CosyVoice README

The project's README from github.com/QwenAudio/CosyVoice. As one markdown file: README.md.

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👉🏻 CosyVoice 👈🏻

Fun-CosyVoice 3.0: Demos; Paper; Modelscope; Huggingface; CV3-Eval

CosyVoice 2.0: Demos; Paper; Modelscope; HuggingFace

CosyVoice 1.0: Demos; Paper; Modelscope; HuggingFace

Highlight🔥

Fun-CosyVoice 3.0 is an advanced text-to-speech (TTS) system based on large language models (LLM), surpassing its predecessor (CosyVoice 2.0) in content consistency, speaker similarity, and prosody naturalness. It is designed for zero-shot multilingual speech synthesis in the wild.

Key Features

Roadmap

Evaluation

Model Open-Source Model Size test-zh
CER (%) ↓		 test-zh
SS (%) ↑		 test-en
WER (%) ↓		 test-en
SS (%) ↑		 test-hard
CER (%) ↓		 test-hard
SS (%) ↑
Human - - 1.26 75.5 2.14 73.4 - -
Seed-TTS ❌ - 1.12 79.6 2.25 76.2 7.59 77.6
MiniMax-Speech ❌ - 0.83 78.3 1.65 69.2 - -
F5-TTS ✅ 0.3B 1.52 74.1 2.00 64.7 8.67 71.3
Spark TTS ✅ 0.5B 1.2 66.0 1.98 57.3 - -
CosyVoice2 ✅ 0.5B 1.45 75.7 2.57 65.9 6.83 72.4
FireRedTTS2 ✅ 1.5B 1.14 73.2 1.95 66.5 - -
Index-TTS2 ✅ 1.5B 1.03 76.5 2.23 70.6 7.12 75.5
VibeVoice-1.5B ✅ 1.5B 1.16 74.4 3.04 68.9 - -
VibeVoice-Realtime ✅ 0.5B - - 2.05 63.3 - -
HiggsAudio-v2 ✅ 3B 1.50 74.0 2.44 67.7 - -
VoxCPM ✅ 0.5B 0.93 77.2 1.85 72.9 8.87 73.0
GLM-TTS ✅ 1.5B 1.03 76.1 - - - -
GLM-TTS RL ✅ 1.5B 0.89 76.4 - - - -
Fun-CosyVoice3-0.5B-2512 ✅ 0.5B 1.21 78.0 2.24 71.8 6.71 75.8
Fun-CosyVoice3-0.5B-2512_RL ✅ 0.5B 0.81 77.4 1.68 69.5 5.44 75.0

Install

Clone and install

Model download

We strongly recommend that you download our pretrained Fun-CosyVoice3-0.5B CosyVoice2-0.5B CosyVoice-300M CosyVoice-300M-SFT CosyVoice-300M-Instruct model and CosyVoice-ttsfrd resource.

# modelscope SDK model download
from modelscope import snapshot_download
snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B')
snapshot_download('iic/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B')
snapshot_download('iic/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M')
snapshot_download('iic/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT')
snapshot_download('iic/CosyVoice-300M-Instruct', local_dir='pretrained_models/CosyVoice-300M-Instruct')
snapshot_download('iic/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd')

# for oversea users, huggingface SDK model download
from huggingface_hub import snapshot_download
snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B')
snapshot_download('FunAudioLLM/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B')
snapshot_download('FunAudioLLM/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M')
snapshot_download('FunAudioLLM/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT')
snapshot_download('FunAudioLLM/CosyVoice-300M-Instruct', local_dir='pretrained_models/CosyVoice-300M-Instruct')
snapshot_download('FunAudioLLM/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd')

Optionally, you can unzip ttsfrd resource and install ttsfrd package for better text normalization performance.

