98 lines
4.0 KiB
Markdown
98 lines
4.0 KiB
Markdown
# CLI
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## 0. Install and global paths settings
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```bash
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git clone https://github.com/litagin02/Style-Bert-VITS2.git
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cd Style-Bert-VITS2
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python -m venv venv
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venv\Scripts\activate
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pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118
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pip install -r requirements.txt
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```
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Then download the necessary models and the default TTS model, and set the global paths.
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```bash
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python initialize.py [--skip_jvnv] [--dataset_root <path>] [--assets_root <path>]
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```
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Optional:
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- `--skip_jvnv`: Skip downloading the default JVNV voice models (use this if you only have to train your own models).
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- `--dataset_root`: Default: `Data`. Root directory of the training dataset. The training dataset of `{model_name}` should be placed in `{dataset_root}/{model_name}`.
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- `--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}`.
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## 1. Dataset preparation
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### 1.1. Slice wavs
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```bash
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python slice.py --model_name <model_name> [-i <input_dir>] [-m <min_sec>] [-M <max_sec>]
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```
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Required:
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- `model_name`: Name of the speaker (to be used as the name of the trained model).
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Optional:
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- `input_dir`: Path to the directory containing the audio files to slice (default: `inputs`)
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- `min_sec`: Minimum duration of the sliced audio files in seconds (default: 2).
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- `max_sec`: Maximum duration of the sliced audio files in seconds (default: 12).
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### 1.2. Transcribe wavs
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```bash
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python transcribe.py --model_name <model_name>
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```
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Required:
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- `model_name`: Name of the speaker (to be used as the name of the trained model).
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Optional
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- `--initial_prompt`: Initial prompt to use for the transcription (default value is specific to Japanese).
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- `--device`: `cuda` or `cpu` (default: `cuda`).
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- `--language`: `jp`, `en`, or `en` (default: `jp`).
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- `--model`: Whisper model, default: `large-v3`
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- `--compute_type`: default: `bfloat16`
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## 2. Preprocess
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```bash
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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] [--freeze_decoder] [--yomi_error <yomi_error>]
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```
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Required:
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- `model_name`: Name of the speaker (to be used as the name of the trained model).
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Optional:
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- `--batch_size`, `-b`: Batch size (default: 2).
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- `--epochs`, `-e`: Number of epochs (default: 100).
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- `--save_every_steps`, `-s`: Save every steps (default: 1000).
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- `--num_processes`: Number of processes (default: half of the number of CPU cores).
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- `--normalize`: Loudness normalize audio.
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- `--trim`: Trim silence.
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- `--freeze_EN_bert`: Freeze English BERT.
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- `--freeze_JP_bert`: Freeze Japanese BERT.
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- `--freeze_ZH_bert`: Freeze Chinese BERT.
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- `--freeze_style`: Freeze style vector.
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- `--freeze_decoder`: Freeze decoder.
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- `--use_jp_extra`: Use JP-Extra model.
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- `--val_per_lang`: Validation data per language (default: 0).
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- `--log_interval`: Log interval (default: 200).
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- `--yomi_error`: How to handle yomi errors (default: `raise`: raise an error after preprocessing all texts, `skip`: skip the texts with errors, `use`: use the texts with errors by ignoring unknown characters).
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## 3. Train
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Training settings are automatically loaded from the above process.
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If NOT using JP-Extra model:
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```bash
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python train_ms.py [--repo_id <username>/<repo_name>]
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```
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If using JP-Extra model:
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```bash
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python train_ms_jp_extra.py [--repo_id <username>/<repo_name>] [--skip_default_style]
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```
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Optional:
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- `--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.
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- `--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).
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