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License: CC BY-NC-SA 4.0

dMelodies Controllability Experiments

About

This repository contains the source code for running the experiments accompanying our ISMIR 2021 paper on disentanglement versus controllability in the context of symbolic music generation using the dMelodies dataset. Refer THIS for additional results not included in the main paper.

Please cite as follows if you are using the code in this repository in any manner.

Ashis Pati, Alexander Lerch. "Is Disentanglement enough? On Latent Representations for Controllable Music Generation”, in Proc. of the 22nd International Society for Music Information Retrieval Conference (ISMIR) , Online, 2021.

@inproceedings{pati2021disentanglement,
  title={Is Disentanglement enough? On Latent Representations for Controllable Music Generation},
  author={Pati, Ashis and Lerch, Alexander},
  booktitle={22nd International Society for Music Information Retrieval Conference (ISMIR)},
  year={2021},
  address={Online},
}

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
  • 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
    • utils: module with model and training utility classes and methods
  • other scripts to train / test the models and generate plots

Usage

TO BE UPDATED.

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.