ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production

TL;DR

This paper presents ProgGP, a Transformer-based model fine-tuned on a progressive metal dataset, capable of generating multi-instrument compositions in GuitarPro format to assist human music production.

Contribution

It introduces a novel application of tokenized GuitarPro data with a Transformer model for genre-specific music generation and demonstrates its practical use in producing a complete metal song.

Findings

Model generates diverse multi-instrument progressive metal parts.

Quantitative and qualitative analyses validate the musical quality.

AI-assisted production results in a fully mixed metal track.

Abstract

Recent work in the field of symbolic music generation has shown value in using a tokenization based on the GuitarPro format, a symbolic representation supporting guitar expressive attributes, as an input and output representation. We extend this work by fine-tuning a pre-trained Transformer model on ProgGP, a custom dataset of 173 progressive metal songs, for the purposes of creating compositions from that genre through a human-AI partnership. Our model is able to generate multiple guitar, bass guitar, drums, piano and orchestral parts. We examine the validity of the generated music using a mixed methods approach by combining quantitative analyses following a computational musicology paradigm and qualitative analyses following a practice-based research paradigm. Finally, we demonstrate the value of the model by using it as a tool to create a progressive metal song, fully produced and mixed by a human metal producer based on AI-generated music.