init

.gitattributes

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 *.7z filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text
+*.mov filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.avro filter=lfs diff=lfs merge=lfs -text
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LICENSE

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+MIT License
+
+Copyright (c) 2024 ICTNLP
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# StreamSpeech
+
+[![arXiv](https://img.shields.io/badge/arXiv-2406.03049-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2406.03049)
+[![project](https://img.shields.io/badge/%F0%9F%8E%A7%20Demo-Listen%20to%20StreamSpeech-orange.svg)](https://ictnlp.github.io/StreamSpeech-site/)
+[![model](https://img.shields.io/badge/%F0%9F%A4%97%20-StreamSpeech_Models-blue.svg)](https://huggingface.co/ICTNLP/StreamSpeech_Models/tree/main)
+[![Hits](https://hitscounter.dev/api/hit?url=https%3A%2F%2Fgithub.com%2Fictnlp%2FStreamSpeech&label=Vistors&icon=graph-up&color=%23dc3545)](https://hits.seeyoufarm.com)
+
+[![twitter](https://img.shields.io/badge/Twitter-@Gorden%20Sun-black?logo=X&logoColor=black)](https://x.com/Gorden_Sun/status/1798742796524007845) [![twitter](https://img.shields.io/badge/Twitter-@imxiaohu-black?logo=X&logoColor=black)](https://x.com/imxiaohu/status/1798999363987124355)
+
+> **Authors**: **[Shaolei Zhang](https://zhangshaolei1998.github.io/), [Qingkai Fang](https://fangqingkai.github.io/), [Shoutao Guo](https://scholar.google.com.hk/citations?user=XwHtPyAAAAAJ&hl), [Zhengrui Ma](https://scholar.google.com.hk/citations?user=dUgq6tEAAAAJ), [Min Zhang](https://scholar.google.com.hk/citations?user=CncXH-YAAAAJ), [Yang Feng*](https://people.ucas.edu.cn/~yangfeng?language=en)**
+
+
+Code for ACL 2024 paper "[StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning](https://arxiv.org/pdf/2406.03049)".
+
+<p align="center" width="100%">
+<img src="./assets/streamspeech.png" alt="StreamSpeech" style="width: 70%; min-width: 300px; display: block; margin: auto;">
+</p>
+<p align="center">
+  🎧 Listen to <a href="https://ictnlp.github.io/StreamSpeech-site/">StreamSpeech's translated speech</a> 🎧 
+</p>
+
+💡**Highlight**:
+1. StreamSpeech achieves **SOTA performance** on both offline and simultaneous speech-to-speech translation.
+2. StreamSpeech performs **streaming ASR**, **simultaneous speech-to-text translation** and **simultaneous speech-to-speech translation** via an "All in One" seamless model.
+3. StreamSpeech can present intermediate results (i.e., ASR or translation results) during simultaneous translation, offering a more comprehensive low-latency communication experience.
+
+## 🔥News
+- [2025.06.17] We are excited to extend the "All-in-One" feature of StreamSpeech to more general multimodal interactions via developing **Stream-Omni**. 👉Refer to [paper](https://arxiv.org/abs/2506.13642), [code & demo](https://github.com/ictnlp/Stream-Omni), [model](https://huggingface.co/ICTNLP/stream-omni-8b) for more details.
+  - Stream-Omni is an GPT-4o-like language-vision-speech chatbot that simultaneously supports interactions across any combination of text, vision, and speech modalities.
+  - Stream-Omni can simultaneously produce intermediate textual results (e.g., ASR transcriptions and model responses) during speech interactions, like the advanced voice service of GPT-4o.
+
+- [2024.06.17] Add [Web GUI demo](./demo), now you can experience StreamSpeech in your local browser.
+- [2024.06.05] [Paper](https://arxiv.org/pdf/2406.03049), [code](https://github.com/ictnlp/StreamSpeech), [models](https://huggingface.co/ICTNLP/StreamSpeech_Models/tree/main) and [demo](https://ictnlp.github.io/StreamSpeech-site/) of StreamSpeech are available!
+
+## ⭐Features
+
+### Support 8 Tasks
+- **Offline**: Speech Recognition (ASR)✅, Speech-to-Text Translation (S2TT)✅, Speech-to-Speech Translation (S2ST)✅, Speech Synthesis (TTS)✅
+- **Simultaneous**: Streaming ASR✅, Simultaneous S2TT✅, Simultaneous S2ST✅, Real-time TTS✅ under any latency (with one model)
+
+### GUI Demo
+
+https://github.com/ictnlp/StreamSpeech/assets/34680227/4d9bdabf-af66-4320-ae7d-0f23e721cd71
+<p align="center">
+  Simultaneously provide ASR, translation, and synthesis results via a seamless model
+</p>
+
+### Case
+
+> **Speech Input**: [example/wavs/common_voice_fr_17301936.mp3](./example/wavs/common_voice_fr_17301936.mp3)
+>
+> **Transcription** (ground truth): jai donc lexpérience des années passées jen dirai un mot tout à lheure
+>
+> **Translation** (ground truth): i therefore have the experience of the passed years i'll say a few words about that later
+
+| StreamSpeech                                    | Simultaneous                                                 | Offline                                                      |
+| ----------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| **Speech Recognition**                          | jai donc expérience des années passé jen dirairai un mot tout à lheure | jai donc lexpérience des années passé jen dirairai un mot tout à lheure |
+| **Speech-to-Text Translation**                  | i therefore have an experience of last years i will tell a word later | so i have the experience in the past years i'll say a word later |
+| **Speech-to-Speech Translation**                | <video src='https://github.com/zhangshaolei1998/StreamSpeech_dev/assets/34680227/ed41ba13-353b-489b-acfa-85563d0cc2cb' width="30%"/>                          | <video src='https://github.com/zhangshaolei1998/StreamSpeech_dev/assets/34680227/ca482ba6-76da-4619-9dfd-24aa2eb3339a' width="30%"/>                          |
+| **Text-to-Speech Synthesis** (*incrementally synthesize speech word by word*) | <video src='https://github.com/zhangshaolei1998/StreamSpeech_dev/assets/34680227/294f1310-eace-4914-be30-5cd798e8592e' width="30%"/>                          | <video src='https://github.com/zhangshaolei1998/StreamSpeech_dev/assets/34680227/52854163-7fc5-4622-a5a6-c133cbd99e58' width="30%"/>                          |
+
+
+
+## ⚙Requirements
+
+- Python == 3.10, PyTorch == 2.0.1, Install fairseq & SimulEval
+
+  ```bash
+  cd fairseq
+  pip install --editable ./ --no-build-isolation
+  cd SimulEval
+  pip install --editable ./
+  ```
+
+## 🚀Quick Start
+
+### 1. Model Download
+
+#### (1) StreamSpeech Models
+
+| Language | UnitY                                                        | StreamSpeech (offline)                                       | StreamSpeech (simultaneous)                                  |
+| -------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| Fr-En    | unity.fr-en.pt [[Huggingface](https://huggingface.co/ICTNLP/StreamSpeech_Models/blob/main/unity.fr-en.pt)] [[Baidu](https://pan.baidu.com/s/10uGYgl0xTej9FP43iKx7Cg?pwd=nkvu)] | streamspeech.offline.fr-en.pt [[Huggingface](https://huggingface.co/ICTNLP/StreamSpeech_Models/blob/main/streamspeech.offline.fr-en.pt)] [[Baidu](https://pan.baidu.com/s/1GFckHGP5SNLuOEj6mbIWhQ?pwd=pwgq)] | streamspeech.simultaneous.fr-en.pt [[Huggingface](https://huggingface.co/ICTNLP/StreamSpeech_Models/blob/main/streamspeech.simultaneous.fr-en.pt)] [[Baidu](https://pan.baidu.com/s/1edCPFljogyDHgGXkUV8_3w?pwd=8gg3)] |
+| Es-En    | unity.es-en.pt [[Huggingface](https://huggingface.co/ICTNLP/StreamSpeech_Models/blob/main/unity.es-en.pt)] [[Baidu](https://pan.baidu.com/s/1RwIEHye8jjw3kiIgrCHA3A?pwd=hde4)] | streamspeech.offline.es-en.pt [[Huggingface](https://huggingface.co/ICTNLP/StreamSpeech_Models/blob/main/streamspeech.offline.es-en.pt)] [[Baidu](https://pan.baidu.com/s/1T89G4NC4J0Ofzcsc8Rt2Ww?pwd=yuhd)] | streamspeech.simultaneous.es-en.pt [[Huggingface](https://huggingface.co/ICTNLP/StreamSpeech_Models/blob/main/streamspeech.simultaneous.es-en.pt)] [[Baidu](https://pan.baidu.com/s/1NbLEVcYWHIdqqLD17P1s9g?pwd=p1pc)] |
+| De-En    | unity.de-en.pt [[Huggingface](https://huggingface.co/ICTNLP/StreamSpeech_Models/blob/main/unity.de-en.pt)] [[Baidu](https://pan.baidu.com/s/1Mg_PBeZ5acEDhl5wRJ_-7w?pwd=egvv)] | streamspeech.offline.de-en.pt [[Huggingface](https://huggingface.co/ICTNLP/StreamSpeech_Models/blob/main/streamspeech.offline.de-en.pt)] [[Baidu](https://pan.baidu.com/s/1mTE4eHuVLJPB7Yg9AackEg?pwd=6ga8)] | streamspeech.simultaneous.de-en.pt [[Huggingface](https://huggingface.co/ICTNLP/StreamSpeech_Models/blob/main/streamspeech.simultaneous.de-en.pt)] [[Baidu](https://pan.baidu.com/s/1DYPMg3mdDopLY70BYQTduQ?pwd=r7kw)] |
