CosyVoice

👉🏻 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

• Language Coverage: Covers 9 common languages (Chinese, English, Japanese, Korean, German, Spanish, French, Italian, Russian), 18+ Chinese dialects/accents (Guangdong, Minnan, Sichuan, Dongbei, Shan3xi, Shan1xi, Shanghai, Tianjin, Shandong, Ningxia, Gansu, etc.) and meanwhile supports both multi-lingual/cross-lingual zero-shot voice cloning.
• Content Consistency & Naturalness: Achieves state-of-the-art performance in content consistency, speaker similarity, and prosody naturalness.
• Pronunciation Inpainting: Supports pronunciation inpainting of Chinese Pinyin and English CMU phonemes, providing more controllability and thus suitable for production use.
• Text Normalization: Supports reading of numbers, special symbols and various text formats without a traditional frontend module.
• Bi-Streaming: Support both text-in streaming and audio-out streaming, and achieves latency as low as 150ms while maintaining high-quality audio output.
• Instruct Support: Supports various instructions such as languages, dialects, emotions, speed, volume, etc.

Roadmap

• 2025/12

  • release Fun-CosyVoice3-0.5B-2512 base model, rl model and its training/inference script
  • release Fun-CosyVoice3-0.5B modelscope gradio space

• 2025/08

  • Thanks to the contribution from NVIDIA Yuekai Zhang, add triton trtllm runtime support and cosyvoice2 grpo training support

• 2025/07

  • release Fun-CosyVoice 3.0 eval set

• 2025/05

  • add CosyVoice2-0.5B vllm support

• 2024/12

  • 25hz CosyVoice2-0.5B released

• 2024/09

  • 25hz CosyVoice-300M base model
  • 25hz CosyVoice-300M voice conversion function

• 2024/08

  • Repetition Aware Sampling(RAS) inference for llm stability
  • Streaming inference mode support, including kv cache and sdpa for rtf optimization

• 2024/07

  • Flow matching training support
  • WeTextProcessing support when ttsfrd is not available
  • Fastapi server and client

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

• Clone the repo

  git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git
  # If you failed to clone the submodule due to network failures, please run the following command until success
  cd CosyVoice
  git submodule update --init --recursive
  

• Install Conda: please see https://docs.conda.io/en/latest/miniconda.html

• Create Conda env:

  conda create -n cosyvoice -y python=3.10
  conda activate cosyvoice
  pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
  
  # If you encounter sox compatibility issues
  # ubuntu
  sudo apt-get install sox libsox-dev
  # centos
  sudo yum install sox sox-devel
  

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.

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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.

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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

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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.