CosyVoice下載 - CosyVoice原始碼下載

舒適之聲

？ CosyVoice 演示 ??

[CosyVoice 論文][CosyVoice Studio][CosyVoice 程式碼]

對於SenseVoice ，請存取 SenseVoice 儲存庫和 SenseVoice 空間。

路線圖

安裝

克隆並安裝

克隆儲存庫

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

安裝Conda：請參閱https://docs.conda.io/en/latest/miniconda.html
創建Conda環境：

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

模型下載

我們強烈建議您下載我們預先訓練的CosyVoice-300M CosyVoice-300M-SFT CosyVoice-300M-Instruct模型和CosyVoice-ttsfrd資源。

如果您是該領域的專家，並且您只對從頭開始訓練自己的 CosyVoice 模型感興趣，則可以跳過此步驟。

 # 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-25Hz' , local_dir = 'pretrained_models/CosyVoice-300M-25Hz' )
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-25Hz.git pretrained_models/CosyVoice-300M-25Hz
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，您可以解壓縮ttsfrd資源並安裝ttsfrd以獲得更好的文字規範化效能。

請注意，這一步並不是必要的。如果您沒有安裝ttsfrd包，我們將預設使用 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

對於zero_shot/cross_lingual推理，請使用CosyVoice-300M模型。對於 sft 推理，請使用CosyVoice-300M-SFT模型。對於指令推理，請使用CosyVoice-300M-Instruct模型。首先，將third_party/Matcha-TTS加入您的PYTHONPATH中。

 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' , load_jit = True , load_onnx = False , fp16 = True )
# sft usage
print ( cosyvoice . list_avaliable_spks ())
# change stream=True for chunk stream inference
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 )):
    torchaudio . save ( 'sft_{}.wav' . format ( i ), j [ 'tts_speech' ], 22050 )

cosyvoice = CosyVoice ( 'pretrained_models/CosyVoice-300M-25Hz' ) # or change to pretrained_models/CosyVoice-300M for 50Hz inference
# zero_shot usage, <|zh|><|en|><|jp|><|yue|><|ko|> for Chinese/English/Japanese/Cantonese/Korean
prompt_speech_16k = load_wav ( 'zero_shot_prompt.wav' , 16000 )
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 )):
    torchaudio . save ( 'zero_shot_{}.wav' . format ( i ), j [ 'tts_speech' ], 22050 )
# cross_lingual usage
prompt_speech_16k = load_wav ( 'cross_lingual_prompt.wav' , 16000 )
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 )):
    torchaudio . save ( 'cross_lingual_{}.wav' . format ( i ), j [ 'tts_speech' ], 22050 )
# vc usage
prompt_speech_16k = load_wav ( 'zero_shot_prompt.wav' , 16000 )
source_speech_16k = load_wav ( 'cross_lingual_prompt.wav' , 16000 )
for i , j in enumerate ( cosyvoice . inference_vc ( source_speech_16k , prompt_speech_16k , stream = False )):
    torchaudio . save ( 'vc_{}.wav' . format ( i ), j [ 'tts_speech' ], 22050 )

cosyvoice = CosyVoice ( 'pretrained_models/CosyVoice-300M-Instruct' )
# instruct usage, support <laughter></laughter><strong></strong>[laughter][breath]
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 )):
    torchaudio . save ( 'instruct_{}.wav' . format ( i ), j [ 'tts_speech' ], 22050 )

開始網路示範

您可以使用我們的網頁演示頁面快速熟悉 CosyVoice。我們在網路演示中支援 sft/zero_shot/cross_lingual/instruct 推理。

詳情請參閱演示網站。

 # 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 - 300 M

進階用法

對於高級用戶，我們在examples/libritts/cosyvoice/run.sh中提供了訓練和推理腳本。您可以按照此食譜熟悉 CosyVoice。

為部署而建置

或者，如果您想要使用 grpc 進行服務部署，可以執行下列步驟。否則，您可以忽略此步驟。

 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 "
cd grpc && python3 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 && 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 >