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License: CC BY-NC-SA 4.0

dMelodies Benchmarking

Code for running benchmarking experiments on the dMelodies dataset. Please cite as follows if you are using the code/data in this repository in any manner.

Ashis Pati, Siddharth Gururani, Alexander Lerch. "dMelodies: A Music Dataset for Disentanglement Learning", 21st International Society for Music Information Retrieval Conference (ISMIR), Montréal, Canada, 2020.

@inproceedings{pati2020dmelodies,
  title={dMelodies: A Music Dataset for Disentanglement Learning},
  author={Pati, Ashis and Gururani, Siddharth and Lerch, Alexander},
  booktitle={21st International Society for Music Information Retrieval Conference (ISMIR)},
  year={2020},
  address={Montréal, Canada}
}

About

This repository contains the source code for running the benchmarking experiments for disentanglement studies using the dMelodies dataset.

Configuration

  • Clone this repository and cd into the root folder of this repository in a terminal window. Run the following commands to initialize the submodules for the dMelodies and dSprites datasets:

    git submodule init
git submodule update

    Alternatively the --recurse-submodules flag can be used with the git clone command while cloning the repository to directly initialize the datasets.

  • Install anaconda or miniconda by following the instruction here.

  • Create a new conda environment using the enviroment.yml file located in the root folder of this repository. The instructions for the same can be found here.

  • Activate the dmelodies environment using the following command:

    conda activate dmelodies

Contents

The contents of this repository are as follows:

  • dmelodies_dataset: submodule containing the dMelodies dataset along with pyTorch dataloader and other helper code
  • dsprites-dataset: submodule containing the dSprites dataset
  • src: contains all the source code related to the different model architectures and trainers
    • dmelodiesvae: model architecture and trainer for the dMelodiesCNN and RNN, also contains the FactorVAE model
    • dspritesvae: model architecure, trainer for the dSpritesVAE, dataloader for the dSprites dataset
    • utils: module with model and training utility classes and methods
  • other scripts to train / test the models and generate plots

Usage

The following scripts can be used to train different models:

  • script_train_dmelodies.py: for training beta-VAE and Annealed-VAE models on the dMelodies dataset using the RNN-based architecture
  • script_train_dmelodies_cnn.py: for training beta-VAE and Annealed-VAE models on the dMelodies dataset using the CNN-based architecture
  • script_train_dmelodies_factor_vae.py: for training Factor-VAE models on the dMelodies dataset using both the CNN and RNN-based architectures
  • script_train_dsprites.py: for training beta-VAE and Annealed-VAE models on the dSprites dataset
  • script_train_dsprites_factor_vae.py: for training Factor-VAE models on the dSprites dataset

Note: To be able to run the training scripts, the dmelodies_dataset folder must be added to the PYTHONPATH. This can be done form the command line by adding PYTHONPATH=dmelodies_dataset before the python command. For example,

PYTHONPATH=dmelodies_dataset python script_train_dmelodies.py

Alternatively, for IDEs such as PyCharm, the required folder can be added using the instructions here.