Earlier quoted context omitted.
Which is unfortunate because it has (on a tiny number of releases) instruments and vocal tags. It's just so unreliable. AllMusic is another decent source for tags, but not instruments. It's the age-old problem with ML/AI: data quality. Garbage in, garbage out. If only we could crowd-sourcev listeners and get them to tag music from a list of available moods, instruments etc. Oh wait .. that's exactly the feature that…
User tagging isn't a panacea either, because people tag inconsistently, and people who tag a lot are probably not very representative. For an extreme example of that, see the boorus. Some machine learning people have become interested in those, since they are huge dataset of extensively tagged material ... or maybe it's the booru people who have become more interested in machine learning. Either way, I'm sure they're…
And don't allow free-text tags. Instead you give a list of available tags - the lowest number needed to describe most tracks. I mean, let people add their own if they want, but you should ignore those while training the model.
I actually think that instead of trying to tag some specific mood (eg "happy") some sounds be a sliding scale between two opposites:
happysad
Instrument tags are easier to understand. Give a list of instruments (or instrument types, because the user might not know precisely which woodwind or percussion instrument it is) with checkboxes beside each.
Some users will be experts because they play woodwind. Let those users apply to become experts, pass a test, "identify the instrument", and if they pass, give them half price subscription as long as they moderate X tunes per week.