MMM: Exploring Conditional Multi-Track Music Generation with the Transformer and the Johann Sebastian Bach Chorales Dataset.

Implementation of the paper "MMM: Exploring Conditional Multi-Track Music Generation with the Transformer" (paper). Uses OpenAI's GPT-2 to compose music.

Contact.

Find me on LinkedIn and say hello.

If you find and issue or have a feature request, report either here on GitHub.

Please be so kind and star the repository if you find it useful.

Acknowledgements.

This repository has been created in cooperation with Pyoneer. I am very grateful!

About.

This repository allows you to train GPT-2 on the Johann Sebastian Bach chorale dataset. You can train both MMMTrack and MMMBar from the paper.

How to run.

Requirements:

pip install transformers
pip install tokenizers
pip install torch
pip install music21
pip install note_seq

Training:

1. Clone this repository git clone https://github.com/AI-Guru/MMM-JSB.git.
2. Train MMMTrack with python train_jsb_mmmtrack.py.
3. Train MMMBar with python train_jsb_mmmbar.py.

Sampling: Run the jupyter notebook.

Training should take roughly one hour on a GPU per model for the JSB dataset.

Pretrained checkpoint.

A pretrained network can be found here: https://ai-guru.s3.eu-central-1.amazonaws.com/mmm-jsb/mmm_jsb_checkpoints.zip

What is missing?

• TensorFlow support is rudimentary.
• Data preprocessing and training on the Lakh dataset.
• Implementation as a tool or a DAW plugin.

License.

Released under the Apache-2.0 License.