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Multi-lingual large voice generation model, providing inference, training and deployment full-stack ability.
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FunAudioLLM/CosyVoice
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CosyVoice 3.0:Demos;Paper;CV3-Eval
CosyVoice 2.0:Demos;Paper;Modelscope;HuggingFace
CosyVoice 1.0:Demos;Paper;Modelscope
CosyVoice 2.0 has been released! Compared to version 1.0, the new version offers more accurate, more stable, faster, and better speech generation capabilities.
- Supported Language: Chinese, English, Japanese, Korean, Chinese dialects (Cantonese, Sichuanese, Shanghainese, Tianjinese, Wuhanese, etc.)
- Crosslingual & Mixlingual:Support zero-shot voice cloning for cross-lingual and code-switching scenarios.
- Bidirectional Streaming Support: CosyVoice 2.0 integrates offline and streaming modeling technologies.
- Rapid First Packet Synthesis: Achieves latency as low as 150ms while maintaining high-quality audio output.
- Improved Pronunciation: Reduces pronunciation errors by 30% to 50% compared to CosyVoice 1.0.
- Benchmark Achievements: Attains the lowest character error rate on the hard test set of the Seed-TTS evaluation set.
- Consistency in Timbre: Ensures reliable voice consistency for zero-shot and cross-language speech synthesis.
- Cross-language Synthesis: Marked improvements compared to version 1.0.
- Enhanced Prosody and Sound Quality: Improved alignment of synthesized audio, raising MOS evaluation scores from 5.4 to 5.53.
- Emotional and Dialectal Flexibility: Now supports more granular emotional controls and accent adjustments.
2025/07
- release cosyvoice 3.0 eval set
2025/05
- add cosyvoice 2.0 vllm support
2024/12
- 25hz cosyvoice 2.0 released
2024/09
- 25hz cosyvoice base model
- 25hz cosyvoice voice conversion model
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
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 successcd CosyVoicegit submodule update --init --recursive
Install Conda: please seehttps://docs.conda.io/en/latest/miniconda.html
Create Conda env:
conda create -n cosyvoice -y python=3.10conda activate cosyvoicepip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com# If you encounter sox compatibility issues# ubuntusudo apt-get install sox libsox-dev# centossudo yum install sox sox-devel
We strongly recommend that you download our pretrainedCosyVoice2-0.5B
CosyVoice-300M
CosyVoice-300M-SFT
CosyVoice-300M-Instruct
model andCosyVoice-ttsfrd
resource.
# SDK模型下载frommodelscopeimportsnapshot_downloadsnapshot_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')
# git模型下载,请确保已安装git lfsmkdir -p pretrained_modelsgit clone https://www.modelscope.cn/iic/CosyVoice2-0.5B.git pretrained_models/CosyVoice2-0.5Bgit clone https://www.modelscope.cn/iic/CosyVoice-300M.git pretrained_models/CosyVoice-300Mgit clone https://www.modelscope.cn/iic/CosyVoice-300M-SFT.git pretrained_models/CosyVoice-300M-SFTgit clone https://www.modelscope.cn/iic/CosyVoice-300M-Instruct.git pretrained_models/CosyVoice-300M-Instructgit clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrd
Optionally, you can unzipttsfrd
resource and installttsfrd
package for better text normalization performance.
Notice that this step is not necessary. If you do not installttsfrd
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.whlpip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl
We strongly recommend usingCosyVoice2-0.5B
for better performance.Follow the code below for detailed usage of each model.
