Music is somewhat more challenging because you have a few other problems that have to be solved in the pipeline, and source separation is still not a 100% solved problem. Beyond that, audio tagging beyond track level artist/genre is a lot harder than image tagging.

Once you have separated sources for a training data set, it's like text generation, except that instead of a single function of sequence position, you have multiple correlated functions of time. Text generation models can barely maintain self consistency from paragraph to paragraph, which is a sequence difference of maybe 200 tokens, now consider moving from token position to a time variable, and adding the requirement that multiple sequences retain coherency both with each other, and with themselves over much larger distances.

There are generative music models, but it's mostly stuff that's been trained on midi files for a specific genre or artist, and the output isn't that impressive.

I am also eagerly awaiting "hark the bloodied angel screams" with blastbeats, shrieks and blistering tremolo guitar, though.