FunAudioLLM/CosyVoice-300M-Instruct

👉🏻 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 Speaker Similarity (%) ↑	test-en WER (%) ↓	test-en Speaker Similarity (%) ↑	test-hard CER (%) ↓	test-hard Speaker Similarity (%) ↑
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

from huggingface_hub import snapshot_download
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

import sys
sys.path.append('third_party/Matcha-TTS')
from cosyvoice.cli.cosyvoice import AutoModel
import torchaudio

""" CosyVoice Usage, check https://fun-audio-llm.github.io/ for more details
"""
cosyvoice = AutoModel(model_dir='pretrained_models/CosyVoice-300M-Instruct')
# instruct usage, support <laughter></laughter><strong></strong>[laughter][breath]
for i, j in enumerate(cosyvoice.inference_instruct('CosyVoice is undergoing a comprehensive upgrade, providing more accurate, stable, faster, and better voice generation capabilities.', '中文男',
                                                    'Theo \'Crimson\', is a fiery, passionate rebel leader. Fights with fervor for justice, but struggles with impulsiveness.<|endofprompt|>')):
    torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
for i, j in enumerate(cosyvoice.inference_instruct('在面对挑战时，他展现了非凡的<strong>勇气</strong>与<strong>智慧</strong>。', '中文男',
                                                    'Theo \'Crimson\', is a fiery, passionate rebel leader. Fights with fervor for justice, but struggles with impulsiveness.<|endofprompt|>')):
    torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)

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}
}

Disclaimer

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