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

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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
这是什么?CosyVoice 是阿里巴巴开源的基于大语言模型的文本转语音系统。它能用 9 种语言以及 18 种以上的中文方言生成自然的语音,支持零样本声音克隆,并附带推理、训练和部署代码。
这是下载就能用的模型,还是需要我自己运行的工具包?两者都是:这个仓库提供预训练模型权重(推荐 Fun-CosyVoice3-0.5B,还有 CosyVoice2-0.5B 和 300M 系列),以及运行、微调和部署这些模型的 Python 代码。
我怎么把它跑起来?克隆仓库,安装 Conda 并创建一个 Conda 环境,下载一个预训练模型——推荐 Fun-CosyVoice3-0.5B——然后参照 example.py 了解各个模型的用法。
安装时有什么坑需要注意?ttsfrd 资源可以改进文本正则化,但它是可选的——没有它的话,仓库默认会回退到 wetext 包。另外,当前模型是 0.5B 级别的,要预留较大的模型下载空间。
我可以在商业产品里用它吗?代码采用 Apache-2.0 协议,但 README 明确说明所提供的内容仅用于学术目的和展示技术能力——如果要做商业化,请先查看模型的使用条款。
我该从哪个模型开始用?Fun-CosyVoice3-0.5B——README 推荐它,而不是 CosyVoice2-0.5B 和更早的 300M 系列(Base、SFT、Instruct)。还有一个 2512 强化学习版本 Fun-CosyVoice3-0.5B-2512RL,在项目自己的评测表里排名第一。
和其他开源 TTS 模型比,它表现怎么样?根据 README 的基准测试表,Fun-CosyVoice3-0.5B-2512RL 在 0.5B 参数规模下,在 test-zh 上达到 0.81% 的 CER,在困难测试集上达到 5.44%——在同一张表里,错误率优于 F5-TTS、Spark TTS、Index-TTS2 和 GLM-TTS。
FunAudioLLM 家族里还有哪些东西?CosyVoice 是 FunAudioLLM 工具包中负责语音生成的部分,同一家族的还有 FunASR(支持 50 多种语言的工业级语音识别)、SenseVoice(超快语音识别,附带情绪和音频事件检测)以及 FunClip(由语音识别驱动的 AI 视频剪辑)。
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, speech-synthesis, voice-cloning, fastapi
Creator
QwenAudio
Size
2.7 MB
Stars
24K
Updated
CommitMay 25
Age
2y
Commits · 90 Days