👉🏻 CosyVoice Demos 👈🏻
[CosyVoice Paper][CosyVoice Studio][CosyVoice Code]
For SenseVoice, visit SenseVoice repo and SenseVoice space.
Clone and install
- Clone the repo
git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git
# If you failed to clone submodule due to network failures, please run 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 python=3.8
conda activate cosyvoice
# pynini is required by WeTextProcessing, use conda to install it as it can be executed on all platform.
conda install -y -c conda-forge pynini==2.1.5
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-develModel download
We strongly recommend that you download our pretrained CosyVoice-300M CosyVoice-300M-SFT CosyVoice-300M-Instruct model and CosyVoice-ttsfrd resource.
If you are expert in this field, and you are only interested in training your own CosyVoice model from scratch, you can skip this step.
# SDK模型下载
from modelscope import snapshot_download
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 lfs
mkdir -p pretrained_models
git clone https://www.modelscope.cn/iic/CosyVoice-300M.git pretrained_models/CosyVoice-300M
git clone https://www.modelscope.cn/iic/CosyVoice-300M-SFT.git pretrained_models/CosyVoice-300M-SFT
git clone https://www.modelscope.cn/iic/CosyVoice-300M-Instruct.git pretrained_models/CosyVoice-300M-Instruct
git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrdOptionaly, you can unzip ttsfrd resouce 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 WeTextProcessing by default.
cd pretrained_models/CosyVoice-ttsfrd/
unzip resource.zip -d .
pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whlBasic Usage
For zero_shot/cross_lingual inference, please use CosyVoice-300M model.
For sft inference, please use CosyVoice-300M-SFT model.
For instruct inference, please use CosyVoice-300M-Instruct model.
First, add third_party/Matcha-TTS to your PYTHONPATH.
export PYTHONPATH=third_party/Matcha-TTSfrom cosyvoice.cli.cosyvoice import CosyVoice
from cosyvoice.utils.file_utils import load_wav
import torchaudio
cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-SFT')
# sft usage
print(cosyvoice.list_avaliable_spks())
output = 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')
torchaudio.save('sft.wav', output['tts_speech'], 22050)
cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M')
# zero_shot usage, <|zh|><|en|><|jp|><|yue|><|ko|> for Chinese/English/Japanese/Cantonese/Korean
prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000)
output = 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)
torchaudio.save('zero_shot.wav', output['tts_speech'], 22050)
# cross_lingual usage
prompt_speech_16k = load_wav('cross_lingual_prompt.wav', 16000)
output = 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)
torchaudio.save('cross_lingual.wav', output['tts_speech'], 22050)
cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-Instruct')
# instruct usage, support <laughter></laughter><strong></strong>[laughter][breath]
output = 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.')
torchaudio.save('instruct.wav', output['tts_speech'], 22050)Start web demo
You can use our web demo page to get familiar with CosyVoice quickly. We support sft/zero_shot/cross_lingual/instruct inference in web demo.
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-300MAdvanced Usage
For advanced user, we have provided train and inference scripts in examples/libritts/cosyvoice/run.sh.
You can get familiar with CosyVoice following this recipie.
Build for deployment
Optionally, if you want to use grpc for service deployment, you can run following steps. Otherwise, you can just ignore this step.
cd runtime/python
docker build -t cosyvoice:v1.0 .
# change iic/CosyVoice-300M to iic/CosyVoice-300M-Instruct if you want to use instruct inference
# for grpc usage
docker 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"
python3 grpc/client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>
# for fastapi usage
docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/fastapi && MODEL_DIR=iic/CosyVoice-300M fastapi dev --port 50000 server.py && sleep infinity"
python3 fastapi/client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>You can directly discuss on Github Issues.
You can also scan the QR code to join our official Dingding chat group.
- 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.
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.