WaveNet

Yet another WaveNet implementation in PyTorch.

The purpose of this implementation is Well-structured, reusable and easily understandable.

• WaveNet Paper
• WaveNet: A Generative Model for Raw Audio

Prerequisites

• System

  • Linux or macOS
  • CPU or (NVIDIA GPU + CUDA CuDNN)
    • It can run on Single CPU/GPU or Multi GPUs.
  • Python 3

• Libraries

  • PyTorch >= 0.3.0
  • librosa >= 0.5.1

Training

python train.py \
    --data_dir=./test/data \
    --output_dir=./outputs

Use python train.py --help to see more options.

Generating

It's just for testing. You need to modify for real world.

python generate.py \
    --model=./outputs/model \
    --seed=./test/data/helloworld.wav \
    --out=./output/helloworld.wav

Use python generate.py --help to see more options.

File structures

networks.py and model.py is main implementations.

• wavenet
  • config.py : Training options
  • networks.py : The neural network architecture of WaveNet
  • model.py : Calculate loss and optimizing
  • utils
    • data.py : Utilities for loading data
  • test
    • Some tests for check if it's correct model like casual, dilated..
• train.py : A script for WaveNet training
• generate.py : A script for generating with pre-trained model

TODO

• [ ] Add some nice samples
• [ ] Global conditions
• [ ] Local conditions
• [ ] Faster generating
• [ ] Parallel WaveNet
• [ ] General Generator

References

• https://github.com/ibab/tensorflow-wavenet
• https://qiita.com/MasaEguchi/items/cd5f7e9735a120f27e2a
• https://github.com/musyoku/wavenet/issues/4
• https://github.com/vincentherrmann/pytorch-wavenet
• http://sergeiturukin.com/2017/03/02/wavenet.html