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.
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.
Clone the repo sh 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
import sys
sys.path.append('third_party/Matcha-TTS')
from cosyvoice.cli.cosyvoice import AutoModel
import torchaudio
""" CosyVoice2 Usage, check https://funaudiollm.github.io/cosyvoice2/ for more details
"""
cosyvoice = AutoModel(model_dir='pretrained_models/CosyVoice2-0.5B')
# NOTE if you want to reproduce the results on https://funaudiollm.github.io/cosyvoice2, please add text_frontend=False during inference
# 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.', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav')):
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('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。', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav')):
torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
# save zero_shot spk for future usage
assert cosyvoice.add_zero_shot_spk('希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', 'my_zero_shot_spk') is True
for i, j in enumerate(cosyvoice.inference_zero_shot('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。', '', '', zero_shot_spk_id='my_zero_shot_spk')):
torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
cosyvoice.save_spkinfo()
# fine grained control, for supported control, check cosyvoice/tokenizer/tokenizer.py#L248
for i, j in enumerate(cosyvoice.inference_cross_lingual('在他讲述那个荒诞故事的过程中，他突然[laughter]停下来，因为他自己也被逗笑了[laughter]。', './asset/zero_shot_prompt.wav')):
torchaudio.save('fine_grained_control_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
# instruct usage
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。', '用四川话说这句话<|endofprompt|>', './asset/zero_shot_prompt.wav')):
torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
# bistream usage, you can use generator as input, this is useful when using text llm model as input
# NOTE you should still have some basic sentence split logic because llm can not handle arbitrary sentence length
def text_generator():
yield '收到好友从远方寄来的生日礼物，'
yield '那份意外的惊喜与深深的祝福'
yield '让我心中充满了甜蜜的快乐，'
yield '笑容如花儿般绽放。'
for i, j in enumerate(cosyvoice.inference_zero_shot(text_generator(), '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_bistream_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
@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}
}
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