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Train locally

Build environment

  1. Make sure you have installed Python==3.8, CMake & C/C++ compilers, ffmpeg;

  2. Clone this repository;

  3. Run pip install -r requirements.txt;

  4. Install GPU version PyTorch: (Make sure you have CUDA 11.6 or 11.7 installed)

    # CUDA 11.6
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
# CUDA 11.7
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117

  5. Install necessary libraries for dealing video data:

    pip install imageio==2.4.1
pip install moviepy

  6. Build monotonic align (necessary for training)

    cd monotonic_align
mkdir monotonic_align
python setup.py build_ext --inplace
cd ..

  7. Download auxiliary data for training

    mkdir pretrained_models
# download data for fine-tuning
wget https://huggingface.co/datasets/Plachta/sampled_audio4ft/resolve/main/sampled_audio4ft_v2.zip
unzip sampled_audio4ft_v2.zip
# create necessary directories
mkdir video_data
mkdir raw_audio
mkdir denoised_audio
mkdir custom_character_voice
mkdir segmented_character_voice

  8. Download pretrained model, available options are:

    CJE: Trilingual (Chinese, Japanese, English)
CJ: Dualigual (Chinese, Japanese)
C: Chinese only

    Linux

    To download CJE model, run the following:

    wget https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/resolve/main/pretrained_models/D_trilingual.pth -O ./pretrained_models/D_0.pth
wget https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/resolve/main/pretrained_models/G_trilingual.pth -O ./pretrained_models/G_0.pth
wget https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/resolve/main/configs/uma_trilingual.json -O ./configs/finetune_speaker.json

    To download CJ model, run the following:

    wget https://huggingface.co/spaces/sayashi/vits-uma-genshin-honkai/resolve/main/model/D_0-p.pth -O ./pretrained_models/D_0.pth
wget https://huggingface.co/spaces/sayashi/vits-uma-genshin-honkai/resolve/main/model/G_0-p.pth -O ./pretrained_models/G_0.pth
wget https://huggingface.co/spaces/sayashi/vits-uma-genshin-honkai/resolve/main/model/config.json -O ./configs/finetune_speaker.json

    To download C model, run the follwoing:

    wget https://huggingface.co/datasets/Plachta/sampled_audio4ft/resolve/main/VITS-Chinese/D_0.pth -O ./pretrained_models/D_0.pth
wget https://huggingface.co/datasets/Plachta/sampled_audio4ft/resolve/main/VITS-Chinese/G_0.pth -O ./pretrained_models/G_0.pth
wget https://huggingface.co/datasets/Plachta/sampled_audio4ft/resolve/main/VITS-Chinese/config.json -O ./configs/finetune_speaker.json

    Windows

    Manually download G_0.pth, D_0.pth, finetune_speaker.json from the URLs in one of the options described above.

    Rename all G models to G_0.pth, D models to D_0.pth, config files (.json) to finetune_speaker.json.
    Put G_0.pth, D_0.pth under pretrained_models directory;
    Put finetune_speaker.json under configs directory

    Please note that when you download one of them, the previous model will be overwritten.

  9. Put your voice data under corresponding directories, see DATA.MD for detailed different uploading options.

    Short audios

    1. Prepare your data according to DATA.MD as a single .zip file;
    2. Put your file under directory ./custom_character_voice/;
    3. run unzip ./custom_character_voice/custom_character_voice.zip -d ./custom_character_voice/

    Long audios

    1. Name your audio files according to DATA.MD;
    2. Put your renamed audio files under directory ./raw_audio/

    Videos

    1. Name your video files according to DATA.MD;
    2. Put your renamed video files under directory ./video_data/

  10. Process all audio data.

python scripts/video2audio.py
python scripts/denoise_audio.py
python scripts/long_audio_transcribe.py --languages "{PRETRAINED_MODEL}" --whisper_size large
python scripts/short_audio_transcribe.py --languages "{PRETRAINED_MODEL}" --whisper_size large
python scripts/resample.py

Replace "{PRETRAINED_MODEL}" with one of {CJ, CJE, C} according to your previous model choice.
Make sure you have a minimum GPU memory of 12GB. If not, change the argument whisper_size to medium or small.

  1. Process all text data.
    If you choose to add auxiliary data, run python preprocess_v2.py --add_auxiliary_data True --languages "{PRETRAINED_MODEL}"
    If not, run python preprocess_v2.py --languages "{PRETRAINED_MODEL}"
    Do replace "{PRETRAINED_MODEL}" with one of {CJ, CJE, C} according to your previous model choice.

  2. Start Training.
    Run python finetune_speaker_v2.py -m ./OUTPUT_MODEL --max_epochs "{Maximum_epochs}" --drop_speaker_embed True
    Do replace {Maximum_epochs} with your desired number of epochs. Empirically, 100 or more is recommended.
    To continue training on previous checkpoint, change the training command to: python finetune_speaker_v2.py -m ./OUTPUT_MODEL --max_epochs "{Maximum_epochs}" --drop_speaker_embed False --cont True. Before you do this, make sure you have previous G_latest.pth and D_latest.pth under ./OUTPUT_MODEL/ directory.
    To view training progress, open a new terminal and cd to the project root directory, run tensorboard --logdir=./OUTPUT_MODEL, then visit localhost:6006 with your web browser.

  3. After training is completed, you can use your model by running:
    python VC_inference.py --model_dir ./OUTPUT_MODEL/G_latest.pth --share True

  4. To clear all audio data, run:

Linux

rm -rf ./custom_character_voice/* ./video_data/* ./raw_audio/* ./denoised_audio/* ./segmented_character_voice/* ./separated/* long_character_anno.txt short_character_anno.txt

Windows

del /Q /S .\custom_character_voice\* .\video_data\* .\raw_audio\* .\denoised_audio\* .\segmented_character_voice\* .\separated\* long_character_anno.txt short_character_anno.txt