+
+#### (2) Unit-based HiFi-GAN Vocoder
+
+| Unit config       | Unit size | Vocoder language | Dataset                                             | Model                                                        |
+| ----------------- | --------- | ---------------- | --------------------------------------------------- | ------------------------------------------------------------ |
+| mHuBERT, layer 11 | 1000      | En               | [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) | [ckpt](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/g_00500000), [config](https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/config.json) |
+
+### 2. Prepare Data and Config (only for test/inference)
+
+#### (1) Config Files
+
+Replace `/data/zhangshaolei/StreamSpeech` in files [configs/fr-en/config_gcmvn.yaml](./configs/fr-en/config_gcmvn.yaml) and [configs/fr-en/config_mtl_asr_st_ctcst.yaml](./configs/fr-en/config_mtl_asr_st_ctcst.yaml) with your local address of StreamSpeech repo.
+
+#### (2) Test Data
+
+Prepare test data following [SimulEval](https://github.com/facebookresearch/SimulEval) format. [example/](./example) provides an example:
+
+- [wav_list.txt](./example/wav_list.txt): Each line records the path of a source speech.
+- [target.txt](./example/target.txt): Each line records the reference text, e.g., target translation or source transcription (used to calculate the metrics).
+
+### 3. Inference with SimulEval
+
+Run these scripts to inference StreamSpeech on streaming ASR, simultaneous S2TT and  simultaneous S2ST.
+
+> `--source-segment-size`: set the chunk size (millisecond) to any value to control the latency
+
+<details>
+<summary>Simultaneous Speech-to-Speech Translation</summary>
+
+`--output-asr-translation`: whether to output the intermediate ASR and translated text results during simultaneous speech-to-speech translation.
+
+```shell
+export CUDA_VISIBLE_DEVICES=0
+
+ROOT=/data/zhangshaolei/StreamSpeech # path to StreamSpeech repo
+PRETRAIN_ROOT=/data/zhangshaolei/pretrain_models 
+VOCODER_CKPT=$PRETRAIN_ROOT/unit-based_HiFi-GAN_vocoder/mHuBERT.layer11.km1000.en/g_00500000 # path to downloaded Unit-based HiFi-GAN Vocoder
+VOCODER_CFG=$PRETRAIN_ROOT/unit-based_HiFi-GAN_vocoder/mHuBERT.layer11.km1000.en/config.json # path to downloaded Unit-based HiFi-GAN Vocoder
+
+LANG=fr
+file=streamspeech.simultaneous.${LANG}-en.pt # path to downloaded StreamSpeech model
+output_dir=$ROOT/res/streamspeech.simultaneous.${LANG}-en/simul-s2st
+
+chunk_size=320 #ms
+PYTHONPATH=$ROOT/fairseq simuleval --data-bin ${ROOT}/configs/${LANG}-en \
+    --user-dir ${ROOT}/researches/ctc_unity --agent-dir ${ROOT}/agent \
+    --source example/wav_list.txt --target example/target.txt \
+    --model-path $file \
+    --config-yaml config_gcmvn.yaml --multitask-config-yaml config_mtl_asr_st_ctcst.yaml \
+    --agent $ROOT/agent/speech_to_speech.streamspeech.agent.py \
+    --vocoder $VOCODER_CKPT --vocoder-cfg $VOCODER_CFG --dur-prediction \
+    --output $output_dir/chunk_size=$chunk_size \
+    --source-segment-size $chunk_size \
+    --quality-metrics ASR_BLEU  --target-speech-lang en --latency-metrics AL AP DAL StartOffset EndOffset LAAL ATD NumChunks DiscontinuitySum DiscontinuityAve DiscontinuityNum RTF \
+    --device gpu --computation-aware \
+    --output-asr-translation True
+```
+
+You should get the following outputs:
+
+```
+fairseq plugins loaded...
+fairseq plugins loaded...
+fairseq plugins loaded...
+fairseq plugins loaded...
+2024-06-06 09:45:46 | INFO     | fairseq.tasks.speech_to_speech | dictionary size: 1,004
+import agents...
+Removing weight norm...
+2024-06-06 09:45:50 | INFO     | agent.tts.vocoder | loaded CodeHiFiGAN checkpoint from /data/zhangshaolei/pretrain_models/unit-based_HiFi-GAN_vocoder/mHuBERT.layer11.km1000.en/g_00500000
+2024-06-06 09:45:50 | INFO     | simuleval.utils.agent | System will run on device: gpu.
+2024-06-06 09:45:50 | INFO     | simuleval.dataloader | Evaluating from speech to speech.
+  0%|                                                                                                                                                                              | 0/2 [00:00<?, ?it/s]
+Streaming ASR: 
+Streaming ASR: 
+Streaming ASR: je
+Simultaneous translation: i would
+Streaming ASR: je voudrais
+Simultaneous translation: i would like to
+Streaming ASR: je voudrais soumettre
+Simultaneous translation: i would like to sub
+Streaming ASR: je voudrais soumettre cette
+Simultaneous translation: i would like to submit
+Streaming ASR: je voudrais soumettre cette idée
+Simultaneous translation: i would like to submit this
+Streaming ASR: je voudrais soumettre cette idée à la
+Simultaneous translation: i would like to submit this idea to
+Streaming ASR: je voudrais soumettre cette idée à la réflexion
+Simultaneous translation: i would like to submit this idea to the
+Streaming ASR: je voudrais soumettre cette idée à la réflexion de
+Simultaneous translation: i would like to submit this idea to the reflection
+Streaming ASR: je voudrais soumettre cette idée à la réflexion de lassemblée
+Simultaneous translation: i would like to submit this idea to the reflection of
+Streaming ASR: je voudrais soumettre cette idée à la réflexion de lassemblée nationale
+Simultaneous translation: i would like to submit this idea to the reflection of the
+Streaming ASR: je voudrais soumettre cette idée à la réflexion de lassemblée nationale
+Simultaneous translation: i would like to submit this idea to the reflection of the national assembly
+ 50%|███████████████████████████████████████████████████████████████████████████████████                                                                                   | 1/2 [00:04<00:04,  4.08s/it]
+Streaming ASR: 
+Streaming ASR: 
+Streaming ASR: 
+Streaming ASR: 
+Streaming ASR: jai donc
+Simultaneous translation: i therefore
+Streaming ASR: jai donc
+Streaming ASR: jai donc expérience des
+Simultaneous translation: i therefore have an experience
+Streaming ASR: jai donc expérience des années
+Streaming ASR: jai donc expérience des années passé
+Simultaneous translation: i therefore have an experience of last
+Streaming ASR: jai donc expérience des années passé jen
+Simultaneous translation: i therefore have an experience of last years
+Streaming ASR: jai donc expérience des années passé jen dirairai
+Simultaneous translation: i therefore have an experience of last years i will
+Streaming ASR: jai donc expérience des années passé jen dirairai un mot
+Simultaneous translation: i therefore have an experience of last years i will tell a
+Streaming ASR: jai donc expérience des années passé jen dirairai un mot tout à lheure
+Simultaneous translation: i therefore have an experience of last years i will tell a word
+Streaming ASR: jai donc expérience des années passé jen dirairai un mot tout à lheure
+Simultaneous translation: i therefore have an experience of last years i will tell a word later
+100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:06<00:00,  3.02s/it]
+2024-06-06 09:45:56 | WARNING  | simuleval.scorer.asr_bleu | Beta feature: Evaluating speech output. Faieseq is required.
+2024-06-06 09:46:12 | INFO | fairseq.tasks.audio_finetuning | Using dict_path : /data/zhangshaolei/.cache/ust_asr/en/dict.ltr.txt
+Transcribing predictions: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.63it/s]
+2024-06-06 09:46:21 | INFO     | simuleval.sentence_level_evaluator | Results:
+ ASR_BLEU       AL    AL_CA    AP  AP_CA      DAL  DAL_CA  StartOffset  StartOffset_CA  EndOffset  EndOffset_CA     LAAL  LAAL_CA      ATD   ATD_CA  NumChunks  NumChunks_CA  DiscontinuitySum  DiscontinuitySum_CA  DiscontinuityAve  DiscontinuityAve_CA  DiscontinuityNum  DiscontinuityNum_CA   RTF  RTF_CA
+   15.448 1724.895 2913.508 0.425  0.776 1358.812 3137.55       1280.0        2213.906     1366.0        1366.0 1724.895 2913.508 1440.146 3389.374        9.5           9.5             110.0                110.0              55.0                 55.0                 1                    1 1.326   1.326
+
+```
+
+Logs and evaluation results are stored in ` $output_dir/chunk_size=$chunk_size`:
