I released new implementation kan-bayashi/ParallelWaveGAN. Please enjoy your hacking!

PYTORCH-WAVENET-VOCODER

This repository is the wavenet-vocoder implementation with pytorch.

You can try the demo recipe in Google colab from now!

Key features

• Support kaldi-like recipe, easy to reproduce the results

• Support multi-gpu training / decoding

• Support world features / mel-spectrogram as auxiliary features

• Support recipes of three public databases

  • CMU Arctic database: egs/arctic
  • LJ Speech database: egs/ljspeech
  • M-AILABS speech database: egs/m-ailabs-speech

Requirements

• python 3.6+
• virtualenv
• cuda 9.0+
• cndnn 7.1+
• nccl 2.0+ (for the use of multi-gpus)

Recommend to use the GPU with 10GB> memory.

Setup

A. Make virtualenv

$ git clone https://github.com/kan-bayashi/PytorchWaveNetVocoder.git
$ cd PytorchWaveNetVocoder/tools
$ make

B. Install with pip

$ git clone https://github.com/kan-bayashi/PytorchWaveNetVocoder.git
$ cd PytorchWaveNetVocoder

# recommend to use with pytorch 1.0.1 because only tested on 1.0.1
$ pip install torch==1.0.1 torchvision==0.2.2
$ pip install -e .

# please make dummy activate file to suppress warning in the recipe
$ mkdir -p tools/venv/bin && touch tools/venv/bin/activate

How-to-run

$ cd egs/arctic/sd
$ ./run.sh

See more detail of the recipes in egs/README.md.

Results

You can listen to samples from kan-bayashi/WaveNetVocoderSamples.

This is the subjective evaluation results using arctic recipe.

Comparison between model type

Effect of the amount of training data

If you want to listen more samples, please access our google drive from here.

Here is the list of samples:

• arctic_raw_16k: original in arctic database
• arctic_sd_16k_world: sd model with world aux feats + noise shaping with world mcep
• arctic_si-open_16k_world: si-open model with world aux feats + noise shaping with world mcep
• arctic_si-close_16k_world: si-close model with world aux feats + noise shaping with world mcep
• arctic_si-close_16k_melspc: si-close model with mel-spectrogram aux feats
• arctic_si-close_16k_melspc_ns: si-close model with mel-spectrogram aux feats + noise shaping with stft mcep
• ljspeech_raw_22.05k: original in ljspeech database
• ljspeech_sd_22.05k_world: sd model with world aux feats + noise shaping with world mcep
• ljspeech_sd_22.05k_melspc: sd model with mel-spectrogram aux feats
• ljspeech_sd_22.05k_melspc_ns: sd model with mel-spectrogram aux feats + noise shaping with stft mcep
• m-ailabs_raw_16k: original in m-ailabs speech database
• m-ailabs_sd_16k_melspc: sd model with mel-spectrogram aux feats

References

Please cite the following articles.

@inproceedings{tamamori2017speaker,
  title={Speaker-dependent WaveNet vocoder},
  author={Tamamori, Akira and Hayashi, Tomoki and Kobayashi, Kazuhiro and Takeda, Kazuya and Toda, Tomoki},
  booktitle={Proceedings of Interspeech},
  pages={1118--1122},
  year={2017}
}
@inproceedings{hayashi2017multi,
  title={An Investigation of Multi-Speaker Training for WaveNet Vocoder},
  author={Hayashi, Tomoki and Tamamori, Akira and Kobayashi, Kazuhiro and Takeda, Kazuya and Toda, Tomoki},
  booktitle={Proc. ASRU 2017},
  year={2017}
}
@article{hayashi2018sp,
  title={複数話者WaveNetボコーダに関する調査}.
  author={林知樹 and 小林和弘 and 玉森聡 and 武田一哉 and 戸田智基},
  journal={電子情報通信学会技術研究報告},
  year={2018}
}

Author

Tomoki Hayashi @ Nagoya University
e-mail:hayashi.tomoki@g.sp.m.is.nagoya-u.ac.jp