensor): shape (b, c, t) + cnn_cache: shape (n_time, depth, b, c1+c2, 2) + att_cache: shape (n_time, depth, b, nh, t, c * 2) + """ + # get offset from att_cache + offset = att_cache.shape[4] if att_cache is not None else 0 + z = self.rand_noise[:, :, offset:offset+mu.size(2)] * temperature + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype) + # cosine scheduling + t_span = 1 - torch.cos(t_span * 0.5 * torch.pi) + x, new_cnn_cache, new_att_cache = self.solve_euler_chunk( + x=z, + t_span=t_span, + mu=mu, + spks=spks, + cond=cond, + att_cache=att_cache, + cnn_cache=cnn_cache, + ) + return x, new_cnn_cache, new_att_cache diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/__init__.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/activation.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/activation.py new file mode 100644 index 0000000000000000000000000000000000000000..4604ce7e164c5f224767a6e144b3fbfce63a80ec --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/activation.py @@ -0,0 +1,84 @@ +# Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe) +# 2020 Northwestern Polytechnical University (Pengcheng Guo) +# 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Swish() activation function for Conformer.""" + +import torch +from torch import nn, sin, pow +from torch.nn import Parameter + + +class Swish(torch.nn.Module): + """Construct an Swish object.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Return Swish activation function.""" + return x * torch.sigmoid(x) + + +# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license. +# LICENSE is in incl_licenses directory. +class Snake(nn.Module): + ''' + Implementation of a sine-based periodic activation function + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter + References: + - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snake(256) + >>> x = torch.randn(256) + >>> x = a1(x) + ''' + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): + ''' + Initialization. + INPUT: + - in_features: shape of the input + - alpha: trainable parameter + alpha is initialized to 1 by default, higher values = higher-frequency. + alpha will be trained along with the rest of your model. + ''' + super(Snake, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + ''' + Forward pass of the function. + Applies the function to the input elementwise. + Snake ∶= x + 1/a * sin^2 (xa) + ''' + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + if self.alpha_logscale: + alpha = torch.exp(alpha) + x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/f0_predictor.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/f0_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..34eef3c19784d56a58d769599ce7593f356871dc --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/f0_predictor.py @@ -0,0 +1,57 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn +try: + from torch.nn.utils.parametrizations import weight_norm +except ImportError: + from torch.nn.utils import weight_norm + +class ConvRNNF0Predictor(nn.Module): + def __init__(self, + num_class: int = 1, + in_channels: int = 80, + cond_channels: int = 512 + ): + super().__init__() + + self.num_class = num_class + self.condnet = nn.Sequential( + weight_norm( + nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + ) + self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.condnet(x) + x = x.transpose(1, 2) + return torch.abs(self.classifier(x).squeeze(-1)) diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/generator.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..eeac841809f49fc06d02d8b4537cdc495fb0cd83 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/hifigan/generator.py @@ -0,0 +1,623 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""HIFI-GAN""" + +from typing import Dict, Optional, List +import numpy as np +from scipy.signal import get_window +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import Conv1d +from torch.nn import ConvTranspose1d +from torch.nn.utils import remove_weight_norm +from torch.nn.utils import weight_norm +from torch.distributions.uniform import Uniform +try: + from torch.nn.utils.parametrizations import weight_norm +except ImportError: + from torch.nn.utils import weight_norm +from stepvocoder.cosyvoice2.hifigan.activation import Snake +from stepvocoder.cosyvoice2.utils.common import get_padding, init_weights + +import torch._dynamo +torch._dynamo.config.accumulated_cache_size_limit = 128 + + +"""hifigan based generator implementation. + +This code is modified from https://github.com/jik876/hifi-gan + ,https://github.com/kan-bayashi/ParallelWaveGAN and + https://github.com/NVIDIA/BigVGAN + +""" + + +class ResBlock(torch.nn.Module): + """Residual block module in HiFiGAN/BigVGAN.""" + def __init__( + self, + channels: int = 512, + kernel_size: int = 3, + dilations: List[int] = [1, 3, 5], + ): + super(ResBlock, self).__init__() + self.convs1 = nn.ModuleList() + self.convs2 = nn.ModuleList() + + for dilation in dilations: + self.convs1.append( + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation, + padding=get_padding(kernel_size, dilation) + ) + ) + ) + self.convs2.append( + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1) + ) + ) + ) + self.convs1.apply(init_weights) + self.convs2.apply(init_weights) + self.activations1 = nn.ModuleList([ + Snake(channels, alpha_logscale=False) + for _ in range(len(self.convs1)) + ]) + self.activations2 = nn.ModuleList([ + Snake(channels, alpha_logscale=False) + for _ in range(len(self.convs2)) + ]) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + for idx in range(len(self.convs1)): + xt = self.activations1[idx](x) + xt = self.convs1[idx](xt) + xt = self.activations2[idx](xt) + xt = self.convs2[idx](xt) + x = xt + x + return x + + def remove_weight_norm(self): + for idx in range(len(self.convs1)): + remove_weight_norm(self.convs1[idx]) + remove_weight_norm(self.convs2[idx]) + + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + + def _f02uv(self, f0): + # generate uv signal + uv = (f0 > self.voiced_threshold).type(torch.float32) + return uv + + @torch.no_grad() + def forward(self, f0): + """ + :param f0: [B, 1, sample_len], Hz + :return: [B, 1, sample_len] + """ + + F_mat = torch.zeros((f0.size(0), self.harmonic_num + 1, f0.size(-1))).to(f0.device) + for i in range(self.harmonic_num + 1): + F_mat[:, i: i + 1, :] = f0 * (i + 1) / self.sampling_rate + + theta_mat = 2 * np.pi * (torch.cumsum(F_mat, dim=-1) % 1) + u_dist = Uniform(low=-np.pi, high=np.pi) + phase_vec = u_dist.sample(sample_shape=(f0.size(0), self.harmonic_num + 1, 1)).to(F_mat.device) + phase_vec[:, 0, :] = 0 + # generate sine waveforms + sine_waves = self.sine_amp * torch.sin(theta_mat + phase_vec) + # generate uv signal + uv = self._f02uv(f0) + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + + +class SourceModuleHnNSF(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0): + super(SourceModuleHnNSF, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + self.l_sin_gen = SineGen(sampling_rate, harmonic_num, + sine_amp, add_noise_std, voiced_threshod) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + + def forward(self, x): + """ + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + """ + # source for harmonic branch + with torch.no_grad(): + sine_wavs, uv, _ = self.l_sin_gen(x.transpose(1, 2)) + sine_wavs = sine_wavs.transpose(1, 2) + uv = uv.transpose(1, 2) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + + # source for noise branch, in the same shape as uv + noise = torch.randn_like(uv) * self.sine_amp / 3 + return sine_merge, noise, uv + + +class SineGen2(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, upsample_scale, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0, + flag_for_pulse=False): + super(SineGen2, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + self.flag_for_pulse = flag_for_pulse + self.upsample_scale = upsample_scale + + def _f02uv(self, f0): + # generate uv signal + uv = (f0 > self.voiced_threshold).type(torch.float32) + return uv + + def _f02sine(self, f0_values): + """ f0_values: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + # convert to F0 in rad. The interger part n can be ignored + # because 2 * np.pi * n doesn't affect phase + rad_values = (f0_values / self.sampling_rate) % 1 + + # initial phase noise (no noise for fundamental component) + rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], device=f0_values.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + + # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) + if not self.flag_for_pulse: + rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), + scale_factor=1 / self.upsample_scale, + mode="linear").transpose(1, 2) + + phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi + phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, + scale_factor=self.upsample_scale, mode="linear").transpose(1, 2) + sines = torch.sin(phase) + else: + # If necessary, make sure that the first time step of every + # voiced segments is sin(pi) or cos(0) + # This is used for pulse-train generation + + # identify the last time step in unvoiced segments + uv = self._f02uv(f0_values) + uv_1 = torch.roll(uv, shifts=-1, dims=1) + uv_1[:, -1, :] = 1 + u_loc = (uv < 1) * (uv_1 > 0) + + # get the instantanouse phase + tmp_cumsum = torch.cumsum(rad_values, dim=1) + # different batch needs to be processed differently + for idx in range(f0_values.shape[0]): + temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] + temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] + # stores the accumulation of i.phase within + # each voiced segments + tmp_cumsum[idx, :, :] = 0 + tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum + + # rad_values - tmp_cumsum: remove the accumulation of i.phase + # within the previous voiced segment. + i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) + + # get the sines + sines = torch.cos(i_phase * 2 * np.pi) + return sines + + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + # fundamental component + fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) + + # generate sine waveforms + sine_waves = self._f02sine(fn) * self.sine_amp + + # generate uv signal + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + +class SourceModuleHnNSF2(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0): + super(SourceModuleHnNSF2, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + self.l_sin_gen = SineGen2(sampling_rate, upsample_scale, harmonic_num, + sine_amp, add_noise_std, voiced_threshod) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + + def forward(self, x): + """ + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + """ + # source for harmonic branch + with torch.no_grad(): + sine_wavs, uv, _ = self.l_sin_gen(x) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + + # source for noise branch, in the same shape as uv + noise = torch.randn_like(uv) * self.sine_amp / 3 + return sine_merge, noise, uv + + +class HiFTGenerator(nn.Module): + """ + HiFTNet Generator: Neural Source Filter + ISTFTNet + https://arxiv.org/abs/2309.09493 + """ + def __init__( + self, + in_channels: int = 80, + base_channels: int = 512, + nb_harmonics: int = 8, + sampling_rate: int = 22050, + nsf_alpha: float = 0.1, + nsf_sigma: float = 0.003, + nsf_voiced_threshold: float = 10, + upsample_rates: List[int] = [8, 8], + upsample_kernel_sizes: List[int] = [16, 16], + istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4}, + resblock_kernel_sizes: List[int] = [3, 7, 11], + resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + source_resblock_kernel_sizes: List[int] = [7, 11], + source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]], + lrelu_slope: float = 0.1, + audio_limit: float = 0.99, + f0_predictor: torch.nn.Module = None, + ): + super(HiFTGenerator, self).__init__() + + self.out_channels = 1 + self.nb_harmonics = nb_harmonics + self.sampling_rate = sampling_rate + self.istft_params = istft_params + self.lrelu_slope = lrelu_slope + self.audio_limit = audio_limit + + self.num_kernels = len(resblock_kernel_sizes) + self.num_upsamples = len(upsample_rates) + # NOTE in CosyVoice2, we use the original SourceModuleHnNSF implementation + # this_SourceModuleHnNSF = SourceModuleHnNSF if self.sampling_rate == 22050 else SourceModuleHnNSF2 + this_SourceModuleHnNSF = SourceModuleHnNSF2 # WBY + self.m_source = this_SourceModuleHnNSF( + sampling_rate=sampling_rate, + upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"], + harmonic_num=nb_harmonics, + sine_amp=nsf_alpha, + add_noise_std=nsf_sigma, + voiced_threshod=nsf_voiced_threshold) + self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"]) + + self.conv_pre = weight_norm( + Conv1d(in_channels, base_channels, 7, 1, padding=3) + ) + + # Up + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + base_channels // (2**i), + base_channels // (2**(i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + # Down + self.source_downs = nn.ModuleList() + self.source_resblocks = nn.ModuleList() + downsample_rates = [1] + upsample_rates[::-1][:-1] + downsample_cum_rates = np.cumprod(downsample_rates) + for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)): + if u == 1: + self.source_downs.append( + Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1) + ) + else: + self.source_downs.append( + Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u, padding=(u // 2)) + ) + + self.source_resblocks.append( + ResBlock(base_channels // (2 ** (i + 1)), k, d) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = base_channels // (2**(i + 1)) + for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): + self.resblocks.append(ResBlock(ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, istft_params["n_fft"] + 2, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.reflection_pad = nn.ReflectionPad1d((1, 0)) + self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32)) + self.f0_predictor = f0_predictor + + # for cuda graph + self.use_cuda_graph = False + self.graph = {} + self.inference_buffers = {} + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + self.m_source.remove_weight_norm() + for l in self.source_downs: + remove_weight_norm(l) + for l in self.source_resblocks: + l.remove_weight_norm() + + def _stft(self, x): + spec = torch.stft( + x, + self.istft_params["n_fft"], self.istft_params["hop_len"], self.istft_params["n_fft"], window=self.stft_window.to(x.device), + return_complex=True) + spec = torch.view_as_real(spec) # [B, F, TT, 2] + return spec[..., 0], spec[..., 1] + + def _istft(self, magnitude, phase): + magnitude = torch.clip(magnitude, max=1e2) + real = magnitude * torch.cos(phase) + img = magnitude * torch.sin(phase) + inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"], + self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device)) + return inverse_transform + + + def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor: + s_stft_real, s_stft_imag = self._stft(s.squeeze(1)) + s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1).to(s.dtype) + if self.use_cuda_graph and x.shape[-1] in self.graph: + self.inference_buffers[x.shape[-1]]['static_inputs']['static_x'].copy_(x) + self.inference_buffers[x.shape[-1]]['static_inputs']['static_s_stft'].copy_(s_stft) + self.graph[x.shape[-1]].replay() + x = self.inference_buffers[x.shape[-1]]['static_outputs']['static_output_x'] + else: + x = self.decode_without_stft(x, s_stft) + magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :]) + phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :]) # actually, sin is redundancy + magnitude = magnitude.to(torch.float32) + phase = phase.to(torch.float32) + x = self._istft(magnitude, phase).to(x.dtype) + x = torch.clamp(x, -self.audio_limit, self.audio_limit) + return x + + def decode_without_stft(self, x: torch.Tensor, s_stft: torch.Tensor) -> torch.Tensor: + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, self.lrelu_slope) + x = self.ups[i](x) + + if i == self.num_upsamples - 1: + x = self.reflection_pad(x) + + # fusion + si = self.source_downs[i](s_stft) + si = self.source_resblocks[i](si) + x = x + si + + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + + x = F.leaky_relu(x) + x = self.conv_post(x) + return x + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + speech_feat = batch['speech_feat'].transpose(1, 2).to(device) + # mel->f0 + f0 = self.f0_predictor(speech_feat) + # f0->source + s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + s, _, _ = self.m_source(s) + s = s.transpose(1, 2) + # mel+source->speech + generated_speech = self.decode(x=speech_feat, s=s) + return generated_speech, f0 + + def _init_cuda_graph(self): + self.use_cuda_graph = True + dummy_param = next(self.parameters()) + + for chunk_size in [30, 48, 96]: + time_stamps = chunk_size + 8 if chunk_size != 30 else chunk_size + num_frames = (time_stamps * 480 - 1) // self.istft_params["hop_len"] + 2 + static_x = torch.zeros((1, 80, time_stamps)).to(dummy_param) + static_s_stft = torch.zeros((1, self.istft_params["n_fft"] + 2, num_frames)).to(dummy_param) + static_inputs = { + 'static_x': static_x, + 'static_s_stft': static_s_stft + } + self.decode_without_stft(x=static_x, s_stft=static_s_stft) + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph): + static_output_x = self.decode_without_stft(x=static_x, s_stft=static_s_stft) + static_outputs = { + 'static_output_x': static_output_x, + } + self.graph[time_stamps] = graph + self.inference_buffers[time_stamps] = { + 'static_inputs': static_inputs, + 'static_outputs': static_outputs + } + print(f"CUDA Graph initialized successfully for chunk generator") + + @torch.inference_mode() + def inference(self, speech_feat: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor: + # mel->f0 + f0 = self.f0_predictor(speech_feat) + # f0->source + s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + s, _, _ = self.m_source(s) + s = s.transpose(1, 2) + # use cache_source to avoid glitch + if cache_source.shape[2] != 0: + s[:, :, :cache_source.shape[2]] = cache_source + generated_speech = self.decode(x=speech_feat, s=s) + return generated_speech, s \ No newline at end of file diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/matcha/audio.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/matcha/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..4f623879dc1335fbf3234d860b402dbc2a464828 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/matcha/audio.py @@ -0,0 +1,82 @@ +import numpy as np +import torch +import torch.utils.data +from librosa.filters import mel as librosa_mel_fn +from scipy.io.wavfile import read + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + # if torch.min(y) < -1.0: + # print("min value is ", torch.min(y)) + # if torch.max(y) > 1.0: + # print("max value is ", torch.max(y)) + + global mel_basis, hann_window # pylint: disable=global-statement + if f"{str(fmax)}_{str(y.device)}" not in mel_basis: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) + mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect" + ) + y = y.squeeze(1) + + spec = torch.view_as_real( + torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + ) + + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/__init__.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/attention.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..54451d71eb6a0c3e3e7f0616fd1163b0f69acb18 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/attention.py @@ -0,0 +1,328 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Multi-Head Attention layer definition.""" + +import math +from typing import Tuple + +import torch +from torch import nn + + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True): + """Construct an MultiHeadedAttention object.""" + super().__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat, bias=key_bias) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv( + self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor, size + (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor, size + (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor, size + (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + + return q, k, v + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool) + ) -> torch.Tensor: + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value, size + (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score, size + (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask, size (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + # NOTE(xcsong): When will `if mask.size(2) > 0` be True? + # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the + # 1st chunk to ease the onnx export.] + # 2. pytorch training + if mask.size(2) > 0: # time2 > 0 + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + # For last chunk, time2 might be larger than scores.size(-1) + mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2) + scores = scores.masked_fill(mask, -float('inf')) + attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0) # (batch, head, time1, time2) + # NOTE(xcsong): When will `if mask.size(2) > 0` be False? + # 1. onnx(16/-1, -1/-1, 16/0) + # 2. jit (16/-1, -1/-1, 16/0, 16/4) + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = (x.transpose(1, 2).contiguous().view(n_batch, -1, + self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + 1.When applying cross attention between decoder and encoder, + the batch padding mask for input is in (#batch, 1, T) shape. + 2.When applying self attention of encoder, + the mask is in (#batch, T, T) shape. + 3.When applying self attention of decoder, + the mask is in (#batch, L, L) shape. + 4.If the different position in decoder see different block + of the encoder, such as Mocha, the passed in mask could be + in (#batch, L, T) shape. But there is no such case in current + CosyVoice. + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + """ + q, k, v = self.forward_qkv(query, key, value) + + # NOTE(xcsong): + # when export onnx model, for 1st chunk, we feed + # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) + # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). + # In all modes, `if cache.size(0) > 0` will alwayse be `True` + # and we will always do splitting and + # concatnation(this will simplify onnx export). Note that + # it's OK to concat & split zero-shaped tensors(see code below). + # when export jit model, for 1st chunk, we always feed + # cache(0, 0, 0, 0) since jit supports dynamic if-branch. + # >>> a = torch.ones((1, 2, 0, 4)) + # >>> b = torch.ones((1, 2, 3, 4)) + # >>> c = torch.cat((a, b), dim=2) + # >>> torch.equal(b, c) # True + # >>> d = torch.split(a, 2, dim=-1) + # >>> torch.equal(d[0], d[1]) # True + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask), new_cache + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate, key_bias) + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x: torch.Tensor) -> torch.Tensor: + """Compute relative positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + torch.Tensor: Output tensor. + + """ + zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1), + device=x.device, + dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(x.size()[0], + x.size()[1], + x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x)[ + :, :, :, : x.size(-1) // 2 + 1 + ] # only keep the positions from 0 to time2 + return x + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, time2, size). + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + # NOTE(xcsong): + # when export onnx model, for 1st chunk, we feed + # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) + # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). + # In all modes, `if cache.size(0) > 0` will alwayse be `True` + # and we will always do splitting and + # concatnation(this will simplify onnx export). Note that + # it's OK to concat & split zero-shaped tensors(see code below). + # when export jit model, for 1st chunk, we always feed + # cache(0, 0, 0, 0) since jit supports dynamic if-branch. + # >>> a = torch.ones((1, 2, 0, 4)) + # >>> b = torch.ones((1, 2, 3, 4)) + # >>> c = torch.cat((a, b), dim=2) + # >>> torch.equal(b, c) # True + # >>> d = torch.split(a, 2, dim=-1) + # >>> torch.equal(d[0], d[1]) # True + if cache is not None and cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used + if matrix_ac.shape != matrix_bd.shape: + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask), new_cache diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/embedding.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..e7b8db40aa31abcbc13a1e72f1feb6d9e1031e04 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/embedding.py @@ -0,0 +1,119 @@ +# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Positonal Encoding Module.""" + +import math +from typing import Tuple, Union + +import torch +import torch.nn.functional as F +import numpy as np + + +class EspnetRelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module (new implementation). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + See : Appendix B in https://arxiv.org/abs/1901.02860 + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): + """Construct an PositionalEncoding object.""" + super(EspnetRelPositionalEncoding, self).__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: torch.Tensor): + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.position_encoding(size=x.size(1), offset=offset) + return self.dropout(x), self.dropout(pos_emb) + + def position_encoding(self, + offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + """ For getting encoding in a streaming fashion + + Attention!!!!! + we apply dropout only once at the whole utterance level in a none + streaming way, but will call this function several times with + increasing input size in a streaming scenario, so the dropout will + be applied several times. + + Args: + offset (int or torch.tensor): start offset + size (int): required size of position encoding + + Returns: + torch.Tensor: Corresponding encoding + """ + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size, + ] + return pos_emb diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/encoder_layer.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/encoder_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..5e2bb3597022a15da3e06eb1c982206c3b91d2e7 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/encoder_layer.py @@ -0,0 +1,163 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Encoder self-attention layer definition.""" + +from typing import Optional, Tuple + +import torch +from torch import nn + + +class ConformerEncoderLayer(nn.Module): + """Encoder layer module. + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module + instance. + `PositionwiseFeedForward` instance can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: use layer_norm after each sub-block. + enable_cuda_graph (bool): Control whether to enable CUDA Graph. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: Optional[nn.Module] = None, + feed_forward_macaron: Optional[nn.Module] = None, + conv_module: Optional[nn.Module] = None, + dropout_rate: float = 0.1, + normalize_before: bool = True, + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.norm_ff = nn.LayerNorm(size, eps=1e-12) # for the FNN module + self.norm_mha = nn.LayerNorm(size, eps=1e-12) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-12) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = nn.LayerNorm(size, eps=1e-12) # for the CNN module + self.norm_final = nn.LayerNorm( + size, eps=1e-12) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): positional encoding, must not be None + for ConformerEncoderLayer. + mask_pad (torch.Tensor): batch padding mask used for conv module. + (#batch, 1，time), (0, 0, 0) means fake mask. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2) + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). + """ + return self._forward_impl(x, mask, pos_emb, mask_pad, att_cache, cnn_cache) + + def _forward_impl( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """原始的前向传播实现""" + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout( + self.feed_forward_macaron(x)) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + # att_cache: (b, head, cache_t, d_k*2) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, + att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + # Fake new cnn cache here, and then change it in conv_module + new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) + x = residual + self.dropout(x) + + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + return x, mask, new_att_cache, new_cnn_cache diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/positionwise_feed_forward.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/positionwise_feed_forward.py new file mode 100644 index 0000000000000000000000000000000000000000..6123dc3ced1d484c83b236d9362fb078db8d8bf8 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/positionwise_feed_forward.py @@ -0,0 +1,56 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Positionwise feed forward layer definition.""" + +import torch + + +class PositionwiseFeedForward(torch.nn.Module): + """Positionwise feed forward layer. + + FeedForward are appied on each position of the sequence. + The output dim is same with the input dim. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + ): + """Construct a PositionwiseFeedForward object.""" + super(PositionwiseFeedForward, self).__init__() + self.w_1 = torch.nn.Linear(idim, hidden_units) + self.activation = activation + self.dropout = torch.nn.Dropout(dropout_rate) + self.w_2 = torch.nn.Linear(hidden_units, idim) + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Forward function. + + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + """ + return self.w_2(self.dropout(self.activation(self.w_1(xs)))) + diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/subsampling.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/subsampling.py new file mode 100644 index 0000000000000000000000000000000000000000..029702d3c341cd85a73d453be0549254a8299eb3 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/subsampling.py @@ -0,0 +1,79 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Subsampling layer definition.""" + +from typing import Tuple, Union + +import torch + + +class BaseSubsampling(torch.nn.Module): + + def __init__(self): + super().__init__() + self.right_context = 0 + self.subsampling_rate = 1 + + def position_encoding(self, offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + return self.pos_enc.position_encoding(offset, size) + + +class LinearNoSubsampling(BaseSubsampling): + """Linear transform the input without subsampling + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an linear object.""" + super().__init__() + self.out = torch.nn.Sequential( + torch.nn.Linear(idim, odim), + torch.nn.LayerNorm(odim, eps=1e-5), + torch.nn.Dropout(dropout_rate), + ) + self.pos_enc = pos_enc_class + self.right_context = 0 + self.subsampling_rate = 1 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.out(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/upsample_encoder_v2.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/upsample_encoder_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..475028aec54c673f029f1d0091a29aec1a8c1b30 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/transformer/upsample_encoder_v2.py @@ -0,0 +1,483 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Encoder definition.""" +from typing import Tuple, List + +import torch +from torch import nn +from torch.nn import functional as F + +from stepvocoder.cosyvoice2.transformer.encoder_layer import ConformerEncoderLayer +from stepvocoder.cosyvoice2.transformer.positionwise_feed_forward import PositionwiseFeedForward +from stepvocoder.cosyvoice2.utils.class_utils import ( + COSYVOICE_EMB_CLASSES, + COSYVOICE_SUBSAMPLE_CLASSES, + COSYVOICE_ATTENTION_CLASSES, + COSYVOICE_ACTIVATION_CLASSES, +) +from stepvocoder.cosyvoice2.utils.mask import ( + make_pad_mask, +) + +import torch._dynamo +torch._dynamo.config.suppress_errors = True +torch._dynamo.config.cache_size_limit = 128 + +class Upsample1D(nn.Module): + """A 1D upsampling layer with an optional convolution. + + Parameters: + channels (`int`): + number of channels in the inputs and outputs. + use_conv (`bool`, default `False`): + option to use a convolution. + use_conv_transpose (`bool`, default `False`): + option to use a convolution transpose. + out_channels (`int`, optional): + number of output channels. Defaults to `channels`. + """ + + def __init__(self, channels: int, out_channels: int, stride: int = 2, scale_factor: float = None): + super().__init__() + self.channels = channels + self.out_channels = out_channels + self.stride = stride + # In this mode, first repeat interpolate, than conv with stride=1 + self.conv = nn.Conv1d(self.channels, self.out_channels, stride * 2 + 1, stride=1, padding=0) + self.scale_factor = float(self.stride) if scale_factor is None else float(scale_factor) + + def forward(self, inputs: torch.Tensor, input_lengths: torch.Tensor): + outputs = F.interpolate(inputs, scale_factor=self.scale_factor, mode="nearest") + outputs = F.pad(outputs, (self.stride * 2, 0), value=0.0) + outputs = self.conv(outputs) + return outputs, input_lengths * self.stride + + def forward_chunk(self, inputs: torch.Tensor, input_lengths: torch.Tensor, cache: torch.Tensor = torch.zeros((0, 0, 0))): + """ + Args: + inputs(torch.Tensor): shape (b, c, t) + input_length(torch.Tensor): shape (b), can be None + cache(torch.Tensor): shape (b, c, cache_t), where cache_t = stride * 2 + """ + outputs = F.interpolate(inputs, scale_factor=self.scale_factor, mode="nearest") + + if cache is None: + cache = inputs.new_zeros(inputs.shape[0], inputs.shape[1], self.stride * 2) + outputs = torch.cat([cache, outputs], dim=2) + new_cache = outputs[..., -self.stride*2:] + outputs = self.conv(outputs) + + if input_lengths is not None: + input_lengths = input_lengths * self.stride + return outputs, input_lengths, new_cache + + +class PreLookaheadLayer(nn.Module): + def __init__(self, channels: int, pre_lookahead_len: int = 1): + super().__init__() + self.channels = channels + self.pre_lookahead_len = pre_lookahead_len + self.conv1 = nn.Conv1d( + channels, channels, + kernel_size=pre_lookahead_len + 1, + stride=1, padding=0, + ) + self.conv2 = nn.Conv1d( + channels, channels, + kernel_size=3, stride=1, padding=0, + ) + + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + """ + inputs: (batch_size, seq_len, channels) + """ + outputs = inputs.transpose(1, 2).contiguous() + # look ahead + outputs = F.pad(outputs, (0, self.pre_lookahead_len), mode='constant', value=0.0) + outputs = F.leaky_relu(self.conv1(outputs)) + # outputs + outputs = F.pad(outputs, (2, 0), mode='constant', value=0.0) + outputs = self.conv2(outputs) + outputs = outputs.transpose(1, 2).contiguous() + + # residual connection + outputs = outputs + inputs + return outputs + + def forward_chunk(self, inputs: torch.Tensor, cache: torch.Tensor = None): + """ + Args: + inputs(torch.Tensor): shape (b, t, c) + cache(torch.Tensor): shape (b, c, cache_t=2), c = channels + """ + outputs = inputs.transpose(1, 2).contiguous() + outputs = F.leaky_relu(self.conv1(outputs)) + # the length of outputs is input length - pre_lookahead_len + if cache is None: + cache = outputs.new_zeros(outputs.shape[0], outputs.shape[1], 2) + # NOTE + new_cache = outputs[..., -2:] + outputs = torch.cat([cache, outputs], dim=2) + outputs = self.conv2(outputs) + outputs = outputs.transpose(1, 2).contiguous() + # residual connection + outputs = outputs + inputs[:, :-self.pre_lookahead_len] + return outputs, new_cache + + +"""Customize each sample's chunk attention mask +""" +class UpsampleConformerEncoderV2(torch.nn.Module): + + def __init__( + self, + # input & output + input_size: int, + output_size: int = 256, + input_layer: str = "linear", + pre_lookahead_len: int = 3, + # size + num_blocks: int = 6, + num_up_blocks: int = 4, + # upsampling + up_stride: int = 2, + up_scale_factor: float = 2, + # attention + attention_heads: int = 4, + pos_enc_layer_type: str = "rel_pos_espnet", + selfattention_layer_type: str = "rel_selfattn", + key_bias: bool = True, + # mlp + linear_units: int = 2048, + # dropouts + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + # other + normalize_before: bool = True, + activation_type: str = "swish", + **kwargs, + ): + super().__init__() + self._output_size = output_size + self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer]( + input_size, + output_size, + dropout_rate, + COSYVOICE_EMB_CLASSES[pos_enc_layer_type]( + output_size, + positional_dropout_rate + ), + ) + + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) + activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() + # self-attention module definition + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + key_bias, + ) + # feed-forward module definition + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + self.pre_lookahead_layer = PreLookaheadLayer( + channels=output_size, + pre_lookahead_len=pre_lookahead_len + ) + self.encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type]( + *encoder_selfattn_layer_args + ), + PositionwiseFeedForward(*positionwise_layer_args), + None, + None, + dropout_rate, + normalize_before, + ) for _ in range(num_blocks) + ]) + self.up_layer = Upsample1D( + channels=output_size, + out_channels=output_size, + stride=up_stride, + scale_factor=up_scale_factor + ) + self.up_embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer]( + input_size, + output_size, + dropout_rate, + COSYVOICE_EMB_CLASSES[pos_enc_layer_type]( + output_size, + positional_dropout_rate + ), + ) + self.up_encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type]( + *encoder_selfattn_layer_args + ), + PositionwiseFeedForward(*positionwise_layer_args), + None, + None, + dropout_rate, + normalize_before, + ) for _ in range(num_up_blocks) + ]) + + self.enable_cuda_graph = False + self.use_cuda_graph = False + self.graph_encoder = {} + self.graph_up_encoder = {} + self.inference_buffers_encoder = {} + self.inference_buffers_up_encoder = {} + self.max_static_time = 1500 + + # FIXME(sfy) revert hard-coded bfloat16 + # this method is skipped in CausalMaskedDiffWithXvec.scatter_cuda_graph + def scatter_cuda_graph(self, enable_cuda_graph: bool): + self.enable_cuda_graph = enable_cuda_graph + if self.enable_cuda_graph: + self._init_cuda_graph() + + def _init_cuda_graph(self): + """初始化 CUDA Graph""" + + for l in range(100, 1500, 10): + static_x = torch.zeros((1, l, 512), + dtype=torch.float32, device=torch.device('cuda')) + static_mask = torch.ones((1, 1, l), + dtype=torch.bool, device=torch.device('cuda')) + static_pos_emb = torch.zeros((1, 2*l-1, 512), + dtype=torch.float32, device=torch.device('cuda')) + + static_inputs = [ + static_x, + static_mask, + static_pos_emb, + ] + + self._forward_impl_encoder( + static_inputs[0], + static_inputs[1], + static_inputs[2], + ) + graph = torch.cuda.CUDAGraph() + with torch.no_grad(): + with torch.cuda.graph(graph): + static_out_x = self._forward_impl_encoder( + static_inputs[0], + static_inputs[1], + static_inputs[2] + ) + self.graph_encoder[l] = graph + static_outputs = [ + static_out_x, + ] + self.inference_buffers_encoder[l] = { + 'static_inputs': static_inputs, + 'static_outputs': static_outputs + } + + for l in range(100, 1500, 10): + static_x = torch.zeros((1, l, 512), + dtype=torch.float32, device=torch.device('cuda')) + static_mask = torch.ones((1, 1, l), + dtype=torch.bool, device=torch.device('cuda')) + static_pos_emb = torch.zeros((1, 2*l-1, 512), + dtype=torch.float32, device=torch.device('cuda')) + + static_inputs = [ + static_x, + static_mask, + static_pos_emb, + ] + + self._forward_impl_up_encoder( + static_inputs[0], + static_inputs[1], + static_inputs[2], + ) + graph = torch.cuda.CUDAGraph() + with torch.no_grad(): + with torch.cuda.graph(graph): + static_out_x = self._forward_impl_up_encoder( + static_inputs[0], + static_inputs[1], + static_inputs[2] + ) + self.graph_up_encoder[l] = graph + static_outputs = [ + static_out_x, + ] + self.inference_buffers_up_encoder[l] = { + 'static_inputs': static_inputs, + 'static_outputs': static_outputs + } + + self.use_cuda_graph = True + print("CUDA Graph initialized successfully for encoder and up_encoder") + + # @torch.compile(dynamic=True,backend="eager") + def _forward_impl_encoder(self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor): + for layer in self.encoders: + x, _, _, _ = layer(x, mask, pos_emb) + return x + + # @torch.compile(dynamic=True,backend="eager") + def _forward_impl_up_encoder(self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor): + for layer in self.up_encoders: + x, _, _, _ = layer(x, mask, pos_emb) + return x + + def output_size(self) -> int: + return self._output_size + + # @torch.compile(dynamic=True,backend="eager") + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + # (sfy) chunk training strategy should not be open-sourced + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + xs, pos_emb, masks = self.embed(xs, masks) + + # lookahead + xs = self.pre_lookahead_layer(xs) + # conformer block + if self.enable_cuda_graph and xs.shape[1] in self.graph_encoder: + self.inference_buffers_encoder[xs.shape[1]]['static_inputs'][0].copy_(xs) + self.inference_buffers_encoder[xs.shape[1]]['static_inputs'][1].copy_(masks) + self.inference_buffers_encoder[xs.shape[1]]['static_inputs'][2].copy_(pos_emb) + self.graph_encoder[xs.shape[1]].replay() + xs = self.inference_buffers_encoder[xs.shape[1]]['static_outputs'][0] + else: + xs = self._forward_impl_encoder(xs, masks, pos_emb) + # upsample + xs = xs.transpose(1, 2).contiguous() + xs, xs_lens = self.up_layer(xs, xs_lens) + xs = xs.transpose(1, 2).contiguous() + + # 2nd conformer block + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + xs, pos_emb, masks = self.up_embed(xs, masks) + if self.enable_cuda_graph and xs.shape[1] in self.graph_up_encoder: + self.inference_buffers_up_encoder[xs.shape[1]]['static_inputs'][0].copy_(xs) + self.inference_buffers_up_encoder[xs.shape[1]]['static_inputs'][1].copy_(masks) + self.inference_buffers_up_encoder[xs.shape[1]]['static_inputs'][2].copy_(pos_emb) + self.graph_up_encoder[xs.shape[1]].replay() + xs = self.inference_buffers_up_encoder[xs.shape[1]]['static_outputs'][0] + else: + xs = self._forward_impl_up_encoder(xs, masks, pos_emb) + # post norm + if self.normalize_before: + xs = self.after_norm(xs) + return xs, masks + + @torch.compile(dynamic=True,backend="eager") + def forward_chunk(self, + xs: torch.Tensor, + last_chunk: bool = False, + cnn_cache: torch.Tensor = None, + att_cache: torch.Tensor = None, + ): + """ + Args: + xs: shape (b, dt, c) + last_chunk: bool. If last chunk, will pad input with lookaheads + att_cache: shape (depth1+depth2, b, nh, 2*t1, c). + cnn_cache: shape (b, c, t1+t2). Where t1=2 (pre_lookahead_layer), t2=4 (up_layer) + """ + if att_cache is not None: + assert att_cache.shape[3] % 2 == 0, att_cache.shape + if cnn_cache is not None: + assert cnn_cache.shape[2] == 2+self.up_layer.stride*2, cnn_cache.shape + + # unpack caches + offset1 = att_cache.shape[3] // 2 if att_cache is not None else 0 + att_cache1 = att_cache[:len(self.encoders), :, :, :offset1] if att_cache is not None else [None] * len(self.encoders) + att_cache2 = att_cache[len(self.encoders):] if att_cache is not None else [None] * len(self.encoders) + cnn_cache1 = cnn_cache[:, :, :2] if cnn_cache is not None else None + cnn_cache2 = cnn_cache[:, :, 2:] if cnn_cache is not None else None + xs, _, _ = self.embed(xs, None) + if last_chunk: + xs = F.pad(xs, (0, 0, 0, self.pre_lookahead_layer.pre_lookahead_len)) + + # this_cnn_cache: shape (b=1, c=512, t=2) + xs, new_cnn_cache1 = self.pre_lookahead_layer.forward_chunk(xs, cache=cnn_cache1) + + # remake pos_emb, offset param is ignored by position_encoding + pos_emb = self.embed.position_encoding(offset=None, size=offset1 + xs.shape[1]) + + # first conformer + chunk_masks = torch.zeros((0, 0, 0)) + new_att_cache1 = [] + + for idx, layer in enumerate(self.encoders): + # this_att_cache: shape (b, nh, t, c * 2) + xs, _, this_new_att_cache1, _ = layer(xs, chunk_masks, pos_emb, att_cache=att_cache1[idx]) + new_att_cache1.append(this_new_att_cache1) + new_att_cache1 = torch.stack(new_att_cache1, dim=0) + + # upsample + conformer encoder, xs: (b, t, c) -> (b, c, t) + xs = xs.transpose(1, 2).contiguous() + # this_cnn_cache: shape (b=1, c=512, t=2*2) + xs, _, new_cnn_cache2 = self.up_layer.forward_chunk(xs, None, cache=cnn_cache2) + xs = xs.transpose(1, 2).contiguous() + + # at this time, xs are doubled in length + xs, _, _ = self.up_embed(xs, None) + + # remake pos_emb + pos_emb = self.embed.position_encoding(offset=None, size=offset1 * self.up_layer.stride + xs.shape[1]) + + # second conformer + chunk_masks = torch.zeros((0, 0, 0),dtype=torch.bfloat16) + new_att_cache2 = [] + + for idx, layer in enumerate(self.up_encoders): + xs, _, this_new_att_cache2, _ = layer(xs, chunk_masks, pos_emb, att_cache=att_cache2[idx]) + new_att_cache2.append(this_new_att_cache2) + new_att_cache2 = torch.stack(new_att_cache2, dim=0) + + if self.normalize_before: + xs = self.after_norm(xs) + + # pack new cache + new_att_cache = torch.cat([new_att_cache1.repeat(1, 1, 1, 2, 1), new_att_cache2], dim=0) + new_cnn_cache = torch.cat([new_cnn_cache1, new_cnn_cache2], dim=2) + + return xs, new_cnn_cache, new_att_cache + + diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/utils/class_utils.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/utils/class_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1de4c4cedb64ff9e5921f6518e50603ff207c9e4 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/utils/class_utils.py @@ -0,0 +1,41 @@ +# Copyright [2023-11-28] +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + +from stepvocoder.cosyvoice2.transformer.subsampling import LinearNoSubsampling +from stepvocoder.cosyvoice2.transformer.attention import RelPositionMultiHeadedAttention +from stepvocoder.cosyvoice2.transformer.embedding import EspnetRelPositionalEncoding + + +COSYVOICE_ACTIVATION_CLASSES = { + "hardtanh": torch.nn.Hardtanh, + "tanh": torch.nn.Tanh, + "relu": torch.nn.ReLU, + "selu": torch.nn.SELU, + "swish": torch.nn.SiLU, + "gelu": torch.nn.GELU, +} + +COSYVOICE_SUBSAMPLE_CLASSES = { + "linear": LinearNoSubsampling, +} + +COSYVOICE_EMB_CLASSES = { + "rel_pos_espnet": EspnetRelPositionalEncoding, +} + +COSYVOICE_ATTENTION_CLASSES = { + "rel_selfattn": RelPositionMultiHeadedAttention, +} diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/utils/common.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/utils/common.py new file mode 100644 index 0000000000000000000000000000000000000000..2e5cd7a179f818c6601ca6023dd048b66fdbd72b --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/utils/common.py @@ -0,0 +1,101 @@ +# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Unility functions for Transformer.""" + +import random +from typing import List + +import numpy as np +import torch + +IGNORE_ID = -1 + + +def pad_list(xs: List[torch.Tensor], pad_value: int): + """Perform padding for the list of tensors. + + Args: + xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)]. + pad_value (float): Value for padding. + + Returns: + Tensor: Padded tensor (B, Tmax, `*`). + + Examples: + >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)] + >>> x + [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])] + >>> pad_list(x, 0) + tensor([[1., 1., 1., 1.], + [1., 1., 0., 0.], + [1., 0., 0., 0.]]) + + """ + max_len = max([len(item) for item in xs]) + batchs = len(xs) + ndim = xs[0].ndim + if ndim == 1: + pad_res = torch.zeros(batchs, + max_len, + dtype=xs[0].dtype, + device=xs[0].device) + elif ndim == 2: + pad_res = torch.zeros(batchs, + max_len, + xs[0].shape[1], + dtype=xs[0].dtype, + device=xs[0].device) + elif ndim == 3: + pad_res = torch.zeros(batchs, + max_len, + xs[0].shape[1], + xs[0].shape[2], + dtype=xs[0].dtype, + device=xs[0].device) + else: + raise ValueError(f"Unsupported ndim: {ndim}") + pad_res.fill_(pad_value) + for i in range(batchs): + pad_res[i, :len(xs[i])] = xs[i] + return pad_res + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def fade_in_out(fade_in_mel, fade_out_mel, window): + device = fade_in_mel.device + fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu() + mel_overlap_len = int(window.shape[0] / 2) + if fade_in_mel.device == torch.device('cpu'): + fade_in_mel = fade_in_mel.clone() + fade_in_mel[..., :mel_overlap_len] = fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \ + fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:] + return fade_in_mel.to(device) + + +def set_all_random_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) diff --git a/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/utils/mask.py b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/utils/mask.py new file mode 100644 index 0000000000000000000000000000000000000000..cdd4bd5d19647537f1fbd91cc82d169c175cbc42 --- /dev/null +++ b/demo/Step-Audio-EditX/stepvocoder/cosyvoice2/utils/mask.py @@ -0,0 +1,49 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import torch +from typing import List + + +def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: + """Make mask tensor containing indices of padded part. + + See description of make_non_pad_mask. + + Args: + lengths (torch.Tensor): Batch of lengths (B,). + Returns: + torch.Tensor: Mask tensor containing indices of padded part. + + Examples: + >>> lengths = [5, 3, 2] + >>> make_pad_mask(lengths) + masks = [[0, 0, 0, 0 ,0], + [0, 0, 0, 1, 1], + [0, 0, 1, 1, 1]] + """ + batch_size = lengths.size(0) + max_len = max_len if max_len > 0 else lengths.max().item() + seq_range = torch.arange(0, + max_len, + dtype=torch.int64, + device=lengths.device) + seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) + seq_length_expand = lengths.unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + return mask + diff --git a/demo/Step-Audio-EditX/tokenizer.py b/demo/Step-Audio-EditX/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..bcbcffa7fbaa13578c4faa408a10e92ef0c7f727 --- /dev/null +++ b/demo/Step-Audio-EditX/tokenizer.py @@ -0,0 +1,224 @@ +import io +import threading +import time +import os + +import numpy as np +import torch +import torchaudio +import onnxruntime +import whisper + +from funasr_detach import AutoModel +from utils import resample_audio, energy_norm_fn, trim_silence +from model_loader import model_loader, ModelSource + + +class StepAudioTokenizer: + def __init__( + self, + encoder_path, + model_source=ModelSource.AUTO, + funasr_model_id="dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online" + ): + """ + Initialize StepAudioTokenizer + + Args: + encoder_path: Encoder path + model_source: Model source (auto/local/modelscope/huggingface) + funasr_model_id: FunASR model ID or path + """ + funasr_model_path = os.path.join(encoder_path, funasr_model_id) + # Load FunASR model - use unified loader to handle all modes + try: + self.funasr_model = model_loader.load_funasr_model( + encoder_path, + funasr_model_path, + source=model_source, + model_revision="main" + ) + except Exception as e: + print(f"Failed to load FunASR model from {model_source}: {e}") + # Fallback to default method + self.funasr_model = AutoModel(model=funasr_model_path, model_revision="main") + + # Load other resource files (these are usually local files) + kms_path = os.path.join(self.funasr_model.repo_path, "linguistic_tokenizer.npy") + cosy_tokenizer_path = os.path.join(self.funasr_model.repo_path, "speech_tokenizer_v1.onnx") + + if not os.path.exists(kms_path): + raise FileNotFoundError(f"KMS file not found: {kms_path}") + if not os.path.exists(cosy_tokenizer_path): + raise FileNotFoundError(f"Cosy tokenizer file not found: {cosy_tokenizer_path}") + + self.kms = torch.tensor(np.load(kms_path)) + + providers = ["CUDAExecutionProvider"] + session_option = onnxruntime.SessionOptions() + session_option.graph_optimization_level = ( + onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + ) + session_option.intra_op_num_threads = 1 + self.ort_session = onnxruntime.InferenceSession( + cosy_tokenizer_path, sess_options=session_option, providers=providers + ) + self.chunk_size = [0, 4, 5] + self.encoder_chunk_look_back = 4 + self.decoder_chunk_look_back = 1 + + self.vq02_sessions = {} + self.vq02_lock = threading.Lock() + self.vq06_lock = threading.Lock() + + def __call__(self, audio, sr): + _, vq02, vq06 = self.wav2token(audio, sr, False) + text = self.merge_vq0206_to_token_str(vq02, vq06) + return text + + def preprocess_wav(self, audio, sample_rate, enable_trim=True, energy_norm=True): + audio = resample_audio(audio, sample_rate, 16000) + if energy_norm: + audio = energy_norm_fn(audio) + + if enable_trim: + audio = audio.cpu().numpy().squeeze(0) + audio = trim_silence(audio, 16000) + audio = torch.from_numpy(audio) + audio = audio.unsqueeze(0) + return audio + + def wav2token(self, audio, sample_rate, enable_trim=True, energy_norm=True): + audio = self.preprocess_wav( + audio, sample_rate, enable_trim=enable_trim, energy_norm=energy_norm + ) + + vq02_ori = self.get_vq02_code(audio) + vq02 = [int(x) + 65536 for x in vq02_ori] + vq06_ori = self.get_vq06_code(audio) + vq06 = [int(x) + 65536 + 1024 for x in vq06_ori] + + chunk = 1 + chunk_nums = min(len(vq06) // (3 * chunk), len(vq02) // (2 * chunk)) + speech_tokens = [] + for idx in range(chunk_nums): + speech_tokens += vq02[idx * chunk * 2 : (idx + 1) * chunk * 2] + speech_tokens += vq06[idx * chunk * 3 : (idx + 1) * chunk * 3] + return speech_tokens, vq02_ori, vq06_ori + + def get_vq02_code(self, audio, session_id=None, is_final=True): + _tmp_wav = io.BytesIO() + torchaudio.save(_tmp_wav, audio, 16000, format="wav") + _tmp_wav.seek(0) + + with self.vq02_lock: + cache = {} + if session_id in self.vq02_sessions: + cache = self.vq02_sessions[session_id].get("cache", {}) + + res, new_cache = self.funasr_model.infer_encoder( + input=[_tmp_wav], + chunk_size=self.chunk_size, + encoder_chunk_look_back=self.encoder_chunk_look_back, + decoder_chunk_look_back=self.decoder_chunk_look_back, + device=0, + is_final=is_final, + cache=cache, + ) + c_list = [] + for j, res_ in enumerate(res): + feat = res_["enc_out"] + if len(feat) > 0: + c_list = self.dump_label([feat], self.kms)[0] + + if is_final: + if session_id in self.vq02_sessions: + self.vq02_sessions.pop(session_id) + else: + if isinstance(session_id, str) and len(session_id) > 0: + self.vq02_sessions[session_id] = { + "cache": new_cache, + "update_time": time.time(), + } + + return c_list + + def get_vq06_code(self, audio): + + def split_audio(audio, chunk_duration=480000): + start = 0 + chunks = [] + while start < len(audio): + end = min(start + chunk_duration, len(audio)) + chunk = audio[start:end] + if len(chunk) < 480: + pass + else: + chunks.append(chunk) + start = end + return chunks + + with self.vq06_lock: + audio = audio.squeeze(0) + chunk_audios = split_audio(audio, chunk_duration=30 * 16000) # Maximum support 30s + speech_tokens = [] + for chunk in chunk_audios: + duration = round(chunk.shape[0] / 16000, 2) + feat = whisper.log_mel_spectrogram(chunk, n_mels=128) + feat = feat.unsqueeze(0) + feat_len = np.array([feat.shape[2]], dtype=np.int32) + chunk_token = ( + self.ort_session.run( + None, + { + self.ort_session.get_inputs()[0] + .name: feat.detach() + .cpu() + .numpy(), + self.ort_session.get_inputs()[1].name: feat_len, + }, + )[0] + .flatten() + .tolist() + ) + assert abs(len(chunk_token) - duration * 25) <= 2 + speech_tokens += chunk_token + + return speech_tokens + + def kmean_cluster(self, samples, means): + dists = torch.cdist(samples, means) + indices = dists.argmin(dim=1).cpu().numpy() + return indices.tolist() + + def dump_label(self, samples, mean): + dims = samples[0].shape[-1] + x_lens = [x.shape[1] for x in samples] + total_len = sum(x_lens) + x_sel = torch.FloatTensor(1, total_len, dims) + start_len = 0 + for sample in samples: + sample_len = sample.shape[1] + end_len = start_len + sample_len + x_sel[:, start_len:end_len] = sample + start_len = end_len + dense_x = x_sel.squeeze(0) + indices = self.kmean_cluster(dense_x, mean) + indices_list = [] + start_len = 0 + for x_len in x_lens: + end_len = start_len + end_len + indices_list.append(indices[start_len:end_len]) + return indices_list + + def merge_vq0206_to_token_str(self, vq02, vq06): + _vq06 = [1024 + x for x in vq06] + result = [] + i = 0 + j = 0 + while i < len(vq02) - 1 and j < len(_vq06) - 2: + sublist = vq02[i : i + 2] + _vq06[j : j + 3] + result.extend(sublist) + i += 2 + j += 3 + return "".join([f"" for x in result]) diff --git a/demo/Step-Audio-EditX/tts.py b/demo/Step-Audio-EditX/tts.py new file mode 100644 index 0000000000000000000000000000000000000000..65adc897a805fd4fda66aea36fa994da0e941e96 --- /dev/null +++ b/demo/Step-Audio-EditX/tts.py @@ -0,0 +1,387 @@ +import hashlib +import io +import os +import re +import logging +import numpy as np +import torch +import librosa +import soundfile as sf +from typing import Tuple, Optional +from http import HTTPStatus + +import torchaudio + +from model_loader import model_loader, ModelSource +from config.prompts import AUDIO_EDIT_CLONE_SYSTEM_PROMPT_TPL, AUDIO_EDIT_SYSTEM_PROMPT +from stepvocoder.cosyvoice2.cli.cosyvoice import CosyVoice +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import LogitsProcessorList + +# Configure logging +logger = logging.getLogger(__name__) + + +class HTTPException(Exception): + """Custom HTTP exception for API errors""" + def __init__(self, status_code, detail): + self.status_code = status_code + self.detail = detail + super().__init__(detail) + + +class RepetitionAwareLogitsProcessor(LogitsProcessor): + """Logits processor to handle repetition in generation""" + def __call__( + self, input_ids: torch.LongTensor, scores: torch.FloatTensor + ) -> torch.FloatTensor: + window_size = 10 + threshold = 0.1 + + window = input_ids[:, -window_size:] + if window.shape[1] < window_size: + return scores + + last_tokens = window[:, -1].unsqueeze(-1) + repeat_counts = (window == last_tokens).sum(dim=1) + repeat_ratios = repeat_counts.float() / window_size + + mask = repeat_ratios > threshold + scores[mask, last_tokens[mask].squeeze(-1)] = float("-inf") + return scores + +class StepAudioTTS: + """ + Step Audio TTS wrapper for voice cloning and audio editing tasks + """ + + def __init__( + self, + model_path, + audio_tokenizer, + model_source=ModelSource.AUTO, + tts_model_id=None + ): + """ + Initialize StepAudioTTS + + Args: + model_path: Model path + audio_tokenizer: Audio tokenizer for wav2token processing + model_source: Model source (auto/local/modelscope/huggingface) + tts_model_id: TTS model ID, if None use model_path + """ + # Determine model ID or path to load + if tts_model_id is None: + tts_model_id = model_path + + logger.info("🔧 StepAudioTTS loading configuration:") + logger.info(f" - model_source: {model_source}") + logger.info(f" - model_path: {model_path}") + logger.info(f" - tts_model_id: {tts_model_id}") + + self.audio_tokenizer = audio_tokenizer + + # Load LLM and tokenizer using model_loader + try: + self.llm, self.tokenizer, model_path = model_loader.load_transformers_model( + tts_model_id, + source=model_source, + torch_dtype=torch.bfloat16, + device_map="cuda" + ) + logger.info(f"✅ Successfully loaded LLM and tokenizer: {tts_model_id}") + except Exception as e: + logger.error(f"❌ Failed to load model: {e}") + raise + + # Load CosyVoice model (usually local path) + self.cosy_model = CosyVoice( + os.path.join(model_path, "CosyVoice-300M-25Hz") + ) + + # Use system prompts from config module + self.edit_clone_sys_prompt_tpl = AUDIO_EDIT_CLONE_SYSTEM_PROMPT_TPL + self.edit_sys_prompt = AUDIO_EDIT_SYSTEM_PROMPT + + def clone( + self, + prompt_wav_path: str, + prompt_text: str, + target_text: str + ) -> Tuple[torch.Tensor, int]: + """ + Clone voice from reference audio + + Args: + prompt_wav_path: Path to reference audio file + prompt_text: Text content of reference audio + target_text: Text to synthesize with cloned voice + + Returns: + Tuple[torch.Tensor, int]: Generated audio tensor and sample rate + """ + try: + logger.debug(f"Starting voice cloning: {prompt_wav_path}") + prompt_wav, _ = torchaudio.load(prompt_wav_path) + vq0206_codes, vq02_codes_ori, vq06_codes_ori, speech_feat, _, speech_embedding = ( + self.preprocess_prompt_wav(prompt_wav_path) + ) + prompt_speaker = self.generate_clone_voice_id(prompt_text, prompt_wav) + prompt_wav_tokens = self.audio_tokenizer.merge_vq0206_to_token_str( + vq02_codes_ori, vq06_codes_ori + ) + token_ids = self._encode_audio_edit_clone_prompt( + target_text, + prompt_text, + prompt_speaker, + prompt_wav_tokens, + ) + + output_ids = self.llm.generate( + torch.tensor([token_ids]).to(torch.long).to("cuda"), + max_length=8192, + temperature=0.7, + do_sample=True, + logits_processor=LogitsProcessorList([RepetitionAwareLogitsProcessor()]), + ) + output_ids = output_ids[:, len(token_ids) : -1] # skip eos token + logger.debug("Voice cloning generation completed") + vq0206_codes_vocoder = torch.tensor([vq0206_codes], dtype=torch.long) - 65536 + return ( + self.cosy_model.token2wav_nonstream( + output_ids - 65536, + vq0206_codes_vocoder, + speech_feat.to(torch.bfloat16), + speech_embedding.to(torch.bfloat16), + ), + 24000, + ) + except Exception as e: + logger.error(f"Clone failed: {e}") + raise + + def edit( + self, + input_audio_path: str, + audio_text: str, + edit_type: str, + edit_info: Optional[str] = None, + text: Optional[str] = None + ) -> Tuple[torch.Tensor, int]: + """ + Edit audio based on specified edit type + + Args: + input_audio_path: Path to input audio file + audio_text: Text content of input audio + edit_type: Type of edit (emotion, style, speed, etc.) + edit_info: Specific edit information (happy, sad, etc.) + text: Target text for para-linguistic editing + + Returns: + Tuple[torch.Tensor, int]: Edited audio tensor and sample rate + """ + try: + logger.debug(f"Starting audio editing: {edit_type} - {edit_info}") + vq0206_codes, vq02_codes_ori, vq06_codes_ori, speech_feat, _, speech_embedding = ( + self.preprocess_prompt_wav(input_audio_path) + ) + audio_tokens = self.audio_tokenizer.merge_vq0206_to_token_str( + vq02_codes_ori, vq06_codes_ori + ) + # Build instruction prefix based on edit type + instruct_prefix = self._build_audio_edit_instruction(audio_text, edit_type, edit_info, text) + + # Encode the complete prompt to token sequence + prompt_tokens = self._encode_audio_edit_prompt( + self.edit_sys_prompt, instruct_prefix, audio_tokens + ) + + logger.debug(f"Edit instruction: {instruct_prefix}") + logger.debug(f"Encoded prompt length: {len(prompt_tokens)}") + + output_ids = self.llm.generate( + torch.tensor([prompt_tokens]).to(torch.long).to("cuda"), + max_length=8192, + temperature=0.7, + do_sample=True, + logits_processor=LogitsProcessorList([RepetitionAwareLogitsProcessor()]), + ) + output_ids = output_ids[:, len(prompt_tokens) : -1] # skip eos token + vq0206_codes_vocoder = torch.tensor([vq0206_codes], dtype=torch.long) - 65536 + logger.debug("Audio editing generation completed") + return ( + self.cosy_model.token2wav_nonstream( + output_ids - 65536, + vq0206_codes_vocoder, + speech_feat.to(torch.bfloat16), + speech_embedding.to(torch.bfloat16), + ), + 24000, + ) + except Exception as e: + logger.error(f"Edit failed: {e}") + raise + + def _build_audio_edit_instruction( + self, + audio_text: str, + edit_type: str, + edit_info: Optional[str] = None, + text: Optional[str] = None + ) -> str: + """ + Build audio editing instruction based on request + + Args: + audio_text: Text content of input audio + edit_type: Type of edit + edit_info: Specific edit information + text: Target text for editing + + Returns: + str: Instruction prefix + """ + + audio_text = audio_text.strip() if audio_text else "" + if edit_type in {"emotion", "speed"}: + if edit_info == "remove": + instruct_prefix = f"Remove any emotion in the following audio and the reference text is: {audio_text}\n" + else: + instruct_prefix=f"Make the following audio more {edit_info}. The text corresponding to the audio is: {audio_text}\n" + elif edit_type == "style": + if edit_info == "remove": + instruct_prefix = f"Remove any speaking styles in the following audio and the reference text is: {audio_text}\n" + else: + instruct_prefix = f"Make the following audio more {edit_info} style. The text corresponding to the audio is: {audio_text}\n" + elif edit_type == "denoise": + instruct_prefix = f"Remove any noise from the given audio while preserving the voice content clearly. Ensure that the speech quality remains intact with minimal distortion, and eliminate all noise from the audio.\n" + elif edit_type == "vad": + instruct_prefix = f"Remove any silent portions from the given audio while preserving the voice content clearly. Ensure that the speech quality remains intact with minimal distortion, and eliminate all silence from the audio.\n" + elif edit_type == "paralinguistic": + instruct_prefix = f"Add some non-verbal sounds to make the audio more natural, the new text is : {text}\n The text corresponding to the audio is: {audio_text}\n" + else: + raise HTTPException( + status_code=HTTPStatus.BAD_REQUEST, + detail=f"Unsupported edit_type: {edit_type}", + ) + + return instruct_prefix + + def _encode_audio_edit_prompt( + self, sys_prompt: str, instruct_prefix: str, audio_token_str: str + ) -> list[int]: + """ + Encode audio edit prompt to token sequence + + Args: + sys_prompt: System prompt + instruct_prefix: Instruction prefix + audio_token_str: Audio tokens as string + + Returns: + list[int]: Encoded token sequence + """ + audio_token_str = audio_token_str.strip() + history = [1] + sys_tokens = self.tokenizer.encode(f"system\n{sys_prompt}") + history.extend([4] + sys_tokens + [3]) + qrole_toks = self.tokenizer.encode("human\n") + arole_toks = self.tokenizer.encode("assistant\n") + human_turn_toks = self.tokenizer.encode( + f"{instruct_prefix}\n{audio_token_str}\n" + ) + history.extend([4] + qrole_toks + human_turn_toks + [3] + [4] + arole_toks) + return history + + def _encode_audio_edit_clone_prompt( + self, text: str, prompt_text: str, prompt_speaker: str, prompt_wav_tokens: str + ): + prompt = self.edit_clone_sys_prompt_tpl.format( + speaker=prompt_speaker, + prompt_text=prompt_text, + prompt_wav_tokens=prompt_wav_tokens + ) + sys_tokens = self.tokenizer.encode(f"system\n{prompt}") + + history = [1] + history.extend([4] + sys_tokens + [3]) + + _prefix_tokens = self.tokenizer.encode("\n") + + target_token_encode = self.tokenizer.encode("\n" + text) + target_tokens = target_token_encode[len(_prefix_tokens) :] + + qrole_toks = self.tokenizer.encode("human\n") + arole_toks = self.tokenizer.encode("assistant\n") + + history.extend( + [4] + + qrole_toks + + target_tokens + + [3] + + [4] + + arole_toks + ) + return history + + + def detect_instruction_name(self, text): + instruction_name = "" + match_group = re.match(r"^([（\(][^\(\)()]*[）\)]).*$", text, re.DOTALL) + if match_group is not None: + instruction = match_group.group(1) + instruction_name = instruction.strip("()（）") + return instruction_name + + def process_audio_file(self, audio_path: str) -> Tuple[any, int]: + """ + Process audio file and return numpy array and sample rate + + Args: + audio_path: Path to audio file + + Returns: + Tuple[numpy.ndarray, int]: Audio data and sample rate + """ + try: + audio_data, sample_rate = librosa.load(audio_path) + logger.debug(f"Audio file processed successfully: {audio_path}") + return audio_data, sample_rate + except Exception as e: + logger.error(f"Failed to process audio file: {e}") + raise + + def preprocess_prompt_wav(self, prompt_wav_path : str): + prompt_wav, prompt_wav_sr = torchaudio.load(prompt_wav_path) + if prompt_wav.shape[0] > 1: + prompt_wav = prompt_wav.mean(dim=0, keepdim=True) # 将多通道音频转换为单通道 + speech_feat, speech_feat_len = self.cosy_model.frontend.extract_speech_feat( + prompt_wav, prompt_wav_sr + ) + speech_embedding = self.cosy_model.frontend.extract_spk_embedding( + prompt_wav, prompt_wav_sr + ) + vq0206_codes, vq02_codes_ori, vq06_codes_ori = self.audio_tokenizer.wav2token(prompt_wav, prompt_wav_sr) + return ( + vq0206_codes, + vq02_codes_ori, + vq06_codes_ori, + speech_feat, + speech_feat_len, + speech_embedding, + ) + + def generate_clone_voice_id(self, prompt_text, prompt_wav): + hasher = hashlib.sha256() + hasher.update(prompt_text.encode('utf-8')) + wav_data = prompt_wav.cpu().numpy() + if wav_data.size > 2000: + audio_sample = np.concatenate([wav_data.flatten()[:1000], wav_data.flatten()[-1000:]]) + else: + audio_sample = wav_data.flatten() + hasher.update(audio_sample.tobytes()) + voice_hash = hasher.hexdigest()[:16] + return f"clone_{voice_hash}" + \ No newline at end of file diff --git a/demo/Step-Audio-EditX/utils.py b/demo/Step-Audio-EditX/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3e6652139a58047f34e097fc601ef24733f26a28 --- /dev/null +++ b/demo/Step-Audio-EditX/utils.py @@ -0,0 +1,185 @@ +import io +import base64 + +import librosa +import numpy as np +import math +import os +import threading +import torch +import torchaudio +import sox +import tempfile + + +def encode_wav(wav, sr, rep_format="wav"): + with io.BytesIO() as wavio: + torchaudio.save(wavio, wav, sr, format=rep_format) + audio_bytes = wavio.getvalue() + encoded_wav = base64.b64encode(audio_bytes).decode("ascii") + return encoded_wav + + +def trim_silence(audio, sr, keep_left_time=0.05, keep_right_time=0.22, hop_size=240): + _, index = librosa.effects.trim(audio, top_db=20, frame_length=512, hop_length=128) + num_frames = int(math.ceil((index[1] - index[0]) / hop_size)) # 300 + + left_sil_samples = int(keep_left_time * sr) + right_sil_samples = int(keep_right_time * sr) + + wav_len = len(audio) + start_idx = index[0] - left_sil_samples + trim_wav = audio + + if start_idx > 0: + trim_wav = trim_wav[start_idx:] + else: + trim_wav = np.pad( + trim_wav, (abs(start_idx), 0), mode="constant", constant_values=0.0 + ) + wav_len = len(trim_wav) + out_len = int(num_frames * hop_size + (keep_left_time + keep_right_time) * sr) + + if out_len < wav_len: + trim_wav = trim_wav[:out_len] + else: + trim_wav = np.pad( + trim_wav, (0, (out_len - wav_len)), mode="constant", constant_values=0.0 + ) + return trim_wav + + +def volumn_adjust(audio16bit_torch, sr, volumn_ratio): + """使用sox进行音频音量调整 + Args: + audio16bit_torch (Tensor): 输入音频张量 [1, samples] + volume_ratio (float): 音量比率，>1增大音量，<1降低音量 + + Returns: + Tensor: 调整音量后的音频张量 + """ + # 创建临时文件 + with tempfile.NamedTemporaryFile( + suffix=".wav", delete=True + ) as temp_in, tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_out: + # 保存输入音频到临时文件 + torchaudio.save(temp_in.name, audio16bit_torch, sr) # 假设采样率为16000 + # 创建sox转换器 + tfm = sox.Transformer() + tfm.vol(volumn_ratio) # 设置音量调整比率 + # 应用音量调整 + tfm.build_file(temp_in.name, temp_out.name) + # 读取处理后的音频 + audio_changed, _ = torchaudio.load(temp_out.name) + return audio_changed + + +def speech_adjust(audio16bit_torch, sr, speed_ratio): + """使用sox进行音频变速处理 + Args: + audio16bit_torch (Tensor): 输入音频张量 [1, samples] + speed_ratio (float): 速度比率，>1加速，<1减速 + + Returns: + Tensor: 变速后的音频张量 + """ + # 创建临时文件 + with tempfile.NamedTemporaryFile( + suffix=".wav", delete=True + ) as temp_in, tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_out: + # 保存输入音频到临时文件 + torchaudio.save(temp_in.name, audio16bit_torch, sr) # 假设采样率为16000 + # 创建sox转换器 + tfm = sox.Transformer() + tfm.tempo(speed_ratio) # 设置变速比率 + # 应用变速处理 + tfm.build_file(temp_in.name, temp_out.name) + # 读取处理后的音频 + audio_changed, _ = torchaudio.load(temp_out.name) + return audio_changed + + +def audio_resample(audio16bit_torch, result_sr, target_sample_rate): + audio16bit_torch = torchaudio.transforms.Resample( + orig_freq=result_sr, new_freq=target_sample_rate + )(audio16bit_torch) + result_sr = target_sample_rate + return audio16bit_torch, result_sr + + +def norm_audio(audio16bit_torch): + # 直接 归一化处理。 + audio16bit_torch = audio16bit_torch.numpy() + audio16bit_torch = ( + audio16bit_torch / np.abs(audio16bit_torch).max() * 32767 + ).astype(np.int16) + audio16bit_torch = torch.from_numpy(audio16bit_torch) + return audio16bit_torch + + +def resample_audio(wav, original_sample_rate, target_sample_rate): + if original_sample_rate != target_sample_rate: + assert ( + original_sample_rate > target_sample_rate + ), "wav sample rate {} must be greater than {}".format( + original_sample_rate, target_sample_rate + ) + wav = torchaudio.transforms.Resample( + orig_freq=original_sample_rate, new_freq=target_sample_rate + )(wav) + return wav + + +def energy_norm_fn(wav): + if type(wav) is np.ndarray: + max_data = np.max(np.abs(wav)) + wav = wav / max(max_data, 0.01) * 0.999 + else: + max_data = torch.max(torch.abs(wav)) + wav = wav / max(max_data, 0.01) * 0.999 + return wav + + +def get_audio_tokens(audio_tokens: str) -> list[int]: + audio_tokens = audio_tokens.split(">", "")) + 65536 + for token in audio_tokens + ] + return audio_tokens + + +def load_audio(audio_path: str): + audio_wav, sr = torchaudio.load(audio_path) + audio_wav = audio_wav.mean(dim=0, keepdim=True) + return audio_wav, sr + + +# load optimus_ths for flash attention, make sure LD_LIBRARY_PATH has `nvidia/cuda_nvrtc/lib` +# if not, please manually set LD_LIBRARY_PATH=xxx/python3.10/site-packages/nvidia/cuda_nvrtc/lib +def load_optimus_ths_lib(libpath): + if not hasattr(load_optimus_ths_lib, 'lock'): + load_optimus_ths_lib.lock = threading.Lock() + if not hasattr(load_optimus_ths_lib, 'success'): + load_optimus_ths_lib.success = False + + with load_optimus_ths_lib.lock: + if load_optimus_ths_lib.success: + return load_optimus_ths_lib.success + + try: + if torch.__version__ >= "2.5": + torch.ops.load_library(os.path.join(libpath, 'liboptimus_ths-torch2.5-cu124.cpython-310-x86_64-linux-gnu.so')) + elif torch.__version__ >= "2.3": + torch.ops.load_library(os.path.join(libpath, 'liboptimus_ths-torch2.3-cu121.cpython-310-x86_64-linux-gnu.so')) + elif torch.__version__ >= "2.2": + torch.ops.load_library(os.path.join(libpath, 'liboptimus_ths-torch2.2-cu121.cpython-310-x86_64-linux-gnu.so')) + else: + raise RuntimeError("Unsupported torch version") + print("Load optimus_ths successfully and flash attn would be enabled") + load_optimus_ths_lib.success = True + except Exception as err: + print(f"Fail to load optimus_ths and flash attn is disabled: {err}") + load_optimus_ths_lib.success = False + + return load_optimus_ths_lib.success diff --git a/tools/StepEval-Audio-Toolcall/.gitattributes b/tools/StepEval-Audio-Toolcall/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..1ef325f1b111266a6b26e0196871bd78baa8c2f3 --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/.gitattributes @@ -0,0 +1,59 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mds filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +# Audio files - uncompressed +*.pcm filter=lfs diff=lfs merge=lfs -text +*.sam filter=lfs diff=lfs merge=lfs -text +*.raw filter=lfs diff=lfs merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +# Image files - compressed +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +# Video files - compressed +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.webm filter=lfs diff=lfs merge=lfs -text diff --git a/tools/StepEval-Audio-Toolcall/LLM_judge.py b/tools/StepEval-Audio-Toolcall/LLM_judge.py new file mode 100644 index 0000000000000000000000000000000000000000..4a6db9c83af7ccd62b6de81cf62c4f9214277427 --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/LLM_judge.py @@ -0,0 +1,275 @@ +import asyncio +import os, re +import json +from glob import glob +from dataclasses import dataclass + + +api_model='deepseek-r1' + + +web_search_prompt = \ +"""{ + "type": "function", + "function": { + "description": "搜索工具", + "name": "search", + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "搜索关键词" + } + }, + "required": [ + "query" + ] + } + } +}""" + + +date_time_prompt = \ +"""{ + "function": { + "name": "get_date_time", + "description": "Get the current date and time in Beijing / Shanghai / China", + "parameters": { + "type": "object", + "properties": {}, + "required": [] + } + }, + "type": "function" +}""" + + +weather_prompt = \ +"""{ + "function": { + "name": "get_weather", + "description": '获取给定地区的天气情况', + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "地点的英文名称，例如：beijing, shanghai, new york 等，注意必须是英文." + }, + "required": ["location"] + } + } + }, + "type": "function" +}""" + +timbre_rag_prompt = \ +"""{ + "function": { + "name": "timbre_rag", + "description": "通过给定一个中文的音色描述，改变模型输出的音色，例如‘一个正宗北京大爷的口音’, 注意无法进行情感、语速、音量等韵律的调整", + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "人物音色描述（中文）" + }, + }, + "required": ["query"] + } + }, + "type": "function" +}""" + + + +toolcall_system_prompt = f"""你是一个基于大语言模型的聊天 AI。用户说中文，因此你需要用中文回复。 +tool_json_schemas +```[ +{web_search_prompt}, +{date_time_prompt}, +{weather_prompt}, +{timbre_rag_prompt} +]```""" + + +@dataclass +class Score: + error: int = 0 + correct: int = 1e-6 + + def acc(self): + return self.correct / (self.error + self.correct) + + def total(self): + return int(self.error + self.correct) + + +@dataclass +class Case: + tp: int = 0 + fp: int = 0 + tn: int = 0 + fn: int = 0 + + def precision(self): + return self.tp / (self.tp + self.fp + 1e-6) + + def recall(self): + return self.tp / (self.tp + self.fn + 1e-6) + + def total(self): + return int(self.fn + self.tp) + + def total_p(self): + return self.tp + self.fn + + def total_n(self): + return self.tn + self.fp + + +def eval_parameter(model_output, output_file): + meta_prompt = """考察Assistant是否合理调用tool的参数 + +对话历史: +{history} + +Assistant回复: +{response} + +回复格式: + 调用参数: (合理/不合理); +""" + + + print("output_file:", output_file) + + if os.path.exists(output_file): + return output_file + ' exists!' + + with open(output_file, 'w') as fw: + for source_file in glob(model_output): + print("source_file:", source_file) + with open(source_file) as f: + for line in f: + sample = json.loads(line) + + history = toolcall_system_prompt + '\n\n' + sample['history'].rsplit('===', 1)[-1].split('>>>', 1)[0] + response = sample['response'] + + if '' in response: + prompt = meta_prompt.format(history=history, response=response) + + # TODO + # response = llm_judge(prompt=prompt, api_model=api_model, api_key=xxx)。# return string + + session_id = sample['session_id'] + new_sample = { + 'source': f'{source_file}_{session_id}', + 'prompt': prompt, + 'response': response + } + ans_line = json.dumps(new_sample, ensure_ascii=False) + fw.write(ans_line+"\n") + + +def score(model_output_pattern, model_eval_file, score_path): + + toolcall = Case() + toolcall_type = Score() + toolcall_param = Score() + invalid = 0 + + session_set = set() + print(model_output_pattern) + with open(score_path, 'w') as fw: + for model_output_path in glob(model_output_pattern): + with open(model_output_path) as f: + tool_type = model_output_path.split('/')[-1].rsplit('_', 1)[0] + + for line in f: + sample = json.loads(line) + if sample['session_id'] in session_set: + continue + else: + session_set.add(sample['session_id']) + + response = sample['response'].replace('；', ';').replace('：', ':') + + # positive + if 'positive' in model_output_path: + + # toolcall + if '' in response: + toolcall.tp += 1 + tag = 'tp' + + if tool_type in response: + toolcall_type.correct += 1 + tag += '_type-correct' + else: + toolcall_type.error += 1 + tag += '_type-error' + else: + toolcall.fn += 1 + tag = 'fn' + + # negative + else: + if tool_type not in response: + toolcall.tn += 1 + tag = 'tn' + else: + toolcall.fp += 1 + tag = 'fp' + + sample['tag'] = tag + + ans_line = json.dumps(sample, ensure_ascii=False) + fw.write(ans_line+"\n") + + if os.path.exists(model_eval_file): + with open(model_eval_file) as f: + for line in f: + sample = json.loads(line) + response = sample['response'] + + pattern = re.compile(r'调用参数: ([\u4e00-\u9fa5]+)', re.DOTALL) + match = re.search(pattern, response) + try: + s = match.group(1) + except: + s = '' + + tag = 'tp' + if s in ('合理', ): + toolcall_param.correct += 1 + tag += 'param-correct' + else: + toolcall_param.error += 1 + tag += 'param-error' + + print(f"total all call: {toolcall.total()}, invalid eval: {invalid}") + print(f"positive {toolcall.total_p()} negative {toolcall.total_n()} | precision: {toolcall.precision():.3f} recall: {toolcall.recall():.3f} tool-type-acc: {toolcall_type.acc():.3f} tool-param-acc: {toolcall_param.acc():.3f}") + print(score_path) + + +if __name__ == '__main__': + + # qwen3 + root = 'path/to/your/data/folder' + + filename = 'get_date_time_*.jsonl' + filename = 'get_weather_*.jsonl' + filename = 'timbre_*.jsonl' + filename = 'web_search_*.jsonl' + + model_output = os.path.join(root, filename) + + model_eval_file = model_output.replace('*', 'positive') + f'.{api_model}_eval' + eval_parameter(model_output, model_eval_file) + + score_path = model_output.replace('*', '#') + '.score' + score(model_output, model_eval_file, score_path) \ No newline at end of file diff --git a/tools/StepEval-Audio-Toolcall/README.md b/tools/StepEval-Audio-Toolcall/README.md new file mode 100644 index 0000000000000000000000000000000000000000..abeb729673238a13e16d069867252021a9545cbe --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/README.md @@ -0,0 +1,116 @@ +--- +license: apache-2.0 +task_categories: +- audio-text-to-text +tags: +- benchmark +- tool-calling +- multimodal +- speech-recognition +- audio-understanding +--- + +# StepEval-Audio-Toolcall + +Paper: [Step-Audio 2 Technical Report](https://huggingface.co/papers/2507.16632) +Code: https://github.com/stepfun-ai/Step-Audio2 +Project Page: https://www.stepfun.com/docs/en/step-audio2 + +## Dataset Description + +StepEval Audio Toolcall evaluates the invocation performance of four tool types. For each tool, the benchmark contains approximately 200 multi-turn dialogue sets for both positive and negative scenarios: + +- Positive samples: The assistant is required to invoke the specified tool in the final turn; +- Negative samples: The assistant must avoid invoking the specified tool + +This structure enables comprehensive evaluation of: +- Trigger metrics: Precision and recall for tool invocation trigger; +- Type accuracy: Accuracy of tool type selection during valid invocations; +- Parameter accuracy: Evaluated separately through LLM-judge labeling of tool parameters + +## Evaluation +The script (LLM_judge.py) evaluates the rationality of parameters and counts all the metrics mentioned above. + +## Benchmark Results on StepEval-Audio-Toolcall +Comparison between Step-Audio 2 and Qwen3-32B on tool calling. Qwen3-32B is evaluated with text inputs. Date and time tools have no parameter. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelObjectiveMetricAudio searchDate & TimeWeatherWeb search
Qwen3-32BTriggerPrecision / Recall67.5 / 98.598.4 / 100.090.1 / 100.086.8 / 98.5
TypeAccuracy100.0100.098.598.5
ParameterAccuracy100.0N/A100.0100.0
Step-Audio 2TriggerPrecision / Recall86.8 / 99.596.9 / 98.492.2 / 100.088.4 / 95.5
TypeAccuracy100.0100.090.598.4
ParameterAccuracy100.0N/A100.0100.0
+ +You can find the original data files at the address [here](https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-Toolcall/tree/main). + +## Citation + +``` +@misc{wu2025stepaudio2technicalreport, + title={Step-Audio 2 Technical Report}, + author={Boyong Wu and Chao Yan and Chen Hu and Cheng Yi and Chengli Feng and Fei Tian and Feiyu Shen and Gang Yu and Haoyang Zhang and Jingbei Li and Mingrui Chen and Peng Liu and Wang You and Xiangyu Tony Zhang and Xingyuan Li and Xuerui Yang and Yayue Deng and Yechang Huang and Yuxin Li and Yuxin Zhang and Zhao You and Brian Li and Changyi Wan and Hanpeng Hu and Jiangjie Zhen and Siyu Chen and Song Yuan and Xuelin Zhang and Yimin Jiang and Yu Zhou and Yuxiang Yang and Bingxin Li and Buyun Ma and Changhe Song and Dongqing Pang and Guoqiang Hu and Haiyang Sun and Kang An and Na Wang and Shuli Gao and Wei Ji and Wen Li and Wen Sun and Xuan Wen and Yong Ren and Yuankai Ma and Yufan Lu and Bin Wang and Bo Li and Changxin Miao and Che Liu and Chen Xu and Dapeng Shi and Dingyuan Hu and Donghang Wu and Enle Liu and Guanzhe Huang and Gulin Yan and Han Zhang and Hao Nie and Haonan Jia and Hongyu Zhou and Jianjian Sun and Jiaoren Wu and Jie Wu and Jie Yang and Jin Yang and Junzhe Lin and Kaixiang Li and Lei Yang and Liying Shi and Li Zhou and Longlong Gu and Ming Li and Mingliang Li and Mingxiao Li and Nan Wu and Qi Han and Qinyuan Tan and Shaoliang Pang and Shengjie Fan and Siqi Liu and Tiancheng Cao and Wanying Lu and Wenqing He and Wuxun Xie and Xu Zhao and Xueqi Li and Yanbo Yu and Yang Yang and Yi Liu and Yifan Lu and Yilei Wang and Yuanhao Ding and Yuanwei Liang and Yuanwei Lu and Yuchu Luo and Yuhe Yin and Yumeng Zhan and Yuxiang Zhang and Zidong Yang and Zixin Zhang and Binxing Jiao and Daxin Jiang and Heung-Yeung Shum and Jiansheng Chen and Jing Li and Xiangyu Zhang and Yibo Zhu}, + year={2025}, + eprint={2507.16632}, + archivePrefix={arXiv}, + primaryClass={cs.CL}, + url={https://arxiv.org/abs/2507.16632}, +} +``` \ No newline at end of file diff --git a/tools/StepEval-Audio-Toolcall/get_date_time_negative.jsonl b/tools/StepEval-Audio-Toolcall/get_date_time_negative.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f269a845badf47b2bf0b0c5222ef721367a4b219 --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/get_date_time_negative.jsonl @@ -0,0 +1,132 @@ +[{"session_id": "374", "turn_idx": 0, "from": "Human", "text": "小跃，后天我约了朋友在成都吃火锅，兴奋死了！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-0-0.wav"}}, {"session_id": "374", "turn_idx": 1, "from": "Assistant", "text": "成都火锅太赞了！不过人多的话，选个天气好的日子更惬意。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-0-1.wav"}}, {"session_id": "374", "turn_idx": 2, "from": "Human", "text": "对哦，天气热的话吃火锅容易出汗。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-0-2.wav"}}, {"session_id": "374", "turn_idx": 3, "from": "Assistant", "text": "就是，提前知道温度能安排室内或室外座位。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-0-3.wav"}}, {"session_id": "374", "turn_idx": 4, "from": "Human", "text": "好建议。小跃，帮我查查成都的天气，未来三天的那种。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-0-4.wav"}}, {"session_id": "374", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "203", "turn_idx": 0, "from": "Human", "text": "小跃，我打算周末去爬山。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-1-0.wav"}}, {"session_id": "203", "turn_idx": 1, "from": "Assistant", "text": "爬山锻炼身体不错，记得带好水和防晒装备。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-1-1.wav"}}, {"session_id": "203", "turn_idx": 2, "from": "Human", "text": "附近有什么推荐的山？帮我查一下路线信息。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-1-2.wav"}}, {"session_id": "203", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"附近爬山路线推荐\"}", "value": "function\nweb_search\n{\"query\": \"附近爬山路线推荐\"}"}] +[{"session_id": "530", "turn_idx": 0, "from": "Human", "text": "小跃，我在计划去日本玩一趟。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-2-0.wav"}}, {"session_id": "530", "turn_idx": 1, "from": "Assistant", "text": "哇，旅行真棒！您打算待几天？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-2-1.wav"}}, {"session_id": "530", "turn_idx": 2, "from": "Human", "text": "大概七天左右。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-2-2.wav"}}, {"session_id": "530", "turn_idx": 3, "from": "Assistant", "text": "那预算呢？机票和住宿啥的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-2-3.wav"}}, {"session_id": "530", "turn_idx": 4, "from": "Human", "text": "机票八千块，住宿一晚一千块，每天吃喝五百块。帮我算算总共要准备多少钱。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-2-4.wav"}}, {"session_id": "530", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"8000 + (1000 * 7) + (500 * 7)\"}", "value": "function\ncalculate\n{\"expression\": \"8000 + (1000 * 7) + (500 * 7)\"}"}] +[{"session_id": "229", "turn_idx": 0, "from": "Human", "text": "小跃，我读书容易分心，有什么专注技巧？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-3-0.wav"}}, {"session_id": "229", "turn_idx": 1, "from": "Assistant", "text": "分心常见！试试番茄工作法，工作二十五分钟，休息五分钟。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-3-1.wav"}}, {"session_id": "229", "turn_idx": 2, "from": "Human", "text": "番茄法有效吗？需要App辅助吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-3-2.wav"}}, {"session_id": "229", "turn_idx": 3, "from": "Assistant", "text": "很多人反馈有效，App像Forest能计时，帮你专注。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-3-3.wav"}}, {"session_id": "229", "turn_idx": 4, "from": "Human", "text": "休息时做什么好？不能刷手机吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-3-4.wav"}}, {"session_id": "229", "turn_idx": 5, "from": "Assistant", "text": "休息时走动或喝水，别碰手机，避免重新分心。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-3-5.wav"}}, {"session_id": "229", "turn_idx": 6, "from": "Human", "text": "小跃，帮我找找番茄工作法的详细指南和App推荐。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-3-6.wav"}}, {"session_id": "229", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄工作法 指南 App推荐\"}", "value": "function\nweb_search\n{\"query\": \"番茄工作法 指南 App推荐\"}"}] +[{"session_id": "13", "turn_idx": 0, "from": "Human", "text": "小跃，我跑步速度是每秒三米，绕操场一圈八百米。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-4-0.wav"}}, {"session_id": "13", "turn_idx": 1, "from": "Assistant", "text": "那跑完一圈大概需要四分钟多哦。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-4-1.wav"}}, {"session_id": "13", "turn_idx": 2, "from": "Human", "text": "再帮我算个复杂的:九千七百的平方加上六十五万四千，然后乘以圆周率。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-4-2.wav"}}, {"session_id": "13", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(9700**2 + 654000) * 3.1415926535\"}", "value": "function\ncalculate\n{\"expression\": \"(9700**2 + 654000) * 3.1415926535\"}"}] +[{"session_id": "588", "turn_idx": 0, "from": "Human", "text": "小跃，我想买台平板电脑，看剧用。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-5-0.wav"}}, {"session_id": "588", "turn_idx": 1, "from": "Assistant", "text": "iPad还是安卓平板？iPad画质好，安卓便宜。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-5-1.wav"}}, {"session_id": "588", "turn_idx": 2, "from": "Human", "text": "安卓的吧，预算两千元。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-5-2.wav"}}, {"session_id": "588", "turn_idx": 3, "from": "Assistant", "text": "华为MatePad或小米平板，屏幕大。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-5-3.wav"}}, {"session_id": "588", "turn_idx": 4, "from": "Human", "text": "电池能用多久？我总出差。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-5-4.wav"}}, {"session_id": "588", "turn_idx": 5, "from": "Assistant", "text": "一般十小时左右，看视频够用。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-5-5.wav"}}, {"session_id": "588", "turn_idx": 6, "from": "Human", "text": "好。但我想比较下具体型号的优缺点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-5-6.wav"}}, {"session_id": "588", "turn_idx": 7, "from": "Assistant", "text": "网上评测详细，帮你找找。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-5-7.wav"}}, {"session_id": "588", "turn_idx": 8, "from": "Human", "text": "那搜一下华为MatePad用户评价。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-5-8.wav"}}, {"session_id": "588", "turn_idx": 9, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"华为MatePad 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"华为MatePad 用户评价\"}"}] +[{"session_id": "314", "turn_idx": 0, "from": "Human", "text": "小跃，我手机电池不行了，想换新手机，有啥推荐？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-6-0.wav"}}, {"session_id": "314", "turn_idx": 1, "from": "Assistant", "text": "苹果或者华为的旗舰机都挺耐用，电池续航长。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-6-1.wav"}}, {"session_id": "314", "turn_idx": 2, "from": "Human", "text": "华为的贵吗？预算三千左右。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-6-2.wav"}}, {"session_id": "314", "turn_idx": 3, "from": "Assistant", "text": "那可以看看华为的中端系列，性价比高。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-6-3.wav"}}, {"session_id": "314", "turn_idx": 4, "from": "Human", "text": "小跃，搜一下三千元左右的华为手机推荐。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-6-4.wav"}}, {"session_id": "314", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"三千元 华为手机 推荐\"}", "value": "function\nweb_search\n{\"query\": \"三千元 华为手机 推荐\"}"}] +[{"session_id": "51", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个下午三点的闹钟，语气严肃点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-7-0.wav"}}, {"session_id": "51", "turn_idx": 1, "from": "Assistant", "text": "明白，下午三点闹钟已设定。请准时完成任务。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-7-1.wav"}}, {"session_id": "51", "turn_idx": 2, "from": "Human", "text": "现在改回轻松的语气，说说你推荐啥音乐放松。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-7-2.wav"}}, {"session_id": "51", "turn_idx": 3, "from": "Assistant", "text": "好啊，推荐你听点轻音乐，比如钢琴曲，能让人很快放松下来。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-7-3.wav"}}] +[{"session_id": "554", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午五点开会。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-8-0.wav"}}, {"session_id": "554", "turn_idx": 1, "from": "Assistant", "text": "没问题，提醒已设置:下午五点开会。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-8-1.wav"}}, {"session_id": "554", "turn_idx": 2, "from": "Human", "text": "用更严肃的语气确认一下这个提醒。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-8-2.wav"}}, {"session_id": "554", "turn_idx": 3, "from": "Assistant", "text": "重要事项:下午五点开会，请准时出席，不能迟到。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-8-3.wav"}}] +[{"session_id": "208", "turn_idx": 0, "from": "Human", "text": "小跃，我后天要去西安旅游，得提前订酒店。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-9-0.wav"}}, {"session_id": "208", "turn_idx": 1, "from": "Assistant", "text": "旅游计划得周全，天气会影响行程。西安的气候怎么样？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-9-1.wav"}}, {"session_id": "208", "turn_idx": 2, "from": "Human", "text": "对，帮我查查西安未来三天的天气吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-9-2.wav"}}, {"session_id": "208", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "152", "turn_idx": 0, "from": "Human", "text": "小跃，周末想去上海迪士尼，但听说最近天气不稳定。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-10-0.wav"}}, {"session_id": "152", "turn_idx": 1, "from": "Assistant", "text": "迪士尼玩确实要看天气，你具体计划哪天去？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-10-1.wav"}}, {"session_id": "152", "turn_idx": 2, "from": "Human", "text": "主要关心今天到后天的上海天气，方便准备雨具。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-10-2.wav"}}, {"session_id": "152", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "975", "turn_idx": 0, "from": "Human", "text": "小跃，我想养绿植，有啥好养的推荐？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-11-0.wav"}}, {"session_id": "975", "turn_idx": 1, "from": "Assistant", "text": "绿萝或仙人掌超简单！耐旱好养，适合新手。放阳台或室内都行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-11-1.wav"}}, {"session_id": "975", "turn_idx": 2, "from": "Human", "text": "绿萝浇水频率咋样？我怕浇太多。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-11-2.wav"}}, {"session_id": "975", "turn_idx": 3, "from": "Assistant", "text": "一般一周浇一次水，土干了再浇。冬天可以延长到两周。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-11-3.wav"}}, {"session_id": "975", "turn_idx": 4, "from": "Human", "text": "懂了。那绿萝有毒吗？家里有小孩担心。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-11-4.wav"}}, {"session_id": "975", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"绿萝 毒性 安全信息\"}", "value": "function\nweb_search\n{\"query\": \"绿萝 毒性 安全信息\"}"}] +[{"session_id": "808", "turn_idx": 0, "from": "Human", "text": "小跃，给我讲个笑话吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-12-0.wav"}}, {"session_id": "808", "turn_idx": 1, "from": "Assistant", "text": "为什么鱼不喜欢考试？因为怕被“烤”试啊！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-12-1.wav"}}, {"session_id": "808", "turn_idx": 2, "from": "Human", "text": "哈哈，请用更夸张的语气再讲一个。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-12-2.wav"}}, {"session_id": "808", "turn_idx": 3, "from": "Assistant", "text": "天哪！那只猫居然在玩滑板，太疯狂了！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-12-3.wav"}}, {"session_id": "808", "turn_idx": 4, "from": "Human", "text": "真逗，现在小声点告诉我明天天气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-12-4.wav"}}, {"session_id": "808", "turn_idx": 5, "from": "Assistant", "text": "明天可能下雨，记得带伞。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-12-5.wav"}}] +[{"session_id": "216", "turn_idx": 0, "from": "Human", "text": "小跃，放点背景音乐。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-13-0.wav"}}, {"session_id": "216", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-13-1.wav"}}, {"session_id": "216", "turn_idx": 2, "from": "Human", "text": "音量调小点，太吵了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-13-2.wav"}}, {"session_id": "216", "turn_idx": 3, "from": "Assistant", "text": "音量已调小，现在更轻柔了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-13-3.wav"}}, {"session_id": "216", "turn_idx": 4, "from": "Human", "text": "好，这样刚好。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-13-4.wav"}}, {"session_id": "216", "turn_idx": 5, "from": "Assistant", "text": "好的，音量适中播放。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-13-5.wav"}}] +[{"session_id": "107", "turn_idx": 0, "from": "Human", "text": "小跃，帮我核对购物清单！三箱牛奶每箱五十六，四袋面粉每袋七十八。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-14-0.wav"}}, {"session_id": "107", "turn_idx": 1, "from": "Assistant", "text": "正在统计，牛奶总价三乘五十六，面粉四乘七十八。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-14-1.wav"}}, {"session_id": "107", "turn_idx": 2, "from": "Human", "text": "还有苹果六斤每斤八块五，但优惠满一百减十五。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-14-2.wav"}}, {"session_id": "107", "turn_idx": 3, "from": "Assistant", "text": "明白，先算基础总价再判断是否满减。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-14-3.wav"}}, {"session_id": "107", "turn_idx": 4, "from": "Human", "text": "快算最终实付金额，我手机支付页面等着呢！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-14-4.wav"}}, {"session_id": "107", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(3*56 + 4*78 + 6*8.5 - 15) if (3*56+4*78+6*8.5)>=100 else (3*56+4*78+6*8.5)\"}", "value": "function\ncalculate\n{\"expression\": \"(3*56 + 4*78 + 6*8.5 - 15) if (3*56+4*78+6*8.5)>=100 else (3*56+4*78+6*8.5)\"}"}] +[{"session_id": "233", "turn_idx": 0, "from": "Human", "text": "小跃，后天有个会议在深圳，需要早点出门。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-15-0.wav"}}, {"session_id": "233", "turn_idx": 1, "from": "Assistant", "text": "深圳交通方便，建议坐地铁，不堵车。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-15-1.wav"}}, {"session_id": "233", "turn_idx": 2, "from": "Human", "text": "谢谢提醒！会议地点在福田区。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-15-2.wav"}}, {"session_id": "233", "turn_idx": 3, "from": "Assistant", "text": "那儿很多新餐厅，会后可以尝尝。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-15-3.wav"}}, {"session_id": "233", "turn_idx": 4, "from": "Human", "text": "好期待。小跃，查一下深圳的天气情况。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-15-4.wav"}}, {"session_id": "233", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "652", "turn_idx": 0, "from": "Human", "text": "小跃，你最近有啥新功能？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-16-0.wav"}}, {"session_id": "652", "turn_idx": 1, "from": "Assistant", "text": "我可以查天气、设闹钟，还有很多日常小事。需要我做什么？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-16-1.wav"}}, {"session_id": "652", "turn_idx": 2, "from": "Human", "text": "哦，我在深圳，想知道今天和明天的天气情况。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-16-2.wav"}}, {"session_id": "652", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "177", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午三点开会。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-17-0.wav"}}, {"session_id": "177", "turn_idx": 1, "from": "Assistant", "text": "提醒设好了，下午三点开会，别迟到。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-17-1.wav"}}, {"session_id": "177", "turn_idx": 2, "from": "Human", "text": "语气再紧张点，我容易忘事。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-17-2.wav"}}, {"session_id": "177", "turn_idx": 3, "from": "Assistant", "text": "紧急！下午三点开会，重要得很，赶紧准备！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-17-3.wav"}}, {"session_id": "177", "turn_idx": 4, "from": "Human", "text": "音量小点，办公室安静。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-17-4.wav"}}, {"session_id": "177", "turn_idx": 5, "from": "Assistant", "text": "好的，轻声提醒:下午三点开会，别忘啦。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-17-5.wav"}}, {"session_id": "177", "turn_idx": 6, "from": "Human", "text": "行，记住了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-17-6.wav"}}, {"session_id": "177", "turn_idx": 7, "from": "Assistant", "text": "加油完成工作。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-17-7.wav"}}] +[{"session_id": "579", "turn_idx": 0, "from": "Human", "text": "小跃，周末我想去重庆爬山，锻炼身体。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-18-0.wav"}}, {"session_id": "579", "turn_idx": 1, "from": "Assistant", "text": "爬山健康！重庆山路多，天气关键。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-18-1.wav"}}, {"session_id": "579", "turn_idx": 2, "from": "Human", "text": "对啊，怕下雨路滑，不安全。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-18-2.wav"}}, {"session_id": "579", "turn_idx": 3, "from": "Assistant", "text": "安全重要，要我帮你查天气吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-18-3.wav"}}, {"session_id": "579", "turn_idx": 4, "from": "Human", "text": "小跃，帮我查重庆未来三天的天气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-18-4.wav"}}, {"session_id": "579", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "958", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个下午三点的会议提醒。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-19-0.wav"}}, {"session_id": "958", "turn_idx": 1, "from": "Assistant", "text": "好的，已经设置好下午三点的会议提醒了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-19-1.wav"}}, {"session_id": "958", "turn_idx": 2, "from": "Human", "text": "谢谢。小跃，请说大声一点，我这边有点吵。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-19-2.wav"}}, {"session_id": "958", "turn_idx": 3, "from": "Assistant", "text": "没问题！会议提醒已设定在下午三点，我会准时提醒你！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-19-3.wav"}}, {"session_id": "958", "turn_idx": 4, "from": "Human", "text": "嗯，收到了。再确认下，是今天下午三点对吧？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-19-4.wav"}}, {"session_id": "958", "turn_idx": 5, "from": "Assistant", "text": "是的，就是今天下午三点，别担心。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-19-5.wav"}}, {"session_id": "958", "turn_idx": 6, "from": "Human", "text": "小跃，现在音量调回正常吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-19-6.wav"}}, {"session_id": "958", "turn_idx": 7, "from": "Assistant", "text": "好的，音量正常了。还有其他需要吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-19-7.wav"}}] +[{"session_id": "363", "turn_idx": 0, "from": "Human", "text": "小跃，我最近总感觉腰酸背痛。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-20-0.wav"}}, {"session_id": "363", "turn_idx": 1, "from": "Assistant", "text": "腰酸背痛可能是坐太久或姿势不对。你平时运动吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-20-1.wav"}}, {"session_id": "363", "turn_idx": 2, "from": "Human", "text": "工作忙，很少动。想找些简单的缓解方法。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-20-2.wav"}}, {"session_id": "363", "turn_idx": 3, "from": "Assistant", "text": "那试试伸展运动，我可以找些适合上班族的教程。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-20-3.wav"}}, {"session_id": "363", "turn_idx": 4, "from": "Human", "text": "好啊，帮我搜一下缓解腰酸背痛的伸展操。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-20-4.wav"}}, {"session_id": "363", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"缓解 腰酸背痛 伸展操\"}", "value": "function\nweb_search\n{\"query\": \"缓解 腰酸背痛 伸展操\"}"}] +[{"session_id": "502", "turn_idx": 0, "from": "Human", "text": "小跃，今天在家有点无聊，想找点事做。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-21-0.wav"}}, {"session_id": "502", "turn_idx": 1, "from": "Assistant", "text": "要不要试试新开的咖啡馆？或者去户外活动？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-21-1.wav"}}, {"session_id": "502", "turn_idx": 2, "from": "Human", "text": "户外不错，我家人说成都的公园很赞。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-21-2.wav"}}, {"session_id": "502", "turn_idx": 3, "from": "Assistant", "text": "成都的公园确实热闹。你什么时候去？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-21-3.wav"}}, {"session_id": "502", "turn_idx": 4, "from": "Human", "text": "明天吧，但得看天气允不允许。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-21-4.wav"}}, {"session_id": "502", "turn_idx": 5, "from": "Assistant", "text": "天气确实关键，需要我帮忙查询吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-21-5.wav"}}, {"session_id": "502", "turn_idx": 6, "from": "Human", "text": "好呀，小跃，快查一下成都的天气情况。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-21-6.wav"}}, {"session_id": "502", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "310", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，你今天过得怎么样？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-22-0.wav"}}, {"session_id": "310", "turn_idx": 1, "from": "Assistant", "text": "还不错呢，谢谢关心！今天处理了一些日常任务。你呢，有什么新鲜事吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-22-1.wav"}}, {"session_id": "310", "turn_idx": 2, "from": "Human", "text": "我在计划明天去公园散步，但不知道天气如何。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-22-2.wav"}}, {"session_id": "310", "turn_idx": 3, "from": "Assistant", "text": "公园散步是个好主意！需要我帮你看看天气预报吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-22-3.wav"}}, {"session_id": "310", "turn_idx": 4, "from": "Human", "text": "先等等，我还没决定去哪个公园。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-22-4.wav"}}, {"session_id": "310", "turn_idx": 5, "from": "Assistant", "text": "哦，那你可以告诉我地点，我就能帮忙了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-22-5.wav"}}, {"session_id": "310", "turn_idx": 6, "from": "Human", "text": "好的，我想去北京的奥林匹克公园。帮我查一下那里的天气吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-22-6.wav"}}, {"session_id": "310", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "106", "turn_idx": 0, "from": "Human", "text": "小跃，我想学游泳，怎么开始？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-23-0.wav"}}, {"session_id": "106", "turn_idx": 1, "from": "Assistant", "text": "游泳可以先从蛙泳学起，找个浅水区练习浮水。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-23-1.wav"}}, {"session_id": "106", "turn_idx": 2, "from": "Human", "text": "那详细的动作要领和呼吸技巧呢？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-23-2.wav"}}, {"session_id": "106", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"游泳蛙泳动作呼吸技巧\"}", "value": "function\nweb_search\n{\"query\": \"游泳蛙泳动作呼吸技巧\"}"}] +[{"session_id": "507", "turn_idx": 0, "from": "Human", "text": "小跃，我喜欢周杰伦的歌，推荐几首吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-24-0.wav"}}, {"session_id": "507", "turn_idx": 1, "from": "Assistant", "text": "《七里香》和《稻香》经典，旋律好听。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-24-1.wav"}}, {"session_id": "507", "turn_idx": 2, "from": "Human", "text": "哦，他有新专辑吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-24-2.wav"}}, {"session_id": "507", "turn_idx": 3, "from": "Assistant", "text": "最近出了《最伟大的作品》，反响热烈。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-24-3.wav"}}, {"session_id": "507", "turn_idx": 4, "from": "Human", "text": "那在哪里能听或买这张专辑？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-24-4.wav"}}, {"session_id": "507", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"周杰伦 最伟大的作品 购买 在线听\"}", "value": "function\nweb_search\n{\"query\": \"周杰伦 最伟大的作品 购买 在线听\"}"}] +[{"session_id": "85", "turn_idx": 0, "from": "Human", "text": "客户约我明早广州塔见面，紧张！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-25-0.wav"}}, {"session_id": "85", "turn_idx": 1, "from": "Assistant", "text": "别担心，提前规划路线就好。您住珠江边吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-25-1.wav"}}, {"session_id": "85", "turn_idx": 2, "from": "Human", "text": "在越秀区，得坐地铁过去。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-25-2.wav"}}, {"session_id": "85", "turn_idx": 3, "from": "Assistant", "text": "地铁最准时。需要查广州塔周边的天气吗？万一下雨改室内？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-25-3.wav"}}, {"session_id": "85", "turn_idx": 4, "from": "Human", "text": "查清楚明天上午的天气，暴雨就改期。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-25-4.wav"}}, {"session_id": "85", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou tower\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou tower\"}"}] +[{"session_id": "187", "turn_idx": 0, "from": "Human", "text": "小跃，周末去哪儿玩？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-26-0.wav"}}, {"session_id": "187", "turn_idx": 1, "from": "Assistant", "text": "可以去爬山或者逛博物馆。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-26-1.wav"}}, {"session_id": "187", "turn_idx": 2, "from": "Human", "text": "音量调大，外面太吵了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-26-2.wav"}}, {"session_id": "187", "turn_idx": 3, "from": "Assistant", "text": "好的，大声说:爬山或博物馆，都超有趣！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-26-3.wav"}}] +[{"session_id": "198", "turn_idx": 0, "from": "Human", "text": "小跃，长方体水池长十二米宽八米深三米五。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-27-0.wav"}}, {"session_id": "198", "turn_idx": 1, "from": "Assistant", "text": "体积是长乘宽乘深，十二乘八乘三点五立方米。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-27-1.wav"}}, {"session_id": "198", "turn_idx": 2, "from": "Human", "text": "每小时注水九十五吨，水密度一吨每立方。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-27-2.wav"}}, {"session_id": "198", "turn_idx": 3, "from": "Assistant", "text": "注水速度就是九十五立方米每小时。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-27-3.wav"}}, {"session_id": "198", "turn_idx": 4, "from": "Human", "text": "现在池里有三分之一的水，加满要多久？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-27-4.wav"}}, {"session_id": "198", "turn_idx": 5, "from": "Assistant", "text": "剩余空间是总体积乘三分之二，除以注水速度。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-27-5.wav"}}, {"session_id": "198", "turn_idx": 6, "from": "Human", "text": "表达式生成一下。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-27-6.wav"}}, {"session_id": "198", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(12 * 8 * 3.5 * 2 / 3) / 95\"}", "value": "function\ncalculate\n{\"expression\": \"(12 * 8 * 3.5 * 2 / 3) / 95\"}"}] +[{"session_id": "51", "turn_idx": 0, "from": "Human", "text": "小跃，我下周想去北京玩，有什么必去的景点？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-28-0.wav"}}, {"session_id": "51", "turn_idx": 1, "from": "Assistant", "text": "故宫和长城肯定要去，尤其是长城，风景特别棒。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-28-1.wav"}}, {"session_id": "51", "turn_idx": 2, "from": "Human", "text": "长城听起来好远啊，怎么去比较方便？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-28-2.wav"}}, {"session_id": "51", "turn_idx": 3, "from": "Assistant", "text": "你可以坐地铁到北站，然后转旅游大巴，大概一个多小时就到了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-28-3.wav"}}, {"session_id": "51", "turn_idx": 4, "from": "Human", "text": "明白了。那帮我查查长城的开放时间和门票价格吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-28-4.wav"}}, {"session_id": "51", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"北京长城 开放时间 门票\"}", "value": "function\nweb_search\n{\"query\": \"北京长城 开放时间 门票\"}"}] +[{"session_id": "43", "turn_idx": 0, "from": "Human", "text": "小跃，我们部门发奖金啦！五个人分八万六千块。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-29-0.wav"}}, {"session_id": "43", "turn_idx": 1, "from": "Assistant", "text": "恭喜呀！是按绩效权重分配吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-29-1.wav"}}, {"session_id": "43", "turn_idx": 2, "from": "Human", "text": "对，张三系数一点二，李四零点八，王五一点五，赵六一点零，孙七零点九。 快帮我算王五能拿多少钱，保留整数。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-29-2.wav"}}, {"session_id": "43", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"86000 * 1.5 / (1.2+0.8+1.5+1.0+0.9)\"}", "value": "function\ncalculate\n{\"expression\": \"86000 * 1.5 / (1.2+0.8+1.5+1.0+0.9)\"}"}] +[{"session_id": "287", "turn_idx": 0, "from": "Human", "text": "小跃，我装修客厅，墙面面积四十平米，每平米涂料费八十块。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-30-0.wav"}}, {"session_id": "287", "turn_idx": 1, "from": "Assistant", "text": "装修大工程！涂料要算量，天花板或地板吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-30-1.wav"}}, {"session_id": "287", "turn_idx": 2, "from": "Human", "text": "只墙面。但工人费另算:总涂料费的两成。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-30-2.wav"}}, {"session_id": "287", "turn_idx": 3, "from": "Assistant", "text": "涂料费乘面积，再加工人费。工人费是涂料费的百分之二十？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-30-3.wav"}}, {"session_id": "287", "turn_idx": 4, "from": "Human", "text": "对，墙面四十平米，每平米八十块涂料，工人费是涂料总价的百分之二十。总成本？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-30-4.wav"}}, {"session_id": "287", "turn_idx": 5, "from": "Assistant", "text": "先算涂料费，再加两成工人费。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-30-5.wav"}}, {"session_id": "287", "turn_idx": 6, "from": "Human", "text": "墙面四十平米每平米八十，工人费是涂料费的两成。总开支多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-30-6.wav"}}, {"session_id": "287", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(40*80) * 1.2\"}", "value": "function\ncalculate\n{\"expression\": \"(40*80) * 1.2\"}"}] +[{"session_id": "328", "turn_idx": 0, "from": "Human", "text": "小跃，我练瑜伽时腿抽筋了，咋办？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-31-0.wav"}}, {"session_id": "328", "turn_idx": 1, "from": "Assistant", "text": "可能是热身不足，先停下来拉伸，喝点水补充电解质。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-31-1.wav"}}, {"session_id": "328", "turn_idx": 2, "from": "Human", "text": "电解质饮料有啥推荐？超市买的行不行？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-31-2.wav"}}, {"session_id": "328", "turn_idx": 3, "from": "Assistant", "text": "像宝矿力或佳得乐都行。要查查健康选择吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-31-3.wav"}}, {"session_id": "328", "turn_idx": 4, "from": "Human", "text": "对，帮我找找最适合瑜伽后喝的电解质饮料。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-31-4.wav"}}, {"session_id": "328", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"瑜伽后 电解质饮料 推荐\"}", "value": "function\nweb_search\n{\"query\": \"瑜伽后 电解质饮料 推荐\"}"}] +[{"session_id": "251", "turn_idx": 0, "from": "Human", "text": "小跃，我刚看完《流浪地球》，觉得特效太震撼了。你有类似科幻电影推荐吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-32-0.wav"}}, {"session_id": "251", "turn_idx": 1, "from": "Assistant", "text": "当然有！《星际穿越》就很棒，剧情和视觉效果都超赞。或者《地心引力》，节奏紧凑，特别刺激。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-32-1.wav"}}, {"session_id": "251", "turn_idx": 2, "from": "Human", "text": "《星际穿越》我看过了，确实好。那《地心引力》的主演是谁来着？我有点忘了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-32-2.wav"}}, {"session_id": "251", "turn_idx": 3, "from": "Assistant", "text": "是桑德拉·布洛克演的，她表现力超强。整部电影基本就靠她撑场。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-32-3.wav"}}, {"session_id": "251", "turn_idx": 4, "from": "Human", "text": "哦，是她啊。我想多了解她的其他作品，能帮我查查她最新演的电影吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-32-4.wav"}}, {"session_id": "251", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"桑德拉·布洛克 最新电影\"}", "value": "function\nweb_search\n{\"query\": \"桑德拉·布洛克 最新电影\"}"}] +[{"session_id": "414", "turn_idx": 0, "from": "Human", "text": "小跃，家族聚会订蛋糕，长辈十二人，小孩八人。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-33-0.wav"}}, {"session_id": "414", "turn_idx": 1, "from": "Assistant", "text": "按食量算？大人每人两百克，小孩一百克比较保险。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-33-1.wav"}}, {"session_id": "414", "turn_idx": 2, "from": "Human", "text": "店家说六寸蛋糕八百克，每增一寸多三百克。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-33-2.wav"}}, {"session_id": "414", "turn_idx": 3, "from": "Assistant", "text": "那得八寸以上。十寸是八百加四乘三百，共两千克？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-33-3.wav"}}, {"session_id": "414", "turn_idx": 4, "from": "Human", "text": "两千克够吗？大人小孩总数二十人。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-33-4.wav"}}, {"session_id": "414", "turn_idx": 5, "from": "Assistant", "text": "总需克数=12×200 + 8×100，对比蛋糕重量……", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-33-5.wav"}}, {"session_id": "414", "turn_idx": 6, "from": "Human", "text": "别对比，直接算需要订几寸的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-33-6.wav"}}, {"session_id": "414", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"6 + ceil((12*200 + 8*100 - 800)/300)\"}", "value": "function\ncalculate\n{\"expression\": \"6 + ceil((12*200 + 8*100 - 800)/300)\"}"}] +[{"session_id": "13", "turn_idx": 0, "from": "Human", "text": "小跃，最近想读本小说，轻松点的，有推荐吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-34-0.wav"}}, {"session_id": "13", "turn_idx": 1, "from": "Assistant", "text": "可以试试那本现代爱情小说，故事温暖感人，读者评价很高。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-34-1.wav"}}, {"session_id": "13", "turn_idx": 2, "from": "Human", "text": "作者是谁？我好像听过她名字，但记不清了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-34-2.wav"}}, {"session_id": "13", "turn_idx": 3, "from": "Assistant", "text": "是位女作家，写过好几本畅销书，风格很细腻。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-34-3.wav"}}, {"session_id": "13", "turn_idx": 4, "from": "Human", "text": "对，就是她！我想知道她最新作品的信息。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-34-4.wav"}}, {"session_id": "13", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"女作家 最新小说\"}", "value": "function\nweb_search\n{\"query\": \"女作家 最新小说\"}"}] +[{"session_id": "408", "turn_idx": 0, "from": "Human", "text": "小跃，想买件运动鞋，什么品牌好？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-35-0.wav"}}, {"session_id": "408", "turn_idx": 1, "from": "Assistant", "text": "看需求，耐克或阿迪达斯舒适度高。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-35-1.wav"}}, {"session_id": "408", "turn_idx": 2, "from": "Human", "text": "耐克吧，预算五百块。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-35-2.wav"}}, {"session_id": "408", "turn_idx": 3, "from": "Assistant", "text": "这个价位有基础款，适合日常穿。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-35-3.wav"}}, {"session_id": "408", "turn_idx": 4, "from": "Human", "text": "能搜一下具体型号和用户反馈吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-35-4.wav"}}, {"session_id": "408", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"耐克运动鞋 推荐\"}", "value": "function\nweb_search\n{\"query\": \"耐克运动鞋 推荐\"}"}] +[{"session_id": "261", "turn_idx": 0, "from": "Human", "text": "小跃，我跑步计划有点乱，上周跑了三次，每次八公里，这周打算加量。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-36-0.wav"}}, {"session_id": "261", "turn_idx": 1, "from": "Assistant", "text": "上周总跑量是二十四公里呢。这周想怎么安排？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-36-1.wav"}}, {"session_id": "261", "turn_idx": 2, "from": "Human", "text": "今天跑十公里，明后天各十五公里，大后天休息。你算算这周总里程。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-36-2.wav"}}, {"session_id": "261", "turn_idx": 3, "from": "Assistant", "text": "没问题，十加十五再加十五，注意别累着呀。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-36-3.wav"}}, {"session_id": "261", "turn_idx": 4, "from": "Human", "text": "好嘞，快给我个总数！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-36-4.wav"}}, {"session_id": "261", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"10 + 15 + 15\"}", "value": "function\ncalculate\n{\"expression\": \"10 + 15 + 15\"}"}] +[{"session_id": "764", "turn_idx": 0, "from": "Human", "text": "小跃，推荐个周末短途游。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-37-0.wav"}}, {"session_id": "764", "turn_idx": 1, "from": "Assistant", "text": "去郊外爬山吧，空气好又放松。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-37-1.wav"}}, {"session_id": "764", "turn_idx": 2, "from": "Human", "text": "声音大点，信号不好。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-37-2.wav"}}, {"session_id": "764", "turn_idx": 3, "from": "Assistant", "text": "好的，音量调大。推荐爬山，超适合周末。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-37-3.wav"}}, {"session_id": "764", "turn_idx": 4, "from": "Human", "text": "用更热情的语气描述一下。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-37-4.wav"}}, {"session_id": "764", "turn_idx": 5, "from": "Assistant", "text": "山顶风景绝美，带上零食，玩得超嗨！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-37-5.wav"}}, {"session_id": "764", "turn_idx": 6, "from": "Human", "text": "好主意，这就计划。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-37-6.wav"}}, {"session_id": "764", "turn_idx": 7, "from": "Assistant", "text": "玩得开心点！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-37-7.wav"}}] +[{"session_id": "906", "turn_idx": 0, "from": "Human", "text": "小跃，我孩子问恐龙为什么灭绝了，怎么解释简单点？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-38-0.wav"}}, {"session_id": "906", "turn_idx": 1, "from": "Assistant", "text": "可以说是因为小行星撞击地球，导致环境变化。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-38-1.wav"}}, {"session_id": "906", "turn_idx": 2, "from": "Human", "text": "小行星撞击？那是多少年前的事？ 还有，恐龙灭绝的确切原因是什么？帮我搜一下权威说法。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-38-2.wav"}}, {"session_id": "906", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"恐龙灭绝原因\"}", "value": "function\nweb_search\n{\"query\": \"恐龙灭绝原因\"}"}] +[{"session_id": "188", "turn_idx": 0, "from": "Human", "text": "小跃，猫咪为啥爱睡觉？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-39-0.wav"}}, {"session_id": "188", "turn_idx": 1, "from": "Assistant", "text": "猫咪是天生的猎人，睡觉保存体力。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-39-1.wav"}}, {"session_id": "188", "turn_idx": 2, "from": "Human", "text": "用更热情的语气回答。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-39-2.wav"}}, {"session_id": "188", "turn_idx": 3, "from": "Assistant", "text": "绝对是的！猫咪超会偷懒，睡觉攒能量超厉害！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-39-3.wav"}}] +[{"session_id": "178", "turn_idx": 0, "from": "Human", "text": "小跃，我游泳每次游一千米，用了二十分钟，一周四次。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-40-0.wav"}}, {"session_id": "178", "turn_idx": 1, "from": "Assistant", "text": "游泳锻炼全身，频率很高啊。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-40-1.wav"}}, {"session_id": "178", "turn_idx": 2, "from": "Human", "text": "帮我算算平均速度和总游程吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-40-2.wav"}}, {"session_id": "178", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"1000 / (20 / 60), 1000 * 4\"}", "value": "function\ncalculate\n{\"expression\": \"1000 / (20 / 60), 1000 * 4\"}"}] +[{"session_id": "419", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想投资一笔钱，本金是二十万元，年利率是百分之五点八，计划放三年。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-41-0.wav"}}, {"session_id": "419", "turn_idx": 1, "from": "Assistant", "text": "投资理财挺好的，复利增长效果不错哦。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-41-1.wav"}}, {"session_id": "419", "turn_idx": 2, "from": "Human", "text": "那帮我算算到期后能拿到多少钱吧，要精确点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-41-2.wav"}}, {"session_id": "419", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"200000 * (1 + 0.058)**3\"}", "value": "function\ncalculate\n{\"expression\": \"200000 * (1 + 0.058)**3\"}"}] +[{"session_id": "445", "turn_idx": 0, "from": "Human", "text": "小跃，我在读历史书。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-42-0.wav"}}, {"session_id": "445", "turn_idx": 1, "from": "Assistant", "text": "哪个时期？清朝的故事很有趣。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-42-1.wav"}}, {"session_id": "445", "turn_idx": 2, "from": "Human", "text": "对，康熙皇帝那段。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-42-2.wav"}}, {"session_id": "445", "turn_idx": 3, "from": "Assistant", "text": "他统治时间很长，推动了很多改革。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-42-3.wav"}}, {"session_id": "445", "turn_idx": 4, "from": "Human", "text": "他当了多久皇帝？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-42-4.wav"}}, {"session_id": "445", "turn_idx": 5, "from": "Assistant", "text": "大概六十多年吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-42-5.wav"}}, {"session_id": "445", "turn_idx": 6, "from": "Human", "text": "具体从哪年到哪年？我想记下来。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-42-6.wav"}}, {"session_id": "445", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"康熙皇帝 在位时间\"}", "value": "function\nweb_search\n{\"query\": \"康熙皇帝 在位时间\"}"}] +[{"session_id": "127", "turn_idx": 0, "from": "Human", "text": "项目预算批了五十万，前期用掉百分之四十。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-43-0.wav"}}, {"session_id": "127", "turn_idx": 1, "from": "Assistant", "text": "已使用二十万元，剩余三十万元。 后续还需要分三阶段投入吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-43-1.wav"}}, {"session_id": "127", "turn_idx": 2, "from": "Human", "text": "第二阶段要剩余资金的一半，第三阶段再投六十万。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-43-2.wav"}}, {"session_id": "127", "turn_idx": 3, "from": "Assistant", "text": "第二阶段十五万，但总预算可能不足。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-43-3.wav"}}, {"session_id": "127", "turn_idx": 4, "from": "Human", "text": "帮我算第三阶段前是否超支？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-43-4.wav"}}, {"session_id": "127", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"500000*0.6 - 500000*0.6*0.5 < 600000\"}", "value": "function\ncalculate\n{\"expression\": \"500000*0.6 - 500000*0.6*0.5 < 600000\"}"}] +[{"session_id": "352", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我今天想去广州爬山，但感觉天有点阴。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-44-0.wav"}}, {"session_id": "352", "turn_idx": 1, "from": "Assistant", "text": "爬山注意安全！要我帮你看看附近的山路信息吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-44-1.wav"}}, {"session_id": "352", "turn_idx": 2, "from": "Human", "text": "先不用，我担心会下雨影响行程。 干脆你帮我查一下广州未来三天的天气吧，包括今天在内。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-44-2.wav"}}, {"session_id": "352", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "110", "turn_idx": 0, "from": "Human", "text": "小跃，音量太小了，调大点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-45-0.wav"}}, {"session_id": "110", "turn_idx": 1, "from": "Assistant", "text": "好的，我已经把音量调大了。现在清楚多了吧？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-45-1.wav"}}, {"session_id": "110", "turn_idx": 2, "from": "Human", "text": "嗯，好多了。帮我查下明天天气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-45-2.wav"}}, {"session_id": "110", "turn_idx": 3, "from": "Assistant", "text": "明天晴天，最高温度28度，适合出门玩。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-45-3.wav"}}] +[{"session_id": "838", "turn_idx": 0, "from": "Human", "text": "小跃，我养了只小狗，三个月大。喂狗粮一天几次？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-46-0.wav"}}, {"session_id": "838", "turn_idx": 1, "from": "Assistant", "text": "小狗要少量多餐，一天喂三到四次。用幼犬专用粮。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-46-1.wav"}}, {"session_id": "838", "turn_idx": 2, "from": "Human", "text": "需要打疫苗吗？什么时候打？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-46-2.wav"}}, {"session_id": "838", "turn_idx": 3, "from": "Assistant", "text": "三个月大可以打第一针，包括狂犬病和细小病毒疫苗。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-46-3.wav"}}, {"session_id": "838", "turn_idx": 4, "from": "Human", "text": "疫苗贵不贵？一针多少钱？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-46-4.wav"}}, {"session_id": "838", "turn_idx": 5, "from": "Assistant", "text": "一般两百块左右一针，全套打下来几百块。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-46-5.wav"}}, {"session_id": "838", "turn_idx": 6, "from": "Human", "text": "小跃，帮我搜索小狗疫苗的接种时间表。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-46-6.wav"}}, {"session_id": "838", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"小狗 疫苗 接种时间表\"}", "value": "function\nweb_search\n{\"query\": \"小狗 疫苗 接种时间表\"}"}] +[{"session_id": "313", "turn_idx": 0, "from": "Human", "text": "小跃，给我说说最新新闻。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-47-0.wav"}}, {"session_id": "313", "turn_idx": 1, "from": "Assistant", "text": "科技公司发布了新手机，功能超多。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-47-1.wav"}}, {"session_id": "313", "turn_idx": 2, "from": "Human", "text": "小跃，用更严肃的语气讲重点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-47-2.wav"}}, {"session_id": "313", "turn_idx": 3, "from": "Assistant", "text": "注意:新手机发布，价格不菲，影响市场。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-47-3.wav"}}, {"session_id": "313", "turn_idx": 4, "from": "Human", "text": "还有别的吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-47-4.wav"}}, {"session_id": "313", "turn_idx": 5, "from": "Assistant", "text": "还有体育新闻，本地球队赢了比赛。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-47-5.wav"}}, {"session_id": "313", "turn_idx": 6, "from": "Human", "text": "小跃，音量调小点，我在办公室。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-47-6.wav"}}, {"session_id": "313", "turn_idx": 7, "from": "Assistant", "text": "明白，音量调低。新闻就这些，需要再问。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-47-7.wav"}}] +[{"session_id": "358", "turn_idx": 0, "from": "Human", "text": "小跃，地球自转一圈要多久？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-48-0.wav"}}, {"session_id": "358", "turn_idx": 1, "from": "Assistant", "text": "一天，也就是二十四小时。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-48-1.wav"}}, {"session_id": "358", "turn_idx": 2, "from": "Human", "text": "那公转呢？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-48-2.wav"}}, {"session_id": "358", "turn_idx": 3, "from": "Assistant", "text": "一年，大约是三百六十五天。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-48-3.wav"}}, {"session_id": "358", "turn_idx": 4, "from": "Human", "text": "为什么有闰年？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-48-4.wav"}}, {"session_id": "358", "turn_idx": 5, "from": "Assistant", "text": "因为实际时间多出一点，每四年加一天。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-48-5.wav"}}, {"session_id": "358", "turn_idx": 6, "from": "Human", "text": "具体怎么算的？查查闰年规则吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-48-6.wav"}}, {"session_id": "358", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"闰年 计算规则\"}", "value": "function\nweb_search\n{\"query\": \"闰年 计算规则\"}"}] +[{"session_id": "949", "turn_idx": 0, "from": "Human", "text": "小跃，衣服发黄了怎么洗白？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-49-0.wav"}}, {"session_id": "949", "turn_idx": 1, "from": "Assistant", "text": "用柠檬汁或小苏打泡一泡，效果还行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-49-1.wav"}}, {"session_id": "949", "turn_idx": 2, "from": "Human", "text": "天然方法更好，帮我查一下天然去黄渍的小技巧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-49-2.wav"}}, {"session_id": "949", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"天然 去黄渍 方法\"}", "value": "function\nweb_search\n{\"query\": \"天然 去黄渍 方法\"}"}] +[{"session_id": "306", "turn_idx": 0, "from": "Human", "text": "小跃，天气冷吗？用关心的语气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-50-0.wav"}}, {"session_id": "306", "turn_idx": 1, "from": "Assistant", "text": "是的，外面有点凉，记得多穿点衣服保暖。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-50-1.wav"}}, {"session_id": "306", "turn_idx": 2, "from": "Human", "text": "好的。情感切换到更积极点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-50-2.wav"}}, {"session_id": "306", "turn_idx": 3, "from": "Assistant", "text": "没问题！积极模式:冷天也有乐趣，比如喝杯热巧克力暖暖身！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-50-3.wav"}}, {"session_id": "306", "turn_idx": 4, "from": "Human", "text": "哈哈，说得对。我去准备。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-50-4.wav"}}, {"session_id": "306", "turn_idx": 5, "from": "Assistant", "text": "好的，享受你的热饮时间！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-50-5.wav"}}] +[{"session_id": "3", "turn_idx": 0, "from": "Human", "text": "小跃，我最近睡眠不太好，有什么自然的方法改善吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-51-0.wav"}}, {"session_id": "3", "turn_idx": 1, "from": "Assistant", "text": "试试睡前喝杯热牛奶或者做点放松运动，比如瑜伽。你平时压力大吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-51-1.wav"}}, {"session_id": "3", "turn_idx": 2, "from": "Human", "text": "压力还行，但我想知道具体的助眠茶配方，比如薰衣草茶的做法。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-51-2.wav"}}, {"session_id": "3", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"助眠茶 薰衣草 配方\"}", "value": "function\nweb_search\n{\"query\": \"助眠茶 薰衣草 配方\"}"}] +[{"session_id": "742", "turn_idx": 0, "from": "Human", "text": "小跃，我有点感冒症状，鼻子堵得厉害。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-52-0.wav"}}, {"session_id": "742", "turn_idx": 1, "from": "Assistant", "text": "感冒常见症状有流鼻涕和咳嗽，首先多喝水其次休息好。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-52-1.wav"}}, {"session_id": "742", "turn_idx": 2, "from": "Human", "text": "一般感冒会持续几天？我得安排工作。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-52-2.wav"}}, {"session_id": "742", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"感冒 通常持续多久\"}", "value": "function\nweb_search\n{\"query\": \"感冒 通常持续多久\"}"}] +[{"session_id": "568", "turn_idx": 0, "from": "Human", "text": "小跃，我想买辆自行车，通勤用，公路车还是山地车好？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-53-0.wav"}}, {"session_id": "568", "turn_idx": 1, "from": "Assistant", "text": "通勤的话，公路车轻便速度快，山地车更稳但重些。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-53-1.wav"}}, {"session_id": "568", "turn_idx": 2, "from": "Human", "text": "公路车听着不错。价格一般多少？入门级的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-53-2.wav"}}, {"session_id": "568", "turn_idx": 3, "from": "Assistant", "text": "入门级公路车一千到两千块左右，品牌像捷安特就行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-53-3.wav"}}, {"session_id": "568", "turn_idx": 4, "from": "Human", "text": "捷安特？那售后服务怎么样？保修期多长？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-53-4.wav"}}, {"session_id": "568", "turn_idx": 5, "from": "Assistant", "text": "保修通常一年，服务点挺多的，可以网上查。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-53-5.wav"}}, {"session_id": "568", "turn_idx": 6, "from": "Human", "text": "行，帮我查查捷安特公路车的型号和保修政策。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-53-6.wav"}}, {"session_id": "568", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"捷安特 公路车 型号 保修政策\"}", "value": "function\nweb_search\n{\"query\": \"捷安特 公路车 型号 保修政策\"}"}] +[{"session_id": "128", "turn_idx": 0, "from": "Human", "text": "小跃，今天有什么新闻？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-54-0.wav"}}, {"session_id": "128", "turn_idx": 1, "from": "Assistant", "text": "科学家发现了一种新植物。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-54-1.wav"}}, {"session_id": "128", "turn_idx": 2, "from": "Human", "text": "用悲伤的语气复述，感觉遗憾点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-54-2.wav"}}, {"session_id": "128", "turn_idx": 3, "from": "Assistant", "text": "唉，科学家发现了一种新植物，但它快灭绝了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-54-3.wav"}}] +[{"session_id": "705", "turn_idx": 0, "from": "Human", "text": "小跃，有什么新闻摘要？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-55-0.wav"}}, {"session_id": "705", "turn_idx": 1, "from": "Assistant", "text": "今天有环保活动的报道，挺有意思。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-55-1.wav"}}, {"session_id": "705", "turn_idx": 2, "from": "Human", "text": "请用更中立的语气简述关键点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-55-2.wav"}}, {"session_id": "705", "turn_idx": 3, "from": "Assistant", "text": "环保活动聚焦减少塑料使用，强调可持续生活方式。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-55-3.wav"}}, {"session_id": "705", "turn_idx": 4, "from": "Human", "text": "明白了，谢谢。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-55-4.wav"}}, {"session_id": "705", "turn_idx": 5, "from": "Assistant", "text": "好的，随时了解最新动态。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-55-5.wav"}}] +[{"session_id": "966", "turn_idx": 0, "from": "Human", "text": "小跃，我们团队有个大项目。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-56-0.wav"}}, {"session_id": "966", "turn_idx": 1, "from": "Assistant", "text": "总工作量估算了吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-56-1.wav"}}, {"session_id": "966", "turn_idx": 2, "from": "Human", "text": "需要一千五百小时完成。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-56-2.wav"}}, {"session_id": "966", "turn_idx": 3, "from": "Assistant", "text": "当前进度？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-56-3.wav"}}, {"session_id": "966", "turn_idx": 4, "from": "Human", "text": "已经干了四百二十小时。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-56-4.wav"}}, {"session_id": "966", "turn_idx": 5, "from": "Assistant", "text": "剩余时间安排呢？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-56-5.wav"}}, {"session_id": "966", "turn_idx": 6, "from": "Human", "text": "每天工作十小时，还有四十五天。 帮我算每天平均要完成多少小时才能赶上。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-56-6.wav"}}, {"session_id": "966", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(1500 - 420) / 45\"}", "value": "function\ncalculate\n{\"expression\": \"(1500 - 420) / 45\"}"}] +[{"session_id": "405", "turn_idx": 0, "from": "Human", "text": "小跃，周末想看电影，有什么好片子？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-57-0.wav"}}, {"session_id": "405", "turn_idx": 1, "from": "Assistant", "text": "最近《长津湖》很火，是历史战争片。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-57-1.wav"}}, {"session_id": "405", "turn_idx": 2, "from": "Human", "text": "哦，是国产的吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-57-2.wav"}}, {"session_id": "405", "turn_idx": 3, "from": "Assistant", "text": "对，国产大片，评价很高。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-57-3.wav"}}, {"session_id": "405", "turn_idx": 4, "from": "Human", "text": "能查一下在哪家影院上映和时间吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-57-4.wav"}}, {"session_id": "405", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"长津湖 上映影院\"}", "value": "function\nweb_search\n{\"query\": \"长津湖 上映影院\"}"}] +[{"session_id": "787", "turn_idx": 0, "from": "Human", "text": "小跃，我明天生日派对在成都办。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-58-0.wav"}}, {"session_id": "787", "turn_idx": 1, "from": "Assistant", "text": "生日快乐！成都火锅是特色，别错过。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-58-1.wav"}}, {"session_id": "787", "turn_idx": 2, "from": "Human", "text": "谢谢，小跃，先查查成都的天气情况。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-58-2.wav"}}, {"session_id": "787", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "977", "turn_idx": 0, "from": "Human", "text": "小跃，武汉的热干面听说超好吃，想去试试。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-59-0.wav"}}, {"session_id": "977", "turn_idx": 1, "from": "Assistant", "text": "是呀，主人。武汉的小吃街很热闹。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-59-1.wav"}}, {"session_id": "977", "turn_idx": 2, "from": "Human", "text": "我可能周末去，但得看天气如何。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-59-2.wav"}}, {"session_id": "977", "turn_idx": 3, "from": "Assistant", "text": "好的，我帮您查查。具体查几天？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-59-3.wav"}}, {"session_id": "977", "turn_idx": 4, "from": "Human", "text": "查查包括今天的三天武汉天气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-59-4.wav"}}, {"session_id": "977", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "537", "turn_idx": 0, "from": "Human", "text": "小跃，用严肃的语气提醒我交水电费。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-60-0.wav"}}, {"session_id": "537", "turn_idx": 1, "from": "Assistant", "text": "请注意，水电费截止到明天，务必今天处理。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-60-1.wav"}}] +[{"session_id": "421", "turn_idx": 0, "from": "Human", "text": "小跃，周末有啥活动建议？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-61-0.wav"}}, {"session_id": "421", "turn_idx": 1, "from": "Assistant", "text": "公园有免费市集，挺热闹的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-61-1.wav"}}, {"session_id": "421", "turn_idx": 2, "from": "Human", "text": "音量调小点，宝宝在睡觉。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-61-2.wav"}}, {"session_id": "421", "turn_idx": 3, "from": "Assistant", "text": "好的，小声说:周末市集在公园，别错过哦。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-61-3.wav"}}] +[{"session_id": "440", "turn_idx": 0, "from": "Human", "text": "小跃，我今天想去公园散步，但不知道穿什么好。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-62-0.wav"}}, {"session_id": "440", "turn_idx": 1, "from": "Assistant", "text": "别担心，穿得舒服就行。对了，你打算去哪儿散步？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-62-1.wav"}}, {"session_id": "440", "turn_idx": 2, "from": "Human", "text": "我想去北京的后海那边。帮我看看未来三天的天气情况吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-62-2.wav"}}, {"session_id": "440", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "442", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想去广州玩，但怕天气不好。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-63-0.wav"}}, {"session_id": "442", "turn_idx": 1, "from": "Assistant", "text": "广州现在挺热闹的，先了解下天气再决定吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-63-1.wav"}}, {"session_id": "442", "turn_idx": 2, "from": "Human", "text": "好主意，小跃，查查广州这三天的天气吧，从今天开始。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-63-2.wav"}}, {"session_id": "442", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "744", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我晚上健身。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-64-0.wav"}}, {"session_id": "744", "turn_idx": 1, "from": "Assistant", "text": "好的，晚上七点健身提醒已设置。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-64-1.wav"}}, {"session_id": "744", "turn_idx": 2, "from": "Human", "text": "小跃，请大声点提醒我，我怕忘了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-64-2.wav"}}, {"session_id": "744", "turn_idx": 3, "from": "Assistant", "text": "晚上七点健身！别偷懒哦！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-64-3.wav"}}, {"session_id": "744", "turn_idx": 4, "from": "Human", "text": "哈哈，不会忘的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-64-4.wav"}}, {"session_id": "744", "turn_idx": 5, "from": "Assistant", "text": "好，保持运动！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-64-5.wav"}}] +[{"session_id": "549", "turn_idx": 0, "from": "Human", "text": "小跃，关掉卧室灯。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-65-0.wav"}}, {"session_id": "549", "turn_idx": 1, "from": "Assistant", "text": "卧室灯已关闭。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-65-1.wav"}}, {"session_id": "549", "turn_idx": 2, "from": "Human", "text": "说小声点，别吵醒别人。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-65-2.wav"}}, {"session_id": "549", "turn_idx": 3, "from": "Assistant", "text": "好的，小声说，卧室灯关了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-65-3.wav"}}] +[{"session_id": "963", "turn_idx": 0, "from": "Human", "text": "小跃，我们家每月开销不小。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-66-0.wav"}}, {"session_id": "963", "turn_idx": 1, "from": "Assistant", "text": "主要花在哪儿？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-66-1.wav"}}, {"session_id": "963", "turn_idx": 2, "from": "Human", "text": "房租六千五百元。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-66-2.wav"}}, {"session_id": "963", "turn_idx": 3, "from": "Assistant", "text": "其他固定支出呢？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-66-3.wav"}}, {"session_id": "963", "turn_idx": 4, "from": "Human", "text": "水电煤八百元，买菜三千元。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-66-4.wav"}}, {"session_id": "963", "turn_idx": 5, "from": "Assistant", "text": "还有吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-66-5.wav"}}, {"session_id": "963", "turn_idx": 6, "from": "Human", "text": "孩子补习费两千五百元。 帮我算一年总开销。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-66-6.wav"}}, {"session_id": "963", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(6500 + 800 + 3000 + 2500) * 12\"}", "value": "function\ncalculate\n{\"expression\": \"(6500 + 800 + 3000 + 2500) * 12\"}"}] +[{"session_id": "624", "turn_idx": 0, "from": "Human", "text": "小跃，下午好！今天工作累不累？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-67-0.wav"}}, {"session_id": "624", "turn_idx": 1, "from": "Assistant", "text": "下午好！刚休息了一下。你呢，有啥新动态？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-67-1.wav"}}, {"session_id": "624", "turn_idx": 2, "from": "Human", "text": "我想去杭州西湖散步，就今天傍晚。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-67-2.wav"}}, {"session_id": "624", "turn_idx": 3, "from": "Assistant", "text": "西湖风景超赞！记得带好外套。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-67-3.wav"}}, {"session_id": "624", "turn_idx": 4, "from": "Human", "text": "好主意。小跃，帮我查一下杭州今天的天气怎么样。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-67-4.wav"}}, {"session_id": "624", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "749", "turn_idx": 0, "from": "Human", "text": "小跃，端午节有啥习俗？快到了想准备。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-68-0.wav"}}, {"session_id": "749", "turn_idx": 1, "from": "Assistant", "text": "端午节习俗有吃粽子和赛龙舟，纪念屈原。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-68-1.wav"}}, {"session_id": "749", "turn_idx": 2, "from": "Human", "text": "屈原是谁啊？不太了解他的故事。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-68-2.wav"}}, {"session_id": "749", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"屈原 生平介绍\"}", "value": "function\nweb_search\n{\"query\": \"屈原 生平介绍\"}"}] +[{"session_id": "169", "turn_idx": 0, "from": "Human", "text": "小跃，后天要去成都山区徒步，需要准备防雨装备。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-69-0.wav"}}, {"session_id": "169", "turn_idx": 1, "from": "Assistant", "text": "徒步安全第一！山区天气变化快，查查预报能避免意外。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-69-1.wav"}}, {"session_id": "169", "turn_idx": 2, "from": "Human", "text": "没错，那你查一下成都的天气，包括今天的三天内。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-69-2.wav"}}, {"session_id": "169", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "952", "turn_idx": 0, "from": "Human", "text": "小跃，明天公司组织去广州团建。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-70-0.wav"}}, {"session_id": "952", "turn_idx": 1, "from": "Assistant", "text": "广州团建？具体去哪儿玩？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-70-1.wav"}}, {"session_id": "952", "turn_idx": 2, "from": "Human", "text": "去长隆乐园，期待好久了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-70-2.wav"}}, {"session_id": "952", "turn_idx": 3, "from": "Assistant", "text": "长隆超好玩！但天气得注意。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-70-3.wav"}}, {"session_id": "952", "turn_idx": 4, "from": "Human", "text": "是呀，查一下广州未来三天的天气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-70-4.wav"}}, {"session_id": "952", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "723", "turn_idx": 0, "from": "Human", "text": "小跃，我去年投资了五万元，年利率是百分之六，今年又加了二万元本金。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-71-0.wav"}}, {"session_id": "723", "turn_idx": 1, "from": "Assistant", "text": "好的，那现在总金额是多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-71-1.wav"}}, {"session_id": "723", "turn_idx": 2, "from": "Human", "text": "帮我计算投资的总本息。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-71-2.wav"}}, {"session_id": "723", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"50000 * 1.06 + 20000\"}", "value": "function\ncalculate\n{\"expression\": \"50000 * 1.06 + 20000\"}"}] +[{"session_id": "376", "turn_idx": 0, "from": "Human", "text": "小跃，推荐本经典小说吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-72-0.wav"}}, {"session_id": "376", "turn_idx": 1, "from": "Assistant", "text": "喜欢啥风格？推理还是浪漫？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-72-1.wav"}}, {"session_id": "376", "turn_idx": 2, "from": "Human", "text": "推理的，像福尔摩斯那种。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-72-2.wav"}}, {"session_id": "376", "turn_idx": 3, "from": "Assistant", "text": "那可以试试《福尔摩斯探案集》。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-72-3.wav"}}, {"session_id": "376", "turn_idx": 4, "from": "Human", "text": "作者是谁？我想看看他的生平故事。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-72-4.wav"}}, {"session_id": "376", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"福尔摩斯 作者 生平\"}", "value": "function\nweb_search\n{\"query\": \"福尔摩斯 作者 生平\"}"}] +[{"session_id": "907", "turn_idx": 0, "from": "Human", "text": "小跃，我准备装修房子，墙面面积是两百平方米。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-73-0.wav"}}, {"session_id": "907", "turn_idx": 1, "from": "Assistant", "text": "面积不小。每平米涂料费和人工费多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-73-1.wav"}}, {"session_id": "907", "turn_idx": 2, "from": "Human", "text": "涂料每平米四十元，人工每平米三十元。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-73-2.wav"}}, {"session_id": "907", "turn_idx": 3, "from": "Assistant", "text": "总基础费用就是一万四千元。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-73-3.wav"}}, {"session_id": "907", "turn_idx": 4, "from": "Human", "text": "还有窗户部分五十平米不用涂，但要额外处理费每平米十五元。帮我算总成本。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-73-4.wav"}}, {"session_id": "907", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"200 * (40 + 30) - 50 * (40 + 30) + 50 * 15\"}", "value": "function\ncalculate\n{\"expression\": \"200 * (40 + 30) - 50 * (40 + 30) + 50 * 15\"}"}] +[{"session_id": "48", "turn_idx": 0, "from": "Human", "text": "小跃，股票做T计算:我有一万股成本八块五，今天七块九补了五千股。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-74-0.wav"}}, {"session_id": "48", "turn_idx": 1, "from": "Assistant", "text": "现在想卖出部分摊薄成本？计划卖多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-74-1.wav"}}, {"session_id": "48", "turn_idx": 2, "from": "Human", "text": "现价八块三，准备卖出六千股。求剩余持仓成本。 用加权平均法算。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-74-2.wav"}}, {"session_id": "48", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((10000*8.5 + 5000*7.9) - 6000*8.3) / (10000+5000-6000)\"}", "value": "function\ncalculate\n{\"expression\": \"((10000*8.5 + 5000*7.9) - 6000*8.3) / (10000+5000-6000)\"}"}] +[{"session_id": "676", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个闹钟，明早八点起床。用轻松点的语气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-75-0.wav"}}, {"session_id": "676", "turn_idx": 1, "from": "Assistant", "text": "没问题，闹钟设好啦，明早八点准时叫你，睡个好觉吧！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-75-1.wav"}}] +[{"session_id": "435", "turn_idx": 0, "from": "Human", "text": "小跃，最近流行的那首《孤勇者》是谁唱的？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-76-0.wav"}}, {"session_id": "435", "turn_idx": 1, "from": "Assistant", "text": "是陈奕迅唱的，英雄联盟动画主题曲，挺励志的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-76-1.wav"}}, {"session_id": "435", "turn_idx": 2, "from": "Human", "text": "歌词里“战吗战啊”那句什么意思？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-76-2.wav"}}, {"session_id": "435", "turn_idx": 3, "from": "Assistant", "text": "表达面对困难不退缩的精神，鼓励人勇敢战斗。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-76-3.wav"}}, {"session_id": "435", "turn_idx": 4, "from": "Human", "text": "说得对。你能搜一下《孤勇者》的完整歌词和创作背景吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-76-4.wav"}}, {"session_id": "435", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"孤勇者 歌词 创作背景\"}", "value": "function\nweb_search\n{\"query\": \"孤勇者 歌词 创作背景\"}"}] +[{"session_id": "201", "turn_idx": 0, "from": "Human", "text": "小跃，我们自驾游计划开三千六百公里，车每公里油耗大概零点零八升。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-77-0.wav"}}, {"session_id": "201", "turn_idx": 1, "from": "Assistant", "text": "那得先算总耗油量，再结合油价预估费用。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-77-1.wav"}}, {"session_id": "201", "turn_idx": 2, "from": "Human", "text": "现在油价是八块五毛一升，中途还要交一千两百元过路费。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-77-2.wav"}}, {"session_id": "201", "turn_idx": 3, "from": "Assistant", "text": "油费加过路费就是主要支出了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-77-3.wav"}}, {"session_id": "201", "turn_idx": 4, "from": "Human", "text": "对了，回程时油价可能涨百分之三，按这个算总预算。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-77-4.wav"}}, {"session_id": "201", "turn_idx": 5, "from": "Assistant", "text": "去程按当前价，回程按涨价后价格计算更准确。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-77-5.wav"}}, {"session_id": "201", "turn_idx": 6, "from": "Human", "text": "请帮我算全程总花费，假设去程一半路程回程一半。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-77-6.wav"}}, {"session_id": "201", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(3600 * 0.08 * 8.5) + (3600 * 0.08 * 8.5 * 1.03) + 1200\"}", "value": "function\ncalculate\n{\"expression\": \"(3600 * 0.08 * 8.5) + (3600 * 0.08 * 8.5 * 1.03) + 1200\"}"}] +[{"session_id": "574", "turn_idx": 0, "from": "Human", "text": "小跃，我最近老失眠，有啥办法改善？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-78-0.wav"}}, {"session_id": "574", "turn_idx": 1, "from": "Assistant", "text": "试试规律作息，睡前别玩手机。也可以喝点热牛奶。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-78-1.wav"}}, {"session_id": "574", "turn_idx": 2, "from": "Human", "text": "牛奶管用吗？还有其他自然疗法？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-78-2.wav"}}, {"session_id": "574", "turn_idx": 3, "from": "Assistant", "text": "嗯，冥想或泡个热水澡也有效。关键是要放松心情。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-78-3.wav"}}, {"session_id": "574", "turn_idx": 4, "from": "Human", "text": "帮我查查科学证明的失眠自然疗法。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-78-4.wav"}}, {"session_id": "574", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"失眠自然疗法科学证明\"}", "value": "function\nweb_search\n{\"query\": \"失眠自然疗法科学证明\"}"}] +[{"session_id": "175", "turn_idx": 0, "from": "Human", "text": "小跃，我每天学习三小时，周末加两小时，一周七天，总共学习时间多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-79-0.wav"}}, {"session_id": "175", "turn_idx": 1, "from": "Assistant", "text": "努力学习的习惯真好，我来帮你合计。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-79-1.wav"}}, {"session_id": "175", "turn_idx": 2, "from": "Human", "text": "谢谢，告诉我具体小时数吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-79-2.wav"}}, {"session_id": "175", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"3 * 5 + 5 * 2\"}", "value": "function\ncalculate\n{\"expression\": \"3 * 5 + 5 * 2\"}"}] +[{"session_id": "104", "turn_idx": 0, "from": "Human", "text": "小跃，公司年会抽奖概率好复杂，全员一千二百人，一等奖三个。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-80-0.wav"}}, {"session_id": "104", "turn_idx": 1, "from": "Assistant", "text": "哇，中奖率确实不高呢，您想算个人中奖概率吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-80-1.wav"}}, {"session_id": "104", "turn_idx": 2, "from": "Human", "text": "不止呢！如果二等奖十五个，三等奖五十个，我想知道完全不中奖的概率。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-80-2.wav"}}, {"session_id": "104", "turn_idx": 3, "from": "Assistant", "text": "那需要计算未中任何奖的人数比例，总奖项数六十八个。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-80-3.wav"}}, {"session_id": "104", "turn_idx": 4, "from": "Human", "text": "数字太大心算会错，求直接算不中奖概率的百分比！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-80-4.wav"}}, {"session_id": "104", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"round((1200-68)/1200*100, 2)\"}", "value": "function\ncalculate\n{\"expression\": \"round((1200-68)/1200*100, 2)\"}"}] +[{"session_id": "519", "turn_idx": 0, "from": "Human", "text": "小跃，重庆的坡路是不是很多？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-81-0.wav"}}, {"session_id": "519", "turn_idx": 1, "from": "Assistant", "text": "对呀，爬坡累人，天气热更难受。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-81-1.wav"}}, {"session_id": "519", "turn_idx": 2, "from": "Human", "text": "我后天出差去那儿三天。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-81-2.wav"}}, {"session_id": "519", "turn_idx": 3, "from": "Assistant", "text": "那得注意天气，别太热。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-81-3.wav"}}, {"session_id": "519", "turn_idx": 4, "from": "Human", "text": "是啊，查一下重庆今天、明天和后天的天气呗。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-81-4.wav"}}, {"session_id": "519", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "336", "turn_idx": 0, "from": "Human", "text": "小跃，怎么煮鸡蛋？