Docs: CLI doc and paperspace guide
This commit is contained in:
litagin02
127
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127
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# For paperspace gradient
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# Based on https://github.com/gradient-ai/base-container
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# Style-Bert-VITS2 are NOT included in this image, only for environment setup
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# ==================================================================
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# Details
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# ------------------------------------------------------------------
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# Ubuntu 22.04, Python 3.10
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# CUDA Toolkit 12.1, CUDNN 8.9.7
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# PyTorch 2.1.2 (cuda 12.1)
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# Jupyter Lab
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# Huggingface CLI
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# Other Python packages in requirements.txt
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# ==================================================================
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# Initial setup
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# ------------------------------------------------------------------
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# Ubuntu 22.04 as base image
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FROM ubuntu:22.04
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# RUN yes| unminimize
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RUN sed -i 's@archive.ubuntu.com@ftp.jaist.ac.jp/pub/Linux@g' /etc/apt/sources.list
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# Set ENV variables
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ENV LANG C.UTF-8
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ENV SHELL=/bin/bash
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ENV DEBIAN_FRONTEND=noninteractive
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ENV APT_INSTALL="apt-get install -y --no-install-recommends"
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ENV PIP_INSTALL="python3 -m pip --no-cache-dir install --upgrade"
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ENV GIT_CLONE="git clone --depth 10"
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# ==================================================================
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# Tools
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# ------------------------------------------------------------------
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RUN apt-get update && \
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$APT_INSTALL \
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build-essential \
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ca-certificates \
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wget \
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git \
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curl \
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unzip \
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zip \
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nano \
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ffmpeg \
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sudo \
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software-properties-common \
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gnupg \
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python3 \
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python3-pip \
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python3-dev
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# ==================================================================
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# Git-lfs
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# ------------------------------------------------------------------
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RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash && \
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$APT_INSTALL git-lfs
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# Add symlink so python and python3 commands use same python3.9 executable
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RUN ln -s /usr/bin/python3 /usr/local/bin/python
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# ==================================================================
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# Installing CUDA packages (CUDA Toolkit 12.1 and CUDNN 8.9.7)
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# ------------------------------------------------------------------
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RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin && \
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mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \
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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 && \
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dpkg -i cuda-repo-ubuntu2204-12-1-local_12.1.0-530.30.02-1_amd64.deb && \
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cp /var/cuda-repo-ubuntu2204-12-1-local/cuda-*-keyring.gpg /usr/share/keyrings/ && \
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apt-get update && \
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$APT_INSTALL cuda && \
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rm cuda-repo-ubuntu2204-12-1-local_12.1.0-530.30.02-1_amd64.deb
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# Installing CUDNN
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RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub && \
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add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /" && \
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apt-get update && \
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$APT_INSTALL libcudnn8=8.9.7.29-1+cuda12.2 \
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libcudnn8-dev=8.9.7.29-1+cuda12.2
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ENV PATH=$PATH:/usr/local/cuda/bin
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ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
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# ==================================================================
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# PyTorch
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# ------------------------------------------------------------------
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# Based on https://pytorch.org/get-started/locally/
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RUN $PIP_INSTALL torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121
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# ==================================================================
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# Jupyter Lab
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# ------------------------------------------------------------------
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RUN $PIP_INSTALL jupyterlab
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# ==================================================================
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# huggingface_cli
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# ------------------------------------------------------------------
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RUN $PIP_INSTALL "huggingface_hub[cli]"
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# ==================================================================
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# Other Python packages
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# ------------------------------------------------------------------
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COPY requirements.txt /tmp/requirements.txt
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RUN $PIP_INSTALL -r /tmp/requirements.txt && \
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rm /tmp/requirements.txt
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# ==================================================================
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# Startup
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# ------------------------------------------------------------------
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EXPOSE 8888 6006
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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
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@@ -90,6 +90,9 @@ model_assets
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### 学習
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- CLIでの学習の詳細は[こちら](docs/CLI.md)を参照してください。
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- 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。
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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。
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- 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。
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@@ -4,7 +4,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Style-Bert-VITS2 (ver 2.2) のGoogle Colabでの学習\n",
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"# Style-Bert-VITS2 (ver 2.3) のGoogle Colabでの学習\n",
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"\n",
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"Google Colab上でStyle-Bert-VITS2の学習を行うことができます。\n",
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"\n",
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@@ -4,7 +4,7 @@ import enum
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# See https://huggingface.co/spaces/gradio/theme-gallery for more themes
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GRADIO_THEME: str = "NoCrypt/miku"
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LATEST_VERSION: str = "2.2"
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LATEST_VERSION: str = "2.3"
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DEFAULT_STYLE: str = "Neutral"
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DEFAULT_STYLE_WEIGHT: float = 5.0
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@@ -67,5 +67,5 @@
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"use_spectral_norm": false,
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"gin_channels": 256
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},
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"version": "2.2"
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"version": "2.3"
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}
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@@ -74,5 +74,5 @@
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"initial_channel": 64
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}
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},
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"version": "2.2-JP-Extra"
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"version": "2.3-JP-Extra"
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}
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92
docs/CLI.md
92
docs/CLI.md
@@ -1,35 +1,49 @@
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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
Normal file
72
docs/paperspace.md
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@@ -0,0 +1,72 @@
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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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@@ -69,11 +69,6 @@ if __name__ == "__main__":
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help="Log interval",
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default=200,
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)
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parser.add_argument(
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"--skip_invalid",
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action="store_true",
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help="Skip invalid",
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)
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args = parser.parse_args()
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@@ -17,7 +17,8 @@ numpy
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psutil
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pyannote.audio>=3.1.0
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pyloudnorm
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pyopenjtalk-prebuilt
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||||
# pyopenjtalk-prebuilt # Should be manually uninstalled
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||||
pyopenjtalk-dict
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||||
pypinyin
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||||
pyworld
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||||
PyYAML
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||||
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||||
Reference in New Issue
Block a user