This implementation is based on CUT, thanks Taesung and Junyan for sharing codes.

We provide a PyTorch implementation of non-parallel voice conversion based on patch-wise contrastive learning and adversarial learning. Compared to baseline CycleGAN-VC, CVC only requires one-way GAN training when it comes to non-parallel one-to-one voice conversion, while improving speech quality and reducing training time.

• Linux or macOS
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

• Clone this repo:

git clone https://github.com/Tinglok/CVC
cd CVC

• Install PyTorch 1.6 and other dependencies.

  For pip users, please type the command pip install -r requirements.txt.

  For Conda users, you can create a new Conda environment using conda env create -f environment.yaml.

• Download pre-trained Parallel WaveGAN vocoder to ./checkpoints/vocoder.

• Download the VCTK dataset

cd dataset
wget https://datashare.is.ed.ac.uk/download/DS_10283_2651.zip
unzip DS_10283_2651.zip
unzip VCTK-Corpus.zip
cp -r ./VCTK-Corpus/wav48/p* ./voice/trainA
cp -r ./VCTK-Corpus/wav48/p* ./voice/trainB

where the speaker folder could be any speakers (e.g. p256, and p270).

• Train the CVC model:

python train.py --dataroot ./datasets/voice --name CVC

The checkpoints will be stored at ./checkpoints/CVC/.

• Test the CVC model:

python test.py --dataroot ./datasets/voice --validation_A_dir ./datasets/voice/trainA --output_A_dir ./checkpoints/CVC/converted_sound

The converted utterance will be saved at ./checkpoints/CVC/converted_sound.

• Train the CycleGAN-VC model:

python train.py --dataroot ./datasets/voice --name CycleGAN --model cycle_gan

• Test the CycleGAN-VC model:

python test.py --dataroot ./datasets/voice --validation_A_dir ./datasets/voice/trainA --output_A_dir ./checkpoints/CycleGAN/converted_sound --model cycle_gan

The converted utterance will be saved at ./checkpoints/CycleGAN/converted_sound.

Pre-trained models on p270-to-p256 and many-to-p249 are avaliable at this URL.

To view loss plots, run tensorboard --logdir=./checkpoints and click the URL https://localhost:6006/.

If you use this code for your research, please cite our paper.

@inproceedings{li2021cvc,
  author={Tingle Li and Yichen Liu and Chenxu Hu and Hang Zhao},
  title={{CVC: Contrastive Learning for Non-Parallel Voice Conversion}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={1324--1328}
}