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ChatTTS webUI & API

A simple local web interface to use ChatTTS for text-to-speech synthesis on the web, supporting mixed Chinese and English text and numbers, and providing an API interface.

The original ChatTTS project

Interface Preview

Sample synthesized voice effects

https://github.com/jianchang512/ChatTTS-ui/assets/3378335/bd6aaef9-a49a-4a81-803a-91e3320bf808

Text and control symbols mixed effect

https://github.com/jianchang512/ChatTTS-ui/assets/3378335/e2a08ea0-32af-4a30-8880-3a91f6cbea55

Windows Pre-packaged Version

1. Download the compressed package from Releases, unzip it, and double-click app.exe to use.
2. Some security software may flag it as a virus, please disable or deploy from source.
3. If you have an Nvidia graphics card with more than 4GB of memory and have installed CUDA11.8+, GPU acceleration will be enabled.

Linux Container Deployment

Installation

1. Clone the project repository

Clone the project to any directory, for example:

bash git clone https://github.com/jianchang512/ChatTTS-ui.git chat-tts-ui

1. Start Runner

Enter the project directory:

bash cd chat-tts-ui

Start the container and view the initialization logs:

```bash For GPU version docker compose -f docker-compose.gpu.yaml up -d

For CPU version
docker compose -f docker-compose.cpu.yaml up -d

docker compose logs -f --no-log-prefix ```

1. Access ChatTTS WebUI

Started at:['0.0.0.0', '9966'], meaning you can access it via IP:9966 of the deployment device, for example:

• Localhost: http://127.0.0.1:9966
• Server: http://192.168.1.100:9966

Update

1. Get the latest code from the main branch:

bash git checkout main git pull origin main

1. Go to the next step and update to the latest image:

```bash docker compose down

For GPU version docker compose -f docker-compose.gpu.yaml up -d --build

For CPU version docker compose -f docker-compose.cpu.yaml up -d --build

docker compose logs -f --no-log-prefix ```

Linux Source Code Deployment

1. Prepare python3.9-3.11 environment. Install FFmpeg
2. Create an empty directory /data/chattts and execute cd /data/chattts && git clone https://github.com/jianchang512/chatTTS-ui ..
3. Create a virtual environment python3 -m venv venv.
4. Activate the virtual environment source ./venv/bin/activate.
5. Install dependencies pip3 install -r requirements.txt.
6. If CUDA acceleration is not needed, execute

pip3 install torch==2.2.0 torchaudio==2.2.0

If CUDA acceleration is needed, execute

``` pip install torch==2.2.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118

pip install nvidia-cublas-cu11 nvidia-cudnn-cu11 ```

Additionally, install CUDA11.8+ ToolKit, search for installation methods or refer to https://juejin.cn/post/7318704408727519270

Besides CUDA, AMD GPU acceleration can also be used by installing ROCm and PyTorch_ROCm version. For AMD GPU, with the help of ROCm, PyTorch works out of the box without further modifications. 1. Refer to https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html to install AMD GPU Driver and ROCm. 2. Then install PyTorch_ROCm version from https://pytorch.org/.

pip3 install torch==2.2.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/rocm6.0

After installation, you can use the command rocm-smi to view the AMD GPUs in the system. The following Torch code(query_gpu.py) can also be used to query the current AMD GPU Device.

``` import torch

print(torch.version)

if torch.cuda.is_available(): device = torch.device("cuda") # a CUDA device object print('Using GPU:', torch.cuda.get_device_name(0)) else: device = torch.device("cpu") print('Using CPU')

torch.cuda.get_device_properties(0)

```

Using the code above, for instance, with AMD Radeon Pro W7900, the device query is as follows.

```

$ python ~/query_gpu.py

2.4.0.dev20240401+rocm6.0

Using GPU: AMD Radeon PRO W7900

```

1. Execute python3 app.py to start. It will automatically open a browser window at http://127.0.0.1:9966. Note: Models are downloaded from modelscope by default without using a proxy, please disable the proxy.

MacOS Source Code Deployment

1. Prepare the python3.9-3.11 environment and install git. Execute command brew install libsndfile git python@3.10. Then continue with

