This tool allows you to complete the entire process of VITS (Data Preprocessing + Whisper ASR + Text Preprocessing + Modification config.json + Training, Inference) with ONE-CLICK!
- A Windows/Linux system with a minimum of
16GB
RAM. - A GPU with at least
12GB
of VRAM. - Python == 3.8
- Anaconda installed.
- PyTorch installed.
- CUDA 11.x installed.
- Zlib DLL installed.
Pytorch install command:
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
CUDA 11.7 Install:
https://developer.nvidia.com/cuda-11-7-0-download-archive
Zlib DLL Install:
https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-zlib-windows
Install pyopenjtalk Manually:
pip install -U pyopenjtalk --no-build-isolation
- Create an Anaconda environment:
conda create -n one-click-vits python=3.8
- Activate the environment:
conda activate one-click-vits
- Clone this repository to your local machine:
git clone https://github.com/ORI-Muchim/One-Click-VITS-Training.git
- Navigate to the cloned directory:
cd One-Click-VITS-Training
- Install the necessary dependencies:
pip install -r requirements.txt
Place the audio files as follows.
.mp3 or .wav files are okay.
One-Click-VITS-Training
├────datasets
│ ├───speaker0
│ │ ├────1.mp3
│ │ └────1.wav
│ └───speaker1
│ │ ├───1.mp3
│ │ └───1.wav
│ ├integral.py
│ └integral_low.py
│
├────vits
├────inference.py
├────main_low.py
├────main_resume.py
├────main.py
├────Readme.md
└────requirements.txt
This is just an example, and it's okay to add more speakers.
To start this tool, use the following command, replacing {language}, {model_name}, and {sample_rate} with your respective values({language: ko, ja, en, zh} / {sample_rate: 22050 / 44100}):
python main.py {language} {model_name} {sample_rate}
For those with low specifications(VRAM < 12GB), please use this code:
python main_low.py {language} {model_name} {sample_rate}
If the data configuration is complete and you want to resume training, enter this code:
python main_resume.py {model_name}
After the model has been trained, you can generate predictions by using the following command, replacing {model_name} and {model_step} with your respective values:
python inference.py {model_name} {model_step}
Or check ./vits/inference.ipynb.
If you want to change the example text used in the reference, modify ./vits/inferencems.py
text
part.
In the repository of CjangCjengh/vits, I made some modifications to the Korean text cleaning method. The other cleaning process is the same by posting it to the CjangCjengh repository, but the cleaner file was modified using the tenebo/g2pk2 library as Korean pronounced.
For more information, please refer to the following repositories: