한국어 버전은 여기로 → README-ko.md

How to use

Clone this repository

git clone https://github.com/ouor/vits.git

Choose cleaners

• Fill "text_cleaners" in config.json
• Initialy "text_cleaners" is set to 'korean_cleaners'. To use alternative cleaners, revise with following step.
• Edit text/symbols.py
• Remove unnecessary imports from text/cleaners.py

Create virtual environment

python -m venv .venv
.\.venv\Scripts\activate

Install pytorch

pip3 install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 --index-url https://download.pytorch.org/whl/cu117

Install requirements

pip install -r requirements.txt

If error occurs while install requirements, Install visual studio build tools and try again.

Build monotonic alignment search

cd monotonic_align
mkdir monotonic_align
python setup.py build_ext --inplace
cd ..

Create datasets

Single speaker

"n_speakers" should be 0 in config.json

path/to/XXX.wav|transcript

• Example

dataset/001.wav|こんにちは。

Mutiple speakers

Speaker id should start from 0

path/to/XXX.wav|speaker id|transcript

• Example

dataset/001.wav|0|こんにちは。

Preprocess

If you need random pick from full filelist..

python random_pick.py --filelist path/to/filelist.txt

# Single speaker
python preprocess.py --text_index 1 --filelists path/to/filelist_train.txt path/to/filelist_val.txt --text_cleaners 'korean_cleaners'

# Mutiple speakers
python preprocess.py --text_index 2 --filelists path/to/filelist_train.txt path/to/filelist_val.txt --text_cleaners 'korean_cleaners'

If you have done this, set "cleaned_text" to true in config.json

Small Tips

• recommand to use pretrained model (you can get pretrained model from huggingface.co)
• If your vram is not enough (less than 40GB)
• do not train with 44100Hz. 22050Hz is good enough.
• make each dataset audio length short. (recommand to use maximum 4 seconds per audio)

Train

# Single speaker
python train.py -c <config> -m <folder>

# Mutiple speakers
python train_ms.py -c <config> -m <folder>

If you want to train from pretrained model, Place 'G_0.pth' and 'D_0.pth' in destination folder before enter train command.

Tensorboard

tensorboard --logdir checkpoints/<folder> --port 6006

Inference

Jupyter notebook

infer.ipynb

Gradio web app

python server.py --config_path path/to/config.json --model_path path/to/model.pth

Running in Docker

docker run -itd --gpus all --name "Container name" -e NVIDIA_DRIVER_CAPABILITIES=compute,utility -e NVIDIA_VISIBLE_DEVICES=all "Image name"