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StarGAN-Voice-Conversion

This is a pytorch implementation of the paper: StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks https://arxiv.org/abs/1806.02169 . Note that the model architecture is a little different from that of the original paper.

Dependencies

  • Python 3.6 (or 3.5)
  • Pytorch 0.4.0
  • pyworld
  • tqdm
  • librosa
  • tensorboardX and tensorboard

Usage

Download Dataset

Download and unzip VCTK corpus to designated directories.

mkdir ./data
wget https://datashare.is.ed.ac.uk/bitstream/handle/10283/2651/VCTK-Corpus.zip?sequence=2&isAllowed=y
unzip VCTK-Corpus.zip -d ./data

If the downloaded VCTK is in tar.gz, run this:

tar -xzvf VCTK-Corpus.tar.gz -C ./data

Preprocess data

We will use Mel-cepstral coefficients(MCEPs) here.

python preprocess.py --sample_rate 16000 \
                    --origin_wavpath data/VCTK-Corpus/wav48 \
                    --target_wavpath data/VCTK-Corpus/wav16 \
                    --mc_dir_train data/mc/train \
                    --mc_dir_test data/mc/test

Train model

Note: you may need to early stop the training process if the training-time test samples sounds good or the you can also see the training loss curves to determine early stop or not.

python main.py

Convert

For example: restore model at step 200000 and specify the source speaker and target speaker to p262 and p272, respectively.

convert.py --resume_iters 200000 --src_spk p262 --trg_spk p272

To-Do list

  • Post some converted samples (Please find some converted samples in the converted_samples folder).

Papers that use this repo:

  1. AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss (ICML2019)
  2. Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion (NeurIPS 2019)
  3. ADAGAN: ADAPTIVE GAN FOR MANY-TO-MANY NON-PARALLEL VOICE CONVERSION (under review for ICLR 2020)