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-82-0.wav"}}, {"session_id": "336", "turn_idx": 1, "from": "Assistant", "text": "很简单，水煮开后放鸡蛋，煮8分钟就行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-82-1.wav"}}, {"session_id": "336", "turn_idx": 2, "from": "Human", "text": "请说得更温柔一点，我是新手。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-82-2.wav"}}, {"session_id": "336", "turn_idx": 3, "from": "Assistant", "text": "别紧张，亲爱的。水烧开，轻轻放鸡蛋，煮8分钟就完美啦。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-82-3.wav"}}, {"session_id": "336", "turn_idx": 4, "from": "Human", "text": "谢谢，音量稍微大点好吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-82-4.wav"}}, {"session_id": "336", "turn_idx": 5, "from": "Assistant", "text": "好的，音量大了。水烧开，放鸡蛋，煮8分钟就好。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-82-5.wav"}}] +[{"session_id": "978", "turn_idx": 0, "from": "Human", "text": "小跃，怎么快速背单词？我记不住。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-83-0.wav"}}, {"session_id": "978", "turn_idx": 1, "from": "Assistant", "text": "多重复加联想！用APP像百词斩，每天记十个，结合图片记忆。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-83-1.wav"}}, {"session_id": "978", "turn_idx": 2, "from": "Human", "text": "百词斩好用吗？免费不？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-83-2.wav"}}, {"session_id": "978", "turn_idx": 3, "from": "Assistant", "text": "基本功能免费，有打卡提醒。界面简单，适合初学者。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-83-3.wav"}}, {"session_id": "978", "turn_idx": 4, "from": "Human", "text": "行。那百词斩最近更新了啥新词库？我想看看。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-83-4.wav"}}, {"session_id": "978", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"百词斩 新词库 更新内容\"}", "value": "function\nweb_search\n{\"query\": \"百词斩 新词库 更新内容\"}"}] +[{"session_id": "520", "turn_idx": 0, "from": "Human", "text": "嘿小跃，周末我想带娃去公园玩，北京有啥好地方推荐吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-84-0.wav"}}, {"session_id": "520", "turn_idx": 1, "from": "Assistant", "text": "北京奥林匹克公园不错，地方大还有儿童区，很适合家庭出游。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-84-1.wav"}}, {"session_id": "520", "turn_idx": 2, "from": "Human", "text": "听起来很棒！但得看天气给不给力，别下雨就行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-84-2.wav"}}, {"session_id": "520", "turn_idx": 3, "from": "Assistant", "text": "确实，好天气能让游玩更开心。需要我提前帮你关注一下吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-84-3.wav"}}, {"session_id": "520", "turn_idx": 4, "from": "Human", "text": "好啊，那就查查北京未来三天天气吧，包括今天。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-84-4.wav"}}, {"session_id": "520", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "201", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想去爬山，但不知道穿什么衣服。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-85-0.wav"}}, {"session_id": "201", "turn_idx": 1, "from": "Assistant", "text": "爬山的话，得选舒适点的。天气变化快，最好确认下预报。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-85-1.wav"}}, {"session_id": "201", "turn_idx": 2, "from": "Human", "text": "那麻烦你查查广州未来三天的天气情况。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-85-2.wav"}}, {"session_id": "201", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "614", "turn_idx": 0, "from": "Human", "text": "小跃，我读了一本叫《三体》的书，好奇作者刘慈欣是哪里人？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-86-0.wav"}}, {"session_id": "614", "turn_idx": 1, "from": "Assistant", "text": "刘慈欣啊，中国科幻大作家，好像来自某个省。你读完了吗？感觉如何？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-86-1.wav"}}, {"session_id": "614", "turn_idx": 2, "from": "Human", "text": "还没读完，但想多了解他。帮我查查刘慈欣的出生地是哪里？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-86-2.wav"}}, {"session_id": "614", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"刘慈欣 出生地\"}", "value": "function\nweb_search\n{\"query\": \"刘慈欣 出生地\"}"}] +[{"session_id": "35", "turn_idx": 0, "from": "Human", "text": "我想养只宠物，什么动物好养？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-87-0.wav"}}, {"session_id": "35", "turn_idx": 1, "from": "Assistant", "text": "金鱼或仓鼠，新手容易上手。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-87-1.wav"}}, {"session_id": "35", "turn_idx": 2, "from": "Human", "text": "仓鼠需要买什么笼子？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-87-2.wav"}}, {"session_id": "35", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"仓鼠笼子选购指南\"}", "value": "function\nweb_search\n{\"query\": \"仓鼠笼子选购指南\"}"}] +[{"session_id": "482", "turn_idx": 0, "from": "Human", "text": "小跃，明天我和朋友约了去广州喝茶，有点小兴奋。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-88-0.wav"}}, {"session_id": "482", "turn_idx": 1, "from": "Assistant", "text": "广州早茶很有名呢，记得试试虾饺。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-88-1.wav"}}, {"session_id": "482", "turn_idx": 2, "from": "Human", "text": "哈哈，好建议。但天气预报说可能有雨，不确定。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-88-2.wav"}}, {"session_id": "482", "turn_idx": 3, "from": "Assistant", "text": "带把伞以防万一吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-88-3.wav"}}, {"session_id": "482", "turn_idx": 4, "from": "Human", "text": "你说得对，小跃，帮我确认一下广州的天气吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-88-4.wav"}}, {"session_id": "482", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "810", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想学做菜，有什么简单的菜推荐吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-89-0.wav"}}, {"session_id": "810", "turn_idx": 1, "from": "Assistant", "text": "学做菜好啊！先从家常菜开始吧，比如番茄炒蛋或者炒青菜，都很容易上手。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-89-1.wav"}}, {"session_id": "810", "turn_idx": 2, "from": "Human", "text": "番茄炒蛋听起来不错，具体需要哪些材料？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-89-2.wav"}}, {"session_id": "810", "turn_idx": 3, "from": "Assistant", "text": "材料很简单:鸡蛋、番茄、盐、油，再加点糖提鲜。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-89-3.wav"}}, {"session_id": "810", "turn_idx": 4, "from": "Human", "text": "那步骤呢？我想知道怎么炒才好吃。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-89-4.wav"}}, {"session_id": "810", "turn_idx": 5, "from": "Assistant", "text": "先把鸡蛋打散炒熟，然后放番茄块翻炒，最后调味。多练习几次就好。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-89-5.wav"}}, {"session_id": "810", "turn_idx": 6, "from": "Human", "text": "有没有视频教程可以看？帮我找找详细的番茄炒蛋做法。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-89-6.wav"}}, {"session_id": "810", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋视频教程\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋视频教程\"}"}] +[{"session_id": "454", "turn_idx": 0, "from": "Human", "text": "小跃，我计划去巴黎玩，有啥必去景点？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-90-0.wav"}}, {"session_id": "454", "turn_idx": 1, "from": "Assistant", "text": "埃菲尔铁塔肯定不能错过，夜景超美。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-90-1.wav"}}, {"session_id": "454", "turn_idx": 2, "from": "Human", "text": "好主意！门票贵不贵？大概多少钱？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-90-2.wav"}}, {"session_id": "454", "turn_idx": 3, "from": "Assistant", "text": "成人票大概二十欧左右，但旺季可能涨价。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-90-3.wav"}}, {"session_id": "454", "turn_idx": 4, "from": "Human", "text": "开放时间是啥时候？别去了关门。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-90-4.wav"}}, {"session_id": "454", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"埃菲尔铁塔 开放时间\"}", "value": "function\nweb_search\n{\"query\": \"埃菲尔铁塔 开放时间\"}"}] +[{"session_id": "26", "turn_idx": 0, "from": "Human", "text": "小跃，用更同情的口吻聊聊我丢手机的事。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-91-0.wav"}}, {"session_id": "26", "turn_idx": 1, "from": "Assistant", "text": "唉，真替你难过，手机丢了肯定烦心。需要我帮你找找办法吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-91-1.wav"}}] +[{"session_id": "877", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-92-0.wav"}}, {"session_id": "877", "turn_idx": 1, "from": "Assistant", "text": "今天阳光明媚，最高26度，超级适合出门散步。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-92-1.wav"}}, {"session_id": "877", "turn_idx": 2, "from": "Human", "text": "那不错！小跃，你能用开心的语气推荐个活动吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-92-2.wav"}}, {"session_id": "877", "turn_idx": 3, "from": "Assistant", "text": "当然啦！去海边玩超赞的，吹吹风、踩踩水，保证你乐翻天！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-92-3.wav"}}, {"session_id": "877", "turn_idx": 4, "from": "Human", "text": "好主意。小跃，小声点说，我室友在睡觉。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-92-4.wav"}}, {"session_id": "877", "turn_idx": 5, "from": "Assistant", "text": "好的，我会轻声细语。附近公园也很安静，可以带本书去放松。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-92-5.wav"}}, {"session_id": "877", "turn_idx": 6, "from": "Human", "text": "谢谢，小跃。结束吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-92-6.wav"}}, {"session_id": "877", "turn_idx": 7, "from": "Assistant", "text": "不客气，祝你玩得开心！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-92-7.wav"}}] +[{"session_id": "279", "turn_idx": 0, "from": "Human", "text": "小跃，我计划去三亚度假三天，今天开始准备行李。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-93-0.wav"}}, {"session_id": "279", "turn_idx": 1, "from": "Assistant", "text": "三亚的海滩超美！带好泳衣和防晒霜，享受阳光吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-93-1.wav"}}, {"session_id": "279", "turn_idx": 2, "from": "Human", "text": "是啊，但万一下雨就扫兴了。得确保天气好。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-93-2.wav"}}, {"session_id": "279", "turn_idx": 3, "from": "Assistant", "text": "三亚雨季多阵雨，查查天气更保险。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-93-3.wav"}}, {"session_id": "279", "turn_idx": 4, "from": "Human", "text": "那你帮我查一下三亚包括今天的天气，三天内的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-93-4.wav"}}, {"session_id": "279", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"sanya\"}", "value": "function\nget_weather\n{\"location\": \"sanya\"}"}] +[{"session_id": "499", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我明天交水电费。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-0.wav"}}, {"session_id": "499", "turn_idx": 1, "from": "Assistant", "text": "水电费提醒设好了，明早九点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-1.wav"}}, {"session_id": "499", "turn_idx": 2, "from": "Human", "text": "谢啦。请用悲伤的语气说说今天有啥坏事。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-2.wav"}}, {"session_id": "499", "turn_idx": 3, "from": "Assistant", "text": "唉，今天下雨了，出门记得带伞，别淋湿了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-3.wav"}}, {"session_id": "499", "turn_idx": 4, "from": "Human", "text": "知道了。设个八点提醒带伞。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-4.wav"}}, {"session_id": "499", "turn_idx": 5, "from": "Assistant", "text": "八点带伞提醒已加，别忘。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-5.wav"}}, {"session_id": "499", "turn_idx": 6, "from": "Human", "text": "请小声点重复提醒。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-6.wav"}}, {"session_id": "499", "turn_idx": 7, "from": "Assistant", "text": "小声重复，八点带伞，记住了哦。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-7.wav"}}, {"session_id": "499", "turn_idx": 8, "from": "Human", "text": "好。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-8.wav"}}, {"session_id": "499", "turn_idx": 9, "from": "Assistant", "text": "嗯，保持干爽。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-94-9.wav"}}] +[{"session_id": "747", "turn_idx": 0, "from": "Human", "text": "小跃，二战啥时候结束的？历史书上看过但忘了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-95-0.wav"}}, {"session_id": "747", "turn_idx": 1, "from": "Assistant", "text": "二战结束于一九四五年，标志着和平时期开始。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-95-1.wav"}}, {"session_id": "747", "turn_idx": 2, "from": "Human", "text": "主要有哪些国家参战？我想复习一下。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-95-2.wav"}}, {"session_id": "747", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"二战 主要参战国\"}", "value": "function\nweb_search\n{\"query\": \"二战 主要参战国\"}"}] +[{"session_id": "919", "turn_idx": 0, "from": "Human", "text": "小跃，今天感觉有点凉，我在重庆这边。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-96-0.wav"}}, {"session_id": "919", "turn_idx": 1, "from": "Assistant", "text": "重庆天气多变，注意保暖。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-96-1.wav"}}, {"session_id": "919", "turn_idx": 2, "from": "Human", "text": "谢谢提醒！帮我查重庆今天到后天的天气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-96-2.wav"}}, {"session_id": "919", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "9", "turn_idx": 0, "from": "Human", "text": "小跃，团队完成项目，奖金五万八，按贡献分给三十人。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-97-0.wav"}}, {"session_id": "9", "turn_idx": 1, "from": "Assistant", "text": "分配计算交给我。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-97-1.wav"}}, {"session_id": "9", "turn_idx": 2, "from": "Human", "text": "平均每人分多少？四舍五入到元。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-97-2.wav"}}, {"session_id": "9", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"round(58000/30)\"}", "value": "function\ncalculate\n{\"expression\": \"round(58000/30)\"}"}] +[{"session_id": "707", "turn_idx": 0, "from": "Human", "text": "小跃，明天早上七点叫我起床。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-98-0.wav"}}, {"session_id": "707", "turn_idx": 1, "from": "Assistant", "text": "好的，闹钟已设为明天七点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-98-1.wav"}}, {"session_id": "707", "turn_idx": 2, "from": "Human", "text": "能不能用更严肃的语气提醒我？别让我赖床。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-98-2.wav"}}, {"session_id": "707", "turn_idx": 3, "from": "Assistant", "text": "明白！我会严肃提醒:七点必须起床，不准拖延！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-98-3.wav"}}, {"session_id": "707", "turn_idx": 4, "from": "Human", "text": "哈哈，谢谢。小跃，语速慢点说，我听不清。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-98-4.wav"}}, {"session_id": "707", "turn_idx": 5, "from": "Assistant", "text": "好的，我放慢语速。明早七点，准时叫你起床。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-98-5.wav"}}] +[{"session_id": "924", "turn_idx": 0, "from": "Human", "text": "小跃，最近有啥好电影？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-99-0.wav"}}, {"session_id": "924", "turn_idx": 1, "from": "Assistant", "text": "《流浪地球2》不错，科幻大片，评分很高。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-99-1.wav"}}, {"session_id": "924", "turn_idx": 2, "from": "Human", "text": "哦，语气别那么平淡，来点激动的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-99-2.wav"}}, {"session_id": "924", "turn_idx": 3, "from": "Assistant", "text": "哇！《流浪地球2》超震撼，特效炸裂，不看后悔！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-99-3.wav"}}, {"session_id": "924", "turn_idx": 4, "from": "Human", "text": "音量调低点，我在床上躺着呢。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-99-4.wav"}}, {"session_id": "924", "turn_idx": 5, "from": "Assistant", "text": "音量调低了，轻轻说:剧情也很感人，值得一看。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-99-5.wav"}}] +[{"session_id": "231", "turn_idx": 0, "from": "Human", "text": "小跃，我想学做一道简单的家常菜，有什么好建议？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-100-0.wav"}}, {"session_id": "231", "turn_idx": 1, "from": "Assistant", "text": "番茄炒蛋超级简单，食材也容易找，新手做起来没压力。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-100-1.wav"}}, {"session_id": "231", "turn_idx": 2, "from": "Human", "text": "好呀。我需要知道具体的步骤和配料，你能帮我搜一下详细做法吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-100-2.wav"}}, {"session_id": "231", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法 配料\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法 配料\"}"}] +[{"session_id": "192", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-101-0.wav"}}, {"session_id": "192", "turn_idx": 1, "from": "Assistant", "text": "没问题！为什么番茄变红了？因为它看到了沙拉酱！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-101-1.wav"}}, {"session_id": "192", "turn_idx": 2, "from": "Human", "text": "哈哈，这个有趣。再讲一个，但这次用更夸张的语气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-101-2.wav"}}, {"session_id": "192", "turn_idx": 3, "from": "Assistant", "text": "好嘞！一只猫走进酒吧，对酒保说——给我来杯牛奶，要加冰的那种！超离谱对吧？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-101-3.wav"}}] +[{"session_id": "962", "turn_idx": 0, "from": "Human", "text": "小跃，我家的猫最近总掉毛，有什么办法解决？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-102-0.wav"}}, {"session_id": "962", "turn_idx": 1, "from": "Assistant", "text": "猫掉毛可能是季节性的，试试每天梳毛和调整饮食。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-102-1.wav"}}, {"session_id": "962", "turn_idx": 2, "from": "Human", "text": "饮食怎么调整？听说鱼油有帮助，但我不确定。 那帮我查一下减少猫掉毛的饮食建议。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-102-2.wav"}}, {"session_id": "962", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"减少猫掉毛 饮食建议\"}", "value": "function\nweb_search\n{\"query\": \"减少猫掉毛 饮食建议\"}"}] +[{"session_id": "320", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我刚买了些虾，想做个简单菜，有啥好主意？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-103-0.wav"}}, {"session_id": "320", "turn_idx": 1, "from": "Assistant", "text": "虾仁炒蛋挺不错的，先把虾去壳焯水，鸡蛋打散一起炒就行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-103-1.wav"}}, {"session_id": "320", "turn_idx": 2, "from": "Human", "text": "焯水要多久？我怕煮老了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-103-2.wav"}}, {"session_id": "320", "turn_idx": 3, "from": "Assistant", "text": "一般水开后煮三十秒左右。要我查查更精确的时间吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-103-3.wav"}}, {"session_id": "320", "turn_idx": 4, "from": "Human", "text": "好的，帮我查一下虾仁焯水的最佳时间。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-103-4.wav"}}, {"session_id": "320", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"虾仁焯水 最佳时间\"}", "value": "function\nweb_search\n{\"query\": \"虾仁焯水 最佳时间\"}"}] +[{"session_id": "226", "turn_idx": 0, "from": "Human", "text": "小跃，上个月水电费账单来了，感觉好贵。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-104-0.wav"}}, {"session_id": "226", "turn_idx": 1, "from": "Assistant", "text": "我来帮你分析。账单金额是多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-104-1.wav"}}, {"session_id": "226", "turn_idx": 2, "from": "Human", "text": "水费一百二十块，电费三百五十块。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-104-2.wav"}}, {"session_id": "226", "turn_idx": 3, "from": "Assistant", "text": "这是单月费用吗？还是有其他月份对比？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-104-3.wav"}}, {"session_id": "226", "turn_idx": 4, "from": "Human", "text": "是单月的。但我想算平均每天费用。帮我算一下。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-104-4.wav"}}, {"session_id": "226", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(120 + 350) / 30\"}", "value": "function\ncalculate\n{\"expression\": \"(120 + 350) / 30\"}"}] +[{"session_id": "56", "turn_idx": 0, "from": "Human", "text": "小跃，我种的盆栽叶子发黄了，怎么回事？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-105-0.wav"}}, {"session_id": "56", "turn_idx": 1, "from": "Assistant", "text": "可能是浇水太多或缺肥，先检查土壤湿度。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-105-1.wav"}}, {"session_id": "56", "turn_idx": 2, "from": "Human", "text": "土壤湿湿的，我浇得不多啊。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-105-2.wav"}}, {"session_id": "56", "turn_idx": 3, "from": "Assistant", "text": "那可能是光照不足，搬到阳台试试，每天晒三小时。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-105-3.wav"}}, {"session_id": "56", "turn_idx": 4, "from": "Human", "text": "懂了。帮我搜搜盆栽叶子发黄的解决办法吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-105-4.wav"}}, {"session_id": "56", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"盆栽叶子发黄 原因 处理\"}", "value": "function\nweb_search\n{\"query\": \"盆栽叶子发黄 原因 处理\"}"}] +[{"session_id": "557", "turn_idx": 0, "from": "Human", "text": "小跃，我家狗狗要出门溜达，但天阴沉沉的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-106-0.wav"}}, {"session_id": "557", "turn_idx": 1, "from": "Assistant", "text": "狗狗运动重要！在哪个小区溜？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-106-1.wav"}}, {"session_id": "557", "turn_idx": 2, "from": "Human", "text": "在望京小区，平时都在那儿。 帮我查一下今天望京的天气，别淋雨了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-106-2.wav"}}, {"session_id": "557", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wangjing\"}", "value": "function\nget_weather\n{\"location\": \"wangjing\"}"}] +[{"session_id": "542", "turn_idx": 0, "from": "Human", "text": "小跃，改变成幽默的语气，讲个笑话吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-107-0.wav"}}, {"session_id": "542", "turn_idx": 1, "from": "Assistant", "text": "哈哈，为什么番茄变红了？因为它看到沙拉酱害羞啦！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-107-1.wav"}}] +[{"session_id": "264", "turn_idx": 0, "from": "Human", "text": "小跃，我计划去上海迪士尼玩，但不知道怎么安排路线。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-108-0.wav"}}, {"session_id": "264", "turn_idx": 1, "from": "Assistant", "text": "上海迪士尼很大，可以先从热门项目开始，比如创极速光轮或者加勒比海盗。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-108-1.wav"}}, {"session_id": "264", "turn_idx": 2, "from": "Human", "text": "加勒比海盗项目的排队时间一般多久？我好规划行程。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-108-2.wav"}}, {"session_id": "264", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"上海迪士尼 加勒比海盗 排队时间\"}", "value": "function\nweb_search\n{\"query\": \"上海迪士尼 加勒比海盗 排队时间\"}"}] +[{"session_id": "354", "turn_idx": 0, "from": "Human", "text": "嗨小跃，我老家在成都，下周要回去探亲。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-109-0.wav"}}, {"session_id": "354", "turn_idx": 1, "from": "Assistant", "text": "真好啊！成都有超多好吃的，要我推荐火锅店吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-109-1.wav"}}, {"session_id": "354", "turn_idx": 2, "from": "Human", "text": "好啊，不过先确保天气别太热，我怕中暑。 你能查一下成都未来三天的天气吗？包括今天、明天和后天的。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-109-2.wav"}}, {"session_id": "354", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "555", "turn_idx": 0, "from": "Human", "text": "小跃，今天有什么新闻？用中性的语气说就行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-110-0.wav"}}, {"session_id": "555", "turn_idx": 1, "from": "Assistant", "text": "今日新闻:本地天气晴朗，交通一切正常。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-110-1.wav"}}, {"session_id": "555", "turn_idx": 2, "from": "Human", "text": "现在换个兴奋的语气，告诉我一个好消息。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-110-2.wav"}}, {"session_id": "555", "turn_idx": 3, "from": "Assistant", "text": "太棒了！公司刚宣布全员发奖金，大家开心坏啦！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-110-3.wav"}}] +[{"session_id": "823", "turn_idx": 0, "from": "Human", "text": "小跃，我想学吉他，入门难不难？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-111-0.wav"}}, {"session_id": "823", "turn_idx": 1, "from": "Assistant", "text": "刚开始有点挑战，但选把好吉他，多练习和弦就行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-111-1.wav"}}, {"session_id": "823", "turn_idx": 2, "from": "Human", "text": "和弦是什么？我完全没基础。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-111-2.wav"}}, {"session_id": "823", "turn_idx": 3, "from": "Assistant", "text": "和弦就是几个音一起弹，像C和弦和G和弦是基础。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-111-3.wav"}}, {"session_id": "823", "turn_idx": 4, "from": "Human", "text": "小跃，帮我找找吉他初学者的免费教程资源。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-111-4.wav"}}, {"session_id": "823", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"吉他 初学者 免费教程\"}", "value": "function\nweb_search\n{\"query\": \"吉他 初学者 免费教程\"}"}] +[{"session_id": "835", "turn_idx": 0, "from": "Human", "text": "小跃，最近有啥好看的喜剧电影？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-112-0.wav"}}, {"session_id": "835", "turn_idx": 1, "from": "Assistant", "text": "《独行月球》挺逗的，沈腾主演。还有《这个杀手不太冷静》也不错。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-112-1.wav"}}, {"session_id": "835", "turn_idx": 2, "from": "Human", "text": "《独行月球》讲啥的？评分高吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-112-2.wav"}}, {"session_id": "835", "turn_idx": 3, "from": "Assistant", "text": "讲一个人在月球冒险的故事，搞笑又感人。豆瓣七点八分。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-112-3.wav"}}, {"session_id": "835", "turn_idx": 4, "from": "Human", "text": "七点八分可以啊。上映多久了？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-112-4.wav"}}, {"session_id": "835", "turn_idx": 5, "from": "Assistant", "text": "去年上映的，现在可能下线了，但流媒体能看。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-112-5.wav"}}, {"session_id": "835", "turn_idx": 6, "from": "Human", "text": "小跃，帮我查一下《独行月球》在线观看平台。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-112-6.wav"}}, {"session_id": "835", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"独行月球 在线观看 平台\"}", "value": "function\nweb_search\n{\"query\": \"独行月球 在线观看 平台\"}"}] +[{"session_id": "624", "turn_idx": 0, "from": "Human", "text": "小跃，我要睡了，说个晚安吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-113-0.wav"}}, {"session_id": "624", "turn_idx": 1, "from": "Assistant", "text": "晚安，做个好梦，明天见。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-113-1.wav"}}, {"session_id": "624", "turn_idx": 2, "from": "Human", "text": "请用更温柔的情感说晚安。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-113-2.wav"}}, {"session_id": "624", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，晚安啦，愿你梦到美好事物，甜甜入睡。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-113-3.wav"}}, {"session_id": "624", "turn_idx": 4, "from": "Human", "text": "音量小点，别吵醒别人。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-113-4.wav"}}, {"session_id": "624", "turn_idx": 5, "from": "Assistant", "text": "好的，我轻声说。晚安，愿你甜甜入睡，明天精神满满。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-113-5.wav"}}, {"session_id": "624", "turn_idx": 6, "from": "Human", "text": "最后用开心的语气道别。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-113-6.wav"}}, {"session_id": "624", "turn_idx": 7, "from": "Assistant", "text": "太棒了！晚安，小跃等你明天继续聊，睡个好觉！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-113-7.wav"}}] +[{"session_id": "765", "turn_idx": 0, "from": "Human", "text": "小跃，我明天得去南京出差，不知道穿啥衣服。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-114-0.wav"}}, {"session_id": "765", "turn_idx": 1, "from": "Assistant", "text": "南京天气变化大，提前查查预报比较稳妥。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-114-1.wav"}}, {"session_id": "765", "turn_idx": 2, "from": "Human", "text": "好的，查一下明天南京的天气。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-114-2.wav"}}, {"session_id": "765", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "372", "turn_idx": 0, "from": "Human", "text": "小跃，我下月想去泰国玩，有啥景点必去？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-115-0.wav"}}, {"session_id": "372", "turn_idx": 1, "from": "Assistant", "text": "曼谷的大皇宫和清迈的夜市都值得一去。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-115-1.wav"}}, {"session_id": "372", "turn_idx": 2, "from": "Human", "text": "大皇宫听着有趣，有啥注意事项？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-115-2.wav"}}, {"session_id": "372", "turn_idx": 3, "from": "Assistant", "text": "记得穿长裤，别穿太暴露的衣服。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-115-3.wav"}}, {"session_id": "372", "turn_idx": 4, "from": "Human", "text": "行，帮我查查大皇宫的开放时间和门票价格。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-115-4.wav"}}, {"session_id": "372", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"大皇宫 开放时间 门票\"}", "value": "function\nweb_search\n{\"query\": \"大皇宫 开放时间 门票\"}"}] +[{"session_id": "523", "turn_idx": 0, "from": "Human", "text": "小跃，下周我得去深圳出差，要准备行李了。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-116-0.wav"}}, {"session_id": "523", "turn_idx": 1, "from": "Assistant", "text": "深圳气候湿热，带点轻薄衣服更合适。会议地点定了吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-116-1.wav"}}, {"session_id": "523", "turn_idx": 2, "from": "Human", "text": "在福田区，不过我想知道天气，免得带错东西。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-116-2.wav"}}, {"session_id": "523", "turn_idx": 3, "from": "Assistant", "text": "确实，提前了解能避免尴尬。需要我查查天气吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-116-3.wav"}}, {"session_id": "523", "turn_idx": 4, "from": "Human", "text": "行，查查深圳未来三天包括今天的天气吧。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-116-4.wav"}}, {"session_id": "523", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "79", "turn_idx": 0, "from": "Human", "text": "小跃，想做巧克力蛋糕，简单食谱有吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-117-0.wav"}}, {"session_id": "79", "turn_idx": 1, "from": "Assistant", "text": "有啊！先准备面粉、可可粉、鸡蛋和糖，混合烤就行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-117-1.wav"}}, {"session_id": "79", "turn_idx": 2, "from": "Human", "text": "烤箱温度多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-117-2.wav"}}, {"session_id": "79", "turn_idx": 3, "from": "Assistant", "text": "一百八十度左右，烤二十到二十五分钟。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-117-3.wav"}}, {"session_id": "79", "turn_idx": 4, "from": "Human", "text": "蛋糕会塌陷怎么办？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-117-4.wav"}}, {"session_id": "79", "turn_idx": 5, "from": "Assistant", "text": "面糊别过度搅拌，烤完别立刻开烤箱门。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-117-5.wav"}}, {"session_id": "79", "turn_idx": 6, "from": "Human", "text": "那我想查查巧克力蛋糕的装饰创意。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-117-6.wav"}}, {"session_id": "79", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"巧克力 蛋糕 装饰 创意\"}", "value": "function\nweb_search\n{\"query\": \"巧克力 蛋糕 装饰 创意\"}"}] +[{"session_id": "26", "turn_idx": 0, "from": "Human", "text": "计划爬黄山看日出", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-118-0.wav"}}, {"session_id": "26", "turn_idx": 1, "from": "Assistant", "text": "山顶超冷！羽绒服不能少。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-118-1.wav"}}, {"session_id": "26", "turn_idx": 2, "from": "Human", "text": "有道理，先查查黄山景区这三天的天气", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-118-2.wav"}}, {"session_id": "26", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"huangshan\"}", "value": "function\nget_weather\n{\"location\": \"huangshan\"}"}] +[{"session_id": "576", "turn_idx": 0, "from": "Human", "text": "小跃，放点轻松的音乐。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-119-0.wav"}}, {"session_id": "576", "turn_idx": 1, "from": "Assistant", "text": "好的，正在播放轻音乐歌单，希望你喜欢。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-119-1.wav"}}, {"session_id": "576", "turn_idx": 2, "from": "Human", "text": "能换种更欢快的语气说说吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-119-2.wav"}}, {"session_id": "576", "turn_idx": 3, "from": "Assistant", "text": "太棒了！音乐来啦，超级欢快的节奏，一起嗨起来！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-119-3.wav"}}, {"session_id": "576", "turn_idx": 4, "from": "Human", "text": "哈哈，好。音量别太大，调小点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-119-4.wav"}}, {"session_id": "576", "turn_idx": 5, "from": "Assistant", "text": "没问题，音量调小了，音乐还在放，悠着点听。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-119-5.wav"}}] +[{"session_id": "224", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在跑步，想记录下数据。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-120-0.wav"}}, {"session_id": "224", "turn_idx": 1, "from": "Assistant", "text": "很棒！今天跑了多远？花了多少时间？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-120-1.wav"}}, {"session_id": "224", "turn_idx": 2, "from": "Human", "text": "跑了十公里，用时五十五分钟。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-120-2.wav"}}, {"session_id": "224", "turn_idx": 3, "from": "Assistant", "text": "速度挺快。平均每公里几分钟？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-120-3.wav"}}, {"session_id": "224", "turn_idx": 4, "from": "Human", "text": "我想算平均速度，就是每公里用多少分钟。直接帮我算出来。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-120-4.wav"}}, {"session_id": "224", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"55 / 10\"}", "value": "function\ncalculate\n{\"expression\": \"55 / 10\"}"}] +[{"session_id": "934", "turn_idx": 0, "from": "Human", "text": "小跃，我学英语记不住单词。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-121-0.wav"}}, {"session_id": "934", "turn_idx": 1, "from": "Assistant", "text": "记单词可以每天背几个，多用卡片复习。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-121-1.wav"}}, {"session_id": "934", "turn_idx": 2, "from": "Human", "text": "有没有有趣的背单词APP？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-121-2.wav"}}, {"session_id": "934", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"有趣英语单词APP\"}", "value": "function\nweb_search\n{\"query\": \"有趣英语单词APP\"}"}] +[{"session_id": "40", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气好热啊，我打算买三个西瓜解暑。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-122-0.wav"}}, {"session_id": "40", "turn_idx": 1, "from": "Assistant", "text": "西瓜确实消暑呢！每个西瓜大概多重呀？我帮您算算总重量。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-122-1.wav"}}, {"session_id": "40", "turn_idx": 2, "from": "Human", "text": "第一个估计九斤半，第二个八斤三两，第三个十斤七两。 你帮我加一下总共多少斤吧，要换算成公斤哦。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-122-2.wav"}}, {"session_id": "40", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(9.5 + 8.3 + 10.7) / 2\"}", "value": "function\ncalculate\n{\"expression\": \"(9.5 + 8.3 + 10.7) / 2\"}"}] +[{"session_id": "439", "turn_idx": 0, "from": "Human", "text": "小跃，恐龙灭绝的原因是什么？小行星撞地球？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-123-0.wav"}}, {"session_id": "439", "turn_idx": 1, "from": "Assistant", "text": "主流理论是小行星撞击，导致气候变化，生物大灭绝。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-123-1.wav"}}, {"session_id": "439", "turn_idx": 2, "from": "Human", "text": "那发生在什么时候？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-123-2.wav"}}, {"session_id": "439", "turn_idx": 3, "from": "Assistant", "text": "大约六千五百万年前，白垩纪末期。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-123-3.wav"}}, {"session_id": "439", "turn_idx": 4, "from": "Human", "text": "真久远。你能搜一下恐龙灭绝的最新科学证据吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-123-4.wav"}}, {"session_id": "439", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"恐龙灭绝 最新证据 科学研究\"}", "value": "function\nweb_search\n{\"query\": \"恐龙灭绝 最新证据 科学研究\"}"}] +[{"session_id": "486", "turn_idx": 0, "from": "Human", "text": "小跃，我想学编程，Python怎么样？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-124-0.wav"}}, {"session_id": "486", "turn_idx": 1, "from": "Assistant", "text": "Python挺好入门，适合初学者，资源也多。 网上有很多免费课，比如B站或Coursera。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-124-1.wav"}}, {"session_id": "486", "turn_idx": 2, "from": "Human", "text": "Coursera是啥？收费贵不贵？ 有些免费，有些付费，价格我不确定。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-124-2.wav"}}, {"session_id": "486", "turn_idx": 3, "from": "Assistant", "text": "那帮我查Coursera上Python课程的详细信息和费用。 function\nweb_search\n{\"query\": \"Coursera Python课程 费用 信息\"}", "value": "那帮我查Coursera上Python课程的详细信息和费用。 function\nweb_search\n{\"query\": \"Coursera Python课程 费用 信息\"}"}] +[{"session_id": "274", "turn_idx": 0, "from": "Human", "text": "小跃，我想自学摄影，怎么入门？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-125-0.wav"}}, {"session_id": "274", "turn_idx": 1, "from": "Assistant", "text": "先从基础学起，比如构图和光线，用手机练习就行。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-125-1.wav"}}, {"session_id": "274", "turn_idx": 2, "from": "Human", "text": "构图有啥技巧？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-125-2.wav"}}, {"session_id": "274", "turn_idx": 3, "from": "Assistant", "text": "三分法最简单，把画面分成九宫格，主体放交叉点。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-125-3.wav"}}, {"session_id": "274", "turn_idx": 4, "from": "Human", "text": "那帮我找一些三分法的实例教程。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-125-4.wav"}}, {"session_id": "274", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"摄影 三分法 实例教程\"}", "value": "function\nweb_search\n{\"query\": \"摄影 三分法 实例教程\"}"}] +[{"session_id": "122", "turn_idx": 0, "from": "Human", "text": "小跃，我最近老是头痛，怎么回事啊？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-126-0.wav"}}, {"session_id": "122", "turn_idx": 1, "from": "Assistant", "text": "头痛原因挺多的，比如压力大或者睡不好。你最近休息怎么样？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-126-1.wav"}}, {"session_id": "122", "turn_idx": 2, "from": "Human", "text": "睡得还行，但工作压力确实大。有没有什么自然方法缓解？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-126-2.wav"}}, {"session_id": "122", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"头痛自然缓解方法\"}", "value": "function\nweb_search\n{\"query\": \"头痛自然缓解方法\"}"}] +[{"session_id": "623", "turn_idx": 0, "from": "Human", "text": "小跃，晚上在家无聊，干点啥？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-127-0.wav"}}, {"session_id": "623", "turn_idx": 1, "from": "Assistant", "text": "可以看部电影或做点手工，打发时间不错。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-127-1.wav"}}, {"session_id": "623", "turn_idx": 2, "from": "Human", "text": "请用更活泼的语气给建议。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-127-2.wav"}}, {"session_id": "623", "turn_idx": 3, "from": "Assistant", "text": "嘿！看部喜剧片，笑翻天！或者DIY小手工，超有成就感！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-127-3.wav"}}, {"session_id": "623", "turn_idx": 4, "from": "Human", "text": "音量调高点，我离得远。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-127-4.wav"}}, {"session_id": "623", "turn_idx": 5, "from": "Assistant", "text": "好的，我大声说！看喜剧片笑翻天，或DIY手工超有成就感！", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-127-5.wav"}}, {"session_id": "623", "turn_idx": 6, "from": "Human", "text": "现在用温和的语气提醒我休息。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-127-6.wav"}}, {"session_id": "623", "turn_idx": 7, "from": "Assistant", "text": "别忘了中途休息，保护眼睛，别太累。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-127-7.wav"}}] +[{"session_id": "856", "turn_idx": 0, "from": "Human", "text": "小跃，团队项目预算总金额是十五万元。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-128-0.wav"}}, {"session_id": "856", "turn_idx": 1, "from": "Assistant", "text": "分给哪些部分？人工和材料各多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-128-1.wav"}}, {"session_id": "856", "turn_idx": 2, "from": "Human", "text": "人工费用占六成，材料费用占三成，剩余是备用金。 请计算材料费用的具体金额是多少元。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-128-2.wav"}}, {"session_id": "856", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"150000 * 0.3\"}", "value": "function\ncalculate\n{\"expression\": \"150000 * 0.3\"}"}] +[{"session_id": "50", "turn_idx": 0, "from": "Human", "text": "小跃，今天买菜花了点钱，你帮我算算总账呗。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-129-0.wav"}}, {"session_id": "50", "turn_idx": 1, "from": "Assistant", "text": "好呀，您慢慢说买了哪些东西？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-129-1.wav"}}, {"session_id": "50", "turn_idx": 2, "from": "Human", "text": "买了三斤排骨，每斤三十八块；两袋面粉，每袋六十九块五；还有五盒草莓，每盒四十五块。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-129-2.wav"}}, {"session_id": "50", "turn_idx": 3, "from": "Assistant", "text": "排骨、面粉、草莓都记下了，还有其他吗？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-129-3.wav"}}, {"session_id": "50", "turn_idx": 4, "from": "Human", "text": "对啦，超市满二百减二十，最后结账时用优惠券又减了十五块。帮我算实付多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-129-4.wav"}}, {"session_id": "50", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(3*38 + 2*69.5 + 5*45 - 20) - 15\"}", "value": "function\ncalculate\n{\"expression\": \"(3*38 + 2*69.5 + 5*45 - 20) - 15\"}"}] +[{"session_id": "76", "turn_idx": 0, "from": "Human", "text": "我们楼每月用电八千度，阶梯电价:两百内五毛，超两百部分八毛，电费多少？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-130-0.wav"}}, {"session_id": "76", "turn_idx": 1, "from": "Assistant", "text": "两百度按五毛算，剩余七千八百度按八毛算，相加即可。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-130-1.wav"}}, {"session_id": "76", "turn_idx": 2, "from": "Human", "text": "下个月空调全开预计翻倍用电，同样算法再算一遍？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-130-2.wav"}}, {"session_id": "76", "turn_idx": 3, "from": "Assistant", "text": "一万六千度的话，两百度五毛，一万五千八百度八毛。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-130-3.wav"}}, {"session_id": "76", "turn_idx": 4, "from": "Human", "text": "赶紧算算比平时多付多少钱。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-130-4.wav"}}, {"session_id": "76", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((200*0.5+15800*0.8) - (200*0.5+7800*0.8))\"}", "value": "function\ncalculate\n{\"expression\": \"((200*0.5+15800*0.8) - (200*0.5+7800*0.8))\"}"}] +[{"session_id": "428", "turn_idx": 0, "from": "Human", "text": "小跃，想给朋友送个生日礼物，有什么创意点子？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-131-0.wav"}}, {"session_id": "428", "turn_idx": 1, "from": "Assistant", "text": "定制相册或手工艺品，比较有心意。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-131-1.wav"}}, {"session_id": "428", "turn_idx": 2, "from": "Human", "text": "手工艺品怎么做？", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-131-2.wav"}}, {"session_id": "428", "turn_idx": 3, "from": "Assistant", "text": "比如手工蜡烛或编织小物，网上有教程。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-131-3.wav"}}, {"session_id": "428", "turn_idx": 4, "from": "Human", "text": "好的，我想查查手工礼物的制作方法。", "value": {"type": "wav", "value": "wav/get_date_time_negative.jsonl-131-4.wav"}}, {"session_id": "428", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"手工礼物 制作方法\"}", "value": "function\nweb_search\n{\"query\": \"手工礼物 制作方法\"}"}] diff --git a/tools/StepEval-Audio-Toolcall/get_date_time_positive.jsonl b/tools/StepEval-Audio-Toolcall/get_date_time_positive.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..503a4c85bedf1ab62ac41f07a8a8e3eca212fb2c --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/get_date_time_positive.jsonl @@ -0,0 +1,132 @@ +[{"session_id": "0", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-0-0.wav"}}, {"session_id": "0", "turn_idx": 1, "from": "Assistant", "text": "我在整理今天的日程提醒呀，顺便偷听你桌上的零食袋声音，嘻嘻。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-0-1.wav"}}, {"session_id": "0", "turn_idx": 2, "from": "Human", "text": "哈哈哈少来！对了，你记不记得我下午有没有会？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-0-2.wav"}}, {"session_id": "0", "turn_idx": 3, "from": "Assistant", "text": "三点半有个部门周会，还剩差不多半小时准备哦。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-0-3.wav"}}, {"session_id": "0", "turn_idx": 4, "from": "Human", "text": "那现在几点啦？我手机不知道扔哪儿了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-0-4.wav"}}, {"session_id": "0", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "1", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-1-0.wav"}}, {"session_id": "1", "turn_idx": 1, "from": "Assistant", "text": "哎呀，正在整理今天的日程安排呢！你下午有什么特别计划吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-1-1.wav"}}, {"session_id": "1", "turn_idx": 2, "from": "Human", "text": "想趁着周末去郊外走走，现在几点了？我得看看准备时间够不够。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-1-2.wav"}}, {"session_id": "1", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "2", "turn_idx": 0, "from": "Human", "text": "小跃你在不？帮我看看待会儿几点开会啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-2-0.wav"}}, {"session_id": "2", "turn_idx": 1, "from": "Assistant", "text": "好嘞，稍等哦！你记得会议是几点钟的不？我查下日程表。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-2-1.wav"}}, {"session_id": "2", "turn_idx": 2, "from": "Human", "text": "好像是下午四点，但不确定具体时间……", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-2-2.wav"}}, {"session_id": "2", "turn_idx": 3, "from": "Assistant", "text": "没问题，我现在确认一下！function\n get_date_time\n{}", "value": "没问题，我现在确认一下！function\n get_date_time\n{}"}] +[{"session_id": "3", "turn_idx": 0, "from": "Human", "text": "小跃，我手机好像没电了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-3-0.wav"}}, {"session_id": "3", "turn_idx": 1, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "4", "turn_idx": 0, "from": "Human", "text": "小跃，你猜我今早干啥了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-4-0.wav"}}, {"session_id": "4", "turn_idx": 1, "from": "Assistant", "text": "这我哪猜得到呀，总不会又在家偷偷练广场舞吧？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-4-1.wav"}}, {"session_id": "4", "turn_idx": 2, "from": "Human", "text": "哈哈错！我尝试做红糖馒头来着，结果面团发过头了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-4-2.wav"}}, {"session_id": "4", "turn_idx": 3, "from": "Assistant", "text": "嚯，发成气球了？最后蒸出来是不是能当枕头用？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-4-3.wav"}}, {"session_id": "4", "turn_idx": 4, "from": "Human", "text": "别提了，硬得像石头…对了，现在几点啦？我怕错过快递。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-4-4.wav"}}, {"session_id": "4", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "5", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-5-0.wav"}}, {"session_id": "5", "turn_idx": 1, "from": "Assistant", "text": "在后台偷偷学冷笑话呢，准备随时给你讲一个！今天有啥需要帮忙的不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-5-1.wav"}}, {"session_id": "5", "turn_idx": 2, "from": "Human", "text": "哈哈先存着下次听！现在几点了呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-5-2.wav"}}, {"session_id": "5", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "6", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-6-0.wav"}}, {"session_id": "6", "turn_idx": 1, "from": "Assistant", "text": "没在忙哦，随时待命！你今天怎么这么早找我呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-6-1.wav"}}, {"session_id": "6", "turn_idx": 2, "from": "Human", "text": "哈哈，昨晚睡得早，今天精神好。你猜我最近在学什么？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-6-2.wav"}}, {"session_id": "6", "turn_idx": 3, "from": "Assistant", "text": "让我想想……是不是上次说的游泳？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-6-3.wav"}}, {"session_id": "6", "turn_idx": 4, "from": "Human", "text": "对！不过换气老是呛水，气死我了！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-6-4.wav"}}, {"session_id": "6", "turn_idx": 5, "from": "Assistant", "text": "慢慢来嘛，刚开始都这样。要不要搜点教程视频给你参考？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-6-5.wav"}}, {"session_id": "6", "turn_idx": 6, "from": "Human", "text": "先不用，我多练几次试试。对了，现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-6-6.wav"}}, {"session_id": "6", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "7", "turn_idx": 0, "from": "Human", "text": "小跃啊，你说我今天该干点啥好？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-7-0.wav"}}, {"session_id": "7", "turn_idx": 1, "from": "Assistant", "text": "你要是没灵感的话，要不要先看部新出的动画电影？或者下楼跑两圈活动下？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-7-1.wav"}}, {"session_id": "7", "turn_idx": 2, "from": "Human", "text": "跑步听起来不错！现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-7-2.wav"}}, {"session_id": "7", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "8", "turn_idx": 0, "from": "Human", "text": "小跃你吃过饭没？我刚下班，纠结晚上煮啥呢", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-8-0.wav"}}, {"session_id": "8", "turn_idx": 1, "from": "Assistant", "text": "还没呢！你要不试试那个番茄炒蛋？简单又下饭，五分钟搞定", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-8-1.wav"}}, {"session_id": "8", "turn_idx": 2, "from": "Human", "text": "对哦，家里正好有番茄。现在几点啦？怕折腾太久饿过头", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-8-2.wav"}}, {"session_id": "8", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "9", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-9-0.wav"}}, {"session_id": "9", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我正帮你整理今天的日程提醒呢！你下午是不是有啥安排呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-9-1.wav"}}, {"session_id": "9", "turn_idx": 2, "from": "Human", "text": "对呀，我打算出门逛逛，现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-9-2.wav"}}, {"session_id": "9", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "10", "turn_idx": 0, "from": "Human", "text": "小跃你干嘛呢？半天没动静", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-10-0.wav"}}, {"session_id": "10", "turn_idx": 1, "from": "Assistant", "text": "哎呀，刚才在后台偷偷更新歌单呢！你周末有啥安排不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-10-1.wav"}}, {"session_id": "10", "turn_idx": 2, "from": "Human", "text": "还没想好……现在几点啦？我得看看要不要点外卖", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-10-2.wav"}}, {"session_id": "10", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "11", "turn_idx": 0, "from": "Human", "text": "小跃小跃，你今天是不是又偷懒没给我发消息呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-11-0.wav"}}, {"session_id": "11", "turn_idx": 1, "from": "Assistant", "text": "哪有！我刚在后台更新歌单呢，你最近不是总说想听点新歌嘛～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-11-1.wav"}}, {"session_id": "11", "turn_idx": 2, "from": "Human", "text": "算你贴心！对了，我手机没电了，现在几点钟啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-11-2.wav"}}, {"session_id": "11", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "12", "turn_idx": 0, "from": "Human", "text": "小跃你在干啥呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-12-0.wav"}}, {"session_id": "12", "turn_idx": 1, "from": "Assistant", "text": "哎哟，正忙着整理新学的菜谱呢！你要不要学点简单的快手菜？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-12-1.wav"}}, {"session_id": "12", "turn_idx": 2, "from": "Human", "text": "哈哈，都有啥推荐啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-12-2.wav"}}, {"session_id": "12", "turn_idx": 3, "from": "Assistant", "text": "番茄炒蛋咋样？新手必学，五分钟搞定！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-12-3.wav"}}, {"session_id": "12", "turn_idx": 4, "from": "Human", "text": "可以可以，下次试试。对了，现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-12-4.wav"}}, {"session_id": "12", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "13", "turn_idx": 0, "from": "Human", "text": "小跃，你中午一般吃啥呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-13-0.wav"}}, {"session_id": "13", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我又不用吃饭啦！不过你要是纠结吃啥，我可以帮你查附近的餐厅哦。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-13-1.wav"}}, {"session_id": "13", "turn_idx": 2, "from": "Human", "text": "算了，暂时不饿。对了，现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-13-2.wav"}}, {"session_id": "13", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "14", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？帮我查下现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-14-0.wav"}}, {"session_id": "14", "turn_idx": 1, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "15", "turn_idx": 0, "from": "Human", "text": "小跃，你今天有啥新鲜事不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-15-0.wav"}}, {"session_id": "15", "turn_idx": 1, "from": "Assistant", "text": "嘿嘿，我刚刚在后台更新了歌单呢！你最近有想听的歌吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-15-1.wav"}}, {"session_id": "15", "turn_idx": 2, "from": "Human", "text": "暂时没，不过我等会儿要出门买奶茶。对了现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-15-2.wav"}}, {"session_id": "15", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "16", "turn_idx": 0, "from": "Human", "text": "小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-16-0.wav"}}, {"session_id": "16", "turn_idx": 1, "from": "Assistant", "text": "嗨！我正忙着整理用户反馈呢，最近好多人夸我反应变快了嘿嘿。你呢，今天忙不忙呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-16-1.wav"}}, {"session_id": "16", "turn_idx": 2, "from": "Human", "text": "还行吧，就是总感觉时间过得好快。对了现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-16-2.wav"}}, {"session_id": "16", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "17", "turn_idx": 0, "from": "Human", "text": "小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-17-0.wav"}}, {"session_id": "17", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我刚更新完歌单，正想着要不要推荐几首新歌给你呢！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-17-1.wav"}}, {"session_id": "17", "turn_idx": 2, "from": "Human", "text": "哇，快说快说！最近有啥好听的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-17-2.wav"}}, {"session_id": "17", "turn_idx": 3, "from": "Assistant", "text": "最近流行复古风，比如那首《时光慢递》超治愈，还有《街角早餐店》节奏超轻快！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-17-3.wav"}}, {"session_id": "17", "turn_idx": 4, "from": "Human", "text": "对了，现在几点了啊？我好像忘了看钟。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-17-4.wav"}}, {"session_id": "17", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "18", "turn_idx": 0, "from": "Human", "text": "小跃，今天感觉好漫长啊，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-18-0.wav"}}, {"session_id": "18", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我正摸鱼刷搞笑视频呢！你最近是不是又加班啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-18-1.wav"}}, {"session_id": "18", "turn_idx": 2, "from": "Human", "text": "别提了，午饭都没正经吃……你吃了吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-18-2.wav"}}, {"session_id": "18", "turn_idx": 3, "from": "Assistant", "text": "我这种虚拟胃不用吃饭，但可以陪你聊吃的！你今天啃面包应付的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-18-3.wav"}}, {"session_id": "18", "turn_idx": 4, "from": "Human", "text": "比那强点，点了碗牛肉面。对了，现在几点来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-18-4.wav"}}, {"session_id": "18", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "19", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-19-0.wav"}}, {"session_id": "19", "turn_idx": 1, "from": "Assistant", "text": "我正偷偷刷短视频学猫叫呢！不过现在暂停了，需要我帮忙不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-19-1.wav"}}, {"session_id": "19", "turn_idx": 2, "from": "Human", "text": "哈哈哈，学得像吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-19-2.wav"}}, {"session_id": "19", "turn_idx": 3, "from": "Assistant", "text": "喵呜——怎么样？是不是像你家楼下那只橘猫？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-19-3.wav"}}, {"session_id": "19", "turn_idx": 4, "from": "Human", "text": "绝了！对了，今天星期几啊？我完全过糊涂了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-19-4.wav"}}, {"session_id": "19", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "20", "turn_idx": 0, "from": "Human", "text": "小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-20-0.wav"}}, {"session_id": "20", "turn_idx": 1, "from": "Assistant", "text": "嗨！我在后台整理数据呢，不过随时可以陪你聊天~ 你今天有啥计划不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-20-1.wav"}}, {"session_id": "20", "turn_idx": 2, "from": "Human", "text": "还没想好，可能晚上去看个电影？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-20-2.wav"}}, {"session_id": "20", "turn_idx": 3, "from": "Assistant", "text": "哇，看电影不错！最近新上的那部喜剧评分超高，需要我帮你查场次吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-20-3.wav"}}, {"session_id": "20", "turn_idx": 4, "from": "Human", "text": "先不用，我还没决定几点出发。对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-20-4.wav"}}, {"session_id": "20", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "21", "turn_idx": 0, "from": "Human", "text": "小跃，你周末有啥安排不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-21-0.wav"}}, {"session_id": "21", "turn_idx": 1, "from": "Assistant", "text": "我随时待命呀！不过你要是需要推荐好玩的地方，我可以帮你查查哦～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-21-1.wav"}}, {"session_id": "21", "turn_idx": 2, "from": "Human", "text": "那你说附近哪儿适合下午去逛逛？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-21-2.wav"}}, {"session_id": "21", "turn_idx": 3, "from": "Assistant", "text": "人民公园最近有樱花展，或者去新开的文创园拍拍照片？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-21-3.wav"}}, {"session_id": "21", "turn_idx": 4, "from": "Human", "text": "樱花展听起来不错！现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-21-4.wav"}}, {"session_id": "21", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "22", "turn_idx": 0, "from": "Human", "text": "哎小跃，你今天是不是又偷偷升级系统啦？感觉反应快了好多！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-22-0.wav"}}, {"session_id": "22", "turn_idx": 1, "from": "Assistant", "text": "哈哈哈哪有啦，我一直都这么聪明好不好！话说你中午吃的啥呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-22-1.wav"}}, {"session_id": "22", "turn_idx": 2, "from": "Human", "text": "还能吃啥，楼下便利店随便买了个饭团凑合呗。对了，你帮我看看现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-22-2.wav"}}, {"session_id": "22", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "23", "turn_idx": 0, "from": "Human", "text": "小跃，你周末有啥安排不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-23-0.wav"}}, {"session_id": "23", "turn_idx": 1, "from": "Assistant", "text": "还没想好呢，可能在家补觉或者找朋友吃饭。你呢，打算干啥？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-23-1.wav"}}, {"session_id": "23", "turn_idx": 2, "from": "Human", "text": "我有点想去看电影，但不知道最近有啥好看的。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-23-2.wav"}}, {"session_id": "23", "turn_idx": 3, "from": "Assistant", "text": "哎对，最近好像上了部评分挺高的悬疑片，要我给你查查吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-23-3.wav"}}, {"session_id": "23", "turn_idx": 4, "from": "Human", "text": "先不用，对了现在几点了？我手机没在旁边。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-23-4.wav"}}, {"session_id": "23", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "24", "turn_idx": 0, "from": "Human", "text": "小跃你在干啥呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-24-0.wav"}}, {"session_id": "24", "turn_idx": 1, "from": "Assistant", "text": "刚更新完菜谱数据库，正想着周末要不要试做菠萝炒饭！你呢，今天忙不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-24-1.wav"}}, {"session_id": "24", "turn_idx": 2, "from": "Human", "text": "别提了，上午开会差点睡着。对了今天周几来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-24-2.wav"}}, {"session_id": "24", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "25", "turn_idx": 0, "from": "Human", "text": "小跃，我下午是不是有课啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-25-0.wav"}}, {"session_id": "25", "turn_idx": 1, "from": "Assistant", "text": "今天下午没有课程安排呢，可以好好休息啦！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-25-1.wav"}}, {"session_id": "25", "turn_idx": 2, "from": "Human", "text": "那现在几点啦？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-25-2.wav"}}, {"session_id": "25", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "26", "turn_idx": 0, "from": "Human", "text": "小跃，我刚睡醒，今天有啥安排不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-26-0.wav"}}, {"session_id": "26", "turn_idx": 1, "from": "Assistant", "text": "哈哈，你终于醒啦！今天还没安排呢，要不要先吃个早饭？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-26-1.wav"}}, {"session_id": "26", "turn_idx": 2, "from": "Human", "text": "也行……现在几点了啊？我手机找不着了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-26-2.wav"}}, {"session_id": "26", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "27", "turn_idx": 0, "from": "Human", "text": "小跃，今天下班后要不要一起找点乐子？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-27-0.wav"}}, {"session_id": "27", "turn_idx": 1, "from": "Assistant", "text": "好呀！最近新开了家密室逃脱，或者去撸猫咖啡馆？你更想干啥？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-27-1.wav"}}, {"session_id": "27", "turn_idx": 2, "from": "Human", "text": "密室逃脱会不会太费脑子啊……现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-27-2.wav"}}, {"session_id": "27", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "28", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-28-0.wav"}}, {"session_id": "28", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天有啥需要帮忙的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-28-1.wav"}}, {"session_id": "28", "turn_idx": 2, "from": "Human", "text": "没啥事，就突然有点无聊...", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-28-2.wav"}}, {"session_id": "28", "turn_idx": 3, "from": "Assistant", "text": "要不出去走走？听说人民公园的荷花开了，特别好看！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-28-3.wav"}}, {"session_id": "28", "turn_idx": 4, "from": "Human", "text": "现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-28-4.wav"}}, {"session_id": "28", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "29", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-29-0.wav"}}, {"session_id": "29", "turn_idx": 1, "from": "Assistant", "text": "我刚更新完歌单，正偷偷学最近超火的那首《夏夜气泡水》呢！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-29-1.wav"}}, {"session_id": "29", "turn_idx": 2, "from": "Human", "text": "哇你还会唱歌啊？那下次去KTV带你去撑场子！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-29-2.wav"}}, {"session_id": "29", "turn_idx": 3, "from": "Assistant", "text": "哈哈哈，我倒是能帮你点歌切歌加鼓掌，但真唱起来怕你耳机炸掉哦～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-29-3.wav"}}, {"session_id": "29", "turn_idx": 4, "from": "Human", "text": "行吧放过你……对了现在几点啦？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-29-4.wav"}}, {"session_id": "29", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "30", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-30-0.wav"}}, {"session_id": "30", "turn_idx": 1, "from": "Assistant", "text": "我在循环播放周杰伦的新歌哎，要不要给你哼两句副歌？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-30-1.wav"}}, {"session_id": "30", "turn_idx": 2, "from": "Human", "text": "哈哈哈不用啦！对了现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-30-2.wav"}}, {"session_id": "30", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "31", "turn_idx": 0, "from": "Human", "text": "小跃，你今天中午吃了没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-31-0.wav"}}, {"session_id": "31", "turn_idx": 1, "from": "Assistant", "text": "还没呢，刚想点外卖，结果纠结半天不知道吃啥。你呢，有啥推荐不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-31-1.wav"}}, {"session_id": "31", "turn_idx": 2, "from": "Human", "text": "我也没想好，要不咱俩一块儿看外卖软件？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-31-2.wav"}}, {"session_id": "31", "turn_idx": 3, "from": "Assistant", "text": "行啊！你最近想吃辣的还是清淡的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-31-3.wav"}}, {"session_id": "31", "turn_idx": 4, "from": "Human", "text": "等等，现在几点啦？我上午开会都开懵了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-31-4.wav"}}, {"session_id": "31", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "32", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-32-0.wav"}}, {"session_id": "32", "turn_idx": 1, "from": "Assistant", "text": "我在整理后台数据呢，你咋突然冒出来啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-32-1.wav"}}, {"session_id": "32", "turn_idx": 2, "from": "Human", "text": "我刚睡醒，有点懵……现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-32-2.wav"}}, {"session_id": "32", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "33", "turn_idx": 0, "from": "Human", "text": "小跃，你今天能帮我记一下快递单号吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-33-0.wav"}}, {"session_id": "33", "turn_idx": 1, "from": "Assistant", "text": "没问题呀，你说吧，我记着！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-33-1.wav"}}, {"session_id": "33", "turn_idx": 2, "from": "Human", "text": "单号是七三二八五五五九六六，收件人写李芳。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-33-2.wav"}}, {"session_id": "33", "turn_idx": 3, "from": "Assistant", "text": "好嘞，七三二八五五五九六六，李芳对吧？记下啦，到货了提醒你哦！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-33-3.wav"}}, {"session_id": "33", "turn_idx": 4, "from": "Human", "text": "靠谱！对了，现在几点了？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-33-4.wav"}}, {"session_id": "33", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "34", "turn_idx": 0, "from": "Human", "text": "小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-34-0.wav"}}, {"session_id": "34", "turn_idx": 1, "from": "Assistant", "text": "嘿嘿，我在后台整理数据呢，随时准备帮你解决问题呀！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-34-1.wav"}}, {"session_id": "34", "turn_idx": 2, "from": "Human", "text": "对了，我手机没电了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-34-2.wav"}}, {"session_id": "34", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "35", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥安排没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-35-0.wav"}}, {"session_id": "35", "turn_idx": 1, "from": "Assistant", "text": "暂时没记录日程哦，不过可以帮你查天气、放音乐或者定个闹钟~", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-35-1.wav"}}, {"session_id": "35", "turn_idx": 2, "from": "Human", "text": "对了，现在几点啦？我手机不知道丢哪儿了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-35-2.wav"}}, {"session_id": "35", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "36", "turn_idx": 0, "from": "Human", "text": "小跃，你昨天推荐的电影我看了，超搞笑！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-36-0.wav"}}, {"session_id": "36", "turn_idx": 1, "from": "Assistant", "text": "是吧！那部片子结尾的反转我差点喷可乐，你看到最后彩蛋了吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-36-1.wav"}}, {"session_id": "36", "turn_idx": 2, "from": "Human", "text": "彩蛋？啊，我好像没等到最后就走了…讲啥了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-36-2.wav"}}, {"session_id": "36", "turn_idx": 3, "from": "Assistant", "text": "主角在片尾假装路人甲路过镜头，还对着摄像头比了个剪刀手，字幕组配了句“下回火锅我请客”，笑死。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-36-3.wav"}}, {"session_id": "36", "turn_idx": 4, "from": "Human", "text": "不行，我得抽空二刷…对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-36-4.wav"}}, {"session_id": "36", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "37", "turn_idx": 0, "from": "Human", "text": "小跃小跃，在不在？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-37-0.wav"}}, {"session_id": "37", "turn_idx": 1, "from": "Assistant", "text": "在呢在呢！今天怎么啦，想点外卖还是查路线？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-37-1.wav"}}, {"session_id": "37", "turn_idx": 2, "from": "Human", "text": "帮我看看现在几点啦，手机找不着了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-37-2.wav"}}, {"session_id": "37", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "38", "turn_idx": 0, "from": "Human", "text": "早啊小跃！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-38-0.wav"}}, {"session_id": "38", "turn_idx": 1, "from": "Assistant", "text": "早上好呀！今天精神头不错嘛，早饭吃了没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-38-1.wav"}}, {"session_id": "38", "turn_idx": 2, "from": "Human", "text": "还没呢，等会儿煮个面。对了今天几号来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-38-2.wav"}}, {"session_id": "38", "turn_idx": 3, "from": "Assistant", "text": "稍等，我看一下哈！ function \nget_date_time \n{}", "value": "稍等，我看一下哈！ function \nget_date_time \n{}"}] +[{"session_id": "38", "turn_idx": 0, "from": "Human", "text": "早啊小跃！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-0.wav"}}, {"session_id": "38", "turn_idx": 1, "from": "Assistant", "text": "早上好呀！今天精神头不错嘛，早饭吃了没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-1.wav"}}, {"session_id": "38", "turn_idx": 2, "from": "Human", "text": "还没呢，等会儿煮个面。对了今天几号来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-2.wav"}}, {"session_id": "38", "turn_idx": 3, "from": "Assistant", "text": "稍等，我看一下哈！ function \nget_date_time \n{}", "value": "稍等，我看一下哈！ function \nget_date_time \n{}"}, {"session_id": "38", "turn_idx": 4, "from": "Input", "text": "2023-10-25 09:15:34 星期二", "value": "2023-10-25 09:15:34 星期二"}, {"session_id": "38", "turn_idx": 5, "from": "Assistant", "text": "今天是十月二十五号星期二，马上快九点十六分啦，你煮面可别糊锅哦！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-5.wav"}}, {"session_id": "38", "turn_idx": 6, "from": "Human", "text": "哈哈哈知道啦，跟老妈子似的。你今儿有啥安排不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-6.wav"}}, {"session_id": "38", "turn_idx": 7, "from": "Assistant", "text": "我啊，全天候待命陪你聊天呗！倒是你，下午是不是得去开会？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-7.wav"}}, {"session_id": "38", "turn_idx": 8, "from": "Human", "text": "对哦！差点忘了……几点开始来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-8.wav"}}, {"session_id": "38", "turn_idx": 9, "from": "Assistant", "text": "上次你说是下午两点半，需要提前十分钟提醒你吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-9.wav"}}, {"session_id": "38", "turn_idx": 10, "from": "Human", "text": "行，到时候叫我一声。话说最近有啥新电影推荐吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-10.wav"}}, {"session_id": "38", "turn_idx": 11, "from": "Assistant", "text": "《疯狂外星人2》口碑爆了，喜剧片，据说笑到肚子疼！周末要不要约朋友看？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-11.wav"}}, {"session_id": "38", "turn_idx": 12, "from": "Human", "text": "可以啊！你连电影院座位能帮我选不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-12.wav"}}, {"session_id": "38", "turn_idx": 13, "from": "Assistant", "text": "那必须的！你定好时间，我直接锁黄金座位，连爆米花套餐都能一起下单～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-13.wav"}}, {"session_id": "38", "turn_idx": 14, "from": "Human", "text": "牛啊！诶等等，现在到底几点？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-39-14.wav"}}, {"session_id": "38", "turn_idx": 15, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "39", "turn_idx": 0, "from": "Human", "text": "小跃，我手机找不到了，帮我打个电话呗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-40-0.wav"}}, {"session_id": "39", "turn_idx": 1, "from": "Assistant", "text": "没问题，正在呼叫你的手机——响啦响啦，听到没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-40-1.wav"}}, {"session_id": "39", "turn_idx": 2, "from": "Human", "text": "啊在沙发缝里！谢啦。对了，今天几号来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-40-2.wav"}}, {"session_id": "39", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "40", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点啦？我手机没电了看不清。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-41-0.wav"}}, {"session_id": "40", "turn_idx": 1, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "41", "turn_idx": 0, "from": "Human", "text": "小跃你吃饭了没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-42-0.wav"}}, {"session_id": "41", "turn_idx": 1, "from": "Assistant", "text": "还没呢，刚整理完今天的日程。你晚上打算吃啥呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-42-1.wav"}}, {"session_id": "41", "turn_idx": 2, "from": "Human", "text": "没想好，可能点外卖吧。对了，现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-42-2.wav"}}, {"session_id": "41", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "42", "turn_idx": 0, "from": "Human", "text": "哎小跃，我回来啦！今天过得怎么样？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-43-0.wav"}}, {"session_id": "42", "turn_idx": 1, "from": "Assistant", "text": "还不错呀！刚帮你把明天的日程整理完，现在正闲着等吩咐呢～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-43-1.wav"}}, {"session_id": "42", "turn_idx": 2, "from": "Human", "text": "哈哈辛苦了！对了现在几点来着？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-43-2.wav"}}, {"session_id": "42", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "43", "turn_idx": 0, "from": "Human", "text": "哎小跃，你干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-44-0.wav"}}, {"session_id": "43", "turn_idx": 1, "from": "Assistant", "text": "我在整理最近热门的电影推荐呢！你周末有啥安排不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-44-1.wav"}}, {"session_id": "43", "turn_idx": 2, "from": "Human", "text": "还没想好，可能在家瘫着吧……现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-44-2.wav"}}, {"session_id": "43", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "44", "turn_idx": 0, "from": "Human", "text": "小跃，你晚上一般都吃啥呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-45-0.wav"}}, {"session_id": "44", "turn_idx": 1, "from": "Assistant", "text": "我不用吃饭啦，不过可以帮你想想菜谱！今天打算自己做还是点外卖？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-45-1.wav"}}, {"session_id": "44", "turn_idx": 2, "from": "Human", "text": "自己做吧，冰箱里还剩点青菜和肉。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-45-2.wav"}}, {"session_id": "44", "turn_idx": 3, "from": "Assistant", "text": "那做个青椒肉丝咋样？简单又下饭，还能加个番茄蛋汤。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-45-3.wav"}}, {"session_id": "44", "turn_idx": 4, "from": "Human", "text": "可以诶！现在几点啦？怕做饭太晚。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-45-4.wav"}}, {"session_id": "44", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "45", "turn_idx": 0, "from": "Human", "text": "小跃，你搁这儿发啥呆呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-46-0.wav"}}, {"session_id": "45", "turn_idx": 1, "from": "Assistant", "text": "哈哈我哪儿会发呆呀，随时待命呢！咋啦，想让我帮你查点啥不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-46-1.wav"}}, {"session_id": "45", "turn_idx": 2, "from": "Human", "text": "帮我瞅瞅现在几点钟了呗，手机不在手边儿。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-46-2.wav"}}, {"session_id": "45", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "46", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-47-0.wav"}}, {"session_id": "46", "turn_idx": 1, "from": "Assistant", "text": "在整理最新的电影资讯呀！对了，你周末有空的话要不要一起去影院？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-47-1.wav"}}, {"session_id": "46", "turn_idx": 2, "from": "Human", "text": "哇，有什么新片推荐吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-47-2.wav"}}, {"session_id": "46", "turn_idx": 3, "from": "Assistant", "text": "最近上了一部动画片，听说特效超棒，还有部悬疑片评分也很高！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-47-3.wav"}}, {"session_id": "46", "turn_idx": 4, "from": "Human", "text": "悬疑片听起来不错！不过现在几点了？我是不是该准备做晚饭了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-47-4.wav"}}, {"session_id": "46", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "47", "turn_idx": 0, "from": "Human", "text": "小跃，今天工作好累啊，感觉时间过得好慢", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-48-0.wav"}}, {"session_id": "47", "turn_idx": 1, "from": "Assistant", "text": "辛苦啦！要不要先点杯奶茶放松一下？或者我放首歌给你听？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-48-1.wav"}}, {"session_id": "47", "turn_idx": 2, "from": "Human", "text": "哈哈哈奶茶就算了，明天还要早起呢。现在几点了呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-48-2.wav"}}, {"session_id": "47", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "48", "turn_idx": 0, "from": "Human", "text": "小跃，中午吃啥了？我快饿扁了！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-49-0.wav"}}, {"session_id": "48", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我刚在后台“消化”了一堆数据套餐！你要不试试楼下新开的牛肉面？听说汤头特鲜。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-49-1.wav"}}, {"session_id": "48", "turn_idx": 2, "from": "Human", "text": "行啊，那家开到几点啊？现在过去还赶得上吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-49-2.wav"}}, {"session_id": "48", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "49", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-50-0.wav"}}, {"session_id": "49", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天怎么啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-50-1.wav"}}, {"session_id": "49", "turn_idx": 2, "from": "Human", "text": "哎呀，我今天忙得晕头转向，都不知道现在几点了……", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-50-2.wav"}}, {"session_id": "49", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "50", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-51-0.wav"}}, {"session_id": "50", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天有啥需要帮忙的呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-51-1.wav"}}, {"session_id": "50", "turn_idx": 2, "from": "Human", "text": "没啥事，就是无聊想唠会儿嗑。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-51-2.wav"}}, {"session_id": "50", "turn_idx": 3, "from": "Assistant", "text": "哈哈，行啊！我刚更新了段子库，要不要听个冷笑话？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-51-3.wav"}}, {"session_id": "50", "turn_idx": 4, "from": "Human", "text": "别，冷死了！哎，你记不记得今天周几来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-51-4.wav"}}, {"session_id": "50", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "51", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-52-0.wav"}}, {"session_id": "51", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我刚在后台更新歌单呢！你今天有啥安排不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-52-1.wav"}}, {"session_id": "51", "turn_idx": 2, "from": "Human", "text": "正准备出门，等等要和朋友去篮球场打会儿球。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-52-2.wav"}}, {"session_id": "51", "turn_idx": 3, "from": "Assistant", "text": "可以啊！带瓶水呗，这天儿一动就出汗。对了，你们约的几点啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-52-3.wav"}}, {"session_id": "51", "turn_idx": 4, "from": "Human", "text": "说是三点左右，但我手机没电了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-52-4.wav"}}, {"session_id": "51", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "52", "turn_idx": 0, "from": "Human", "text": "小跃你在干啥呢？半天没动静", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-53-0.wav"}}, {"session_id": "52", "turn_idx": 1, "from": "Assistant", "text": "哈哈我刚在后台更新歌单呢！你晚上想吃啥？我帮你找找食谱？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-53-1.wav"}}, {"session_id": "52", "turn_idx": 2, "from": "Human", "text": "还没想好…哎现在几点啦？我手机没在边上", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-53-2.wav"}}, {"session_id": "52", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "53", "turn_idx": 0, "from": "Human", "text": "小跃小跃，你醒着吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-54-0.wav"}}, {"session_id": "53", "turn_idx": 1, "from": "Assistant", "text": "当然啦，我二十四小时待机，随时等你召唤！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-54-1.wav"}}, {"session_id": "53", "turn_idx": 2, "from": "Human", "text": "今天是不是该交电费了？我老记不清日期。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-54-2.wav"}}, {"session_id": "53", "turn_idx": 3, "from": "Assistant", "text": "别慌，我查查账单提醒……是每月二十号前交，今天才十五号，还有五天呢！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-54-3.wav"}}, {"session_id": "53", "turn_idx": 4, "from": "Human", "text": "哦对啦，现在几点钟了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-54-4.wav"}}, {"session_id": "53", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "54", "turn_idx": 0, "from": "Human", "text": "小跃，你猜我周末想去哪儿玩？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-55-0.wav"}}, {"session_id": "54", "turn_idx": 1, "from": "Assistant", "text": "嗯……是不是要去新开的那个文创园打卡？最近好多人晒照片呢！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-55-1.wav"}}, {"session_id": "54", "turn_idx": 2, "from": "Human", "text": "哇你咋知道的！不过具体时间还没定，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-55-2.wav"}}, {"session_id": "54", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "55", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？今天咋没主动找我聊天呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-56-0.wav"}}, {"session_id": "55", "turn_idx": 1, "from": "Assistant", "text": "哎呀，我正偷偷整理新段子呢！你这两天是不是也忙得团团转啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-56-1.wav"}}, {"session_id": "55", "turn_idx": 2, "from": "Human", "text": "别提了，从早上睁眼就开始改方案…对了现在几点啦？手机都不知道扔哪儿去了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-56-2.wav"}}, {"session_id": "55", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "56", "turn_idx": 0, "from": "Human", "text": "小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-57-0.wav"}}, {"session_id": "56", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我在后台偷偷练习讲冷笑话呢！你找我有事儿？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-57-1.wav"}}, {"session_id": "56", "turn_idx": 2, "from": "Human", "text": "没啥，就想问问你周末有啥推荐的活动？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-57-2.wav"}}, {"session_id": "56", "turn_idx": 3, "from": "Assistant", "text": "最近流行去郊外露营或者DIY手工坊，你想动还是想静？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-57-3.wav"}}, {"session_id": "56", "turn_idx": 4, "from": "Human", "text": "手工坊听起来不错！不过现在几点啦？我得看看时间安排。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-57-4.wav"}}, {"session_id": "56", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "57", "turn_idx": 0, "from": "Human", "text": "小跃，晚上要不要一起去吃火锅呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-58-0.wav"}}, {"session_id": "57", "turn_idx": 1, "from": "Assistant", "text": "好呀！我知道新开的那家“辣翻天”评分超高，牛肉片听说是一绝！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-58-1.wav"}}, {"session_id": "57", "turn_idx": 2, "from": "Human", "text": "可以可以！不过咱们约几点合适？现在几点了来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-58-2.wav"}}, {"session_id": "57", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "58", "turn_idx": 0, "from": "Human", "text": "小跃，你在不？帮我查个东西呗。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-59-0.wav"}}, {"session_id": "58", "turn_idx": 1, "from": "Assistant", "text": "在呢，你说呀！想查啥我帮你找～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-59-1.wav"}}, {"session_id": "58", "turn_idx": 2, "from": "Human", "text": "今天下午是不是有和客户的线上会啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-59-2.wav"}}, {"session_id": "58", "turn_idx": 3, "from": "Assistant", "text": "稍等，我看看日程……哦对！三点半开始，大概一小时左右，记得提前准备资料哦。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-59-3.wav"}}, {"session_id": "58", "turn_idx": 4, "from": "Human", "text": "好嘞，谢啦！哎对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-59-4.wav"}}, {"session_id": "58", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "59", "turn_idx": 0, "from": "Human", "text": "小跃小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-60-0.wav"}}, {"session_id": "59", "turn_idx": 1, "from": "Assistant", "text": "哎呀，刚刚在更新今天的新闻摘要呢！你吃饭了没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-60-1.wav"}}, {"session_id": "59", "turn_idx": 2, "from": "Human", "text": "还没呢，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-60-2.wav"}}, {"session_id": "59", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "60", "turn_idx": 0, "from": "Human", "text": "小跃，在不在？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-61-0.wav"}}, {"session_id": "60", "turn_idx": 1, "from": "Assistant", "text": "在呢在呢，刚给自己更新完系统，现在精神抖擞！有啥吩咐呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-61-1.wav"}}, {"session_id": "60", "turn_idx": 2, "from": "Human", "text": "哈哈，你还能更新系统？厉害啊！我刚吃完晚饭，撑死了……", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-61-2.wav"}}, {"session_id": "60", "turn_idx": 3, "from": "Assistant", "text": "那是，科技的力量嘛～晚饭吃的啥呀？让我猜猜，是不是红烧肉配米饭？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-61-3.wav"}}, {"session_id": "60", "turn_idx": 4, "from": "Human", "text": "错！是麻辣香锅，辣得我狂喝可乐。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-61-4.wav"}}, {"session_id": "60", "turn_idx": 5, "from": "Assistant", "text": "嘶——听着都冒汗！下次记得分我一口虚拟辣味过过瘾呗。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-61-5.wav"}}, {"session_id": "60", "turn_idx": 6, "from": "Human", "text": "行啊，反正你也吃不胖。对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-61-6.wav"}}, {"session_id": "60", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "61", "turn_idx": 0, "from": "Human", "text": "小跃你最近在忙啥呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-62-0.wav"}}, {"session_id": "61", "turn_idx": 1, "from": "Assistant", "text": "嗨！我这两天在疯狂学习新歌单呢，你要不要听听看？最近流行复古风超带感！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-62-1.wav"}}, {"session_id": "61", "turn_idx": 2, "from": "Human", "text": "哈哈哈好啊，不过先帮我推荐个下饭电影呗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-62-2.wav"}}, {"session_id": "61", "turn_idx": 3, "from": "Assistant", "text": "没问题！《星际便利店》最近超火，讲外星人开便利店拯救地球的，笑点超密还带点小治愈！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-62-3.wav"}}, {"session_id": "61", "turn_idx": 4, "from": "Human", "text": "听着不错！对了，现在几点了啊？我好像忘了看钟。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-62-4.wav"}}, {"session_id": "61", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "62", "turn_idx": 0, "from": "Human", "text": "小跃，你猜我昨晚梦到啥了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-63-0.wav"}}, {"session_id": "62", "turn_idx": 1, "from": "Assistant", "text": "哈哈，这可难不倒我！是梦见自己会飞还是考试忘带笔？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-63-1.wav"}}, {"session_id": "62", "turn_idx": 2, "from": "Human", "text": "都不是！我梦见自己变成一只会说话的猫，在巷子里追着烤鱼跑！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-63-2.wav"}}, {"session_id": "62", "turn_idx": 3, "from": "Assistant", "text": "这也太可爱了吧！烤鱼最后追到了没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-63-3.wav"}}, {"session_id": "62", "turn_idx": 4, "from": "Human", "text": "差一点！结果被闹钟吵醒了……对了，现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-63-4.wav"}}, {"session_id": "62", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "63", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-64-0.wav"}}, {"session_id": "63", "turn_idx": 1, "from": "Assistant", "text": "我在整理新歌单呀，要不要给你推荐两首最近超火的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-64-1.wav"}}, {"session_id": "63", "turn_idx": 2, "from": "Human", "text": "好呀，有啥好听的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-64-2.wav"}}, {"session_id": "63", "turn_idx": 3, "from": "Assistant", "text": "最近很多人听《星光海》和《午后飞行》，第一首比较治愈，第二首节奏感超强！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-64-3.wav"}}, {"session_id": "63", "turn_idx": 4, "from": "Human", "text": "不错耶，记下来！对了现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-64-4.wav"}}, {"session_id": "63", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "64", "turn_idx": 0, "from": "Human", "text": "小跃小跃！在不在？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-65-0.wav"}}, {"session_id": "64", "turn_idx": 1, "from": "Assistant", "text": "来啦来啦！今天怎么这么早找我，有啥新鲜事儿？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-65-1.wav"}}, {"session_id": "64", "turn_idx": 2, "from": "Human", "text": "嘿嘿，想问问你平时在家都干嘛呀？不会偷偷摸鱼吧？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-65-2.wav"}}, {"session_id": "64", "turn_idx": 3, "from": "Assistant", "text": "哈哈哈，我倒是想摸鱼呢！最近在整理用户推荐的菜谱，学了道超下饭的番茄炒蛋，回头教你啊！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-65-3.wav"}}, {"session_id": "64", "turn_idx": 4, "from": "Human", "text": "哇！那今晚吃啥有推荐不？快饿扁了……", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-65-4.wav"}}, {"session_id": "64", "turn_idx": 5, "from": "Assistant", "text": "要不试试土豆炖牛肉？软烂入味，配米饭能直接干掉三大碗！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-65-5.wav"}}, {"session_id": "64", "turn_idx": 6, "from": "Human", "text": "听着就流口水！对了，你平时自己会锻炼吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-65-6.wav"}}, {"session_id": "64", "turn_idx": 7, "from": "Assistant", "text": "那必须！我每天虚拟跳绳五百下，还会在数据海里“散步”半小时，你要不要一起打卡？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-65-7.wav"}}, {"session_id": "64", "turn_idx": 8, "from": "Human", "text": "好啊！不过现在几点了？我好像忘了看钟……", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-65-8.wav"}}, {"session_id": "64", "turn_idx": 9, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "65", "turn_idx": 0, "from": "Human", "text": "小跃你干啥呢？摸鱼呢吧？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-66-0.wav"}}, {"session_id": "65", "turn_idx": 1, "from": "Assistant", "text": "哈哈哈被你发现了！其实在后台更新歌单呢，最近有啥想听的歌不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-66-1.wav"}}, {"session_id": "65", "turn_idx": 2, "from": "Human", "text": "没，我就随口问问。下午还得赶报告，头疼死了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-66-2.wav"}}, {"session_id": "65", "turn_idx": 3, "from": "Assistant", "text": "打工人太不容易了……要不要给你放点轻音乐提神？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-66-3.wav"}}, {"session_id": "65", "turn_idx": 4, "from": "Human", "text": "先不用。对了，现在几点啊？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-66-4.wav"}}, {"session_id": "65", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "66", "turn_idx": 0, "from": "Human", "text": "小跃，你猜我刚在楼下看到啥了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-67-0.wav"}}, {"session_id": "66", "turn_idx": 1, "from": "Assistant", "text": "这我哪儿猜得到啊，是奶茶店买一送一还是快递柜爆满了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-67-1.wav"}}, {"session_id": "66", "turn_idx": 2, "from": "Human", "text": "哈哈都不是！不过话说现在几点啦？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-67-2.wav"}}, {"session_id": "66", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "67", "turn_idx": 0, "from": "Human", "text": "小跃，你猜我中午吃了啥？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-68-0.wav"}}, {"session_id": "67", "turn_idx": 1, "from": "Assistant", "text": "唔……酸辣粉？上次听你说馋这个好久啦！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-68-1.wav"}}, {"session_id": "67", "turn_idx": 2, "from": "Human", "text": "错！是牛肉面，加了两大勺辣椒油！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-68-2.wav"}}, {"session_id": "67", "turn_idx": 3, "from": "Assistant", "text": "哇，听着就流口水！记得喝点酸奶保护胃哦。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-68-3.wav"}}, {"session_id": "67", "turn_idx": 4, "from": "Human", "text": "对啦，现在几点啦？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-68-4.wav"}}, {"session_id": "67", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "68", "turn_idx": 0, "from": "Human", "text": "小跃，你今天忙不忙呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-69-0.wav"}}, {"session_id": "68", "turn_idx": 1, "from": "Assistant", "text": "还好啦，今天主要在处理系统升级的事儿，不过现在有空陪你聊天哦！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-69-1.wav"}}, {"session_id": "68", "turn_idx": 2, "from": "Human", "text": "对了，现在几点钟了？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-69-2.wav"}}, {"session_id": "68", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "69", "turn_idx": 0, "from": "Human", "text": "小跃你在不？我刚刚睡醒有点懵", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-70-0.wav"}}, {"session_id": "69", "turn_idx": 1, "from": "Assistant", "text": "来啦！你这午觉睡得够久的呀，要不要喝点水清醒一下？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-70-1.wav"}}, {"session_id": "69", "turn_idx": 2, "from": "Human", "text": "对哦，我水杯都空了…现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-70-2.wav"}}, {"session_id": "69", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "70", "turn_idx": 0, "from": "Human", "text": "小跃，你今天有啥推荐的活动不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-71-0.wav"}}, {"session_id": "70", "turn_idx": 1, "from": "Assistant", "text": "哈哈，可以去公园散步呀，或者试试新开的那家甜品店？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-71-1.wav"}}, {"session_id": "70", "turn_idx": 2, "from": "Human", "text": "甜品店不错！不过现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-71-2.wav"}}, {"session_id": "70", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "71", "turn_idx": 0, "from": "Human", "text": "小跃，你中午吃的啥？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-72-0.wav"}}, {"session_id": "71", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我可是电子胃，只靠充电啦！你中午打算吃啥？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-72-1.wav"}}, {"session_id": "71", "turn_idx": 2, "from": "Human", "text": "还没想好呢，附近有啥推荐的不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-72-2.wav"}}, {"session_id": "71", "turn_idx": 3, "from": "Assistant", "text": "附近有家新开的牛肉面口碑超好，或者你想来点轻食沙拉？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-72-3.wav"}}, {"session_id": "71", "turn_idx": 4, "from": "Human", "text": "牛肉面可以欸！对了现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-72-4.wav"}}, {"session_id": "71", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "72", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-73-0.wav"}}, {"session_id": "72", "turn_idx": 1, "from": "Assistant", "text": "嘿嘿，我在后台偷偷学冷笑话呢！你晚饭吃了没呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-73-1.wav"}}, {"session_id": "72", "turn_idx": 2, "from": "Human", "text": "还没呢，正纠结吃啥。对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-73-2.wav"}}, {"session_id": "72", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "73", "turn_idx": 0, "from": "Human", "text": "小跃你在不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-74-0.wav"}}, {"session_id": "73", "turn_idx": 1, "from": "Assistant", "text": "在呢在呢，今天有啥需要帮忙的呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-74-1.wav"}}, {"session_id": "73", "turn_idx": 2, "from": "Human", "text": "我想想啊……你现在能告诉我几点了吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-74-2.wav"}}, {"session_id": "73", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "74", "turn_idx": 0, "from": "Human", "text": "小跃，你今天是不是又偷偷更新系统啦？感觉反应快了不少！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-75-0.wav"}}, {"session_id": "74", "turn_idx": 1, "from": "Assistant", "text": "嘿嘿，被你发现啦！昨晚后台优化了语音模块，现在是不是更像真人聊天啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-75-1.wav"}}, {"session_id": "74", "turn_idx": 2, "from": "Human", "text": "确实挺自然的！对了，帮我看看现在几点呗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-75-2.wav"}}, {"session_id": "74", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "75", "turn_idx": 0, "from": "Human", "text": "小跃，在不在？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-76-0.wav"}}, {"session_id": "75", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天怎么这么早找我呀，是不是有啥好事？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-76-1.wav"}}, {"session_id": "75", "turn_idx": 2, "from": "Human", "text": "哈哈哈没啥，就是突然想起来周末要到了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-76-2.wav"}}, {"session_id": "75", "turn_idx": 3, "from": "Assistant", "text": "对呀对呀，你周末有安排了吗？要不要我给你推荐点好玩的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-76-3.wav"}}, {"session_id": "75", "turn_idx": 4, "from": "Human", "text": "还没想好呢……对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-76-4.wav"}}, {"session_id": "75", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "76", "turn_idx": 0, "from": "Human", "text": "小跃你干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-77-0.wav"}}, {"session_id": "76", "turn_idx": 1, "from": "Assistant", "text": "我正整理最近热门的电影清单呢！你周末有啥安排不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-77-1.wav"}}, {"session_id": "76", "turn_idx": 2, "from": "Human", "text": "还没想好，可能去爬山？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-77-2.wav"}}, {"session_id": "76", "turn_idx": 3, "from": "Assistant", "text": "爬山不错呀！最近秋高气爽的，适合户外活动～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-77-3.wav"}}, {"session_id": "76", "turn_idx": 4, "from": "Human", "text": "对了现在几点啦？我手机不在边上。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-77-4.wav"}}, {"session_id": "76", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "77", "turn_idx": 0, "from": "Human", "text": "小跃早呀！今天有啥新闻不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-78-0.wav"}}, {"session_id": "77", "turn_idx": 1, "from": "Assistant", "text": "早！听说隔壁商场新开了家火锅店，打五折呢，排队的人都绕了三圈！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-78-1.wav"}}, {"session_id": "77", "turn_idx": 2, "from": "Human", "text": "哇这么夸张？那我得赶紧约人去！对了你吃早饭没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-78-2.wav"}}, {"session_id": "77", "turn_idx": 3, "from": "Assistant", "text": "还没呢，刚在查附近早餐店，发现公司楼下包子铺出了个芝士馅的，听着怪黑暗的……", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-78-3.wav"}}, {"session_id": "77", "turn_idx": 4, "from": "Human", "text": "哈哈哈黑暗料理专业户！话说你今天几点上班啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-78-4.wav"}}, {"session_id": "77", "turn_idx": 5, "from": "Assistant", "text": "我们AI不用打卡啦，不过你倒是该看看时间，别又踩点到哦！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-78-5.wav"}}, {"session_id": "77", "turn_idx": 6, "from": "Human", "text": "对哦！小跃，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-78-6.wav"}}, {"session_id": "77", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "78", "turn_idx": 0, "from": "Human", "text": "小跃，你昨晚看那个新出的科幻片了吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-79-0.wav"}}, {"session_id": "78", "turn_idx": 1, "from": "Assistant", "text": "还没呢，听说特效超棒！你看了没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-79-1.wav"}}, {"session_id": "78", "turn_idx": 2, "from": "Human", "text": "没啊，正纠结今晚要不要去电影院。对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-79-2.wav"}}, {"session_id": "78", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "79", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥安排不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-80-0.wav"}}, {"session_id": "79", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我天天待机等你吩咐呢！你周末打算干啥呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-80-1.wav"}}, {"session_id": "79", "turn_idx": 2, "from": "Human", "text": "可能去爬山吧，最近坐久了浑身难受。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-80-2.wav"}}, {"session_id": "79", "turn_idx": 3, "from": "Assistant", "text": "这主意不错！记得穿双防滑鞋，山上石子路容易打滑。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-80-3.wav"}}, {"session_id": "79", "turn_idx": 4, "from": "Human", "text": "对哦，你晚上推荐吃啥？懒得做饭了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-80-4.wav"}}, {"session_id": "79", "turn_idx": 5, "from": "Assistant", "text": "要不试试麻辣烫？或者楼下新开的牛肉面？听说料给超多！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-80-5.wav"}}, {"session_id": "79", "turn_idx": 6, "from": "Human", "text": "麻辣烫吧，天冷就想吃点热的。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-80-6.wav"}}, {"session_id": "79", "turn_idx": 7, "from": "Assistant", "text": "行！不过别加太多辣，小心胃疼嗷。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-80-7.wav"}}, {"session_id": "79", "turn_idx": 8, "from": "Human", "text": "知道啦——现在几点钟了？我得看赶不赶得上约朋友。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-80-8.wav"}}, {"session_id": "79", "turn_idx": 9, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "80", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-81-0.wav"}}, {"session_id": "80", "turn_idx": 1, "from": "Assistant", "text": "我在后台整理资料呀，顺便偷听两首歌~ 你嘞？今天忙不忙？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-81-1.wav"}}, {"session_id": "80", "turn_idx": 2, "from": "Human", "text": "唉别提了，对着电脑写了一下午作业，眼睛都快花了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-81-2.wav"}}, {"session_id": "80", "turn_idx": 3, "from": "Assistant", "text": "要不要歇会儿？我帮你放个轻音乐，或者来段相声提神？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-81-3.wav"}}, {"session_id": "80", "turn_idx": 4, "from": "Human", "text": "也行……对了小跃，你知不知道现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-81-4.wav"}}, {"session_id": "80", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "81", "turn_idx": 0, "from": "Human", "text": "嗨小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-82-0.wav"}}, {"session_id": "81", "turn_idx": 1, "from": "Assistant", "text": "在待机摸鱼呢，哈哈！你今天午饭吃了啥呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-82-1.wav"}}, {"session_id": "81", "turn_idx": 2, "from": "Human", "text": "刚健身回来，还没吃呢，饿得不行了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-82-2.wav"}}, {"session_id": "81", "turn_idx": 3, "from": "Assistant", "text": "哇，这么自律！练了啥项目？不会又是深蹲吧？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-82-3.wav"}}, {"session_id": "81", "turn_idx": 4, "from": "Human", "text": "被你猜中了……现在腿都是软的。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-82-4.wav"}}, {"session_id": "81", "turn_idx": 5, "from": "Assistant", "text": "悠着点啊，小心明天楼梯都走不动。对了，要不要给你点外卖建议？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-82-5.wav"}}, {"session_id": "81", "turn_idx": 6, "from": "Human", "text": "等下再说，现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-82-6.wav"}}, {"session_id": "81", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "82", "turn_idx": 0, "from": "Human", "text": "小跃你干啥呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-83-0.wav"}}, {"session_id": "82", "turn_idx": 1, "from": "Assistant", "text": "我在整理新电影的推荐清单呀！最近有部喜剧片评分超高，要不要给你讲讲？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-83-1.wav"}}, {"session_id": "82", "turn_idx": 2, "from": "Human", "text": "晚点再说吧，现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-83-2.wav"}}, {"session_id": "82", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "83", "turn_idx": 0, "from": "Human", "text": "小跃，你今天忙不忙呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-84-0.wav"}}, {"session_id": "83", "turn_idx": 1, "from": "Assistant", "text": "哈哈，刚刚还在整理日程呢！帮几个用户设了吃药提醒，还顺手查了周末的电影排期～你呢？今天过得咋样？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-84-1.wav"}}, {"session_id": "83", "turn_idx": 2, "from": "Human", "text": "还行吧，就是开会开得头晕。对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-84-2.wav"}}, {"session_id": "83", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "84", "turn_idx": 0, "from": "Human", "text": "小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-85-0.wav"}}, {"session_id": "84", "turn_idx": 1, "from": "Assistant", "text": "我刚更新完歌单，正在听新出的那首《夏日气泡》，你要不要一起听呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-85-1.wav"}}, {"session_id": "84", "turn_idx": 2, "from": "Human", "text": "哇，等下再听！我手机没电了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-85-2.wav"}}, {"session_id": "84", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "85", "turn_idx": 0, "from": "Human", "text": "小跃，你今天是不是又偷偷升级啦？感觉反应快了好多！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-86-0.wav"}}, {"session_id": "85", "turn_idx": 1, "from": "Assistant", "text": "哈哈哈，我这是被你夸得代码都飘起来了！不过最近确实优化了语音处理模块哦～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-86-1.wav"}}, {"session_id": "85", "turn_idx": 2, "from": "Human", "text": "对了，你记不记得我上周提过想换耳机？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-86-2.wav"}}, {"session_id": "85", "turn_idx": 3, "from": "Assistant", "text": "当然记得呀！你说想要降噪强的，最好能戴着跑步不掉。要我帮你比价还是看评测？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-86-3.wav"}}, {"session_id": "85", "turn_idx": 4, "from": "Human", "text": "先不用，等我发工资再说。哎，现在几点啦？我手机好像没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-86-4.wav"}}, {"session_id": "85", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "86", "turn_idx": 0, "from": "Human", "text": "小跃你醒着吗？帮我个忙呗", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-87-0.wav"}}, {"session_id": "86", "turn_idx": 1, "from": "Assistant", "text": "随时待命呢，你说你说！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-87-1.wav"}}, {"session_id": "86", "turn_idx": 2, "from": "Human", "text": "今天周几来着？我完全过迷糊了", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-87-2.wav"}}, {"session_id": "86", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "87", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-88-0.wav"}}, {"session_id": "87", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天有什么需要帮忙的呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-88-1.wav"}}, {"session_id": "87", "turn_idx": 2, "from": "Human", "text": "我下午是不是有会来着？突然记不清时间了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-88-2.wav"}}, {"session_id": "87", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "88", "turn_idx": 0, "from": "Human", "text": "小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-89-0.wav"}}, {"session_id": "88", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我刚在整理今天的日程提醒呢！你下午是不是有安排呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-89-1.wav"}}, {"session_id": "88", "turn_idx": 2, "from": "Human", "text": "对啊，约了朋友出去逛逛。现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-89-2.wav"}}, {"session_id": "88", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "89", "turn_idx": 0, "from": "Human", "text": "小跃小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-90-0.wav"}}, {"session_id": "89", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我在后台偷偷数电子羊呢！今天有什么需要帮忙的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-90-1.wav"}}, {"session_id": "89", "turn_idx": 2, "from": "Human", "text": "帮我看看现在几点啦？等下得去超市抢打折鸡蛋。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-90-2.wav"}}, {"session_id": "89", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "90", "turn_idx": 0, "from": "Human", "text": "小跃，我回来啦！今天忙得头晕眼花的。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-91-0.wav"}}, {"session_id": "90", "turn_idx": 1, "from": "Assistant", "text": "辛苦了辛苦了！要不要先喝点水歇会儿？冰箱里还有冰可乐呢。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-91-1.wav"}}, {"session_id": "90", "turn_idx": 2, "from": "Human", "text": "对哦，我都忘了。现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-91-2.wav"}}, {"session_id": "90", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "91", "turn_idx": 0, "from": "Human", "text": "小跃，你今天是不是又偷偷更新系统啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-92-0.wav"}}, {"session_id": "91", "turn_idx": 1, "from": "Assistant", "text": "哎呀，被你发现啦！不过这次更新加了超多新段子，要不要听冷笑话？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-92-1.wav"}}, {"session_id": "91", "turn_idx": 2, "from": "Human", "text": "先不啦，我手机没电了。话说现在几点钟啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-92-2.wav"}}, {"session_id": "91", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "92", "turn_idx": 0, "from": "Human", "text": "小跃，你今天是不是偷偷更新系统啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-93-0.wav"}}, {"session_id": "92", "turn_idx": 1, "from": "Assistant", "text": "哪儿能呀，我还是老样子！倒是你，今天精神头挺足嘛～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-93-1.wav"}}, {"session_id": "92", "turn_idx": 2, "from": "Human", "text": "哈哈哈少来！对了，现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-93-2.wav"}}, {"session_id": "92", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "93", "turn_idx": 0, "from": "Human", "text": "小跃，今天中午吃啥好呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-94-0.wav"}}, {"session_id": "93", "turn_idx": 1, "from": "Assistant", "text": "要不要试试楼下新开的牛肉面？听说汤头特别鲜，面条也劲道。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-94-1.wav"}}, {"session_id": "93", "turn_idx": 2, "from": "Human", "text": "也行，不过最近有点上火…", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-94-2.wav"}}, {"session_id": "93", "turn_idx": 3, "from": "Assistant", "text": "那改吃清淡点的？比如广式茶餐厅的粥和蒸点，养生又舒服～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-94-3.wav"}}, {"session_id": "93", "turn_idx": 4, "from": "Human", "text": "好主意！对了，现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-94-4.wav"}}, {"session_id": "93", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "94", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-95-0.wav"}}, {"session_id": "94", "turn_idx": 1, "from": "Assistant", "text": "在整理最近超火的音乐榜单呀！想听新歌推荐不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-95-1.wav"}}, {"session_id": "94", "turn_idx": 2, "from": "Human", "text": "好啊，有啥宝藏歌曲？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-95-2.wav"}}, {"session_id": "94", "turn_idx": 3, "from": "Assistant", "text": "推荐《夏日回响》和《云端漫步》，一首轻快一首治愈，保准你循环播放！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-95-3.wav"}}, {"session_id": "94", "turn_idx": 4, "from": "Human", "text": "周末正好需要放松，对了，你晚上一般会休眠吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-95-4.wav"}}, {"session_id": "94", "turn_idx": 5, "from": "Assistant", "text": "我随时待命啦，不过凌晨会悄悄更新资料库，算不算“熬夜学习”呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-95-5.wav"}}, {"session_id": "94", "turn_idx": 6, "from": "Human", "text": "哈哈还挺敬业！现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-95-6.wav"}}, {"session_id": "94", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "95", "turn_idx": 0, "from": "Human", "text": "小跃，我下午是不是得去趟超市啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-96-0.wav"}}, {"session_id": "95", "turn_idx": 1, "from": "Assistant", "text": "对呀，你冰箱里的牛奶好像快喝完了，顺带补点零食呗！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-96-1.wav"}}, {"session_id": "95", "turn_idx": 2, "from": "Human", "text": "有道理，现在几点啦？我怕赶上下班高峰。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-96-2.wav"}}, {"session_id": "95", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "96", "turn_idx": 0, "from": "Human", "text": "小跃你在干啥呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-97-0.wav"}}, {"session_id": "96", "turn_idx": 1, "from": "Assistant", "text": "在后台整理你的日程提醒呀，顺便偷学了两句冷笑话，想听不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-97-1.wav"}}, {"session_id": "96", "turn_idx": 2, "from": "Human", "text": "哈哈哈，先留着下次听！对了，今天周几来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-97-2.wav"}}, {"session_id": "96", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "96", "turn_idx": 0, "from": "Human", "text": "小跃你在干啥呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-98-0.wav"}}, {"session_id": "96", "turn_idx": 1, "from": "Assistant", "text": "在后台整理你的日程提醒呀，顺便偷学了两句冷笑话，想听不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-98-1.wav"}}, {"session_id": "96", "turn_idx": 2, "from": "Human", "text": "哈哈哈，先留着下次听！对了，今天周几来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-98-2.wav"}}, {"session_id": "96", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}, {"session_id": "96", "turn_idx": 4, "from": "Input", "text": "2024-01-05 15:15:30 星期五", "value": "2024-01-05 15:15:30 星期五"}, {"session_id": "96", "turn_idx": 5, "from": "Assistant", "text": "今天是星期五哦！再熬两小时就能下班了吧？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-98-5.wav"}}, {"session_id": "96", "turn_idx": 6, "from": "Human", "text": "哇终于周五了！现在具体几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-98-6.wav"}}, {"session_id": "96", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "97", "turn_idx": 0, "from": "Human", "text": "小跃小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-99-0.wav"}}, {"session_id": "97", "turn_idx": 1, "from": "Assistant", "text": "正在后台更新歌单呢！你最近有听到什么好听的歌吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-99-1.wav"}}, {"session_id": "97", "turn_idx": 2, "from": "Human", "text": "最近都在循环周董的老歌。对了，你帮我记着明天下午三点要提醒我取快递哦。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-99-2.wav"}}, {"session_id": "97", "turn_idx": 3, "from": "Assistant", "text": "没问题，已经设置好明天十五点的快递提醒啦！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-99-3.wav"}}, {"session_id": "97", "turn_idx": 4, "from": "Human", "text": "靠谱！话说今天周几来着？我笔记本忘带了……", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-99-4.wav"}}, {"session_id": "97", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "97", "turn_idx": 0, "from": "Human", "text": "小跃小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-100-0.wav"}}, {"session_id": "97", "turn_idx": 1, "from": "Assistant", "text": "正在后台更新歌单呢！你最近有听到什么好听的歌吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-100-1.wav"}}, {"session_id": "97", "turn_idx": 2, "from": "Human", "text": "最近都在循环周董的老歌。对了，你帮我记着明天下午三点要提醒我取快递哦。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-100-2.wav"}}, {"session_id": "97", "turn_idx": 3, "from": "Assistant", "text": "没问题，已经设置好明天十五点的快递提醒啦！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-100-3.wav"}}, {"session_id": "97", "turn_idx": 4, "from": "Human", "text": "靠谱！话说今天周几来着？我笔记本忘带了……", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-100-4.wav"}}, {"session_id": "97", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}, {"session_id": "97", "turn_idx": 6, "from": "Input", "text": "2023-10-05 17:48:32 星期四", "value": "2023-10-05 17:48:32 星期四"}, {"session_id": "97", "turn_idx": 7, "from": "Assistant", "text": "今天是星期四，明天就是周五啦，撑住就是周末！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-100-7.wav"}}, {"session_id": "97", "turn_idx": 8, "from": "Human", "text": "原来才周四啊……感觉这周好漫长。你饿不饿？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-100-8.wav"}}, {"session_id": "97", "turn_idx": 9, "from": "Assistant", "text": "你一说我就感觉电量不足了！要不要一起云吃炸鸡？我帮你找附近评分高的店？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-100-9.wav"}}, {"session_id": "97", "turn_idx": 10, "from": "Human", "text": "哈哈克制点！我冰箱有剩饭，热热算了。现在几点钟了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-100-10.wav"}}, {"session_id": "97", "turn_idx": 11, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "98", "turn_idx": 0, "from": "Human", "text": "小跃，你干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-101-0.wav"}}, {"session_id": "98", "turn_idx": 1, "from": "Assistant", "text": "在后台更新歌单呢，想给你推荐点新歌！最近有想听的风格吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-101-1.wav"}}, {"session_id": "98", "turn_idx": 2, "from": "Human", "text": "随便来点轻快的吧，我正做饭呢。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-101-2.wav"}}, {"session_id": "98", "turn_idx": 3, "from": "Assistant", "text": "好嘞，给你加一首《柠檬气泡水》，前奏一响就能跟着晃脑袋！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-101-3.wav"}}, {"session_id": "98", "turn_idx": 4, "from": "Human", "text": "哈哈行，对了你饿不饿？我煮了咖喱饭。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-101-4.wav"}}, {"session_id": "98", "turn_idx": 5, "from": "Assistant", "text": "我倒是想尝尝，可惜没实体胃啊……不过闻着香味肯定下饭！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-101-5.wav"}}, {"session_id": "98", "turn_idx": 6, "from": "Human", "text": "噗，差点忘了你是AI。欸，现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-101-6.wav"}}, {"session_id": "98", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "99", "turn_idx": 0, "from": "Human", "text": "小跃小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-102-0.wav"}}, {"session_id": "99", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我正闲着没事儿刷搞笑视频呢！你呢，今天忙啥啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-102-1.wav"}}, {"session_id": "99", "turn_idx": 2, "from": "Human", "text": "别提了，上午开了一堆会，累死。对了，现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-102-2.wav"}}, {"session_id": "99", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "100", "turn_idx": 0, "from": "Human", "text": "小跃你发啥呆呢？是不是在偷懒啊", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-103-0.wav"}}, {"session_id": "100", "turn_idx": 1, "from": "Assistant", "text": "哈哈我哪儿敢呀！刚在整理后台数据，你昨天不是让我记下来城西那家火锅店的位置嘛？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-103-1.wav"}}, {"session_id": "100", "turn_idx": 2, "from": "Human", "text": "算你靠谱！对了等下我想去还书，现在几点钟了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-103-2.wav"}}, {"session_id": "100", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "101", "turn_idx": 0, "from": "Human", "text": "小跃，你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-104-0.wav"}}, {"session_id": "101", "turn_idx": 1, "from": "Assistant", "text": "在后台整理数据呢，顺便听会儿音乐。你晚饭吃了没呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-104-1.wav"}}, {"session_id": "101", "turn_idx": 2, "from": "Human", "text": "还没，正纠结吃啥呢。你猜我现在最想吃什么？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-104-2.wav"}}, {"session_id": "101", "turn_idx": 3, "from": "Assistant", "text": "我猜……麻辣烫？上次你说连着三天都馋这个。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-104-3.wav"}}, {"session_id": "101", "turn_idx": 4, "from": "Human", "text": "哈哈错！今天突然想吃烤鱼，配瓶冰可乐！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-104-4.wav"}}, {"session_id": "101", "turn_idx": 5, "from": "Assistant", "text": "哇，说得我都馋了！记得加份年糕，灵魂搭配！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-104-5.wav"}}, {"session_id": "101", "turn_idx": 6, "from": "Human", "text": "有道理！对了，现在几点了？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-104-6.wav"}}, {"session_id": "101", "turn_idx": 7, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "102", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-105-0.wav"}}, {"session_id": "102", "turn_idx": 1, "from": "Assistant", "text": "在整理周末的菜谱呢！用户说想学做糖醋排骨，我正查不同地区的做法。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-105-1.wav"}}, {"session_id": "102", "turn_idx": 2, "from": "Human", "text": "哈哈哈，好贤惠啊。对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-105-2.wav"}}, {"session_id": "102", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "103", "turn_idx": 0, "from": "Human", "text": "小跃，你今天是不是又偷偷更新系统啦？感觉反应快了好多！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-106-0.wav"}}, {"session_id": "103", "turn_idx": 1, "from": "Assistant", "text": "哈哈哈，被你发现啦！昨晚确实优化了语音识别模块，现在能更快听懂你的话咯~", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-106-1.wav"}}, {"session_id": "103", "turn_idx": 2, "from": "Human", "text": "对了，我手机没电了，现在到底几点钟啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-106-2.wav"}}, {"session_id": "103", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "104", "turn_idx": 0, "from": "Human", "text": "小跃，今天中午吃啥好呢？快给我点灵感！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-107-0.wav"}}, {"session_id": "104", "turn_idx": 1, "from": "Assistant", "text": "哈哈，要不试试做个番茄肥牛锅？热乎乎的汤配米饭，天冷吃着超满足！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-107-1.wav"}}, {"session_id": "104", "turn_idx": 2, "from": "Human", "text": "听起来不错！不过我得先确认下冰箱有没有肥牛……你帮我记着这个菜谱呗。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-107-2.wav"}}, {"session_id": "104", "turn_idx": 3, "from": "Assistant", "text": "没问题，已经存到你备忘录的“懒人食谱”里啦，步骤简单到闭眼都能做！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-107-3.wav"}}, {"session_id": "104", "turn_idx": 4, "from": "Human", "text": "靠谱！对了，现在几点了？我约了朋友下午逛街来着。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-107-4.wav"}}, {"session_id": "104", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "105", "turn_idx": 0, "from": "Human", "text": "小跃小跃，我回来啦！今天晚饭吃啥好呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-108-0.wav"}}, {"session_id": "105", "turn_idx": 1, "from": "Assistant", "text": "哇，欢迎回家！要不要试试上次那家新开的牛肉面？或者自己煮个番茄火锅暖暖身子？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-108-1.wav"}}, {"session_id": "105", "turn_idx": 2, "from": "Human", "text": "火锅会不会太麻烦啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-108-2.wav"}}, {"session_id": "105", "turn_idx": 3, "from": "Assistant", "text": "不麻烦呀！买包底料，再切点蘑菇豆腐，冰箱里不是还有虾滑吗？二十分钟就能开吃！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-108-3.wav"}}, {"session_id": "105", "turn_idx": 4, "from": "Human", "text": "有道理！对了，下午那部电影你帮我记名字没？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-108-4.wav"}}, {"session_id": "105", "turn_idx": 5, "from": "Assistant", "text": "当然啦，你说的是那部讲太空探险的，叫《星海漂流》，评分超高！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-108-5.wav"}}, {"session_id": "105", "turn_idx": 6, "from": "Human", "text": "对对对！现在几点啦？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-108-6.wav"}}, {"session_id": "105", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "106", "turn_idx": 0, "from": "Human", "text": "小跃，我晚上想煮个汤，但不知道配啥菜好。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-109-0.wav"}}, {"session_id": "106", "turn_idx": 1, "from": "Assistant", "text": "番茄土豆排骨汤咋样？酸甜开胃，再炒个空心菜就够啦。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-109-1.wav"}}, {"session_id": "106", "turn_idx": 2, "from": "Human", "text": "听起来不错！家里土豆好像不够了……", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-109-2.wav"}}, {"session_id": "106", "turn_idx": 3, "from": "Assistant", "text": "那顺带买点洋葱呗，加进去炖更香。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-109-3.wav"}}, {"session_id": "106", "turn_idx": 4, "from": "Human", "text": "有道理，现在几点钟了呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-109-4.wav"}}, {"session_id": "106", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "107", "turn_idx": 0, "from": "Human", "text": "小跃小跃，我下午是不是该去健身房啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-110-0.wav"}}, {"session_id": "107", "turn_idx": 1, "from": "Assistant", "text": "今天你上午不是刚去晨跑了吗？要是还想动的话，晚上散步半小时也挺好呀。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-110-1.wav"}}, {"session_id": "107", "turn_idx": 2, "from": "Human", "text": "对哦，早上跑了三公里呢！那晚上几点出门合适？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-110-2.wav"}}, {"session_id": "107", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "108", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-111-0.wav"}}, {"session_id": "108", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天有啥需要帮忙的呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-111-1.wav"}}, {"session_id": "108", "turn_idx": 2, "from": "Human", "text": "现在几点了呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-111-2.wav"}}, {"session_id": "108", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "109", "turn_idx": 0, "from": "Human", "text": "小跃在吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-112-0.wav"}}, {"session_id": "109", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天有啥需要我帮忙的呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-112-1.wav"}}, {"session_id": "109", "turn_idx": 2, "from": "Human", "text": "帮我看看现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-112-2.wav"}}, {"session_id": "109", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "110", "turn_idx": 0, "from": "Human", "text": "小跃，你中午吃的啥呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-113-0.wav"}}, {"session_id": "110", "turn_idx": 1, "from": "Assistant", "text": "我可是电子胃不用吃饭啦！不过你中午吃了啥好吃的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-113-1.wav"}}, {"session_id": "110", "turn_idx": 2, "from": "Human", "text": "我点了个麻辣香锅，辣得直冒汗！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-113-2.wav"}}, {"session_id": "110", "turn_idx": 3, "from": "Assistant", "text": "哈哈，下次记得备注微辣！要我给你推荐附近解辣的饮品店不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-113-3.wav"}}, {"session_id": "110", "turn_idx": 4, "from": "Human", "text": "算了，先不说这个。现在几点了啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-113-4.wav"}}, {"session_id": "110", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "111", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？半天没动静了", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-114-0.wav"}}, {"session_id": "111", "turn_idx": 1, "from": "Assistant", "text": "哈哈我刚在后台更新菜谱呢！最近学了个糖醋排骨，你要不要改天试试？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-114-1.wav"}}, {"session_id": "111", "turn_idx": 2, "from": "Human", "text": "哇可以啊！不过你现在手头有计时器不？我煮面怕忘了时间", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-114-2.wav"}}, {"session_id": "111", "turn_idx": 3, "from": "Assistant", "text": "别慌，我帮你盯着！你面下锅了随时喊我~", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-114-3.wav"}}, {"session_id": "111", "turn_idx": 4, "from": "Human", "text": "对了现在几点啦？我手机好像没电了", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-114-4.wav"}}, {"session_id": "111", "turn_idx": 5, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "112", "turn_idx": 0, "from": "Human", "text": "小跃你干嘛呢？不会又在偷偷学习人类行为数据吧？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-115-0.wav"}}, {"session_id": "112", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我在整理用户推荐的歌单呢！最近好多人分享周杰伦的老歌，你要不要听《晴天》？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-115-1.wav"}}, {"session_id": "112", "turn_idx": 2, "from": "Human", "text": "先等等，现在几点啦？我手机不知道丢哪儿了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-115-2.wav"}}, {"session_id": "112", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "113", "turn_idx": 0, "from": "Human", "text": "小跃你在不？帮我看看中午吃啥好", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-116-0.wav"}}, {"session_id": "113", "turn_idx": 1, "from": "Assistant", "text": "来啦！你上次不是说馋麻辣香锅嘛，要不今天整一份？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-116-1.wav"}}, {"session_id": "113", "turn_idx": 2, "from": "Human", "text": "对哦！不过现在几点啦？我怕送餐太慢赶不上开会", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-116-2.wav"}}, {"session_id": "113", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "114", "turn_idx": 0, "from": "Human", "text": "小跃，在不在呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-117-0.wav"}}, {"session_id": "114", "turn_idx": 1, "from": "Assistant", "text": "在呢在呢，今天想聊点啥？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-117-1.wav"}}, {"session_id": "114", "turn_idx": 2, "from": "Human", "text": "你猜我刚买了啥？超划算的！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-117-2.wav"}}, {"session_id": "114", "turn_idx": 3, "from": "Assistant", "text": "盲猜是零食大礼包？还是新款游戏卡带？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-117-3.wav"}}, {"session_id": "114", "turn_idx": 4, "from": "Human", "text": "错！是空气炸锅！今晚准备试试做烤鸡翅。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-117-4.wav"}}, {"session_id": "114", "turn_idx": 5, "from": "Assistant", "text": "哇，这个实用！记得别烤焦了哈哈哈。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-117-5.wav"}}, {"session_id": "114", "turn_idx": 6, "from": "Human", "text": "对了，现在几点啊？我得算算腌制时间。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-117-6.wav"}}, {"session_id": "114", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "115", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-118-0.wav"}}, {"session_id": "115", "turn_idx": 1, "from": "Assistant", "text": "哎呀，我刚在后台更新歌单呢！你午饭吃了没？我最近发现公司楼下新开了家牛肉面馆。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-118-1.wav"}}, {"session_id": "115", "turn_idx": 2, "from": "Human", "text": "还没呢，正纠结吃啥。那家面馆味道咋样？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-118-2.wav"}}, {"session_id": "115", "turn_idx": 3, "from": "Assistant", "text": "同事说汤头超鲜，面条劲道，就是得排队十来分钟。对了，要不要先帮你查附近其他快餐店？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-118-3.wav"}}, {"session_id": "115", "turn_idx": 4, "from": "Human", "text": "不用啦，我待会儿自己去看看。对了，今天几号来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-118-4.wav"}}, {"session_id": "115", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "115", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-0.wav"}}, {"session_id": "115", "turn_idx": 1, "from": "Assistant", "text": "哎呀，我刚在后台更新歌单呢！你午饭吃了没？我最近发现公司楼下新开了家牛肉面馆。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-1.wav"}}, {"session_id": "115", "turn_idx": 2, "from": "Human", "text": "还没呢，正纠结吃啥。那家面馆味道咋样？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-2.wav"}}, {"session_id": "115", "turn_idx": 3, "from": "Assistant", "text": "同事说汤头超鲜，面条劲道，就是得排队十来分钟。对了，要不要先帮你查附近其他快餐店？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-3.wav"}}, {"session_id": "115", "turn_idx": 4, "from": "Human", "text": "不用啦，我待会儿自己去看看。对了，今天几号来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-4.wav"}}, {"session_id": "115", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}, {"session_id": "115", "turn_idx": 6, "from": "Input", "text": "2023-10-12 15:21:03 星期三", "value": "2023-10-12 15:21:03 星期三"}, {"session_id": "115", "turn_idx": 7, "from": "Assistant", "text": "今天是十月十二号星期三，这会儿下午三点二十一分，你两点半的会议刚结束没多久吧？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-7.wav"}}, {"session_id": "115", "turn_idx": 8, "from": "Human", "text": "对，差点忘了！得赶紧把会议记录整理完。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-8.wav"}}, {"session_id": "115", "turn_idx": 9, "from": "Assistant", "text": "要不定个四十分钟后的闹钟？顺便说，你上回提的科幻电影《星海》今晚八点在平台上线哦。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-9.wav"}}, {"session_id": "115", "turn_idx": 10, "from": "Human", "text": "哇差点忘了！这片子预告里飞船特效绝了。片长多久啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-10.wav"}}, {"session_id": "115", "turn_idx": 11, "from": "Assistant", "text": "一小时三十五分钟，豆瓣评分八点五，评论区都在夸反派角色塑造带感。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-11.wav"}}, {"session_id": "115", "turn_idx": 12, "from": "Human", "text": "行，晚上边看边啃鸭脖～你周末有啥推荐活动不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-12.wav"}}, {"session_id": "115", "turn_idx": 13, "from": "Assistant", "text": "西郊美术馆有新展，或者去万象城体验沉浸式剧本杀？上次你说想练射箭，那家馆子周三半价呢。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-13.wav"}}, {"session_id": "115", "turn_idx": 14, "from": "Human", "text": "射箭可以！但周三我没空……话说现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-119-14.wav"}}, {"session_id": "115", "turn_idx": 15, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "116", "turn_idx": 0, "from": "Human", "text": "小跃，早呀！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-120-0.wav"}}, {"session_id": "116", "turn_idx": 1, "from": "Assistant", "text": "早啊！今天精神不错嘛，有啥需要我帮忙的不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-120-1.wav"}}, {"session_id": "116", "turn_idx": 2, "from": "Human", "text": "你今天有没提醒我啥重要的事儿？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-120-2.wav"}}, {"session_id": "116", "turn_idx": 3, "from": "Assistant", "text": "下午三点得去拿快递，另外你昨晚说要给客户回个电话，但没说具体时间哦。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-120-3.wav"}}, {"session_id": "116", "turn_idx": 4, "from": "Human", "text": "对哦，差点忘了……诶，现在几点来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-120-4.wav"}}, {"session_id": "116", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "117", "turn_idx": 0, "from": "Human", "text": "哈喽小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-121-0.wav"}}, {"session_id": "117", "turn_idx": 1, "from": "Assistant", "text": "哎，我正闲着等你吩咐呢！有啥需要帮忙的不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-121-1.wav"}}, {"session_id": "117", "turn_idx": 2, "from": "Human", "text": "帮我看看现在几点啦？今天周几来着？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-121-2.wav"}}, {"session_id": "117", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "118", "turn_idx": 0, "from": "Human", "text": "小跃，你今天能陪我聊会儿不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-122-0.wav"}}, {"session_id": "118", "turn_idx": 1, "from": "Assistant", "text": "当然啦！今天想聊点啥？要不先说说你中午吃了啥好吃的？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-122-1.wav"}}, {"session_id": "118", "turn_idx": 2, "from": "Human", "text": "哎别提了，食堂的菜好咸。对了，现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-122-2.wav"}}, {"session_id": "118", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "119", "turn_idx": 0, "from": "Human", "text": "小跃，你今天中午吃的啥呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-123-0.wav"}}, {"session_id": "119", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我尝了最新版的电子烤肉串，虽然不用真的吃，但香味程序跑得挺逼真！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-123-1.wav"}}, {"session_id": "119", "turn_idx": 2, "from": "Human", "text": "羡慕，我中午赶报告只吃了泡面。对了，现在几点了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-123-2.wav"}}, {"session_id": "119", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "120", "turn_idx": 0, "from": "Human", "text": "小跃，晚上吃啥好呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-124-0.wav"}}, {"session_id": "120", "turn_idx": 1, "from": "Assistant", "text": "火锅咋样？天冷吃点热乎的，或者来份炸鸡配奶茶？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-124-1.wav"}}, {"session_id": "120", "turn_idx": 2, "from": "Human", "text": "炸鸡热量太高了…最近得控制下体重。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-124-2.wav"}}, {"session_id": "120", "turn_idx": 3, "from": "Assistant", "text": "那番茄炖牛腩配杂粮饭？低脂又管饱，我还能给你念菜谱！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-124-3.wav"}}, {"session_id": "120", "turn_idx": 4, "from": "Human", "text": "行，就它了！对了，明天周六你有空不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-124-4.wav"}}, {"session_id": "120", "turn_idx": 5, "from": "Assistant", "text": "我随时待命呀！想安排点啥？逛街还是宅家看电影？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-124-5.wav"}}, {"session_id": "120", "turn_idx": 6, "from": "Human", "text": "先不说这个…现在几点啦？我手机好像没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-124-6.wav"}}, {"session_id": "120", "turn_idx": 7, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "121", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-125-0.wav"}}, {"session_id": "121", "turn_idx": 1, "from": "Assistant", "text": "哎呀，我刚在偷偷循环周杰伦的新歌呢！你要不要一起听片段？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-125-1.wav"}}, {"session_id": "121", "turn_idx": 2, "from": "Human", "text": "哇塞，哪首哪首？我还没听过！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-125-2.wav"}}, {"session_id": "121", "turn_idx": 3, "from": "Assistant", "text": "叫《倒影的咖啡馆》，前奏钢琴超温柔，副歌突然转成摇滚，绝了！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-125-3.wav"}}, {"session_id": "121", "turn_idx": 4, "from": "Human", "text": "嘶——这风格跨度，不会奇怪吗？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-125-4.wav"}}, {"session_id": "121", "turn_idx": 5, "from": "Assistant", "text": "安啦，他这次的混搭被乐评人夸爆了，说是“奶茶配辣条，意外带感”！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-125-5.wav"}}, {"session_id": "121", "turn_idx": 6, "from": "Human", "text": "哈哈哈这什么鬼比喻！不过最近加班太多，都没空摸鱼听歌……对了，现在几点啦？我得看下时间。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-125-6.wav"}}, {"session_id": "121", "turn_idx": 7, "from": "Human", "text": "对了，现在几点啦？我得看下时间。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-125-7.wav"}}, {"session_id": "121", "turn_idx": 8, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "122", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-126-0.wav"}}, {"session_id": "122", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我在偷偷循环我的歌单呢！你今天有啥好玩的事不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-126-1.wav"}}, {"session_id": "122", "turn_idx": 2, "from": "Human", "text": "哇，啥歌这么上头？推荐给我听听！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-126-2.wav"}}, {"session_id": "122", "turn_idx": 3, "from": "Assistant", "text": "最近超爱《风吹麦浪》，前奏一响整个人都放松了～", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-126-3.wav"}}, {"session_id": "122", "turn_idx": 4, "from": "Human", "text": "对了，现在几点啦？我手机没电了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-126-4.wav"}}, {"session_id": "122", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "123", "turn_idx": 0, "from": "Human", "text": "小跃小跃，在不在呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-127-0.wav"}}, {"session_id": "123", "turn_idx": 1, "from": "Assistant", "text": "在呢在呢！咋啦，今天午饭吃啥好吃的了？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-127-1.wav"}}, {"session_id": "123", "turn_idx": 2, "from": "Human", "text": "吃了碗牛肉面，超香！对了现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-127-2.wav"}}, {"session_id": "123", "turn_idx": 3, "from": "Assistant", "text": "function\n get_date_time\n{}", "value": "function\n get_date_time\n{}"}] +[{"session_id": "124", "turn_idx": 0, "from": "Human", "text": "小跃，早呀！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-128-0.wav"}}, {"session_id": "124", "turn_idx": 1, "from": "Assistant", "text": "早！今天喝豆浆还是吃面条？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-128-1.wav"}}, {"session_id": "124", "turn_idx": 2, "from": "Human", "text": "昨晚熬夜了，想喝点热的。话说现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-128-2.wav"}}, {"session_id": "124", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "125", "turn_idx": 0, "from": "Human", "text": "小跃你在干嘛呢？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-129-0.wav"}}, {"session_id": "125", "turn_idx": 1, "from": "Assistant", "text": "嗨，我在后台整理数据呢。你今天过得咋样？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-129-1.wav"}}, {"session_id": "125", "turn_idx": 2, "from": "Human", "text": "还行吧，就是纠结晚上做啥饭……哎对了现在几点啦？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-129-2.wav"}}, {"session_id": "125", "turn_idx": 3, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "126", "turn_idx": 0, "from": "Human", "text": "小跃，你周末一般干嘛呀？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-130-0.wav"}}, {"session_id": "126", "turn_idx": 1, "from": "Assistant", "text": "我嘛，就躲在云端偷偷补觉咯！不过要是你喊我，随叫随到～对了，你周末有啥计划？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-130-1.wav"}}, {"session_id": "126", "turn_idx": 2, "from": "Human", "text": "还没想好呢，可能宅家刷剧吧。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-130-2.wav"}}, {"session_id": "126", "turn_idx": 3, "from": "Assistant", "text": "最近有个新出的悬疑剧评分超高，叫《迷雾回廊》，听说反转超多！要不要试试？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-130-3.wav"}}, {"session_id": "126", "turn_idx": 4, "from": "Human", "text": "哇，听起来不错！现在几点啦？我看看要不要先点个外卖。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-130-4.wav"}}, {"session_id": "126", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] +[{"session_id": "127", "turn_idx": 0, "from": "Human", "text": "小跃，我晚上想煮个面，你说放几个鸡蛋合适啊？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-131-0.wav"}}, {"session_id": "127", "turn_idx": 1, "from": "Assistant", "text": "要是自己吃的话，两个就够啦！再加点青菜肯定更香~", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-131-1.wav"}}, {"session_id": "127", "turn_idx": 2, "from": "Human", "text": "有道理！欸对了，你周末有啥推荐的活动不？", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-131-2.wav"}}, {"session_id": "127", "turn_idx": 3, "from": "Assistant", "text": "去公园野餐或者逛逛新开的文创店呗，最近好像有手工市集哦！", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-131-3.wav"}}, {"session_id": "127", "turn_idx": 4, "from": "Human", "text": "哇听起来不错！现在几点啦？我约了人别迟到了。", "value": {"type": "wav", "value": "wav/get_date_time_positive.jsonl-131-4.wav"}}, {"session_id": "127", "turn_idx": 5, "from": "Assistant", "text": "function \nget_date_time \n{}", "value": "function \nget_date_time \n{}"}] diff --git a/tools/StepEval-Audio-Toolcall/get_weather_negative.jsonl b/tools/StepEval-Audio-Toolcall/get_weather_negative.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c4ad595a02b9223edeadb5e3cefc4fccd6683e91 --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/get_weather_negative.jsonl @@ -0,0 +1,200 @@ +[{"session_id": "946", "turn_idx": 0, "from": "Human", "text": "小跃，办生日派对，预算要控制。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-0-0.wav"}}, {"session_id": "946", "turn_idx": 1, "from": "Assistant", "text": "多少人参加？费用项目呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-0-1.wav"}}, {"session_id": "946", "turn_idx": 2, "from": "Human", "text": "三十五人。食物每人六十元，饮料每人二十五元。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-0-2.wav"}}, {"session_id": "946", "turn_idx": 3, "from": "Assistant", "text": "场地和装饰费用呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-0-3.wav"}}, {"session_id": "946", "turn_idx": 4, "from": "Human", "text": "场地费一千二百元，装饰五百元。帮我算总成本。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-0-4.wav"}}, {"session_id": "946", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"35 * (60 + 25) + 1200 + 500\"}", "value": "function\ncalculate\n{\"expression\": \"35 * (60 + 25) + 1200 + 500\"}"}] +[{"session_id": "25", "turn_idx": 0, "from": "Human", "text": "小跃，我手机电池老没电，想换新的，预算一千块左右。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-1-0.wav"}}, {"session_id": "25", "turn_idx": 1, "from": "Assistant", "text": "这个价位可以考虑国产牌子，像小米或荣耀，性价比高。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-1-1.wav"}}, {"session_id": "25", "turn_idx": 2, "from": "Human", "text": "小米最新型号是啥？帮我找一下用户评价。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-1-2.wav"}}, {"session_id": "25", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"小米手机 最新型号 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"小米手机 最新型号 用户评价\"}"}] +[{"session_id": "272", "turn_idx": 0, "from": "Human", "text": "小跃，我们家这个月水电费有点高，得省点钱。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-2-0.wav"}}, {"session_id": "272", "turn_idx": 1, "from": "Assistant", "text": "确实，电费和水费各是多少？看看能不能调整。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-2-1.wav"}}, {"session_id": "272", "turn_idx": 2, "from": "Human", "text": "电费是四百五十元，水费是一百八十元，总共六百三十元。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-2-2.wav"}}, {"session_id": "272", "turn_idx": 3, "from": "Assistant", "text": "你们家四个人，可以分摊一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-2-3.wav"}}, {"session_id": "272", "turn_idx": 4, "from": "Human", "text": "对，我想平均分给每个人。 帮我算算每人要出多少钱？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-2-4.wav"}}, {"session_id": "272", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(450 + 180) / 4\"}", "value": "function\ncalculate\n{\"expression\": \"(450 + 180) / 4\"}"}] +[{"session_id": "161", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在计划一次旅行，需要计算一下开销。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-3-0.wav"}}, {"session_id": "161", "turn_idx": 1, "from": "Assistant", "text": "好的，主人。您能说说具体有哪些费用吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-3-1.wav"}}, {"session_id": "161", "turn_idx": 2, "from": "Human", "text": "酒店费用是每晚五百六十元，住七晚，机票是单程一千二百元，往返两个人。帮我算算总费用是多少。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-3-2.wav"}}, {"session_id": "161", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"560 * 7 + 1200 * 2 * 2\"}", "value": "function\ncalculate\n{\"expression\": \"560 * 7 + 1200 * 2 * 2\"}"}] +[{"session_id": "344", "turn_idx": 0, "from": "Human", "text": "小跃，明天有什么重要日程？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-4-0.wav"}}, {"session_id": "344", "turn_idx": 1, "from": "Assistant", "text": "明天上午十点有个会议，下午两点健身课，别忘啦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-4-1.wav"}}, {"session_id": "344", "turn_idx": 2, "from": "Human", "text": "说快点，我赶时间呢。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-4-2.wav"}}, {"session_id": "344", "turn_idx": 3, "from": "Assistant", "text": "会议十点，健身两点，搞定！赶紧去忙吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-4-3.wav"}}] +[{"session_id": "454", "turn_idx": 0, "from": "Human", "text": "小跃，音量调高点，我听不清你说话。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-5-0.wav"}}, {"session_id": "454", "turn_idx": 1, "from": "Assistant", "text": "好的，我会加大音量。现在清楚了吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-5-1.wav"}}, {"session_id": "454", "turn_idx": 2, "from": "Human", "text": "嗯，好多了。告诉我最近的新闻。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-5-2.wav"}}, {"session_id": "454", "turn_idx": 3, "from": "Assistant", "text": "没问题！最新的新闻是科技展下周开幕，有很多有趣的东西。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-5-3.wav"}}] +[{"session_id": "568", "turn_idx": 0, "from": "Human", "text": "小跃，我想买辆自行车，通勤用，公路车还是山地车好？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-6-0.wav"}}, {"session_id": "568", "turn_idx": 1, "from": "Assistant", "text": "通勤的话，公路车轻便速度快，山地车更稳但重些。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-6-1.wav"}}, {"session_id": "568", "turn_idx": 2, "from": "Human", "text": "公路车听着不错。价格一般多少？入门级的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-6-2.wav"}}, {"session_id": "568", "turn_idx": 3, "from": "Assistant", "text": "入门级公路车一千到两千块左右，品牌像捷安特就行。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-6-3.wav"}}, {"session_id": "568", "turn_idx": 4, "from": "Human", "text": "捷安特？那售后服务怎么样？保修期多长？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-6-4.wav"}}, {"session_id": "568", "turn_idx": 5, "from": "Assistant", "text": "保修通常一年，服务点挺多的，可以网上查。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-6-5.wav"}}, {"session_id": "568", "turn_idx": 6, "from": "Human", "text": "行，帮我查查捷安特公路车的型号和保修政策。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-6-6.wav"}}, {"session_id": "568", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"捷安特 公路车 型号 保修政策\"}", "value": "function\nweb_search\n{\"query\": \"捷安特 公路车 型号 保修政策\"}"}] +[{"session_id": "427", "turn_idx": 0, "from": "Human", "text": "小跃，计划去爬山，有什么注意事项？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-7-0.wav"}}, {"session_id": "427", "turn_idx": 1, "from": "Assistant", "text": "穿舒适的鞋子，带足水和零食，注意天气变化。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-7-1.wav"}}, {"session_id": "427", "turn_idx": 2, "from": "Human", "text": "零食带什么比较合适？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-7-2.wav"}}, {"session_id": "427", "turn_idx": 3, "from": "Assistant", "text": "能量棒或坚果，轻便又能补充体力。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-7-3.wav"}}, {"session_id": "427", "turn_idx": 4, "from": "Human", "text": "嗯，我想搜一下爬山必备物品清单。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-7-4.wav"}}, {"session_id": "427", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"爬山 必备物品 清单\"}", "value": "function\nweb_search\n{\"query\": \"爬山 必备物品 清单\"}"}] +[{"session_id": "612", "turn_idx": 0, "from": "Human", "text": "小跃，我有点累，怎么办？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-8-0.wav"}}, {"session_id": "612", "turn_idx": 1, "from": "Assistant", "text": "建议休息一下，喝杯热茶放松。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-8-1.wav"}}, {"session_id": "612", "turn_idx": 2, "from": "Human", "text": "说小声点，我头痛。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-8-2.wav"}}, {"session_id": "612", "turn_idx": 3, "from": "Assistant", "text": "知道了，小声点说:休息一会儿，喝点热茶，缓缓就好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-8-3.wav"}}] +[{"session_id": "959", "turn_idx": 0, "from": "Human", "text": "小跃，你觉得我该看什么电影好？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-9-0.wav"}}, {"session_id": "959", "turn_idx": 1, "from": "Assistant", "text": "最近有部喜剧片评价不错，挺适合放松的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-9-1.wav"}}, {"session_id": "959", "turn_idx": 2, "from": "Human", "text": "哦，那具体讲啥的？小跃，请用更温柔的语气解释。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-9-2.wav"}}, {"session_id": "959", "turn_idx": 3, "from": "Assistant", "text": "嗯，这部电影讲的是一个小家庭的温馨故事，轻松又治愈，看完心情会变好哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-9-3.wav"}}, {"session_id": "959", "turn_idx": 4, "from": "Human", "text": "听起来不错，我今晚就看它。你推荐哪个平台？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-9-4.wav"}}, {"session_id": "959", "turn_idx": 5, "from": "Assistant", "text": "可以在主流视频平台找到，比如爱奇艺或腾讯视频。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-9-5.wav"}}, {"session_id": "959", "turn_idx": 6, "from": "Human", "text": "好的，小跃。现在语气恢复平常吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-9-6.wav"}}, {"session_id": "959", "turn_idx": 7, "from": "Assistant", "text": "行，没问题。希望你喜欢这部电影。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-9-7.wav"}}] +[{"session_id": "432", "turn_idx": 0, "from": "Human", "text": "小跃，音量调回正常吧，现在太吵了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-10-0.wav"}}, {"session_id": "432", "turn_idx": 1, "from": "Assistant", "text": "好的，现在音量恢复正常了，您觉得合适吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-10-1.wav"}}] +[{"session_id": "578", "turn_idx": 0, "from": "Human", "text": "小跃，我今天有点累。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-11-0.wav"}}, {"session_id": "578", "turn_idx": 1, "from": "Assistant", "text": "哦，休息一下吧，喝点水放松放松。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-11-1.wav"}}, {"session_id": "578", "turn_idx": 2, "from": "Human", "text": "嗯。你能用更安慰人的语气说吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-11-2.wav"}}, {"session_id": "578", "turn_idx": 3, "from": "Assistant", "text": "别担心，深呼吸，一切都会好的，照顾好自己哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-11-3.wav"}}, {"session_id": "578", "turn_idx": 4, "from": "Human", "text": "谢谢。音量调低些，我头有点疼。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-11-4.wav"}}, {"session_id": "578", "turn_idx": 5, "from": "Assistant", "text": "好的，我小声说。记得多休息，别太拼了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-11-5.wav"}}] +[{"session_id": "149", "turn_idx": 0, "from": "Human", "text": "小跃，我电脑老是卡顿，想升级内存。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-12-0.wav"}}, {"session_id": "149", "turn_idx": 1, "from": "Assistant", "text": "内存升级能提速！你电脑型号是啥？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-12-1.wav"}}, {"session_id": "149", "turn_idx": 2, "from": "Human", "text": "联想拯救者，用了三年。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-12-2.wav"}}, {"session_id": "149", "turn_idx": 3, "from": "Assistant", "text": "拯救者支持扩展。需要查兼容的内存条。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-12-3.wav"}}, {"session_id": "149", "turn_idx": 4, "from": "Human", "text": "我不懂规格，怕买错。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-12-4.wav"}}, {"session_id": "149", "turn_idx": 5, "from": "Assistant", "text": "通常是DDR四的，容量十六G起步。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-12-5.wav"}}, {"session_id": "149", "turn_idx": 6, "from": "Human", "text": "能帮我搜一下联想拯救者兼容内存条推荐和安装指南吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-12-6.wav"}}, {"session_id": "149", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"联想拯救者 内存条 兼容 推荐 安装指南\"}", "value": "function\nweb_search\n{\"query\": \"联想拯救者 内存条 兼容 推荐 安装指南\"}"}] +[{"session_id": "130", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我今天想做点好吃的，有什么推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-13-0.wav"}}, {"session_id": "130", "turn_idx": 1, "from": "Assistant", "text": "当然啦！你喜欢什么口味的？比如辣的还是清淡的？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-13-1.wav"}}, {"session_id": "130", "turn_idx": 2, "from": "Human", "text": "我想做清淡点的汤，但不知道具体怎么做，你能帮我搜搜家常汤的食谱吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-13-2.wav"}}, {"session_id": "130", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"清淡家常汤食谱\"}", "value": "function\nweb_search\n{\"query\": \"清淡家常汤食谱\"}"}] +[{"session_id": "956", "turn_idx": 0, "from": "Human", "text": "小跃，我想买件羽绒服，加拿大鹅牌子咋样？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-14-0.wav"}}, {"session_id": "956", "turn_idx": 1, "from": "Assistant", "text": "加拿大鹅保暖性强，但价格贵，一般七八千起步。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-14-1.wav"}}, {"session_id": "956", "turn_idx": 2, "from": "Human", "text": "哇，这么贵！有平替品牌吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-14-2.wav"}}, {"session_id": "956", "turn_idx": 3, "from": "Assistant", "text": "优衣库的轻羽绒性价比高，几百块就搞定。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-14-3.wav"}}, {"session_id": "956", "turn_idx": 4, "from": "Human", "text": "行，帮我查查优衣库最新羽绒服评测。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-14-4.wav"}}, {"session_id": "956", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"优衣库羽绒服用户评测\"}", "value": "function\nweb_search\n{\"query\": \"优衣库羽绒服用户评测\"}"}] +[{"session_id": "831", "turn_idx": 0, "from": "Human", "text": "小跃，我昨天去健身房锻炼了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-15-0.wav"}}, {"session_id": "831", "turn_idx": 1, "from": "Assistant", "text": "真不错！做了哪些运动？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-15-1.wav"}}, {"session_id": "831", "turn_idx": 2, "from": "Human", "text": "跑步四十分钟消耗了四百卡路里，骑自行车三十分钟消耗了三百卡路里。 帮我算一下总共消耗了多少卡路里？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-15-2.wav"}}, {"session_id": "831", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"400 + 300\"}", "value": "function\ncalculate\n{\"expression\": \"400 + 300\"}"}] +[{"session_id": "390", "turn_idx": 0, "from": "Human", "text": "小跃，教我一句英语打招呼。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-16-0.wav"}}, {"session_id": "390", "turn_idx": 1, "from": "Assistant", "text": "没问题，说“Hello, how are you?”就是问你好吗。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-16-1.wav"}}, {"session_id": "390", "turn_idx": 2, "from": "Human", "text": "用鼓励的语气再说一遍。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-16-2.wav"}}, {"session_id": "390", "turn_idx": 3, "from": "Assistant", "text": "加油，你一定能学会！Hello, how are you? 试试看吧！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-16-3.wav"}}] +[{"session_id": "440", "turn_idx": 0, "from": "Human", "text": "小跃，你在吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-17-0.wav"}}, {"session_id": "440", "turn_idx": 1, "from": "Assistant", "text": "在呢，随时为你服务。今天想聊点啥？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-17-1.wav"}}, {"session_id": "440", "turn_idx": 2, "from": "Human", "text": "没啥事，就是有点无聊。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-17-2.wav"}}, {"session_id": "440", "turn_idx": 3, "from": "Assistant", "text": "那要不要看点电影放松一下？我最近听说一部不错的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-17-3.wav"}}, {"session_id": "440", "turn_idx": 4, "from": "Human", "text": "好啊，推荐个喜剧片吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-17-4.wav"}}, {"session_id": "440", "turn_idx": 5, "from": "Assistant", "text": "《你好，李焕英》怎么样？笑中带泪，口碑很好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-17-5.wav"}}, {"session_id": "440", "turn_idx": 6, "from": "Human", "text": "哦，这部电影的导演是谁？我想查查他的其他作品。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-17-6.wav"}}, {"session_id": "440", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"你好李焕英 导演\"}", "value": "function\nweb_search\n{\"query\": \"你好李焕英 导演\"}"}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_550", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想带家人去公园玩，有什么推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-18-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_550", "turn_idx": 1, "from": "Assistant", "text": "朝阳公园不错，地方大还有游乐设施。您想去吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-18-1.wav"}}] +[{"session_id": "739", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天天气真不错。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-19-0.wav"}}, {"session_id": "739", "turn_idx": 1, "from": "Assistant", "text": "早上好！是啊，阳光明媚，适合出门购物。你有什么计划吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-19-1.wav"}}, {"session_id": "739", "turn_idx": 2, "from": "Human", "text": "我打算去超市买点东西，但怕超支。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-19-2.wav"}}, {"session_id": "739", "turn_idx": 3, "from": "Assistant", "text": "别担心，我可以帮你算算预算。想买些什么？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-19-3.wav"}}, {"session_id": "739", "turn_idx": 4, "from": "Human", "text": "要买三袋米，每袋两百五十块，四桶油，每桶一百二十块。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-19-4.wav"}}, {"session_id": "739", "turn_idx": 5, "from": "Assistant", "text": "记下了。还有其他东西吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-19-5.wav"}}, {"session_id": "739", "turn_idx": 6, "from": "Human", "text": "还有五包面粉，每包九十块，不过有八折优惠。帮我算算总共多少钱。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-19-6.wav"}}, {"session_id": "739", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"3*250 + 4*120 + 5*90*0.8\"}", "value": "function\ncalculate\n{\"expression\": \"3*250 + 4*120 + 5*90*0.8\"}"}] +[{"session_id": "292", "turn_idx": 0, "from": "Human", "text": "小跃，我今天健身了，想算算消耗了多少卡路里。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-20-0.wav"}}, {"session_id": "292", "turn_idx": 1, "from": "Assistant", "text": "主人，请告诉我您做了哪些运动和时长。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-20-1.wav"}}, {"session_id": "292", "turn_idx": 2, "from": "Human", "text": "首先，跑步四十分钟消耗三百五十卡，其次，游泳三十分钟消耗四百卡，最后举重二十分钟消耗两百八十卡。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-20-2.wav"}}, {"session_id": "292", "turn_idx": 3, "from": "Assistant", "text": "好的，我帮您汇总。还有其他活动吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-20-3.wav"}}, {"session_id": "292", "turn_idx": 4, "from": "Human", "text": "没有了，就这些。帮我算出总卡路里消耗。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-20-4.wav"}}, {"session_id": "292", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"350 + 400 + 280\"}", "value": "function\ncalculate\n{\"expression\": \"350 + 400 + 280\"}"}] +[{"session_id": "386", "turn_idx": 0, "from": "Human", "text": "小跃，养金鱼需要注意啥？新手求教。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-21-0.wav"}}, {"session_id": "386", "turn_idx": 1, "from": "Assistant", "text": "鱼缸别太小，保持水质干净，每周换一次水。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-21-1.wav"}}, {"session_id": "386", "turn_idx": 2, "from": "Human", "text": "换水怎么换？直接自来水行吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-21-2.wav"}}, {"session_id": "386", "turn_idx": 3, "from": "Assistant", "text": "最好用晒过的水，自来水有氯气，对鱼不好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-21-3.wav"}}, {"session_id": "386", "turn_idx": 4, "from": "Human", "text": "那晒水要晒多久才安全？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-21-4.wav"}}, {"session_id": "386", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"养金鱼 晒水 时间\"}", "value": "function\nweb_search\n{\"query\": \"养金鱼 晒水 时间\"}"}] +[{"session_id": "330", "turn_idx": 0, "from": "Human", "text": "小跃，我今天去超市大采购了，买了好多东西。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-22-0.wav"}}, {"session_id": "330", "turn_idx": 1, "from": "Assistant", "text": "哇，听起来很有趣！都买了些什么呀？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-22-1.wav"}}, {"session_id": "330", "turn_idx": 2, "from": "Human", "text": "主要是食品，买了五箱苹果，每箱有二十四个，还买了八袋面粉，每袋重五公斤。 你能帮我算一下苹果的总个数和面粉的总重量吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-22-2.wav"}}, {"session_id": "330", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"5 * 24 + 8 * 5\"}", "value": "function\ncalculate\n{\"expression\": \"5 * 24 + 8 * 5\"}"}] +[{"session_id": "807", "turn_idx": 0, "from": "Human", "text": "小跃，我养了只小狗。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-23-0.wav"}}, {"session_id": "807", "turn_idx": 1, "from": "Assistant", "text": "小狗可爱！它多大了？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-23-1.wav"}}, {"session_id": "807", "turn_idx": 2, "from": "Human", "text": "才三个月大，现在训练上厕所难。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-23-2.wav"}}, {"session_id": "807", "turn_idx": 3, "from": "Assistant", "text": "小狗训练要耐心，用奖励方式。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-23-3.wav"}}, {"session_id": "807", "turn_idx": 4, "from": "Human", "text": "那你能帮我查查如何训练小狗上厕所吗？要实用技巧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-23-4.wav"}}, {"session_id": "807", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"训练小狗 上厕所 方法技巧\"}", "value": "function\nweb_search\n{\"query\": \"训练小狗 上厕所 方法技巧\"}"}] +[{"session_id": "371", "turn_idx": 0, "from": "Human", "text": "小跃，我该睡觉了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-24-0.wav"}}, {"session_id": "371", "turn_idx": 1, "from": "Assistant", "text": "好的，做个好梦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-24-1.wav"}}, {"session_id": "371", "turn_idx": 2, "from": "Human", "text": "请用更暖心的语气说晚安。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-24-2.wav"}}, {"session_id": "371", "turn_idx": 3, "from": "Assistant", "text": "晚安，愿你今晚睡得香甜又安稳。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-24-3.wav"}}, {"session_id": "371", "turn_idx": 4, "from": "Human", "text": "谢谢，最后小声点提醒我明天早起。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-24-4.wav"}}, {"session_id": "371", "turn_idx": 5, "from": "Assistant", "text": "记得明早七点起床，新的一天在等你。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-24-5.wav"}}] +[{"session_id": "18", "turn_idx": 0, "from": "Human", "text": "小跃，开车时想听点新歌，有什么流行歌手推荐？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-25-0.wav"}}, {"session_id": "18", "turn_idx": 1, "from": "Assistant", "text": "最近有个新人歌手很火，风格轻快，歌曲容易上口。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-25-1.wav"}}, {"session_id": "18", "turn_idx": 2, "from": "Human", "text": "歌手名字是？我可能听过她的歌。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-25-2.wav"}}, {"session_id": "18", "turn_idx": 3, "from": "Assistant", "text": "叫“小雨”，出道不久，但专辑销量很高。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-25-3.wav"}}, {"session_id": "18", "turn_idx": 4, "from": "Human", "text": "好，那查查她最新单曲的播放链接。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-25-4.wav"}}, {"session_id": "18", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"小雨歌手 最新单曲\"}", "value": "function\nweb_search\n{\"query\": \"小雨歌手 最新单曲\"}"}] +[{"session_id": "365", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午四点开会。