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# CosyVoice
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## 👉🏻 [CosyVoice Demos](https://fun-audio-llm.github.io/) 👈🏻
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[[CosyVoice Paper](https://fun-audio-llm.github.io/pdf/CosyVoice_v1.pdf)][[CosyVoice Studio](https://www.modelscope.cn/studios/iic/CosyVoice-300M)][[CosyVoice Code](https://github.com/FunAudioLLM/CosyVoice)]
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For `SenseVoice`, visit [SenseVoice repo](https://github.com/FunAudioLLM/SenseVoice) and [SenseVoice space](https://www.modelscope.cn/studios/iic/SenseVoice).
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## Install
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**Clone and install**
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- Clone the repo
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``` sh
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git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git
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# If you failed to clone submodule due to network failures, please run following command until success
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cd CosyVoice
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git submodule update --init --recursive
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```
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- Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
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- Create Conda env:
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``` sh
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conda create -n cosyvoice python=3.8
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conda activate cosyvoice
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# pynini is required by WeTextProcessing, use conda to install it as it can be executed on all platform.
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conda install -y -c conda-forge pynini==2.1.5
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pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
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# If you encounter sox compatibility issues
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# ubuntu
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sudo apt-get install sox libsox-dev
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# centos
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sudo yum install sox sox-devel
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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-300M-Instruct', local_dir='pretrained_models/CosyVoice-300M-Instruct')
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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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git clone https://www.modelscope.cn/iic/CosyVoice-300M-Instruct.git pretrained_models/CosyVoice-300M-Instruct
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git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrd
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```
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Optionaly, you can unzip `ttsfrd` resouce and install `ttsfrd` package for better text normalization performance.
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Notice that this step is not necessary. If you do not install `ttsfrd` package, we will use WeTextProcessing by default.
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``` sh
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cd pretrained_models/CosyVoice-ttsfrd/
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unzip resource.zip -d .
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pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whl
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```
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**Basic Usage**
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For zero_shot/cross_lingual inference, please use `CosyVoice-300M` model.
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For sft inference, please use `CosyVoice-300M-SFT` model.
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For instruct inference, please use `CosyVoice-300M-Instruct` model.
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First, add `third_party/Matcha-TTS` to your `PYTHONPATH`.
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``` sh
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export PYTHONPATH=third_party/Matcha-TTS
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```
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``` python
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from cosyvoice.cli.cosyvoice import CosyVoice
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from cosyvoice.utils.file_utils import load_wav
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import torchaudio
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cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-SFT')
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# sft usage
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print(cosyvoice.list_avaliable_spks())
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# change stream=True for chunk stream inference
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for i, j in enumerate(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)):
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torchaudio.save('sft_{}.wav'.format(i), j['tts_speech'], 22050)
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cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M')
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# zero_shot usage, <|zh|><|en|><|jp|><|yue|><|ko|> for Chinese/English/Japanese/Cantonese/Korean
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prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000)
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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。', prompt_speech_16k, stream=False)):
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torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], 22050)
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# cross_lingual usage
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prompt_speech_16k = load_wav('cross_lingual_prompt.wav', 16000)
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for i, j in enumerate(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)):
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torchaudio.save('cross_lingual_{}.wav'.format(i), j['tts_speech'], 22050)
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cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-Instruct')
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# instruct usage, support <laughter></laughter><strong></strong>[laughter][breath]
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for i, j in enumerate(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)):
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torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], 22050)
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```
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**Start web demo**
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You can use our web demo page to get familiar with CosyVoice quickly.
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We support sft/zero_shot/cross_lingual/instruct inference in web demo.
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Please see the demo website for details.
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``` python
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# change iic/CosyVoice-300M-SFT for sft inference, or iic/CosyVoice-300M-Instruct for instruct inference
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python3 webui.py --port 50000 --model_dir pretrained_models/CosyVoice-300M
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```
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**Advanced Usage**
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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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you can run following steps. Otherwise, you can just ignore this step.
