From 442ad6ac276ec2622334b8b490a5604211619517 Mon Sep 17 00:00:00 2001 From: litagin02 Date: Mon, 19 Feb 2024 20:15:20 +0900 Subject: [PATCH] Docs: CLI doc and paperspace guide --- Dockerfile | 127 ---------------------------------- README.md | 3 + colab.ipynb | 2 +- common/constants.py | 2 +- configs/config.json | 2 +- configs/configs_jp_extra.json | 2 +- docs/CLI.md | 92 ++++++++++++++++-------- docs/paperspace.md | 72 +++++++++++++++++++ preprocess_all.py | 5 -- requirements.txt | 3 +- 10 files changed, 146 insertions(+), 164 deletions(-) delete mode 100644 Dockerfile create mode 100644 docs/paperspace.md diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 863425d..0000000 --- a/Dockerfile +++ /dev/null @@ -1,127 +0,0 @@ -# For paperspace gradient -# Based on https://github.com/gradient-ai/base-container -# Style-Bert-VITS2 are NOT included in this image, only for environment setup - -# ================================================================== -# Details -# ------------------------------------------------------------------ -# Ubuntu 22.04, Python 3.10 -# CUDA Toolkit 12.1, CUDNN 8.9.7 -# PyTorch 2.1.2 (cuda 12.1) -# Jupyter Lab -# Huggingface CLI -# Other Python packages in requirements.txt - -# ================================================================== -# Initial setup -# ------------------------------------------------------------------ - -# Ubuntu 22.04 as base image -FROM ubuntu:22.04 -# RUN yes| unminimize - -RUN sed -i 's@archive.ubuntu.com@ftp.jaist.ac.jp/pub/Linux@g' /etc/apt/sources.list - -# Set ENV variables -ENV LANG C.UTF-8 -ENV SHELL=/bin/bash -ENV DEBIAN_FRONTEND=noninteractive - -ENV APT_INSTALL="apt-get install -y --no-install-recommends" -ENV PIP_INSTALL="python3 -m pip --no-cache-dir install --upgrade" -ENV GIT_CLONE="git clone --depth 10" - - -# ================================================================== -# Tools -# ------------------------------------------------------------------ - -RUN apt-get update && \ - $APT_INSTALL \ - build-essential \ - ca-certificates \ - wget \ - git \ - curl \ - unzip \ - zip \ - nano \ - ffmpeg \ - sudo \ - software-properties-common \ - gnupg \ - python3 \ - python3-pip \ - python3-dev - -# ================================================================== -# Git-lfs -# ------------------------------------------------------------------ - -RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash && \ - $APT_INSTALL git-lfs - -# Add symlink so python and python3 commands use same python3.9 executable -RUN ln -s /usr/bin/python3 /usr/local/bin/python - - -# ================================================================== -# Installing CUDA packages (CUDA Toolkit 12.1 and CUDNN 8.9.7) -# ------------------------------------------------------------------ -RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin && \ - mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \ - wget https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda-repo-ubuntu2204-12-1-local_12.1.0-530.30.02-1_amd64.deb && \ - dpkg -i cuda-repo-ubuntu2204-12-1-local_12.1.0-530.30.02-1_amd64.deb && \ - cp /var/cuda-repo-ubuntu2204-12-1-local/cuda-*-keyring.gpg /usr/share/keyrings/ && \ - apt-get update && \ - $APT_INSTALL cuda && \ - rm cuda-repo-ubuntu2204-12-1-local_12.1.0-530.30.02-1_amd64.deb - -# Installing CUDNN -RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub && \ - add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /" && \ - apt-get update && \ - $APT_INSTALL libcudnn8=8.9.7.29-1+cuda12.2 \ - libcudnn8-dev=8.9.7.29-1+cuda12.2 - - -ENV PATH=$PATH:/usr/local/cuda/bin -ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH - - -# ================================================================== -# PyTorch -# ------------------------------------------------------------------ - -# Based on https://pytorch.org/get-started/locally/ - -RUN $PIP_INSTALL torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121 - -# ================================================================== -# Jupyter Lab -# ------------------------------------------------------------------ - -RUN $PIP_INSTALL jupyterlab - -# ================================================================== -# huggingface_cli -# ------------------------------------------------------------------ - -RUN $PIP_INSTALL "huggingface_hub[cli]" - -# ================================================================== -# Other Python packages -# ------------------------------------------------------------------ - -COPY requirements.txt /tmp/requirements.txt - -RUN $PIP_INSTALL -r /tmp/requirements.txt && \ - rm /tmp/requirements.txt - -# ================================================================== -# Startup -# ------------------------------------------------------------------ - -EXPOSE 8888 6006 - -CMD jupyter lab --allow-root --ip=0.0.0.0 --no-browser --ServerApp.trust_xheaders=True --ServerApp.disable_check_xsrf=False --ServerApp.allow_remote_access=True --ServerApp.allow_origin='*' --ServerApp.allow_credentials=True \ No newline at end of file diff --git a/README.md b/README.md index bf2a05f..edd5ba5 100644 --- a/README.md +++ b/README.md @@ -90,6 +90,9 @@ model_assets ### 学習 +- CLIでの学習の詳細は[こちら](docs/CLI.md)を参照してください。 +- paperspace上での学習の詳細は[こちら](docs/paperspace.md)を参照してください。 + 学習には2-14秒程度の音声ファイルが複数と、それらの書き起こしデータが必要です。 - 既存コーパスなどですでに分割された音声ファイルと書き起こしデータがある場合はそのまま（必要に応じて書き起こしファイルを修正して）使えます。下の「学習WebUI」を参照してください。 diff --git a/colab.ipynb b/colab.ipynb index f5cab3e..1a5369a 100644 --- a/colab.ipynb +++ b/colab.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Style-Bert-VITS2 (ver 2.2) のGoogle Colabでの学習\n", + "# Style-Bert-VITS2 (ver 2.3) のGoogle Colabでの学習\n", "\n", "Google Colab上でStyle-Bert-VITS2の学習を行うことができます。\n", "\n", diff --git a/common/constants.py b/common/constants.py index d2d6352..cd118e2 100644 --- a/common/constants.py +++ b/common/constants.py @@ -4,7 +4,7 @@ import enum # See https://huggingface.co/spaces/gradio/theme-gallery for more themes GRADIO_THEME: str = "NoCrypt/miku" -LATEST_VERSION: str = "2.2" +LATEST_VERSION: str = "2.3" DEFAULT_STYLE: str = "Neutral" DEFAULT_STYLE_WEIGHT: float = 5.0 diff --git a/configs/config.json b/configs/config.json index 6aa647c..5196633 100644 --- a/configs/config.json +++ b/configs/config.json @@ -67,5 +67,5 @@ "use_spectral_norm": false, "gin_channels": 256 }, - "version": "2.2" + "version": "2.3" } diff --git a/configs/configs_jp_extra.json b/configs/configs_jp_extra.json index 7e2698e..490f4d6 100644 --- a/configs/configs_jp_extra.json +++ b/configs/configs_jp_extra.json @@ -74,5 +74,5 @@ "initial_channel": 64 } }, - "version": "2.2-JP-Extra" + "version": "2.3-JP-Extra" } diff --git a/docs/CLI.md b/docs/CLI.md index ca955e1..3873475 100644 --- a/docs/CLI.md +++ b/docs/CLI.md @@ -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 -o -m -M +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 ] [--assets_root ] +``` 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 -o --speaker_name +python slice.py --model_name [-i ] [-m ] [-M ] ``` 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 +``` +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 -o [--normalize] [--trim] +python preprocess_all.py -m [--use_jp_extra] [-b ] [-e ] [-s ] [--num_processes ] [--normalize] [--trim] [--val_per_lang ] [--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 /] +``` + +If using JP-Extra model: +```bash +python train_ms_jp_extra.py [--repo_id /] +``` + +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. diff --git a/docs/paperspace.md b/docs/paperspace.md new file mode 100644 index 0000000..d67253e --- /dev/null +++ b/docs/paperspace.md @@ -0,0 +1,72 @@ +# Paperspace gradient で学習する + +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。 + +## 事前準備 +- Paperspace のアカウントを作成し必要なら課金する +- Projectを作る +- NotebookはStart from Scratchを選択して空いてるGPUマシンを選ぶ + +## 使い方 + +以下では次のような方針でやっています。 + +- `/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。 +- `/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。 +- 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。 + +### 1. 環境を作る + +まずは永続ストレージにgit clone +```bash +mkdir -p /storage/sbv2 +cd /storage/sbv2 +git clone https://github.com/litagin02/Style-Bert-VITS2.git +``` +環境構築（デフォルトはPyTorch 1.x系、Python 3.9の模様） +```bash +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 +``` +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 +```bash +python initialize.py --skip_jvnv --dataset_root /notebooks/Data --assets_root /notebooks/model_assets +``` + +### 2. データセットの準備 +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。 +```bash +cd /nodtebooks +huggingface-cli login # 事前にトークンが必要 +huggingface-cli download username/voices Foo.zip --repo-type dataset --local-dir . +``` + +- 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 +```bash +mkdir -p Data/Foo +unzip Foo.zip -d Data/Foo +rm Foo.zip +cd /storage/sbv2/Style-Bert-VITS2 +``` + +- zipファイルが音声ファイルのみの場合 +```bash +mkdir inputs +unzip Foo.zip -d inputs +cd /storage/sbv2/Style-Bert-VITS2 +python slice.py --model_name Foo -i /notebooks/inputs +python transcribe.py --model_name Foo +``` + +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）。 +```bash +python preprocess_all.py --model_name Foo -b 6 -e 300 --use_jp_extra +``` + +### 3. 学習 + +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。 +```bash +python train_ms_jp_extra.py --repo_id username/sbv2-private +``` +(JP-Extraでない場合は`train_ms.py`を使う) diff --git a/preprocess_all.py b/preprocess_all.py index 8ff4e34..954af0b 100644 --- a/preprocess_all.py +++ b/preprocess_all.py @@ -69,11 +69,6 @@ if __name__ == "__main__": help="Log interval", default=200, ) - parser.add_argument( - "--skip_invalid", - action="store_true", - help="Skip invalid", - ) args = parser.parse_args() diff --git a/requirements.txt b/requirements.txt index 62b4d60..f5325d5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,7 +17,8 @@ numpy psutil pyannote.audio>=3.1.0 pyloudnorm -pyopenjtalk-prebuilt +# pyopenjtalk-prebuilt # Should be manually uninstalled +pyopenjtalk-dict pypinyin pyworld PyYAML