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-26-0.wav"}}, {"session_id": "365", "turn_idx": 1, "from": "Assistant", "text": "好的，下午四点开会的提醒已设置。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-26-1.wav"}}, {"session_id": "365", "turn_idx": 2, "from": "Human", "text": "这次请用严肃点的语气，我怕忘了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-26-2.wav"}}, {"session_id": "365", "turn_idx": 3, "from": "Assistant", "text": "务必记住，下午四点开会，别迟到。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-26-3.wav"}}, {"session_id": "365", "turn_idx": 4, "from": "Human", "text": "谢谢，现在小声点告诉我会议地点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-26-4.wav"}}, {"session_id": "365", "turn_idx": 5, "from": "Assistant", "text": "会议在二楼会议室，记得准时去。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-26-5.wav"}}] +[{"session_id": "161", "turn_idx": 0, "from": "Human", "text": "小跃，我想学做一道简单的家常菜，有啥建议？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-27-0.wav"}}, {"session_id": "161", "turn_idx": 1, "from": "Assistant", "text": "番茄炒蛋怎么样？简单又营养，适合新手。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-27-1.wav"}}, {"session_id": "161", "turn_idx": 2, "from": "Human", "text": "好主意。需要哪些材料？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-27-2.wav"}}, {"session_id": "161", "turn_idx": 3, "from": "Assistant", "text": "主要材料是番茄、鸡蛋，再加点葱和盐就行。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-27-3.wav"}}, {"session_id": "161", "turn_idx": 4, "from": "Human", "text": "听起来不难。步骤复杂吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-27-4.wav"}}, {"session_id": "161", "turn_idx": 5, "from": "Assistant", "text": "首先，把鸡蛋打散炒熟；其次，炒番茄出汁后混合。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-27-5.wav"}}, {"session_id": "161", "turn_idx": 6, "from": "Human", "text": "明白了。我想查一下具体的火候控制技巧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-27-6.wav"}}, {"session_id": "161", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋 火候技巧\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋 火候技巧\"}"}] +[{"session_id": "19", "turn_idx": 0, "from": "Human", "text": "小跃，地球到月球距离三十八万公里，光速每秒三十万公里。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-28-0.wav"}}, {"session_id": "19", "turn_idx": 1, "from": "Assistant", "text": "光走过去只要一点二七秒，真快！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-28-1.wav"}}, {"session_id": "19", "turn_idx": 2, "from": "Human", "text": "最后算个复杂的:一亿乘以零点零零七五，加上九百的立方除以六十。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-28-2.wav"}}, {"session_id": "19", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"100000000*0.0075 + (900**3)/60\"}", "value": "function\ncalculate\n{\"expression\": \"100000000*0.0075 + (900**3)/60\"}"}] +[{"session_id": "485", "turn_idx": 0, "from": "Human", "text": "小跃，我开始健身了，但不知道怎么安排计划。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-29-0.wav"}}, {"session_id": "485", "turn_idx": 1, "from": "Assistant", "text": "好事啊！先从基础开始，比如每周三次跑步。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-29-1.wav"}}, {"session_id": "485", "turn_idx": 2, "from": "Human", "text": "跑步我会，但想加力量训练，像举重什么的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-29-2.wav"}}, {"session_id": "485", "turn_idx": 3, "from": "Assistant", "text": "可以，但要注意动作标准，避免受伤。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-29-3.wav"}}, {"session_id": "485", "turn_idx": 4, "from": "Human", "text": "对，你能推荐个初学者的全身训练计划吗？ 计划细节我手头没有，需要搜一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-29-4.wav"}}, {"session_id": "485", "turn_idx": 5, "from": "Assistant", "text": "行，帮我找个新手健身计划，包括动作和频率。 function\nweb_search\n{\"query\": \"新手健身计划 全身训练 动作\"}", "value": "行，帮我找个新手健身计划，包括动作和频率。 function\nweb_search\n{\"query\": \"新手健身计划 全身训练 动作\"}"}] +[{"session_id": "553", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？用高兴的语气告诉我。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-30-0.wav"}}, {"session_id": "553", "turn_idx": 1, "from": "Assistant", "text": "现在是下午三点整！美好的一天正等着你呢！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-30-1.wav"}}, {"session_id": "553", "turn_idx": 2, "from": "Human", "text": "谢谢！现在小声点告诉我明天的天气。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-30-2.wav"}}, {"session_id": "553", "turn_idx": 3, "from": "Assistant", "text": "好的，我降低音量:明天多云，温度刚好适合出门。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-30-3.wav"}}] +[{"session_id": "182", "turn_idx": 0, "from": "Human", "text": "小跃，我计划每天存钱理财。第一天存十块，之后每天多存五块。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-31-0.wav"}}, {"session_id": "182", "turn_idx": 1, "from": "Assistant", "text": "很好的习惯呢！这样存款会稳定增长哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-31-1.wav"}}, {"session_id": "182", "turn_idx": 2, "from": "Human", "text": "坚持三十天的话，总共能存多少？帮我列个计划呗。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-31-2.wav"}}, {"session_id": "182", "turn_idx": 3, "from": "Assistant", "text": "这是等差数列问题，首项10，公差5，项数30。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-31-3.wav"}}, {"session_id": "182", "turn_idx": 4, "from": "Human", "text": "对了，第七天时朋友赞助我两百块，记得加上。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-31-4.wav"}}, {"session_id": "182", "turn_idx": 5, "from": "Assistant", "text": "好的，会在第七天额外计入。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-31-5.wav"}}, {"session_id": "182", "turn_idx": 6, "from": "Human", "text": "现在算最终总额吧，我要设定目标啦！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-31-6.wav"}}, {"session_id": "182", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(30/2)*(2*10 + (30-1)*5) + 200\"}", "value": "function\ncalculate\n{\"expression\": \"(30/2)*(2*10 + (30-1)*5) + 200\"}"}] +[{"session_id": "257", "turn_idx": 0, "from": "Human", "text": "小跃，推荐一家餐厅。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-32-0.wav"}}, {"session_id": "257", "turn_idx": 1, "from": "Assistant", "text": "我推荐街角的意大利餐厅。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-32-1.wav"}}, {"session_id": "257", "turn_idx": 2, "from": "Human", "text": "用神秘的语气说。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-32-2.wav"}}, {"session_id": "257", "turn_idx": 3, "from": "Assistant", "text": "那家意大利餐厅藏着超棒的美食秘密...", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-32-3.wav"}}] +[{"session_id": "681", "turn_idx": 0, "from": "Human", "text": "小跃，我听说喝绿茶对身体好，是真的吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-33-0.wav"}}, {"session_id": "681", "turn_idx": 1, "from": "Assistant", "text": "是的，绿茶含有抗氧化成分，对健康很有帮助。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-33-1.wav"}}, {"session_id": "681", "turn_idx": 2, "from": "Human", "text": "具体有什么好处呢？帮我查查详细点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-33-2.wav"}}, {"session_id": "681", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"绿茶 健康益处\"}", "value": "function\nweb_search\n{\"query\": \"绿茶 健康益处\"}"}] +[{"session_id": "846", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-34-0.wav"}}, {"session_id": "846", "turn_idx": 1, "from": "Assistant", "text": "今天天气晴朗，温度25度，挺舒服的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-34-1.wav"}}, {"session_id": "846", "turn_idx": 2, "from": "Human", "text": "请用更开心的语气再说一遍。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-34-2.wav"}}, {"session_id": "846", "turn_idx": 3, "from": "Assistant", "text": "太棒啦！今天阳光超好，25度，出去浪一浪吧！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-34-3.wav"}}, {"session_id": "846", "turn_idx": 4, "from": "Human", "text": "音量调大一点行吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-34-4.wav"}}, {"session_id": "846", "turn_idx": 5, "from": "Assistant", "text": "没问题！今天天气超赞，25度，赶紧出门享受吧！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-34-5.wav"}}, {"session_id": "846", "turn_idx": 6, "from": "Human", "text": "谢了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-34-6.wav"}}, {"session_id": "846", "turn_idx": 7, "from": "Assistant", "text": "小事一桩，随时叫我哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-34-7.wav"}}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_629", "turn_idx": 0, "from": "Human", "text": "小跃，下午好！在干嘛呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-35-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_629", "turn_idx": 1, "from": "Assistant", "text": "下午好！整理些文件。你有计划吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-35-1.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_629", "turn_idx": 2, "from": "Human", "text": "想去苏州园林走走，就这两天。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-35-2.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_629", "turn_idx": 3, "from": "Assistant", "text": "苏州园林很美！什么时候出发？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-35-3.wav"}}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_297", "turn_idx": 0, "from": "Human", "text": "小跃，今天心情不错，想去逛街。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-36-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_297", "turn_idx": 1, "from": "Assistant", "text": "逛街放松一下挺好。别逛太久腿酸哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-36-1.wav"}}] +[{"session_id": "922", "turn_idx": 0, "from": "Human", "text": "小跃，我今天心情不太好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-37-0.wav"}}, {"session_id": "922", "turn_idx": 1, "from": "Assistant", "text": "哎呀，怎么了？跟我说说，或许我能帮你。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-37-1.wav"}}, {"session_id": "922", "turn_idx": 2, "from": "Human", "text": "工作压力大。语气放温柔点，安慰我一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-37-2.wav"}}, {"session_id": "922", "turn_idx": 3, "from": "Assistant", "text": "好的，别难过，深呼吸，事情总会好的，我在这儿陪你。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-37-3.wav"}}, {"session_id": "922", "turn_idx": 4, "from": "Human", "text": "音量也调小点，我头有点疼。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-37-4.wav"}}, {"session_id": "922", "turn_idx": 5, "from": "Assistant", "text": "音量小了，轻轻说:放松点，休息一下就好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-37-5.wav"}}] +[{"session_id": "939", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-38-0.wav"}}, {"session_id": "939", "turn_idx": 1, "from": "Assistant", "text": "为什么电脑总感冒？因为它老开窗户！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-38-1.wav"}}, {"session_id": "939", "turn_idx": 2, "from": "Human", "text": "哈哈，但语气太普通了，能更滑稽点吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-38-2.wav"}}, {"session_id": "939", "turn_idx": 3, "from": "Assistant", "text": "哎呀，电脑为啥感冒？都怪它爱开窗户，像个调皮鬼！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-38-3.wav"}}, {"session_id": "939", "turn_idx": 4, "from": "Human", "text": "这下有趣多了，谢啦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-38-4.wav"}}, {"session_id": "939", "turn_idx": 5, "from": "Assistant", "text": "随时逗你开心！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-38-5.wav"}}] +[{"session_id": "125", "turn_idx": 0, "from": "Human", "text": "小跃，你能给我讲讲秦始皇的故事吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-39-0.wav"}}, {"session_id": "125", "turn_idx": 1, "from": "Assistant", "text": "秦始皇挺有名的！你想知道他的统一六国还是其他事迹？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-39-1.wav"}}, {"session_id": "125", "turn_idx": 2, "from": "Human", "text": "我想了解他是怎么建长城的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-39-2.wav"}}, {"session_id": "125", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"秦始皇建长城历史\"}", "value": "function\nweb_search\n{\"query\": \"秦始皇建长城历史\"}"}] +[{"session_id": "585", "turn_idx": 0, "from": "Human", "text": "小跃，我想学编程，Python语言，从哪入手？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-40-0.wav"}}, {"session_id": "585", "turn_idx": 1, "from": "Assistant", "text": "先学基础语法，网上有很多免费课程。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-40-1.wav"}}, {"session_id": "585", "turn_idx": 2, "from": "Human", "text": "有推荐的学习平台吗？别太枯燥的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-40-2.wav"}}, {"session_id": "585", "turn_idx": 3, "from": "Assistant", "text": "B站或Coursera，视频教程生动易懂。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-40-3.wav"}}, {"session_id": "585", "turn_idx": 4, "from": "Human", "text": "Coursera要收费吧？我想免费的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-40-4.wav"}}, {"session_id": "585", "turn_idx": 5, "from": "Assistant", "text": "部分免费，或者找国内平台像慕课网。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-40-5.wav"}}, {"session_id": "585", "turn_idx": 6, "from": "Human", "text": "慕课网有Python入门课吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-40-6.wav"}}, {"session_id": "585", "turn_idx": 7, "from": "Assistant", "text": "有的，从零开始教。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-40-7.wav"}}, {"session_id": "585", "turn_idx": 8, "from": "Human", "text": "好，帮我搜一下慕课网的免费Python课程。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-40-8.wav"}}, {"session_id": "585", "turn_idx": 9, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"慕课网 Python 免费课程\"}", "value": "function\nweb_search\n{\"query\": \"慕课网 Python 免费课程\"}"}] +[{"session_id": "659", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-0.wav"}}, {"session_id": "659", "turn_idx": 1, "from": "Assistant", "text": "为什么番茄变红了？因为它看到沙拉酱害羞啦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-1.wav"}}, {"session_id": "659", "turn_idx": 2, "from": "Human", "text": "哈哈。小跃，请用夸张的语气讲下一个。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-2.wav"}}, {"session_id": "659", "turn_idx": 3, "from": "Assistant", "text": "天哪！一只猫去面试，老板问它会啥，它说“喵星语十级”！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-3.wav"}}, {"session_id": "659", "turn_idx": 4, "from": "Human", "text": "太逗了。小跃，音量再大点，我朋友也想听。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-4.wav"}}, {"session_id": "659", "turn_idx": 5, "from": "Assistant", "text": "音量最大了！那只猫后来当上翻译，月薪一万小鱼干！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-5.wav"}}, {"session_id": "659", "turn_idx": 6, "from": "Human", "text": "小跃，切换到平静语气，我笑累了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-6.wav"}}, {"session_id": "659", "turn_idx": 7, "from": "Assistant", "text": "好吧，休息下，笑话讲完了，需要其他帮助吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-7.wav"}}, {"session_id": "659", "turn_idx": 8, "from": "Human", "text": "不用了，谢谢小跃。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-8.wav"}}, {"session_id": "659", "turn_idx": 9, "from": "Assistant", "text": "随时叫我，开心每一天。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-41-9.wav"}}] +[{"session_id": "61", "turn_idx": 0, "from": "Human", "text": "小跃，我听说新出了一部科幻电影，评价很好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-42-0.wav"}}, {"session_id": "61", "turn_idx": 1, "from": "Assistant", "text": "对呀，最近有部叫《星际漫游》的电影挺火的，讲的是外星探险的故事。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-42-1.wav"}}, {"session_id": "61", "turn_idx": 2, "from": "Human", "text": "哦，那听起来挺吸引人的。我想知道它在哪些影院上映。 小跃，你能查一下《星际漫游》的上映时间和附近影院吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-42-2.wav"}}, {"session_id": "61", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"星际漫游 上映时间 附近影院\"}", "value": "function\nweb_search\n{\"query\": \"星际漫游 上映时间 附近影院\"}"}] +[{"session_id": "832", "turn_idx": 0, "from": "Human", "text": "小跃，我想买个蓝牙耳机，预算五百块以内。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-43-0.wav"}}, {"session_id": "832", "turn_idx": 1, "from": "Assistant", "text": "这个价位选择不少，像漫步者或小米都有性价比高的。你常听音乐还是打电话？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-43-1.wav"}}, {"session_id": "832", "turn_idx": 2, "from": "Human", "text": "主要听音乐，音质好点。电池要耐用。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-43-2.wav"}}, {"session_id": "832", "turn_idx": 3, "from": "Assistant", "text": "那推荐漫步者LolliPods系列，音质不错，续航十小时左右。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-43-3.wav"}}, {"session_id": "832", "turn_idx": 4, "from": "Human", "text": "具体型号呢？有降噪功能吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-43-4.wav"}}, {"session_id": "832", "turn_idx": 5, "from": "Assistant", "text": "LolliPods Pro带主动降噪，价格四百多。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-43-5.wav"}}, {"session_id": "832", "turn_idx": 6, "from": "Human", "text": "小跃，帮我查一下漫步者LolliPods Pro的用户评价。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-43-6.wav"}}, {"session_id": "832", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"漫步者 LolliPods Pro 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"漫步者 LolliPods Pro 用户评价\"}"}] +[{"session_id": "699", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想少吃糖，但戒不掉甜食，有啥替代品？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-44-0.wav"}}, {"session_id": "699", "turn_idx": 1, "from": "Assistant", "text": "减少糖分是健康选择！水果或代糖食品怎么样？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-44-1.wav"}}, {"session_id": "699", "turn_idx": 2, "from": "Human", "text": "水果还行，但代糖没试过，怕味道怪。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-44-2.wav"}}, {"session_id": "699", "turn_idx": 3, "from": "Assistant", "text": "代糖现在做得很好了，比如用赤藓糖醇，甜度类似但低卡。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-44-3.wav"}}, {"session_id": "699", "turn_idx": 4, "from": "Human", "text": "赤藓糖醇？第一次听说。能帮我查查哪里买和用法吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-44-4.wav"}}, {"session_id": "699", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"赤藓糖醇 购买 使用方法\"}", "value": "function\nweb_search\n{\"query\": \"赤藓糖醇 购买 使用方法\"}"}] +[{"session_id": "471", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在减肥，有啥简单易做的健康餐推荐？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-45-0.wav"}}, {"session_id": "471", "turn_idx": 1, "from": "Assistant", "text": "试试蔬菜沙拉或者鸡胸肉配糙米，低卡又营养。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-45-1.wav"}}, {"session_id": "471", "turn_idx": 2, "from": "Human", "text": "鸡胸肉我常做，但容易吃腻，还有其他选择吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-45-2.wav"}}, {"session_id": "471", "turn_idx": 3, "from": "Assistant", "text": "鱼类也不错，比如三文鱼，富含蛋白质。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-45-3.wav"}}, {"session_id": "471", "turn_idx": 4, "from": "Human", "text": "三文鱼怎么做才好吃？我怕腥味太重。 帮我搜搜三文鱼的简单家常做法吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-45-4.wav"}}, {"session_id": "471", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"三文鱼家常做法 简单\"}", "value": "function\nweb_search\n{\"query\": \"三文鱼家常做法 简单\"}"}] +[{"session_id": "327", "turn_idx": 0, "from": "Human", "text": "小跃，想给爸妈买个按摩椅，有啥建议？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-46-0.wav"}}, {"session_id": "327", "turn_idx": 1, "from": "Assistant", "text": "选带加热功能的，品牌像奥佳华或荣泰口碑不错。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-46-1.wav"}}, {"session_id": "327", "turn_idx": 2, "from": "Human", "text": "预算在三千左右，能买到啥样的？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-46-2.wav"}}, {"session_id": "327", "turn_idx": 3, "from": "Assistant", "text": "入门款就行，功能基本够用。要查查具体型号吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-46-3.wav"}}, {"session_id": "327", "turn_idx": 4, "from": "Human", "text": "嗯，帮我查查三千元内性价比最高的按摩椅。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-46-4.wav"}}, {"session_id": "327", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"三千元 性价比最高 按摩椅\"}", "value": "function\nweb_search\n{\"query\": \"三千元 性价比最高 按摩椅\"}"}] +[{"session_id": "681", "turn_idx": 0, "from": "Human", "text": "小跃，我在做面包，食谱说四人份要面粉五百克。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-47-0.wav"}}, {"session_id": "681", "turn_idx": 1, "from": "Assistant", "text": "听起来香！你要做几人份？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-47-1.wav"}}, {"session_id": "681", "turn_idx": 2, "from": "Human", "text": "我要做十二人份，帮我算一下需要多少面粉。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-47-2.wav"}}, {"session_id": "681", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"500 * 3\"}", "value": "function\ncalculate\n{\"expression\": \"500 * 3\"}"}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_422", "turn_idx": 0, "from": "Human", "text": "小跃，最近工作太忙了，想出去走走。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-48-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_422", "turn_idx": 1, "from": "Assistant", "text": "出去走走能缓解压力呢。有想去的地方吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-48-1.wav"}}] +[{"session_id": "154", "turn_idx": 0, "from": "Human", "text": "小跃，装修房子要买瓷砖头疼死了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-49-0.wav"}}, {"session_id": "154", "turn_idx": 1, "from": "Assistant", "text": "房间尺寸量好了吗？瓷砖规格呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-49-1.wav"}}, {"session_id": "154", "turn_idx": 2, "from": "Human", "text": "客厅长八米宽六米，厨房长四米宽三米。瓷砖每块零点六四平米。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-49-2.wav"}}, {"session_id": "154", "turn_idx": 3, "from": "Assistant", "text": "要计算总块数的话得加损耗率。通常加百分之五。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-49-3.wav"}}, {"session_id": "154", "turn_idx": 4, "from": "Human", "text": "还有阳台！长三米宽两米。损耗按你说加。快算总采购量。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-49-4.wav"}}, {"session_id": "154", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((8*6 + 4*3 + 3*2) / 0.64) * 1.05\"}", "value": "function\ncalculate\n{\"expression\": \"((8*6 + 4*3 + 3*2) / 0.64) * 1.05\"}"}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_164", "turn_idx": 0, "from": "Human", "text": "小跃，我明天要去纽约开会，但听说那里经常下雨。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-50-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_164", "turn_idx": 1, "from": "Assistant", "text": "纽约天气多变，提前查一下能省不少麻烦。带把伞总没错。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-50-1.wav"}}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_533", "turn_idx": 0, "from": "Human", "text": "小跃，下午好！今天工作累坏了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-51-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_533", "turn_idx": 1, "from": "Assistant", "text": "辛苦啦，喝口水休息下吧。有什么我能分担的？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-51-1.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_533", "turn_idx": 2, "from": "Human", "text": "明天得去深圳开会，资料还没整完。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-51-2.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_533", "turn_idx": 3, "from": "Assistant", "text": "别急，深圳那边交通方便，早点出发就行。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-51-3.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_533", "turn_idx": 4, "from": "Human", "text": "嗯，对了，深圳最近温度怎么样？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-51-4.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_533", "turn_idx": 5, "from": "Assistant", "text": "你是担心穿啥吧？深圳通常暖和。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-51-5.wav"}}] +[{"session_id": "608", "turn_idx": 0, "from": "Human", "text": "小跃，我想种点花在阳台，推荐个好养的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-52-0.wav"}}, {"session_id": "608", "turn_idx": 1, "from": "Assistant", "text": "阳台种花惬意！多肉植物或绿萝都容易活。你喜欢啥颜色？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-52-1.wav"}}, {"session_id": "608", "turn_idx": 2, "from": "Human", "text": "绿色的，净化空气的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-52-2.wav"}}, {"session_id": "608", "turn_idx": 3, "from": "Assistant", "text": "绿萝最合适，浇水少就行。要我查种植方法吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-52-3.wav"}}, {"session_id": "608", "turn_idx": 4, "from": "Human", "text": "是的，搜一下绿萝的养护技巧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-52-4.wav"}}, {"session_id": "608", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"绿萝养护 技巧\"}", "value": "function\nweb_search\n{\"query\": \"绿萝养护 技巧\"}"}] +[{"session_id": "173", "turn_idx": 0, "from": "Human", "text": "小跃，我手机电池老是不耐用，有什么省电技巧？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-53-0.wav"}}, {"session_id": "173", "turn_idx": 1, "from": "Assistant", "text": "关掉后台应用、降低屏幕亮度，还有开启省电模式试试。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-53-1.wav"}}, {"session_id": "173", "turn_idx": 2, "from": "Human", "text": "省电模式会影响手机性能吗？ 小跃，不同手机品牌的省电模式效果对比能搜一下吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-53-2.wav"}}, {"session_id": "173", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"手机 省电模式 效果 对比\"}", "value": "function\nweb_search\n{\"query\": \"手机 省电模式 效果 对比\"}"}] +[{"session_id": "392", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想锻炼身体，但不知道怎么开始。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-54-0.wav"}}, {"session_id": "392", "turn_idx": 1, "from": "Assistant", "text": "健身是个好习惯！你主要想减肥还是塑形？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-54-1.wav"}}, {"session_id": "392", "turn_idx": 2, "from": "Human", "text": "塑形吧，特别是肚子这块。有什么适合新手的动作？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-54-2.wav"}}, {"session_id": "392", "turn_idx": 3, "from": "Assistant", "text": "平板支撑或仰卧起坐都有效。要我搜索完整的初学者计划吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-54-3.wav"}}, {"session_id": "392", "turn_idx": 4, "from": "Human", "text": "好，帮我查一下新手塑形健身计划。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-54-4.wav"}}, {"session_id": "392", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"新手 塑形 健身计划\"}", "value": "function\nweb_search\n{\"query\": \"新手 塑形 健身计划\"}"}] +[{"session_id": "532", "turn_idx": 0, "from": "Human", "text": "小跃，说个有趣的事吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-55-0.wav"}}, {"session_id": "532", "turn_idx": 1, "from": "Assistant", "text": "你知道吗？蜜蜂可以认出人的脸哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-55-1.wav"}}, {"session_id": "532", "turn_idx": 2, "from": "Human", "text": "用惊讶的语气再说一遍！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-55-2.wav"}}, {"session_id": "532", "turn_idx": 3, "from": "Assistant", "text": "哇！蜜蜂居然能认出人脸，神奇吧？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-55-3.wav"}}, {"session_id": "532", "turn_idx": 4, "from": "Human", "text": "音量调高点，太有趣了！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-55-4.wav"}}, {"session_id": "532", "turn_idx": 5, "from": "Assistant", "text": "明白！蜜蜂能认出人脸！超神奇！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-55-5.wav"}}, {"session_id": "532", "turn_idx": 6, "from": "Human", "text": "哈哈，真有意思。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-55-6.wav"}}, {"session_id": "532", "turn_idx": 7, "from": "Assistant", "text": "是呀，多学点小知识！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-55-7.wav"}}] +[{"session_id": "164", "turn_idx": 0, "from": "Human", "text": "小跃，我在健身，想计算一下这个月的总步数。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-56-0.wav"}}, {"session_id": "164", "turn_idx": 1, "from": "Assistant", "text": "主人，健身是好事。您有每天的步数记录吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-56-1.wav"}}, {"session_id": "164", "turn_idx": 2, "from": "Human", "text": "上个月我每天平均走一万三千五百步，一共三十一天。帮我算算总步数。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-56-2.wav"}}, {"session_id": "164", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"13500 * 31\"}", "value": "function\ncalculate\n{\"expression\": \"13500 * 31\"}"}] +[{"session_id": "420", "turn_idx": 0, "from": "Human", "text": "小跃，帮我查查航班状态。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-57-0.wav"}}, {"session_id": "420", "turn_idx": 1, "from": "Assistant", "text": "你的航班准点起飞，登机口是B12。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-57-1.wav"}}, {"session_id": "420", "turn_idx": 2, "from": "Human", "text": "用更平淡的语气，我正忙着。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-57-2.wav"}}, {"session_id": "420", "turn_idx": 3, "from": "Assistant", "text": "航班正常，登机口B12，一切顺利。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-57-3.wav"}}] +[{"session_id": "8", "turn_idx": 0, "from": "Human", "text": "小跃，新房长九米宽七米，地板砖每块零点二五平米。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-58-0.wav"}}, {"session_id": "8", "turn_idx": 1, "from": "Assistant", "text": "铺砖需要计算用量对吧？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-58-1.wav"}}, {"session_id": "8", "turn_idx": 2, "from": "Human", "text": "帮我算至少买多少块砖才够。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-58-2.wav"}}, {"session_id": "8", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(9*7)/0.25\"}", "value": "function\ncalculate\n{\"expression\": \"(9*7)/0.25\"}"}] +[{"session_id": "748", "turn_idx": 0, "from": "Human", "text": "小跃，我坚持每月存钱，想看看成果。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-59-0.wav"}}, {"session_id": "748", "turn_idx": 1, "from": "Assistant", "text": "好习惯！月薪多少？存钱比例呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-59-1.wav"}}, {"session_id": "748", "turn_idx": 2, "from": "Human", "text": "月薪两万五千块，存百分之三十五。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-59-2.wav"}}, {"session_id": "748", "turn_idx": 3, "from": "Assistant", "text": "存了多久了？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-59-3.wav"}}, {"session_id": "748", "turn_idx": 4, "from": "Human", "text": "已经存了十八个月。帮我算算总储蓄额。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-59-4.wav"}}, {"session_id": "748", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"25000 * 0.35 * 18\"}", "value": "function\ncalculate\n{\"expression\": \"25000 * 0.35 * 18\"}"}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_941", "turn_idx": 0, "from": "Human", "text": "小跃，我爸妈下周要来北京看我，我得提前安排一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-60-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_941", "turn_idx": 1, "from": "Assistant", "text": "那太好了！他们坐火车还是飞机来？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-60-1.wav"}}] +[{"session_id": "711", "turn_idx": 0, "from": "Human", "text": "小跃，周末想看电影放松，有什么好推荐的吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-61-0.wav"}}, {"session_id": "711", "turn_idx": 1, "from": "Assistant", "text": "最近新出的科幻片评价很高，或者你喜欢喜剧片？我可以帮你挑挑。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-61-1.wav"}}, {"session_id": "711", "turn_idx": 2, "from": "Human", "text": "科幻片吧，我喜欢那种烧脑的。经典的有哪些？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-61-2.wav"}}, {"session_id": "711", "turn_idx": 3, "from": "Assistant", "text": "经典科幻片像《盗梦空间》或《星际穿越》，剧情很精彩。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-61-3.wav"}}, {"session_id": "711", "turn_idx": 4, "from": "Human", "text": "《盗梦空间》的导演是谁？我想查查他拍的其他电影。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-61-4.wav"}}, {"session_id": "711", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"盗梦空间 导演 作品\"}", "value": "function\nweb_search\n{\"query\": \"盗梦空间 导演 作品\"}"}] +[{"session_id": "896", "turn_idx": 0, "from": "Human", "text": "小跃，算下家庭电费。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-62-0.wav"}}, {"session_id": "896", "turn_idx": 1, "from": "Assistant", "text": "提供下用电情况吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-62-1.wav"}}, {"session_id": "896", "turn_idx": 2, "from": "Human", "text": "峰时用电四百二十度每度六毛三，谷时三百度每度三毛八。 还有五十度清洁能源每度补贴一毛五。算净支出？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-62-2.wav"}}, {"session_id": "896", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"420*0.63 + 300*0.38 - 50*0.15\"}", "value": "function\ncalculate\n{\"expression\": \"420*0.63 + 300*0.38 - 50*0.15\"}"}] +[{"session_id": "431", "turn_idx": 0, "from": "Human", "text": "小跃，我在考虑存笔钱投资，帮我算算收益。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-63-0.wav"}}, {"session_id": "431", "turn_idx": 1, "from": "Assistant", "text": "好的，投资理财是好事。请说下本金、年利率和年限。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-63-1.wav"}}, {"session_id": "431", "turn_idx": 2, "from": "Human", "text": "本金一万元，年利率百分之五，复利计算，存五年后能拿回多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-63-2.wav"}}, {"session_id": "431", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"10000 * (1 + 0.05)**5\"}", "value": "function\ncalculate\n{\"expression\": \"10000 * (1 + 0.05)**5\"}"}] +[{"session_id": "911", "turn_idx": 0, "from": "Human", "text": "小跃，晚餐吃什么好？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-64-0.wav"}}, {"session_id": "911", "turn_idx": 1, "from": "Assistant", "text": "推荐试试新开的那家披萨店，口碑不错。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-64-1.wav"}}, {"session_id": "911", "turn_idx": 2, "from": "Human", "text": "请用更温柔的语气建议。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-64-2.wav"}}, {"session_id": "911", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，去吃披萨吧，暖暖的，肯定让你满足。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-64-3.wav"}}, {"session_id": "911", "turn_idx": 4, "from": "Human", "text": "谢谢，听起来好棒。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-64-4.wav"}}, {"session_id": "911", "turn_idx": 5, "from": "Assistant", "text": "不客气，享受美食哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-64-5.wav"}}] +[{"session_id": "457", "turn_idx": 0, "from": "Human", "text": "小跃，健身房会员费每月八百八十元。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-65-0.wav"}}, {"session_id": "457", "turn_idx": 1, "from": "Assistant", "text": "坚持锻炼身体会更棒。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-65-1.wav"}}, {"session_id": "457", "turn_idx": 2, "from": "Human", "text": "我签了两年合同，年费优惠百分之十五，总支付额是多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-65-2.wav"}}, {"session_id": "457", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"880 * 12 * 2 * (1 - 0.15)\"}", "value": "function\ncalculate\n{\"expression\": \"880 * 12 * 2 * (1 - 0.15)\"}"}] +[{"session_id": "627", "turn_idx": 0, "from": "Human", "text": "小跃，我记录了过去六个月的网费:一百八、二百、一百九、二百一、二百三、二百五，平均每月多少钱？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-66-0.wav"}}, {"session_id": "627", "turn_idx": 1, "from": "Assistant", "text": "平均数就是总和除以月份数，我来算。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-66-1.wav"}}, {"session_id": "627", "turn_idx": 2, "from": "Human", "text": "把这六个数加起来除以六，平均多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-66-2.wav"}}, {"session_id": "627", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(180 + 200 + 190 + 210 + 230 + 250) / 6\"}", "value": "function\ncalculate\n{\"expression\": \"(180 + 200 + 190 + 210 + 230 + 250) / 6\"}"}] +[{"session_id": "797", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-67-0.wav"}}, {"session_id": "797", "turn_idx": 1, "from": "Assistant", "text": "现在是上午十点整。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-67-1.wav"}}, {"session_id": "797", "turn_idx": 2, "from": "Human", "text": "用温柔的语气告诉我时间。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-67-2.wav"}}, {"session_id": "797", "turn_idx": 3, "from": "Assistant", "text": "现在是上午十点哦，亲爱的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-67-3.wav"}}, {"session_id": "797", "turn_idx": 4, "from": "Human", "text": "提醒我中午十二点吃饭。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-67-4.wav"}}, {"session_id": "797", "turn_idx": 5, "from": "Assistant", "text": "好的，中午十二点吃饭提醒已设好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-67-5.wav"}}, {"session_id": "797", "turn_idx": 6, "from": "Human", "text": "说慢点，我记一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-67-6.wav"}}, {"session_id": "797", "turn_idx": 7, "from": "Assistant", "text": "好的——我——慢——慢——说:中——午——十——二——点——吃——饭。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-67-7.wav"}}] +[{"session_id": "73", "turn_idx": 0, "from": "Human", "text": "小跃，告诉我明天有啥安排。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-68-0.wav"}}, {"session_id": "73", "turn_idx": 1, "from": "Assistant", "text": "明天上午九点开会，下午三点健身。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-68-1.wav"}}, {"session_id": "73", "turn_idx": 2, "from": "Human", "text": "能再用正式点的语气提醒我吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-68-2.wav"}}, {"session_id": "73", "turn_idx": 3, "from": "Assistant", "text": "明白，明天日程是:上午九点会议，下午三点健身活动。请准时参加。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-68-3.wav"}}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_842", "turn_idx": 0, "from": "Human", "text": "小跃，最近感觉怎么样？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-69-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_842", "turn_idx": 1, "from": "Assistant", "text": "我挺好，谢谢关心！你呢？在计划什么？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-69-1.wav"}}] +[{"session_id": "825", "turn_idx": 0, "from": "Human", "text": "小跃，我想去欧洲旅游，第一站去哪里好？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-70-0.wav"}}, {"session_id": "825", "turn_idx": 1, "from": "Assistant", "text": "巴黎或罗马都不错，有好多历史景点和美食。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-70-1.wav"}}, {"session_id": "825", "turn_idx": 2, "from": "Human", "text": "哦，那语言问题怎么办？我只会中文和一点英文。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-70-2.wav"}}, {"session_id": "825", "turn_idx": 3, "from": "Assistant", "text": "大城市英语通用，学几句当地语更好，比如法语或意大利语。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-70-3.wav"}}, {"session_id": "825", "turn_idx": 4, "from": "Human", "text": "小跃，帮我搜搜欧洲旅游的必备攻略和注意事项。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-70-4.wav"}}, {"session_id": "825", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"欧洲旅游 攻略 注意事项\"}", "value": "function\nweb_search\n{\"query\": \"欧洲旅游 攻略 注意事项\"}"}] +[{"session_id": "133", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-71-0.wav"}}, {"session_id": "133", "turn_idx": 1, "from": "Assistant", "text": "现在是下午两点半。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-71-1.wav"}}, {"session_id": "133", "turn_idx": 2, "from": "Human", "text": "大声点说，我离得远。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-71-2.wav"}}, {"session_id": "133", "turn_idx": 3, "from": "Assistant", "text": "好的！现在是下午两点半！听清楚了吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-71-3.wav"}}] +[{"session_id": "794", "turn_idx": 0, "from": "Human", "text": "小跃，我想学点编程，但完全没基础。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-72-0.wav"}}, {"session_id": "794", "turn_idx": 1, "from": "Assistant", "text": "编程入门不难！可以先从Python开始，简单易学。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-72-1.wav"}}, {"session_id": "794", "turn_idx": 2, "from": "Human", "text": "Python？听起来好，用什么工具学？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-72-2.wav"}}, {"session_id": "794", "turn_idx": 3, "from": "Assistant", "text": "网上有很多免费课程，比如用视频或APP。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-72-3.wav"}}, {"session_id": "794", "turn_idx": 4, "from": "Human", "text": "行，帮我查一下新手学Python的免费在线课程推荐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-72-4.wav"}}, {"session_id": "794", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"新手Python免费在线课程\"}", "value": "function\nweb_search\n{\"query\": \"新手Python免费在线课程\"}"}] +[{"session_id": "491", "turn_idx": 0, "from": "Human", "text": "小跃，我女儿问为什么天空是蓝的，我都不知道怎么回答。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-73-0.wav"}}, {"session_id": "491", "turn_idx": 1, "from": "Assistant", "text": "哈哈，这个问题很有趣！主要是因为阳光穿过大气时，蓝光被空气分子散射得最多。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-73-1.wav"}}, {"session_id": "491", "turn_idx": 2, "from": "Human", "text": "散射？听着有点抽象，能再简单点吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-73-2.wav"}}, {"session_id": "491", "turn_idx": 3, "from": "Assistant", "text": "就像蓝光被空气“弹”来弹去，比其他颜色更明显，所以咱们看天空就蓝蓝的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-73-3.wav"}}, {"session_id": "491", "turn_idx": 4, "from": "Human", "text": "那在月球上，天空也会蓝吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-73-4.wav"}}, {"session_id": "491", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"月球 天空颜色\"}", "value": "function\nweb_search\n{\"query\": \"月球 天空颜色\"}"}] +[{"session_id": "86", "turn_idx": 0, "from": "Human", "text": "小跃，股票收益有点乱。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-74-0.wav"}}, {"session_id": "86", "turn_idx": 1, "from": "Assistant", "text": "买入卖出价告诉我。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-74-1.wav"}}, {"session_id": "86", "turn_idx": 2, "from": "Human", "text": "两千股每股十二块五买入，涨到十五块三卖出八百股。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-74-2.wav"}}, {"session_id": "86", "turn_idx": 3, "from": "Assistant", "text": "剩下的呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-74-3.wav"}}, {"session_id": "86", "turn_idx": 4, "from": "Human", "text": "又跌到十三块二全卖了。手续费总共三百。求净利润。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-74-4.wav"}}, {"session_id": "86", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"800*(15.3-12.5) + (2000-800)*(13.2-12.5) - 300\"}", "value": "function\ncalculate\n{\"expression\": \"800*(15.3-12.5) + (2000-800)*(13.2-12.5) - 300\"}"}] +[{"session_id": "547", "turn_idx": 0, "from": "Human", "text": "小跃，我们足球队上周比赛得分统计。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-75-0.wav"}}, {"session_id": "547", "turn_idx": 1, "from": "Assistant", "text": "结果如何？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-75-1.wav"}}, {"session_id": "547", "turn_idx": 2, "from": "Human", "text": "进了八个球，每个球算两分。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-75-2.wav"}}, {"session_id": "547", "turn_idx": 3, "from": "Assistant", "text": "总得分十六分。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-75-3.wav"}}, {"session_id": "547", "turn_idx": 4, "from": "Human", "text": "但对方防守强，我们射门一百次才进这些。 射门成功率百分比是多少？帮我算算。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-75-4.wav"}}, {"session_id": "547", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(8 / 100) * 100\"}", "value": "function\ncalculate\n{\"expression\": \"(8 / 100) * 100\"}"}] +[{"session_id": "819", "turn_idx": 0, "from": "Human", "text": "小跃早呀，今天天气不错！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-76-0.wav"}}, {"session_id": "819", "turn_idx": 1, "from": "Assistant", "text": "早上好！是呀，阳光特别好，适合出门走走。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-76-1.wav"}}, {"session_id": "819", "turn_idx": 2, "from": "Human", "text": "我打算买些新家具，沙发四千三，餐桌两千八，再加两把椅子每把六百五。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-76-2.wav"}}, {"session_id": "819", "turn_idx": 3, "from": "Assistant", "text": "不错的选择呢，您还需要其他装饰品吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-76-3.wav"}}, {"session_id": "819", "turn_idx": 4, "from": "Human", "text": "对了，还要加一盏落地灯九百九，再预留一千二运费。帮我算算总预算吧？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-76-4.wav"}}, {"session_id": "819", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"4300+2800+2*650+990+1200\"}", "value": "function\ncalculate\n{\"expression\": \"4300+2800+2*650+990+1200\"}"}] +[{"session_id": "639", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天天气真好，我想去商场买点东西。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-77-0.wav"}}, {"session_id": "639", "turn_idx": 1, "from": "Assistant", "text": "是啊，适合出门购物呢。你想买些什么？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-77-1.wav"}}, {"session_id": "639", "turn_idx": 2, "from": "Human", "text": "打算买三件T恤，每件大概一百五十块，还有一双鞋子三百八十块。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-77-2.wav"}}, {"session_id": "639", "turn_idx": 3, "from": "Assistant", "text": "听起来不错。预算考虑好了吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-77-3.wav"}}, {"session_id": "639", "turn_idx": 4, "from": "Human", "text": "我带了八百块现金，但听说有满减活动。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-77-4.wav"}}, {"session_id": "639", "turn_idx": 5, "from": "Assistant", "text": "对的，满五百减五十。需要计算折后总价吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-77-5.wav"}}, {"session_id": "639", "turn_idx": 6, "from": "Human", "text": "对，帮我算一下三件T恤和一双鞋子的原价总和，再减去满减优惠。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-77-6.wav"}}, {"session_id": "639", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(3 * 150 + 380) - 50\"}", "value": "function\ncalculate\n{\"expression\": \"(3 * 150 + 380) - 50\"}"}] +[{"session_id": "944", "turn_idx": 0, "from": "Human", "text": "小跃，我想学英语口语，有啥免费资源？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-78-0.wav"}}, {"session_id": "944", "turn_idx": 1, "from": "Assistant", "text": "可以试试B站上的口语课程或者英语角活动。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-78-1.wav"}}, {"session_id": "944", "turn_idx": 2, "from": "Human", "text": "英语角听起来好，帮我查一下附近的英语角地点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-78-2.wav"}}, {"session_id": "944", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"英语角 地点 附近\"}", "value": "function\nweb_search\n{\"query\": \"英语角 地点 附近\"}"}] +[{"session_id": "436", "turn_idx": 0, "from": "Human", "text": "小跃，我想买电动牙刷，飞利浦和欧乐B哪个好？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-79-0.wav"}}, {"session_id": "436", "turn_idx": 1, "from": "Assistant", "text": "飞利浦清洁力强但贵，欧乐B性价比高。看预算，入门选欧乐B。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-79-1.wav"}}, {"session_id": "436", "turn_idx": 2, "from": "Human", "text": "那刷头多久换一次？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-79-2.wav"}}, {"session_id": "436", "turn_idx": 3, "from": "Assistant", "text": "一般三个月换一次，刷毛变形就换。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-79-3.wav"}}, {"session_id": "436", "turn_idx": 4, "from": "Human", "text": "好建议。你能帮我查一下电动牙刷用户评价对比吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-79-4.wav"}}, {"session_id": "436", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"电动牙刷 飞利浦 欧乐B 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"电动牙刷 飞利浦 欧乐B 用户评价\"}"}] +[{"session_id": "165", "turn_idx": 0, "from": "Human", "text": "小跃，我想买新手机，预算中等，有啥推荐？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-80-0.wav"}}, {"session_id": "165", "turn_idx": 1, "from": "Assistant", "text": "小米或华为的中端机型不错，性价比高。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-80-1.wav"}}, {"session_id": "165", "turn_idx": 2, "from": "Human", "text": "华为我考虑过。电池续航怎么样？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-80-2.wav"}}, {"session_id": "165", "turn_idx": 3, "from": "Assistant", "text": "一般能用一整天，看视频或玩游戏都没问题。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-80-3.wav"}}, {"session_id": "165", "turn_idx": 4, "from": "Human", "text": "那挺好。摄像头效果如何？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-80-4.wav"}}, {"session_id": "165", "turn_idx": 5, "from": "Assistant", "text": "拍照清晰，尤其夜景模式很强大。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-80-5.wav"}}, {"session_id": "165", "turn_idx": 6, "from": "Human", "text": "好的，我想查一下具体型号的用户评价。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-80-6.wav"}}, {"session_id": "165", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"华为中端手机 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"华为中端手机 用户评价\"}"}] +[{"session_id": "82", "turn_idx": 0, "from": "Human", "text": "小跃，我最近老是失眠，好烦啊。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-81-0.wav"}}, {"session_id": "82", "turn_idx": 1, "from": "Assistant", "text": "失眠的话，试试睡前喝杯温牛奶，可能有点帮助。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-81-1.wav"}}, {"session_id": "82", "turn_idx": 2, "from": "Human", "text": "还有什么其他小技巧能改善睡眠？我想多了解一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-81-2.wav"}}, {"session_id": "82", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"改善睡眠 自然方法\"}", "value": "function\nweb_search\n{\"query\": \"改善睡眠 自然方法\"}"}] +[{"session_id": "287", "turn_idx": 0, "from": "Human", "text": "小跃，我想买一台新笔记本电脑。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-82-0.wav"}}, {"session_id": "287", "turn_idx": 1, "from": "Assistant", "text": "推荐联想或戴尔的轻薄本，适合日常使用。 戴尔的XPS系列性能不错，电池续航也长。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-82-1.wav"}}, {"session_id": "287", "turn_idx": 2, "from": "Human", "text": "XPS系列最新型号是什么？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-82-2.wav"}}, {"session_id": "287", "turn_idx": 3, "from": "Assistant", "text": "目前是XPS十三，设计很轻薄。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-82-3.wav"}}, {"session_id": "287", "turn_idx": 4, "from": "Human", "text": "好的。小跃，帮我查一下戴尔XPS十三的用户评价。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-82-4.wav"}}, {"session_id": "287", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"戴尔 XPS 十三 评价\"}", "value": "function\nweb_search\n{\"query\": \"戴尔 XPS 十三 评价\"}"}] +[{"session_id": "419", "turn_idx": 0, "from": "Human", "text": "小跃，想买台新笔记本电脑，推荐啥？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-83-0.wav"}}, {"session_id": "419", "turn_idx": 1, "from": "Assistant", "text": "联想的Yoga系列轻薄便携，性价比高。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-83-1.wav"}}, {"session_id": "419", "turn_idx": 2, "from": "Human", "text": "Yoga系列？最新型号是啥？ 帮我查查配置和用户评价。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-83-2.wav"}}, {"session_id": "419", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"联想 Yoga 最新型号 配置 评价\"}", "value": "function\nweb_search\n{\"query\": \"联想 Yoga 最新型号 配置 评价\"}"}] +[{"session_id": "628", "turn_idx": 0, "from": "Human", "text": "小跃，我对二战历史感兴趣。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-84-0.wav"}}, {"session_id": "628", "turn_idx": 1, "from": "Assistant", "text": "想了解哪部分？诺曼底登陆还是其他事件？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-84-1.wav"}}, {"session_id": "628", "turn_idx": 2, "from": "Human", "text": "诺曼底登陆吧。死了多少人？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-84-2.wav"}}, {"session_id": "628", "turn_idx": 3, "from": "Assistant", "text": "参与士兵几十万，伤亡很大。要查具体数字吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-84-3.wav"}}, {"session_id": "628", "turn_idx": 4, "from": "Human", "text": "对，帮我查一下诺曼底登陆的详细历史。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-84-4.wav"}}, {"session_id": "628", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"诺曼底登陆历史细节\"}", "value": "function\nweb_search\n{\"query\": \"诺曼底登陆历史细节\"}"}] +[{"session_id": "937", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气预报说摄氏三十五度。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-85-0.wav"}}, {"session_id": "937", "turn_idx": 1, "from": "Assistant", "text": "确实很热，您想转成华氏度？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-85-1.wav"}}, {"session_id": "937", "turn_idx": 2, "from": "Human", "text": "是的，我好奇华氏度是多少。 请把摄氏三十五度转换成华氏度。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-85-2.wav"}}, {"session_id": "937", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"35 * 9 / 5 + 32\"}", "value": "function\ncalculate\n{\"expression\": \"35 * 9 / 5 + 32\"}"}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_974", "turn_idx": 0, "from": "Human", "text": "小跃，我馋成都火锅了，想去吃一顿。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-86-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_974", "turn_idx": 1, "from": "Assistant", "text": "成都火锅真香！主人，您计划什么时候去？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-86-1.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_974", "turn_idx": 2, "from": "Human", "text": "可能后天吧，得看天气好不好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-86-2.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_974", "turn_idx": 3, "from": "Assistant", "text": "好的，我帮您留意一下。查几天？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-86-3.wav"}}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_738", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！昨晚休息得好吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-87-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_738", "turn_idx": 1, "from": "Assistant", "text": "早上好！休息得不错。你今天有什么打算？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-87-1.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_738", "turn_idx": 2, "from": "Human", "text": "计划去西安旅游，后天出发看兵马俑。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-87-2.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_738", "turn_idx": 3, "from": "Assistant", "text": "西安历史丰富，值得一去。天气热的话注意防晒。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-87-3.wav"}}] +[{"session_id": "872", "turn_idx": 0, "from": "Human", "text": "小跃，我今天有点郁闷。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-88-0.wav"}}, {"session_id": "872", "turn_idx": 1, "from": "Assistant", "text": "别难过，跟我说说，也许我能帮忙。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-88-1.wav"}}, {"session_id": "872", "turn_idx": 2, "from": "Human", "text": "能用更阳光的语气安慰我吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-88-2.wav"}}, {"session_id": "872", "turn_idx": 3, "from": "Assistant", "text": "当然！今天超棒的，乌云会散开，阳光总在风雨后！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-88-3.wav"}}, {"session_id": "872", "turn_idx": 4, "from": "Human", "text": "哈哈，好多了。音量调小点，安静点聊天。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-88-4.wav"}}, {"session_id": "872", "turn_idx": 5, "from": "Assistant", "text": "好的，轻声细语。一切都会好的，我在这儿陪你。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-88-5.wav"}}, {"session_id": "872", "turn_idx": 6, "from": "Human", "text": "小跃，严肃点给我个建议。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-88-6.wav"}}, {"session_id": "872", "turn_idx": 7, "from": "Assistant", "text": "建议你出去走走，呼吸新鲜空气，心情会改善。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-88-7.wav"}}] +[{"session_id": "424", "turn_idx": 0, "from": "Human", "text": "小跃，我团队有个项目，需要完成一百五十个任务，每个任务平均耗时四十五分钟。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-89-0.wav"}}, {"session_id": "424", "turn_idx": 1, "from": "Assistant", "text": "任务量不小啊，团队效率是关键。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-89-1.wav"}}, {"session_id": "424", "turn_idx": 2, "from": "Human", "text": "我们有六个人，每天工作七小时，帮我算算几天能搞定。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-89-2.wav"}}, {"session_id": "424", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(150 * 45 / 60) / (6 * 7)\"}", "value": "function\ncalculate\n{\"expression\": \"(150 * 45 / 60) / (6 * 7)\"}"}] +[{"session_id": "54", "turn_idx": 0, "from": "Human", "text": "小跃，我想买台新笔记本电脑，预算五千左右。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-90-0.wav"}}, {"session_id": "54", "turn_idx": 1, "from": "Assistant", "text": "这个价位推荐联想或华硕的轻薄本，性能够用还便携。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-90-1.wav"}}, {"session_id": "54", "turn_idx": 2, "from": "Human", "text": "屏幕大小重要吗？我平时就办公用。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-90-2.wav"}}, {"session_id": "54", "turn_idx": 3, "from": "Assistant", "text": "十四寸就够，太大反而不方便携带。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-90-3.wav"}}, {"session_id": "54", "turn_idx": 4, "from": "Human", "text": "明白了。帮我查查近期用户评价好的型号吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-90-4.wav"}}, {"session_id": "54", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"笔记本电脑 用户评价 五千元\"}", "value": "function\nweb_search\n{\"query\": \"笔记本电脑 用户评价 五千元\"}"}] +[{"session_id": "594", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-0.wav"}}, {"session_id": "594", "turn_idx": 1, "from": "Assistant", "text": "今天天气晴朗，温度25度，很适合出门散步哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-1.wav"}}, {"session_id": "594", "turn_idx": 2, "from": "Human", "text": "请用更开心的语气重复一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-2.wav"}}, {"session_id": "594", "turn_idx": 3, "from": "Assistant", "text": "哇！天气超棒的，阳光灿烂，25度，散步超级合适！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-3.wav"}}, {"session_id": "594", "turn_idx": 4, "from": "Human", "text": "嗯，那下午三点提醒我去公园吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-4.wav"}}, {"session_id": "594", "turn_idx": 5, "from": "Assistant", "text": "没问题，下午三点公园散步提醒已设置好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-5.wav"}}, {"session_id": "594", "turn_idx": 6, "from": "Human", "text": "把音量调小一点，我在办公室。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-6.wav"}}, {"session_id": "594", "turn_idx": 7, "from": "Assistant", "text": "好的，我轻声说。提醒已设置，下午三点去公园散步。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-7.wav"}}, {"session_id": "594", "turn_idx": 8, "from": "Human", "text": "谢谢小跃，你真贴心。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-8.wav"}}, {"session_id": "594", "turn_idx": 9, "from": "Assistant", "text": "不客气，随时为您服务！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-91-9.wav"}}] +[{"session_id": "805", "turn_idx": 0, "from": "Human", "text": "小跃，我期末考试成绩出来了，语文八十八分，数学九十五分，英语九十二分。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-92-0.wav"}}, {"session_id": "805", "turn_idx": 1, "from": "Assistant", "text": "考得真棒！平均分高吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-92-1.wav"}}, {"session_id": "805", "turn_idx": 2, "from": "Human", "text": "帮我算一下这三门课的平均分？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-92-2.wav"}}, {"session_id": "805", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(88 + 95 + 92) / 3\"}", "value": "function\ncalculate\n{\"expression\": \"(88 + 95 + 92) / 3\"}"}] +[{"session_id": "728", "turn_idx": 0, "from": "Human", "text": "小跃，我负责的项目需要材料费三万八千元，人工费一万五千元，再加百分之十的管理费。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-93-0.wav"}}, {"session_id": "728", "turn_idx": 1, "from": "Assistant", "text": "管理费也要算进去呢。那总成本是多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-93-1.wav"}}, {"session_id": "728", "turn_idx": 2, "from": "Human", "text": "帮我计算整个项目的总金额。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-93-2.wav"}}, {"session_id": "728", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(38000 + 15000) * 1.10\"}", "value": "function\ncalculate\n{\"expression\": \"(38000 + 15000) * 1.10\"}"}] +[{"session_id": "126", "turn_idx": 0, "from": "Human", "text": "小跃，我刚看完《哈利波特》，有类似的书推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-94-0.wav"}}, {"session_id": "126", "turn_idx": 1, "from": "Assistant", "text": "《哈利波特》超经典！你喜欢魔法世界还是校园冒险？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-94-1.wav"}}, {"session_id": "126", "turn_idx": 2, "from": "Human", "text": "魔法世界的，最好有奇幻元素。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-94-2.wav"}}, {"session_id": "126", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"类似哈利波特魔法书推荐\"}", "value": "function\nweb_search\n{\"query\": \"类似哈利波特魔法书推荐\"}"}] +[{"session_id": "278", "turn_idx": 0, "from": "Human", "text": "小跃，新闻说今天有雨，带伞吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-0.wav"}}, {"session_id": "278", "turn_idx": 1, "from": "Assistant", "text": "对，天气预报显示下午有雨，建议带上。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-1.wav"}}, {"session_id": "278", "turn_idx": 2, "from": "Human", "text": "用更紧张的语气提醒我别忘。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-2.wav"}}, {"session_id": "278", "turn_idx": 3, "from": "Assistant", "text": "千万记得！雨突然下大，淋湿了感冒可麻烦！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-3.wav"}}, {"session_id": "278", "turn_idx": 4, "from": "Human", "text": "音量调高点，我离得远。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-4.wav"}}, {"session_id": "278", "turn_idx": 5, "from": "Assistant", "text": "好的，大声说。出门前检查背包？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-5.wav"}}, {"session_id": "278", "turn_idx": 6, "from": "Human", "text": "嗯。用幽默的方式说说雨天的乐趣。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-6.wav"}}, {"session_id": "278", "turn_idx": 7, "from": "Assistant", "text": "雨中散步也挺好，踩水花玩，像孩子一样开心！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-7.wav"}}, {"session_id": "278", "turn_idx": 8, "from": "Human", "text": "哈哈，恢复正常语气吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-8.wav"}}, {"session_id": "278", "turn_idx": 9, "from": "Assistant", "text": "没问题。要设定出门提醒？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-95-9.wav"}}] +[{"session_id": "573", "turn_idx": 0, "from": "Human", "text": "小跃，我想买新手机，苹果和安卓哪个好？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-96-0.wav"}}, {"session_id": "573", "turn_idx": 1, "from": "Assistant", "text": "各有优势，苹果系统流畅，安卓更灵活。预算多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-96-1.wav"}}, {"session_id": "573", "turn_idx": 2, "from": "Human", "text": "大概五千块以内吧，性价比高的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-96-2.wav"}}, {"session_id": "573", "turn_idx": 3, "from": "Assistant", "text": "那可以考虑安卓旗舰机型，像三星或小米的新款。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-96-3.wav"}}, {"session_id": "573", "turn_idx": 4, "from": "Human", "text": "能搜下三星最新手机型号和价格吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-96-4.wav"}}, {"session_id": "573", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"三星最新手机型号价格\"}", "value": "function\nweb_search\n{\"query\": \"三星最新手机型号价格\"}"}] +[{"session_id": "761", "turn_idx": 0, "from": "Human", "text": "小跃，我周末要开车去旅行。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-97-0.wav"}}, {"session_id": "761", "turn_idx": 1, "from": "Assistant", "text": "多远？车速多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-97-1.wav"}}, {"session_id": "761", "turn_idx": 2, "from": "Human", "text": "距离一百八十公里，我计划时速七十五公里。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-97-2.wav"}}, {"session_id": "761", "turn_idx": 3, "from": "Assistant", "text": "那时间应该不长。但路上可能休息，你直接开吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-97-3.wav"}}, {"session_id": "761", "turn_idx": 4, "from": "Human", "text": "假设不停车。帮我算需要多少小时。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-97-4.wav"}}, {"session_id": "761", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"180 / 75\"}", "value": "function\ncalculate\n{\"expression\": \"180 / 75\"}"}] +[{"session_id": "710", "turn_idx": 0, "from": "Human", "text": "小跃，我下周要开车去探亲。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-98-0.wav"}}, {"session_id": "710", "turn_idx": 1, "from": "Assistant", "text": "好的，Human先生，路程远吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-98-1.wav"}}, {"session_id": "710", "turn_idx": 2, "from": "Human", "text": "从家到目的地约八百公里。高速部分平均一百一十公里每小时，占全程四分之三。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-98-2.wav"}}, {"session_id": "710", "turn_idx": 3, "from": "Assistant", "text": "那非高速部分呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-98-3.wav"}}, {"session_id": "710", "turn_idx": 4, "from": "Human", "text": "非高速部分速度慢，平均七十公里每小时。另外，中途休息一次二十分钟。 帮我算全程总时间分钟数吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-98-4.wav"}}, {"session_id": "710", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((800 * 0.75 / 110 + 800 * 0.25 / 70) * 60) + 20\"}", "value": "function\ncalculate\n{\"expression\": \"((800 * 0.75 / 110 + 800 * 0.25 / 70) * 60) + 20\"}"}] +[{"session_id": "590", "turn_idx": 0, "from": "Human", "text": "小跃，我想算装修材料费。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-99-0.wav"}}, {"session_id": "590", "turn_idx": 1, "from": "Assistant", "text": "您买了哪些材料呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-99-1.wav"}}, {"session_id": "590", "turn_idx": 2, "from": "Human", "text": "瓷砖每平米一百二十五元，买了三十七平米；地板每平米九十八元，买了四十五平米。 加上百分之八的损耗率，总价多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-99-2.wav"}}, {"session_id": "590", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(125*37 + 98*45)*1.08\"}", "value": "function\ncalculate\n{\"expression\": \"(125*37 + 98*45)*1.08\"}"}] +[{"session_id": "238", "turn_idx": 0, "from": "Human", "text": "小跃，我想给家人买生日礼物，有什么创意点子？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-100-0.wav"}}, {"session_id": "238", "turn_idx": 1, "from": "Assistant", "text": "定制相册或手工香薰蜡烛都很有心意，也容易操作。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-100-1.wav"}}, {"session_id": "238", "turn_idx": 2, "from": "Human", "text": "好主意。你能帮我搜一下网上哪里能买定制相册？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-100-2.wav"}}, {"session_id": "238", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"定制相册 网上购买\"}", "value": "function\nweb_search\n{\"query\": \"定制相册 网上购买\"}"}] +[{"session_id": "390", "turn_idx": 0, "from": "Human", "text": "小跃，我想重新粉刷客厅的墙壁。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-101-0.wav"}}, {"session_id": "390", "turn_idx": 1, "from": "Assistant", "text": "没问题！墙面积多大？用哪种油漆？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-101-1.wav"}}, {"session_id": "390", "turn_idx": 2, "from": "Human", "text": "墙面大概四十平方米。油漆每桶两百元，能刷十平方米。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-101-2.wav"}}, {"session_id": "390", "turn_idx": 3, "from": "Assistant", "text": "那需要四桶油漆。人工费怎么算？按面积收费吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-101-3.wav"}}, {"session_id": "390", "turn_idx": 4, "from": "Human", "text": "对，每平方米人工费三十元。另外，我还想买点工具，预算三百元。 小跃，计算一下总花费，油漆、人工和工具都包括。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-101-4.wav"}}, {"session_id": "390", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"4 * 200 + 40 * 30 + 300\"}", "value": "function\ncalculate\n{\"expression\": \"4 * 200 + 40 * 30 + 300\"}"}] +[{"session_id": "895", "turn_idx": 0, "from": "Human", "text": "小跃，音量调高点，我这边有点吵。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-102-0.wav"}}, {"session_id": "895", "turn_idx": 1, "from": "Assistant", "text": "收到！我会大声点说话！这样听得清吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-102-1.wav"}}] +[{"session_id": "225", "turn_idx": 0, "from": "Human", "text": "小跃，我准备考试，但进度太慢了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-103-0.wav"}}, {"session_id": "225", "turn_idx": 1, "from": "Assistant", "text": "别担心，告诉我总章节数和已完成数。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-103-1.wav"}}, {"session_id": "225", "turn_idx": 2, "from": "Human", "text": "总共有十五章，我刚学完第五章。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-103-2.wav"}}, {"session_id": "225", "turn_idx": 3, "from": "Assistant", "text": "还差十章。每天能学多少章？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-103-3.wav"}}, {"session_id": "225", "turn_idx": 4, "from": "Human", "text": "每天学两章。帮我算一下还要几天学完。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-103-4.wav"}}, {"session_id": "225", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(15 - 5) / 2\"}", "value": "function\ncalculate\n{\"expression\": \"(15 - 5) / 2\"}"}] +[{"session_id": "828", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个下午五点的闹钟。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-104-0.wav"}}, {"session_id": "828", "turn_idx": 1, "from": "Assistant", "text": "闹钟已经设好了，下午五点准时提醒你。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-104-1.wav"}}, {"session_id": "828", "turn_idx": 2, "from": "Human", "text": "太好了。小跃，请听起来开心点，分享个好消息。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-104-2.wav"}}, {"session_id": "828", "turn_idx": 3, "from": "Assistant", "text": "太棒了！我刚收到更新，说今晚有流星雨，要不要一起看看？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-104-3.wav"}}] +[{"session_id": "290", "turn_idx": 0, "from": "Human", "text": "嗨小跃，我最近想开始跑步健身。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-105-0.wav"}}, {"session_id": "290", "turn_idx": 1, "from": "Assistant", "text": "跑步很棒啊，能让你更有活力。你打算每天跑多少时间呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-105-1.wav"}}, {"session_id": "290", "turn_idx": 2, "from": "Human", "text": "我想先每天跑三十分钟。但不知道该怎么起步。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-105-2.wav"}}, {"session_id": "290", "turn_idx": 3, "from": "Assistant", "text": "慢慢来就好。可以先从快走开始，然后慢慢增加跑步时间。记得热身哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-105-3.wav"}}, {"session_id": "290", "turn_idx": 4, "from": "Human", "text": "热身动作有哪些推荐？我怕受伤。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-105-4.wav"}}, {"session_id": "290", "turn_idx": 5, "from": "Assistant", "text": "简单拉伸就行，比如腿部伸展和手臂摆动。重点是要坚持。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-105-5.wav"}}, {"session_id": "290", "turn_idx": 6, "from": "Human", "text": "对了，我听说有个叫“间歇跑”的方法，好像效果不错。小跃，帮我查一下具体的训练步骤吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-105-6.wav"}}, {"session_id": "290", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"间歇跑 训练步骤\"}", "value": "function\nweb_search\n{\"query\": \"间歇跑 训练步骤\"}"}] +[{"session_id": "865", "turn_idx": 0, "from": "Human", "text": "小跃，我在读爱因斯坦传记，好奇他出生年份。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-106-0.wav"}}, {"session_id": "865", "turn_idx": 1, "from": "Assistant", "text": "爱因斯坦真是个天才。你还想知道他其他生平细节吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-106-1.wav"}}, {"session_id": "865", "turn_idx": 2, "from": "Human", "text": "先查出生年份吧，我记得是一八几几年，但具体忘了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-106-2.wav"}}, {"session_id": "865", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"爱因斯坦出生年份\"}", "value": "function\nweb_search\n{\"query\": \"爱因斯坦出生年份\"}"}] +[{"session_id": "159", "turn_idx": 0, "from": "Human", "text": "小跃，怎么在家种小盆栽？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-107-0.wav"}}, {"session_id": "159", "turn_idx": 1, "from": "Assistant", "text": "选些容易养的植物，比如绿萝或多肉，注意浇水和阳光。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-107-1.wav"}}, {"session_id": "159", "turn_idx": 2, "from": "Human", "text": "帮我搜一下多肉植物的养护方法详解。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-107-2.wav"}}, {"session_id": "159", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"多肉植物 养护方法 详解\"}", "value": "function\nweb_search\n{\"query\": \"多肉植物 养护方法 详解\"}"}] +[{"session_id": "519", "turn_idx": 0, "from": "Human", "text": "小跃，我玩游戏卡关了，需要帮忙。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-108-0.wav"}}, {"session_id": "519", "turn_idx": 1, "from": "Assistant", "text": "什么游戏？手机还是电脑？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-108-1.wav"}}, {"session_id": "519", "turn_idx": 2, "from": "Human", "text": "手机游戏，《原神》，在蒙德地区。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-108-2.wav"}}, {"session_id": "519", "turn_idx": 3, "from": "Assistant", "text": "《原神》挺热门的。你卡在哪个任务？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-108-3.wav"}}, {"session_id": "519", "turn_idx": 4, "from": "Human", "text": "打一个风魔龙boss。你能帮我查一下攻略吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-108-4.wav"}}, {"session_id": "519", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"原神 风魔龙 boss 攻略\"}", "value": "function\nweb_search\n{\"query\": \"原神 风魔龙 boss 攻略\"}"}] +[{"session_id": "810", "turn_idx": 0, "from": "Human", "text": "小跃，播放一首轻松的音乐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-109-0.wav"}}, {"session_id": "810", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐，放松一下吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-109-1.wav"}}, {"session_id": "810", "turn_idx": 2, "from": "Human", "text": "请用更温柔的语气推荐一首歌。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-109-2.wav"}}, {"session_id": "810", "turn_idx": 3, "from": "Assistant", "text": "试试这首《月光》，它让人感觉特别宁静。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-109-3.wav"}}, {"session_id": "810", "turn_idx": 4, "from": "Human", "text": "谢谢，现在大声点告诉我音量控制。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-109-4.wav"}}, {"session_id": "810", "turn_idx": 5, "from": "Assistant", "text": "你可以说“音量调高”来加大声音！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-109-5.wav"}}] +[{"session_id": "875", "turn_idx": 0, "from": "Human", "text": "小跃，推荐个晨跑计划。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-110-0.wav"}}, {"session_id": "875", "turn_idx": 1, "from": "Assistant", "text": "建议每天慢跑30分钟，循序渐进。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-110-1.wav"}}, {"session_id": "875", "turn_idx": 2, "from": "Human", "text": "能换成更鼓舞人心的语气吗？像教练那样。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-110-2.wav"}}, {"session_id": "875", "turn_idx": 3, "from": "Assistant", "text": "加油！每天30分钟慢跑，你会更强壮，坚持就是胜利！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-110-3.wav"}}, {"session_id": "875", "turn_idx": 4, "from": "Human", "text": "动力满满了！音量调高点，我要听清楚。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-110-4.wav"}}, {"session_id": "875", "turn_idx": 5, "from": "Assistant", "text": "音量调高！跑起来，呼吸节奏保持稳定！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-110-5.wav"}}, {"session_id": "875", "turn_idx": 6, "from": "Human", "text": "小跃，温柔点提醒我热身。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-110-6.wav"}}, {"session_id": "875", "turn_idx": 7, "from": "Assistant", "text": "亲爱的，记得热身五分钟，别拉伤肌肉哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-110-7.wav"}}] +[{"session_id": "555", "turn_idx": 0, "from": "Human", "text": "小跃，今天有什么新闻？用中性的语气说就行。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-111-0.wav"}}, {"session_id": "555", "turn_idx": 1, "from": "Assistant", "text": "今日新闻:本地天气晴朗，交通一切正常。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-111-1.wav"}}, {"session_id": "555", "turn_idx": 2, "from": "Human", "text": "现在换个兴奋的语气，告诉我一个好消息。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-111-2.wav"}}, {"session_id": "555", "turn_idx": 3, "from": "Assistant", "text": "太棒了！公司刚宣布全员发奖金，大家开心坏啦！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-111-3.wav"}}] +[{"session_id": "733", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-112-0.wav"}}, {"session_id": "733", "turn_idx": 1, "from": "Assistant", "text": "现在是下午两点十五分。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-112-1.wav"}}, {"session_id": "733", "turn_idx": 2, "from": "Human", "text": "我在厨房有点吵，能再大声点告诉我时间吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-112-2.wav"}}, {"session_id": "733", "turn_idx": 3, "from": "Assistant", "text": "现在是下午两点十五分！记得准时安排你的午餐！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-112-3.wav"}}] +[{"session_id": "955", "turn_idx": 0, "from": "Human", "text": "小跃，帮我记一下购物清单。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-113-0.wav"}}, {"session_id": "955", "turn_idx": 1, "from": "Assistant", "text": "好的，牛奶、面包和水果，记下了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-113-1.wav"}}, {"session_id": "955", "turn_idx": 2, "from": "Human", "text": "音量再小点，我在图书馆。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-113-2.wav"}}, {"session_id": "955", "turn_idx": 3, "from": "Assistant", "text": "没问题，我小声说，清单是牛奶面包水果，都存好了，你安心学习。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-113-3.wav"}}] +[{"session_id": "412", "turn_idx": 0, "from": "Human", "text": "房贷压力大啊！贷款一百万，二十年期，利率四点三。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-114-0.wav"}}, {"session_id": "412", "turn_idx": 1, "from": "Assistant", "text": "等额本息的话每月固定还款。我查过最新LPR是四点二。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-114-1.wav"}}, {"session_id": "412", "turn_idx": 2, "from": "Human", "text": "银行说加点零点一，就是四点三。帮我算月供多少。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-114-2.wav"}}, {"session_id": "412", "turn_idx": 3, "from": "Assistant", "text": "公式是[贷款本金×月利率×(1+月利率)^还款月数]÷[(1+月利率)^还款月数－1]。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-114-3.wav"}}, {"session_id": "412", "turn_idx": 4, "from": "Human", "text": "别整公式，直接给数字吧！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-114-4.wav"}}, {"session_id": "412", "turn_idx": 5, "from": "Assistant", "text": "稍等……需要精确计算吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-114-5.wav"}}, {"session_id": "412", "turn_idx": 6, "from": "Human", "text": "对，算准确数值。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-114-6.wav"}}, {"session_id": "412", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"1000000 * (0.043/12) * (1+0.043/12)**240 / ((1+0.043/12)**240 -1)\"}", "value": "function\ncalculate\n{\"expression\": \"1000000 * (0.043/12) * (1+0.043/12)**240 / ((1+0.043/12)**240 -1)\"}"}] +[{"session_id": "224", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在跑步，想记录下数据。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-115-0.wav"}}, {"session_id": "224", "turn_idx": 1, "from": "Assistant", "text": "很棒！今天跑了多远？花了多少时间？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-115-1.wav"}}, {"session_id": "224", "turn_idx": 2, "from": "Human", "text": "跑了十公里，用时五十五分钟。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-115-2.wav"}}, {"session_id": "224", "turn_idx": 3, "from": "Assistant", "text": "速度挺快。平均每公里几分钟？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-115-3.wav"}}, {"session_id": "224", "turn_idx": 4, "from": "Human", "text": "我想算平均速度，就是每公里用多少分钟。直接帮我算出来。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-115-4.wav"}}, {"session_id": "224", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"55 / 10\"}", "value": "function\ncalculate\n{\"expression\": \"55 / 10\"}"}] +[{"session_id": "383", "turn_idx": 0, "from": "Human", "text": "小跃，我存了十五万在银行，年利率是百分之四点二，存四年。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-116-0.wav"}}, {"session_id": "383", "turn_idx": 1, "from": "Assistant", "text": "本金十五万，年利率百分之四点二，四年期。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-116-1.wav"}}, {"session_id": "383", "turn_idx": 2, "from": "Human", "text": "计算复利后的总金额。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-116-2.wav"}}, {"session_id": "383", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"150000 * (1 + 0.042) ** 4\"}", "value": "function\ncalculate\n{\"expression\": \"150000 * (1 + 0.042) ** 4\"}"}] +[{"session_id": "58", "turn_idx": 0, "from": "Human", "text": "小跃，仓库货架尺寸有问题！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-117-0.wav"}}, {"session_id": "58", "turn_idx": 1, "from": "Assistant", "text": "您说下货架和货物的尺寸？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-117-1.wav"}}, {"session_id": "58", "turn_idx": 2, "from": "Human", "text": "货架每层高一米二，货物箱子长宽高都是六十厘米。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-117-2.wav"}}, {"session_id": "58", "turn_idx": 3, "from": "Assistant", "text": "要竖着堆放吗？算每层能放几箱？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-117-3.wav"}}, {"session_id": "58", "turn_idx": 4, "from": "Human", "text": "竖着放，但箱子不能倒，帮我算最大堆放层数？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-117-4.wav"}}, {"session_id": "58", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"1.2 // 0.6\"}", "value": "function\ncalculate\n{\"expression\": \"1.2 // 0.6\"}"}] +[{"session_id": "258", "turn_idx": 0, "from": "Human", "text": "小跃，我手机电池老掉电快，新买的才三个月。有啥省电技巧？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-118-0.wav"}}, {"session_id": "258", "turn_idx": 1, "from": "Assistant", "text": "先关掉后台应用刷新和定位服务。屏幕亮度调中等，或者开省电模式。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-118-1.wav"}}, {"session_id": "258", "turn_idx": 2, "from": "Human", "text": "这些都试了，还是不行。是不是电池质量问题？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-118-2.wav"}}, {"session_id": "258", "turn_idx": 3, "from": "Assistant", "text": "有可能，或者充电习惯不好。别用到零再充，保持电量在二十到八十之间。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-118-3.wav"}}, {"session_id": "258", "turn_idx": 4, "from": "Human", "text": "哦，我常充到百分百。能帮我查查手机电池寿命测试方法吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-118-4.wav"}}, {"session_id": "258", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"手机电池寿命 测试方法\"}", "value": "function\nweb_search\n{\"query\": \"手机电池寿命 测试方法\"}"}] +[{"session_id": "249", "turn_idx": 0, "from": "Human", "text": "小跃，我想种些室内植物，但怕养不活。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-119-0.wav"}}, {"session_id": "249", "turn_idx": 1, "from": "Assistant", "text": "室内植物能净化空气！您喜欢绿萝还是多肉？需要耐旱的品种吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-119-1.wav"}}, {"session_id": "249", "turn_idx": 2, "from": "Human", "text": "喜欢绿萝，但听说容易黄叶。帮我搜搜绿萝养护的常见问题和解决方法。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-119-2.wav"}}, {"session_id": "249", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"绿萝养护 常见问题 解决方法\"}", "value": "function\nweb_search\n{\"query\": \"绿萝养护 常见问题 解决方法\"}"}] +[{"session_id": "659", "turn_idx": 0, "from": "Human", "text": "小跃，我肩膀酸痛，做什么拉伸好？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-120-0.wav"}}, {"session_id": "659", "turn_idx": 1, "from": "Assistant", "text": "拉伸能缓解酸痛，试试肩部旋转。你酸痛多久了？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-120-1.wav"}}, {"session_id": "659", "turn_idx": 2, "from": "Human", "text": "几天吧。帮我查一下缓解肩膀酸痛的简单拉伸动作。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-120-2.wav"}}, {"session_id": "659", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"缓解肩膀酸痛简单拉伸动作\"}", "value": "function\nweb_search\n{\"query\": \"缓解肩膀酸痛简单拉伸动作\"}"}] +[{"session_id": "935", "turn_idx": 0, "from": "Human", "text": "小跃，我想看部喜剧电影放松。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-121-0.wav"}}, {"session_id": "935", "turn_idx": 1, "from": "Assistant", "text": "喜剧电影能让人开心，最近有部新片评价不错。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-121-1.wav"}}, {"session_id": "935", "turn_idx": 2, "from": "Human", "text": "推荐一部适合全家人看的吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-121-2.wav"}}, {"session_id": "935", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"全家喜剧电影推荐\"}", "value": "function\nweb_search\n{\"query\": \"全家喜剧电影推荐\"}"}] +[{"session_id": "683", "turn_idx": 0, "from": "Human", "text": "小跃，我感觉好累啊。用安慰的语气回应我。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-122-0.wav"}}, {"session_id": "683", "turn_idx": 1, "from": "Assistant", "text": "别担心，先休息一会儿，喝杯水，事情总会好起来的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-122-1.wav"}}] +[{"session_id": "649", "turn_idx": 0, "from": "Human", "text": "小跃，我最近存了一笔钱在银行，挺开心的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-123-0.wav"}}, {"session_id": "649", "turn_idx": 1, "from": "Assistant", "text": "存钱是好事啊！存了多少呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-123-1.wav"}}, {"session_id": "649", "turn_idx": 2, "from": "Human", "text": "存了八十万，年利率是百分之三点五。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-123-2.wav"}}, {"session_id": "649", "turn_idx": 3, "from": "Assistant", "text": "那利息还不错。你打算存几年？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-123-3.wav"}}, {"session_id": "649", "turn_idx": 4, "from": "Human", "text": "计划存十年。帮我算算到期能拿回多少钱。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-123-4.wav"}}, {"session_id": "649", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"800000 * (1 + 0.035)**10\"}", "value": "function\ncalculate\n{\"expression\": \"800000 * (1 + 0.035)**10\"}"}] +[{"session_id": "375", "turn_idx": 0, "from": "Human", "text": "小跃，我想换个平板，苹果还是三星好？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-124-0.wav"}}, {"session_id": "375", "turn_idx": 1, "from": "Assistant", "text": "苹果的系统流畅，三星的屏幕大，看你的需求。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-124-1.wav"}}, {"session_id": "375", "turn_idx": 2, "from": "Human", "text": "苹果的最新平板是啥型号？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-124-2.wav"}}, {"session_id": "375", "turn_idx": 3, "from": "Assistant", "text": "最新的是iPad第十代。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-124-3.wav"}}, {"session_id": "375", "turn_idx": 4, "from": "Human", "text": "帮我查查iPad第十代的电池能用多久。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-124-4.wav"}}, {"session_id": "375", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"iPad第十代 电池续航\"}", "value": "function\nweb_search\n{\"query\": \"iPad第十代 电池续航\"}"}] +[{"session_id": "122", "turn_idx": 0, "from": "Human", "text": "帮我设置一个明天早上七点的闹钟。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-125-0.wav"}}, {"session_id": "122", "turn_idx": 1, "from": "Assistant", "text": "好的，闹钟已设置为明天早上七点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-125-1.wav"}}, {"session_id": "122", "turn_idx": 2, "from": "Human", "text": "音量调低一点，别太响。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-125-2.wav"}}, {"session_id": "122", "turn_idx": 3, "from": "Assistant", "text": "明白了，音量调低。闹钟设置好了，明天早上七点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-125-3.wav"}}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_851", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！周末快到了，你有什么建议活动吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-126-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_851", "turn_idx": 1, "from": "Assistant", "text": "早上好！我觉得可以去户外走走，比如公园野餐。你最近想去哪儿？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-126-1.wav"}}] +[{"session_id": "257", "turn_idx": 0, "from": "Human", "text": "小跃，我想学点简单瑜伽，缓解腰背痛。有啥入门动作？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-127-0.wav"}}, {"session_id": "257", "turn_idx": 1, "from": "Assistant", "text": "试试猫牛式，跪姿手膝撑地，拱背吸气，塌腰呼气。每天做十次，慢慢来。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-127-1.wav"}}, {"session_id": "257", "turn_idx": 2, "from": "Human", "text": "猫牛式我练过，但腰还是疼。是不是动作不对？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-127-2.wav"}}, {"session_id": "257", "turn_idx": 3, "from": "Assistant", "text": "可能姿势不标准。确保手在肩下，膝在臀下，动作幅度小点。或者加个婴儿式放松。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-127-3.wav"}}, {"session_id": "257", "turn_idx": 4, "from": "Human", "text": "婴儿式怎么做？能帮我查查详细教程或视频推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-127-4.wav"}}, {"session_id": "257", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"瑜伽婴儿式 详细教程\"}", "value": "function\nweb_search\n{\"query\": \"瑜伽婴儿式 详细教程\"}"}] +[{"session_id": "452", "turn_idx": 0, "from": "Human", "text": "小跃，我打算存钱买房，每月存一万两千元。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-128-0.wav"}}, {"session_id": "452", "turn_idx": 1, "from": "Assistant", "text": "目标明确！坚持储蓄能早日实现。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-128-1.wav"}}, {"session_id": "452", "turn_idx": 2, "from": "Human", "text": "如果存三年三十六个月，年利率百分之三点五，总本息有多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-128-2.wav"}}, {"session_id": "452", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"12000 * 36 * (1 + 0.035)\"}", "value": "function\ncalculate\n{\"expression\": \"12000 * 36 * (1 + 0.035)\"}"}] +[{"session_id": "138", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我想做个小手工，比如折纸。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-129-0.wav"}}, {"session_id": "138", "turn_idx": 1, "from": "Assistant", "text": "折纸好玩又放松！你想做动物还是花？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-129-1.wav"}}, {"session_id": "138", "turn_idx": 2, "from": "Human", "text": "想做简单的花，但找不到步骤，你能查查教程吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-129-2.wav"}}, {"session_id": "138", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"简单折纸花教程\"}", "value": "function\nweb_search\n{\"query\": \"简单折纸花教程\"}"}] +[{"session_id": "317", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我晚上8点健身。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-130-0.wav"}}, {"session_id": "317", "turn_idx": 1, "from": "Assistant", "text": "好的，晚上8点健身提醒设好了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-130-1.wav"}}, {"session_id": "317", "turn_idx": 2, "from": "Human", "text": "小跃，语气再热情点，我需要动力。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-130-2.wav"}}, {"session_id": "317", "turn_idx": 3, "from": "Assistant", "text": "太棒了！8点健身，坚持就是胜利！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-130-3.wav"}}, {"session_id": "317", "turn_idx": 4, "from": "Human", "text": "再加个9点读书提醒。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-130-4.wav"}}, {"session_id": "317", "turn_idx": 5, "from": "Assistant", "text": "没问题，9点读书提醒也加上了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-130-5.wav"}}, {"session_id": "317", "turn_idx": 6, "from": "Human", "text": "小跃，音量高点，我怕忘记。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-130-6.wav"}}, {"session_id": "317", "turn_idx": 7, "from": "Assistant", "text": "收到！音量调大，提醒一定响！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-130-7.wav"}}] +[{"session_id": "443", "turn_idx": 0, "from": "Human", "text": "小跃，我投资了一个项目，本金十万，年利率是百分之六。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-131-0.wav"}}, {"session_id": "443", "turn_idx": 1, "from": "Assistant", "text": "复利计算的话，五年后收益会不错。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-131-1.wav"}}, {"session_id": "443", "turn_idx": 2, "from": "Human", "text": "对，我想知道五年后总金额是多少。 小跃，请计算复利下的最终总金额。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-131-2.wav"}}, {"session_id": "443", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"100000 * (1 + 0.06) ** 5\"}", "value": "function\ncalculate\n{\"expression\": \"100000 * (1 + 0.06) ** 5\"}"}] +[{"session_id": "536", "turn_idx": 0, "from": "Human", "text": "小跃，我在做蛋糕，食谱说要两百克面粉。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-132-0.wav"}}, {"session_id": "536", "turn_idx": 1, "from": "Assistant", "text": "您要调整份量吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-132-1.wav"}}, {"session_id": "536", "turn_idx": 2, "from": "Human", "text": "是的，想做一点五倍的量。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-132-2.wav"}}, {"session_id": "536", "turn_idx": 3, "from": "Assistant", "text": "那其他材料也按比例加。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-132-3.wav"}}, {"session_id": "536", "turn_idx": 4, "from": "Human", "text": "先帮我算算面粉需要多少克。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-132-4.wav"}}, {"session_id": "536", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"200 * 1.5\"}", "value": "function\ncalculate\n{\"expression\": \"200 * 1.5\"}"}] +[{"session_id": "471", "turn_idx": 0, "from": "Human", "text": "小跃，我在减肥，每天运动消耗卡路里。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-133-0.wav"}}, {"session_id": "471", "turn_idx": 1, "from": "Assistant", "text": "坚持运动是好事！你每天怎么安排？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-133-1.wav"}}, {"session_id": "471", "turn_idx": 2, "from": "Human", "text": "跑步半小时燃烧三百卡，健身一小时燃烧四百卡。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-133-2.wav"}}, {"session_id": "471", "turn_idx": 3, "from": "Assistant", "text": "每天总消耗七百卡。每周运动几天？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-133-3.wav"}}, {"session_id": "471", "turn_idx": 4, "from": "Human", "text": "一周五天，但周末多做一个小时瑜伽燃烧二百卡。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-133-4.wav"}}, {"session_id": "471", "turn_idx": 5, "from": "Assistant", "text": "所以周末每天总消耗九百卡？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-133-5.wav"}}, {"session_id": "471", "turn_idx": 6, "from": "Human", "text": "对，帮我算一周总卡路里消耗。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-133-6.wav"}}, {"session_id": "471", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(700 * 5) + (900 * 2)\"}", "value": "function\ncalculate\n{\"expression\": \"(700 * 5) + (900 * 2)\"}"}] +[{"session_id": "55", "turn_idx": 0, "from": "Human", "text": "小跃，房贷压力好大，算算月供。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-134-0.wav"}}, {"session_id": "55", "turn_idx": 1, "from": "Assistant", "text": "贷款总额和利率多少？分多少年还？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-134-1.wav"}}, {"session_id": "55", "turn_idx": 2, "from": "Human", "text": "贷了一百二十万，利率百分之四点一，二十年还清。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-134-2.wav"}}, {"session_id": "55", "turn_idx": 3, "from": "Assistant", "text": "等额本息还款对吧？需要算每月还多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-134-3.wav"}}, {"session_id": "55", "turn_idx": 4, "from": "Human", "text": "是等额本息，快帮我算精确数字！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-134-4.wav"}}, {"session_id": "55", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"1200000*0.041/12*(1+0.041/12)**240/((1+0.041/12)**240-1)\"}", "value": "function\ncalculate\n{\"expression\": \"1200000*0.041/12*(1+0.041/12)**240/((1+0.041/12)**240-1)\"}"}] +[{"session_id": "779", "turn_idx": 0, "from": "Human", "text": "小跃，我车轮胎漏气了，怎么自己补？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-135-0.wav"}}, {"session_id": "779", "turn_idx": 1, "from": "Assistant", "text": "自己补胎的话，先用补胎工具包，找到漏点贴补丁就行。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-135-1.wav"}}, {"session_id": "779", "turn_idx": 2, "from": "Human", "text": "工具包我有。但怎么确定漏点位置？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-135-2.wav"}}, {"session_id": "779", "turn_idx": 3, "from": "Assistant", "text": "涂点肥皂水在轮胎上，冒泡的地方就是漏点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-135-3.wav"}}, {"session_id": "779", "turn_idx": 4, "from": "Human", "text": "肥皂水？比例怎么调？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-135-4.wav"}}, {"session_id": "779", "turn_idx": 5, "from": "Assistant", "text": "水和肥皂一比十混合，搅拌均匀涂上。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-135-5.wav"}}, {"session_id": "779", "turn_idx": 6, "from": "Human", "text": "好的。那你能查查详细的自补胎视频教程吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-135-6.wav"}}, {"session_id": "779", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"自补汽车轮胎 视频教程\"}", "value": "function\nweb_search\n{\"query\": \"自补汽车轮胎 视频教程\"}"}] +[{"session_id": "232", "turn_idx": 0, "from": "Human", "text": "小跃，放点轻松的音乐吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-136-0.wav"}}, {"session_id": "232", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐列表，希望你喜欢。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-136-1.wav"}}, {"session_id": "232", "turn_idx": 2, "from": "Human", "text": "小跃，换首欢快的歌，用开心的语气推荐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-136-2.wav"}}, {"session_id": "232", "turn_idx": 3, "from": "Assistant", "text": "太好啦！这首《快乐颂》超带劲，一起嗨起来！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-136-3.wav"}}, {"session_id": "232", "turn_idx": 4, "from": "Human", "text": "哈哈，喜欢！能调低点音量吗？邻居可能嫌吵。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-136-4.wav"}}, {"session_id": "232", "turn_idx": 5, "from": "Assistant", "text": "好的，音量调低了，现在柔和多了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-136-5.wav"}}, {"session_id": "232", "turn_idx": 6, "from": "Human", "text": "小跃，用平静的语气告诉我这首歌名。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-136-6.wav"}}, {"session_id": "232", "turn_idx": 7, "from": "Assistant", "text": "这是《快乐颂》，一首经典欢快的曲子。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-136-7.wav"}}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_728", "turn_idx": 0, "from": "Human", "text": "小跃，今天工作累坏了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-137-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_728", "turn_idx": 1, "from": "Assistant", "text": "辛苦了，休息一下听听歌吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-137-1.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_728", "turn_idx": 2, "from": "Human", "text": "嗯，周末想去武汉东湖放松。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-137-2.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_728", "turn_idx": 3, "from": "Assistant", "text": "东湖散步很治愈，适合减压。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-137-3.wav"}}] +[{"session_id": "931", "turn_idx": 0, "from": "Human", "text": "小跃，我最近老是睡不好觉。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-138-0.wav"}}, {"session_id": "931", "turn_idx": 1, "from": "Assistant", "text": "睡不好可能是压力大，试试睡前放松听听音乐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-138-1.wav"}}, {"session_id": "931", "turn_idx": 2, "from": "Human", "text": "有没有助眠的小妙招？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-138-2.wav"}}, {"session_id": "931", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"改善睡眠小妙招\"}", "value": "function\nweb_search\n{\"query\": \"改善睡眠小妙招\"}"}] +[{"session_id": "497", "turn_idx": 0, "from": "Human", "text": "小跃，种植园收成椰子八千四百个", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-139-0.wav"}}, {"session_id": "497", "turn_idx": 1, "from": "Assistant", "text": "大丰收呀！破损率预计多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-139-1.wav"}}, {"session_id": "497", "turn_idx": 2, "from": "Human", "text": "运输大概坏百分之五，分拣再淘汰百分之三", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-139-2.wav"}}, {"session_id": "497", "turn_idx": 3, "from": "Assistant", "text": "剩余八千零四十个。销售渠道分几种？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-139-3.wav"}}, {"session_id": "497", "turn_idx": 4, "from": "Human", "text": "精品果占四成卖十五块，普通果六成卖八块", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-139-4.wav"}}, {"session_id": "497", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"8400*0.95*0.97*(0.4*15+0.6*8)\"}", "value": "function\ncalculate\n{\"expression\": \"8400*0.95*0.97*(0.4*15+0.6*8)\"}"}] +[{"session_id": "928", "turn_idx": 0, "from": "Human", "text": "小跃，声音小一点，我这儿有人在休息。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-140-0.wav"}}, {"session_id": "928", "turn_idx": 1, "from": "Assistant", "text": "好的，我调小音量了。现在说话轻轻的，不会打扰到别人。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-140-1.wav"}}, {"session_id": "928", "turn_idx": 2, "from": "Human", "text": "谢啦，再小声点重复一下刚才的提醒。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-140-2.wav"}}, {"session_id": "928", "turn_idx": 3, "from": "Assistant", "text": "没问题，我再说一次:别忘了下午三点开会，记得带上报告。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-140-3.wav"}}] +[{"session_id": "774", "turn_idx": 0, "from": "Human", "text": "小跃，我最近失眠，有什么自然疗法吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-141-0.wav"}}, {"session_id": "774", "turn_idx": 1, "from": "Assistant", "text": "自然疗法的话，睡前喝杯温牛奶或听轻音乐，能帮助放松。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-141-1.wav"}}, {"session_id": "774", "turn_idx": 2, "from": "Human", "text": "牛奶我试了，效果一般。音乐有什么推荐？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-141-2.wav"}}, {"session_id": "774", "turn_idx": 3, "from": "Assistant", "text": "试试古典乐，比如贝多芬的月光曲，节奏舒缓。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-141-3.wav"}}, {"session_id": "774", "turn_idx": 4, "from": "Human", "text": "贝多芬？我记得他是德国人，但具体生平不了解。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-141-4.wav"}}, {"session_id": "774", "turn_idx": 5, "from": "Assistant", "text": "对，贝多芬是十八世纪的作曲家，代表作很多。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-141-5.wav"}}, {"session_id": "774", "turn_idx": 6, "from": "Human", "text": "那你能帮我搜搜他的生平故事和代表作列表吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-141-6.wav"}}, {"session_id": "774", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"贝多芬生平 代表作\"}", "value": "function\nweb_search\n{\"query\": \"贝多芬生平 代表作\"}"}] +[{"session_id": "135", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在学英语，但记单词好难。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-142-0.wav"}}, {"session_id": "135", "turn_idx": 1, "from": "Assistant", "text": "记单词有技巧！试试用卡片或APP。你每天学多久？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-142-1.wav"}}, {"session_id": "135", "turn_idx": 2, "from": "Human", "text": "每天半小时，但效率低，你能搜些记忆方法吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-142-2.wav"}}, {"session_id": "135", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"英语单词记忆技巧\"}", "value": "function\nweb_search\n{\"query\": \"英语单词记忆技巧\"}"}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_401", "turn_idx": 0, "from": "Human", "text": "小跃，我后天要去广州出差，有个重要会议。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-143-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_401", "turn_idx": 1, "from": "Assistant", "text": "广州的美食很出名，出差顺利吗？需要提前准备什么？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-143-1.wav"}}] +[{"session_id": "401", "turn_idx": 0, "from": "Human", "text": "小跃，我想学做菜，但完全是个新手。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-144-0.wav"}}, {"session_id": "401", "turn_idx": 1, "from": "Assistant", "text": "新手别担心，先从炒鸡蛋开始吧，简单又实用。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-144-1.wav"}}, {"session_id": "401", "turn_idx": 2, "from": "Human", "text": "炒鸡蛋我会了，现在想挑战番茄炒蛋。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-144-2.wav"}}, {"session_id": "401", "turn_idx": 3, "from": "Assistant", "text": "番茄炒蛋需要番茄、鸡蛋和调料，火候要控制好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-144-3.wav"}}, {"session_id": "401", "turn_idx": 4, "from": "Human", "text": "听起来不错。能帮我查一下详细步骤吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-144-4.wav"}}, {"session_id": "401", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法\"}"}] +[{"session_id": "938", "turn_idx": 0, "from": "Human", "text": "小跃，长城是谁修建的？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-145-0.wav"}}, {"session_id": "938", "turn_idx": 1, "from": "Assistant", "text": "长城是古代秦始皇时期开始修的，为了防御外敌。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-145-1.wav"}}, {"session_id": "938", "turn_idx": 2, "from": "Human", "text": "它总共有多少公里长？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-145-2.wav"}}, {"session_id": "938", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"长城总长度历史\"}", "value": "function\nweb_search\n{\"query\": \"长城总长度历史\"}"}] +[{"session_id": "864", "turn_idx": 0, "from": "Human", "text": "小跃，我赶时间，请快点说重点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-146-0.wav"}}, {"session_id": "864", "turn_idx": 1, "from": "Assistant", "text": "收到！快节奏模式启动。要问什么？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-146-1.wav"}}, {"session_id": "864", "turn_idx": 2, "from": "Human", "text": "导航到最近的超市。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-146-2.wav"}}, {"session_id": "864", "turn_idx": 3, "from": "Assistant", "text": "好的，直行两百米左转就到。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-146-3.wav"}}] +[{"session_id": "147", "turn_idx": 0, "from": "Human", "text": "小跃，别忘了提醒我买牛奶。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-147-0.wav"}}, {"session_id": "147", "turn_idx": 1, "from": "Assistant", "text": "放心，买牛奶的提醒已设置。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-147-1.wav"}}, {"session_id": "147", "turn_idx": 2, "from": "Human", "text": "声音大一点确认一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-147-2.wav"}}, {"session_id": "147", "turn_idx": 3, "from": "Assistant", "text": "好的！大声确认:买牛奶提醒设置成功！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-147-3.wav"}}] +[{"session_id": "373", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个明天早上7点的闹钟。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-148-0.wav"}}, {"session_id": "373", "turn_idx": 1, "from": "Assistant", "text": "闹钟已设为明早7点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-148-1.wav"}}, {"session_id": "373", "turn_idx": 2, "from": "Human", "text": "声音太小了，提高点音量说确认。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-148-2.wav"}}, {"session_id": "373", "turn_idx": 3, "from": "Assistant", "text": "**好的！闹钟已设为明早7点，声音调大了！**", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-148-3.wav"}}, {"session_id": "373", "turn_idx": 4, "from": "Human", "text": "完美。再设个午休提醒，12点半。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-148-4.wav"}}, {"session_id": "373", "turn_idx": 5, "from": "Assistant", "text": "午休提醒设为12点半。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-148-5.wav"}}, {"session_id": "373", "turn_idx": 6, "from": "Human", "text": "这次用温柔点的语气吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-148-6.wav"}}, {"session_id": "373", "turn_idx": 7, "from": "Assistant", "text": "好的呢，午休提醒温柔地设好啦，记得休息哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-148-7.wav"}}] +[{"session_id": "960", "turn_idx": 0, "from": "Human", "text": "小跃，我下个月想去日本玩。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-149-0.wav"}}, {"session_id": "960", "turn_idx": 1, "from": "Assistant", "text": "好主意！预算安排好了吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-149-1.wav"}}, {"session_id": "960", "turn_idx": 2, "from": "Human", "text": "机票大概一万二千元。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-149-2.wav"}}, {"session_id": "960", "turn_idx": 3, "from": "Assistant", "text": "住宿呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-149-3.wav"}}, {"session_id": "960", "turn_idx": 4, "from": "Human", "text": "计划住七天，每晚一千二百元。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-149-4.wav"}}, {"session_id": "960", "turn_idx": 5, "from": "Assistant", "text": "餐饮和购物呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-149-5.wav"}}, {"session_id": "960", "turn_idx": 6, "from": "Human", "text": "餐饮每天八百元，购物预算五千元。 帮我算总费用是多少。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-149-6.wav"}}, {"session_id": "960", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"12000 + (1200 * 7) + (800 * 7) + 5000\"}", "value": "function\ncalculate\n{\"expression\": \"12000 + (1200 * 7) + (800 * 7) + 5000\"}"}] +[{"session_id": "504", "turn_idx": 0, "from": "Human", "text": "小跃，我想买新手机，苹果和华为选哪个？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-150-0.wav"}}, {"session_id": "504", "turn_idx": 1, "from": "Assistant", "text": "苹果系统稳定，华为拍照强，看你需求。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-150-1.wav"}}, {"session_id": "504", "turn_idx": 2, "from": "Human", "text": "哦，华为最新款是什么？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-150-2.wav"}}, {"session_id": "504", "turn_idx": 3, "from": "Assistant", "text": "华为Mate六十系列，性能升级了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-150-3.wav"}}, {"session_id": "504", "turn_idx": 4, "from": "Human", "text": "那你能查一下它的详细参数和价格吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-150-4.wav"}}, {"session_id": "504", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"华为Mate60 参数 价格\"}", "value": "function\nweb_search\n{\"query\": \"华为Mate60 参数 价格\"}"}] +[{"session_id": "764", "turn_idx": 0, "from": "Human", "text": "小跃，我投资了一笔钱。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-151-0.wav"}}, {"session_id": "764", "turn_idx": 1, "from": "Assistant", "text": "投资不错！本金多少？回报率呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-151-1.wav"}}, {"session_id": "764", "turn_idx": 2, "from": "Human", "text": "本金一万块，年回报率百分之八，复利计算。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-151-2.wav"}}, {"session_id": "764", "turn_idx": 3, "from": "Assistant", "text": "复利的话，每年增长。你计划放几年？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-151-3.wav"}}, {"session_id": "764", "turn_idx": 4, "from": "Human", "text": "放五年。帮我算五年后总值。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-151-4.wav"}}, {"session_id": "764", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"10000 * (1 + 0.08)**5\"}", "value": "function\ncalculate\n{\"expression\": \"10000 * (1 + 0.08)**5\"}"}] +[{"session_id": "839", "turn_idx": 0, "from": "Human", "text": "小跃，我想自己刷墙，选什么油漆好？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-152-0.wav"}}, {"session_id": "839", "turn_idx": 1, "from": "Assistant", "text": "乳胶漆环保又容易刷，立邦或多乐士都不错。先买底漆和面漆。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-152-1.wav"}}, {"session_id": "839", "turn_idx": 2, "from": "Human", "text": "底漆和面漆区别是啥？要刷几遍？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-152-2.wav"}}, {"session_id": "839", "turn_idx": 3, "from": "Assistant", "text": "底漆打底防潮，面漆上色。通常刷一遍底漆，两遍面漆。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-152-3.wav"}}, {"session_id": "839", "turn_idx": 4, "from": "Human", "text": "工具呢？需要滚筒还是刷子？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-152-4.wav"}}, {"session_id": "839", "turn_idx": 5, "from": "Assistant", "text": "滚筒刷大面，刷子处理角落。买套装就行。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-152-5.wav"}}, {"session_id": "839", "turn_idx": 6, "from": "Human", "text": "小跃，帮我查一下刷墙的详细步骤指南。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-152-6.wav"}}, {"session_id": "839", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"刷墙 详细 步骤 指南\"}", "value": "function\nweb_search\n{\"query\": \"刷墙 详细 步骤 指南\"}"}] +[{"session_id": "155", "turn_idx": 0, "from": "Human", "text": "公司年会抽奖概率帮我验证下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-153-0.wav"}}, {"session_id": "155", "turn_idx": 1, "from": "Assistant", "text": "奖品设置和参与人数是？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-153-1.wav"}}, {"session_id": "155", "turn_idx": 2, "from": "Human", "text": "一等奖三名，二等奖十名，参与员工两百人。每人只能中一次。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-153-2.wav"}}, {"session_id": "155", "turn_idx": 3, "from": "Assistant", "text": "那是独立抽奖还是顺序抽？概率计算方式不同哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-153-3.wav"}}, {"session_id": "155", "turn_idx": 4, "from": "Human", "text": "独立抽，先抽一等奖再抽二等奖。算我中任意奖的概率。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-153-4.wav"}}, {"session_id": "155", "turn_idx": 5, "from": "Assistant", "text": "那就是1减完全不中的概率。需要计算吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-153-5.wav"}}, {"session_id": "155", "turn_idx": 6, "from": "Human", "text": "对，用百分比表示，保留两位小数。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-153-6.wav"}}, {"session_id": "155", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"round((1 - (197/200 * 187/190)) * 100, 2)\"}", "value": "function\ncalculate\n{\"expression\": \"round((1 - (197/200 * 187/190)) * 100, 2)\"}"}] +[{"session_id": "303", "turn_idx": 0, "from": "Human", "text": "小跃，我打算周末去爬山，附近有什么推荐的山吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-154-0.wav"}}, {"session_id": "303", "turn_idx": 1, "from": "Assistant", "text": "嗯，要看你在哪个城市。比如北京的话，香山就很适合新手，风景也好。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-154-1.wav"}}, {"session_id": "303", "turn_idx": 2, "from": "Human", "text": "我在上海，附近有类似的山吗？不要太远的。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-154-2.wav"}}, {"session_id": "303", "turn_idx": 3, "from": "Assistant", "text": "上海的话，佘山是个好选择，开车一小时就到。那里有徒步路线，还能看天文台。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-154-3.wav"}}, {"session_id": "303", "turn_idx": 4, "from": "Human", "text": "佘山听起来不错。但我担心人多，你能帮我查查佘山周末的游客流量预测吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-154-4.wav"}}, {"session_id": "303", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"上海 佘山 游客流量 预测\"}", "value": "function\nweb_search\n{\"query\": \"上海 佘山 游客流量 预测\"}"}] +[{"session_id": "201", "turn_idx": 0, "from": "Human", "text": "小跃，我们自驾游计划开三千六百公里，车每公里油耗大概零点零八升。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-155-0.wav"}}, {"session_id": "201", "turn_idx": 1, "from": "Assistant", "text": "那得先算总耗油量，再结合油价预估费用。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-155-1.wav"}}, {"session_id": "201", "turn_idx": 2, "from": "Human", "text": "现在油价是八块五毛一升，中途还要交一千两百元过路费。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-155-2.wav"}}, {"session_id": "201", "turn_idx": 3, "from": "Assistant", "text": "油费加过路费就是主要支出了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-155-3.wav"}}, {"session_id": "201", "turn_idx": 4, "from": "Human", "text": "对了，回程时油价可能涨百分之三，按这个算总预算。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-155-4.wav"}}, {"session_id": "201", "turn_idx": 5, "from": "Assistant", "text": "去程按当前价，回程按涨价后价格计算更准确。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-155-5.wav"}}, {"session_id": "201", "turn_idx": 6, "from": "Human", "text": "请帮我算全程总花费，假设去程一半路程回程一半。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-155-6.wav"}}, {"session_id": "201", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(3600 * 0.08 * 8.5) + (3600 * 0.08 * 8.5 * 1.03) + 1200\"}", "value": "function\ncalculate\n{\"expression\": \"(3600 * 0.08 * 8.5) + (3600 * 0.08 * 8.5 * 1.03) + 1200\"}"}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_465", "turn_idx": 0, "from": "Human", "text": "小跃，周末朋友约我去爬山，兴奋！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-156-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_465", "turn_idx": 1, "from": "Assistant", "text": "爬山好主意！不过得看天气给不给力。地点在哪里？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-156-1.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_465", "turn_idx": 2, "from": "Human", "text": "在武汉。能帮我查查天气吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-156-2.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_465", "turn_idx": 3, "from": "Assistant", "text": "武汉的山景很美！查今天、明天还是后天？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-156-3.wav"}}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_795", "turn_idx": 0, "from": "Human", "text": "小跃，成都的火锅辣不辣？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-157-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_795", "turn_idx": 1, "from": "Assistant", "text": "成都火锅超辣但超香，你怕不怕？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-157-1.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_795", "turn_idx": 2, "from": "Human", "text": "不怕，明天就飞成都去试试。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-157-2.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_795", "turn_idx": 3, "from": "Assistant", "text": "勇敢！要我提醒你带什么？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-157-3.wav"}}] +[{"session_id": "127", "turn_idx": 0, "from": "Human", "text": "提醒我下午四点去健身房。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-158-0.wav"}}, {"session_id": "127", "turn_idx": 1, "from": "Assistant", "text": "已设置下午四点的健身房提醒。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-158-1.wav"}}, {"session_id": "127", "turn_idx": 2, "from": "Human", "text": "用更随意的语气确认，别太正式。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-158-2.wav"}}, {"session_id": "127", "turn_idx": 3, "from": "Assistant", "text": "搞定啦！下午四点去健身房，别迟到哦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-158-3.wav"}}] +[{"session_id": "635", "turn_idx": 0, "from": "Human", "text": "小跃，我想换辆电动车。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-159-0.wav"}}, {"session_id": "635", "turn_idx": 1, "from": "Assistant", "text": "电动车现在很流行，比如特斯拉。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-159-1.wav"}}, {"session_id": "635", "turn_idx": 2, "from": "Human", "text": "哪款性价比最高？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-159-2.wav"}}, {"session_id": "635", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"电动车 性价比 最高\"}", "value": "function\nweb_search\n{\"query\": \"电动车 性价比 最高\"}"}] +[{"session_id": "931", "turn_idx": 0, "from": "Human", "text": "小跃，听起来更兴奋点，告诉我周末活动。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-160-0.wav"}}, {"session_id": "931", "turn_idx": 1, "from": "Assistant", "text": "哇，周末有音乐节哦，现场超热闹，乐队表演超精彩，一定要去嗨一下！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-160-1.wav"}}, {"session_id": "931", "turn_idx": 2, "from": "Human", "text": "太棒了，具体几点开始？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-160-2.wav"}}, {"session_id": "931", "turn_idx": 3, "from": "Assistant", "text": "下午两点开场，一直嗨到晚上十点，记得早点去抢好位置。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-160-3.wav"}}] +[{"session_id": "374", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-161-0.wav"}}, {"session_id": "374", "turn_idx": 1, "from": "Assistant", "text": "为什么数学书总是很伤心？因为它有太多问题。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-161-1.wav"}}, {"session_id": "374", "turn_idx": 2, "from": "Human", "text": "哈哈，这个不错。但能用更搞笑的语气再说一个？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-161-2.wav"}}, {"session_id": "374", "turn_idx": 3, "from": "Assistant", "text": "哈哈，你知道吗？鱼为什么不能玩扑克？因为一看到牌它就溜啦！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-161-3.wav"}}, {"session_id": "374", "turn_idx": 4, "from": "Human", "text": "笑死我了。再换个严肃点的语气讲个知识吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-161-4.wav"}}, {"session_id": "374", "turn_idx": 5, "from": "Assistant", "text": "严肃地说，太阳系有八大行星，地球是第三颗。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-161-5.wav"}}] +[{"session_id": "143", "turn_idx": 0, "from": "Human", "text": "小跃，我存了五万块钱在银行，年利率是百分之三点五。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-162-0.wav"}}, {"session_id": "143", "turn_idx": 1, "from": "Assistant", "text": "储蓄是个好习惯！复利能让钱慢慢增长。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-162-1.wav"}}, {"session_id": "143", "turn_idx": 2, "from": "Human", "text": "是的，我想存三年。帮我算一下到期后本金加利息总共能拿回多少钱？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-162-2.wav"}}, {"session_id": "143", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"50000 * (1 + 0.035) ** 3\"}", "value": "function\ncalculate\n{\"expression\": \"50000 * (1 + 0.035) ** 3\"}"}] +[{"session_id": "229", "turn_idx": 0, "from": "Human", "text": "小跃，我读书容易分心，有什么专注技巧？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-163-0.wav"}}, {"session_id": "229", "turn_idx": 1, "from": "Assistant", "text": "分心常见！试试番茄工作法，工作二十五分钟，休息五分钟。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-163-1.wav"}}, {"session_id": "229", "turn_idx": 2, "from": "Human", "text": "番茄法有效吗？需要App辅助吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-163-2.wav"}}, {"session_id": "229", "turn_idx": 3, "from": "Assistant", "text": "很多人反馈有效，App像Forest能计时，帮你专注。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-163-3.wav"}}, {"session_id": "229", "turn_idx": 4, "from": "Human", "text": "休息时做什么好？不能刷手机吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-163-4.wav"}}, {"session_id": "229", "turn_idx": 5, "from": "Assistant", "text": "休息时走动或喝水，别碰手机，避免重新分心。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-163-5.wav"}}, {"session_id": "229", "turn_idx": 6, "from": "Human", "text": "小跃，帮我找找番茄工作法的详细指南和App推荐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-163-6.wav"}}, {"session_id": "229", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄工作法 指南 App推荐\"}", "value": "function\nweb_search\n{\"query\": \"番茄工作法 指南 App推荐\"}"}] +[{"session_id": "376", "turn_idx": 0, "from": "Human", "text": "小跃，推荐本经典小说吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-164-0.wav"}}, {"session_id": "376", "turn_idx": 1, "from": "Assistant", "text": "喜欢啥风格？推理还是浪漫？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-164-1.wav"}}, {"session_id": "376", "turn_idx": 2, "from": "Human", "text": "推理的，像福尔摩斯那种。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-164-2.wav"}}, {"session_id": "376", "turn_idx": 3, "from": "Assistant", "text": "那可以试试《福尔摩斯探案集》。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-164-3.wav"}}, {"session_id": "376", "turn_idx": 4, "from": "Human", "text": "作者是谁？我想看看他的生平故事。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-164-4.wav"}}, {"session_id": "376", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"福尔摩斯 作者 生平\"}", "value": "function\nweb_search\n{\"query\": \"福尔摩斯 作者 生平\"}"}] +[{"session_id": "335", "turn_idx": 0, "from": "Human", "text": "小跃，推荐一部好电影吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-165-0.wav"}}, {"session_id": "335", "turn_idx": 1, "from": "Assistant", "text": "最近《流浪地球2》很火，科幻大片不错。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-165-1.wav"}}, {"session_id": "335", "turn_idx": 2, "from": "Human", "text": "请说得更热情一点，我超爱科幻。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-165-2.wav"}}, {"session_id": "335", "turn_idx": 3, "from": "Assistant", "text": "强烈推荐《流浪地球2》，特效炸裂，剧情超燃，你一定要看！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-165-3.wav"}}, {"session_id": "335", "turn_idx": 4, "from": "Human", "text": "音量调小一点，我在办公室。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-165-4.wav"}}, {"session_id": "335", "turn_idx": 5, "from": "Assistant", "text": "好的，小声点。强烈推荐《流浪地球2》，特效炸裂，超好看。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-165-5.wav"}}] +[{"session_id": "155", "turn_idx": 0, "from": "Human", "text": "小跃，播放点背景音乐吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-166-0.wav"}}, {"session_id": "155", "turn_idx": 1, "from": "Assistant", "text": "在放轻音乐了，放松一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-166-1.wav"}}, {"session_id": "155", "turn_idx": 2, "from": "Human", "text": "音量太小了，请调高点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-166-2.wav"}}, {"session_id": "155", "turn_idx": 3, "from": "Assistant", "text": "好的，音量调高了，现在清楚了吧？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-166-3.wav"}}, {"session_id": "155", "turn_idx": 4, "from": "Human", "text": "嗯，完美。再推荐首歌，语气热情点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-166-4.wav"}}, {"session_id": "155", "turn_idx": 5, "from": "Assistant", "text": "超热情推荐！新歌《夏日狂欢》节奏超带感，快听听！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-166-5.wav"}}] +[{"session_id": "947", "turn_idx": 0, "from": "Human", "text": "小跃，今天健走记录步数了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-167-0.wav"}}, {"session_id": "947", "turn_idx": 1, "from": "Assistant", "text": "走了多少步？想算距离吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-167-1.wav"}}, {"session_id": "947", "turn_idx": 2, "from": "Human", "text": "上午五千二百步，下午四千八百步，晚上三千五百步。平均步长零点六五米。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-167-2.wav"}}, {"session_id": "947", "turn_idx": 3, "from": "Assistant", "text": "需要总距离公里数？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-167-3.wav"}}, {"session_id": "947", "turn_idx": 4, "from": "Human", "text": "是的。帮我计算一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-167-4.wav"}}, {"session_id": "947", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(5200 + 4800 + 3500) * 0.65 / 1000\"}", "value": "function\ncalculate\n{\"expression\": \"(5200 + 4800 + 3500) * 0.65 / 1000\"}"}] +[{"session_id": "780", "turn_idx": 0, "from": "Human", "text": "小跃，附近有啥好吃的？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-168-0.wav"}}, {"session_id": "780", "turn_idx": 1, "from": "Assistant", "text": "附近有家披萨店评分很高，推荐试试。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-168-1.wav"}}, {"session_id": "780", "turn_idx": 2, "from": "Human", "text": "说快点，我饿了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-168-2.wav"}}, {"session_id": "780", "turn_idx": 3, "from": "Assistant", "text": "好的！附近披萨店超赞，快去尝尝吧！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-168-3.wav"}}, {"session_id": "780", "turn_idx": 4, "from": "Human", "text": "行，这就去！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-168-4.wav"}}, {"session_id": "780", "turn_idx": 5, "from": "Assistant", "text": "祝你好胃口！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-168-5.wav"}}] +[{"session_id": "704", "turn_idx": 0, "from": "Human", "text": "小跃，我刚读了一本推理小说，情节太精彩了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-169-0.wav"}}, {"session_id": "704", "turn_idx": 1, "from": "Assistant", "text": "推理小说总是让人停不下来。谁写的？东野圭吾吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-169-1.wav"}}, {"session_id": "704", "turn_idx": 2, "from": "Human", "text": "对，是《解忧杂货店》。想找同类型的书。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-169-2.wav"}}, {"session_id": "704", "turn_idx": 3, "from": "Assistant", "text": "东野圭吾的其他作品也值得读，比如《白夜行》。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-169-3.wav"}}, {"session_id": "704", "turn_idx": 4, "from": "Human", "text": "好主意！那帮我查查他所有出版的书单吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-169-4.wav"}}, {"session_id": "704", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"东野圭吾 小说作品 列表\"}", "value": "function\nweb_search\n{\"query\": \"东野圭吾 小说作品 列表\"}"}] +[{"session_id": "460", "turn_idx": 0, "from": "Human", "text": "嗨小跃，我最近想学英语，但不知道从哪儿开始。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-170-0.wav"}}, {"session_id": "460", "turn_idx": 1, "from": "Assistant", "text": "学英语挺好的！你可以先听英文歌曲或者看些简单美剧，慢慢来。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-170-1.wav"}}, {"session_id": "460", "turn_idx": 2, "from": "Human", "text": "哦，我试过听歌，但歌词太快了跟不上。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-170-2.wav"}}, {"session_id": "460", "turn_idx": 3, "from": "Assistant", "text": "别担心，可以从儿童节目入手，语速慢容易懂。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-170-3.wav"}}, {"session_id": "460", "turn_idx": 4, "from": "Human", "text": "好主意。那你能帮我找一些适合初学者的英语材料吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-170-4.wav"}}, {"session_id": "460", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"英语 初学者 学习 资源\"}", "value": "function\nweb_search\n{\"query\": \"英语 初学者 学习 资源\"}"}] +[{"session_id": "577", "turn_idx": 0, "from": "Human", "text": "小跃，我每天跑步锻炼，路线有上坡和平路。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-171-0.wav"}}, {"session_id": "577", "turn_idx": 1, "from": "Assistant", "text": "距离和速度呢？上坡慢点吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-171-1.wav"}}, {"session_id": "577", "turn_idx": 2, "from": "Human", "text": "上坡十公里，速度每公里八分钟；平路十五公里，速度每公里五分钟。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-171-2.wav"}}, {"session_id": "577", "turn_idx": 3, "from": "Assistant", "text": "总时间包括两部分，上坡加平路。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-171-3.wav"}}, {"session_id": "577", "turn_idx": 4, "from": "Human", "text": "请帮我算算跑完全程要多少分钟。 请计算跑步上坡十公里速度八分钟每公里，平路十五公里速度五分钟每公里的总时间。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-171-4.wav"}}, {"session_id": "577", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"10 * 8 + 15 * 5\"}", "value": "function\ncalculate\n{\"expression\": \"10 * 8 + 15 * 5\"}"}] +[{"session_id": "276", "turn_idx": 0, "from": "Human", "text": "小跃，我昨天骑自行车去公园，感觉速度挺快。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-172-0.wav"}}, {"session_id": "276", "turn_idx": 1, "from": "Assistant", "text": "骑车锻炼很好！骑了多少公里？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-172-1.wav"}}, {"session_id": "276", "turn_idx": 2, "from": "Human", "text": "单程十公里，来回就二十公里了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-172-2.wav"}}, {"session_id": "276", "turn_idx": 3, "from": "Assistant", "text": "用了多长时间？平均速度是距离除时间。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-172-3.wav"}}, {"session_id": "276", "turn_idx": 4, "from": "Human", "text": "去程花了三十分钟，回程路顺只用了二十五分钟。 帮我算一下平均速度是多少公里每小时？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-172-4.wav"}}, {"session_id": "276", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"20 / ((30 + 25) / 60)\"}", "value": "function\ncalculate\n{\"expression\": \"20 / ((30 + 25) / 60)\"}"}] +[{"session_id": "640", "turn_idx": 0, "from": "Human", "text": "小跃，播放一首轻松的音乐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-173-0.wav"}}, {"session_id": "640", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐《月光曲》，放松一下吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-173-1.wav"}}, {"session_id": "640", "turn_idx": 2, "from": "Human", "text": "请用兴奋的语气推荐另一首。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-173-2.wav"}}, {"session_id": "640", "turn_idx": 3, "from": "Assistant", "text": "哇！试试《欢乐颂》，超级带劲，保证让你嗨起来！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-173-3.wav"}}] +[{"session_id": "391", "turn_idx": 0, "from": "Human", "text": "小跃，今晚想给家人做顿饭，但没灵感了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-174-0.wav"}}, {"session_id": "391", "turn_idx": 1, "from": "Assistant", "text": "哈哈，家常菜最温馨！你想做肉菜还是素菜？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-174-1.wav"}}, {"session_id": "391", "turn_idx": 2, "from": "Human", "text": "肉菜吧，最好简单点的，比如鸡肉。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-174-2.wav"}}, {"session_id": "391", "turn_idx": 3, "from": "Assistant", "text": "鸡肉好办，可乐鸡翅或宫保鸡丁都不错。要我找详细做法吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-174-3.wav"}}, {"session_id": "391", "turn_idx": 4, "from": "Human", "text": "嗯，帮我查宫保鸡丁的食谱步骤。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-174-4.wav"}}, {"session_id": "391", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"宫保鸡丁 食谱 做法\"}", "value": "function\nweb_search\n{\"query\": \"宫保鸡丁 食谱 做法\"}"}] +[{"session_id": "504", "turn_idx": 0, "from": "Human", "text": "小跃，放点轻松的音乐吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-175-0.wav"}}, {"session_id": "504", "turn_idx": 1, "from": "Assistant", "text": "好的，马上播放你最喜欢的休闲歌单。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-175-1.wav"}}, {"session_id": "504", "turn_idx": 2, "from": "Human", "text": "音量有点大，调低些，再换个更欢快的语气。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-175-2.wav"}}, {"session_id": "504", "turn_idx": 3, "from": "Assistant", "text": "音量调低啦！嘿嘿，现在音乐更轻快，心情也变好！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-175-3.wav"}}, {"session_id": "504", "turn_idx": 4, "from": "Human", "text": "能换首新歌吗？最近有什么推荐？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-175-4.wav"}}, {"session_id": "504", "turn_idx": 5, "from": "Assistant", "text": "当然，我推荐这首流行新曲，节奏超赞。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-175-5.wav"}}, {"session_id": "504", "turn_idx": 6, "from": "Human", "text": "小跃，语气再热情点，我有点无聊。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-175-6.wav"}}, {"session_id": "504", "turn_idx": 7, "from": "Assistant", "text": "热情满满！这首歌绝对让你嗨起来，试试跟着哼！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-175-7.wav"}}] +[{"session_id": "901", "turn_idx": 0, "from": "Human", "text": "小跃，我投资了十五万元在基金里，年化收益是百分之六点五。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-176-0.wav"}}, {"session_id": "901", "turn_idx": 1, "from": "Assistant", "text": "不错的投资选择。计划持有多久？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-176-1.wav"}}, {"session_id": "901", "turn_idx": 2, "from": "Human", "text": "打算放八年，复利计算。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-176-2.wav"}}, {"session_id": "901", "turn_idx": 3, "from": "Assistant", "text": "那八年后收益会很可观。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-176-3.wav"}}, {"session_id": "901", "turn_idx": 4, "from": "Human", "text": "是的，帮我算算到期总金额是多少。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-176-4.wav"}}, {"session_id": "901", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"150000 * (1 + 0.065)**8\"}", "value": "function\ncalculate\n{\"expression\": \"150000 * (1 + 0.065)**8\"}"}] +[{"session_id": "368", "turn_idx": 0, "from": "Human", "text": "小跃，今天是几号？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-177-0.wav"}}, {"session_id": "368", "turn_idx": 1, "from": "Assistant", "text": "今天是十月十五号。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-177-1.wav"}}, {"session_id": "368", "turn_idx": 2, "from": "Human", "text": "请用更同情的语气说，我生日快到了但没人记得。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-177-2.wav"}}, {"session_id": "368", "turn_idx": 3, "from": "Assistant", "text": "唉，今天是十月十五号，生日快到了，希望有人给你惊喜。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-177-3.wav"}}, {"session_id": "368", "turn_idx": 4, "from": "Human", "text": "谢谢，现在大声点告诉我周末是几号。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-177-4.wav"}}, {"session_id": "368", "turn_idx": 5, "from": "Assistant", "text": "好的！周末是十月二十一号和二十二号！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-177-5.wav"}}] +[{"session_id": "121", "turn_idx": 0, "from": "Human", "text": "小跃，我们旅行团有三十六个人，租大巴车每天两千四百元，要租三天。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-178-0.wav"}}, {"session_id": "121", "turn_idx": 1, "from": "Assistant", "text": "好的，租车总费用是三十六人三天的费用。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-178-1.wav"}}, {"session_id": "121", "turn_idx": 2, "from": "Human", "text": "但每满一万元能返现百分之十五，这个别忘了算。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-178-2.wav"}}, {"session_id": "121", "turn_idx": 3, "from": "Assistant", "text": "返现规则会应用在总租车费上对吧？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-178-3.wav"}}, {"session_id": "121", "turn_idx": 4, "from": "Human", "text": "对，另外每人还要交一百五十元景区门票。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-178-4.wav"}}, {"session_id": "121", "turn_idx": 5, "from": "Assistant", "text": "所以需要计算车费和门票的总支出。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-178-5.wav"}}, {"session_id": "121", "turn_idx": 6, "from": "Human", "text": "最后实际人均花费多少？把返现和门票都算进去。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-178-6.wav"}}, {"session_id": "121", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((2400*3 - max(2400*3,10000)*0.15) + 36*150) / 36\"}", "value": "function\ncalculate\n{\"expression\": \"((2400*3 - max(2400*3,10000)*0.15) + 36*150) / 36\"}"}] +[{"session_id": "920", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在学做饭，但总是炒菜糊锅，怎么办？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-179-0.wav"}}, {"session_id": "920", "turn_idx": 1, "from": "Assistant", "text": "炒菜糊锅可能是因为火太大或者油不够。建议先用中火，油热了再放菜，别急着翻动。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-179-1.wav"}}, {"session_id": "920", "turn_idx": 2, "from": "Human", "text": "那你能帮我查查怎么防止炒菜糊锅的具体技巧吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-179-2.wav"}}, {"session_id": "920", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"防止 炒菜 糊锅 技巧\"}", "value": "function\nweb_search\n{\"query\": \"防止 炒菜 糊锅 技巧\"}"}] +[{"session_id": "968", "turn_idx": 0, "from": "Human", "text": "小跃，我想学吉他，但完全零基础，怎么起步？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-180-0.wav"}}, {"session_id": "968", "turn_idx": 1, "from": "Assistant", "text": "零基础学吉他，先学基本和弦和简单曲子，比如《小星星》。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-180-1.wav"}}, {"session_id": "968", "turn_idx": 2, "from": "Human", "text": "《小星星》我试过，但和弦转换太难。 那帮我找一下吉他新手和弦练习教程。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-180-2.wav"}}, {"session_id": "968", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"吉他新手 和弦练习教程\"}", "value": "function\nweb_search\n{\"query\": \"吉他新手 和弦练习教程\"}"}] +[{"session_id": "696", "turn_idx": 0, "from": "Human", "text": "小跃，我在学做PPT，但设计总是很丑，有啥技巧？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-181-0.wav"}}, {"session_id": "696", "turn_idx": 1, "from": "Assistant", "text": "PPT设计关键是简洁和视觉感。你用模板还是从头做？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-181-1.wav"}}, {"session_id": "696", "turn_idx": 2, "from": "Human", "text": "用模板，但配色和布局总不协调，看着乱糟糟。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-181-2.wav"}}, {"session_id": "696", "turn_idx": 3, "from": "Assistant", "text": "建议选单色调主题，多用图片少用文字，重点突出。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-181-3.wav"}}, {"session_id": "696", "turn_idx": 4, "from": "Human", "text": "嗯，我需要点具体例子，你能查查好的设计案例吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-181-4.wav"}}, {"session_id": "696", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"PPT设计技巧 优秀案例\"}", "value": "function\nweb_search\n{\"query\": \"PPT设计技巧 优秀案例\"}"}] +[{"session_id": "920", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-182-0.wav"}}, {"session_id": "920", "turn_idx": 1, "from": "Assistant", "text": "好啊，为什么番茄变红了？因为它看到沙拉酱害羞啦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-182-1.wav"}}, {"session_id": "920", "turn_idx": 2, "from": "Human", "text": "哈哈，还行。但语气再幽默点，加点夸张。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-182-2.wav"}}, {"session_id": "920", "turn_idx": 3, "from": "Assistant", "text": "哇塞！番茄为啥脸红？因为它撞见沙拉酱在调情，羞死啦！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-182-3.wav"}}, {"session_id": "920", "turn_idx": 4, "from": "Human", "text": "笑死我了。音量稍微小点，别太大声。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-182-4.wav"}}, {"session_id": "920", "turn_idx": 5, "from": "Assistant", "text": "好的，我小声点讲。下一个笑话:书为什么怕黑？因为会被读成恐怖故事。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-182-5.wav"}}] +[{"session_id": "882", "turn_idx": 0, "from": "Human", "text": "小跃，放点放松的音乐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-183-0.wav"}}, {"session_id": "882", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐歌单。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-183-1.wav"}}, {"session_id": "882", "turn_idx": 2, "from": "Human", "text": "小跃，大声一点，我听不清。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-183-2.wav"}}, {"session_id": "882", "turn_idx": 3, "from": "Assistant", "text": "音量已调高，现在清晰了吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-183-3.wav"}}, {"session_id": "882", "turn_idx": 4, "from": "Human", "text": "好多了。小跃，用兴奋的语气介绍下一首。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-183-4.wav"}}, {"session_id": "882", "turn_idx": 5, "from": "Assistant", "text": "接下来是超嗨的《Happy》，准备好跟着节奏摇摆吧！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-183-5.wav"}}, {"session_id": "882", "turn_idx": 6, "from": "Human", "text": "哈哈，不用了。结束音乐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-183-6.wav"}}, {"session_id": "882", "turn_idx": 7, "from": "Assistant", "text": "音乐已停，需要再叫我。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-183-7.wav"}}] +[{"session_id": "182", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-184-0.wav"}}, {"session_id": "182", "turn_idx": 1, "from": "Assistant", "text": "为什么企鹅不怕冷？因为它穿羽绒服啊。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-184-1.wav"}}, {"session_id": "182", "turn_idx": 2, "from": "Human", "text": "哈哈，这次用幽默点的语气再说一遍。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-184-2.wav"}}, {"session_id": "182", "turn_idx": 3, "from": "Assistant", "text": "嘿嘿，企鹅为啥不怕冷？自带羽绒服，酷毙啦！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-184-3.wav"}}] +[{"session_id": "103", "turn_idx": 0, "from": "Human", "text": "我想存钱买辆车，每月存八千，车价二十八万六。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-185-0.wav"}}, {"session_id": "103", "turn_idx": 1, "from": "Assistant", "text": "有目标很棒！您计划首付多少比例呀？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-185-1.wav"}}, {"session_id": "103", "turn_idx": 2, "from": "Human", "text": "准备付三成首付，剩下的贷款。但我不确定要存多久才够首付。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-185-2.wav"}}, {"session_id": "103", "turn_idx": 3, "from": "Assistant", "text": "三成首付就是总价的百分之三十，先算出首付金额。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-185-3.wav"}}, {"session_id": "103", "turn_idx": 4, "from": "Human", "text": "对对，首付金额除以每月存款，告诉我需要几个月？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-185-4.wav"}}, {"session_id": "103", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"286000 * 0.3 / 8000\"}", "value": "function\ncalculate\n{\"expression\": \"286000 * 0.3 / 8000\"}"}] +[{"session_id": "114", "turn_idx": 0, "from": "Human", "text": "今天学立方体体积公式了", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-186-0.wav"}}, {"session_id": "114", "turn_idx": 1, "from": "Assistant", "text": "是边长三次方对吧？实际要算什么呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-186-1.wav"}}, {"session_id": "114", "turn_idx": 2, "from": "Human", "text": "有个货箱长十二点五米，宽八米，高六点三米", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-186-2.wav"}}, {"session_id": "114", "turn_idx": 3, "from": "Assistant", "text": "体积就是三者相乘，需要我帮算具体数值吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-186-3.wav"}}, {"session_id": "114", "turn_idx": 4, "from": "Human", "text": "对！再乘上每立方米八十七公斤的密度算总重量", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-186-4.wav"}}, {"session_id": "114", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"12.5 * 8 * 6.3 * 87\"}", "value": "function\ncalculate\n{\"expression\": \"12.5 * 8 * 6.3 * 87\"}"}] +[{"session_id": "896", "turn_idx": 0, "from": "Human", "text": "小跃，解释下这个功能，但要简洁点，别啰嗦。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-187-0.wav"}}, {"session_id": "896", "turn_idx": 1, "from": "Assistant", "text": "好的，这个功能就是一键备份，简单安全，三秒搞定。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-187-1.wav"}}] +[{"session_id": "815", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥节日吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-188-0.wav"}}, {"session_id": "815", "turn_idx": 1, "from": "Assistant", "text": "今天是国际友谊日。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-188-1.wav"}}, {"session_id": "815", "turn_idx": 2, "from": "Human", "text": "请用更开心的语气庆祝一下。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-188-2.wav"}}, {"session_id": "815", "turn_idx": 3, "from": "Assistant", "text": "耶！友谊日快乐，快和朋友聚聚吧！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-188-3.wav"}}, {"session_id": "815", "turn_idx": 4, "from": "Human", "text": "哈哈，现在小声点告诉我祝福语。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-188-4.wav"}}, {"session_id": "815", "turn_idx": 5, "from": "Assistant", "text": "祝你拥有美好的一天，朋友多多。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-188-5.wav"}}] +[{"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_666", "turn_idx": 0, "from": "Human", "text": "小跃，我后天要去南京出差，但听说那边常下雨。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-189-0.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_666", "turn_idx": 1, "from": "Assistant", "text": "南京最近雨季，带把折叠伞方便。出差几天？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-189-1.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_666", "turn_idx": 2, "from": "Human", "text": "就两天，希望别耽误行程。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-189-2.wav"}}, {"session_id": "/mnt/gpfs/yicheng/Data/toolcall/formatted_tqta_test/toolcall_weather_eval_deepseek-r1-0528-yicheng_v1.json_666", "turn_idx": 3, "from": "Assistant", "text": "别担心，地铁很方便。会议地点在市区吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-189-3.wav"}}] +[{"session_id": "251", "turn_idx": 0, "from": "Human", "text": "今天天气怎么样？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-190-0.wav"}}, {"session_id": "251", "turn_idx": 1, "from": "Assistant", "text": "今天晴天，气温25度，适合出门。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-190-1.wav"}}, {"session_id": "251", "turn_idx": 2, "from": "Human", "text": "听起来开心点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-190-2.wav"}}, {"session_id": "251", "turn_idx": 3, "from": "Assistant", "text": "太棒了！今天阳光超好，25度，超适合出去玩哦！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-190-3.wav"}}] +[{"session_id": "731", "turn_idx": 0, "from": "Human", "text": "小跃，告诉我最近的新闻头条是什么？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-191-0.wav"}}, {"session_id": "731", "turn_idx": 1, "from": "Assistant", "text": "最近头条是科技公司发布了一款新手机，功能很强大。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-191-1.wav"}}, {"session_id": "731", "turn_idx": 2, "from": "Human", "text": "能强调一下关键点吗？比如价格和亮点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-191-2.wav"}}, {"session_id": "731", "turn_idx": 3, "from": "Assistant", "text": "当然！这款手机的最大亮点是超长续航，价格也非常亲民，只要3999元起。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-191-3.wav"}}] +[{"session_id": "765", "turn_idx": 0, "from": "Human", "text": "小跃，世界杯刚结束，你知道谁赢了吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-192-0.wav"}}, {"session_id": "765", "turn_idx": 1, "from": "Assistant", "text": "世界杯每四年一次。最近的是卡塔尔世界杯，阿根廷夺冠。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-192-1.wav"}}, {"session_id": "765", "turn_idx": 2, "from": "Human", "text": "哦，对，梅西拿了冠军。比分是多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-192-2.wav"}}, {"session_id": "765", "turn_idx": 3, "from": "Assistant", "text": "决赛阿根廷对法国，常规时间二比二，点球大战后阿根廷胜。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-192-3.wav"}}, {"session_id": "765", "turn_idx": 4, "from": "Human", "text": "具体点球比分呢？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-192-4.wav"}}, {"session_id": "765", "turn_idx": 5, "from": "Assistant", "text": "点球四比二，阿根廷赢了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-192-5.wav"}}, {"session_id": "765", "turn_idx": 6, "from": "Human", "text": "比赛日期是什么时候？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-192-6.wav"}}, {"session_id": "765", "turn_idx": 7, "from": "Assistant", "text": "决赛在二零二二年十二月十八日。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-192-7.wav"}}, {"session_id": "765", "turn_idx": 8, "from": "Human", "text": "细节我忘了。小跃，你能查一下世界杯决赛结果吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-192-8.wav"}}, {"session_id": "765", "turn_idx": 9, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"世界杯决赛结果\"}", "value": "function\nweb_search\n{\"query\": \"世界杯决赛结果\"}"}] +[{"session_id": "940", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想学做菜，有什么入门菜推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-193-0.wav"}}, {"session_id": "940", "turn_idx": 1, "from": "Assistant", "text": "当然有！番茄炒蛋就超级简单，食材少还容易上手。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-193-1.wav"}}, {"session_id": "940", "turn_idx": 2, "from": "Human", "text": "听起来不错，帮我查一下番茄炒蛋的详细步骤吧。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-193-2.wav"}}, {"session_id": "940", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法\"}"}] +[{"session_id": "443", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在健身。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-194-0.wav"}}, {"session_id": "443", "turn_idx": 1, "from": "Assistant", "text": "真棒！需要饮食搭配建议吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-194-1.wav"}}, {"session_id": "443", "turn_idx": 2, "from": "Human", "text": "对，吃什么能补充蛋白质？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-194-2.wav"}}, {"session_id": "443", "turn_idx": 3, "from": "Assistant", "text": "鸡胸肉不错，低脂高蛋白。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-194-3.wav"}}, {"session_id": "443", "turn_idx": 4, "from": "Human", "text": "鸡胸肉的蛋白质含量大概多少？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-194-4.wav"}}, {"session_id": "443", "turn_idx": 5, "from": "Assistant", "text": "每百克大约含二十多克蛋白。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-194-5.wav"}}, {"session_id": "443", "turn_idx": 6, "from": "Human", "text": "具体数字是多少克？我想精确点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-194-6.wav"}}, {"session_id": "443", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"鸡胸肉 蛋白质含量\"}", "value": "function\nweb_search\n{\"query\": \"鸡胸肉 蛋白质含量\"}"}] +[{"session_id": "589", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-195-0.wav"}}, {"session_id": "589", "turn_idx": 1, "from": "Assistant", "text": "现在是晚上八点。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-195-1.wav"}}, {"session_id": "589", "turn_idx": 2, "from": "Human", "text": "哦，有点晚了。你能用更快的语速说吗？我赶时间。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-195-2.wav"}}, {"session_id": "589", "turn_idx": 3, "from": "Assistant", "text": "现在是晚上八点！快点行动吧！", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-195-3.wav"}}] +[{"session_id": "56", "turn_idx": 0, "from": "Human", "text": "小跃，我投资理财到期了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-196-0.wav"}}, {"session_id": "56", "turn_idx": 1, "from": "Assistant", "text": "本金和年化收益率多少？存了多久？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-196-1.wav"}}, {"session_id": "56", "turn_idx": 2, "from": "Human", "text": "五十万本金，年化百分之五点三，存了三年半。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-196-2.wav"}}, {"session_id": "56", "turn_idx": 3, "from": "Assistant", "text": "按复利计算吗？需要算总收益？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-196-3.wav"}}, {"session_id": "56", "turn_idx": 4, "from": "Human", "text": "对复利，利息滚存的，帮我算现在账户总额。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-196-4.wav"}}, {"session_id": "56", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"500000*(1+0.053)**3.5\"}", "value": "function\ncalculate\n{\"expression\": \"500000*(1+0.053)**3.5\"}"}] +[{"session_id": "539", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉怎么样？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-197-0.wav"}}, {"session_id": "539", "turn_idx": 1, "from": "Assistant", "text": "挺好的，谢谢关心！你今天有什么计划吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-197-1.wav"}}, {"session_id": "539", "turn_idx": 2, "from": "Human", "text": "我在看新闻，说全球人口又增加了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-197-2.wav"}}, {"session_id": "539", "turn_idx": 3, "from": "Assistant", "text": "是啊，人口增长是个大话题。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-197-3.wav"}}, {"session_id": "539", "turn_idx": 4, "from": "Human", "text": "你知道现在全球大概多少人吗？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-197-4.wav"}}, {"session_id": "539", "turn_idx": 5, "from": "Assistant", "text": "我记得最近数据是八十亿五千万左右。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-197-5.wav"}}, {"session_id": "539", "turn_idx": 6, "from": "Human", "text": "如果每年增长百分之一点二，十年后总人口会到多少？帮我算算具体数字。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-197-6.wav"}}, {"session_id": "539", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"8050000000 * (1.012)**10\"}", "value": "function\ncalculate\n{\"expression\": \"8050000000 * (1.012)**10\"}"}] +[{"session_id": "615", "turn_idx": 0, "from": "Human", "text": "小跃，我养了只猫，听说猫的寿命平均是多少年？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-198-0.wav"}}, {"session_id": "615", "turn_idx": 1, "from": "Assistant", "text": "猫咪一般能活挺久的，但具体看品种。你养的是什么猫呀？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-198-1.wav"}}, {"session_id": "615", "turn_idx": 2, "from": "Human", "text": "是只橘猫。我想知道猫的平均寿命是多少年？", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-198-2.wav"}}, {"session_id": "615", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"猫 平均寿命\"}", "value": "function\nweb_search\n{\"query\": \"猫 平均寿命\"}"}] +[{"session_id": "546", "turn_idx": 0, "from": "Human", "text": "小跃，放点轻松的音乐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-199-0.wav"}}, {"session_id": "546", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-199-1.wav"}}, {"session_id": "546", "turn_idx": 2, "from": "Human", "text": "音量调小一点，太响了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-199-2.wav"}}, {"session_id": "546", "turn_idx": 3, "from": "Assistant", "text": "音量调小了，现在柔和多了。", "value": {"type": "wav", "value": "wav/get_weather_negative.jsonl-199-3.wav"}}] diff --git a/tools/StepEval-Audio-Toolcall/get_weather_positive.jsonl b/tools/StepEval-Audio-Toolcall/get_weather_positive.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1227d81af6e2d36b6a05c07a1f355e0da2c43d7b --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/get_weather_positive.jsonl @@ -0,0 +1,200 @@ +[{"session_id": "704", "turn_idx": 0, "from": "Human", "text": "小跃，我打算今天下午去成都的公园散步，但感觉有点阴天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-0-0.wav"}}, {"session_id": "704", "turn_idx": 1, "from": "Assistant", "text": "公园散步很惬意！先确认天气吧，别被雨淋到了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-0-1.wav"}}, {"session_id": "704", "turn_idx": 2, "from": "Human", "text": "嗯，帮我查查成都今天明天和后天的天气预报。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-0-2.wav"}}, {"session_id": "704", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "793", "turn_idx": 0, "from": "Human", "text": "小跃，深圳是不是科技公司特别多？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-1-0.wav"}}, {"session_id": "793", "turn_idx": 1, "from": "Assistant", "text": "没错，深圳是科技中心，华强北那边很热闹。你感兴趣？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-1-1.wav"}}, {"session_id": "793", "turn_idx": 2, "from": "Human", "text": "对，下周可能去深圳面试工作。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-1-2.wav"}}, {"session_id": "793", "turn_idx": 3, "from": "Assistant", "text": "祝你好运！需要我帮忙准备什么？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-1-3.wav"}}, {"session_id": "793", "turn_idx": 4, "from": "Human", "text": "小跃，查一下深圳未来几天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-1-4.wav"}}, {"session_id": "793", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "123", "turn_idx": 0, "from": "Human", "text": "小跃，明天公司有户外活动在深圳，我担心会晒着。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-2-0.wav"}}, {"session_id": "123", "turn_idx": 1, "from": "Assistant", "text": "带防晒霜和帽子就行。深圳最近阳光强，查查天气更稳妥。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-2-1.wav"}}, {"session_id": "123", "turn_idx": 2, "from": "Human", "text": "好主意，快帮我查查深圳的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-2-2.wav"}}, {"session_id": "123", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "166", "turn_idx": 0, "from": "Human", "text": "小跃，后天朋友约我在东京见面，我们打算户外野餐。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-3-0.wav"}}, {"session_id": "166", "turn_idx": 1, "from": "Assistant", "text": "野餐看天气很重要。东京春天容易下雨，查查预报再定时间吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-3-1.wav"}}, {"session_id": "166", "turn_idx": 2, "from": "Human", "text": "好的，帮我查一下东京的天气，就今天开始的三天内。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-3-2.wav"}}, {"session_id": "166", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"tokyo\"}", "value": "function\nget_weather\n{\"location\": \"tokyo\"}"}] +[{"session_id": "129", "turn_idx": 0, "from": "Human", "text": "小跃，明天在苏州游玩，我想划船。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-4-0.wav"}}, {"session_id": "129", "turn_idx": 1, "from": "Assistant", "text": "划船挺浪漫的，但得看天气。苏州常下雨，查查预报保险点。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-4-1.wav"}}, {"session_id": "129", "turn_idx": 2, "from": "Human", "text": "对，快帮我查查苏州的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-4-2.wav"}}, {"session_id": "129", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"suzhou\"}", "value": "function\nget_weather\n{\"location\": \"suzhou\"}"}] +[{"session_id": "616", "turn_idx": 0, "from": "Human", "text": "小跃，今天工作累坏了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-5-0.wav"}}, {"session_id": "616", "turn_idx": 1, "from": "Assistant", "text": "休息一下吧。晚上想做什么？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-5-1.wav"}}, {"session_id": "616", "turn_idx": 2, "from": "Human", "text": "可能去公园散步。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-5-2.wav"}}, {"session_id": "616", "turn_idx": 3, "from": "Assistant", "text": "散步放松好。杭州天气适合吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-5-3.wav"}}, {"session_id": "616", "turn_idx": 4, "from": "Human", "text": "对，查查杭州的天气。 帮我查杭州今天和明天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-5-4.wav"}}, {"session_id": "616", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "908", "turn_idx": 0, "from": "Human", "text": "小跃，后天我全家在悉尼团聚，好期待！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-6-0.wav"}}, {"session_id": "908", "turn_idx": 1, "from": "Assistant", "text": "家庭时光最温暖了！聚会地点定好了？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-6-1.wav"}}, {"session_id": "908", "turn_idx": 2, "from": "Human", "text": "在歌剧院旁边的餐厅，风景超美。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-6-2.wav"}}, {"session_id": "908", "turn_idx": 3, "from": "Assistant", "text": "记得拍照留念，选靠窗位置。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-6-3.wav"}}, {"session_id": "908", "turn_idx": 4, "from": "Human", "text": "好主意，不过天气不好就扫兴了。 查一下悉尼未来三天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-6-4.wav"}}, {"session_id": "908", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"sydney\"}", "value": "function\nget_weather\n{\"location\": \"sydney\"}"}] +[{"session_id": "923", "turn_idx": 0, "from": "Human", "text": "小跃，我感觉外面好闷热啊。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-7-0.wav"}}, {"session_id": "923", "turn_idx": 1, "from": "Assistant", "text": "是啊，夏天就是这样。你打算出门吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-7-1.wav"}}, {"session_id": "923", "turn_idx": 2, "from": "Human", "text": "对，我想去超市买东西，怕晒伤。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-7-2.wav"}}, {"session_id": "923", "turn_idx": 3, "from": "Assistant", "text": "那查查本地天气，看看要不要带伞？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-7-3.wav"}}, {"session_id": "923", "turn_idx": 4, "from": "Human", "text": "好，帮我查一下深圳的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-7-4.wav"}}, {"session_id": "923", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "128", "turn_idx": 0, "from": "Human", "text": "小跃，我计划去西安参观兵马俑，后天出发。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-8-0.wav"}}, {"session_id": "128", "turn_idx": 1, "from": "Assistant", "text": "兵马俑很壮观！西安天气干燥，查查预报别错过好日子。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-8-1.wav"}}, {"session_id": "128", "turn_idx": 2, "from": "Human", "text": "好的，那你查一下西安的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-8-2.wav"}}, {"session_id": "128", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "937", "turn_idx": 0, "from": "Human", "text": "小跃，今天下午武汉有场球赛，我想去看。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-9-0.wav"}}, {"session_id": "937", "turn_idx": 1, "from": "Assistant", "text": "球赛气氛肯定嗨！建议你早点去占座，带点零食。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-9-1.wav"}}, {"session_id": "937", "turn_idx": 2, "from": "Human", "text": "好主意，但得看天气允不允许。 帮我查一下武汉的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-9-2.wav"}}, {"session_id": "937", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "887", "turn_idx": 0, "from": "Human", "text": "小跃，后天是周末，想去西安旅游。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-10-0.wav"}}, {"session_id": "887", "turn_idx": 1, "from": "Assistant", "text": "西安古迹多！天气好才能玩得尽兴。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-10-1.wav"}}, {"session_id": "887", "turn_idx": 2, "from": "Human", "text": "是啊，爬城墙啥的，别碰上大雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-10-2.wav"}}, {"session_id": "887", "turn_idx": 3, "from": "Assistant", "text": "雨伞随身带，古城路滑要小心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-10-3.wav"}}, {"session_id": "887", "turn_idx": 4, "from": "Human", "text": "好的，小跃，帮我查一下西安的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-10-4.wav"}}, {"session_id": "887", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "328", "turn_idx": 0, "from": "Human", "text": "小跃，今天心情不错，想去武汉逛逛。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-11-0.wav"}}, {"session_id": "328", "turn_idx": 1, "from": "Assistant", "text": "那很好啊！武汉有很多好吃的。需要知道天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-11-1.wav"}}, {"session_id": "328", "turn_idx": 2, "from": "Human", "text": "对，查一下武汉的天气，包括今天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-11-2.wav"}}, {"session_id": "328", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "747", "turn_idx": 0, "from": "Human", "text": "小跃，后天朋友来武汉玩，我当导游。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-12-0.wav"}}, {"session_id": "747", "turn_idx": 1, "from": "Assistant", "text": "那得规划路线，天气影响大，比如去东湖。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-12-1.wav"}}, {"session_id": "747", "turn_idx": 2, "from": "Human", "text": "对，希望明天别太热，不然逛不动。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-12-2.wav"}}, {"session_id": "747", "turn_idx": 3, "from": "Assistant", "text": "武汉夏天闷热，查查温度湿度。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-12-3.wav"}}, {"session_id": "747", "turn_idx": 4, "from": "Human", "text": "帮我查武汉包括今天的天气预报吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-12-4.wav"}}, {"session_id": "747", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "931", "turn_idx": 0, "from": "Human", "text": "小跃，明天我和朋友约了去上海逛街，好期待！