``` brew install ffmpeg

export PATH="/usr/local/opt/python@3.10/bin:$PATH"

source ~/.bash_profile

source ~/.zshrc

```

1. Create an empty directory /data/chattts and execute command cd /data/chattts && git clone https://github.com/jianchang512/chatTTS-ui ..
2. Create a virtual environment python3 -m venv venv.
3. Activate the virtual environment source ./venv/bin/activate.
4. Install dependencies pip3 install -r requirements.txt.
5. Install torch pip3 install torch==2.2.0 torchaudio==2.2.0.
6. Execute python3 app.py to start. It will automatically open a browser window at http://127.0.0.1:9966. Note: Models are downloaded from modelscope by default without using a proxy, please disable the proxy.

Windows Source Code Deployment

1. Download python3.9-3.11, make sure to check Add Python to environment variables during installation. install ffmpeg.exe
2. Download and install git from https://github.com/git-for-windows/git/releases/download/v2.45.1.windows.1/Git-2.45.1-64-bit.exe.
3. Create an empty folder D:/chattts and enter it, type cmd in the address bar and press Enter. In the cmd window that pops up, execute command git clone https://github.com/jianchang512/chatTTS-ui ..
4. Create a virtual environment by executing command python -m venv venv.
5. Activate the virtual environment by executing .\venv\scripts\activate.
6. Install dependencies by executing pip install -r requirements.txt.
7. If CUDA acceleration is not needed,

execute pip install torch==2.2.0 torchaudio==2.2.0.

If CUDA acceleration is needed, execute

pip install torch==2.2.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118.

Additionally, install CUDA11.8+ ToolKit, search for installation methods or refer to https://juejin.cn/post/7318704408727519270.

1. Execute python app.py to start. It will automatically open a browser window at http://127.0.0.1:9966. Note: Models are downloaded from modelscope by default without using a proxy, please disable the proxy.

Deployment Notes

1. install ffmpeg since 0.96

2. If the GPU memory is below 4GB, it will forcefully use the CPU.

3. Under Windows or Linux, if the memory is more than 4GB and it is an Nvidia graphics card, but the source code deployment still uses CPU, you may try uninstalling torch first and then reinstalling it. Uninstall with pip uninstall -y torch torchaudio, then reinstall the CUDA version of torch pip install torch==2.2.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118. CUDA11.8+ must be installed.

4. By default, it checks whether modelscope can be connected. If so, models are downloaded from modelscope; otherwise, models are downloaded from huggingface.co.

FAQs and Troubleshooting

Modify HTTP Address

The default address is http://127.0.0.1:9966. If you want to modify it, open the .env file in the directory and change WEB_ADDRESS=127.0.0.1:9966 to the appropriate IP and port, such as changing to WEB_ADDRESS=192.168.0.10:9966 for LAN access.

Using API Requests v0.5+

Method: POST

URL: http://127.0.0.1:9966/tts

Parameters:

text: str| Required, text to synthesize.

voice: int| Optional, default 2222. Determines the voice digit, choose from 2222 | 7869 | 6653 | 4099 | 5099, or any input will randomly use a voice.

prompt: str| Optional, default empty. Sets laughter, pause, etc., like [oral_2][laugh_0][break_6].

temperature: float| Optional, default 0.3.

top_p: float| Optional, default 0.7.

top_k: int| Optional, default 20.

skip_refine: int| Optional, default 0. 1=skip refine text, 0=do not skip.

custom_voice: int| Optional, default 0. Sets a custom seed value for obtaining the voice, must be a positive integer. If set, it will take precedence over voice.

Response: JSON

Success: {code:0,msg:ok,audio_files:[dict1,dict2]}

where audio_files is an array of dictionaries, each element dict is {filename:absolute path to wav file, url:downloadable wav URL}

Failure:

{code:1,msg:error reason}

# API Call Code

import requests

res = requests.post('http://127.0.0.1:9966/tts', data={
  "text": "No need to fill if unsure",
  "prompt": "",
  "voice": "3333",
  "temperature": 0.3,
  "top_p": 0.7,
  "top_k": 20,
  "skip_refine": 0,
  "custom_voice": 0,
})
print(res.json())

#ok
{code:0, msg:'ok', audio_files:[{filename: E:/python/chattts/static/wavs/20240601-22_12_12-c7456293f7b5e4dfd3ff83bbd884a23e.wav, url: http://127.0.0.1:9966/static/wavs/20240601-22_12_12-c7456293f7b5e4dfd3ff83bbd884a23e.wav}]}

#error
{code:1, msg:"error"}

Using in pyVideoTrans software

Upgrade pyVideoTrans to 1.82+ https://github.com/jianchang512/pyvideotrans

1. Click Menu-Settings-ChatTTS and fill in the request address, which should by default be http://127.0.0.1:9966.
2. After ensuring there are no issues, select ChatTTS on the main interface.