  Recent advances in text-to-music editing, which employ text queries to modify
music (e.g.\ by changing its style or adjusting instrumental components),
present unique challenges and opportunities for AI-assisted music creation.
Previous approaches in this domain have been constrained by the necessity to
train specific editing models from scratch, which is both resource-intensive
and inefficient; other research uses large language models to predict edited
music, resulting in imprecise audio reconstruction. To Combine the strengths
and address these limitations, we introduce Instruct-MusicGen, a novel approach
that finetunes a pretrained MusicGen model to efficiently follow editing
instructions such as adding, removing, or separating stems. Our approach
involves a modification of the original MusicGen architecture by incorporating
a text fusion module and an audio fusion module, which allow the model to
process instruction texts and audio inputs concurrently and yield the desired
edited music. Remarkably, Instruct-MusicGen only introduces 8% new parameters
to the original MusicGen model and only trains for 5K steps, yet it achieves
superior performance across all tasks compared to existing baselines, and
demonstrates performance comparable to the models trained for specific tasks.
This advancement not only enhances the efficiency of text-to-music editing but
also broadens the applicability of music language models in dynamic music
production environments.