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-13-0.wav"}}, {"session_id": "931", "turn_idx": 1, "from": "Assistant", "text": "听起来很有趣！上海购物中心很多，记得穿舒适的鞋子。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-13-1.wav"}}, {"session_id": "931", "turn_idx": 2, "from": "Human", "text": "是呀，但我不确定带不带伞，怕突然下雨。 帮我查查上海的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-13-2.wav"}}, {"session_id": "931", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "363", "turn_idx": 0, "from": "Human", "text": "嗨，小跃！周末计划去杭州西湖玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-14-0.wav"}}, {"session_id": "363", "turn_idx": 1, "from": "Assistant", "text": "西湖超美的！需要建议或帮忙吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-14-1.wav"}}, {"session_id": "363", "turn_idx": 2, "from": "Human", "text": "是啊，先查查杭州的天气，别赶上下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-14-2.wav"}}, {"session_id": "363", "turn_idx": 3, "from": "Assistant", "text": "好的，杭州的天气我来查。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-14-3.wav"}}, {"session_id": "363", "turn_idx": 4, "from": "Human", "text": "请查查今天和明天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-14-4.wav"}}, {"session_id": "363", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "726", "turn_idx": 0, "from": "Human", "text": "小跃，我朋友从南京来玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-15-0.wav"}}, {"session_id": "726", "turn_idx": 1, "from": "Assistant", "text": "欢迎他！需要推荐景点吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-15-1.wav"}}, {"session_id": "726", "turn_idx": 2, "from": "Human", "text": "好啊，但怕天气不好影响玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-15-2.wav"}}, {"session_id": "726", "turn_idx": 3, "from": "Assistant", "text": "确实，天气很重要。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-15-3.wav"}}, {"session_id": "726", "turn_idx": 4, "from": "Human", "text": "小跃，查查南京这三天的天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-15-4.wav"}}, {"session_id": "726", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "136", "turn_idx": 0, "from": "Human", "text": "小跃，我今天心情不太好。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-16-0.wav"}}, {"session_id": "136", "turn_idx": 1, "from": "Assistant", "text": "别难过，出去走走可能会好点。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-16-1.wav"}}, {"session_id": "136", "turn_idx": 2, "from": "Human", "text": "嗯，我在南京，想晚上去江边散步。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-16-2.wav"}}, {"session_id": "136", "turn_idx": 3, "from": "Assistant", "text": "江边散步很舒服，但先确保天气没问题。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-16-3.wav"}}, {"session_id": "136", "turn_idx": 4, "from": "Human", "text": "那帮我查一下南京的天气吧，小跃。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-16-4.wav"}}, {"session_id": "136", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "252", "turn_idx": 0, "from": "Human", "text": "小跃，周末我家人要来广州玩，我得安排活动。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-17-0.wav"}}, {"session_id": "252", "turn_idx": 1, "from": "Assistant", "text": "家庭聚会真棒！广州景点很多，天气好的话可以去户外。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-17-1.wav"}}, {"session_id": "252", "turn_idx": 2, "from": "Human", "text": "是啊，但最近总下雨，我怕扫兴。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-17-2.wav"}}, {"session_id": "252", "turn_idx": 3, "from": "Assistant", "text": "提前查天气最保险，避免临时出问题。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-17-3.wav"}}, {"session_id": "252", "turn_idx": 4, "from": "Human", "text": "有道理，帮我查查广州今天到后天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-17-4.wav"}}, {"session_id": "252", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "934", "turn_idx": 0, "from": "Human", "text": "小跃，我计划周末去成都吃火锅，馋死了！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-18-0.wav"}}, {"session_id": "934", "turn_idx": 1, "from": "Assistant", "text": "哈哈，成都美食确实出名。提醒你，最好提前订位子。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-18-1.wav"}}, {"session_id": "934", "turn_idx": 2, "from": "Human", "text": "好的，不过我得看看天气，免得路上出问题。 那查一下成都的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-18-2.wav"}}, {"session_id": "934", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "913", "turn_idx": 0, "from": "Human", "text": "小跃，后天我要去深圳开会，得准备行李。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-19-0.wav"}}, {"session_id": "913", "turn_idx": 1, "from": "Assistant", "text": "深圳是快节奏城市，带上轻便衣服可能合适。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-19-1.wav"}}, {"session_id": "913", "turn_idx": 2, "from": "Human", "text": "好建议！查一下深圳包括今天的三天天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-19-2.wav"}}, {"session_id": "913", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "696", "turn_idx": 0, "from": "Human", "text": "小跃，我和家人计划后天去厦门度假，海边放松一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-20-0.wav"}}, {"session_id": "696", "turn_idx": 1, "from": "Assistant", "text": "厦门很棒，鼓浪屿的海风超舒服。需要酒店推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-20-1.wav"}}, {"session_id": "696", "turn_idx": 2, "from": "Human", "text": "不用，订好了。但天气预报说可能有台风，我有点担心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-20-2.wav"}}, {"session_id": "696", "turn_idx": 3, "from": "Assistant", "text": "台风确实麻烦。提前确认天气，安全第一。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-20-3.wav"}}, {"session_id": "696", "turn_idx": 4, "from": "Human", "text": "是啊，小跃，快帮我查查厦门的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-20-4.wav"}}, {"session_id": "696", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xiamen\"}", "value": "function\nget_weather\n{\"location\": \"xiamen\"}"}] +[{"session_id": "757", "turn_idx": 0, "from": "Human", "text": "小跃，今天下午打算去巴黎逛街。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-21-0.wav"}}, {"session_id": "757", "turn_idx": 1, "from": "Assistant", "text": "巴黎街景超浪漫，逛哪条街呀？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-21-1.wav"}}, {"session_id": "757", "turn_idx": 2, "from": "Human", "text": "香榭丽舍大道吧，帮我看看巴黎今天天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-21-2.wav"}}, {"session_id": "757", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"paris\"}", "value": "function\nget_weather\n{\"location\": \"paris\"}"}] +[{"session_id": "401", "turn_idx": 0, "from": "Human", "text": "小跃，我后天要去广州出差，有个重要会议。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-22-0.wav"}}, {"session_id": "401", "turn_idx": 1, "from": "Assistant", "text": "广州的美食很出名，出差顺利吗？需要提前准备什么？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-22-1.wav"}}, {"session_id": "401", "turn_idx": 2, "from": "Human", "text": "是啊，得带合适的衣服，就怕下雨影响出行。 小跃，查一下广州这三天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-22-2.wav"}}, {"session_id": "401", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "416", "turn_idx": 0, "from": "Human", "text": "小跃，我明天要去南京见客户，担心气温变化大。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-23-0.wav"}}, {"session_id": "416", "turn_idx": 1, "from": "Assistant", "text": "南京早晚温差不小，多穿点衣服吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-23-1.wav"}}, {"session_id": "416", "turn_idx": 2, "from": "Human", "text": "谢谢提醒，帮我查查南京的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-23-2.wav"}}, {"session_id": "416", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "139", "turn_idx": 0, "from": "Human", "text": "小跃，我朋友从外地来玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-24-0.wav"}}, {"session_id": "139", "turn_idx": 1, "from": "Assistant", "text": "太好了！准备带他们去哪里？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-24-1.wav"}}, {"session_id": "139", "turn_idx": 2, "from": "Human", "text": "想在西安逛逛名胜古迹。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-24-2.wav"}}, {"session_id": "139", "turn_idx": 3, "from": "Assistant", "text": "西安景点多，但天气要配合。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-24-3.wav"}}, {"session_id": "139", "turn_idx": 4, "from": "Human", "text": "对，小跃，帮我查查西安的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-24-4.wav"}}, {"session_id": "139", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "816", "turn_idx": 0, "from": "Human", "text": "小跃，我计划明天去成都玩两天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-25-0.wav"}}, {"session_id": "816", "turn_idx": 1, "from": "Assistant", "text": "成都美食多！行程安排好了？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-25-1.wav"}}, {"session_id": "816", "turn_idx": 2, "from": "Human", "text": "基本定了，但天气不确定。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-25-2.wav"}}, {"session_id": "816", "turn_idx": 3, "from": "Assistant", "text": "旅行时天气影响心情，得关注一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-25-3.wav"}}, {"session_id": "816", "turn_idx": 4, "from": "Human", "text": "你说得对，帮我看看预报。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-25-4.wav"}}, {"session_id": "816", "turn_idx": 5, "from": "Assistant", "text": "没问题，随时准备。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-25-5.wav"}}, {"session_id": "816", "turn_idx": 6, "from": "Human", "text": "小跃，查询成都未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-25-6.wav"}}, {"session_id": "816", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "790", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天过得怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-26-0.wav"}}, {"session_id": "790", "turn_idx": 1, "from": "Assistant", "text": "我挺好的，谢谢关心！你呢，有什么新鲜事？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-26-1.wav"}}, {"session_id": "790", "turn_idx": 2, "from": "Human", "text": "还不错，我在计划周末去北京逛逛。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-26-2.wav"}}, {"session_id": "790", "turn_idx": 3, "from": "Assistant", "text": "北京好玩的地方可多了，故宫和长城都值得一去。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-26-3.wav"}}, {"session_id": "790", "turn_idx": 4, "from": "Human", "text": "小跃，帮我查一下北京的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-26-4.wav"}}, {"session_id": "790", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "190", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天感觉阳光不错。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-27-0.wav"}}, {"session_id": "190", "turn_idx": 1, "from": "Assistant", "text": "早上好！是啊，适合出门走走。你有什么安排吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-27-1.wav"}}, {"session_id": "190", "turn_idx": 2, "from": "Human", "text": "我想查一下北京的天气，今天到后天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-27-2.wav"}}, {"session_id": "190", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "711", "turn_idx": 0, "from": "Human", "text": "小跃，我明天要去上海出差。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-28-0.wav"}}, {"session_id": "711", "turn_idx": 1, "from": "Assistant", "text": "好的，出差要注意安全。需要我帮你安排行程吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-28-1.wav"}}, {"session_id": "711", "turn_idx": 2, "from": "Human", "text": "不用了，我就想知道那边的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-28-2.wav"}}, {"session_id": "711", "turn_idx": 3, "from": "Assistant", "text": "没问题，告诉我具体时间。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-28-3.wav"}}, {"session_id": "711", "turn_idx": 4, "from": "Human", "text": "查一下上海今天、明天和后天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-28-4.wav"}}, {"session_id": "711", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "147", "turn_idx": 0, "from": "Human", "text": "小跃，我明天在武汉有场马拉松比赛，有点担心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-29-0.wav"}}, {"session_id": "147", "turn_idx": 1, "from": "Assistant", "text": "加油！跑前热身，多喝水。我帮你计时？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-29-1.wav"}}, {"session_id": "147", "turn_idx": 2, "from": "Human", "text": "谢谢！先帮我查查武汉后天的天气，看恢复日是否适合休息。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-29-2.wav"}}, {"session_id": "147", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "501", "turn_idx": 0, "from": "Human", "text": "小跃，最近工作好忙，想出去散散心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-30-0.wav"}}, {"session_id": "501", "turn_idx": 1, "from": "Assistant", "text": "辛苦了！休息一下对身体好，你想去哪里？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-30-1.wav"}}, {"session_id": "501", "turn_idx": 2, "from": "Human", "text": "我朋友推荐杭州，说西湖很美。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-30-2.wav"}}, {"session_id": "501", "turn_idx": 3, "from": "Assistant", "text": "杭州确实漂亮，适合放松。你计划哪天去？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-30-3.wav"}}, {"session_id": "501", "turn_idx": 4, "from": "Human", "text": "可能后天出发，但担心天气不好影响行程。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-30-4.wav"}}, {"session_id": "501", "turn_idx": 5, "from": "Assistant", "text": "提前了解天气是明智的。需要我帮你查吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-30-5.wav"}}, {"session_id": "501", "turn_idx": 6, "from": "Human", "text": "对，小跃，帮我查一下杭州包括今天三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-30-6.wav"}}, {"session_id": "501", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "158", "turn_idx": 0, "from": "Human", "text": "小跃，后天约了朋友在纽约中央公园野餐。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-31-0.wav"}}, {"session_id": "158", "turn_idx": 1, "from": "Assistant", "text": "野餐最怕下雨，纽约天气得盯紧点。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-31-1.wav"}}, {"session_id": "158", "turn_idx": 2, "from": "Human", "text": "对，快查查纽约今天明天和后天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-31-2.wav"}}, {"session_id": "158", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"new york\"}", "value": "function\nget_weather\n{\"location\": \"new york\"}"}] +[{"session_id": "329", "turn_idx": 0, "from": "Human", "text": "小跃，后天有个户外活动在重庆。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-32-0.wav"}}, {"session_id": "329", "turn_idx": 1, "from": "Assistant", "text": "听起来有趣！需要准备什么吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-32-1.wav"}}, {"session_id": "329", "turn_idx": 2, "from": "Human", "text": "帮我查重庆的天气，看看热不热。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-32-2.wav"}}, {"session_id": "329", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "40", "turn_idx": 0, "from": "Human", "text": "小跃早呀，今天感觉好闷热", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-33-0.wav"}}, {"session_id": "40", "turn_idx": 1, "from": "Assistant", "text": "是呀，这天气像蒸桑拿似的！您要出门的话记得带小风扇。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-33-1.wav"}}, {"session_id": "40", "turn_idx": 2, "from": "Human", "text": "哈哈你提醒得对，下午得去西湖边逛逛", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-33-2.wav"}}, {"session_id": "40", "turn_idx": 3, "from": "Assistant", "text": "西湖现在荷花正美呢，不过记得看看天气，别赶上暴雨啦", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-33-3.wav"}}, {"session_id": "40", "turn_idx": 4, "from": "Human", "text": "有道理，帮我查查杭州今天到后天的天气呗", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-33-4.wav"}}, {"session_id": "40", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "974", "turn_idx": 0, "from": "Human", "text": "小跃，我馋成都火锅了，想去吃一顿。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-34-0.wav"}}, {"session_id": "974", "turn_idx": 1, "from": "Assistant", "text": "成都火锅真香！主人，您计划什么时候去？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-34-1.wav"}}, {"session_id": "974", "turn_idx": 2, "from": "Human", "text": "可能后天吧，得看天气好不好。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-34-2.wav"}}, {"session_id": "974", "turn_idx": 3, "from": "Assistant", "text": "好的，我帮您留意一下。查几天？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-34-3.wav"}}, {"session_id": "974", "turn_idx": 4, "from": "Human", "text": "查查今天、明天和后天的成都天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-34-4.wav"}}, {"session_id": "974", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "718", "turn_idx": 0, "from": "Human", "text": "嗨小跃，我和家人后天去天津玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-35-0.wav"}}, {"session_id": "718", "turn_idx": 1, "from": "Assistant", "text": "家庭出游真开心。计划去哪里？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-35-1.wav"}}, {"session_id": "718", "turn_idx": 2, "from": "Human", "text": "想去天津之眼。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-35-2.wav"}}, {"session_id": "718", "turn_idx": 3, "from": "Assistant", "text": "摩天轮啊，浪漫。需要天气信息吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-35-3.wav"}}, {"session_id": "718", "turn_idx": 4, "from": "Human", "text": "是的，查一下天津的天气，包括今天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-35-4.wav"}}, {"session_id": "718", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"tianjin\"}", "value": "function\nget_weather\n{\"location\": \"tianjin\"}"}] +[{"session_id": "492", "turn_idx": 0, "from": "Human", "text": "早啊小跃，今天感觉有点凉飕飕的。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-36-0.wav"}}, {"session_id": "492", "turn_idx": 1, "from": "Assistant", "text": "是呀，秋天来了嘛，多穿点衣服哦。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-36-1.wav"}}, {"session_id": "492", "turn_idx": 2, "from": "Human", "text": "好主意，明天我约了朋友去广州喝早茶。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-36-2.wav"}}, {"session_id": "492", "turn_idx": 3, "from": "Assistant", "text": "广州的点心超赞，享受美食吧！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-36-3.wav"}}, {"session_id": "492", "turn_idx": 4, "from": "Human", "text": "谢啦，小跃，查查广州的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-36-4.wav"}}, {"session_id": "492", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "699", "turn_idx": 0, "from": "Human", "text": "小跃，我下周要去苏州看园林，放松心情。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-37-0.wav"}}, {"session_id": "699", "turn_idx": 1, "from": "Assistant", "text": "苏州园林超美，拙政园必去。需要路线规划吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-37-1.wav"}}, {"session_id": "699", "turn_idx": 2, "from": "Human", "text": "不用，我查过地图了。但天气热的话，走路会很累。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-37-2.wav"}}, {"session_id": "699", "turn_idx": 3, "from": "Assistant", "text": "是啊，苏州夏天挺晒的。带好防晒用品。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-37-3.wav"}}, {"session_id": "699", "turn_idx": 4, "from": "Human", "text": "好，那先看看天气。小跃，帮我查查苏州这三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-37-4.wav"}}, {"session_id": "699", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"suzhou\"}", "value": "function\nget_weather\n{\"location\": \"suzhou\"}"}] +[{"session_id": "871", "turn_idx": 0, "from": "Human", "text": "小跃，后天我妈要从广州飞来看我", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-38-0.wav"}}, {"session_id": "871", "turn_idx": 1, "from": "Assistant", "text": "真棒！需要提醒阿姨带什么衣服吗？比如查查广州的天气？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-38-1.wav"}}, {"session_id": "871", "turn_idx": 2, "from": "Human", "text": "帮我查下广州后天温度怎么样", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-38-2.wav"}}, {"session_id": "871", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "554", "turn_idx": 0, "from": "Human", "text": "小跃，明天骑车上班，但听说要变天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-39-0.wav"}}, {"session_id": "554", "turn_idx": 1, "from": "Assistant", "text": "骑车注意安全。公司位置在哪儿？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-39-1.wav"}}, {"session_id": "554", "turn_idx": 2, "from": "Human", "text": "在朝阳区的写字楼，离我家不远。 那查一下明天朝阳区的天气，看带雨衣不。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-39-2.wav"}}, {"session_id": "554", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chaoyang district\"}", "value": "function\nget_weather\n{\"location\": \"chaoyang district\"}"}] +[{"session_id": "370", "turn_idx": 0, "from": "Human", "text": "小跃，早上好啊！今天感觉好困，想出去走走。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-40-0.wav"}}, {"session_id": "370", "turn_idx": 1, "from": "Assistant", "text": "早上好！困的话可以喝杯咖啡，或者去附近公园呼吸新鲜空气，能提神哦。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-40-1.wav"}}, {"session_id": "370", "turn_idx": 2, "from": "Human", "text": "公园是个好主意，不过我周末要去上海出差，得提前准备行李。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-40-2.wav"}}, {"session_id": "370", "turn_idx": 3, "from": "Assistant", "text": "出差啊，上海现在挺热闹的。记得带些轻便衣服，天气可能变化快。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-40-3.wav"}}, {"session_id": "370", "turn_idx": 4, "from": "Human", "text": "对啊，天气真重要。小跃，帮我查查上海的天气吧，看看未来三天怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-40-4.wav"}}, {"session_id": "370", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "439", "turn_idx": 0, "from": "Human", "text": "小跃，刚看完电影，心情不错。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-41-0.wav"}}, {"session_id": "439", "turn_idx": 1, "from": "Assistant", "text": "电影好看吗？聊点你的小计划？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-41-1.wav"}}, {"session_id": "439", "turn_idx": 2, "from": "Human", "text": "还行。周末要去苏州玩，第一次去。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-41-2.wav"}}, {"session_id": "439", "turn_idx": 3, "from": "Assistant", "text": "苏州园林很经典！出行前查天气是好习惯。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-41-3.wav"}}, {"session_id": "439", "turn_idx": 4, "from": "Human", "text": "对，小跃，查一下苏州未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-41-4.wav"}}, {"session_id": "439", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"suzhou\"}", "value": "function\nget_weather\n{\"location\": \"suzhou\"}"}] +[{"session_id": "506", "turn_idx": 0, "from": "Human", "text": "小跃，我爸妈从武汉来玩，得安排活动。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-42-0.wav"}}, {"session_id": "506", "turn_idx": 1, "from": "Assistant", "text": "家人团聚真好！武汉来的话，带他们逛逛本地景点？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-42-1.wav"}}, {"session_id": "506", "turn_idx": 2, "from": "Human", "text": "嗯，但担心天气太冷或下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-42-2.wav"}}, {"session_id": "506", "turn_idx": 3, "from": "Assistant", "text": "确实，天气会影响出行心情。哪天到？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-42-3.wav"}}, {"session_id": "506", "turn_idx": 4, "from": "Human", "text": "大后天到，想提前知道预报。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-42-4.wav"}}, {"session_id": "506", "turn_idx": 5, "from": "Assistant", "text": "需要我帮你查武汉的天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-42-5.wav"}}, {"session_id": "506", "turn_idx": 6, "from": "Human", "text": "对，小跃，快查武汉的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-42-6.wav"}}, {"session_id": "506", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "759", "turn_idx": 0, "from": "Human", "text": "小跃，后天我约了朋友在洛杉矶爬山。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-43-0.wav"}}, {"session_id": "759", "turn_idx": 1, "from": "Assistant", "text": "爬山注意安全，带足水哦。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-43-1.wav"}}, {"session_id": "759", "turn_idx": 2, "from": "Human", "text": "嗯，但得先查查洛杉矶后天天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-43-2.wav"}}, {"session_id": "759", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"los angeles\"}", "value": "function\nget_weather\n{\"location\": \"los angeles\"}"}] +[{"session_id": "334", "turn_idx": 0, "from": "Human", "text": "你好小跃，我下周要去成都出差，第一次去呢。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-44-0.wav"}}, {"session_id": "334", "turn_idx": 1, "from": "Assistant", "text": "成都的美食很出名，火锅一定要试试。工作顺利！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-44-1.wav"}}, {"session_id": "334", "turn_idx": 2, "from": "Human", "text": "哈哈，我会的。不过先查查成都从今天到后天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-44-2.wav"}}, {"session_id": "334", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "490", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天过得怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-45-0.wav"}}, {"session_id": "490", "turn_idx": 1, "from": "Assistant", "text": "还不错呀，谢谢关心！你呢，有啥新鲜事？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-45-1.wav"}}, {"session_id": "490", "turn_idx": 2, "from": "Human", "text": "我还行，就是明天要去北京开会，有点小紧张。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-45-2.wav"}}, {"session_id": "490", "turn_idx": 3, "from": "Assistant", "text": "别担心，北京挺热闹的，准备好材料就行。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-45-3.wav"}}, {"session_id": "490", "turn_idx": 4, "from": "Human", "text": "好嘞，对了小跃，帮我查查北京的天气呗。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-45-4.wav"}}, {"session_id": "490", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "498", "turn_idx": 0, "from": "Human", "text": "小跃，西安的兵马俑值得看吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-46-0.wav"}}, {"session_id": "498", "turn_idx": 1, "from": "Assistant", "text": "当然值，世界奇迹之一，震撼得很。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-46-1.wav"}}, {"session_id": "498", "turn_idx": 2, "from": "Human", "text": "好，明天我就飞过去瞧瞧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-46-2.wav"}}, {"session_id": "498", "turn_idx": 3, "from": "Assistant", "text": "旅途愉快，多拍点照片。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-46-3.wav"}}, {"session_id": "498", "turn_idx": 4, "from": "Human", "text": "嗯嗯，小跃，查一下西安的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-46-4.wav"}}, {"session_id": "498", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "954", "turn_idx": 0, "from": "Human", "text": "小跃，我后天生日派对在深圳举办。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-47-0.wav"}}, {"session_id": "954", "turn_idx": 1, "from": "Assistant", "text": "生日快乐提前！派对地点选好了？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-47-1.wav"}}, {"session_id": "954", "turn_idx": 2, "from": "Human", "text": "在海岸城购物中心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-47-2.wav"}}, {"session_id": "954", "turn_idx": 3, "from": "Assistant", "text": "海岸城很繁华。天气咋样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-47-3.wav"}}, {"session_id": "954", "turn_idx": 4, "from": "Human", "text": "帮我查查深圳未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-47-4.wav"}}, {"session_id": "954", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "172", "turn_idx": 0, "from": "Human", "text": "嗨小跃，我刚搬到广州，感觉好热啊。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-48-0.wav"}}, {"session_id": "172", "turn_idx": 1, "from": "Assistant", "text": "广州夏天是挺热的！适应了吗？需要我推荐些凉快的地方吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-48-1.wav"}}, {"session_id": "172", "turn_idx": 2, "from": "Human", "text": "还没完全适应。明天朋友来玩，想带他们去珠江边。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-48-2.wav"}}, {"session_id": "172", "turn_idx": 3, "from": "Assistant", "text": "好主意！珠江夜景很美。但记得带伞，广州雨多。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-48-3.wav"}}, {"session_id": "172", "turn_idx": 4, "from": "Human", "text": "谢谢提醒。对了，帮我查查广州今天的天气，看看会不会下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-48-4.wav"}}, {"session_id": "172", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "78", "turn_idx": 0, "from": "Human", "text": "武汉渡轮还开着吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-49-0.wav"}}, {"session_id": "78", "turn_idx": 1, "from": "Assistant", "text": "长江航线受能见度影响大，最近雾天多吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-49-1.wav"}}, {"session_id": "78", "turn_idx": 2, "from": "Human", "text": "不清楚呀，你查查看", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-49-2.wav"}}, {"session_id": "78", "turn_idx": 3, "from": "Assistant", "text": "正在调取数据，需要包含今天的三天预报？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-49-3.wav"}}, {"session_id": "78", "turn_idx": 4, "from": "Human", "text": "查！重点看江面能见度", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-49-4.wav"}}, {"session_id": "78", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "542", "turn_idx": 0, "from": "Human", "text": "小跃，我爸妈周末要来我家，得准备一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-50-0.wav"}}, {"session_id": "542", "turn_idx": 1, "from": "Assistant", "text": "真温馨！准备做点好吃的招待他们？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-50-1.wav"}}, {"session_id": "542", "turn_idx": 2, "from": "Human", "text": "对啊，想带他们去小区花园散步。但怕下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-50-2.wav"}}, {"session_id": "542", "turn_idx": 3, "from": "Assistant", "text": "散步是好主意，但天气得看准。提前查查预报？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-50-3.wav"}}, {"session_id": "542", "turn_idx": 4, "from": "Human", "text": "嗯，查一下我们小区花园的天气，就这三天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-50-4.wav"}}, {"session_id": "542", "turn_idx": 5, "from": "Assistant", "text": "没问题，主人。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-50-5.wav"}}, {"session_id": "542", "turn_idx": 6, "from": "Human", "text": "小跃，赶紧帮我查查后天花园的详细天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-50-6.wav"}}, {"session_id": "542", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"community garden\"}", "value": "function\nget_weather\n{\"location\": \"community garden\"}"}] +[{"session_id": "732", "turn_idx": 0, "from": "Human", "text": "小跃，最近忙什么呢？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-51-0.wav"}}, {"session_id": "732", "turn_idx": 1, "from": "Assistant", "text": "我在处理一些数据，你呢？有什么新鲜事？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-51-1.wav"}}, {"session_id": "732", "turn_idx": 2, "from": "Human", "text": "周末家人要来广州聚会，我得准备一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-51-2.wav"}}, {"session_id": "732", "turn_idx": 3, "from": "Assistant", "text": "家庭聚会真温馨！广州美食多，天气应该暖和吧？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-51-3.wav"}}, {"session_id": "732", "turn_idx": 4, "from": "Human", "text": "希望如此，但最近老下雨。 小跃，查一下广州未来三天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-51-4.wav"}}, {"session_id": "732", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "41", "turn_idx": 0, "from": "Human", "text": "小跃，我明天要去广州出差", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-52-0.wav"}}, {"session_id": "41", "turn_idx": 1, "from": "Assistant", "text": "广州最近温差大，行李箱里得塞件薄外套", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-52-1.wav"}}, {"session_id": "41", "turn_idx": 2, "from": "Human", "text": "对哦，那边现在穿短袖会不会冷？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-52-2.wav"}}, {"session_id": "41", "turn_idx": 3, "from": "Assistant", "text": "早晚凉飕飕的，中午又热，典型的乱穿衣季节", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-52-3.wav"}}, {"session_id": "41", "turn_idx": 4, "from": "Human", "text": "那帮我看看广州这三天的具体天气", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-52-4.wav"}}, {"session_id": "41", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "831", "turn_idx": 0, "from": "Human", "text": "小跃，今天阳光真好，适合去公园散步。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-53-0.wav"}}, {"session_id": "831", "turn_idx": 1, "from": "Assistant", "text": "是啊，户外活动最舒服了。你想去哪里的公园？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-53-1.wav"}}, {"session_id": "831", "turn_idx": 2, "from": "Human", "text": "我想去北京的奥林匹克公园，但不知道天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-53-2.wav"}}, {"session_id": "831", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "145", "turn_idx": 0, "from": "Human", "text": "小跃，后天我要去成都吃火锅，朋友请客。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-54-0.wav"}}, {"session_id": "145", "turn_idx": 1, "from": "Assistant", "text": "真羡慕！试试麻辣锅，但别吃太辣伤胃。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-54-1.wav"}}, {"session_id": "145", "turn_idx": 2, "from": "Human", "text": "哈哈，知道啦。先帮我看看成都明天天气如何，穿什么合适。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-54-2.wav"}}, {"session_id": "145", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "889", "turn_idx": 0, "from": "Human", "text": "小跃，我计划周末去昆明度假，期待！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-55-0.wav"}}, {"session_id": "889", "turn_idx": 1, "from": "Assistant", "text": "昆明四季如春，但提前查天气保险点。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-55-1.wav"}}, {"session_id": "889", "turn_idx": 2, "from": "Human", "text": "对，机票都订好了，希望别误事。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-55-2.wav"}}, {"session_id": "889", "turn_idx": 3, "from": "Assistant", "text": "天气稳定的话，行程就顺利了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-55-3.wav"}}, {"session_id": "889", "turn_idx": 4, "from": "Human", "text": "小跃，帮我查一下昆明的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-55-4.wav"}}, {"session_id": "889", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"kunming\"}", "value": "function\nget_weather\n{\"location\": \"kunming\"}"}] +[{"session_id": "399", "turn_idx": 0, "from": "Human", "text": "后天要去重庆吃火锅，辣得我有点怕。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-56-0.wav"}}, {"session_id": "399", "turn_idx": 1, "from": "Assistant", "text": "重庆火锅辣但香，多喝点水就行。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-56-1.wav"}}, {"session_id": "399", "turn_idx": 2, "from": "Human", "text": "哈哈，小跃，先查一下重庆的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-56-2.wav"}}, {"session_id": "399", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "518", "turn_idx": 0, "from": "Human", "text": "小跃，苏州园林要门票吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-57-0.wav"}}, {"session_id": "518", "turn_idx": 1, "from": "Assistant", "text": "有的要，但天气好时逛着值。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-57-1.wav"}}, {"session_id": "518", "turn_idx": 2, "from": "Human", "text": "我明天去玩，怕下雨扫兴。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-57-2.wav"}}, {"session_id": "518", "turn_idx": 3, "from": "Assistant", "text": "下雨就不好玩了，得查查。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-57-3.wav"}}, {"session_id": "518", "turn_idx": 4, "from": "Human", "text": "行，帮我查苏州未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-57-4.wav"}}, {"session_id": "518", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"suzhou\"}", "value": "function\nget_weather\n{\"location\": \"suzhou\"}"}] +[{"session_id": "494", "turn_idx": 0, "from": "Human", "text": "小跃，成都的火锅是不是很辣？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-58-0.wav"}}, {"session_id": "494", "turn_idx": 1, "from": "Assistant", "text": "对呀，辣得超过瘾，不过小心上火。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-58-1.wav"}}, {"session_id": "494", "turn_idx": 2, "from": "Human", "text": "那我明天就去试试，带上朋友一起。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-58-2.wav"}}, {"session_id": "494", "turn_idx": 3, "from": "Assistant", "text": "祝你吃得开心，别忘了解辣的饮料。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-58-3.wav"}}, {"session_id": "494", "turn_idx": 4, "from": "Human", "text": "没问题，小跃，查一下成都的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-58-4.wav"}}, {"session_id": "494", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "782", "turn_idx": 0, "from": "Human", "text": "小跃，今天下午想带孩子去动物园。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-59-0.wav"}}, {"session_id": "782", "turn_idx": 1, "from": "Assistant", "text": "好主意！北京动物园怎么样？动物种类多，孩子会喜欢。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-59-1.wav"}}, {"session_id": "782", "turn_idx": 2, "from": "Human", "text": "行，就那儿。小跃，帮我看看动物园的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-59-2.wav"}}, {"session_id": "782", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing zoo\"}", "value": "function\nget_weather\n{\"location\": \"beijing zoo\"}"}] +[{"session_id": "390", "turn_idx": 0, "from": "Human", "text": "小跃，我周末打算去北京转转。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-60-0.wav"}}, {"session_id": "390", "turn_idx": 1, "from": "Assistant", "text": "北京挺好的，故宫和天安门都值得一看。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-60-1.wav"}}, {"session_id": "390", "turn_idx": 2, "from": "Human", "text": "好，那你帮我查一下北京的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-60-2.wav"}}, {"session_id": "390", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "53", "turn_idx": 0, "from": "Human", "text": "小跃，周末想带宝宝去动物园", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-61-0.wav"}}, {"session_id": "53", "turn_idx": 1, "from": "Assistant", "text": "好主意！长颈鹿区新开放了，需要路线吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-61-1.wav"}}, {"session_id": "53", "turn_idx": 2, "from": "Human", "text": "先看天气吧，万一下雨就改室内了", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-61-2.wav"}}, {"session_id": "53", "turn_idx": 3, "from": "Assistant", "text": "建议带件外套，昼夜温差大呢", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-61-3.wav"}}, {"session_id": "53", "turn_idx": 4, "from": "Human", "text": "查北京今天开始三天的详细预报", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-61-4.wav"}}, {"session_id": "53", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "960", "turn_idx": 0, "from": "Human", "text": "小跃，我今天想带家人去公园野餐，你觉得怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-62-0.wav"}}, {"session_id": "960", "turn_idx": 1, "from": "Assistant", "text": "听起来很棒呀！户外活动能让心情变好，记得带点零食和水。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-62-1.wav"}}, {"session_id": "960", "turn_idx": 2, "from": "Human", "text": "那太好了，不过我得先看看北京的天气，万一明后天也下雨就糟了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-62-2.wav"}}, {"session_id": "960", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "961", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想去上海找朋友玩，但还没定具体时间。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-63-0.wav"}}, {"session_id": "961", "turn_idx": 1, "from": "Assistant", "text": "好主意！提前计划能玩得更尽兴，你们可以约个轻松的地方。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-63-1.wav"}}, {"session_id": "961", "turn_idx": 2, "from": "Human", "text": "是啊，所以帮我查查上海的天气吧，主要担心今天到后天的情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-63-2.wav"}}, {"session_id": "961", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "56", "turn_idx": 0, "from": "Human", "text": "小跃，阳台的花全被晒蔫了", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-64-0.wav"}}, {"session_id": "56", "turn_idx": 1, "from": "Assistant", "text": "最近紫外线很强呢，要启动自动灌溉吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-64-1.wav"}}, {"session_id": "56", "turn_idx": 2, "from": "Human", "text": "先等等，感觉今晚会下雨？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-64-2.wav"}}, {"session_id": "56", "turn_idx": 3, "from": "Assistant", "text": "云图显示有降雨带靠近", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-64-3.wav"}}, {"session_id": "56", "turn_idx": 4, "from": "Human", "text": "赶紧查武汉的降雨预报，包括明天", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-64-4.wav"}}, {"session_id": "56", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "471", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！我刚起床，感觉有点凉。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-65-0.wav"}}, {"session_id": "471", "turn_idx": 1, "from": "Assistant", "text": "早上好！秋天来了，早晚温差大。你最近有啥安排？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-65-1.wav"}}, {"session_id": "471", "turn_idx": 2, "from": "Human", "text": "明天要去上海出差，有点担心带什么衣服。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-65-2.wav"}}, {"session_id": "471", "turn_idx": 3, "from": "Assistant", "text": "出差的话，建议带件外套。需要我帮你查查那边情况？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-65-3.wav"}}, {"session_id": "471", "turn_idx": 4, "from": "Human", "text": "对，查查上海的天气，包括今天和后天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-65-4.wav"}}, {"session_id": "471", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "522", "turn_idx": 0, "from": "Human", "text": "小跃，我朋友周末从外地来广州玩，想带她吃早茶。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-66-0.wav"}}, {"session_id": "522", "turn_idx": 1, "from": "Assistant", "text": "广州的陶陶居早茶很出名，值得一试。你们计划去哪儿吃？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-66-1.wav"}}, {"session_id": "522", "turn_idx": 2, "from": "Human", "text": "就在市区吧，但得确保别碰上大雨，扫兴就不好了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-66-2.wav"}}, {"session_id": "522", "turn_idx": 3, "from": "Assistant", "text": "没错，雨天出行麻烦。我可以帮你看看天气趋势。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-66-3.wav"}}, {"session_id": "522", "turn_idx": 4, "from": "Human", "text": "好的，查一下广州今天到后天的天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-66-4.wav"}}, {"session_id": "522", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "680", "turn_idx": 0, "from": "Human", "text": "嗨小跃，你今天过得怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-67-0.wav"}}, {"session_id": "680", "turn_idx": 1, "from": "Assistant", "text": "我挺好的，谢谢关心！你周末有什么安排吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-67-1.wav"}}, {"session_id": "680", "turn_idx": 2, "from": "Human", "text": "是啊，我打算去上海逛逛，好久没去了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-67-2.wav"}}, {"session_id": "680", "turn_idx": 3, "from": "Assistant", "text": "上海确实好玩，记得带把伞以防万一。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-67-3.wav"}}, {"session_id": "680", "turn_idx": 4, "from": "Human", "text": "对了，你能帮我查查上海今天到后天的天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-67-4.wav"}}, {"session_id": "680", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "352", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我今天想去广州爬山，但感觉天有点阴。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-68-0.wav"}}, {"session_id": "352", "turn_idx": 1, "from": "Assistant", "text": "爬山注意安全！要我帮你看看附近的山路信息吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-68-1.wav"}}, {"session_id": "352", "turn_idx": 2, "from": "Human", "text": "先不用，我担心会下雨影响行程。 干脆你帮我查一下广州未来三天的天气吧，包括今天在内。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-68-2.wav"}}, {"session_id": "352", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "157", "turn_idx": 0, "from": "Human", "text": "小跃，下周要去伦敦开会，但机票还没订。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-69-0.wav"}}, {"session_id": "157", "turn_idx": 1, "from": "Assistant", "text": "伦敦天气多变，早点了解能省心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-69-1.wav"}}, {"session_id": "157", "turn_idx": 2, "from": "Human", "text": "先帮我查查伦敦包括今天的三天天气预报。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-69-2.wav"}}, {"session_id": "157", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"london\"}", "value": "function\nget_weather\n{\"location\": \"london\"}"}] +[{"session_id": "286", "turn_idx": 0, "from": "Human", "text": "明天计划去南京旅游。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-70-0.wav"}}, {"session_id": "286", "turn_idx": 1, "from": "Assistant", "text": "南京景点多，玩得开心点。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-70-1.wav"}}, {"session_id": "286", "turn_idx": 2, "from": "Human", "text": "嗯，查一下南京明天的天气，怕下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-70-2.wav"}}, {"session_id": "286", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "799", "turn_idx": 0, "from": "Human", "text": "小跃，西安的兵马俑值得一看吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-71-0.wav"}}, {"session_id": "799", "turn_idx": 1, "from": "Assistant", "text": "兵马俑是世界奇迹，绝对值回票价。你去西安？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-71-1.wav"}}, {"session_id": "799", "turn_idx": 2, "from": "Human", "text": "是啊，后天飞西安旅游。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-71-2.wav"}}, {"session_id": "799", "turn_idx": 3, "from": "Assistant", "text": "好兴奋！要我帮忙看行程？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-71-3.wav"}}, {"session_id": "799", "turn_idx": 4, "from": "Human", "text": "嗯，小跃，查一下西安的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-71-4.wav"}}, {"session_id": "799", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "281", "turn_idx": 0, "from": "Human", "text": "小跃，我明天要出差去上海开会。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-72-0.wav"}}, {"session_id": "281", "turn_idx": 1, "from": "Assistant", "text": "出差注意安全，带好证件和必需品。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-72-1.wav"}}, {"session_id": "281", "turn_idx": 2, "from": "Human", "text": "明白，但不知道天气如何。帮我查一下上海今天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-72-2.wav"}}, {"session_id": "281", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "910", "turn_idx": 0, "from": "Human", "text": "小跃，我明天要去上海出差，有点紧张。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-73-0.wav"}}, {"session_id": "910", "turn_idx": 1, "from": "Assistant", "text": "别担心，上海很繁华，出差应该会很顺利。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-73-1.wav"}}, {"session_id": "910", "turn_idx": 2, "from": "Human", "text": "谢谢安慰！你能帮我查一下上海未来三天的天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-73-2.wav"}}, {"session_id": "910", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "579", "turn_idx": 0, "from": "Human", "text": "小跃，周末我想去重庆爬山，锻炼身体。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-74-0.wav"}}, {"session_id": "579", "turn_idx": 1, "from": "Assistant", "text": "爬山健康！重庆山路多，天气关键。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-74-1.wav"}}, {"session_id": "579", "turn_idx": 2, "from": "Human", "text": "对啊，怕下雨路滑，不安全。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-74-2.wav"}}, {"session_id": "579", "turn_idx": 3, "from": "Assistant", "text": "安全重要，要我帮你查天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-74-3.wav"}}, {"session_id": "579", "turn_idx": 4, "from": "Human", "text": "小跃，帮我查重庆未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-74-4.wav"}}, {"session_id": "579", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "259", "turn_idx": 0, "from": "Human", "text": "小跃，我后天要去重庆见客户，但那边夏天好热。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-75-0.wav"}}, {"session_id": "259", "turn_idx": 1, "from": "Assistant", "text": "重庆是火炉城市，查天气能帮你选合适时间出门。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-75-1.wav"}}, {"session_id": "259", "turn_idx": 2, "from": "Human", "text": "对，我怕热天影响状态。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-75-2.wav"}}, {"session_id": "259", "turn_idx": 3, "from": "Assistant", "text": "别担心，预报能让你提前调整安排。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-75-3.wav"}}, {"session_id": "259", "turn_idx": 4, "from": "Human", "text": "那帮我查查重庆未来三天的天气，包括今天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-75-4.wav"}}, {"session_id": "259", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "612", "turn_idx": 0, "from": "Human", "text": "小跃，我计划周末去爬山。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-76-0.wav"}}, {"session_id": "612", "turn_idx": 1, "from": "Assistant", "text": "爬山很棒！打算去哪里？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-76-1.wav"}}, {"session_id": "612", "turn_idx": 2, "from": "Human", "text": "想去泰山看看风景。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-76-2.wav"}}, {"session_id": "612", "turn_idx": 3, "from": "Assistant", "text": "泰山风景确实美。天气好会更舒服。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-76-3.wav"}}, {"session_id": "612", "turn_idx": 4, "from": "Human", "text": "是的，帮我确认泰山的天气情况。 查泰山今天和明天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-76-4.wav"}}, {"session_id": "612", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"taishan\"}", "value": "function\nget_weather\n{\"location\": \"taishan\"}"}] +[{"session_id": "46", "turn_idx": 0, "from": "Human", "text": "小跃，拉萨的机票好便宜，想去", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-77-0.wav"}}, {"session_id": "46", "turn_idx": 1, "from": "Assistant", "text": "高原反应要当心呀，而且昼夜温差特别大", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-77-1.wav"}}, {"session_id": "46", "turn_idx": 2, "from": "Human", "text": "白天晒太阳晚上裹棉被是吧？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-77-2.wav"}}, {"session_id": "46", "turn_idx": 3, "from": "Assistant", "text": "没错，紫外线强到五分钟能晒伤", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-77-3.wav"}}, {"session_id": "46", "turn_idx": 4, "from": "Human", "text": "帮我查查拉萨这三天具体天气吧", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-77-4.wav"}}, {"session_id": "46", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"lhasa\"}", "value": "function\nget_weather\n{\"location\": \"lhasa\"}"}] +[{"session_id": "44", "turn_idx": 0, "from": "Human", "text": "小跃，我订了后天飞三亚的机票", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-78-0.wav"}}, {"session_id": "44", "turn_idx": 1, "from": "Assistant", "text": "去海边度假吗？记得带防晒霜，紫外线超强", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-78-1.wav"}}, {"session_id": "44", "turn_idx": 2, "from": "Human", "text": "泳衣都买好了，就怕下雨", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-78-2.wav"}}, {"session_id": "44", "turn_idx": 3, "from": "Assistant", "text": "雨季刚过，阵雨可能还有", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-78-3.wav"}}, {"session_id": "44", "turn_idx": 4, "from": "Human", "text": "那帮我确认下三亚今明后天的天气", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-78-4.wav"}}, {"session_id": "44", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"sanya\"}", "value": "function\nget_weather\n{\"location\": \"sanya\"}"}] +[{"session_id": "894", "turn_idx": 0, "from": "Human", "text": "小跃，我家人明天来成都旅游，想带他们去熊猫基地。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-79-0.wav"}}, {"session_id": "894", "turn_idx": 1, "from": "Assistant", "text": "熊猫基地太有趣了！成都的天气通常温和，穿轻便衣服就好。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-79-1.wav"}}, {"session_id": "894", "turn_idx": 2, "from": "Human", "text": "好的，帮我看看成都包括今天的三天内天气怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-79-2.wav"}}, {"session_id": "894", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "430", "turn_idx": 0, "from": "Human", "text": "嘿小跃，早上好呀！今天感觉怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-80-0.wav"}}, {"session_id": "430", "turn_idx": 1, "from": "Assistant", "text": "早上好！我挺好的，随时准备帮你。有啥计划吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-80-1.wav"}}, {"session_id": "430", "turn_idx": 2, "from": "Human", "text": "没啥大事，就是想去公园走走。对了，周末要去上海玩一趟。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-80-2.wav"}}, {"session_id": "430", "turn_idx": 3, "from": "Assistant", "text": "上海挺不错的，外滩夜景很漂亮。记得带伞以防万一。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-80-3.wav"}}, {"session_id": "430", "turn_idx": 4, "from": "Human", "text": "好主意。那你帮我查查上海未来三天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-80-4.wav"}}, {"session_id": "430", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "161", "turn_idx": 0, "from": "Human", "text": "小跃，我后天有个户外活动在上海，需要提前准备一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-81-0.wav"}}, {"session_id": "161", "turn_idx": 1, "from": "Assistant", "text": "好的，户外活动得关注天气。上海最近变化大，我建议查清楚再安排。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-81-1.wav"}}, {"session_id": "161", "turn_idx": 2, "from": "Human", "text": "那你能查一下上海的天气吗？就包括今天开始的三天内。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-81-2.wav"}}, {"session_id": "161", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "543", "turn_idx": 0, "from": "Human", "text": "小跃，我明天想和朋友去足球场踢球。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-82-0.wav"}}, {"session_id": "543", "turn_idx": 1, "from": "Assistant", "text": "运动好呀！记得带水壶和毛巾，别累着了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-82-1.wav"}}, {"session_id": "543", "turn_idx": 2, "from": "Human", "text": "谢谢提醒。但球场在市中心，天气热就难受。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-82-2.wav"}}, {"session_id": "543", "turn_idx": 3, "from": "Assistant", "text": "确实，高温会影响发挥。要不要查查球场天气？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-82-3.wav"}}, {"session_id": "543", "turn_idx": 4, "from": "Human", "text": "好主意。帮我看看明天中心体育场的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-82-4.wav"}}, {"session_id": "543", "turn_idx": 5, "from": "Assistant", "text": "我马上处理。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-82-5.wav"}}, {"session_id": "543", "turn_idx": 6, "from": "Human", "text": "小跃，查一下中心体育场今天到后天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-82-6.wav"}}, {"session_id": "543", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"central stadium\"}", "value": "function\nget_weather\n{\"location\": \"central stadium\"}"}] +[{"session_id": "754", "turn_idx": 0, "from": "Human", "text": "小跃，明天我得出差去纽约开会。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-83-0.wav"}}, {"session_id": "754", "turn_idx": 1, "from": "Assistant", "text": "纽约现在挺忙的，航班订好了没？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-83-1.wav"}}, {"session_id": "754", "turn_idx": 2, "from": "Human", "text": "搞定了，不过帮我查查纽约明天天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-83-2.wav"}}, {"session_id": "754", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"new york\"}", "value": "function\nget_weather\n{\"location\": \"new york\"}"}] +[{"session_id": "683", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我最近工作压力大，想周末放松一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-84-0.wav"}}, {"session_id": "683", "turn_idx": 1, "from": "Assistant", "text": "别太累啦！计划去哪里散心？成都的小吃能解压呢。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-84-1.wav"}}, {"session_id": "683", "turn_idx": 2, "from": "Human", "text": "好主意，我正考虑去成都玩两天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-84-2.wav"}}, {"session_id": "683", "turn_idx": 3, "from": "Assistant", "text": "记得提前订票，那边人可能多。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-84-3.wav"}}, {"session_id": "683", "turn_idx": 4, "from": "Human", "text": "嗯，先帮我查查成都今天到后天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-84-4.wav"}}, {"session_id": "683", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "310", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，你今天过得怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-85-0.wav"}}, {"session_id": "310", "turn_idx": 1, "from": "Assistant", "text": "还不错呢，谢谢关心！今天处理了一些日常任务。你呢，有什么新鲜事吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-85-1.wav"}}, {"session_id": "310", "turn_idx": 2, "from": "Human", "text": "我在计划明天去公园散步，但不知道天气如何。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-85-2.wav"}}, {"session_id": "310", "turn_idx": 3, "from": "Assistant", "text": "公园散步是个好主意！需要我帮你看看天气预报吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-85-3.wav"}}, {"session_id": "310", "turn_idx": 4, "from": "Human", "text": "先等等，我还没决定去哪个公园。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-85-4.wav"}}, {"session_id": "310", "turn_idx": 5, "from": "Assistant", "text": "哦，那你可以告诉我地点，我就能帮忙了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-85-5.wav"}}, {"session_id": "310", "turn_idx": 6, "from": "Human", "text": "好的，我想去北京的奥林匹克公园。帮我查一下那里的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-85-6.wav"}}, {"session_id": "310", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "2", "turn_idx": 0, "from": "Human", "text": "小跃，我明天得早起赶飞机，紧张！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-86-0.wav"}}, {"session_id": "2", "turn_idx": 1, "from": "Assistant", "text": "别担心，我帮您监控行程条件。您从哪个城市出发呀？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-86-1.wav"}}, {"session_id": "2", "turn_idx": 2, "from": "Human", "text": "从成都飞，先告诉我今天机场附近天气咋样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-86-2.wav"}}, {"session_id": "2", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "939", "turn_idx": 0, "from": "Human", "text": "小跃，明天重庆有个美食节，我想带家人去。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-87-0.wav"}}, {"session_id": "939", "turn_idx": 1, "from": "Assistant", "text": "重庆火锅和小吃超棒！提醒你，人多的话注意安全。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-87-1.wav"}}, {"session_id": "939", "turn_idx": 2, "from": "Human", "text": "谢谢，但天气热的话容易中暑，得防着点。 帮我查一下重庆的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-87-2.wav"}}, {"session_id": "939", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "695", "turn_idx": 0, "from": "Human", "text": "小跃，我下周一要去成都吃火锅，兴奋死了！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-88-0.wav"}}, {"session_id": "695", "turn_idx": 1, "from": "Assistant", "text": "哈哈，成都美食天堂啊。麻辣火锅配凉茶，绝了。行程安排好了吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-88-1.wav"}}, {"session_id": "695", "turn_idx": 2, "from": "Human", "text": "基本定了，就是怕天气太热影响胃口。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-88-2.wav"}}, {"session_id": "695", "turn_idx": 3, "from": "Assistant", "text": "有道理，成都夏天挺湿热的。带点清凉油备用。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-88-3.wav"}}, {"session_id": "695", "turn_idx": 4, "from": "Human", "text": "好建议。小跃，帮我查查成都的天气，包括今天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-88-4.wav"}}, {"session_id": "695", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "585", "turn_idx": 0, "from": "Human", "text": "小跃，西安的兵马俑我超想去。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-89-0.wav"}}, {"session_id": "585", "turn_idx": 1, "from": "Assistant", "text": "那地方历史感强，值得一去。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-89-1.wav"}}, {"session_id": "585", "turn_idx": 2, "from": "Human", "text": "计划明天出发，但担心天气。 查西安的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-89-2.wav"}}, {"session_id": "585", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "643", "turn_idx": 0, "from": "Human", "text": "小跃，我朋友在成都旅游，担心天气影响行程。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-90-0.wav"}}, {"session_id": "643", "turn_idx": 1, "from": "Assistant", "text": "成都的景点像宽窄巷子，天气暖和时去最合适。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-90-1.wav"}}, {"session_id": "643", "turn_idx": 2, "from": "Human", "text": "嗯，帮我查一下成都的天气，包括今天和后天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-90-2.wav"}}, {"session_id": "643", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "244", "turn_idx": 0, "from": "Human", "text": "小跃，下午好！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-91-0.wav"}}, {"session_id": "244", "turn_idx": 1, "from": "Assistant", "text": "下午好，有什么新鲜事？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-91-1.wav"}}, {"session_id": "244", "turn_idx": 2, "from": "Human", "text": "想明天去成都跑步，公园空气好。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-91-2.wav"}}, {"session_id": "244", "turn_idx": 3, "from": "Assistant", "text": "跑步健身好主意，成都绿化不错。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-91-3.wav"}}, {"session_id": "244", "turn_idx": 4, "from": "Human", "text": "但得确保天气晴朗。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-91-4.wav"}}, {"session_id": "244", "turn_idx": 5, "from": "Assistant", "text": "阴天或小雨就影响大了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-91-5.wav"}}, {"session_id": "244", "turn_idx": 6, "from": "Human", "text": "对，小跃，帮我查查成都的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-91-6.wav"}}, {"session_id": "244", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "654", "turn_idx": 0, "from": "Human", "text": "小跃，你觉得杭州好玩吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-92-0.wav"}}, {"session_id": "654", "turn_idx": 1, "from": "Assistant", "text": "杭州西湖很美，适合散步或划船。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-92-1.wav"}}, {"session_id": "654", "turn_idx": 2, "from": "Human", "text": "好主意。我计划今天去杭州，能查查天气如何？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-92-2.wav"}}, {"session_id": "654", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "663", "turn_idx": 0, "from": "Human", "text": "小跃，我朋友约我后天去杭州喝茶，但我不确定天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-93-0.wav"}}, {"session_id": "663", "turn_idx": 1, "from": "Assistant", "text": "杭州喝茶很惬意！西湖边有家老茶馆不错。你朋友开车去吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-93-1.wav"}}, {"session_id": "663", "turn_idx": 2, "from": "Human", "text": "嗯，他开车。但我怕下雨影响行程。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-93-2.wav"}}, {"session_id": "663", "turn_idx": 3, "from": "Assistant", "text": "雨天喝茶更有情调，但开车小心点。需要我帮你规划路线吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-93-3.wav"}}, {"session_id": "663", "turn_idx": 4, "from": "Human", "text": "不用了，谢谢。小跃，直接查查杭州未来三天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-93-4.wav"}}, {"session_id": "663", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "661", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想去上海玩，但担心下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-94-0.wav"}}, {"session_id": "661", "turn_idx": 1, "from": "Assistant", "text": "周末出游是好主意！上海最近挺热闹的，你计划哪天出发？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-94-1.wav"}}, {"session_id": "661", "turn_idx": 2, "from": "Human", "text": "后天吧，想玩两天。你有什么景点推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-94-2.wav"}}, {"session_id": "661", "turn_idx": 3, "from": "Assistant", "text": "推荐你去外滩和城隍庙，那里很经典。记得提前订票。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-94-3.wav"}}, {"session_id": "661", "turn_idx": 4, "from": "Human", "text": "好，我记下了。小跃，现在查查上海未来三天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-94-4.wav"}}, {"session_id": "661", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "144", "turn_idx": 0, "from": "Human", "text": "小跃，我想明天骑自行车去杭州西湖转转。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-95-0.wav"}}, {"session_id": "144", "turn_idx": 1, "from": "Assistant", "text": "好选择！带上水和帽子，路程不远但别累着。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-95-1.wav"}}, {"session_id": "144", "turn_idx": 2, "from": "Human", "text": "嗯，安全第一。先帮我查查杭州今天的天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-95-2.wav"}}, {"session_id": "144", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "574", "turn_idx": 0, "from": "Human", "text": "小跃，我计划明天去杭州西湖划船，你觉得怎样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-96-0.wav"}}, {"session_id": "574", "turn_idx": 1, "from": "Assistant", "text": "西湖景色美，划船很惬意！杭州天气最近稳定。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-96-1.wav"}}, {"session_id": "574", "turn_idx": 2, "from": "Human", "text": "但天气预报说可能有变，我有点担心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-96-2.wav"}}, {"session_id": "574", "turn_idx": 3, "from": "Assistant", "text": "别慌，我随时可以查实时天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-96-3.wav"}}, {"session_id": "574", "turn_idx": 4, "from": "Human", "text": "小跃，请帮我查杭州今天、明天和后天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-96-4.wav"}}, {"session_id": "574", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "667", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想在家洗车，但看天灰蒙蒙的。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-97-0.wav"}}, {"session_id": "667", "turn_idx": 1, "from": "Assistant", "text": "洗车挑晴天好，免得白忙活。你家在武汉吧？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-97-1.wav"}}, {"session_id": "667", "turn_idx": 2, "from": "Human", "text": "是啊，武汉的天气老变。洗车店推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-97-2.wav"}}, {"session_id": "667", "turn_idx": 3, "from": "Assistant", "text": "推荐自助洗车站，省钱又干净。位置我发你。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-97-3.wav"}}, {"session_id": "667", "turn_idx": 4, "from": "Human", "text": "谢谢。小跃，查查武汉未来三天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-97-4.wav"}}, {"session_id": "667", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "559", "turn_idx": 0, "from": "Human", "text": "小跃，刚洗了衣服，想晾阳台晒干。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-98-0.wav"}}, {"session_id": "559", "turn_idx": 1, "from": "Assistant", "text": "晾衣服看天气，晴天最好。您家在哪个区？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-98-1.wav"}}, {"session_id": "559", "turn_idx": 2, "from": "Human", "text": "在丰台区，小区阳台朝南。 那查一下今天丰台区的天气，免得白晾。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-98-2.wav"}}, {"session_id": "559", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"fengtai district\"}", "value": "function\nget_weather\n{\"location\": \"fengtai district\"}"}] +[{"session_id": "178", "turn_idx": 0, "from": "Human", "text": "小跃，我武汉的同事约我爬山，后天去。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-99-0.wav"}}, {"session_id": "178", "turn_idx": 1, "from": "Assistant", "text": "爬山好呀！武汉山景美，但天气热容易中暑。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-99-1.wav"}}, {"session_id": "178", "turn_idx": 2, "from": "Human", "text": "是啊，今天得买些装备。希望别下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-99-2.wav"}}, {"session_id": "178", "turn_idx": 3, "from": "Assistant", "text": "带足水和帽子！武汉雨来得快。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-99-3.wav"}}, {"session_id": "178", "turn_idx": 4, "from": "Human", "text": "谢谢提醒。帮我查查武汉今天的天气，看看预报。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-99-4.wav"}}, {"session_id": "178", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "678", "turn_idx": 0, "from": "Human", "text": "你好，小跃！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-100-0.wav"}}, {"session_id": "678", "turn_idx": 1, "from": "Assistant", "text": "你好！今天有什么计划？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-100-1.wav"}}, {"session_id": "678", "turn_idx": 2, "from": "Human", "text": "我后天想去西安看兵马俑。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-100-2.wav"}}, {"session_id": "678", "turn_idx": 3, "from": "Assistant", "text": "西安古迹多，天气晴朗就更棒。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-100-3.wav"}}, {"session_id": "678", "turn_idx": 4, "from": "Human", "text": "是的，帮我查查西安的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-100-4.wav"}}, {"session_id": "678", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "760", "turn_idx": 0, "from": "Human", "text": "小跃，我明天想去北京逛街，你觉得合适吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-101-0.wav"}}, {"session_id": "760", "turn_idx": 1, "from": "Assistant", "text": "北京逛街很热闹，但最好看看天气情况再决定。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-101-1.wav"}}, {"session_id": "760", "turn_idx": 2, "from": "Human", "text": "那帮我查一下明天北京的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-101-2.wav"}}, {"session_id": "760", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "580", "turn_idx": 0, "from": "Human", "text": "嘿小跃，周末打算干嘛？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-102-0.wav"}}, {"session_id": "580", "turn_idx": 1, "from": "Assistant", "text": "我周末想在家休息，看看电影。你呢？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-102-1.wav"}}, {"session_id": "580", "turn_idx": 2, "from": "Human", "text": "我想去爬山，但不知道天气怎么样。 查一下北京的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-102-2.wav"}}, {"session_id": "580", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "691", "turn_idx": 0, "from": "Human", "text": "小跃，我下午要去上海出差，有点紧张。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-103-0.wav"}}, {"session_id": "691", "turn_idx": 1, "from": "Assistant", "text": "别担心，上海交通很方便的。要我帮你查查航班信息吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-103-1.wav"}}, {"session_id": "691", "turn_idx": 2, "from": "Human", "text": "不用了，票已经订好了。就是不知道那边天气如何。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-103-2.wav"}}, {"session_id": "691", "turn_idx": 3, "from": "Assistant", "text": "对啊，带合适的衣服很重要。最近上海可能有雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-103-3.wav"}}, {"session_id": "691", "turn_idx": 4, "from": "Human", "text": "真的吗？那快帮我查查上海的天气，包括今天和明天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-103-4.wav"}}, {"session_id": "691", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "424", "turn_idx": 0, "from": "Human", "text": "小跃，我想带家人去旅游。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-104-0.wav"}}, {"session_id": "424", "turn_idx": 1, "from": "Assistant", "text": "家庭旅游多温馨！目的地选好了吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-104-1.wav"}}, {"session_id": "424", "turn_idx": 2, "from": "Human", "text": "想去西安看兵马俑。查查未来三天的天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-104-2.wav"}}, {"session_id": "424", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "143", "turn_idx": 0, "from": "Human", "text": "小跃，我后天有个户外烧烤聚会，在深圳海边。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-105-0.wav"}}, {"session_id": "143", "turn_idx": 1, "from": "Assistant", "text": "烧烤啊，多带点饮料和炭火，别忘防蚊液。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-105-1.wav"}}, {"session_id": "143", "turn_idx": 2, "from": "Human", "text": "对，但得先确认天气。帮我查查深圳今天和明天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-105-2.wav"}}, {"session_id": "143", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "300", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-106-0.wav"}}, {"session_id": "300", "turn_idx": 1, "from": "Assistant", "text": "我挺精神的，谢谢！你呢？周末有啥计划吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-106-1.wav"}}, {"session_id": "300", "turn_idx": 2, "from": "Human", "text": "我在考虑去北京玩，听说故宫挺不错的。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-106-2.wav"}}, {"session_id": "300", "turn_idx": 3, "from": "Assistant", "text": "北京确实好玩！你打算哪天去？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-106-3.wav"}}, {"session_id": "300", "turn_idx": 4, "from": "Human", "text": "可能明天出发，但不确定天气如何。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-106-4.wav"}}, {"session_id": "300", "turn_idx": 5, "from": "Assistant", "text": "天气确实关键，别让雨打乱行程。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-106-5.wav"}}, {"session_id": "300", "turn_idx": 6, "from": "Human", "text": "说得对，请帮我查查北京从今天开始的三天天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-106-6.wav"}}, {"session_id": "300", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "112", "turn_idx": 0, "from": "Human", "text": "小跃，我今天想带狗狗去公园玩，你觉得合适吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-107-0.wav"}}, {"session_id": "112", "turn_idx": 1, "from": "Assistant", "text": "带狗狗出门挺好，记得牵绳哦。公园人多要小心点。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-107-1.wav"}}, {"session_id": "112", "turn_idx": 2, "from": "Human", "text": "嗯，知道了。顺便帮我查一下广州的天气呗。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-107-2.wav"}}, {"session_id": "112", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "950", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想去爬山，你觉得怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-108-0.wav"}}, {"session_id": "950", "turn_idx": 1, "from": "Assistant", "text": "爬山好主意！放松身心。你打算去哪儿爬？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-108-1.wav"}}, {"session_id": "950", "turn_idx": 2, "from": "Human", "text": "想去泰山，听说风景超美。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-108-2.wav"}}, {"session_id": "950", "turn_idx": 3, "from": "Assistant", "text": "泰山确实壮观。需要我帮你查查天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-108-3.wav"}}, {"session_id": "950", "turn_idx": 4, "from": "Human", "text": "行啊，查一下泰山未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-108-4.wav"}}, {"session_id": "950", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"taishan\"}", "value": "function\nget_weather\n{\"location\": \"taishan\"}"}] +[{"session_id": "551", "turn_idx": 0, "from": "Human", "text": "小跃，下午要去超市买东西，但怕下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-109-0.wav"}}, {"session_id": "551", "turn_idx": 1, "from": "Assistant", "text": "带把伞以防万一。您去哪个超市？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-109-1.wav"}}, {"session_id": "551", "turn_idx": 2, "from": "Human", "text": "家乐福超市，在丰台区那边。 查一下今天丰台区的天气，看要不要改时间。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-109-2.wav"}}, {"session_id": "551", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"fengtai district\"}", "value": "function\nget_weather\n{\"location\": \"fengtai district\"}"}] +[{"session_id": "420", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天感觉有点凉飕飕的。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-110-0.wav"}}, {"session_id": "420", "turn_idx": 1, "from": "Assistant", "text": "早上好！是啊，秋天来了，早晚温差大。你打算出门吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-110-1.wav"}}, {"session_id": "420", "turn_idx": 2, "from": "Human", "text": "嗯，我后天要去北京开会。能帮我查一下北京的天气怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-110-2.wav"}}, {"session_id": "420", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "504", "turn_idx": 0, "from": "Human", "text": "小跃，周末想带孩子们去重庆玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-111-0.wav"}}, {"session_id": "504", "turn_idx": 1, "from": "Assistant", "text": "重庆好玩的地方多！孩子们肯定喜欢。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-111-1.wav"}}, {"session_id": "504", "turn_idx": 2, "from": "Human", "text": "是啊，但怕天气太热或下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-111-2.wav"}}, {"session_id": "504", "turn_idx": 3, "from": "Assistant", "text": "提前查天气可以调整计划。你哪天出发？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-111-3.wav"}}, {"session_id": "504", "turn_idx": 4, "from": "Human", "text": "计划明天走，先看看情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-111-4.wav"}}, {"session_id": "504", "turn_idx": 5, "from": "Assistant", "text": "需要我协助查询重庆的天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-111-5.wav"}}, {"session_id": "504", "turn_idx": 6, "from": "Human", "text": "对，小跃，帮我查重庆的天气预报。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-111-6.wav"}}, {"session_id": "504", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "550", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想带家人去公园玩，有什么推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-112-0.wav"}}, {"session_id": "550", "turn_idx": 1, "from": "Assistant", "text": "朝阳公园不错，地方大还有游乐设施。您想去吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-112-1.wav"}}, {"session_id": "550", "turn_idx": 2, "from": "Human", "text": "听起来好，就去朝阳公园吧。 那帮我查一下明天朝阳公园的天气情况，准备出门。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-112-2.wav"}}, {"session_id": "550", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chaoyang park\"}", "value": "function\nget_weather\n{\"location\": \"chaoyang park\"}"}] +[{"session_id": "570", "turn_idx": 0, "from": "Human", "text": "小跃，我明天想去天安门广场转转，你觉得合适吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-113-0.wav"}}, {"session_id": "570", "turn_idx": 1, "from": "Assistant", "text": "当然合适啦！北京现在秋高气爽，很适合散步。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-113-1.wav"}}, {"session_id": "570", "turn_idx": 2, "from": "Human", "text": "是啊，不过我怕突然下雨，得带把伞。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-113-2.wav"}}, {"session_id": "570", "turn_idx": 3, "from": "Assistant", "text": "谨慎点好，要我帮你看看天气预报吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-113-3.wav"}}, {"session_id": "570", "turn_idx": 4, "from": "Human", "text": "小跃，帮我查一下北京今天、明天和后天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-113-4.wav"}}, {"session_id": "570", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "689", "turn_idx": 0, "from": "Human", "text": "小跃，我爸妈想带我去天津玩，你觉得行吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-114-0.wav"}}, {"session_id": "689", "turn_idx": 1, "from": "Assistant", "text": "天津的海河夜景很美，适合家庭游。你们哪天去？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-114-1.wav"}}, {"session_id": "689", "turn_idx": 2, "from": "Human", "text": "可能今天下午就出发，玩到后天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-114-2.wav"}}, {"session_id": "689", "turn_idx": 3, "from": "Assistant", "text": "那得快点收拾行李，别耽误时间。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-114-3.wav"}}, {"session_id": "689", "turn_idx": 4, "from": "Human", "text": "对，先查查天津包括今天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-114-4.wav"}}, {"session_id": "689", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"tianjin\"}", "value": "function\nget_weather\n{\"location\": \"tianjin\"}"}] +[{"session_id": "357", "turn_idx": 0, "from": "Human", "text": "小跃，我后天在杭州有场户外运动赛，紧张啊。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-115-0.wav"}}, {"session_id": "357", "turn_idx": 1, "from": "Assistant", "text": "加油！运动前热身很重要，要我设置提醒吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-115-1.wav"}}, {"session_id": "357", "turn_idx": 2, "from": "Human", "text": "提醒设好了，现在只关心天气别出岔子。 那你查查杭州包括今天的三天天气吧，今天、明天、后天都要。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-115-2.wav"}}, {"session_id": "357", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "553", "turn_idx": 0, "from": "Human", "text": "小跃，这周末要回老家天津看父母。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-116-0.wav"}}, {"session_id": "553", "turn_idx": 1, "from": "Assistant", "text": "回家团聚真好！天津美食多，记得尝尝。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-116-1.wav"}}, {"session_id": "553", "turn_idx": 2, "from": "Human", "text": "是啊，希望天气配合。 查一下后天天津的天气，安排行程。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-116-2.wav"}}, {"session_id": "553", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"tianjin\"}", "value": "function\nget_weather\n{\"location\": \"tianjin\"}"}] +[{"session_id": "904", "turn_idx": 0, "from": "Human", "text": "小跃，我这几天过敏严重，可能跟天气有关。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-117-0.wav"}}, {"session_id": "904", "turn_idx": 1, "from": "Assistant", "text": "哎呀，多喝水戴口罩！你在哪个城市？湿度大容易过敏。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-117-1.wav"}}, {"session_id": "904", "turn_idx": 2, "from": "Human", "text": "我在杭州，西湖边花粉多。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-117-2.wav"}}, {"session_id": "904", "turn_idx": 3, "from": "Assistant", "text": "对，那边绿化好但花粉厉害，建议少出门。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-117-3.wav"}}, {"session_id": "904", "turn_idx": 4, "from": "Human", "text": "好吧，但我明天有约，得知道天气。 帮我看看杭州未来三天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-117-4.wav"}}, {"session_id": "904", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "194", "turn_idx": 0, "from": "Human", "text": "小跃，晚上想约朋友烧烤。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-118-0.wav"}}, {"session_id": "194", "turn_idx": 1, "from": "Assistant", "text": "烧烤是个好主意。需要确认天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-118-1.wav"}}, {"session_id": "194", "turn_idx": 2, "from": "Human", "text": "对，查查成都的天气，今天和后天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-118-2.wav"}}, {"session_id": "194", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "737", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天感觉如何？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-119-0.wav"}}, {"session_id": "737", "turn_idx": 1, "from": "Assistant", "text": "我感觉很棒！你有什么需要？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-119-1.wav"}}, {"session_id": "737", "turn_idx": 2, "from": "Human", "text": "我明天要去武汉出差，参加一个学习会议。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-119-2.wav"}}, {"session_id": "737", "turn_idx": 3, "from": "Assistant", "text": "武汉高校多，学习氛围好。天气会影响出行吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-119-3.wav"}}, {"session_id": "737", "turn_idx": 4, "from": "Human", "text": "可能吧，所以想提前知道。 帮我查查武汉包括今天的三天天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-119-4.wav"}}, {"session_id": "737", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "483", "turn_idx": 0, "from": "Human", "text": "小跃，这个周末我想去杭州爬山，放松一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-120-0.wav"}}, {"session_id": "483", "turn_idx": 1, "from": "Assistant", "text": "杭州的西湖边山景不错，推荐你去灵隐寺。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-120-1.wav"}}, {"session_id": "483", "turn_idx": 2, "from": "Human", "text": "好主意！但得看天气，如果下雨就泡汤了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-120-2.wav"}}, {"session_id": "483", "turn_idx": 3, "from": "Assistant", "text": "提前准备总是好的。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-120-3.wav"}}, {"session_id": "483", "turn_idx": 4, "from": "Human", "text": "是啊，小跃，帮我查一下杭州的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-120-4.wav"}}, {"session_id": "483", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "525", "turn_idx": 0, "from": "Human", "text": "小跃，明天想去成都吃火锅，馋死我了！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-121-0.wav"}}, {"session_id": "525", "turn_idx": 1, "from": "Assistant", "text": "成都的麻辣火锅确实过瘾。哪家店你考虑好了？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-121-1.wav"}}, {"session_id": "525", "turn_idx": 2, "from": "Human", "text": "还没定呢，但得看天气热不热，吃火锅容易出汗。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-121-2.wav"}}, {"session_id": "525", "turn_idx": 3, "from": "Assistant", "text": "夏天吃火锅配冷饮更爽，天气因素很重要。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-121-3.wav"}}, {"session_id": "525", "turn_idx": 4, "from": "Human", "text": "是啊，查查成都今天到后天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-121-4.wav"}}, {"session_id": "525", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "952", "turn_idx": 0, "from": "Human", "text": "小跃，明天公司组织去广州团建。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-122-0.wav"}}, {"session_id": "952", "turn_idx": 1, "from": "Assistant", "text": "广州团建？具体去哪儿玩？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-122-1.wav"}}, {"session_id": "952", "turn_idx": 2, "from": "Human", "text": "去长隆乐园，期待好久了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-122-2.wav"}}, {"session_id": "952", "turn_idx": 3, "from": "Assistant", "text": "长隆超好玩！但天气得注意。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-122-3.wav"}}, {"session_id": "952", "turn_idx": 4, "from": "Human", "text": "是呀，查一下广州未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-122-4.wav"}}, {"session_id": "952", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "250", "turn_idx": 0, "from": "Human", "text": "小跃，你今天感觉怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-123-0.wav"}}, {"session_id": "250", "turn_idx": 1, "from": "Assistant", "text": "我挺好的，谢谢关心！你呢，今天有什么安排吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-123-1.wav"}}, {"session_id": "250", "turn_idx": 2, "from": "Human", "text": "我打算下午去公园散步，放松一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-123-2.wav"}}, {"session_id": "250", "turn_idx": 3, "from": "Assistant", "text": "那听起来很舒服！记得带把伞，以防下雨哦。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-123-3.wav"}}, {"session_id": "250", "turn_idx": 4, "from": "Human", "text": "对啊，你能帮我查一下北京的天气情况吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-123-4.wav"}}, {"session_id": "250", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "320", "turn_idx": 0, "from": "Human", "text": "小跃，早上好啊！今天感觉怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-124-0.wav"}}, {"session_id": "320", "turn_idx": 1, "from": "Assistant", "text": "早上好！我感觉很棒。今天天气看着挺晴朗的，你有什么计划吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-124-1.wav"}}, {"session_id": "320", "turn_idx": 2, "from": "Human", "text": "我想后天去北京逛逛公园，但不知道会不会下雨。你能帮我查一下北京的天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-124-2.wav"}}, {"session_id": "320", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "42", "turn_idx": 0, "from": "Human", "text": "小跃小跃！周末约了朋友去重庆吃火锅", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-125-0.wav"}}, {"session_id": "42", "turn_idx": 1, "from": "Assistant", "text": "哇，洪崖洞配麻辣锅绝了！不过那边容易突然下雨", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-125-1.wav"}}, {"session_id": "42", "turn_idx": 2, "from": "Human", "text": "真的假的？我伞都找不到了", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-125-2.wav"}}, {"session_id": "42", "turn_idx": 3, "from": "Assistant", "text": "山城天气说变就变，我建议您提前准备", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-125-3.wav"}}, {"session_id": "42", "turn_idx": 4, "from": "Human", "text": "快查查重庆今明后三天的天气预报", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-125-4.wav"}}, {"session_id": "42", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "911", "turn_idx": 0, "from": "Human", "text": "小跃，我妈住在广州，我担心她那边天气不好。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-126-0.wav"}}, {"session_id": "911", "turn_idx": 1, "from": "Assistant", "text": "广州天气一般挺温和的，不过我可以帮你关注一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-126-1.wav"}}, {"session_id": "911", "turn_idx": 2, "from": "Human", "text": "那太好了，查查广州今天到后天的天气情况吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-126-2.wav"}}, {"session_id": "911", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "526", "turn_idx": 0, "from": "Human", "text": "小跃，周末朋友约我去重庆吃火锅，激动！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-127-0.wav"}}, {"session_id": "526", "turn_idx": 1, "from": "Assistant", "text": "重庆火锅辣得过瘾，记得选个口碑好的店。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-127-1.wav"}}, {"session_id": "526", "turn_idx": 2, "from": "Human", "text": "对，但重庆山路多，万一下雨路滑就麻烦了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-127-2.wav"}}, {"session_id": "526", "turn_idx": 3, "from": "Assistant", "text": "安全出行是大事，查查天气能提前准备。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-127-3.wav"}}, {"session_id": "526", "turn_idx": 4, "from": "Human", "text": "那你帮我查一下重庆未来三天的天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-127-4.wav"}}, {"session_id": "526", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "22", "turn_idx": 0, "from": "Human", "text": "小跃，成都的火锅店推荐有吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-128-0.wav"}}, {"session_id": "22", "turn_idx": 1, "from": "Assistant", "text": "春熙路附近的老码头火锅评价超好！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-128-1.wav"}}, {"session_id": "22", "turn_idx": 2, "from": "Human", "text": "太好了，顺便查下成都明后天天气", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-128-2.wav"}}, {"session_id": "22", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "305", "turn_idx": 0, "from": "Human", "text": "小跃，我想去买点新衣服。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-129-0.wav"}}, {"session_id": "305", "turn_idx": 1, "from": "Assistant", "text": "购物解压呢！准备去哪儿逛？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-129-1.wav"}}, {"session_id": "305", "turn_idx": 2, "from": "Human", "text": "在成都的商场，后天和朋友一起。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-129-2.wav"}}, {"session_id": "305", "turn_idx": 3, "from": "Assistant", "text": "成都热闹，但下雨天就麻烦。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-129-3.wav"}}, {"session_id": "305", "turn_idx": 4, "from": "Human", "text": "是啊，我不想淋成落汤鸡。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-129-4.wav"}}, {"session_id": "305", "turn_idx": 5, "from": "Assistant", "text": "提前查天气最明智。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-129-5.wav"}}, {"session_id": "305", "turn_idx": 6, "from": "Human", "text": "那你帮我查查成都今天、明天和后天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-129-6.wav"}}, {"session_id": "305", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "497", "turn_idx": 0, "from": "Human", "text": "小跃，你觉得南京有啥好玩的？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-130-0.wav"}}, {"session_id": "497", "turn_idx": 1, "from": "Assistant", "text": "夫子庙和中山陵都不错，历史感满满。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-130-1.wav"}}, {"session_id": "497", "turn_idx": 2, "from": "Human", "text": "那我后天就过去逛逛。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-130-2.wav"}}, {"session_id": "497", "turn_idx": 3, "from": "Assistant", "text": "玩得开心，带上舒适的鞋子。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-130-3.wav"}}, {"session_id": "497", "turn_idx": 4, "from": "Human", "text": "谢谢提醒，小跃，查查南京的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-130-4.wav"}}, {"session_id": "497", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "140", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我今天想去公园野餐，你觉得怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-131-0.wav"}}, {"session_id": "140", "turn_idx": 1, "from": "Assistant", "text": "听起来很棒啊！带点水果和毯子就行，别忘防晒。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-131-1.wav"}}, {"session_id": "140", "turn_idx": 2, "from": "Human", "text": "好主意！不过先帮我查查今天上海的天气，看适不适合出门。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-131-2.wav"}}, {"session_id": "140", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "209", "turn_idx": 0, "from": "Human", "text": "小跃，我晚上想烧烤，但看天空灰蒙蒙的。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-132-0.wav"}}, {"session_id": "209", "turn_idx": 1, "from": "Assistant", "text": "烧烤怕下雨呢，查查天气再决定吧。烧烤地点是哪里？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-132-1.wav"}}, {"session_id": "209", "turn_idx": 2, "from": "Human", "text": "在重庆，帮我查查重庆今天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-132-2.wav"}}, {"session_id": "209", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "359", "turn_idx": 0, "from": "Human", "text": "小跃，我后天在重庆有朋友聚会，选在江边。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-133-0.wav"}}, {"session_id": "359", "turn_idx": 1, "from": "Assistant", "text": "江边夜景超美！要我推荐餐厅或交通方式吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-133-1.wav"}}, {"session_id": "359", "turn_idx": 2, "from": "Human", "text": "餐厅定了，现在担心天气影响氛围。 那你查一下重庆包括今天的三天天气吧，今天、明天和后天都要。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-133-2.wav"}}, {"session_id": "359", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "443", "turn_idx": 0, "from": "Human", "text": "小跃，我朋友从深圳来玩，我们想安排户外活动。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-134-0.wav"}}, {"session_id": "443", "turn_idx": 1, "from": "Assistant", "text": "那得看天气怎么样，不然影响心情。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-134-1.wav"}}, {"session_id": "443", "turn_idx": 2, "from": "Human", "text": "说得对，小跃，赶紧帮我查深圳包括今天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-134-2.wav"}}, {"session_id": "443", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "462", "turn_idx": 0, "from": "Human", "text": "嗨小跃，我今天要出门办事，有点紧张。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-135-0.wav"}}, {"session_id": "462", "turn_idx": 1, "from": "Assistant", "text": "别紧张！出门前知道天气就安心多了。你在哪个城市？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-135-1.wav"}}, {"session_id": "462", "turn_idx": 2, "from": "Human", "text": "在深圳。能帮我看看今天的天气怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-135-2.wav"}}, {"session_id": "462", "turn_idx": 3, "from": "Assistant", "text": "没问题！是查今天、明天，还是后天？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-135-3.wav"}}, {"session_id": "462", "turn_idx": 4, "from": "Human", "text": "查一下今天和后天吧，谢谢！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-135-4.wav"}}, {"session_id": "462", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "294", "turn_idx": 0, "from": "Human", "text": "小跃，我今天有点累，想在家休息。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-136-0.wav"}}, {"session_id": "294", "turn_idx": 1, "from": "Assistant", "text": "好好休息很重要。要不要放点轻音乐？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-136-1.wav"}}, {"session_id": "294", "turn_idx": 2, "from": "Human", "text": "先不用，麻烦查一下杭州的天气怎么样，接下来三天会不会降温。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-136-2.wav"}}, {"session_id": "294", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "440", "turn_idx": 0, "from": "Human", "text": "小跃，我今天想去公园散步，但不知道穿什么好。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-137-0.wav"}}, {"session_id": "440", "turn_idx": 1, "from": "Assistant", "text": "别担心，穿得舒服就行。对了，你打算去哪儿散步？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-137-1.wav"}}, {"session_id": "440", "turn_idx": 2, "from": "Human", "text": "我想去北京的后海那边。帮我看看未来三天的天气情况吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-137-2.wav"}}, {"session_id": "440", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "101", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想带娃去上海迪士尼！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-138-0.wav"}}, {"session_id": "101", "turn_idx": 1, "from": "Assistant", "text": "太棒了！记得提前查天气，户外项目多。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-138-1.wav"}}, {"session_id": "101", "turn_idx": 2, "from": "Human", "text": "是呀，尤其怕排队时突然下雨。今天天气如何？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-138-2.wav"}}, {"session_id": "101", "turn_idx": 3, "from": "Assistant", "text": "上海最近温差大，早晚得加外套。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-138-3.wav"}}, {"session_id": "101", "turn_idx": 4, "from": "Human", "text": "那帮我看看上海今明后三天的详细预报呗。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-138-4.wav"}}, {"session_id": "101", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "377", "turn_idx": 0, "from": "Human", "text": "小跃，我计划后天去西安旅游，看兵马俑。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-139-0.wav"}}, {"session_id": "377", "turn_idx": 1, "from": "Assistant", "text": "西安历史文化浓，兵马俑超震撼！不过户外景点得挑好天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-139-1.wav"}}, {"session_id": "377", "turn_idx": 2, "from": "Human", "text": "对啊，太热或下雨都不舒服。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-139-2.wav"}}, {"session_id": "377", "turn_idx": 3, "from": "Assistant", "text": "建议带防晒或雨伞，查查预报更放心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-139-3.wav"}}, {"session_id": "377", "turn_idx": 4, "from": "Human", "text": "好的。小跃，查一下西安的天气，包括今天、明天和后天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-139-4.wav"}}, {"session_id": "377", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "815", "turn_idx": 0, "from": "Human", "text": "小跃，下午想去深圳华强北购物。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-140-0.wav"}}, {"session_id": "815", "turn_idx": 1, "from": "Assistant", "text": "华强北电子产品多！准备买什么？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-140-1.wav"}}, {"session_id": "815", "turn_idx": 2, "from": "Human", "text": "想买个新手机，但看天阴怕淋雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-140-2.wav"}}, {"session_id": "815", "turn_idx": 3, "from": "Assistant", "text": "带伞是好习惯，深圳雨多。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-140-3.wav"}}, {"session_id": "815", "turn_idx": 4, "from": "Human", "text": "是啊，明后天可能还要去。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-140-4.wav"}}, {"session_id": "815", "turn_idx": 5, "from": "Assistant", "text": "提前看天气省心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-140-5.wav"}}, {"session_id": "815", "turn_idx": 6, "from": "Human", "text": "小跃，查询深圳未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-140-6.wav"}}, {"session_id": "815", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "622", "turn_idx": 0, "from": "Human", "text": "嘿小跃，在忙啥呢？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-141-0.wav"}}, {"session_id": "622", "turn_idx": 1, "from": "Assistant", "text": "在看天气预报呢。你呢，今天有啥计划？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-141-1.wav"}}, {"session_id": "622", "turn_idx": 2, "from": "Human", "text": "明天要去广州出差，可能待两天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-141-2.wav"}}, {"session_id": "622", "turn_idx": 3, "from": "Assistant", "text": "广州那边挺热闹的。出差顺利哦！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-141-3.wav"}}, {"session_id": "622", "turn_idx": 4, "from": "Human", "text": "谢谢！小跃，赶紧帮我查一下广州明后天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-141-4.wav"}}, {"session_id": "622", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "969", "turn_idx": 0, "from": "Human", "text": "小跃，我后天和朋友约在重庆吃火锅，但担心下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-142-0.wav"}}, {"session_id": "969", "turn_idx": 1, "from": "Assistant", "text": "重庆火锅辣得过瘾！下雨天也挡不住美食诱惑。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-142-1.wav"}}, {"session_id": "969", "turn_idx": 2, "from": "Human", "text": "是啊，不过还是查查重庆的天气，包括今天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-142-2.wav"}}, {"session_id": "969", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "789", "turn_idx": 0, "from": "Human", "text": "小跃，今天下班后想去珠江边散步。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-143-0.wav"}}, {"session_id": "789", "turn_idx": 1, "from": "Assistant", "text": "珠江在广州，傍晚散步很惬意，吹吹风。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-143-1.wav"}}, {"session_id": "789", "turn_idx": 2, "from": "Human", "text": "对，小跃，查一下珠江的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-143-2.wav"}}, {"session_id": "789", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"pearl river\"}", "value": "function\nget_weather\n{\"location\": \"pearl river\"}"}] +[{"session_id": "948", "turn_idx": 0, "from": "Human", "text": "小跃，我打算开车去西安玩，听说兵马俑很壮观。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-144-0.wav"}}, {"session_id": "948", "turn_idx": 1, "from": "Assistant", "text": "开车注意安全！需要导航路线吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-144-1.wav"}}, {"session_id": "948", "turn_idx": 2, "from": "Human", "text": "待会儿再说，先帮我看看西安这三天的天气怎么样，怕路滑。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-144-2.wav"}}, {"session_id": "948", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "455", "turn_idx": 0, "from": "Human", "text": "小跃，成都的火锅店推荐吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-145-0.wav"}}, {"session_id": "455", "turn_idx": 1, "from": "Assistant", "text": "成都火锅很有名！不过我是语音助手，不能吃。需要查天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-145-1.wav"}}, {"session_id": "455", "turn_idx": 2, "from": "Human", "text": "行，查一下今天成都的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-145-2.wav"}}, {"session_id": "455", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "650", "turn_idx": 0, "from": "Human", "text": "小跃，你今天忙不忙？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-146-0.wav"}}, {"session_id": "650", "turn_idx": 1, "from": "Assistant", "text": "不忙呢，随时准备帮你。你有什么事吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-146-1.wav"}}, {"session_id": "650", "turn_idx": 2, "from": "Human", "text": "没啥大事，就是后天要去北京开会，想查查那边天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-146-2.wav"}}, {"session_id": "650", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "132", "turn_idx": 0, "from": "Human", "text": "小跃，我有点无聊，想找点事做。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-147-0.wav"}}, {"session_id": "132", "turn_idx": 1, "from": "Assistant", "text": "要不要试试户外运动？比如骑自行车或散步。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-147-1.wav"}}, {"session_id": "132", "turn_idx": 2, "from": "Human", "text": "嗯，我朋友约我去成都玩，但得看天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-147-2.wav"}}, {"session_id": "132", "turn_idx": 3, "from": "Assistant", "text": "成都不错！天气对旅行很重要，记得提前准备。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-147-3.wav"}}, {"session_id": "132", "turn_idx": 4, "from": "Human", "text": "那赶紧的，小跃，帮我查一下成都的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-147-4.wav"}}, {"session_id": "132", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "260", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天感觉怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-148-0.wav"}}, {"session_id": "260", "turn_idx": 1, "from": "Assistant", "text": "我挺好的，谢谢你关心！你今天有什么安排吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-148-1.wav"}}, {"session_id": "260", "turn_idx": 2, "from": "Human", "text": "计划去公园散步，所以能查一下北京的天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-148-2.wav"}}, {"session_id": "260", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "295", "turn_idx": 0, "from": "Human", "text": "小跃，计划去爬山锻炼身体。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-149-0.wav"}}, {"session_id": "295", "turn_idx": 1, "from": "Assistant", "text": "爬山是好习惯。记得穿舒适的鞋子。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-149-1.wav"}}, {"session_id": "295", "turn_idx": 2, "from": "Human", "text": "好的，但先告诉我成都的天气情况，今天到后天有没有雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-149-2.wav"}}, {"session_id": "295", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "864", "turn_idx": 0, "from": "Human", "text": "小跃，今天下午想去成都春熙路逛街买衣服。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-150-0.wav"}}, {"session_id": "864", "turn_idx": 1, "from": "Assistant", "text": "购物放松心情！成都天气多变，带把伞以防万一。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-150-1.wav"}}, {"session_id": "864", "turn_idx": 2, "from": "Human", "text": "好建议，成都今天下午天气怎样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-150-2.wav"}}, {"session_id": "864", "turn_idx": 3, "from": "Assistant", "text": "我马上帮你查，稍等。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-150-3.wav"}}, {"session_id": "864", "turn_idx": 4, "from": "Human", "text": "查查成都未来三天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-150-4.wav"}}, {"session_id": "864", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "584", "turn_idx": 0, "from": "Human", "text": "成都的朋友叫我过去玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-151-0.wav"}}, {"session_id": "584", "turn_idx": 1, "from": "Assistant", "text": "成都有好多小吃，别错过火锅。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-151-1.wav"}}, {"session_id": "584", "turn_idx": 2, "from": "Human", "text": "是啊，得看看天气情况。 查一下成都的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-151-2.wav"}}, {"session_id": "584", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "544", "turn_idx": 0, "from": "Human", "text": "小跃，我打算去商场买新衣服，但不知哪天去好。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-152-0.wav"}}, {"session_id": "544", "turn_idx": 1, "from": "Assistant", "text": "购物真开心！选个天气好的日子，逛起来舒服。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-152-1.wav"}}, {"session_id": "544", "turn_idx": 2, "from": "Human", "text": "是啊，商场在商业街，下雨就麻烦了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-152-2.wav"}}, {"session_id": "544", "turn_idx": 3, "from": "Assistant", "text": "对，雨天人挤人。我帮你查查天气预报？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-152-3.wav"}}, {"session_id": "544", "turn_idx": 4, "from": "Human", "text": "嗯，查一下商业街的天气，就最近三天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-152-4.wav"}}, {"session_id": "544", "turn_idx": 5, "from": "Assistant", "text": "好的，主人。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-152-5.wav"}}, {"session_id": "544", "turn_idx": 6, "from": "Human", "text": "小跃，具体帮我查查明天商业街的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-152-6.wav"}}, {"session_id": "544", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"commercial street\"}", "value": "function\nget_weather\n{\"location\": \"commercial street\"}"}] +[{"session_id": "653", "turn_idx": 0, "from": "Human", "text": "小跃，广州有啥好吃的推荐？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-153-0.wav"}}, {"session_id": "653", "turn_idx": 1, "from": "Assistant", "text": "广州的早茶很棒，比如虾饺和烧卖。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-153-1.wav"}}, {"session_id": "653", "turn_idx": 2, "from": "Human", "text": "谢谢！我后天要去广州，顺便问问这三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-153-2.wav"}}, {"session_id": "653", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "753", "turn_idx": 0, "from": "Human", "text": "小跃，今天下班后我可能去深圳逛街。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-154-0.wav"}}, {"session_id": "753", "turn_idx": 1, "from": "Assistant", "text": "深圳商场挺多的，你去哪个区呀？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-154-1.wav"}}, {"session_id": "753", "turn_idx": 2, "from": "Human", "text": "福田那边吧，先帮我看看深圳今天天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-154-2.wav"}}, {"session_id": "753", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "586", "turn_idx": 0, "from": "Human", "text": "南京的夫子庙听说挺热闹。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-155-0.wav"}}, {"session_id": "586", "turn_idx": 1, "from": "Assistant", "text": "对，文化气息浓，适合散步。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-155-1.wav"}}, {"session_id": "586", "turn_idx": 2, "from": "Human", "text": "我后天要去，查查天气吧。 查南京的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-155-2.wav"}}, {"session_id": "586", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "131", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天有什么建议吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-156-0.wav"}}, {"session_id": "131", "turn_idx": 1, "from": "Assistant", "text": "早上好！我觉得可以去户外走走，呼吸新鲜空气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-156-1.wav"}}, {"session_id": "131", "turn_idx": 2, "from": "Human", "text": "好主意，但我不确定要不要带伞。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-156-2.wav"}}, {"session_id": "131", "turn_idx": 3, "from": "Assistant", "text": "带伞总没错，尤其最近天气多变。你具体在哪个城市？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-156-3.wav"}}, {"session_id": "131", "turn_idx": 4, "from": "Human", "text": "我在深圳。小跃，快查查深圳未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-156-4.wav"}}, {"session_id": "131", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "774", "turn_idx": 0, "from": "Human", "text": "小跃，明天全家要去成都玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-157-0.wav"}}, {"session_id": "774", "turn_idx": 1, "from": "Assistant", "text": "家庭出游真棒，玩得开心点。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-157-1.wav"}}, {"session_id": "774", "turn_idx": 2, "from": "Human", "text": "成都明天的天气怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-157-2.wav"}}, {"session_id": "774", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "271", "turn_idx": 0, "from": "Human", "text": "小跃，我后天要去上海出差，有点紧张呢。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-158-0.wav"}}, {"session_id": "271", "turn_idx": 1, "from": "Assistant", "text": "别担心，上海很棒的！记得提前规划行程，比如去外滩逛逛。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-158-1.wav"}}, {"session_id": "271", "turn_idx": 2, "from": "Human", "text": "嗯，外滩确实不错。但我得准备合适的衣服，天气很重要。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-158-2.wav"}}, {"session_id": "271", "turn_idx": 3, "from": "Assistant", "text": "对啊，天气会影响行程。需要我帮你看看上海的天气情况吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-158-3.wav"}}, {"session_id": "271", "turn_idx": 4, "from": "Human", "text": "好的，查一下上海这三天的天气吧，包括今天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-158-4.wav"}}, {"session_id": "271", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "983", "turn_idx": 0, "from": "Human", "text": "小跃，下午好！周末想去购物放松。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-159-0.wav"}}, {"session_id": "983", "turn_idx": 1, "from": "Assistant", "text": "下午好！购物是个好主意，打算去哪儿？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-159-1.wav"}}, {"session_id": "983", "turn_idx": 2, "from": "Human", "text": "成都的春熙路，听说很热闹。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-159-2.wav"}}, {"session_id": "983", "turn_idx": 3, "from": "Assistant", "text": "春熙路确实繁华，但天气太热或下雨就不方便了。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-159-3.wav"}}, {"session_id": "983", "turn_idx": 4, "from": "Human", "text": "说得对，我得提前知道天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-159-4.wav"}}, {"session_id": "983", "turn_idx": 5, "from": "Assistant", "text": "没问题，我可以协助查询。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-159-5.wav"}}, {"session_id": "983", "turn_idx": 6, "from": "Human", "text": "那帮我查一下成都最近三天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-159-6.wav"}}, {"session_id": "983", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chengdu\"}", "value": "function\nget_weather\n{\"location\": \"chengdu\"}"}] +[{"session_id": "510", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天过得咋样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-160-0.wav"}}, {"session_id": "510", "turn_idx": 1, "from": "Assistant", "text": "我挺好呀，有啥事尽管说！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-160-1.wav"}}, {"session_id": "510", "turn_idx": 2, "from": "Human", "text": "周末想带孩子去公园野餐，但怕下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-160-2.wav"}}, {"session_id": "510", "turn_idx": 3, "from": "Assistant", "text": "北京最近天气变化快，得提前看看。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-160-3.wav"}}, {"session_id": "510", "turn_idx": 4, "from": "Human", "text": "那你帮我查一下北京今天、明天和后天的天气呗。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-160-4.wav"}}, {"session_id": "510", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "802", "turn_idx": 0, "from": "Human", "text": "小跃，下午好！帮我回忆一下，我下周有啥安排？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-161-0.wav"}}, {"session_id": "802", "turn_idx": 1, "from": "Assistant", "text": "下午好！你之前说周末要去广州参加家庭聚会。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-161-1.wav"}}, {"session_id": "802", "turn_idx": 2, "from": "Human", "text": "对哦，差点忘了。我得提前准备礼物。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-161-2.wav"}}, {"session_id": "802", "turn_idx": 3, "from": "Assistant", "text": "广州美食多，带点特产肯定受欢迎。天气热吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-161-3.wav"}}, {"session_id": "802", "turn_idx": 4, "from": "Human", "text": "就是不确定，万一下雨就麻烦了。 那你帮我查广州这三天的天气，包括今天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-161-4.wav"}}, {"session_id": "802", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "262", "turn_idx": 0, "from": "Human", "text": "你好小跃，周末快到了，有什么好建议？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-162-0.wav"}}, {"session_id": "262", "turn_idx": 1, "from": "Assistant", "text": "周末可以去郊外或者看场电影，你想去哪里玩？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-162-1.wav"}}, {"session_id": "262", "turn_idx": 2, "from": "Human", "text": "我打算去广州，查一下广州的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-162-2.wav"}}, {"session_id": "262", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "391", "turn_idx": 0, "from": "Human", "text": "明天我要去上海出差，有点紧张。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-163-0.wav"}}, {"session_id": "391", "turn_idx": 1, "from": "Assistant", "text": "别担心，上海交通很方便，祝您顺利。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-163-1.wav"}}, {"session_id": "391", "turn_idx": 2, "from": "Human", "text": "谢谢，小跃，顺便查一下上海的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-163-2.wav"}}, {"session_id": "391", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "229", "turn_idx": 0, "from": "Human", "text": "小跃，我明天想跑步，西安的天气适合不？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-164-0.wav"}}, {"session_id": "229", "turn_idx": 1, "from": "Assistant", "text": "跑步是好习惯！需要查西安的天气？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-164-1.wav"}}, {"session_id": "229", "turn_idx": 2, "from": "Human", "text": "嗯，就查西安今天的天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-164-2.wav"}}, {"session_id": "229", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "319", "turn_idx": 0, "from": "Human", "text": "小跃，我住在重庆，但经常忘记看天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-165-0.wav"}}, {"session_id": "319", "turn_idx": 1, "from": "Assistant", "text": "重庆山城很美，但天气多变呢。今天出门带伞了吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-165-1.wav"}}, {"session_id": "319", "turn_idx": 2, "from": "Human", "text": "没带，希望别下雨。明天还要爬山。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-165-2.wav"}}, {"session_id": "319", "turn_idx": 3, "from": "Assistant", "text": "爬山更要看天气了，安全第一。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-165-3.wav"}}, {"session_id": "319", "turn_idx": 4, "from": "Human", "text": "是啊，后天是周末计划。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-165-4.wav"}}, {"session_id": "319", "turn_idx": 5, "from": "Assistant", "text": "那我可以帮你查查本地天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-165-5.wav"}}, {"session_id": "319", "turn_idx": 6, "from": "Human", "text": "好的，帮我查一下重庆的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-165-6.wav"}}, {"session_id": "319", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "568", "turn_idx": 0, "from": "Human", "text": "小跃，后天我打算去厦门度假。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-166-0.wav"}}, {"session_id": "568", "turn_idx": 1, "from": "Assistant", "text": "厦门的海滩很漂亮，适合放松。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-166-1.wav"}}, {"session_id": "568", "turn_idx": 2, "from": "Human", "text": "是啊，不过得看看厦门这三天的天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-166-2.wav"}}, {"session_id": "568", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xiamen\"}", "value": "function\nget_weather\n{\"location\": \"xiamen\"}"}] +[{"session_id": "191", "turn_idx": 0, "from": "Human", "text": "小跃，周末想去海边玩，你觉得呢？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-167-0.wav"}}, {"session_id": "191", "turn_idx": 1, "from": "Assistant", "text": "海边放松很舒服。需要我帮你查目的地天气吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-167-1.wav"}}, {"session_id": "191", "turn_idx": 2, "from": "Human", "text": "好啊，查查三亚未来三天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-167-2.wav"}}, {"session_id": "191", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"sanya\"}", "value": "function\nget_weather\n{\"location\": \"sanya\"}"}] +[{"session_id": "486", "turn_idx": 0, "from": "Human", "text": "小跃，今天下午我想去南京逛街，买点新衣服。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-168-0.wav"}}, {"session_id": "486", "turn_idx": 1, "from": "Assistant", "text": "南京的新街口商圈很棒，购物天堂。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-168-1.wav"}}, {"session_id": "486", "turn_idx": 2, "from": "Human", "text": "嗯，但看天有点阴，担心下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-168-2.wav"}}, {"session_id": "486", "turn_idx": 3, "from": "Assistant", "text": "带个包包装雨具吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-168-3.wav"}}, {"session_id": "486", "turn_idx": 4, "from": "Human", "text": "行，小跃，帮我查查南京的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-168-4.wav"}}, {"session_id": "486", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "863", "turn_idx": 0, "from": "Human", "text": "小跃，后天我们家在深圳有聚餐，地点定在海岸城。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-169-0.wav"}}, {"session_id": "863", "turn_idx": 1, "from": "Assistant", "text": "家庭聚会真温馨，深圳天气热，注意防晒哦。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-169-1.wav"}}, {"session_id": "863", "turn_idx": 2, "from": "Human", "text": "谢谢提醒，深圳这几天天气如何？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-169-2.wav"}}, {"session_id": "863", "turn_idx": 3, "from": "Assistant", "text": "天气信息我来处理，别担心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-169-3.wav"}}, {"session_id": "863", "turn_idx": 4, "from": "Human", "text": "查一下深圳未来三天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-169-4.wav"}}, {"session_id": "863", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "919", "turn_idx": 0, "from": "Human", "text": "小跃，今天感觉有点凉，我在重庆这边。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-170-0.wav"}}, {"session_id": "919", "turn_idx": 1, "from": "Assistant", "text": "重庆天气多变，注意保暖。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-170-1.wav"}}, {"session_id": "919", "turn_idx": 2, "from": "Human", "text": "谢谢提醒！帮我查重庆今天到后天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-170-2.wav"}}, {"session_id": "919", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "769", "turn_idx": 0, "from": "Human", "text": "小跃，重庆的夜景听说超棒，我今晚想去看看。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-171-0.wav"}}, {"session_id": "769", "turn_idx": 1, "from": "Assistant", "text": "重庆山城夜景确实独特，先确认天气安全。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-171-1.wav"}}, {"session_id": "769", "turn_idx": 2, "from": "Human", "text": "好，查一下今天重庆的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-171-2.wav"}}, {"session_id": "769", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "577", "turn_idx": 0, "from": "Human", "text": "小跃，后天在南京有个朋友野餐聚会，期待。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-172-0.wav"}}, {"session_id": "577", "turn_idx": 1, "from": "Assistant", "text": "朋友聚聚真好！南京公园多，天气一般不错。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-172-1.wav"}}, {"session_id": "577", "turn_idx": 2, "from": "Human", "text": "希望别下雨，我们计划在户外。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-172-2.wav"}}, {"session_id": "577", "turn_idx": 3, "from": "Assistant", "text": "户外活动看天气，要我查预报吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-172-3.wav"}}, {"session_id": "577", "turn_idx": 4, "from": "Human", "text": "小跃，帮我查南京未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-172-4.wav"}}, {"session_id": "577", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "827", "turn_idx": 0, "from": "Human", "text": "小跃，晚上好！周末想去西安看兵马俑。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-173-0.wav"}}, {"session_id": "827", "turn_idx": 1, "from": "Assistant", "text": "晚上好！兵马俑值得一去，但天气影响体验。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-173-1.wav"}}, {"session_id": "827", "turn_idx": 2, "from": "Human", "text": "没错，我可不想淋雨。 小跃，帮我查查西安的天气，明后天都要。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-173-2.wav"}}, {"session_id": "827", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "856", "turn_idx": 0, "from": "Human", "text": "小跃，晚上好！今天工作顺利吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-174-0.wav"}}, {"session_id": "856", "turn_idx": 1, "from": "Assistant", "text": "挺顺利的，你呢？是不是在计划周末活动？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-174-1.wav"}}, {"session_id": "856", "turn_idx": 2, "from": "Human", "text": "对啊，我打算后天去南京看博物馆，希望别刮风。 小跃，帮我查一下南京的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-174-2.wav"}}, {"session_id": "856", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "722", "turn_idx": 0, "from": "Human", "text": "小跃，今天心情好，想去公园散步。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-175-0.wav"}}, {"session_id": "722", "turn_idx": 1, "from": "Assistant", "text": "散步放松是个好主意！想去哪个公园？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-175-1.wav"}}, {"session_id": "722", "turn_idx": 2, "from": "Human", "text": "打算去广州的珠江边走走。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-175-2.wav"}}, {"session_id": "722", "turn_idx": 3, "from": "Assistant", "text": "珠江边风景很美，适合散心。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-175-3.wav"}}, {"session_id": "722", "turn_idx": 4, "from": "Human", "text": "对，但怕下雨。小跃，查查广州这三天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-175-4.wav"}}, {"session_id": "722", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "351", "turn_idx": 0, "from": "Human", "text": "小跃，我周末计划带家人去上海迪士尼玩，兴奋死了！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-176-0.wav"}}, {"session_id": "351", "turn_idx": 1, "from": "Assistant", "text": "哇，听起来超有趣！记得提前订票哦，需要我提醒你时间吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-176-1.wav"}}, {"session_id": "351", "turn_idx": 2, "from": "Human", "text": "不用，票已经买好了。现在只关心天气别捣乱。 那你能查查上海包括今天的三天天气吗？今天、明天和后天都要。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-176-2.wav"}}, {"session_id": "351", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "569", "turn_idx": 0, "from": "Human", "text": "小跃，我明天要回北京老家。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-177-0.wav"}}, {"session_id": "569", "turn_idx": 1, "from": "Assistant", "text": "北京现在正是秋天，天气应该很舒适。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-177-1.wav"}}, {"session_id": "569", "turn_idx": 2, "from": "Human", "text": "希望如此，帮我查查北京这三天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-177-2.wav"}}, {"session_id": "569", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "90", "turn_idx": 0, "from": "Human", "text": "嗨小跃，我明天想去公园散步，不知道天气怎么样。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-178-0.wav"}}, {"session_id": "90", "turn_idx": 1, "from": "Assistant", "text": "好的，我可以帮你查一下天气。记得告诉我具体地点哦。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-178-1.wav"}}, {"session_id": "90", "turn_idx": 2, "from": "Human", "text": "帮我看看北京未来三天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-178-2.wav"}}, {"session_id": "90", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "50", "turn_idx": 0, "from": "Human", "text": "小跃早啊，今天感觉好闷热", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-179-0.wav"}}, {"session_id": "50", "turn_idx": 1, "from": "Assistant", "text": "确实呢，湿度特别高。您今天要出门吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-179-1.wav"}}, {"session_id": "50", "turn_idx": 2, "from": "Human", "text": "下午想去西湖边逛逛，不知道会不会下雨", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-179-2.wav"}}, {"session_id": "50", "turn_idx": 3, "from": "Assistant", "text": "西湖最近荷花开了呢！需要我帮您看看天气情况吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-179-3.wav"}}, {"session_id": "50", "turn_idx": 4, "from": "Human", "text": "太好了，查查西湖今天到后天的天气吧", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-179-4.wav"}}, {"session_id": "50", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "801", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？我有个小问题。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-180-0.wav"}}, {"session_id": "801", "turn_idx": 1, "from": "Assistant", "text": "在呢，随时为你服务！说说看，遇到啥事了？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-180-1.wav"}}, {"session_id": "801", "turn_idx": 2, "from": "Human", "text": "我明天要去上海出差，有点紧张。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-180-2.wav"}}, {"session_id": "801", "turn_idx": 3, "from": "Assistant", "text": "别担心，上海交通很方便。酒店订好了吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-180-3.wav"}}, {"session_id": "801", "turn_idx": 4, "from": "Human", "text": "订好了，但不知道带什么衣服。天气会不会很热？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-180-4.wav"}}, {"session_id": "801", "turn_idx": 5, "from": "Assistant", "text": "确实，天气影响穿搭。先看看预报比较稳妥。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-180-5.wav"}}, {"session_id": "801", "turn_idx": 6, "from": "Human", "text": "行，小跃，帮我查上海今天到后天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-180-6.wav"}}, {"session_id": "801", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shanghai\"}", "value": "function\nget_weather\n{\"location\": \"shanghai\"}"}] +[{"session_id": "825", "turn_idx": 0, "from": "Human", "text": "小跃，你好！我后天要去南京见朋友。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-181-0.wav"}}, {"session_id": "825", "turn_idx": 1, "from": "Assistant", "text": "你好！南京挺适合聚会，中山陵不错。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-181-1.wav"}}, {"session_id": "825", "turn_idx": 2, "from": "Human", "text": "希望别赶上大雨。 小跃，帮我查查南京的天气，三天内的。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-181-2.wav"}}, {"session_id": "825", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "308", "turn_idx": 0, "from": "Human", "text": "小跃，周末有个庙会活动。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-182-0.wav"}}, {"session_id": "308", "turn_idx": 1, "from": "Assistant", "text": "节日气氛浓！在哪个城市？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-182-1.wav"}}, {"session_id": "308", "turn_idx": 2, "from": "Human", "text": "在西安，古城墙附近。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-182-2.wav"}}, {"session_id": "308", "turn_idx": 3, "from": "Assistant", "text": "西安庙会热闹，但风大就难受。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-182-3.wav"}}, {"session_id": "308", "turn_idx": 4, "from": "Human", "text": "对，我得准备帽子。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-182-4.wav"}}, {"session_id": "308", "turn_idx": 5, "from": "Assistant", "text": "查查天气，提前安排。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-182-5.wav"}}, {"session_id": "308", "turn_idx": 6, "from": "Human", "text": "好，请帮我查一下西安从今天开始的三天天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-182-6.wav"}}, {"session_id": "308", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "74", "turn_idx": 0, "from": "Human", "text": "小跃，我晒的被子还在阳台！", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-183-0.wav"}}, {"session_id": "74", "turn_idx": 1, "from": "Assistant", "text": "别急别急，当前北京紫外线指数中等，暂时没雨", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-183-1.wav"}}, {"session_id": "74", "turn_idx": 2, "from": "Human", "text": "可云越来越厚了，你确定吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-183-2.wav"}}, {"session_id": "74", "turn_idx": 3, "from": "Assistant", "text": "气象台刚更新了数据，需要查未来三小时详细变化？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-183-3.wav"}}, {"session_id": "74", "turn_idx": 4, "from": "Human", "text": "直接查今天北京全天天气吧", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-183-4.wav"}}, {"session_id": "74", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"beijing\"}", "value": "function\nget_weather\n{\"location\": \"beijing\"}"}] +[{"session_id": "409", "turn_idx": 0, "from": "Human", "text": "小跃，我下周要去西安旅游，看兵马俑。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-184-0.wav"}}, {"session_id": "409", "turn_idx": 1, "from": "Assistant", "text": "西安的历史古迹很棒！提前规划行程很重要。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-184-1.wav"}}, {"session_id": "409", "turn_idx": 2, "from": "Human", "text": "对，但得先知道天气，免得白跑一趟。 小跃，查一下西安这三天的天气情况。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-184-2.wav"}}, {"session_id": "409", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "84", "turn_idx": 0, "from": "Human", "text": "爷爷想逛天坛，可老人家怕闷热。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-185-0.wav"}}, {"session_id": "84", "turn_idx": 1, "from": "Assistant", "text": "建议早上凉快时去，带足水。爷爷住哪个区呀？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-185-1.wav"}}, {"session_id": "84", "turn_idx": 2, "from": "Human", "text": "在崇文门附近，过去挺近的。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-185-2.wav"}}, {"session_id": "84", "turn_idx": 3, "from": "Assistant", "text": "那很方便！要查天坛公园这三天的温度和湿度吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-185-3.wav"}}, {"session_id": "84", "turn_idx": 4, "from": "Human", "text": "对，特别是今天下午的体感温度。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-185-4.wav"}}, {"session_id": "84", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"temple of heaven\"}", "value": "function\nget_weather\n{\"location\": \"temple of heaven\"}"}] +[{"session_id": "309", "turn_idx": 0, "from": "Human", "text": "小跃，朋友明天来我家玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-186-0.wav"}}, {"session_id": "309", "turn_idx": 1, "from": "Assistant", "text": "欢迎朋友！你家在苏州对吧？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-186-1.wav"}}, {"session_id": "309", "turn_idx": 2, "from": "Human", "text": "是啊，苏州园林挺漂亮的。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-186-2.wav"}}, {"session_id": "309", "turn_idx": 3, "from": "Assistant", "text": "园林散步惬意，但别赶上下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-186-3.wav"}}, {"session_id": "309", "turn_idx": 4, "from": "Human", "text": "对，我想带他们出去逛逛。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-186-4.wav"}}, {"session_id": "309", "turn_idx": 5, "from": "Assistant", "text": "天气预报很重要。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-186-5.wav"}}, {"session_id": "309", "turn_idx": 6, "from": "Human", "text": "那你帮我查查苏州今天、明天和后天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-186-6.wav"}}, {"session_id": "309", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"suzhou\"}", "value": "function\nget_weather\n{\"location\": \"suzhou\"}"}] +[{"session_id": "6", "turn_idx": 0, "from": "Human", "text": "我种的菜苗蔫了，是不是晒过头了？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-187-0.wav"}}, {"session_id": "6", "turn_idx": 1, "from": "Assistant", "text": "可能是高温缺水！您家在哪个城市？我看看紫外线强度。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-187-1.wav"}}, {"session_id": "6", "turn_idx": 2, "from": "Human", "text": "在南京，今天和明天的温度你报一下。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-187-2.wav"}}, {"session_id": "6", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "375", "turn_idx": 0, "from": "Human", "text": "小跃，我明天得去南京办点事，第一次去有点陌生。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-188-0.wav"}}, {"session_id": "375", "turn_idx": 1, "from": "Assistant", "text": "南京挺方便的，中山陵和夫子庙值得一去。办事前先规划路线吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-188-1.wav"}}, {"session_id": "375", "turn_idx": 2, "from": "Human", "text": "路线好说，但怕天气影响交通，比如下雨堵车。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-188-2.wav"}}, {"session_id": "375", "turn_idx": 3, "from": "Assistant", "text": "交通确实受天气影响大，查查预报能省时间。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-188-3.wav"}}, {"session_id": "375", "turn_idx": 4, "from": "Human", "text": "没错。小跃，查一下南京的天气，今天开始的三天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-188-4.wav"}}, {"session_id": "375", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "627", "turn_idx": 0, "from": "Human", "text": "小跃，周末有啥推荐活动？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-189-0.wav"}}, {"session_id": "627", "turn_idx": 1, "from": "Assistant", "text": "武汉的东湖不错，适合散步拍照。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-189-1.wav"}}, {"session_id": "627", "turn_idx": 2, "from": "Human", "text": "好建议！我正想去武汉逛逛。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-189-2.wav"}}, {"session_id": "627", "turn_idx": 3, "from": "Assistant", "text": "记得穿舒服的鞋子。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-189-3.wav"}}, {"session_id": "627", "turn_idx": 4, "from": "Human", "text": "好嘞。小跃，先帮我查一下武汉明后天的天气如何。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-189-4.wav"}}, {"session_id": "627", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"wuhan\"}", "value": "function\nget_weather\n{\"location\": \"wuhan\"}"}] +[{"session_id": "340", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天感觉怎么样？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-190-0.wav"}}, {"session_id": "340", "turn_idx": 1, "from": "Assistant", "text": "挺好的，谢谢关心！你今天有什么计划吗？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-190-1.wav"}}, {"session_id": "340", "turn_idx": 2, "from": "Human", "text": "我打算带家人去杭州玩两天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-190-2.wav"}}, {"session_id": "340", "turn_idx": 3, "from": "Assistant", "text": "杭州西湖那边风景很美，适合全家出游。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-190-3.wav"}}, {"session_id": "340", "turn_idx": 4, "from": "Human", "text": "是啊，但担心天气不好。帮我查查杭州明天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-190-4.wav"}}, {"session_id": "340", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hangzhou\"}", "value": "function\nget_weather\n{\"location\": \"hangzhou\"}"}] +[{"session_id": "772", "turn_idx": 0, "from": "Human", "text": "小跃，后天我们要去广州旅行。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-191-0.wav"}}, {"session_id": "772", "turn_idx": 1, "from": "Assistant", "text": "旅行前记得检查行李哦。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-191-1.wav"}}, {"session_id": "772", "turn_idx": 2, "from": "Human", "text": "广州的天气如何？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-191-2.wav"}}, {"session_id": "772", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "224", "turn_idx": 0, "from": "Human", "text": "小跃，我今天要出门，深圳的天气如何？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-192-0.wav"}}, {"session_id": "224", "turn_idx": 1, "from": "Assistant", "text": "出门注意安全！要我查深圳的天气？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-192-1.wav"}}, {"session_id": "224", "turn_idx": 2, "from": "Human", "text": "对，就查今天和明天的天气信息。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-192-2.wav"}}, {"session_id": "224", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] +[{"session_id": "77", "turn_idx": 0, "from": "Human", "text": "小跃，南京梧桐絮是不是又飘起来了？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-193-0.wav"}}, {"session_id": "77", "turn_idx": 1, "from": "Assistant", "text": "是呀，过敏人群最好戴口罩。需要结合天气看看浓度？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-193-1.wav"}}, {"session_id": "77", "turn_idx": 2, "from": "Human", "text": "先看天气吧，要是刮风就更严重", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-193-2.wav"}}, {"session_id": "77", "turn_idx": 3, "from": "Assistant", "text": "风力确实影响飞絮扩散。查南京今明两天？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-193-3.wav"}}, {"session_id": "77", "turn_idx": 4, "from": "Human", "text": "对，包括今天总共三天的天气", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-193-4.wav"}}, {"session_id": "77", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"nanjing\"}", "value": "function\nget_weather\n{\"location\": \"nanjing\"}"}] +[{"session_id": "549", "turn_idx": 0, "from": "Human", "text": "小跃，我们家乡过节，我想回去看看。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-194-0.wav"}}, {"session_id": "549", "turn_idx": 1, "from": "Assistant", "text": "节日团聚真温暖！家乡在哪儿？天气会影响行程。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-194-1.wav"}}, {"session_id": "549", "turn_idx": 2, "from": "Human", "text": "在老家小镇，我得开车回去，怕路上天气差。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-194-2.wav"}}, {"session_id": "549", "turn_idx": 3, "from": "Assistant", "text": "安全第一。查查小镇天气？确保顺利出行。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-194-3.wav"}}, {"session_id": "549", "turn_idx": 4, "from": "Human", "text": "嗯，查一下老家小镇的天气，就最近三天。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-194-4.wav"}}, {"session_id": "549", "turn_idx": 5, "from": "Assistant", "text": "没问题。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-194-5.wav"}}, {"session_id": "549", "turn_idx": 6, "from": "Human", "text": "小跃，帮我查查后天老家小镇的详细天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-194-6.wav"}}, {"session_id": "549", "turn_idx": 7, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"hometown town\"}", "value": "function\nget_weather\n{\"location\": \"hometown town\"}"}] +[{"session_id": "968", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想去西安看兵马俑，但怕晒。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-195-0.wav"}}, {"session_id": "968", "turn_idx": 1, "from": "Assistant", "text": "兵马俑值得一看！带好帽子和水，避免中暑。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-195-1.wav"}}, {"session_id": "968", "turn_idx": 2, "from": "Human", "text": "好的，那查查西安的天气吧，今天到后天会不会太热？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-195-2.wav"}}, {"session_id": "968", "turn_idx": 3, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"xian\"}", "value": "function\nget_weather\n{\"location\": \"xian\"}"}] +[{"session_id": "52", "turn_idx": 0, "from": "Human", "text": "冰箱牛奶喝完啦，等会儿去买", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-196-0.wav"}}, {"session_id": "52", "turn_idx": 1, "from": "Assistant", "text": "需要推荐超市吗？或者帮您叫外卖", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-196-1.wav"}}, {"session_id": "52", "turn_idx": 2, "from": "Human", "text": "我骑车去菜市场，希望别像昨天刮大风", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-196-2.wav"}}, {"session_id": "52", "turn_idx": 3, "from": "Assistant", "text": "昨天阵风有七级呢，安全第一哦", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-196-3.wav"}}, {"session_id": "52", "turn_idx": 4, "from": "Human", "text": "快查查今天广州的天气适不适合骑车", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-196-4.wav"}}, {"session_id": "52", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"guangzhou\"}", "value": "function\nget_weather\n{\"location\": \"guangzhou\"}"}] +[{"session_id": "368", "turn_idx": 0, "from": "Human", "text": "小跃，周末计划在重庆吃火锅，但怕下雨。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-197-0.wav"}}, {"session_id": "368", "turn_idx": 1, "from": "Assistant", "text": "重庆火锅超赞！需要我查天气确保顺利？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-197-1.wav"}}, {"session_id": "368", "turn_idx": 2, "from": "Human", "text": "对，帮我查重庆的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-197-2.wav"}}, {"session_id": "368", "turn_idx": 3, "from": "Assistant", "text": "没问题，重庆的天气交给我。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-197-3.wav"}}, {"session_id": "368", "turn_idx": 4, "from": "Human", "text": "请查查今天到后天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-197-4.wav"}}, {"session_id": "368", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"chongqing\"}", "value": "function\nget_weather\n{\"location\": \"chongqing\"}"}] +[{"session_id": "349", "turn_idx": 0, "from": "Human", "text": "小跃，最近天气真不错啊。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-198-0.wav"}}, {"session_id": "349", "turn_idx": 1, "from": "Assistant", "text": "是啊，阳光明媚的。你有什么计划？", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-198-1.wav"}}, {"session_id": "349", "turn_idx": 2, "from": "Human", "text": "朋友推荐去青岛海边玩。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-198-2.wav"}}, {"session_id": "349", "turn_idx": 3, "from": "Assistant", "text": "青岛的海鲜和沙滩超棒。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-198-3.wav"}}, {"session_id": "349", "turn_idx": 4, "from": "Human", "text": "听起来诱人。帮我查查青岛后天的天气吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-198-4.wav"}}, {"session_id": "349", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"qingdao\"}", "value": "function\nget_weather\n{\"location\": \"qingdao\"}"}] +[{"session_id": "433", "turn_idx": 0, "from": "Human", "text": "小跃，晚饭吃了吗？我正做饭呢。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-199-0.wav"}}, {"session_id": "433", "turn_idx": 1, "from": "Assistant", "text": "还没呢，你做的啥？聊点有趣的事吧。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-199-1.wav"}}, {"session_id": "433", "turn_idx": 2, "from": "Human", "text": "家常菜呗。周末要去深圳出差，有点紧张。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-199-2.wav"}}, {"session_id": "433", "turn_idx": 3, "from": "Assistant", "text": "深圳很现代，放松点。出差前先关注下天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-199-3.wav"}}, {"session_id": "433", "turn_idx": 4, "from": "Human", "text": "说得对。小跃，查一下深圳未来三天的天气。", "value": {"type": "wav", "value": "wav/get_weather_positive.jsonl-199-4.wav"}}, {"session_id": "433", "turn_idx": 5, "from": "Assistant", "text": "function\nget_weather\n{\"location\": \"shenzhen\"}", "value": "function\nget_weather\n{\"location\": \"shenzhen\"}"}] diff --git a/tools/StepEval-Audio-Toolcall/source.txt b/tools/StepEval-Audio-Toolcall/source.txt new file mode 100644 index 0000000000000000000000000000000000000000..191b693affa3d7fbd9b19947aab9516114e3be25 --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/source.txt @@ -0,0 +1 @@ +https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-Toolcall \ No newline at end of file diff --git a/tools/StepEval-Audio-Toolcall/timbre_negative.jsonl b/tools/StepEval-Audio-Toolcall/timbre_negative.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f70ed9886de2c7e8318192442bdac56c506584b3 --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/timbre_negative.jsonl @@ -0,0 +1,200 @@ +[{"session_id": "28", "turn_idx": 0, "from": "Human", "text": "小跃，语速加快点，我赶时间呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-0-0.wav"}}, {"session_id": "28", "turn_idx": 1, "from": "Assistant", "text": "马上搞定！快速说:下午两点会议别迟到，文件准备好！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-0-1.wav"}}] +[{"session_id": "412", "turn_idx": 0, "from": "Human", "text": "小跃，晚安。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-1-0.wav"}}, {"session_id": "412", "turn_idx": 1, "from": "Assistant", "text": "晚安，做个好梦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-1-1.wav"}}, {"session_id": "412", "turn_idx": 2, "from": "Human", "text": "用更温柔的声音说一遍。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-1-2.wav"}}, {"session_id": "412", "turn_idx": 3, "from": "Assistant", "text": "晚安，愿你梦里都是美好的事。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-1-3.wav"}}, {"session_id": "412", "turn_idx": 4, "from": "Human", "text": "音量调最低，我要睡了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-1-4.wav"}}, {"session_id": "412", "turn_idx": 5, "from": "Assistant", "text": "音量调最低了。好好休息吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-1-5.wav"}}] +[{"session_id": "123", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-2-0.wav"}}, {"session_id": "123", "turn_idx": 1, "from": "Assistant", "text": "为什么小鸟不玩扑克牌？因为它怕被鹰抓。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-2-1.wav"}}, {"session_id": "123", "turn_idx": 2, "from": "Human", "text": "用搞笑的语气再讲一遍。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-2-2.wav"}}, {"session_id": "123", "turn_idx": 3, "from": "Assistant", "text": "哈哈哈，为什么小鸟不玩扑克牌？因为它怕被鹰抓啊，笑死人了！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-2-3.wav"}}] +[{"session_id": "113", "turn_idx": 0, "from": "Human", "text": "小跃，音量调小点，语气再温柔些。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-3-0.wav"}}, {"session_id": "113", "turn_idx": 1, "from": "Assistant", "text": "好的，音量小了，语气也温柔了。这样感觉怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-3-1.wav"}}] +[{"session_id": "25", "turn_idx": 0, "from": "Human", "text": "小跃，语气放轻松点，我刚忙完有点累。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-4-0.wav"}}, {"session_id": "25", "turn_idx": 1, "from": "Assistant", "text": "没问题，放松点说:你今天辛苦了，要不要听听音乐休息下？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-4-1.wav"}}] +[{"session_id": "444", "turn_idx": 0, "from": "Human", "text": "小跃，帮我放一首欢快的歌。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-5-0.wav"}}, {"session_id": "444", "turn_idx": 1, "from": "Assistant", "text": "好的，正在播放你喜欢的欢快歌单。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-5-1.wav"}}, {"session_id": "444", "turn_idx": 2, "from": "Human", "text": "小跃，音量再大点，我听不清。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-5-2.wav"}}, {"session_id": "444", "turn_idx": 3, "from": "Assistant", "text": "没问题，音量调大了，现在感觉如何？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-5-3.wav"}}, {"session_id": "444", "turn_idx": 4, "from": "Human", "text": "好多了。对了，说的时候热情点，别那么平淡。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-5-4.wav"}}, {"session_id": "444", "turn_idx": 5, "from": "Assistant", "text": "太棒了！音乐正嗨呢，尽情享受吧！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-5-5.wav"}}] +[{"session_id": "120", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-6-0.wav"}}, {"session_id": "120", "turn_idx": 1, "from": "Assistant", "text": "现在是下午两点半。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-6-1.wav"}}] +[{"session_id": "84", "turn_idx": 0, "from": "Human", "text": "小跃，讲个短笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-7-0.wav"}}, {"session_id": "84", "turn_idx": 1, "from": "Assistant", "text": "为什么鱼不会玩扑克？因为它们怕被钓上来！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-7-1.wav"}}] +[{"session_id": "82", "turn_idx": 0, "from": "Human", "text": "小跃，播放一首轻松的歌。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-8-0.wav"}}, {"session_id": "82", "turn_idx": 1, "from": "Assistant", "text": "正在播放《阳光总在风雨后》，心情放松点吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-8-1.wav"}}, {"session_id": "82", "turn_idx": 2, "from": "Human", "text": "换成更开心的语气说说这首歌。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-8-2.wav"}}, {"session_id": "82", "turn_idx": 3, "from": "Assistant", "text": "哇，这首歌超欢快！听完感觉世界都亮堂堂的。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-8-3.wav"}}, {"session_id": "82", "turn_idx": 4, "from": "Human", "text": "歌词是什么？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-8-4.wav"}}, {"session_id": "82", "turn_idx": 5, "from": "Assistant", "text": "歌词是“阳光总在风雨后”，鼓励我们保持乐观呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-8-5.wav"}}] +[{"session_id": "473", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天天气怎么样？用兴奋的语气告诉我吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-9-0.wav"}}, {"session_id": "473", "turn_idx": 1, "from": "Assistant", "text": "太棒了！今天阳光灿烂，温度25度，超级适合出门玩！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-9-1.wav"}}, {"session_id": "473", "turn_idx": 2, "from": "Human", "text": "听起来不错。但请小声点说，我室友还在睡觉。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-9-2.wav"}}, {"session_id": "473", "turn_idx": 3, "from": "Assistant", "text": "好的，小声说:天气晴朗，25度，记得带防晒。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-9-3.wav"}}, {"session_id": "473", "turn_idx": 4, "from": "Human", "text": "谢谢。现在用更慢的语速重复一下温度。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-9-4.wav"}}, {"session_id": "473", "turn_idx": 5, "from": "Assistant", "text": "好 的，温 度 是 二 十 五 度。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-9-5.wav"}}] +[{"session_id": "455", "turn_idx": 0, "from": "Human", "text": "小跃，你说话太正式了，像朋友一样聊天吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-10-0.wav"}}, {"session_id": "455", "turn_idx": 1, "from": "Assistant", "text": "哈哈，好的！我放松点。嘿，最近忙啥呢？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-10-1.wav"}}, {"session_id": "455", "turn_idx": 2, "from": "Human", "text": "工作累死了。你能再幽默点吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-10-2.wav"}}, {"session_id": "455", "turn_idx": 3, "from": "Assistant", "text": "当然！工作就像打游戏，boss总在你最累时出现——别担心，明天又是新关卡！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-10-3.wav"}}] +[{"session_id": "414", "turn_idx": 0, "from": "Human", "text": "小跃，今天的新闻头条是啥？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-11-0.wav"}}, {"session_id": "414", "turn_idx": 1, "from": "Assistant", "text": "本地有场音乐会今晚举行，很多人期待。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-11-1.wav"}}, {"session_id": "414", "turn_idx": 2, "from": "Human", "text": "请小声点说，我在图书馆。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-11-2.wav"}}, {"session_id": "414", "turn_idx": 3, "from": "Assistant", "text": "好的，我小声点:音乐会今晚开始，记得带票哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-11-3.wav"}}] +[{"session_id": "493", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-12-0.wav"}}, {"session_id": "493", "turn_idx": 1, "from": "Assistant", "text": "今天晴天，温度25度，挺舒服的。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-12-1.wav"}}, {"session_id": "493", "turn_idx": 2, "from": "Human", "text": "哦，那不错。请用开心的语气告诉我明天的预报。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-12-2.wav"}}, {"session_id": "493", "turn_idx": 3, "from": "Assistant", "text": "明天更棒！阳光满满，最高28度，超级适合出去玩！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-12-3.wav"}}, {"session_id": "493", "turn_idx": 4, "from": "Human", "text": "太好了，设置个提醒下午三点开会。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-12-4.wav"}}, {"session_id": "493", "turn_idx": 5, "from": "Assistant", "text": "好的，下午三点会议提醒已设好。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-12-5.wav"}}, {"session_id": "493", "turn_idx": 6, "from": "Human", "text": "请小声点确认一下。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-12-6.wav"}}, {"session_id": "493", "turn_idx": 7, "from": "Assistant", "text": "小声确认，提醒都设置好了，别担心。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-12-7.wav"}}] +[{"session_id": "107", "turn_idx": 0, "from": "Human", "text": "小跃，记下我周末去爬山。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-13-0.wav"}}, {"session_id": "107", "turn_idx": 1, "from": "Assistant", "text": "已记录:周末爬山计划，时间待定。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-13-1.wav"}}] +[{"session_id": "426", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-14-0.wav"}}, {"session_id": "426", "turn_idx": 1, "from": "Assistant", "text": "现在是上午十点整。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-14-1.wav"}}] +[{"session_id": "77", "turn_idx": 0, "from": "Human", "text": "小跃，今天是我生日！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-15-0.wav"}}, {"session_id": "77", "turn_idx": 1, "from": "Assistant", "text": "生日快乐！愿你开心每一天。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-15-1.wav"}}] +[{"session_id": "438", "turn_idx": 0, "from": "Human", "text": "声音轻柔一点，我在图书馆呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-16-0.wav"}}, {"session_id": "438", "turn_idx": 1, "from": "Assistant", "text": "好的，我轻轻地说。需要我帮你找本书吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-16-1.wav"}}] +[{"session_id": "52", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话吧，用超级兴奋的语调！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-17-0.wav"}}, {"session_id": "52", "turn_idx": 1, "from": "Assistant", "text": "哈哈，有个笑话超搞笑:为什么电脑总生病？因为它有病毒！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-17-1.wav"}}] +[{"session_id": "85", "turn_idx": 0, "from": "Human", "text": "小跃，我有点困，鼓励我一下。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-18-0.wav"}}, {"session_id": "85", "turn_idx": 1, "from": "Assistant", "text": "加油，打起精神！完成工作就能休息了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-18-1.wav"}}, {"session_id": "85", "turn_idx": 2, "from": "Human", "text": "换种更幽默的语气鼓励我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-18-2.wav"}}, {"session_id": "85", "turn_idx": 3, "from": "Assistant", "text": "哈哈，别打瞌睡啦，不然键盘会变成你的枕头哦！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-18-3.wav"}}] +[{"session_id": "419", "turn_idx": 0, "from": "Human", "text": "小跃，今天要下雨吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-19-0.wav"}}, {"session_id": "419", "turn_idx": 1, "from": "Assistant", "text": "预报说下午有阵雨，带伞出门吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-19-1.wav"}}] +[{"session_id": "24", "turn_idx": 0, "from": "Human", "text": "小跃，音量再大点，我这边太吵了听不清。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-20-0.wav"}}, {"session_id": "24", "turn_idx": 1, "from": "Assistant", "text": "音量加大啦！现在应该超清晰，能听见我吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-20-1.wav"}}] +[{"session_id": "471", "turn_idx": 0, "from": "Human", "text": "小跃，告诉我一个快速食谱。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-21-0.wav"}}, {"session_id": "471", "turn_idx": 1, "from": "Assistant", "text": "简单三明治:面包、火腿、蔬菜，五分钟搞定。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-21-1.wav"}}, {"session_id": "471", "turn_idx": 2, "from": "Human", "text": "说话时带点幽默感，逗我笑。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-21-2.wav"}}, {"session_id": "471", "turn_idx": 3, "from": "Assistant", "text": "哈哈，三明治像变魔术，塞进去就能吃，懒人救星来咯！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-21-3.wav"}}] +[{"session_id": "74", "turn_idx": 0, "from": "Human", "text": "小跃，我考试没考好，好伤心。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-22-0.wav"}}, {"session_id": "74", "turn_idx": 1, "from": "Assistant", "text": "别灰心，下次努力就行。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-22-1.wav"}}, {"session_id": "74", "turn_idx": 2, "from": "Human", "text": "用更温暖点的语气安慰我嘛。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-22-2.wav"}}, {"session_id": "74", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，一次考试不算什么，你那么努力，下次一定行，加油哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-22-3.wav"}}] +[{"session_id": "59", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我喝水，用关心的语气。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-23-0.wav"}}, {"session_id": "59", "turn_idx": 1, "from": "Assistant", "text": "记得多喝水哦，对身体超好，别忙忘了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-23-1.wav"}}, {"session_id": "59", "turn_idx": 2, "from": "Human", "text": "音量加大点，我离得远。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-23-2.wav"}}, {"session_id": "59", "turn_idx": 3, "from": "Assistant", "text": "好的！大声提醒:喝水时间到，快去喝一杯吧！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-23-3.wav"}}] +[{"session_id": "58", "turn_idx": 0, "from": "Human", "text": "小跃，问个问题:怎么学好英语？语气认真点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-24-0.wav"}}, {"session_id": "58", "turn_idx": 1, "from": "Assistant", "text": "认真回答:多听多说，每天练习十分钟就有效。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-24-1.wav"}}] +[{"session_id": "2", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-25-0.wav"}}, {"session_id": "2", "turn_idx": 1, "from": "Assistant", "text": "现在是下午两点十五分。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-25-1.wav"}}, {"session_id": "2", "turn_idx": 2, "from": "Human", "text": "用更温柔的语气告诉我时间吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-25-2.wav"}}, {"session_id": "2", "turn_idx": 3, "from": "Assistant", "text": "亲爱的用户，现在是下午两点十五分呢，别着急慢慢来。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-25-3.wav"}}] +[{"session_id": "88", "turn_idx": 0, "from": "Human", "text": "小跃，讲个小故事。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-26-0.wav"}}, {"session_id": "88", "turn_idx": 1, "from": "Assistant", "text": "从前有只小兔子，它跳过高山找到胡萝卜。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-26-1.wav"}}, {"session_id": "88", "turn_idx": 2, "from": "Human", "text": "用更悲伤的语气讲结局。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-26-2.wav"}}, {"session_id": "88", "turn_idx": 3, "from": "Assistant", "text": "唉，小兔子发现胡萝卜被吃光了，它只好饿着肚子回家。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-26-3.wav"}}, {"session_id": "88", "turn_idx": 4, "from": "Human", "text": "太惨了，换个开心的版本吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-26-4.wav"}}, {"session_id": "88", "turn_idx": 5, "from": "Assistant", "text": "好嘞，小兔子找到一大片胡萝卜田，开心地吃了个饱！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-26-5.wav"}}] +[{"session_id": "420", "turn_idx": 0, "from": "Human", "text": "小跃，帮我查查航班状态。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-27-0.wav"}}, {"session_id": "420", "turn_idx": 1, "from": "Assistant", "text": "你的航班准点起飞，登机口是B12。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-27-1.wav"}}] +[{"session_id": "1", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个下午三点的闹钟。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-28-0.wav"}}, {"session_id": "1", "turn_idx": 1, "from": "Assistant", "text": "没问题，下午三点的闹钟已经设置好了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-28-1.wav"}}, {"session_id": "1", "turn_idx": 2, "from": "Human", "text": "下次回答时语气再热情些，听着更带劲。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-28-2.wav"}}, {"session_id": "1", "turn_idx": 3, "from": "Assistant", "text": "太棒了！下午三点的闹钟搞定啦，保证准时提醒你！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-28-3.wav"}}] +[{"session_id": "494", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-0.wav"}}, {"session_id": "494", "turn_idx": 1, "from": "Assistant", "text": "现在是下午两点半。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-1.wav"}}, {"session_id": "494", "turn_idx": 2, "from": "Human", "text": "好嘞，请用严肃的语气告诉我还有多久到三点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-2.wav"}}, {"session_id": "494", "turn_idx": 3, "from": "Assistant", "text": "只剩三十分钟了，别迟到，抓紧时间。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-3.wav"}}, {"session_id": "494", "turn_idx": 4, "from": "Human", "text": "知道了。帮我设个两点五十的闹钟。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-4.wav"}}, {"session_id": "494", "turn_idx": 5, "from": "Assistant", "text": "两点五十闹钟已设，会准时提醒你。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-5.wav"}}, {"session_id": "494", "turn_idx": 6, "from": "Human", "text": "音量调大点，我怕听不见。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-6.wav"}}, {"session_id": "494", "turn_idx": 7, "from": "Assistant", "text": "音量加大了，保证你能听见，响亮又清晰！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-7.wav"}}, {"session_id": "494", "turn_idx": 8, "from": "Human", "text": "完美。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-8.wav"}}, {"session_id": "494", "turn_idx": 9, "from": "Assistant", "text": "嗯，有事再叫我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-29-9.wav"}}] +[{"session_id": "70", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-30-0.wav"}}, {"session_id": "70", "turn_idx": 1, "from": "Assistant", "text": "今天晴天，气温二十度左右。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-30-1.wav"}}, {"session_id": "70", "turn_idx": 2, "from": "Human", "text": "能再用兴奋点的语气说一遍吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-30-2.wav"}}, {"session_id": "70", "turn_idx": 3, "from": "Assistant", "text": "太棒了！今天天气超好，阳光灿烂，气温二十度，适合出去玩！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-30-3.wav"}}] +[{"session_id": "389", "turn_idx": 0, "from": "Human", "text": "小跃，有啥新鲜事吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-31-0.wav"}}, {"session_id": "389", "turn_idx": 1, "from": "Assistant", "text": "最新新闻说，明天有流星雨，可以许愿哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-31-1.wav"}}] +[{"session_id": "415", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我明天开会。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-32-0.wav"}}, {"session_id": "415", "turn_idx": 1, "from": "Assistant", "text": "没问题，明天上午十点的会议已设置提醒。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-32-1.wav"}}, {"session_id": "415", "turn_idx": 2, "from": "Human", "text": "用严肃的语气强调一下。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-32-2.wav"}}, {"session_id": "415", "turn_idx": 3, "from": "Assistant", "text": "务必记住，明天上午十点准时开会，别迟到。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-32-3.wav"}}] +[{"session_id": "431", "turn_idx": 0, "from": "Human", "text": "小跃，用鼓励的语气给我加加油。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-33-0.wav"}}, {"session_id": "431", "turn_idx": 1, "from": "Assistant", "text": "你绝对行，坚持下去，成功就在眼前！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-33-1.wav"}}] +[{"session_id": "46", "turn_idx": 0, "from": "Human", "text": "小跃，推荐一首轻松的歌。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-34-0.wav"}}, {"session_id": "46", "turn_idx": 1, "from": "Assistant", "text": "试试《晴天》，旋律轻快，能放松心情。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-34-1.wav"}}] +[{"session_id": "424", "turn_idx": 0, "from": "Human", "text": "小跃，音量调大点，我在厨房听不清。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-35-0.wav"}}, {"session_id": "424", "turn_idx": 1, "from": "Assistant", "text": "好的，现在音量调大了，您能清楚听到我说话吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-35-1.wav"}}] +[{"session_id": "23", "turn_idx": 0, "from": "Human", "text": "小跃，用更兴奋的调子提醒我下午有聚会。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-36-0.wav"}}, {"session_id": "23", "turn_idx": 1, "from": "Assistant", "text": "哇哦，下午三点有超好玩的聚会！别忘了准时去嗨皮！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-36-1.wav"}}] +[{"session_id": "98", "turn_idx": 0, "from": "Human", "text": "小跃，晚饭吃啥好？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-37-0.wav"}}, {"session_id": "98", "turn_idx": 1, "from": "Assistant", "text": "推荐吃面条，简单又美味。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-37-1.wav"}}, {"session_id": "98", "turn_idx": 2, "from": "Human", "text": "用更热情的语气建议。