+
+```
+$output_dir/chunk_size=$chunk_size
+├── wavs/
+│   ├── 0_pred.wav # generated speech
+│   ├── 1_pred.wav 
+│   ├── 0_pred.txt # asr transcription for ASR-BLEU tookit
+│   ├── 1_pred.txt 
+├── config.yaml
+├── asr_transcripts.txt # ASR-BLEU transcription results
+├── metrics.tsv
+├── scores.tsv
+├── asr_cmd.bash
+└── instances.log # logs of Simul-S2ST
+```
+
+</details>
+
+<details>
+<summary>Simultaneous Speech-to-Text Translation</summary>
+
+```shell
+export CUDA_VISIBLE_DEVICES=0
+
+ROOT=/data/zhangshaolei/StreamSpeech # path to StreamSpeech repo
+
+LANG=fr
+file=streamspeech.simultaneous.${LANG}-en.pt # path to downloaded StreamSpeech model
+output_dir=$ROOT/res/streamspeech.simultaneous.${LANG}-en/simul-s2tt
+
+chunk_size=320 #ms
+PYTHONPATH=$ROOT/fairseq simuleval --data-bin ${ROOT}/configs/${LANG}-en \
+    --user-dir ${ROOT}/researches/ctc_unity --agent-dir ${ROOT}/agent \
+    --source example/wav_list.txt --target example/target.txt \
+    --model-path $file \
+    --config-yaml config_gcmvn.yaml --multitask-config-yaml config_mtl_asr_st_ctcst.yaml \
+    --agent $ROOT/agent/speech_to_text.s2tt.streamspeech.agent.py\
+    --output $output_dir/chunk_size=$chunk_size \
+    --source-segment-size $chunk_size \
+    --quality-metrics BLEU  --latency-metrics AL AP DAL StartOffset EndOffset LAAL ATD NumChunks RTF \
+    --device gpu --computation-aware 
+```
+</details>
+
+<details>
+<summary>Streaming ASR</summary>
+
+```shell
+export CUDA_VISIBLE_DEVICES=0
+
+ROOT=/data/zhangshaolei/StreamSpeech # path to StreamSpeech repo
+
+LANG=fr
+file=streamspeech.simultaneous.${LANG}-en.pt # path to downloaded StreamSpeech model
+output_dir=$ROOT/res/streamspeech.simultaneous.${LANG}-en/streaming-asr
+
+chunk_size=320 #ms
+PYTHONPATH=$ROOT/fairseq simuleval --data-bin ${ROOT}/configs/${LANG}-en \
+    --user-dir ${ROOT}/researches/ctc_unity --agent-dir ${ROOT}/agent \
+    --source example/wav_list.txt --target example/source.txt \
+    --model-path $file \
+    --config-yaml config_gcmvn.yaml --multitask-config-yaml config_mtl_asr_st_ctcst.yaml \
+    --agent $ROOT/agent/speech_to_text.asr.streamspeech.agent.py\
+    --output $output_dir/chunk_size=$chunk_size \
+    --source-segment-size $chunk_size \
+    --quality-metrics BLEU  --latency-metrics AL AP DAL StartOffset EndOffset LAAL ATD NumChunks RTF \
+    --device gpu --computation-aware 
+```
+</details>
+
+## 🎈Develop Your Own StreamSpeech
+
+### 1. Data Preprocess
+
+- Follow [`./preprocess_scripts`](./preprocess_scripts) to process CVSS-C data. 
+
+### 2. Training
+
+> [!Note]
+> You can directly use the [downloaded StreamSpeech model](#1-model-download) for evaluation and skip training.
+
+<p align="center" width="100%">
+<img src="./assets/model.png" alt="model" style="width: 100%; min-width: 300px; display: block; margin: auto;">
+</p>
+
+- Follow [`researches/ctc_unity/train_scripts/train.simul-s2st.sh`](./researches/ctc_unity/train_scripts/train.simul-s2st.sh) to train StreamSpeech for simultaneous speech-to-speech translation.
+- Follow [`researches/ctc_unity/train_scripts/train.offline-s2st.sh`](./researches/ctc_unity/train_scripts/train.offline-s2st.sh) to train StreamSpeech for offline speech-to-speech translation.
+- We also provide some other StreamSpeech variants and baseline implementations.
+
+| Model             | --user-dir                 | --arch                            | Description                                                  |
+| ----------------- | -------------------------- | --------------------------------- | ------------------------------------------------------------ |
+| **Translatotron 2** | `researches/translatotron` | `s2spect2_conformer_modified`     | [Translatotron 2](https://proceedings.mlr.press/v162/jia22b.html) |
+| **UnitY**         | `researches/translatotron` | `unity_conformer_modified`        | [UnitY](https://aclanthology.org/2023.acl-long.872/)         |
+| **Uni-UnitY**     | `researches/uni_unity`     | `uni_unity_conformer`             | Change all encoders in UnitY into unidirectional             |
+| **Chunk-UnitY**   | `researches/chunk_unity`   | `chunk_unity_conformer`           | Change the Conformer in UnitY into Chunk-based Conformer     |
+| **StreamSpeech**  | `researches/ctc_unity`     | `streamspeech`                    | StreamSpeech                                                 |
+| **StreamSpeech (cascade)** | `researches/ctc_unity` | `streamspeech_cascade` | Cascaded StreamSpeech of S2TT and TTS. TTS module can be used independently for real-time TTS given incremental text. |
+| **HMT**           | `researches/hmt`           | `hmt_transformer_iwslt_de_en`     | [HMT](https://openreview.net/forum?id=9y0HFvaAYD6): strong simultaneous text-to-text translation method |
+| **DiSeg**         | `researches/diseg`         | `convtransformer_espnet_base_seg` | [DiSeg](https://aclanthology.org/2023.findings-acl.485/): strong simultaneous speech-to-text translation method |
+
+> [!Tip]
+> The `train_scripts/` and `test_scripts/` in directory `--user-dir` give the training and testing scripts for each model.
+> Refer to official repo of [UnitY](https://github.com/facebookresearch/fairseq/blob/main/fairseq/models/speech_to_speech/s2s_conformer_unity.py), [Translatotron 2](https://github.com/facebookresearch/fairseq/blob/main/fairseq/models/speech_to_speech/s2s_conformer_translatotron2.py), [HMT](https://github.com/ictnlp/HMT) and [DiSeg](https://github.com/ictnlp/DiSeg) for more details.
+
+### 3. Evaluation
+
+#### (1) Offline Evaluation
+
+Follow [`pred.offline-s2st.sh`](./researches/ctc_unity/test_scripts/pred.offline-s2st.sh) to evaluate the offline performance of StreamSpeech on ASR, S2TT and S2ST.
+
+#### (2) Simultaneous Evaluation
+
+A trained StreamSpeech model can be used for streaming ASR, simultaneous speech-to-text translation and simultaneous speech-to-speech translation. We provide [agent/](./agent) for these three tasks:
+
+- `agent/speech_to_speech.streamspeech.agent.py`: simultaneous speech-to-speech translation
+- `agent/speech_to_text.s2tt.streamspeech.agent.py`: simultaneous speech-to-text translation
+- `agent/speech_to_text.asr.streamspeech.agent.py`: streaming ASR
+
+Follow [`simuleval.simul-s2st.sh`](./researches/ctc_unity/test_scripts/simuleval.simul-s2st.sh), [`simuleval.simul-s2tt.sh`](./researches/ctc_unity/test_scripts/simuleval.simul-s2tt.sh), [`simuleval.streaming-asr.sh`](./researches/ctc_unity/test_scripts/simuleval.streaming-asr.sh)  to evaluate StreamSpeech.
+
+### 4. Our Results
+
+Our project page ([https://ictnlp.github.io/StreamSpeech-site/](https://ictnlp.github.io/StreamSpeech-site/)) provides some translated speech generated by StreamSpeech, listen to it 🎧.
+
+#### (1) Offline Speech-to-Speech Translation  ( ASR-BLEU: quality )
+
+<p align="center" width="100%">
+<img src="./assets/offline_results.png" alt="offline" style="width: 100%; min-width: 300px; display: block; margin: auto;">
+</p>
+
+#### (2) Simultaneous Speech-to-Speech Translation  ( AL: latency  |  ASR-BLEU: quality )
+
+<p align="center" width="100%">
+<img src="./assets/simultaneous_results.png" alt="simul" style="width: 100%; min-width: 300px; display: block; margin: auto;">
+</p>
+
+#### (3) Simultaneous Speech-to-Text Translation  ( AL: latency  |  BLEU: quality )
+
+<p align="center" width="100%">
+<img src="./assets/s2tt.png" alt="simul" style="width: 38%; min-width: 300px; display: block; margin: auto;">
+</p>
+
+#### (4) Streaming ASR  ( AL: latency  |  WER: quality )
+
+<p align="center" width="100%">
+<img src="./assets/asr.png" alt="simul" style="width: 50%; min-width: 300px; display: block; margin: auto;">
+</p>
+
+## 🖋Citation
+
+If you have any questions, please feel free to submit an issue or contact `zhangshaolei20z@ict.ac.cn`.
+
+If our work is useful for you, please cite as:
+
+```
+@inproceedings{streamspeech,
+      title={StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning}, 
+      author={Shaolei Zhang and Qingkai Fang and Shoutao Guo and Zhengrui Ma and Min Zhang and Yang Feng},
+      year={2024},
+      booktitle = {Proceedings of the 62th Annual Meeting of the Association for Computational Linguistics (Long Papers)},
+      publisher = {Association for Computational Linguistics}
+}
+```

