tuna2134/sbv2-v2
1
0
Fork 0
Code Issues Pull Requests 1 Actions Packages Projects Releases Wiki Activity

Docs: CLI doc and paperspace guide

Browse Source
This commit is contained in:
litagin02
2024-02-19 20:15:20 +09:00
parent 6ee8125dff
commit 442ad6ac27
10 changed files with 146 additions and 164 deletions
Download Patch File Download Diff File Expand all files Collapse all files

92
docs/CLI.md
View File

@@ -1,35 +1,49 @@
# CLI
**WIP**
## Dataset
`Dataset.bat` webui (`python webui_dataset.py`) consists of **slice audio** and **transcribe wavs**.
### Slice audio
## 0. Install and global paths settings
```bash
python slice.py -i <input_dir> -o <output_dir> -m <min_sec> -M <max_sec>
git clone https://github.com/litagin02/Style-Bert-VITS2.git
cd Style-Bert-VITS2
python -m venv venv
venv\Scripts\activate
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
```
Required:
- `input_dir`: Path to the directory containing the audio files to slice.
- `output_dir`: Path to the directory where the sliced audio files will be saved.
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>]
```
Optional:
- `min_sec`: Minimum duration of the sliced audio files in seconds (default 2).
- `max_sec`: Maximum duration of the sliced audio files in seconds (default 12).
- `--skip_jvnv`: Skip downloading the default JVNV 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}`.
### Transcribe wavs
## 1. Dataset preparation
### 1.1. Slice wavs
```bash
python transcribe.py -i <input_dir> -o <output_file> --speaker_name <speaker_name>
python slice.py --model_name <model_name> [-i <input_dir>] [-m <min_sec>] [-M <max_sec>]
```
Required:
- `input_dir`: Path to the directory containing the audio files to transcribe.
- `output_file`: Path to the file where the transcriptions will be saved.
- `speaker_name`: Name of the speaker.
- `model_name`: Name of the speaker (to be used as the name of the trained model).
Optional:
- `input_dir`: Path to the directory containing the audio files to slice (default: `inputs`)
- `min_sec`: Minimum duration of the sliced audio files in seconds (default: 2).
- `max_sec`: Maximum duration of the sliced audio files in seconds (default: 12).
### 1.2. Transcribe wavs
```bash
python transcribe.py --model_name <model_name>
```
Required:
- `model_name`: Name of the speaker (to be used as the name of the trained model).
Optional
- `--initial_prompt`: Initial prompt to use for the transcription (default value is specific to Japanese).
@@ -38,19 +52,43 @@ Optional
- `--model`: Whisper model, default: `large-v3`
- `--compute_type`: default: `bfloat16`
## Train
## 2. Preprocess
`Train.bat` webui (`python webui_train.py`) consists of the following.
### Preprocess audio
```bash
python resample.py -i <input_dir> -o <output_dir> [--normalize] [--trim]
python preprocess_all.py -m <model_name> [--use_jp_extra] [-b <batch_size>] [-e <epochs>] [-s <save_every_steps>] [--num_processes <num_processes>] [--normalize] [--trim] [--val_per_lang <val_per_lang>] [--log_interval <log_interval>] [--freeze_EN_bert] [--freeze_JP_bert] [--freeze_ZH_bert] [--freeze_style]
```
Required:
- `input_dir`: Path to the directory containing the audio files to preprocess.
- `output_dir`: Path to the directory where the preprocessed audio files will be saved.
- `model_name`: Name of the speaker (to be used as the name of the trained model).
TO BE WRITTEN (WIP)
Optional:
- `--batch_size`, `-b`: Batch size (default: 2).
- `--epochs`, `-e`: Number of epochs (default: 100).
- `--save_every_steps`, `-s`: Save every steps (default: 1000).
- `--num_processes`: Number of processes (default: half of the number of CPU cores).
- `--normalize`: Loudness normalize audio.
- `--trim`: Trim silence.
- `--freeze_EN_bert`: Freeze English BERT.
- `--freeze_JP_bert`: Freeze Japanese BERT.
- `--freeze_ZH_bert`: Freeze Chinese BERT.
- `--freeze_style`: Freeze style vector.
- `--use_jp_extra`: Use JP-Extra model.
- `--val_per_lang`: Validation data per language (default: 0).
- `--log_interval`: Log interval (default: 200).
これいる？
## 3. Train
Training settings are automatically loaded from the above process.
If NOT using JP-Extra model:
```bash
python train_ms.py [--repo_id <username>/<repo_name>]
```
If using JP-Extra model:
```bash
python train_ms_jp_extra.py [--repo_id <username>/<repo_name>]
```
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.