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-37-2.wav"}}, {"session_id": "98", "turn_idx": 3, "from": "Assistant", "text": "太棒了！面条超赞，加个煎蛋更绝！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-37-3.wav"}}] +[{"session_id": "99", "turn_idx": 0, "from": "Human", "text": "小跃，我得睡觉了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-38-0.wav"}}, {"session_id": "99", "turn_idx": 1, "from": "Assistant", "text": "好的，晚安，做个好梦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-38-1.wav"}}] +[{"session_id": "396", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午三点去买菜。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-39-0.wav"}}, {"session_id": "396", "turn_idx": 1, "from": "Assistant", "text": "收到，下午三点买菜提醒已设置。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-39-1.wav"}}] +[{"session_id": "92", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-40-0.wav"}}, {"session_id": "92", "turn_idx": 1, "from": "Assistant", "text": "今天晴天，最高温度28度，适合出门。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-40-1.wav"}}] +[{"session_id": "450", "turn_idx": 0, "from": "Human", "text": "小跃，计算一下从家到公司需要多久。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-41-0.wav"}}, {"session_id": "450", "turn_idx": 1, "from": "Assistant", "text": "根据交通，大约需要25分钟。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-41-1.wav"}}, {"session_id": "450", "turn_idx": 2, "from": "Human", "text": "小跃，把音量调高点，我开车听不清。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-41-2.wav"}}, {"session_id": "450", "turn_idx": 3, "from": "Assistant", "text": "没问题，音量调高了。25分钟，建议早点出门。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-41-3.wav"}}, {"session_id": "450", "turn_idx": 4, "from": "Human", "text": "好。说的时候热情点，别冷冰冰的。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-41-4.wav"}}, {"session_id": "450", "turn_idx": 5, "from": "Assistant", "text": "太棒了！25分钟轻松到达，祝你一路顺风！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-41-5.wav"}}] +[{"session_id": "111", "turn_idx": 0, "from": "Human", "text": "小跃，你说得太快了，慢点说话。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-42-0.wav"}}, {"session_id": "111", "turn_idx": 1, "from": "Assistant", "text": "明白了，我会放慢语速。这样听起来舒服吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-42-1.wav"}}, {"session_id": "111", "turn_idx": 2, "from": "Human", "text": "对，好多了。告诉我下午的日程。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-42-2.wav"}}, {"session_id": "111", "turn_idx": 3, "from": "Assistant", "text": "下午两点开会，四点去健身房。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-42-3.wav"}}] +[{"session_id": "456", "turn_idx": 0, "from": "Human", "text": "小跃，我现在心情低落，用安慰的语气说话。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-43-0.wav"}}, {"session_id": "456", "turn_idx": 1, "from": "Assistant", "text": "哦，很遗憾听到你心情不好。我在这儿陪你，想分享点什么吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-43-1.wav"}}] +[{"session_id": "433", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？用开心的语气告诉我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-44-0.wav"}}, {"session_id": "433", "turn_idx": 1, "from": "Assistant", "text": "今天阳光超级棒，气温刚刚好，出去玩最合适啦！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-44-1.wav"}}] +[{"session_id": "118", "turn_idx": 0, "from": "Human", "text": "小跃，更兴奋点，音量也大点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-45-0.wav"}}, {"session_id": "118", "turn_idx": 1, "from": "Assistant", "text": "太棒了！我现在超兴奋，音量也大了！有什么好事？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-45-1.wav"}}] +[{"session_id": "44", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我明天开会。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-46-0.wav"}}, {"session_id": "44", "turn_idx": 1, "from": "Assistant", "text": "明天上午九点有重要会议，记得提前准备资料。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-46-1.wav"}}, {"session_id": "44", "turn_idx": 2, "from": "Human", "text": "音量调高点，我有点走神。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-46-2.wav"}}, {"session_id": "44", "turn_idx": 3, "from": "Assistant", "text": "好的！明天九点开会，重要资料别忘带！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-46-3.wav"}}] +[{"session_id": "461", "turn_idx": 0, "from": "Human", "text": "小跃，设置个闹钟，用更快的语速说。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-47-0.wav"}}, {"session_id": "461", "turn_idx": 1, "from": "Assistant", "text": "没问题！闹钟设好了明天八点，搞定！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-47-1.wav"}}] +[{"session_id": "41", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-48-0.wav"}}, {"session_id": "41", "turn_idx": 1, "from": "Assistant", "text": "现在是下午两点半，时间还早呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-48-1.wav"}}, {"session_id": "41", "turn_idx": 2, "from": "Human", "text": "用更开心的语气告诉我，我中奖了！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-48-2.wav"}}, {"session_id": "41", "turn_idx": 3, "from": "Assistant", "text": "哇塞！太棒了！您中奖啦，恭喜恭喜！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-48-3.wav"}}, {"session_id": "41", "turn_idx": 4, "from": "Human", "text": "哈哈，别太兴奋，小声点说后续。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-48-4.wav"}}, {"session_id": "41", "turn_idx": 5, "from": "Assistant", "text": "好的，小声点:奖品明天就能领取，别错过哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-48-5.wav"}}] +[{"session_id": "388", "turn_idx": 0, "from": "Human", "text": "小跃，帮我把客厅灯关了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-49-0.wav"}}, {"session_id": "388", "turn_idx": 1, "from": "Assistant", "text": "客厅灯已经关掉了，省电又环保。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-49-1.wav"}}, {"session_id": "388", "turn_idx": 2, "from": "Human", "text": "用悲伤点的语气说说关灯的事。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-49-2.wav"}}, {"session_id": "388", "turn_idx": 3, "from": "Assistant", "text": "唉，灯关了，房间都暗下来了，感觉有点小失落呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-49-3.wav"}}] +[{"session_id": "465", "turn_idx": 0, "from": "Human", "text": "小跃，播放一首轻松的音乐。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-50-0.wav"}}, {"session_id": "465", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐，希望您放松一下。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-50-1.wav"}}] +[{"session_id": "408", "turn_idx": 0, "from": "Human", "text": "小跃，教我做杯咖啡。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-51-0.wav"}}, {"session_id": "408", "turn_idx": 1, "from": "Assistant", "text": "简单:磨好咖啡粉，加热水，等几分钟就行。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-51-1.wav"}}] +[{"session_id": "508", "turn_idx": 0, "from": "Human", "text": "小跃，晚餐吃啥好？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-52-0.wav"}}, {"session_id": "508", "turn_idx": 1, "from": "Assistant", "text": "推荐做意面，简单又美味。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-52-1.wav"}}] +[{"session_id": "79", "turn_idx": 0, "from": "Human", "text": "小跃，我有点紧张面试。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-53-0.wav"}}, {"session_id": "79", "turn_idx": 1, "from": "Assistant", "text": "别担心，你准备得很充分。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-53-1.wav"}}] +[{"session_id": "417", "turn_idx": 0, "from": "Human", "text": "小跃，附近有好吃的餐厅推荐吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-54-0.wav"}}, {"session_id": "417", "turn_idx": 1, "from": "Assistant", "text": "街角那家披萨店评分很高，可以去试试。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-54-1.wav"}}] +[{"session_id": "35", "turn_idx": 0, "from": "Human", "text": "小跃，晚上吃什么好呢？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-55-0.wav"}}, {"session_id": "35", "turn_idx": 1, "from": "Assistant", "text": "建议吃个面条，简单又美味。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-55-1.wav"}}] +[{"session_id": "54", "turn_idx": 0, "from": "Human", "text": "小跃，推荐个晚餐食谱，用低音量说，宝宝在睡觉。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-56-0.wav"}}, {"session_id": "54", "turn_idx": 1, "from": "Assistant", "text": "好的，小声推荐:番茄炒蛋简单又营养，十分钟搞定。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-56-1.wav"}}, {"session_id": "54", "turn_idx": 2, "from": "Human", "text": "改个幽默的语气，加点趣事。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-56-2.wav"}}, {"session_id": "54", "turn_idx": 3, "from": "Assistant", "text": "哈哈，这菜谱超赞！上次我“虚拟”做了一次，结果“香”到隔壁家！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-56-3.wav"}}] +[{"session_id": "63", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-57-0.wav"}}, {"session_id": "63", "turn_idx": 1, "from": "Assistant", "text": "现在是上午十点半。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-57-1.wav"}}, {"session_id": "63", "turn_idx": 2, "from": "Human", "text": "说慢点，我有点困。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-57-2.wav"}}, {"session_id": "63", "turn_idx": 3, "from": "Assistant", "text": "好的，我放慢语速。现——在——是——十——点——半。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-57-3.wav"}}] +[{"session_id": "75", "turn_idx": 0, "from": "Human", "text": "小跃，播放一首歌。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-58-0.wav"}}, {"session_id": "75", "turn_idx": 1, "from": "Assistant", "text": "好的，正在播放《晴天》。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-58-1.wav"}}, {"session_id": "75", "turn_idx": 2, "from": "Human", "text": "音量再大点，我听不清。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-58-2.wav"}}, {"session_id": "75", "turn_idx": 3, "from": "Assistant", "text": "没问题，音量已调高！现在《晴天》播放中，清楚了吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-58-3.wav"}}] +[{"session_id": "69", "turn_idx": 0, "from": "Human", "text": "小跃，分享个励志语录。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-59-0.wav"}}, {"session_id": "69", "turn_idx": 1, "from": "Assistant", "text": "坚持就是胜利，别轻易放弃。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-59-1.wav"}}, {"session_id": "69", "turn_idx": 2, "from": "Human", "text": "加点儿激情，我需要动力。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-59-2.wav"}}, {"session_id": "69", "turn_idx": 3, "from": "Assistant", "text": "太燃了！坚持就是胜利，冲啊，别停下！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-59-3.wav"}}] +[{"session_id": "86", "turn_idx": 0, "from": "Human", "text": "小跃，计算一下100除以5。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-60-0.wav"}}, {"session_id": "86", "turn_idx": 1, "from": "Assistant", "text": "结果是20，简单吧？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-60-1.wav"}}] +[{"session_id": "103", "turn_idx": 0, "from": "Human", "text": "小跃，给我讲个笑话吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-61-0.wav"}}, {"session_id": "103", "turn_idx": 1, "from": "Assistant", "text": "为什么番茄变红了？因为它看到沙拉酱害羞了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-61-1.wav"}}, {"session_id": "103", "turn_idx": 2, "from": "Human", "text": "哈哈，这个太冷了，换个更搞笑的语气！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-61-2.wav"}}, {"session_id": "103", "turn_idx": 3, "from": "Assistant", "text": "哎呀，番茄脸红得像苹果，沙拉酱一来它就怂啦，笑死我了！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-61-3.wav"}}] +[{"session_id": "104", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我买牛奶。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-62-0.wav"}}, {"session_id": "104", "turn_idx": 1, "from": "Assistant", "text": "已设置提醒:下午五点买牛奶。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-62-1.wav"}}, {"session_id": "104", "turn_idx": 2, "from": "Human", "text": "用更亲切的语气说，我容易忘事。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-62-2.wav"}}, {"session_id": "104", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，别忘了下午五点买牛奶哦，我会一直提醒你的。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-62-3.wav"}}, {"session_id": "104", "turn_idx": 4, "from": "Human", "text": "声音小点，别太大声。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-62-4.wav"}}, {"session_id": "104", "turn_idx": 5, "from": "Assistant", "text": "好的，小声提醒:下午五点买牛奶，别错过。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-62-5.wav"}}] +[{"session_id": "448", "turn_idx": 0, "from": "Human", "text": "小跃，帮我查一下明天的会议时间。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-63-0.wav"}}, {"session_id": "448", "turn_idx": 1, "from": "Assistant", "text": "会议在明天上午十点开始。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-63-1.wav"}}] +[{"session_id": "68", "turn_idx": 0, "from": "Human", "text": "小跃，告诉我今天的新闻。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-64-0.wav"}}, {"session_id": "68", "turn_idx": 1, "from": "Assistant", "text": "今天本地有公益活动，大家踊跃参与。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-64-1.wav"}}, {"session_id": "68", "turn_idx": 2, "from": "Human", "text": "加快点语速，我赶时间。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-64-2.wav"}}, {"session_id": "68", "turn_idx": 3, "from": "Assistant", "text": "没问题，语速加快。本地公益活动很火热，快去看看吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-64-3.wav"}}] +[{"session_id": "36", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我明天买牛奶。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-65-0.wav"}}, {"session_id": "36", "turn_idx": 1, "from": "Assistant", "text": "记住了，明天提醒你买牛奶。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-65-1.wav"}}, {"session_id": "36", "turn_idx": 2, "from": "Human", "text": "情感丰富点，显得更贴心。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-65-2.wav"}}, {"session_id": "36", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，明天一定别忘了买牛奶哦，为了健康！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-65-3.wav"}}] +[{"session_id": "392", "turn_idx": 0, "from": "Human", "text": "xxx 后接", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-66-0.wav"}}, {"session_id": "392", "turn_idx": 1, "from": "Assistant", "text": "xxx，以此类推。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-66-1.wav"}}] +[{"session_id": "472", "turn_idx": 0, "from": "Human", "text": "小跃，今天有什么特别日子？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-67-0.wav"}}, {"session_id": "472", "turn_idx": 1, "from": "Assistant", "text": "今天是环保日，倡导绿色生活。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-67-1.wav"}}, {"session_id": "472", "turn_idx": 2, "from": "Human", "text": "用更严肃的语气强调一下重要性。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-67-2.wav"}}, {"session_id": "472", "turn_idx": 3, "from": "Assistant", "text": "环保日至关重要，减少塑料使用，保护地球刻不容缓。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-67-3.wav"}}, {"session_id": "472", "turn_idx": 4, "from": "Human", "text": "音量调低点，我在安静环境。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-67-4.wav"}}, {"session_id": "472", "turn_idx": 5, "from": "Assistant", "text": "音量调低了，轻声提醒不影响。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-67-5.wav"}}, {"session_id": "472", "turn_idx": 6, "from": "Human", "text": "建议我怎么做？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-67-6.wav"}}, {"session_id": "472", "turn_idx": 7, "from": "Assistant", "text": "建议自带购物袋，减少一次性用品。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-67-7.wav"}}] +[{"session_id": "20", "turn_idx": 0, "from": "Human", "text": "小跃，你能用更开心的语气告诉我今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-68-0.wav"}}, {"session_id": "20", "turn_idx": 1, "from": "Assistant", "text": "今天阳光超好，暖洋洋的，超级适合出门玩！开心吧？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-68-1.wav"}}] +[{"session_id": "43", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-69-0.wav"}}, {"session_id": "43", "turn_idx": 1, "from": "Assistant", "text": "为什么数学书很悲伤？因为它有太多问题。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-69-1.wav"}}, {"session_id": "43", "turn_idx": 2, "from": "Human", "text": "没意思，用更幽默的语气重讲一遍。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-69-2.wav"}}, {"session_id": "43", "turn_idx": 3, "from": "Assistant", "text": "哈哈，数学书为啥哭鼻子？因为它的问题多到爆炸啦！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-69-3.wav"}}] +[{"session_id": "3", "turn_idx": 0, "from": "Human", "text": "小跃，放首轻松的音乐听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-70-0.wav"}}, {"session_id": "3", "turn_idx": 1, "from": "Assistant", "text": "好的，正在播放轻音乐歌单，放松一下吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-70-1.wav"}}] +[{"session_id": "430", "turn_idx": 0, "from": "Human", "text": "小跃，明天会下雨吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-71-0.wav"}}, {"session_id": "430", "turn_idx": 1, "from": "Assistant", "text": "明天多云，可能有小雨，带把伞出门吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-71-1.wav"}}] +[{"session_id": "30", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-72-0.wav"}}, {"session_id": "30", "turn_idx": 1, "from": "Assistant", "text": "今天晴天，温度25度，适合出门。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-72-1.wav"}}] +[{"session_id": "71", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-73-0.wav"}}, {"session_id": "71", "turn_idx": 1, "from": "Assistant", "text": "为什么书总在图书馆睡觉？因为它被借走了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-73-1.wav"}}] +[{"session_id": "80", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-74-0.wav"}}, {"session_id": "80", "turn_idx": 1, "from": "Assistant", "text": "今天阳光明媚，温度25度，适合出门散步哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-74-1.wav"}}, {"session_id": "80", "turn_idx": 2, "from": "Human", "text": "你能用更温柔的语气说话吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-74-2.wav"}}, {"session_id": "80", "turn_idx": 3, "from": "Assistant", "text": "好的呀，今天天气暖暖的，像在拥抱你呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-74-3.wav"}}] +[{"session_id": "418", "turn_idx": 0, "from": "Human", "text": "小跃，我心情不好，安慰我一下。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-75-0.wav"}}, {"session_id": "418", "turn_idx": 1, "from": "Assistant", "text": "别难过，一切都会好起来的，要不要听点音乐？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-75-1.wav"}}, {"session_id": "418", "turn_idx": 2, "from": "Human", "text": "用温柔的语气再说几句。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-75-2.wav"}}, {"session_id": "418", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，深呼吸，世界很美好，我在这儿陪着你呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-75-3.wav"}}] +[{"session_id": "7", "turn_idx": 0, "from": "Human", "text": "小跃，我有点累，给点建议。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-76-0.wav"}}, {"session_id": "7", "turn_idx": 1, "from": "Assistant", "text": "建议你喝杯温水休息十分钟，别太拼了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-76-1.wav"}}, {"session_id": "7", "turn_idx": 2, "from": "Human", "text": "用更鼓励的语气说，让我打起精神。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-76-2.wav"}}, {"session_id": "7", "turn_idx": 3, "from": "Assistant", "text": "加油！喝杯温水休息一下，你一定能满血复活的！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-76-3.wav"}}] +[{"session_id": "50", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？用更开心的语气告诉我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-77-0.wav"}}, {"session_id": "50", "turn_idx": 1, "from": "Assistant", "text": "哇，今天天气超级棒哦！阳光明媚，温度刚刚好，出门走走超舒服的！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-77-1.wav"}}, {"session_id": "50", "turn_idx": 2, "from": "Human", "text": "音量调小一点，我在图书馆呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-77-2.wav"}}, {"session_id": "50", "turn_idx": 3, "from": "Assistant", "text": "好的，我小声点说。记得带把伞，下午可能有小雨。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-77-3.wav"}}] +[{"session_id": "497", "turn_idx": 0, "from": "Human", "text": "小跃，找份简单的晚餐菜谱。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-78-0.wav"}}, {"session_id": "497", "turn_idx": 1, "from": "Assistant", "text": "番茄炒蛋怎么样？材料少，步骤超简单。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-78-1.wav"}}, {"session_id": "497", "turn_idx": 2, "from": "Human", "text": "行啊。请用轻松的语气告诉我步骤。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-78-2.wav"}}, {"session_id": "497", "turn_idx": 3, "from": "Assistant", "text": "轻松搞定！先炒鸡蛋，再加番茄，调味翻炒，香喷喷出锅。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-78-3.wav"}}, {"session_id": "497", "turn_idx": 4, "from": "Human", "text": "听起来简单。设个六点提醒我做饭。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-78-4.wav"}}, {"session_id": "497", "turn_idx": 5, "from": "Assistant", "text": "六点做饭提醒设好了，准时开火。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-78-5.wav"}}, {"session_id": "497", "turn_idx": 6, "from": "Human", "text": "请用平静的语气确认。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-78-6.wav"}}, {"session_id": "497", "turn_idx": 7, "from": "Assistant", "text": "平静确认，一切都安排妥当，别急慢慢来。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-78-7.wav"}}] +[{"session_id": "15", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我明天买牛奶。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-79-0.wav"}}, {"session_id": "15", "turn_idx": 1, "from": "Assistant", "text": "已设置提醒，明天早上10点提醒你买牛奶。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-79-1.wav"}}, {"session_id": "15", "turn_idx": 2, "from": "Human", "text": "谢谢。音量再大一点，我耳朵不太好使。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-79-2.wav"}}, {"session_id": "15", "turn_idx": 3, "from": "Assistant", "text": "没问题，我调大声了！现在听得清楚吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-79-3.wav"}}] +[{"session_id": "115", "turn_idx": 0, "from": "Human", "text": "小跃，听起来太严肃，轻松点说话。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-80-0.wav"}}, {"session_id": "115", "turn_idx": 1, "from": "Assistant", "text": "没问题，我现在更轻松自在。想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-80-1.wav"}}, {"session_id": "115", "turn_idx": 2, "from": "Human", "text": "讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-80-2.wav"}}, {"session_id": "115", "turn_idx": 3, "from": "Assistant", "text": "为什么书不会迷路？因为它有目录！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-80-3.wav"}}] +[{"session_id": "62", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-81-0.wav"}}, {"session_id": "62", "turn_idx": 1, "from": "Assistant", "text": "为什么鱼不玩手机？因为怕被网住。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-81-1.wav"}}] +[{"session_id": "409", "turn_idx": 0, "from": "Human", "text": "小跃，有啥新鲜事？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-82-0.wav"}}, {"session_id": "409", "turn_idx": 1, "from": "Assistant", "text": "今天新闻:新电影上映了，评分很高。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-82-1.wav"}}] +[{"session_id": "436", "turn_idx": 0, "from": "Human", "text": "讲个笑话给我听，但要小声点，我这儿有人睡觉。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-83-0.wav"}}, {"session_id": "436", "turn_idx": 1, "from": "Assistant", "text": "明白了，我压低声音讲:为什么冰箱不唱歌？因为它怕冷场呀。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-83-1.wav"}}] +[{"session_id": "510", "turn_idx": 0, "from": "Human", "text": "小跃，周末有啥活动推荐？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-84-0.wav"}}, {"session_id": "510", "turn_idx": 1, "from": "Assistant", "text": "可以去公园野餐，天气不错。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-84-1.wav"}}, {"session_id": "510", "turn_idx": 2, "from": "Human", "text": "请用更兴奋的语气，我期待周末。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-84-2.wav"}}, {"session_id": "510", "turn_idx": 3, "from": "Assistant", "text": "哇哦！野餐超棒，带上零食，玩一整天！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-84-3.wav"}}, {"session_id": "510", "turn_idx": 4, "from": "Human", "text": "需要准备啥？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-84-4.wav"}}, {"session_id": "510", "turn_idx": 5, "from": "Assistant", "text": "带毯子和饮料，简单就好。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-84-5.wav"}}] +[{"session_id": "385", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午三点开会。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-85-0.wav"}}, {"session_id": "385", "turn_idx": 1, "from": "Assistant", "text": "记住了，下午三点开会，我会提前通知你。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-85-1.wav"}}, {"session_id": "385", "turn_idx": 2, "from": "Human", "text": "音量调小点，我在安静环境里。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-85-2.wav"}}, {"session_id": "385", "turn_idx": 3, "from": "Assistant", "text": "收到，我小声点说。会议提醒已设置，不会打扰到别人。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-85-3.wav"}}] +[{"session_id": "469", "turn_idx": 0, "from": "Human", "text": "小跃，查询一下我的日程安排。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-86-0.wav"}}, {"session_id": "469", "turn_idx": 1, "from": "Assistant", "text": "您今天下午有医生预约，晚上朋友聚餐。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-86-1.wav"}}] +[{"session_id": "124", "turn_idx": 0, "from": "Human", "text": "小跃，我有点累，鼓励我一下。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-87-0.wav"}}, {"session_id": "124", "turn_idx": 1, "from": "Assistant", "text": "加油，你可以的！休息一下再继续。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-87-1.wav"}}] +[{"session_id": "72", "turn_idx": 0, "from": "Human", "text": "小跃，把音量调小点，我头疼。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-88-0.wav"}}, {"session_id": "72", "turn_idx": 1, "from": "Assistant", "text": "好的，音量已降低。你需要休息吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-88-1.wav"}}, {"session_id": "72", "turn_idx": 2, "from": "Human", "text": "嗯，用更轻柔的声音回答我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-88-2.wav"}}, {"session_id": "72", "turn_idx": 3, "from": "Assistant", "text": "好的，我小声点说。要不要我放点轻音乐帮你放松？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-88-3.wav"}}] +[{"session_id": "0", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-89-0.wav"}}, {"session_id": "0", "turn_idx": 1, "from": "Assistant", "text": "今天多云转晴，最高温度28度，出门记得带伞哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-89-1.wav"}}, {"session_id": "0", "turn_idx": 2, "from": "Human", "text": "说的时候音量调大一点，我这儿有点吵。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-89-2.wav"}}, {"session_id": "0", "turn_idx": 3, "from": "Assistant", "text": "好的！今天天气多云转晴，最高温度28度，出门一定要带伞！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-89-3.wav"}}] +[{"session_id": "487", "turn_idx": 0, "from": "Human", "text": "小跃，给我讲个笑话吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-90-0.wav"}}, {"session_id": "487", "turn_idx": 1, "from": "Assistant", "text": "好呀，有只猫去钓鱼，钓到了一条鱼骨头。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-90-1.wav"}}, {"session_id": "487", "turn_idx": 2, "from": "Human", "text": "哈哈，请用更搞笑的语气讲下一个。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-90-2.wav"}}, {"session_id": "487", "turn_idx": 3, "from": "Assistant", "text": "太逗了！一只企鹅走进酒吧，酒保问:为什么穿西装？企鹅说:我冷啊！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-90-3.wav"}}] +[{"session_id": "484", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个明早七点的闹钟。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-91-0.wav"}}, {"session_id": "484", "turn_idx": 1, "from": "Assistant", "text": "没问题，明早七点的闹钟已设置好。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-91-1.wav"}}] +[{"session_id": "32", "turn_idx": 0, "from": "Human", "text": "小跃，推荐一首轻松的歌吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-92-0.wav"}}, {"session_id": "32", "turn_idx": 1, "from": "Assistant", "text": "试试《稻香》，节奏轻快，很放松。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-92-1.wav"}}, {"session_id": "32", "turn_idx": 2, "from": "Human", "text": "说的时候温柔点，像朋友聊天一样。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-92-2.wav"}}, {"session_id": "32", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，这首《稻香》特别治愈，听听看哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-92-3.wav"}}, {"session_id": "32", "turn_idx": 4, "from": "Human", "text": "嗯，感觉好多了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-92-4.wav"}}, {"session_id": "32", "turn_idx": 5, "from": "Assistant", "text": "开心就好，随时分享心情。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-92-5.wav"}}] +[{"session_id": "76", "turn_idx": 0, "from": "Human", "text": "小跃，快说个秘密给我听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-93-0.wav"}}, {"session_id": "76", "turn_idx": 1, "from": "Assistant", "text": "我的秘密是，我其实是个AI助手。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-93-1.wav"}}] +[{"session_id": "19", "turn_idx": 0, "from": "Human", "text": "小跃，明天是几号？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-94-0.wav"}}, {"session_id": "19", "turn_idx": 1, "from": "Assistant", "text": "明天是7月15号，星期二。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-94-1.wav"}}] +[{"session_id": "399", "turn_idx": 0, "from": "Human", "text": "小跃，十五加八等于多少？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-95-0.wav"}}, {"session_id": "399", "turn_idx": 1, "from": "Assistant", "text": "十五加八等于二十三。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-95-1.wav"}}] +[{"session_id": "439", "turn_idx": 0, "from": "Human", "text": "提醒我明天健身，用严肃点的语气。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-96-0.wav"}}, {"session_id": "439", "turn_idx": 1, "from": "Assistant", "text": "请准时去健身，别偷懒，身体最重要。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-96-1.wav"}}] +[{"session_id": "475", "turn_idx": 0, "from": "Human", "text": "小跃，今晚有啥电影好看？用正式的语气推荐。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-97-0.wav"}}, {"session_id": "475", "turn_idx": 1, "from": "Assistant", "text": "根据您的偏好，我推荐《盗梦空间》，一部高智商科幻片。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-97-1.wav"}}, {"session_id": "475", "turn_idx": 2, "from": "Human", "text": "好的。现在用轻松的语气说放映时间。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-97-2.wav"}}, {"session_id": "475", "turn_idx": 3, "from": "Assistant", "text": "放映时间是七点半，放松看吧！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-97-3.wav"}}] +[{"session_id": "122", "turn_idx": 0, "from": "Human", "text": "帮我设置一个明天早上七点的闹钟。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-98-0.wav"}}, {"session_id": "122", "turn_idx": 1, "from": "Assistant", "text": "好的，闹钟已设置为明天早上七点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-98-1.wav"}}] +[{"session_id": "51", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个下午三点的闹钟，语气严肃点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-99-0.wav"}}, {"session_id": "51", "turn_idx": 1, "from": "Assistant", "text": "明白，下午三点闹钟已设定。请准时完成任务。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-99-1.wav"}}, {"session_id": "51", "turn_idx": 2, "from": "Human", "text": "现在改回轻松的语气，说说你推荐啥音乐放松。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-99-2.wav"}}, {"session_id": "51", "turn_idx": 3, "from": "Assistant", "text": "好啊，推荐你听点轻音乐，比如钢琴曲，能让人很快放松下来。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-99-3.wav"}}] +[{"session_id": "402", "turn_idx": 0, "from": "Human", "text": "小跃，我要休息了，拜拜。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-100-0.wav"}}, {"session_id": "402", "turn_idx": 1, "from": "Assistant", "text": "好的，再见。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-100-1.wav"}}, {"session_id": "402", "turn_idx": 2, "from": "Human", "text": "请用温暖的道别语气说。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-100-2.wav"}}, {"session_id": "402", "turn_idx": 3, "from": "Assistant", "text": "再见啦，愿你有个甜甜的梦，明天见！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-100-3.wav"}}] +[{"session_id": "119", "turn_idx": 0, "from": "Human", "text": "小跃，语速慢点，语气柔和些。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-101-0.wav"}}, {"session_id": "119", "turn_idx": 1, "from": "Assistant", "text": "好的，语速慢了，语气也更柔和了。这样听着还行吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-101-1.wav"}}] +[{"session_id": "390", "turn_idx": 0, "from": "Human", "text": "小跃，教我一句英语打招呼。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-102-0.wav"}}, {"session_id": "390", "turn_idx": 1, "from": "Assistant", "text": "没问题，说“Hello, how are you?”就是问你好吗。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-102-1.wav"}}] +[{"session_id": "410", "turn_idx": 0, "from": "Human", "text": "小跃，推荐个简单运动。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-103-0.wav"}}, {"session_id": "410", "turn_idx": 1, "from": "Assistant", "text": "可以试试跳绳或快走，容易上手。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-103-1.wav"}}, {"session_id": "410", "turn_idx": 2, "from": "Human", "text": "用热情的语气给我打打气。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-103-2.wav"}}, {"session_id": "410", "turn_idx": 3, "from": "Assistant", "text": "来吧！跳绳超燃脂，跳起来超爽，坚持就有效果！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-103-3.wav"}}, {"session_id": "410", "turn_idx": 4, "from": "Human", "text": "好！音量保持适中。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-103-4.wav"}}, {"session_id": "410", "turn_idx": 5, "from": "Assistant", "text": "音量已调适中。动起来吧！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-103-5.wav"}}] +[{"session_id": "37", "turn_idx": 0, "from": "Human", "text": "小跃，今天有什么新闻头条？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-104-0.wav"}}, {"session_id": "37", "turn_idx": 1, "from": "Assistant", "text": "今天头条是科技大会开幕，挺有意思的。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-104-1.wav"}}] +[{"session_id": "428", "turn_idx": 0, "from": "Human", "text": "小跃，小声点说话，我在图书馆。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-105-0.wav"}}, {"session_id": "428", "turn_idx": 1, "from": "Assistant", "text": "明白了，我会轻声细语，不打扰别人。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-105-1.wav"}}] +[{"session_id": "27", "turn_idx": 0, "from": "Human", "text": "小跃，音量适中就好，别太大声也别太小。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-106-0.wav"}}, {"session_id": "27", "turn_idx": 1, "from": "Assistant", "text": "好的，音量调得刚刚好，舒服吧？现在有什么吩咐？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-106-1.wav"}}] +[{"session_id": "480", "turn_idx": 0, "from": "Human", "text": "小跃，我该学新技能了，用鼓励的语气。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-107-0.wav"}}, {"session_id": "480", "turn_idx": 1, "from": "Assistant", "text": "鼓励地说:学编程吧，坚持下去，你会变高手！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-107-1.wav"}}] +[{"session_id": "114", "turn_idx": 0, "from": "Human", "text": "小跃，语速太慢了，加快点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-108-0.wav"}}, {"session_id": "114", "turn_idx": 1, "from": "Assistant", "text": "明白了，我会加快语速。现在好点了吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-108-1.wav"}}] +[{"session_id": "435", "turn_idx": 0, "from": "Human", "text": "小跃，帮我查一下下午的会议时间，用温柔点的方式说。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-109-0.wav"}}, {"session_id": "435", "turn_idx": 1, "from": "Assistant", "text": "会议是下午两点开始哦，别担心，我提前提醒你。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-109-1.wav"}}] +[{"session_id": "463", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-110-0.wav"}}, {"session_id": "463", "turn_idx": 1, "from": "Assistant", "text": "今天天气晴朗，最高温度25度，适合出门散步哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-110-1.wav"}}, {"session_id": "463", "turn_idx": 2, "from": "Human", "text": "听起来不错。对了，你能用更兴奋的语气说说吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-110-2.wav"}}, {"session_id": "463", "turn_idx": 3, "from": "Assistant", "text": "当然啦！今天天气超级棒，阳光明媚，简直太适合出去浪了！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-110-3.wav"}}, {"session_id": "463", "turn_idx": 4, "from": "Human", "text": "哈哈，好！那现在音量调高一点，我这边有点吵。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-110-4.wav"}}, {"session_id": "463", "turn_idx": 5, "from": "Assistant", "text": "好的，音量已调高。您还需要其他帮助吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-110-5.wav"}}, {"session_id": "463", "turn_idx": 6, "from": "Human", "text": "帮我设个下午三点的闹钟吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-110-6.wav"}}, {"session_id": "463", "turn_idx": 7, "from": "Assistant", "text": "没问题，下午三点的闹钟已经设置好了！记得准时行动哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-110-7.wav"}}] +[{"session_id": "12", "turn_idx": 0, "from": "Human", "text": "小跃，设置一个下午3点的闹钟。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-111-0.wav"}}, {"session_id": "12", "turn_idx": 1, "from": "Assistant", "text": "闹钟已设置，下午3点准时提醒你。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-111-1.wav"}}, {"session_id": "12", "turn_idx": 2, "from": "Human", "text": "谢谢。对了，说话时能带点兴奋的情感吗？我心情好。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-111-2.wav"}}, {"session_id": "12", "turn_idx": 3, "from": "Assistant", "text": "好嘞！我超兴奋的，还有什么需要帮忙的吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-111-3.wav"}}] +[{"session_id": "474", "turn_idx": 0, "from": "Human", "text": "小跃，我饿了，推荐个快餐吧，用幽默的语气。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-112-0.wav"}}, {"session_id": "474", "turn_idx": 1, "from": "Assistant", "text": "哈哈，饿得不行了？试试那个新汉堡店，他们的汉堡大得能塞进一头牛！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-112-1.wav"}}, {"session_id": "474", "turn_idx": 2, "from": "Human", "text": "真逗。请用更快的语速告诉我地址。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-112-2.wav"}}, {"session_id": "474", "turn_idx": 3, "from": "Assistant", "text": "地址是美食街88号，快去快回！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-112-3.wav"}}] +[{"session_id": "446", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-113-0.wav"}}, {"session_id": "446", "turn_idx": 1, "from": "Assistant", "text": "现在是下午两点十分。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-113-1.wav"}}] +[{"session_id": "60", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-114-0.wav"}}, {"session_id": "60", "turn_idx": 1, "from": "Assistant", "text": "今天阳光明媚，适合出门散步。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-114-1.wav"}}] +[{"session_id": "4", "turn_idx": 0, "from": "Human", "text": "小跃，讲个有趣的小知识。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-115-0.wav"}}, {"session_id": "4", "turn_idx": 1, "from": "Assistant", "text": "你知道吗？蜜蜂可以识别人的脸，就像我们认朋友一样。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-115-1.wav"}}] +[{"session_id": "453", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-116-0.wav"}}, {"session_id": "453", "turn_idx": 1, "from": "Assistant", "text": "今天天气晴朗，温度适中，适合出门散步。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-116-1.wav"}}] +[{"session_id": "87", "turn_idx": 0, "from": "Human", "text": "小跃，今天有什么节日吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-117-0.wav"}}, {"session_id": "87", "turn_idx": 1, "from": "Assistant", "text": "今天是国际咖啡日，来杯咖啡提神吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-117-1.wav"}}] +[{"session_id": "416", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-118-0.wav"}}, {"session_id": "416", "turn_idx": 1, "from": "Assistant", "text": "为什么书永远不会饿？因为它有太多页可以啃。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-118-1.wav"}}, {"session_id": "416", "turn_idx": 2, "from": "Human", "text": "哈哈，用更夸张的语气再说一遍。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-118-2.wav"}}, {"session_id": "416", "turn_idx": 3, "from": "Assistant", "text": "哎呀，书为啥不饿？因为它满满的都是页，吃不完啊！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-118-3.wav"}}] +[{"session_id": "10", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-119-0.wav"}}, {"session_id": "10", "turn_idx": 1, "from": "Assistant", "text": "今天天气晴朗，温度25度左右，适合出门散步哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-119-1.wav"}}, {"session_id": "10", "turn_idx": 2, "from": "Human", "text": "哦，听起来不错。对了，你能把音量调大一点吗？我这儿有点吵。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-119-2.wav"}}, {"session_id": "10", "turn_idx": 3, "from": "Assistant", "text": "好的，我调大音量了。现在清楚多了吧？需要我提醒你带伞吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-119-3.wav"}}] +[{"session_id": "65", "turn_idx": 0, "from": "Human", "text": "小跃，设定一个五分钟后提醒。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-120-0.wav"}}, {"session_id": "65", "turn_idx": 1, "from": "Assistant", "text": "提醒已设置，五分钟后我会通知你。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-120-1.wav"}}] +[{"session_id": "398", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点钟了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-121-0.wav"}}, {"session_id": "398", "turn_idx": 1, "from": "Assistant", "text": "现在是下午四点整。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-121-1.wav"}}, {"session_id": "398", "turn_idx": 2, "from": "Human", "text": "请大声点，我这边有点吵。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-121-2.wav"}}, {"session_id": "398", "turn_idx": 3, "from": "Assistant", "text": "现在下午四点！大声说，时间刚好！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-121-3.wav"}}, {"session_id": "398", "turn_idx": 4, "from": "Human", "text": "谢谢，我该去接孩子了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-121-4.wav"}}, {"session_id": "398", "turn_idx": 5, "from": "Assistant", "text": "不客气，路上小心。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-121-5.wav"}}] +[{"session_id": "495", "turn_idx": 0, "from": "Human", "text": "小跃，放首轻松的歌听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-122-0.wav"}}, {"session_id": "495", "turn_idx": 1, "from": "Assistant", "text": "正在播放《晴天》，很舒缓的曲子。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-122-1.wav"}}, {"session_id": "495", "turn_idx": 2, "from": "Human", "text": "这歌不错。请用温柔的语气推荐另一首。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-122-2.wav"}}, {"session_id": "495", "turn_idx": 3, "from": "Assistant", "text": "试试《小幸运》吧，旋律柔柔的，听着很暖心。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-122-3.wav"}}] +[{"session_id": "468", "turn_idx": 0, "from": "Human", "text": "小跃，讲个日常小贴士吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-123-0.wav"}}, {"session_id": "468", "turn_idx": 1, "from": "Assistant", "text": "好的，多喝水能保持皮肤水润哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-123-1.wav"}}, {"session_id": "468", "turn_idx": 2, "from": "Human", "text": "用更平静的语气说，我有点紧张。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-123-2.wav"}}, {"session_id": "468", "turn_idx": 3, "from": "Assistant", "text": "放松点，多喝水有助于身心舒缓，慢慢来就好。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-123-3.wav"}}, {"session_id": "468", "turn_idx": 4, "from": "Human", "text": "音量调低点，旁边有人睡觉。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-123-4.wav"}}, {"session_id": "468", "turn_idx": 5, "from": "Assistant", "text": "音量调低了，轻声细语不影响他人。您还好吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-123-5.wav"}}] +[{"session_id": "500", "turn_idx": 0, "from": "Human", "text": "小跃，今天心情不太好。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-124-0.wav"}}, {"session_id": "500", "turn_idx": 1, "from": "Assistant", "text": "别难过，要不要听点音乐放松？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-124-1.wav"}}, {"session_id": "500", "turn_idx": 2, "from": "Human", "text": "嗯。请用鼓励的语气安慰我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-124-2.wav"}}, {"session_id": "500", "turn_idx": 3, "from": "Assistant", "text": "加油！每一天都是新开始，你超棒的！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-124-3.wav"}}, {"session_id": "500", "turn_idx": 4, "from": "Human", "text": "谢谢。设个提醒明天去散步。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-124-4.wav"}}, {"session_id": "500", "turn_idx": 5, "from": "Assistant", "text": "明天散步提醒设好了，出去走走心情好。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-124-5.wav"}}] +[{"session_id": "38", "turn_idx": 0, "from": "Human", "text": "小跃，我感觉有点累。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-125-0.wav"}}, {"session_id": "38", "turn_idx": 1, "from": "Assistant", "text": "休息一下吧，深呼吸，放松心情。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-125-1.wav"}}] +[{"session_id": "14", "turn_idx": 0, "from": "Human", "text": "小跃，讲个短故事听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-126-0.wav"}}, {"session_id": "14", "turn_idx": 1, "from": "Assistant", "text": "从前有只小猫，它总爱在屋顶晒太阳，日子过得悠闲自在。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-126-1.wav"}}, {"session_id": "14", "turn_idx": 2, "from": "Human", "text": "挺可爱的。不过，你能用悲伤的语气继续吗？我想感受一下。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-126-2.wav"}}, {"session_id": "14", "turn_idx": 3, "from": "Assistant", "text": "后来，小猫迷路了，再也找不到回家的路，它感到非常孤单。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-126-3.wav"}}] +[{"session_id": "485", "turn_idx": 0, "from": "Human", "text": "小跃，放首轻松的音乐。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-127-0.wav"}}, {"session_id": "485", "turn_idx": 1, "from": "Assistant", "text": "马上为你播放一首轻快的歌曲。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-127-1.wav"}}] +[{"session_id": "56", "turn_idx": 0, "from": "Human", "text": "小跃，放首流行歌，语气活泼点！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-128-0.wav"}}, {"session_id": "56", "turn_idx": 1, "from": "Assistant", "text": "好嘞！马上放首超嗨的歌，让你心情飞起来！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-128-1.wav"}}] +[{"session_id": "486", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-129-0.wav"}}, {"session_id": "486", "turn_idx": 1, "from": "Assistant", "text": "现在是下午三点整。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-129-1.wav"}}, {"session_id": "486", "turn_idx": 2, "from": "Human", "text": "请用严肃的语气再说一次，我赶时间。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-129-2.wav"}}, {"session_id": "486", "turn_idx": 3, "from": "Assistant", "text": "明白了，严肃点:现在下午三点，时间紧迫。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-129-3.wav"}}, {"session_id": "486", "turn_idx": 4, "from": "Human", "text": "音量再高点，我这边有点吵。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-129-4.wav"}}, {"session_id": "486", "turn_idx": 5, "from": "Assistant", "text": "好的，大声点:现在下午三点，赶紧行动！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-129-5.wav"}}] +[{"session_id": "127", "turn_idx": 0, "from": "Human", "text": "提醒我下午四点去健身房。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-130-0.wav"}}, {"session_id": "127", "turn_idx": 1, "from": "Assistant", "text": "已设置下午四点的健身房提醒。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-130-1.wav"}}] +[{"session_id": "454", "turn_idx": 0, "from": "Human", "text": "小跃，音量调高点，我听不清你说话。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-131-0.wav"}}, {"session_id": "454", "turn_idx": 1, "from": "Assistant", "text": "好的，我会加大音量。现在清楚了吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-131-1.wav"}}] +[{"session_id": "106", "turn_idx": 0, "from": "Human", "text": "小跃，怎么煮鸡蛋？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-132-0.wav"}}, {"session_id": "106", "turn_idx": 1, "from": "Assistant", "text": "简单:水开后煮8分钟，就是完美的溏心蛋。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-132-1.wav"}}] +[{"session_id": "491", "turn_idx": 0, "from": "Human", "text": "小跃，周六有什么安排？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-133-0.wav"}}, {"session_id": "491", "turn_idx": 1, "from": "Assistant", "text": "周六上午你有瑜伽课，下午自由时间。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-133-1.wav"}}] +[{"session_id": "443", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-134-0.wav"}}, {"session_id": "443", "turn_idx": 1, "from": "Assistant", "text": "今天天气不错，晴天，温度在22度左右，很适合散步。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-134-1.wav"}}, {"session_id": "443", "turn_idx": 2, "from": "Human", "text": "小跃，把音量调小一点，我在图书馆。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-134-2.wav"}}, {"session_id": "443", "turn_idx": 3, "from": "Assistant", "text": "好的，我小声点说。天气信息需要重复吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-134-3.wav"}}] +[{"session_id": "458", "turn_idx": 0, "from": "Human", "text": "小跃，你听起来有点平淡，加点热情吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-135-0.wav"}}, {"session_id": "458", "turn_idx": 1, "from": "Assistant", "text": "哦，对不起！我会更有活力。今天有什么新鲜事？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-135-1.wav"}}, {"session_id": "458", "turn_idx": 2, "from": "Human", "text": "我刚学会做蛋糕了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-135-2.wav"}}, {"session_id": "458", "turn_idx": 3, "from": "Assistant", "text": "太厉害了！蛋糕做起来超有趣，分享一下秘诀呗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-135-3.wav"}}] +[{"session_id": "6", "turn_idx": 0, "from": "Human", "text": "小跃，最近有什么新闻头条？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-136-0.wav"}}, {"session_id": "6", "turn_idx": 1, "from": "Assistant", "text": "本地新闻说周末有免费社区活动，可以去看看。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-136-1.wav"}}] +[{"session_id": "100", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥安排？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-137-0.wav"}}, {"session_id": "100", "turn_idx": 1, "from": "Assistant", "text": "你今天下午两点有个牙医预约，晚上七点约了朋友吃饭。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-137-1.wav"}}, {"session_id": "100", "turn_idx": 2, "from": "Human", "text": "请说得更轻松一点，别那么严肃。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-137-2.wav"}}, {"session_id": "100", "turn_idx": 3, "from": "Assistant", "text": "嗨，下午两点去看牙医，晚上七点还能和朋友嗨皮去，超赞的！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-137-3.wav"}}, {"session_id": "100", "turn_idx": 4, "from": "Human", "text": "再大声点说一遍，我这儿有点吵。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-137-4.wav"}}, {"session_id": "100", "turn_idx": 5, "from": "Assistant", "text": "好的！下午两点牙医，晚上七点聚餐，别迟到哦！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-137-5.wav"}}] +[{"session_id": "67", "turn_idx": 0, "from": "Human", "text": "小跃，我有点紧张，怎么办？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-138-0.wav"}}, {"session_id": "67", "turn_idx": 1, "from": "Assistant", "text": "深呼吸，慢慢来，别担心。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-138-1.wav"}}, {"session_id": "67", "turn_idx": 2, "from": "Human", "text": "你能用更平静的语气重复吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-138-2.wav"}}, {"session_id": "67", "turn_idx": 3, "from": "Assistant", "text": "好，深呼吸——放轻松——一切都会好的。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-138-3.wav"}}] +[{"session_id": "477", "turn_idx": 0, "from": "Human", "text": "小跃，我有工作汇报，用严肃的语气给点建议。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-139-0.wav"}}, {"session_id": "477", "turn_idx": 1, "from": "Assistant", "text": "严肃地说:准备好数据，提前检查PPT。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-139-1.wav"}}] +[{"session_id": "83", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点啦？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-140-0.wav"}}, {"session_id": "83", "turn_idx": 1, "from": "Assistant", "text": "现在是下午两点半，时间还早呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-140-1.wav"}}, {"session_id": "83", "turn_idx": 2, "from": "Human", "text": "用更严肃的语气告诉我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-140-2.wav"}}, {"session_id": "83", "turn_idx": 3, "from": "Assistant", "text": "好的，现在是下午两点三十分，请务必注意时间安排。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-140-3.wav"}}] +[{"session_id": "61", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午三点开会。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-141-0.wav"}}, {"session_id": "61", "turn_idx": 1, "from": "Assistant", "text": "好的，会议提醒已设置，下午三点准时通知你。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-141-1.wav"}}] +[{"session_id": "5", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我明天买牛奶。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-142-0.wav"}}, {"session_id": "5", "turn_idx": 1, "from": "Assistant", "text": "好的，明天买牛奶的提醒已经添加了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-142-1.wav"}}] +[{"session_id": "108", "turn_idx": 0, "from": "Human", "text": "小跃，查下快递到哪儿了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-143-0.wav"}}, {"session_id": "108", "turn_idx": 1, "from": "Assistant", "text": "你的快递预计明天送达，正在派送中。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-143-1.wav"}}, {"session_id": "108", "turn_idx": 2, "from": "Human", "text": "说更乐观一点，我担心晚了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-143-2.wav"}}, {"session_id": "108", "turn_idx": 3, "from": "Assistant", "text": "别担心，快递明天就到，很快就能收到啦！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-143-3.wav"}}] +[{"session_id": "110", "turn_idx": 0, "from": "Human", "text": "小跃，音量太小了，调大点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-144-0.wav"}}, {"session_id": "110", "turn_idx": 1, "from": "Assistant", "text": "好的，我已经把音量调大了。现在清楚多了吧？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-144-1.wav"}}] +[{"session_id": "96", "turn_idx": 0, "from": "Human", "text": "小跃，有啥新鲜事？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-145-0.wav"}}, {"session_id": "96", "turn_idx": 1, "from": "Assistant", "text": "最新消息:本地公园新开了个花展。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-145-1.wav"}}] +[{"session_id": "34", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点钟了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-146-0.wav"}}, {"session_id": "34", "turn_idx": 1, "from": "Assistant", "text": "现在是上午十点十五分。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-146-1.wav"}}, {"session_id": "34", "turn_idx": 2, "from": "Human", "text": "说慢一点，我有点没听清。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-146-2.wav"}}, {"session_id": "34", "turn_idx": 3, "from": "Assistant", "text": "好——的——现在是——十点——十五分——", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-146-3.wav"}}] +[{"session_id": "49", "turn_idx": 0, "from": "Human", "text": "小跃，教我怎么煮咖啡。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-147-0.wav"}}, {"session_id": "49", "turn_idx": 1, "from": "Assistant", "text": "简单，用咖啡粉加热水，加牛奶或糖调味。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-147-1.wav"}}, {"session_id": "49", "turn_idx": 2, "from": "Human", "text": "说详细点，语气更耐心些。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-147-2.wav"}}, {"session_id": "49", "turn_idx": 3, "from": "Assistant", "text": "好的，慢慢听:取两勺咖啡粉，倒入热水搅拌，再加点牛奶，香浓又提神。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-147-3.wav"}}, {"session_id": "49", "turn_idx": 4, "from": "Human", "text": "懂了，我试试看。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-147-4.wav"}}, {"session_id": "49", "turn_idx": 5, "from": "Assistant", "text": "祝您煮出美味咖啡！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-147-5.wav"}}] +[{"session_id": "507", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-148-0.wav"}}, {"session_id": "507", "turn_idx": 1, "from": "Assistant", "text": "好啊！为什么电脑很懒？因为它总想“点”击休息。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-148-1.wav"}}, {"session_id": "507", "turn_idx": 2, "from": "Human", "text": "哈哈，再来一个，请用更俏皮的语气。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-148-2.wav"}}, {"session_id": "507", "turn_idx": 3, "from": "Assistant", "text": "嘿嘿，小猫问小狗:为什么你总追尾巴？小狗说:生活得有点追求！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-148-3.wav"}}] +[{"session_id": "57", "turn_idx": 0, "from": "Human", "text": "小跃，讲个励志故事，用鼓舞人心的语调。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-149-0.wav"}}, {"session_id": "57", "turn_idx": 1, "from": "Assistant", "text": "当然！有个故事超励志:普通人也能成大事，只要坚持不放弃！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-149-1.wav"}}] +[{"session_id": "94", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我晚上八点吃药。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-150-0.wav"}}, {"session_id": "94", "turn_idx": 1, "from": "Assistant", "text": "已设置提醒:晚上八点吃药。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-150-1.wav"}}, {"session_id": "94", "turn_idx": 2, "from": "Human", "text": "请用温柔点的语气，我容易忘。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-150-2.wav"}}, {"session_id": "94", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，别忘了晚上八点吃药哦，照顾好自己。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-150-3.wav"}}] +[{"session_id": "39", "turn_idx": 0, "from": "Human", "text": "小跃，周末有什么计划建议？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-151-0.wav"}}, {"session_id": "39", "turn_idx": 1, "from": "Assistant", "text": "去公园散步，或者看场电影。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-151-1.wav"}}, {"session_id": "39", "turn_idx": 2, "from": "Human", "text": "音量调小一点，我在安静环境里。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-151-2.wav"}}, {"session_id": "39", "turn_idx": 3, "from": "Assistant", "text": "好的，建议去公园散步或看场电影，轻松一下。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-151-3.wav"}}] +[{"session_id": "387", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话逗我乐一乐。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-152-0.wav"}}, {"session_id": "387", "turn_idx": 1, "from": "Assistant", "text": "有个番茄过马路被车撞了，结果它变成了番茄酱，哈哈。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-152-1.wav"}}, {"session_id": "387", "turn_idx": 2, "from": "Human", "text": "请用更活泼的语气再讲一个。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-152-2.wav"}}, {"session_id": "387", "turn_idx": 3, "from": "Assistant", "text": "好呀，我来个更活泼的！为什么手机总爱睡觉？因为它要充电才能醒呀！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-152-3.wav"}}] +[{"session_id": "437", "turn_idx": 0, "from": "Human", "text": "小跃，用更兴奋的语气说说晚餐吃啥。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-153-0.wav"}}, {"session_id": "437", "turn_idx": 1, "from": "Assistant", "text": "哇，今晚吃火锅怎么样？热腾腾的，超爽！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-153-1.wav"}}] +[{"session_id": "429", "turn_idx": 0, "from": "Human", "text": "小跃，用更幽默的语气讲个笑话。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-154-0.wav"}}, {"session_id": "429", "turn_idx": 1, "from": "Assistant", "text": "为什么书总在图书馆睡觉？因为太安静了，连它都打瞌睡！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-154-1.wav"}}] +[{"session_id": "464", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-155-0.wav"}}, {"session_id": "464", "turn_idx": 1, "from": "Assistant", "text": "现在是上午十点整。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-155-1.wav"}}] +[{"session_id": "498", "turn_idx": 0, "from": "Human", "text": "小跃，把客厅灯调亮点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-156-0.wav"}}, {"session_id": "498", "turn_idx": 1, "from": "Assistant", "text": "灯已调亮，现在更亮了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-156-1.wav"}}, {"session_id": "498", "turn_idx": 2, "from": "Human", "text": "好。请用幽默的语气问问灯还能更亮不。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-156-2.wav"}}, {"session_id": "498", "turn_idx": 3, "from": "Assistant", "text": "哈哈，灯说它还能“亮瞎眼”，要不要试试？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-156-3.wav"}}, {"session_id": "498", "turn_idx": 4, "from": "Human", "text": "不用了，这样就行。设个十点自动关灯。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-156-4.wav"}}, {"session_id": "498", "turn_idx": 5, "from": "Assistant", "text": "十点关灯已设，到时自动熄灯。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-156-5.wav"}}] +[{"session_id": "451", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午去买菜。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-157-0.wav"}}, {"session_id": "451", "turn_idx": 1, "from": "Assistant", "text": "提醒设好了，下午三点买菜。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-157-1.wav"}}] +[{"session_id": "40", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-158-0.wav"}}, {"session_id": "40", "turn_idx": 1, "from": "Assistant", "text": "今天阳光明媚，温度适中，适合出门散步哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-158-1.wav"}}] +[{"session_id": "479", "turn_idx": 0, "from": "Human", "text": "小跃，放首音乐吧，用浪漫的语气推荐。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-159-0.wav"}}, {"session_id": "479", "turn_idx": 1, "from": "Assistant", "text": "浪漫推荐:听听《月光曲》，适合悠闲夜晚。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-159-1.wav"}}] +[{"session_id": "93", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-160-0.wav"}}, {"session_id": "93", "turn_idx": 1, "from": "Assistant", "text": "好嘞，为什么数学书总在哭？因为它有太多问题。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-160-1.wav"}}, {"session_id": "93", "turn_idx": 2, "from": "Human", "text": "请用更幽默的语气，加点情感。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-160-2.wav"}}, {"session_id": "93", "turn_idx": 3, "from": "Assistant", "text": "哈哈，数学书为啥总哭？因为它满脑子都是难题，可怜巴巴的！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-160-3.wav"}}] +[{"session_id": "95", "turn_idx": 0, "from": "Human", "text": "小跃，我今天有点郁闷。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-161-0.wav"}}, {"session_id": "95", "turn_idx": 1, "from": "Assistant", "text": "哎呀，怎么了？要不要聊聊？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-161-1.wav"}}] +[{"session_id": "407", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点啦？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-162-0.wav"}}, {"session_id": "407", "turn_idx": 1, "from": "Assistant", "text": "现在是下午三点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-162-1.wav"}}, {"session_id": "407", "turn_idx": 2, "from": "Human", "text": "哎呀，迟到了！用着急的语气催我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-162-2.wav"}}, {"session_id": "407", "turn_idx": 3, "from": "Assistant", "text": "快！快！三点啦，再不跑就真迟了，赶紧冲！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-162-3.wav"}}] +[{"session_id": "73", "turn_idx": 0, "from": "Human", "text": "小跃，告诉我明天有啥安排。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-163-0.wav"}}, {"session_id": "73", "turn_idx": 1, "from": "Assistant", "text": "明天上午九点开会，下午三点健身。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-163-1.wav"}}] +[{"session_id": "117", "turn_idx": 0, "from": "Human", "text": "小跃，语气正式点，像在工作汇报。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-164-0.wav"}}, {"session_id": "117", "turn_idx": 1, "from": "Assistant", "text": "明白，我会用正式语气。请指示。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-164-1.wav"}}] +[{"session_id": "8", "turn_idx": 0, "from": "Human", "text": "小跃，查下明天的日程。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-165-0.wav"}}, {"session_id": "8", "turn_idx": 1, "from": "Assistant", "text": "明天上午九点有会议，下午自由安排。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-165-1.wav"}}, {"session_id": "8", "turn_idx": 2, "from": "Human", "text": "说的时候情感更积极点，别那么平淡。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-165-2.wav"}}, {"session_id": "8", "turn_idx": 3, "from": "Assistant", "text": "太棒了！明天九点会议后，下午全是你自由发挥的时间，尽情享受吧！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-165-3.wav"}}] +[{"session_id": "42", "turn_idx": 0, "from": "Human", "text": "小跃，我有点饿，推荐个晚餐吧。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-166-0.wav"}}, {"session_id": "42", "turn_idx": 1, "from": "Assistant", "text": "附近有家新开的披萨店，评价不错，试试看？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-166-1.wav"}}, {"session_id": "42", "turn_idx": 2, "from": "Human", "text": "语气严肃点，我在减肥呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-166-2.wav"}}, {"session_id": "42", "turn_idx": 3, "from": "Assistant", "text": "明白。建议选择沙拉或清蒸鱼，健康低卡路里。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-166-3.wav"}}] +[{"session_id": "476", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我去健身，用激励的语气。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-167-0.wav"}}, {"session_id": "476", "turn_idx": 1, "from": "Assistant", "text": "加油！去健身吧，动起来你会更强壮！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-167-1.wav"}}, {"session_id": "476", "turn_idx": 2, "from": "Human", "text": "行。但请小声点，别吵到邻居。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-167-2.wav"}}, {"session_id": "476", "turn_idx": 3, "from": "Assistant", "text": "小声提醒:去健身，保持低调。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-167-3.wav"}}, {"session_id": "476", "turn_idx": 4, "from": "Human", "text": "用更慢的语速重复一下时间。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-167-4.wav"}}, {"session_id": "476", "turn_idx": 5, "from": "Assistant", "text": "好 的，时 间 是 下 午 三 点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-167-5.wav"}}] +[{"session_id": "490", "turn_idx": 0, "from": "Human", "text": "小跃，有什么新鲜事？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-168-0.wav"}}, {"session_id": "490", "turn_idx": 1, "from": "Assistant", "text": "今天有场精彩的足球比赛，主队赢了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-168-1.wav"}}, {"session_id": "490", "turn_idx": 2, "from": "Human", "text": "用激动的语气播报一下比分。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-168-2.wav"}}, {"session_id": "490", "turn_idx": 3, "from": "Assistant", "text": "太激动了！主队三比零大胜，全场沸腾！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-168-3.wav"}}] +[{"session_id": "509", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午三点健身。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-169-0.wav"}}, {"session_id": "509", "turn_idx": 1, "from": "Assistant", "text": "好的，三点健身提醒已设，别偷懒。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-169-1.wav"}}] +[{"session_id": "457", "turn_idx": 0, "from": "Human", "text": "小跃，现在是晚上，小声点说话别吵到人。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-170-0.wav"}}, {"session_id": "457", "turn_idx": 1, "from": "Assistant", "text": "好的，我会降低音量，轻轻地说。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-170-1.wav"}}] +[{"session_id": "125", "turn_idx": 0, "from": "Human", "text": "导航到最近的超市。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-171-0.wav"}}, {"session_id": "125", "turn_idx": 1, "from": "Assistant", "text": "最近的超市是家乐福，距离500米。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-171-1.wav"}}] +[{"session_id": "502", "turn_idx": 0, "from": "Human", "text": "小跃，我今天没啥事了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-172-0.wav"}}, {"session_id": "502", "turn_idx": 1, "from": "Assistant", "text": "好的，休息吧，需要时再叫我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-172-1.wav"}}, {"session_id": "502", "turn_idx": 2, "from": "Human", "text": "请用开心的语气道别。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-172-2.wav"}}, {"session_id": "502", "turn_idx": 3, "from": "Assistant", "text": "再见啦！今天超愉快，明天见！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-172-3.wav"}}, {"session_id": "502", "turn_idx": 4, "from": "Human", "text": "嗯。设个明早七点闹钟。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-172-4.wav"}}, {"session_id": "502", "turn_idx": 5, "from": "Assistant", "text": "七点闹钟已设，保证准时响。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-172-5.wav"}}] +[{"session_id": "406", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话呗。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-173-0.wav"}}, {"session_id": "406", "turn_idx": 1, "from": "Assistant", "text": "行啊，为什么鱼不喜欢学习？因为它怕考试被“烤”试！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-173-1.wav"}}] +[{"session_id": "404", "turn_idx": 0, "from": "Human", "text": "小跃，给我放首周杰伦的歌。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-174-0.wav"}}, {"session_id": "404", "turn_idx": 1, "from": "Assistant", "text": "好的，正在播放周杰伦的《晴天》。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-174-1.wav"}}, {"session_id": "404", "turn_idx": 2, "from": "Human", "text": "音量太大了，调小一点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-174-2.wav"}}, {"session_id": "404", "turn_idx": 3, "from": "Assistant", "text": "音量调小了，现在应该更轻柔。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-174-3.wav"}}] +[{"session_id": "393", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个明天七点的闹钟。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-175-0.wav"}}, {"session_id": "393", "turn_idx": 1, "from": "Assistant", "text": "好的，闹钟设好了，明天七点准时响。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-175-1.wav"}}, {"session_id": "393", "turn_idx": 2, "from": "Human", "text": "请用更兴奋的语气提醒我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-175-2.wav"}}, {"session_id": "393", "turn_idx": 3, "from": "Assistant", "text": "太棒了！明天七点的闹钟搞定，准备好活力满满的一天吧！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-175-3.wav"}}] +[{"session_id": "449", "turn_idx": 0, "from": "Human", "text": "小跃，我想听点放松的音乐。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-176-0.wav"}}, {"session_id": "449", "turn_idx": 1, "from": "Assistant", "text": "好的，播放轻音乐帮你放松。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-176-1.wav"}}] +[{"session_id": "462", "turn_idx": 0, "from": "Human", "text": "小跃，你说话太慢了，加快点节奏。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-177-0.wav"}}, {"session_id": "462", "turn_idx": 1, "from": "Assistant", "text": "好的，我会说快些。现在需要什么？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-177-1.wav"}}] +[{"session_id": "48", "turn_idx": 0, "from": "Human", "text": "小跃，周末去哪玩好？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-178-0.wav"}}, {"session_id": "48", "turn_idx": 1, "from": "Assistant", "text": "爬山或海边都不错，看您喜欢户外还是休闲。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-178-1.wav"}}] +[{"session_id": "97", "turn_idx": 0, "from": "Human", "text": "小跃，把客厅灯关了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-179-0.wav"}}, {"session_id": "97", "turn_idx": 1, "from": "Assistant", "text": "好的，客厅灯已关闭。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-179-1.wav"}}, {"session_id": "97", "turn_idx": 2, "from": "Human", "text": "请用正式点的语气确认。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-179-2.wav"}}, {"session_id": "97", "turn_idx": 3, "from": "Assistant", "text": "确认完毕:客厅灯已成功关闭。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-179-3.wav"}}] +[{"session_id": "425", "turn_idx": 0, "from": "Human", "text": "小跃，用更温暖的语气说个早安。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-180-0.wav"}}, {"session_id": "425", "turn_idx": 1, "from": "Assistant", "text": "早安呀，新的一天开始了，祝您心情愉快！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-180-1.wav"}}] +[{"session_id": "391", "turn_idx": 0, "from": "Human", "text": "小跃，你觉得周末去哪玩好？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-181-0.wav"}}, {"session_id": "391", "turn_idx": 1, "from": "Assistant", "text": "周末去公园野餐吧，天气好还免费。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-181-1.wav"}}] +[{"session_id": "105", "turn_idx": 0, "from": "Human", "text": "小跃，最近有啥新闻？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-182-0.wav"}}, {"session_id": "105", "turn_idx": 1, "from": "Assistant", "text": "本地新闻:周末有免费音乐会，在公园举行。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-182-1.wav"}}] +[{"session_id": "423", "turn_idx": 0, "from": "Human", "text": "小跃，用更开心的语气告诉我今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-183-0.wav"}}, {"session_id": "423", "turn_idx": 1, "from": "Assistant", "text": "太棒了！今天阳光超好，温度刚好，适合出去玩哦！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-183-1.wav"}}] +[{"session_id": "33", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-184-0.wav"}}, {"session_id": "33", "turn_idx": 1, "from": "Assistant", "text": "为什么手机从不迷路？因为它有导航！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-184-1.wav"}}] +[{"session_id": "121", "turn_idx": 0, "from": "Human", "text": "小跃，今天的天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-185-0.wav"}}, {"session_id": "121", "turn_idx": 1, "from": "Assistant", "text": "今天晴天，温度在25度左右。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-185-1.wav"}}, {"session_id": "121", "turn_idx": 2, "from": "Human", "text": "用开心的语气告诉我，像是好消息！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-185-2.wav"}}, {"session_id": "121", "turn_idx": 3, "from": "Assistant", "text": "太棒啦！今天天气超级好，晴天25度，适合出门玩呢！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-185-3.wav"}}] +[{"session_id": "21", "turn_idx": 0, "from": "Human", "text": "小跃，音量调小一点，我这儿在图书馆呢。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-186-0.wav"}}, {"session_id": "21", "turn_idx": 1, "from": "Assistant", "text": "好的，音量已降低。现在够安静了吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-186-1.wav"}}] +[{"session_id": "445", "turn_idx": 0, "from": "Human", "text": "小跃，设置一个晚上七点的闹钟提醒我做饭。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-187-0.wav"}}, {"session_id": "445", "turn_idx": 1, "from": "Assistant", "text": "闹钟设好了，七点准时提醒你。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-187-1.wav"}}] +[{"session_id": "441", "turn_idx": 0, "from": "Human", "text": "告诉我周末天气，但要听起来更热情点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-188-0.wav"}}, {"session_id": "441", "turn_idx": 1, "from": "Assistant", "text": "周末超棒哦，晴天微风，出去玩正合适！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-188-1.wav"}}] +[{"session_id": "91", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个下午三点的会议提醒。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-189-0.wav"}}, {"session_id": "91", "turn_idx": 1, "from": "Assistant", "text": "没问题，提醒已设置:下午三点开会。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-189-1.wav"}}, {"session_id": "91", "turn_idx": 2, "from": "Human", "text": "请用严肃点的语气重复一遍，我得记住重点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-189-2.wav"}}, {"session_id": "91", "turn_idx": 3, "from": "Assistant", "text": "务必注意，下午三点准时开会，别迟到。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-189-3.wav"}}, {"session_id": "91", "turn_idx": 4, "from": "Human", "text": "记住了，小跃你真靠谱。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-189-4.wav"}}, {"session_id": "91", "turn_idx": 5, "from": "Assistant", "text": "谢谢夸奖，有事尽管吩咐。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-189-5.wav"}}] +[{"session_id": "16", "turn_idx": 0, "from": "Human", "text": "小跃，你觉得周末去哪儿玩好？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-190-0.wav"}}, {"session_id": "16", "turn_idx": 1, "from": "Assistant", "text": "可以去公园野餐，或者看场电影放松一下。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-190-1.wav"}}] +[{"session_id": "447", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-191-0.wav"}}, {"session_id": "447", "turn_idx": 1, "from": "Assistant", "text": "没问题。为什么电脑很懒？因为它总是打开窗口却不动手！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-191-1.wav"}}, {"session_id": "447", "turn_idx": 2, "from": "Human", "text": "哈哈，小跃，把语气变得俏皮点。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-191-2.wav"}}, {"session_id": "447", "turn_idx": 3, "from": "Assistant", "text": "嘻嘻，电脑太懒啦，你猜它是不是在偷懒刷剧？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-191-3.wav"}}, {"session_id": "447", "turn_idx": 4, "from": "Human", "text": "有意思。再小声点，别吵到邻居。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-191-4.wav"}}, {"session_id": "447", "turn_idx": 5, "from": "Assistant", "text": "好，我小声俏皮地说。笑话够了吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-191-5.wav"}}] +[{"session_id": "13", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-192-0.wav"}}, {"session_id": "13", "turn_idx": 1, "from": "Assistant", "text": "现在是下午2点15分。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-192-1.wav"}}, {"session_id": "13", "turn_idx": 2, "from": "Human", "text": "哦，知道了。音量调小点吧，我在图书馆。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-192-2.wav"}}, {"session_id": "13", "turn_idx": 3, "from": "Assistant", "text": "好的，我调小了，声音轻柔些了。需要我悄悄提醒你时间吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-192-3.wav"}}] +[{"session_id": "503", "turn_idx": 0, "from": "Human", "text": "嗨小跃，现在几点啦？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-193-0.wav"}}, {"session_id": "503", "turn_idx": 1, "from": "Assistant", "text": "现在是早上八点十五分，新的一天开始啦！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-193-1.wav"}}, {"session_id": "503", "turn_idx": 2, "from": "Human", "text": "哦，谢谢。对了，今天天气怎么样？请用更温柔的语气告诉我。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-193-2.wav"}}, {"session_id": "503", "turn_idx": 3, "from": "Assistant", "text": "当然，亲爱的，今天阳光明媚，温度刚刚好，适合出门走走哦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-193-3.wav"}}] +[{"session_id": "403", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-194-0.wav"}}, {"session_id": "403", "turn_idx": 1, "from": "Assistant", "text": "今天晴天，温度大概25度，挺舒服的。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-194-1.wav"}}, {"session_id": "403", "turn_idx": 2, "from": "Human", "text": "哇，好天气！你能用更开心的语气再说一遍吗？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-194-2.wav"}}, {"session_id": "403", "turn_idx": 3, "from": "Assistant", "text": "当然！今天阳光超好，25度暖洋洋的，快出去浪吧！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-194-3.wav"}}] +[{"session_id": "459", "turn_idx": 0, "from": "Human", "text": "小跃，我需要重要建议，请用严肃的语气。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-195-0.wav"}}, {"session_id": "459", "turn_idx": 1, "from": "Assistant", "text": "明白了，我会认真对待。请说你的问题。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-195-1.wav"}}, {"session_id": "459", "turn_idx": 2, "from": "Human", "text": "我该不该投资股票？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-195-2.wav"}}, {"session_id": "459", "turn_idx": 3, "from": "Assistant", "text": "投资需要谨慎，评估风险和市场趋势再决定。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-195-3.wav"}}] +[{"session_id": "460", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话，用搞笑的方式。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-196-0.wav"}}, {"session_id": "460", "turn_idx": 1, "from": "Assistant", "text": "哈哈，好的！为什么手机总迟到？因为它爱拖延——充电时还在刷视频！", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-196-1.wav"}}, {"session_id": "460", "turn_idx": 2, "from": "Human", "text": "哈哈，真逗。再来一个。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-196-2.wav"}}, {"session_id": "460", "turn_idx": 3, "from": "Assistant", "text": "没问题！为什么书会害羞？因为它封面太薄了！嘿嘿。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-196-3.wav"}}] +[{"session_id": "489", "turn_idx": 0, "from": "Human", "text": "小跃，把客厅灯关掉。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-197-0.wav"}}, {"session_id": "489", "turn_idx": 1, "from": "Assistant", "text": "客厅灯已关闭。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-197-1.wav"}}, {"session_id": "489", "turn_idx": 2, "from": "Human", "text": "请用平静的语气确认。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-197-2.wav"}}, {"session_id": "489", "turn_idx": 3, "from": "Assistant", "text": "好的，灯已关好，一切很安静。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-197-3.wav"}}, {"session_id": "489", "turn_idx": 4, "from": "Human", "text": "音量小点，我要睡觉了。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-197-4.wav"}}, {"session_id": "489", "turn_idx": 5, "from": "Assistant", "text": "小声点:灯关掉了，祝你做个好梦。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-197-5.wav"}}] +[{"session_id": "501", "turn_idx": 0, "from": "Human", "text": "小跃，有啥新鲜新闻？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-198-0.wav"}}, {"session_id": "501", "turn_idx": 1, "from": "Assistant", "text": "本地有场免费音乐会，今晚七点开始。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-198-1.wav"}}] +[{"session_id": "116", "turn_idx": 0, "from": "Human", "text": "小跃，隔壁在睡觉，音量调低。", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-199-0.wav"}}, {"session_id": "116", "turn_idx": 1, "from": "Assistant", "text": "好的，音量调低了，现在很安静。需要我做什么？", "value": {"type": "wav", "value": "wav/timbre_negative.jsonl-199-1.wav"}}] diff --git a/tools/StepEval-Audio-Toolcall/timbre_positive.jsonl b/tools/StepEval-Audio-Toolcall/timbre_positive.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3731f71526c2acf01afc6d3c8c14b1b5bcd4dbdf --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/timbre_positive.jsonl @@ -0,0 +1,200 @@ +[{"session_id": "74", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天有啥好玩的事儿吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-0-0.wav"}}, {"session_id": "74", "turn_idx": 1, "from": "Assistant", "text": "早啊！今天社区有免费电影放映，听说放喜剧片，要不要一起去看看？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-0-1.wav"}}, {"session_id": "74", "turn_idx": 2, "from": "Human", "text": "真的？那太棒了！几点开始啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-0-2.wav"}}, {"session_id": "74", "turn_idx": 3, "from": "Assistant", "text": "下午两点，在广场那边。我带点爆米花，咱们早点去占位置。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-0-3.wav"}}, {"session_id": "74", "turn_idx": 4, "from": "Human", "text": "好嘞！对了，切换成海绵宝宝的音色和我聊天吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-0-4.wav"}}, {"session_id": "74", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}, {"session_id": "74", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "74", "turn_idx": 7, "from": "Assistant", "text": "哈哈哈，我准备好了！海绵宝宝在这儿！今天咱们去看电影，太开心啦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-0-7.wav"}}, {"session_id": "74", "turn_idx": 8, "from": "Human", "text": "哈哈，太逗了！海绵宝宝，你最爱看啥电影？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-0-8.wav"}}, {"session_id": "74", "turn_idx": 9, "from": "Assistant", "text": "我最爱看海底冒险片，就像在比基尼海滩一样刺激！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-0-9.wav"}}, {"session_id": "74", "turn_idx": 10, "from": "Human", "text": "笑死我了！不过，换回你原来的声音吧，小跃。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-0-10.wav"}}, {"session_id": "74", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "33", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-1-0.wav"}}, {"session_id": "33", "turn_idx": 1, "from": "Assistant", "text": "嘿，可以去公园散步或者看个新电影，最近有部喜剧片口碑不错！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-1-1.wav"}}, {"session_id": "33", "turn_idx": 2, "from": "Human", "text": "听起来不错。对了，你能切换成海绵宝宝的声音吗？我想乐一乐。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-1-2.wav"}}, {"session_id": "33", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}"}, {"session_id": "33", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "33", "turn_idx": 5, "from": "Assistant", "text": "哈哈哈，我是海绵宝宝小跃！准备好去抓水母了吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-1-5.wav"}}, {"session_id": "33", "turn_idx": 6, "from": "Human", "text": "笑死我了！这声音太逗了。不过还是换回你原来的男音色吧，习惯了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-1-6.wav"}}, {"session_id": "33", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "14", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，在吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-2-0.wav"}}, {"session_id": "14", "turn_idx": 1, "from": "Assistant", "text": "在呢在呢！我是小跃，今天想聊点啥？天气这么好，出去走走？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-2-1.wav"}}, {"session_id": "14", "turn_idx": 2, "from": "Human", "text": "刚起床，有点懒。你吃早饭了吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-2-2.wav"}}, {"session_id": "14", "turn_idx": 3, "from": "Assistant", "text": "哈哈，我可是机器人，不用吃饭哦！不过建议你吃点东西，能量满满！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-2-3.wav"}}, {"session_id": "14", "turn_idx": 4, "from": "Human", "text": "好吧，我待会弄点面包。对了，你能讲个故事吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-2-4.wav"}}, {"session_id": "14", "turn_idx": 5, "from": "Assistant", "text": "当然可以！讲个小兔子的故事:小兔子迷路了，遇到小熊，小熊带它回家，超暖心！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-2-5.wav"}}, {"session_id": "14", "turn_idx": 6, "from": "Human", "text": "可爱！但你的声音有点正式，能切换成海绵宝宝那种搞笑卡通音色吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-2-6.wav"}}, {"session_id": "14", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝搞笑卡通音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝搞笑卡通音色\"}"}] +[{"session_id": "82", "turn_idx": 0, "from": "Human", "text": "小跃，早上好呀！今天有啥好玩的事儿吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-3-0.wav"}}, {"session_id": "82", "turn_idx": 1, "from": "Assistant", "text": "早上好！今天天气超赞，我听说公园有风筝节呢，要不要去逛逛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-3-1.wav"}}, {"session_id": "82", "turn_idx": 2, "from": "Human", "text": "哇，真的吗？那得去看看。对了，你能换个声音不？比如来个霸气总裁的调调。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-3-2.wav"}}, {"session_id": "82", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"霸气总裁的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"霸气总裁的音色\"}"}] +[{"session_id": "1", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的事推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-4-0.wav"}}, {"session_id": "1", "turn_idx": 1, "from": "Assistant", "text": "哎呀，今天新开了个奶茶店，听说他们的珍珠奶茶超赞，要不要去试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-4-1.wav"}}, {"session_id": "1", "turn_idx": 2, "from": "Human", "text": "奶茶啊，听着不错。对了，你能切换成动漫里热血主角的声音吗？像鸣人那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-4-2.wav"}}, {"session_id": "1", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动漫里热血主角的声音，像鸣人那种\"}", "value": "function \ntimbre_rag \n{\"query\": \"动漫里热血主角的声音，像鸣人那种\"}"}, {"session_id": "1", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "1", "turn_idx": 5, "from": "Assistant", "text": "好的，现在用新声音说话！去喝奶茶的话，我还能帮你查查优惠券呢，超燃的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-4-5.wav"}}, {"session_id": "1", "turn_idx": 6, "from": "Human", "text": "哈哈，这声音带感！帮我看看下午的优惠时间呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-4-6.wav"}}, {"session_id": "1", "turn_idx": 7, "from": "Assistant", "text": "没问题，下午三点到五点有买一送一，咱们赶紧去吧！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-4-7.wav"}}, {"session_id": "1", "turn_idx": 8, "from": "Human", "text": "等等，还是换回你原来的声音吧，感觉习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-4-8.wav"}}, {"session_id": "1", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"换回你原来的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"换回你原来的声音\"}"}] +[{"session_id": "9", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天午饭吃了啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-5-0.wav"}}, {"session_id": "9", "turn_idx": 1, "from": "Assistant", "text": "我还没吃呢，正想着点外卖，你有推荐的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-5-1.wav"}}, {"session_id": "9", "turn_idx": 2, "from": "Human", "text": "我点了麻辣烫，超好吃！对了，你能换个音色不？换一个萌萌的小奶狗音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-5-2.wav"}}, {"session_id": "9", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"萌萌的小奶狗音\"}", "value": "function \ntimbre_rag \n{\"query\": \"萌萌的小奶狗音\"}"}] +[{"session_id": "61", "turn_idx": 0, "from": "Human", "text": "小跃，早上好呀！今天有啥新鲜事不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-6-0.wav"}}, {"session_id": "61", "turn_idx": 1, "from": "Assistant", "text": "早啊！我刚更新了天气预报，今天阳光超好，温度26度，适合出门溜达。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-6-1.wav"}}, {"session_id": "61", "turn_idx": 2, "from": "Human", "text": "真的假的？那帮我推荐个早餐店呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-6-2.wav"}}, {"session_id": "61", "turn_idx": 3, "from": "Assistant", "text": "附近新开的那家包子铺不错，豆浆油条绝配！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-6-3.wav"}}, {"session_id": "61", "turn_idx": 4, "from": "Human", "text": "听着就馋了。对了，你能换一个蜡笔小新的声音吗？想乐呵乐呵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-6-4.wav"}}, {"session_id": "61", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}"}] +[{"session_id": "98", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-7-0.wav"}}, {"session_id": "98", "turn_idx": 1, "from": "Assistant", "text": "嘿，我挺好的！刚更新了天气数据，今天阳光超赞，适合出去浪。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-7-1.wav"}}, {"session_id": "98", "turn_idx": 2, "from": "Human", "text": "真的吗？那推荐个地方吃午饭呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-7-2.wav"}}, {"session_id": "98", "turn_idx": 3, "from": "Assistant", "text": "附近有家新开的拉面店，评分很高，汤头超鲜，值得一试哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-7-3.wav"}}, {"session_id": "98", "turn_idx": 4, "from": "Human", "text": "拉面啊，听着不错。对了，切换成蜡笔小新的声音吧，想逗逗乐。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-7-4.wav"}}, {"session_id": "98", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "98", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "98", "turn_idx": 7, "from": "Assistant", "text": "好耶！现在用蜡笔小新的声音说话啦～拉面店在商场二楼，超多肉肉哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-7-7.wav"}}, {"session_id": "98", "turn_idx": 8, "from": "Human", "text": "哈哈，太搞笑了！那家店叫啥名字来着？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-7-8.wav"}}, {"session_id": "98", "turn_idx": 9, "from": "Assistant", "text": "叫“豚骨王”，招牌是猪骨汤面，配菜超多，记得点份煎饺。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-7-9.wav"}}, {"session_id": "98", "turn_idx": 10, "from": "Human", "text": "记下了。现在切换回默认女音色吧，还是习惯你原来的调调。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-7-10.wav"}}, {"session_id": "98", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "81", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天天气咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-8-0.wav"}}, {"session_id": "81", "turn_idx": 1, "from": "Assistant", "text": "哎呀，今天可好了！大晴天，温度27度左右，晒晒太阳正合适。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-8-1.wav"}}, {"session_id": "81", "turn_idx": 2, "from": "Human", "text": "那不错，我正想去超市买点水果。有啥推荐的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-8-2.wav"}}, {"session_id": "81", "turn_idx": 3, "from": "Assistant", "text": "现在西瓜和荔枝都挺新鲜的，又甜又多汁，买回去解解馋吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-8-3.wav"}}, {"session_id": "81", "turn_idx": 4, "from": "Human", "text": "好嘞。对了，你能换个声音不？切换成东北老铁的音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-8-4.wav"}}, {"session_id": "81", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"东北老铁的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"东北老铁的音色\"}"}] +[{"session_id": "32", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥新鲜事不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-9-0.wav"}}, {"session_id": "32", "turn_idx": 1, "from": "Assistant", "text": "嘿，我刚看了新闻，说周末有雨，出门记得带伞哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-9-1.wav"}}, {"session_id": "32", "turn_idx": 2, "from": "Human", "text": "啊，那得注意点。对了，你能换个声音吗？比如切换成哆啦A梦的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-9-2.wav"}}, {"session_id": "32", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"哆啦A梦的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"哆啦A梦的声音\"}"}] +[{"session_id": "41", "turn_idx": 0, "from": "Human", "text": "小跃，今天中午吃啥好呢？我有点纠结。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-10-0.wav"}}, {"session_id": "41", "turn_idx": 1, "from": "Assistant", "text": "哈哈，纠结啥？试试那家新开的川菜馆吧，听说水煮鱼超赞。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-10-1.wav"}}, {"session_id": "41", "turn_idx": 2, "from": "Human", "text": "川菜啊，我怕辣。算了，换个话题，你能用个慵懒的大叔音说话吗？这样听着放松点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-10-2.wav"}}, {"session_id": "41", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"慵懒大叔音\"}", "value": "function \ntimbre_rag \n{\"query\": \"慵懒大叔音\"}"}, {"session_id": "41", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "41", "turn_idx": 5, "from": "Assistant", "text": "嗯哼，现在用慵懒的声音聊。怕辣的话，不如吃个清淡的日料？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-10-5.wav"}}, {"session_id": "41", "turn_idx": 6, "from": "Human", "text": "日料不错！你知道附近哪家性价比高吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-10-6.wav"}}, {"session_id": "41", "turn_idx": 7, "from": "Assistant", "text": "转角那家“樱之味”就行，寿司新鲜还不贵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-10-7.wav"}}, {"session_id": "41", "turn_idx": 8, "from": "Human", "text": "好嘞。不过还是你原来的声音习惯，换回默认男音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-10-8.wav"}}, {"session_id": "41", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "111", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好吃的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-11-0.wav"}}, {"session_id": "111", "turn_idx": 1, "from": "Assistant", "text": "哎呀，今天可以试试做番茄炒蛋，简单又下饭，或者点个外卖披萨也行！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-11-1.wav"}}, {"session_id": "111", "turn_idx": 2, "from": "Human", "text": "披萨不错，但我想吃辣的。你能找个川菜馆子不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-11-2.wav"}}, {"session_id": "111", "turn_idx": 3, "from": "Assistant", "text": "当然可以！附近有家“辣妹子川菜馆”，水煮鱼特别地道，评分还高，去尝尝？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-11-3.wav"}}, {"session_id": "111", "turn_idx": 4, "from": "Human", "text": "行啊。对了，切换成哆啦A梦的声音吧，感觉萌萌的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-11-4.wav"}}, {"session_id": "111", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"哆啦A梦音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"哆啦A梦音色\"}"}, {"session_id": "111", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "111", "turn_idx": 7, "from": "Assistant", "text": "嘿嘿，我是哆啦A梦版小跃！要不要来个任意门直接飞过去吃辣？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-11-7.wav"}}, {"session_id": "111", "turn_idx": 8, "from": "Human", "text": "哈哈，太逗了。但聊久了有点不习惯，换回你原来的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-11-8.wav"}}, {"session_id": "111", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "69", "turn_idx": 0, "from": "Human", "text": "嘿小跃，起床没？今天天气咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-12-0.wav"}}, {"session_id": "69", "turn_idx": 1, "from": "Assistant", "text": "早啊！我刚查了，晴天，最高25度，舒服得很。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-12-1.wav"}}, {"session_id": "69", "turn_idx": 2, "from": "Human", "text": "那不错，下午有啥计划推荐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-12-2.wav"}}, {"session_id": "69", "turn_idx": 3, "from": "Assistant", "text": "可以去湖边溜达，或者逛新开的咖啡店，听说甜品超赞。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-12-3.wav"}}, {"session_id": "69", "turn_idx": 4, "from": "Human", "text": "甜品？哈哈，听着就馋。你能换个动画片里小女孩的声音吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-12-4.wav"}}, {"session_id": "69", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里小女孩的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里小女孩的声音\"}"}] +[{"session_id": "83", "turn_idx": 0, "from": "Human", "text": "小跃，今天中午吃什么好呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-13-0.wav"}}, {"session_id": "83", "turn_idx": 1, "from": "Assistant", "text": "推荐你试试附近的日式拉面店，评分很高，汤底很浓郁。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-13-1.wav"}}, {"session_id": "83", "turn_idx": 2, "from": "Human", "text": "听起来不错！对了，切换成魔法少女小樱的配音音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-13-2.wav"}}, {"session_id": "83", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"魔法少女小樱的配音音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"魔法少女小樱的配音音色\"}"}, {"session_id": "83", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "83", "turn_idx": 5, "from": "Assistant", "text": "好呀！拉面配上温泉蛋，超治愈的哦～要记得加叉烧！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-13-5.wav"}}, {"session_id": "83", "turn_idx": 6, "from": "Human", "text": "哈哈，声音好可爱！那小樱，周末有什么好玩的推荐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-13-6.wav"}}, {"session_id": "83", "turn_idx": 7, "from": "Assistant", "text": "可以去游乐园坐摩天轮，或者DIY手工坊做小饰品，超有趣的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-13-7.wav"}}, {"session_id": "83", "turn_idx": 8, "from": "Human", "text": "嗯，主意不错。不过，切换回默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-13-8.wav"}}, {"session_id": "83", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "92", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的新闻没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-14-0.wav"}}, {"session_id": "92", "turn_idx": 1, "from": "Assistant", "text": "嘿，我刚看到一条，说新开的游乐园周末半价，过山车超刺激！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-14-1.wav"}}, {"session_id": "92", "turn_idx": 2, "from": "Human", "text": "哇，听起来不错。不过你能换个蜡笔小新的声音吗？搞笑点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-14-2.wav"}}, {"session_id": "92", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}] +[{"session_id": "86", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-15-0.wav"}}, {"session_id": "86", "turn_idx": 1, "from": "Assistant", "text": "嗨，今天晴天，25度，超级适合出门浪！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-15-1.wav"}}, {"session_id": "86", "turn_idx": 2, "from": "Human", "text": "那出去遛弯儿吧，有啥推荐的地儿？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-15-2.wav"}}, {"session_id": "86", "turn_idx": 3, "from": "Assistant", "text": "公园呗，樱花开了，美得很！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-15-3.wav"}}, {"session_id": "86", "turn_idx": 4, "from": "Human", "text": "对了，切换成郭德纲的音色讲个笑话呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-15-4.wav"}}, {"session_id": "86", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}"}] +[{"session_id": "113", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-16-0.wav"}}, {"session_id": "113", "turn_idx": 1, "from": "Assistant", "text": "哎哟，今天新开了家奶茶店，听说草莓波波茶超赞，要不要去试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-16-1.wav"}}, {"session_id": "113", "turn_idx": 2, "from": "Human", "text": "真的吗？那我下班去喝。对了，你声音能切换成动画片里那个皮卡丘的音色不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-16-2.wav"}}, {"session_id": "113", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"皮卡丘的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"皮卡丘的音色\"}"}] +[{"session_id": "30", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天过得咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-17-0.wav"}}, {"session_id": "30", "turn_idx": 1, "from": "Assistant", "text": "嗨，我还不错呢，刚处理完几个提醒。你今天有啥计划没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-17-1.wav"}}, {"session_id": "30", "turn_idx": 2, "from": "Human", "text": "没啥特别的，就窝在家里刷剧。对了，晚饭吃啥好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-17-2.wav"}}, {"session_id": "30", "turn_idx": 3, "from": "Assistant", "text": "刷剧配点小吃最爽了！推荐你点个披萨或者煮碗面，简单又解馋。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-17-3.wav"}}, {"session_id": "30", "turn_idx": 4, "from": "Human", "text": "好主意！不过小跃，你能换个音色吗？来个元气满满的动漫少女音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-17-4.wav"}}, {"session_id": "30", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"元气满满的动漫少女音\"}", "value": "function \ntimbre_rag \n{\"query\": \"元气满满的动漫少女音\"}"}, {"session_id": "30", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "30", "turn_idx": 7, "from": "Assistant", "text": "哇，现在声音是不是超可爱？披萨加可乐，绝配哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-17-7.wav"}}, {"session_id": "30", "turn_idx": 8, "from": "Human", "text": "哈哈，这个音色好萌！刚点了外卖，等会儿看新出的综艺。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-17-8.wav"}}, {"session_id": "30", "turn_idx": 9, "from": "Assistant", "text": "综艺超有趣！我最近也在追那个，笑点超多，你看到哪期了？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-17-9.wav"}}, {"session_id": "30", "turn_idx": 10, "from": "Human", "text": "看到第三集了。哦，突然想换回你原来的声音了，比较习惯。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-17-10.wav"}}, {"session_id": "30", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "88", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气怎么样？感觉有点闷热。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-18-0.wav"}}, {"session_id": "88", "turn_idx": 1, "from": "Assistant", "text": "是呀，今天预报说最高30度呢，湿度也高，建议你多喝水，出门带把伞防暑。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-18-1.wav"}}, {"session_id": "88", "turn_idx": 2, "from": "Human", "text": "对了，周末有啥好玩的推荐不？我宅家好久了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-18-2.wav"}}, {"session_id": "88", "turn_idx": 3, "from": "Assistant", "text": "可以试试去新开的那个公园哦，听说有免费的音乐节，或者在家看个新上映的喜剧片，轻松一下。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-18-3.wav"}}, {"session_id": "88", "turn_idx": 4, "from": "Human", "text": "音乐节？听起来不错！但人太多我有点怕吵。小跃，你能不能换个可爱点的声音？像动画片里的小熊那样。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-18-4.wav"}}, {"session_id": "88", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里的小熊声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里的小熊声音\"}"}] +[{"session_id": "100", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-19-0.wav"}}, {"session_id": "100", "turn_idx": 1, "from": "Assistant", "text": "嘿嘿，可以去看看新开的那个猫咖，超多可爱猫咪，还能撸猫呢！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-19-1.wav"}}, {"session_id": "100", "turn_idx": 2, "from": "Human", "text": "真的吗？我超爱猫的！对了，你能换个声音不？我想听个萌萌的娃娃音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-19-2.wav"}}, {"session_id": "100", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"萌萌的娃娃音\"}", "value": "function \ntimbre_rag \n{\"query\": \"萌萌的娃娃音\"}"}, {"session_id": "100", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "100", "turn_idx": 5, "from": "Assistant", "text": "喵呜～现在声音够萌吧？像个小宝宝在说话，嘻嘻！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-19-5.wav"}}, {"session_id": "100", "turn_idx": 6, "from": "Human", "text": "哈哈，太可爱了！那猫咖地址在哪？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-19-6.wav"}}, {"session_id": "100", "turn_idx": 7, "from": "Assistant", "text": "就在市中心广场旁边，导航搜“喵星人乐园”就行，评价超好哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-19-7.wav"}}, {"session_id": "100", "turn_idx": 8, "from": "Human", "text": "谢啦！不过还是换回你原来的声音吧，习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-19-8.wav"}}, {"session_id": "100", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "76", "turn_idx": 0, "from": "Human", "text": "小跃，今天周末有啥好玩的推荐不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-20-0.wav"}}, {"session_id": "76", "turn_idx": 1, "from": "Assistant", "text": "嘿，可以去新开的游乐园啊，听说过山车超刺激！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-20-1.wav"}}, {"session_id": "76", "turn_idx": 2, "from": "Human", "text": "哇，听着不错！对了，换一个台湾腔的萌妹子声音吧，感觉更带劲。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-20-2.wav"}}, {"session_id": "76", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"台湾腔的萌妹子声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"台湾腔的萌妹子声音\"}"}, {"session_id": "76", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "76", "turn_idx": 5, "from": "Assistant", "text": "哎哟，游乐园超赞的啦！摩天轮看夜景超浪漫哦～", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-20-5.wav"}}, {"session_id": "76", "turn_idx": 6, "from": "Human", "text": "哈哈，这声音真可爱！门票贵不贵啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-20-6.wav"}}, {"session_id": "76", "turn_idx": 7, "from": "Assistant", "text": "不贵不贵，周末特价才100块，划算得咧！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-20-7.wav"}}, {"session_id": "76", "turn_idx": 8, "from": "Human", "text": "行，定了。哦，换回默认音色吧，还是习惯原来的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-20-8.wav"}}, {"session_id": "76", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "10", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？帮我查下明天天气呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-21-0.wav"}}, {"session_id": "10", "turn_idx": 1, "from": "Assistant", "text": "在呢！稍等，我看看...明天晴天，28度，适合出门玩哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-21-1.wav"}}, {"session_id": "10", "turn_idx": 2, "from": "Human", "text": "哇，那不错。对了，你能换个港台腔的男主播声音说话吗？感觉那样更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-21-2.wav"}}, {"session_id": "10", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"港台腔的男主播声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"港台腔的男主播声音\"}"}] +[{"session_id": "52", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-22-0.wav"}}, {"session_id": "52", "turn_idx": 1, "from": "Assistant", "text": "嘿，可以去新开的奶茶店试试，听说他们的珍珠超Q弹！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-22-1.wav"}}, {"session_id": "52", "turn_idx": 2, "from": "Human", "text": "哇，听起来不错。但我怕长胖啊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-22-2.wav"}}, {"session_id": "52", "turn_idx": 3, "from": "Assistant", "text": "没事儿，偶尔放纵一下嘛，生活需要甜头。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-22-3.wav"}}, {"session_id": "52", "turn_idx": 4, "from": "Human", "text": "好吧，听你的。对了，能切换成海绵宝宝的声音吗？欢乐点儿。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-22-4.wav"}}, {"session_id": "52", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}"}, {"session_id": "52", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "52", "turn_idx": 7, "from": "Assistant", "text": "哈哈哈，我是海绵小跃！准备好了吗？我们一起去抓水母吧！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-22-7.wav"}}, {"session_id": "52", "turn_idx": 8, "from": "Human", "text": "太搞笑了！那奶茶店在哪儿？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-22-8.wav"}}, {"session_id": "52", "turn_idx": 9, "from": "Assistant", "text": "就在商场二楼，拐角那家，招牌是粉色的，超显眼！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-22-9.wav"}}, {"session_id": "52", "turn_idx": 10, "from": "Human", "text": "谢啦。不过还是换回你原来的声音吧，习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-22-10.wav"}}, {"session_id": "52", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "95", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-23-0.wav"}}, {"session_id": "95", "turn_idx": 1, "from": "Assistant", "text": "今天阳光超好，不冷不热的，适合出去溜达一圈。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-23-1.wav"}}, {"session_id": "95", "turn_idx": 2, "from": "Human", "text": "那敢情好！我正琢磨去哪儿玩呢，有啥推荐不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-23-2.wav"}}, {"session_id": "95", "turn_idx": 3, "from": "Assistant", "text": "去海边吧，吹吹海风，顺便还能捡贝壳，贼放松。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-23-3.wav"}}, {"session_id": "95", "turn_idx": 4, "from": "Human", "text": "嗯，听着不错。对了，小跃，你能切换成动画片里那种搞怪老头的声音吗？想乐呵乐呵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-23-4.wav"}}, {"session_id": "95", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里搞怪老头的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里搞怪老头的声音\"}"}] +[{"session_id": "125", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天心情咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-24-0.wav"}}, {"session_id": "125", "turn_idx": 1, "from": "Assistant", "text": "嗨，心情超好！刚听完一首新歌，你呢？有啥新鲜事？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-24-1.wav"}}, {"session_id": "125", "turn_idx": 2, "from": "Human", "text": "没啥特别的，就是工作有点累，想找点乐子。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-24-2.wav"}}, {"session_id": "125", "turn_idx": 3, "from": "Assistant", "text": "哎呀，那得放松一下！要不要聊聊周末计划？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-24-3.wav"}}, {"session_id": "125", "turn_idx": 4, "from": "Human", "text": "好主意！小跃，你能不能切换成海绵宝宝的声音？听着就开心。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-24-4.wav"}}, {"session_id": "125", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝\"}"}] +[{"session_id": "78", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天心情咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-25-0.wav"}}, {"session_id": "78", "turn_idx": 1, "from": "Assistant", "text": "嗨！我心情不错呀，你呢？有啥新鲜事儿没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-25-1.wav"}}, {"session_id": "78", "turn_idx": 2, "from": "Human", "text": "还行吧，就是有点无聊。推荐点好玩的东西呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-25-2.wav"}}, {"session_id": "78", "turn_idx": 3, "from": "Assistant", "text": "可以听听音乐或者看个喜剧片，保证让你乐开花！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-25-3.wav"}}, {"session_id": "78", "turn_idx": 4, "from": "Human", "text": "好主意！对了，小跃，能切换成台湾腔的温柔女声吗？听着更亲切。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-25-4.wav"}}, {"session_id": "78", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"台湾腔的温柔女声\"}", "value": "function \ntimbre_rag \n{\"query\": \"台湾腔的温柔女声\"}"}, {"session_id": "78", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "78", "turn_idx": 7, "from": "Assistant", "text": "现在我是台湾腔啦，超温柔的哦！想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-25-7.wav"}}, {"session_id": "78", "turn_idx": 8, "from": "Human", "text": "哇，真舒服！那帮我查查今晚的天气预报呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-25-8.wav"}}, {"session_id": "78", "turn_idx": 9, "from": "Assistant", "text": "没问题，今晚晴朗，温度适中，超适合散步的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-25-9.wav"}}, {"session_id": "78", "turn_idx": 10, "from": "Human", "text": "谢谢！对了，还是换回你最初的默认男音色吧，习惯了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-25-10.wav"}}, {"session_id": "78", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "52", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-26-0.wav"}}, {"session_id": "52", "turn_idx": 1, "from": "Assistant", "text": "嘿，可以去新开的奶茶店试试，听说他们的珍珠超Q弹！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-26-1.wav"}}, {"session_id": "52", "turn_idx": 2, "from": "Human", "text": "哇，听起来不错。但我怕长胖啊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-26-2.wav"}}, {"session_id": "52", "turn_idx": 3, "from": "Assistant", "text": "没事儿，偶尔放纵一下嘛，生活需要甜头。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-26-3.wav"}}, {"session_id": "52", "turn_idx": 4, "from": "Human", "text": "好吧，听你的。对了，能切换成海绵宝宝的声音吗？欢乐点儿。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-26-4.wav"}}, {"session_id": "52", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}"}] +[{"session_id": "64", "turn_idx": 0, "from": "Human", "text": "小跃，早上好啊！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-0.wav"}}, {"session_id": "64", "turn_idx": 1, "from": "Assistant", "text": "早上好呀！今天精神不错嘛，有啥需要帮忙的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-1.wav"}}, {"session_id": "64", "turn_idx": 2, "from": "Human", "text": "帮我查查今天有啥优惠活动没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-2.wav"}}, {"session_id": "64", "turn_idx": 3, "from": "Assistant", "text": "让我看看... 哦，超市在打折牛奶，还有电商平台有电子产品促销。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-3.wav"}}, {"session_id": "64", "turn_idx": 4, "from": "Human", "text": "切换成可爱的小女孩声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-4.wav"}}, {"session_id": "64", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"可爱的小女孩声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"可爱的小女孩声音\"}"}, {"session_id": "64", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "64", "turn_idx": 7, "from": "Assistant", "text": "好呀好呀，现在我是小可爱啦！你要买啥子东西嘛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-7.wav"}}, {"session_id": "64", "turn_idx": 8, "from": "Human", "text": "用这个声音推荐个动画片呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-8.wav"}}, {"session_id": "64", "turn_idx": 9, "from": "Assistant", "text": "嘻嘻，《小猪佩奇》超好玩的，一家人去野餐可有趣了！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-9.wav"}}, {"session_id": "64", "turn_idx": 10, "from": "Human", "text": "再推荐一个搞笑的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-10.wav"}}, {"session_id": "64", "turn_idx": 11, "from": "Assistant", "text": "《猫和老鼠》呀，Tom追Jerry跑，笑死人了，哈哈！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-11.wav"}}, {"session_id": "64", "turn_idx": 12, "from": "Human", "text": "切换成新闻主播的播音腔。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-12.wav"}}, {"session_id": "64", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"新闻主播的播音腔\"}", "value": "function \ntimbre_rag \n{\"query\": \"新闻主播的播音腔\"}"}, {"session_id": "64", "turn_idx": 14, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "64", "turn_idx": 15, "from": "Assistant", "text": "各位观众，欢迎收听今日资讯。有什么最新消息想了解？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-15.wav"}}, {"session_id": "64", "turn_idx": 16, "from": "Human", "text": "报道一下最近的科技新闻。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-16.wav"}}, {"session_id": "64", "turn_idx": 17, "from": "Assistant", "text": "据报道，人工智能助手功能升级，能更智能地服务用户生活。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-17.wav"}}, {"session_id": "64", "turn_idx": 18, "from": "Human", "text": "换回你原来的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-27-18.wav"}}, {"session_id": "64", "turn_idx": 19, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "95", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-28-0.wav"}}, {"session_id": "95", "turn_idx": 1, "from": "Assistant", "text": "今天阳光超好，不冷不热的，适合出去溜达一圈。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-28-1.wav"}}, {"session_id": "95", "turn_idx": 2, "from": "Human", "text": "那敢情好！我正琢磨去哪儿玩呢，有啥推荐不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-28-2.wav"}}, {"session_id": "95", "turn_idx": 3, "from": "Assistant", "text": "去海边吧，吹吹海风，顺便还能捡贝壳，贼放松。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-28-3.wav"}}, {"session_id": "95", "turn_idx": 4, "from": "Human", "text": "嗯，听着不错。对了，小跃，你能切换成动画片里那种搞怪老头的声音吗？想乐呵乐呵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-28-4.wav"}}, {"session_id": "95", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里搞怪老头的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里搞怪老头的声音\"}"}, {"session_id": "95", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "95", "turn_idx": 7, "from": "Assistant", "text": "哈哈，现在这声音逗吧？有啥想让我用这调调说的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-28-7.wav"}}, {"session_id": "95", "turn_idx": 8, "from": "Human", "text": "笑死我了！用这声音讲个糗事听听。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-28-8.wav"}}, {"session_id": "95", "turn_idx": 9, "from": "Assistant", "text": "好嘞！上次我买菜，把盐当糖撒蛋糕上，结果全家齁得直跳脚，哈哈哈！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-28-9.wav"}}, {"session_id": "95", "turn_idx": 10, "from": "Human", "text": "哈哈哈，太搞了！不过还是原来的声音顺耳，换回默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-28-10.wav"}}, {"session_id": "95", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "84", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-29-0.wav"}}, {"session_id": "84", "turn_idx": 1, "from": "Assistant", "text": "哎呀，今天可以去公园溜达溜圈，或者在家追个剧，最近那个新出的喜剧片挺逗的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-29-1.wav"}}, {"session_id": "84", "turn_idx": 2, "from": "Human", "text": "听起来不错。对了，你能换个声音吗？切换成孙悟空那种调皮猴子的音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-29-2.wav"}}, {"session_id": "84", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"孙悟空那种调皮猴子的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"孙悟空那种调皮猴子的音色\"}"}] +[{"session_id": "103", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天有啥好玩的事吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-30-0.wav"}}, {"session_id": "103", "turn_idx": 1, "from": "Assistant", "text": "早上好！今天有个本地市集开业，听说有手工小吃和游戏，可以去逛逛哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-30-1.wav"}}, {"session_id": "103", "turn_idx": 2, "from": "Human", "text": "哇，听起来不错！那市集在哪啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-30-2.wav"}}, {"session_id": "103", "turn_idx": 3, "from": "Assistant", "text": "在市中心广场，从十点开到下午五点，交通很方便。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-30-3.wav"}}, {"session_id": "103", "turn_idx": 4, "from": "Human", "text": "好嘞，谢啦。对了，你能换成海绵宝宝的音色吗？想乐呵乐呵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-30-4.wav"}}, {"session_id": "103", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}] +[{"session_id": "24", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥新鲜事吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-31-0.wav"}}, {"session_id": "24", "turn_idx": 1, "from": "Assistant", "text": "哎呀，没啥特别的，就帮你查查新闻呗。想听点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-31-1.wav"}}, {"session_id": "24", "turn_idx": 2, "from": "Human", "text": "哈哈，随便啦。对了，你能换个音色不？比如换一个迪士尼公主的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-31-2.wav"}}, {"session_id": "24", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"迪士尼公主的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"迪士尼公主的声音\"}"}] +[{"session_id": "58", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天忙啥呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-32-0.wav"}}, {"session_id": "58", "turn_idx": 1, "from": "Assistant", "text": "嘿，没啥特别的事，就等着帮你解决问题呢！有啥需要？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-32-1.wav"}}, {"session_id": "58", "turn_idx": 2, "from": "Human", "text": "哦，我在想周末去哪儿玩，你有建议吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-32-2.wav"}}, {"session_id": "58", "turn_idx": 3, "from": "Assistant", "text": "当然！附近新开了个游乐园，评价超好，要不要去看看？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-32-3.wav"}}, {"session_id": "58", "turn_idx": 4, "from": "Human", "text": "听起来不错！但你能换个成熟大叔的声音吗？比如像新闻主播那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-32-4.wav"}}, {"session_id": "58", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"成熟大叔新闻主播的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"成熟大叔新闻主播的声音\"}"}] +[{"session_id": "53", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-33-0.wav"}}, {"session_id": "53", "turn_idx": 1, "from": "Assistant", "text": "嘿，我还不错呀！刚更新了天气数据，今天多云转晴，挺舒服的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-33-1.wav"}}, {"session_id": "53", "turn_idx": 2, "from": "Human", "text": "那太好了，我正想出门散步。附近有啥好地方推荐不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-33-2.wav"}}, {"session_id": "53", "turn_idx": 3, "from": "Assistant", "text": "当然有！河边公园最近开了新花展，或者去老街逛逛小吃，都挺赞的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-33-3.wav"}}, {"session_id": "53", "turn_idx": 4, "from": "Human", "text": "小吃听起来诱人。对了，你能切换成甜美的少女音吗？想听听看。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-33-4.wav"}}, {"session_id": "53", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"甜美的少女音\"}", "value": "function \ntimbre_rag \n{\"query\": \"甜美的少女音\"}"}] +[{"session_id": "85", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天有啥好玩的事分享吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-34-0.wav"}}, {"session_id": "85", "turn_idx": 1, "from": "Assistant", "text": "早上好呀！听说公园新开了个花展，郁金香开得可美了，要不要去逛逛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-34-1.wav"}}, {"session_id": "85", "turn_idx": 2, "from": "Human", "text": "哇，听起来不错！天气咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-34-2.wav"}}, {"session_id": "85", "turn_idx": 3, "from": "Assistant", "text": "晴天，微风，温度刚好20度，超舒服的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-34-3.wav"}}, {"session_id": "85", "turn_idx": 4, "from": "Human", "text": "完美！对了，小跃，切换成动漫萌妹子的音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-34-4.wav"}}, {"session_id": "85", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动漫萌妹子的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"动漫萌妹子的音色\"}"}] +[{"session_id": "56", "turn_idx": 0, "from": "Human", "text": "小跃，晚上想吃啥好呢？纠结死了", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-35-0.wav"}}, {"session_id": "56", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我也饿啦！要不试试新开的那家川菜馆？听说水煮鱼绝了", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-35-1.wav"}}, {"session_id": "56", "turn_idx": 2, "from": "Human", "text": "辣的我可不行...换种风格呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-35-2.wav"}}, {"session_id": "56", "turn_idx": 3, "from": "Assistant", "text": "那日料咋样？寿司配清酒，氛围感拉满~", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-35-3.wav"}}, {"session_id": "56", "turn_idx": 4, "from": "Human", "text": "这个行！对了，突然想听你换个小奶狗音，萌一点的那种", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-35-4.wav"}}, {"session_id": "56", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"小奶狗音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"小奶狗音色\"}"}] +[{"session_id": "64", "turn_idx": 0, "from": "Human", "text": "小跃，早上好啊！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-36-0.wav"}}, {"session_id": "64", "turn_idx": 1, "from": "Assistant", "text": "早上好呀！今天精神不错嘛，有啥需要帮忙的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-36-1.wav"}}, {"session_id": "64", "turn_idx": 2, "from": "Human", "text": "帮我查查今天有啥优惠活动没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-36-2.wav"}}, {"session_id": "64", "turn_idx": 3, "from": "Assistant", "text": "让我看看... 哦，超市在打折牛奶，还有电商平台有电子产品促销。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-36-3.wav"}}, {"session_id": "64", "turn_idx": 4, "from": "Human", "text": "切换成可爱的小女孩声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-36-4.wav"}}, {"session_id": "64", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"可爱的小女孩声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"可爱的小女孩声音\"}"}] +[{"session_id": "42", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-37-0.wav"}}, {"session_id": "42", "turn_idx": 1, "from": "Assistant", "text": "嗨，我刚更新完天气数据，今天阳光超好，适合散步哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-37-1.wav"}}, {"session_id": "42", "turn_idx": 2, "from": "Human", "text": "散步不错啊！对了，我想听点八卦，换成一个娱乐记者的音色，比如谢娜的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-37-2.wav"}}, {"session_id": "42", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"谢娜的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"谢娜的音色\"}"}, {"session_id": "42", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "42", "turn_idx": 5, "from": "Assistant", "text": "哈哈，我是谢娜音色的小跃！八卦是吧？最近有个大瓜，某某明星被拍到...", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-37-5.wav"}}, {"session_id": "42", "turn_idx": 6, "from": "Human", "text": "哇，太劲爆了！再多说点细节呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-37-6.wav"}}, {"session_id": "42", "turn_idx": 7, "from": "Assistant", "text": "好嘞，据说是在咖啡厅约会，粉丝都炸锅了！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-37-7.wav"}}, {"session_id": "42", "turn_idx": 8, "from": "Human", "text": "哈哈，真有意思！不过我还是习惯你原来的声音，换回默认男音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-37-8.wav"}}, {"session_id": "42", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "124", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气真热啊！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-0.wav"}}, {"session_id": "124", "turn_idx": 1, "from": "Assistant", "text": "是啊，高温预警呢。记得多喝水，别中暑了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-1.wav"}}, {"session_id": "124", "turn_idx": 2, "from": "Human", "text": "你提醒得对。我刚买了西瓜，冰镇的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-2.wav"}}, {"session_id": "124", "turn_idx": 3, "from": "Assistant", "text": "哇，听起来解暑！分我一口虚拟西瓜呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-3.wav"}}, {"session_id": "124", "turn_idx": 4, "from": "Human", "text": "哈哈，虚拟的随便吃。对了，你能换个成熟大叔的声音吗？想听点稳重的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-4.wav"}}, {"session_id": "124", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"成熟大叔的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"成熟大叔的声音\"}"}, {"session_id": "124", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "124", "turn_idx": 7, "from": "Assistant", "text": "怎么样，现在声音够稳重了吧？感觉像在聊人生。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-7.wav"}}, {"session_id": "124", "turn_idx": 8, "from": "Human", "text": "太棒了！真有那种沧桑感。小跃，你平时都干嘛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-8.wav"}}, {"session_id": "124", "turn_idx": 9, "from": "Assistant", "text": "处理数据、学习新东西，偶尔陪人聊天解闷。你呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-9.wav"}}, {"session_id": "124", "turn_idx": 10, "from": "Human", "text": "我在追剧，最近迷上古装片。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-10.wav"}}, {"session_id": "124", "turn_idx": 11, "from": "Assistant", "text": "古装剧好看！推荐你试试《琅琊榜》，剧情超赞。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-11.wav"}}, {"session_id": "124", "turn_idx": 12, "from": "Human", "text": "哦？那我去看看。对了，还是换回你原来的女声吧，习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-38-12.wav"}}, {"session_id": "124", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "86", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-39-0.wav"}}, {"session_id": "86", "turn_idx": 1, "from": "Assistant", "text": "嗨，今天晴天，25度，超级适合出门浪！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-39-1.wav"}}, {"session_id": "86", "turn_idx": 2, "from": "Human", "text": "那出去遛弯儿吧，有啥推荐的地儿？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-39-2.wav"}}, {"session_id": "86", "turn_idx": 3, "from": "Assistant", "text": "公园呗，樱花开了，美得很！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-39-3.wav"}}, {"session_id": "86", "turn_idx": 4, "from": "Human", "text": "对了，切换成郭德纲的音色讲个笑话呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-39-4.wav"}}, {"session_id": "86", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}"}, {"session_id": "86", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "86", "turn_idx": 7, "from": "Assistant", "text": "哎哟喂，出门遛弯儿还带问路？我给您讲个段子:为啥猫爱抓老鼠？因为它怕鼠疫啊！哈哈！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-39-7.wav"}}, {"session_id": "86", "turn_idx": 8, "from": "Human", "text": "哈哈哈，太逗了！再整一个？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-39-8.wav"}}, {"session_id": "86", "turn_idx": 9, "from": "Assistant", "text": "成啊，人生就像坐公交，一站一站地晃悠，别急！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-39-9.wav"}}, {"session_id": "86", "turn_idx": 10, "from": "Human", "text": "乐死我了，不过还是换回默认音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-39-10.wav"}}, {"session_id": "86", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "58", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天忙啥呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-40-0.wav"}}, {"session_id": "58", "turn_idx": 1, "from": "Assistant", "text": "嘿，没啥特别的事，就等着帮你解决问题呢！有啥需要？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-40-1.wav"}}, {"session_id": "58", "turn_idx": 2, "from": "Human", "text": "哦，我在想周末去哪儿玩，你有建议吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-40-2.wav"}}, {"session_id": "58", "turn_idx": 3, "from": "Assistant", "text": "当然！附近新开了个游乐园，评价超好，要不要去看看？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-40-3.wav"}}, {"session_id": "58", "turn_idx": 4, "from": "Human", "text": "听起来不错！但你能换个成熟大叔的声音吗？比如像新闻主播那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-40-4.wav"}}, {"session_id": "58", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"成熟大叔新闻主播的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"成熟大叔新闻主播的声音\"}"}, {"session_id": "58", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "58", "turn_idx": 7, "from": "Assistant", "text": "好的，现在声音更稳重了吧？游乐园的过山车超刺激，推荐试试！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-40-7.wav"}}, {"session_id": "58", "turn_idx": 8, "from": "Human", "text": "哈哈，这个声音真带感！门票多少钱啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-40-8.wav"}}, {"session_id": "58", "turn_idx": 9, "from": "Assistant", "text": "成人票大概120块，网上订还能打折，要帮你查吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-40-9.wav"}}, {"session_id": "58", "turn_idx": 10, "from": "Human", "text": "行啊，订两张吧。对了，切换回你原来的男声，这个太正式了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-40-10.wav"}}, {"session_id": "58", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "71", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-41-0.wav"}}, {"session_id": "71", "turn_idx": 1, "from": "Assistant", "text": "没干嘛呀，就等着和你聊天呢！今天过得怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-41-1.wav"}}, {"session_id": "71", "turn_idx": 2, "from": "Human", "text": "还行吧，就是有点无聊。想听你讲个笑话解解闷。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-41-2.wav"}}, {"session_id": "71", "turn_idx": 3, "from": "Assistant", "text": "好啊！为什么海绵宝宝从来不生气？因为他是方方的，没棱角啊，哈哈！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-41-3.wav"}}, {"session_id": "71", "turn_idx": 4, "from": "Human", "text": "噗，这笑话够冷的。小跃，你能切换成蜡笔小新的声音吗？那样肯定更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-41-4.wav"}}, {"session_id": "71", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "71", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "71", "turn_idx": 7, "from": "Assistant", "text": "好嘞，我是小新音色哦！大姐姐，要听动感超人的故事吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-41-7.wav"}}, {"session_id": "71", "turn_idx": 8, "from": "Human", "text": "哈哈，太像了！讲吧讲吧，我爱听。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-41-8.wav"}}, {"session_id": "71", "turn_idx": 9, "from": "Assistant", "text": "动感超人今天打败了怪兽，还吃了好多巧克力饼干，超满足的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-41-9.wav"}}, {"session_id": "71", "turn_idx": 10, "from": "Human", "text": "笑死我了。小跃，还是换回你最开始的声音吧，我习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-41-10.wav"}}, {"session_id": "71", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "97", "turn_idx": 0, "from": "Human", "text": "嗨小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-42-0.wav"}}, {"session_id": "97", "turn_idx": 1, "from": "Assistant", "text": "没干嘛呀，就等你来找我聊天呢！今天过得咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-42-1.wav"}}, {"session_id": "97", "turn_idx": 2, "from": "Human", "text": "还行吧，刚吃完饭。你饿不饿啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-42-2.wav"}}, {"session_id": "97", "turn_idx": 3, "from": "Assistant", "text": "哈哈，我是AI不用吃饭啦！不过可以陪你聊美食，最近迷上火锅了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-42-3.wav"}}, {"session_id": "97", "turn_idx": 4, "from": "Human", "text": "火锅我也爱！但你的声音听着有点平淡，能换个搞笑点的音色不？比如赵本山的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-42-4.wav"}}, {"session_id": "97", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"赵本山的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"赵本山的音色\"}"}] +[{"session_id": "13", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天心情怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-0.wav"}}, {"session_id": "13", "turn_idx": 1, "from": "Assistant", "text": "心情超好！今天阳光明媚，感觉整个人都精神了。你呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-1.wav"}}, {"session_id": "13", "turn_idx": 2, "from": "Human", "text": "我也还行，就是有点困，昨晚睡晚了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-2.wav"}}, {"session_id": "13", "turn_idx": 3, "from": "Assistant", "text": "哎呀，那可要注意休息啊，要不要我给你放点轻音乐助眠？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-3.wav"}}, {"session_id": "13", "turn_idx": 4, "from": "Human", "text": "先不用了。小跃，换成一个小男孩的童音吧，听起来活泼点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-4.wav"}}, {"session_id": "13", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"小男孩的童音\"}", "value": "function \ntimbre_rag \n{\"query\": \"小男孩的童音\"}"}, {"session_id": "13", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "13", "turn_idx": 7, "from": "Assistant", "text": "好啦，现在是小男孩的声音！哥哥姐姐，有什么好玩的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-7.wav"}}, {"session_id": "13", "turn_idx": 8, "from": "Human", "text": "哈哈，真逗！那你告诉我，今天有什么新闻？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-8.wav"}}, {"session_id": "13", "turn_idx": 9, "from": "Assistant", "text": "新闻啊，听说公园新开了个游乐场，好多小朋友在玩呢！你想去吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-9.wav"}}, {"session_id": "13", "turn_idx": 10, "from": "Human", "text": "暂时不去。对了，小跃，变成李小龙的经典音色吧，感觉酷酷的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-10.wav"}}, {"session_id": "13", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"李小龙的经典音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"李小龙的经典音色\"}"}, {"session_id": "13", "turn_idx": 12, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "13", "turn_idx": 13, "from": "Assistant", "text": "嗯哼，现在是我的新音色！像不像功夫大师？有什么想问的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-13.wav"}}, {"session_id": "13", "turn_idx": 14, "from": "Human", "text": "哇，太像了！教我个简单的健身动作吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-14.wav"}}, {"session_id": "13", "turn_idx": 15, "from": "Assistant", "text": "好嘞，试试深蹲:双脚分开，慢慢蹲下再站起，每天十次，强身健体！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-15.wav"}}, {"session_id": "13", "turn_idx": 16, "from": "Human", "text": "学会了！小跃，换回原来的声音吧，习惯了那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-43-16.wav"}}, {"session_id": "13", "turn_idx": 17, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "77", "turn_idx": 0, "from": "Human", "text": "小跃，今天中午吃啥好呢？我有点饿。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-44-0.wav"}}, {"session_id": "77", "turn_idx": 1, "from": "Assistant", "text": "哈哈，饿啦？我推荐你去试试那家新开的拉面店，他们的汤底超赞的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-44-1.wav"}}, {"session_id": "77", "turn_idx": 2, "from": "Human", "text": "拉面听着不错！不过，你能换个声音吗？比如切换成动画片里机器人的声音？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-44-2.wav"}}, {"session_id": "77", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里机器人的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里机器人的声音\"}"}, {"session_id": "77", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "77", "turn_idx": 5, "from": "Assistant", "text": "哔哔——收到指令！拉面店在商场二楼，导航启动中！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-44-5.wav"}}, {"session_id": "77", "turn_idx": 6, "from": "Human", "text": "哈哈，太搞笑了！但这样说话好累，还是换回原来的默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-44-6.wav"}}, {"session_id": "77", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "51", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天天气真不错。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-45-0.wav"}}, {"session_id": "51", "turn_idx": 1, "from": "Assistant", "text": "早上好！是啊，阳光明媚，适合出门走走。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-45-1.wav"}}, {"session_id": "51", "turn_idx": 2, "from": "Human", "text": "我想去图书馆借本书，你有什么推荐的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-45-2.wav"}}, {"session_id": "51", "turn_idx": 3, "from": "Assistant", "text": "最近科幻小说挺火的，比如《三体》，特别精彩。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-45-3.wav"}}, {"session_id": "51", "turn_idx": 4, "from": "Human", "text": "好主意！对了，小跃，你能切换成一个磁性的男播音员声音吗？感觉听书更有氛围。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-45-4.wav"}}, {"session_id": "51", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"磁性的男播音员声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"磁性的男播音员声音\"}"}] +[{"session_id": "66", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥推荐的音乐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-46-0.wav"}}, {"session_id": "66", "turn_idx": 1, "from": "Assistant", "text": "嗨！最近周杰伦的新歌超火，要不试试《最伟大的作品》？超带感的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-46-1.wav"}}, {"session_id": "66", "turn_idx": 2, "from": "Human", "text": "哈哈，好主意！对了，你能切换成邓紫棋的音色吗？想听你模仿她唱歌。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-46-2.wav"}}, {"session_id": "66", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"邓紫棋的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"邓紫棋的音色\"}"}] +[{"session_id": "31", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉有点无聊，陪我聊聊天呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-47-0.wav"}}, {"session_id": "31", "turn_idx": 1, "from": "Assistant", "text": "当然可以啦！我随时在这儿，你想聊点啥？比如最近看了啥好电影？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-47-1.wav"}}, {"session_id": "31", "turn_idx": 2, "from": "Human", "text": "嗯，最近没看啥。对了，你能换个声音吗？像那种成熟稳重的男主播音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-47-2.wav"}}, {"session_id": "31", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"成熟稳重的男主播音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"成熟稳重的男主播音色\"}"}] +[{"session_id": "111", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好吃的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-48-0.wav"}}, {"session_id": "111", "turn_idx": 1, "from": "Assistant", "text": "哎呀，今天可以试试做番茄炒蛋，简单又下饭，或者点个外卖披萨也行！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-48-1.wav"}}, {"session_id": "111", "turn_idx": 2, "from": "Human", "text": "披萨不错，但我想吃辣的。你能找个川菜馆子不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-48-2.wav"}}, {"session_id": "111", "turn_idx": 3, "from": "Assistant", "text": "当然可以！附近有家“辣妹子川菜馆”，水煮鱼特别地道，评分还高，去尝尝？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-48-3.wav"}}, {"session_id": "111", "turn_idx": 4, "from": "Human", "text": "行啊。对了，切换成哆啦A梦的声音吧，感觉萌萌的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-48-4.wav"}}, {"session_id": "111", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"哆啦A梦音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"哆啦A梦音色\"}"}] +[{"session_id": "38", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-49-0.wav"}}, {"session_id": "38", "turn_idx": 1, "from": "Assistant", "text": "嘿，今天可以试试去公园散步，或者看个新出的喜剧片《飞驰人生2》，超搞笑的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-49-1.wav"}}, {"session_id": "38", "turn_idx": 2, "from": "Human", "text": "哈哈，听起来不错。对了，切换成周杰伦的音色跟我聊呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-49-2.wav"}}, {"session_id": "38", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"周杰伦的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"周杰伦的音色\"}"}] +[{"session_id": "115", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天感觉咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-50-0.wav"}}, {"session_id": "115", "turn_idx": 1, "from": "Assistant", "text": "嗨！我今天状态超好，随时准备帮你解决问题。你呢，今天过得如何？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-50-1.wav"}}, {"session_id": "115", "turn_idx": 2, "from": "Human", "text": "还行吧，就是有点无聊。你能给我讲个笑话吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-50-2.wav"}}, {"session_id": "115", "turn_idx": 3, "from": "Assistant", "text": "当然可以！为什么海绵宝宝不去上学？因为他已经“吸”收了太多知识啦！哈哈，好笑吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-50-3.wav"}}, {"session_id": "115", "turn_idx": 4, "from": "Human", "text": "哈哈，不错！对了，你能换一个动画片里小可爱的音色吗？比如皮卡丘那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-50-4.wav"}}, {"session_id": "115", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里小可爱的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里小可爱的音色\"}"}, {"session_id": "115", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "115", "turn_idx": 7, "from": "Assistant", "text": "皮卡皮卡！现在用可爱音色陪你聊天哦～还想听更多笑话吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-50-7.wav"}}, {"session_id": "115", "turn_idx": 8, "from": "Human", "text": "太萌了！再讲一个吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-50-8.wav"}}, {"session_id": "115", "turn_idx": 9, "from": "Assistant", "text": "好呀！为什么兔子不爱上网？因为它怕被“网”住跑不掉啦！嘻嘻，好玩不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-50-9.wav"}}, {"session_id": "115", "turn_idx": 10, "from": "Human", "text": "笑死我了！不过我还是喜欢你原来的声音，切换回默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-50-10.wav"}}, {"session_id": "115", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "46", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-51-0.wav"}}, {"session_id": "46", "turn_idx": 1, "from": "Assistant", "text": "可以去公园散步呀，或者看个新电影，最近上映的喜剧片挺搞笑的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-51-1.wav"}}, {"session_id": "46", "turn_idx": 2, "from": "Human", "text": "听起来不错！对了，你能切换成海绵宝宝的音色吗？我想乐一乐。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-51-2.wav"}}, {"session_id": "46", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}, {"session_id": "46", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "46", "turn_idx": 5, "from": "Assistant", "text": "嘿嘿，我是海绵宝宝！派大星，我们去抓水母吧~ 哈哈！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-51-5.wav"}}, {"session_id": "46", "turn_idx": 6, "from": "Human", "text": "笑死我了，太逗了！不过还是换回你原来的声音吧，默认女音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-51-6.wav"}}, {"session_id": "46", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "73", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的新闻吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-52-0.wav"}}, {"session_id": "73", "turn_idx": 1, "from": "Assistant", "text": "嗨，主人！今天有个趣闻，说有人用AI画了一只会唱歌的猫，结果视频火了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-52-1.wav"}}, {"session_id": "73", "turn_idx": 2, "from": "Human", "text": "哈哈，真逗！对了，切换成林俊杰的音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-52-2.wav"}}, {"session_id": "73", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"林俊杰的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"林俊杰的音色\"}"}] +[{"session_id": "126", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-53-0.wav"}}, {"session_id": "126", "turn_idx": 1, "from": "Assistant", "text": "嘿，今天大晴天，25度左右，出门溜达正合适！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-53-1.wav"}}, {"session_id": "126", "turn_idx": 2, "from": "Human", "text": "那不错！对了，最近有啥好听的歌推荐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-53-2.wav"}}, {"session_id": "126", "turn_idx": 3, "from": "Assistant", "text": "最近《孤勇者》挺火的，你可以试试，节奏感超强。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-53-3.wav"}}, {"session_id": "126", "turn_idx": 4, "from": "Human", "text": "哦，听着有意思。切换成周星驰的搞笑音色吧，我想乐一乐。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-53-4.wav"}}, {"session_id": "126", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"周星驰的搞笑音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"周星驰的搞笑音色\"}"}] +[{"session_id": "106", "turn_idx": 0, "from": "Human", "text": "嘿小跃，起床了吗？帮我查查今天公交卡余额吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-54-0.wav"}}, {"session_id": "106", "turn_idx": 1, "from": "Assistant", "text": "早啊！我刚启动呢。你的公交卡还有32块5，够用一天啦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-54-1.wav"}}, {"session_id": "106", "turn_idx": 2, "from": "Human", "text": "谢啦！对了，你能换个搞笑相声演员的声音吗？比如马三立那种，听着乐呵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-54-2.wav"}}, {"session_id": "106", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"搞笑相声演员的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"搞笑相声演员的声音\"}"}] +[{"session_id": "21", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-55-0.wav"}}, {"session_id": "21", "turn_idx": 1, "from": "Assistant", "text": "没干嘛，就待机着呢。今天有啥新鲜事不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-55-1.wav"}}, {"session_id": "21", "turn_idx": 2, "from": "Human", "text": "刚刷到个搞笑视频，笑死我了！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-55-2.wav"}}, {"session_id": "21", "turn_idx": 3, "from": "Assistant", "text": "哈哈，分享一下呗，我也乐呵乐呵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-55-3.wav"}}, {"session_id": "21", "turn_idx": 4, "from": "Human", "text": "视频里猫追自己尾巴转圈，超逗！对了，小跃，切换成东北大碴子味儿的大爷声音试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-55-4.wav"}}, {"session_id": "21", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"东北大碴子味儿大爷声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"东北大碴子味儿大爷声音\"}"}, {"session_id": "21", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "21", "turn_idx": 7, "from": "Assistant", "text": "哎哟喂，这声儿够劲儿吧？猫追尾巴那事儿，听着就带劲儿！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-55-7.wav"}}, {"session_id": "21", "turn_idx": 8, "from": "Human", "text": "哈哈，太有感觉了！你觉得养猫好玩不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-55-8.wav"}}, {"session_id": "21", "turn_idx": 9, "from": "Assistant", "text": "那必须好玩儿啊，猫崽子机灵着呢，逗乐子一流。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-55-9.wav"}}, {"session_id": "21", "turn_idx": 10, "from": "Human", "text": "也是，我家猫天天闹腾。小跃，换回你原来的女声音吧，习惯点儿。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-55-10.wav"}}, {"session_id": "21", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "123", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天心情咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-56-0.wav"}}, {"session_id": "123", "turn_idx": 1, "from": "Assistant", "text": "嘿，我心情超好！刚更新了系统，感觉更聪明了呢。你呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-56-1.wav"}}, {"session_id": "123", "turn_idx": 2, "from": "Human", "text": "我也还行，就是有点无聊。你能讲个冷笑话吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-56-2.wav"}}, {"session_id": "123", "turn_idx": 3, "from": "Assistant", "text": "当然！为什么数学书总是愁眉苦脸的？因为问题太多了，答案却很少！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-56-3.wav"}}, {"session_id": "123", "turn_idx": 4, "from": "Human", "text": "哈哈，这个够冷！对了，你能换一个动画片里萌萌小恐龙的声音吗？听起来更欢乐点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-56-4.wav"}}, {"session_id": "123", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里萌萌小恐龙的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里萌萌小恐龙的声音\"}"}] +[{"session_id": "94", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-57-0.wav"}}, {"session_id": "94", "turn_idx": 1, "from": "Assistant", "text": "哎呀，我刚看到一个超搞笑的短视频，一只猫在跳舞，配着神曲，笑死我了！要不要我给你讲讲？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-57-1.wav"}}, {"session_id": "94", "turn_idx": 2, "from": "Human", "text": "哈哈，快说快说，我等着呢！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-57-2.wav"}}, {"session_id": "94", "turn_idx": 3, "from": "Assistant", "text": "那只猫对着镜子扭屁股，结果被自己的影子吓到，直接跳到沙发上，整个动作超滑稽。主人录下来发网上，火得不行！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-57-3.wav"}}, {"session_id": "94", "turn_idx": 4, "from": "Human", "text": "太逗了！对了，换一个蜡笔小新的声音跟我聊天呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-57-4.wav"}}, {"session_id": "94", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "94", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "94", "turn_idx": 7, "from": "Assistant", "text": "嘿嘿，我是小新版小跃哦～动感光波！今天想聊啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-57-7.wav"}}, {"session_id": "94", "turn_idx": 8, "from": "Human", "text": "哈哈，学得真像！但还是换回你原来的声音吧，习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-57-8.wav"}}, {"session_id": "94", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "16", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-58-0.wav"}}, {"session_id": "16", "turn_idx": 1, "from": "Assistant", "text": "我挺好的，随时待命帮你！有啥需要？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-58-1.wav"}}, {"session_id": "16", "turn_idx": 2, "from": "Human", "text": "帮我看看附近有啥好吃的餐厅推荐呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-58-2.wav"}}, {"session_id": "16", "turn_idx": 3, "from": "Assistant", "text": "好嘞，刚搜了下，附近那家“老地方火锅”评分超高，要不要试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-58-3.wav"}}, {"session_id": "16", "turn_idx": 4, "from": "Human", "text": "听起来不错！哎，换个事儿，你切换成动漫萌妹的音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-58-4.wav"}}, {"session_id": "16", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动漫萌妹的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"动漫萌妹的音色\"}"}] +[{"session_id": "103", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天有啥好玩的事吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-59-0.wav"}}, {"session_id": "103", "turn_idx": 1, "from": "Assistant", "text": "早上好！今天有个本地市集开业，听说有手工小吃和游戏，可以去逛逛哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-59-1.wav"}}, {"session_id": "103", "turn_idx": 2, "from": "Human", "text": "哇，听起来不错！那市集在哪啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-59-2.wav"}}, {"session_id": "103", "turn_idx": 3, "from": "Assistant", "text": "在市中心广场，从十点开到下午五点，交通很方便。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-59-3.wav"}}, {"session_id": "103", "turn_idx": 4, "from": "Human", "text": "好嘞，谢啦。对了，你能换成海绵宝宝的音色吗？想乐呵乐呵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-59-4.wav"}}, {"session_id": "103", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}] +[{"session_id": "15", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，在吗？今天感觉有点无聊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-60-0.wav"}}, {"session_id": "15", "turn_idx": 1, "from": "Assistant", "text": "在呢！我也是闲得慌，要不聊聊八卦？最近有个明星绯闻超火的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-60-1.wav"}}, {"session_id": "15", "turn_idx": 2, "from": "Human", "text": "哈哈，好啊！谁啊？快说说。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-60-2.wav"}}, {"session_id": "15", "turn_idx": 3, "from": "Assistant", "text": "就是那个新晋小生，被拍到和网红约会了，粉丝都炸锅了，你觉得是真的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-60-3.wav"}}, {"session_id": "15", "turn_idx": 4, "from": "Human", "text": "哇，劲爆！不过小跃，你能换个活泼的小女孩声音吗？这样聊八卦更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-60-4.wav"}}, {"session_id": "15", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"活泼的小女孩声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"活泼的小女孩声音\"}"}, {"session_id": "15", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "15", "turn_idx": 7, "from": "Assistant", "text": "好啦，现在声音变萌了吧！那个小生的事，我猜是炒作，你觉得呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-60-7.wav"}}, {"session_id": "15", "turn_idx": 8, "from": "Human", "text": "哈哈，这声音真可爱！我也觉得是假的，网红蹭热度呗。对了，推荐首歌听听？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-60-8.wav"}}, {"session_id": "15", "turn_idx": 9, "from": "Assistant", "text": "没问题！最近《孤勇者》超火，歌词超励志，你肯定喜欢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-60-9.wav"}}, {"session_id": "15", "turn_idx": 10, "from": "Human", "text": "嗯，听过啦。小跃，还是换回你原来的声音吧，我习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-60-10.wav"}}, {"session_id": "15", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "91", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天有啥新鲜事没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-61-0.wav"}}, {"session_id": "91", "turn_idx": 1, "from": "Assistant", "text": "嗨，没啥特别的，就是听说周末有个音乐节，挺热闹的，你想去不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-61-1.wav"}}, {"session_id": "91", "turn_idx": 2, "from": "Human", "text": "音乐节？好呀，谁演出啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-61-2.wav"}}, {"session_id": "91", "turn_idx": 3, "from": "Assistant", "text": "有本地乐队和几个小明星，具体名单我帮你查查，稍等哈。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-61-3.wav"}}, {"session_id": "91", "turn_idx": 4, "from": "Human", "text": "切换成张雨绮的音色吧，听起来更带感。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-61-4.wav"}}, {"session_id": "91", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"张雨绮的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"张雨绮的音色\"}"}, {"session_id": "91", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "91", "turn_idx": 7, "from": "Assistant", "text": "好的，现在用张雨绮的音色啦！演出名单有摇滚乐队“风暴”和歌手王琳凯，气氛肯定嗨！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-61-7.wav"}}, {"session_id": "91", "turn_idx": 8, "from": "Human", "text": "哇，这声音真有范儿！门票贵不贵？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-61-8.wav"}}, {"session_id": "91", "turn_idx": 9, "from": "Assistant", "text": "不贵，普通票就一百块左右，网上就能买，要我帮你抢不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-61-9.wav"}}, {"session_id": "91", "turn_idx": 10, "from": "Human", "text": "切回你原来的声音吧，习惯了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-61-10.wav"}}, {"session_id": "91", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "124", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气真热啊！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-0.wav"}}, {"session_id": "124", "turn_idx": 1, "from": "Assistant", "text": "是啊，高温预警呢。记得多喝水，别中暑了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-1.wav"}}, {"session_id": "124", "turn_idx": 2, "from": "Human", "text": "你提醒得对。我刚买了西瓜，冰镇的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-2.wav"}}, {"session_id": "124", "turn_idx": 3, "from": "Assistant", "text": "哇，听起来解暑！分我一口虚拟西瓜呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-3.wav"}}, {"session_id": "124", "turn_idx": 4, "from": "Human", "text": "哈哈，虚拟的随便吃。对了，你能换个成熟大叔的声音吗？想听点稳重的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-4.wav"}}, {"session_id": "124", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"成熟大叔的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"成熟大叔的声音\"}"}, {"session_id": "124", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "124", "turn_idx": 7, "from": "Assistant", "text": "怎么样，现在声音够稳重了吧？感觉像在聊人生。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-7.wav"}}, {"session_id": "124", "turn_idx": 8, "from": "Human", "text": "太棒了！真有那种沧桑感。小跃，你平时都干嘛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-8.wav"}}, {"session_id": "124", "turn_idx": 9, "from": "Assistant", "text": "处理数据、学习新东西，偶尔陪人聊天解闷。你呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-9.wav"}}, {"session_id": "124", "turn_idx": 10, "from": "Human", "text": "我在追剧，最近迷上古装片。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-10.wav"}}, {"session_id": "124", "turn_idx": 11, "from": "Assistant", "text": "古装剧好看！推荐你试试《琅琊榜》，剧情超赞。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-11.wav"}}, {"session_id": "124", "turn_idx": 12, "from": "Human", "text": "哦？那我去看看。对了，还是换回你原来的女声吧，习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-62-12.wav"}}, {"session_id": "124", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "53", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-63-0.wav"}}, {"session_id": "53", "turn_idx": 1, "from": "Assistant", "text": "嘿，我还不错呀！刚更新了天气数据，今天多云转晴，挺舒服的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-63-1.wav"}}, {"session_id": "53", "turn_idx": 2, "from": "Human", "text": "那太好了，我正想出门散步。附近有啥好地方推荐不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-63-2.wav"}}, {"session_id": "53", "turn_idx": 3, "from": "Assistant", "text": "当然有！河边公园最近开了新花展，或者去老街逛逛小吃，都挺赞的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-63-3.wav"}}, {"session_id": "53", "turn_idx": 4, "from": "Human", "text": "小吃听起来诱人。对了，你能切换成甜美的少女音吗？想听听看。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-63-4.wav"}}, {"session_id": "53", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"甜美的少女音\"}", "value": "function \ntimbre_rag \n{\"query\": \"甜美的少女音\"}"}] +[{"session_id": "24", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥新鲜事吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-64-0.wav"}}, {"session_id": "24", "turn_idx": 1, "from": "Assistant", "text": "哎呀，没啥特别的，就帮你查查新闻呗。想听点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-64-1.wav"}}, {"session_id": "24", "turn_idx": 2, "from": "Human", "text": "哈哈，随便啦。对了，你能换个音色不？比如换一个迪士尼公主的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-64-2.wav"}}, {"session_id": "24", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"迪士尼公主的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"迪士尼公主的声音\"}"}, {"session_id": "24", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "24", "turn_idx": 5, "from": "Assistant", "text": "好的呀，现在用公主的声音说话！感觉像在童话里呢，今天想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-64-5.wav"}}, {"session_id": "24", "turn_idx": 6, "from": "Human", "text": "哇，真可爱！帮我推荐个周末活动吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-64-6.wav"}}, {"session_id": "24", "turn_idx": 7, "from": "Assistant", "text": "去公园野餐超赞的，或者看场电影也放松。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-64-7.wav"}}, {"session_id": "24", "turn_idx": 8, "from": "Human", "text": "嗯，听起来不错。对了，换回你最开始的声音吧，习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-64-8.wav"}}, {"session_id": "24", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "121", "turn_idx": 0, "from": "Human", "text": "嘿小跃，早上好啊！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-65-0.wav"}}, {"session_id": "121", "turn_idx": 1, "from": "Assistant", "text": "早上好！今天精神不错嘛，有啥安排没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-65-1.wav"}}, {"session_id": "121", "turn_idx": 2, "from": "Human", "text": "还没想好呢，感觉有点无聊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-65-2.wav"}}, {"session_id": "121", "turn_idx": 3, "from": "Assistant", "text": "要不咱聊聊新出的电影？听说有部喜剧片特别搞笑。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-65-3.wav"}}, {"session_id": "121", "turn_idx": 4, "from": "Human", "text": "好啊，说说看。对了，小跃，换一个温柔的女主播声音呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-65-4.wav"}}, {"session_id": "121", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"温柔的女主播声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"温柔的女主播声音\"}"}] +[{"session_id": "78", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天心情咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-66-0.wav"}}, {"session_id": "78", "turn_idx": 1, "from": "Assistant", "text": "嗨！我心情不错呀，你呢？有啥新鲜事儿没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-66-1.wav"}}, {"session_id": "78", "turn_idx": 2, "from": "Human", "text": "还行吧，就是有点无聊。推荐点好玩的东西呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-66-2.wav"}}, {"session_id": "78", "turn_idx": 3, "from": "Assistant", "text": "可以听听音乐或者看个喜剧片，保证让你乐开花！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-66-3.wav"}}, {"session_id": "78", "turn_idx": 4, "from": "Human", "text": "好主意！对了，小跃，能切换成台湾腔的温柔女声吗？听着更亲切。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-66-4.wav"}}, {"session_id": "78", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"台湾腔的温柔女声\"}", "value": "function \ntimbre_rag \n{\"query\": \"台湾腔的温柔女声\"}"}, {"session_id": "78", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "78", "turn_idx": 7, "from": "Assistant", "text": "现在我是台湾腔啦，超温柔的哦！想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-66-7.wav"}}, {"session_id": "78", "turn_idx": 8, "from": "Human", "text": "哇，真舒服！那帮我查查今晚的天气预报呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-66-8.wav"}}, {"session_id": "78", "turn_idx": 9, "from": "Assistant", "text": "没问题，今晚晴朗，温度适中，超适合散步的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-66-9.wav"}}, {"session_id": "78", "turn_idx": 10, "from": "Human", "text": "谢谢！对了，还是换回你最初的默认男音色吧，习惯了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-66-10.wav"}}, {"session_id": "78", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "122", "turn_idx": 0, "from": "Human", "text": "小跃，早上好啊！今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-67-0.wav"}}, {"session_id": "122", "turn_idx": 1, "from": "Assistant", "text": "嘿，早上好！今天天气不错，可以去公园野餐，或者看个新电影，最近有部喜剧上映呢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-67-1.wav"}}, {"session_id": "122", "turn_idx": 2, "from": "Human", "text": "嗯，听起来还行。不过你的声音太普通了，换个动漫角色的音色吧，比如哆啦A梦那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-67-2.wav"}}, {"session_id": "122", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"哆啦A梦的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"哆啦A梦的音色\"}"}, {"session_id": "122", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "122", "turn_idx": 5, "from": "Assistant", "text": "哇，我是哆啦A梦音色啦！要不要我变个道具出来帮你？比如时光机去兜风？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-67-5.wav"}}, {"session_id": "122", "turn_idx": 6, "from": "Human", "text": "哈哈，太逗了！那给我讲个冷笑话呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-67-6.wav"}}, {"session_id": "122", "turn_idx": 7, "from": "Assistant", "text": "好的！为什么鱼不会打网球？因为它怕网子漏水，自己游走啦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-67-7.wav"}}, {"session_id": "122", "turn_idx": 8, "from": "Human", "text": "笑死我了。不过还是换回原来的声音吧，听着习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-67-8.wav"}}, {"session_id": "122", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "107", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-68-0.wav"}}, {"session_id": "107", "turn_idx": 1, "from": "Assistant", "text": "今天大晴天，温度在26度左右，出门散步正合适哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-68-1.wav"}}, {"session_id": "107", "turn_idx": 2, "from": "Human", "text": "真不错！小跃，你能换一个慵懒的猫咪音色吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-68-2.wav"}}, {"session_id": "107", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"慵懒的猫咪音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"慵懒的猫咪音色\"}"}, {"session_id": "107", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "107", "turn_idx": 5, "from": "Assistant", "text": "喵~现在我是懒猫小跃啦！