siwtom/ConvS2S-VC

ConvS2S-VC

This repository provides an official PyTorch implementation for ConvS2S-VC.

ConvS2S-VC is a parallel many-to-many voice conversion (VC) method using a fully convolutional sequence-to-sequence (ConvS2S) model. The current version performs VC by first modifying the mel-spectrogram of input speech in accordance with a target speaker or style index, and then generating a waveform using a speaker-independent neural vocoder (Parallel WaveGAN or HiFi-GAN) from the converted mel-spectrogram.

Audio samples are available here.

Papers

• Hirokazu Kameoka, Kou Tanaka, Takuhiro Kaneko, "FastS2S-VC: Streaming Non-Autoregressive Sequence-to-Sequence Voice Conversion," arXiv:2104.06900 [cs.SD], 2021. [Paper]

• Hirokazu Kameoka, Kou Tanaka, Damian Kwasny, Takuhiro Kaneko, Nobukatsu Hojo, "ConvS2S-VC: Fully Convolutional Sequence-to-Sequence Voice Conversion," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 1849-1863, Jun. 2020. [Paper]

Preparation

Requirements

• See requirements.txt.

Dataset

1. Setup your training and test sets. The data structure should look like:

/path/to/dataset/training
├── spk_1
│   ├── utt1.wav
│   ...
├── spk_2
│   ├── utt1.wav
│   ...
└── spk_N
    ├── utt1.wav
    ...
    
/path/to/dataset/test
├── spk_1
│   ├── utt1.wav
│   ...
├── spk_2
│   ├── utt1.wav
│   ...
└── spk_N
    ├── utt1.wav
    ...

Waveform generator

1. Place a copy of the directory parallel_wavegan from https://github.com/kan-bayashi/ParallelWaveGAN in pwg/.
2. HifiGAN models trained on several databases can be found here. Once these are downloaded, place them in pwg/egs/. Please contact me if you have any problems downloading.
3. Optionally, Parallel WaveGAN can be used instead for waveform generation. The trained models are available here. Once these are downloaded, place them in pwg/egs/.

Main

Train

To run all stages for model training, execute:

./recipes/run_train.sh [-g gpu] [-s stage] [-e exp_name]

• Options:

  -g: GPU device (default: -1)
  #    -1 indicates CPU
  -s: Stage to start (0 or 1)
  #    Stages 0 and 1 correspond to feature extraction and model training, respectively.
  -e: Experiment name (default: "exp1")
  #    This name will be used at test time to specify which trained model to load.

• Examples:

  # To run the training from scratch with the default settings:
  ./recipes/run_train.sh
  
  # To skip the feature extraction stage:
  ./recipes/run_train.sh -s 1
  
  # To set the gpu device to, say, 0:
  ./recipes/run_train.sh -g 0

See other scripts in recipes for examples of training on different datasets.

To monitor the training process, use tensorboard:

tensorboard [--logdir log_path]

Test

To perform conversion, execute:

./recipes/run_test.sh [-g gpu] [-e exp_name] [-c checkpoint] [-a attention_mode] [-v vocoder]

• Options:

  -g: GPU device (default: -1)
  #    -1 indicates CPU
  -e: Experiment name (e.g., "exp1")
  -c: Model checkpoint to load (default: 0)
  #    0 indicates the newest model
  -a: Attention mode ("r", "f", or "d")
  #    The modes in which the attention matrix is processed during conversion
  #    r: raw attention (default)
  #    f: windowed attention
  #    d: exactly diagonal attention
  -v: Vocoder type ("hfg" or "pwg")
  #    The type of vocoder used for waveform generation
  #    hfg: HiFi-GAN (default)
  #    pwg: Parallel WaveGAN

• Examples:

  # To perform conversion with the default settings:
  ./recipes/run_test.sh -g 0 -e exp1
  
  # To enable attention windowing:
  ./recipes/run_test.sh -g 0 -e exp1 -a f
  
  # To use Parallel WaveGAN as an alternative for waveform generation:
  ./recipes/run_test.sh -g 0 -e exp1 -v pwg

Citation

If you find this work useful for your research, please cite our papers.

@Article{Kameoka2021arXiv_FastS2S-VC,
  author={Hirokazu Kameoka and Kou Tanaka and Takuhiro Kaneko},
  journal={arXiv:2104.06900 [cs.SD]}, 
  title={FastS2S-VC: Streaming Non-Autoregressive Sequence-to-Sequence Voice Conversion}, 
  year={2021},
  month=apr
}
@Article{Kameoka2020IEEETrans_ConvS2S-VC,
  author={Hirokazu Kameoka and Kou Tanaka and Damian Kwasny and Takuhiro Kaneko and Nobukatsu Hojo},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, 
  title={{ConvS2S-VC}: Fully Convolutional Sequence-to-Sequence Voice Conversion}, 
  year={2020},
  month=jun,
  volume={28},
  pages={1849-1863}
}

Author

Hirokazu Kameoka (@kamepong)

E-mail: kame.hirokazu@gmail.com