docs/CLI.md

CLI

0. Install and global paths settings

git clone https://github.com/litagin02/Style-Bert-VITS2.git
cd Style-Bert-VITS2
python -m venv venv
venv\Scripts\activate
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.

python initialize.py [--skip_default_models] [--dataset_root <path>] [--assets_root <path>]

Optional:

• --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}.

1. Dataset preparation

1.1. Slice audio files

The following audio formats are supported: ".wav", ".flac", ".mp3", ".ogg", ".opus", ".m4a".

python slice.py --model_name <model_name> [-i <input_dir>] [-m <min_sec>] [-M <max_sec>] [--time_suffix]

Required:

• 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).
• --time_suffix: Make the filename end with -start_ms-end_ms when saving wav.

1.2. Transcribe audio files

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).
• --device: cuda or cpu (default: cuda).
• --language: jp, en, or en (default: jp).
• --model: Whisper model, default: large-v3
• --compute_type: default: bfloat16. Only used if not --use_hf_whisper.
• --use_hf_whisper: Use Hugging Face's whisper model instead of default faster-whisper (HF whisper is faster but requires more VRAM).
• --batch_size: Batch size (default: 16). Only used if --use_hf_whisper.
• --num_beams: Beam size (default: 1).
• --no_repeat_ngram_size: N-gram size for no repeat (default: 10).

2. Preprocess

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>]

Required:

• model_name: Name of the speaker (to be used as the name of the trained model).

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.
• --freeze_decoder: Freeze decoder.
• --use_jp_extra: Use JP-Extra model.
• --val_per_lang: Validation data per language (default: 0).
• --log_interval: Log interval (default: 200).
• --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).

3. Train

Training settings are automatically loaded from the above process.

If NOT using JP-Extra model:

python train_ms.py [--repo_id <username>/<repo_name>]

If using JP-Extra model:

python train_ms_jp_extra.py [--repo_id <username>/<repo_name>] [--skip_default_style]

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 has been already made).