SimulEval/.github/workflows/main.yml

@@ -0,0 +1,46 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: build
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version}}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        sudo apt-get install libsndfile1
+        python -m pip install --upgrade pip
+        pip install flake8 pytest black
+        pip install -e .
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with black
+      run: black --check --diff .
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest simuleval/test/test_agent.py
+        pytest simuleval/test/test_agent_pipeline.py
+        pytest simuleval/test/test_evaluator.py
+        pytest simuleval/test/test_remote_evaluation.py

SimulEval/.gitignore

@@ -0,0 +1,139 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+.vscode

SimulEval/CHANGELOG.md

@@ -0,0 +1,10 @@
+1.0.0 (September 25, 2020)
+
+* Initial release.
+
+1.0.1 (February 8, 2021)
+
+* Change CLI command
+    * Change `simuleval-server` to `simuleval --server-only`
+    * Change `simuleval-client` to `simuleval --client-only`
+* Fix some typos

SimulEval/CODE_OF_CONDUCT.md

@@ -0,0 +1,45 @@
+# Open Source Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment include:
+
+Using welcoming and inclusive language
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+
+## Our Responsibilities
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+
+## Scope
+
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+
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+
+Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project’s leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org

SimulEval/CONTRIBUTING.md

@@ -0,0 +1,20 @@
+# Contributing to Facebook AI SimulEval
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `master`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+## License
+By contributing to Facebook AI SimulEval, you agree that your contributions will
+be licensed under the LICENSE file in the root directory of this source tree.

SimulEval/LICENSE

@@ -0,0 +1,427 @@
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SimulEval/README.md

@@ -0,0 +1,35 @@
+# SimulEval
+[![](https://github.com/facebookresearch/SimulEval/workflows/build/badge.svg)](https://github.com/facebookresearch/SimulEval/actions)
+
+SimulEval is a general evaluation framework for simultaneous translation on text and speech. Full documentation can be found [here](https://simuleval.readthedocs.io/en/v1.1.0/).
+
+## Installation
+```
+git clone https://github.com/facebookresearch/SimulEval.git
+cd SimulEval
+pip install -e .
+```
+
+## Quick Start
+Following is the evaluation of a [dummy agent](examples/quick_start) which operates wait-k (k = 3) policy and generates random words until the length of the generated words is the same as the number of all the source words.
+```shell
+cd examples/quick_start
+simuleval --source source.txt --target target.txt --agent first_agent.py
+```
+
+# License
+
+SimulEval is licensed under Creative Commons BY-SA 4.0.
+
+# Citation
+
+Please cite as:
+
+```bibtex
+@inproceedings{simuleval2020,
+  title = {Simuleval: An evaluation toolkit for simultaneous translation},
+  author = {Xutai Ma, Mohammad Javad Dousti, Changhan Wang, Jiatao Gu, Juan Pino},
+  booktitle = {Proceedings of the EMNLP},
+  year = {2020},
+}
+```

SimulEval/docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

SimulEval/docs/conf.py

@@ -0,0 +1,40 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = "SimulEval"
+copyright = "Facebook AI Research (FAIR)"
+author = "Facebook AI Research (FAIR)"
+release = "1.1.0"
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+    "sphinx_rtd_theme",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.napoleon",
+    "sphinxarg.ext",
+]
+
+# templates_path = ['_templates']
+exclude_patterns = []
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = "sphinx_rtd_theme"
+# html_static_path = ['_static']

SimulEval/docs/index.rst

@@ -0,0 +1,30 @@
+SimulEval documentation
+=======================
+
+SimulEval is a general evaluation framework for simultaneous translation.
+
+.. toctree::
+   :maxdepth: 2
+   :glob:
+   :caption: Get started
+
+   installation
+   quick_start
+
+.. toctree::
+   :maxdepth: 2
+   :caption: User's guide
+
+   user_guide/introduction
+   user_guide/agent
+   user_guide/evaluator
+   user_guide/dataloader
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials
+
+   tutorials/remote_evaluation
+   tutorials/speech_to_text
+   tutorials/speech_to_speech
+

SimulEval/docs/installation.rst

@@ -0,0 +1,21 @@
+Installation
+============
+
+From pip
+--------
+
+.. code-block:: bash
+
+    pip install simuleval
+
+
+From source
+-----------
+
+.. code-block:: bash
+
+    git clone https://github.com/facebookresearch/SimulEval.git
+    cd SimulEval
+    pip install -e .
+
+

SimulEval/docs/make.bat

@@ -0,0 +1,41 @@
+:: Copyright (c) Facebook, Inc. and its affiliates.
+:: All rights reserved.
+::
+:: This source code is licensed under the license found in the
+:: LICENSE file in the root directory of this source tree.
+
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

SimulEval/docs/quick_start.rst

@@ -0,0 +1,49 @@
+.. _first-agent:
+
+Quick Start
+===========
+
+This section will introduce a minimal example on how to use SimulEval for simultaneous translation evaluation.
+The code in the example can be found in :code:`examples/quick_start`.
+
+The agent in SimulEval is core for simultaneous evaluation.
+It's a carrier of user's simultaneous system.
+The user has to implement the agent based on their system for evaluation.
+The example simultaneous system is a dummy wait-k agent, which
+
+- Runs `wait-k <https://aclanthology.org/P19-1289/>`_ policy.
+- Generates random characters the policy decide to write.
+- Stops the generation k predictions after source input. For simplicity, we just set :code:`k=3` in this example.
+
+The implementation of this agent is shown as follow.
+
+.. literalinclude:: ../examples/quick_start/first_agent.py
+   :language: python
+   :lines: 6-
+
+There two essential components for an agent:
+
+- :code:`states`:  The attribute keeps track of the source and target information.
+- :code:`policy`:  The method makes decisions when the there is a new source segment.
+
+Once the agent is implemented and saved at :code:`first_agent.py`,
+run the following command for latency evaluation on:
+
+.. code-block:: bash
+
+    simuleval --source source.txt --reference target.txt --agent first_agent.py
+
+where :code:`--source` is the input file while :code:`--target` is the reference file.
+
+By default, the SimulEval will give the following output --- one quality and three latency metrics.
+
+.. code-block:: bash
+
+   2022-12-05 13:43:58 | INFO | simuleval.cli | Evaluate system: DummyWaitkTextAgent
+   2022-12-05 13:43:58 | INFO | simuleval.dataloader | Evaluating from text to text.
+   2022-12-05 13:43:58 | INFO | simuleval.sentence_level_evaluator | Results:
+   BLEU  AL    AP  DAL
+   1.541 3.0 0.688  3.0
+
+The average lagging is expected since we are running an wait-3 system where the source and target always have the same length.
+Notice that we have a very low yet random BLEU score. It's because we are randomly generate the output.

SimulEval/docs/requirements.txt

@@ -0,0 +1,2 @@
+sphinx-argparse==0.4.0
+-e .

SimulEval/docs/tutorials/remote_evaluation.rst

@@ -0,0 +1,44 @@
+Remote Evaluation
+=================
+
+Stand Alone Agent
+-----------------
+The agent can run in stand alone mode,
+by using :code:`--standalone` option.
+The SimulEval will kickoff a server that host the agent.
+For instance, with the agent in :ref:`first-agent`,
+
+.. code-block:: bash
+
+    > simuleval --standalone --remote-port 8888 --agent first_agent.py.py
+    2022-12-06 19:12:26 | INFO | simuleval.cli | Evaluate system: DummyWaitkTextAgent
+    2022-12-06 19:12:26 | INFO | simuleval.agent_server | Simultaneous Translation Server Started (process id 53902). Listening to port 8888
+
+For detailed RESTful APIs, please see (TODO)
+
+Docker
+-----------------
+You can also use a docker image to run the simuleval.
+An minimal example of :code:`Dockerfile` is
+
+.. literalinclude:: ../../examples/quick_start/Dockerfile
+   :language: docker
+
+Build and run the docker image:
+
+.. code-block:: bash
+
+    cd examples/quick_start && docker build -t simuleval_agent .
+    docker run -p 8888:8888 simuleval_agent:latest
+
+Remote Evaluation
+------------------
+If there is an agent server or docker image available,
+(let's say the one we just kickoff at localhost:8888)
+We can start a remote evaluator as follow. For simplicity we assume they are on the same machine
+
+.. code-block:: bash
+
+    simuleval --remote-eval --remote-port 8888 \
+        --source source.txt --target target.txt \
+        --source-type text --target-type text

