Upload QOR-TTS-0.6B (based on Qwen/Qwen3-TTS-12Hz-0.6B-Base)

README.md

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+---
+license: apache-2.0
+language:
+  - en
+  - zh
+  - ja
+  - ko
+  - de
+  - fr
+  - es
+  - pt
+  - ru
+  - it
+tags:
+  - tts
+  - voice-cloning
+  - text-to-speech
+  - qor
+base_model: Qwen/Qwen3-TTS-12Hz-0.6B-Base
+pipeline_tag: text-to-speech
+---
+
+# QOR-TTS-0.6B
+
+QOR-TTS 0.6B — Fast local voice cloning (~2 GB)
+
+## About
+
+QOR-TTS is a voice cloning text-to-speech model, part of the [QOR AI system](https://github.com/QORResearch/qor).
+It enables local, offline voice cloning — record a short voice sample and generate speech in that voice.
+
+This model is based on **Qwen/Qwen3-TTS-12Hz-0.6B-Base** by Alibaba Cloud (Apache 2.0 license), repackaged for easy use with the QOR Voice Studio.
+
+## Quick Start
+
+```python
+# With QOR Voice Studio (recommended):
+# 1. Start QOR server: python -m qor serve
+# 2. Open Voice Studio in browser
+# 3. Go to Models tab → Download → Load
+# 4. Record your voice → Test Clone
+
+# Direct Python usage:
+from qor.qwen_tts import QwenTTSEngine
+
+engine = QwenTTSEngine()
+engine.load_model("0.6B")
+wav_path, duration = engine.generate(
+    text="Hello, this is my cloned voice!",
+    reference_audio="my_voice_sample.wav",
+    reference_text="What I said in the sample",
+    language="en",
+)
+```
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Parameters | 0.6 billion |
+| Size | ~2 GB |
+| License | Apache 2.0 |
+| Languages | English, Chinese, Japanese, Korean, German, French, Spanish, Portuguese, Russian, Italian |
+| Supports | Voice cloning, text-to-speech, instruction-guided delivery |
+| Device | CUDA, DirectML (Windows), CPU |
+| Based on | [Qwen/Qwen3-TTS-12Hz-0.6B-Base](https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base) |
+
+## Attribution
+
+This model is a redistribution of **Qwen/Qwen3-TTS-12Hz-0.6B-Base** by Alibaba Cloud / Qwen Team.
+Original model released under [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
+
+We thank the Qwen team for their excellent work on voice synthesis.
+
+## License
+
+Apache 2.0 — same as the original model. See [LICENSE](LICENSE) for full text.

README_ORIGINAL.md

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+---
+license: apache-2.0
+pipeline_tag: text-to-speech
+language:
+- zh
+- en
+- ja
+- ko
+- de
+- fr
+- ru
+- pt
+- es
+- it
+tags:
+- audio
+- tts
+- voice-clone
+---
+
+# Qwen3-TTS-12Hz-0.6B-Base
+
+[**Qwen3-TTS Technical Report**](https://huggingface.co/papers/2601.15621) | [**GitHub Repository**](https://github.com/QwenLM/Qwen3-TTS) | [**Hugging Face Demo**](https://huggingface.co/spaces/Qwen/Qwen3-TTS)
+
+Qwen3-TTS is a family of advanced multilingual, controllable, robust, and streaming text-to-speech models. Trained on over 5 million hours of speech data spanning 10 languages, Qwen3-TTS supports state-of-the-art 3-second voice cloning and description-based control.
+
+This specific checkpoint is the **0.6B Base model**, which is capable of rapid voice cloning from a user-provided audio input.
+
+## Quickstart
+
+### Installation
+
+```bash
+pip install -U qwen-tts
+# Optional: for optimized performance
+pip install -U flash-attn --no-build-isolation
+```
+
+### Sample Usage (Voice Clone)
+
+To clone a voice and synthesize new content using the Base model, you can use the following code snippet:
+
+```python
+import torch
+import soundfile as sf
+from qwen_tts import Qwen3TTSModel
+
+# Load the model
+model = Qwen3TTSModel.from_pretrained(
+    "Qwen/Qwen3-TTS-12Hz-0.6B-Base",
+    device_map="cuda:0",
+    dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+)
+
+# Reference audio for cloning
+ref_audio = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/clone.wav"
+ref_text  = "Okay. Yeah. I resent you. I love you. I respect you. But you know what? You blew it! And thanks to you."
+
+# Generate speech
+wavs, sr = model.generate_voice_clone(
+    text="I am solving the equation: x = [-b ± √(b²-4ac)] / 2a? Nobody can — it's a disaster (◍•͈⌔•͈◍), very sad!",
+    language="English",
+    ref_audio=ref_audio,
+    ref_text=ref_text,
+)
+
+# Save the resulting audio
+sf.write("output_voice_clone.wav", wavs[0], sr)
+```
+
+## Overview
+### Introduction
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/qwen3_tts_introduction.png" width="90%"/>
+<p>
+
+Qwen3-TTS covers 10 major languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian) as well as multiple dialectal voice profiles to meet global application needs. Key features:
+
+* **Powerful Speech Representation**: Powered by the self-developed Qwen3-TTS-Tokenizer-12Hz, it achieves efficient acoustic compression and high-dimensional semantic modeling.
+* **Universal End-to-End Architecture**: Utilizing a discrete multi-codebook LM architecture, it realizes full-information end-to-end speech modeling.
+* **Extreme Low-Latency Streaming Generation**: End-to-end synthesis latency as low as 97ms, meeting the rigorous demands of real-time interactive scenarios.
+* **Intelligent Text Understanding and Voice Control**: Supports speech generation driven by natural language instructions, allowing for flexible control over multi-dimensional acoustic attributes.
+
+### Model Architecture
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/overview.png" width="80%"/>
+<p>
+
+## Citation
+
+If you find this work useful, please consider citing the technical report:
+
+```BibTeX
+@article{Qwen3-TTS,
+  title={Qwen3-TTS Technical Report},
+  author={Hangrui Hu and Xinfa Zhu and Ting He and Dake Guo and Bin Zhang and Xiong Wang and Zhifang Guo and Ziyue Jiang and Hongkun Hao and Zishan Guo and Xinyu Zhang and Pei Zhang and Baosong Yang and Jin Xu and Jingren Zhou and Junyang Lin},
+  journal={arXiv preprint arXiv:2601.15621},
+  year={2026}
+}
+```

config.json

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+    "Qwen3TTSForConditionalGeneration"
+  ],
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+}

generation_config.json

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preprocessor_config.json

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+  "return_attention_mask": true
+}

speech_tokenizer/config.json

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+    },
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+      "normalized": false,
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+    },
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+      "normalized": false,
+      "rstrip": false,
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+    },
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151672": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151673": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151674": {
+      "content": "<tts_text_bos_single>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151675": {
+      "content": "<|audio_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>",
+    "<|audio_start|>",
+    "<|audio_end|>",
+    "<tts_pad>",
+    "<tts_text_bos>",
+    "<tts_text_bos_single>",
+    "<|audio_pad|>"
+  ],
+  "extra_special_tokens": {
+    "image_token": "<|image_pad|>",
+    "audio_token": "<|audio_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>",
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>"
+  },
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+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "image_token": "<|image_pad|>",
+  "audio_token": "<|audio_pad|>",
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>",
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>"
+}