Notice that this step is not necessary. If you do not install ttsfrd package, we will use wetext by default.

cd pretrained_models/CosyVoice-ttsfrd/
unzip resource.zip -d .
pip install ttsfrd_dependency-0.1-py3-none-any.whl
pip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl

Basic Usage

We strongly recommend using Fun-CosyVoice3-0.5B for better performance. Follow the code in example.py for detailed usage of each model.

python example.py

vLLM Usage

CosyVoice2/3 now supports vLLM 0.11.x+ (V1 engine) and vLLM 0.9.0 (legacy). Older vllm version(<0.9.0) do not support CosyVoice inference, and versions in between (e.g., 0.10.x) are not tested.

Notice that vllm has a lot of specific requirements. You can create a new env to in case your hardward do not support vllm and old env is corrupted.

conda create -n cosyvoice_vllm --clone cosyvoice
conda activate cosyvoice_vllm
# for vllm==0.9.0
pip install vllm==v0.9.0 transformers==4.51.3 numpy==1.26.4 -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
# for vllm>=0.11.0
pip install vllm==v0.11.0 transformers==4.57.1 numpy==1.26.4 -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
python vllm_example.py

Start web demo

You can use our web demo page to get familiar with CosyVoice quickly.

Please see the demo website for details.

# change iic/CosyVoice-300M-SFT for sft inference, or iic/CosyVoice-300M-Instruct for instruct inference
python3 webui.py --port 50000 --model_dir pretrained_models/CosyVoice-300M

Advanced Usage

For advanced users, we have provided training and inference scripts in examples/libritts.

Build for deployment

Optionally, if you want service deployment, You can run the following steps.

cd runtime/python
docker build -t cosyvoice:v1.0 .
# change iic/CosyVoice-300M to iic/CosyVoice-300M-Instruct if you want to use instruct inference
# for grpc usage
docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/grpc && python3 server.py --port 50000 --max_conc 4 --model_dir iic/CosyVoice-300M && sleep infinity"
cd grpc && python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>
# for fastapi usage
docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/fastapi && python3 server.py --port 50000 --model_dir iic/CosyVoice-300M && sleep infinity"
cd fastapi && python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>

Using Nvidia TensorRT-LLM for deployment

Using TensorRT-LLM to accelerate cosyvoice2 llm could give 4x acceleration comparing with huggingface transformers implementation. To quick start:

cd runtime/triton_trtllm
docker compose up -d

For more details, you could check here

Discussion & Communication

You can directly discuss on Github Issues.

You can also scan the QR code to join our official Dingding chat group.

Acknowledge

  1. We borrowed a lot of code from FunASR.
  2. We borrowed a lot of code from FunCodec.
  3. We borrowed a lot of code from Matcha-TTS.
  4. We borrowed a lot of code from AcademiCodec.
  5. We borrowed a lot of code from WeNet.

Citations

@article{du2024cosyvoice,
  title={Cosyvoice: A scalable multilingual zero-shot text-to-speech synthesizer based on supervised semantic tokens},
  author={Du, Zhihao and Chen, Qian and Zhang, Shiliang and Hu, Kai and Lu, Heng and Yang, Yexin and Hu, Hangrui and Zheng, Siqi and Gu, Yue and Ma, Ziyang and others},
  journal={arXiv preprint arXiv:2407.05407},
  year={2024}
}

@article{du2024cosyvoice,
  title={Cosyvoice 2: Scalable streaming speech synthesis with large language models},
  author={Du, Zhihao and Wang, Yuxuan and Chen, Qian and Shi, Xian and Lv, Xiang and Zhao, Tianyu and Gao, Zhifu and Yang, Yexin and Gao, Changfeng and Wang, Hui and others},
  journal={arXiv preprint arXiv:2412.10117},
  year={2024}
}

@article{du2025cosyvoice,
  title={CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training},
  author={Du, Zhihao and Gao, Changfeng and Wang, Yuxuan and Yu, Fan and Zhao, Tianyu and Wang, Hao and Lv, Xiang and Wang, Hui and Shi, Xian and An, Keyu and others},
  journal={arXiv preprint arXiv:2505.17589},
  year={2025}
}