SimulEval/docs/tutorials/speech_to_speech.rst

@@ -0,0 +1,2 @@
+Speech-to-Speech
+================

SimulEval/docs/tutorials/speech_to_text.rst

@@ -0,0 +1,2 @@
+Speech-to-Text
+==============

SimulEval/docs/user_guide/agent.rst

@@ -0,0 +1,198 @@
+Agent
+=====
+
+To evaluate the simultaneous translation system,
+the users need to implement agent class which operate the system logics.
+This section will introduce how to implement an agent.
+
+Source-Target Types
+-------------------
+First of all,
+we must declare the source and target types of the agent class.
+It can be done by inheriting from
+
+- One of the following four built-in agent types
+
+    - :class:`simuleval.agents.TextToTextAgent`
+    - :class:`simuleval.agents.SpeechToTextAgent`
+    - :class:`simuleval.agents.TextToSpeechAgent`
+    - :class:`simuleval.agents.SpeechToSpeechAgent`
+
+- Or :class:`simuleval.agents.GenericAgent`, with explicit declaration of :code:`source_type` and  :code:`target_type`.
+
+The follow two examples are equivalent.
+
+.. code-block:: python
+
+    from simuleval import simuleval
+    from simuleval.agents import GenericAgent
+
+    class MySpeechToTextAgent(GenericAgent):
+        source_type = "Speech"
+        target_type = "Text"
+        ....
+
+.. code-block:: python
+
+    from simuleval.agents import SpeechToSpeechAgent
+
+    class MySpeechToTextAgent(SpeechToSpeechAgent):
+        ....
+
+.. _agent_policy:
+
+Policy
+------
+
+The agent must have a :code:`policy` method which must return one of two actions, :code:`ReadAction` and :code:`WriteAction`.
+For example, an agent with a :code:`policy` method should look like this
+
+.. code-block:: python
+
+    class MySpeechToTextAgent(SpeechToSpeechAgent):
+        def policy(self):
+            if do_we_need_more_input(self.states):
+                return ReadAction()
+            else:
+                prediction = generate_a_token(self.states)
+                finished = is_sentence_finished(self.states)
+                return WriteAction(prediction, finished=finished)
+
+
+..
+    .. autoclass:: simuleval.agents.actions.WriteAction
+
+..
+    .. autoclass:: simuleval.agents.actions.ReadAction
+
+States
+------------
+Each agent has the attribute the :code:`states` to keep track of the progress of decoding.
+The :code:`states` attribute will be reset at the beginning of each sentence.
+SimulEval provide an built-in states :class:`simuleval.agents.states.AgentStates`,
+which has some basic attributes such source and target sequences.
+The users can also define customized states with :code:`Agent.build_states` method:
+
+.. code-block:: python
+
+    from simuleval.agents.states import AgentStates
+    from dataclasses import dataclass
+
+    @dataclass
+    class MyComplicatedStates(AgentStates)
+        some_very_useful_variable: int
+
+        def reset(self):
+            super().reset()
+            # also remember to reset the value
+            some_very_useful_variable = 0
+
+    class MySpeechToTextAgent(SpeechToSpeechAgent):
+        def build_states(self):
+            return MyComplicatedStates(0)
+
+        def policy(self):
+            some_very_useful_variable = self.states.some_very_useful_variable
+            ...
+            self.states.some_very_useful_variable = new_value
+            ...
+
+..
+    .. autoclass:: simuleval.agents.states.AgentStates
+        :members:
+
+
+Pipeline
+--------
+The simultaneous system can consist several different components.
+For instance, a simultaneous speech-to-text translation can have a streaming automatic speech recognition system and simultaneous text-to-text translation system.
+SimulEval introduces the agent pipeline to support this function.
+The following is a minimal example.
+We concatenate two wait-k systems with different rates (:code:`k=2` and :code:`k=3`)
+Note that if there are more than one agent class define,
+the :code:`@entrypoint` decorator has to be used to determine the entry point
+
+.. literalinclude:: ../../examples/quick_start/agent_pipeline.py
+   :language: python
+   :lines: 7-
+
+Customized Arguments
+-----------------------
+
+It is often the case that we need to pass some customized arguments for the system to configure different settings.
+The agent class has a built-in static method :code:`add_args` for this purpose.
+The following is an updated version of the dummy agent from :ref:`first-agent`.
+
+.. literalinclude:: ../../examples/quick_start/agent_with_configs.py
+   :language: python
+   :lines: 6-
+
+Then just simply pass the arguments through command line as follow.
+
+.. code-block:: bash
+
+    simuleval \
+        --source source.txt --source target.txt \ # data arguments
+        --agent dummy_waitk_text_agent_v2.py \
+        --waitk 3 --vocab data/dict.txt # agent arguments
+
+Load Agents from Python Class
+-----------------------------
+
+If you have the agent class in the python environment, for instance 
+
+.. literalinclude:: ../../examples/quick_start/agent_with_configs.py
+   :language: python
+   :lines: 6-
+
+You can also start the evaluation with following command
+
+.. code-block:: bash
+
+    simuleval \
+        --source source.txt --source target.txt \ # data arguments
+        --agent-class DummyWaitkTextAgent \
+        --waitk 3 --vocab data/dict.txt # agent arguments
+
+
+Load Agents from Directory
+--------------------------
+
+Agent can also be loaded from a directory, which will be referred to as system directory.
+The system directory should have everything required to start the agent. Again use the following agent as example
+
+.. literalinclude:: ../../examples/quick_start/agent_with_configs.py
+   :language: python
+   :lines: 6-
+
+and the system directory has 
+
+.. code-block:: bash
+
+    > ls ${system_dir}
+    main.yaml dict.txt 
+
+Where the `main.yaml` has all the command line options. The path will be the relative path to the `${system_dir}`.
+
+.. code-block:: yaml
+
+    waitk: 3
+    vocab: dict.txt
+
+The agent can then be started as following
+
+.. code-block:: bash
+
+    simuleval \
+        --source source.txt --source target.txt \ # data arguments
+        --system-dir ${system_dir}
+
+By default, the `main.yaml` will be read. You can also have multiple YAML files in the system directory and pass them through command line arguments
+
+.. code-block:: bash
+     > ls ${system_dir}
+    main.yaml dict.txt v1.yaml 
+
+    > simuleval \
+        --source source.txt --source target.txt \ # data arguments
+        --system-dir ${system_dir} --system-config v1.yaml

SimulEval/docs/user_guide/dataloader.rst

@@ -0,0 +1,5 @@
+Dataloader
+===========
+There are two ways to load data.
+
+.. autoclass:: simuleval.data.dataloader.GenericDataloader

SimulEval/docs/user_guide/evaluator.rst

@@ -0,0 +1,44 @@
+Evaluator
+=========
+
+The evaluation in SimulEval implemented as the Evaluator shown below.
+It runs on sentence level, and will score the translation on quality and latency.
+The user can use :code:`--quality-metrics` and :code:`--latency-metrics` to choose the metrics.
+The final results along with the logs will be saved at :code:`--output` if given.
+
+.. autoclass:: simuleval.evaluator.evaluator.SentenceLevelEvaluator
+
+Quality Scorers
+---------------
+
+.. autoclass:: simuleval.evaluator.scorers.quality_scorer.SacreBLEUScorer
+.. autoclass:: simuleval.evaluator.scorers.quality_scorer.ASRSacreBLEUScorer
+
+Latency Scorers
+---------------
+
+.. autoclass:: simuleval.evaluator.scorers.latency_scorer.ALScorer
+   :members:
+
+.. autoclass:: simuleval.evaluator.scorers.latency_scorer.APScorer
+   :members:
+
+.. autoclass:: simuleval.evaluator.scorers.latency_scorer.DALScorer
+   :members:
+
+Customized Scorers
+------------------
+To add customized scorers, the user can use :code:`@register_latency_scorer` or :code:`@register_quality_scorer` to decorate a scorer class.
+and use :code:`--quality-metrics` and :code:`--latency-metrics` to call the scorer. For example:
+
+.. literalinclude:: ../../examples/quick_start/agent_with_new_metrics.py
+   :lines: 6-
+
+.. code-block:: bash
+
+    > simuleval --source source.txt --target target.txt --agent agent_with_new_metrics.py --latency-metrics RTF
+    2022-12-06 12:56:01 | INFO | simuleval.cli | Evaluate system: DummyWaitkTextAgent
+    2022-12-06 12:56:01 | INFO | simuleval.dataloader | Evaluating from text to text.
+    2022-12-06 12:56:01 | INFO | simuleval.sentence_level_evaluator | Results:
+    BLEU   RTF
+    1.593 1.078

SimulEval/docs/user_guide/introduction.rst

@@ -0,0 +1,35 @@
+Introduction
+============
+Different from offline translation system, the evaluation of simultaneous translation requires incremental decoding with an streaming input.
+The simultaneous introduce the a front-end / back-end setup, shown as follow.
+
+The back-end contains one or multiple user-defined agents which make decisions of whether to generate prediction at a certain point.
+The agent can also considered as queue, where the input are keep pushed in and policy decides the timing to pop the output.
+
+The front-end on the other side, represent the source of input and recipient of the system prediction.
+In deployment, the front-end can be web page or cell phone app.
+In SimulEval, the front-end is the evaluator , which feeds streaming input to back-end, receive prediction and track the delays.
+The front-end and back-end can run separately for different purpose.
+
+The evaluation process can summarized as follow pseudocode
+
+.. code-block:: python
+
+    for instance in evaluator.instances:
+        while not instance.finished:
+            input_segment = instance.send_source()
+            prediction = agent.pushpop(input_segment)
+            if prediction is not None:
+                instance.receive_prediction(prediction)
+
+    results = [scorer.score() for scorer in evaluate.scorers]
+
+
+
+The common usage of SimulEval is as follow
+
+.. code-block:: bash
+
+    simuleval DATALOADER_OPTIONS EVALUATOR_OPTIONS --agent $AGENT_FILE AGENT_OPTIONS
+
+We will introduce the usage of the toolkit based on these three major components: Agent, Dataloader and Evaluator.

