CosyVoice

• GitHub：https://github.com/FunAudioLLM/CosyVoice

安装

克隆并安装

• 克隆仓库

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

• 安装 Conda: 参见 https://docs.conda.io/en/latest/miniconda.html
• 创建 Conda 环境:

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-devel

下载模型

我们强烈建议您下载我们的预训练模型 CosyVoice-300M CosyVoice-300M-SFT CosyVoice-300M-Instruct 和 CosyVoice-ttsfrd 资源.

如果您是该领域的专家，并且只对从头开始训练自己的 CosyVoice 模型感兴趣，那么您可以跳过此步骤。.

# 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-ttsfrd

或者，您可以解压 ttsfrd 资源并安装ttsfrd包以获得更好的文本规范化性能。

注意，这一步不是必须的，如果你没有安装ttsfrd，我们默认使用WeTextProcessing。

cd pretrained_models/CosyVoice-ttsfrd/
unzip resource.zip -d .
pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whl

基本用法

对于零样本/跨语言推理，请使用CosyVoice-300M模型。
对于 sft 推理，请使用CosyVoice-300M-SFT模型。
对于指令推理，请使用CosyVoice-300M-Instruct模型。
首先，添加third_party/Matcha-TTS到您的PYTHONPATH。

export PYTHONPATH=third_party/Matcha-TTS

from 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)

开始网页示例

您可以使用我们的网页演示页面快速熟悉 CosyVoice。我们在网页演示中支持 sft/zero_shot/cross_lingual/instruct 推理。

详细信息请参阅演示网站。

# 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-300M

高级用法

对于高级用户，我们在examples/libritts/cosyvoice/run.sh。您可以按照此方法熟悉 CosyVoice。

为部署而构建

或者，如果您想使用 grpc 进行服务部署，您可以运行以下步骤。否则，您可以忽略此步骤。

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
docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python && python3 server.py --port 50000 --max_conc 4 --model_dir iic/CosyVoice-300M && sleep infinity"
python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>