importsyssys.path.append('third_party/Matcha-TTS')fromcosyvoice.cli.cosyvoiceimportCosyVoice,CosyVoice2fromcosyvoice.utils.file_utilsimportload_wavimporttorchaudio
cosyvoice=CosyVoice2('pretrained_models/CosyVoice2-0.5B',load_jit=False,load_trt=False,load_vllm=False,fp16=False)# NOTE if you want to reproduce the results on https://funaudiollm.github.io/cosyvoice2, please add text_frontend=False during inference# zero_shot usageprompt_speech_16k=load_wav('./asset/zero_shot_prompt.wav',16000)fori,jinenumerate(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。',prompt_speech_16k,stream=False)):torchaudio.save('zero_shot_{}.wav'.format(i),j['tts_speech'],cosyvoice.sample_rate)# save zero_shot spk for future usageassertcosyvoice.add_zero_shot_spk('希望你以后能够做的比我还好呦。',prompt_speech_16k,'my_zero_shot_spk')isTruefori,jinenumerate(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',stream=False)):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#L248fori,jinenumerate(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]。',prompt_speech_16k,stream=False)):torchaudio.save('fine_grained_control_{}.wav'.format(i),j['tts_speech'],cosyvoice.sample_rate)# instruct usagefori,jinenumerate(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',prompt_speech_16k,stream=False)):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 lengthdeftext_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。'fori,jinenumerate(cosyvoice.inference_zero_shot(text_generator(),'希望你以后能够做的比我还好呦。',prompt_speech_16k,stream=False)):torchaudio.save('zero_shot_{}.wav'.format(i),j['tts_speech'],cosyvoice.sample_rate)
If you want to use vllm for inference, please installvllm==v0.9.0
. Older vllm version do not support CosyVoice2 inference.
Notice thatvllm==v0.9.0
has a lot of specific requirements, for exampletorch==2.7.0
. 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 cosyvoiceconda activate cosyvoice_vllmpip install vllm==v0.9.0 -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.compython vllm_example.py
cosyvoice=CosyVoice('pretrained_models/CosyVoice-300M-SFT',load_jit=False,load_trt=False,fp16=False)# sft usageprint(cosyvoice.list_available_spks())# change stream=True for chunk stream inferencefori,jinenumerate(cosyvoice.inference_sft('你好,我是通义生成式语音大模型,请问有什么可以帮您的吗?','中文女',stream=False)):torchaudio.save('sft_{}.wav'.format(i),j['tts_speech'],cosyvoice.sample_rate)cosyvoice=CosyVoice('pretrained_models/CosyVoice-300M')# zero_shot usage, <|zh|><|en|><|jp|><|yue|><|ko|> for Chinese/English/Japanese/Cantonese/Koreanprompt_speech_16k=load_wav('./asset/zero_shot_prompt.wav',16000)fori,jinenumerate(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。',prompt_speech_16k,stream=False)):torchaudio.save('zero_shot_{}.wav'.format(i),j['tts_speech'],cosyvoice.sample_rate)# cross_lingual usageprompt_speech_16k=load_wav('./asset/cross_lingual_prompt.wav',16000)fori,jinenumerate(cosyvoice.inference_cross_lingual('<|en|>And then later on, fully acquiring that company. So keeping management in line, interest in line with the asset that\'s coming into the family is a reason why sometimes we don\'t buy the whole thing.',prompt_speech_16k,stream=False)):torchaudio.save('cross_lingual_{}.wav'.format(i),j['tts_speech'],cosyvoice.sample_rate)# vc usageprompt_speech_16k=load_wav('./asset/zero_shot_prompt.wav',16000)source_speech_16k=load_wav('./asset/cross_lingual_prompt.wav',16000)fori,jinenumerate(cosyvoice.inference_vc(source_speech_16k,prompt_speech_16k,stream=False)):torchaudio.save('vc_{}.wav'.format(i),j['tts_speech'],cosyvoice.sample_rate)cosyvoice=CosyVoice('pretrained_models/CosyVoice-300M-Instruct')# instruct usage, support <laughter></laughter><strong></strong>[laughter][breath]fori,jinenumerate(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.',stream=False)):torchaudio.save('instruct_{}.wav'.format(i),j['tts_speech'],cosyvoice.sample_rate)
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 inferencepython3webui.py--port50000--model_dirpretrained_models/CosyVoice-300M
For advanced users, we have provided training and inference scripts inexamples/libritts/cosyvoice/run.sh
.
Optionally, if you want service deployment,You can run the following steps.
cd runtime/pythondocker build -t cosyvoice:v1.0.# change iic/CosyVoice-300M to iic/CosyVoice-300M-Instruct if you want to use instruct inference# for grpc usagedocker 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 usagedocker 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>
You can directly discuss onGithub Issues.
You can also scan the QR code to join our official Dingding chat group.
- We borrowed a lot of code fromFunASR.
- We borrowed a lot of code fromFunCodec.
- We borrowed a lot of code fromMatcha-TTS.
- We borrowed a lot of code fromAcademiCodec.
- We borrowed a lot of code fromWeNet.
@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}}
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.
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Multi-lingual large voice generation model, providing inference, training and deployment full-stack ability.
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