Show HN: I trained an AI model on 120M+ songs from iTunes

Re:Show HN: I trained an AI model on 120M+ songs from iTunes by macrolime on Friday February 03, 2023 @07:39AM (#34640298)

I've tried with Discogs and found it to work pretty well. Kinda similar to what OP did just the "embedding" vectors was created by the Genre/Styles on Discogs. I didn't have a Vector database though, so it was kinda very slow. On Discogs those tags are per album and not per track. To create a playlist of say 10 songs similar to a song, I'd find the ten closest albums, then search for them on last.fm and pick the most popular track on each to add to the playlist.

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Re:Show HN: I trained an AI model on 120M+ songs from iTunes by alastairp on Friday February 03, 2023 @08:44AM (#34640948)

A similar embeddings model based on Discogs genre/style data is the Effnet-Discogs model made at the Music Technology Group at Universitat Pompeu Fabra: https://replicate.com/mtg/effnet-discogs

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Re:Show HN: I trained an AI model on 120M+ songs from iTunes by raffraffraff on Friday February 03, 2023 @09:02AM (#34641148)

Per-album metadata is useless for a lot of stuff that I like. It's even useless for a lot of The Beatles stuff because they tend to have a range of styles on an album and tended to bring in weird instruments on individual tracks.

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