/so-vits-svc-fork

so-vits-svc fork with REALTIME support (voice changer) and greatly improved interface.

Primary LanguagePythonMIT LicenseMIT

SoftVC VITS Singing Voice Conversion Fork

CI Status Documentation Status Test coverage percentage

Poetry black pre-commit

PyPI Version Supported Python versions License

A fork of so-vits-svc with realtime support and greatly improved interface. Based on branch 4.0 (v1) and the models are compatible.

Installation

Install this via pip (or your favourite package manager):

pip install -U torch torchaudio --index-url https://download.pytorch.org/whl/cu117
pip install so-vits-svc-fork

Features not available in the original repo

  • Realtime voice conversion
  • GUI available
  • Unified command-line interface (no need to run Python scripts)
  • Ready to use just by installing with pip.
  • Automatically download pretrained base model and HuBERT model
  • Code completely formatted with black, isort, autoflake etc.
  • Other minor differences

Usage

Inference

GUI

GUI

GUI launches with the following command:

svcg

CLI

  • Realtime (from microphone)
svc vc --model-path <model-path>
  • File
svc --model-path <model-path> source.wav

Training

Use of Google Colab is recommended. (To train locally, you need at least 12GB of VRAM.)

Google Colab

Open In Colab

Local

Place your dataset like dataset_raw/{speaker_id}/{wav_file}.wav and run:

svc pre-resample
svc pre-config
svc pre-hubert
svc train

Further help

For more details, run svc -h or svc <subcommand> -h.

> svc -h
Usage: svc [OPTIONS] COMMAND [ARGS]...

  so-vits-svc allows any folder structure for training data.
  However, the following folder structure is recommended.
      When training: dataset_raw/{speaker_name}/{wav_name}.wav
      When inference: configs/44k/config.json, logs/44k/G_XXXX.pth
  If the folder structure is followed, you DO NOT NEED TO SPECIFY model path, config path, etc.
  (The latest model will be automatically loaded.)
  To train a model, run pre-resample, pre-config, pre-hubert, train.
  To infer a model, run infer.

Options:
  -h, --help  Show this message and exit.

Commands:
  clean          Clean up files, only useful if you are using the default file structure
  infer          Inference
  onnx           Export model to onnx
  pre-config     Preprocessing part 2: config
  pre-hubert     Preprocessing part 3: hubert If the HuBERT model is not found, it will be...
  pre-resample   Preprocessing part 1: resample
  train          Train model If D_0.pth or G_0.pth not found, automatically download from hub.
  train-cluster  Train k-means clustering
  vc             Realtime inference from microphone

Contributors ✨

Thanks goes to these wonderful people (emoji key):

34j
34j
💻 🤔 📖

This project follows the all-contributors specification. Contributions of any kind welcome!