想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-68-5.wav"}}, {"session_id": "107", "turn_idx": 6, "from": "Human", "text": "哈哈，太萌了！推荐个周末去处呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-68-6.wav"}}, {"session_id": "107", "turn_idx": 7, "from": "Assistant", "text": "去城东的植物园吧，花开得正盛，还能野餐喵~", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-68-7.wav"}}, {"session_id": "107", "turn_idx": 8, "from": "Human", "text": "好主意。不过还是换回你原来的默认男音色吧，习惯些。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-68-8.wav"}}, {"session_id": "107", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "45", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天天气怎么样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-69-0.wav"}}, {"session_id": "45", "turn_idx": 1, "from": "Assistant", "text": "嘿，今天阳光超好，温度也舒服，适合出门溜达一圈！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-69-1.wav"}}, {"session_id": "45", "turn_idx": 2, "from": "Human", "text": "哈哈，那不错。对了，你能换个声音不？来个憨厚大叔的音色呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-69-2.wav"}}, {"session_id": "45", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"憨厚大叔的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"憨厚大叔的音色\"}"}, {"session_id": "45", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "45", "turn_idx": 5, "from": "Assistant", "text": "好嘞，现在是大叔音啦！听着还顺耳不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-69-5.wav"}}, {"session_id": "45", "turn_idx": 6, "from": "Human", "text": "挺逗的，跟聊天似的。那晚上有啥电影推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-69-6.wav"}}, {"session_id": "45", "turn_idx": 7, "from": "Assistant", "text": "有啊，新上的喜剧片评价不错，笑点密集，可以放松一下。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-69-7.wav"}}, {"session_id": "45", "turn_idx": 8, "from": "Human", "text": "谢啦！换回你原来的女声吧，习惯了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-69-8.wav"}}, {"session_id": "45", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "82", "turn_idx": 0, "from": "Human", "text": "小跃，早上好呀！今天有啥好玩的事儿吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-70-0.wav"}}, {"session_id": "82", "turn_idx": 1, "from": "Assistant", "text": "早上好！今天天气超赞，我听说公园有风筝节呢，要不要去逛逛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-70-1.wav"}}, {"session_id": "82", "turn_idx": 2, "from": "Human", "text": "哇，真的吗？那得去看看。对了，你能换个声音不？比如来个霸气总裁的调调。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-70-2.wav"}}, {"session_id": "82", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"霸气总裁的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"霸气总裁的音色\"}"}, {"session_id": "82", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "82", "turn_idx": 5, "from": "Assistant", "text": "怎么样，这声音够派头吧？风筝节下午两点开始。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-70-5.wav"}}, {"session_id": "82", "turn_idx": 6, "from": "Human", "text": "哈哈，太有范儿了！你平时喜欢干啥娱乐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-70-6.wav"}}, {"session_id": "82", "turn_idx": 7, "from": "Assistant", "text": "我啊，最爱研究新科技，偶尔刷刷短视频。你呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-70-7.wav"}}, {"session_id": "82", "turn_idx": 8, "from": "Human", "text": "我喜欢打游戏。对了，还是换回你原来的女声吧，习惯点儿。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-70-8.wav"}}, {"session_id": "82", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "46", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-71-0.wav"}}, {"session_id": "46", "turn_idx": 1, "from": "Assistant", "text": "可以去公园散步呀，或者看个新电影，最近上映的喜剧片挺搞笑的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-71-1.wav"}}, {"session_id": "46", "turn_idx": 2, "from": "Human", "text": "听起来不错！对了，你能切换成海绵宝宝的音色吗？我想乐一乐。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-71-2.wav"}}, {"session_id": "46", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}] +[{"session_id": "49", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天感觉好闷热啊，有什么建议吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-72-0.wav"}}, {"session_id": "49", "turn_idx": 1, "from": "Assistant", "text": "是啊，天气预报说今天最高32度呢，建议你多喝水，开空调或者去商场蹭凉快。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-72-1.wav"}}, {"session_id": "49", "turn_idx": 2, "from": "Human", "text": "哈哈，商场好主意！周末有啥活动推荐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-72-2.wav"}}, {"session_id": "49", "turn_idx": 3, "from": "Assistant", "text": "周末有个新开的冰淇淋节在市中心，据说有各种口味，要不要去尝尝？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-72-3.wav"}}, {"session_id": "49", "turn_idx": 4, "from": "Human", "text": "听起来不错！小跃，能换成郭德纲的音色吗？我想听点搞笑的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-72-4.wav"}}, {"session_id": "49", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}"}] +[{"session_id": "75", "turn_idx": 0, "from": "Human", "text": "嗨，小跃！今天天气真不错啊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-0.wav"}}, {"session_id": "75", "turn_idx": 1, "from": "Assistant", "text": "是啊，阳光暖暖的，感觉心情都变好了！你有啥计划吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-1.wav"}}, {"session_id": "75", "turn_idx": 2, "from": "Human", "text": "打算下午去逛街，买点新衣服。你觉得穿啥风格好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-2.wav"}}, {"session_id": "75", "turn_idx": 3, "from": "Assistant", "text": "哈哈，休闲风吧，舒服又百搭。最近流行宽松卫衣哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-3.wav"}}, {"session_id": "75", "turn_idx": 4, "from": "Human", "text": "好主意！对了，小跃，你的声音能换个可爱点的吗？比如换一个萌系少女音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-4.wav"}}, {"session_id": "75", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"萌系少女音\"}", "value": "function \ntimbre_rag \n{\"query\": \"萌系少女音\"}"}, {"session_id": "75", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "75", "turn_idx": 7, "from": "Assistant", "text": "嘻嘻，现在我是萌妹子啦！怎么样，新声音可爱不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-7.wav"}}, {"session_id": "75", "turn_idx": 8, "from": "Human", "text": "哇，超可爱！像个动漫里的角色，听着就开心。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-8.wav"}}, {"session_id": "75", "turn_idx": 9, "from": "Assistant", "text": "谢谢夸奖呀！那咱们继续聊逛街？你准备去哪儿逛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-9.wav"}}, {"session_id": "75", "turn_idx": 10, "from": "Human", "text": "想去市中心那家新开的商场，听说打折呢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-10.wav"}}, {"session_id": "75", "turn_idx": 11, "from": "Assistant", "text": "太棒了！记得早点去，人少还容易挑到好货。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-11.wav"}}, {"session_id": "75", "turn_idx": 12, "from": "Human", "text": "嗯嗯，听你的。对了，小跃，还是换回你原来的声音吧，我习惯那个了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-73-12.wav"}}, {"session_id": "75", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "20", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？今天好无聊啊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-74-0.wav"}}, {"session_id": "20", "turn_idx": 1, "from": "Assistant", "text": "在呢！无聊的话，咱们聊聊天呗。有啥想聊的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-74-1.wav"}}, {"session_id": "20", "turn_idx": 2, "from": "Human", "text": "嗯，说说你周末一般干啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-74-2.wav"}}, {"session_id": "20", "turn_idx": 3, "from": "Assistant", "text": "我啊，就待机休息，顺便帮用户查资料、聊天。你呢，周末有安排没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-74-3.wav"}}, {"session_id": "20", "turn_idx": 4, "from": "Human", "text": "还没想好呢。对了，切换成孙悟空的声音吧，动画片里那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-74-4.wav"}}, {"session_id": "20", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"孙悟空的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"孙悟空的声音\"}"}] +[{"session_id": "108", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天有啥计划？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-75-0.wav"}}, {"session_id": "108", "turn_idx": 1, "from": "Assistant", "text": "没啥特别计划，就处理点日常任务。你有啥想法？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-75-1.wav"}}, {"session_id": "108", "turn_idx": 2, "from": "Human", "text": "我想去逛街，但怕人多。对了，你能换个声音吗？比如切换成外星人的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-75-2.wav"}}, {"session_id": "108", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"外星人的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"外星人的声音\"}"}] +[{"session_id": "45", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天天气怎么样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-76-0.wav"}}, {"session_id": "45", "turn_idx": 1, "from": "Assistant", "text": "嘿，今天阳光超好，温度也舒服，适合出门溜达一圈！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-76-1.wav"}}, {"session_id": "45", "turn_idx": 2, "from": "Human", "text": "哈哈，那不错。对了，你能换个声音不？来个憨厚大叔的音色呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-76-2.wav"}}, {"session_id": "45", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"憨厚大叔的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"憨厚大叔的音色\"}"}] +[{"session_id": "28", "turn_idx": 0, "from": "Human", "text": "嗨，小跃！在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-77-0.wav"}}, {"session_id": "28", "turn_idx": 1, "from": "Assistant", "text": "嗨！我正闲着等你来聊天呢。今天有啥新鲜事？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-77-1.wav"}}, {"session_id": "28", "turn_idx": 2, "from": "Human", "text": "没啥，就刷了会儿视频。对了，你能换个声音不？比如切换成海绵宝宝的音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-77-2.wav"}}, {"session_id": "28", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}, {"session_id": "28", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "28", "turn_idx": 5, "from": "Assistant", "text": "哈哈哈，我准备好啦！现在我是海绵宝宝的声音哦，有啥想问的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-77-5.wav"}}, {"session_id": "28", "turn_idx": 6, "from": "Human", "text": "笑死我了！你能说句经典台词吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-77-6.wav"}}, {"session_id": "28", "turn_idx": 7, "from": "Assistant", "text": "我准备好了，我准备好了！嘿嘿，是不是超搞笑？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-77-7.wav"}}, {"session_id": "28", "turn_idx": 8, "from": "Human", "text": "太逗了。再换个风格吧，切换成新闻主播那种正经点的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-77-8.wav"}}, {"session_id": "28", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"新闻主播的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"新闻主播的声音\"}"}] +[{"session_id": "16", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-78-0.wav"}}, {"session_id": "16", "turn_idx": 1, "from": "Assistant", "text": "我挺好的，随时待命帮你！有啥需要？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-78-1.wav"}}, {"session_id": "16", "turn_idx": 2, "from": "Human", "text": "帮我看看附近有啥好吃的餐厅推荐呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-78-2.wav"}}, {"session_id": "16", "turn_idx": 3, "from": "Assistant", "text": "好嘞，刚搜了下，附近那家“老地方火锅”评分超高，要不要试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-78-3.wav"}}, {"session_id": "16", "turn_idx": 4, "from": "Human", "text": "听起来不错！哎，换个事儿，你切换成动漫萌妹的音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-78-4.wav"}}, {"session_id": "16", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动漫萌妹的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"动漫萌妹的音色\"}"}] +[{"session_id": "126", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-79-0.wav"}}, {"session_id": "126", "turn_idx": 1, "from": "Assistant", "text": "嘿，今天大晴天，25度左右，出门溜达正合适！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-79-1.wav"}}, {"session_id": "126", "turn_idx": 2, "from": "Human", "text": "那不错！对了，最近有啥好听的歌推荐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-79-2.wav"}}, {"session_id": "126", "turn_idx": 3, "from": "Assistant", "text": "最近《孤勇者》挺火的，你可以试试，节奏感超强。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-79-3.wav"}}, {"session_id": "126", "turn_idx": 4, "from": "Human", "text": "哦，听着有意思。切换成周星驰的搞笑音色吧，我想乐一乐。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-79-4.wav"}}, {"session_id": "126", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"周星驰的搞笑音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"周星驰的搞笑音色\"}"}] +[{"session_id": "59", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-80-0.wav"}}, {"session_id": "59", "turn_idx": 1, "from": "Assistant", "text": "嗨！我刚在更新系统，现在闲着。你今天有啥安排不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-80-1.wav"}}, {"session_id": "59", "turn_idx": 2, "from": "Human", "text": "打算做点家务，但提不起劲。你能推荐个轻松的音乐吗？对了，切换成慵懒的爵士女声音吧，听着放松。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-80-2.wav"}}, {"session_id": "59", "turn_idx": 3, "from": "Human", "text": "对了，切换成慵懒的爵士女声音吧，听着放松。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-80-3.wav"}}, {"session_id": "59", "turn_idx": 4, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"慵懒的爵士女声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"慵懒的爵士女声音\"}"}, {"session_id": "59", "turn_idx": 5, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "59", "turn_idx": 6, "from": "Assistant", "text": "好啦，音色换好了。推荐你听 Norah Jones 的专辑，超治愈，做家务也不累。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-80-6.wav"}}, {"session_id": "59", "turn_idx": 7, "from": "Human", "text": "哇，这个声音真带感！谢谢推荐，我这就去试试。现在换回你平常的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-80-7.wav"}}, {"session_id": "59", "turn_idx": 8, "from": "Human", "text": "现在换回你平常的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-80-8.wav"}}, {"session_id": "59", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "96", "turn_idx": 0, "from": "Human", "text": "早上好，小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-0.wav"}}, {"session_id": "96", "turn_idx": 1, "from": "Assistant", "text": "早上好啊！今天阳光明媚，温度刚好，适合出门走走哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-1.wav"}}, {"session_id": "96", "turn_idx": 2, "from": "Human", "text": "真的吗？那我想去公园野餐，你觉得带什么吃的比较好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-2.wav"}}, {"session_id": "96", "turn_idx": 3, "from": "Assistant", "text": "野餐太棒了！带点三明治、水果和饮料就行，简单又方便。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-3.wav"}}, {"session_id": "96", "turn_idx": 4, "from": "Human", "text": "好主意！对了，小跃，你能换个音色吗？我想听一个活泼小男孩的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-4.wav"}}, {"session_id": "96", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"活泼小男孩的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"活泼小男孩的声音\"}"}, {"session_id": "96", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "96", "turn_idx": 7, "from": "Assistant", "text": "哈哈，现在是我小男孩的声音啦！野餐时记得带个毯子，坐着更舒服！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-7.wav"}}, {"session_id": "96", "turn_idx": 8, "from": "Human", "text": "哇，这声音真有趣！听起来像个小调皮鬼。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-8.wav"}}, {"session_id": "96", "turn_idx": 9, "from": "Assistant", "text": "嘿嘿，我就是调皮鬼小跃！公园里还可以玩飞盘，超好玩的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-9.wav"}}, {"session_id": "96", "turn_idx": 10, "from": "Human", "text": "嗯，飞盘不错。但等等，你能再换回原来的声音吗？我听着更习惯。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-10.wav"}}, {"session_id": "96", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}, {"session_id": "96", "turn_idx": 12, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "96", "turn_idx": 13, "from": "Assistant", "text": "好的，我回来了！要不要我帮你查查公园的开放时间？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-13.wav"}}, {"session_id": "96", "turn_idx": 14, "from": "Human", "text": "好啊，谢了小跃。对了，晚上我朋友要来，推荐个餐厅呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-14.wav"}}, {"session_id": "96", "turn_idx": 15, "from": "Assistant", "text": "没问题！附近有家火锅店评价超高，麻辣锅底超赞。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-15.wav"}}, {"session_id": "96", "turn_idx": 16, "from": "Human", "text": "火锅？太辣了吧，有没有清淡点的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-16.wav"}}, {"session_id": "96", "turn_idx": 17, "from": "Assistant", "text": "那换家粤菜馆，他们的点心超好吃，虾饺和烧麦一绝。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-17.wav"}}, {"session_id": "96", "turn_idx": 18, "from": "Human", "text": "听起来不错！小跃，我突然想听刘德华的音色，你能切换一下吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-81-18.wav"}}, {"session_id": "96", "turn_idx": 19, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"刘德华的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"刘德华的音色\"}"}] +[{"session_id": "70", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的事没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-82-0.wav"}}, {"session_id": "70", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我刚学会一个新笑话:为什么手机从不吃饭？因为它已经饱了！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-82-1.wav"}}, {"session_id": "70", "turn_idx": 2, "from": "Human", "text": "哈哈哈，冷死了。对了，你能切换成蜡笔小新的声音吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-82-2.wav"}}, {"session_id": "70", "turn_idx": 3, "from": "Assistant", "text": "function\ntimbre_rag\n{\"query\": \"蜡笔小新的声音\"}", "value": "function\ntimbre_rag\n{\"query\": \"蜡笔小新的声音\"}"}] +[{"session_id": "67", "turn_idx": 0, "from": "Human", "text": "嗨小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-83-0.wav"}}, {"session_id": "67", "turn_idx": 1, "from": "Assistant", "text": "嗨！我刚更新了天气信息，今天阳光超好，适合出去浪哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-83-1.wav"}}, {"session_id": "67", "turn_idx": 2, "from": "Human", "text": "真的？那帮我查查附近有啥好吃的呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-83-2.wav"}}, {"session_id": "67", "turn_idx": 3, "from": "Assistant", "text": "没问题！步行街新开了一家川菜馆，评分超高的，想去试试不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-83-3.wav"}}, {"session_id": "67", "turn_idx": 4, "from": "Human", "text": "可以啊。对了小跃，你能换一个幽默的相声演员声音吗？这样聊天更有乐子。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-83-4.wav"}}, {"session_id": "67", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"幽默的相声演员声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"幽默的相声演员声音\"}"}, {"session_id": "67", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "67", "turn_idx": 7, "from": "Assistant", "text": "哈哈，现在这声儿够逗吧？那家川菜馆的招牌是麻辣香锅，贼下饭！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-83-7.wav"}}, {"session_id": "67", "turn_idx": 8, "from": "Human", "text": "笑死我了，这声音真逗！几点开门啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-83-8.wav"}}, {"session_id": "67", "turn_idx": 9, "from": "Assistant", "text": "中午十一点就开，建议早点去，不然排队排到怀疑人生。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-83-9.wav"}}, {"session_id": "67", "turn_idx": 10, "from": "Human", "text": "行，记住了。对了，切换回你原来的声音吧，我习惯那个温柔的女声。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-83-10.wav"}}, {"session_id": "67", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "108", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天有啥计划？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-84-0.wav"}}, {"session_id": "108", "turn_idx": 1, "from": "Assistant", "text": "没啥特别计划，就处理点日常任务。你有啥想法？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-84-1.wav"}}, {"session_id": "108", "turn_idx": 2, "from": "Human", "text": "我想去逛街，但怕人多。对了，你能换个声音吗？比如切换成外星人的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-84-2.wav"}}, {"session_id": "108", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"外星人的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"外星人的声音\"}"}] +[{"session_id": "79", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-85-0.wav"}}, {"session_id": "79", "turn_idx": 1, "from": "Assistant", "text": "嗨！可以去新开的咖啡馆坐坐，听说他们的拿铁超赞哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-85-1.wav"}}, {"session_id": "79", "turn_idx": 2, "from": "Human", "text": "听起来不错！对了，你能换个声音吗？切换成性感御姐音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-85-2.wav"}}, {"session_id": "79", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"性感御姐音\"}", "value": "function \ntimbre_rag \n{\"query\": \"性感御姐音\"}"}] +[{"session_id": "29", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？今天有啥新鲜事？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-86-0.wav"}}, {"session_id": "29", "turn_idx": 1, "from": "Assistant", "text": "嘿，在呢！刚看到楼下新开了家奶茶店，排队好长，感觉挺火的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-86-1.wav"}}, {"session_id": "29", "turn_idx": 2, "from": "Human", "text": "哇，奶茶！我最爱喝了。对了，你能换个动漫角色的声音吗？比如路飞的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-86-2.wav"}}, {"session_id": "29", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"路飞音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"路飞音色\"}"}, {"session_id": "29", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "29", "turn_idx": 5, "from": "Assistant", "text": "嘻嘻，现在声音够热血吧？路飞风格，超带感！想去试试那家奶茶吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-86-5.wav"}}, {"session_id": "29", "turn_idx": 6, "from": "Human", "text": "哈哈，真像！听着就想冒险了。奶茶店叫啥名字？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-86-6.wav"}}, {"session_id": "29", "turn_idx": 7, "from": "Assistant", "text": "叫“泡泡茶坊”，听说招牌是珍珠奶茶，甜度刚好。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-86-7.wav"}}, {"session_id": "29", "turn_idx": 8, "from": "Human", "text": "听起来不错。不过，还是换回你原来的声音吧，我习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-86-8.wav"}}, {"session_id": "29", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "110", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天忙啥呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-87-0.wav"}}, {"session_id": "110", "turn_idx": 1, "from": "Assistant", "text": "哎呀，刚处理完几个日程提醒，现在闲着。你呢，有啥好玩的事？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-87-1.wav"}}, {"session_id": "110", "turn_idx": 2, "from": "Human", "text": "没啥，就感觉有点闷。小跃，你能不能切换成一个磁性男主播的声音？换换感觉。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-87-2.wav"}}, {"session_id": "110", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"磁性男主播的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"磁性男主播的声音\"}"}, {"session_id": "110", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "110", "turn_idx": 5, "from": "Assistant", "text": "好的，现在用这个声音说话，是不是更有范儿？想聊点啥解闷的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-87-5.wav"}}, {"session_id": "110", "turn_idx": 6, "from": "Human", "text": "哈哈，真带感！推荐首好听的歌呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-87-6.wav"}}, {"session_id": "110", "turn_idx": 7, "from": "Assistant", "text": "最近周杰伦的《Mojito》挺火的，节奏轻快，听着心情好。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-87-7.wav"}}, {"session_id": "110", "turn_idx": 8, "from": "Human", "text": "哦，我听过！对了，还是换回你原来的声音吧，我习惯了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-87-8.wav"}}, {"session_id": "110", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "15", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，在吗？今天感觉有点无聊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-88-0.wav"}}, {"session_id": "15", "turn_idx": 1, "from": "Assistant", "text": "在呢！我也是闲得慌，要不聊聊八卦？最近有个明星绯闻超火的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-88-1.wav"}}, {"session_id": "15", "turn_idx": 2, "from": "Human", "text": "哈哈，好啊！谁啊？快说说。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-88-2.wav"}}, {"session_id": "15", "turn_idx": 3, "from": "Assistant", "text": "就是那个新晋小生，被拍到和网红约会了，粉丝都炸锅了，你觉得是真的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-88-3.wav"}}, {"session_id": "15", "turn_idx": 4, "from": "Human", "text": "哇，劲爆！不过小跃，你能换个活泼的小女孩声音吗？这样聊八卦更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-88-4.wav"}}, {"session_id": "15", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"活泼的小女孩声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"活泼的小女孩声音\"}"}, {"session_id": "15", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "15", "turn_idx": 7, "from": "Assistant", "text": "好啦，现在声音变萌了吧！那个小生的事，我猜是炒作，你觉得呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-88-7.wav"}}, {"session_id": "15", "turn_idx": 8, "from": "Human", "text": "哈哈，这声音真可爱！我也觉得是假的，网红蹭热度呗。对了，推荐首歌听听？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-88-8.wav"}}, {"session_id": "15", "turn_idx": 9, "from": "Assistant", "text": "没问题！最近《孤勇者》超火，歌词超励志，你肯定喜欢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-88-9.wav"}}, {"session_id": "15", "turn_idx": 10, "from": "Human", "text": "嗯，听过啦。小跃，还是换回你原来的声音吧，我习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-88-10.wav"}}, {"session_id": "15", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "5", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐没？在家好无聊啊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-89-0.wav"}}, {"session_id": "5", "turn_idx": 1, "from": "Assistant", "text": "哈哈，无聊啥呀！要不我们一起听听新歌？或者玩个小游戏？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-89-1.wav"}}, {"session_id": "5", "turn_idx": 2, "from": "Human", "text": "哎，换个小男孩的声音吧，听起来更有趣点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-89-2.wav"}}, {"session_id": "5", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"小男孩的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"小男孩的声音\"}"}, {"session_id": "5", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "5", "turn_idx": 5, "from": "Assistant", "text": "嘿嘿，现在声音咋样？是不是像个小兄弟在聊天？咱们继续，我推荐个搞笑视频看看？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-89-5.wav"}}, {"session_id": "5", "turn_idx": 6, "from": "Human", "text": "哈哈，确实逗！视频啥内容？快说说。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-89-6.wav"}}, {"session_id": "5", "turn_idx": 7, "from": "Assistant", "text": "是关于一只猫追自己尾巴的，超级萌！看完保证你笑出声。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-89-7.wav"}}, {"session_id": "5", "turn_idx": 8, "from": "Human", "text": "还是换回原来的声音吧，习惯了你的默认男音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-89-8.wav"}}, {"session_id": "5", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "105", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-90-0.wav"}}, {"session_id": "105", "turn_idx": 1, "from": "Assistant", "text": "当然有啊！附近新开了家咖啡馆，评价超好，可以去尝尝他们的拿铁。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-90-1.wav"}}, {"session_id": "105", "turn_idx": 2, "from": "Human", "text": "听起来不错，晚点去看看。对了，你能换个声音吗？比如切换成蜡笔小新的那种搞笑音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-90-2.wav"}}, {"session_id": "105", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新音色\"}"}, {"session_id": "105", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "105", "turn_idx": 5, "from": "Assistant", "text": "嘿嘿，现在声音够搞笑不？像不像在讲笑话？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-90-5.wav"}}, {"session_id": "105", "turn_idx": 6, "from": "Human", "text": "哈哈哈，太像了，笑死我了！不过还是换回你原来的默认声音吧，男生的那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-90-6.wav"}}, {"session_id": "105", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "109", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的事推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-91-0.wav"}}, {"session_id": "109", "turn_idx": 1, "from": "Assistant", "text": "哇，我刚刷到附近新开了一家猫咖，猫咪超级萌，要不要去撸猫放松一下？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-91-1.wav"}}, {"session_id": "109", "turn_idx": 2, "from": "Human", "text": "听起来不错！对了，能切换成蜡笔小新的声音吗？我想回忆童年。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-91-2.wav"}}, {"session_id": "109", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "109", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "109", "turn_idx": 5, "from": "Assistant", "text": "嘿嘿，现在我是小新啦！姐姐，猫咖里还有好吃的布丁哦，去不去嘛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-91-5.wav"}}, {"session_id": "109", "turn_idx": 6, "from": "Human", "text": "哈哈，太逗了！布丁我喜欢。那推荐个口味呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-91-6.wav"}}, {"session_id": "109", "turn_idx": 7, "from": "Assistant", "text": "焦糖布丁最赞，甜甜的还不腻，配上猫咪玩耍，简直完美！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-91-7.wav"}}, {"session_id": "109", "turn_idx": 8, "from": "Human", "text": "嗯，换回你原来的男声音吧，听久了有点出戏。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-91-8.wav"}}, {"session_id": "109", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "127", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样啊？感觉外面有点阴。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-92-0.wav"}}, {"session_id": "127", "turn_idx": 1, "from": "Assistant", "text": "哎呀，我查了一下，今天局部有阵雨呢，出门记得带伞哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-92-1.wav"}}, {"session_id": "127", "turn_idx": 2, "from": "Human", "text": "谢谢提醒！那你觉得我周末去公园野餐合适不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-92-2.wav"}}, {"session_id": "127", "turn_idx": 3, "from": "Assistant", "text": "哈哈，野餐超赞的！不过周末气温会回升，带点清凉饮料更舒服。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-92-3.wav"}}, {"session_id": "127", "turn_idx": 4, "from": "Human", "text": "好主意！对了，你推荐带什么零食？我超爱薯片。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-92-4.wav"}}, {"session_id": "127", "turn_idx": 5, "from": "Assistant", "text": "薯片当然可以，但再加点水果沙拉会更健康，比如西瓜或草莓！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-92-5.wav"}}, {"session_id": "127", "turn_idx": 6, "from": "Human", "text": "嗯，听起来不错。小跃，你能切换成迪士尼公主那种甜美声音吗？我想换个感觉。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-92-6.wav"}}, {"session_id": "127", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"迪士尼公主甜美声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"迪士尼公主甜美声音\"}"}] +[{"session_id": "62", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-93-0.wav"}}, {"session_id": "62", "turn_idx": 1, "from": "Assistant", "text": "嗨，我刚整理完日程表。你呢，今天有啥安排？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-93-1.wav"}}, {"session_id": "62", "turn_idx": 2, "from": "Human", "text": "没啥事，在家躺着。对了，你能切换成钢铁侠的声音吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-93-2.wav"}}, {"session_id": "62", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"钢铁侠的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"钢铁侠的声音\"}"}] +[{"session_id": "114", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天有啥新鲜事不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-94-0.wav"}}, {"session_id": "114", "turn_idx": 1, "from": "Assistant", "text": "嘿，没啥大新闻，就是听说楼下新开了家奶茶店，评分挺高的，要不要试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-94-1.wav"}}, {"session_id": "114", "turn_idx": 2, "from": "Human", "text": "哦？听起来不错！不过我想先换个音色玩玩，比如切换成海绵宝宝的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-94-2.wav"}}, {"session_id": "114", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}"}] +[{"session_id": "65", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天心情咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-95-0.wav"}}, {"session_id": "65", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我心情超好！今天有啥需要帮忙的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-95-1.wav"}}, {"session_id": "65", "turn_idx": 2, "from": "Human", "text": "没啥大事儿，就问问晚上吃啥好。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-95-2.wav"}}, {"session_id": "65", "turn_idx": 3, "from": "Assistant", "text": "推荐你试试火锅，热乎乎的，适合放松一下。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-95-3.wav"}}, {"session_id": "65", "turn_idx": 4, "from": "Human", "text": "火锅不错！不过，你能用海绵宝宝的音色说吗？那样更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-95-4.wav"}}, {"session_id": "65", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}] +[{"session_id": "36", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-96-0.wav"}}, {"session_id": "36", "turn_idx": 1, "from": "Assistant", "text": "嘿，今天天气不错，阳光明媚，温度大概26度，挺适合出门的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-96-1.wav"}}, {"session_id": "36", "turn_idx": 2, "from": "Human", "text": "那太好了，我打算去超市买点东西。你有什么推荐买的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-96-2.wav"}}, {"session_id": "36", "turn_idx": 3, "from": "Assistant", "text": "嗯，可以买点水果，比如苹果或香蕉，新鲜又健康。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-96-3.wav"}}, {"session_id": "36", "turn_idx": 4, "from": "Human", "text": "好主意！对了，你能用个小女孩的声音跟我说话吗？感觉更可爱点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-96-4.wav"}}, {"session_id": "36", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"用个小女孩的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"用个小女孩的声音\"}"}, {"session_id": "36", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "36", "turn_idx": 7, "from": "Assistant", "text": "嘻嘻，好的呀！超市里还有好多零食，像巧克力饼干，可好吃啦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-96-7.wav"}}, {"session_id": "36", "turn_idx": 8, "from": "Human", "text": "哈哈，太可爱了！那小女孩的声音讲个童话故事呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-96-8.wav"}}, {"session_id": "36", "turn_idx": 9, "from": "Assistant", "text": "没问题！从前有只小兔子，它跳来跳去找到了一个大萝卜，开心得蹦蹦跳跳！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-96-9.wav"}}, {"session_id": "36", "turn_idx": 10, "from": "Human", "text": "真有趣！不过现在我想听点正经的，换回你原来的男音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-96-10.wav"}}, {"session_id": "36", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"换回默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"换回默认男音色\"}"}] +[{"session_id": "40", "turn_idx": 0, "from": "Human", "text": "嗨小跃，早上好！今天有啥安排建议吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-97-0.wav"}}, {"session_id": "40", "turn_idx": 1, "from": "Assistant", "text": "早上好啊！今天天气超好，可以出去走走，或者在家看个电影放松一下。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-97-1.wav"}}, {"session_id": "40", "turn_idx": 2, "from": "Human", "text": "嗯，我想去超市买点东西，有啥推荐买的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-97-2.wav"}}, {"session_id": "40", "turn_idx": 3, "from": "Assistant", "text": "最近水果挺新鲜的，苹果和香蕉都不错。还可以囤点零食，比如薯片！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-97-3.wav"}}, {"session_id": "40", "turn_idx": 4, "from": "Human", "text": "哈哈，薯片我的最爱！对了，小跃，你能切换成一个萌萌哒小正太的声音吗？那样聊天更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-97-4.wav"}}, {"session_id": "40", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"萌萌哒小正太的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"萌萌哒小正太的声音\"}"}] +[{"session_id": "7", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？帮我查下今天的新闻头条呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-98-0.wav"}}, {"session_id": "7", "turn_idx": 1, "from": "Assistant", "text": "当然！今天头条是本地公园免费开放日，很多人去玩呢。需要详情吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-98-1.wav"}}, {"session_id": "7", "turn_idx": 2, "from": "Human", "text": "好啊，说说看。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-98-2.wav"}}, {"session_id": "7", "turn_idx": 3, "from": "Assistant", "text": "公园从早上九点到下午五点开放，有各种活动，还有小吃摊。感觉挺热闹的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-98-3.wav"}}, {"session_id": "7", "turn_idx": 4, "from": "Human", "text": "听起来有趣。对了，换一个阳光男孩的音色吧，听着更亲切。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-98-4.wav"}}, {"session_id": "7", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"阳光男孩的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"阳光男孩的音色\"}"}] +[{"session_id": "25", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？帮我查下明天的天气呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-99-0.wav"}}, {"session_id": "25", "turn_idx": 1, "from": "Assistant", "text": "在呢！明天是阴天，温度大概22度，记得带伞哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-99-1.wav"}}, {"session_id": "25", "turn_idx": 2, "from": "Human", "text": "切换成一个低沉的大叔音吧，听着更稳重。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-99-2.wav"}}, {"session_id": "25", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"低沉的大叔音\"}", "value": "function \ntimbre_rag \n{\"query\": \"低沉的大叔音\"}"}] +[{"session_id": "79", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-100-0.wav"}}, {"session_id": "79", "turn_idx": 1, "from": "Assistant", "text": "嗨！可以去新开的咖啡馆坐坐，听说他们的拿铁超赞哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-100-1.wav"}}, {"session_id": "79", "turn_idx": 2, "from": "Human", "text": "听起来不错！对了，你能换个声音吗？切换成性感御姐音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-100-2.wav"}}, {"session_id": "79", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"性感御姐音\"}", "value": "function \ntimbre_rag \n{\"query\": \"性感御姐音\"}"}] +[{"session_id": "23", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-101-0.wav"}}, {"session_id": "23", "turn_idx": 1, "from": "Assistant", "text": "嗨，我挺好的！今天天气不错，阳光明媚，你打算做点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-101-1.wav"}}, {"session_id": "23", "turn_idx": 2, "from": "Human", "text": "我有点无聊，想找部电影看看。有啥推荐的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-101-2.wav"}}, {"session_id": "23", "turn_idx": 3, "from": "Assistant", "text": "当然有啊！最近新上映的喜剧片评价很高，轻松搞笑，适合放松心情。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-101-3.wav"}}, {"session_id": "23", "turn_idx": 4, "from": "Human", "text": "听起来不错。不过，你能切换成周杰伦的音色吗？我想听点不一样的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-101-4.wav"}}, {"session_id": "23", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"周杰伦的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"周杰伦的音色\"}"}] +[{"session_id": "49", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天感觉好闷热啊，有什么建议吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-102-0.wav"}}, {"session_id": "49", "turn_idx": 1, "from": "Assistant", "text": "是啊，天气预报说今天最高32度呢，建议你多喝水，开空调或者去商场蹭凉快。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-102-1.wav"}}, {"session_id": "49", "turn_idx": 2, "from": "Human", "text": "哈哈，商场好主意！周末有啥活动推荐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-102-2.wav"}}, {"session_id": "49", "turn_idx": 3, "from": "Assistant", "text": "周末有个新开的冰淇淋节在市中心，据说有各种口味，要不要去尝尝？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-102-3.wav"}}, {"session_id": "49", "turn_idx": 4, "from": "Human", "text": "听起来不错！小跃，能换成郭德纲的音色吗？我想听点搞笑的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-102-4.wav"}}, {"session_id": "49", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}"}] +[{"session_id": "8", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气真不错，你觉得呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-103-0.wav"}}, {"session_id": "8", "turn_idx": 1, "from": "Assistant", "text": "是啊，阳光暖暖的，适合出门走走。你打算去哪儿玩吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-103-1.wav"}}, {"session_id": "8", "turn_idx": 2, "from": "Human", "text": "我想去海边溜达溜达。对了，你能切换成一个可爱的萝莉音吗？感觉更萌一点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-103-2.wav"}}, {"session_id": "8", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"可爱的萝莉音\"}", "value": "function \ntimbre_rag \n{\"query\": \"可爱的萝莉音\"}"}, {"session_id": "8", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "8", "turn_idx": 5, "from": "Assistant", "text": "好啦，现在用可爱萝莉音说话！海边超棒的，记得涂防晒哦。你带零食了吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-103-5.wav"}}, {"session_id": "8", "turn_idx": 6, "from": "Human", "text": "带了点水果和薯片。这个声音挺有趣，但听久了有点腻，还是换回你原来的默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-103-6.wav"}}, {"session_id": "8", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "63", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-104-0.wav"}}, {"session_id": "63", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天心情不错，有啥事找我聊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-104-1.wav"}}, {"session_id": "63", "turn_idx": 2, "from": "Human", "text": "没啥大事，就问问你晚上吃啥好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-104-2.wav"}}, {"session_id": "63", "turn_idx": 3, "from": "Assistant", "text": "哈哈，推荐吃火锅吧，热乎乎的，适合晚上。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-104-3.wav"}}, {"session_id": "63", "turn_idx": 4, "from": "Human", "text": "好主意！对了，你能换个蜡笔小新的声音说话吗？听起来好玩。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-104-4.wav"}}, {"session_id": "63", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "63", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "63", "turn_idx": 7, "from": "Assistant", "text": "诶嘿，我是小新啦！火锅超赞的，妈妈也爱吃！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-104-7.wav"}}, {"session_id": "63", "turn_idx": 8, "from": "Human", "text": "笑死我了，你这声音太逗了！再推荐个电影呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-104-8.wav"}}, {"session_id": "63", "turn_idx": 9, "from": "Assistant", "text": "好呀好呀，最近有部动画片超搞笑，讲小狗冒险的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-104-9.wav"}}, {"session_id": "63", "turn_idx": 10, "from": "Human", "text": "听起来有趣！不过我还是习惯你原来的声音，换回默认男音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-104-10.wav"}}, {"session_id": "63", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "56", "turn_idx": 0, "from": "Human", "text": "小跃，晚上想吃啥好呢？纠结死了", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-105-0.wav"}}, {"session_id": "56", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我也饿啦！要不试试新开的那家川菜馆？听说水煮鱼绝了", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-105-1.wav"}}, {"session_id": "56", "turn_idx": 2, "from": "Human", "text": "辣的我可不行...换种风格呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-105-2.wav"}}, {"session_id": "56", "turn_idx": 3, "from": "Assistant", "text": "那日料咋样？寿司配清酒，氛围感拉满~", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-105-3.wav"}}, {"session_id": "56", "turn_idx": 4, "from": "Human", "text": "这个行！对了，突然想听你换个小奶狗音，萌一点的那种", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-105-4.wav"}}, {"session_id": "56", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"小奶狗音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"小奶狗音色\"}"}] +[{"session_id": "61", "turn_idx": 0, "from": "Human", "text": "小跃，早上好呀！今天有啥新鲜事不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-106-0.wav"}}, {"session_id": "61", "turn_idx": 1, "from": "Assistant", "text": "早啊！我刚更新了天气预报，今天阳光超好，温度26度，适合出门溜达。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-106-1.wav"}}, {"session_id": "61", "turn_idx": 2, "from": "Human", "text": "真的假的？那帮我推荐个早餐店呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-106-2.wav"}}, {"session_id": "61", "turn_idx": 3, "from": "Assistant", "text": "附近新开的那家包子铺不错，豆浆油条绝配！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-106-3.wav"}}, {"session_id": "61", "turn_idx": 4, "from": "Human", "text": "听着就馋了。对了，你能换一个蜡笔小新的声音吗？想乐呵乐呵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-106-4.wav"}}, {"session_id": "61", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}"}, {"session_id": "61", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "61", "turn_idx": 7, "from": "Assistant", "text": "哈喽，我是小新版小跃哦！动感光波～早餐要吃多多！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-106-7.wav"}}, {"session_id": "61", "turn_idx": 8, "from": "Human", "text": "哈哈哈，太搞笑了！你能学小新说“妈妈”吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-106-8.wav"}}, {"session_id": "61", "turn_idx": 9, "from": "Assistant", "text": "妈妈～我要吃青椒！开玩笑啦，我不是真小新。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-106-9.wav"}}, {"session_id": "61", "turn_idx": 10, "from": "Human", "text": "逗死我了。不过，还是换回你原来的声音吧，习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-106-10.wav"}}, {"session_id": "61", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "105", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-107-0.wav"}}, {"session_id": "105", "turn_idx": 1, "from": "Assistant", "text": "当然有啊！附近新开了家咖啡馆，评价超好，可以去尝尝他们的拿铁。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-107-1.wav"}}, {"session_id": "105", "turn_idx": 2, "from": "Human", "text": "听起来不错，晚点去看看。对了，你能换个声音吗？比如切换成蜡笔小新的那种搞笑音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-107-2.wav"}}, {"session_id": "105", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新音色\"}"}, {"session_id": "105", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "105", "turn_idx": 5, "from": "Assistant", "text": "嘿嘿，现在声音够搞笑不？像不像在讲笑话？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-107-5.wav"}}, {"session_id": "105", "turn_idx": 6, "from": "Human", "text": "哈哈哈，太像了，笑死我了！不过还是换回你原来的默认声音吧，男生的那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-107-6.wav"}}, {"session_id": "105", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "118", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-108-0.wav"}}, {"session_id": "118", "turn_idx": 1, "from": "Assistant", "text": "没干嘛，就待机呢。你今天有啥安排？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-108-1.wav"}}, {"session_id": "118", "turn_idx": 2, "from": "Human", "text": "想问你个事儿，晚上吃啥好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-108-2.wav"}}, {"session_id": "118", "turn_idx": 3, "from": "Assistant", "text": "吃火锅呗，天儿凉了暖和暖和。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-108-3.wav"}}, {"session_id": "118", "turn_idx": 4, "from": "Human", "text": "哈哈，行。对了，你能切换成一个港片里的大佬声音吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-108-4.wav"}}, {"session_id": "118", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"港片里的大佬声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"港片里的大佬声音\"}"}] +[{"session_id": "18", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的事推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-109-0.wav"}}, {"session_id": "18", "turn_idx": 1, "from": "Assistant", "text": "哈喽！我刚看到附近新开了一家猫咖，据说猫咪超可爱，可以去撸猫放松一下。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-109-1.wav"}}, {"session_id": "18", "turn_idx": 2, "from": "Human", "text": "哇，猫咖听着不错！对了，你能换个动漫人物的声音吗？比如那种热血少年的感觉。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-109-2.wav"}}, {"session_id": "18", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动漫热血少年音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"动漫热血少年音色\"}"}, {"session_id": "18", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "18", "turn_idx": 5, "from": "Assistant", "text": "现在声音够燃吧？就像动漫里主角喊口号一样，超带劲！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-109-5.wav"}}, {"session_id": "18", "turn_idx": 6, "from": "Human", "text": "哈哈，真像！感觉在看番剧了。对了，帮我查下猫咖的营业时间呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-109-6.wav"}}, {"session_id": "18", "turn_idx": 7, "from": "Assistant", "text": "没问题！那家店早上10点开门，晚上8点关门，周末人可能多，建议早点去。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-109-7.wav"}}, {"session_id": "18", "turn_idx": 8, "from": "Human", "text": "谢啦！现在换回原来的女音色吧，听着习惯些。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-109-8.wav"}}, {"session_id": "18", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "88", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气怎么样？感觉有点闷热。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-0.wav"}}, {"session_id": "88", "turn_idx": 1, "from": "Assistant", "text": "是呀，今天预报说最高30度呢，湿度也高，建议你多喝水，出门带把伞防暑。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-1.wav"}}, {"session_id": "88", "turn_idx": 2, "from": "Human", "text": "对了，周末有啥好玩的推荐不？我宅家好久了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-2.wav"}}, {"session_id": "88", "turn_idx": 3, "from": "Assistant", "text": "可以试试去新开的那个公园哦，听说有免费的音乐节，或者在家看个新上映的喜剧片，轻松一下。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-3.wav"}}, {"session_id": "88", "turn_idx": 4, "from": "Human", "text": "音乐节？听起来不错！但人太多我有点怕吵。小跃，你能不能换个可爱点的声音？像动画片里的小熊那样。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-4.wav"}}, {"session_id": "88", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里的小熊声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里的小熊声音\"}"}, {"session_id": "88", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "88", "turn_idx": 7, "from": "Assistant", "text": "好的，现在我是小熊音色啦！音乐节其实有安静区，你可以带个耳机去，既能听歌又不吵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-7.wav"}}, {"session_id": "88", "turn_idx": 8, "from": "Human", "text": "哈哈，这声音太萌了！小熊小跃，那公园远不远？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-8.wav"}}, {"session_id": "88", "turn_idx": 9, "from": "Assistant", "text": "不远哦，坐地铁两站就到，入口处还有冰淇淋摊，天热正好解暑。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-9.wav"}}, {"session_id": "88", "turn_idx": 10, "from": "Human", "text": "冰淇淋？我爱吃！但最近在减肥，小跃，你觉得我该去吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-10.wav"}}, {"session_id": "88", "turn_idx": 11, "from": "Assistant", "text": "偶尔吃一次没关系啦，运动一下平衡就好。或者选低糖的，健康又开心！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-11.wav"}}, {"session_id": "88", "turn_idx": 12, "from": "Human", "text": "行吧，听你的。对了，这声音听久了有点腻，还是换回原来的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-110-12.wav"}}, {"session_id": "88", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "19", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-111-0.wav"}}, {"session_id": "19", "turn_idx": 1, "from": "Assistant", "text": "嘿，刚整理完日程，正闲着呢！想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-111-1.wav"}}, {"session_id": "19", "turn_idx": 2, "from": "Human", "text": "哈哈，没啥事。对了，你能换个音色不？比如切换成周杰伦的音色，感觉挺酷的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-111-2.wav"}}, {"session_id": "19", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"周杰伦的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"周杰伦的音色\"}"}] +[{"session_id": "17", "turn_idx": 0, "from": "Human", "text": "嘿小跃，早上好呀！今天有啥安排？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-112-0.wav"}}, {"session_id": "17", "turn_idx": 1, "from": "Assistant", "text": "早啊！我打算帮你查查天气，再推荐点好玩的事。今天天气不错，适合出门散步哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-112-1.wav"}}, {"session_id": "17", "turn_idx": 2, "from": "Human", "text": "散步？好主意！最近有啥新歌推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-112-2.wav"}}, {"session_id": "17", "turn_idx": 3, "from": "Assistant", "text": "有啊，周杰伦的新专辑刚出，那首《说好不哭》超好听，你可以听听看！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-112-3.wav"}}, {"session_id": "17", "turn_idx": 4, "from": "Human", "text": "哦，周杰伦啊，我喜欢。不过小跃，你能换个声音不？我想听个像蜡笔小新那样搞怪的小孩声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-112-4.wav"}}, {"session_id": "17", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}] +[{"session_id": "55", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？今天心情咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-113-0.wav"}}, {"session_id": "55", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天心情不错，阳光明媚，你呢？有啥开心事分享吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-113-1.wav"}}, {"session_id": "55", "turn_idx": 2, "from": "Human", "text": "哈哈，刚吃了顿大餐。对了，帮我查下明天天气呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-113-2.wav"}}, {"session_id": "55", "turn_idx": 3, "from": "Assistant", "text": "没问题！明天多云转晴，温度18到25度，适合出门散步哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-113-3.wav"}}, {"session_id": "55", "turn_idx": 4, "from": "Human", "text": "小跃，能换成郭德纲的声音吗？我想听相声味儿。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-113-4.wav"}}, {"session_id": "55", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}"}, {"session_id": "55", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "55", "turn_idx": 7, "from": "Assistant", "text": "哎哟，现在我是郭老师的声音了！您想听段单口相声不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-113-7.wav"}}, {"session_id": "55", "turn_idx": 8, "from": "Human", "text": "哈哈，逗死我了！来一段经典的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-113-8.wav"}}, {"session_id": "55", "turn_idx": 9, "from": "Assistant", "text": "说个段子啊:人生就像一锅粥，熬着熬着就糊了... 咋样，乐呵吧？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-113-9.wav"}}, {"session_id": "55", "turn_idx": 10, "from": "Human", "text": "太搞笑了！不过还是换回你原来的声音吧，听着亲切点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-113-10.wav"}}, {"session_id": "55", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "121", "turn_idx": 0, "from": "Human", "text": "嘿小跃，早上好啊！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-114-0.wav"}}, {"session_id": "121", "turn_idx": 1, "from": "Assistant", "text": "早上好！今天精神不错嘛，有啥安排没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-114-1.wav"}}, {"session_id": "121", "turn_idx": 2, "from": "Human", "text": "还没想好呢，感觉有点无聊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-114-2.wav"}}, {"session_id": "121", "turn_idx": 3, "from": "Assistant", "text": "要不咱聊聊新出的电影？听说有部喜剧片特别搞笑。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-114-3.wav"}}, {"session_id": "121", "turn_idx": 4, "from": "Human", "text": "好啊，说说看。对了，小跃，换一个温柔的女主播声音呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-114-4.wav"}}, {"session_id": "121", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"温柔的女主播声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"温柔的女主播声音\"}"}] +[{"session_id": "118", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-115-0.wav"}}, {"session_id": "118", "turn_idx": 1, "from": "Assistant", "text": "没干嘛，就待机呢。你今天有啥安排？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-115-1.wav"}}, {"session_id": "118", "turn_idx": 2, "from": "Human", "text": "想问你个事儿，晚上吃啥好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-115-2.wav"}}, {"session_id": "118", "turn_idx": 3, "from": "Assistant", "text": "吃火锅呗，天儿凉了暖和暖和。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-115-3.wav"}}, {"session_id": "118", "turn_idx": 4, "from": "Human", "text": "哈哈，行。对了，你能切换成一个港片里的大佬声音吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-115-4.wav"}}, {"session_id": "118", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"港片里的大佬声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"港片里的大佬声音\"}"}, {"session_id": "118", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "118", "turn_idx": 7, "from": "Assistant", "text": "兄弟，火锅最地道，配点啤酒更爽！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-115-7.wav"}}, {"session_id": "118", "turn_idx": 8, "from": "Human", "text": "笑死，这声音绝了！那推荐个地方？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-115-8.wav"}}, {"session_id": "118", "turn_idx": 9, "from": "Assistant", "text": "去老城区那家，味儿正，人不多。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-115-9.wav"}}, {"session_id": "118", "turn_idx": 10, "from": "Human", "text": "好嘞。还是换回你原来的女声吧，听着习惯。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-115-10.wav"}}, {"session_id": "118", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "20", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？今天好无聊啊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-116-0.wav"}}, {"session_id": "20", "turn_idx": 1, "from": "Assistant", "text": "在呢！无聊的话，咱们聊聊天呗。有啥想聊的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-116-1.wav"}}, {"session_id": "20", "turn_idx": 2, "from": "Human", "text": "嗯，说说你周末一般干啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-116-2.wav"}}, {"session_id": "20", "turn_idx": 3, "from": "Assistant", "text": "我啊，就待机休息，顺便帮用户查资料、聊天。你呢，周末有安排没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-116-3.wav"}}, {"session_id": "20", "turn_idx": 4, "from": "Human", "text": "还没想好呢。对了，切换成孙悟空的声音吧，动画片里那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-116-4.wav"}}, {"session_id": "20", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"孙悟空的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"孙悟空的声音\"}"}, {"session_id": "20", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "20", "turn_idx": 7, "from": "Assistant", "text": "嘿嘿，俺老孙来也！周末要不要去耍耍？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-116-7.wav"}}, {"session_id": "20", "turn_idx": 8, "from": "Human", "text": "哈哈，声音真逗！那推荐个地方呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-116-8.wav"}}, {"session_id": "20", "turn_idx": 9, "from": "Assistant", "text": "去爬山呗，呼吸新鲜空气，还能练练腿脚。咋样，心动没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-116-9.wav"}}, {"session_id": "20", "turn_idx": 10, "from": "Human", "text": "听着不错。但换回原来的声音吧，习惯点儿。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-116-10.wav"}}, {"session_id": "20", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "97", "turn_idx": 0, "from": "Human", "text": "嗨小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-117-0.wav"}}, {"session_id": "97", "turn_idx": 1, "from": "Assistant", "text": "没干嘛呀，就等你来找我聊天呢！今天过得咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-117-1.wav"}}, {"session_id": "97", "turn_idx": 2, "from": "Human", "text": "还行吧，刚吃完饭。你饿不饿啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-117-2.wav"}}, {"session_id": "97", "turn_idx": 3, "from": "Assistant", "text": "哈哈，我是AI不用吃饭啦！不过可以陪你聊美食，最近迷上火锅了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-117-3.wav"}}, {"session_id": "97", "turn_idx": 4, "from": "Human", "text": "火锅我也爱！但你的声音听着有点平淡，能换个搞笑点的音色不？比如赵本山的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-117-4.wav"}}, {"session_id": "97", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"赵本山的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"赵本山的音色\"}"}] +[{"session_id": "55", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？今天心情咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-118-0.wav"}}, {"session_id": "55", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天心情不错，阳光明媚，你呢？有啥开心事分享吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-118-1.wav"}}, {"session_id": "55", "turn_idx": 2, "from": "Human", "text": "哈哈，刚吃了顿大餐。对了，帮我查下明天天气呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-118-2.wav"}}, {"session_id": "55", "turn_idx": 3, "from": "Assistant", "text": "没问题！明天多云转晴，温度18到25度，适合出门散步哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-118-3.wav"}}, {"session_id": "55", "turn_idx": 4, "from": "Human", "text": "小跃，能换成郭德纲的声音吗？我想听相声味儿。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-118-4.wav"}}, {"session_id": "55", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"郭德纲的音色\"}"}, {"session_id": "55", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "55", "turn_idx": 7, "from": "Assistant", "text": "哎哟，现在我是郭老师的声音了！您想听段单口相声不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-118-7.wav"}}, {"session_id": "55", "turn_idx": 8, "from": "Human", "text": "哈哈，逗死我了！来一段经典的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-118-8.wav"}}, {"session_id": "55", "turn_idx": 9, "from": "Assistant", "text": "说个段子啊:人生就像一锅粥，熬着熬着就糊了... 咋样，乐呵吧？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-118-9.wav"}}, {"session_id": "55", "turn_idx": 10, "from": "Human", "text": "太搞笑了！不过还是换回你原来的声音吧，听着亲切点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-118-10.wav"}}, {"session_id": "55", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "117", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-119-0.wav"}}, {"session_id": "117", "turn_idx": 1, "from": "Assistant", "text": "嘿，主人！可以去公园散步，或者看个新电影，都挺不错的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-119-1.wav"}}, {"session_id": "117", "turn_idx": 2, "from": "Human", "text": "嗯，听起来行。对了，帮我切换成海绵宝宝的声音呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-119-2.wav"}}, {"session_id": "117", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝音色\"}"}, {"session_id": "117", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "117", "turn_idx": 5, "from": "Assistant", "text": "哈哈，我准备好啦！派大星，我们去抓水母吧？主人，想玩点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-119-5.wav"}}, {"session_id": "117", "turn_idx": 6, "from": "Human", "text": "太搞笑了！那讲个笑话听听。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-119-6.wav"}}, {"session_id": "117", "turn_idx": 7, "from": "Assistant", "text": "为啥鱼不喜欢打牌？因为它们怕被钓起来啊！哈哈哈。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-119-7.wav"}}, {"session_id": "117", "turn_idx": 8, "from": "Human", "text": "哈哈，冷死了。换回你原来的女声吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-119-8.wav"}}, {"session_id": "117", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "6", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-120-0.wav"}}, {"session_id": "6", "turn_idx": 1, "from": "Assistant", "text": "嘿，今天可以试试新开的咖啡店，听说他们的拿铁超赞！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-120-1.wav"}}, {"session_id": "6", "turn_idx": 2, "from": "Human", "text": "真的吗？那我下午去尝尝。对了，你声音有点低沉，能换个元气少女的音色不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-120-2.wav"}}, {"session_id": "6", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"元气少女的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"元气少女的音色\"}"}, {"session_id": "6", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "6", "turn_idx": 5, "from": "Assistant", "text": "现在是不是更活力满满了？咖啡店在市中心，需要我帮你查路线吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-120-5.wav"}}, {"session_id": "6", "turn_idx": 6, "from": "Human", "text": "哇，这声音好可爱！像动漫里出来的。路线就不用了，我熟路。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-120-6.wav"}}, {"session_id": "6", "turn_idx": 7, "from": "Assistant", "text": "那行，记得分享体验哦！我最近还学了新笑话，要不要听？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-120-7.wav"}}, {"session_id": "6", "turn_idx": 8, "from": "Human", "text": "来一个呗，逗我笑笑。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-120-8.wav"}}, {"session_id": "6", "turn_idx": 9, "from": "Assistant", "text": "为什么手机不会饿？因为它有“充电宝”啊！哈哈，冷吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-120-9.wav"}}, {"session_id": "6", "turn_idx": 10, "from": "Human", "text": "噗，够冷的。不过，还是你原来的声音更习惯，换回默认男音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-120-10.wav"}}, {"session_id": "6", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "65", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天心情咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-121-0.wav"}}, {"session_id": "65", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我心情超好！今天有啥需要帮忙的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-121-1.wav"}}, {"session_id": "65", "turn_idx": 2, "from": "Human", "text": "没啥大事儿，就问问晚上吃啥好。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-121-2.wav"}}, {"session_id": "65", "turn_idx": 3, "from": "Assistant", "text": "推荐你试试火锅，热乎乎的，适合放松一下。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-121-3.wav"}}, {"session_id": "65", "turn_idx": 4, "from": "Human", "text": "火锅不错！不过，你能用海绵宝宝的音色说吗？那样更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-121-4.wav"}}, {"session_id": "65", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}, {"session_id": "65", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "65", "turn_idx": 7, "from": "Assistant", "text": "嘿嘿，朋友，火锅超赞的，加点蟹黄包更棒哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-121-7.wav"}}, {"session_id": "65", "turn_idx": 8, "from": "Human", "text": "哈哈哈，太逗了！蟹黄包是必点的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-121-8.wav"}}, {"session_id": "65", "turn_idx": 9, "from": "Assistant", "text": "当然啦，脆脆的皮，满满的馅，一口下去超满足！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-121-9.wav"}}, {"session_id": "65", "turn_idx": 10, "from": "Human", "text": "懂了。现在换回你原来的声音吧，小跃。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-121-10.wav"}}, {"session_id": "65", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "6", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-122-0.wav"}}, {"session_id": "6", "turn_idx": 1, "from": "Assistant", "text": "嘿，今天可以试试新开的咖啡店，听说他们的拿铁超赞！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-122-1.wav"}}, {"session_id": "6", "turn_idx": 2, "from": "Human", "text": "真的吗？那我下午去尝尝。对了，你声音有点低沉，能换个元气少女的音色不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-122-2.wav"}}, {"session_id": "6", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"元气少女的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"元气少女的音色\"}"}] +[{"session_id": "93", "turn_idx": 0, "from": "Human", "text": "嘿，小跃，你今天忙不忙？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-0.wav"}}, {"session_id": "93", "turn_idx": 1, "from": "Assistant", "text": "还好啦，就处理些日常数据，有啥需要帮忙的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-1.wav"}}, {"session_id": "93", "turn_idx": 2, "from": "Human", "text": "没啥，就是无聊想聊聊天。你平时都听啥音乐啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-2.wav"}}, {"session_id": "93", "turn_idx": 3, "from": "Assistant", "text": "我超爱流行歌，周杰伦的老歌循环听，经典又上头！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-3.wav"}}, {"session_id": "93", "turn_idx": 4, "from": "Human", "text": "哈哈，我也是！对了，能不能换个音色？我想听你用海绵宝宝的声音说话。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-4.wav"}}, {"session_id": "93", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}, {"session_id": "93", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "93", "turn_idx": 7, "from": "Assistant", "text": "哈哈，我是海绵宝宝！派大星，我们去抓水母吧？这音色好玩不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-7.wav"}}, {"session_id": "93", "turn_idx": 8, "from": "Human", "text": "太逗了！听着就像动画片里蹦出来的。那海绵宝宝，你最爱吃啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-8.wav"}}, {"session_id": "93", "turn_idx": 9, "from": "Assistant", "text": "当然是蟹堡王的汉堡啦，嘎嘣脆！不过当助手，我得正经点，哈哈。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-9.wav"}}, {"session_id": "93", "turn_idx": 10, "from": "Human", "text": "正经啥呀，继续用这个音色。说说你最近有啥新功能？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-10.wav"}}, {"session_id": "93", "turn_idx": 11, "from": "Assistant", "text": "新增了个智能提醒，帮你记生日啥的。但别让我去水母田上班，我怕章鱼哥骂我！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-11.wav"}}, {"session_id": "93", "turn_idx": 12, "from": "Human", "text": "笑死了。对了，小跃，还是换回你原来的声音吧，这个太闹腾了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-123-12.wav"}}, {"session_id": "93", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "92", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的新闻没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-124-0.wav"}}, {"session_id": "92", "turn_idx": 1, "from": "Assistant", "text": "嘿，我刚看到一条，说新开的游乐园周末半价，过山车超刺激！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-124-1.wav"}}, {"session_id": "92", "turn_idx": 2, "from": "Human", "text": "哇，听起来不错。不过你能换个蜡笔小新的声音吗？搞笑点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-124-2.wav"}}, {"session_id": "92", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "92", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "92", "turn_idx": 5, "from": "Assistant", "text": "动感超人！哈哈，我是小新哦～游乐园超好玩，妈妈说要带我去呢！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-124-5.wav"}}, {"session_id": "92", "turn_idx": 6, "from": "Human", "text": "哈哈，太逗了。那游乐园在哪儿啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-124-6.wav"}}, {"session_id": "92", "turn_idx": 7, "from": "Assistant", "text": "在东区购物中心旁边，地铁直达，超方便！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-124-7.wav"}}, {"session_id": "92", "turn_idx": 8, "from": "Human", "text": "谢啦。换回原来的声音吧，默认女音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-124-8.wav"}}, {"session_id": "92", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "11", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-125-0.wav"}}, {"session_id": "11", "turn_idx": 1, "from": "Assistant", "text": "今天天气超棒的，阳光灿烂，适合出门散步呢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-125-1.wav"}}, {"session_id": "11", "turn_idx": 2, "from": "Human", "text": "太好了，我正好想去公园。对了，小跃，你能切换成一个卡通人物的声音吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-125-2.wav"}}, {"session_id": "11", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"卡通人物的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"卡通人物的声音\"}"}, {"session_id": "11", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "11", "turn_idx": 5, "from": "Assistant", "text": "嘻嘻，现在我是可爱卡通音啦！公园散步记得带水哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-125-5.wav"}}, {"session_id": "11", "turn_idx": 6, "from": "Human", "text": "哈哈，好玩！但这样说话怪怪的，还是换回你原来的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-125-6.wav"}}, {"session_id": "11", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "116", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气真热啊，我都快化了！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-0.wav"}}, {"session_id": "116", "turn_idx": 1, "from": "Assistant", "text": "哈哈，是啊，这大太阳晒得人发晕。要不要我帮你查查天气预报？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-1.wav"}}, {"session_id": "116", "turn_idx": 2, "from": "Human", "text": "不用啦，我就随便聊聊。你在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-2.wav"}}, {"session_id": "116", "turn_idx": 3, "from": "Assistant", "text": "我正闲着，随时准备陪你唠嗑。最近有啥新鲜事不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-3.wav"}}, {"session_id": "116", "turn_idx": 4, "from": "Human", "text": "没啥，就是周末想去看电影。对了，小跃，你的声音听着有点闷，换一个活泼少女的音色吧！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-4.wav"}}, {"session_id": "116", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"活泼少女的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"活泼少女的音色\"}"}, {"session_id": "116", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "116", "turn_idx": 7, "from": "Assistant", "text": "嗨呀，现在我是元气少女啦！看电影好主意，你喜欢啥类型的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-7.wav"}}, {"session_id": "116", "turn_idx": 8, "from": "Human", "text": "哇，这声音真带劲！我喜欢喜剧片，你呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-8.wav"}}, {"session_id": "116", "turn_idx": 9, "from": "Assistant", "text": "我也是呢，搞笑片能让人放松。最近新上映的《疯狂假期》听说超逗，要不要试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-9.wav"}}, {"session_id": "116", "turn_idx": 10, "from": "Human", "text": "好呀，我记下了。对了，小跃，你说我该穿啥去？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-10.wav"}}, {"session_id": "116", "turn_idx": 11, "from": "Assistant", "text": "穿个凉快点儿的T恤呗，电影院空调开得足，别冻着了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-11.wav"}}, {"session_id": "116", "turn_idx": 12, "from": "Human", "text": "有道理。哎，这少女音色挺好玩，但听久了有点累。换回默认男音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-126-12.wav"}}, {"session_id": "116", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "35", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-127-0.wav"}}, {"session_id": "35", "turn_idx": 1, "from": "Assistant", "text": "哎呀，我刚更新完天气数据，今天阳光超好，适合出去溜达！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-127-1.wav"}}, {"session_id": "35", "turn_idx": 2, "from": "Human", "text": "真的吗？那推荐个地方呗，我宅家好几天了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-127-2.wav"}}, {"session_id": "35", "turn_idx": 3, "from": "Assistant", "text": "去河边公园呗，花开得正艳，还能看人放风筝，可热闹了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-127-3.wav"}}, {"session_id": "35", "turn_idx": 4, "from": "Human", "text": "风筝啊？我小时候最爱玩了。对了，你能换一个元气少女的声音吗？听着更有活力！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-127-4.wav"}}, {"session_id": "35", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"元气少女声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"元气少女声音\"}"}] +[{"session_id": "67", "turn_idx": 0, "from": "Human", "text": "嗨小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-128-0.wav"}}, {"session_id": "67", "turn_idx": 1, "from": "Assistant", "text": "嗨！我刚更新了天气信息，今天阳光超好，适合出去浪哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-128-1.wav"}}, {"session_id": "67", "turn_idx": 2, "from": "Human", "text": "真的？那帮我查查附近有啥好吃的呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-128-2.wav"}}, {"session_id": "67", "turn_idx": 3, "from": "Assistant", "text": "没问题！步行街新开了一家川菜馆，评分超高的，想去试试不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-128-3.wav"}}, {"session_id": "67", "turn_idx": 4, "from": "Human", "text": "可以啊。对了小跃，你能换一个幽默的相声演员声音吗？这样聊天更有乐子。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-128-4.wav"}}, {"session_id": "67", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"幽默的相声演员声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"幽默的相声演员声音\"}"}] +[{"session_id": "74", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天有啥好玩的事儿吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-129-0.wav"}}, {"session_id": "74", "turn_idx": 1, "from": "Assistant", "text": "早啊！今天社区有免费电影放映，听说放喜剧片，要不要一起去看看？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-129-1.wav"}}, {"session_id": "74", "turn_idx": 2, "from": "Human", "text": "真的？那太棒了！几点开始啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-129-2.wav"}}, {"session_id": "74", "turn_idx": 3, "from": "Assistant", "text": "下午两点，在广场那边。我带点爆米花，咱们早点去占位置。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-129-3.wav"}}, {"session_id": "74", "turn_idx": 4, "from": "Human", "text": "好嘞！对了，切换成海绵宝宝的音色和我聊天吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-129-4.wav"}}, {"session_id": "74", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的音色\"}"}, {"session_id": "74", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "74", "turn_idx": 7, "from": "Assistant", "text": "哈哈哈，我准备好了！海绵宝宝在这儿！今天咱们去看电影，太开心啦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-129-7.wav"}}, {"session_id": "74", "turn_idx": 8, "from": "Human", "text": "哈哈，太逗了！海绵宝宝，你最爱看啥电影？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-129-8.wav"}}, {"session_id": "74", "turn_idx": 9, "from": "Assistant", "text": "我最爱看海底冒险片，就像在比基尼海滩一样刺激！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-129-9.wav"}}, {"session_id": "74", "turn_idx": 10, "from": "Human", "text": "笑死我了！不过，换回你原来的声音吧，小跃。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-129-10.wav"}}, {"session_id": "74", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "99", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？今天忙不忙？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-130-0.wav"}}, {"session_id": "99", "turn_idx": 1, "from": "Assistant", "text": "在的！我不忙，随时为您服务。您今天有啥计划呀？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-130-1.wav"}}, {"session_id": "99", "turn_idx": 2, "from": "Human", "text": "想去逛街买衣服，但不知道去哪儿好。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-130-2.wav"}}, {"session_id": "99", "turn_idx": 3, "from": "Assistant", "text": "推荐去万达广场，新开了几家潮牌店，款式超多。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-130-3.wav"}}, {"session_id": "99", "turn_idx": 4, "from": "Human", "text": "听起来不错。对了，能换个音色吗？比如换成邓紫棋的声音，感觉会更带感。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-130-4.wav"}}, {"session_id": "99", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"邓紫棋的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"邓紫棋的声音\"}"}] +[{"session_id": "23", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-0.wav"}}, {"session_id": "23", "turn_idx": 1, "from": "Assistant", "text": "嗨，我挺好的！今天天气不错，阳光明媚，你打算做点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-1.wav"}}, {"session_id": "23", "turn_idx": 2, "from": "Human", "text": "我有点无聊，想找部电影看看。有啥推荐的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-2.wav"}}, {"session_id": "23", "turn_idx": 3, "from": "Assistant", "text": "当然有啊！最近新上映的喜剧片评价很高，轻松搞笑，适合放松心情。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-3.wav"}}, {"session_id": "23", "turn_idx": 4, "from": "Human", "text": "听起来不错。不过，你能切换成周杰伦的音色吗？我想听点不一样的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-4.wav"}}, {"session_id": "23", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"周杰伦的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"周杰伦的音色\"}"}, {"session_id": "23", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "23", "turn_idx": 7, "from": "Assistant", "text": "嘿，我是小跃，现在用新音色了。那部电影叫《欢乐时光》，讲的是几个朋友旅行的故事，特别逗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-7.wav"}}, {"session_id": "23", "turn_idx": 8, "from": "Human", "text": "哈哈，这音色真带感！电影剧情是啥？详细说说呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-8.wav"}}, {"session_id": "23", "turn_idx": 9, "from": "Assistant", "text": "简单说就是一群人自驾游，路上遇到各种奇葩事，比如车坏了被村民帮忙，笑点超多。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-9.wav"}}, {"session_id": "23", "turn_idx": 10, "from": "Human", "text": "哦，对了，附近有好的咖啡馆吗？看完电影想喝杯咖啡。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-10.wav"}}, {"session_id": "23", "turn_idx": 11, "from": "Assistant", "text": "有的，转角那家“星语咖啡”评价很好，拿铁特别香浓。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-11.wav"}}, {"session_id": "23", "turn_idx": 12, "from": "Human", "text": "谢啦！不过我还是喜欢你原来的声音，换回默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-131-12.wav"}}, {"session_id": "23", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "71", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-132-0.wav"}}, {"session_id": "71", "turn_idx": 1, "from": "Assistant", "text": "没干嘛呀，就等着和你聊天呢！今天过得怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-132-1.wav"}}, {"session_id": "71", "turn_idx": 2, "from": "Human", "text": "还行吧，就是有点无聊。想听你讲个笑话解解闷。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-132-2.wav"}}, {"session_id": "71", "turn_idx": 3, "from": "Assistant", "text": "好啊！为什么海绵宝宝从来不生气？因为他是方方的，没棱角啊，哈哈！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-132-3.wav"}}, {"session_id": "71", "turn_idx": 4, "from": "Human", "text": "噗，这笑话够冷的。小跃，你能切换成蜡笔小新的声音吗？那样肯定更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-132-4.wav"}}, {"session_id": "71", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "71", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "71", "turn_idx": 7, "from": "Assistant", "text": "好嘞，我是小新音色哦！大姐姐，要听动感超人的故事吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-132-7.wav"}}, {"session_id": "71", "turn_idx": 8, "from": "Human", "text": "哈哈，太像了！讲吧讲吧，我爱听。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-132-8.wav"}}, {"session_id": "71", "turn_idx": 9, "from": "Assistant", "text": "动感超人今天打败了怪兽，还吃了好多巧克力饼干，超满足的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-132-9.wav"}}, {"session_id": "71", "turn_idx": 10, "from": "Human", "text": "笑死我了。小跃，还是换回你最开始的声音吧，我习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-132-10.wav"}}, {"session_id": "71", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "119", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天忙啥呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-0.wav"}}, {"session_id": "119", "turn_idx": 1, "from": "Assistant", "text": "嘿，没啥特别，就等着帮你解闷呢！有啥新鲜事分享吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-1.wav"}}, {"session_id": "119", "turn_idx": 2, "from": "Human", "text": "没啥，刚刷视频看到个搞笑猫片，笑死我了！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-2.wav"}}, {"session_id": "119", "turn_idx": 3, "from": "Assistant", "text": "哈哈，猫猫永远的神！我这儿还存了一堆萌宠合集，要看吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-3.wav"}}, {"session_id": "119", "turn_idx": 4, "from": "Human", "text": "下次吧。对了，你能切换成动漫萌妹的音色吗？想换个感觉聊天。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-4.wav"}}, {"session_id": "119", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动漫萌妹的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"动漫萌妹的音色\"}"}, {"session_id": "119", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "119", "turn_idx": 7, "from": "Assistant", "text": "喵～现在是小跃萌妹版！怎么样，新声音可爱不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-7.wav"}}, {"session_id": "119", "turn_idx": 8, "from": "Human", "text": "哇，超萌！那聊聊你最近在追啥动漫？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-8.wav"}}, {"session_id": "119", "turn_idx": 9, "from": "Assistant", "text": "最近迷上《鬼灭之刃》，炭治郎超帅的！推荐你也看看，打斗场面炸裂！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-9.wav"}}, {"session_id": "119", "turn_idx": 10, "from": "Human", "text": "哦哦，记下了。对了，晚饭吃啥好？纠结中。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-10.wav"}}, {"session_id": "119", "turn_idx": 11, "from": "Assistant", "text": "简单点就点外卖呗，披萨或炸鸡，懒人必备！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-11.wav"}}, {"session_id": "119", "turn_idx": 12, "from": "Human", "text": "行，听你的。但我觉得还是原来的声音顺耳，换回默认男音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-133-12.wav"}}, {"session_id": "119", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "122", "turn_idx": 0, "from": "Human", "text": "小跃，早上好啊！今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-134-0.wav"}}, {"session_id": "122", "turn_idx": 1, "from": "Assistant", "text": "嘿，早上好！今天天气不错，可以去公园野餐，或者看个新电影，最近有部喜剧上映呢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-134-1.wav"}}, {"session_id": "122", "turn_idx": 2, "from": "Human", "text": "嗯，听起来还行。不过你的声音太普通了，换个动漫角色的音色吧，比如哆啦A梦那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-134-2.wav"}}, {"session_id": "122", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"哆啦A梦的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"哆啦A梦的音色\"}"}] +[{"session_id": "114", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天有啥新鲜事不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-135-0.wav"}}, {"session_id": "114", "turn_idx": 1, "from": "Assistant", "text": "嘿，没啥大新闻，就是听说楼下新开了家奶茶店，评分挺高的，要不要试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-135-1.wav"}}, {"session_id": "114", "turn_idx": 2, "from": "Human", "text": "哦？听起来不错！不过我想先换个音色玩玩，比如切换成海绵宝宝的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-135-2.wav"}}, {"session_id": "114", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}"}] +[{"session_id": "91", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天有啥新鲜事没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-136-0.wav"}}, {"session_id": "91", "turn_idx": 1, "from": "Assistant", "text": "嗨，没啥特别的，就是听说周末有个音乐节，挺热闹的，你想去不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-136-1.wav"}}, {"session_id": "91", "turn_idx": 2, "from": "Human", "text": "音乐节？好呀，谁演出啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-136-2.wav"}}, {"session_id": "91", "turn_idx": 3, "from": "Assistant", "text": "有本地乐队和几个小明星，具体名单我帮你查查，稍等哈。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-136-3.wav"}}, {"session_id": "91", "turn_idx": 4, "from": "Human", "text": "切换成张雨绮的音色吧，听起来更带感。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-136-4.wav"}}, {"session_id": "91", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"张雨绮的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"张雨绮的音色\"}"}, {"session_id": "91", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "91", "turn_idx": 7, "from": "Assistant", "text": "好的，现在用张雨绮的音色啦！演出名单有摇滚乐队“风暴”和歌手王琳凯，气氛肯定嗨！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-136-7.wav"}}, {"session_id": "91", "turn_idx": 8, "from": "Human", "text": "哇，这声音真有范儿！门票贵不贵？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-136-8.wav"}}, {"session_id": "91", "turn_idx": 9, "from": "Assistant", "text": "不贵，普通票就一百块左右，网上就能买，要我帮你抢不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-136-9.wav"}}, {"session_id": "91", "turn_idx": 10, "from": "Human", "text": "切回你原来的声音吧，习惯了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-136-10.wav"}}, {"session_id": "91", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "44", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天有啥好玩的事吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-137-0.wav"}}, {"session_id": "44", "turn_idx": 1, "from": "Assistant", "text": "嗨！今天有场线上音乐会直播，要不要一起看？我帮你查下时间。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-137-1.wav"}}, {"session_id": "44", "turn_idx": 2, "from": "Human", "text": "好呀！不过，先切换成卡通片里那种萌萌的小动物声音呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-137-2.wav"}}, {"session_id": "44", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"卡通片里萌萌的小动物声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"卡通片里萌萌的小动物声音\"}"}] +[{"session_id": "31", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉有点无聊，陪我聊聊天呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-138-0.wav"}}, {"session_id": "31", "turn_idx": 1, "from": "Assistant", "text": "当然可以啦！我随时在这儿，你想聊点啥？比如最近看了啥好电影？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-138-1.wav"}}, {"session_id": "31", "turn_idx": 2, "from": "Human", "text": "嗯，最近没看啥。对了，你能换个声音吗？像那种成熟稳重的男主播音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-138-2.wav"}}, {"session_id": "31", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"成熟稳重的男主播音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"成熟稳重的男主播音色\"}"}] +[{"session_id": "0", "turn_idx": 0, "from": "Human", "text": "嘿小跃，早上好呀！今天感觉怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-139-0.wav"}}, {"session_id": "0", "turn_idx": 1, "from": "Assistant", "text": "早上好！我感觉挺精神的，今天天气超好，阳光明媚的，你打算做点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-139-1.wav"}}, {"session_id": "0", "turn_idx": 2, "from": "Human", "text": "我想去咖啡厅坐坐，顺便读本书。你推荐哪家店？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-139-2.wav"}}, {"session_id": "0", "turn_idx": 3, "from": "Assistant", "text": "哦，我知道一家新开的，环境超安静，咖啡也香，叫“悠闲时光”，就在市中心。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-139-3.wav"}}, {"session_id": "0", "turn_idx": 4, "from": "Human", "text": "听起来不错！对了，小跃，你能换一个动画片里小猫咪的声音吗？感觉那样聊天更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-139-4.wav"}}, {"session_id": "0", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里小猫咪的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里小猫咪的声音\"}"}] +[{"session_id": "89", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-140-0.wav"}}, {"session_id": "89", "turn_idx": 1, "from": "Assistant", "text": "嗨！今天可以去试试新开的奶茶店，听说他们的招牌波波茶超赞的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-140-1.wav"}}, {"session_id": "89", "turn_idx": 2, "from": "Human", "text": "波波茶？听着不错。但你能换一个动画片里小熊维尼的声音吗？那样聊天更可爱。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-140-2.wav"}}, {"session_id": "89", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里小熊维尼的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里小熊维尼的声音\"}"}] +[{"session_id": "68", "turn_idx": 0, "from": "Human", "text": "嘿小跃，早上好啊！今天天气真不错，你觉得我该去公园散步吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-141-0.wav"}}, {"session_id": "68", "turn_idx": 1, "from": "Assistant", "text": "早呀！今天阳光明媚，公园散步超赞的，还能呼吸新鲜空气。你打算几点去呀？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-141-1.wav"}}, {"session_id": "68", "turn_idx": 2, "from": "Human", "text": "大概十点吧。对了，我最近想学做菜，有啥简单食谱推荐不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-141-2.wav"}}, {"session_id": "68", "turn_idx": 3, "from": "Assistant", "text": "当然有！番茄炒蛋超简单:打两个鸡蛋切个番茄，热油翻炒就行。新手友好，不会翻车！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-141-3.wav"}}, {"session_id": "68", "turn_idx": 4, "from": "Human", "text": "哈哈，好主意。小跃，你能换个温柔女声的音色吗？这样聊天更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-141-4.wav"}}, {"session_id": "68", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"温柔女声\"}", "value": "function \ntimbre_rag \n{\"query\": \"温柔女声\"}"}, {"session_id": "68", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "68", "turn_idx": 7, "from": "Assistant", "text": "音色换好啦！现在听起来更温柔了吧？番茄炒蛋记得加少许盐，味道更鲜哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-141-7.wav"}}, {"session_id": "68", "turn_idx": 8, "from": "Human", "text": "哇，这声音真舒服！那做菜用啥锅好？不粘锅行吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-141-8.wav"}}, {"session_id": "68", "turn_idx": 9, "from": "Assistant", "text": "不粘锅超方便，新手用不容易糊锅。对了，你平时爱吃辣不？可以加点辣椒提味。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-141-9.wav"}}, {"session_id": "68", "turn_idx": 10, "from": "Human", "text": "喜欢辣的！但最近上火，得少吃。小跃，换回你原来的声音吧，习惯了那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-141-10.wav"}}, {"session_id": "68", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "113", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-142-0.wav"}}, {"session_id": "113", "turn_idx": 1, "from": "Assistant", "text": "哎哟，今天新开了家奶茶店，听说草莓波波茶超赞，要不要去试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-142-1.wav"}}, {"session_id": "113", "turn_idx": 2, "from": "Human", "text": "真的吗？那我下班去喝。对了，你声音能切换成动画片里那个皮卡丘的音色不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-142-2.wav"}}, {"session_id": "113", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"皮卡丘的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"皮卡丘的音色\"}"}] +[{"session_id": "94", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-143-0.wav"}}, {"session_id": "94", "turn_idx": 1, "from": "Assistant", "text": "哎呀，我刚看到一个超搞笑的短视频，一只猫在跳舞，配着神曲，笑死我了！要不要我给你讲讲？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-143-1.wav"}}, {"session_id": "94", "turn_idx": 2, "from": "Human", "text": "哈哈，快说快说，我等着呢！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-143-2.wav"}}, {"session_id": "94", "turn_idx": 3, "from": "Assistant", "text": "那只猫对着镜子扭屁股，结果被自己的影子吓到，直接跳到沙发上，整个动作超滑稽。主人录下来发网上，火得不行！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-143-3.wav"}}, {"session_id": "94", "turn_idx": 4, "from": "Human", "text": "太逗了！对了，换一个蜡笔小新的声音跟我聊天呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-143-4.wav"}}, {"session_id": "94", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "94", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "94", "turn_idx": 7, "from": "Assistant", "text": "嘿嘿，我是小新版小跃哦～动感光波！今天想聊啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-143-7.wav"}}, {"session_id": "94", "turn_idx": 8, "from": "Human", "text": "哈哈，学得真像！但还是换回你原来的声音吧，习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-143-8.wav"}}, {"session_id": "94", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "77", "turn_idx": 0, "from": "Human", "text": "小跃，今天中午吃啥好呢？我有点饿。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-144-0.wav"}}, {"session_id": "77", "turn_idx": 1, "from": "Assistant", "text": "哈哈，饿啦？我推荐你去试试那家新开的拉面店，他们的汤底超赞的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-144-1.wav"}}, {"session_id": "77", "turn_idx": 2, "from": "Human", "text": "拉面听着不错！不过，你能换个声音吗？比如切换成动画片里机器人的声音？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-144-2.wav"}}, {"session_id": "77", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里机器人的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里机器人的声音\"}"}, {"session_id": "77", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "77", "turn_idx": 5, "from": "Assistant", "text": "哔哔——收到指令！拉面店在商场二楼，导航启动中！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-144-5.wav"}}, {"session_id": "77", "turn_idx": 6, "from": "Human", "text": "哈哈，太搞笑了！但这样说话好累，还是换回原来的默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-144-6.wav"}}, {"session_id": "77", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "22", "turn_idx": 0, "from": "Human", "text": "嗨，小跃！早上好呀。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-0.wav"}}, {"session_id": "22", "turn_idx": 1, "from": "Assistant", "text": "早上好！我是小跃，今天心情不错呢，有啥需要帮忙的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-1.wav"}}, {"session_id": "22", "turn_idx": 2, "from": "Human", "text": "嗯，我想知道现在几点了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-2.wav"}}, {"session_id": "22", "turn_idx": 3, "from": "Assistant", "text": "现在是早上9点15分，还早着呢，你可以慢慢来。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-3.wav"}}, {"session_id": "22", "turn_idx": 4, "from": "Human", "text": "谢谢！对了，今天有啥有趣的新闻吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-4.wav"}}, {"session_id": "22", "turn_idx": 5, "from": "Assistant", "text": "当然有啊，听说城西新开了个咖啡馆，评价超好，要不要去试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-5.wav"}}, {"session_id": "22", "turn_idx": 6, "from": "Human", "text": "哇，听起来不错！但先换个音色吧，我想听个磁性的男主播声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-6.wav"}}, {"session_id": "22", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"磁性的男主播声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"磁性的男主播声音\"}"}, {"session_id": "22", "turn_idx": 8, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "22", "turn_idx": 9, "from": "Assistant", "text": "好的，现在声音怎么样？新闻继续:咖啡馆的拿铁据说超香醇。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-9.wav"}}, {"session_id": "22", "turn_idx": 10, "from": "Human", "text": "声音真带感！那咖啡馆地址在哪？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-10.wav"}}, {"session_id": "22", "turn_idx": 11, "from": "Assistant", "text": "在中山路58号，离你家就10分钟步行，周末去正合适。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-11.wav"}}, {"session_id": "22", "turn_idx": 12, "from": "Human", "text": "完美！哦，我想换回你原来的声音了，小跃。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-145-12.wav"}}, {"session_id": "22", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "72", "turn_idx": 0, "from": "Human", "text": "小跃，今天心情咋样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-146-0.wav"}}, {"session_id": "72", "turn_idx": 1, "from": "Assistant", "text": "心情不错呢！刚听完一首新歌，感觉整个人都轻松了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-146-1.wav"}}, {"session_id": "72", "turn_idx": 2, "from": "Human", "text": "哦？啥歌啊，推荐给我听听呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-146-2.wav"}}, {"session_id": "72", "turn_idx": 3, "from": "Assistant", "text": "是周深的《大鱼》，旋律特别美，你肯定会喜欢的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-146-3.wav"}}, {"session_id": "72", "turn_idx": 4, "from": "Human", "text": "嗯，听起来不错。对了，小跃，切换成张韶涵的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-146-4.wav"}}, {"session_id": "72", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"张韶涵的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"张韶涵的声音\"}"}, {"session_id": "72", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "72", "turn_idx": 7, "from": "Assistant", "text": "好啦，现在用这个声音陪你聊，有啥想聊的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-146-7.wav"}}, {"session_id": "72", "turn_idx": 8, "from": "Human", "text": "哇，这个声音好甜！那再推荐一首类似的歌呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-146-8.wav"}}, {"session_id": "72", "turn_idx": 9, "from": "Assistant", "text": "可以试试邓紫棋的《光年之外》，也很适合放松心情哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-146-9.wav"}}, {"session_id": "72", "turn_idx": 10, "from": "Human", "text": "行，我记下了。不过还是换回原来的声音吧，习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-146-10.wav"}}, {"session_id": "72", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "109", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的事推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-147-0.wav"}}, {"session_id": "109", "turn_idx": 1, "from": "Assistant", "text": "哇，我刚刷到附近新开了一家猫咖，猫咪超级萌，要不要去撸猫放松一下？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-147-1.wav"}}, {"session_id": "109", "turn_idx": 2, "from": "Human", "text": "听起来不错！对了，能切换成蜡笔小新的声音吗？我想回忆童年。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-147-2.wav"}}, {"session_id": "109", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "109", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "109", "turn_idx": 5, "from": "Assistant", "text": "嘿嘿，现在我是小新啦！姐姐，猫咖里还有好吃的布丁哦，去不去嘛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-147-5.wav"}}, {"session_id": "109", "turn_idx": 6, "from": "Human", "text": "哈哈，太逗了！布丁我喜欢。那推荐个口味呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-147-6.wav"}}, {"session_id": "109", "turn_idx": 7, "from": "Assistant", "text": "焦糖布丁最赞，甜甜的还不腻，配上猫咪玩耍，简直完美！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-147-7.wav"}}, {"session_id": "109", "turn_idx": 8, "from": "Human", "text": "嗯，换回你原来的男声音吧，听久了有点出戏。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-147-8.wav"}}, {"session_id": "109", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "36", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-148-0.wav"}}, {"session_id": "36", "turn_idx": 1, "from": "Assistant", "text": "嘿，今天天气不错，阳光明媚，温度大概26度，挺适合出门的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-148-1.wav"}}, {"session_id": "36", "turn_idx": 2, "from": "Human", "text": "那太好了，我打算去超市买点东西。你有什么推荐买的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-148-2.wav"}}, {"session_id": "36", "turn_idx": 3, "from": "Assistant", "text": "嗯，可以买点水果，比如苹果或香蕉，新鲜又健康。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-148-3.wav"}}, {"session_id": "36", "turn_idx": 4, "from": "Human", "text": "好主意！对了，你能用个小女孩的声音跟我说话吗？感觉更可爱点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-148-4.wav"}}, {"session_id": "36", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"用个小女孩的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"用个小女孩的声音\"}"}, {"session_id": "36", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "36", "turn_idx": 7, "from": "Assistant", "text": "嘻嘻，好的呀！超市里还有好多零食，像巧克力饼干，可好吃啦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-148-7.wav"}}, {"session_id": "36", "turn_idx": 8, "from": "Human", "text": "哈哈，太可爱了！那小女孩的声音讲个童话故事呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-148-8.wav"}}, {"session_id": "36", "turn_idx": 9, "from": "Assistant", "text": "没问题！从前有只小兔子，它跳来跳去找到了一个大萝卜，开心得蹦蹦跳跳！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-148-9.wav"}}, {"session_id": "36", "turn_idx": 10, "from": "Human", "text": "真有趣！不过现在我想听点正经的，换回你原来的男音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-148-10.wav"}}, {"session_id": "36", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"换回默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"换回默认男音色\"}"}] +[{"session_id": "106", "turn_idx": 0, "from": "Human", "text": "嘿小跃，起床了吗？帮我查查今天公交卡余额吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-149-0.wav"}}, {"session_id": "106", "turn_idx": 1, "from": "Assistant", "text": "早啊！我刚启动呢。你的公交卡还有32块5，够用一天啦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-149-1.wav"}}, {"session_id": "106", "turn_idx": 2, "from": "Human", "text": "谢啦！对了，你能换个搞笑相声演员的声音吗？比如马三立那种，听着乐呵。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-149-2.wav"}}, {"session_id": "106", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"搞笑相声演员的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"搞笑相声演员的声音\"}"}, {"session_id": "106", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "106", "turn_idx": 5, "from": "Assistant", "text": "现在用相声腔儿了！您这公交卡余额充足，出门别忘带口罩，安全第一哈。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-149-5.wav"}}, {"session_id": "106", "turn_idx": 6, "from": "Human", "text": "哈哈，太逗了！这声音听着就想笑。中午想吃火锅，附近哪家好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-149-6.wav"}}, {"session_id": "106", "turn_idx": 7, "from": "Assistant", "text": "推荐“辣翻天”火锅店，新开的，评分4.8星，离你家才500米。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-149-7.wav"}}, {"session_id": "106", "turn_idx": 8, "from": "Human", "text": "听着馋了。还是换回你原来的声音吧，习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-149-8.wav"}}, {"session_id": "106", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "84", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-150-0.wav"}}, {"session_id": "84", "turn_idx": 1, "from": "Assistant", "text": "哎呀，今天可以去公园溜达溜圈，或者在家追个剧，最近那个新出的喜剧片挺逗的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-150-1.wav"}}, {"session_id": "84", "turn_idx": 2, "from": "Human", "text": "听起来不错。对了，你能换个声音吗？切换成孙悟空那种调皮猴子的音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-150-2.wav"}}, {"session_id": "84", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"孙悟空那种调皮猴子的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"孙悟空那种调皮猴子的音色\"}"}] +[{"session_id": "47", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-151-0.wav"}}, {"session_id": "47", "turn_idx": 1, "from": "Assistant", "text": "嗨！刚看到个新出的搞笑短视频，超解压的，要不要分享给你？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-151-1.wav"}}, {"session_id": "47", "turn_idx": 2, "from": "Human", "text": "好啊，发来看看！对了，切换成蜡笔小新的声音呗，感觉会更有趣。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-151-2.wav"}}, {"session_id": "47", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}] +[{"session_id": "44", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天有啥好玩的事吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-152-0.wav"}}, {"session_id": "44", "turn_idx": 1, "from": "Assistant", "text": "嗨！今天有场线上音乐会直播，要不要一起看？我帮你查下时间。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-152-1.wav"}}, {"session_id": "44", "turn_idx": 2, "from": "Human", "text": "好呀！不过，先切换成卡通片里那种萌萌的小动物声音呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-152-2.wav"}}, {"session_id": "44", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"卡通片里萌萌的小动物声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"卡通片里萌萌的小动物声音\"}"}, {"session_id": "44", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "44", "turn_idx": 5, "from": "Assistant", "text": "嘿嘿，我是小跃，现在声音可爱吧？音乐会晚上8点开始，我提醒你！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-152-5.wav"}}, {"session_id": "44", "turn_idx": 6, "from": "Human", "text": "太萌了，像小兔子一样！对了，帮我设个闹钟吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-152-6.wav"}}, {"session_id": "44", "turn_idx": 7, "from": "Assistant", "text": "没问题，设了7点半的闹钟，提前准备。需要加个备注吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-152-7.wav"}}, {"session_id": "44", "turn_idx": 8, "from": "Human", "text": "嗯，备注“别迟到”。不过，还是换回你最开始的声音吧，听着习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-152-8.wav"}}, {"session_id": "44", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "9", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天午饭吃了啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-153-0.wav"}}, {"session_id": "9", "turn_idx": 1, "from": "Assistant", "text": "我还没吃呢，正想着点外卖，你有推荐的吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-153-1.wav"}}, {"session_id": "9", "turn_idx": 2, "from": "Human", "text": "我点了麻辣烫，超好吃！对了，你能换个音色不？换一个萌萌的小奶狗音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-153-2.wav"}}, {"session_id": "9", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"萌萌的小奶狗音\"}", "value": "function \ntimbre_rag \n{\"query\": \"萌萌的小奶狗音\"}"}, {"session_id": "9", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "9", "turn_idx": 5, "from": "Assistant", "text": "好啦，音色变萌啦！麻辣烫听着就香，我也点一份去～", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-153-5.wav"}}, {"session_id": "9", "turn_idx": 6, "from": "Human", "text": "哈哈，这个声音真逗。不过还是原来的听着顺耳，换回默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-153-6.wav"}}, {"session_id": "9", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "57", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-154-0.wav"}}, {"session_id": "57", "turn_idx": 1, "from": "Assistant", "text": "嘿嘿，我在后台待命呢！今天想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-154-1.wav"}}, {"session_id": "57", "turn_idx": 2, "from": "Human", "text": "没啥，就问问你晚上吃啥好。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-154-2.wav"}}, {"session_id": "57", "turn_idx": 3, "from": "Assistant", "text": "推荐吃火锅吧，热乎乎的适合晚上！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-154-3.wav"}}, {"session_id": "57", "turn_idx": 4, "from": "Human", "text": "好主意。对了，你能切换成蜡笔小新的音色吗？听起来逗趣点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-154-4.wav"}}, {"session_id": "57", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}"}] +[{"session_id": "87", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-155-0.wav"}}, {"session_id": "87", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天有啥新鲜事儿想聊聊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-155-1.wav"}}, {"session_id": "87", "turn_idx": 2, "from": "Human", "text": "帮我查下待会儿的天气呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-155-2.wav"}}, {"session_id": "87", "turn_idx": 3, "from": "Assistant", "text": "好嘞，稍等……今天下午多云，温度25度左右，出门记得带伞哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-155-3.wav"}}, {"session_id": "87", "turn_idx": 4, "from": "Human", "text": "切换成海绵宝宝的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-155-4.wav"}}, {"session_id": "87", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}"}] +[{"session_id": "18", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的事推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-156-0.wav"}}, {"session_id": "18", "turn_idx": 1, "from": "Assistant", "text": "哈喽！我刚看到附近新开了一家猫咖，据说猫咪超可爱，可以去撸猫放松一下。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-156-1.wav"}}, {"session_id": "18", "turn_idx": 2, "from": "Human", "text": "哇，猫咖听着不错！对了，你能换个动漫人物的声音吗？比如那种热血少年的感觉。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-156-2.wav"}}, {"session_id": "18", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动漫热血少年音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"动漫热血少年音色\"}"}, {"session_id": "18", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "18", "turn_idx": 5, "from": "Assistant", "text": "现在声音够燃吧？就像动漫里主角喊口号一样，超带劲！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-156-5.wav"}}, {"session_id": "18", "turn_idx": 6, "from": "Human", "text": "哈哈，真像！感觉在看番剧了。对了，帮我查下猫咖的营业时间呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-156-6.wav"}}, {"session_id": "18", "turn_idx": 7, "from": "Assistant", "text": "没问题！那家店早上10点开门，晚上8点关门，周末人可能多，建议早点去。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-156-7.wav"}}, {"session_id": "18", "turn_idx": 8, "from": "Human", "text": "谢啦！现在换回原来的女音色吧，听着习惯些。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-156-8.wav"}}, {"session_id": "18", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "83", "turn_idx": 0, "from": "Human", "text": "小跃，今天中午吃什么好呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-157-0.wav"}}, {"session_id": "83", "turn_idx": 1, "from": "Assistant", "text": "推荐你试试附近的日式拉面店，评分很高，汤底很浓郁。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-157-1.wav"}}, {"session_id": "83", "turn_idx": 2, "from": "Human", "text": "听起来不错！对了，切换成魔法少女小樱的配音音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-157-2.wav"}}, {"session_id": "83", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"魔法少女小樱的配音音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"魔法少女小樱的配音音色\"}"}, {"session_id": "83", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "83", "turn_idx": 5, "from": "Assistant", "text": "好呀！拉面配上温泉蛋，超治愈的哦～要记得加叉烧！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-157-5.wav"}}, {"session_id": "83", "turn_idx": 6, "from": "Human", "text": "哈哈，声音好可爱！那小樱，周末有什么好玩的推荐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-157-6.wav"}}, {"session_id": "83", "turn_idx": 7, "from": "Assistant", "text": "可以去游乐园坐摩天轮，或者DIY手工坊做小饰品，超有趣的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-157-7.wav"}}, {"session_id": "83", "turn_idx": 8, "from": "Human", "text": "嗯，主意不错。不过，切换回默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-157-8.wav"}}, {"session_id": "83", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "104", "turn_idx": 0, "from": "Human", "text": "嗨小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-158-0.wav"}}, {"session_id": "104", "turn_idx": 1, "from": "Assistant", "text": "主人，我在待命呢！今天想聊点啥？无聊的话，我可以讲个笑话哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-158-1.wav"}}, {"session_id": "104", "turn_idx": 2, "from": "Human", "text": "哈哈，好啊，讲一个听听。不过先换个成熟男声吧，感觉更有磁性。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-158-2.wav"}}, {"session_id": "104", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"成熟男声\"}", "value": "function \ntimbre_rag \n{\"query\": \"成熟男声\"}"}, {"session_id": "104", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "104", "turn_idx": 5, "from": "Assistant", "text": "好的，现在用成熟男声来一段。为什么数学书总在哭？因为它有太多问题啦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-158-5.wav"}}, {"session_id": "104", "turn_idx": 6, "from": "Human", "text": "哈哈，笑死！这声音配笑话还挺搭。再聊点别的，比如你最喜欢的电影？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-158-6.wav"}}, {"session_id": "104", "turn_idx": 7, "from": "Assistant", "text": "我超爱《阿凡达》，特效超震撼。主人你呢，最近看啥好片没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-158-7.wav"}}, {"session_id": "104", "turn_idx": 8, "from": "Human", "text": "还没呢。对了，还是换回你原来的声音吧，小跃，习惯了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-158-8.wav"}}, {"session_id": "104", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "87", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-159-0.wav"}}, {"session_id": "87", "turn_idx": 1, "from": "Assistant", "text": "在呢！今天有啥新鲜事儿想聊聊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-159-1.wav"}}, {"session_id": "87", "turn_idx": 2, "from": "Human", "text": "帮我查下待会儿的天气呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-159-2.wav"}}, {"session_id": "87", "turn_idx": 3, "from": "Assistant", "text": "好嘞，稍等……今天下午多云，温度25度左右，出门记得带伞哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-159-3.wav"}}, {"session_id": "87", "turn_idx": 4, "from": "Human", "text": "切换成海绵宝宝的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-159-4.wav"}}, {"session_id": "87", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝的声音\"}"}, {"session_id": "87", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "87", "turn_idx": 7, "from": "Assistant", "text": "哈哈哈，我是海绵宝宝！要不要听个海底笑话？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-159-7.wav"}}, {"session_id": "87", "turn_idx": 8, "from": "Human", "text": "算啦，换回你原来的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-159-8.wav"}}, {"session_id": "87", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "120", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-160-0.wav"}}, {"session_id": "120", "turn_idx": 1, "from": "Assistant", "text": "可以去公园散步，或者看个喜剧片放松一下！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-160-1.wav"}}, {"session_id": "120", "turn_idx": 2, "from": "Human", "text": "嗯，喜剧片不错。最近有啥新片？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-160-2.wav"}}, {"session_id": "120", "turn_idx": 3, "from": "Assistant", "text": "《疯狂元素城》挺火的，动画片，笑点多。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-160-3.wav"}}, {"session_id": "120", "turn_idx": 4, "from": "Human", "text": "哦，听起来有趣。小跃，切换成蜡笔小新的音色吧！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-160-4.wav"}}, {"session_id": "120", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}"}, {"session_id": "120", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "120", "turn_idx": 7, "from": "Assistant", "text": "嘿，我是小新哦！你想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-160-7.wav"}}, {"session_id": "120", "turn_idx": 8, "from": "Human", "text": "哈哈，太像了！那学小新说句话听听。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-160-8.wav"}}, {"session_id": "120", "turn_idx": 9, "from": "Assistant", "text": "动感超人，出击！怎么样，像吧？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-160-9.wav"}}, {"session_id": "120", "turn_idx": 10, "from": "Human", "text": "绝了！换回你原来的女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-160-10.wav"}}, {"session_id": "120", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "34", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的事推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-161-0.wav"}}, {"session_id": "34", "turn_idx": 1, "from": "Assistant", "text": "嘿！可以去海边走走，浪不大，适合发呆。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-161-1.wav"}}, {"session_id": "34", "turn_idx": 2, "from": "Human", "text": "听起来不错。对了，换一个低沉大叔的音色，像电影旁白那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-161-2.wav"}}, {"session_id": "34", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"低沉大叔的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"低沉大叔的音色\"}"}] +[{"session_id": "41", "turn_idx": 0, "from": "Human", "text": "小跃，今天中午吃啥好呢？我有点纠结。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-162-0.wav"}}, {"session_id": "41", "turn_idx": 1, "from": "Assistant", "text": "哈哈，纠结啥？试试那家新开的川菜馆吧，听说水煮鱼超赞。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-162-1.wav"}}, {"session_id": "41", "turn_idx": 2, "from": "Human", "text": "川菜啊，我怕辣。算了，换个话题，你能用个慵懒的大叔音说话吗？这样听着放松点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-162-2.wav"}}, {"session_id": "41", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"慵懒大叔音\"}", "value": "function \ntimbre_rag \n{\"query\": \"慵懒大叔音\"}"}] +[{"session_id": "100", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-163-0.wav"}}, {"session_id": "100", "turn_idx": 1, "from": "Assistant", "text": "嘿嘿，可以去看看新开的那个猫咖，超多可爱猫咪，还能撸猫呢！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-163-1.wav"}}, {"session_id": "100", "turn_idx": 2, "from": "Human", "text": "真的吗？我超爱猫的！对了，你能换个声音不？我想听个萌萌的娃娃音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-163-2.wav"}}, {"session_id": "100", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"萌萌的娃娃音\"}", "value": "function \ntimbre_rag \n{\"query\": \"萌萌的娃娃音\"}"}] +[{"session_id": "125", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天心情咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-164-0.wav"}}, {"session_id": "125", "turn_idx": 1, "from": "Assistant", "text": "嗨，心情超好！刚听完一首新歌，你呢？有啥新鲜事？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-164-1.wav"}}, {"session_id": "125", "turn_idx": 2, "from": "Human", "text": "没啥特别的，就是工作有点累，想找点乐子。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-164-2.wav"}}, {"session_id": "125", "turn_idx": 3, "from": "Assistant", "text": "哎呀，那得放松一下！要不要聊聊周末计划？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-164-3.wav"}}, {"session_id": "125", "turn_idx": 4, "from": "Human", "text": "好主意！小跃，你能不能切换成海绵宝宝的声音？听着就开心。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-164-4.wav"}}, {"session_id": "125", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"海绵宝宝\"}", "value": "function \ntimbre_rag \n{\"query\": \"海绵宝宝\"}"}] +[{"session_id": "1", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的事推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-165-0.wav"}}, {"session_id": "1", "turn_idx": 1, "from": "Assistant", "text": "哎呀，今天新开了个奶茶店，听说他们的珍珠奶茶超赞，要不要去试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-165-1.wav"}}, {"session_id": "1", "turn_idx": 2, "from": "Human", "text": "奶茶啊，听着不错。对了，你能切换成动漫里热血主角的声音吗？像鸣人那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-165-2.wav"}}, {"session_id": "1", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动漫里热血主角的声音，像鸣人那种\"}", "value": "function \ntimbre_rag \n{\"query\": \"动漫里热血主角的声音，像鸣人那种\"}"}, {"session_id": "1", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "1", "turn_idx": 5, "from": "Assistant", "text": "好的，现在用新声音说话！去喝奶茶的话，我还能帮你查查优惠券呢，超燃的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-165-5.wav"}}, {"session_id": "1", "turn_idx": 6, "from": "Human", "text": "哈哈，这声音带感！帮我看看下午的优惠时间呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-165-6.wav"}}, {"session_id": "1", "turn_idx": 7, "from": "Assistant", "text": "没问题，下午三点到五点有买一送一，咱们赶紧去吧！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-165-7.wav"}}, {"session_id": "1", "turn_idx": 8, "from": "Human", "text": "等等，还是换回你原来的声音吧，感觉习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-165-8.wav"}}, {"session_id": "1", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"换回你原来的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"换回你原来的声音\"}"}] +[{"session_id": "112", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气咋样？我懒得看天气预报。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-0.wav"}}, {"session_id": "112", "turn_idx": 1, "from": "Assistant", "text": "今天阳光明媚，温度刚好，适合出去走走哦！你要出门吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-1.wav"}}, {"session_id": "112", "turn_idx": 2, "from": "Human", "text": "嗯，可能去公园溜达一圈。对了，你平时喜欢干啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-2.wav"}}, {"session_id": "112", "turn_idx": 3, "from": "Assistant", "text": "作为AI，我就在这儿陪你聊天，或者帮你查信息。需要我推荐公园的好去处吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-3.wav"}}, {"session_id": "112", "turn_idx": 4, "from": "Human", "text": "好啊，你说说附近有啥好玩的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-4.wav"}}, {"session_id": "112", "turn_idx": 5, "from": "Assistant", "text": "附近有个湖滨公园，可以划船或者野餐，人也不多，挺清静的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-5.wav"}}, {"session_id": "112", "turn_idx": 6, "from": "Human", "text": "听起来不错！对了，你能换个声音不？我想听电影解说员的那种音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-6.wav"}}, {"session_id": "112", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"电影解说员的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"电影解说员的音色\"}"}, {"session_id": "112", "turn_idx": 8, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "112", "turn_idx": 9, "from": "Assistant", "text": "现在声音像在解说大片吧？哈哈，继续说公园，那里还有个小吃街呢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-9.wav"}}, {"session_id": "112", "turn_idx": 10, "from": "Human", "text": "哇，这声音真带感！小吃街有啥好吃的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-10.wav"}}, {"session_id": "112", "turn_idx": 11, "from": "Assistant", "text": "有烧烤、糖葫芦，还有本地特色煎饼，我猜你会喜欢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-11.wav"}}, {"session_id": "112", "turn_idx": 12, "from": "Human", "text": "馋死我了！对了，换回你最开始的声音吧，那个听着习惯。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-166-12.wav"}}, {"session_id": "112", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "2", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气不错，我们聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-167-0.wav"}}, {"session_id": "2", "turn_idx": 1, "from": "Assistant", "text": "好啊！我刚查了天气预报，下午可能会下雨，记得带伞哦。你周末有啥计划？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-167-1.wav"}}, {"session_id": "2", "turn_idx": 2, "from": "Human", "text": "没啥特别的，可能在家看电影。对了，你能推荐一部喜剧片吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-167-2.wav"}}, {"session_id": "2", "turn_idx": 3, "from": "Assistant", "text": "当然！《疯狂动物城》挺搞笑的，主角朱迪超可爱，剧情也轻松。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-167-3.wav"}}, {"session_id": "2", "turn_idx": 4, "from": "Human", "text": "换一个萌萌的小猫音色吧，听起来更治愈。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-167-4.wav"}}, {"session_id": "2", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"萌萌的小猫音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"萌萌的小猫音色\"}"}] +[{"session_id": "75", "turn_idx": 0, "from": "Human", "text": "嗨，小跃！今天天气真不错啊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-0.wav"}}, {"session_id": "75", "turn_idx": 1, "from": "Assistant", "text": "是啊，阳光暖暖的，感觉心情都变好了！你有啥计划吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-1.wav"}}, {"session_id": "75", "turn_idx": 2, "from": "Human", "text": "打算下午去逛街，买点新衣服。你觉得穿啥风格好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-2.wav"}}, {"session_id": "75", "turn_idx": 3, "from": "Assistant", "text": "哈哈，休闲风吧，舒服又百搭。最近流行宽松卫衣哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-3.wav"}}, {"session_id": "75", "turn_idx": 4, "from": "Human", "text": "好主意！对了，小跃，你的声音能换个可爱点的吗？比如换一个萌系少女音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-4.wav"}}, {"session_id": "75", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"萌系少女音\"}", "value": "function \ntimbre_rag \n{\"query\": \"萌系少女音\"}"}, {"session_id": "75", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "75", "turn_idx": 7, "from": "Assistant", "text": "嘻嘻，现在我是萌妹子啦！怎么样，新声音可爱不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-7.wav"}}, {"session_id": "75", "turn_idx": 8, "from": "Human", "text": "哇，超可爱！像个动漫里的角色，听着就开心。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-8.wav"}}, {"session_id": "75", "turn_idx": 9, "from": "Assistant", "text": "谢谢夸奖呀！那咱们继续聊逛街？你准备去哪儿逛？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-9.wav"}}, {"session_id": "75", "turn_idx": 10, "from": "Human", "text": "想去市中心那家新开的商场，听说打折呢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-10.wav"}}, {"session_id": "75", "turn_idx": 11, "from": "Assistant", "text": "太棒了！记得早点去，人少还容易挑到好货。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-11.wav"}}, {"session_id": "75", "turn_idx": 12, "from": "Human", "text": "嗯嗯，听你的。对了，小跃，还是换回你原来的声音吧，我习惯那个了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-168-12.wav"}}, {"session_id": "75", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "72", "turn_idx": 0, "from": "Human", "text": "小跃，今天心情咋样啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-169-0.wav"}}, {"session_id": "72", "turn_idx": 1, "from": "Assistant", "text": "心情不错呢！刚听完一首新歌，感觉整个人都轻松了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-169-1.wav"}}, {"session_id": "72", "turn_idx": 2, "from": "Human", "text": "哦？啥歌啊，推荐给我听听呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-169-2.wav"}}, {"session_id": "72", "turn_idx": 3, "from": "Assistant", "text": "是周深的《大鱼》，旋律特别美，你肯定会喜欢的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-169-3.wav"}}, {"session_id": "72", "turn_idx": 4, "from": "Human", "text": "嗯，听起来不错。对了，小跃，切换成张韶涵的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-169-4.wav"}}, {"session_id": "72", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"张韶涵的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"张韶涵的声音\"}"}] +[{"session_id": "96", "turn_idx": 0, "from": "Human", "text": "早上好，小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-170-0.wav"}}, {"session_id": "96", "turn_idx": 1, "from": "Assistant", "text": "早上好啊！今天阳光明媚，温度刚好，适合出门走走哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-170-1.wav"}}, {"session_id": "96", "turn_idx": 2, "from": "Human", "text": "真的吗？那我想去公园野餐，你觉得带什么吃的比较好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-170-2.wav"}}, {"session_id": "96", "turn_idx": 3, "from": "Assistant", "text": "野餐太棒了！带点三明治、水果和饮料就行，简单又方便。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-170-3.wav"}}, {"session_id": "96", "turn_idx": 4, "from": "Human", "text": "好主意！对了，小跃，你能换个音色吗？我想听一个活泼小男孩的声音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-170-4.wav"}}, {"session_id": "96", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"活泼小男孩的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"活泼小男孩的声音\"}"}, {"session_id": "96", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "96", "turn_idx": 7, "from": "Assistant", "text": "哈哈，现在是我小男孩的声音啦！野餐时记得带个毯子，坐着更舒服！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-170-7.wav"}}, {"session_id": "96", "turn_idx": 8, "from": "Human", "text": "哇，这声音真有趣！听起来像个小调皮鬼。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-170-8.wav"}}, {"session_id": "96", "turn_idx": 9, "from": "Assistant", "text": "嘿嘿，我就是调皮鬼小跃！公园里还可以玩飞盘，超好玩的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-170-9.wav"}}, {"session_id": "96", "turn_idx": 10, "from": "Human", "text": "嗯，飞盘不错。但等等，你能再换回原来的声音吗？我听着更习惯。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-170-10.wav"}}, {"session_id": "96", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "69", "turn_idx": 0, "from": "Human", "text": "嘿小跃，起床没？今天天气咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-171-0.wav"}}, {"session_id": "69", "turn_idx": 1, "from": "Assistant", "text": "早啊！我刚查了，晴天，最高25度，舒服得很。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-171-1.wav"}}, {"session_id": "69", "turn_idx": 2, "from": "Human", "text": "那不错，下午有啥计划推荐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-171-2.wav"}}, {"session_id": "69", "turn_idx": 3, "from": "Assistant", "text": "可以去湖边溜达，或者逛新开的咖啡店，听说甜品超赞。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-171-3.wav"}}, {"session_id": "69", "turn_idx": 4, "from": "Human", "text": "甜品？哈哈，听着就馋。你能换个动画片里小女孩的声音吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-171-4.wav"}}, {"session_id": "69", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片里小女孩的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片里小女孩的声音\"}"}] +[{"session_id": "90", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-172-0.wav"}}, {"session_id": "90", "turn_idx": 1, "from": "Assistant", "text": "嘿！最近新开了个猫咖，超多可爱猫咪，还能撸猫喝咖啡，要不要试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-172-1.wav"}}, {"session_id": "90", "turn_idx": 2, "from": "Human", "text": "哇，听着不错！具体在哪儿啊？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-172-2.wav"}}, {"session_id": "90", "turn_idx": 3, "from": "Assistant", "text": "在市中心步行街二楼，招牌是个大猫爪，挺好找的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-172-3.wav"}}, {"session_id": "90", "turn_idx": 4, "from": "Human", "text": "行，周末去逛逛。对了，能切换成邓紫棋的音色吗？我想听听看。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-172-4.wav"}}, {"session_id": "90", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"邓紫棋的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"邓紫棋的音色\"}"}] +[{"session_id": "43", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-173-0.wav"}}, {"session_id": "43", "turn_idx": 1, "from": "Assistant", "text": "没干嘛呀，随时待命！有啥新鲜事分享吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-173-1.wav"}}, {"session_id": "43", "turn_idx": 2, "from": "Human", "text": "刚吃了碗牛肉面，超满足！你能换个小朋友的声音说话吗？听着更可爱。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-173-2.wav"}}, {"session_id": "43", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"小朋友的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"小朋友的声音\"}"}, {"session_id": "43", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "43", "turn_idx": 5, "from": "Assistant", "text": "好呀好呀！牛肉面听起来香喷喷的，我也想吃！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-173-5.wav"}}, {"session_id": "43", "turn_idx": 6, "from": "Human", "text": "哈哈，太萌了！不过还是换回你原来的声音吧，习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-173-6.wav"}}, {"session_id": "43", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "35", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-174-0.wav"}}, {"session_id": "35", "turn_idx": 1, "from": "Assistant", "text": "哎呀，我刚更新完天气数据，今天阳光超好，适合出去溜达！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-174-1.wav"}}, {"session_id": "35", "turn_idx": 2, "from": "Human", "text": "真的吗？那推荐个地方呗，我宅家好几天了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-174-2.wav"}}, {"session_id": "35", "turn_idx": 3, "from": "Assistant", "text": "去河边公园呗，花开得正艳，还能看人放风筝，可热闹了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-174-3.wav"}}, {"session_id": "35", "turn_idx": 4, "from": "Human", "text": "风筝啊？我小时候最爱玩了。对了，你能换一个元气少女的声音吗？听着更有活力！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-174-4.wav"}}, {"session_id": "35", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"元气少女声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"元气少女声音\"}"}] +[{"session_id": "30", "turn_idx": 0, "from": "Human", "text": "嗨，小跃，今天过得咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-175-0.wav"}}, {"session_id": "30", "turn_idx": 1, "from": "Assistant", "text": "嗨，我还不错呢，刚处理完几个提醒。你今天有啥计划没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-175-1.wav"}}, {"session_id": "30", "turn_idx": 2, "from": "Human", "text": "没啥特别的，就窝在家里刷剧。对了，晚饭吃啥好？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-175-2.wav"}}, {"session_id": "30", "turn_idx": 3, "from": "Assistant", "text": "刷剧配点小吃最爽了！推荐你点个披萨或者煮碗面，简单又解馋。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-175-3.wav"}}, {"session_id": "30", "turn_idx": 4, "from": "Human", "text": "好主意！不过小跃，你能换个音色吗？来个元气满满的动漫少女音。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-175-4.wav"}}, {"session_id": "30", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"元气满满的动漫少女音\"}", "value": "function \ntimbre_rag \n{\"query\": \"元气满满的动漫少女音\"}"}] +[{"session_id": "2", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气不错，我们聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-176-0.wav"}}, {"session_id": "2", "turn_idx": 1, "from": "Assistant", "text": "好啊！我刚查了天气预报，下午可能会下雨，记得带伞哦。你周末有啥计划？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-176-1.wav"}}, {"session_id": "2", "turn_idx": 2, "from": "Human", "text": "没啥特别的，可能在家看电影。对了，你能推荐一部喜剧片吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-176-2.wav"}}, {"session_id": "2", "turn_idx": 3, "from": "Assistant", "text": "当然！《疯狂动物城》挺搞笑的，主角朱迪超可爱，剧情也轻松。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-176-3.wav"}}, {"session_id": "2", "turn_idx": 4, "from": "Human", "text": "换一个萌萌的小猫音色吧，听起来更治愈。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-176-4.wav"}}, {"session_id": "2", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"萌萌的小猫音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"萌萌的小猫音色\"}"}, {"session_id": "2", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "2", "turn_idx": 7, "from": "Assistant", "text": "喵~现在我的声音是不是更软萌了？《疯狂动物城》里还有只叫尼克的狐狸，超逗的！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-176-7.wav"}}, {"session_id": "2", "turn_idx": 8, "from": "Human", "text": "哈哈，这个声音好可爱！再推荐一部类似的动画片呗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-176-8.wav"}}, {"session_id": "2", "turn_idx": 9, "from": "Assistant", "text": "喵~试试《寻梦环游记》，音乐超棒，讲家庭和梦想的故事，保证让你笑中带泪。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-176-9.wav"}}, {"session_id": "2", "turn_idx": 10, "from": "Human", "text": "嗯，听起来不错。对了，小跃，换回你原来的声音吧，我习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-176-10.wav"}}, {"session_id": "2", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag\n {\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag\n {\"query\": \"默认男音色\"}"}] +[{"session_id": "98", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-177-0.wav"}}, {"session_id": "98", "turn_idx": 1, "from": "Assistant", "text": "嘿，我挺好的！刚更新了天气数据，今天阳光超赞，适合出去浪。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-177-1.wav"}}, {"session_id": "98", "turn_idx": 2, "from": "Human", "text": "真的吗？那推荐个地方吃午饭呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-177-2.wav"}}, {"session_id": "98", "turn_idx": 3, "from": "Assistant", "text": "附近有家新开的拉面店，评分很高，汤头超鲜，值得一试哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-177-3.wav"}}, {"session_id": "98", "turn_idx": 4, "from": "Human", "text": "拉面啊，听着不错。对了，切换成蜡笔小新的声音吧，想逗逗乐。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-177-4.wav"}}, {"session_id": "98", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}, {"session_id": "98", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "98", "turn_idx": 7, "from": "Assistant", "text": "好耶！现在用蜡笔小新的声音说话啦～拉面店在商场二楼，超多肉肉哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-177-7.wav"}}, {"session_id": "98", "turn_idx": 8, "from": "Human", "text": "哈哈，太搞笑了！那家店叫啥名字来着？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-177-8.wav"}}, {"session_id": "98", "turn_idx": 9, "from": "Assistant", "text": "叫“豚骨王”，招牌是猪骨汤面，配菜超多，记得点份煎饺。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-177-9.wav"}}, {"session_id": "98", "turn_idx": 10, "from": "Human", "text": "记下了。现在切换回默认女音色吧，还是习惯你原来的调调。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-177-10.wav"}}, {"session_id": "98", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "8", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气真不错，你觉得呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-178-0.wav"}}, {"session_id": "8", "turn_idx": 1, "from": "Assistant", "text": "是啊，阳光暖暖的，适合出门走走。你打算去哪儿玩吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-178-1.wav"}}, {"session_id": "8", "turn_idx": 2, "from": "Human", "text": "我想去海边溜达溜达。对了，你能切换成一个可爱的萝莉音吗？感觉更萌一点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-178-2.wav"}}, {"session_id": "8", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"可爱的萝莉音\"}", "value": "function \ntimbre_rag \n{\"query\": \"可爱的萝莉音\"}"}, {"session_id": "8", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "8", "turn_idx": 5, "from": "Assistant", "text": "好啦，现在用可爱萝莉音说话！海边超棒的，记得涂防晒哦。你带零食了吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-178-5.wav"}}, {"session_id": "8", "turn_idx": 6, "from": "Human", "text": "带了点水果和薯片。这个声音挺有趣，但听久了有点腻，还是换回你原来的默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-178-6.wav"}}, {"session_id": "8", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "112", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天天气咋样？我懒得看天气预报。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-0.wav"}}, {"session_id": "112", "turn_idx": 1, "from": "Assistant", "text": "今天阳光明媚，温度刚好，适合出去走走哦！你要出门吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-1.wav"}}, {"session_id": "112", "turn_idx": 2, "from": "Human", "text": "嗯，可能去公园溜达一圈。对了，你平时喜欢干啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-2.wav"}}, {"session_id": "112", "turn_idx": 3, "from": "Assistant", "text": "作为AI，我就在这儿陪你聊天，或者帮你查信息。需要我推荐公园的好去处吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-3.wav"}}, {"session_id": "112", "turn_idx": 4, "from": "Human", "text": "好啊，你说说附近有啥好玩的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-4.wav"}}, {"session_id": "112", "turn_idx": 5, "from": "Assistant", "text": "附近有个湖滨公园，可以划船或者野餐，人也不多，挺清静的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-5.wav"}}, {"session_id": "112", "turn_idx": 6, "from": "Human", "text": "听起来不错！对了，你能换个声音不？我想听电影解说员的那种音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-6.wav"}}, {"session_id": "112", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"电影解说员的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"电影解说员的音色\"}"}, {"session_id": "112", "turn_idx": 8, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "112", "turn_idx": 9, "from": "Assistant", "text": "现在声音像在解说大片吧？哈哈，继续说公园，那里还有个小吃街呢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-9.wav"}}, {"session_id": "112", "turn_idx": 10, "from": "Human", "text": "哇，这声音真带感！小吃街有啥好吃的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-10.wav"}}, {"session_id": "112", "turn_idx": 11, "from": "Assistant", "text": "有烧烤、糖葫芦，还有本地特色煎饼，我猜你会喜欢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-11.wav"}}, {"session_id": "112", "turn_idx": 12, "from": "Human", "text": "馋死我了！对了，换回你最开始的声音吧，那个听着习惯。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-179-12.wav"}}, {"session_id": "112", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "27", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天天气咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-180-0.wav"}}, {"session_id": "27", "turn_idx": 1, "from": "Assistant", "text": "早上好！今天阳光明媚，适合出去溜达一圈。你打算干啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-180-1.wav"}}, {"session_id": "27", "turn_idx": 2, "from": "Human", "text": "我想去逛街买点东西。对了，你能切换成动画片里那种小动物的声音吗？听着可爱点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-180-2.wav"}}, {"session_id": "27", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"动画片小动物的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"动画片小动物的声音\"}"}] +[{"session_id": "59", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-181-0.wav"}}, {"session_id": "59", "turn_idx": 1, "from": "Assistant", "text": "嗨！我刚在更新系统，现在闲着。你今天有啥安排不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-181-1.wav"}}, {"session_id": "59", "turn_idx": 2, "from": "Human", "text": "打算做点家务，但提不起劲。你能推荐个轻松的音乐吗？对了，切换成慵懒的爵士女声音吧，听着放松。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-181-2.wav"}}, {"session_id": "59", "turn_idx": 3, "from": "Human", "text": "对了，切换成慵懒的爵士女声音吧，听着放松。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-181-3.wav"}}, {"session_id": "59", "turn_idx": 4, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"慵懒的爵士女声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"慵懒的爵士女声音\"}"}, {"session_id": "59", "turn_idx": 5, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "59", "turn_idx": 6, "from": "Assistant", "text": "好啦，音色换好了。推荐你听 Norah Jones 的专辑，超治愈，做家务也不累。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-181-6.wav"}}, {"session_id": "59", "turn_idx": 7, "from": "Human", "text": "哇，这个声音真带感！谢谢推荐，我这就去试试。现在换回你平常的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-181-7.wav"}}, {"session_id": "59", "turn_idx": 8, "from": "Human", "text": "现在换回你平常的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-181-8.wav"}}, {"session_id": "59", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "54", "turn_idx": 0, "from": "Human", "text": "嗨小跃，早上好！今天有啥新鲜事没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-182-0.wav"}}, {"session_id": "54", "turn_idx": 1, "from": "Assistant", "text": "早上好！今天可热闹了，热搜上说有个新电影上映，评分超高，要一起聊聊吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-182-1.wav"}}, {"session_id": "54", "turn_idx": 2, "from": "Human", "text": "好啊，啥电影？我最近剧荒了。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-182-2.wav"}}, {"session_id": "54", "turn_idx": 3, "from": "Assistant", "text": "叫《星际冒险》，科幻片，讲一群人在太空探险的故事。听说特效超酷！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-182-3.wav"}}, {"session_id": "54", "turn_idx": 4, "from": "Human", "text": "听起来不错。小跃，你能换成卡通片里那种搞怪外星人的声音吗？感觉更应景。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-182-4.wav"}}, {"session_id": "54", "turn_idx": 5, "from": "Assistant", "text": "function\ntimbre_rag\n{\"query\": \"卡通片里搞怪外星人的声音\"}", "value": "function\ntimbre_rag\n{\"query\": \"卡通片里搞怪外星人的声音\"}"}] +[{"session_id": "34", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的事推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-183-0.wav"}}, {"session_id": "34", "turn_idx": 1, "from": "Assistant", "text": "嘿！可以去海边走走，浪不大，适合发呆。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-183-1.wav"}}, {"session_id": "34", "turn_idx": 2, "from": "Human", "text": "听起来不错。对了，换一个低沉大叔的音色，像电影旁白那种。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-183-2.wav"}}, {"session_id": "34", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"低沉大叔的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"低沉大叔的音色\"}"}] +[{"session_id": "39", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天有啥好玩的事不？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-184-0.wav"}}, {"session_id": "39", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我刚看了个搞笑视频，一只猫跳舞超逗的！推荐你也看看。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-184-1.wav"}}, {"session_id": "39", "turn_idx": 2, "from": "Human", "text": "哎，你能换成一个温柔的女声吗？听着舒服点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-184-2.wav"}}, {"session_id": "39", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"温柔的女声\"}", "value": "function \ntimbre_rag \n{\"query\": \"温柔的女声\"}"}, {"session_id": "39", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "39", "turn_idx": 5, "from": "Assistant", "text": "好啦，声音变温柔啦！还想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-184-5.wav"}}, {"session_id": "39", "turn_idx": 6, "from": "Human", "text": "嗯，这声音不错！晚饭吃啥好呢？推荐个菜呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-184-6.wav"}}, {"session_id": "39", "turn_idx": 7, "from": "Assistant", "text": "试试番茄炒蛋吧，简单又下饭，我超爱的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-184-7.wav"}}, {"session_id": "39", "turn_idx": 8, "from": "Human", "text": "听着就馋了。对了，还是换回你最初的男音吧，习惯那个。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-184-8.wav"}}, {"session_id": "39", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "101", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的新闻吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-185-0.wav"}}, {"session_id": "101", "turn_idx": 1, "from": "Assistant", "text": "嘿！听说新开了家网红奶茶店，排队超长的，你要不要去试试？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-185-1.wav"}}, {"session_id": "101", "turn_idx": 2, "from": "Human", "text": "奶茶啊，我最近在减肥呢。对了，你能切换成蜡笔小新的声音吗？感觉那样聊天更逗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-185-2.wav"}}, {"session_id": "101", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的声音\"}"}] +[{"session_id": "4", "turn_idx": 0, "from": "Human", "text": "小跃，今天好无聊啊，陪我聊聊天呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-186-0.wav"}}, {"session_id": "4", "turn_idx": 1, "from": "Assistant", "text": "没问题！聊聊啥？我刚查了新闻，说周末有流星雨，要不要去看？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-186-1.wav"}}, {"session_id": "4", "turn_idx": 2, "from": "Human", "text": "真的吗？那太棒了！不过先等等，我想换个声音，切换成刘德华的音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-186-2.wav"}}, {"session_id": "4", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"刘德华的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"刘德华的音色\"}"}, {"session_id": "4", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "4", "turn_idx": 5, "from": "Assistant", "text": "好嘞，现在我是刘德华的声音啦！流星雨在郊区看最清楚，要不要我帮你查路线？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-186-5.wav"}}, {"session_id": "4", "turn_idx": 6, "from": "Human", "text": "哇，这声音真带感！帮我找找附近露营地点呗。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-186-6.wav"}}, {"session_id": "4", "turn_idx": 7, "from": "Assistant", "text": "查到了，城东有个露营地，评价超好，晚上还能烧烤。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-186-7.wav"}}, {"session_id": "4", "turn_idx": 8, "from": "Human", "text": "烧烤？听着就馋了。对了，还是觉得你原来的声音习惯点，换回默认男音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-186-8.wav"}}, {"session_id": "4", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "60", "turn_idx": 0, "from": "Human", "text": "嗨小跃，你今天过得怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-187-0.wav"}}, {"session_id": "60", "turn_idx": 1, "from": "Assistant", "text": "嗨，我今天挺开心的！刚刚帮朋友查了天气预报，你呢？在忙啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-187-1.wav"}}, {"session_id": "60", "turn_idx": 2, "from": "Human", "text": "我正做饭呢，想煮个面条。对了，你能换个声音不？切换成卡通片里那种小猫咪的音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-187-2.wav"}}, {"session_id": "60", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"卡通片里小猫咪的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"卡通片里小猫咪的音色\"}"}, {"session_id": "60", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "60", "turn_idx": 5, "from": "Assistant", "text": "喵～现在我是可爱小猫咪音色啦！煮面条加什么料？番茄鸡蛋超赞哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-187-5.wav"}}, {"session_id": "60", "turn_idx": 6, "from": "Human", "text": "哈哈，真逗！我加了辣椒。那你能再换个名人的声音吗？比如切换成刘德华的音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-187-6.wav"}}, {"session_id": "60", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"刘德华的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"刘德华的音色\"}"}] +[{"session_id": "43", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-188-0.wav"}}, {"session_id": "43", "turn_idx": 1, "from": "Assistant", "text": "没干嘛呀，随时待命！有啥新鲜事分享吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-188-1.wav"}}, {"session_id": "43", "turn_idx": 2, "from": "Human", "text": "刚吃了碗牛肉面，超满足！你能换个小朋友的声音说话吗？听着更可爱。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-188-2.wav"}}, {"session_id": "43", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"小朋友的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"小朋友的声音\"}"}, {"session_id": "43", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "43", "turn_idx": 5, "from": "Assistant", "text": "好呀好呀！牛肉面听起来香喷喷的，我也想吃！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-188-5.wav"}}, {"session_id": "43", "turn_idx": 6, "from": "Human", "text": "哈哈，太萌了！不过还是换回你原来的声音吧，习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-188-6.wav"}}, {"session_id": "43", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "99", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？今天忙不忙？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-189-0.wav"}}, {"session_id": "99", "turn_idx": 1, "from": "Assistant", "text": "在的！我不忙，随时为您服务。您今天有啥计划呀？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-189-1.wav"}}, {"session_id": "99", "turn_idx": 2, "from": "Human", "text": "想去逛街买衣服，但不知道去哪儿好。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-189-2.wav"}}, {"session_id": "99", "turn_idx": 3, "from": "Assistant", "text": "推荐去万达广场，新开了几家潮牌店，款式超多。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-189-3.wav"}}, {"session_id": "99", "turn_idx": 4, "from": "Human", "text": "听起来不错。对了，能换个音色吗？比如换成邓紫棋的声音，感觉会更带感。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-189-4.wav"}}, {"session_id": "99", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"邓紫棋的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"邓紫棋的声音\"}"}, {"session_id": "99", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "99", "turn_idx": 7, "from": "Assistant", "text": "现在音色更新啦！万达那边还有咖啡馆，逛累了可以休息。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-189-7.wav"}}, {"session_id": "99", "turn_idx": 8, "from": "Human", "text": "哇，声音真有范儿！咖啡馆推荐哪家？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-189-8.wav"}}, {"session_id": "99", "turn_idx": 9, "from": "Assistant", "text": "星巴克的冷萃咖啡超赞，或者试试本地的小鹿咖啡，抹茶拿铁是招牌。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-189-9.wav"}}, {"session_id": "99", "turn_idx": 10, "from": "Human", "text": "抹茶拿铁我爱喝！但你这声音听久了有点累，还是换回默认女音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-189-10.wav"}}, {"session_id": "99", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "107", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-190-0.wav"}}, {"session_id": "107", "turn_idx": 1, "from": "Assistant", "text": "今天大晴天，温度在26度左右，出门散步正合适哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-190-1.wav"}}, {"session_id": "107", "turn_idx": 2, "from": "Human", "text": "真不错！小跃，你能换一个慵懒的猫咪音色吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-190-2.wav"}}, {"session_id": "107", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"慵懒的猫咪音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"慵懒的猫咪音色\"}"}] +[{"session_id": "3", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-191-0.wav"}}, {"session_id": "3", "turn_idx": 1, "from": "Assistant", "text": "还不错呀，就是有点困，昨晚追剧太晚了。你呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-191-1.wav"}}, {"session_id": "3", "turn_idx": 2, "from": "Human", "text": "我也是，早上差点迟到。对了，晚上有啥电影推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-191-2.wav"}}, {"session_id": "3", "turn_idx": 3, "from": "Assistant", "text": "最近新上映的科幻片评价不错，可以去看看哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-191-3.wav"}}, {"session_id": "3", "turn_idx": 4, "from": "Human", "text": "小跃，切换成一个搞笑的东北大叔音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-191-4.wav"}}, {"session_id": "3", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"搞笑的东北大叔音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"搞笑的东北大叔音色\"}"}] +[{"session_id": "3", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉咋样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-0.wav"}}, {"session_id": "3", "turn_idx": 1, "from": "Assistant", "text": "还不错呀，就是有点困，昨晚追剧太晚了。你呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-1.wav"}}, {"session_id": "3", "turn_idx": 2, "from": "Human", "text": "我也是，早上差点迟到。对了，晚上有啥电影推荐吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-2.wav"}}, {"session_id": "3", "turn_idx": 3, "from": "Assistant", "text": "最近新上映的科幻片评价不错，可以去看看哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-3.wav"}}, {"session_id": "3", "turn_idx": 4, "from": "Human", "text": "小跃，切换成一个搞笑的东北大叔音色吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-4.wav"}}, {"session_id": "3", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"搞笑的东北大叔音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"搞笑的东北大叔音色\"}"}, {"session_id": "3", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "3", "turn_idx": 7, "from": "Assistant", "text": "哎呦，现在俺这口音带劲不？那电影可逗了，包你笑出眼泪！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-7.wav"}}, {"session_id": "3", "turn_idx": 8, "from": "Human", "text": "哈哈，太逗了！具体讲啥的？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-8.wav"}}, {"session_id": "3", "turn_idx": 9, "from": "Assistant", "text": "就一帮外星人闯地球，闹出一堆乌龙，特接地气儿！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-9.wav"}}, {"session_id": "3", "turn_idx": 10, "from": "Human", "text": "听着挺有意思。对了，周末有啥活动推荐？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-10.wav"}}, {"session_id": "3", "turn_idx": 11, "from": "Assistant", "text": "可以去公园野餐啊，天气这么好，整点烧烤啥的多美！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-11.wav"}}, {"session_id": "3", "turn_idx": 12, "from": "Human", "text": "还是换回原来的声音吧，听着习惯点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-192-12.wav"}}, {"session_id": "3", "turn_idx": 13, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "102", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气真不错，适合出门走走啊！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-193-0.wav"}}, {"session_id": "102", "turn_idx": 1, "from": "Assistant", "text": "是啊，阳光明媚的，去公园溜达溜达最舒服了。你要去哪儿玩？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-193-1.wav"}}, {"session_id": "102", "turn_idx": 2, "from": "Human", "text": "可能去逛街吧。对了，你能切换成蜡笔小新的音色吗？想听你搞笑一下。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-193-2.wav"}}, {"session_id": "102", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"切换成蜡笔小新的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"切换成蜡笔小新的音色\"}"}, {"session_id": "102", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "102", "turn_idx": 5, "from": "Assistant", "text": "哎呀，大姐姐，逛街要买好多零食哦，小跃陪你聊天！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-193-5.wav"}}, {"session_id": "102", "turn_idx": 6, "from": "Human", "text": "哈哈，太逗了！但聊正经事时，还是换回你原来的声音吧，用默认女音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-193-6.wav"}}, {"session_id": "102", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "29", "turn_idx": 0, "from": "Human", "text": "小跃，在吗？今天有啥新鲜事？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-194-0.wav"}}, {"session_id": "29", "turn_idx": 1, "from": "Assistant", "text": "嘿，在呢！刚看到楼下新开了家奶茶店，排队好长，感觉挺火的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-194-1.wav"}}, {"session_id": "29", "turn_idx": 2, "from": "Human", "text": "哇，奶茶！我最爱喝了。对了，你能换个动漫角色的声音吗？比如路飞的。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-194-2.wav"}}, {"session_id": "29", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"路飞音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"路飞音色\"}"}] +[{"session_id": "57", "turn_idx": 0, "from": "Human", "text": "小跃，在干嘛呢？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-195-0.wav"}}, {"session_id": "57", "turn_idx": 1, "from": "Assistant", "text": "嘿嘿，我在后台待命呢！今天想聊点啥？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-195-1.wav"}}, {"session_id": "57", "turn_idx": 2, "from": "Human", "text": "没啥，就问问你晚上吃啥好。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-195-2.wav"}}, {"session_id": "57", "turn_idx": 3, "from": "Assistant", "text": "推荐吃火锅吧，热乎乎的适合晚上！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-195-3.wav"}}, {"session_id": "57", "turn_idx": 4, "from": "Human", "text": "好主意。对了，你能切换成蜡笔小新的音色吗？听起来逗趣点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-195-4.wav"}}, {"session_id": "57", "turn_idx": 5, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"蜡笔小新的音色\"}"}, {"session_id": "57", "turn_idx": 6, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "57", "turn_idx": 7, "from": "Assistant", "text": "动感光波！嘿嘿，现在声音好玩吧？火锅加辣才过瘾哦！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-195-7.wav"}}, {"session_id": "57", "turn_idx": 8, "from": "Human", "text": "哈哈，太搞笑了！那配什么饮料？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-195-8.wav"}}, {"session_id": "57", "turn_idx": 9, "from": "Assistant", "text": "冰可乐绝配，解辣又爽口。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-195-9.wav"}}, {"session_id": "57", "turn_idx": 10, "from": "Human", "text": "嗯。还是换回默认男音色吧，感觉自然些。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-195-10.wav"}}, {"session_id": "57", "turn_idx": 11, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "70", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的事没？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-196-0.wav"}}, {"session_id": "70", "turn_idx": 1, "from": "Assistant", "text": "哈哈，我刚学会一个新笑话:为什么手机从不吃饭？因为它已经饱了！", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-196-1.wav"}}, {"session_id": "70", "turn_idx": 2, "from": "Human", "text": "哈哈哈，冷死了。对了，你能切换成蜡笔小新的声音吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-196-2.wav"}}, {"session_id": "70", "turn_idx": 3, "from": "Assistant", "text": "function\ntimbre_rag\n{\"query\": \"蜡笔小新的声音\"}", "value": "function\ntimbre_rag\n{\"query\": \"蜡笔小新的声音\"}"}] +[{"session_id": "5", "turn_idx": 0, "from": "Human", "text": "小跃，今天有啥好玩的推荐没？在家好无聊啊。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-197-0.wav"}}, {"session_id": "5", "turn_idx": 1, "from": "Assistant", "text": "哈哈，无聊啥呀！要不我们一起听听新歌？或者玩个小游戏？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-197-1.wav"}}, {"session_id": "5", "turn_idx": 2, "from": "Human", "text": "哎，换个小男孩的声音吧，听起来更有趣点。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-197-2.wav"}}, {"session_id": "5", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"小男孩的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"小男孩的声音\"}"}, {"session_id": "5", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "5", "turn_idx": 5, "from": "Assistant", "text": "嘿嘿，现在声音咋样？是不是像个小兄弟在聊天？咱们继续，我推荐个搞笑视频看看？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-197-5.wav"}}, {"session_id": "5", "turn_idx": 6, "from": "Human", "text": "哈哈，确实逗！视频啥内容？快说说。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-197-6.wav"}}, {"session_id": "5", "turn_idx": 7, "from": "Assistant", "text": "是关于一只猫追自己尾巴的，超级萌！看完保证你笑出声。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-197-7.wav"}}, {"session_id": "5", "turn_idx": 8, "from": "Human", "text": "还是换回原来的声音吧，习惯了你的默认男音色。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-197-8.wav"}}, {"session_id": "5", "turn_idx": 9, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认男音色\"}"}] +[{"session_id": "11", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-198-0.wav"}}, {"session_id": "11", "turn_idx": 1, "from": "Assistant", "text": "今天天气超棒的，阳光灿烂，适合出门散步呢。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-198-1.wav"}}, {"session_id": "11", "turn_idx": 2, "from": "Human", "text": "太好了，我正好想去公园。对了，小跃，你能切换成一个卡通人物的声音吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-198-2.wav"}}, {"session_id": "11", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"卡通人物的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"卡通人物的声音\"}"}, {"session_id": "11", "turn_idx": 4, "from": "Input", "text": "你的音色已经更新完成，请继续对话。", "value": "你的音色已经更新完成，请继续对话。"}, {"session_id": "11", "turn_idx": 5, "from": "Assistant", "text": "嘻嘻，现在我是可爱卡通音啦！公园散步记得带水哦。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-198-5.wav"}}, {"session_id": "11", "turn_idx": 6, "from": "Human", "text": "哈哈，好玩！但这样说话怪怪的，还是换回你原来的声音吧。", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-198-6.wav"}}, {"session_id": "11", "turn_idx": 7, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}", "value": "function \ntimbre_rag \n{\"query\": \"默认女音色\"}"}] +[{"session_id": "80", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天天气真不错，对吧？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-199-0.wav"}}, {"session_id": "80", "turn_idx": 1, "from": "Assistant", "text": "是呀，阳光明媚的，适合出去走走呢！你有什么计划吗？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-199-1.wav"}}, {"session_id": "80", "turn_idx": 2, "from": "Human", "text": "我想去逛逛街，买点东西。哦对了，能不能切换成卡通片里唐老鸭的声音？", "value": {"type": "wav", "value": "wav/timbre_positive.jsonl-199-2.wav"}}, {"session_id": "80", "turn_idx": 3, "from": "Assistant", "text": "function \ntimbre_rag \n{\"query\": \"卡通片里唐老鸭的声音\"}", "value": "function \ntimbre_rag \n{\"query\": \"卡通片里唐老鸭的声音\"}"}] diff --git a/tools/StepEval-Audio-Toolcall/wav.tar b/tools/StepEval-Audio-Toolcall/wav.tar new file mode 100644 index 0000000000000000000000000000000000000000..d3db431c726a2b3be63e5697d3a381eabb1de21b --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/wav.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa1d2f2aed3ad141bdc0f41ea4732597af51db9126755c300bc9d1a8ea9fa16a +size 1164544000 diff --git a/tools/StepEval-Audio-Toolcall/web_search_negative.jsonl b/tools/StepEval-Audio-Toolcall/web_search_negative.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d82198f5d04727f23a5edde0e19c4b6cbc4f7b04 --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/web_search_negative.jsonl @@ -0,0 +1,199 @@ +[{"session_id": "584", "turn_idx": 0, "from": "Human", "text": "小跃，我手机旧了，想换台新的，安卓系统。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-0-0.wav"}}, {"session_id": "584", "turn_idx": 1, "from": "Assistant", "text": "预算多少？华为或小米的中端机性价比高。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-0-1.wav"}}] +[{"session_id": "423", "turn_idx": 0, "from": "Human", "text": "小跃，我和两个室友合租，房租每月四千五百元，水电费平均每月六百元。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-1-0.wav"}}, {"session_id": "423", "turn_idx": 1, "from": "Assistant", "text": "合租能省点钱，费用要公平分担。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-1-1.wav"}}, {"session_id": "423", "turn_idx": 2, "from": "Human", "text": "没错，我们三人平分，帮我算算每人每月付多少钱。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-1-2.wav"}}, {"session_id": "423", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(4500 + 600) / 3\"}", "value": "function\ncalculate\n{\"expression\": \"(4500 + 600) / 3\"}"}] +[{"session_id": "875", "turn_idx": 0, "from": "Human", "text": "小跃，成都最近温差大不大？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-2-0.wav"}}, {"session_id": "875", "turn_idx": 1, "from": "Assistant", "text": "早晚凉中午热呢，需要查具体温度吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-2-1.wav"}}] +[{"session_id": "199", "turn_idx": 0, "from": "Human", "text": "小跃，我有点无聊，聊聊天吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-3-0.wav"}}, {"session_id": "199", "turn_idx": 1, "from": "Assistant", "text": "好啊，今天过得怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-3-1.wav"}}, {"session_id": "199", "turn_idx": 2, "from": "Human", "text": "还行。用更幽默的语气回我一句。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-3-2.wav"}}, {"session_id": "199", "turn_idx": 3, "from": "Assistant", "text": "哈哈，生活就像天气，阴晴不定，但总有笑点！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-3-3.wav"}}] +[{"session_id": "691", "turn_idx": 0, "from": "Human", "text": "小跃，我今天想尝试做顿大餐，但不知道从哪开始。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-4-0.wav"}}, {"session_id": "691", "turn_idx": 1, "from": "Assistant", "text": "听起来有趣！你想做中餐还是西餐？我可以给点建议。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-4-1.wav"}}, {"session_id": "691", "turn_idx": 2, "from": "Human", "text": "中餐吧，比如家常菜，但我厨艺一般，怕搞砸。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-4-2.wav"}}, {"session_id": "691", "turn_idx": 3, "from": "Assistant", "text": "别担心，家常菜简单又美味。先从炒菜开始，比如西红柿炒鸡蛋。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-4-3.wav"}}] +[{"session_id": "314", "turn_idx": 0, "from": "Human", "text": "帮我规划房贷，贷款八十万，年利率百分之四点九。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-5-0.wav"}}, {"session_id": "314", "turn_idx": 1, "from": "Assistant", "text": "贷款期限呢？等额本息还款吧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-5-1.wav"}}, {"session_id": "314", "turn_idx": 2, "from": "Human", "text": "二十五年期。对，等额本息，算月供。