SimulEval/examples/quick_start/Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.8
+RUN apt-get update \
+&& apt-get upgrade -y \
+&& apt-get install -y \
+&& apt-get -y install apt-utils gcc libpq-dev libsndfile-dev
+RUN git clone https://github.com/facebookresearch/SimulEval.git
+WORKDIR SimulEval
+RUN git checkout v1.1.0
+RUN pip install -e .
+CMD ["simuleval", "--standalone", "--remote-port", "8888", "--agent", "examples/quick_start/first_agent.py.py"]

SimulEval/examples/quick_start/agent_pipeline.py

@@ -0,0 +1,39 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+from simuleval.utils import entrypoint
+from simuleval.agents import TextToTextAgent
+from simuleval.agents.actions import ReadAction, WriteAction
+from simuleval.agents import AgentPipeline
+
+
+class DummyWaitkTextAgent(TextToTextAgent):
+    waitk = 0
+    vocab = [chr(i) for i in range(ord("A"), ord("Z") + 1)]
+
+    def policy(self):
+        lagging = len(self.states.source) - len(self.states.target)
+
+        if lagging >= self.waitk or self.states.source_finished:
+            prediction = random.choice(self.vocab)
+
+            return WriteAction(prediction, finished=(lagging <= 1))
+        else:
+            return ReadAction()
+
+
+class DummyWait2TextAgent(DummyWaitkTextAgent):
+    waitk = 2
+
+
+class DummyWait4TextAgent(DummyWaitkTextAgent):
+    waitk = 4
+
+
+@entrypoint
+class DummyPipeline(AgentPipeline):
+    pipeline = [DummyWait2TextAgent, DummyWait4TextAgent]

SimulEval/examples/quick_start/agent_with_configs.py

@@ -0,0 +1,39 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+from simuleval.utils import entrypoint
+from simuleval.agents import TextToTextAgent
+from simuleval.agents.actions import ReadAction, WriteAction
+from argparse import Namespace, ArgumentParser
+
+
+@entrypoint
+class DummyWaitkTextAgent(TextToTextAgent):
+    def __init__(self, args: Namespace):
+        """Initialize your agent here.
+        For example loading model, vocab, etc
+        """
+        super().__init__(args)
+        self.waitk = args.waitk
+        with open(args.vocab) as f:
+            self.vocab = [line.strip() for line in f]
+
+    @staticmethod
+    def add_args(parser: ArgumentParser):
+        """Add customized command line arguments"""
+        parser.add_argument("--waitk", type=int, default=3)
+        parser.add_argument("--vocab", type=str)
+
+    def policy(self):
+        lagging = len(self.states.source) - len(self.states.target)
+
+        if lagging >= self.waitk or self.states.source_finished:
+            prediction = random.choice(self.vocab)
+
+            return WriteAction(prediction, finished=(lagging <= 1))
+        else:
+            return ReadAction()

SimulEval/examples/quick_start/agent_with_new_metrics.py

@@ -0,0 +1,47 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+from statistics import mean
+from simuleval.utils import entrypoint
+from simuleval.evaluator.scorers.latency_scorer import (
+    register_latency_scorer,
+    LatencyScorer,
+)
+from simuleval.agents import TextToTextAgent
+from simuleval.agents.actions import ReadAction, WriteAction
+
+
+@register_latency_scorer("RTF")
+class RTFScorer(LatencyScorer):
+    """Real time factor
+
+    Usage:
+        --latency-metrics RTF
+    """
+
+    def __call__(self, instances) -> float:
+        scores = []
+        for ins in instances.values():
+            scores.append(ins.delays[-1] / ins.source_length)
+
+        return mean(scores)
+
+
+@entrypoint
+class DummyWaitkTextAgent(TextToTextAgent):
+    waitk = 3
+    vocab = [chr(i) for i in range(ord("A"), ord("Z") + 1)]
+
+    def policy(self):
+        lagging = len(self.states.source) - len(self.states.target)
+
+        if lagging >= self.waitk or self.states.source_finished:
+            prediction = random.choice(self.vocab)
+
+            return WriteAction(prediction, finished=(lagging <= 1))
+        else:
+            return ReadAction()

SimulEval/examples/quick_start/dict.txt

@@ -0,0 +1,26 @@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z

SimulEval/examples/quick_start/first_agent.py

@@ -0,0 +1,26 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+from simuleval.utils import entrypoint
+from simuleval.agents import TextToTextAgent
+from simuleval.agents.actions import ReadAction, WriteAction
+
+
+@entrypoint
+class DummyWaitkTextAgent(TextToTextAgent):
+    waitk = 3
+    vocab = [chr(i) for i in range(ord("A"), ord("Z") + 1)]
+
+    def policy(self):
+        lagging = len(self.states.source) - len(self.states.target)
+
+        if lagging >= self.waitk or self.states.source_finished:
+            prediction = random.choice(self.vocab)
+
+            return WriteAction(prediction, finished=(lagging <= 1))
+        else:
+            return ReadAction()

SimulEval/examples/quick_start/readme.md

@@ -0,0 +1,47 @@
+# Quick Start
+Following are some minimal examples to use SimulEval. More details can be found [here](https://simuleval.readthedocs.io/en/v1.1.0/quick_start.html).
+
+## First Agent
+To evaluate a text-to-text wait-3 system with random output:
+
+```
+> simuleval --source source.txt --target target.txt --agent first_agent.py
+
+2022-12-05 13:43:58 | INFO | simuleval.cli | Evaluate system: DummyWaitkTextAgent
+2022-12-05 13:43:58 | INFO | simuleval.dataloader | Evaluating from text to text.
+2022-12-05 13:43:58 | INFO | simuleval.sentence_level_evaluator | Results:
+BLEU  AL    AP  DAL
+1.541 3.0 0.688  3.0
+
+```
+
+## Agent with Command Line Arguments
+```
+simuleval --source source.txt --target target.txt --agent agent_with_configs.py --waitk 3 --vocab dict.txt
+```
+
+## Agent Pipeline
+```
+simuleval --source source.txt --target target.txt --agent agent_pipeline.py
+```
+
+## Agent with New Metrics
+```
+simuleval --source source.txt --target target.txt --agent agent_with_new_metrics.py
+```
+
+## Standalone Agent & Remote Evaluation
+Start an agent server:
+```
+simuleval --standalone --remote-port 8888 --agent agent_with_new_metrics.py
+```
+Or with docker
+```
+docker build -t simuleval_agent .
+docker run -p 8888:8888 simuleval_agent:latest
+```
+
+Start a remote evaluator:
+```
+simuleval --remote-eval --source source.txt --target target.txt --source-type text --target-type text --remote-port 8888
+```

SimulEval/examples/quick_start/source.txt

@@ -0,0 +1,10 @@
+Z U S N B Y X Q L O T
+M A J F P G O Y V R H M Z O T A
+M O W A O I D H H B O F
+N Q N I P C O H A A
+G B O J H P W C I A L V
+P T Z D E E N T B Y G Z R K
+F S H U K R W K S B R K M B B Q F C O U
+M H O L W Z G J Y X J B I
+A V B F E S F E W Q C S
+F N O I E Z B R S C V N S

SimulEval/examples/quick_start/target.txt

@@ -0,0 +1,10 @@
+Z U S N B Y X Q L O T
+M A J F P G O Y V R H M Z O T A
+M O W A O I D H H B O F
+N Q N I P C O H A A
+G B O J H P W C I A L V
+P T Z D E E N T B Y G Z R K
+F S H U K R W K S B R K M B B Q F C O U
+M H O L W Z G J Y X J B I
+A V B F E S F E W Q C S
+F N O I E Z B R S C V N S

