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All Projects → mazzzystar → Wavegan Pytorch

mazzzystar / Wavegan Pytorch

PyTorch implementation of " Synthesizing Audio with Generative Adversarial Networks"

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gangenerative-model

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WaveGAN-pytorch

PyTorch implementation of Synthesizing Audio with Generative Adversarial Networks(Chris Donahue, Feb 2018).

Befor running, make sure you have the sc09 dataset, and put that dataset under your current filepath.

Quick Start:

  1. Installation
sudo apt-get install libav-tools
  1. Download dataset
  1. Run

For sc09 task, make sure sc09 dataset under your current project filepath befor run your code.

$ python train.py

Training time

  • For SC09 dataset, 4 X Tesla P40 takes nearly 2 days to get reasonable result.
  • For piano piano dataset, 2 X Tesla P40 takes 3-6 hours to get reasonable result.
  • Increase the BATCH_SIZE from 10 to 32 or 64 can acquire shorter per-epoch time on multiple-GPU but slower gradient descent learning rate.

Results

Generated "0-9": https://soundcloud.com/mazzzystar/sets/dcgan-sc09

Generated piano: https://soundcloud.com/mazzzystar/sets/wavegan-piano

Loss curve:

Architecture

TODO

  • [ ] Add some evaluation experiments, eg. inception score.

Contributions

This repo is based on chrisdonahue's and jtcramer's implementation.

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