MusRec: Zero-Shot Text-to-Music Editing via Rectified Flow and Diffusion Transformers

TL;DR

MusRec is a novel zero-shot text-to-music editing model that leverages rectified flow and diffusion transformers to perform diverse editing tasks on real-world music without task-specific retraining.

Contribution

It introduces MusRec, the first zero-shot model for text-based music editing using advanced diffusion techniques, overcoming limitations of prior models.

Findings

Outperforms existing methods in content preservation

Maintains structural consistency during editing

Achieves high editing fidelity

Abstract

Music editing has emerged as an important and practical area of artificial intelligence, with applications ranging from video game and film music production to personalizing existing tracks according to user preferences. However, existing models face significant limitations, such as being restricted to editing synthesized music generated by their own models, requiring highly precise prompts, or necessitating task-specific retraining, thus lacking true zero-shot capability. leveraging recent advances in rectified flow and diffusion transformers, we introduce MusRec, a zero-shot text-to-music editing model capable of performing diverse editing tasks on real-world music efficiently and effectively. Experimental results demonstrate that our approach outperforms existing methods in preserving musical content, structural consistency, and editing fidelity, establishing a strong foundation for controllable music editing in real-world scenarios.