SimulEval/examples/speech_to_speech/english_counter_agent.py

@@ -0,0 +1,69 @@
+from simuleval.utils import entrypoint
+from simuleval.data.segments import SpeechSegment
+from simuleval.agents import SpeechToSpeechAgent
+from simuleval.agents.actions import WriteAction, ReadAction
+from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
+from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
+
+
+class TTSModel:
+    def __init__(self):
+        models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
+            "facebook/fastspeech2-en-ljspeech",
+            arg_overrides={"vocoder": "hifigan", "fp16": False},
+        )
+        TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
+        self.tts_generator = task.build_generator(models, cfg)
+        self.tts_task = task
+        self.tts_model = models[0]
+        self.tts_model.to("cpu")
+        self.tts_generator.vocoder.to("cpu")
+
+    def synthesize(self, text):
+        sample = TTSHubInterface.get_model_input(self.tts_task, text)
+        if sample["net_input"]["src_lengths"][0] == 0:
+            return [], 0
+        for key in sample["net_input"].keys():
+            if sample["net_input"][key] is not None:
+                sample["net_input"][key] = sample["net_input"][key].to("cpu")
+
+            wav, rate = TTSHubInterface.get_prediction(
+                self.tts_task, self.tts_model, self.tts_generator, sample
+            )
+            wav = wav.tolist()
+            return wav, rate
+
+
+@entrypoint
+class EnglishSpeechCounter(SpeechToSpeechAgent):
+    """
+    Incrementally feed text to this offline Fastspeech2 TTS model,
+    with a minimum numbers of phonemes every chunk.
+    """
+
+    def __init__(self, args):
+        super().__init__(args)
+        self.wait_seconds = args.wait_seconds
+        self.tts_model = TTSModel()
+
+    @staticmethod
+    def add_args(parser):
+        parser.add_argument("--wait-seconds", default=1, type=int)
+
+    def policy(self):
+        length_in_seconds = round(
+            len(self.states.source) / self.states.source_sample_rate
+        )
+        if not self.states.source_finished and length_in_seconds < self.wait_seconds:
+            return ReadAction()
+        samples, fs = self.tts_model.synthesize(f"{length_in_seconds} mississippi")
+
+        # A SpeechSegment has to be returned for speech-to-speech translation system
+        return WriteAction(
+            SpeechSegment(
+                content=samples,
+                sample_rate=fs,
+                finished=self.states.source_finished,
+            ),
+            finished=self.states.source_finished,
+        )

SimulEval/examples/speech_to_speech/eval.sh

@@ -0,0 +1,6 @@
+simuleval \
+    --agent english_counter_agent.py --output output \
+    --source source.txt --target reference/en.txt --source-segment-size 1000\
+    --quality-metrics WHISPER_ASR_BLEU \
+    --target-speech-lang en --transcript-lowercase --transcript-non-punctuation --whisper-model-size large \
+    --latency-metrics StartOffset EndOffset ATD

SimulEval/examples/speech_to_speech/readme.md

@@ -0,0 +1,66 @@
+## Simultaneous Speech-to-Speech Translation
+
+This tutorial provides a minimal example on how to evaluate a simultaneous speech-to-speech translation system.
+
+### Requirements
+
+To run this example, the following package is required
+
+- [`whisper`](https://github.com/openai/whisper): for quality evaluation (`WHISPER_ASR_BLEU`).
+
+### Agent
+
+The speech-to-speech agent ([english_counter_agent.py](english_counter_agent.py)) in this example is a counter, which generates a piece of audio every second after an initial wait.
+The policy of the agent is show follow. The agent will wait for `self.wait_seconds` seconds,
+and generate the audio of `{length_in_seconds} mississippi` every second afterward.
+
+```python
+ def policy(self):
+        length_in_seconds = round(
+            len(self.states.source) / self.states.source_sample_rate
+        )
+        if not self.states.source_finished and length_in_seconds < self.wait_seconds:
+            return ReadAction()
+        print(length_in_seconds)
+        samples, fs = self.tts_model.synthesize(f"{length_in_seconds} mississippi")
+
+        # A SpeechSegment has to be returned for speech-to-speech translation system
+        return WriteAction(
+            SpeechSegment(
+                content=samples,
+                sample_rate=fs,
+                finished=self.states.source_finished,
+            ),
+            finished=self.states.source_finished,
+        )
+```
+
+Notice that for speech output agent, the `WriteAction` has to contain a `SpeechSegment` class.
+
+### Evaluation
+
+The following command will start an evaluation
+
+```bash
+simuleval \
+    --agent english_counter_agent.py --output output \
+    --source source.txt --target reference/en.txt --source-segment-size 1000\
+    --quality-metrics WHISPER_ASR_BLEU \
+    --target-speech-lang en --transcript-lowercase --transcript-non-punctuation\
+    --latency-metrics StartOffset EndOffset ATD
+```
+
+For quality evaluation, we use ASR_BLEU, that is transcribing the speech output and compute BLEU score with the reference text. To use this feature, `whisper` has to be installed.
+
+We use three metrics for latency evaluation
+
+- `StartOffset`: The starting offset of translation comparing with source audio
+- `EndOffset`: The ending offset of translation comparing with source audio
+- `ATD`: Average Token Delay
+
+The results of the evaluation should be as following. The transcripts and alignments can be found in the `output` directory.
+
+```
+ WHISPER_ASR_BLEU  StartOffset  EndOffset      ATD
+            100.0       1000.0   1490.703 1248.261
+```

SimulEval/examples/speech_to_speech/reference/de.txt

@@ -0,0 +1 @@
+ein Mississippi zwei Mississippi drei Mississippi vier Mississippi fünf Mississippi sechs Mississippi sieben Mississippi

SimulEval/examples/speech_to_speech/reference/en.txt

@@ -0,0 +1 @@
+one mississippi two mississippi three mississippi four mississippi five mississippi six mississippi seven mississippi

SimulEval/examples/speech_to_speech/reference/ja.txt

@@ -0,0 +1 @@
+1 ミシシッピ 2 ミシシッピ 3 ミシシッピ 4 ミシシッピ 5 ミシシッピ 6 ミシシッピ 7 ミシシッピ

SimulEval/examples/speech_to_speech/reference/zh.txt

@@ -0,0 +1 @@
+一密西西比二密西西比三密西西比四密西西比五密西西比六密西西比七密西西比

SimulEval/examples/speech_to_speech/source.txt

@@ -0,0 +1 @@
+test.wav

SimulEval/examples/speech_to_speech/test.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:354152b48e1575f4ca3590b76700d46ef302dca9c183b5526f810c636c5d88f7
+size 302124

SimulEval/setup.cfg

@@ -0,0 +1,2 @@
+[tool:pytest]
+flake8-max-line-length = 127

SimulEval/setup.py

@@ -0,0 +1,43 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import setuptools
+
+setuptools.setup(
+    python_requires=">3.7.0",
+    name="simuleval",
+    version="1.1.0",
+    author="Xutai Ma",
+    entry_points={
+        "console_scripts": [
+            "simuleval = simuleval.cli:main",
+        ],
+    },
+    install_requires=[
+        "pytest",
+        "pytest-cov",
+        "sacrebleu==2.3.1",
+        "tornado",
+        "soundfile",
+        "pandas",
+        "requests",
+        "pytest-flake8",
+        "textgrid",
+        "tqdm==4.64.1",
+        "pyyaml",
+        "bitarray==2.6.0",
+        "yt-dlp",
+        "pydub",
+    ],
+    package=setuptools.find_packages(
+        exclude=[
+            "examples",
+            "examples.*",
+            "docs",
+            "docs.*",
+        ]
+    ),
+)

SimulEval/simuleval/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.

SimulEval/simuleval/agents/__init__.py

@@ -0,0 +1,16 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .agent import (  # noqa
+    GenericAgent,
+    SpeechToTextAgent,
+    SpeechToSpeechAgent,
+    TextToSpeechAgent,
+    TextToTextAgent,
+)
+from .states import AgentStates  # noqa
+from .actions import Action, ReadAction, WriteAction  # noqa
+from .pipeline import AgentPipeline  # noqa

SimulEval/simuleval/agents/actions.py

@@ -0,0 +1,60 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Union, List
+from dataclasses import dataclass
+from simuleval.data.segments import Segment
+
+
+class Action:
+    """
+    Abstract Action class
+    """
+
+    def is_read(self) -> bool:
+        """
+        Whether the action is a read action
+
+        Returns:
+            bool: True if the action is a read action.
+        """
+        assert NotImplementedError
+
+
+class ReadAction(Action):
+    """
+    Action to return when policy decide to read one more source segment.
+    The only way to use it is to return :code:`ReadAction()`
+    """
+
+    def is_read(self) -> bool:
+        return True
+
+    def __repr__(self) -> str:
+        return "ReadAction()"
+
+
+@dataclass
+class WriteAction(Action):
+    """
+    Action to return when policy decide to generate a prediction
+
+    Args:
+        content (Union[str, List[float]]): The prediction.
+        finished (bool): Indicates if current sentence is finished.
+
+    .. note:: For text the prediction a str; for speech, it's a list.
+
+    """
+
+    content: Union[str, List[float], Segment]
+    finished: bool
+
+    def is_read(self) -> bool:
+        return False
+
+    def __repr__(self) -> str:
+        return f"WriteAction({self.content}, finished={self.finished})"