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``` sh
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cd runtime/python
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docker build -t cosyvoice:v1.0 .
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# change iic/CosyVoice-300M to iic/CosyVoice-300M-Instruct if you want to use instruct inference
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# for grpc usage
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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"
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cd grpc && python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>
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# for fastapi usage
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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"
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cd fastapi && python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>
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```
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## Discussion & Communication
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You can directly discuss on [Github Issues](https://github.com/FunAudioLLM/CosyVoice/issues).
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You can also scan the QR code to join our official Dingding chat group.
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<img src="./asset/dingding.png" width="250px">
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## Acknowledge
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1. We borrowed a lot of code from [FunASR](https://github.com/modelscope/FunASR).
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2. We borrowed a lot of code from [FunCodec](https://github.com/modelscope/FunCodec).
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3. We borrowed a lot of code from [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS).
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4. We borrowed a lot of code from [AcademiCodec](https://github.com/yangdongchao/AcademiCodec).
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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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{"framework":"Pytorch","task":"text-to-speech"}
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# set random seed, so that you may reproduce your result.
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__set_seed1: !apply:random.seed [1986]
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__set_seed2: !apply:numpy.random.seed [1986]
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__set_seed3: !apply:torch.manual_seed [1986]
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__set_seed4: !apply:torch.cuda.manual_seed_all [1986]
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# fixed params
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sample_rate: 22050
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text_encoder_input_size: 512
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llm_input_size: 1024
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llm_output_size: 1024
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spk_embed_dim: 192
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# model params
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# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
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# for system/third_party class/function, we do not require this.
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llm: !new:cosyvoice.llm.llm.TransformerLM
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text_encoder_input_size: !ref <text_encoder_input_size>
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llm_input_size: !ref <llm_input_size>
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llm_output_size: !ref <llm_output_size>
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text_token_size: 51866
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speech_token_size: 4096
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length_normalized_loss: True
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lsm_weight: 0
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spk_embed_dim: !ref <spk_embed_dim>
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text_encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder
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input_size: !ref <text_encoder_input_size>
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output_size: 1024
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attention_heads: 16
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linear_units: 4096
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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normalize_before: True
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input_layer: 'linear'
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pos_enc_layer_type: 'rel_pos_espnet'
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selfattention_layer_type: 'rel_selfattn'
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use_cnn_module: False
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macaron_style: False
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use_dynamic_chunk: False
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use_dynamic_left_chunk: False
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static_chunk_size: 1
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llm: !new:cosyvoice.transformer.encoder.TransformerEncoder
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input_size: !ref <llm_input_size>
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output_size: !ref <llm_output_size>
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attention_heads: 16
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linear_units: 4096
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num_blocks: 14
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: 'linear_legacy'
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pos_enc_layer_type: 'rel_pos_espnet'
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selfattention_layer_type: 'rel_selfattn'
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static_chunk_size: 1
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sampling: !name:cosyvoice.utils.common.ras_sampling
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top_p: 0.8
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top_k: 25
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win_size: 10
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tau_r: 0.1
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flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec
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input_size: 512
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output_size: 80
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spk_embed_dim: !ref <spk_embed_dim>
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output_type: 'mel'
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vocab_size: 4096
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input_frame_rate: 50
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only_mask_loss: True
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encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder
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output_size: 512
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attention_heads: 8
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.1
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normalize_before: True
