Generative structured lottery

This repository contains experiments on enhancing the lottery ticket hypothesis for deep generative models. It also supports the DaFX 2020 submission with code, sound examples and supplementary informations

Supporting webpage

For a better viewing experience, please visit the corresponding supporting website.

It embeds the following:

• Supplementary figures
• Audio examples
  • Reconstruction
  • Interpolation

You can also directly parse through the different sub-directories of the main docs directory.

Dataset

The examples in the paper have been computed on different audio datasets.

Code

Dependencies

Python

Code has been developed with Python 3.7. It should work with other versions of Python 3, but has not been tested. Moreover, we rely on several third-party libraries, listed in requirements.txt. They can be installed with

$ pip install -r requirements.txt

As our experiments are coded in PyTorch, no additional library is required to run them on GPU (provided you already have CUDA installed).

Usage

The code is mostly divided into two scripts train.py and evaluate.py. The first script train.py allows to train a model from scratch as described in the paper. The second script evaluate.py allows to generate the figures of the papers, and also all the supporting additional materials visible on the supporting page) of this repository.

train.py arguments

Pre-trained models

Note that a set of pre-trained models are availble in the code/results folder.

Models details

As discussed in the paper, the very large amount of baseline models implemented did not allow to provide all the parameters for reference models (which are defined in the source code). However, we provide these details inside the documentation page in the models details section