SimulEval/simuleval/agents/agent.py

@@ -0,0 +1,216 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from inspect import signature
+from argparse import Namespace, ArgumentParser
+from simuleval.data.segments import Segment, TextSegment, SpeechSegment, EmptySegment
+from typing import Optional
+from .states import AgentStates
+from .actions import Action
+
+
+SEGMENT_TYPE_DICT = {"text": TextSegment, "speech": SpeechSegment}
+
+
+class GenericAgent:
+    """
+    Generic Agent class.
+    """
+
+    source_type = None
+    target_type = None
+
+    def __init__(self, args: Optional[Namespace] = None) -> None:
+        if args is not None:
+            self.args = args
+        assert self.source_type
+        assert self.target_type
+        self.device = "cpu"
+
+        self.states = self.build_states()
+        self.reset()
+
+    def build_states(self) -> AgentStates:
+        """
+        Build states instance for agent
+
+        Returns:
+            AgentStates: agent states
+        """
+        return AgentStates()
+
+    def reset(self) -> None:
+        """
+        Reset agent, called every time when a new sentence coming in.
+        """
+        self.states.reset()
+
+    def policy(self, states: Optional[AgentStates] = None) -> Action:
+        """
+        The policy to make decision every time
+        when the system has new input.
+        The function has to return an Action instance
+
+        Args:
+            states (Optional[AgentStates]): an optional states for stateless agent
+
+        Returns:
+            Action: The actions to make at certain point.
+
+        .. note:
+
+            WriteAction means that the system has a prediction.
+            ReadAction means that the system needs more source.
+            When states are provided, the agent will become stateless and ignore self.states.
+        """
+        assert NotImplementedError
+
+    def push(
+        self, source_segment: Segment, states: Optional[AgentStates] = None
+    ) -> None:
+        """
+        The function to process the incoming information.
+
+        Args:
+            source_info (dict): incoming information dictionary
+            states (Optional[AgentStates]): an optional states for stateless agent
+        """
+        if states is None:
+            states = self.states
+        states.update_source(source_segment)
+
+    def pop(self, states: Optional[AgentStates] = None) -> Segment:
+        """
+        The function to generate system output.
+        By default, it first runs policy,
+        and than returns the output segment.
+        If the policy decide to read,
+        it will return an empty segment.
+
+        Args:
+            states (Optional[AgentStates]): an optional states for stateless agent
+
+        Returns:
+            Segment: segment to return.
+        """
+        if len(signature(self.policy).parameters) == 0:
+            is_stateless = False
+            if states:
+                raise RuntimeError("Feeding states to stateful agents.")
+        else:
+            is_stateless = True
+
+        if states is None:
+            states = self.states
+
+        if states.target_finished:
+            return EmptySegment(finished=True)
+
+        if is_stateless:
+            action = self.policy(states)
+        else:
+            action = self.policy()
+
+        if not isinstance(action, Action):
+            raise RuntimeError(
+                f"The return value of {self.policy.__qualname__} is not an {Action.__qualname__} instance"
+            )
+        if action.is_read():
+            return EmptySegment()
+        else:
+            if isinstance(action.content, Segment):
+                return action.content
+
+            segment = SEGMENT_TYPE_DICT[self.target_type](
+                index=0, content=action.content, finished=action.finished
+            )
+            states.update_target(segment)
+            return segment
+
+    def pushpop(
+        self, segment: Segment, states: Optional[AgentStates] = None
+    ) -> Segment:
+        """
+        Operate pop immediately after push.
+
+        Args:
+            segment (Segment): input segment
+
+        Returns:
+            Segment: output segment
+        """
+        self.push(segment, states)
+        return self.pop(states)
+
+    @staticmethod
+    def add_args(parser: ArgumentParser):
+        """
+        Add agent arguments to parser.
+        Has to be a static method.
+
+        Args:
+            parser (ArgumentParser): cli argument parser
+        """
+        pass
+
+    @classmethod
+    def from_args(cls, args):
+        return cls(args)
+
+    def to(self, device: str, *args, **kwargs) -> None:
+        """
+        Move agent to specified device.
+
+        Args:
+            device (str): Device to move agent to.
+        """
+        pass
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}[{self.source_type} -> {self.target_type}]"
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+
+class SpeechToTextAgent(GenericAgent):
+    """
+    Same as generic agent, but with explicit types
+    speech -> text
+    """
+
+    source_type: str = "speech"
+    target_type: str = "text"
+
+
+class SpeechToSpeechAgent(GenericAgent):
+    """
+    Same as generic agent, but with explicit types
+    speech -> speech
+    """
+
+    source_type: str = "speech"
+    target_type: str = "speech"
+
+
+class TextToSpeechAgent(GenericAgent):
+    """
+    Same as generic agent, but with explicit types
+    text -> speech
+    """
+
+    source_type: str = "text"
+    target_type: str = "speech"
+
+
+class TextToTextAgent(GenericAgent):
+    """
+    Same as generic agent, but with explicit types
+    text -> text
+    """
+
+    source_type: str = "text"
+    target_type: str = "text"

SimulEval/simuleval/agents/pipeline.py

@@ -0,0 +1,90 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import List, Optional
+from simuleval.data.segments import Segment
+from .agent import GenericAgent, AgentStates
+
+
+class AgentPipeline(GenericAgent):
+    """A pipeline of agents
+
+    Attributes:
+        pipeline (list): a list of agent classes.
+
+    """
+
+    pipeline: List = []
+
+    def __init__(self, module_list: List[GenericAgent]) -> None:
+        self.module_list = module_list
+        self.check_pipeline_types()
+
+    def check_pipeline_types(self):
+        if len(self.pipeline) > 1:
+            for i in range(1, len(self.pipeline)):
+                if (
+                    self.module_list[i].source_type
+                    != self.module_list[i - 1].target_type
+                ):
+                    raise RuntimeError(
+                        f"{self.module_list[i]}.source_type({self.module_list[i].source_type}) != {self.pipeline[i-1]}.target_type({self.pipeline[i - 1].target_type}"  # noqa F401
+                    )
+
+    @property
+    def source_type(self) -> Optional[str]:
+        return self.module_list[0].source_type
+
+    @property
+    def target_type(self) -> Optional[str]:
+        return self.module_list[-1].target_type
+
+    def reset(self) -> None:
+        for module in self.module_list:
+            module.reset()
+
+    def build_states(self) -> List[AgentStates]:
+        return [module.build_states() for module in self.module_list]
+
+    def push(
+        self, segment: Segment, states: Optional[List[Optional[AgentStates]]] = None
+    ) -> None:
+        if states is None:
+            states = [None for _ in self.module_list]
+        else:
+            assert len(states) == len(self.module_list)
+
+        for index, module in enumerate(self.module_list[:-1]):
+            segment = module.pushpop(segment, states[index])
+        self.module_list[-1].push(segment, states[-1])
+
+    def pop(self, states: Optional[List[Optional[AgentStates]]] = None) -> Segment:
+        if states is None:
+            last_states = None
+        else:
+            assert len(states) == len(self.module_list)
+            last_states = states[-1]
+
+        return self.module_list[-1].pop(last_states)
+
+    @classmethod
+    def add_args(cls, parser) -> None:
+        for module_class in cls.pipeline:
+            module_class.add_args(parser)
+
+    @classmethod
+    def from_args(cls, args):
+        assert len(cls.pipeline) > 0
+        return cls([module_class.from_args(args) for module_class in cls.pipeline])
+
+    def __repr__(self) -> str:
+        pipline_str = "\n\t".join(
+            "\t".join(str(module).splitlines(True)) for module in self.module_list
+        )
+        return f"{self.__class__.__name__}(\n\t{pipline_str}\n)"
+
+    def __str__(self) -> str:
+        return self.__repr__()

SimulEval/simuleval/agents/service.py

@@ -0,0 +1,59 @@
+# 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 os
+import json
+import logging
+from tornado import web, ioloop
+from simuleval.data.segments import segment_from_json_string
+from simuleval import options
+
+logger = logging.getLogger("simuleval.agent_server")
+
+
+class SystemHandler(web.RequestHandler):
+    def initialize(self, system):
+        self.system = system
+
+    def get(self):
+        self.write(json.dumps({"info": str(self.system)}))
+
+
+class ResetHandle(SystemHandler):
+    def post(self):
+        self.system.reset()
+
+
+class OutputHandler(SystemHandler):
+    def get(self):
+        output_segment = self.system.pop()
+        self.write(output_segment.json())
+
+
+class InputHandler(SystemHandler):
+    def put(self):
+        segment = segment_from_json_string(self.request.body)
+        self.system.push(segment)
+
+
+def start_agent_service(system):
+    parser = options.general_parser()
+    options.add_evaluator_args(parser)
+    args, _ = parser.parse_known_args()
+    app = web.Application(
+        [
+            (r"/reset", ResetHandle, {"system": system}),
+            (r"/input", InputHandler, {"system": system}),
+            (r"/output", OutputHandler, {"system": system}),
+            (r"/", SystemHandler, {"system": system}),
+        ],
+        debug=False,
+    )
+
+    app.listen(args.remote_port, max_buffer_size=1024**3)
+
+    logger.info(
+        f"Simultaneous Translation Server Started (process id {os.getpid()}). Listening to port {args.remote_port} "
+    )
+    ioloop.IOLoop.current().start()