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Running
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Zero
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README.md
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@@ -34,13 +34,13 @@ sudo yum install sox sox-devel
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```
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**Model download**
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> *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.*
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We strongly recommend that you download our pretrained `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `CosyVoice-ttsfrd` resource.
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``` python
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from modelscope import snapshot_download
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snapshot_download('iic/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M')
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snapshot_download('iic/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT')
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snapshot_download('iic/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd')
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```
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Download models with git, you should install [git lfs](https://github.com/git-lfs/git-lfs?utm_source=CosyVoice_site&utm_medium=download_models&utm_campaign=gitlfs#installing) first.
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``` sh
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mkdir -p pretrained_models
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git clone https://www.modelscope.cn/iic/CosyVoice-300M.git pretrained_models/CosyVoice-300M
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git clone https://www.modelscope.cn/iic/CosyVoice-300M-SFT.git pretrained_models/CosyVoice-300M-SFT
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For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`.
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You can get familiar with CosyVoice following this recipie.
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**Serve with FastAPI**
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The `runtime/python/fastapi_server.py` file contains the http API build with `FastAPI`.
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```sh
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cd runtime/python
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# Set inference model
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export MODEL_DIR=pretrained_models/CosyVoice-300M-Instruct
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# For development
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fastapi dev --port 6006 fastapi_server.py
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# For production
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fastapi run --port 6006 fastapi_server.py
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# Call the API with python client
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python fastapi_client.py --api_base http://127.0.0.1:6006 --mode cross_lingual --tts_wav ./demo.wav
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```
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**Build for deployment**
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Optionally, if you want to use grpc for service deployment,
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5. We borrowed a lot of code from [WeNet](https://github.com/wenet-e2e/wenet).
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## Disclaimer
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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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```
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**Model download**
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We strongly recommend that you download our pretrained `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `CosyVoice-ttsfrd` resource.
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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.
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``` python
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# SDK模型下载
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from modelscope import snapshot_download
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snapshot_download('iic/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M')
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snapshot_download('iic/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT')
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snapshot_download('iic/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd')
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```
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``` sh
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# git模型下载,请确保已安装git lfs
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mkdir -p pretrained_models
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git clone https://www.modelscope.cn/iic/CosyVoice-300M.git pretrained_models/CosyVoice-300M
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git clone https://www.modelscope.cn/iic/CosyVoice-300M-SFT.git pretrained_models/CosyVoice-300M-SFT
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For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`.
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You can get familiar with CosyVoice following this recipie.
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**Build for deployment**
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Optionally, if you want to use grpc for service deployment,
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5. We borrowed a lot of code from [WeNet](https://github.com/wenet-e2e/wenet).
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## Disclaimer
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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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