@inproceedings{lyu2025build,
  title={Build LLM-Based Zero-Shot Streaming TTS System with Cosyvoice},
  author={Lyu, Xiang and Wang, Yuxuan and Zhao, Tianyu and Wang, Hao and Liu, Huadai and Du, Zhihao},
  booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--2},
  year={2025},
  organization={IEEE}
}

Ecosystem

CosyVoice is part of the FunAudioLLM family — a complete speech AI toolkit:

Project Description Stars
FunASR Industrial speech recognition — 50+ languages, speaker diarization, streaming
Fun-ASR-Nano End-to-end LLM-based ASR — 31 languages, hotwords, vLLM streaming
SenseVoice Ultra-fast ASR + emotion + audio event detection
FunClip AI video clipping powered by speech recognition

Disclaimer

The content provided above is for academic purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. If any content infringes on your rights, please contact us to request its removal.

CosyVoice (QwenAudio/CosyVoice)

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24K ·GitHub
CosyVoice
Что это такое?CosyVoice — открытая система синтеза речи из текста от Alibaba на основе LLM. Она генерирует естественную речь на 9 языках, включая 18+ китайских диалектов, поддерживает клонирование голоса zero-shot и поставляется с кодом для инференса, обучения и развёртывания.
Это модель, которую я скачиваю, или набор инструментов, который запускаю сам?И то и другое: репозиторий содержит предобученные чекпоинты (рекомендуется Fun-CosyVoice3-0.5B, также есть CosyVoice2-0.5B и серия 300M) и Python-код для запуска, дообучения и развёртывания этих моделей.
Как мне это запустить?Склонируйте репозиторий, установите Conda и создайте окружение Conda, скачайте предобученную модель — рекомендуется Fun-CosyVoice3-0.5B, — а затем посмотрите example.py с примерами использования каждой модели.
Есть ли подводные камни при установке?Ресурс ttsfrd улучшает нормализацию текста, но он необязателен — без него репозиторий по умолчанию переключается на пакет wetext. Также будьте готовы к скачиванию моделей большого размера: текущие модели относятся к классу 0.5B.
Могу ли я использовать это в коммерческих продуктах?Код распространяется под Apache-2.0, однако в README прямо сказано, что предоставляемый контент предназначен для академических целей и демонстрации технических возможностей — перед выпуском чего-либо коммерческого проверьте условия использования моделей.
С какого чекпоинта лучше начать?С Fun-CosyVoice3-0.5B — README рекомендует его вместо CosyVoice2-0.5B и более старой серии 300M (Base, SFT, Instruct). Вариант с обучением с подкреплением Fun-CosyVoice3-0.5B-2512RL возглавляет собственную таблицу оценок проекта.
Как он сравнивается с другими открытыми моделями TTS?Согласно таблице бенчмарков в README, Fun-CosyVoice3-0.5B-2512RL достигает CER 0,81% на test-zh и 5,44% на сложном тестовом наборе при 0,5B параметров — это лучше по частоте ошибок, чем F5-TTS, Spark TTS, Index-TTS2 и GLM-TTS из той же таблицы.
Что ещё есть в семействе FunAudioLLM?CosyVoice отвечает в наборе FunAudioLLM за генерацию речи; рядом с ним FunASR (промышленное распознавание речи на 50+ языках), SenseVoice (сверхбыстрый ASR плюс определение эмоций и аудиособытий) и FunClip (ИИ-нарезка видео на основе распознавания речи).
What are similar projects?ChatTTS, VoxCPM, MOSS-TTS-Nano, MOSS-TTS, index-tts, OpenVoice, ebook2audiobook, dia
Which topics is it in?tts-model, zero-shot-tts, gpt-4o, voice-conversion, alibaba, korean, japanese, audio-generation, english, voice-cloning, speech-synthesis, fastapi
Creator
QwenAudio
Size
2.7 MB
Stars
24K
Updated
CommitMay 25
Age
2y
Commits · 90 Days