Really cool, quite surprising that this hasn't been done (well) before afaik.
This is the Spotify Radio feature. i.e. find a song and start a radio from that song/album/artist
Show HN: I trained an AI model on 120M+ songs from iTunes
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Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#32Loading … Beef it up before posting on HN?
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#33This is very interesting, but unfortunately I haven't had the greatest luck in finding new songs I would enjoy listening to. It absolutely finds similar sounding tracks, but it doesn't distinguish which part of the song made it enjoyable. There's no tempo consistency or genre consistency or even main instrument/vocal timbre consistency between recommendations. I think locking one or more of those dimensions would all…
Hey thanks for the feedback! I definitely have a lot of improvement to do on the model, it currently performs better for some styles/genres of music than others.
But the model architecture I'm using is kinda outdated as well, gotta iterate on it more to improve it further!
I'm also thinking of letting users upvote/downvote results, which can also help improve quality on the ranking side.
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#34How did you download that many songs? Wouldn't that be something on the order of 360 terabytes?
Also curious about this... where did you get the dataset?
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#35Exciting to see AI used this way. My main feedback is I'd look at incorporating other factors to rank results, not purely how similar it sounds. Audiophiles might prefer a pure similarity ranking, but that could be offered as a non-default setting if anything.
e.g. I'm sometimes seeing several essentially identical tracks at the top of recommendations (also mentioned in a comment by rayshan). You probably want to penalise tracks like that so they're pushed well down the list, i.e. penalise matches by metadata similarity (artist, title, etc).
OTOH I think it should boost results from more popular songs/artists, so the top result is less likely to be an obscure result that happens to sound similar. Some might argue it's a good thing to discover/highlight obscure artists, but for most users, it's more practical to recommend results that are already "proven" to be appealing. More obscure results could still be blended in if highlighting them is seen as a goal of the project.
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#36What model/architecture did you use? Also curious about the process with that. Did you use an off the shelf model (like Jukebox) to embed the tracks?
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#37[deleted]
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#38How did you scrape the audio of 120M songs? That sounds expensive?
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#39Tried Erreur 404 by L'Impératrice[1], and I noticed the beat of the other recommended songs were eerily similar!! I'd argue your project is actually too good.
Where Spotify's Discover Weekly tried to connect you to music other people listen to {B, C, D, ...} because you've listened to a particular song {A} {B...->A}, your model quite literally tried to find other music {A₂} that sounds like what you're looking for {A₁} {A₂->A₁}.
Edit: OK, some of these are actually pretty dope...
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#40Not even loading