CosyVoice-ttsfrd/README.md

# CosyVoice
## 👉🏻 [CosyVoice Demos](https://fun-audio-llm.github.io/) 👈🏻
[[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)]

For `SenseVoice`, visit [SenseVoice repo](https://github.com/FunAudioLLM/SenseVoice) and [SenseVoice space](https://www.modelscope.cn/studios/iic/SenseVoice).

## Install

**Clone and install**

- Clone the repo
``` sh

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:

``` sh

conda create -n cosyvoice python=3.8

conda activate cosyvoice

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

```

**Model download**

We strongly recommand that you download our pretrained `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `speech_kantts_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.

``` python

# 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/speech_kantts_ttsfrd', local_dir='pretrained_models/speech_kantts_ttsfrd')

```

``` sh

# 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/speech_kantts_ttsfrd.git pretrained_models/speech_kantts_ttsfrd

```

Unzip `ttsfrd` resouce and install `ttsfrd` package
``` sh

cd pretrained_models/speech_kantts_ttsfrd/

unzip resource.zip -d .

pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whl

```

**Basic 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/AcademiCodec` and `third_party/Matcha-TTS` to your `PYTHONPATH`.

``` sh

export PYTHONPATH=third_party/AcademiCodec:third_party/Matcha-TTS

```

``` python

from cosyvoice.cli.cosyvoice import CosyVoice

from cosyvoice.utils.file_utils import load_wav

import torchaudio



cosyvoice = CosyVoice('speech_tts/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('speech_tts/CosyVoice-300M')

# zero_shot usage

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('speech_tts/CosyVoice-300M-Instruct')

# instruct usage

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.

``` python

# change speech_tts/CosyVoice-300M-SFT for sft inference, or speech_tts/CosyVoice-300M-Instruct for instruct inference

python3 webui.py --port 50000 --model_dir speech_tts/CosyVoice-300M

```

**Advanced 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.

``` sh

cd runtime/python

docker build -t cosyvoice:v1.0 .

# change speech_tts/CosyVoice-300M to speech_tts/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 speech_tts/CosyVoice-300M && sleep infinity"

python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>

```

## Discussion & Communication

You can directly discuss on [Github Issues](https://github.com/FunAudioLLM/CosyVoice/issues).

You can also scan the QR code to join our officla Dingding chat group.

<img src="./asset/dingding.png" width="250px">

## Acknowledge

1. We borrowed a lot of code from [FunASR](https://github.com/modelscope/FunASR).
2. We borrowed a lot of code from [FunCodec](https://github.com/modelscope/FunCodec).
3. We borrowed a lot of code from [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS).
4. We borrowed a lot of code from [AcademiCodec](https://github.com/yangdongchao/AcademiCodec).
5. We borrowed a lot of code from [WeNet](https://github.com/wenet-e2e/wenet).

## Disclaimer
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.