This is the official ICRCycleGAN-VC implementation repository with PyTorch.
Due to the results and poor performance of voice conversion models in Persian VC, we proposed ICRCycleGAN-VC, which is implemented with careful attention to the structure of Persian speech. This is the last method for non-parallel audio conversion using CycleGAN-VC. This context-independent method is based on cycle adversarial generative networks and uses the Inception-ResNet module in the cyclic structure to extract features through different filters in parallel. The depth of this method is less than other CycleGAN-VC methods, especially MaskCycleGAN-VC. The performance of this method in Persian and English VC is excellent compared to the previous methods.
Figure1: ICRCycleGAN-VC Generator Architecture
Figure2: Inception-ResNet Blocks in Generator
Figure3: ICRCycleGAN-VC Discriminator Architecture
Clone the repository.
git clone https://github.com/nafiuny/ICRCycleGAN-VC.git
cd ICRCycleGAN-VC
Create the conda environment.
conda env create -f environment.yml
conda activate ICRCycleGAN-VC
We used FarsDat dataset for tests in Persian language and VCC2018 for English VC.
You can customize your training and testing with other datasets. The data structure should be as follows:
VCC2018/VCC2018_evaluation
├── VCC2SF1
│ ├── 10001.wav
│ ...
| ...
└── VCC2SM2
├── 10001.wav
...
VCC2018/VCC2018_training
├── VCC2SF1
│ ├── 10001.wav
│ ...
│ ...
└── VCC2SM2
├── 10001.wav
...
Download the VCC2018 dataset from this command line. We need two male and two female speakers and at least 10 wavs files for each.
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
To expedite training, we preprocess the dataset by converting waveforms to melspectograms, then save the spectrograms as pickle files <speaker_id>normalized.pickle and normalization statistics (mean, std) as npz files <speaker_id>_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 VCC2SM1 VCC2SM2
python data_preprocessing/preprocess_vcc2018.py \
--data_directory vcc2018/vcc2018_evaluation \
--preprocessed_data_directory vcc2018_preprocessed/vcc2018_evaluation \
--speaker_ids VCC2SF1 VCC2SF2 VCC2SM1 VCC2SM2
Train ICRCycleGAN-VC to convert between <speaker_A_id> and <speaker_B_id>. You should start to get excellent results after only several hundred epochs.
python -W ignore::UserWarning -m icr_cyclegan_vc.train \
--name icr_cyclegan_vc_<speaker_id_A>_<speaker_id_B> \
--seed 0 \
--save_dir results/ \
--preprocessed_data_dir vcc2018_preprocessed/vcc2018_training/ \
--speaker_A_id <speaker_A_id> \
--speaker_B_id <speaker_B_id> \
--epochs_per_save 100 \
--epochs_per_plot 10 \
--num_epochs 100000 \
--batch_size 1 \
--generator_lr 2e-4 \
--discriminator_lr 1e-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
Test your trained ICRCycleGAN-VC by converting between <speaker_A_id> and <speaker_B_id> on the evaluation dataset. Your converted .wav files are stored in results/<name>/converted_audio.
python -W ignore::UserWarning -m icr_cyclegan_vc.test \
--name icr_cyclegan_vc_VCC2SF2_VCC2SF1 \
--save_dir results/ \
--preprocessed_data_dir vcc2018_preprocessed/vcc2018_evaluation \
--gpu_ids 0 \
--speaker_A_id VCC2SF2 \
--speaker_B_id VCC2SF1 \
--ckpt_dir results/icr_cyclegan_vc_VCC2SF2_VCC2SF1/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.
├── 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 ICRCycleGAN-VC (inherits train_arg_parser)
│ ├── cycleGAN_test_arg_parser <- arg parser for testing ICRCycleGAN-VC (inherits base_arg_parser)
│
├── bash_scripts
│ ├── icr_cyclegan_train.sh <- sample script to train ICRCycleGAN-VC
│ ├── icr_cyclegan_test.sh <- sample script to test ICRCycleGAN-VC
│
├── data_preprocessing
│ ├── preprocess_vcc2018.py <- preprocess VCC2018 dataset
│
├── dataset
│ ├── vc_dataset.py <- torch dataset class for ICRCycleGAN-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
│
├── icr_cyclegan_vc
│ ├── model.py <- defines ICRCycleGAN-VC model architecture
│ ├── train.py <- training script for ICRCycleGAN-VC
│ ├── test.py <- training script for ICRCycleGAN-VC
│ ├── utils.py <- utility functions to train and test ICRCycleGAN-VC