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input_layer: 'linear'
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pos_enc_layer_type: 'rel_pos_espnet'
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selfattention_layer_type: 'rel_selfattn'
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input_size: 512
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use_cnn_module: False
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macaron_style: False
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length_regulator: !new:cosyvoice.flow.length_regulator.InterpolateRegulator
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channels: 80
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sampling_ratios: [1, 1, 1, 1]
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decoder: !new:cosyvoice.flow.flow_matching.ConditionalCFM
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in_channels: 240
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n_spks: 1
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spk_emb_dim: 80
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cfm_params: !new:omegaconf.DictConfig
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content:
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sigma_min: 1e-06
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solver: 'euler'
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t_scheduler: 'cosine'
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training_cfg_rate: 0.2
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inference_cfg_rate: 0.7
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reg_loss_type: 'l1'
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estimator: !new:cosyvoice.flow.decoder.ConditionalDecoder
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in_channels: 320
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out_channels: 80
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channels: [256, 256]
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dropout: 0.0
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attention_head_dim: 64
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n_blocks: 4
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num_mid_blocks: 12
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num_heads: 8
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act_fn: 'gelu'
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hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
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in_channels: 80
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base_channels: 512
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nb_harmonics: 8
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sampling_rate: !ref <sample_rate>
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nsf_alpha: 0.1
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nsf_sigma: 0.003
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nsf_voiced_threshold: 10
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upsample_rates: [8, 8]
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upsample_kernel_sizes: [16, 16]
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istft_params:
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n_fft: 16
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hop_len: 4
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resblock_kernel_sizes: [3, 7, 11]
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resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
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source_resblock_kernel_sizes: [7, 11]
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source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
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lrelu_slope: 0.1
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audio_limit: 0.99
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f0_predictor: !new:cosyvoice.hifigan.f0_predictor.ConvRNNF0Predictor
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num_class: 1
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in_channels: 80
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cond_channels: 512
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# processor functions
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parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
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get_tokenizer: !name:whisper.tokenizer.get_tokenizer
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multilingual: True
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num_languages: 100
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language: 'en'
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task: 'transcribe'
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allowed_special: 'all'
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tokenize: !name:cosyvoice.dataset.processor.tokenize
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get_tokenizer: !ref <get_tokenizer>
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allowed_special: !ref <allowed_special>
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filter: !name:cosyvoice.dataset.processor.filter
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max_length: 40960
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min_length: 0
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token_max_length: 200
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token_min_length: 1
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resample: !name:cosyvoice.dataset.processor.resample
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resample_rate: !ref <sample_rate>
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feat_extractor: !name:matcha.utils.audio.mel_spectrogram
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n_fft: 1024
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||||
num_mels: 80
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sampling_rate: !ref <sample_rate>
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hop_size: 256
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win_size: 1024
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fmin: 0
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fmax: 8000
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center: False
|
||||
compute_fbank: !name:cosyvoice.dataset.processor.compute_fbank
|
||||
feat_extractor: !ref <feat_extractor>
|
||||
parse_embedding: !name:cosyvoice.dataset.processor.parse_embedding
|
||||
normalize: True
|
||||
shuffle: !name:cosyvoice.dataset.processor.shuffle
|
||||
shuffle_size: 1000
|
||||
sort: !name:cosyvoice.dataset.processor.sort
|
||||
sort_size: 500 # sort_size should be less than shuffle_size
|
||||
batch: !name:cosyvoice.dataset.processor.batch
|
||||
batch_type: 'dynamic'
|
||||
max_frames_in_batch: 2000
|
||||
padding: !name:cosyvoice.dataset.processor.padding
|
||||
|
||||
# dataset processor pipeline
|
||||
data_pipeline: [
|
||||
!ref <parquet_opener>,
|
||||
!ref <tokenize>,
|
||||
!ref <filter>,
|
||||
!ref <resample>,
|
||||
!ref <compute_fbank>,
|
||||
!ref <parse_embedding>,
|
||||
!ref <shuffle>,
|
||||
!ref <sort>,
|
||||
!ref <batch>,
|
||||
!ref <padding>,
|
||||
]
|
||||
|
||||
# train conf
|
||||
train_conf:
|
||||
optim: adam
|
||||
optim_conf:
|
||||
lr: 0.001
|
||||
scheduler: warmuplr
|
||||
scheduler_conf:
|
||||
warmup_steps: 2500
|
||||
max_epoch: 200
|
||||
grad_clip: 5
|
||||
accum_grad: 2
|
||||
log_interval: 100
|
||||
save_per_step: -1
|
||||
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Reference in New Issue