Implementation of Kaneko et al.'s MaskCycleGAN-VC model for non-parallel voice conversion.

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Deep LearningMaskCycleGAN-VC
Overview

MaskCycleGAN-VC

Unofficial PyTorch implementation of Kaneko et al.'s MaskCycleGAN-VC (2021) for non-parallel voice conversion.

MaskCycleGAN-VC is the state of the art method for non-parallel voice conversion using CycleGAN. It is trained using a novel auxiliary task of filling in frames (FIF) by applying a temporal mask to the input Mel-spectrogram. It demonstrates marked improvements over prior models such as CycleGAN-VC (2018), CycleGAN-VC2 (2019), and CycleGAN-VC3 (2020).


Figure1: MaskCycleGAN-VC Training




Figure2: MaskCycleGAN-VC Generator Architecture




Figure3: MaskCycleGAN-VC PatchGAN Discriminator Architecture



Paper: https://arxiv.org/pdf/2102.12841.pdf

Repository Contributors: Claire Pajot, Hikaru Hotta, Sofian Zalouk

Setup

Clone the repository.

git clone [email protected]:GANtastic3/MaskCycleGAN-VC.git
cd MaskCycleGAN-VC

Create the conda environment.

conda env create -f environment.yml
conda activate MaskCycleGAN-VC

VCC2018 Dataset

The authors of the paper used the dataset from the Spoke task of Voice Conversion Challenge 2018 (VCC2018). This is a dataset of non-parallel utterances from 6 male and 6 female speakers. Each speaker utters approximately 80 sentences.

Download the dataset from the command line.

wget --no-check-certificate https://datashare.ed.ac.uk/bitstream/handle/10283/3061/vcc2018_database_training.zip?sequence=2&isAllowed=y
wget --no-check-certificate https://datashare.ed.ac.uk/bitstream/handle/10283/3061/vcc2018_database_evaluation.zip?sequence=3&isAllowed=y
wget --no-check-certificate https://datashare.ed.ac.uk/bitstream/handle/10283/3061/vcc2018_database_reference.zip?sequence=5&isAllowed=y

Unzip the dataset file.

mkdir vcc2018
apt-get install unzip
unzip vcc2018_database_training.zip?sequence=2 -d vcc2018/
unzip vcc2018_database_evaluation.zip?sequence=3 -d vcc2018/
unzip vcc2018_database_reference.zip?sequence=5 -d vcc2018/
mv -v vcc2018/vcc2018_reference/* vcc2018/vcc2018_evaluation
rm -rf vcc2018/vcc2018_reference

Data Preprocessing

To expedite training, we preprocess the dataset by converting waveforms to melspectograms, then save the spectrograms as pickle files normalized.pickle and normalization statistics (mean, std) as npz files _norm_stats.npz. We convert waveforms to spectrograms using a melgan vocoder to ensure that you can decode voice converted spectrograms to waveform and listen to your samples during inference.

python data_preprocessing/preprocess_vcc2018.py \
  --data_directory vcc2018/vcc2018_training \
  --preprocessed_data_directory vcc2018_preprocessed/vcc2018_training \
  --speaker_ids VCC2SF1 VCC2SF2 VCC2SF3 VCC2SF4 VCC2SM1 VCC2SM2 VCC2SM3 VCC2SM4 VCC2TF1 VCC2TF2 VCC2TM1 VCC2TM2

python data_preprocessing/preprocess_vcc2018.py \
  --data_directory vcc2018/vcc2018_evaluation \
  --preprocessed_data_directory vcc2018_preprocessed/vcc2018_evaluation \
  --speaker_ids VCC2SF1 VCC2SF2 VCC2SF3 VCC2SF4 VCC2SM1 VCC2SM2 VCC2SM3 VCC2SM4 VCC2TF1 VCC2TF2 VCC2TM1 VCC2TM2

Training

Train MaskCycleGAN-VC to convert between and . You should start to get excellent results after only several hundred epochs.

python -W ignore::UserWarning -m mask_cyclegan_vc.train \
    --name mask_cyclegan_vc__ \
    --seed 0 \
    --save_dir results/ \
    --preprocessed_data_dir vcc2018_preprocessed/vcc2018_training/ \
    --speaker_A_id  \
    --speaker_B_id  \
    --epochs_per_save 100 \
    --epochs_per_plot 10 \
    --num_epochs 6172 \
    --batch_size 1 \
    --lr 5e-4 \
    --decay_after 1e4 \
    --sample_rate 22050 \
    --num_frames 64 \
    --max_mask_len 25 \
    --gpu_ids 0 \

To continue training from a previous checkpoint in the case that training is suspended, add the argument --continue_train while keeping all others the same. The model saver class will automatically load the most recently saved checkpoint and resume training.

Launch Tensorboard in a separate terminal window.

tensorboard --logdir results/logs

Testing

Test your trained MaskCycleGAN-VC by converting between and on the evaluation dataset. Your converted .wav files are stored in results//converted_audio.

python -W ignore::UserWarning -m mask_cyclegan_vc.test \
    --name mask_cyclegan_vc_VCC2SF3_VCC2TF1 \
    --save_dir results/ \
    --preprocessed_data_dir vcc2018_preprocessed/vcc2018_evaluation \
    --gpu_ids 0 \
    --speaker_A_id VCC2SF3 \
    --speaker_B_id VCC2TF1 \
    --ckpt_dir /data1/cycleGAN_VC3/mask_cyclegan_vc_VCC2SF3_VCC2TF1/ckpts \
    --load_epoch 500 \
    --model_name generator_A2B \

Toggle between A->B and B->A conversion by setting --model_name as either generator_A2B or generator_B2A.

Select the epoch to load your model from by setting --load_epoch.

Code Organization

├── README.md                       <- Top-level README.
├── environment.yml                 <- Conda environment
├── .gitignore
├── LICENSE
|
├── args
│   ├── base_arg_parser             <- arg parser
│   ├── train_arg_parser            <- arg parser for training (inherits base_arg_parser)
│   ├── cycleGAN_train_arg_parser   <- arg parser for training MaskCycleGAN-VC (inherits train_arg_parser)
│   ├── cycleGAN_test_arg_parser    <- arg parser for testing MaskCycleGAN-VC (inherits base_arg_parser)
│
├── bash_scripts
│   ├── mask_cyclegan_train.sh      <- sample script to train MaskCycleGAN-VC
│   ├── mask_cyclegan_test.sh       <- sample script to test MaskCycleGAN-VC
│
├── data_preprocessing
│   ├── preprocess_vcc2018.py       <- preprocess VCC2018 dataset
│
├── dataset
│   ├── vc_dataset.py               <- torch dataset class for MaskCycleGAN-VC
│
├── logger
│   ├── base_logger.sh              <- logging to Tensorboard
│   ├── train_logger.sh             <- logging to Tensorboard during training (inherits base_logger)
│
├── saver
│   ├── model_saver.py              <- saves and loads models
│
├── mask_cyclegan_vc
│   ├── model.py                    <- defines MaskCycleGAN-VC model architecture
│   ├── train.py                    <- training script for MaskCycleGAN-VC
│   ├── test.py                     <- training script for MaskCycleGAN-VC
│   ├── utils.py                    <- utility functions to train and test MaskCycleGAN-VC

Acknowledgements

This repository was inspired by jackaduma's implementation of CycleGAN-VC2.

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