Docs: CLI doc and paperspace guide
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litagin02
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docs/CLI.md
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docs/CLI.md
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# CLI
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**WIP**
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## Dataset
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`Dataset.bat` webui (`python webui_dataset.py`) consists of **slice audio** and **transcribe wavs**.
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### Slice audio
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## 0. Install and global paths settings
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```bash
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python slice.py -i <input_dir> -o <output_dir> -m <min_sec> -M <max_sec>
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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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Required:
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- `input_dir`: Path to the directory containing the audio files to slice.
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- `output_dir`: Path to the directory where the sliced audio files will be saved.
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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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- `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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- `--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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### Transcribe wavs
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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 transcribe.py -i <input_dir> -o <output_file> --speaker_name <speaker_name>
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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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- `input_dir`: Path to the directory containing the audio files to transcribe.
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- `output_file`: Path to the file where the transcriptions will be saved.
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- `speaker_name`: Name of the speaker.
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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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@@ -38,19 +52,43 @@ Optional
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- `--model`: Whisper model, default: `large-v3`
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- `--compute_type`: default: `bfloat16`
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## Train
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## 2. Preprocess
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`Train.bat` webui (`python webui_train.py`) consists of the following.
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### Preprocess audio
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```bash
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python resample.py -i <input_dir> -o <output_dir> [--normalize] [--trim]
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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]
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```
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Required:
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- `input_dir`: Path to the directory containing the audio files to preprocess.
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- `output_dir`: Path to the directory where the preprocessed audio files will be saved.
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- `model_name`: Name of the speaker (to be used as the name of the trained model).
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TO BE WRITTEN (WIP)
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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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- `--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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これいる？
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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>]
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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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72
docs/paperspace.md
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# Paperspace gradient で学習する
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詳しいコマンドの叩き方は[こちら](CLI.md)を参照してください。
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## 事前準備
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- Paperspace のアカウントを作成し必要なら課金する
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- Projectを作る
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- NotebookはStart from Scratchを選択して空いてるGPUマシンを選ぶ
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## 使い方
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以下では次のような方針でやっています。
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- `/storage/`は永続ストレージなので、事前学習モデルとかを含めてリポジトリをクローンするとよい。
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- `/notebooks/`は一時ストレージなので、データセットやその結果を保存する。
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- hugging faceアカウントを作り、（プライベートな）リポジトリを作って、学習元データを置いたり、学習結果を随時アップロードする。
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### 1. 環境を作る
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まずは永続ストレージにgit clone
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```bash
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mkdir -p /storage/sbv2
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cd /storage/sbv2
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git clone https://github.com/litagin02/Style-Bert-VITS2.git
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```
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環境構築（デフォルトはPyTorch 1.x系、Python 3.9の模様）
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```bash
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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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事前学習済みモデル等のダウンロード、またパスを`/notebooks/`以下のものに設定
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```bash
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python initialize.py --skip_jvnv --dataset_root /notebooks/Data --assets_root /notebooks/model_assets
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```
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### 2. データセットの準備
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以下では`username/voices`というデータセットリポジトリにある`Foo.zip`というデータセットを使うことを想定しています。
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```bash
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cd /nodtebooks
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huggingface-cli login # 事前にトークンが必要
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huggingface-cli download username/voices Foo.zip --repo-type dataset --local-dir .
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```
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- zipファイル中身が既に`raw`と`esd.list`があるデータ（スライス・書き起こし済み）の場合
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```bash
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mkdir -p Data/Foo
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unzip Foo.zip -d Data/Foo
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rm Foo.zip
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cd /storage/sbv2/Style-Bert-VITS2
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```
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- zipファイルが音声ファイルのみの場合
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```bash
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mkdir inputs
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unzip Foo.zip -d inputs
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cd /storage/sbv2/Style-Bert-VITS2
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python slice.py --model_name Foo -i /notebooks/inputs
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python transcribe.py --model_name Foo
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```
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それが終わったら、以下のコマンドで一括前処理を行う（パラメータは各自お好み、バッチサイズ6でVRAM 16GBギリくらい）。
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```bash
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python preprocess_all.py --model_name Foo -b 6 -e 300 --use_jp_extra
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```
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### 3. 学習
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Hugging faceの`username/sbv2-private`というモデルリポジトリに学習済みモデルをアップロードすることを想定しています。事前に`huggingface-cli login`でログインしておくこと。
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```bash
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python train_ms_jp_extra.py --repo_id username/sbv2-private
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```
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(JP-Extraでない場合は`train_ms.py`を使う)
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