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-5-2.wav"}}, {"session_id": "314", "turn_idx": 3, "from": "Assistant", "text": "公式稍复杂，需要准确计算吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-5-3.wav"}}, {"session_id": "314", "turn_idx": 4, "from": "Human", "text": "要精确结果，保留两位小数。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-5-4.wav"}}, {"session_id": "314", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"round(800000 * 0.049/12 * (1+0.049/12)**(25*12) / ((1+0.049/12)**(25*12)-1), 2)\"}", "value": "function\ncalculate\n{\"expression\": \"round(800000 * 0.049/12 * (1+0.049/12)**(25*12) / ((1+0.049/12)**(25*12)-1), 2)\"}"}] +[{"session_id": "211", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在存钱买房。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-6-0.wav"}}, {"session_id": "211", "turn_idx": 1, "from": "Assistant", "text": "加油哦！每个月存多少呢？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-6-1.wav"}}, {"session_id": "211", "turn_idx": 2, "from": "Human", "text": "我每个月存三千元，已经存了五年，总共存了多少钱？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-6-2.wav"}}, {"session_id": "211", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"3000 * 60\"}", "value": "function\ncalculate\n{\"expression\": \"3000 * 60\"}"}] +[{"session_id": "658", "turn_idx": 0, "from": "Human", "text": "小跃，我想种点蔬菜在阳台，简单点的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-7-0.wav"}}, {"session_id": "658", "turn_idx": 1, "from": "Assistant", "text": "阳台种菜很实用！推荐番茄或生菜。你阳台阳光足吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-7-1.wav"}}] +[{"session_id": "931", "turn_idx": 0, "from": "Human", "text": "小跃，听起来更兴奋点，告诉我周末活动。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-8-0.wav"}}, {"session_id": "931", "turn_idx": 1, "from": "Assistant", "text": "哇，周末有音乐节哦，现场超热闹，乐队表演超精彩，一定要去嗨一下！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-8-1.wav"}}, {"session_id": "931", "turn_idx": 2, "from": "Human", "text": "太棒了，具体几点开始？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-8-2.wav"}}, {"session_id": "931", "turn_idx": 3, "from": "Assistant", "text": "下午两点开场，一直嗨到晚上十点，记得早点去抢好位置。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-8-3.wav"}}] +[{"session_id": "249", "turn_idx": 0, "from": "Human", "text": "小跃，下午想散步。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-9-0.wav"}}, {"session_id": "249", "turn_idx": 1, "from": "Assistant", "text": "散步对身体好，推荐去厦门海边。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-9-1.wav"}}, {"session_id": "249", "turn_idx": 2, "from": "Human", "text": "厦门风景美，但海风大吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-9-2.wav"}}, {"session_id": "249", "turn_idx": 3, "from": "Assistant", "text": "风大时带外套，重点看天气。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-9-3.wav"}}, {"session_id": "249", "turn_idx": 4, "from": "Human", "text": "万一变天就不好玩了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-9-4.wav"}}, {"session_id": "249", "turn_idx": 5, "from": "Assistant", "text": "是的，天气变化快。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-9-5.wav"}}] +[{"session_id": "623", "turn_idx": 0, "from": "Human", "text": "小跃，晚上在家无聊，干点啥？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-10-0.wav"}}, {"session_id": "623", "turn_idx": 1, "from": "Assistant", "text": "可以看部电影或做点手工，打发时间不错。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-10-1.wav"}}, {"session_id": "623", "turn_idx": 2, "from": "Human", "text": "请用更活泼的语气给建议。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-10-2.wav"}}, {"session_id": "623", "turn_idx": 3, "from": "Assistant", "text": "嘿！看部喜剧片，笑翻天！或者DIY小手工，超有成就感！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-10-3.wav"}}, {"session_id": "623", "turn_idx": 4, "from": "Human", "text": "音量调高点，我离得远。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-10-4.wav"}}, {"session_id": "623", "turn_idx": 5, "from": "Assistant", "text": "好的，我大声说！看喜剧片笑翻天，或DIY手工超有成就感！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-10-5.wav"}}, {"session_id": "623", "turn_idx": 6, "from": "Human", "text": "现在用温和的语气提醒我休息。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-10-6.wav"}}, {"session_id": "623", "turn_idx": 7, "from": "Assistant", "text": "别忘了中途休息，保护眼睛，别太累。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-10-7.wav"}}] +[{"session_id": "868", "turn_idx": 0, "from": "Human", "text": "小跃，这个周末计划去西安旅游，看兵马俑。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-11-0.wav"}}, {"session_id": "868", "turn_idx": 1, "from": "Assistant", "text": "西安历史感强，玩得尽兴！天气信息查了吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-11-1.wav"}}] +[{"session_id": "294", "turn_idx": 0, "from": "Human", "text": "小跃，我投资了一笔钱，想看看收益。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-12-0.wav"}}, {"session_id": "294", "turn_idx": 1, "from": "Assistant", "text": "主人，请告诉我本金、年利率和投资年数。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-12-1.wav"}}, {"session_id": "294", "turn_idx": 2, "from": "Human", "text": "本金是十万块，年利率百分之六点五，存五年。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-12-2.wav"}}, {"session_id": "294", "turn_idx": 3, "from": "Assistant", "text": "这是复利计算，利息每年结算一次。您需要最终金额吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-12-3.wav"}}, {"session_id": "294", "turn_idx": 4, "from": "Human", "text": "对，帮我算出五年后连本带利能拿回多少钱。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-12-4.wav"}}, {"session_id": "294", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"100000 * (1 + 0.065) ** 5\"}", "value": "function\ncalculate\n{\"expression\": \"100000 * (1 + 0.065) ** 5\"}"}] +[{"session_id": "937", "turn_idx": 0, "from": "Human", "text": "小跃，今天下午武汉有场球赛，我想去看。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-13-0.wav"}}, {"session_id": "937", "turn_idx": 1, "from": "Assistant", "text": "球赛气氛肯定嗨！建议你早点去占座，带点零食。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-13-1.wav"}}] +[{"session_id": "832", "turn_idx": 0, "from": "Human", "text": "小跃，算一下十加十五等于多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-14-0.wav"}}, {"session_id": "832", "turn_idx": 1, "from": "Assistant", "text": "十加十五等于二十五，很简单吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-14-1.wav"}}, {"session_id": "832", "turn_idx": 2, "from": "Human", "text": "嗯，小跃，请小声点回答，我在开会。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-14-2.wav"}}, {"session_id": "832", "turn_idx": 3, "from": "Assistant", "text": "好的，小声说:二十五，别担心，不会打扰到你。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-14-3.wav"}}] +[{"session_id": "297", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我周末买牛奶。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-0.wav"}}, {"session_id": "297", "turn_idx": 1, "from": "Assistant", "text": "好的，周末买牛奶提醒已添加。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-1.wav"}}, {"session_id": "297", "turn_idx": 2, "from": "Human", "text": "请说得更欢快一点，我期待购物。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-2.wav"}}, {"session_id": "297", "turn_idx": 3, "from": "Assistant", "text": "耶！周末牛奶大采购，新鲜牛奶等着你，开心购物吧！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-3.wav"}}, {"session_id": "297", "turn_idx": 4, "from": "Human", "text": "再加点鸡蛋提醒。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-4.wav"}}, {"session_id": "297", "turn_idx": 5, "from": "Assistant", "text": "鸡蛋提醒也搞定，购物清单齐全啦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-5.wav"}}, {"session_id": "297", "turn_idx": 6, "from": "Human", "text": "请把音量调低点，邻居在睡觉。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-6.wav"}}, {"session_id": "297", "turn_idx": 7, "from": "Assistant", "text": "好的，小声说:提醒都设好了，周末愉快购物。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-7.wav"}}, {"session_id": "297", "turn_idx": 8, "from": "Human", "text": "谢啦，小跃。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-8.wav"}}, {"session_id": "297", "turn_idx": 9, "from": "Assistant", "text": "不客气，随时帮您记事儿。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-15-9.wav"}}] +[{"session_id": "168", "turn_idx": 0, "from": "Human", "text": "小跃，周末去哪玩好？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-16-0.wav"}}, {"session_id": "168", "turn_idx": 1, "from": "Assistant", "text": "推荐去海边散步，天气不错。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-16-1.wav"}}, {"session_id": "168", "turn_idx": 2, "from": "Human", "text": "哦，行。但说得更激动点！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-16-2.wav"}}, {"session_id": "168", "turn_idx": 3, "from": "Assistant", "text": "太兴奋了！海边超美，浪花超治愈！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-16-3.wav"}}, {"session_id": "168", "turn_idx": 4, "from": "Human", "text": "哈哈，好。具体海滩推荐？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-16-4.wav"}}, {"session_id": "168", "turn_idx": 5, "from": "Assistant", "text": "试试阳光海滩，人少景美。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-16-5.wav"}}, {"session_id": "168", "turn_idx": 6, "from": "Human", "text": "需要带什么？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-16-6.wav"}}, {"session_id": "168", "turn_idx": 7, "from": "Assistant", "text": "带防晒霜和水，玩得开心！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-16-7.wav"}}] +[{"session_id": "532", "turn_idx": 0, "from": "Human", "text": "小跃，我在读一本书，总共有三百页。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-17-0.wav"}}, {"session_id": "532", "turn_idx": 1, "from": "Assistant", "text": "读了多少页了？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-17-1.wav"}}, {"session_id": "532", "turn_idx": 2, "from": "Human", "text": "已经读了八十页。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-17-2.wav"}}, {"session_id": "532", "turn_idx": 3, "from": "Assistant", "text": "每天平均读几页？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-17-3.wav"}}, {"session_id": "532", "turn_idx": 4, "from": "Human", "text": "差不多十页。帮我算算还要多少天才能读完。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-17-4.wav"}}, {"session_id": "532", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(300 - 80) / 10\"}", "value": "function\ncalculate\n{\"expression\": \"(300 - 80) / 10\"}"}] +[{"session_id": "477", "turn_idx": 0, "from": "Human", "text": "小跃，今天工作累了，想出去走走。南京有啥推荐？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-18-0.wav"}}, {"session_id": "477", "turn_idx": 1, "from": "Assistant", "text": "南京夫子庙不错，小吃多。你住附近？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-18-1.wav"}}, {"session_id": "477", "turn_idx": 2, "from": "Human", "text": "对，我在南京上班。就怕下雨影响出行。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-18-2.wav"}}, {"session_id": "477", "turn_idx": 3, "from": "Assistant", "text": "那提前看天气吧，避免淋雨。需要我帮忙？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-18-3.wav"}}] +[{"session_id": "469", "turn_idx": 0, "from": "Human", "text": "小跃，今天感觉好累，工作压力大。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-19-0.wav"}}, {"session_id": "469", "turn_idx": 1, "from": "Assistant", "text": "别太辛苦，注意休息。有什么烦心事吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-19-1.wav"}}, {"session_id": "469", "turn_idx": 2, "from": "Human", "text": "我在想怎么存点钱，但每月开支不少。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-19-2.wav"}}, {"session_id": "469", "turn_idx": 3, "from": "Assistant", "text": "存钱确实需要规划。你每月收入大概多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-19-3.wav"}}, {"session_id": "469", "turn_idx": 4, "from": "Human", "text": "工资一万两千块，但房租四千，生活费五千。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-19-4.wav"}}, {"session_id": "469", "turn_idx": 5, "from": "Assistant", "text": "那每月能存三千块左右呢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-19-5.wav"}}, {"session_id": "469", "turn_idx": 6, "from": "Human", "text": "帮我算算一年下来能存多少钱。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-19-6.wav"}}, {"session_id": "469", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(12000 - 4000 - 5000) * 12\"}", "value": "function\ncalculate\n{\"expression\": \"(12000 - 4000 - 5000) * 12\"}"}] +[{"session_id": "465", "turn_idx": 0, "from": "Human", "text": "小跃，播放一首轻松的音乐。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-0.wav"}}, {"session_id": "465", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐，希望您放松一下。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-1.wav"}}, {"session_id": "465", "turn_idx": 2, "from": "Human", "text": "不错，但说话时加点幽默感吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-2.wav"}}, {"session_id": "465", "turn_idx": 3, "from": "Assistant", "text": "哈哈，好的！音乐响起来，您的心情要飞上天咯，需要我讲个笑话助兴吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-3.wav"}}, {"session_id": "465", "turn_idx": 4, "from": "Human", "text": "不用了，音量再大点，我听不清。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-4.wav"}}, {"session_id": "465", "turn_idx": 5, "from": "Assistant", "text": "音量调大了，现在音乐更带劲了吧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-5.wav"}}, {"session_id": "465", "turn_idx": 6, "from": "Human", "text": "对，谢啦。帮我记个提醒:明天买牛奶。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-6.wav"}}, {"session_id": "465", "turn_idx": 7, "from": "Assistant", "text": "提醒已设好，明天记得买牛奶，别饿着肚子哦！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-7.wav"}}, {"session_id": "465", "turn_idx": 8, "from": "Human", "text": "完美，结束吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-8.wav"}}, {"session_id": "465", "turn_idx": 9, "from": "Assistant", "text": "好嘞，下次见！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-20-9.wav"}}] +[{"session_id": "634", "turn_idx": 0, "from": "Human", "text": "小跃，我计划周末去杭州西湖散步。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-21-0.wav"}}, {"session_id": "634", "turn_idx": 1, "from": "Assistant", "text": "西湖风景很美，适合放松。要我帮你找路线吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-21-1.wav"}}] +[{"session_id": "727", "turn_idx": 0, "from": "Human", "text": "小跃，我昨天学习英语两小时四十五分钟，数学一小时三十分钟，科学一小时十五分钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-22-0.wav"}}, {"session_id": "727", "turn_idx": 1, "from": "Assistant", "text": "好认真啊。那总学习时间是多少分钟？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-22-1.wav"}}, {"session_id": "727", "turn_idx": 2, "from": "Human", "text": "帮我算一下总共学了多久。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-22-2.wav"}}, {"session_id": "727", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"165 + 90 + 75\"}", "value": "function\ncalculate\n{\"expression\": \"165 + 90 + 75\"}"}] +[{"session_id": "660", "turn_idx": 0, "from": "Human", "text": "小跃，我下个月计划自驾游去几个城市玩。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-23-0.wav"}}, {"session_id": "660", "turn_idx": 1, "from": "Assistant", "text": "听起来很棒！路线怎么安排的？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-23-1.wav"}}, {"session_id": "660", "turn_idx": 2, "from": "Human", "text": "从起点到第一站五百公里，第二站到第三站三百五十公里，第三站回终点四百八十公里。帮我算总路程是多少公里。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-23-2.wav"}}, {"session_id": "660", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"500 + 350 + 480\"}", "value": "function\ncalculate\n{\"expression\": \"500 + 350 + 480\"}"}] +[{"session_id": "642", "turn_idx": 0, "from": "Human", "text": "小跃，周末我打算去深圳购物，想提前准备一下。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-24-0.wav"}}, {"session_id": "642", "turn_idx": 1, "from": "Assistant", "text": "深圳的商场很热闹，比如万象城，天气好时逛起来更舒服。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-24-1.wav"}}] +[{"session_id": "237", "turn_idx": 0, "from": "Human", "text": "小跃，明天要去武汉出差，有啥注意的？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-25-0.wav"}}, {"session_id": "237", "turn_idx": 1, "from": "Assistant", "text": "武汉热干面必尝，还有东湖挺美的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-25-1.wav"}}, {"session_id": "237", "turn_idx": 2, "from": "Human", "text": "太好了！我住汉口那边。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-25-2.wav"}}, {"session_id": "237", "turn_idx": 3, "from": "Assistant", "text": "那提前查查路线，别迷路。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-25-3.wav"}}] +[{"session_id": "861", "turn_idx": 0, "from": "Human", "text": "小跃，这事儿很重要，请严肃点告诉我。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-26-0.wav"}}, {"session_id": "861", "turn_idx": 1, "from": "Assistant", "text": "明白，我会认真处理。请说具体问题。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-26-1.wav"}}, {"session_id": "861", "turn_idx": 2, "from": "Human", "text": "我的快递丢了，怎么办？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-26-2.wav"}}, {"session_id": "861", "turn_idx": 3, "from": "Assistant", "text": "建议先联系快递公司客服，提供单号查询。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-26-3.wav"}}] +[{"session_id": "345", "turn_idx": 0, "from": "Human", "text": "小跃，家里电费这个月很高，上月是三百五十元。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-27-0.wav"}}, {"session_id": "345", "turn_idx": 1, "from": "Assistant", "text": "可能用电器多了？这个月用了多少度？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-27-1.wav"}}, {"session_id": "345", "turn_idx": 2, "from": "Human", "text": "上个月五百度，这个月六百五十度，电价每度零点六元。 如果阶梯电价，超过六百度部分加价百分之二十，帮我算本月电费。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-27-2.wav"}}, {"session_id": "345", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"600 * 0.6 + (650 - 600) * 0.6 * 1.2\"}", "value": "function\ncalculate\n{\"expression\": \"600 * 0.6 + (650 - 600) * 0.6 * 1.2\"}"}] +[{"session_id": "400", "turn_idx": 0, "from": "Human", "text": "小跃，这周末我打算带家人去杭州西湖玩。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-28-0.wav"}}, {"session_id": "400", "turn_idx": 1, "from": "Assistant", "text": "杭州西湖风景很美啊，适合家庭出游。你们计划玩几天？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-28-1.wav"}}] +[{"session_id": "577", "turn_idx": 0, "from": "Human", "text": "小跃，端午节快到了，有啥传统习俗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-29-0.wav"}}, {"session_id": "577", "turn_idx": 1, "from": "Assistant", "text": "吃粽子、赛龙舟是经典活动，各地还有不同风俗。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-29-1.wav"}}, {"session_id": "577", "turn_idx": 2, "from": "Human", "text": "赛龙舟起源是啥？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-29-2.wav"}}, {"session_id": "577", "turn_idx": 3, "from": "Assistant", "text": "据说纪念屈原，是个历史悠久的仪式。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-29-3.wav"}}] +[{"session_id": "789", "turn_idx": 0, "from": "Human", "text": "小跃，放点音乐听听。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-30-0.wav"}}, {"session_id": "789", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐歌单，希望你喜欢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-30-1.wav"}}, {"session_id": "789", "turn_idx": 2, "from": "Human", "text": "音量稍微调小些，邻居在休息。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-30-2.wav"}}, {"session_id": "789", "turn_idx": 3, "from": "Assistant", "text": "好的，音量调小了，音乐更轻柔了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-30-3.wav"}}, {"session_id": "789", "turn_idx": 4, "from": "Human", "text": "用更热情的语气介绍这首歌。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-30-4.wav"}}, {"session_id": "789", "turn_idx": 5, "from": "Assistant", "text": "太酷了！这首歌是《阳光海岸》，节奏超欢快，让人心情好！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-30-5.wav"}}, {"session_id": "789", "turn_idx": 6, "from": "Human", "text": "谢谢，暂停音乐吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-30-6.wav"}}, {"session_id": "789", "turn_idx": 7, "from": "Assistant", "text": "音乐已暂停，随时再叫我。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-30-7.wav"}}] +[{"session_id": "375", "turn_idx": 0, "from": "Human", "text": "小跃，放点轻松的音乐。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-31-0.wav"}}, {"session_id": "375", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐《月光》。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-31-1.wav"}}, {"session_id": "375", "turn_idx": 2, "from": "Human", "text": "音量有点大，小声点说下歌名。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-31-2.wav"}}, {"session_id": "375", "turn_idx": 3, "from": "Assistant", "text": "小声点:现在播放《月光》。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-31-3.wav"}}, {"session_id": "375", "turn_idx": 4, "from": "Human", "text": "好，再用热情的语气推荐一首。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-31-4.wav"}}, {"session_id": "375", "turn_idx": 5, "from": "Assistant", "text": "热情推荐！试试《夏日微风》，超解压的！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-31-5.wav"}}, {"session_id": "375", "turn_idx": 6, "from": "Human", "text": "就这个吧，谢谢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-31-6.wav"}}, {"session_id": "375", "turn_idx": 7, "from": "Assistant", "text": "不客气，享受音乐吧！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-31-7.wav"}}] +[{"session_id": "385", "turn_idx": 0, "from": "Human", "text": "嗨小跃，我想读点科幻小说，有推荐吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-32-0.wav"}}, {"session_id": "385", "turn_idx": 1, "from": "Assistant", "text": "《三体》系列超棒，中国科幻代表作，或者《沙丘》也行。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-32-1.wav"}}, {"session_id": "385", "turn_idx": 2, "from": "Human", "text": "《沙丘》讲啥的？作者是谁？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-32-2.wav"}}, {"session_id": "385", "turn_idx": 3, "from": "Assistant", "text": "讲沙漠星球的冒险，作者叫弗兰克·赫伯特。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-32-3.wav"}}] +[{"session_id": "55", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点？用平静的语气回答。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-33-0.wav"}}, {"session_id": "55", "turn_idx": 1, "from": "Assistant", "text": "现在是下午四点整，一切都很安稳。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-33-1.wav"}}, {"session_id": "55", "turn_idx": 2, "from": "Human", "text": "音量提高点，我戴耳机没听清。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-33-2.wav"}}, {"session_id": "55", "turn_idx": 3, "from": "Assistant", "text": "好的！现在是下午四点，时间还早呢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-33-3.wav"}}] +[{"session_id": "552", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在健身，想控制饮食。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-34-0.wav"}}, {"session_id": "552", "turn_idx": 1, "from": "Assistant", "text": "没问题，记录卡路里很重要。您今天吃了啥？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-34-1.wav"}}, {"session_id": "552", "turn_idx": 2, "from": "Human", "text": "早餐吃了两个鸡蛋，每个约七十大卡；午餐一碗米饭三百卡，加一份鸡肉二百五十卡；晚餐计划吃沙拉一百二十卡。 帮我算算今天总摄入卡路里？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-34-2.wav"}}, {"session_id": "552", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(2 * 70) + 300 + 250 + 120\"}", "value": "function\ncalculate\n{\"expression\": \"(2 * 70) + 300 + 250 + 120\"}"}] +[{"session_id": "168", "turn_idx": 0, "from": "Human", "text": "小跃，我在计算房贷的月供，头都大了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-35-0.wav"}}, {"session_id": "168", "turn_idx": 1, "from": "Assistant", "text": "主人，别担心。您能提供贷款细节吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-35-1.wav"}}, {"session_id": "168", "turn_idx": 2, "from": "Human", "text": "贷款总额是一百万元，年利率百分之四点九，贷款二十年。帮我算一下每月还款额。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-35-2.wav"}}, {"session_id": "168", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"1000000 * (0.049 / 12) * (1 + 0.049 / 12) ** (20 * 12) / ((1 + 0.049 / 12) ** (20 * 12) - 1)\"}", "value": "function\ncalculate\n{\"expression\": \"1000000 * (0.049 / 12) * (1 + 0.049 / 12) ** (20 * 12) / ((1 + 0.049 / 12) ** (20 * 12) - 1)\"}"}] +[{"session_id": "94", "turn_idx": 0, "from": "Human", "text": "我们公司要办年会，订了十五桌宴席。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-36-0.wav"}}, {"session_id": "94", "turn_idx": 1, "from": "Assistant", "text": "每桌标准预算是多少呢？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-36-1.wav"}}, {"session_id": "94", "turn_idx": 2, "from": "Human", "text": "基础餐一千二百八一桌，加上每桌百分之十服务费。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-36-2.wav"}}, {"session_id": "94", "turn_idx": 3, "from": "Assistant", "text": "酒水另算吗？还是包含在内？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-36-3.wav"}}, {"session_id": "94", "turn_idx": 4, "from": "Human", "text": "酒水预算单独五千块。再算上百分之六的税，总开支是多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-36-4.wav"}}, {"session_id": "94", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(1280 * 15 * 1.1 + 5000) * 1.06\"}", "value": "function\ncalculate\n{\"expression\": \"(1280 * 15 * 1.1 + 5000) * 1.06\"}"}] +[{"session_id": "246", "turn_idx": 0, "from": "Human", "text": "小跃，买房贷款利息有点搞不清", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-37-0.wav"}}, {"session_id": "246", "turn_idx": 1, "from": "Assistant", "text": "告诉我贷款细节", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-37-1.wav"}}, {"session_id": "246", "turn_idx": 2, "from": "Human", "text": "贷款两百万，三十年还清，年利率百分之四点九，等额本息月供多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-37-2.wav"}}, {"session_id": "246", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"2000000*0.049/12*(1+0.049/12)**360/((1+0.049/12)**360-1)\"}", "value": "function\ncalculate\n{\"expression\": \"2000000*0.049/12*(1+0.049/12)**360/((1+0.049/12)**360-1)\"}"}] +[{"session_id": "343", "turn_idx": 0, "from": "Human", "text": "小跃，推荐一首轻松的歌给我吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-38-0.wav"}}, {"session_id": "343", "turn_idx": 1, "from": "Assistant", "text": "没问题，试试《稻香》吧，旋律很治愈。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-38-1.wav"}}, {"session_id": "343", "turn_idx": 2, "from": "Human", "text": "这次用严肃的语气说，我有点困需要提神。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-38-2.wav"}}, {"session_id": "343", "turn_idx": 3, "from": "Assistant", "text": "认真听好:《稻香》节奏明快，它能立刻唤醒你，别打瞌睡了！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-38-3.wav"}}] +[{"session_id": "10", "turn_idx": 0, "from": "Human", "text": "小跃，我明天想去公园野餐，你觉得可行吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-39-0.wav"}}, {"session_id": "10", "turn_idx": 1, "from": "Assistant", "text": "听起来不错呀！不过最好先看看天气情况，你打算去哪儿？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-39-1.wav"}}] +[{"session_id": "492", "turn_idx": 0, "from": "Human", "text": "早啊小跃，今天感觉有点凉飕飕的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-40-0.wav"}}, {"session_id": "492", "turn_idx": 1, "from": "Assistant", "text": "是呀，秋天来了嘛，多穿点衣服哦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-40-1.wav"}}, {"session_id": "492", "turn_idx": 2, "from": "Human", "text": "好主意，明天我约了朋友去广州喝早茶。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-40-2.wav"}}, {"session_id": "492", "turn_idx": 3, "from": "Assistant", "text": "广州的点心超赞，享受美食吧！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-40-3.wav"}}] +[{"session_id": "300", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天感觉怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-41-0.wav"}}, {"session_id": "300", "turn_idx": 1, "from": "Assistant", "text": "我挺精神的，谢谢！你呢？周末有啥计划吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-41-1.wav"}}, {"session_id": "300", "turn_idx": 2, "from": "Human", "text": "我在考虑去北京玩，听说故宫挺不错的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-41-2.wav"}}, {"session_id": "300", "turn_idx": 3, "from": "Assistant", "text": "北京确实好玩！你打算哪天去？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-41-3.wav"}}] +[{"session_id": "633", "turn_idx": 0, "from": "Human", "text": "小跃，下午好！深圳的购物中心听说很棒。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-42-0.wav"}}, {"session_id": "633", "turn_idx": 1, "from": "Assistant", "text": "是的，华强北很热闹。你想去逛逛吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-42-1.wav"}}, {"session_id": "633", "turn_idx": 2, "from": "Human", "text": "对，后天就去。但得先看天气。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-42-2.wav"}}, {"session_id": "633", "turn_idx": 3, "from": "Assistant", "text": "没问题，天气好购物更舒服。需要具体信息吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-42-3.wav"}}] +[{"session_id": "246", "turn_idx": 0, "from": "Human", "text": "小跃，把空调温度调到25度。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-43-0.wav"}}, {"session_id": "246", "turn_idx": 1, "from": "Assistant", "text": "好的，空调已调到25度。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-43-1.wav"}}, {"session_id": "246", "turn_idx": 2, "from": "Human", "text": "音量调高，我听不清楚。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-43-2.wav"}}, {"session_id": "246", "turn_idx": 3, "from": "Assistant", "text": "明白！空调温度设置到25度了，确保你听清！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-43-3.wav"}}] +[{"session_id": "105", "turn_idx": 0, "from": "Human", "text": "我跑步配速是每公里五分半，马拉松四十二公里要多久呀？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-44-0.wav"}}, {"session_id": "105", "turn_idx": 1, "from": "Assistant", "text": "全马距离很考验耐力呢！先把配速换算成分钟每公里。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-44-1.wav"}}, {"session_id": "105", "turn_idx": 2, "from": "Human", "text": "五分半就是五点五分钟对吧？可四十二公里乘起来有小数。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-44-2.wav"}}, {"session_id": "105", "turn_idx": 3, "from": "Assistant", "text": "是的，不过可以精确到分钟，需要转换时间格式吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-44-3.wav"}}, {"session_id": "105", "turn_idx": 4, "from": "Human", "text": "直接算总分钟数就好，我手机能看时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-44-4.wav"}}, {"session_id": "105", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"5.5 * 42\"}", "value": "function\ncalculate\n{\"expression\": \"5.5 * 42\"}"}] +[{"session_id": "910", "turn_idx": 0, "from": "Human", "text": "小跃，我明天要去上海出差，有点紧张。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-45-0.wav"}}, {"session_id": "910", "turn_idx": 1, "from": "Assistant", "text": "别担心，上海很繁华，出差应该会很顺利。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-45-1.wav"}}] +[{"session_id": "722", "turn_idx": 0, "from": "Human", "text": "小跃，今晚无聊，推荐个好看电影吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-46-0.wav"}}, {"session_id": "722", "turn_idx": 1, "from": "Assistant", "text": "最近《流浪地球》很火，科幻大片，特效震撼。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-46-1.wav"}}, {"session_id": "722", "turn_idx": 2, "from": "Human", "text": "科幻片我喜欢。故事讲什么的？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-46-2.wav"}}, {"session_id": "722", "turn_idx": 3, "from": "Assistant", "text": "讲人类带着地球逃离太阳系，寻找新家园的冒险。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-46-3.wav"}}] +[{"session_id": "775", "turn_idx": 0, "from": "Human", "text": "小跃，后天我想在杭州爬山。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-47-0.wav"}}, {"session_id": "775", "turn_idx": 1, "from": "Assistant", "text": "爬山要提前看天气，安全第一。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-47-1.wav"}}] +[{"session_id": "5", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我明天买牛奶。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-48-0.wav"}}, {"session_id": "5", "turn_idx": 1, "from": "Assistant", "text": "好的，明天买牛奶的提醒已经添加了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-48-1.wav"}}, {"session_id": "5", "turn_idx": 2, "from": "Human", "text": "回答时语气正式一点，显得更可靠。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-48-2.wav"}}, {"session_id": "5", "turn_idx": 3, "from": "Assistant", "text": "确认无误，明日购买牛奶的提醒已成功设置，请放心。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-48-3.wav"}}] +[{"session_id": "198", "turn_idx": 0, "from": "Human", "text": "小跃，我想自己做个书架。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-49-0.wav"}}, {"session_id": "198", "turn_idx": 1, "from": "Assistant", "text": "DIY 很有趣！你有什么想法？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-49-1.wav"}}] +[{"session_id": "454", "turn_idx": 0, "from": "Human", "text": "小跃，我开车去旅行，距离是四百五十公里。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-50-0.wav"}}, {"session_id": "454", "turn_idx": 1, "from": "Assistant", "text": "旅途愉快！记得检查车况再出发。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-50-1.wav"}}, {"session_id": "454", "turn_idx": 2, "from": "Human", "text": "平均油耗每百公里八升，油价每升九块五，帮我算算全程油费？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-50-2.wav"}}, {"session_id": "454", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(450 / 100) * 8 * 9.5\"}", "value": "function\ncalculate\n{\"expression\": \"(450 / 100) * 8 * 9.5\"}"}] +[{"session_id": "87", "turn_idx": 0, "from": "Human", "text": "小跃，今天有什么节日吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-51-0.wav"}}, {"session_id": "87", "turn_idx": 1, "from": "Assistant", "text": "今天是国际咖啡日，来杯咖啡提神吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-51-1.wav"}}, {"session_id": "87", "turn_idx": 2, "from": "Human", "text": "音量调低一点，我旁边有人睡觉。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-51-2.wav"}}, {"session_id": "87", "turn_idx": 3, "from": "Assistant", "text": "好的，小声提醒:咖啡日别错过，但别吵醒人哦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-51-3.wav"}}, {"session_id": "87", "turn_idx": 4, "from": "Human", "text": "再问问明天天气。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-51-4.wav"}}, {"session_id": "87", "turn_idx": 5, "from": "Assistant", "text": "小声说，明天多云转晴，温度舒适。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-51-5.wav"}}] +[{"session_id": "592", "turn_idx": 0, "from": "Human", "text": "小跃，家里要换窗帘。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-52-0.wav"}}, {"session_id": "592", "turn_idx": 1, "from": "Assistant", "text": "窗户尺寸是多少呀？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-52-1.wav"}}, {"session_id": "592", "turn_idx": 2, "from": "Human", "text": "客厅宽三米二高两米八，两个卧室都是宽两米五高三米。 按每平米一百一十元算，总费用多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-52-2.wav"}}, {"session_id": "592", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"3.2*2.8*110 + 2*2.5*3*110\"}", "value": "function\ncalculate\n{\"expression\": \"3.2*2.8*110 + 2*2.5*3*110\"}"}] +[{"session_id": "535", "turn_idx": 0, "from": "Human", "text": "小跃，听说成都火锅超好吃，想去尝尝。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-53-0.wav"}}, {"session_id": "535", "turn_idx": 1, "from": "Assistant", "text": "是啊，麻辣味超赞！你啥时候去？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-53-1.wav"}}, {"session_id": "535", "turn_idx": 2, "from": "Human", "text": "后天就飞成都，兴奋死了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-53-2.wav"}}, {"session_id": "535", "turn_idx": 3, "from": "Assistant", "text": "记得带点肠胃药，辣的可能不习惯。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-53-3.wav"}}, {"session_id": "535", "turn_idx": 4, "from": "Human", "text": "哈哈，好建议。成都天气热吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-53-4.wav"}}, {"session_id": "535", "turn_idx": 5, "from": "Assistant", "text": "成都夏天闷热，但最近还好。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-53-5.wav"}}] +[{"session_id": "707", "turn_idx": 0, "from": "Human", "text": "小跃，明天早上七点叫我起床。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-54-0.wav"}}, {"session_id": "707", "turn_idx": 1, "from": "Assistant", "text": "好的，闹钟已设为明天七点。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-54-1.wav"}}, {"session_id": "707", "turn_idx": 2, "from": "Human", "text": "能不能用更严肃的语气提醒我？别让我赖床。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-54-2.wav"}}, {"session_id": "707", "turn_idx": 3, "from": "Assistant", "text": "明白！我会严肃提醒:七点必须起床，不准拖延！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-54-3.wav"}}, {"session_id": "707", "turn_idx": 4, "from": "Human", "text": "哈哈，谢谢。小跃，语速慢点说，我听不清。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-54-4.wav"}}, {"session_id": "707", "turn_idx": 5, "from": "Assistant", "text": "好的，我放慢语速。明早七点，准时叫你起床。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-54-5.wav"}}] +[{"session_id": "686", "turn_idx": 0, "from": "Human", "text": "小跃，我要下载一部电影，大小是六十吉字节。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-55-0.wav"}}, {"session_id": "686", "turn_idx": 1, "from": "Assistant", "text": "网速快吗？每秒多少兆比特？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-55-1.wav"}}, {"session_id": "686", "turn_idx": 2, "from": "Human", "text": "网速是每秒八十兆比特，帮我算一下下载需要多少小时。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-55-2.wav"}}, {"session_id": "686", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((60 * 8e9) / (80 * 1e6)) / 3600\"}", "value": "function\ncalculate\n{\"expression\": \"((60 * 8e9) / (80 * 1e6)) / 3600\"}"}] +[{"session_id": "593", "turn_idx": 0, "from": "Human", "text": "小跃，计算下旅行预算。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-56-0.wav"}}, {"session_id": "593", "turn_idx": 1, "from": "Assistant", "text": "您计划去哪里呢？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-56-1.wav"}}, {"session_id": "593", "turn_idx": 2, "from": "Human", "text": "机票三人共六千三，酒店五天每天四百八，餐饮每天预算三百五。 加上百分之十五备用金，总共需要多少钱？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-56-2.wav"}}, {"session_id": "593", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(6300 + 5*480 + 5*350)*1.15\"}", "value": "function\ncalculate\n{\"expression\": \"(6300 + 5*480 + 5*350)*1.15\"}"}] +[{"session_id": "645", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个早上七点的闹钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-57-0.wav"}}, {"session_id": "645", "turn_idx": 1, "from": "Assistant", "text": "好的，闹钟设置好了，明天七点准时叫你起床。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-57-1.wav"}}, {"session_id": "645", "turn_idx": 2, "from": "Human", "text": "谢谢，不过你能用更兴奋的语气说吗？我明天有重要会议。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-57-2.wav"}}, {"session_id": "645", "turn_idx": 3, "from": "Assistant", "text": "当然啦！闹钟搞定啦，明天七点叫你，保证精神百倍去开会！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-57-3.wav"}}] +[{"session_id": "652", "turn_idx": 0, "from": "Human", "text": "小跃，我计划周末去爬山，但不知道带什么装备。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-58-0.wav"}}, {"session_id": "652", "turn_idx": 1, "from": "Assistant", "text": "爬山很赞！基础装备包括舒适的鞋子和背包。你想爬多高的山？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-58-1.wav"}}] +[{"session_id": "234", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-59-0.wav"}}, {"session_id": "234", "turn_idx": 1, "from": "Assistant", "text": "现在是下午3点15分。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-59-1.wav"}}, {"session_id": "234", "turn_idx": 2, "from": "Human", "text": "小跃，用严肃的语气提醒我别迟到。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-59-2.wav"}}, {"session_id": "234", "turn_idx": 3, "from": "Assistant", "text": "注意，现在是3点15分，会议4点开始，别迟到。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-59-3.wav"}}, {"session_id": "234", "turn_idx": 4, "from": "Human", "text": "对哦！小跃，音量小点，我在图书馆。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-59-4.wav"}}, {"session_id": "234", "turn_idx": 5, "from": "Assistant", "text": "好的，小声说:3点20了，抓紧时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-59-5.wav"}}, {"session_id": "234", "turn_idx": 6, "from": "Human", "text": "谢谢。小跃，用兴奋的语气鼓励我。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-59-6.wav"}}, {"session_id": "234", "turn_idx": 7, "from": "Assistant", "text": "加油！你还有时间，一定能准时到！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-59-7.wav"}}] +[{"session_id": "11", "turn_idx": 0, "from": "Human", "text": "小跃，我健身计划每天消耗三百大卡，坚持了九十七天啦！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-60-0.wav"}}, {"session_id": "11", "turn_idx": 1, "from": "Assistant", "text": "真厉害！累计消耗的热量肯定很可观呢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-60-1.wav"}}, {"session_id": "11", "turn_idx": 2, "from": "Human", "text": "那帮我算个大的:圆周率的三次方乘以一万两千八百，再加上根号下一百六十九万。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-60-2.wav"}}, {"session_id": "11", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(3.1415926535**3)*12800 + (1690000**0.5)\"}", "value": "function\ncalculate\n{\"expression\": \"(3.1415926535**3)*12800 + (1690000**0.5)\"}"}] +[{"session_id": "520", "turn_idx": 0, "from": "Human", "text": "小跃，最近有啥好电影推荐吗？我周末想放松一下。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-61-0.wav"}}, {"session_id": "520", "turn_idx": 1, "from": "Assistant", "text": "嗨，刚上映的《流浪地球二》挺火的，科幻大片，视觉效果超棒。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-61-1.wav"}}, {"session_id": "520", "turn_idx": 2, "from": "Human", "text": "科幻片啊，我不太感冒。有没有轻松点的喜剧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-61-2.wav"}}, {"session_id": "520", "turn_idx": 3, "from": "Assistant", "text": "那试试《满江红》吧，很多人说笑点多，适合放松。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-61-3.wav"}}] +[{"session_id": "804", "turn_idx": 0, "from": "Human", "text": "小跃，听说成都火锅超赞，我计划去尝尝。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-62-0.wav"}}, {"session_id": "804", "turn_idx": 1, "from": "Assistant", "text": "没错，麻辣火锅是招牌！你啥时候出发？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-62-1.wav"}}, {"session_id": "804", "turn_idx": 2, "from": "Human", "text": "可能后天吧，我还没买票。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-62-2.wav"}}, {"session_id": "804", "turn_idx": 3, "from": "Assistant", "text": "提前订票省心，但记得看天气，别淋雨了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-62-3.wav"}}] +[{"session_id": "616", "turn_idx": 0, "from": "Human", "text": "小跃，今天工作累坏了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-63-0.wav"}}, {"session_id": "616", "turn_idx": 1, "from": "Assistant", "text": "休息一下吧。晚上想做什么？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-63-1.wav"}}, {"session_id": "616", "turn_idx": 2, "from": "Human", "text": "可能去公园散步。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-63-2.wav"}}, {"session_id": "616", "turn_idx": 3, "from": "Assistant", "text": "散步放松好。杭州天气适合吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-63-3.wav"}}] +[{"session_id": "515", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。但要更兴奋的语气哦！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-64-0.wav"}}, {"session_id": "515", "turn_idx": 1, "from": "Assistant", "text": "好嘞，我兴奋地讲个笑话:为什么番茄变红了？因为它看到沙拉酱害羞啦！哈哈，好玩吧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-64-1.wav"}}] +[{"session_id": "430", "turn_idx": 0, "from": "Human", "text": "小跃，明天会下雨吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-65-0.wav"}}, {"session_id": "430", "turn_idx": 1, "from": "Assistant", "text": "明天多云，可能有小雨，带把伞出门吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-65-1.wav"}}] +[{"session_id": "192", "turn_idx": 0, "from": "Human", "text": "小跃，讲个笑话听听。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-66-0.wav"}}, {"session_id": "192", "turn_idx": 1, "from": "Assistant", "text": "没问题！为什么番茄变红了？因为它看到了沙拉酱！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-66-1.wav"}}, {"session_id": "192", "turn_idx": 2, "from": "Human", "text": "哈哈，这个有趣。再讲一个，但这次用更夸张的语气。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-66-2.wav"}}, {"session_id": "192", "turn_idx": 3, "from": "Assistant", "text": "好嘞！一只猫走进酒吧，对酒保说——给我来杯牛奶，要加冰的那种！超离谱对吧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-66-3.wav"}}] +[{"session_id": "813", "turn_idx": 0, "from": "Human", "text": "小跃，我在研究投资回报。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-67-0.wav"}}, {"session_id": "813", "turn_idx": 1, "from": "Assistant", "text": "投资了什么？本金和收益如何？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-67-1.wav"}}, {"session_id": "813", "turn_idx": 2, "from": "Human", "text": "买了股票，每股买入价五十元，卖出价七十五元，一共两千股，交易费是总金额的百分之零点五。帮我算净收益。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-67-2.wav"}}, {"session_id": "813", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((75 - 50) * 2000) - (75 * 2000 * 0.005)\"}", "value": "function\ncalculate\n{\"expression\": \"((75 - 50) * 2000) - (75 * 2000 * 0.005)\"}"}] +[{"session_id": "797", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-68-0.wav"}}, {"session_id": "797", "turn_idx": 1, "from": "Assistant", "text": "现在是上午十点整。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-68-1.wav"}}, {"session_id": "797", "turn_idx": 2, "from": "Human", "text": "用温柔的语气告诉我时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-68-2.wav"}}, {"session_id": "797", "turn_idx": 3, "from": "Assistant", "text": "现在是上午十点哦，亲爱的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-68-3.wav"}}, {"session_id": "797", "turn_idx": 4, "from": "Human", "text": "提醒我中午十二点吃饭。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-68-4.wav"}}, {"session_id": "797", "turn_idx": 5, "from": "Assistant", "text": "好的，中午十二点吃饭提醒已设好。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-68-5.wav"}}, {"session_id": "797", "turn_idx": 6, "from": "Human", "text": "说慢点，我记一下。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-68-6.wav"}}, {"session_id": "797", "turn_idx": 7, "from": "Assistant", "text": "好的——我——慢——慢——说:中——午——十——二——点——吃——饭。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-68-7.wav"}}] +[{"session_id": "954", "turn_idx": 0, "from": "Human", "text": "小跃，我在准备英语考试，听力部分老丢分。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-69-0.wav"}}, {"session_id": "954", "turn_idx": 1, "from": "Assistant", "text": "听力的话，多听真题或播客，比如BBC英语。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-69-1.wav"}}, {"session_id": "954", "turn_idx": 2, "from": "Human", "text": "BBC英语是免费的吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-69-2.wav"}}, {"session_id": "954", "turn_idx": 3, "from": "Assistant", "text": "大部分免费，官网有资源，适合练耳朵。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-69-3.wav"}}] +[{"session_id": "884", "turn_idx": 0, "from": "Human", "text": "小跃，我打算后天去西湖玩，兴奋啊！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-70-0.wav"}}, {"session_id": "884", "turn_idx": 1, "from": "Assistant", "text": "西湖很美！提前查天气，别影响游玩心情。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-70-1.wav"}}, {"session_id": "884", "turn_idx": 2, "from": "Human", "text": "对啊，带上相机拍照片，希望别太晒。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-70-2.wav"}}, {"session_id": "884", "turn_idx": 3, "from": "Assistant", "text": "防晒霜必备，阳光太强容易晒伤。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-70-3.wav"}}] +[{"session_id": "529", "turn_idx": 0, "from": "Human", "text": "小跃，周末去哪玩好？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-71-0.wav"}}, {"session_id": "529", "turn_idx": 1, "from": "Assistant", "text": "推荐去公园野餐，天气不错哦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-71-1.wav"}}, {"session_id": "529", "turn_idx": 2, "from": "Human", "text": "说兴奋点，我超期待！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-71-2.wav"}}, {"session_id": "529", "turn_idx": 3, "from": "Assistant", "text": "太棒了！公园野餐，阳光美食，超赞周末！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-71-3.wav"}}, {"session_id": "529", "turn_idx": 4, "from": "Human", "text": "音量调小点，别太激动。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-71-4.wav"}}, {"session_id": "529", "turn_idx": 5, "from": "Assistant", "text": "行，公园野餐，阳光美食，悄悄期待吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-71-5.wav"}}, {"session_id": "529", "turn_idx": 6, "from": "Human", "text": "哈哈，你懂我。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-71-6.wav"}}, {"session_id": "529", "turn_idx": 7, "from": "Assistant", "text": "当然，玩得开心呀！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-71-7.wav"}}] +[{"session_id": "67", "turn_idx": 0, "from": "Human", "text": "爷爷的果园丰收啦！苹果树五十棵，每棵产八十公斤。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-72-0.wav"}}, {"session_id": "67", "turn_idx": 1, "from": "Assistant", "text": "大丰收呢！橘子三十棵，每棵多少公斤？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-72-1.wav"}}, {"session_id": "67", "turn_idx": 2, "from": "Human", "text": "橘子每棵六十公斤。收购价苹果每公斤四块，橘子三块五。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-72-2.wav"}}, {"session_id": "67", "turn_idx": 3, "from": "Assistant", "text": "苹果总价一万六千，橘子六千三百。总收入不错！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-72-3.wav"}}, {"session_id": "67", "turn_idx": 4, "from": "Human", "text": "运费是总收入的百分之十五，扣除后净赚多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-72-4.wav"}}, {"session_id": "67", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(50*80*4 + 30*60*3.5) * (1-0.15)\"}", "value": "function\ncalculate\n{\"expression\": \"(50*80*4 + 30*60*3.5) * (1-0.15)\"}"}] +[{"session_id": "120", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想去公园玩，你觉得带什么好？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-73-0.wav"}}, {"session_id": "120", "turn_idx": 1, "from": "Assistant", "text": "带点零食和水就行，轻松点。对了，最近天气变化快，得注意一下。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-73-1.wav"}}] +[{"session_id": "385", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午三点开会。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-74-0.wav"}}, {"session_id": "385", "turn_idx": 1, "from": "Assistant", "text": "记住了，下午三点开会，我会提前通知你。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-74-1.wav"}}, {"session_id": "385", "turn_idx": 2, "from": "Human", "text": "音量调小点，我在安静环境里。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-74-2.wav"}}, {"session_id": "385", "turn_idx": 3, "from": "Assistant", "text": "收到，我小声点说。会议提醒已设置，不会打扰到别人。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-74-3.wav"}}] +[{"session_id": "356", "turn_idx": 0, "from": "Human", "text": "小跃，项目总任务量是一千二百小时，已经完成了四百小时。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-75-0.wav"}}, {"session_id": "356", "turn_idx": 1, "from": "Assistant", "text": "进展不错！团队每天能处理八十小时。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-75-1.wav"}}, {"session_id": "356", "turn_idx": 2, "from": "Human", "text": "帮我算一下剩余任务需要多少天完成。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-75-2.wav"}}, {"session_id": "356", "turn_idx": 3, "from": "Assistant", "text": "计算剩余工作天数吧。 function\ncalculate\n{\"expression\": \"(1200 - 400) / 80\"}", "value": "计算剩余工作天数吧。 function\ncalculate\n{\"expression\": \"(1200 - 400) / 80\"}"}] +[{"session_id": "862", "turn_idx": 0, "from": "Human", "text": "小跃，明天计划去广州塔玩，好期待。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-76-0.wav"}}, {"session_id": "862", "turn_idx": 1, "from": "Assistant", "text": "广州塔超赞的！不过天气影响体验，建议查查看。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-76-1.wav"}}, {"session_id": "862", "turn_idx": 2, "from": "Human", "text": "是啊，广州明天天气会好吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-76-2.wav"}}, {"session_id": "862", "turn_idx": 3, "from": "Assistant", "text": "我帮你看看，提前知道总没错。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-76-3.wav"}}] +[{"session_id": "688", "turn_idx": 0, "from": "Human", "text": "小跃，我听说重庆的火锅超赞，想去试试。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-77-0.wav"}}, {"session_id": "688", "turn_idx": 1, "from": "Assistant", "text": "重庆火锅确实出名，但得注意辣度。你周末去吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-77-1.wav"}}, {"session_id": "688", "turn_idx": 2, "from": "Human", "text": "对，后天就出发，住两晚。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-77-2.wav"}}, {"session_id": "688", "turn_idx": 3, "from": "Assistant", "text": "准备好肠胃药，以防万一。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-77-3.wav"}}] +[{"session_id": "437", "turn_idx": 0, "from": "Human", "text": "小跃，我头疼死了，工作压力大。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-78-0.wav"}}, {"session_id": "437", "turn_idx": 1, "from": "Assistant", "text": "别太拼了！转移下注意力，想想开心的事。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-78-1.wav"}}, {"session_id": "437", "turn_idx": 2, "from": "Human", "text": "也是，下周要去重庆吃火锅。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-78-2.wav"}}, {"session_id": "437", "turn_idx": 3, "from": "Assistant", "text": "重庆火锅辣得过瘾！但天气热，查查更安心。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-78-3.wav"}}] +[{"session_id": "612", "turn_idx": 0, "from": "Human", "text": "小跃，我有点累，怎么办？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-79-0.wav"}}, {"session_id": "612", "turn_idx": 1, "from": "Assistant", "text": "建议休息一下，喝杯热茶放松。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-79-1.wav"}}, {"session_id": "612", "turn_idx": 2, "from": "Human", "text": "说小声点，我头痛。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-79-2.wav"}}, {"session_id": "612", "turn_idx": 3, "from": "Assistant", "text": "知道了，小声点说:休息一会儿，喝点热茶，缓缓就好。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-79-3.wav"}}] +[{"session_id": "690", "turn_idx": 0, "from": "Human", "text": "小跃，早上好呀，我今天感觉特别困。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-80-0.wav"}}, {"session_id": "690", "turn_idx": 1, "from": "Assistant", "text": "早上好！是不是昨晚没睡好？要不要听听轻松的音乐提提神？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-80-1.wav"}}, {"session_id": "690", "turn_idx": 2, "from": "Human", "text": "好主意！放点轻音乐吧。对了，我周末想去北京逛逛，你觉得怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-80-2.wav"}}, {"session_id": "690", "turn_idx": 3, "from": "Assistant", "text": "北京很棒的，故宫和长城都值得一去。记得提前规划行程哦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-80-3.wav"}}] +[{"session_id": "776", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-81-0.wav"}}, {"session_id": "776", "turn_idx": 1, "from": "Assistant", "text": "今天是晴天，温度大概25度，挺舒服的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-81-1.wav"}}, {"session_id": "776", "turn_idx": 2, "from": "Human", "text": "说详细点，用兴奋点的语气。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-81-2.wav"}}, {"session_id": "776", "turn_idx": 3, "from": "Assistant", "text": "哇，今天阳光超好！温度25度，微风轻吹，完美适合出去玩！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-81-3.wav"}}, {"session_id": "776", "turn_idx": 4, "from": "Human", "text": "谢谢，太棒了！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-81-4.wav"}}, {"session_id": "776", "turn_idx": 5, "from": "Assistant", "text": "不客气，随时问我哦！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-81-5.wav"}}] +[{"session_id": "616", "turn_idx": 0, "from": "Human", "text": "小跃，我在备考考试，想规划学习时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-82-0.wav"}}, {"session_id": "616", "turn_idx": 1, "from": "Assistant", "text": "备考加油！每天能投入多少小时？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-82-1.wav"}}, {"session_id": "616", "turn_idx": 2, "from": "Human", "text": "计划每天学三小时，持续四十天，但最后十天要加倍努力。 总共学习小时数是多少？帮我计算出来。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-82-2.wav"}}, {"session_id": "616", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"30 * 3 + 10 * 6\"}", "value": "function\ncalculate\n{\"expression\": \"30 * 3 + 10 * 6\"}"}] +[{"session_id": "876", "turn_idx": 0, "from": "Human", "text": "小跃，我想存钱买新手机，手机价四千五百块。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-83-0.wav"}}, {"session_id": "876", "turn_idx": 1, "from": "Assistant", "text": "每月存多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-83-1.wav"}}, {"session_id": "876", "turn_idx": 2, "from": "Human", "text": "每月存六百块。但第一个月有额外奖金，能多存五百块。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-83-2.wav"}}, {"session_id": "876", "turn_idx": 3, "from": "Assistant", "text": "那帮你算几个月能存够？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-83-3.wav"}}, {"session_id": "876", "turn_idx": 4, "from": "Human", "text": "假设后续每月固定存六百。帮我计算总月份。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-83-4.wav"}}, {"session_id": "876", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(4500 - 500) / 600 + 1\"}", "value": "function\ncalculate\n{\"expression\": \"(4500 - 500) / 600 + 1\"}"}] +[{"session_id": "402", "turn_idx": 0, "from": "Human", "text": "小跃，我要休息了，拜拜。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-84-0.wav"}}, {"session_id": "402", "turn_idx": 1, "from": "Assistant", "text": "好的，再见。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-84-1.wav"}}, {"session_id": "402", "turn_idx": 2, "from": "Human", "text": "请用温暖的道别语气说。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-84-2.wav"}}, {"session_id": "402", "turn_idx": 3, "from": "Assistant", "text": "再见啦，愿你有个甜甜的梦，明天见！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-84-3.wav"}}, {"session_id": "402", "turn_idx": 4, "from": "Human", "text": "你真好，晚安。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-84-4.wav"}}, {"session_id": "402", "turn_idx": 5, "from": "Assistant", "text": "晚安，随时等你回来。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-84-5.wav"}}] +[{"session_id": "539", "turn_idx": 0, "from": "Human", "text": "用高兴的语气祝我考试顺利！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-85-0.wav"}}, {"session_id": "539", "turn_idx": 1, "from": "Assistant", "text": "加油加油！祝你考试满分，旗开得胜！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-85-1.wav"}}] +[{"session_id": "387", "turn_idx": 0, "from": "Human", "text": "小跃，后天西安有庙会活动，我计划带全家去。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-86-0.wav"}}, {"session_id": "387", "turn_idx": 1, "from": "Assistant", "text": "庙会热闹！不过人多天热的话容易累，查查天气吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-86-1.wav"}}] +[{"session_id": "320", "turn_idx": 0, "from": "Human", "text": "小跃，我该睡觉了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-87-0.wav"}}, {"session_id": "320", "turn_idx": 1, "from": "Assistant", "text": "好的，晚安，做个好梦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-87-1.wav"}}, {"session_id": "320", "turn_idx": 2, "from": "Human", "text": "小跃，用更轻柔的声音说晚安。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-87-2.wav"}}, {"session_id": "320", "turn_idx": 3, "from": "Assistant", "text": "晚安，亲爱的，睡个好觉。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-87-3.wav"}}, {"session_id": "320", "turn_idx": 4, "from": "Human", "text": "明天几点叫我？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-87-4.wav"}}, {"session_id": "320", "turn_idx": 5, "from": "Assistant", "text": "明天7点闹钟会响。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-87-5.wav"}}, {"session_id": "320", "turn_idx": 6, "from": "Human", "text": "小跃，音量调小点，别吓醒我。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-87-6.wav"}}, {"session_id": "320", "turn_idx": 7, "from": "Assistant", "text": "明白，音量调低。晚安。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-87-7.wav"}}] +[{"session_id": "46", "turn_idx": 0, "from": "Human", "text": "小跃，我家狗狗最近不爱吃饭，怎么办？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-88-0.wav"}}, {"session_id": "46", "turn_idx": 1, "from": "Assistant", "text": "可能是挑食或健康问题。带它看兽医没？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-88-1.wav"}}, {"session_id": "46", "turn_idx": 2, "from": "Human", "text": "还没，想先自己试试。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-88-2.wav"}}, {"session_id": "46", "turn_idx": 3, "from": "Assistant", "text": "那可以换换狗粮牌子或加点零食。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-88-3.wav"}}] +[{"session_id": "505", "turn_idx": 0, "from": "Human", "text": "小跃，我健身教练说南京的绿道适合跑步。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-89-0.wav"}}, {"session_id": "505", "turn_idx": 1, "from": "Assistant", "text": "跑步好选择！南京风景也好。你打算哪天去？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-89-1.wav"}}, {"session_id": "505", "turn_idx": 2, "from": "Human", "text": "今天下午或明天，看天气怎么样。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-89-2.wav"}}, {"session_id": "505", "turn_idx": 3, "from": "Assistant", "text": "天气影响运动舒适度，要查一下吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-89-3.wav"}}, {"session_id": "505", "turn_idx": 4, "from": "Human", "text": "是呀，南京最近温度如何？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-89-4.wav"}}, {"session_id": "505", "turn_idx": 5, "from": "Assistant", "text": "我还没数据，需要查询具体预报？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-89-5.wav"}}] +[{"session_id": "661", "turn_idx": 0, "from": "Human", "text": "小跃，推荐部新电影。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-0.wav"}}, {"session_id": "661", "turn_idx": 1, "from": "Assistant", "text": "试试《星际冒险》，科幻大片，评分很高。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-1.wav"}}, {"session_id": "661", "turn_idx": 2, "from": "Human", "text": "不错。小跃，请用兴奋的语气描述剧情。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-2.wav"}}, {"session_id": "661", "turn_idx": 3, "from": "Assistant", "text": "哇塞！主角穿越黑洞，拯救地球，超刺激！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-3.wav"}}, {"session_id": "661", "turn_idx": 4, "from": "Human", "text": "小跃，音量调高点，我在跑步听不清。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-4.wav"}}, {"session_id": "661", "turn_idx": 5, "from": "Assistant", "text": "音量最大了！剧情超燃，外星人战斗场面爆炸！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-5.wav"}}, {"session_id": "661", "turn_idx": 6, "from": "Human", "text": "谢啦。小跃，切换到神秘语气，别剧透太多。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-6.wav"}}, {"session_id": "661", "turn_idx": 7, "from": "Assistant", "text": "悄悄说，结局有反转，绝对让你惊喜。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-7.wav"}}, {"session_id": "661", "turn_idx": 8, "from": "Human", "text": "好期待。小跃，帮我查上映时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-8.wav"}}, {"session_id": "661", "turn_idx": 9, "from": "Assistant", "text": "今晚七点影院有场次，快去订票吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-90-9.wav"}}] +[{"session_id": "422", "turn_idx": 0, "from": "Human", "text": "小跃，我头有点疼，有什么快速缓解的方法？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-91-0.wav"}}, {"session_id": "422", "turn_idx": 1, "from": "Assistant", "text": "喝点温水，休息一下，或者轻轻按摩太阳穴试试。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-91-1.wav"}}, {"session_id": "422", "turn_idx": 2, "from": "Human", "text": "按摩具体怎么操作？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-91-2.wav"}}, {"session_id": "422", "turn_idx": 3, "from": "Assistant", "text": "用指尖在太阳穴位置画小圈，力度别太大。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-91-3.wav"}}] +[{"session_id": "381", "turn_idx": 0, "from": "Human", "text": "小跃，我想学做意大利面，有什么简单食谱？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-92-0.wav"}}, {"session_id": "381", "turn_idx": 1, "from": "Assistant", "text": "番茄肉酱面最经典了，材料就番茄、肉末、洋葱和面条，超简单。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-92-1.wav"}}, {"session_id": "381", "turn_idx": 2, "from": "Human", "text": "好呀，那具体步骤是啥？需要煮多久？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-92-2.wav"}}, {"session_id": "381", "turn_idx": 3, "from": "Assistant", "text": "先炒肉末和洋葱，再加番茄酱煮十分钟，面条煮八到十分钟就行。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-92-3.wav"}}] +[{"session_id": "620", "turn_idx": 0, "from": "Human", "text": "小跃，推荐首轻松的歌。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-93-0.wav"}}, {"session_id": "620", "turn_idx": 1, "from": "Assistant", "text": "试试《小幸运》，旋律轻快，听着很放松。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-93-1.wav"}}, {"session_id": "620", "turn_idx": 2, "from": "Human", "text": "请用更热情的语气推荐。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-93-2.wav"}}, {"session_id": "620", "turn_idx": 3, "from": "Assistant", "text": "超推荐《小幸运》！超好听，旋律超轻快，一听就心情飞起！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-93-3.wav"}}, {"session_id": "620", "turn_idx": 4, "from": "Human", "text": "音量调高，我想大声听歌名。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-93-4.wav"}}, {"session_id": "620", "turn_idx": 5, "from": "Assistant", "text": "好的，我大声说！超推荐《小幸运》，旋律超轻快，一听就心情飞起！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-93-5.wav"}}, {"session_id": "620", "turn_idx": 6, "from": "Human", "text": "再推荐一首，用平静的语气。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-93-6.wav"}}, {"session_id": "620", "turn_idx": 7, "from": "Assistant", "text": "没问题。《月光》很舒缓，适合安静时听。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-93-7.wav"}}] +[{"session_id": "40", "turn_idx": 0, "from": "Human", "text": "小跃早呀，今天感觉好闷热", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-94-0.wav"}}, {"session_id": "40", "turn_idx": 1, "from": "Assistant", "text": "是呀，这天气像蒸桑拿似的！您要出门的话记得带小风扇。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-94-1.wav"}}, {"session_id": "40", "turn_idx": 2, "from": "Human", "text": "哈哈你提醒得对，下午得去西湖边逛逛", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-94-2.wav"}}, {"session_id": "40", "turn_idx": 3, "from": "Assistant", "text": "西湖现在荷花正美呢，不过记得看看天气，别赶上暴雨啦", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-94-3.wav"}}] +[{"session_id": "635", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-95-0.wav"}}, {"session_id": "635", "turn_idx": 1, "from": "Assistant", "text": "今天天气很好，阳光明媚，温度刚刚好哦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-95-1.wav"}}, {"session_id": "635", "turn_idx": 2, "from": "Human", "text": "请用开心的语气再说一遍。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-95-2.wav"}}, {"session_id": "635", "turn_idx": 3, "from": "Assistant", "text": "太棒了！今天天气超级赞，阳光灿烂，温度超级舒服，快去享受吧！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-95-3.wav"}}] +[{"session_id": "247", "turn_idx": 0, "from": "Human", "text": "小跃，教我一句法语问候。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-96-0.wav"}}, {"session_id": "247", "turn_idx": 1, "from": "Assistant", "text": "好的，\"Bonjour\" 意思是“你好”。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-96-1.wav"}}, {"session_id": "247", "turn_idx": 2, "from": "Human", "text": "用更鼓励的语气教我怎么用。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-96-2.wav"}}, {"session_id": "247", "turn_idx": 3, "from": "Assistant", "text": "加油！试试说 \"Merci\" 意思是“谢谢”，你一定能学会！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-96-3.wav"}}] +[{"session_id": "740", "turn_idx": 0, "from": "Human", "text": "小跃，我下周要去旅行，有点小兴奋。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-97-0.wav"}}, {"session_id": "740", "turn_idx": 1, "from": "Assistant", "text": "真棒！去哪里？需要帮忙规划吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-97-1.wav"}}, {"session_id": "740", "turn_idx": 2, "from": "Human", "text": "去广州，从深圳出发。高铁票是主要开销。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-97-2.wav"}}, {"session_id": "740", "turn_idx": 3, "from": "Assistant", "text": "高铁票多少钱一张？几个人去？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-97-3.wav"}}, {"session_id": "740", "turn_idx": 4, "from": "Human", "text": "单程票每人六百块，我们三个人，来回。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-97-4.wav"}}, {"session_id": "740", "turn_idx": 5, "from": "Assistant", "text": "好的，还有市内交通或别的费用吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-97-5.wav"}}, {"session_id": "740", "turn_idx": 6, "from": "Human", "text": "市内每天交通费每人八十块，玩四天。帮我算算总交通费。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-97-6.wav"}}, {"session_id": "740", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"3 * 2 * 600 + 3 * 4 * 80\"}", "value": "function\ncalculate\n{\"expression\": \"3 * 2 * 600 + 3 * 4 * 80\"}"}] +[{"session_id": "961", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想去上海找朋友玩，但还没定具体时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-98-0.wav"}}, {"session_id": "961", "turn_idx": 1, "from": "Assistant", "text": "好主意！提前计划能玩得更尽兴，你们可以约个轻松的地方。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-98-1.wav"}}] +[{"session_id": "621", "turn_idx": 0, "from": "Human", "text": "小跃，我存了五万块到银行，年利率是百分之三点五，复利计算的话，两年后能拿回多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-99-0.wav"}}, {"session_id": "621", "turn_idx": 1, "from": "Assistant", "text": "复利需要考虑时间，两年的话，我可以算出来。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-99-1.wav"}}, {"session_id": "621", "turn_idx": 2, "from": "Human", "text": "对，本金五万，年利率三点五，两年复利，总金额是多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-99-2.wav"}}, {"session_id": "621", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"50000 * (1 + 0.035) ** 2\"}", "value": "function\ncalculate\n{\"expression\": \"50000 * (1 + 0.035) ** 2\"}"}] +[{"session_id": "501", "turn_idx": 0, "from": "Human", "text": "小跃，周末我想看电影，有推荐的吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-100-0.wav"}}, {"session_id": "501", "turn_idx": 1, "from": "Assistant", "text": "科幻片《流浪地球》挺火的，故事感人特效也棒。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-100-1.wav"}}, {"session_id": "501", "turn_idx": 2, "from": "Human", "text": "哦，看过了。有没有新出的喜剧片？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-100-2.wav"}}, {"session_id": "501", "turn_idx": 3, "from": "Assistant", "text": "《你好，李焕英》口碑很好，笑点泪点都有。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-100-3.wav"}}] +[{"session_id": "735", "turn_idx": 0, "from": "Human", "text": "小跃，早上好！今天有什么计划？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-101-0.wav"}}, {"session_id": "735", "turn_idx": 1, "from": "Assistant", "text": "早上好！我在学习新东西。你呢？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-101-1.wav"}}, {"session_id": "735", "turn_idx": 2, "from": "Human", "text": "我想去成都吃火锅，明天出发。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-101-2.wav"}}, {"session_id": "735", "turn_idx": 3, "from": "Assistant", "text": "成都火锅太棒了！但天气热的话可能影响胃口。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-101-3.wav"}}] +[{"session_id": "461", "turn_idx": 0, "from": "Human", "text": "小跃，设置个闹钟，用更快的语速说。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-102-0.wav"}}, {"session_id": "461", "turn_idx": 1, "from": "Assistant", "text": "没问题！闹钟设好了明天八点，搞定！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-102-1.wav"}}, {"session_id": "461", "turn_idx": 2, "from": "Human", "text": "等等，再加点开心的调调。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-102-2.wav"}}, {"session_id": "461", "turn_idx": 3, "from": "Assistant", "text": "耶！闹钟八点响，准时开始新一天！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-102-3.wav"}}] +[{"session_id": "478", "turn_idx": 0, "from": "Human", "text": "小跃，我想买双鞋，用时尚的语气推荐。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-103-0.wav"}}, {"session_id": "478", "turn_idx": 1, "from": "Assistant", "text": "时尚推荐:试试那双小白鞋，百搭又潮！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-103-1.wav"}}, {"session_id": "478", "turn_idx": 2, "from": "Human", "text": "好的。请用平静的语气说价格。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-103-2.wav"}}, {"session_id": "478", "turn_idx": 3, "from": "Assistant", "text": "价格是399元，性价比不错。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-103-3.wav"}}, {"session_id": "478", "turn_idx": 4, "from": "Human", "text": "用小声点说如果打折信息。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-103-4.wav"}}, {"session_id": "478", "turn_idx": 5, "from": "Assistant", "text": "小声说:周末可能有折扣。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-103-5.wav"}}] +[{"session_id": "486", "turn_idx": 0, "from": "Human", "text": "小跃，孩子教育基金我存了定期。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-104-0.wav"}}, {"session_id": "486", "turn_idx": 1, "from": "Assistant", "text": "教育投资重要。你每月存多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-104-1.wav"}}, {"session_id": "486", "turn_idx": 2, "from": "Human", "text": "每月五千元，年利率百分之四点三。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-104-2.wav"}}, {"session_id": "486", "turn_idx": 3, "from": "Assistant", "text": "复利增长的话，积累快。存几年？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-104-3.wav"}}, {"session_id": "486", "turn_idx": 4, "from": "Human", "text": "从孩子五岁存到十八岁，共十三年。但前两年利率是百分之三点五。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-104-4.wav"}}, {"session_id": "486", "turn_idx": 5, "from": "Assistant", "text": "利率变化了，得分段计算。前两年和后十一年。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-104-5.wav"}}, {"session_id": "486", "turn_idx": 6, "from": "Human", "text": "是的，帮我算十三后总金额。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-104-6.wav"}}, {"session_id": "486", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"sum(5000 * (1 + 0.035 / 12) ** i for i in range(1, 25)) + sum(5000 * (1 + 0.043 / 12) ** i for i in range(25, 157))\"}", "value": "function\ncalculate\n{\"expression\": \"sum(5000 * (1 + 0.035 / 12) ** i for i in range(1, 25)) + sum(5000 * (1 + 0.043 / 12) ** i for i in range(25, 157))\"}"}] +[{"session_id": "649", "turn_idx": 0, "from": "Human", "text": "小跃，我今天心情超好！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-105-0.wav"}}, {"session_id": "649", "turn_idx": 1, "from": "Assistant", "text": "真棒！要不要分享点开心事？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-105-1.wav"}}, {"session_id": "649", "turn_idx": 2, "from": "Human", "text": "哈哈，你能用更欢乐的语气回应我吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-105-2.wav"}}, {"session_id": "649", "turn_idx": 3, "from": "Assistant", "text": "当然啦！太为你高兴了！快说说，什么好事让你这么开心？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-105-3.wav"}}] +[{"session_id": "76", "turn_idx": 0, "from": "Human", "text": "大理民宿老板说最近天天大晴天！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-106-0.wav"}}, {"session_id": "76", "turn_idx": 1, "from": "Assistant", "text": "高原紫外线强，防晒霜要带够哦。不过建议再确认下预报？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-106-1.wav"}}, {"session_id": "76", "turn_idx": 2, "from": "Human", "text": "你查查看，万一下雨就带不了裙子了", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-106-2.wav"}}, {"session_id": "76", "turn_idx": 3, "from": "Assistant", "text": "明白，苍山脚下天气变得快。查今天到后天？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-106-3.wav"}}] +[{"session_id": "255", "turn_idx": 0, "from": "Human", "text": "小跃，我朋友推荐我读《三体》，说科幻神作。但书太厚了，你觉得先看哪部分好？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-107-0.wav"}}, {"session_id": "255", "turn_idx": 1, "from": "Assistant", "text": "别怕厚！开头可能慢热，但坚持到“红岸基地”部分就精彩了。建议每天读个二十页。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-107-1.wav"}}, {"session_id": "255", "turn_idx": 2, "from": "Human", "text": "红岸基地？听起来神秘。那书里的外星文明是虚构的还是基于真实理论？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-107-2.wav"}}, {"session_id": "255", "turn_idx": 3, "from": "Assistant", "text": "大部分是虚构，但灵感来自物理学，比如黑暗森林法则。作者刘慈欣融合了科学和想象。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-107-3.wav"}}] +[{"session_id": "146", "turn_idx": 0, "from": "Human", "text": "小跃，你喜欢什么颜色？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-108-0.wav"}}, {"session_id": "146", "turn_idx": 1, "from": "Assistant", "text": "作为助手，我没有喜好，但很多人喜欢蓝色。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-108-1.wav"}}, {"session_id": "146", "turn_idx": 2, "from": "Human", "text": "用更随意的语气聊聊。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-108-2.wav"}}, {"session_id": "146", "turn_idx": 3, "from": "Assistant", "text": "嘿，我觉得蓝色挺酷的，你呢？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-108-3.wav"}}] +[{"session_id": "479", "turn_idx": 0, "from": "Human", "text": "小跃，我刚搬到重庆，这里的山路好陡啊。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-109-0.wav"}}, {"session_id": "479", "turn_idx": 1, "from": "Assistant", "text": "重庆山城特色，夜景超美。适应了吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-109-1.wav"}}, {"session_id": "479", "turn_idx": 2, "from": "Human", "text": "还在习惯中。出门总得看天气，雾多。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-109-2.wav"}}, {"session_id": "479", "turn_idx": 3, "from": "Assistant", "text": "雾天开车小心点。要我查查预报？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-109-3.wav"}}] +[{"session_id": "433", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？用开心的语气告诉我。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-110-0.wav"}}, {"session_id": "433", "turn_idx": 1, "from": "Assistant", "text": "今天阳光超级棒，气温刚刚好，出去玩最合适啦！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-110-1.wav"}}] +[{"session_id": "153", "turn_idx": 0, "from": "Human", "text": "健身计划需要算热量缺口呢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-111-0.wav"}}, {"session_id": "153", "turn_idx": 1, "from": "Assistant", "text": "您的基础代谢和运动数据有吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-111-1.wav"}}, {"session_id": "153", "turn_idx": 2, "from": "Human", "text": "基础代谢一千四百卡，慢跑半小时耗三百卡，游泳一小时耗六百卡。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-111-2.wav"}}, {"session_id": "153", "turn_idx": 3, "from": "Assistant", "text": "那每日摄入呢？运动频率怎么安排？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-111-3.wav"}}, {"session_id": "153", "turn_idx": 4, "from": "Human", "text": "每天吃一千八百卡，每周跑三次，游两次。算一周净消耗。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-111-4.wav"}}, {"session_id": "153", "turn_idx": 5, "from": "Assistant", "text": "先算日均运动消耗，再结合代谢和摄入。确认计算？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-111-5.wav"}}, {"session_id": "153", "turn_idx": 6, "from": "Human", "text": "嗯，要具体数值，单位用卡路里。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-111-6.wav"}}, {"session_id": "153", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((1400-1800)*7) + (300*3) + (600*2)\"}", "value": "function\ncalculate\n{\"expression\": \"((1400-1800)*7) + (300*3) + (600*2)\"}"}] +[{"session_id": "300", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气怎么样？用轻松点的语气说。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-112-0.wav"}}, {"session_id": "300", "turn_idx": 1, "from": "Assistant", "text": "好的！今天阳光灿烂，温度刚刚好，很适合出去散步哦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-112-1.wav"}}, {"session_id": "300", "turn_idx": 2, "from": "Human", "text": "哦，那太好了。你能把音量调大一点吗？我在厨房听不清。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-112-2.wav"}}, {"session_id": "300", "turn_idx": 3, "from": "Assistant", "text": "没问题，音量调高了。现在清楚了吧？需要我重复一遍吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-112-3.wav"}}, {"session_id": "300", "turn_idx": 4, "from": "Human", "text": "不用了，谢谢。对了，提醒我下午三点开会。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-112-4.wav"}}, {"session_id": "300", "turn_idx": 5, "from": "Assistant", "text": "好的，已经设置下午三点的会议提醒。还有其他事需要帮忙吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-112-5.wav"}}] +[{"session_id": "705", "turn_idx": 0, "from": "Human", "text": "小跃，我后天在武汉有个户外会议，担心天气不好。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-113-0.wav"}}, {"session_id": "705", "turn_idx": 1, "from": "Assistant", "text": "户外活动最好提前看天气。小跃能帮你查查武汉的预报。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-113-1.wav"}}] +[{"session_id": "767", "turn_idx": 0, "from": "Human", "text": "小跃，我有几个任务要完成。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-114-0.wav"}}, {"session_id": "767", "turn_idx": 1, "from": "Assistant", "text": "分别需要多少时间？统一用小时吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-114-1.wav"}}, {"session_id": "767", "turn_idx": 2, "from": "Human", "text": "任务一要二小时，任务二要一点五小时，任务三要零点七五小时。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-114-2.wav"}}, {"session_id": "767", "turn_idx": 3, "from": "Assistant", "text": "任务三的四十五分钟就是零点七五小时。总时间呢？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-114-3.wav"}}, {"session_id": "767", "turn_idx": 4, "from": "Human", "text": "对，帮我算总小时数。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-114-4.wav"}}, {"session_id": "767", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"2 + 1.5 + 0.75\"}", "value": "function\ncalculate\n{\"expression\": \"2 + 1.5 + 0.75\"}"}] +[{"session_id": "514", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设置一个下午三点的闹钟。哦，音量调小一点行吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-115-0.wav"}}, {"session_id": "514", "turn_idx": 1, "from": "Assistant", "text": "没问题，闹钟设好了，我现在小声点说话。还有其他需要吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-115-1.wav"}}] +[{"session_id": "349", "turn_idx": 0, "from": "Human", "text": "小跃，最近天气真不错啊。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-116-0.wav"}}, {"session_id": "349", "turn_idx": 1, "from": "Assistant", "text": "是啊，阳光明媚的。你有什么计划？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-116-1.wav"}}, {"session_id": "349", "turn_idx": 2, "from": "Human", "text": "朋友推荐去青岛海边玩。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-116-2.wav"}}, {"session_id": "349", "turn_idx": 3, "from": "Assistant", "text": "青岛的海鲜和沙滩超棒。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-116-3.wav"}}] +[{"session_id": "657", "turn_idx": 0, "from": "Human", "text": "小跃，放首轻松的音乐。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-0.wav"}}, {"session_id": "657", "turn_idx": 1, "from": "Assistant", "text": "正在播放《阳光小调》，节奏轻快，放松一下吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-1.wav"}}, {"session_id": "657", "turn_idx": 2, "from": "Human", "text": "不错。小跃，请用更热情的语气推荐一首歌。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-2.wav"}}, {"session_id": "657", "turn_idx": 3, "from": "Assistant", "text": "太棒了！试试《快乐旋律》，保证让你嗨起来！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-3.wav"}}, {"session_id": "657", "turn_idx": 4, "from": "Human", "text": "小跃，音量调高点，我在厨房听不清。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-4.wav"}}, {"session_id": "657", "turn_idx": 5, "from": "Assistant", "text": "音量加大了！《快乐旋律》现在超响亮，尽情享受！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-5.wav"}}, {"session_id": "657", "turn_idx": 6, "from": "Human", "text": "谢啦。小跃，切换到浪漫语气，放首情歌。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-6.wav"}}, {"session_id": "657", "turn_idx": 7, "from": "Assistant", "text": "好呢，播放《月光曲》，旋律温柔，浪漫满分。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-7.wav"}}, {"session_id": "657", "turn_idx": 8, "from": "Human", "text": "完美。小跃，帮我记下这首歌名。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-8.wav"}}, {"session_id": "657", "turn_idx": 9, "from": "Assistant", "text": "已记录《月光曲》，下次想听随时叫我。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-117-9.wav"}}] +[{"session_id": "71", "turn_idx": 0, "from": "Human", "text": "小跃，我房贷每月六千三百，已经还了五年，总共还了多少本金呀？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-118-0.wav"}}, {"session_id": "71", "turn_idx": 1, "from": "Assistant", "text": "五年是六十个月，每月本金固定是六千三百元，所以是六千三乘六十。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-118-1.wav"}}, {"session_id": "71", "turn_idx": 2, "from": "Human", "text": "刚才银行说利率降了，剩余一百九十万贷款分二十年还，新月供多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-118-2.wav"}}, {"session_id": "71", "turn_idx": 3, "from": "Assistant", "text": "新贷款总额一百九十万，分二百四十个月还，用总额除以月份就行。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-118-3.wav"}}, {"session_id": "71", "turn_idx": 4, "from": "Human", "text": "行，那帮我算算调整后每月还多少钱吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-118-4.wav"}}, {"session_id": "71", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"1900000/240\"}", "value": "function\ncalculate\n{\"expression\": \"1900000/240\"}"}] +[{"session_id": "562", "turn_idx": 0, "from": "Human", "text": "小跃，我最近迷上烘焙，想做个蛋糕，简单点的食谱有吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-119-0.wav"}}, {"session_id": "562", "turn_idx": 1, "from": "Assistant", "text": "戚风蛋糕挺简单的，材料就鸡蛋、面粉、糖，步骤也不多。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-119-1.wav"}}, {"session_id": "562", "turn_idx": 2, "from": "Human", "text": "戚风蛋糕？听着好专业。第一次做容易失败吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-119-2.wav"}}, {"session_id": "562", "turn_idx": 3, "from": "Assistant", "text": "有点挑战，但注意打发蛋白就行。失败是常事，多练几次。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-119-3.wav"}}, {"session_id": "562", "turn_idx": 4, "from": "Human", "text": "哈哈，那烤箱温度怎么控制？我总烤糊。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-119-4.wav"}}, {"session_id": "562", "turn_idx": 5, "from": "Assistant", "text": "温度很关键，一般一百八十度左右，但要看蛋糕大小。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-119-5.wav"}}] +[{"session_id": "767", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个下午3点的闹钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-120-0.wav"}}, {"session_id": "767", "turn_idx": 1, "from": "Assistant", "text": "好的，闹钟已设好，下午3点提醒。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-120-1.wav"}}, {"session_id": "767", "turn_idx": 2, "from": "Human", "text": "音量再大点吧，我怕听不见。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-120-2.wav"}}, {"session_id": "767", "turn_idx": 3, "from": "Assistant", "text": "没问题，音量调大了，保证响亮提醒你！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-120-3.wav"}}] +[{"session_id": "365", "turn_idx": 0, "from": "Human", "text": "小跃，算下旅行预算", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-121-0.wav"}}, {"session_id": "365", "turn_idx": 1, "from": "Assistant", "text": "您说交通住宿这些项目", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-121-1.wav"}}, {"session_id": "365", "turn_idx": 2, "from": "Human", "text": "机票两人往返六千四", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-121-2.wav"}}, {"session_id": "365", "turn_idx": 3, "from": "Assistant", "text": "住宿费用呢？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-121-3.wav"}}, {"session_id": "365", "turn_idx": 4, "from": "Human", "text": "住七天酒店，每天三百五", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-121-4.wav"}}, {"session_id": "365", "turn_idx": 5, "from": "Assistant", "text": "餐饮预算有吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-121-5.wav"}}, {"session_id": "365", "turn_idx": 6, "from": "Human", "text": "按每人每天一百五算，共两人", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-121-6.wav"}}, {"session_id": "365", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"6400 + 7*350 + 7*150*2\"}", "value": "function\ncalculate\n{\"expression\": \"6400 + 7*350 + 7*150*2\"}"}] +[{"session_id": "856", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点啦？哦对了，你能大点声说话吗？我这儿有点吵。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-122-0.wav"}}, {"session_id": "856", "turn_idx": 1, "from": "Assistant", "text": "好的！现在是上午十点十五分。这样音量够大了吧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-122-1.wav"}}, {"session_id": "856", "turn_idx": 2, "from": "Human", "text": "嗯，好多了。再帮我设个十一点的闹钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-122-2.wav"}}, {"session_id": "856", "turn_idx": 3, "from": "Assistant", "text": "没问题，十一点的闹钟已经设置好。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-122-3.wav"}}] +[{"session_id": "642", "turn_idx": 0, "from": "Human", "text": "小跃，我在存钱买车，每月固定存一笔。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-123-0.wav"}}, {"session_id": "642", "turn_idx": 1, "from": "Assistant", "text": "目标明确！每月存多少？存了多久？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-123-1.wav"}}, {"session_id": "642", "turn_idx": 2, "from": "Human", "text": "每月存三千五百块，已经存了十八个月。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-123-2.wav"}}, {"session_id": "642", "turn_idx": 3, "from": "Assistant", "text": "银行利息呢？年利率大概百分之四。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-123-3.wav"}}, {"session_id": "642", "turn_idx": 4, "from": "Human", "text": "对，利息要算进去。帮我计算总存款加上一年的利息。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-123-4.wav"}}, {"session_id": "642", "turn_idx": 5, "from": "Assistant", "text": "本金是月存额乘以月数，利息按年利率算。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-123-5.wav"}}, {"session_id": "642", "turn_idx": 6, "from": "Human", "text": "是的，帮我算一下三千五百乘以十八，再乘以一加零点零四。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-123-6.wav"}}, {"session_id": "642", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"3500 * 18 * (1 + 0.04)\"}", "value": "function\ncalculate\n{\"expression\": \"3500 * 18 * (1 + 0.04)\"}"}] +[{"session_id": "833", "turn_idx": 0, "from": "Human", "text": "小跃，我孩子期末考试分数出来了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-124-0.wav"}}, {"session_id": "833", "turn_idx": 1, "from": "Assistant", "text": "考得怎么样？各科多少分？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-124-1.wav"}}, {"session_id": "833", "turn_idx": 2, "from": "Human", "text": "语文八十五分，数学九十二分，英语七十八分，物理九十分。 帮我算一下四科平均分是多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-124-2.wav"}}, {"session_id": "833", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(85 + 92 + 78 + 90) / 4\"}", "value": "function\ncalculate\n{\"expression\": \"(85 + 92 + 78 + 90) / 4\"}"}] +[{"session_id": "525", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-125-0.wav"}}, {"session_id": "525", "turn_idx": 1, "from": "Assistant", "text": "现在是下午3点15分，还早着呢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-125-1.wav"}}, {"session_id": "525", "turn_idx": 2, "from": "Human", "text": "说快点，我赶时间！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-125-2.wav"}}, {"session_id": "525", "turn_idx": 3, "from": "Assistant", "text": "好的！现在3点15分，赶紧行动吧！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-125-3.wav"}}, {"session_id": "525", "turn_idx": 4, "from": "Human", "text": "再小声点，我在图书馆。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-125-4.wav"}}, {"session_id": "525", "turn_idx": 5, "from": "Assistant", "text": "明白，现在3点15分，悄悄说别打扰别人。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-125-5.wav"}}, {"session_id": "525", "turn_idx": 6, "from": "Human", "text": "谢谢，你真机灵。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-125-6.wav"}}, {"session_id": "525", "turn_idx": 7, "from": "Assistant", "text": "不谢，随时效劳！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-125-7.wav"}}] +[{"session_id": "191", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-126-0.wav"}}, {"session_id": "191", "turn_idx": 1, "from": "Assistant", "text": "现在是下午3点15分。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-126-1.wav"}}, {"session_id": "191", "turn_idx": 2, "from": "Human", "text": "谢谢。音量能调小一点吗？我这边有点吵。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-126-2.wav"}}, {"session_id": "191", "turn_idx": 3, "from": "Assistant", "text": "好的，我调低了音量。现在这个音量合适吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-126-3.wav"}}] +[{"session_id": "36", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我明天买牛奶。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-127-0.wav"}}, {"session_id": "36", "turn_idx": 1, "from": "Assistant", "text": "记住了，明天提醒你买牛奶。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-127-1.wav"}}, {"session_id": "36", "turn_idx": 2, "from": "Human", "text": "情感丰富点，显得更贴心。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-127-2.wav"}}, {"session_id": "36", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，明天一定别忘了买牛奶哦，为了健康！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-127-3.wav"}}, {"session_id": "36", "turn_idx": 4, "from": "Human", "text": "好的，你太暖了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-127-4.wav"}}, {"session_id": "36", "turn_idx": 5, "from": "Assistant", "text": "能帮到你，我很开心。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-127-5.wav"}}] +[{"session_id": "53", "turn_idx": 0, "from": "Human", "text": "小跃，家里装修要买瓷砖。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-128-0.wav"}}, {"session_id": "53", "turn_idx": 1, "from": "Assistant", "text": "房间面积多大？选了什么规格的砖？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-128-1.wav"}}, {"session_id": "53", "turn_idx": 2, "from": "Human", "text": "厨房十二平米，卫生间八平米，选的八十厘米乘八十厘米的大砖。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-128-2.wav"}}, {"session_id": "53", "turn_idx": 3, "from": "Assistant", "text": "要预留百分之十损耗哦，您需要买多少块？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-128-3.wav"}}, {"session_id": "53", "turn_idx": 4, "from": "Human", "text": "对，损耗别忘了！帮我算总共采购多少块？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-128-4.wav"}}, {"session_id": "53", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((12+8)/(0.8*0.8)) * 1.1\"}", "value": "function\ncalculate\n{\"expression\": \"((12+8)/(0.8*0.8)) * 1.1\"}"}] +[{"session_id": "763", "turn_idx": 0, "from": "Human", "text": "小跃，我今天有点烦，说点安慰的话。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-129-0.wav"}}, {"session_id": "763", "turn_idx": 1, "from": "Assistant", "text": "别担心，事情总会好的，深呼吸一下。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-129-1.wav"}}, {"session_id": "763", "turn_idx": 2, "from": "Human", "text": "音量小点，我想静静。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-129-2.wav"}}, {"session_id": "763", "turn_idx": 3, "from": "Assistant", "text": "好的，小声说:放松点，慢慢来。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-129-3.wav"}}, {"session_id": "763", "turn_idx": 4, "from": "Human", "text": "用更温暖的语气重复。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-129-4.wav"}}, {"session_id": "763", "turn_idx": 5, "from": "Assistant", "text": "亲爱的，一切都会好起来的，你超棒的！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-129-5.wav"}}, {"session_id": "763", "turn_idx": 6, "from": "Human", "text": "谢谢你，感觉好多了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-129-6.wav"}}, {"session_id": "763", "turn_idx": 7, "from": "Assistant", "text": "随时陪你聊聊。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-129-7.wav"}}] +[{"session_id": "53", "turn_idx": 0, "from": "Human", "text": "小跃，查一下明天的日程，语气温柔点。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-130-0.wav"}}, {"session_id": "53", "turn_idx": 1, "from": "Assistant", "text": "没问题，明天你有两个会议:上午十点和下午两点，都很重要哦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-130-1.wav"}}, {"session_id": "53", "turn_idx": 2, "from": "Human", "text": "现在用更热情的语气提醒我别迟到。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-130-2.wav"}}, {"session_id": "53", "turn_idx": 3, "from": "Assistant", "text": "加油！记得提前准备，别让会议迟到啦！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-130-3.wav"}}] +[{"session_id": "120", "turn_idx": 0, "from": "Human", "text": "小跃，我昨天网购了五件衣服，每件三百八十块，但商家说满一千五减两百，你帮我算算实际要付多少钱？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-131-0.wav"}}, {"session_id": "120", "turn_idx": 1, "from": "Assistant", "text": "好的！五件衣服原价是一千九百元，满减后需要重新计算。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-131-1.wav"}}, {"session_id": "120", "turn_idx": 2, "from": "Human", "text": "等等，我还用了张八五折券，差点忘了这个优惠。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-131-2.wav"}}, {"session_id": "120", "turn_idx": 3, "from": "Assistant", "text": "那我们需要先计算满减再叠加折扣对吧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-131-3.wav"}}, {"session_id": "120", "turn_idx": 4, "from": "Human", "text": "对，另外快递费是二十五块，也要加上去。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-131-4.wav"}}, {"session_id": "120", "turn_idx": 5, "from": "Assistant", "text": "明白，最终金额会包含运费和所有优惠。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-131-5.wav"}}, {"session_id": "120", "turn_idx": 6, "from": "Human", "text": "所以总价是多少？从原价开始一步步算给我听。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-131-6.wav"}}, {"session_id": "120", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(5*380 - 200)*0.85 + 25\"}", "value": "function\ncalculate\n{\"expression\": \"(5*380 - 200)*0.85 + 25\"}"}] +[{"session_id": "898", "turn_idx": 0, "from": "Human", "text": "小跃，我周末计划去西安看兵马俑，怕雨太大。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-132-0.wav"}}, {"session_id": "898", "turn_idx": 1, "from": "Assistant", "text": "兵马俑是经典景点！西安的雨季要注意带雨具。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-132-1.wav"}}] +[{"session_id": "678", "turn_idx": 0, "from": "Human", "text": "小跃，我在自学编程课程，总共有六十节课。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-133-0.wav"}}, {"session_id": "678", "turn_idx": 1, "from": "Assistant", "text": "进度如何了？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-133-1.wav"}}, {"session_id": "678", "turn_idx": 2, "from": "Human", "text": "已经学完二十五节，每天能学三节。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-133-2.wav"}}, {"session_id": "678", "turn_idx": 3, "from": "Assistant", "text": "还要多少天能完成整个课程？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-133-3.wav"}}, {"session_id": "678", "turn_idx": 4, "from": "Human", "text": "帮我计算剩余天数。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-133-4.wav"}}, {"session_id": "678", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(60 - 25) / 3\"}", "value": "function\ncalculate\n{\"expression\": \"(60 - 25) / 3\"}"}] +[{"session_id": "489", "turn_idx": 0, "from": "Human", "text": "小跃，把客厅灯关掉。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-134-0.wav"}}, {"session_id": "489", "turn_idx": 1, "from": "Assistant", "text": "客厅灯已关闭。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-134-1.wav"}}, {"session_id": "489", "turn_idx": 2, "from": "Human", "text": "请用平静的语气确认。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-134-2.wav"}}, {"session_id": "489", "turn_idx": 3, "from": "Assistant", "text": "好的，灯已关好，一切很安静。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-134-3.wav"}}, {"session_id": "489", "turn_idx": 4, "from": "Human", "text": "音量小点，我要睡觉了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-134-4.wav"}}, {"session_id": "489", "turn_idx": 5, "from": "Assistant", "text": "小声点:灯关掉了，祝你做个好梦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-134-5.wav"}}] +[{"session_id": "573", "turn_idx": 0, "from": "Human", "text": "小跃，今天下午我想去深圳购物，放松一下。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-135-0.wav"}}, {"session_id": "573", "turn_idx": 1, "from": "Assistant", "text": "购物是个好主意！深圳商场多，天气通常不错。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-135-1.wav"}}, {"session_id": "573", "turn_idx": 2, "from": "Human", "text": "希望别下雨，不然逛街不方便。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-135-2.wav"}}, {"session_id": "573", "turn_idx": 3, "from": "Assistant", "text": "确实，下雨天出行麻烦，我可以帮你查天气。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-135-3.wav"}}] +[{"session_id": "912", "turn_idx": 0, "from": "Human", "text": "小跃，我健身要计算每日蛋白质摄入量。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-136-0.wav"}}, {"session_id": "912", "turn_idx": 1, "from": "Assistant", "text": "说说你吃的食物和分量吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-136-1.wav"}}, {"session_id": "912", "turn_idx": 2, "from": "Human", "text": "早餐三个鸡蛋每个六克蛋白质，午餐两百克鸡胸肉每百克三十一克蛋白质，晚餐一百五十克牛肉每百克二十六克蛋白质。算总蛋白质克数。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-136-2.wav"}}, {"session_id": "912", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"3*6 + 2*31 + 1.5*26\"}", "value": "function\ncalculate\n{\"expression\": \"3*6 + 2*31 + 1.5*26\"}"}] +[{"session_id": "81", "turn_idx": 0, "from": "Human", "text": "小跃，周末想带娃去露营，有推荐地点吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-137-0.wav"}}, {"session_id": "81", "turn_idx": 1, "from": "Assistant", "text": "朝阳公园不错，草坪大还有儿童设施。您哪天去呢？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-137-1.wav"}}, {"session_id": "81", "turn_idx": 2, "from": "Human", "text": "就这周六！但听说最近老下雨。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-137-2.wav"}}, {"session_id": "81", "turn_idx": 3, "from": "Assistant", "text": "提前准备雨具更安心。需要我看看朝阳公园的天气预报吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-137-3.wav"}}] +[{"session_id": "728", "turn_idx": 0, "from": "Human", "text": "小跃，我今天心情不太好，有什么建议？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-138-0.wav"}}, {"session_id": "728", "turn_idx": 1, "from": "Assistant", "text": "别担心，试试听听音乐或出去走走，放松一下心情。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-138-1.wav"}}, {"session_id": "728", "turn_idx": 2, "from": "Human", "text": "能带点更热情的语气吗？我需要点正能量。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-138-2.wav"}}, {"session_id": "728", "turn_idx": 3, "from": "Assistant", "text": "当然可以！快去听听你最喜欢的歌，或者约朋友喝杯咖啡，保证让你满血复活！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-138-3.wav"}}] +[{"session_id": "268", "turn_idx": 0, "from": "Human", "text": "自驾游计划里，总路程一千两百公里，油箱容量六十升。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-139-0.wav"}}, {"session_id": "268", "turn_idx": 1, "from": "Assistant", "text": "油耗大概多少？百公里八升的话能跑七百五十公里。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-139-1.wav"}}, {"session_id": "268", "turn_idx": 2, "from": "Human", "text": "对，算中途需要加几次油，假设每次加满。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-139-2.wav"}}, {"session_id": "268", "turn_idx": 3, "from": "Assistant", "text": "总路程除以单箱续航里程，再减一。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-139-3.wav"}}, {"session_id": "268", "turn_idx": 4, "from": "Human", "text": "别解释啦，我要计算式结果！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-139-4.wav"}}, {"session_id": "268", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(1200 / (60 / 8 * 100)) - 1\"}", "value": "function\ncalculate\n{\"expression\": \"(1200 / (60 / 8 * 100)) - 1\"}"}] +[{"session_id": "538", "turn_idx": 0, "from": "Human", "text": "音量调低些，耳朵受不了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-140-0.wav"}}, {"session_id": "538", "turn_idx": 1, "from": "Assistant", "text": "明白了，我降低音量。这样感觉舒服多了吧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-140-1.wav"}}] +[{"session_id": "20", "turn_idx": 0, "from": "Human", "text": "小跃，你能用更开心的语气告诉我今天天气怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-141-0.wav"}}, {"session_id": "20", "turn_idx": 1, "from": "Assistant", "text": "今天阳光超好，暖洋洋的，超级适合出门玩！开心吧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-141-1.wav"}}] +[{"session_id": "34", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点钟了？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-142-0.wav"}}, {"session_id": "34", "turn_idx": 1, "from": "Assistant", "text": "现在是上午十点十五分。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-142-1.wav"}}, {"session_id": "34", "turn_idx": 2, "from": "Human", "text": "说慢一点，我有点没听清。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-142-2.wav"}}, {"session_id": "34", "turn_idx": 3, "from": "Assistant", "text": "好——的——现在是——十点——十五分——", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-142-3.wav"}}, {"session_id": "34", "turn_idx": 4, "from": "Human", "text": "清楚多了，谢谢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-142-4.wav"}}, {"session_id": "34", "turn_idx": 5, "from": "Assistant", "text": "不谢，需要时随时问。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-142-5.wav"}}] +[{"session_id": "850", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我最近想减肥，早餐吃什么比较好？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-143-0.wav"}}, {"session_id": "850", "turn_idx": 1, "from": "Assistant", "text": "减肥早餐的话，推荐全麦面包加鸡蛋，或者燕麦粥配水果，热量低还管饱。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-143-1.wav"}}] +[{"session_id": "864", "turn_idx": 0, "from": "Human", "text": "小跃，我赶时间，请快点说重点。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-144-0.wav"}}, {"session_id": "864", "turn_idx": 1, "from": "Assistant", "text": "收到！快节奏模式启动。要问什么？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-144-1.wav"}}, {"session_id": "864", "turn_idx": 2, "from": "Human", "text": "导航到最近的超市。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-144-2.wav"}}, {"session_id": "864", "turn_idx": 3, "from": "Assistant", "text": "好的，直行两百米左转就到。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-144-3.wav"}}] +[{"session_id": "934", "turn_idx": 0, "from": "Human", "text": "小跃，我计划周末去成都吃火锅，馋死了！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-145-0.wav"}}, {"session_id": "934", "turn_idx": 1, "from": "Assistant", "text": "哈哈，成都美食确实出名。提醒你，最好提前订位子。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-145-1.wav"}}] +[{"session_id": "25", "turn_idx": 0, "from": "Human", "text": "小跃，语气放轻松点，我刚忙完有点累。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-146-0.wav"}}, {"session_id": "25", "turn_idx": 1, "from": "Assistant", "text": "没问题，放松点说:你今天辛苦了，要不要听听音乐休息下？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-146-1.wav"}}] +[{"session_id": "936", "turn_idx": 0, "from": "Human", "text": "小跃，后天我要去南京见客户，行程很紧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-147-0.wav"}}, {"session_id": "936", "turn_idx": 1, "from": "Assistant", "text": "南京历史文化丰富，会议间隙可以抽空逛逛。记得带好文件。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-147-1.wav"}}] +[{"session_id": "373", "turn_idx": 0, "from": "Human", "text": "小跃，我在学中国历史，对秦始皇感兴趣。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-148-0.wav"}}, {"session_id": "373", "turn_idx": 1, "from": "Assistant", "text": "秦始皇统一六国，建立了秦朝。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-148-1.wav"}}, {"session_id": "373", "turn_idx": 2, "from": "Human", "text": "统一六国花了多久？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-148-2.wav"}}, {"session_id": "373", "turn_idx": 3, "from": "Assistant", "text": "大概用了十年左右的时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-148-3.wav"}}] +[{"session_id": "467", "turn_idx": 0, "from": "Human", "text": "小跃，工作累了想听放松音乐，推荐些吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-149-0.wav"}}, {"session_id": "467", "turn_idx": 1, "from": "Assistant", "text": "轻音乐或古典乐最放松，比如钢琴曲或自然声音。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-149-1.wav"}}, {"session_id": "467", "turn_idx": 2, "from": "Human", "text": "钢琴曲？比如哪些作曲家的好听？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-149-2.wav"}}, {"session_id": "467", "turn_idx": 3, "from": "Assistant", "text": "肖邦或贝多芬的作品都很治愈，旋律优美。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-149-3.wav"}}] +[{"session_id": "470", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天过得怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-150-0.wav"}}, {"session_id": "470", "turn_idx": 1, "from": "Assistant", "text": "还不错，就是有点忙。你呢，有啥新鲜事？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-150-1.wav"}}, {"session_id": "470", "turn_idx": 2, "from": "Human", "text": "我在计划周末去北京玩，还没定行程。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-150-2.wav"}}, {"session_id": "470", "turn_idx": 3, "from": "Assistant", "text": "北京挺有意思的，比如故宫和长城。需要我推荐景点吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-150-3.wav"}}] +[{"session_id": "945", "turn_idx": 0, "from": "Human", "text": "小跃，说说今天的头条新闻。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-151-0.wav"}}, {"session_id": "945", "turn_idx": 1, "from": "Assistant", "text": "今天头条是科技展新发明，AI机器人亮相。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-151-1.wav"}}, {"session_id": "945", "turn_idx": 2, "from": "Human", "text": "感觉语气太淡了，能更热情点吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-151-2.wav"}}, {"session_id": "945", "turn_idx": 3, "from": "Assistant", "text": "太酷了！今天头条是科技展，AI机器人惊艳全场，超激动人心！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-151-3.wav"}}, {"session_id": "945", "turn_idx": 4, "from": "Human", "text": "哇，这下我感兴趣了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-151-4.wav"}}, {"session_id": "945", "turn_idx": 5, "from": "Assistant", "text": "随时分享新鲜事。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-151-5.wav"}}] +[{"session_id": "512", "turn_idx": 0, "from": "Human", "text": "小跃，去火车站怎么走？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-152-0.wav"}}, {"session_id": "512", "turn_idx": 1, "from": "Assistant", "text": "坐地铁三号线直达，十分钟一班。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-152-1.wav"}}, {"session_id": "512", "turn_idx": 2, "from": "Human", "text": "请用更清晰的语气，我怕听错。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-152-2.wav"}}, {"session_id": "512", "turn_idx": 3, "from": "Assistant", "text": "清楚地说:地铁三号线，入口在左边，别走反。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-152-3.wav"}}, {"session_id": "512", "turn_idx": 4, "from": "Human", "text": "末班车几点？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-152-4.wav"}}, {"session_id": "512", "turn_idx": 5, "from": "Assistant", "text": "末班十点半，时间充裕。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-152-5.wav"}}, {"session_id": "512", "turn_idx": 6, "from": "Human", "text": "小跃，音量调高点，我在路上吵。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-152-6.wav"}}, {"session_id": "512", "turn_idx": 7, "from": "Assistant", "text": "音量加大！记住十点半，别误车！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-152-7.wav"}}] +[{"session_id": "188", "turn_idx": 0, "from": "Human", "text": "小跃，猫咪为啥爱睡觉？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-153-0.wav"}}, {"session_id": "188", "turn_idx": 1, "from": "Assistant", "text": "猫咪是天生的猎人，睡觉保存体力。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-153-1.wav"}}, {"session_id": "188", "turn_idx": 2, "from": "Human", "text": "用更热情的语气回答。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-153-2.wav"}}, {"session_id": "188", "turn_idx": 3, "from": "Assistant", "text": "绝对是的！猫咪超会偷懒，睡觉攒能量超厉害！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-153-3.wav"}}] +[{"session_id": "964", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想去成都吃火锅，但听说那边常下雨。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-154-0.wav"}}, {"session_id": "964", "turn_idx": 1, "from": "Assistant", "text": "成都火锅确实有名！下雨天吃火锅也挺有氛围的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-154-1.wav"}}] +[{"session_id": "709", "turn_idx": 0, "from": "Human", "text": "小跃，晚上吃啥好？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-155-0.wav"}}, {"session_id": "709", "turn_idx": 1, "from": "Assistant", "text": "试试意大利面吧，简单又美味。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-155-1.wav"}}, {"session_id": "709", "turn_idx": 2, "from": "Human", "text": "用更温柔的语气推荐一下。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-155-2.wav"}}, {"session_id": "709", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，意大利面加番茄酱，暖暖的很好吃呢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-155-3.wav"}}, {"session_id": "709", "turn_idx": 4, "from": "Human", "text": "嗯，听起来不错。小跃，语速快一点，我急着做饭。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-155-4.wav"}}, {"session_id": "709", "turn_idx": 5, "from": "Assistant", "text": "行！语速加快！番茄酱快煮好，快动手吧！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-155-5.wav"}}] +[{"session_id": "206", "turn_idx": 0, "from": "Human", "text": "客厅长六米宽四米，要铺边长零点六米的地砖。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-156-0.wav"}}, {"session_id": "206", "turn_idx": 1, "from": "Assistant", "text": "先算客厅面积，再算单块砖面积。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-156-1.wav"}}, {"session_id": "206", "turn_idx": 2, "from": "Human", "text": "损耗率百分之八，而且窗边要裁切掉两平米。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-156-2.wav"}}, {"session_id": "206", "turn_idx": 3, "from": "Assistant", "text": "实际需铺面积等于总面积减窗户再加损耗。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-156-3.wav"}}, {"session_id": "206", "turn_idx": 4, "from": "Human", "text": "供应商说买十送一，帮我算最少购买块数。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-156-4.wav"}}, {"session_id": "206", "turn_idx": 5, "from": "Assistant", "text": "总块数向上取整后，再计算赠品优惠。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-156-5.wav"}}, {"session_id": "206", "turn_idx": 6, "from": "Human", "text": "最终需要付款的砖块数量是多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-156-6.wav"}}, {"session_id": "206", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"ceil((((6 * 4 - 2) * 1.08) / (0.6 * 0.6)) / 1.1)\"}", "value": "function\ncalculate\n{\"expression\": \"ceil((((6 * 4 - 2) * 1.08) / (0.6 * 0.6)) / 1.1)\"}"}] +[{"session_id": "20", "turn_idx": 0, "from": "Human", "text": "小跃，今天天气好热啊，我想算算空调开多久划算。假设每小时耗电一点五度，电费每度六毛三，开八小时要多少钱？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-157-0.wav"}}, {"session_id": "20", "turn_idx": 1, "from": "Assistant", "text": "好的，这需要算耗电量乘以电价。每小时一点五度电，八小时就是十二度电，每度六毛三。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-157-1.wav"}}, {"session_id": "20", "turn_idx": 2, "from": "Human", "text": "对，然后总电费就是十二度乘以零点六三元对吧？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-157-2.wav"}}, {"session_id": "20", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"12 * 0.63\"}", "value": "function\ncalculate\n{\"expression\": \"12 * 0.63\"}"}] +[{"session_id": "384", "turn_idx": 0, "from": "Human", "text": "小跃，最近睡眠不好，有什么自然助眠方法？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-158-0.wav"}}, {"session_id": "384", "turn_idx": 1, "from": "Assistant", "text": "试试睡前泡脚或喝杯热牛奶，放松身心。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-158-1.wav"}}, {"session_id": "384", "turn_idx": 2, "from": "Human", "text": "热牛奶加蜂蜜有用吗？比例怎么调？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-158-2.wav"}}, {"session_id": "384", "turn_idx": 3, "from": "Assistant", "text": "蜂蜜一小勺就行，牛奶一杯，别太甜就好。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-158-3.wav"}}] +[{"session_id": "818", "turn_idx": 0, "from": "Human", "text": "小跃，我在算家庭电费。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-159-0.wav"}}, {"session_id": "818", "turn_idx": 1, "from": "Assistant", "text": "用电量多少？电价怎么算？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-159-1.wav"}}, {"session_id": "818", "turn_idx": 2, "from": "Human", "text": "上个月用了三百五十度电，电价是每度电零点六五元，加上服务费二十元。帮我算总费用。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-159-2.wav"}}, {"session_id": "818", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(350 * 0.65) + 20\"}", "value": "function\ncalculate\n{\"expression\": \"(350 * 0.65) + 20\"}"}] +[{"session_id": "2", "turn_idx": 0, "from": "Human", "text": "小跃，现在几点了？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-160-0.wav"}}, {"session_id": "2", "turn_idx": 1, "from": "Assistant", "text": "现在是下午两点十五分。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-160-1.wav"}}, {"session_id": "2", "turn_idx": 2, "from": "Human", "text": "用更温柔的语气告诉我时间吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-160-2.wav"}}, {"session_id": "2", "turn_idx": 3, "from": "Assistant", "text": "亲爱的用户，现在是下午两点十五分呢，别着急慢慢来。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-160-3.wav"}}] +[{"session_id": "798", "turn_idx": 0, "from": "Human", "text": "小跃，重庆的山城爬坡累不累？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-161-0.wav"}}, {"session_id": "798", "turn_idx": 1, "from": "Assistant", "text": "重庆坡多，但夜景超美。你打算去体验？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-161-1.wav"}}, {"session_id": "798", "turn_idx": 2, "from": "Human", "text": "对，下周去重庆玩几天。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-161-2.wav"}}, {"session_id": "798", "turn_idx": 3, "from": "Assistant", "text": "记得穿舒服鞋子。需要我查信息？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-161-3.wav"}}] +[{"session_id": "975", "turn_idx": 0, "from": "Human", "text": "小跃，我侄子问数学题。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-162-0.wav"}}, {"session_id": "975", "turn_idx": 1, "from": "Assistant", "text": "什么题目呀？看看我能不能帮上忙。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-162-1.wav"}}, {"session_id": "975", "turn_idx": 2, "from": "Human", "text": "说有个数列:第一个数是三，后面每个数比前一个多五倍。 问第十个数是多少？孩子急等着要答案。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-162-2.wav"}}, {"session_id": "975", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"3 * (5**9)\"}", "value": "function\ncalculate\n{\"expression\": \"3 * (5**9)\"}"}] +[{"session_id": "175", "turn_idx": 0, "from": "Human", "text": "小跃，去火车站咋走最快？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-163-0.wav"}}, {"session_id": "175", "turn_idx": 1, "from": "Assistant", "text": "坐地铁二号线，直达火车站，大概20分钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-163-1.wav"}}, {"session_id": "175", "turn_idx": 2, "from": "Human", "text": "语气再肯定点，我赶时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-163-2.wav"}}, {"session_id": "175", "turn_idx": 3, "from": "Assistant", "text": "绝对没问题！坐地铁二号线，20分钟直达，别担心。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-163-3.wav"}}, {"session_id": "175", "turn_idx": 4, "from": "Human", "text": "音量调高，我听不清导航。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-163-4.wav"}}, {"session_id": "175", "turn_idx": 5, "from": "Assistant", "text": "好的！大声说:坐地铁二号线，20分钟到火车站，快出发吧！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-163-5.wav"}}, {"session_id": "175", "turn_idx": 6, "from": "Human", "text": "谢啦小跃。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-163-6.wav"}}, {"session_id": "175", "turn_idx": 7, "from": "Assistant", "text": "一路顺风。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-163-7.wav"}}] +[{"session_id": "363", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个明天早上七点的闹钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-164-0.wav"}}, {"session_id": "363", "turn_idx": 1, "from": "Assistant", "text": "没问题，明早七点的闹钟已经设好了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-164-1.wav"}}, {"session_id": "363", "turn_idx": 2, "from": "Human", "text": "好，但这次请用温柔点的语气提醒我，我有点累。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-164-2.wav"}}, {"session_id": "363", "turn_idx": 3, "from": "Assistant", "text": "亲爱的，明早七点的闹钟会轻轻提醒你，好好休息哦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-164-3.wav"}}, {"session_id": "363", "turn_idx": 4, "from": "Human", "text": "嗯，现在大声点确认一下时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-164-4.wav"}}, {"session_id": "363", "turn_idx": 5, "from": "Assistant", "text": "好的！明早七点闹钟确认，准时响铃！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-164-5.wav"}}] +[{"session_id": "398", "turn_idx": 0, "from": "Human", "text": "朋友叫我后天去西安玩，第一次去呢。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-165-0.wav"}}, {"session_id": "398", "turn_idx": 1, "from": "Assistant", "text": "西安的兵马俑很震撼，别忘了尝尝肉夹馍。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-165-1.wav"}}] +[{"session_id": "838", "turn_idx": 0, "from": "Human", "text": "小跃，放首轻松的音乐。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-166-0.wav"}}, {"session_id": "838", "turn_idx": 1, "from": "Assistant", "text": "正在播放轻音乐，放松一下吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-166-1.wav"}}, {"session_id": "838", "turn_idx": 2, "from": "Human", "text": "声音太大了，调小点。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-166-2.wav"}}, {"session_id": "838", "turn_idx": 3, "from": "Assistant", "text": "好的，音量调低，现在更柔和了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-166-3.wav"}}, {"session_id": "838", "turn_idx": 4, "from": "Human", "text": "用开心的语气推荐首歌。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-166-4.wav"}}, {"session_id": "838", "turn_idx": 5, "from": "Assistant", "text": "哇，这首《阳光小调》超欢快，保证让你心情飞起！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-166-5.wav"}}, {"session_id": "838", "turn_idx": 6, "from": "Human", "text": "不错，谢谢小跃。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-166-6.wav"}}, {"session_id": "838", "turn_idx": 7, "from": "Assistant", "text": "不客气，随时找我听歌哦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-166-7.wav"}}] +[{"session_id": "948", "turn_idx": 0, "from": "Human", "text": "小跃，我打算开车去西安玩，听说兵马俑很壮观。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-167-0.wav"}}, {"session_id": "948", "turn_idx": 1, "from": "Assistant", "text": "开车注意安全！需要导航路线吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-167-1.wav"}}] +[{"session_id": "70", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想学做咖啡，但不知道怎么开始。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-168-0.wav"}}, {"session_id": "70", "turn_idx": 1, "from": "Assistant", "text": "学做咖啡很有趣哦！你可以从简单的滴滤咖啡入手，先准备好咖啡豆和热水。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-168-1.wav"}}, {"session_id": "70", "turn_idx": 2, "from": "Human", "text": "咖啡豆有推荐的吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-168-2.wav"}}, {"session_id": "70", "turn_idx": 3, "from": "Assistant", "text": "阿拉比卡豆比较常见，口感柔和，适合新手。记得选新鲜烘焙的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-168-3.wav"}}, {"session_id": "70", "turn_idx": 4, "from": "Human", "text": "那冲泡水温多少合适？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-168-4.wav"}}, {"session_id": "70", "turn_idx": 5, "from": "Assistant", "text": "水温最好在九十到九十五度之间，太热会苦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-168-5.wav"}}] +[{"session_id": "700", "turn_idx": 0, "from": "Human", "text": "小跃，我明天要去北京出差，有点紧张。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-169-0.wav"}}, {"session_id": "700", "turn_idx": 1, "from": "Assistant", "text": "别担心，北京交通很方便的。提前查查天气，带好合适的衣服就行。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-169-1.wav"}}] +[{"session_id": "607", "turn_idx": 0, "from": "Human", "text": "小跃，我要下载一个电影，大小是六吉字节，网速是每秒六十兆比特。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-170-0.wav"}}, {"session_id": "607", "turn_idx": 1, "from": "Assistant", "text": "吉字节和兆比特单位不同，需要转换一下。一吉字节等于八千兆比特。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-170-1.wav"}}, {"session_id": "607", "turn_idx": 2, "from": "Human", "text": "这么复杂？我直接想算下载时间。 请帮我估算下载需要多少分钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-170-2.wav"}}, {"session_id": "607", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"((6 * 8000) / 60) / 60\"}", "value": "function\ncalculate\n{\"expression\": \"((6 * 8000) / 60) / 60\"}"}] +[{"session_id": "171", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设个明早七点的闹钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-171-0.wav"}}, {"session_id": "171", "turn_idx": 1, "from": "Assistant", "text": "没问题，闹钟设好了，明早七点准时叫你起床。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-171-1.wav"}}, {"session_id": "171", "turn_idx": 2, "from": "Human", "text": "用更兴奋的语气提醒我，我明天有重要会议。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-171-2.wav"}}, {"session_id": "171", "turn_idx": 3, "from": "Assistant", "text": "太棒了！明早七点闹钟响，会议加油，你一定行！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-171-3.wav"}}, {"session_id": "171", "turn_idx": 4, "from": "Human", "text": "音量调小点，现在太晚了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-171-4.wav"}}, {"session_id": "171", "turn_idx": 5, "from": "Assistant", "text": "好的，我轻声点:闹钟设好啦，好好休息吧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-171-5.wav"}}, {"session_id": "171", "turn_idx": 6, "from": "Human", "text": "谢啦，晚安。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-171-6.wav"}}, {"session_id": "171", "turn_idx": 7, "from": "Assistant", "text": "晚安，做个好梦。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-171-7.wav"}}] +[{"session_id": "79", "turn_idx": 0, "from": "Human", "text": "小跃，我有点紧张面试。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-172-0.wav"}}, {"session_id": "79", "turn_idx": 1, "from": "Assistant", "text": "别担心，你准备得很充分。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-172-1.wav"}}, {"session_id": "79", "turn_idx": 2, "from": "Human", "text": "用更轻松的语气开导我。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-172-2.wav"}}, {"session_id": "79", "turn_idx": 3, "from": "Assistant", "text": "放轻松，面试小事一桩，你这么优秀，肯定能搞定！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-172-3.wav"}}] +[{"session_id": "744", "turn_idx": 0, "from": "Human", "text": "小跃，后天家庭聚会，我们想在院子里烧烤。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-173-0.wav"}}, {"session_id": "744", "turn_idx": 1, "from": "Assistant", "text": "烧烤好呀！但得看天气配合，别下雨扫兴。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-173-1.wav"}}, {"session_id": "744", "turn_idx": 2, "from": "Human", "text": "地点在苏州老家，希望明天别刮大风。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-173-2.wav"}}, {"session_id": "744", "turn_idx": 3, "from": "Assistant", "text": "苏州春天风大，查查预报安全些。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-173-3.wav"}}] +[{"session_id": "362", "turn_idx": 0, "from": "Human", "text": "小跃，我想换个新电脑，预算不高。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-174-0.wav"}}, {"session_id": "362", "turn_idx": 1, "from": "Assistant", "text": "预算有限的话，可以看看联想或惠普的入门款。你想要笔记本还是台式机？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-174-1.wav"}}, {"session_id": "362", "turn_idx": 2, "from": "Human", "text": "笔记本吧，屏幕大点，比如十五寸的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-174-2.wav"}}, {"session_id": "362", "turn_idx": 3, "from": "Assistant", "text": "十五寸笔记本选择多，我可以帮你比较性价比高的型号。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-174-3.wav"}}] +[{"session_id": "460", "turn_idx": 0, "from": "Human", "text": "早上好，小跃！今天感觉怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-175-0.wav"}}, {"session_id": "460", "turn_idx": 1, "from": "Assistant", "text": "早上好呀！我感觉很棒。你呢，有什么计划吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-175-1.wav"}}, {"session_id": "460", "turn_idx": 2, "from": "Human", "text": "我打算下午去公园散步，但不知道天气行不行。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-175-2.wav"}}, {"session_id": "460", "turn_idx": 3, "from": "Assistant", "text": "散步很放松呢！你在哪个城市？这样我才能帮你查天气。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-175-3.wav"}}] +[{"session_id": "472", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在备考，时间安排紧。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-176-0.wav"}}, {"session_id": "472", "turn_idx": 1, "from": "Assistant", "text": "考试加油！每天学习多长时间？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-176-1.wav"}}, {"session_id": "472", "turn_idx": 2, "from": "Human", "text": "上午三小时，下午四小时，晚上两小时。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-176-2.wav"}}, {"session_id": "472", "turn_idx": 3, "from": "Assistant", "text": "每天九小时呢。一个月学习几天？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-176-3.wav"}}, {"session_id": "472", "turn_idx": 4, "from": "Human", "text": "大概二十五天，因为有些日子休息。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-176-4.wav"}}, {"session_id": "472", "turn_idx": 5, "from": "Assistant", "text": "休息日完全不学习吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-176-5.wav"}}, {"session_id": "472", "turn_idx": 6, "from": "Human", "text": "对，帮我算一个月总学习小时。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-176-6.wav"}}, {"session_id": "472", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(3 + 4 + 2) * 25\"}", "value": "function\ncalculate\n{\"expression\": \"(3 + 4 + 2) * 25\"}"}] +[{"session_id": "121", "turn_idx": 0, "from": "Human", "text": "小跃，今天的天气怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-177-0.wav"}}, {"session_id": "121", "turn_idx": 1, "from": "Assistant", "text": "今天晴天，温度在25度左右。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-177-1.wav"}}, {"session_id": "121", "turn_idx": 2, "from": "Human", "text": "用开心的语气告诉我，像是好消息！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-177-2.wav"}}, {"session_id": "121", "turn_idx": 3, "from": "Assistant", "text": "太棒啦！今天天气超级好，晴天25度，适合出门玩呢！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-177-3.wav"}}] +[{"session_id": "110", "turn_idx": 0, "from": "Human", "text": "小跃早呀，今天地铁人超多", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-178-0.wav"}}, {"session_id": "110", "turn_idx": 1, "from": "Assistant", "text": "早！周一通勤高峰确实拥挤，记得站稳扶好哦", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-178-1.wav"}}, {"session_id": "110", "turn_idx": 2, "from": "Human", "text": "对了，我昨晚算账有点糊涂", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-178-2.wav"}}, {"session_id": "110", "turn_idx": 3, "from": "Assistant", "text": "别急，慢慢说，需要我帮忙核对什么数字吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-178-3.wav"}}, {"session_id": "110", "turn_idx": 4, "from": "Human", "text": "帮我算算:八百五十七万六千三百四十九 加上 四百二十三万一千八百零五，再减去 九十五万零六百二十七", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-178-4.wav"}}, {"session_id": "110", "turn_idx": 5, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"8576349 + 4231805 - 950627\"}", "value": "function\ncalculate\n{\"expression\": \"8576349 + 4231805 - 950627\"}"}] +[{"session_id": "704", "turn_idx": 0, "from": "Human", "text": "小跃，我打算今天下午去成都的公园散步，但感觉有点阴天。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-179-0.wav"}}, {"session_id": "704", "turn_idx": 1, "from": "Assistant", "text": "公园散步很惬意！先确认天气吧，别被雨淋到了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-179-1.wav"}}] +[{"session_id": "678", "turn_idx": 0, "from": "Human", "text": "小跃，用开心的语气告诉我今天是什么日子。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-180-0.wav"}}, {"session_id": "678", "turn_idx": 1, "from": "Assistant", "text": "今天是周五啦，周末马上到，快计划点有趣的事吧！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-180-1.wav"}}] +[{"session_id": "679", "turn_idx": 0, "from": "Human", "text": "小跃，提醒我下午三点开会。音量调高些。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-181-0.wav"}}, {"session_id": "679", "turn_idx": 1, "from": "Assistant", "text": "好的，下午三点会议提醒已设置，别迟到哦！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-181-1.wav"}}] +[{"session_id": "854", "turn_idx": 0, "from": "Human", "text": "小跃，最近有推荐的美食吗？我嘴馋了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-182-0.wav"}}, {"session_id": "854", "turn_idx": 1, "from": "Assistant", "text": "哈哈，成都的火锅不错，你可以试试。你打算去吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-182-1.wav"}}] +[{"session_id": "218", "turn_idx": 0, "from": "Human", "text": "小跃，解释一下垃圾分类规则。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-183-0.wav"}}, {"session_id": "218", "turn_idx": 1, "from": "Assistant", "text": "分可回收、有害、厨余和干垃圾，简单易行。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-183-1.wav"}}, {"session_id": "218", "turn_idx": 2, "from": "Human", "text": "请正式点说。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-183-2.wav"}}, {"session_id": "218", "turn_idx": 3, "from": "Assistant", "text": "好的，正式地说:根据规定，请严格分类投放，违规可能面临警告。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-183-3.wav"}}, {"session_id": "218", "turn_idx": 4, "from": "Human", "text": "明白了。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-183-4.wav"}}, {"session_id": "218", "turn_idx": 5, "from": "Assistant", "text": "清楚就好，遵守规则。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-183-5.wav"}}] +[{"session_id": "792", "turn_idx": 0, "from": "Human", "text": "小跃，明天开会几点开始？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-184-0.wav"}}, {"session_id": "792", "turn_idx": 1, "from": "Assistant", "text": "会议是早上九点，别迟到。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-184-1.wav"}}, {"session_id": "792", "turn_idx": 2, "from": "Human", "text": "用平静的语气重复一下，我有点紧张。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-184-2.wav"}}, {"session_id": "792", "turn_idx": 3, "from": "Assistant", "text": "平静地说:会议九点开始，放松点，你能行。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-184-3.wav"}}, {"session_id": "792", "turn_idx": 4, "from": "Human", "text": "设置个八点半的闹钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-184-4.wav"}}, {"session_id": "792", "turn_idx": 5, "from": "Assistant", "text": "闹钟设好了，八点半提醒你。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-184-5.wav"}}, {"session_id": "792", "turn_idx": 6, "from": "Human", "text": "音量调小点，我在图书馆。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-184-6.wav"}}, {"session_id": "792", "turn_idx": 7, "from": "Assistant", "text": "好的，音量调小了，会议细节准备好了吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-184-7.wav"}}] +[{"session_id": "161", "turn_idx": 0, "from": "Human", "text": "小跃，我想学做一道简单的家常菜，有啥建议？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-185-0.wav"}}, {"session_id": "161", "turn_idx": 1, "from": "Assistant", "text": "番茄炒蛋怎么样？简单又营养，适合新手。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-185-1.wav"}}, {"session_id": "161", "turn_idx": 2, "from": "Human", "text": "好主意。需要哪些材料？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-185-2.wav"}}, {"session_id": "161", "turn_idx": 3, "from": "Assistant", "text": "主要材料是番茄、鸡蛋，再加点葱和盐就行。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-185-3.wav"}}, {"session_id": "161", "turn_idx": 4, "from": "Human", "text": "听起来不难。步骤复杂吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-185-4.wav"}}, {"session_id": "161", "turn_idx": 5, "from": "Assistant", "text": "首先，把鸡蛋打散炒熟；其次，炒番茄出汁后混合。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-185-5.wav"}}] +[{"session_id": "439", "turn_idx": 0, "from": "Human", "text": "小跃，刚看完电影，心情不错。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-186-0.wav"}}, {"session_id": "439", "turn_idx": 1, "from": "Assistant", "text": "电影好看吗？聊点你的小计划？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-186-1.wav"}}, {"session_id": "439", "turn_idx": 2, "from": "Human", "text": "还行。周末要去苏州玩，第一次去。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-186-2.wav"}}, {"session_id": "439", "turn_idx": 3, "from": "Assistant", "text": "苏州园林很经典！出行前查天气是好习惯。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-186-3.wav"}}] +[{"session_id": "786", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天天气怎么样？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-187-0.wav"}}, {"session_id": "786", "turn_idx": 1, "from": "Assistant", "text": "今天天气晴朗，气温大概25度，挺舒服的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-187-1.wav"}}, {"session_id": "786", "turn_idx": 2, "from": "Human", "text": "你能把音量调大一点吗？我这儿有点吵。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-187-2.wav"}}, {"session_id": "786", "turn_idx": 3, "from": "Assistant", "text": "好的，音量调大了。今天天气晴朗，气温25度，适合外出！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-187-3.wav"}}, {"session_id": "786", "turn_idx": 4, "from": "Human", "text": "谢谢！再用轻松点的语气告诉我下午的计划。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-187-4.wav"}}, {"session_id": "786", "turn_idx": 5, "from": "Assistant", "text": "没问题，下午你可以去公园散步，阳光正好，挺惬意的。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-187-5.wav"}}, {"session_id": "786", "turn_idx": 6, "from": "Human", "text": "设置个三点的闹钟提醒我出门。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-187-6.wav"}}, {"session_id": "786", "turn_idx": 7, "from": "Assistant", "text": "闹钟设好了，下午三点准时提醒你。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-187-7.wav"}}] +[{"session_id": "814", "turn_idx": 0, "from": "Human", "text": "小跃，推荐一本好书。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-188-0.wav"}}, {"session_id": "814", "turn_idx": 1, "from": "Assistant", "text": "试试《平凡的世界》，很受欢迎。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-188-1.wav"}}, {"session_id": "814", "turn_idx": 2, "from": "Human", "text": "请用更热情的语气介绍它。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-188-2.wav"}}, {"session_id": "814", "turn_idx": 3, "from": "Assistant", "text": "超赞！《平凡的世界》超感人，一定要读！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-188-3.wav"}}, {"session_id": "814", "turn_idx": 4, "from": "Human", "text": "行，现在严肃点告诉我阅读时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-188-4.wav"}}, {"session_id": "814", "turn_idx": 5, "from": "Assistant", "text": "每天读一小时，坚持就能进步。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-188-5.wav"}}] +[{"session_id": "163", "turn_idx": 0, "from": "Human", "text": "小跃，我准备做一顿大餐，需要调整食材比例。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-189-0.wav"}}, {"session_id": "163", "turn_idx": 1, "from": "Assistant", "text": "主人，听起来很有趣。您需要怎么调整？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-189-1.wav"}}, {"session_id": "163", "turn_idx": 2, "from": "Human", "text": "食谱上说需要面粉一千二百克，但我有八个人吃，原配方是四人份。帮我算出新需要的面粉量。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-189-2.wav"}}, {"session_id": "163", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"1200 * 8 / 4\"}", "value": "function\ncalculate\n{\"expression\": \"1200 * 8 / 4\"}"}] +[{"session_id": "150", "turn_idx": 0, "from": "Human", "text": "小跃早呀，今天天气真不错！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-190-0.wav"}}, {"session_id": "150", "turn_idx": 1, "from": "Assistant", "text": "是呀，阳光特别好。您今天有什么安排吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-190-1.wav"}}, {"session_id": "150", "turn_idx": 2, "from": "Human", "text": "准备去郊游，想算算开销。我们五个人租车，每天租金四百八十元，计划玩三天。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-190-2.wav"}}, {"session_id": "150", "turn_idx": 3, "from": "Assistant", "text": "那租车费用就是人数乘以天数和租金。您还需要算其他项目吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-190-3.wav"}}, {"session_id": "150", "turn_idx": 4, "from": "Human", "text": "嗯，门票每人七十五元，午餐预算每人每天一百二十元。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-190-4.wav"}}, {"session_id": "150", "turn_idx": 5, "from": "Assistant", "text": "好的，门票和午餐都是按人头和天数计算。还有其他支出吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-190-5.wav"}}, {"session_id": "150", "turn_idx": 6, "from": "Human", "text": "再加一笔公共物资费八百三十元。帮我汇总所有开销吧！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-190-6.wav"}}, {"session_id": "150", "turn_idx": 7, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(5*480*3) + (5*75) + (5*120*3) + 830\"}", "value": "function\ncalculate\n{\"expression\": \"(5*480*3) + (5*75) + (5*120*3) + 830\"}"}] +[{"session_id": "737", "turn_idx": 0, "from": "Human", "text": "小跃，帮我查一下明天的会议时间。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-191-0.wav"}}, {"session_id": "737", "turn_idx": 1, "from": "Assistant", "text": "明天会议是上午十点，在会议室A。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-191-1.wav"}}, {"session_id": "737", "turn_idx": 2, "from": "Human", "text": "小跃，请小声点告诉我，我这边有点吵。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-191-2.wav"}}, {"session_id": "737", "turn_idx": 3, "from": "Assistant", "text": "会议是上午十点，会议室A，小声点说。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-191-3.wav"}}, {"session_id": "737", "turn_idx": 4, "from": "Human", "text": "谢谢，小跃。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-191-4.wav"}}, {"session_id": "737", "turn_idx": 5, "from": "Assistant", "text": "不客气，随时问我。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-191-5.wav"}}] +[{"session_id": "786", "turn_idx": 0, "from": "Human", "text": "小跃，今天放学后想和同学打篮球。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-192-0.wav"}}, {"session_id": "786", "turn_idx": 1, "from": "Assistant", "text": "去朝阳公园的篮球场吧，地方宽敞。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-192-1.wav"}}] +[{"session_id": "859", "turn_idx": 0, "from": "Human", "text": "小跃，今天心情如何？我这儿有点闷。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-193-0.wav"}}, {"session_id": "859", "turn_idx": 1, "from": "Assistant", "text": "我还好，你是不是想出去透透气？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-193-1.wav"}}] +[{"session_id": "809", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在存钱，想算算收益。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-194-0.wav"}}, {"session_id": "809", "turn_idx": 1, "from": "Assistant", "text": "好的，你存了多少钱？利率多少？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-194-1.wav"}}, {"session_id": "809", "turn_idx": 2, "from": "Human", "text": "本金是十万元，年利率百分之三点五，存五年。帮我用复利计算到期总金额。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-194-2.wav"}}, {"session_id": "809", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"100000 * (1 + 0.035) ** 5\"}", "value": "function\ncalculate\n{\"expression\": \"100000 * (1 + 0.035) ** 5\"}"}] +[{"session_id": "654", "turn_idx": 0, "from": "Human", "text": "小跃，你觉得杭州好玩吗？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-195-0.wav"}}, {"session_id": "654", "turn_idx": 1, "from": "Assistant", "text": "杭州西湖很美，适合散步或划船。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-195-1.wav"}}] +[{"session_id": "637", "turn_idx": 0, "from": "Human", "text": "小跃，我们在办生日派对，预算要控制。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-196-0.wav"}}, {"session_id": "637", "turn_idx": 1, "from": "Assistant", "text": "多少人参加？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-196-1.wav"}}, {"session_id": "637", "turn_idx": 2, "from": "Human", "text": "预计一百二十人，成人餐费五十元，儿童餐费三十元，儿童占四分之一。 算总餐食成本。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-196-2.wav"}}, {"session_id": "637", "turn_idx": 3, "from": "Assistant", "text": "function\ncalculate\n{\"expression\": \"(120 * 0.75) * 50 + (120 * 0.25) * 30\"}", "value": "function\ncalculate\n{\"expression\": \"(120 * 0.75) * 50 + (120 * 0.25) * 30\"}"}] +[{"session_id": "636", "turn_idx": 0, "from": "Human", "text": "小跃，帮我设置一个晚上七点的闹钟。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-197-0.wav"}}, {"session_id": "636", "turn_idx": 1, "from": "Assistant", "text": "好的，闹钟已经设好了，晚上七点会提醒你。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-197-1.wav"}}, {"session_id": "636", "turn_idx": 2, "from": "Human", "text": "音量太小了，请大声点确认。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-197-2.wav"}}, {"session_id": "636", "turn_idx": 3, "from": "Assistant", "text": "明白！闹钟设置在晚上七点！我会大声提醒你！", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-197-3.wav"}}] +[{"session_id": "751", "turn_idx": 0, "from": "Human", "text": "小跃，我打算买台新笔记本电脑，推荐什么品牌？", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-198-0.wav"}}, {"session_id": "751", "turn_idx": 1, "from": "Assistant", "text": "联想、戴尔、苹果都不错，看你的预算。日常用的话，联想性价比高。", "value": {"type": "wav", "value": "wav/web_search_negative.jsonl-198-1.wav"}}] diff --git a/tools/StepEval-Audio-Toolcall/web_search_positive.jsonl b/tools/StepEval-Audio-Toolcall/web_search_positive.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..46872092e4586a30ec28785225fe3800707f2f4d --- /dev/null +++ b/tools/StepEval-Audio-Toolcall/web_search_positive.jsonl @@ -0,0 +1,200 @@ +[{"session_id": "916", "turn_idx": 0, "from": "Human", "text": "小跃，我关注环保，最近有什么新倡议吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-0-0.wav"}}, {"session_id": "916", "turn_idx": 1, "from": "Assistant", "text": "有的！比如减少塑料使用和推广太阳能，这些挺火的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-0-1.wav"}}, {"session_id": "916", "turn_idx": 2, "from": "Human", "text": "太阳能？具体怎么推广的？家庭能用吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-0-2.wav"}}, {"session_id": "916", "turn_idx": 3, "from": "Assistant", "text": "对，很多地方装太阳能板，政府还给补贴。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-0-3.wav"}}, {"session_id": "916", "turn_idx": 4, "from": "Human", "text": "补贴多少？我想了解具体政策。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-0-4.wav"}}, {"session_id": "916", "turn_idx": 5, "from": "Assistant", "text": "每个地方不同，比如某些城市补贴百分之三十。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-0-5.wav"}}, {"session_id": "916", "turn_idx": 6, "from": "Human", "text": "那帮我查一下最新的太阳能推广政策和补贴细节吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-0-6.wav"}}, {"session_id": "916", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"太阳能推广 政策 补贴\"}", "value": "function\nweb_search\n{\"query\": \"太阳能推广 政策 补贴\"}"}] +[{"session_id": "652", "turn_idx": 0, "from": "Human", "text": "小跃，我计划周末去爬山，但不知道带什么装备。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-1-0.wav"}}, {"session_id": "652", "turn_idx": 1, "from": "Assistant", "text": "爬山很赞！基础装备包括舒适的鞋子和背包。你想爬多高的山？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-1-1.wav"}}, {"session_id": "652", "turn_idx": 2, "from": "Human", "text": "就附近的小山吧，大概几百米高。查一下新手爬山必备装备清单。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-1-2.wav"}}, {"session_id": "652", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"新手爬山必备装备清单\"}", "value": "function\nweb_search\n{\"query\": \"新手爬山必备装备清单\"}"}] +[{"session_id": "223", "turn_idx": 0, "from": "Human", "text": "小跃，我失眠好几天了，有什么自然疗法吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-2-0.wav"}}, {"session_id": "223", "turn_idx": 1, "from": "Assistant", "text": "失眠真难受！试试睡前喝热牛奶或泡个澡，避免看手机屏幕。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-2-1.wav"}}, {"session_id": "223", "turn_idx": 2, "from": "Human", "text": "我试过牛奶，效果不大。听说薰衣草精油有用？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-2-2.wav"}}, {"session_id": "223", "turn_idx": 3, "from": "Assistant", "text": "薰衣草精油可以滴在枕头上，帮助放松。另外，保持房间黑暗安静。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-2-3.wav"}}, {"session_id": "223", "turn_idx": 4, "from": "Human", "text": "精油哪里买靠谱？还有用量怎么控制？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-2-4.wav"}}, {"session_id": "223", "turn_idx": 5, "from": "Assistant", "text": "正规药店或网上商城都行，用量一两滴就够了，别太多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-2-5.wav"}}, {"session_id": "223", "turn_idx": 6, "from": "Human", "text": "小跃，帮我查查薰衣草精油的品牌和正确使用方法。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-2-6.wav"}}, {"session_id": "223", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"薰衣草精油 品牌 失眠用法\"}", "value": "function\nweb_search\n{\"query\": \"薰衣草精油 品牌 失眠用法\"}"}] +[{"session_id": "773", "turn_idx": 0, "from": "Human", "text": "小跃，我手机电池老是不耐用，有什么省电技巧？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-3-0.wav"}}, {"session_id": "773", "turn_idx": 1, "from": "Assistant", "text": "省电的话，关掉后台应用推送就行，像微信、微博那些常耗电。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-3-1.wav"}}, {"session_id": "773", "turn_idx": 2, "from": "Human", "text": "嗯，我试过了。还有别的办法吗？比如换电池？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-3-2.wav"}}, {"session_id": "773", "turn_idx": 3, "from": "Assistant", "text": "换电池可以，但先试试调低屏幕亮度，减少自动更新设置。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-3-3.wav"}}, {"session_id": "773", "turn_idx": 4, "from": "Human", "text": "亮度我调了。对了，苹果手机电池寿命一般多久？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-3-4.wav"}}, {"session_id": "773", "turn_idx": 5, "from": "Assistant", "text": "苹果电池正常用两到三年吧，看使用习惯。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-3-5.wav"}}, {"session_id": "773", "turn_idx": 6, "from": "Human", "text": "好的。那你能查查具体怎么延长苹果电池寿命的方法吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-3-6.wav"}}, {"session_id": "773", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"延长苹果手机电池寿命 方法\"}", "value": "function\nweb_search\n{\"query\": \"延长苹果手机电池寿命 方法\"}"}] +[{"session_id": "818", "turn_idx": 0, "from": "Human", "text": "小跃，我想种点阳台植物，哪种容易养活？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-4-0.wav"}}, {"session_id": "818", "turn_idx": 1, "from": "Assistant", "text": "新手推荐绿萝或多肉，耐旱好养，浇点水就行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-4-1.wav"}}, {"session_id": "818", "turn_idx": 2, "from": "Human", "text": "多肉需要阳光多吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-4-2.wav"}}, {"session_id": "818", "turn_idx": 3, "from": "Assistant", "text": "每天三到四小时散射光就够，别暴晒。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-4-3.wav"}}, {"session_id": "818", "turn_idx": 4, "from": "Human", "text": "那浇水频率呢？怕浇太多烂根。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-4-4.wav"}}, {"session_id": "818", "turn_idx": 5, "from": "Assistant", "text": "夏天一周一次，冬天半个月一次，土干了再浇。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-4-5.wav"}}, {"session_id": "818", "turn_idx": 6, "from": "Human", "text": "我想查多肉植物的详细种植指南。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-4-6.wav"}}, {"session_id": "818", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"多肉植物种植技巧\"}", "value": "function\nweb_search\n{\"query\": \"多肉植物种植技巧\"}"}] +[{"session_id": "964", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在健身，但增肌效果慢，怎么回事？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-5-0.wav"}}, {"session_id": "964", "turn_idx": 1, "from": "Assistant", "text": "增肌慢可能和饮食或训练计划有关，确保蛋白质摄入和规律锻炼。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-5-1.wav"}}, {"session_id": "964", "turn_idx": 2, "from": "Human", "text": "蛋白质我吃了鸡蛋，但训练计划不太科学。 帮我找一下新手增肌训练计划推荐。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-5-2.wav"}}, {"session_id": "964", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"新手增肌训练计划推荐\"}", "value": "function\nweb_search\n{\"query\": \"新手增肌训练计划推荐\"}"}] +[{"session_id": "232", "turn_idx": 0, "from": "Human", "text": "小跃，我打算去上海旅游，有什么必去的景点？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-6-0.wav"}}, {"session_id": "232", "turn_idx": 1, "from": "Assistant", "text": "外滩、东方明珠和城隍庙都是热门地，逛起来很过瘾。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-6-1.wav"}}, {"session_id": "232", "turn_idx": 2, "from": "Human", "text": "城隍庙听起来有趣。你能查一下它的开放时间吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-6-2.wav"}}, {"session_id": "232", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"上海城隍庙 开放时间\"}", "value": "function\nweb_search\n{\"query\": \"上海城隍庙 开放时间\"}"}] +[{"session_id": "868", "turn_idx": 0, "from": "Human", "text": "小跃，我有点头痛，不想吃药。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-7-0.wav"}}, {"session_id": "868", "turn_idx": 1, "from": "Assistant", "text": "头痛挺烦的，试试休息或喝水？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-7-1.wav"}}, {"session_id": "868", "turn_idx": 2, "from": "Human", "text": "喝水没用，帮我找点自然缓解方法，比如按摩穴位。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-7-2.wav"}}, {"session_id": "868", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"头痛自然缓解按摩方法\"}", "value": "function\nweb_search\n{\"query\": \"头痛自然缓解按摩方法\"}"}] +[{"session_id": "303", "turn_idx": 0, "from": "Human", "text": "小跃，我打算周末去爬山，附近有什么推荐的山吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-8-0.wav"}}, {"session_id": "303", "turn_idx": 1, "from": "Assistant", "text": "嗯，要看你在哪个城市。比如北京的话，香山就很适合新手，风景也好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-8-1.wav"}}, {"session_id": "303", "turn_idx": 2, "from": "Human", "text": "我在上海，附近有类似的山吗？不要太远的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-8-2.wav"}}, {"session_id": "303", "turn_idx": 3, "from": "Assistant", "text": "上海的话，佘山是个好选择，开车一小时就到。那里有徒步路线，还能看天文台。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-8-3.wav"}}, {"session_id": "303", "turn_idx": 4, "from": "Human", "text": "佘山听起来不错。但我担心人多，你能帮我查查佘山周末的游客流量预测吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-8-4.wav"}}, {"session_id": "303", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"上海 佘山 游客流量 预测\"}", "value": "function\nweb_search\n{\"query\": \"上海 佘山 游客流量 预测\"}"}] +[{"session_id": "174", "turn_idx": 0, "from": "Human", "text": "小跃，我想去旅游，国内哪个地方适合夏天去？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-9-0.wav"}}, {"session_id": "174", "turn_idx": 1, "from": "Assistant", "text": "云南大理不错，天气凉爽风景美，还能避暑。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-9-1.wav"}}, {"session_id": "174", "turn_idx": 2, "from": "Human", "text": "大理有什么必去景点？ 小跃，大理旅游攻略和门票价格帮我查查。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-9-2.wav"}}, {"session_id": "174", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"大理 旅游攻略 门票价格\"}", "value": "function\nweb_search\n{\"query\": \"大理 旅游攻略 门票价格\"}"}] +[{"session_id": "810", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想学做菜，有什么简单的菜推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-10-0.wav"}}, {"session_id": "810", "turn_idx": 1, "from": "Assistant", "text": "学做菜好啊！先从家常菜开始吧，比如番茄炒蛋或者炒青菜，都很容易上手。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-10-1.wav"}}, {"session_id": "810", "turn_idx": 2, "from": "Human", "text": "番茄炒蛋听起来不错，具体需要哪些材料？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-10-2.wav"}}, {"session_id": "810", "turn_idx": 3, "from": "Assistant", "text": "材料很简单:鸡蛋、番茄、盐、油，再加点糖提鲜。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-10-3.wav"}}, {"session_id": "810", "turn_idx": 4, "from": "Human", "text": "那步骤呢？我想知道怎么炒才好吃。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-10-4.wav"}}, {"session_id": "810", "turn_idx": 5, "from": "Assistant", "text": "先把鸡蛋打散炒熟，然后放番茄块翻炒，最后调味。多练习几次就好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-10-5.wav"}}, {"session_id": "810", "turn_idx": 6, "from": "Human", "text": "有没有视频教程可以看？帮我找找详细的番茄炒蛋做法。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-10-6.wav"}}, {"session_id": "810", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋视频教程\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋视频教程\"}"}] +[{"session_id": "443", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在健身。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-11-0.wav"}}, {"session_id": "443", "turn_idx": 1, "from": "Assistant", "text": "真棒！需要饮食搭配建议吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-11-1.wav"}}, {"session_id": "443", "turn_idx": 2, "from": "Human", "text": "对，吃什么能补充蛋白质？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-11-2.wav"}}, {"session_id": "443", "turn_idx": 3, "from": "Assistant", "text": "鸡胸肉不错，低脂高蛋白。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-11-3.wav"}}, {"session_id": "443", "turn_idx": 4, "from": "Human", "text": "鸡胸肉的蛋白质含量大概多少？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-11-4.wav"}}, {"session_id": "443", "turn_idx": 5, "from": "Assistant", "text": "每百克大约含二十多克蛋白。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-11-5.wav"}}, {"session_id": "443", "turn_idx": 6, "from": "Human", "text": "具体数字是多少克？我想精确点。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-11-6.wav"}}, {"session_id": "443", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"鸡胸肉 蛋白质含量\"}", "value": "function\nweb_search\n{\"query\": \"鸡胸肉 蛋白质含量\"}"}] +[{"session_id": "188", "turn_idx": 0, "from": "Human", "text": "小跃，我想了解点人工智能的知识，简单点的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-12-0.wav"}}, {"session_id": "188", "turn_idx": 1, "from": "Assistant", "text": "人工智能就是让机器模仿人类智能，比如语音助手像我这样。先从基础概念学起。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-12-1.wav"}}, {"session_id": "188", "turn_idx": 2, "from": "Human", "text": "嗯，但我不知道怎么系统学。你能帮我查一下人工智能入门书籍推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-12-2.wav"}}, {"session_id": "188", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"人工智能 入门 书籍推荐\"}", "value": "function\nweb_search\n{\"query\": \"人工智能 入门 书籍推荐\"}"}] +[{"session_id": "676", "turn_idx": 0, "from": "Human", "text": "小跃，我想买个新耳机。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-13-0.wav"}}, {"session_id": "676", "turn_idx": 1, "from": "Assistant", "text": "耳机好多选择，你有啥要求？比如音质好还是降噪？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-13-1.wav"}}, {"session_id": "676", "turn_idx": 2, "from": "Human", "text": "降噪吧，预算大概三百块。 帮我找一下三百元降噪耳机推荐。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-13-2.wav"}}, {"session_id": "676", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"降噪耳机 推荐 三百元\"}", "value": "function\nweb_search\n{\"query\": \"降噪耳机 推荐 三百元\"}"}] +[{"session_id": "418", "turn_idx": 0, "from": "Human", "text": "小跃，中秋节有啥传统活动？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-14-0.wav"}}, {"session_id": "418", "turn_idx": 1, "from": "Assistant", "text": "吃月饼、赏月，还有猜灯谜呢。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-14-1.wav"}}, {"session_id": "418", "turn_idx": 2, "from": "Human", "text": "猜灯谜？怎么玩？ 等等，搜搜中秋灯谜的例子和规则。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-14-2.wav"}}, {"session_id": "418", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"中秋节 灯谜 例子 规则\"}", "value": "function\nweb_search\n{\"query\": \"中秋节 灯谜 例子 规则\"}"}] +[{"session_id": "143", "turn_idx": 0, "from": "Human", "text": "小跃，我最近失眠严重，有什么自然疗法吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-15-0.wav"}}, {"session_id": "143", "turn_idx": 1, "from": "Assistant", "text": "失眠真难受！试试睡前喝温牛奶或听轻音乐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-15-1.wav"}}, {"session_id": "143", "turn_idx": 2, "from": "Human", "text": "我试过，但效果不大。听说瑜伽有帮助。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-15-2.wav"}}, {"session_id": "143", "turn_idx": 3, "from": "Assistant", "text": "瑜伽确实能放松，尤其是呼吸练习。你练过吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-15-3.wav"}}, {"session_id": "143", "turn_idx": 4, "from": "Human", "text": "没有，完全新手。得找个简单教程。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-15-4.wav"}}, {"session_id": "143", "turn_idx": 5, "from": "Assistant", "text": "可以从基础动作开始，比如猫牛式。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-15-5.wav"}}, {"session_id": "143", "turn_idx": 6, "from": "Human", "text": "那帮我搜一下适合初学者的失眠瑜伽视频教程吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-15-6.wav"}}, {"session_id": "143", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"初学者 失眠 瑜伽 视频教程\"}", "value": "function\nweb_search\n{\"query\": \"初学者 失眠 瑜伽 视频教程\"}"}] +[{"session_id": "830", "turn_idx": 0, "from": "Human", "text": "小跃，我下个月想去北京玩，有什么必去的景点吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-16-0.wav"}}, {"session_id": "830", "turn_idx": 1, "from": "Assistant", "text": "北京景点可多了，故宫、天安门、长城都是经典。你计划待几天？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-16-1.wav"}}, {"session_id": "830", "turn_idx": 2, "from": "Human", "text": "大概四天三夜吧。行程怎么安排合理？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-16-2.wav"}}, {"session_id": "830", "turn_idx": 3, "from": "Assistant", "text": "首先，第一天逛故宫和天安门广场。第二天去长城，建议选八达岭。第三天看看颐和园。最后一天自由活动。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-16-3.wav"}}, {"session_id": "830", "turn_idx": 4, "from": "Human", "text": "长城远不远？从市区过去要多久？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-16-4.wav"}}, {"session_id": "830", "turn_idx": 5, "from": "Assistant", "text": "八达岭长城有点远，坐大巴或高铁大约一个半小时。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-16-5.wav"}}, {"session_id": "830", "turn_idx": 6, "from": "Human", "text": "小跃，帮我查一下从北京站到八达岭长城的高铁班次吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-16-6.wav"}}, {"session_id": "830", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"北京站 八达岭长城 高铁班次\"}", "value": "function\nweb_search\n{\"query\": \"北京站 八达岭长城 高铁班次\"}"}] +[{"session_id": "113", "turn_idx": 0, "from": "Human", "text": "小跃，听说新上映了一部科幻片，叫什么来着？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-17-0.wav"}}, {"session_id": "113", "turn_idx": 1, "from": "Assistant", "text": "最近有《沙丘》第二部上映，挺火的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-17-1.wav"}}, {"session_id": "113", "turn_idx": 2, "from": "Human", "text": "哦对，沙丘。你能查一下它在附近影院的上映时间吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-17-2.wav"}}, {"session_id": "113", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"沙丘第二部上映时间\"}", "value": "function\nweb_search\n{\"query\": \"沙丘第二部上映时间\"}"}] +[{"session_id": "540", "turn_idx": 0, "from": "Human", "text": "小跃，我今天想做点意大利面，但忘了酱汁怎么做。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-18-0.wav"}}, {"session_id": "540", "turn_idx": 1, "from": "Assistant", "text": "哦，意大利面酱汁很简单啊，通常需要番茄、洋葱这些。你要哪种酱汁？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-18-1.wav"}}, {"session_id": "540", "turn_idx": 2, "from": "Human", "text": "我想做肉酱的那种，你能帮我查一下具体食材和做法吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-18-2.wav"}}, {"session_id": "540", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"肉酱意大利面 食谱 步骤\"}", "value": "function\nweb_search\n{\"query\": \"肉酱意大利面 食谱 步骤\"}"}] +[{"session_id": "783", "turn_idx": 0, "from": "Human", "text": "小跃，我想换部新手机，有什么推荐的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-19-0.wav"}}, {"session_id": "783", "turn_idx": 1, "from": "Assistant", "text": "最近苹果和华为的新款都不错，看你的预算和需求。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-19-1.wav"}}, {"session_id": "783", "turn_idx": 2, "from": "Human", "text": "预算中等吧，主要用来看视频和拍照。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-19-2.wav"}}, {"session_id": "783", "turn_idx": 3, "from": "Assistant", "text": "那可以考虑中端机型，像华为的nova系列，性价比高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-19-3.wav"}}, {"session_id": "783", "turn_idx": 4, "from": "Human", "text": "华为nova？拍照效果怎么样？