ClassicGAN

A GAN for generating classical music. Works with piano rolls of midi files. Basic structure is a mixture of MuseGAN and StackGAN++.

Getting Started

Prerequisites

tensorflow-gpu
tqdm
numpy
pretty-midi
matplotlib

Installing

None necessary. Clone this repository.

Dataset Generation

For your own datasets, put all midi files under /Classics.

Then run

python3 Data.py

to convert them into .tfrecord files.

Efficient compression has been integrated, tfrecord files are now generated at a ratio of approximately 1:100. (Ex: 220MB of midi files are converted into a single 20GB tfrecord file.)

Note: these tfrecord files are highly compressible, and the entire tfrecord file compresses to a 56MB .7z file.

Training

Run

python3 ClassicGAN.py

For timeline file generations, add -r.

For concatenation of all generated midi files, add -c.

Sampling

Make sure your checkpoint is under /Checkpoints.

Select a midi file for encoding.

Then, run

python3 ClassicGAN.py -s /path/to/midi

TODOs

Integrate Gradient Checkpointing - Completed. Removed after benchmarks showed no significant speedup/memory advantage.

Convert generated results to midi - Completed.

Faster datasets using tf.data.Dataset - Completed.

Integrate VAEs - Completed. Removed after training instability.

TTUR - Completed.

Currently tinkering with Spectral Normalization.

Authors

• Rick-McCoy - Initial work - Rick-McCoy

License

This project is licensed under the MIT License.

Acknowledgments

• Jin-Jung Kim - General structure of code adapted - golbin
• Ishaan Gulrajani - Loss function code used - igul222
• MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment, Hao-Wen Dong et al. - Idea of stacking generators - link
• StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks, Han Zhang et al. - Idea of multiple levels - link
• Improved Training of Wasserstein GANs, Ishaan Gulrajani et al. - WGAN-GP used. - link
• SPECTRAL NORMALIZATION FOR GENERATIVE ADVERSARIAL NETWORKS, Takeru Miyato et al. - Spectral normalization - link
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, Martin Heusel et al. - TTUR - link
• Nhat M. Nguyen - Spectral Normalization code used - minhnhat93
• Amazon - Provider of funding
• Everyone Else I Questioned - Thanks!