Beatrice V2 Training Webui

This webui is designed to train models for Beatrice v2 which is compatible with w-okada's realtime voice changing client v2: https://github.com/w-okada/voice-changer

The code to train beatrice models is adapted from: https://huggingface.co/fierce-cats/beatrice-trainer

The latest version of w-okada is 2.0.61-alpha as of writing this readme

Requirements

• Nvidia GPU (any 3rd to 4th gen should be sufficient)
• The Ultimate Vocal Remover - https://github.com/Anjok07/ultimatevocalremovergui

Installation

As with a majority of my packages/repos, official support will be for Windows only. Linux shouldn't have much of an issue, just some pathing changes may be necessary. Pull request are accepted, though, I won't be able to actively maintain any Linux additions.

Windows Package

Will be available for Youtube Channel Members at the Supporter (Package) level: https://www.youtube.com/channel/UCwNdsF7ZXOlrTKhSoGJPnlQ/join

1. After downloading the zip file, unzip it.
2. Launch the webui with launch_webui.bat

Windows Manual Installation

Prerequisites

• Python 3.11 - https://www.python.org/downloads/release/python-3119/
• git - https://git-scm.com/downloads

1. Clone the repository

   git clone https://github.com/JarodMica/beatrice_trainer_webui.git

2. Navigate into the repo

   cd .\beatrice_trainer_webui\

3. Setup a virtual environment, specifying python 3.11

   py -3.11 -m venv venv

4. Activate venv. If you've never run venv before on windows powershell, you will need to change ExecutionPolicy to RemoteSigned: https://learn.microsoft.com/en-us/answers/questions/506985/powershell-execution-setting-is-overridden-by-a-po

   .\venv\Scripts\activate

5. Run the requirements.txt

   pip install -r .\requirements.txt

6. Uninstall and reinstall torch manually. Other packages will install torch without cuda, to enable cuda, you need the prebuilt wheels.

   torch 2.4.0 causes issues with CTranslate2 (causes issue with whisperx) so make sure you do this step

   pip uninstall torch
   pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121

7. Initialized submodules

   git submodule init
   git submodule update --remote

8. Install submodules into venv

   pip install .\modules\beatrice_trainer\
   pip install .\modules\gradio_utils\

9. Grab the assets from the original beatrice HuggingFace repo at this hash here: https://huggingface.co/fierce-cats/beatrice-trainer/tree/be628e89d162d0d1aa038f57f19e1f578b7e6328

   The easiest way is to clone the repo, checkout at that specific hash, then copy and paste assets into the root folder of the beatrice trainer webui

   git clone https://huggingface.co/fierce-cats/beatrice-trainer.git
   cd beatrice-trainer
   git checkout be628e89d162d0d1aa038f57f19e1f578b7e6328
   cd ..

   The folder structure should look like this:

   beatrice_trainer_webui\assets

10. Run the webui

    python webui.py

11. (Optional) Make a .bat file to automatically run the webui.py each time without having to activate venv each time. How to: https://www.windowscentral.com/how-create-and-run-batch-file-windows-10

    call venv\Scripts\activate
    python webui.py

Usage

There are 3 tabs: Create Dataset, Train, and Settings.

Create Dataset

This tab is where you create your dataset. Follow the steps below to get a feel for doing this.

1. Obtain audio data to train on the speaker
   • This can be a podcast, audiobook, youtube video, etc. Basically, anything that has audio (even songs, but not recommended)
   • One large audio file is recommended, but several smaller files can be used too.
2. Navigate into the WebUI datasets folder in the file explorer. Create a new folder in here and name it whatever you want the final model to be named. Open this now empty folder.
3. Decide how many speakers you want inside of this beatrice model (as beatrice can be multispeaker) and then create a new folder for each speaker you want. Then, place audio files of each speaker into their respective folders.
   • For example, let's say from step 2, you want the model to be called elden_ring and you have audio files for two speakers, melina and ranni
   • The folder structure would look like this:

     elden_ring\ranni\<many audio files of ranni>
     elden_ring\melina\<many audio files of ranni>

4. Now launch the training webui. In the Dataset to Process dropdown, select the freshly created dataset from steps 1-3 (if you don't see it, click Refresh Datasets Available)
   • If you run into any errors here, you may have setup the folder structure incorrectly
5. Click Begin Process and it will start curating a dataset. The output will be placed in your training folder
   • Manual install users will incur an additional download for the whisper model that is used to split the datasaet.
6. After some time, you should see something like Dataset creation completed successfully in the Progress Console window.
7. Congrats, your first dataset has been completed!

I haven't run into any issues at this step, so if you do, please open an issue in the github tab

Train

The Create Dataset step should be completed before this proceeding here. If you don't see anything in the dropdown menu, click Refresh Training Datasets Available and then choose the dataset to train on.

You could just click Start Training and use the defaults, but I would adjust some of the settings based on what the webui says.

Settings

Dark Mode - Toggle on/off Dark Mode

Toggle Custom Theme - Toggle on/off custom theme

Acknowledgements

This would not be possible without w-okada and his contributors. Huge thanks to them for creating this powerful open-source tool: https://github.com/w-okada/voice-changer

License

Everything I've coded it MIT. Check w-okada for any licenses involving his tools (the voice changer client and beatrice)

Audio files used here are directly from Libritts-r: https://www.openslr.org/141/ which retains a license of CC BY 4.0.