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litagin02
2024-06-01 20:08:31 +09:00
parent a271b8a41c
commit 31b709eef6
5 changed files with 26 additions and 147 deletions
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docs/CLI.md
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@@ -7,17 +7,17 @@ git clone https://github.com/litagin02/Style-Bert-VITS2.git
cd Style-Bert-VITS2
python -m venv venv
venv\Scripts\activate
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
```
Then download the necessary models and the default TTS model, and set the global paths.
```bash
python initialize.py [--skip_jvnv] [--dataset_root <path>] [--assets_root <path>]
python initialize.py [--skip_default_models] [--dataset_root <path>] [--assets_root <path>]
```
Optional:
- `--skip_jvnv`: Skip downloading the default JVNV voice models (use this if you only have to train your own models).
- `--skip_default_models`: Skip downloading the default voice models (use this if you only have to train your own models).
- `--dataset_root`: Default: `Data`. Root directory of the training dataset. The training dataset of `{model_name}` should be placed in `{dataset_root}/{model_name}`.
- `--assets_root`: Default: `model_assets`. Root directory of the model assets (for inference). In training, the model assets will be saved to `{assets_root}/{model_name}`, and in inference, we load all the models from `{assets_root}`.
@@ -26,7 +26,7 @@ Optional:
### 1.1. Slice audio files
The following audio formats are supported: ".wav", ".flac", ".mp3", ".ogg", ".opus".
The following audio formats are supported: ".wav", ".flac", ".mp3", ".ogg", ".opus", ".m4a".
```bash
python slice.py --model_name <model_name> [-i <input_dir>] [-m <min_sec>] [-M <max_sec>] [--time_suffix]
```
@@ -101,4 +101,4 @@ python train_ms_jp_extra.py [--repo_id <username>/<repo_name>] [--skip_default_s
Optional:
- `--repo_id`: Hugging Face repository ID to upload the trained model to. You should have logged in using `huggingface-cli login` before running this command.
- `--skip_default_style`: Skip making the default style vector. Use this if you want to resume training (since the default style vector is already made).
- `--skip_default_style`: Skip making the default style vector. Use this if you want to resume training (since the default style vector has been already made).