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Audio Style Transfer

For Special Problems 8903, GTCMT, Georgia Tech

Introduction

For this project, we attempt to do style transfer with audio. We trained a Very Deep Convolutional Neural Network with raw audio to classify pitches. In addition to the above network we trained an example implementation of the SoundNet. We used the NSynth dataset from Google's Magenta team for this project.

We then use these networks to perform style transfer.

All of our models were trained on an NVIDIA V100 GPU on Paperspace.

Requirements

We used the following:

  • pytorch (for training and deep learning)
  • Librosa (for sound loading and STFTs in notebook)
  • scikit image (for weight denoising in our notebook)
  • Tensorboard X for logging

We recommend training on a GPU. Our networks are large enough to a point where it takes a V100 GPU nearly 15 minutes to run through an epoch.

Training and Dataset

  • Download the nsynth dataset. Please use the JSON/WAV version of the dataset.
  • Extract your train, valid and test tar archives into the `./data/nsynth/`` folder.
  • The fastest way to start training is to use train.sh. Just run ./train.sh to get going.
  • Parameters such as batch-size and number of epochs are accessible as arguments to train.py.
  • To run style transfer, use StyleTransferProject.ipynb.

Files and Folders

Files:

  • train.py is our training routine file.
  • utils.py contains the code for validation, dataset loading and helpers for saving files.
  • VGG_pytorch.py and Alvin.py are our model files. Running alvin_big from the models folder requires VGG_pytorch.py. Alvin.py contains both models for training.

Folders:

  • The data folder is where you will be storing the nsynth dataset.
  • The models folder contains the pth models we prepared for submission. Additionally, it's where models are saved after every epoch.
  • The test_data folder contains some example files you can use for style transfer. Some of the examples are from the nsynth dataset.
  • The runs folder is where you'd be storing your tensorboard runs.

Authors:

Ashvala Vinay, Avneesh Sarwate, Antoine de Meeus