Name: Fun-CosyVoice3-0.5B-2512
Availability: InStock

👉🏻 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

sh
1git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git 2# If you failed to clone the submodule due to network failures, please run the following command until success 3cd CosyVoice 4git submodule update --init --recursive
Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
Create Conda env:

sh
1conda create -n cosyvoice -y python=3.10 2conda activate cosyvoice 3pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com 4 5# If you encounter sox compatibility issues 6# ubuntu 7sudo apt-get install sox libsox-dev 8# centos 9sudo yum install sox sox-devel

Model download

python

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/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

python

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

""" CosyVoice3 Usage, check https://funaudiollm.github.io/cosyvoice3/ for more details
"""
cosyvoice = AutoModel(model_dir='pretrained_models/Fun-CosyVoice3-0.5B')
# en zero_shot usage
for i, j in enumerate(cosyvoice.inference_zero_shot('CosyVoice is undergoing a comprehensive upgrade, providing more accurate, stable, faster, and better voice generation capabilities.', 'You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。',
                                                    './asset/zero_shot_prompt.wav', stream=False)):
    torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
# zh zero_shot usage
for i, j in enumerate(cosyvoice.inference_zero_shot('八百标兵奔北坡，北坡炮兵并排跑，炮兵怕把标兵碰，标兵怕碰炮兵炮。', 'You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。',
                                                    './asset/zero_shot_prompt.wav', stream=False)):
    torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)

# fine grained control, for supported control, check cosyvoice/tokenizer/tokenizer.py#L280
for i, j in enumerate(cosyvoice.inference_cross_lingual('You are a helpful assistant.<|endofprompt|>[breath]因为他们那一辈人[breath]在乡里面住的要习惯一点，[breath]邻居都很活络，[breath]嗯，都很熟悉。[breath]',
                                                        './asset/zero_shot_prompt.wav', stream=False)):
    torchaudio.save('fine_grained_control_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)

# instruct usage, for supported control, check cosyvoice/utils/common.py#L28
for i, j in enumerate(cosyvoice.inference_instruct2('好少咯，一般系放嗰啲国庆啊，中秋嗰啲可能会咯。', 'You are a helpful assistant. 请用广东话表达。<|endofprompt|>',
                                                    './asset/zero_shot_prompt.wav', stream=False)):
    torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
for i, j in enumerate(cosyvoice.inference_instruct2('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', 'You are a helpful assistant. 请用尽可能快地语速说一句话。<|endofprompt|>',
                                                    './asset/zero_shot_prompt.wav', stream=False)):
    torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)

# hotfix usage
for i, j in enumerate(cosyvoice.inference_zero_shot('高管也通过电话、短信、微信等方式对报道[j][ǐ]予好评。', 'You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。',
                                                    './asset/zero_shot_prompt.wav', stream=False)):
    torchaudio.save('hotfix_{}.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

We borrowed a lot of code from FunASR.
We borrowed a lot of code from FunCodec.
We borrowed a lot of code from Matcha-TTS.
We borrowed a lot of code from AcademiCodec.
We borrowed a lot of code from WeNet.

Citations

bibtex

@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

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.

zhchhe/Fun-CosyVoice3-0.5B-2512