AdaIN-VC

Implementation of the paper One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization modified from the official one and heavily based on https://github.com/cyhuang-tw/AdaIN-VC.

Requires at least python 3.6. For other dependencies, see requirements.txt.

Differences from the official implementation

The main difference from the official implementation is the improvements of audio quality due to the use of a neural vocoder (universal vocoder, whose code was from yistLin/universal-vocoder).

Besides, this implementation supports torch.jit, so the full model can be loaded with simply one line:

model = torch.jit.load(model_path)

Usage

The main contribution of this repo is organizing instructions for preprocessing, training and inference into a runnable notebook.

Also, usage instructions are automatically available using python adain-vc.py --help thanks to python-fire.

Reference

Please cite the paper if you find AdaIN-VC useful.

@article{chou2019one,
  title={One-shot voice conversion by separating speaker and content representations with instance normalization},
  author={Chou, Ju-chieh and Yeh, Cheng-chieh and Lee, Hung-yi},
  journal={arXiv preprint arXiv:1904.05742},
  year={2019}
}