MIDIformers

Table of Contents

• MIDIformers
  • Table of Contents
  • DeepMixer
    • Installation
    • Notebook
    • Output samples
  • Support

DeepMixer

The project involves the usage of the open-source MusicBERT model to perform mask prediction tasks for MIDI task with customizability.

Installation

git clone https://github.com/tripathiarpan20/midiformers.git
cd midiformers/DeepMixer/models/musicbert
./setup.sh

Notebook

The live version of Colab notebook utilising the scripts in DeepMixer/models/musicbert can be accessed from this link :

The notebook supports customisability on top of the original MusicBERT codebase, like masking chosen percentage of random notes from either whole MIDI stream/ notes from selected instruments based on user preference.

Other features include:

• Option to leaving notes from the beginning min_bar_mask masks out of the masking pool to provide more initial context for mask prediction.
• Prediction modes with trade-off between speed and quality of predictions.
• Sampling strategies like Temperature, Top-k and Nuclues (Top-p) added for mask predictions.
• Filtering invalid prediction for more consistent results.
• Song segment selection and multi-program/ins masking.

Output samples

Some of the samples from the above notebook along with the reference pieces can be found in a Drive folder , the songs are copyrighted by the respective owners.

A few of our favorites are embedded below:

• Shock (Attack on Titan):
  • Original & Remix:

Shock_-_Attack_On_Titan.mp4

Shock_-_Attack_On_Titan_program0_40percentmaskpred.mp4

• Bohemian Rhapsody (Queen):
  • Original & Remix:

BohemianRhapsody.mp4

bohemian_ckpt2_program128_92percentmaskpred_minbarmask4.mp4

• Unforgiven 2 (Metallica):
  • Original & Remix:

Unforgiven2_track_filtered.1.mp4

Unforgiven2_program26_75percentmaskpred_minbarmask4_Vanilla.1.mp4

Support

There are many ways to support a project - starring⭐️ the GitHub repo is just one.