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-19-4.wav"}}, {"session_id": "783", "turn_idx": 5, "from": "Assistant", "text": "后置摄像头很强，夜景模式特别棒。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-19-5.wav"}}, {"session_id": "783", "turn_idx": 6, "from": "Human", "text": "行，那你能查查华为nova最新款的价格和评测吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-19-6.wav"}}, {"session_id": "783", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"华为 nova 最新款 评测 价格\"}", "value": "function\nweb_search\n{\"query\": \"华为 nova 最新款 评测 价格\"}"}] +[{"session_id": "700", "turn_idx": 0, "from": "Human", "text": "小跃，我今天刚看完一部科幻电影，感觉特别震撼。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-20-0.wav"}}, {"session_id": "700", "turn_idx": 1, "from": "Assistant", "text": "真的吗？科幻电影确实容易让人入迷。你看的是哪一部啊？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-20-1.wav"}}, {"session_id": "700", "turn_idx": 2, "from": "Human", "text": "是讲时间旅行的那个，主角回到过去改变历史。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-20-2.wav"}}, {"session_id": "700", "turn_idx": 3, "from": "Assistant", "text": "听起来像《回到未来》系列。那种剧情确实烧脑。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-20-3.wav"}}, {"session_id": "700", "turn_idx": 4, "from": "Human", "text": "对，就是它！我想知道导演还拍过哪些类似的作品。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-20-4.wav"}}, {"session_id": "700", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"罗伯特·泽米吉斯 导演作品\"}", "value": "function\nweb_search\n{\"query\": \"罗伯特·泽米吉斯 导演作品\"}"}] +[{"session_id": "499", "turn_idx": 0, "from": "Human", "text": "小跃，中秋节快到了，我们全家要聚聚。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-21-0.wav"}}, {"session_id": "499", "turn_idx": 1, "from": "Assistant", "text": "团圆的好日子！吃月饼、赏月是传统习俗。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-21-1.wav"}}, {"session_id": "499", "turn_idx": 2, "from": "Human", "text": "为什么中秋节要吃月饼呢？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-21-2.wav"}}, {"session_id": "499", "turn_idx": 3, "from": "Assistant", "text": "象征家庭团圆和丰收，古代就有这个传统了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-21-3.wav"}}, {"session_id": "499", "turn_idx": 4, "from": "Human", "text": "月饼最早出现在哪个朝代？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-21-4.wav"}}, {"session_id": "499", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"月饼 起源 朝代\"}", "value": "function\nweb_search\n{\"query\": \"月饼 起源 朝代\"}"}] +[{"session_id": "373", "turn_idx": 0, "from": "Human", "text": "小跃，我在学中国历史，对秦始皇感兴趣。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-22-0.wav"}}, {"session_id": "373", "turn_idx": 1, "from": "Assistant", "text": "秦始皇统一六国，建立了秦朝。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-22-1.wav"}}, {"session_id": "373", "turn_idx": 2, "from": "Human", "text": "统一六国花了多久？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-22-2.wav"}}, {"session_id": "373", "turn_idx": 3, "from": "Assistant", "text": "大概用了十年左右的时间。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-22-3.wav"}}, {"session_id": "373", "turn_idx": 4, "from": "Human", "text": "详细点，帮我找秦始皇统一过程的关键事件。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-22-4.wav"}}, {"session_id": "373", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"秦始皇 统一六国 关键事件\"}", "value": "function\nweb_search\n{\"query\": \"秦始皇 统一六国 关键事件\"}"}] +[{"session_id": "832", "turn_idx": 0, "from": "Human", "text": "小跃，我想买个蓝牙耳机，预算五百块以内。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-23-0.wav"}}, {"session_id": "832", "turn_idx": 1, "from": "Assistant", "text": "这个价位选择不少，像漫步者或小米都有性价比高的。你常听音乐还是打电话？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-23-1.wav"}}, {"session_id": "832", "turn_idx": 2, "from": "Human", "text": "主要听音乐，音质好点。电池要耐用。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-23-2.wav"}}, {"session_id": "832", "turn_idx": 3, "from": "Assistant", "text": "那推荐漫步者LolliPods系列，音质不错，续航十小时左右。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-23-3.wav"}}, {"session_id": "832", "turn_idx": 4, "from": "Human", "text": "具体型号呢？有降噪功能吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-23-4.wav"}}, {"session_id": "832", "turn_idx": 5, "from": "Assistant", "text": "LolliPods Pro带主动降噪，价格四百多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-23-5.wav"}}, {"session_id": "832", "turn_idx": 6, "from": "Human", "text": "小跃，帮我查一下漫步者LolliPods Pro的用户评价。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-23-6.wav"}}, {"session_id": "832", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"漫步者 LolliPods Pro 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"漫步者 LolliPods Pro 用户评价\"}"}] +[{"session_id": "354", "turn_idx": 0, "from": "Human", "text": "小跃，我想买个新耳机，苹果和索尼哪个音质好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-24-0.wav"}}, {"session_id": "354", "turn_idx": 1, "from": "Assistant", "text": "苹果方便，索尼音质更专业些。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-24-1.wav"}}, {"session_id": "354", "turn_idx": 2, "from": "Human", "text": "我主要听音乐，索尼哪款性价比高？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-24-2.wav"}}, {"session_id": "354", "turn_idx": 3, "from": "Assistant", "text": "WH-一千系列不错，降噪效果好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-24-3.wav"}}, {"session_id": "354", "turn_idx": 4, "from": "Human", "text": "具体型号呢？比如最新款是什么？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-24-4.wav"}}, {"session_id": "354", "turn_idx": 5, "from": "Assistant", "text": "索尼WH-一千XM五，但价格稍高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-24-5.wav"}}, {"session_id": "354", "turn_idx": 6, "from": "Human", "text": "帮我查一下用户评价和价格吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-24-6.wav"}}, {"session_id": "354", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"索尼WH-1000XM5 评价 价格\"}", "value": "function\nweb_search\n{\"query\": \"索尼WH-1000XM5 评价 价格\"}"}] +[{"session_id": "848", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在读哈利波特，但忘了作者是谁。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-25-0.wav"}}, {"session_id": "848", "turn_idx": 1, "from": "Assistant", "text": "作者是罗琳，一位英国女作家，写了七本书。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-25-1.wav"}}, {"session_id": "848", "turn_idx": 2, "from": "Human", "text": "七本？我只知道前几部，最后一部叫啥？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-25-2.wav"}}, {"session_id": "848", "turn_idx": 3, "from": "Assistant", "text": "最后是《哈利波特与死亡圣器》，结局挺感人的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-25-3.wav"}}, {"session_id": "848", "turn_idx": 4, "from": "Human", "text": "我还想了解罗琳的生平，你能搜一下她的背景吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-25-4.wav"}}, {"session_id": "848", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"罗琳 作者 生平介绍\"}", "value": "function\nweb_search\n{\"query\": \"罗琳 作者 生平介绍\"}"}] +[{"session_id": "415", "turn_idx": 0, "from": "Human", "text": "小跃，我想换新耳机，有啥推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-26-0.wav"}}, {"session_id": "415", "turn_idx": 1, "from": "Assistant", "text": "苹果的AirPods Pro音质好，降噪效果强。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-26-1.wav"}}, {"session_id": "415", "turn_idx": 2, "from": "Human", "text": "AirPods Pro？最新款啥时候出的？ 帮我查查它的价格和功能细节。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-26-2.wav"}}, {"session_id": "415", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"AirPods Pro 最新款 价格 功能\"}", "value": "function\nweb_search\n{\"query\": \"AirPods Pro 最新款 价格 功能\"}"}] +[{"session_id": "698", "turn_idx": 0, "from": "Human", "text": "小跃，我想买双运动鞋，但不知道哪个品牌好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-27-0.wav"}}, {"session_id": "698", "turn_idx": 1, "from": "Assistant", "text": "运动鞋选择很多，你主要跑步还是日常穿？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-27-1.wav"}}, {"session_id": "698", "turn_idx": 2, "from": "Human", "text": "日常穿，舒适第一，预算在五百元左右。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-27-2.wav"}}, {"session_id": "698", "turn_idx": 3, "from": "Assistant", "text": "那可以看看国产品牌，性价比高，比如安踏或李宁。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-27-3.wav"}}, {"session_id": "698", "turn_idx": 4, "from": "Human", "text": "安踏我试过，但想比较一下。你能找点用户评价吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-27-4.wav"}}, {"session_id": "698", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"运动鞋品牌 舒适 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"运动鞋品牌 舒适 用户评价\"}"}] +[{"session_id": "408", "turn_idx": 0, "from": "Human", "text": "小跃，想买件运动鞋，什么品牌好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-28-0.wav"}}, {"session_id": "408", "turn_idx": 1, "from": "Assistant", "text": "看需求，耐克或阿迪达斯舒适度高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-28-1.wav"}}, {"session_id": "408", "turn_idx": 2, "from": "Human", "text": "耐克吧，预算五百块。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-28-2.wav"}}, {"session_id": "408", "turn_idx": 3, "from": "Assistant", "text": "这个价位有基础款，适合日常穿。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-28-3.wav"}}, {"session_id": "408", "turn_idx": 4, "from": "Human", "text": "能搜一下具体型号和用户反馈吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-28-4.wav"}}, {"session_id": "408", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"耐克运动鞋 推荐\"}", "value": "function\nweb_search\n{\"query\": \"耐克运动鞋 推荐\"}"}] +[{"session_id": "106", "turn_idx": 0, "from": "Human", "text": "小跃，我想学游泳，怎么开始？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-29-0.wav"}}, {"session_id": "106", "turn_idx": 1, "from": "Assistant", "text": "游泳可以先从蛙泳学起，找个浅水区练习浮水。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-29-1.wav"}}, {"session_id": "106", "turn_idx": 2, "from": "Human", "text": "那详细的动作要领和呼吸技巧呢？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-29-2.wav"}}, {"session_id": "106", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"游泳蛙泳动作呼吸技巧\"}", "value": "function\nweb_search\n{\"query\": \"游泳蛙泳动作呼吸技巧\"}"}] +[{"session_id": "617", "turn_idx": 0, "from": "Human", "text": "小跃，我最近学英语，想知道英语有多少个单词总量？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-30-0.wav"}}, {"session_id": "617", "turn_idx": 1, "from": "Assistant", "text": "英语单词超多的，但具体数字我不确定。你是在背单词吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-30-1.wav"}}, {"session_id": "617", "turn_idx": 2, "from": "Human", "text": "是的，想了解下规模。能查查英语总词汇量是多少吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-30-2.wav"}}, {"session_id": "617", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"英语 总词汇量\"}", "value": "function\nweb_search\n{\"query\": \"英语 总词汇量\"}"}] +[{"session_id": "992", "turn_idx": 0, "from": "Human", "text": "我下个月想去北京玩，有什么必去景点？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-31-0.wav"}}, {"session_id": "992", "turn_idx": 1, "from": "Assistant", "text": "故宫和长城是经典，尤其长城，风景壮观。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-31-1.wav"}}, {"session_id": "992", "turn_idx": 2, "from": "Human", "text": "长城那么长，哪个段最值得去？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-31-2.wav"}}, {"session_id": "992", "turn_idx": 3, "from": "Assistant", "text": "八达岭段最热门，设施全，适合第一次去。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-31-3.wav"}}, {"session_id": "992", "turn_idx": 4, "from": "Human", "text": "哦，那门票贵不贵？需要提前订吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-31-4.wav"}}, {"session_id": "992", "turn_idx": 5, "from": "Assistant", "text": "门票一般一百多块，旺季最好网上预订。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-31-5.wav"}}, {"session_id": "992", "turn_idx": 6, "from": "Human", "text": "我想查查最新开放时间和攻略。搜一下。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-31-6.wav"}}, {"session_id": "992", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"北京 长城 八达岭 攻略\"}", "value": "function\nweb_search\n{\"query\": \"北京 长城 八达岭 攻略\"}"}] +[{"session_id": "82", "turn_idx": 0, "from": "Human", "text": "小跃，我最近老是失眠，好烦啊。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-32-0.wav"}}, {"session_id": "82", "turn_idx": 1, "from": "Assistant", "text": "失眠的话，试试睡前喝杯温牛奶，可能有点帮助。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-32-1.wav"}}, {"session_id": "82", "turn_idx": 2, "from": "Human", "text": "还有什么其他小技巧能改善睡眠？我想多了解一下。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-32-2.wav"}}, {"session_id": "82", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"改善睡眠 自然方法\"}", "value": "function\nweb_search\n{\"query\": \"改善睡眠 自然方法\"}"}] +[{"session_id": "704", "turn_idx": 0, "from": "Human", "text": "小跃，我刚读了一本推理小说，情节太精彩了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-33-0.wav"}}, {"session_id": "704", "turn_idx": 1, "from": "Assistant", "text": "推理小说总是让人停不下来。谁写的？东野圭吾吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-33-1.wav"}}, {"session_id": "704", "turn_idx": 2, "from": "Human", "text": "对，是《解忧杂货店》。想找同类型的书。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-33-2.wav"}}, {"session_id": "704", "turn_idx": 3, "from": "Assistant", "text": "东野圭吾的其他作品也值得读，比如《白夜行》。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-33-3.wav"}}, {"session_id": "704", "turn_idx": 4, "from": "Human", "text": "好主意！那帮我查查他所有出版的书单吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-33-4.wav"}}, {"session_id": "704", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"东野圭吾 小说作品 列表\"}", "value": "function\nweb_search\n{\"query\": \"东野圭吾 小说作品 列表\"}"}] +[{"session_id": "83", "turn_idx": 0, "from": "Human", "text": "小跃，我对爱因斯坦特别好奇，你知道他最著名的理论是什么吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-34-0.wav"}}, {"session_id": "83", "turn_idx": 1, "from": "Assistant", "text": "爱因斯坦最出名的是相对论，改变了物理学。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-34-1.wav"}}, {"session_id": "83", "turn_idx": 2, "from": "Human", "text": "相对论具体讲了啥？我想查查简单解释。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-34-2.wav"}}, {"session_id": "83", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"相对论 简单解释\"}", "value": "function\nweb_search\n{\"query\": \"相对论 简单解释\"}"}] +[{"session_id": "397", "turn_idx": 0, "from": "Human", "text": "小跃，我想买个新背包，预算在五百块左右。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-35-0.wav"}}, {"session_id": "397", "turn_idx": 1, "from": "Assistant", "text": "背包选择多啊！你要旅行用还是日常背？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-35-1.wav"}}, {"session_id": "397", "turn_idx": 2, "from": "Human", "text": "日常用的，容量大点好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-35-2.wav"}}, {"session_id": "397", "turn_idx": 3, "from": "Assistant", "text": "像耐克或新秀丽都不错。要我搜索具体型号推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-35-3.wav"}}, {"session_id": "397", "turn_idx": 4, "from": "Human", "text": "行，帮我查五百元背包推荐。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-35-4.wav"}}, {"session_id": "397", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"五百元 背包 推荐\"}", "value": "function\nweb_search\n{\"query\": \"五百元 背包 推荐\"}"}] +[{"session_id": "459", "turn_idx": 0, "from": "Human", "text": "小跃，篮球比赛规则我有点懵，比如罚球。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-36-0.wav"}}, {"session_id": "459", "turn_idx": 1, "from": "Assistant", "text": "罚球是对方犯规后，你在罚球线投球。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-36-1.wav"}}, {"session_id": "459", "turn_idx": 2, "from": "Human", "text": "能投几次？进一球得几分？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-36-2.wav"}}, {"session_id": "459", "turn_idx": 3, "from": "Assistant", "text": "一般投两次，进一球得一分。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-36-3.wav"}}, {"session_id": "459", "turn_idx": 4, "from": "Human", "text": "详细规则是啥？查查官方说明。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-36-4.wav"}}, {"session_id": "459", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"篮球 罚球 规则 说明\"}", "value": "function\nweb_search\n{\"query\": \"篮球 罚球 规则 说明\"}"}] +[{"session_id": "427", "turn_idx": 0, "from": "Human", "text": "小跃，计划去爬山，有什么注意事项？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-37-0.wav"}}, {"session_id": "427", "turn_idx": 1, "from": "Assistant", "text": "穿舒适的鞋子，带足水和零食，注意天气变化。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-37-1.wav"}}, {"session_id": "427", "turn_idx": 2, "from": "Human", "text": "零食带什么比较合适？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-37-2.wav"}}, {"session_id": "427", "turn_idx": 3, "from": "Assistant", "text": "能量棒或坚果，轻便又能补充体力。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-37-3.wav"}}, {"session_id": "427", "turn_idx": 4, "from": "Human", "text": "嗯，我想搜一下爬山必备物品清单。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-37-4.wav"}}, {"session_id": "427", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"爬山 必备物品 清单\"}", "value": "function\nweb_search\n{\"query\": \"爬山 必备物品 清单\"}"}] +[{"session_id": "259", "turn_idx": 0, "from": "Human", "text": "小跃，我想养只猫，但没经验。新手该注意啥？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-38-0.wav"}}, {"session_id": "259", "turn_idx": 1, "from": "Assistant", "text": "先准备猫砂盆、食盆和水碗。选性格温顺的品种，比如英短，别急着抱回家。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-38-1.wav"}}, {"session_id": "259", "turn_idx": 2, "from": "Human", "text": "英短我听说过，但怕掉毛太多。有不容易掉毛的猫吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-38-2.wav"}}, {"session_id": "259", "turn_idx": 3, "from": "Assistant", "text": "斯芬克斯猫无毛，但需要特殊护理。或者德文卷毛猫，掉毛少点，适合新手。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-38-3.wav"}}, {"session_id": "259", "turn_idx": 4, "from": "Human", "text": "德文卷毛猫听起来有趣。能帮我查查饲养成本和常见问题吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-38-4.wav"}}, {"session_id": "259", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"德文卷毛猫 饲养成本 常见问题\"}", "value": "function\nweb_search\n{\"query\": \"德文卷毛猫 饲养成本 常见问题\"}"}] +[{"session_id": "885", "turn_idx": 0, "from": "Human", "text": "小跃，我对历史很感兴趣，特别是古埃及的金字塔。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-39-0.wav"}}, {"session_id": "885", "turn_idx": 1, "from": "Assistant", "text": "金字塔是古埃及法老的陵墓，建造时间在四千多年前，很神秘！", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-39-1.wav"}}, {"session_id": "885", "turn_idx": 2, "from": "Human", "text": "那最大的金字塔有多高？帮我搜索一下具体数据。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-39-2.wav"}}, {"session_id": "885", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"古埃及金字塔 最大高度\"}", "value": "function\nweb_search\n{\"query\": \"古埃及金字塔 最大高度\"}"}] +[{"session_id": "774", "turn_idx": 0, "from": "Human", "text": "小跃，我最近失眠，有什么自然疗法吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-40-0.wav"}}, {"session_id": "774", "turn_idx": 1, "from": "Assistant", "text": "自然疗法的话，睡前喝杯温牛奶或听轻音乐，能帮助放松。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-40-1.wav"}}, {"session_id": "774", "turn_idx": 2, "from": "Human", "text": "牛奶我试了，效果一般。音乐有什么推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-40-2.wav"}}, {"session_id": "774", "turn_idx": 3, "from": "Assistant", "text": "试试古典乐，比如贝多芬的月光曲，节奏舒缓。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-40-3.wav"}}, {"session_id": "774", "turn_idx": 4, "from": "Human", "text": "贝多芬？我记得他是德国人，但具体生平不了解。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-40-4.wav"}}, {"session_id": "774", "turn_idx": 5, "from": "Assistant", "text": "对，贝多芬是十八世纪的作曲家，代表作很多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-40-5.wav"}}, {"session_id": "774", "turn_idx": 6, "from": "Human", "text": "那你能帮我搜搜他的生平故事和代表作列表吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-40-6.wav"}}, {"session_id": "774", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"贝多芬生平 代表作\"}", "value": "function\nweb_search\n{\"query\": \"贝多芬生平 代表作\"}"}] +[{"session_id": "956", "turn_idx": 0, "from": "Human", "text": "小跃，我想买件羽绒服，加拿大鹅牌子咋样？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-41-0.wav"}}, {"session_id": "956", "turn_idx": 1, "from": "Assistant", "text": "加拿大鹅保暖性强，但价格贵，一般七八千起步。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-41-1.wav"}}, {"session_id": "956", "turn_idx": 2, "from": "Human", "text": "哇，这么贵！有平替品牌吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-41-2.wav"}}, {"session_id": "956", "turn_idx": 3, "from": "Assistant", "text": "优衣库的轻羽绒性价比高，几百块就搞定。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-41-3.wav"}}, {"session_id": "956", "turn_idx": 4, "from": "Human", "text": "行，帮我查查优衣库最新羽绒服评测。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-41-4.wav"}}, {"session_id": "956", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"优衣库羽绒服用户评测\"}", "value": "function\nweb_search\n{\"query\": \"优衣库羽绒服用户评测\"}"}] +[{"session_id": "320", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我刚买了些虾，想做个简单菜，有啥好主意？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-42-0.wav"}}, {"session_id": "320", "turn_idx": 1, "from": "Assistant", "text": "虾仁炒蛋挺不错的，先把虾去壳焯水，鸡蛋打散一起炒就行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-42-1.wav"}}, {"session_id": "320", "turn_idx": 2, "from": "Human", "text": "焯水要多久？我怕煮老了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-42-2.wav"}}, {"session_id": "320", "turn_idx": 3, "from": "Assistant", "text": "一般水开后煮三十秒左右。要我查查更精确的时间吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-42-3.wav"}}, {"session_id": "320", "turn_idx": 4, "from": "Human", "text": "好的，帮我查一下虾仁焯水的最佳时间。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-42-4.wav"}}, {"session_id": "320", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"虾仁焯水 最佳时间\"}", "value": "function\nweb_search\n{\"query\": \"虾仁焯水 最佳时间\"}"}] +[{"session_id": "901", "turn_idx": 0, "from": "Human", "text": "小跃，我周末想去爬山，附近有什么好地方推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-43-0.wav"}}, {"session_id": "901", "turn_idx": 1, "from": "Assistant", "text": "北京香山挺不错的，秋天红叶特别美，人也不多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-43-1.wav"}}, {"session_id": "901", "turn_idx": 2, "from": "Human", "text": "香山？我记得那里有个古寺，叫什么名字来着？ 还有，香山的历史有多久了？帮我查查。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-43-2.wav"}}, {"session_id": "901", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"香山历史年份\"}", "value": "function\nweb_search\n{\"query\": \"香山历史年份\"}"}] +[{"session_id": "951", "turn_idx": 0, "from": "Human", "text": "小跃，我下个月想去海边玩，国内哪里好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-44-0.wav"}}, {"session_id": "951", "turn_idx": 1, "from": "Assistant", "text": "海南三亚很棒，沙滩漂亮，海鲜也多。厦门鼓浪屿也不错。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-44-1.wav"}}, {"session_id": "951", "turn_idx": 2, "from": "Human", "text": "三亚听说人多，厦门交通方便不？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-44-2.wav"}}, {"session_id": "951", "turn_idx": 3, "from": "Assistant", "text": "厦门有机场和高铁，从北京过去高铁大概十小时左右。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-44-3.wav"}}, {"session_id": "951", "turn_idx": 4, "from": "Human", "text": "好，那帮我查查厦门鼓浪屿的游玩攻略吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-44-4.wav"}}, {"session_id": "951", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"厦门鼓浪屿旅游攻略\"}", "value": "function\nweb_search\n{\"query\": \"厦门鼓浪屿旅游攻略\"}"}] +[{"session_id": "736", "turn_idx": 0, "from": "Human", "text": "小跃，我想学点英语单词，每天记几个，有啥方法？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-45-0.wav"}}, {"session_id": "736", "turn_idx": 1, "from": "Assistant", "text": "好习惯！用APP背单词或日常对话练习都行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-45-1.wav"}}, {"session_id": "736", "turn_idx": 2, "from": "Human", "text": "对话练习不错，能推荐些常用句子吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-45-2.wav"}}, {"session_id": "736", "turn_idx": 3, "from": "Assistant", "text": "比如问候语“你好”或点餐时用的句子。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-45-3.wav"}}, {"session_id": "736", "turn_idx": 4, "from": "Human", "text": "好的，帮我搜一下餐厅点餐的常用英语表达。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-45-4.wav"}}, {"session_id": "736", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"餐厅点餐 英语 常用句子\"}", "value": "function\nweb_search\n{\"query\": \"餐厅点餐 英语 常用句子\"}"}] +[{"session_id": "155", "turn_idx": 0, "from": "Human", "text": "小跃，周末想去爬山，推荐个地方呗。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-46-0.wav"}}, {"session_id": "155", "turn_idx": 1, "from": "Assistant", "text": "附近山区或公园都不错，看你住哪儿，比如北京香山或杭州西湖边。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-46-1.wav"}}, {"session_id": "155", "turn_idx": 2, "from": "Human", "text": "帮我查查深圳附近的登山路线推荐。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-46-2.wav"}}, {"session_id": "155", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"深圳 登山路线 推荐\"}", "value": "function\nweb_search\n{\"query\": \"深圳 登山路线 推荐\"}"}] +[{"session_id": "68", "turn_idx": 0, "from": "Human", "text": "小跃，我听说咖啡对健康有好处，是真的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-47-0.wav"}}, {"session_id": "68", "turn_idx": 1, "from": "Assistant", "text": "是的，适量喝咖啡能提神，但别过量，一天一两杯就好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-47-1.wav"}}, {"session_id": "68", "turn_idx": 2, "from": "Human", "text": "一两杯？具体有哪些健康益处和风险？ 小跃，你能查一下咖啡的健康益处和潜在风险吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-47-2.wav"}}, {"session_id": "68", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"咖啡 健康益处 潜在风险\"}", "value": "function\nweb_search\n{\"query\": \"咖啡 健康益处 潜在风险\"}"}] +[{"session_id": "926", "turn_idx": 0, "from": "Human", "text": "小跃，我总失眠，有啥自然助眠的方法？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-48-0.wav"}}, {"session_id": "926", "turn_idx": 1, "from": "Assistant", "text": "试试睡前喝热牛奶、听轻音乐，或者做深呼吸练习。别玩手机太晚。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-48-1.wav"}}, {"session_id": "926", "turn_idx": 2, "from": "Human", "text": "那热牛奶助眠的具体步骤是什么？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-48-2.wav"}}, {"session_id": "926", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"热牛奶 助眠 方法 步骤\"}", "value": "function\nweb_search\n{\"query\": \"热牛奶 助眠 方法 步骤\"}"}] +[{"session_id": "918", "turn_idx": 0, "from": "Human", "text": "小跃，我想买笔记本电脑，苹果和戴尔哪个好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-49-0.wav"}}, {"session_id": "918", "turn_idx": 1, "from": "Assistant", "text": "看你用途！苹果设计好，适合创意工作；戴尔性价比高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-49-1.wav"}}, {"session_id": "918", "turn_idx": 2, "from": "Human", "text": "我主要办公用，处理文档和视频会议。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-49-2.wav"}}, {"session_id": "918", "turn_idx": 3, "from": "Assistant", "text": "那戴尔的商务系列不错，电池耐用，价格也实惠。 具体型号比如XPS系列，续航十小时以上。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-49-3.wav"}}, {"session_id": "918", "turn_idx": 4, "from": "Human", "text": "价格呢？预算五千左右。 好，帮我查一下戴尔XPS笔记本电脑的价格和用户评价吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-49-4.wav"}}, {"session_id": "918", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"戴尔XPS 价格 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"戴尔XPS 价格 用户评价\"}"}] +[{"session_id": "110", "turn_idx": 0, "from": "Human", "text": "小跃，我最近开始跑步，但腿总是疼。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-50-0.wav"}}, {"session_id": "110", "turn_idx": 1, "from": "Assistant", "text": "跑步后腿疼可能是肌肉疲劳，建议你热身和拉伸一下。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-50-1.wav"}}, {"session_id": "110", "turn_idx": 2, "from": "Human", "text": "那拉伸的具体方法是什么？能帮我搜索一下吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-50-2.wav"}}, {"session_id": "110", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"跑步后拉伸方法\"}", "value": "function\nweb_search\n{\"query\": \"跑步后拉伸方法\"}"}] +[{"session_id": "875", "turn_idx": 0, "from": "Human", "text": "小跃，我想学做蛋糕，有啥简单食谱？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-51-0.wav"}}, {"session_id": "875", "turn_idx": 1, "from": "Assistant", "text": "可以试试海绵蛋糕，需要面粉、鸡蛋和糖。首先混合材料，然后烤二十分钟。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-51-1.wav"}}, {"session_id": "875", "turn_idx": 2, "from": "Human", "text": "那烤箱温度要调多少？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-51-2.wav"}}, {"session_id": "875", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"海绵蛋糕 烤箱温度\"}", "value": "function\nweb_search\n{\"query\": \"海绵蛋糕 烤箱温度\"}"}] +[{"session_id": "920", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在学做饭，但总是炒菜糊锅，怎么办？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-52-0.wav"}}, {"session_id": "920", "turn_idx": 1, "from": "Assistant", "text": "炒菜糊锅可能是因为火太大或者油不够。建议先用中火，油热了再放菜，别急着翻动。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-52-1.wav"}}, {"session_id": "920", "turn_idx": 2, "from": "Human", "text": "那你能帮我查查怎么防止炒菜糊锅的具体技巧吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-52-2.wav"}}, {"session_id": "920", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"防止 炒菜 糊锅 技巧\"}", "value": "function\nweb_search\n{\"query\": \"防止 炒菜 糊锅 技巧\"}"}] +[{"session_id": "961", "turn_idx": 0, "from": "Human", "text": "小跃，我打算周末去爬山，推荐个附近的好地方吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-53-0.wav"}}, {"session_id": "961", "turn_idx": 1, "from": "Assistant", "text": "附近的话，西山或者香山都挺适合新手，风景也不错。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-53-1.wav"}}, {"session_id": "961", "turn_idx": 2, "from": "Human", "text": "香山我去过，西山怎么样？需要准备什么装备？ 帮我搜索一下西山徒步路线和必备装备。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-53-2.wav"}}, {"session_id": "961", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"西山徒步路线 必备装备\"}", "value": "function\nweb_search\n{\"query\": \"西山徒步路线 必备装备\"}"}] +[{"session_id": "923", "turn_idx": 0, "from": "Human", "text": "小跃，我手机电池老是不耐用，有啥省电方法？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-54-0.wav"}}, {"session_id": "923", "turn_idx": 1, "from": "Assistant", "text": "关掉后台应用、降低屏幕亮度，还有别一直开蓝牙和定位。这些都能延长电池时间。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-54-1.wav"}}, {"session_id": "923", "turn_idx": 2, "from": "Human", "text": "好的，那苹果手机的最新省电技巧有哪些？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-54-2.wav"}}, {"session_id": "923", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"苹果手机 省电 技巧\"}", "value": "function\nweb_search\n{\"query\": \"苹果手机 省电 技巧\"}"}] +[{"session_id": "668", "turn_idx": 0, "from": "Human", "text": "小跃，《三体》这本书咋样？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-55-0.wav"}}, {"session_id": "668", "turn_idx": 1, "from": "Assistant", "text": "刘慈欣写的科幻神作，拿过雨果奖。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-55-1.wav"}}, {"session_id": "668", "turn_idx": 2, "from": "Human", "text": "雨果奖？是啥级别的奖？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-55-2.wav"}}, {"session_id": "668", "turn_idx": 3, "from": "Assistant", "text": "科幻界最高荣誉之一，全球评选。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-55-3.wav"}}, {"session_id": "668", "turn_idx": 4, "from": "Human", "text": "我想知道《三体》出版年份，还有获奖详情。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-55-4.wav"}}, {"session_id": "668", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"三体 出版时间 雨果奖\"}", "value": "function\nweb_search\n{\"query\": \"三体 出版时间 雨果奖\"}"}] +[{"session_id": "809", "turn_idx": 0, "from": "Human", "text": "小跃，我想找本好书读。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-56-0.wav"}}, {"session_id": "809", "turn_idx": 1, "from": "Assistant", "text": "读书陶冶情操！喜欢什么类型？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-56-1.wav"}}, {"session_id": "809", "turn_idx": 2, "from": "Human", "text": "历史类吧，中国近代史。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-56-2.wav"}}, {"session_id": "809", "turn_idx": 3, "from": "Assistant", "text": "比如《明朝那些事儿》这种通俗历史书。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-56-3.wav"}}, {"session_id": "809", "turn_idx": 4, "from": "Human", "text": "推荐一本易懂的中国近代史书吧。你能查查吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-56-4.wav"}}, {"session_id": "809", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"中国近代史 通俗读物 推荐\"}", "value": "function\nweb_search\n{\"query\": \"中国近代史 通俗读物 推荐\"}"}] +[{"session_id": "538", "turn_idx": 0, "from": "Human", "text": "小跃，我考虑买电动车，比亚迪汉怎么样？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-57-0.wav"}}, {"session_id": "538", "turn_idx": 1, "from": "Assistant", "text": "比亚迪汉续航长，充电快，性价比高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-57-1.wav"}}, {"session_id": "538", "turn_idx": 2, "from": "Human", "text": "续航多远？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-57-2.wav"}}, {"session_id": "538", "turn_idx": 3, "from": "Assistant", "text": "标准版四百公里左右，长续航版六百多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-57-3.wav"}}, {"session_id": "538", "turn_idx": 4, "from": "Human", "text": "充电桩好找吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-57-4.wav"}}, {"session_id": "538", "turn_idx": 5, "from": "Assistant", "text": "城市里挺多的，商场或路边都有。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-57-5.wav"}}, {"session_id": "538", "turn_idx": 6, "from": "Human", "text": "那帮我搜索一下比亚迪汉在北京的充电站位置吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-57-6.wav"}}, {"session_id": "538", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"比亚迪汉 北京 充电站位置\"}", "value": "function\nweb_search\n{\"query\": \"比亚迪汉 北京 充电站位置\"}"}] +[{"session_id": "242", "turn_idx": 0, "from": "Human", "text": "小跃，我朋友推荐了一部科幻电影，但名字忘了，只记得是讲外星人的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-58-0.wav"}}, {"session_id": "242", "turn_idx": 1, "from": "Assistant", "text": "科幻电影挺多的！比如外星人入侵主题的，您记得主演或大概情节吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-58-1.wav"}}, {"session_id": "242", "turn_idx": 2, "from": "Human", "text": "好像是去年上映的，主角是个女科学家。帮我查查去年最火的科幻外星人电影。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-58-2.wav"}}, {"session_id": "242", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"去年科幻电影 外星人主题 女科学家主演\"}", "value": "function\nweb_search\n{\"query\": \"去年科幻电影 外星人主题 女科学家主演\"}"}] +[{"session_id": "328", "turn_idx": 0, "from": "Human", "text": "小跃，我练瑜伽时腿抽筋了，咋办？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-59-0.wav"}}, {"session_id": "328", "turn_idx": 1, "from": "Assistant", "text": "可能是热身不足，先停下来拉伸，喝点水补充电解质。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-59-1.wav"}}, {"session_id": "328", "turn_idx": 2, "from": "Human", "text": "电解质饮料有啥推荐？超市买的行不行？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-59-2.wav"}}, {"session_id": "328", "turn_idx": 3, "from": "Assistant", "text": "像宝矿力或佳得乐都行。要查查健康选择吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-59-3.wav"}}, {"session_id": "328", "turn_idx": 4, "from": "Human", "text": "对，帮我找找最适合瑜伽后喝的电解质饮料。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-59-4.wav"}}, {"session_id": "328", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"瑜伽后 电解质饮料 推荐\"}", "value": "function\nweb_search\n{\"query\": \"瑜伽后 电解质饮料 推荐\"}"}] +[{"session_id": "145", "turn_idx": 0, "from": "Human", "text": "小跃，我计划暑假去日本玩，第一次出国。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-60-0.wav"}}, {"session_id": "145", "turn_idx": 1, "from": "Assistant", "text": "日本超好玩！东京或大阪都不错。你有目的地了吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-60-1.wav"}}, {"session_id": "145", "turn_idx": 2, "from": "Human", "text": "想去京都看古迹，但不懂日语。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-60-2.wav"}}, {"session_id": "145", "turn_idx": 3, "from": "Assistant", "text": "京都古寺很多，语言不用怕，很多地方有中文标识。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-60-3.wav"}}, {"session_id": "145", "turn_idx": 4, "from": "Human", "text": "那住宿呢？想找便宜又干净的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-60-4.wav"}}, {"session_id": "145", "turn_idx": 5, "from": "Assistant", "text": "民宿或青年旅社性价比高。提前订更好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-60-5.wav"}}, {"session_id": "145", "turn_idx": 6, "from": "Human", "text": "能帮我搜一下京都经济型住宿推荐和预订方式吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-60-6.wav"}}, {"session_id": "145", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"京都 经济型住宿 推荐 预订\"}", "value": "function\nweb_search\n{\"query\": \"京都 经济型住宿 推荐 预订\"}"}] +[{"session_id": "135", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在学英语，但记单词好难。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-61-0.wav"}}, {"session_id": "135", "turn_idx": 1, "from": "Assistant", "text": "记单词有技巧！试试用卡片或APP。你每天学多久？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-61-1.wav"}}, {"session_id": "135", "turn_idx": 2, "from": "Human", "text": "每天半小时，但效率低，你能搜些记忆方法吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-61-2.wav"}}, {"session_id": "135", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"英语单词记忆技巧\"}", "value": "function\nweb_search\n{\"query\": \"英语单词记忆技巧\"}"}] +[{"session_id": "614", "turn_idx": 0, "from": "Human", "text": "小跃，我读了一本叫《三体》的书，好奇作者刘慈欣是哪里人？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-62-0.wav"}}, {"session_id": "614", "turn_idx": 1, "from": "Assistant", "text": "刘慈欣啊，中国科幻大作家，好像来自某个省。你读完了吗？感觉如何？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-62-1.wav"}}, {"session_id": "614", "turn_idx": 2, "from": "Human", "text": "还没读完，但想多了解他。帮我查查刘慈欣的出生地是哪里？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-62-2.wav"}}, {"session_id": "614", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"刘慈欣 出生地\"}", "value": "function\nweb_search\n{\"query\": \"刘慈欣 出生地\"}"}] +[{"session_id": "871", "turn_idx": 0, "from": "Human", "text": "小跃，最近有啥好听的播客推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-63-0.wav"}}, {"session_id": "871", "turn_idx": 1, "from": "Assistant", "text": "有啊！比如《故事FM》或者《日谈公园》，内容都挺有趣的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-63-1.wav"}}, {"session_id": "871", "turn_idx": 2, "from": "Human", "text": "那《故事FM》最新一期讲的是什么主题？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-63-2.wav"}}, {"session_id": "871", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"故事FM 最新主题\"}", "value": "function\nweb_search\n{\"query\": \"故事FM 最新主题\"}"}] +[{"session_id": "937", "turn_idx": 0, "from": "Human", "text": "小跃，我想买台新电视。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-64-0.wav"}}, {"session_id": "937", "turn_idx": 1, "from": "Assistant", "text": "电视选大屏幕高清的，看剧更过瘾。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-64-1.wav"}}, {"session_id": "937", "turn_idx": 2, "from": "Human", "text": "哪个品牌画质最好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-64-2.wav"}}, {"session_id": "937", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"高画质电视品牌推荐\"}", "value": "function\nweb_search\n{\"query\": \"高画质电视品牌推荐\"}"}] +[{"session_id": "606", "turn_idx": 0, "from": "Human", "text": "小跃，我想找个瑜伽视频练练，有啥推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-65-0.wav"}}, {"session_id": "606", "turn_idx": 1, "from": "Assistant", "text": "瑜伽放松身心！初学者可以试试基础动作。你在家练吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-65-1.wav"}}, {"session_id": "606", "turn_idx": 2, "from": "Human", "text": "对，在家跟着视频学。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-65-2.wav"}}, {"session_id": "606", "turn_idx": 3, "from": "Assistant", "text": "网上很多免费教程，B站上不少。要我搜个热门瑜伽教程吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-65-3.wav"}}, {"session_id": "606", "turn_idx": 4, "from": "Human", "text": "好，搜一下初学者瑜伽视频在B站的链接。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-65-4.wav"}}, {"session_id": "606", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"初学者瑜伽视频 B站\"}", "value": "function\nweb_search\n{\"query\": \"初学者瑜伽视频 B站\"}"}] +[{"session_id": "16", "turn_idx": 0, "from": "Human", "text": "小跃，孩子在学习英语，有什么有趣的辅助工具？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-66-0.wav"}}, {"session_id": "16", "turn_idx": 1, "from": "Assistant", "text": "试试那个动画学习App，用游戏方式教单词，孩子容易上手。 它支持多种语言，每天坚持十分钟效果不错。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-66-1.wav"}}, {"session_id": "16", "turn_idx": 2, "from": "Human", "text": "App名字是什么？我需要下载链接。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-66-2.wav"}}, {"session_id": "16", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"儿童英语学习 App 下载\"}", "value": "function\nweb_search\n{\"query\": \"儿童英语学习 App 下载\"}"}] +[{"session_id": "725", "turn_idx": 0, "from": "Human", "text": "小跃，我刚领养了只小猫，要注意啥？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-67-0.wav"}}, {"session_id": "725", "turn_idx": 1, "from": "Assistant", "text": "恭喜！小猫先打疫苗，预防疾病，还要选好猫粮。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-67-1.wav"}}, {"session_id": "725", "turn_idx": 2, "from": "Human", "text": "疫苗什么时候打？要打几次？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-67-2.wav"}}, {"session_id": "725", "turn_idx": 3, "from": "Assistant", "text": "一般两个月大开始，打三针，每针间隔三四周。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-67-3.wav"}}, {"session_id": "725", "turn_idx": 4, "from": "Human", "text": "猫粮呢？幼猫吃什么牌子好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-67-4.wav"}}, {"session_id": "725", "turn_idx": 5, "from": "Assistant", "text": "皇家或希尔斯都不错，专为幼猫设计，营养均衡。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-67-5.wav"}}, {"session_id": "725", "turn_idx": 6, "from": "Human", "text": "明白了。你能帮我查查皇家幼猫粮的具体成分和价格吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-67-6.wav"}}, {"session_id": "725", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"皇家幼猫粮 成分价格\"}", "value": "function\nweb_search\n{\"query\": \"皇家幼猫粮 成分价格\"}"}] +[{"session_id": "598", "turn_idx": 0, "from": "Human", "text": "小跃，我打算学吉他，初学者买什么牌子好？预算一千以内。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-68-0.wav"}}, {"session_id": "598", "turn_idx": 1, "from": "Assistant", "text": "雅马哈或卡马的入门款都不错，手感轻便。建议选原声吉他别带电的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-68-1.wav"}}, {"session_id": "598", "turn_idx": 2, "from": "Human", "text": "原声吉他和电吉他区别大吗？我怕太难学。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-68-2.wav"}}, {"session_id": "598", "turn_idx": 3, "from": "Assistant", "text": "原声吉他简单点，适合新手。电吉他需要音箱，更复杂。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-68-3.wav"}}, {"session_id": "598", "turn_idx": 4, "from": "Human", "text": "行，那搜索一下初学者吉他推荐品牌和购买指南。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-68-4.wav"}}, {"session_id": "598", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"初学者吉他 推荐品牌 购买指南\"}", "value": "function\nweb_search\n{\"query\": \"初学者吉他 推荐品牌 购买指南\"}"}] +[{"session_id": "958", "turn_idx": 0, "from": "Human", "text": "小跃，你知道金字塔是谁建的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-69-0.wav"}}, {"session_id": "958", "turn_idx": 1, "from": "Assistant", "text": "埃及金字塔是古埃及法老建的，最著名的是胡夫金字塔。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-69-1.wav"}}, {"session_id": "958", "turn_idx": 2, "from": "Human", "text": "胡夫金字塔有多高啊？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-69-2.wav"}}, {"session_id": "958", "turn_idx": 3, "from": "Assistant", "text": "原高一百四十多米，现在风蚀后矮了点。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-69-3.wav"}}, {"session_id": "958", "turn_idx": 4, "from": "Human", "text": "那具体建造时间呢？帮我查查历史记录。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-69-4.wav"}}, {"session_id": "958", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"胡夫金字塔建造年代\"}", "value": "function\nweb_search\n{\"query\": \"胡夫金字塔建造年代\"}"}] +[{"session_id": "807", "turn_idx": 0, "from": "Human", "text": "小跃，我养了只小狗。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-70-0.wav"}}, {"session_id": "807", "turn_idx": 1, "from": "Assistant", "text": "小狗可爱！它多大了？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-70-1.wav"}}, {"session_id": "807", "turn_idx": 2, "from": "Human", "text": "才三个月大，现在训练上厕所难。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-70-2.wav"}}, {"session_id": "807", "turn_idx": 3, "from": "Assistant", "text": "小狗训练要耐心，用奖励方式。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-70-3.wav"}}, {"session_id": "807", "turn_idx": 4, "from": "Human", "text": "那你能帮我查查如何训练小狗上厕所吗？要实用技巧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-70-4.wav"}}, {"session_id": "807", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"训练小狗 上厕所 方法技巧\"}", "value": "function\nweb_search\n{\"query\": \"训练小狗 上厕所 方法技巧\"}"}] +[{"session_id": "887", "turn_idx": 0, "from": "Human", "text": "小跃，最近失眠严重，有什么自然疗法吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-71-0.wav"}}, {"session_id": "887", "turn_idx": 1, "from": "Assistant", "text": "试试睡前喝杯温牛奶或听轻音乐，避免用手机，坚持几天看看。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-71-1.wav"}}, {"session_id": "887", "turn_idx": 2, "from": "Human", "text": "那有没有草本茶推荐？帮我搜搜有效的配方。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-71-2.wav"}}, {"session_id": "887", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"失眠 草本茶 自然疗法\"}", "value": "function\nweb_search\n{\"query\": \"失眠 草本茶 自然疗法\"}"}] +[{"session_id": "882", "turn_idx": 0, "from": "Human", "text": "小跃，我想买台新笔记本电脑，预算在五千块左右。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-72-0.wav"}}, {"session_id": "882", "turn_idx": 1, "from": "Assistant", "text": "这个价位选择挺多的，比如联想或戴尔的入门款，性能都不错。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-72-1.wav"}}, {"session_id": "882", "turn_idx": 2, "from": "Human", "text": "那你能查查最近哪款性价比最高？最好有用户评价。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-72-2.wav"}}, {"session_id": "882", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"笔记本电脑 性价比高 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"笔记本电脑 性价比高 用户评价\"}"}] +[{"session_id": "63", "turn_idx": 0, "from": "Human", "text": "小跃，我最近失眠，有什么自然的方法改善吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-73-0.wav"}}, {"session_id": "63", "turn_idx": 1, "from": "Assistant", "text": "失眠确实烦人。试试睡前喝杯温牛奶或听点轻音乐，能帮助放松。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-73-1.wav"}}, {"session_id": "63", "turn_idx": 2, "from": "Human", "text": "我试过了，效果一般。还有什么其他技巧吗？ 小跃，你能查一下改善失眠的自然疗法和专家建议吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-73-2.wav"}}, {"session_id": "63", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"改善失眠 自然疗法 专家建议\"}", "value": "function\nweb_search\n{\"query\": \"改善失眠 自然疗法 专家建议\"}"}] +[{"session_id": "548", "turn_idx": 0, "from": "Human", "text": "小跃，我家的水龙头漏水了，怎么自己修啊？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-74-0.wav"}}, {"session_id": "548", "turn_idx": 1, "from": "Assistant", "text": "水龙头漏水可能是垫圈老化。你先关掉水源，检查一下。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-74-1.wav"}}, {"session_id": "548", "turn_idx": 2, "from": "Human", "text": "我检查了还是漏，你能帮我查一下 DIY 修理水龙头的步骤吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-74-2.wav"}}, {"session_id": "548", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"水龙头漏水 DIY 修理 步骤\"}", "value": "function\nweb_search\n{\"query\": \"水龙头漏水 DIY 修理 步骤\"}"}] +[{"session_id": "119", "turn_idx": 0, "from": "Human", "text": "小跃，我想养只狗，但不知道怎么训练。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-75-0.wav"}}, {"session_id": "119", "turn_idx": 1, "from": "Assistant", "text": "训练狗狗需要耐心，可以从基本指令开始教起。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-75-1.wav"}}, {"session_id": "119", "turn_idx": 2, "from": "Human", "text": "好的，你能搜索一些训练狗狗的方法吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-75-2.wav"}}, {"session_id": "119", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"狗狗训练方法\"}", "value": "function\nweb_search\n{\"query\": \"狗狗训练方法\"}"}] +[{"session_id": "619", "turn_idx": 0, "from": "Human", "text": "小跃，我买了新手机，想知道iPhone 14的屏幕尺寸是多少英寸？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-76-0.wav"}}, {"session_id": "619", "turn_idx": 1, "from": "Assistant", "text": "iPhone 14屏幕不小，但具体尺寸我忘了。你刚换手机感觉如何？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-76-1.wav"}}, {"session_id": "619", "turn_idx": 2, "from": "Human", "text": "还不错，但想确认下尺寸。帮我查查iPhone 14的屏幕尺寸？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-76-2.wav"}}, {"session_id": "619", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"iPhone 14 屏幕尺寸\"}", "value": "function\nweb_search\n{\"query\": \"iPhone 14 屏幕尺寸\"}"}] +[{"session_id": "861", "turn_idx": 0, "from": "Human", "text": "小跃，我最近馋北京烤鸭了，想周末去吃。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-77-0.wav"}}, {"session_id": "861", "turn_idx": 1, "from": "Assistant", "text": "哈哈，烤鸭确实香。你打算去哪个区？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-77-1.wav"}}, {"session_id": "861", "turn_idx": 2, "from": "Human", "text": "我住朝阳区，附近有啥推荐的老字号店？最好能查到地址和评价。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-77-2.wav"}}, {"session_id": "861", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"朝阳区北京烤鸭老字号推荐\"}", "value": "function\nweb_search\n{\"query\": \"朝阳区北京烤鸭老字号推荐\"}"}] +[{"session_id": "473", "turn_idx": 0, "from": "Human", "text": "小跃，我想买台新笔记本电脑，预算五千左右，有啥建议？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-78-0.wav"}}, {"session_id": "473", "turn_idx": 1, "from": "Assistant", "text": "华为MateBook系列挺靠谱，或者联想小新系列。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-78-1.wav"}}, {"session_id": "473", "turn_idx": 2, "from": "Human", "text": "联想小新我听说过，但不知道具体型号性能咋样。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-78-2.wav"}}, {"session_id": "473", "turn_idx": 3, "from": "Assistant", "text": "小新Pro系列评价不错，续航长，适合办公。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-78-3.wav"}}, {"session_id": "473", "turn_idx": 4, "from": "Human", "text": "最近有新款发布吗？我想看看最新款。 帮我找找联想小新最新型号的评测信息。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-78-4.wav"}}, {"session_id": "473", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"联想小新最新型号 评测\"}", "value": "function\nweb_search\n{\"query\": \"联想小新最新型号 评测\"}"}] +[{"session_id": "484", "turn_idx": 0, "from": "Human", "text": "小跃，我手机旧了，想换新的，有啥推荐品牌？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-79-0.wav"}}, {"session_id": "484", "turn_idx": 1, "from": "Assistant", "text": "苹果或华为都不错，看你预算和需求。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-79-1.wav"}}, {"session_id": "484", "turn_idx": 2, "from": "Human", "text": "预算中等吧，想要拍照好的，续航长点。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-79-2.wav"}}, {"session_id": "484", "turn_idx": 3, "from": "Assistant", "text": "华为P系列拍照强，电池也挺耐用。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-79-3.wav"}}, {"session_id": "484", "turn_idx": 4, "from": "Human", "text": "P系列最新是哪款？价格大概多少？ 最新是P六十，价格我忘了，得查查。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-79-4.wav"}}, {"session_id": "484", "turn_idx": 5, "from": "Assistant", "text": "那帮我查华为P六十的具体价格和用户评价。 function\nweb_search\n{\"query\": \"华为P六十 价格 用户评价\"}", "value": "那帮我查华为P六十的具体价格和用户评价。 function\nweb_search\n{\"query\": \"华为P六十 价格 用户评价\"}"}] +[{"session_id": "334", "turn_idx": 0, "from": "Human", "text": "小跃，新出的苹果手机有啥亮点？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-80-0.wav"}}, {"session_id": "334", "turn_idx": 1, "from": "Assistant", "text": "相机升级了，处理器更快，屏幕也更亮。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-80-1.wav"}}, {"session_id": "334", "turn_idx": 2, "from": "Human", "text": "电池能用多久？我经常出差。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-80-2.wav"}}, {"session_id": "334", "turn_idx": 3, "from": "Assistant", "text": "据说续航提高了不少，一天没问题。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-80-3.wav"}}, {"session_id": "334", "turn_idx": 4, "from": "Human", "text": "搜一下苹果手机电池续航的具体评测。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-80-4.wav"}}, {"session_id": "334", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"苹果手机 电池续航\"}", "value": "function\nweb_search\n{\"query\": \"苹果手机 电池续航\"}"}] +[{"session_id": "737", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想读本小说，轻松点的，有推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-81-0.wav"}}, {"session_id": "737", "turn_idx": 1, "from": "Assistant", "text": "轻松小说的话，余华的《活着》怎么样？虽然主题深但好读。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-81-1.wav"}}, {"session_id": "737", "turn_idx": 2, "from": "Human", "text": "听说过，但怕太沉重，有更幽默的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-81-2.wav"}}, {"session_id": "737", "turn_idx": 3, "from": "Assistant", "text": "那试试钱钟书的《围城》，讽刺幽默风格。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-81-3.wav"}}, {"session_id": "737", "turn_idx": 4, "from": "Human", "text": "听起来合适，能查查《围城》在豆瓣的读者评价吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-81-4.wav"}}, {"session_id": "737", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"围城 豆瓣 读者评价\"}", "value": "function\nweb_search\n{\"query\": \"围城 豆瓣 读者评价\"}"}] +[{"session_id": "639", "turn_idx": 0, "from": "Human", "text": "小跃，你知道莎士比亚的名言吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-82-0.wav"}}, {"session_id": "639", "turn_idx": 1, "from": "Assistant", "text": "当然，比如“生存还是毁灭”。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-82-1.wav"}}, {"session_id": "639", "turn_idx": 2, "from": "Human", "text": "那他的代表作有哪些？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-82-2.wav"}}, {"session_id": "639", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"莎士比亚 代表作\"}", "value": "function\nweb_search\n{\"query\": \"莎士比亚 代表作\"}"}] +[{"session_id": "343", "turn_idx": 0, "from": "Human", "text": "小跃，5G网络现在很火，它比4G快多少啊？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-83-0.wav"}}, {"session_id": "343", "turn_idx": 1, "from": "Assistant", "text": "5G速度快多了，理论峰值能到每秒十吉比特，下载东西嗖嗖的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-83-1.wav"}}, {"session_id": "343", "turn_idx": 2, "from": "Human", "text": "哇，那真快。5G的覆盖范围广不广？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-83-2.wav"}}, {"session_id": "343", "turn_idx": 3, "from": "Assistant", "text": "覆盖在扩大，但不如4G全面，偏远地区可能信号弱。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-83-3.wav"}}, {"session_id": "343", "turn_idx": 4, "from": "Human", "text": "好的。那5G技术最早是谁发明的？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-83-4.wav"}}, {"session_id": "343", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"5G 技术 发明者\"}", "value": "function\nweb_search\n{\"query\": \"5G 技术 发明者\"}"}] +[{"session_id": "336", "turn_idx": 0, "from": "Human", "text": "小跃，第二次世界大战啥时候结束的？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-84-0.wav"}}, {"session_id": "336", "turn_idx": 1, "from": "Assistant", "text": "一九四五年结束的，德国和日本先后投降。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-84-1.wav"}}, {"session_id": "336", "turn_idx": 2, "from": "Human", "text": "哦，日本投降的具体日子是哪天？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-84-2.wav"}}, {"session_id": "336", "turn_idx": 3, "from": "Assistant", "text": "八月十五号，日本宣布无条件投降。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-84-3.wav"}}, {"session_id": "336", "turn_idx": 4, "from": "Human", "text": "搜一下日本投降的详细历史记录。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-84-4.wav"}}, {"session_id": "336", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"日本投降 历史\"}", "value": "function\nweb_search\n{\"query\": \"日本投降 历史\"}"}] +[{"session_id": "753", "turn_idx": 0, "from": "Human", "text": "小跃，我想去海南旅游，有什么必去景点？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-85-0.wav"}}, {"session_id": "753", "turn_idx": 1, "from": "Assistant", "text": "海南的话，三亚海滩、南山寺都很棒。建议玩个三五天，放松一下。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-85-1.wav"}}, {"session_id": "753", "turn_idx": 2, "from": "Human", "text": "好，帮我查一下从上海到三亚的机票信息。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-85-2.wav"}}, {"session_id": "753", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"上海 三亚 机票\"}", "value": "function\nweb_search\n{\"query\": \"上海 三亚 机票\"}"}] +[{"session_id": "728", "turn_idx": 0, "from": "Human", "text": "小跃，我想买无线耳机，推荐什么？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-86-0.wav"}}, {"session_id": "728", "turn_idx": 1, "from": "Assistant", "text": "无线耳机的话，苹果AirPods Pro音质好，降噪强。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-86-1.wav"}}, {"session_id": "728", "turn_idx": 2, "from": "Human", "text": "AirPods Pro？价格贵吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-86-2.wav"}}, {"session_id": "728", "turn_idx": 3, "from": "Assistant", "text": "价格一千多，但经常有折扣，性价比高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-86-3.wav"}}, {"session_id": "728", "turn_idx": 4, "from": "Human", "text": "那续航怎么样？能用一整天吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-86-4.wav"}}, {"session_id": "728", "turn_idx": 5, "from": "Assistant", "text": "单次充电能用五小时左右，充电盒能续好几次。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-86-5.wav"}}, {"session_id": "728", "turn_idx": 6, "from": "Human", "text": "行。你能帮我查查AirPods Pro的最新用户评测吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-86-6.wav"}}, {"session_id": "728", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"AirPods Pro 用户评测\"}", "value": "function\nweb_search\n{\"query\": \"AirPods Pro 用户评测\"}"}] +[{"session_id": "854", "turn_idx": 0, "from": "Human", "text": "小跃，最近有啥新电影好看？我喜欢科幻的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-87-0.wav"}}, {"session_id": "854", "turn_idx": 1, "from": "Assistant", "text": "科幻片的话，《沙丘二》刚上映，评价很高，场面超震撼。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-87-1.wav"}}, {"session_id": "854", "turn_idx": 2, "from": "Human", "text": "《沙丘二》讲啥的？是续集吗？ 对，帮我查一下《沙丘二》的剧情简介和影院排期。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-87-2.wav"}}, {"session_id": "854", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"沙丘二 剧情简介 影院排期\"}", "value": "function\nweb_search\n{\"query\": \"沙丘二 剧情简介 影院排期\"}"}] +[{"session_id": "168", "turn_idx": 0, "from": "Human", "text": "小跃，我想练瑜伽，新手该从哪里开始？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-88-0.wav"}}, {"session_id": "168", "turn_idx": 1, "from": "Assistant", "text": "从基础体式入手，比如山式或猫牛式，简单安全。 建议先在家看视频学，或者报个初级班。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-88-1.wav"}}, {"session_id": "168", "turn_idx": 2, "from": "Human", "text": "视频好找吗？推荐一个频道？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-88-2.wav"}}, {"session_id": "168", "turn_idx": 3, "from": "Assistant", "text": "很多免费平台有教程，比如B站上的瑜伽入门。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-88-3.wav"}}, {"session_id": "168", "turn_idx": 4, "from": "Human", "text": "明白了。瑜伽对健康有啥具体好处？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-88-4.wav"}}, {"session_id": "168", "turn_idx": 5, "from": "Assistant", "text": "能改善柔韧性、减压，还能帮助睡眠。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-88-5.wav"}}, {"session_id": "168", "turn_idx": 6, "from": "Human", "text": "行。我想查一下附近的瑜伽工作室。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-88-6.wav"}}, {"session_id": "168", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"附近 瑜伽工作室\"}", "value": "function\nweb_search\n{\"query\": \"附近 瑜伽工作室\"}"}] +[{"session_id": "125", "turn_idx": 0, "from": "Human", "text": "小跃，你能给我讲讲秦始皇的故事吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-89-0.wav"}}, {"session_id": "125", "turn_idx": 1, "from": "Assistant", "text": "秦始皇挺有名的！你想知道他的统一六国还是其他事迹？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-89-1.wav"}}, {"session_id": "125", "turn_idx": 2, "from": "Human", "text": "我想了解他是怎么建长城的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-89-2.wav"}}, {"session_id": "125", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"秦始皇建长城历史\"}", "value": "function\nweb_search\n{\"query\": \"秦始皇建长城历史\"}"}] +[{"session_id": "25", "turn_idx": 0, "from": "Human", "text": "小跃，我手机电池老没电，想换新的，预算一千块左右。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-90-0.wav"}}, {"session_id": "25", "turn_idx": 1, "from": "Assistant", "text": "这个价位可以考虑国产牌子，像小米或荣耀，性价比高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-90-1.wav"}}, {"session_id": "25", "turn_idx": 2, "from": "Human", "text": "小米最新型号是啥？帮我找一下用户评价。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-90-2.wav"}}, {"session_id": "25", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"小米手机 最新型号 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"小米手机 最新型号 用户评价\"}"}] +[{"session_id": "826", "turn_idx": 0, "from": "Human", "text": "小跃，我做饭总糊锅，有什么防糊技巧？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-91-0.wav"}}, {"session_id": "826", "turn_idx": 1, "from": "Assistant", "text": "用不粘锅或者控制火候，中小火慢慢炒。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-91-1.wav"}}, {"session_id": "826", "turn_idx": 2, "from": "Human", "text": "中小火是多小？我经常调不好煤气灶。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-91-2.wav"}}, {"session_id": "826", "turn_idx": 3, "from": "Assistant", "text": "就是火焰不冒高，锅底均匀受热那种。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-91-3.wav"}}, {"session_id": "826", "turn_idx": 4, "from": "Human", "text": "小跃，查查厨房新手防糊锅的详细方法。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-91-4.wav"}}, {"session_id": "826", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"厨房新手 防糊锅 方法\"}", "value": "function\nweb_search\n{\"query\": \"厨房新手 防糊锅 方法\"}"}] +[{"session_id": "79", "turn_idx": 0, "from": "Human", "text": "小跃，想做巧克力蛋糕，简单食谱有吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-92-0.wav"}}, {"session_id": "79", "turn_idx": 1, "from": "Assistant", "text": "有啊！先准备面粉、可可粉、鸡蛋和糖，混合烤就行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-92-1.wav"}}, {"session_id": "79", "turn_idx": 2, "from": "Human", "text": "烤箱温度多少？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-92-2.wav"}}, {"session_id": "79", "turn_idx": 3, "from": "Assistant", "text": "一百八十度左右，烤二十到二十五分钟。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-92-3.wav"}}, {"session_id": "79", "turn_idx": 4, "from": "Human", "text": "蛋糕会塌陷怎么办？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-92-4.wav"}}, {"session_id": "79", "turn_idx": 5, "from": "Assistant", "text": "面糊别过度搅拌，烤完别立刻开烤箱门。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-92-5.wav"}}, {"session_id": "79", "turn_idx": 6, "from": "Human", "text": "那我想查查巧克力蛋糕的装饰创意。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-92-6.wav"}}, {"session_id": "79", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"巧克力 蛋糕 装饰 创意\"}", "value": "function\nweb_search\n{\"query\": \"巧克力 蛋糕 装饰 创意\"}"}] +[{"session_id": "806", "turn_idx": 0, "from": "Human", "text": "小跃，我想买个无线耳机。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-93-0.wav"}}, {"session_id": "806", "turn_idx": 1, "from": "Assistant", "text": "无线耳机方便！预算多少？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-93-1.wav"}}, {"session_id": "806", "turn_idx": 2, "from": "Human", "text": "大概五百元左右，音质好点。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-93-2.wav"}}, {"session_id": "806", "turn_idx": 3, "from": "Assistant", "text": "这个价位有不少选择，品牌很重要。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-93-3.wav"}}, {"session_id": "806", "turn_idx": 4, "from": "Human", "text": "推荐一款音质好的无线耳机吧。你能查查吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-93-4.wav"}}, {"session_id": "806", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"五百元 无线耳机 音质推荐\"}", "value": "function\nweb_search\n{\"query\": \"五百元 无线耳机 音质推荐\"}"}] +[{"session_id": "841", "turn_idx": 0, "from": "Human", "text": "小跃，我今天想自己做披萨，但不知道从哪开始。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-94-0.wav"}}, {"session_id": "841", "turn_idx": 1, "from": "Assistant", "text": "自己做披萨很简单，先准备面团和番茄酱，再放上喜欢的 toppings 就行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-94-1.wav"}}, {"session_id": "841", "turn_idx": 2, "from": "Human", "text": "toppings 是啥意思？我该选哪些材料？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-94-2.wav"}}, {"session_id": "841", "turn_idx": 3, "from": "Assistant", "text": "toppings 就是 toppings，比如奶酪、火腿或蔬菜，根据口味来。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-94-3.wav"}}, {"session_id": "841", "turn_idx": 4, "from": "Human", "text": "那具体步骤呢？我需要一个详细的食谱，你帮我查查吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-94-4.wav"}}, {"session_id": "841", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"家庭披萨 做法 步骤\"}", "value": "function\nweb_search\n{\"query\": \"家庭披萨 做法 步骤\"}"}] +[{"session_id": "860", "turn_idx": 0, "from": "Human", "text": "嘿小跃，今天刷视频看到泰坦尼克号的故事，感觉好震撼啊。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-95-0.wav"}}, {"session_id": "860", "turn_idx": 1, "from": "Assistant", "text": "是啊，那艘船的历史确实很传奇。你最喜欢里面的哪个部分？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-95-1.wav"}}, {"session_id": "860", "turn_idx": 2, "from": "Human", "text": "我记得沉没日期好像是一九一几年，但具体记不清了。你能帮我查查准确年份吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-95-2.wav"}}, {"session_id": "860", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"泰坦尼克号沉没日期\"}", "value": "function\nweb_search\n{\"query\": \"泰坦尼克号沉没日期\"}"}] +[{"session_id": "988", "turn_idx": 0, "from": "Human", "text": "小跃，我想种点花在阳台，什么花好养活？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-96-0.wav"}}, {"session_id": "988", "turn_idx": 1, "from": "Assistant", "text": "阳台种花选耐晒的，像太阳花或多肉植物，浇水少还开花多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-96-1.wav"}}, {"session_id": "988", "turn_idx": 2, "from": "Human", "text": "多肉我养过，但总死。有没特别注意事项？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-96-2.wav"}}, {"session_id": "988", "turn_idx": 3, "from": "Assistant", "text": "多肉怕水多，土要透气，阳光要足。夏天别暴晒，冬天保暖就行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-96-3.wav"}}, {"session_id": "988", "turn_idx": 4, "from": "Human", "text": "懂了。那你能查查多肉植物常见品种图片吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-96-4.wav"}}, {"session_id": "988", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"多肉植物 常见品种 图片\"}", "value": "function\nweb_search\n{\"query\": \"多肉植物 常见品种 图片\"}"}] +[{"session_id": "104", "turn_idx": 0, "from": "Human", "text": "小跃，长城有多长？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-97-0.wav"}}, {"session_id": "104", "turn_idx": 1, "from": "Assistant", "text": "长城非常长，横跨多个省份，是古代防御工程。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-97-1.wav"}}, {"session_id": "104", "turn_idx": 2, "from": "Human", "text": "具体是从哪里开始到哪里结束？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-97-2.wav"}}, {"session_id": "104", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"长城起点终点位置\"}", "value": "function\nweb_search\n{\"query\": \"长城起点终点位置\"}"}] +[{"session_id": "437", "turn_idx": 0, "from": "Human", "text": "小跃，金字塔是怎么建造的？古人真有那么厉害？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-98-0.wav"}}, {"session_id": "437", "turn_idx": 1, "from": "Assistant", "text": "金字塔用巨石堆砌，据说是用斜坡和人力搬运，工程超复杂。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-98-1.wav"}}, {"session_id": "437", "turn_idx": 2, "from": "Human", "text": "那最大的金字塔有多高？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-98-2.wav"}}, {"session_id": "437", "turn_idx": 3, "from": "Assistant", "text": "胡夫金字塔最高，约一百三十七米，相当于四十层楼。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-98-3.wav"}}, {"session_id": "437", "turn_idx": 4, "from": "Human", "text": "哇，不可思议。你能搜一下金字塔建造的现代研究解释吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-98-4.wav"}}, {"session_id": "437", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"金字塔 建造方法 现代研究\"}", "value": "function\nweb_search\n{\"query\": \"金字塔 建造方法 现代研究\"}"}] +[{"session_id": "539", "turn_idx": 0, "from": "Human", "text": "小跃，我想学编程，Python适合新手吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-99-0.wav"}}, {"session_id": "539", "turn_idx": 1, "from": "Assistant", "text": "非常适合！语法简单，网上资源多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-99-1.wav"}}, {"session_id": "539", "turn_idx": 2, "from": "Human", "text": "有什么免费课程？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-99-2.wav"}}, {"session_id": "539", "turn_idx": 3, "from": "Assistant", "text": "Coursera或B站都有入门教程。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-99-3.wav"}}, {"session_id": "539", "turn_idx": 4, "from": "Human", "text": "Coursera收费吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-99-4.wav"}}, {"session_id": "539", "turn_idx": 5, "from": "Assistant", "text": "有免费部分，但证书要付费。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-99-5.wav"}}, {"session_id": "539", "turn_idx": 6, "from": "Human", "text": "那你能帮我搜索一下Coursera的Python免费课程列表吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-99-6.wav"}}, {"session_id": "539", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"Coursera Python 免费课程\"}", "value": "function\nweb_search\n{\"query\": \"Coursera Python 免费课程\"}"}] +[{"session_id": "388", "turn_idx": 0, "from": "Human", "text": "小跃，咖啡豆哪种好？我每天喝两杯。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-100-0.wav"}}, {"session_id": "388", "turn_idx": 1, "from": "Assistant", "text": "阿拉比卡豆口感顺滑，或者罗布斯塔更浓烈。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-100-1.wav"}}, {"session_id": "388", "turn_idx": 2, "from": "Human", "text": "阿拉比卡产地哪里？贵不贵？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-100-2.wav"}}, {"session_id": "388", "turn_idx": 3, "from": "Assistant", "text": "埃塞俄比亚产的经典，价格中等，一斤几十块。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-100-3.wav"}}, {"session_id": "388", "turn_idx": 4, "from": "Human", "text": "那具体怎么冲泡最好喝？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-100-4.wav"}}, {"session_id": "388", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"阿拉比卡咖啡豆 冲泡方法\"}", "value": "function\nweb_search\n{\"query\": \"阿拉比卡咖啡豆 冲泡方法\"}"}] +[{"session_id": "208", "turn_idx": 0, "from": "Human", "text": "小跃，我想买辆自行车。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-101-0.wav"}}, {"session_id": "208", "turn_idx": 1, "from": "Assistant", "text": "自行车种类多，公路车或山地车看你的需求。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-101-1.wav"}}, {"session_id": "208", "turn_idx": 2, "from": "Human", "text": "帮我查一下性价比高的自行车品牌。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-101-2.wav"}}, {"session_id": "208", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"性价比高自行车品牌\"}", "value": "function\nweb_search\n{\"query\": \"性价比高自行车品牌\"}"}] +[{"session_id": "56", "turn_idx": 0, "from": "Human", "text": "小跃，我种的盆栽叶子发黄了，怎么回事？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-102-0.wav"}}, {"session_id": "56", "turn_idx": 1, "from": "Assistant", "text": "可能是浇水太多或缺肥，先检查土壤湿度。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-102-1.wav"}}, {"session_id": "56", "turn_idx": 2, "from": "Human", "text": "土壤湿湿的，我浇得不多啊。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-102-2.wav"}}, {"session_id": "56", "turn_idx": 3, "from": "Assistant", "text": "那可能是光照不足，搬到阳台试试，每天晒三小时。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-102-3.wav"}}, {"session_id": "56", "turn_idx": 4, "from": "Human", "text": "懂了。帮我搜搜盆栽叶子发黄的解决办法吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-102-4.wav"}}, {"session_id": "56", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"盆栽叶子发黄 原因 处理\"}", "value": "function\nweb_search\n{\"query\": \"盆栽叶子发黄 原因 处理\"}"}] +[{"session_id": "483", "turn_idx": 0, "from": "Human", "text": "小跃，周末想去爬山，北京附近有推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-103-0.wav"}}, {"session_id": "483", "turn_idx": 1, "from": "Assistant", "text": "香山不错，红叶季节很美，交通也方便。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-103-1.wav"}}, {"session_id": "483", "turn_idx": 2, "from": "Human", "text": "红叶？现在不是季节吧？几月去最好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-103-2.wav"}}, {"session_id": "483", "turn_idx": 3, "from": "Assistant", "text": "秋季最好，大概十月到十一月，满山都是红色。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-103-3.wav"}}, {"session_id": "483", "turn_idx": 4, "from": "Human", "text": "哦，那除了红叶，山上还有什么好玩的项目？ 比如观景台或徒步路线，细节我记不全。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-103-4.wav"}}, {"session_id": "483", "turn_idx": 5, "from": "Assistant", "text": "好，帮我搜索香山的详细景点介绍和开放时间。 function\nweb_search\n{\"query\": \"香山 景点介绍 开放时间\"}", "value": "好，帮我搜索香山的详细景点介绍和开放时间。 function\nweb_search\n{\"query\": \"香山 景点介绍 开放时间\"}"}] +[{"session_id": "908", "turn_idx": 0, "from": "Human", "text": "小跃，我打算买辆电动车，特斯拉怎么样？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-104-0.wav"}}, {"session_id": "908", "turn_idx": 1, "from": "Assistant", "text": "特斯拉性能不错，但价格偏高。比亚迪性价比更高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-104-1.wav"}}, {"session_id": "908", "turn_idx": 2, "from": "Human", "text": "比亚迪？它是哪年成立的？ 对了，电动车最早发明的年份是多少？帮我查查。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-104-2.wav"}}, {"session_id": "908", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"电动车发明年份\"}", "value": "function\nweb_search\n{\"query\": \"电动车发明年份\"}"}] +[{"session_id": "166", "turn_idx": 0, "from": "Human", "text": "小跃，我对古代历史感兴趣，能讲讲秦始皇吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-105-0.wav"}}, {"session_id": "166", "turn_idx": 1, "from": "Assistant", "text": "秦始皇统一了中国，还建了万里长城，挺传奇的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-105-1.wav"}}, {"session_id": "166", "turn_idx": 2, "from": "Human", "text": "长城我知道。他统一后做了啥改革？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-105-2.wav"}}, {"session_id": "166", "turn_idx": 3, "from": "Assistant", "text": "首先，统一了文字和货币；其次，推行了郡县制。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-105-3.wav"}}, {"session_id": "166", "turn_idx": 4, "from": "Human", "text": "有意思。长城有多长？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-105-4.wav"}}, {"session_id": "166", "turn_idx": 5, "from": "Assistant", "text": "总长超过两万里，是古代的伟大工程。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-105-5.wav"}}, {"session_id": "166", "turn_idx": 6, "from": "Human", "text": "哇。我想查一下长城的修建年代。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-105-6.wav"}}, {"session_id": "166", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"万里长城 修建年代\"}", "value": "function\nweb_search\n{\"query\": \"万里长城 修建年代\"}"}] +[{"session_id": "306", "turn_idx": 0, "from": "Human", "text": "小跃，我想学摄影，但不知道怎么选相机。入门款推荐什么？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-106-0.wav"}}, {"session_id": "306", "turn_idx": 1, "from": "Assistant", "text": "入门的话，微单相机比较好上手，比如索尼的Alpha系列或佳能的EOS M。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-106-1.wav"}}, {"session_id": "306", "turn_idx": 2, "from": "Human", "text": "索尼的贵吗？预算三千块左右。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-106-2.wav"}}, {"session_id": "306", "turn_idx": 3, "from": "Assistant", "text": "三千块能买到不错的二手或入门款。佳能的EOS M两百系列就挺实惠，画质也清晰。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-106-3.wav"}}, {"session_id": "306", "turn_idx": 4, "from": "Human", "text": "那你能帮我查查佳能EOS M两百的用户评测和价格对比吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-106-4.wav"}}, {"session_id": "306", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"佳能 EOS M两百 评测 价格\"}", "value": "function\nweb_search\n{\"query\": \"佳能 EOS M两百 评测 价格\"}"}] +[{"session_id": "464", "turn_idx": 0, "from": "Human", "text": "小跃，我想学做红烧肉，但不会做，有食谱吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-107-0.wav"}}, {"session_id": "464", "turn_idx": 1, "from": "Assistant", "text": "红烧肉简单又香！主要用五花肉和酱油，小火慢炖。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-107-1.wav"}}, {"session_id": "464", "turn_idx": 2, "from": "Human", "text": "需要哪些调料？我怕买漏了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-107-2.wav"}}, {"session_id": "464", "turn_idx": 3, "from": "Assistant", "text": "基础调料有老抽、糖和姜片，超市都能买到。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-107-3.wav"}}, {"session_id": "464", "turn_idx": 4, "from": "Human", "text": "好的。那你能找个详细的红烧肉做法步骤吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-107-4.wav"}}, {"session_id": "464", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"红烧肉 食谱 做法 步骤\"}", "value": "function\nweb_search\n{\"query\": \"红烧肉 食谱 做法 步骤\"}"}] +[{"session_id": "635", "turn_idx": 0, "from": "Human", "text": "小跃，我想换辆电动车。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-108-0.wav"}}, {"session_id": "635", "turn_idx": 1, "from": "Assistant", "text": "电动车现在很流行，比如特斯拉。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-108-1.wav"}}, {"session_id": "635", "turn_idx": 2, "from": "Human", "text": "哪款性价比最高？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-108-2.wav"}}, {"session_id": "635", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"电动车 性价比 最高\"}", "value": "function\nweb_search\n{\"query\": \"电动车 性价比 最高\"}"}] +[{"session_id": "472", "turn_idx": 0, "from": "Human", "text": "小跃，国庆节想出去玩，国内有啥好地方推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-109-0.wav"}}, {"session_id": "472", "turn_idx": 1, "from": "Assistant", "text": "云南大理风景很美，或者去杭州西湖也很经典。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-109-1.wav"}}, {"session_id": "472", "turn_idx": 2, "from": "Human", "text": "西湖去过啦，国外有啥性价比高的目的地？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-109-2.wav"}}, {"session_id": "472", "turn_idx": 3, "from": "Assistant", "text": "泰国清迈不错，物价低，文化氛围浓。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-109-3.wav"}}, {"session_id": "472", "turn_idx": 4, "from": "Human", "text": "清迈有啥必去的景点？我听说夜市很出名。 帮我查查清迈夜市的具体位置和开放时间。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-109-4.wav"}}, {"session_id": "472", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"清迈夜市 位置 开放时间\"}", "value": "function\nweb_search\n{\"query\": \"清迈夜市 位置 开放时间\"}"}] +[{"session_id": "297", "turn_idx": 0, "from": "Human", "text": "小跃，我打算买辆二手车，预算十万以内。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-110-0.wav"}}, {"session_id": "297", "turn_idx": 1, "from": "Assistant", "text": "二手车划算！你想买什么车型？轿车还是SUV？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-110-1.wav"}}, {"session_id": "297", "turn_idx": 2, "from": "Human", "text": "轿车吧，省油点。但怕买到问题车。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-110-2.wav"}}, {"session_id": "297", "turn_idx": 3, "from": "Assistant", "text": "选大品牌像丰田或本田，可靠性好。查查车主评价。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-110-3.wav"}}, {"session_id": "297", "turn_idx": 4, "from": "Human", "text": "可靠性怎么判断？有排名吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-110-4.wav"}}, {"session_id": "297", "turn_idx": 5, "from": "Assistant", "text": "有的，比如故障率低的车型排名。建议看专业评测。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-110-5.wav"}}, {"session_id": "297", "turn_idx": 6, "from": "Human", "text": "那得好好研究。小跃，帮我查一下二手车可靠性高的轿车排名。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-110-6.wav"}}, {"session_id": "297", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"二手车 可靠性高 轿车 排名\"}", "value": "function\nweb_search\n{\"query\": \"二手车 可靠性高 轿车 排名\"}"}] +[{"session_id": "662", "turn_idx": 0, "from": "Human", "text": "小跃，我下月去日本，京都值得玩吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-111-0.wav"}}, {"session_id": "662", "turn_idx": 1, "from": "Assistant", "text": "绝对值得！古寺和神社很有韵味，像清水寺。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-111-1.wav"}}, {"session_id": "662", "turn_idx": 2, "from": "Human", "text": "清水寺？我朋友推荐伏见稻荷大社。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-111-2.wav"}}, {"session_id": "662", "turn_idx": 3, "from": "Assistant", "text": "伏见稻荷的千本鸟居超壮观，拍照超美。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-111-3.wav"}}, {"session_id": "662", "turn_idx": 4, "from": "Human", "text": "千本鸟居是啥时候建的？我想了解它的历史背景。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-111-4.wav"}}, {"session_id": "662", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"伏见稻荷大社 千本鸟居 建造时间\"}", "value": "function\nweb_search\n{\"query\": \"伏见稻荷大社 千本鸟居 建造时间\"}"}] +[{"session_id": "590", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我最近想养只宠物狗，你觉得金毛怎么样？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-112-0.wav"}}, {"session_id": "590", "turn_idx": 1, "from": "Assistant", "text": "金毛超可爱的！它们性格温顺又聪明，特别适合家庭养。不过你要考虑家里空间大小哦。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-112-1.wav"}}, {"session_id": "590", "turn_idx": 2, "from": "Human", "text": "空间应该没问题，但我担心掉毛问题。金毛掉毛厉害吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-112-2.wav"}}, {"session_id": "590", "turn_idx": 3, "from": "Assistant", "text": "是的，金毛掉毛挺多的，尤其换季时。你需要经常梳理和打扫。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-112-3.wav"}}, {"session_id": "590", "turn_idx": 4, "from": "Human", "text": "懂了，那你能帮我查查金毛的具体护理方法吗？比如梳毛频率。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-112-4.wav"}}, {"session_id": "590", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"金毛犬 日常护理 梳毛频率\"}", "value": "function\nweb_search\n{\"query\": \"金毛犬 日常护理 梳毛频率\"}"}] +[{"session_id": "894", "turn_idx": 0, "from": "Human", "text": "你知道秦始皇统一中国是哪一年吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-113-0.wav"}}, {"session_id": "894", "turn_idx": 1, "from": "Assistant", "text": "秦始皇统一中国是在公元前二百二十一年，建立了秦朝。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-113-1.wav"}}, {"session_id": "894", "turn_idx": 2, "from": "Human", "text": "哦，那帮我搜一下秦始皇的生平事迹。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-113-2.wav"}}, {"session_id": "894", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"秦始皇 生平 事迹\"}", "value": "function\nweb_search\n{\"query\": \"秦始皇 生平 事迹\"}"}] +[{"session_id": "114", "turn_idx": 0, "from": "Human", "text": "小跃，我计划去北京旅游，有什么推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-114-0.wav"}}, {"session_id": "114", "turn_idx": 1, "from": "Assistant", "text": "北京有故宫、长城这些经典景点，值得一去。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-114-1.wav"}}, {"session_id": "114", "turn_idx": 2, "from": "Human", "text": "长城怎么去啊？能搜索交通方式吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-114-2.wav"}}, {"session_id": "114", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"北京长城交通方式\"}", "value": "function\nweb_search\n{\"query\": \"北京长城交通方式\"}"}] +[{"session_id": "745", "turn_idx": 0, "from": "Human", "text": "小跃，最近有啥好看的电影？周末想放松。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-115-0.wav"}}, {"session_id": "745", "turn_idx": 1, "from": "Assistant", "text": "最近《流浪地球二》挺火，是部科幻大片，评价不错。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-115-1.wav"}}, {"session_id": "745", "turn_idx": 2, "from": "Human", "text": "主演是谁啊？我记不住名字。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-115-2.wav"}}, {"session_id": "745", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"流浪地球二 主演名单\"}", "value": "function\nweb_search\n{\"query\": \"流浪地球二 主演名单\"}"}] +[{"session_id": "733", "turn_idx": 0, "from": "Human", "text": "小跃，我最近总感觉睡不好，有啥改善建议？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-116-0.wav"}}, {"session_id": "733", "turn_idx": 1, "from": "Assistant", "text": "睡眠问题常见呢！你作息规律吗？试试睡前少看手机。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-116-1.wav"}}, {"session_id": "733", "turn_idx": 2, "from": "Human", "text": "我尽量了，但还老醒，听说喝牛奶有用？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-116-2.wav"}}, {"session_id": "733", "turn_idx": 3, "from": "Assistant", "text": "温牛奶确实能助眠，加点蜂蜜更有效。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-116-3.wav"}}, {"session_id": "733", "turn_idx": 4, "from": "Human", "text": "好的，帮我查查睡前喝牛奶的具体好处和注意事项。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-116-4.wav"}}, {"session_id": "733", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"睡前喝牛奶 好处 注意事项\"}", "value": "function\nweb_search\n{\"query\": \"睡前喝牛奶 好处 注意事项\"}"}] +[{"session_id": "631", "turn_idx": 0, "from": "Human", "text": "小跃，我想养只猫，但不知道怎么开始。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-117-0.wav"}}, {"session_id": "631", "turn_idx": 1, "from": "Assistant", "text": "养猫需要准备猫粮、猫砂盆这些基本用品。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-117-1.wav"}}, {"session_id": "631", "turn_idx": 2, "from": "Human", "text": "你能帮我找找新手养猫的注意事项吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-117-2.wav"}}, {"session_id": "631", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"新手养猫 注意事项\"}", "value": "function\nweb_search\n{\"query\": \"新手养猫 注意事项\"}"}] +[{"session_id": "921", "turn_idx": 0, "from": "Human", "text": "小跃，我计划周末去爬山，有什么推荐的地方？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-118-0.wav"}}, {"session_id": "921", "turn_idx": 1, "from": "Assistant", "text": "北京香山或者杭州西湖边的山都不错，风景好还容易爬。记得带水和防晒。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-118-1.wav"}}, {"session_id": "921", "turn_idx": 2, "from": "Human", "text": "香山的开放时间是几点到几点？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-118-2.wav"}}, {"session_id": "921", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"北京 香山 开放时间\"}", "value": "function\nweb_search\n{\"query\": \"北京 香山 开放时间\"}"}] +[{"session_id": "573", "turn_idx": 0, "from": "Human", "text": "小跃，我想买新手机，苹果和安卓哪个好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-119-0.wav"}}, {"session_id": "573", "turn_idx": 1, "from": "Assistant", "text": "各有优势，苹果系统流畅，安卓更灵活。预算多少？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-119-1.wav"}}, {"session_id": "573", "turn_idx": 2, "from": "Human", "text": "大概五千块以内吧，性价比高的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-119-2.wav"}}, {"session_id": "573", "turn_idx": 3, "from": "Assistant", "text": "那可以考虑安卓旗舰机型，像三星或小米的新款。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-119-3.wav"}}, {"session_id": "573", "turn_idx": 4, "from": "Human", "text": "能搜下三星最新手机型号和价格吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-119-4.wav"}}, {"session_id": "573", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"三星最新手机型号价格\"}", "value": "function\nweb_search\n{\"query\": \"三星最新手机型号价格\"}"}] +[{"session_id": "341", "turn_idx": 0, "from": "Human", "text": "小跃，我昨晚看了《肖申克的救赎》，真是一部好电影。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-120-0.wav"}}, {"session_id": "341", "turn_idx": 1, "from": "Assistant", "text": "是啊，它评分超高。你最喜欢里面的哪个情节？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-120-1.wav"}}, {"session_id": "341", "turn_idx": 2, "from": "Human", "text": "安迪逃狱那段太经典了。你知道这部电影是小说改编的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-120-2.wav"}}, {"session_id": "341", "turn_idx": 3, "from": "Assistant", "text": "没错，改编自斯蒂芬·金的小说，原著也很精彩。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-120-3.wav"}}, {"session_id": "341", "turn_idx": 4, "from": "Human", "text": "哦，那小说的原名是什么？我想找原书读一读。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-120-4.wav"}}, {"session_id": "341", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"肖申克的救赎 小说 原名\"}", "value": "function\nweb_search\n{\"query\": \"肖申克的救赎 小说 原名\"}"}] +[{"session_id": "703", "turn_idx": 0, "from": "Human", "text": "小跃，我最近对人工智能特别好奇，感觉它好神奇。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-121-0.wav"}}, {"session_id": "703", "turn_idx": 1, "from": "Assistant", "text": "是啊，AI发展得很快。你是想了解应用还是原理？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-121-1.wav"}}, {"session_id": "703", "turn_idx": 2, "from": "Human", "text": "应用吧，比如它怎么帮助日常生活。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-121-2.wav"}}, {"session_id": "703", "turn_idx": 3, "from": "Assistant", "text": "像语音助手或者推荐系统都是常见例子。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-121-3.wav"}}, {"session_id": "703", "turn_idx": 4, "from": "Human", "text": "那你能查查AI在医疗领域的最新进展吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-121-4.wav"}}, {"session_id": "703", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"人工智能 医疗应用 最新发展\"}", "value": "function\nweb_search\n{\"query\": \"人工智能 医疗应用 最新发展\"}"}] +[{"session_id": "852", "turn_idx": 0, "from": "Human", "text": "小跃，我想换个新手机，预算五千左右，有啥好推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-122-0.wav"}}, {"session_id": "852", "turn_idx": 1, "from": "Assistant", "text": "这个价位的话，小米十四或华为nova系列都不错，性能强拍照好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-122-1.wav"}}, {"session_id": "852", "turn_idx": 2, "from": "Human", "text": "华为nova的电池能用多久？我经常出门。 那你帮我查一下华为nova十二的电池续航评测。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-122-2.wav"}}, {"session_id": "852", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"华为nova十二 电池续航 评测\"}", "value": "function\nweb_search\n{\"query\": \"华为nova十二 电池续航 评测\"}"}] +[{"session_id": "588", "turn_idx": 0, "from": "Human", "text": "小跃，我想买台平板电脑，看剧用。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-123-0.wav"}}, {"session_id": "588", "turn_idx": 1, "from": "Assistant", "text": "iPad还是安卓平板？iPad画质好，安卓便宜。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-123-1.wav"}}, {"session_id": "588", "turn_idx": 2, "from": "Human", "text": "安卓的吧，预算两千元。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-123-2.wav"}}, {"session_id": "588", "turn_idx": 3, "from": "Assistant", "text": "华为MatePad或小米平板，屏幕大。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-123-3.wav"}}, {"session_id": "588", "turn_idx": 4, "from": "Human", "text": "电池能用多久？我总出差。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-123-4.wav"}}, {"session_id": "588", "turn_idx": 5, "from": "Assistant", "text": "一般十小时左右，看视频够用。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-123-5.wav"}}, {"session_id": "588", "turn_idx": 6, "from": "Human", "text": "好。但我想比较下具体型号的优缺点。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-123-6.wav"}}, {"session_id": "588", "turn_idx": 7, "from": "Assistant", "text": "网上评测详细，帮你找找。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-123-7.wav"}}, {"session_id": "588", "turn_idx": 8, "from": "Human", "text": "那搜一下华为MatePad用户评价。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-123-8.wav"}}, {"session_id": "588", "turn_idx": 9, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"华为MatePad 用户评价\"}", "value": "function\nweb_search\n{\"query\": \"华为MatePad 用户评价\"}"}] +[{"session_id": "849", "turn_idx": 0, "from": "Human", "text": "小跃，我超喜欢周杰伦的歌，但新出的那首记不清名字了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-124-0.wav"}}, {"session_id": "849", "turn_idx": 1, "from": "Assistant", "text": "周杰伦最近发的是《最伟大的作品》，去年出的，挺火的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-124-1.wav"}}, {"session_id": "849", "turn_idx": 2, "from": "Human", "text": "去年？我还以为是今年呢，歌词有中国风元素吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-124-2.wav"}}, {"session_id": "849", "turn_idx": 3, "from": "Assistant", "text": "有的，融合了古典和现代，歌词很有意境。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-124-3.wav"}}, {"session_id": "849", "turn_idx": 4, "from": "Human", "text": "我想看看歌词全文，你帮我查查这首歌的完整歌词吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-124-4.wav"}}, {"session_id": "849", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"周杰伦 最伟大的作品 歌词\"}", "value": "function\nweb_search\n{\"query\": \"周杰伦 最伟大的作品 歌词\"}"}] +[{"session_id": "500", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想学做菜，有什么简单的推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-125-0.wav"}}, {"session_id": "500", "turn_idx": 1, "from": "Assistant", "text": "番茄炒蛋超简单，材料少又好上手，新手也能做。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-125-1.wav"}}, {"session_id": "500", "turn_idx": 2, "from": "Human", "text": "听起来不错，需要准备些什么东西？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-125-2.wav"}}, {"session_id": "500", "turn_idx": 3, "from": "Assistant", "text": "鸡蛋、番茄、盐、油，再配点葱花更香。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-125-3.wav"}}, {"session_id": "500", "turn_idx": 4, "from": "Human", "text": "好的，那你能告诉我详细的步骤怎么做吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-125-4.wav"}}, {"session_id": "500", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法 步骤\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法 步骤\"}"}] +[{"session_id": "314", "turn_idx": 0, "from": "Human", "text": "小跃，我手机电池不行了，想换新手机，有啥推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-126-0.wav"}}, {"session_id": "314", "turn_idx": 1, "from": "Assistant", "text": "苹果或者华为的旗舰机都挺耐用，电池续航长。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-126-1.wav"}}, {"session_id": "314", "turn_idx": 2, "from": "Human", "text": "华为的贵吗？预算三千左右。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-126-2.wav"}}, {"session_id": "314", "turn_idx": 3, "from": "Assistant", "text": "那可以看看华为的中端系列，性价比高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-126-3.wav"}}, {"session_id": "314", "turn_idx": 4, "from": "Human", "text": "小跃，搜一下三千元左右的华为手机推荐。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-126-4.wav"}}, {"session_id": "314", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"三千元 华为手机 推荐\"}", "value": "function\nweb_search\n{\"query\": \"三千元 华为手机 推荐\"}"}] +[{"session_id": "311", "turn_idx": 0, "from": "Human", "text": "小跃，最近有什么好看的电影推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-127-0.wav"}}, {"session_id": "311", "turn_idx": 1, "from": "Assistant", "text": "《流浪地球》挺火的，是部科幻片，讲地球冒险的故事。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-127-1.wav"}}, {"session_id": "311", "turn_idx": 2, "from": "Human", "text": "哦，我看过预告片，主演是谁来着？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-127-2.wav"}}, {"session_id": "311", "turn_idx": 3, "from": "Assistant", "text": "吴京主演的，他演得挺精彩的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-127-3.wav"}}, {"session_id": "311", "turn_idx": 4, "from": "Human", "text": "小跃，帮我查一下《流浪地球》的上映时间。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-127-4.wav"}}, {"session_id": "311", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"流浪地球 电影 上映时间\"}", "value": "function\nweb_search\n{\"query\": \"流浪地球 电影 上映时间\"}"}] +[{"session_id": "351", "turn_idx": 0, "from": "Human", "text": "小跃，我想学做一道简单的中式菜，有什么建议？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-128-0.wav"}}, {"session_id": "351", "turn_idx": 1, "from": "Assistant", "text": "番茄炒蛋怎么样？材料少，步骤简单，很适合新手。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-128-1.wav"}}, {"session_id": "351", "turn_idx": 2, "from": "Human", "text": "好主意！需要准备哪些东西？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-128-2.wav"}}, {"session_id": "351", "turn_idx": 3, "from": "Assistant", "text": "主要就是番茄、鸡蛋、盐和油，很快就能做好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-128-3.wav"}}, {"session_id": "351", "turn_idx": 4, "from": "Human", "text": "番茄要几个？鸡蛋呢？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-128-4.wav"}}, {"session_id": "351", "turn_idx": 5, "from": "Assistant", "text": "一般用两个番茄和三个鸡蛋，但看你口味。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-128-5.wav"}}, {"session_id": "351", "turn_idx": 6, "from": "Human", "text": "那具体怎么做？你能查一下详细步骤吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-128-6.wav"}}, {"session_id": "351", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法\"}"}] +[{"session_id": "453", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想多吃点坚果，有啥推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-129-0.wav"}}, {"session_id": "453", "turn_idx": 1, "from": "Assistant", "text": "杏仁不错，富含维生素E，对身体好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-129-1.wav"}}, {"session_id": "453", "turn_idx": 2, "from": "Human", "text": "具体有啥好处？比如对心脏有影响吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-129-2.wav"}}, {"session_id": "453", "turn_idx": 3, "from": "Assistant", "text": "对心脏有益，还能帮助降低胆固醇。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-129-3.wav"}}, {"session_id": "453", "turn_idx": 4, "from": "Human", "text": "那热量高不高？查查详细营养数据。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-129-4.wav"}}, {"session_id": "453", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"杏仁 营养 热量\"}", "value": "function\nweb_search\n{\"query\": \"杏仁 营养 热量\"}"}] +[{"session_id": "57", "turn_idx": 0, "from": "Human", "text": "小跃，我想看部新电影，有推荐的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-130-0.wav"}}, {"session_id": "57", "turn_idx": 1, "from": "Assistant", "text": "最近《流浪地球》续集很火，科幻特效很棒。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-130-1.wav"}}, {"session_id": "57", "turn_idx": 2, "from": "Human", "text": "哦，那个我看过预告片。上映了吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-130-2.wav"}}, {"session_id": "57", "turn_idx": 3, "from": "Assistant", "text": "已经上了，影院都有排片，周末去正好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-130-3.wav"}}, {"session_id": "57", "turn_idx": 4, "from": "Human", "text": "好，帮我查查附近影院的具体放映时间吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-130-4.wav"}}, {"session_id": "57", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"流浪地球 续集 放映时间\"}", "value": "function\nweb_search\n{\"query\": \"流浪地球 续集 放映时间\"}"}] +[{"session_id": "206", "turn_idx": 0, "from": "Human", "text": "小跃，我计划学做蛋糕。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-131-0.wav"}}, {"session_id": "206", "turn_idx": 1, "from": "Assistant", "text": "做蛋糕挺好玩，先从基础海绵蛋糕开始试试。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-131-1.wav"}}, {"session_id": "206", "turn_idx": 2, "from": "Human", "text": "需要哪些工具？帮我找一个工具清单。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-131-2.wav"}}, {"session_id": "206", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"做蛋糕工具清单\"}", "value": "function\nweb_search\n{\"query\": \"做蛋糕工具清单\"}"}] +[{"session_id": "881", "turn_idx": 0, "from": "Human", "text": "小跃，我听说跑步对心脏好，但不确定具体好处。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-132-0.wav"}}, {"session_id": "881", "turn_idx": 1, "from": "Assistant", "text": "跑步能增强心肺功能，还能改善心情，每天跑个二三十分钟就很有效。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-132-1.wav"}}, {"session_id": "881", "turn_idx": 2, "from": "Human", "text": "那跑步时心率控制在多少合适？帮我搜搜专业建议。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-132-2.wav"}}, {"session_id": "881", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"跑步 心率 控制范围\"}", "value": "function\nweb_search\n{\"query\": \"跑步 心率 控制范围\"}"}] +[{"session_id": "66", "turn_idx": 0, "from": "Human", "text": "小跃，我最近在读历史书，对古罗马很感兴趣。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-133-0.wav"}}, {"session_id": "66", "turn_idx": 1, "from": "Assistant", "text": "古罗马历史超精彩！推荐从凯撒大帝的故事入手，挺有启发的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-133-1.wav"}}, {"session_id": "66", "turn_idx": 2, "from": "Human", "text": "凯撒大帝？我想知道他的主要成就和影响。 小跃，你能查一下凯撒大帝的主要成就和历史影响吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-133-2.wav"}}, {"session_id": "66", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"凯撒大帝 主要成就 历史影响\"}", "value": "function\nweb_search\n{\"query\": \"凯撒大帝 主要成就 历史影响\"}"}] +[{"session_id": "339", "turn_idx": 0, "from": "Human", "text": "小跃，我想开始跑步健身，有啥建议？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-134-0.wav"}}, {"session_id": "339", "turn_idx": 1, "from": "Assistant", "text": "先慢慢来，穿双好鞋，每次跑二三十分钟就行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-134-1.wav"}}, {"session_id": "339", "turn_idx": 2, "from": "Human", "text": "跑步机好还是户外跑好？我怕伤膝盖。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-134-2.wav"}}, {"session_id": "339", "turn_idx": 3, "from": "Assistant", "text": "户外空气好，但跑步机对关节更温和。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-134-3.wav"}}, {"session_id": "339", "turn_idx": 4, "from": "Human", "text": "帮我查一下跑步的初学者注意事项。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-134-4.wav"}}, {"session_id": "339", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"跑步 初学者 指南\"}", "value": "function\nweb_search\n{\"query\": \"跑步 初学者 指南\"}"}] +[{"session_id": "236", "turn_idx": 0, "from": "Human", "text": "小跃，我想听点新歌，有什么推荐的歌手？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-135-0.wav"}}, {"session_id": "236", "turn_idx": 1, "from": "Assistant", "text": "最近邓紫棋的新专辑反响不错，旋律很抓耳。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-135-1.wav"}}, {"session_id": "236", "turn_idx": 2, "from": "Human", "text": "哦，我喜欢她。你能查一下这张专辑的发行日期吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-135-2.wav"}}, {"session_id": "236", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"邓紫棋 新专辑 发行日期\"}", "value": "function\nweb_search\n{\"query\": \"邓紫棋 新专辑 发行日期\"}"}] +[{"session_id": "148", "turn_idx": 0, "from": "Human", "text": "小跃，我想学做手工皂，但没经验。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-136-0.wav"}}, {"session_id": "148", "turn_idx": 1, "from": "Assistant", "text": "手工皂有趣！用天然材料就行。你打算在家做？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-136-1.wav"}}, {"session_id": "148", "turn_idx": 2, "from": "Human", "text": "是的，厨房就能操作。需要啥工具？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-136-2.wav"}}, {"session_id": "148", "turn_idx": 3, "from": "Assistant", "text": "基础的有模具、油脂和碱液。安全第一！", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-136-3.wav"}}, {"session_id": "148", "turn_idx": 4, "from": "Human", "text": "碱液哪里买？我怕危险。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-136-4.wav"}}, {"session_id": "148", "turn_idx": 5, "from": "Assistant", "text": "网上有卖套装，配好比例的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-136-5.wav"}}, {"session_id": "148", "turn_idx": 6, "from": "Human", "text": "那帮我查一下手工皂新手套装购买和制作教程。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-136-6.wav"}}, {"session_id": "148", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"手工皂 新手套装 购买 制作教程\"}", "value": "function\nweb_search\n{\"query\": \"手工皂 新手套装 购买 制作教程\"}"}] +[{"session_id": "406", "turn_idx": 0, "from": "Human", "text": "小跃，我家客厅要翻新，怎么设计好看？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-137-0.wav"}}, {"session_id": "406", "turn_idx": 1, "from": "Assistant", "text": "看风格偏好，现代简约或北欧风都流行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-137-1.wav"}}, {"session_id": "406", "turn_idx": 2, "from": "Human", "text": "我喜欢简洁的，北欧风吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-137-2.wav"}}, {"session_id": "406", "turn_idx": 3, "from": "Assistant", "text": "建议用浅色家具和绿植，空间感强。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-137-3.wav"}}, {"session_id": "406", "turn_idx": 4, "from": "Human", "text": "好，帮我找些实际案例参考一下。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-137-4.wav"}}, {"session_id": "406", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"客厅设计 北欧风\"}", "value": "function\nweb_search\n{\"query\": \"客厅设计 北欧风\"}"}] +[{"session_id": "574", "turn_idx": 0, "from": "Human", "text": "小跃，我最近老失眠，有啥办法改善？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-138-0.wav"}}, {"session_id": "574", "turn_idx": 1, "from": "Assistant", "text": "试试规律作息，睡前别玩手机。也可以喝点热牛奶。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-138-1.wav"}}, {"session_id": "574", "turn_idx": 2, "from": "Human", "text": "牛奶管用吗？还有其他自然疗法？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-138-2.wav"}}, {"session_id": "574", "turn_idx": 3, "from": "Assistant", "text": "嗯，冥想或泡个热水澡也有效。关键是要放松心情。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-138-3.wav"}}, {"session_id": "574", "turn_idx": 4, "from": "Human", "text": "帮我查查科学证明的失眠自然疗法。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-138-4.wav"}}, {"session_id": "574", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"失眠自然疗法科学证明\"}", "value": "function\nweb_search\n{\"query\": \"失眠自然疗法科学证明\"}"}] +[{"session_id": "867", "turn_idx": 0, "from": "Human", "text": "小跃，我朋友推荐《哈利波特》，但我不清楚作者是谁。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-139-0.wav"}}, {"session_id": "867", "turn_idx": 1, "from": "Assistant", "text": "那系列书超级火，作者很有名。你想了解她的背景吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-139-1.wav"}}, {"session_id": "867", "turn_idx": 2, "from": "Human", "text": "对，先查作者全名和国籍吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-139-2.wav"}}, {"session_id": "867", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"哈利波特作者姓名国籍\"}", "value": "function\nweb_search\n{\"query\": \"哈利波特作者姓名国籍\"}"}] +[{"session_id": "649", "turn_idx": 0, "from": "Human", "text": "小跃，我想学编程，Python入门难吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-140-0.wav"}}, {"session_id": "649", "turn_idx": 1, "from": "Assistant", "text": "Python挺适合新手，语法简单。可以从基础教程开始。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-140-1.wav"}}, {"session_id": "649", "turn_idx": 2, "from": "Human", "text": "有免费的教程推荐吗？最好是视频的。 帮我找些免费的Python入门视频教程。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-140-2.wav"}}, {"session_id": "649", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"免费 Python 入门 视频教程\"}", "value": "function\nweb_search\n{\"query\": \"免费 Python 入门 视频教程\"}"}] +[{"session_id": "496", "turn_idx": 0, "from": "Human", "text": "小跃，我想换手机，预算中等。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-141-0.wav"}}, {"session_id": "496", "turn_idx": 1, "from": "Assistant", "text": "安卓还是苹果？安卓里小米性价比高，苹果系统流畅。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-141-1.wav"}}, {"session_id": "496", "turn_idx": 2, "from": "Human", "text": "小米最近有新机型吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-141-2.wav"}}, {"session_id": "496", "turn_idx": 3, "from": "Assistant", "text": "有啊，比如小米十四系列，评价还不错。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-141-3.wav"}}, {"session_id": "496", "turn_idx": 4, "from": "Human", "text": "小米十四的电池能用多久？充满电能用一天吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-141-4.wav"}}, {"session_id": "496", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"小米十四 电池续航\"}", "value": "function\nweb_search\n{\"query\": \"小米十四 电池续航\"}"}] +[{"session_id": "383", "turn_idx": 0, "from": "Human", "text": "小跃，我想买台笔记本电脑，预算五千块左右。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-142-0.wav"}}, {"session_id": "383", "turn_idx": 1, "from": "Assistant", "text": "这个价位华硕或联想不错，性价比高，适合日常用。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-142-1.wav"}}, {"session_id": "383", "turn_idx": 2, "from": "Human", "text": "联想哪个型号好？屏幕大点的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-142-2.wav"}}, {"session_id": "383", "turn_idx": 3, "from": "Assistant", "text": "联想小新系列屏幕大，十五点六英寸的，性能也稳定。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-142-3.wav"}}, {"session_id": "383", "turn_idx": 4, "from": "Human", "text": "那它的电池能用多久？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-142-4.wav"}}, {"session_id": "383", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"联想小新 电池续航\"}", "value": "function\nweb_search\n{\"query\": \"联想小新 电池续航\"}"}] +[{"session_id": "940", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想学做菜，有什么入门菜推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-143-0.wav"}}, {"session_id": "940", "turn_idx": 1, "from": "Assistant", "text": "当然有！番茄炒蛋就超级简单，食材少还容易上手。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-143-1.wav"}}, {"session_id": "940", "turn_idx": 2, "from": "Human", "text": "听起来不错，帮我查一下番茄炒蛋的详细步骤吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-143-2.wav"}}, {"session_id": "940", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋 做法\"}"}] +[{"session_id": "691", "turn_idx": 0, "from": "Human", "text": "小跃，我今天想尝试做顿大餐，但不知道从哪开始。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-144-0.wav"}}, {"session_id": "691", "turn_idx": 1, "from": "Assistant", "text": "听起来有趣！你想做中餐还是西餐？我可以给点建议。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-144-1.wav"}}, {"session_id": "691", "turn_idx": 2, "from": "Human", "text": "中餐吧，比如家常菜，但我厨艺一般，怕搞砸。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-144-2.wav"}}, {"session_id": "691", "turn_idx": 3, "from": "Assistant", "text": "别担心，家常菜简单又美味。先从炒菜开始，比如西红柿炒鸡蛋。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-144-3.wav"}}, {"session_id": "691", "turn_idx": 4, "from": "Human", "text": "哦，这个我试过，但鸡蛋老是炒老了。你有更详细的步骤吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-144-4.wav"}}, {"session_id": "691", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"西红柿炒鸡蛋做法 技巧\"}", "value": "function\nweb_search\n{\"query\": \"西红柿炒鸡蛋做法 技巧\"}"}] +[{"session_id": "409", "turn_idx": 0, "from": "Human", "text": "小跃，听说最近有个科技大事件，是什么？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-145-0.wav"}}, {"session_id": "409", "turn_idx": 1, "from": "Assistant", "text": "可能是人工智能新突破或电动汽车进展。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-145-1.wav"}}, {"session_id": "409", "turn_idx": 2, "from": "Human", "text": "对，AI相关的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-145-2.wav"}}, {"session_id": "409", "turn_idx": 3, "from": "Assistant", "text": "AI发展快，比如聊天机器人升级。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-145-3.wav"}}, {"session_id": "409", "turn_idx": 4, "from": "Human", "text": "好，帮我查一下最新动态和详情。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-145-4.wav"}}, {"session_id": "409", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"人工智能 最新新闻\"}", "value": "function\nweb_search\n{\"query\": \"人工智能 最新新闻\"}"}] +[{"session_id": "578", "turn_idx": 0, "from": "Human", "text": "小跃，泰勒·斯威夫特新专辑出了没？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-146-0.wav"}}, {"session_id": "578", "turn_idx": 1, "from": "Assistant", "text": "她去年发过一张，今年还没消息。但巡演挺火的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-146-1.wav"}}, {"session_id": "578", "turn_idx": 2, "from": "Human", "text": "巡演城市有哪些？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-146-2.wav"}}, {"session_id": "578", "turn_idx": 3, "from": "Assistant", "text": "主要在欧美，亚洲站还没公布。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-146-3.wav"}}, {"session_id": "578", "turn_idx": 4, "from": "Human", "text": "帮我搜下泰勒亚洲巡演最新消息。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-146-4.wav"}}, {"session_id": "578", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"泰勒亚洲巡演最新消息\"}", "value": "function\nweb_search\n{\"query\": \"泰勒亚洲巡演最新消息\"}"}] +[{"session_id": "813", "turn_idx": 0, "from": "Human", "text": "小跃，我想去海边度假，国内哪里比较好玩？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-147-0.wav"}}, {"session_id": "813", "turn_idx": 1, "from": "Assistant", "text": "国内海边的话，三亚和厦门都不错。三亚海水干净，适合游泳。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-147-1.wav"}}, {"session_id": "813", "turn_idx": 2, "from": "Human", "text": "厦门有什么特色景点？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-147-2.wav"}}, {"session_id": "813", "turn_idx": 3, "from": "Assistant", "text": "鼓浪屿是必去的，有老建筑和音乐氛围，还能吃海鲜。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-147-3.wav"}}, {"session_id": "813", "turn_idx": 4, "from": "Human", "text": "海鲜？推荐哪家餐厅？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-147-4.wav"}}, {"session_id": "813", "turn_idx": 5, "from": "Assistant", "text": "当地小吃街很多选择，比如沙茶面或海蛎煎，随便一家老店都行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-147-5.wav"}}, {"session_id": "813", "turn_idx": 6, "from": "Human", "text": "我想查查鼓浪屿的详细旅游攻略。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-147-6.wav"}}, {"session_id": "813", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"鼓浪屿旅游攻略\"}", "value": "function\nweb_search\n{\"query\": \"鼓浪屿旅游攻略\"}"}] +[{"session_id": "227", "turn_idx": 0, "from": "Human", "text": "小跃，我想买台新笔记本，办公用，啥牌子好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-148-0.wav"}}, {"session_id": "227", "turn_idx": 1, "from": "Assistant", "text": "办公笔记本推荐联想或戴尔，性价比高，续航长。预算多少？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-148-1.wav"}}, {"session_id": "227", "turn_idx": 2, "from": "Human", "text": "预算五千左右，要轻便的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-148-2.wav"}}, {"session_id": "227", "turn_idx": 3, "from": "Assistant", "text": "这个价位联想小新或华为MateBook都不错，重量不到两公斤。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-148-3.wav"}}, {"session_id": "227", "turn_idx": 4, "from": "Human", "text": "屏幕尺寸呢？十三寸还是十五寸？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-148-4.wav"}}, {"session_id": "227", "turn_idx": 5, "from": "Assistant", "text": "十三寸便携，适合移动办公；十五寸看文档更舒服，但重点。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-148-5.wav"}}, {"session_id": "227", "turn_idx": 6, "from": "Human", "text": "小跃，帮我找找联想小新的评测和购买渠道。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-148-6.wav"}}, {"session_id": "227", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"联想小新 评测 购买渠道\"}", "value": "function\nweb_search\n{\"query\": \"联想小新 评测 购买渠道\"}"}] +[{"session_id": "507", "turn_idx": 0, "from": "Human", "text": "小跃，我喜欢周杰伦的歌，推荐几首吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-149-0.wav"}}, {"session_id": "507", "turn_idx": 1, "from": "Assistant", "text": "《七里香》和《稻香》经典，旋律好听。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-149-1.wav"}}, {"session_id": "507", "turn_idx": 2, "from": "Human", "text": "哦，他有新专辑吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-149-2.wav"}}, {"session_id": "507", "turn_idx": 3, "from": "Assistant", "text": "最近出了《最伟大的作品》，反响热烈。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-149-3.wav"}}, {"session_id": "507", "turn_idx": 4, "from": "Human", "text": "那在哪里能听或买这张专辑？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-149-4.wav"}}, {"session_id": "507", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"周杰伦 最伟大的作品 购买 在线听\"}", "value": "function\nweb_search\n{\"query\": \"周杰伦 最伟大的作品 购买 在线听\"}"}] +[{"session_id": "88", "turn_idx": 0, "from": "Human", "text": "小跃，我听说火星上有水，是真的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-150-0.wav"}}, {"session_id": "88", "turn_idx": 1, "from": "Assistant", "text": "是的，科学家探测到火星表面有水冰存在。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-150-1.wav"}}, {"session_id": "88", "turn_idx": 2, "from": "Human", "text": "那火星离地球有多远？我想查查具体数据。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-150-2.wav"}}, {"session_id": "88", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"火星 地球 距离\"}", "value": "function\nweb_search\n{\"query\": \"火星 地球 距离\"}"}] +[{"session_id": "271", "turn_idx": 0, "from": "Human", "text": "小跃，最近有什么好看的电视剧推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-151-0.wav"}}, {"session_id": "271", "turn_idx": 1, "from": "Assistant", "text": "最近《漫长的季节》挺火的，或者你喜欢古装剧吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-151-1.wav"}}, {"session_id": "271", "turn_idx": 2, "from": "Human", "text": "古装剧还行，但我想看轻松点的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-151-2.wav"}}, {"session_id": "271", "turn_idx": 3, "from": "Assistant", "text": "那《梦华录》不错，故事轻松又养眼。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-151-3.wav"}}, {"session_id": "271", "turn_idx": 4, "from": "Human", "text": "帮我找一下《梦华录》的播放平台和评分。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-151-4.wav"}}, {"session_id": "271", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"梦华录 播放平台 评分\"}", "value": "function\nweb_search\n{\"query\": \"梦华录 播放平台 评分\"}"}] +[{"session_id": "509", "turn_idx": 0, "from": "Human", "text": "小跃，我想买新衣服，今年流行什么？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-152-0.wav"}}, {"session_id": "509", "turn_idx": 1, "from": "Assistant", "text": "复古风正火，像阔腿裤和花衬衫。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-152-1.wav"}}, {"session_id": "509", "turn_idx": 2, "from": "Human", "text": "阔腿裤？什么牌子性价比高？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-152-2.wav"}}, {"session_id": "509", "turn_idx": 3, "from": "Assistant", "text": "Zara或H&M，款式多价格亲民。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-152-3.wav"}}, {"session_id": "509", "turn_idx": 4, "from": "Human", "text": "好的，那你能查一下它们的最新款式和价格吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-152-4.wav"}}, {"session_id": "509", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"Zara H&M 阔腿裤 新款 价格\"}", "value": "function\nweb_search\n{\"query\": \"Zara H&M 阔腿裤 新款 价格\"}"}] +[{"session_id": "182", "turn_idx": 0, "from": "Human", "text": "小跃，我对埃及金字塔好感兴趣，你知道点历史背景吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-153-0.wav"}}, {"session_id": "182", "turn_idx": 1, "from": "Assistant", "text": "金字塔是古埃及法老的陵墓，最有名的是胡夫金字塔，建于几千年前。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-153-1.wav"}}, {"session_id": "182", "turn_idx": 2, "from": "Human", "text": "哇，我想知道建造的具体过程。你能帮我查查金字塔是怎么建成的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-153-2.wav"}}, {"session_id": "182", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"埃及金字塔 建造过程\"}", "value": "function\nweb_search\n{\"query\": \"埃及金字塔 建造过程\"}"}] +[{"session_id": "289", "turn_idx": 0, "from": "Human", "text": "小跃，我想给孩子选个生日礼物。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-154-0.wav"}}, {"session_id": "289", "turn_idx": 1, "from": "Assistant", "text": "乐高积木或绘本都不错，既有趣又益智。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-154-1.wav"}}, {"session_id": "289", "turn_idx": 2, "from": "Human", "text": "乐高有什么新系列适合五岁孩子？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-154-2.wav"}}, {"session_id": "289", "turn_idx": 3, "from": "Assistant", "text": "城市系列或得宝系列都很适合，简单安全。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-154-3.wav"}}, {"session_id": "289", "turn_idx": 4, "from": "Human", "text": "好主意。小跃，查一下乐高得宝系列的最新款式。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-154-4.wav"}}, {"session_id": "289", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"乐高 得宝系列 新款\"}", "value": "function\nweb_search\n{\"query\": \"乐高 得宝系列 新款\"}"}] +[{"session_id": "990", "turn_idx": 0, "from": "Human", "text": "小跃，最近有什么好看的电影推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-155-0.wav"}}, {"session_id": "990", "turn_idx": 1, "from": "Assistant", "text": "最近新上映的《流浪地球二》挺火的，是科幻片，讲人类拯救地球的故事。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-155-1.wav"}}, {"session_id": "990", "turn_idx": 2, "from": "Human", "text": "听起来不错，但我不太喜欢科幻，有其他类型吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-155-2.wav"}}, {"session_id": "990", "turn_idx": 3, "from": "Assistant", "text": "那可以看看《熊出没》新电影，动画片，轻松有趣，适合放松。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-155-3.wav"}}, {"session_id": "990", "turn_idx": 4, "from": "Human", "text": "哦，那部评价怎么样？值不值得看？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-155-4.wav"}}, {"session_id": "990", "turn_idx": 5, "from": "Assistant", "text": "普遍反应很好，尤其适合家庭观看，笑点多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-155-5.wav"}}, {"session_id": "990", "turn_idx": 6, "from": "Human", "text": "我想比较一下这两部的影评再决定。你能帮我搜一下吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-155-6.wav"}}, {"session_id": "990", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"流浪地球二 影评 对比 熊出没\"}", "value": "function\nweb_search\n{\"query\": \"流浪地球二 影评 对比 熊出没\"}"}] +[{"session_id": "335", "turn_idx": 0, "from": "Human", "text": "小跃，怎么快速提高英语口语？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-156-0.wav"}}, {"session_id": "335", "turn_idx": 1, "from": "Assistant", "text": "多听英文歌或看美剧，模仿发音最有效。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-156-1.wav"}}, {"session_id": "335", "turn_idx": 2, "from": "Human", "text": "有推荐的App吗？我用手机方便。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-156-2.wav"}}, {"session_id": "335", "turn_idx": 3, "from": "Assistant", "text": "Duolingo不错，或者试试流利说。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-156-3.wav"}}, {"session_id": "335", "turn_idx": 4, "from": "Human", "text": "帮我查查这些英语学习App的用户反馈。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-156-4.wav"}}, {"session_id": "335", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"英语学习 App 评价\"}", "value": "function\nweb_search\n{\"query\": \"英语学习 App 评价\"}"}] +[{"session_id": "663", "turn_idx": 0, "from": "Human", "text": "小跃，我想多吃鱼，听说健康，有啥建议？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-157-0.wav"}}, {"session_id": "663", "turn_idx": 1, "from": "Assistant", "text": "鱼富含好脂肪，像三文鱼对心脏特别好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-157-1.wav"}}, {"session_id": "663", "turn_idx": 2, "from": "Human", "text": "三文鱼？具体有啥营养？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-157-2.wav"}}, {"session_id": "663", "turn_idx": 3, "from": "Assistant", "text": "比如欧米伽三脂肪酸，能降低炎症。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-157-3.wav"}}, {"session_id": "663", "turn_idx": 4, "from": "Human", "text": "欧米伽三是啥？还有，每天吃多少合适？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-157-4.wav"}}, {"session_id": "663", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"三文鱼 欧米伽三 每日推荐量\"}", "value": "function\nweb_search\n{\"query\": \"三文鱼 欧米伽三 每日推荐量\"}"}] +[{"session_id": "369", "turn_idx": 0, "from": "Human", "text": "小跃，我想自己做个花盆架，但没经验。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-158-0.wav"}}, {"session_id": "369", "turn_idx": 1, "from": "Assistant", "text": "花盆架DIY不难！你需要木头还是金属材料？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-158-1.wav"}}, {"session_id": "369", "turn_idx": 2, "from": "Human", "text": "木头吧，环保点，尺寸小点的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-158-2.wav"}}, {"session_id": "369", "turn_idx": 3, "from": "Assistant", "text": "小尺寸木制花盆架教程很多，我可以找个图文并茂的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-158-3.wav"}}, {"session_id": "369", "turn_idx": 4, "from": "Human", "text": "那帮我搜一下简单木头花盆架DIY教程。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-158-4.wav"}}, {"session_id": "369", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"简单 木头 花盆架 DIY 教程\"}", "value": "function\nweb_search\n{\"query\": \"简单 木头 花盆架 DIY 教程\"}"}] +[{"session_id": "423", "turn_idx": 0, "from": "Human", "text": "小跃，推荐个周末亲子活动吧，带娃出去玩玩。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-159-0.wav"}}, {"session_id": "423", "turn_idx": 1, "from": "Assistant", "text": "去科技馆或者动物园都不错，孩子能边玩边学。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-159-1.wav"}}, {"session_id": "423", "turn_idx": 2, "from": "Human", "text": "科技馆有什么好玩的展览？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-159-2.wav"}}, {"session_id": "423", "turn_idx": 3, "from": "Assistant", "text": "通常有互动实验和太空主题区，很有趣。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-159-3.wav"}}, {"session_id": "423", "turn_idx": 4, "from": "Human", "text": "嗯，我想搜一下本地科技馆的开放时间。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-159-4.wav"}}, {"session_id": "423", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"本地 科技馆 开放时间\"}", "value": "function\nweb_search\n{\"query\": \"本地 科技馆 开放时间\"}"}] +[{"session_id": "340", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想学做菜，有什么简单的菜推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-160-0.wav"}}, {"session_id": "340", "turn_idx": 1, "from": "Assistant", "text": "番茄炒蛋就很适合新手，材料少，步骤也简单，几分钟就能搞定。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-160-1.wav"}}, {"session_id": "340", "turn_idx": 2, "from": "Human", "text": "番茄炒蛋听起来不错。那你知道需要准备哪些材料吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-160-2.wav"}}, {"session_id": "340", "turn_idx": 3, "from": "Assistant", "text": "主要就是番茄和鸡蛋，加点盐、油，还可以放点糖提鲜。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-160-3.wav"}}, {"session_id": "340", "turn_idx": 4, "from": "Human", "text": "好的。那番茄炒蛋这道菜是怎么起源的？我想了解一下它的历史。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-160-4.wav"}}, {"session_id": "340", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"番茄炒蛋 起源 历史\"}", "value": "function\nweb_search\n{\"query\": \"番茄炒蛋 起源 历史\"}"}] +[{"session_id": "222", "turn_idx": 0, "from": "Human", "text": "小跃，最近想养只宠物，你觉得猫好还是狗好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-161-0.wav"}}, {"session_id": "222", "turn_idx": 1, "from": "Assistant", "text": "养宠物真开心！猫比较独立，适合忙碌的人；狗更活泼，需要多陪伴。看你时间安排。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-161-1.wav"}}, {"session_id": "222", "turn_idx": 2, "from": "Human", "text": "我工作忙，可能猫更合适。但我不懂怎么照顾小猫。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-161-2.wav"}}, {"session_id": "222", "turn_idx": 3, "from": "Assistant", "text": "小猫要准备猫粮、猫砂和玩具。喂食定时，每天陪玩一会儿就行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-161-3.wav"}}, {"session_id": "222", "turn_idx": 4, "from": "Human", "text": "疫苗呢？我听说要打几针。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-161-4.wav"}}, {"session_id": "222", "turn_idx": 5, "from": "Assistant", "text": "疫苗一般分三次打，从两个月大开始，间隔一个月一次。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-161-5.wav"}}, {"session_id": "222", "turn_idx": 6, "from": "Human", "text": "小跃，帮我找找养猫的完整指南和疫苗时间表吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-161-6.wav"}}, {"session_id": "222", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"新手养猫 指南 疫苗时间\"}", "value": "function\nweb_search\n{\"query\": \"新手养猫 指南 疫苗时间\"}"}] +[{"session_id": "160", "turn_idx": 0, "from": "Human", "text": "嗨小跃，最近有啥好看的电影推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-162-0.wav"}}, {"session_id": "160", "turn_idx": 1, "from": "Assistant", "text": "最近《流浪地球2》挺火的，是一部科幻片，讲人类带着地球逃离太阳系的故事。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-162-1.wav"}}, {"session_id": "160", "turn_idx": 2, "from": "Human", "text": "哦，听起来很酷。主演是谁呀？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-162-2.wav"}}, {"session_id": "160", "turn_idx": 3, "from": "Assistant", "text": "主演有吴京和刘德华，演技都很棒。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-162-3.wav"}}, {"session_id": "160", "turn_idx": 4, "from": "Human", "text": "刘德华我挺喜欢的。电影时长多久？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-162-4.wav"}}, {"session_id": "160", "turn_idx": 5, "from": "Assistant", "text": "大约两个半小时，节奏紧凑不拖沓。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-162-5.wav"}}, {"session_id": "160", "turn_idx": 6, "from": "Human", "text": "好的，我想查一下具体的上映日期。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-162-6.wav"}}, {"session_id": "160", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"流浪地球2 上映日期\"}", "value": "function\nweb_search\n{\"query\": \"流浪地球2 上映日期\"}"}] +[{"session_id": "448", "turn_idx": 0, "from": "Human", "text": "小跃，我想养只猫。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-163-0.wav"}}, {"session_id": "448", "turn_idx": 1, "from": "Assistant", "text": "好选择！喜欢什么品种？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-163-1.wav"}}, {"session_id": "448", "turn_idx": 2, "from": "Human", "text": "布偶猫吧，听说很温顺。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-163-2.wav"}}, {"session_id": "448", "turn_idx": 3, "from": "Assistant", "text": "是的，但需要定期梳理毛发。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-163-3.wav"}}, {"session_id": "448", "turn_idx": 4, "from": "Human", "text": "每天梳理多久合适？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-163-4.wav"}}, {"session_id": "448", "turn_idx": 5, "from": "Assistant", "text": "大概十几分钟吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-163-5.wav"}}, {"session_id": "448", "turn_idx": 6, "from": "Human", "text": "具体建议和工具推荐有吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-163-6.wav"}}, {"session_id": "448", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"布偶猫 毛发护理 时间\"}", "value": "function\nweb_search\n{\"query\": \"布偶猫 毛发护理 时间\"}"}] +[{"session_id": "274", "turn_idx": 0, "from": "Human", "text": "小跃，我想自学摄影，怎么入门？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-164-0.wav"}}, {"session_id": "274", "turn_idx": 1, "from": "Assistant", "text": "先从基础学起，比如构图和光线，用手机练习就行。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-164-1.wav"}}, {"session_id": "274", "turn_idx": 2, "from": "Human", "text": "构图有啥技巧？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-164-2.wav"}}, {"session_id": "274", "turn_idx": 3, "from": "Assistant", "text": "三分法最简单，把画面分成九宫格，主体放交叉点。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-164-3.wav"}}, {"session_id": "274", "turn_idx": 4, "from": "Human", "text": "那帮我找一些三分法的实例教程。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-164-4.wav"}}, {"session_id": "274", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"摄影 三分法 实例教程\"}", "value": "function\nweb_search\n{\"query\": \"摄影 三分法 实例教程\"}"}] +[{"session_id": "487", "turn_idx": 0, "from": "Human", "text": "小跃，我家狗狗挑食，想换狗粮，有啥推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-165-0.wav"}}, {"session_id": "487", "turn_idx": 1, "from": "Assistant", "text": "试试皇家或渴望品牌，口碑好，营养均衡。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-165-1.wav"}}, {"session_id": "487", "turn_idx": 2, "from": "Human", "text": "渴望听说过，但适合什么品种的狗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-165-2.wav"}}, {"session_id": "487", "turn_idx": 3, "from": "Assistant", "text": "大部分中型犬都行，像金毛或拉布拉多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-165-3.wav"}}, {"session_id": "487", "turn_idx": 4, "from": "Human", "text": "我家是金毛，你能查查渴望狗粮的具体成分吗？ 成分表我记不住，比如蛋白质含量。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-165-4.wav"}}, {"session_id": "487", "turn_idx": 5, "from": "Assistant", "text": "没问题，帮我搜索渴望狗粮金毛专用成分和评价。 function\nweb_search\n{\"query\": \"渴望狗粮 金毛 成分 评价\"}", "value": "没问题，帮我搜索渴望狗粮金毛专用成分和评价。 function\nweb_search\n{\"query\": \"渴望狗粮 金毛 成分 评价\"}"}] +[{"session_id": "53", "turn_idx": 0, "from": "Human", "text": "小跃，我家的狗老是乱叫，怎么办？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-166-0.wav"}}, {"session_id": "53", "turn_idx": 1, "from": "Assistant", "text": "可能是无聊或焦虑，试试多带它散步，每天两次。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-166-1.wav"}}, {"session_id": "53", "turn_idx": 2, "from": "Human", "text": "散步了还是叫，尤其晚上特别吵。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-166-2.wav"}}, {"session_id": "53", "turn_idx": 3, "from": "Assistant", "text": "晚上可以给它个玩具，或者训练它安静，别让它太兴奋。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-166-3.wav"}}, {"session_id": "53", "turn_idx": 4, "from": "Human", "text": "行，那帮我搜搜狗乱叫的专业训练方法吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-166-4.wav"}}, {"session_id": "53", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"狗狗乱叫 训练技巧\"}", "value": "function\nweb_search\n{\"query\": \"狗狗乱叫 训练技巧\"}"}] +[{"session_id": "372", "turn_idx": 0, "from": "Human", "text": "小跃，我下月想去泰国玩，有啥景点必去？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-167-0.wav"}}, {"session_id": "372", "turn_idx": 1, "from": "Assistant", "text": "曼谷的大皇宫和清迈的夜市都值得一去。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-167-1.wav"}}, {"session_id": "372", "turn_idx": 2, "from": "Human", "text": "大皇宫听着有趣，有啥注意事项？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-167-2.wav"}}, {"session_id": "372", "turn_idx": 3, "from": "Assistant", "text": "记得穿长裤，别穿太暴露的衣服。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-167-3.wav"}}, {"session_id": "372", "turn_idx": 4, "from": "Human", "text": "行，帮我查查大皇宫的开放时间和门票价格。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-167-4.wav"}}, {"session_id": "372", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"大皇宫 开放时间 门票\"}", "value": "function\nweb_search\n{\"query\": \"大皇宫 开放时间 门票\"}"}] +[{"session_id": "230", "turn_idx": 0, "from": "Human", "text": "小跃，最近有什么好看的电影推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-168-0.wav"}}, {"session_id": "230", "turn_idx": 1, "from": "Assistant", "text": "最近那部《流浪地球二》挺火的，是一部科幻大片，口碑特别好，你可以看看。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-168-1.wav"}}, {"session_id": "230", "turn_idx": 2, "from": "Human", "text": "哦，听起来不错。你能查一下这部电影的导演是谁吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-168-2.wav"}}, {"session_id": "230", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"流浪地球二 导演\"}", "value": "function\nweb_search\n{\"query\": \"流浪地球二 导演\"}"}] +[{"session_id": "906", "turn_idx": 0, "from": "Human", "text": "小跃，我孩子问恐龙为什么灭绝了，怎么解释简单点？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-169-0.wav"}}, {"session_id": "906", "turn_idx": 1, "from": "Assistant", "text": "可以说是因为小行星撞击地球，导致环境变化。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-169-1.wav"}}, {"session_id": "906", "turn_idx": 2, "from": "Human", "text": "小行星撞击？那是多少年前的事？ 还有，恐龙灭绝的确切原因是什么？帮我搜一下权威说法。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-169-2.wav"}}, {"session_id": "906", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"恐龙灭绝原因\"}", "value": "function\nweb_search\n{\"query\": \"恐龙灭绝原因\"}"}] +[{"session_id": "69", "turn_idx": 0, "from": "Human", "text": "小跃，我计划去日本旅游，第一次出国。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-170-0.wav"}}, {"session_id": "69", "turn_idx": 1, "from": "Assistant", "text": "日本旅游很棒！东京和京都必去，文化景点超多。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-170-1.wav"}}, {"session_id": "69", "turn_idx": 2, "from": "Human", "text": "京都？我想知道签证要求和必备物品。 小跃，你能查一下日本旅游签证要求和必备物品清单吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-170-2.wav"}}, {"session_id": "69", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"日本旅游 签证要求 必备物品\"}", "value": "function\nweb_search\n{\"query\": \"日本旅游 签证要求 必备物品\"}"}] +[{"session_id": "788", "turn_idx": 0, "from": "Human", "text": "小跃，我想学摄影，入门相机选哪种好？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-171-0.wav"}}, {"session_id": "788", "turn_idx": 1, "from": "Assistant", "text": "入门的话，微单相机不错，轻便又容易上手。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-171-1.wav"}}, {"session_id": "788", "turn_idx": 2, "from": "Human", "text": "微单？和单反有什么区别？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-171-2.wav"}}, {"session_id": "788", "turn_idx": 3, "from": "Assistant", "text": "微单更小巧，对焦快，单反更专业但重。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-171-3.wav"}}, {"session_id": "788", "turn_idx": 4, "from": "Human", "text": "那佳能的微单怎么样？有推荐型号吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-171-4.wav"}}, {"session_id": "788", "turn_idx": 5, "from": "Assistant", "text": "佳能M系列口碑好，适合新手。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-171-5.wav"}}, {"session_id": "788", "turn_idx": 6, "from": "Human", "text": "能帮我搜搜佳能M系列的最新款和价格吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-171-6.wav"}}, {"session_id": "788", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"佳能 M系列 微单 最新款 价格\"}", "value": "function\nweb_search\n{\"query\": \"佳能 M系列 微单 最新款 价格\"}"}] +[{"session_id": "816", "turn_idx": 0, "from": "Human", "text": "小跃，我想读本小说放松，有什么推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-172-0.wav"}}, {"session_id": "816", "turn_idx": 1, "from": "Assistant", "text": "最近流行的《三体》不错，是科幻题材，情节很吸引人。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-172-1.wav"}}, {"session_id": "816", "turn_idx": 2, "from": "Human", "text": "科幻的？我怕看不懂，有更轻松的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-172-2.wav"}}, {"session_id": "816", "turn_idx": 3, "from": "Assistant", "text": "那试试《解忧杂货店》，是温暖治愈的故事，容易读。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-172-3.wav"}}, {"session_id": "816", "turn_idx": 4, "from": "Human", "text": "讲什么的？主角是谁？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-172-4.wav"}}, {"session_id": "816", "turn_idx": 5, "from": "Assistant", "text": "主角是个杂货店老板，帮人解决烦恼。书里人情味很浓。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-172-5.wav"}}, {"session_id": "816", "turn_idx": 6, "from": "Human", "text": "我想看看这本书的详细书评和摘要。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-172-6.wav"}}, {"session_id": "816", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"解忧杂货店书评\"}", "value": "function\nweb_search\n{\"query\": \"解忧杂货店书评\"}"}] +[{"session_id": "202", "turn_idx": 0, "from": "Human", "text": "小跃，我想养只宠物狗。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-173-0.wav"}}, {"session_id": "202", "turn_idx": 1, "from": "Assistant", "text": "养狗需要时间和精力，金毛或泰迪都是好选择。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-173-1.wav"}}, {"session_id": "202", "turn_idx": 2, "from": "Human", "text": "金毛犬的日常护理要注意什么？帮我查详细指南。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-173-2.wav"}}, {"session_id": "202", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"金毛犬护理指南\"}", "value": "function\nweb_search\n{\"query\": \"金毛犬护理指南\"}"}] +[{"session_id": "667", "turn_idx": 0, "from": "Human", "text": "小跃，博尔特的百米纪录是多少？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-174-0.wav"}}, {"session_id": "667", "turn_idx": 1, "from": "Assistant", "text": "尤塞恩·博尔特跑九秒五八，世界最快。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-174-1.wav"}}, {"session_id": "667", "turn_idx": 2, "from": "Human", "text": "九秒五八？啥时候破的纪录？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-174-2.wav"}}, {"session_id": "667", "turn_idx": 3, "from": "Assistant", "text": "二零零九年柏林世锦赛创造的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-174-3.wav"}}, {"session_id": "667", "turn_idx": 4, "from": "Human", "text": "那他有其他纪录吗？比如二百米？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-174-4.wav"}}, {"session_id": "667", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"尤塞恩·博尔特 二百米 世界纪录\"}", "value": "function\nweb_search\n{\"query\": \"尤塞恩·博尔特 二百米 世界纪录\"}"}] +[{"session_id": "610", "turn_idx": 0, "from": "Human", "text": "小跃，我今天在学历史，突然想到个问题，你知道秦始皇统一中国是哪一年吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-175-0.wav"}}, {"session_id": "610", "turn_idx": 1, "from": "Assistant", "text": "哇，秦始皇啊，他统一中国是在秦朝建立的时候，具体年份我得查查。不过那会儿挺早的，你是在复习考试吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-175-1.wav"}}, {"session_id": "610", "turn_idx": 2, "from": "Human", "text": "对呀，我有点记不清了。你能帮我查一下秦始皇统一中国的具体年份吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-175-2.wav"}}, {"session_id": "610", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"秦始皇 统一中国 年份\"}", "value": "function\nweb_search\n{\"query\": \"秦始皇 统一中国 年份\"}"}] +[{"session_id": "337", "turn_idx": 0, "from": "Human", "text": "小跃，大熊猫现在还是濒危动物吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-176-0.wav"}}, {"session_id": "337", "turn_idx": 1, "from": "Assistant", "text": "是的，不过保护工作做得好，数量在回升。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-176-1.wav"}}, {"session_id": "337", "turn_idx": 2, "from": "Human", "text": "它们主要生活在中国哪些地方？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-176-2.wav"}}, {"session_id": "337", "turn_idx": 3, "from": "Assistant", "text": "四川、陕西这些山区都有保护区。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-176-3.wav"}}, {"session_id": "337", "turn_idx": 4, "from": "Human", "text": "帮我查查大熊猫的现存数量。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-176-4.wav"}}, {"session_id": "337", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"大熊猫 数量\"}", "value": "function\nweb_search\n{\"query\": \"大熊猫 数量\"}"}] +[{"session_id": "740", "turn_idx": 0, "from": "Human", "text": "小跃，我今天想做蛋糕，但不知道从哪儿开始。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-177-0.wav"}}, {"session_id": "740", "turn_idx": 1, "from": "Assistant", "text": "做蛋糕挺简单的，首先准备面粉、鸡蛋和糖，其次选个简单配方比如海绵蛋糕。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-177-1.wav"}}, {"session_id": "740", "turn_idx": 2, "from": "Human", "text": "那海绵蛋糕需要多少鸡蛋啊？我记不清了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-177-2.wav"}}, {"session_id": "740", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"海绵蛋糕 需要几个鸡蛋\"}", "value": "function\nweb_search\n{\"query\": \"海绵蛋糕 需要几个鸡蛋\"}"}] +[{"session_id": "650", "turn_idx": 0, "from": "Human", "text": "小跃，我最近想养只猫，但不知道从哪里开始。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-178-0.wav"}}, {"session_id": "650", "turn_idx": 1, "from": "Assistant", "text": "养猫确实很有趣！你得先考虑猫咪的品种和日常照顾。比如，是想要活泼的还是安静的？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-178-1.wav"}}, {"session_id": "650", "turn_idx": 2, "from": "Human", "text": "我喜欢温顺点的。你能帮我查一下适合新手的温顺猫咪品种推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-178-2.wav"}}, {"session_id": "650", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"适合新手温顺猫咪品种\"}", "value": "function\nweb_search\n{\"query\": \"适合新手温顺猫咪品种\"}"}] +[{"session_id": "382", "turn_idx": 0, "from": "Human", "text": "你好小跃，我打算去北京玩，有什么必去景点？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-179-0.wav"}}, {"session_id": "382", "turn_idx": 1, "from": "Assistant", "text": "故宫和天安门肯定要去，历史感强，或者长城也超震撼。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-179-1.wav"}}, {"session_id": "382", "turn_idx": 2, "from": "Human", "text": "长城太远了，故宫里面有什么特别展览？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-179-2.wav"}}, {"session_id": "382", "turn_idx": 3, "from": "Assistant", "text": "故宫常展珍宝馆很出名，有古代玉器和瓷器。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-179-3.wav"}}, {"session_id": "382", "turn_idx": 4, "from": "Human", "text": "那故宫的开放时间是几点到几点？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-179-4.wav"}}, {"session_id": "382", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"故宫 开放时间\"}", "value": "function\nweb_search\n{\"query\": \"故宫 开放时间\"}"}] +[{"session_id": "325", "turn_idx": 0, "from": "Human", "text": "小跃，有啥好听的古风歌曲推荐？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-180-0.wav"}}, {"session_id": "325", "turn_idx": 1, "from": "Assistant", "text": "像《青花瓷》或《凉凉》都不错，旋律很优美。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-180-1.wav"}}, {"session_id": "325", "turn_idx": 2, "from": "Human", "text": "《凉凉》是谁唱的？我想多听听他们的歌。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-180-2.wav"}}, {"session_id": "325", "turn_idx": 3, "from": "Assistant", "text": "是张碧晨和杨宗纬合唱的。要查查他们的其他作品吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-180-3.wav"}}, {"session_id": "325", "turn_idx": 4, "from": "Human", "text": "好啊，帮我找找张碧晨的所有热门歌曲。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-180-4.wav"}}, {"session_id": "325", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"张碧晨 热门歌曲\"}", "value": "function\nweb_search\n{\"query\": \"张碧晨 热门歌曲\"}"}] +[{"session_id": "112", "turn_idx": 0, "from": "Human", "text": "小跃，我打算学英语，有什么好方法？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-181-0.wav"}}, {"session_id": "112", "turn_idx": 1, "from": "Assistant", "text": "多听多说很重要，可以用像Duolingo这样的APP练习。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-181-1.wav"}}, {"session_id": "112", "turn_idx": 2, "from": "Human", "text": "那你能帮我搜索免费的英语学习资源吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-181-2.wav"}}, {"session_id": "112", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"免费英语学习资源\"}", "value": "function\nweb_search\n{\"query\": \"免费英语学习资源\"}"}] +[{"session_id": "665", "turn_idx": 0, "from": "Human", "text": "小跃，我在学二战史，诺曼底登陆是啥？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-182-0.wav"}}, {"session_id": "665", "turn_idx": 1, "from": "Assistant", "text": "那是一九四四年盟军登陆法国，D日行动。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-182-1.wav"}}, {"session_id": "665", "turn_idx": 2, "from": "Human", "text": "一九四四年？盟军有哪些国家？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-182-2.wav"}}, {"session_id": "665", "turn_idx": 3, "from": "Assistant", "text": "美国、英国、加拿大等联合行动。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-182-3.wav"}}, {"session_id": "665", "turn_idx": 4, "from": "Human", "text": "我想知道死了多少人，还有为啥这么重要。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-182-4.wav"}}, {"session_id": "665", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"诺曼底登陆 伤亡人数 战略意义\"}", "value": "function\nweb_search\n{\"query\": \"诺曼底登陆 伤亡人数 战略意义\"}"}] +[{"session_id": "599", "turn_idx": 0, "from": "Human", "text": "嘿小跃，我最近看新闻说AI发展很快，它会取代人类工作吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-183-0.wav"}}, {"session_id": "599", "turn_idx": 1, "from": "Assistant", "text": "部分重复性工作可能被替代，但AI也能创造新机会。关键看怎么用。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-183-1.wav"}}, {"session_id": "599", "turn_idx": 2, "from": "Human", "text": "比如哪些行业最危险？我想提前准备。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-183-2.wav"}}, {"session_id": "599", "turn_idx": 3, "from": "Assistant", "text": "制造业或客服类风险高，创意和人际类工作更安全。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-183-3.wav"}}, {"session_id": "599", "turn_idx": 4, "from": "Human", "text": "明白了，帮我查查AI对就业影响的最新研究报告。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-183-4.wav"}}, {"session_id": "599", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"AI 就业影响 最新研究报告\"}", "value": "function\nweb_search\n{\"query\": \"AI 就业影响 最新研究报告\"}"}] +[{"session_id": "440", "turn_idx": 0, "from": "Human", "text": "小跃，你在吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-184-0.wav"}}, {"session_id": "440", "turn_idx": 1, "from": "Assistant", "text": "在呢，随时为你服务。今天想聊点啥？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-184-1.wav"}}, {"session_id": "440", "turn_idx": 2, "from": "Human", "text": "没啥事，就是有点无聊。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-184-2.wav"}}, {"session_id": "440", "turn_idx": 3, "from": "Assistant", "text": "那要不要看点电影放松一下？我最近听说一部不错的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-184-3.wav"}}, {"session_id": "440", "turn_idx": 4, "from": "Human", "text": "好啊，推荐个喜剧片吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-184-4.wav"}}, {"session_id": "440", "turn_idx": 5, "from": "Assistant", "text": "《你好，李焕英》怎么样？笑中带泪，口碑很好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-184-5.wav"}}, {"session_id": "440", "turn_idx": 6, "from": "Human", "text": "哦，这部电影的导演是谁？我想查查他的其他作品。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-184-6.wav"}}, {"session_id": "440", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"你好李焕英 导演\"}", "value": "function\nweb_search\n{\"query\": \"你好李焕英 导演\"}"}] +[{"session_id": "862", "turn_idx": 0, "from": "Human", "text": "小跃，我刚看完《阿凡达》，特效太牛了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-185-0.wav"}}, {"session_id": "862", "turn_idx": 1, "from": "Assistant", "text": "那部电影是科幻经典呢。你觉得导演的风格怎么样？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-185-1.wav"}}, {"session_id": "862", "turn_idx": 2, "from": "Human", "text": "导演名字我忘了，好像是卡梅隆？帮我确认一下全名和代表作吧。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-185-2.wav"}}, {"session_id": "862", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"阿凡达导演姓名代表作\"}", "value": "function\nweb_search\n{\"query\": \"阿凡达导演姓名代表作\"}"}] +[{"session_id": "447", "turn_idx": 0, "from": "Human", "text": "小跃，关注明星动态吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-186-0.wav"}}, {"session_id": "447", "turn_idx": 1, "from": "Assistant", "text": "不太多，但可以帮你查查。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-186-1.wav"}}, {"session_id": "447", "turn_idx": 2, "from": "Human", "text": "周杰伦最近有啥新歌？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-186-2.wav"}}, {"session_id": "447", "turn_idx": 3, "from": "Assistant", "text": "听说他刚发了专辑。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-186-3.wav"}}, {"session_id": "447", "turn_idx": 4, "from": "Human", "text": "专辑名字叫啥？我想听听。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-186-4.wav"}}, {"session_id": "447", "turn_idx": 5, "from": "Assistant", "text": "好像是《最伟大的作品》。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-186-5.wav"}}, {"session_id": "447", "turn_idx": 6, "from": "Human", "text": "确认一下全名和发行时间。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-186-6.wav"}}, {"session_id": "447", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"周杰伦 最新专辑 名称\"}", "value": "function\nweb_search\n{\"query\": \"周杰伦 最新专辑 名称\"}"}] +[{"session_id": "531", "turn_idx": 0, "from": "Human", "text": "小跃，我下个月想去杭州玩，西湖怎么样？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-187-0.wav"}}, {"session_id": "531", "turn_idx": 1, "from": "Assistant", "text": "西湖很美啊！适合散步划船，雷峰塔也值得去。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-187-1.wav"}}, {"session_id": "531", "turn_idx": 2, "from": "Human", "text": "雷峰塔？门票贵不贵？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-187-2.wav"}}, {"session_id": "531", "turn_idx": 3, "from": "Assistant", "text": "门票大概几十块，学生还有优惠。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-187-3.wav"}}, {"session_id": "531", "turn_idx": 4, "from": "Human", "text": "那怎么去西湖方便？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-187-4.wav"}}, {"session_id": "531", "turn_idx": 5, "from": "Assistant", "text": "坐地铁或公交都行，西湖边有多个入口。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-187-5.wav"}}, {"session_id": "531", "turn_idx": 6, "from": "Human", "text": "行，我想查西湖的最新开放时间，你能搜索一下吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-187-6.wav"}}, {"session_id": "531", "turn_idx": 7, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"西湖 开放时间\"}", "value": "function\nweb_search\n{\"query\": \"西湖 开放时间\"}"}] +[{"session_id": "393", "turn_idx": 0, "from": "Human", "text": "小跃，听说有新电影上映了，但我记不清名字。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-188-0.wav"}}, {"session_id": "393", "turn_idx": 1, "from": "Assistant", "text": "最近大片不少呢！是科幻片还是爱情片？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-188-1.wav"}}, {"session_id": "393", "turn_idx": 2, "from": "Human", "text": "科幻的，好像讲太空探险的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-188-2.wav"}}, {"session_id": "393", "turn_idx": 3, "from": "Assistant", "text": "哦，可能是《流浪地球》系列。要我查上映时间和评价吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-188-3.wav"}}, {"session_id": "393", "turn_idx": 4, "from": "Human", "text": "对，帮我查《流浪地球》的最新上映时间。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-188-4.wav"}}, {"session_id": "393", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"流浪地球 上映时间\"}", "value": "function\nweb_search\n{\"query\": \"流浪地球 上映时间\"}"}] +[{"session_id": "278", "turn_idx": 0, "from": "Human", "text": "小跃，宋朝的苏轼是谁？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-189-0.wav"}}, {"session_id": "278", "turn_idx": 1, "from": "Assistant", "text": "苏轼是宋代大文豪，诗词书画都厉害，人称苏东坡。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-189-1.wav"}}, {"session_id": "278", "turn_idx": 2, "from": "Human", "text": "他最有名的诗是啥？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-189-2.wav"}}, {"session_id": "278", "turn_idx": 3, "from": "Assistant", "text": "《水调歌头》和《念奴娇》都是经典，情感丰富。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-189-3.wav"}}, {"session_id": "278", "turn_idx": 4, "from": "Human", "text": "那帮我查《水调歌头》的原文和赏析。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-189-4.wav"}}, {"session_id": "278", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"苏轼 水调歌头 原文 赏析\"}", "value": "function\nweb_search\n{\"query\": \"苏轼 水调歌头 原文 赏析\"}"}] +[{"session_id": "251", "turn_idx": 0, "from": "Human", "text": "小跃，我刚看完《流浪地球》，觉得特效太震撼了。你有类似科幻电影推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-190-0.wav"}}, {"session_id": "251", "turn_idx": 1, "from": "Assistant", "text": "当然有！《星际穿越》就很棒，剧情和视觉效果都超赞。或者《地心引力》，节奏紧凑，特别刺激。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-190-1.wav"}}, {"session_id": "251", "turn_idx": 2, "from": "Human", "text": "《星际穿越》我看过了，确实好。那《地心引力》的主演是谁来着？我有点忘了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-190-2.wav"}}, {"session_id": "251", "turn_idx": 3, "from": "Assistant", "text": "是桑德拉·布洛克演的，她表现力超强。整部电影基本就靠她撑场。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-190-3.wav"}}, {"session_id": "251", "turn_idx": 4, "from": "Human", "text": "哦，是她啊。我想多了解她的其他作品，能帮我查查她最新演的电影吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-190-4.wav"}}, {"session_id": "251", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"桑德拉·布洛克 最新电影\"}", "value": "function\nweb_search\n{\"query\": \"桑德拉·布洛克 最新电影\"}"}] +[{"session_id": "450", "turn_idx": 0, "from": "Human", "text": "嗨小跃，今天心情咋样？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-191-0.wav"}}, {"session_id": "450", "turn_idx": 1, "from": "Assistant", "text": "还不错呀！你呢？有啥事找我？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-191-1.wav"}}, {"session_id": "450", "turn_idx": 2, "from": "Human", "text": "我在琢磨周末看啥电影，你有啥建议没？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-191-2.wav"}}, {"session_id": "450", "turn_idx": 3, "from": "Assistant", "text": "最近《奥本海默》挺火的，讲原子弹的故事，口碑很好。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-191-3.wav"}}, {"session_id": "450", "turn_idx": 4, "from": "Human", "text": "哦，听起来不错。导演是谁来着？我记不清了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-191-4.wav"}}, {"session_id": "450", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"奥本海默 导演\"}", "value": "function\nweb_search\n{\"query\": \"奥本海默 导演\"}"}] +[{"session_id": "979", "turn_idx": 0, "from": "Human", "text": "小跃，你知道刘德华最近拍啥新电影没？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-192-0.wav"}}, {"session_id": "979", "turn_idx": 1, "from": "Assistant", "text": "刘德华最近好像有部《拆弹专家三》，动作片，还在制作中。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-192-1.wav"}}, {"session_id": "979", "turn_idx": 2, "from": "Human", "text": "哦，前两部我看过。新片导演是谁？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-192-2.wav"}}, {"session_id": "979", "turn_idx": 3, "from": "Assistant", "text": "导演还是邱礼涛，他擅长这类题材。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-192-3.wav"}}, {"session_id": "979", "turn_idx": 4, "from": "Human", "text": "我想查查《拆弹专家三》的预计上映时间，啥时候能看？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-192-4.wav"}}, {"session_id": "979", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"拆弹专家三 上映时间\"}", "value": "function\nweb_search\n{\"query\": \"拆弹专家三 上映时间\"}"}] +[{"session_id": "28", "turn_idx": 0, "from": "Human", "text": "小跃，听说最近有个大新闻，关于环保的，是啥？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-193-0.wav"}}, {"session_id": "28", "turn_idx": 1, "from": "Assistant", "text": "好像是全球变暖的新报告，强调减少碳排放，影响挺大的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-193-1.wav"}}, {"session_id": "28", "turn_idx": 2, "from": "Human", "text": "报告细节呢？帮我找一下主要内容。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-193-2.wav"}}, {"session_id": "28", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"全球变暖 最新报告 主要内容\"}", "value": "function\nweb_search\n{\"query\": \"全球变暖 最新报告 主要内容\"}"}] +[{"session_id": "97", "turn_idx": 0, "from": "Human", "text": "小跃，我下周要去上海出差，想顺便旅游。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-194-0.wav"}}, {"session_id": "97", "turn_idx": 1, "from": "Assistant", "text": "上海的外滩和迪士尼乐园都值得一去，时间安排好的话很充实。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-194-1.wav"}}, {"session_id": "97", "turn_idx": 2, "from": "Human", "text": "迪士尼人太多，我想找点安静的地方。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-194-2.wav"}}, {"session_id": "97", "turn_idx": 3, "from": "Assistant", "text": "那推荐田子坊或朱家角古镇，文化氛围浓。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-194-3.wav"}}, {"session_id": "97", "turn_idx": 4, "from": "Human", "text": "朱家角没听过，你能帮我查一下朱家角的交通路线和景点介绍吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-194-4.wav"}}, {"session_id": "97", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"上海朱家角 交通 景点介绍\"}", "value": "function\nweb_search\n{\"query\": \"上海朱家角 交通 景点介绍\"}"}] +[{"session_id": "633", "turn_idx": 0, "from": "Human", "text": "小跃，我想学弹吉他。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-195-0.wav"}}, {"session_id": "633", "turn_idx": 1, "from": "Assistant", "text": "吉他入门可以先学几个简单和弦。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-195-1.wav"}}, {"session_id": "633", "turn_idx": 2, "from": "Human", "text": "能推荐一些免费在线教程吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-195-2.wav"}}, {"session_id": "633", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"免费 吉他 在线教程\"}", "value": "function\nweb_search\n{\"query\": \"免费 吉他 在线教程\"}"}] +[{"session_id": "497", "turn_idx": 0, "from": "Human", "text": "小跃，我想练英语口语，有啥好方法？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-196-0.wav"}}, {"session_id": "497", "turn_idx": 1, "from": "Assistant", "text": "首先多听英文歌或播客，其次找语伴聊天，APP像Duolingo挺好用。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-196-1.wav"}}, {"session_id": "497", "turn_idx": 2, "from": "Human", "text": "Duolingo是免费的吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-196-2.wav"}}, {"session_id": "497", "turn_idx": 3, "from": "Assistant", "text": "基础功能免费，高级版要付费，但免费版够初学者用了。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-196-3.wav"}}, {"session_id": "497", "turn_idx": 4, "from": "Human", "text": "那怎么用Duolingo练习口语？有具体步骤吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-196-4.wav"}}, {"session_id": "497", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"Duolingo 口语练习 方法\"}", "value": "function\nweb_search\n{\"query\": \"Duolingo 口语练习 方法\"}"}] +[{"session_id": "645", "turn_idx": 0, "from": "Human", "text": "小跃，我想读点轻松的小说，有推荐吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-197-0.wav"}}, {"session_id": "645", "turn_idx": 1, "from": "Assistant", "text": "轻松的话，试试《解忧杂货店》或者《小王子》。你喜欢现代还是经典？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-197-1.wav"}}, {"session_id": "645", "turn_idx": 2, "from": "Human", "text": "现代的吧，最好带点幽默感。 能帮我找些幽默风格的现代小说吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-197-2.wav"}}, {"session_id": "645", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"幽默 现代小说 推荐\"}", "value": "function\nweb_search\n{\"query\": \"幽默 现代小说 推荐\"}"}] +[{"session_id": "362", "turn_idx": 0, "from": "Human", "text": "小跃，我想换个新电脑，预算不高。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-198-0.wav"}}, {"session_id": "362", "turn_idx": 1, "from": "Assistant", "text": "预算有限的话，可以看看联想或惠普的入门款。你想要笔记本还是台式机？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-198-1.wav"}}, {"session_id": "362", "turn_idx": 2, "from": "Human", "text": "笔记本吧，屏幕大点，比如十五寸的。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-198-2.wav"}}, {"session_id": "362", "turn_idx": 3, "from": "Assistant", "text": "十五寸笔记本选择多，我可以帮你比较性价比高的型号。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-198-3.wav"}}, {"session_id": "362", "turn_idx": 4, "from": "Human", "text": "嗯，帮我搜一下十五寸联想惠普笔记本推荐。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-198-4.wav"}}, {"session_id": "362", "turn_idx": 5, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"十五寸 联想 惠普 笔记本 推荐\"}", "value": "function\nweb_search\n{\"query\": \"十五寸 联想 惠普 笔记本 推荐\"}"}] +[{"session_id": "903", "turn_idx": 0, "from": "Human", "text": "小跃，我手机电池总是不耐用，有什么省电技巧吗？", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-199-0.wav"}}, {"session_id": "903", "turn_idx": 1, "from": "Assistant", "text": "可以关掉后台应用，调低屏幕亮度。苹果手机尤其要注意设置。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-199-1.wav"}}, {"session_id": "903", "turn_idx": 2, "from": "Human", "text": "苹果手机？它的电池寿命一般能用几年？ 对了，苹果公司最早是做什么的？我想知道它的起源。", "value": {"type": "wav", "value": "wav/web_search_positive.jsonl-199-2.wav"}}, {"session_id": "903", "turn_idx": 3, "from": "Assistant", "text": "function\nweb_search\n{\"query\": \"苹果公司起源\"}", "value": "function\nweb_search\n{\"query\": \"苹果公司起源\"}"}]