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DALL-E for Playlists

naturallanguageplaylist.com

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Re: DALL-E for Playlists

#71

Post by scubbo »

nlplaylist wrote: post #25

OP HERE! Solved the hugging to death issue. Reached the Spotify 11k playlist limit. Deleting old playlists and the site should be working as intended now. THANK YOU whoever posted this! Currently working on: Fixing the recommendation scoring function. Right now it's giving hit or miss responses. I think the problem is with my cross encoder "reranker" is not doing its job the right way. I'll fix the passages it looks…

It's still hugged-to-death so I can't see it, but based on responses, this looks extremely cool!

I know that monetization is a sticky topic for many people, so please don't be offended if this isn't something you're looking for, but I have a bunch of contacts in a major music streaming service's recommendation and personalization teams - if you'd like an intro to discuss this with them (either selling the idea/implementation, or using it as a resume-item for a job), drop me an email (in my about box).

Re: DALL-E for Playlists

#73

Post by egypturnash »

posting this here so I can see if this generated playlist is, indeed, "chock full of farts" when I have time to listen to some music later today

if anyone listens to this before I get a chance, please weigh in on how farty you think this is

https://open.spotify.com/playlist/088WOoTJQ5y2YradJpWAtc?go=...

Re: DALL-E for Playlists

#74

Post by at_a_remove »

oaththrowaway wrote: post #41

Back in the day there was a project called Tomahawk Player that tied into a service called Echo Nest (which I believe got purchased and shut down by Spotify). For me that was the peak of music discovery. I haven't been able to replicate it yet.

AcousticBrainz and Echo Nest, both gone now. It seems like only Pandora's Music Genome is still functioning.

I know this is a Hard Problem, but I also think it is a problem with a great deal of payoff. Art (music, movies, books, etc) is unique in that someone will very much want "more of the same" but not the exact same thing (the identical song). People want the same cheeseburger over and over again, but not the same bit of music. Being able to say "more of that" and actually get the right results back, rather than "more of what other people who liked that song liked" would be a huge boon, but so far, we just don't have the analysis to automate that kind of thing.

It's also hard when it comes to identifying what features must be conserved. On Reddit, you'll get questions, say in the /r/horror subreddit, such as "I really liked May; more like this!" and I will ask, "More with Angela Bettis? More by director Lucky McKee? More about body parts? More where the lead undergoes some late stage transition from average to alluring? What, in particular, did you want more of?"

You can sort of replicate this on Discogs by looking up band members and trying to hunt down later or earlier projects from them, but again, that's just a proxy for a particular quality.

Re: DALL-E for Playlists

#76

Post by wizofaus »

btown wrote: post #26
jtvjan wrote: post #15

Earlier quoted context omitted.

I find it hard to believe that some indie techno band _hasn't_ made a song called "internal server error".

https://open.spotify.com/track/2FF1ROYkhiRcU6zJSkBnBU is exactly what you're looking for

Courtesy of you know who...

    There are several songs that have been named after computer terminology, some examples include:

    "404" by The Notwist
    "Blue Screen of Death" by The Flashbulb
    "Error" by Depeche Mode
    "Fatal System Error" by Fear Factory
    "File Not Found" by John Foxx
    "HTTP Error 503" by The Postal Service
    "Kernel Panic" by The Faint
    "Server Error" by The Radio Company
    "System Error" by Covenant
    "404 Error" by The Algorithm
I'm not actually convinced any of these songs really exist? There is an album "File Not Found" by "Division By Zero" though.

Re: DALL-E for Playlists

#77

Post by ibestvina »

nlplaylist wrote: post #44
jdminhbg wrote: post #35

Earlier quoted context omitted.

I’d love to know at a high level how you went about implementing this. Is it just using OpenAI’s built-in music knowledge? Did you do any of your own classification?

TLDR: Lots of musical metadata converted into paragraphs and the SentenceTransformers Retrieve & Re-Rank Pipeline.( https://www.sbert.net/index.html ) The sentence embeddings are calculated using a Bidirectional Encoder Representation Transformer (BERT) model. There's a pre-trained model for this network trained on over 1 billion sentences from the internet that is publicly available, (thanks Microsoft) . The model t…

Incredible work, and great explanation, thank you. Could you comment more on the cost of running this (e.g. per query or per hour, or however it's set up)? Where are you running the model from?

Also, could you provide more details on the cross encoder used for reranking?

Btw, if you already have all these song embeddings, it would be very interesting to be able to pick a song, and get all the similar ones in a playlist (sort of like "Song radio" on Spotify)!

Re: DALL-E for Playlists

#78

Post by dzango »

nlplaylist wrote: post #44
jdminhbg wrote: post #35

Earlier quoted context omitted.

I’d love to know at a high level how you went about implementing this. Is it just using OpenAI’s built-in music knowledge? Did you do any of your own classification?

TLDR: Lots of musical metadata converted into paragraphs and the SentenceTransformers Retrieve & Re-Rank Pipeline.( https://www.sbert.net/index.html ) The sentence embeddings are calculated using a Bidirectional Encoder Representation Transformer (BERT) model. There's a pre-trained model for this network trained on over 1 billion sentences from the internet that is publicly available, (thanks Microsoft) . The model t…

Very impressive. I'm running into an issue where if I try to tell it to exclude yacht rock, it gives me yacht rock. Is that hard to train?

Re: DALL-E for Playlists

#80

Post by LunarAurora »

Nice work !

So The OP said here that this is based on a “semantic” enhancing of an (already rich) 35k tracks DB.

Music platforms in general do not allow people to describe/annotate tracks verbally (people would love to "let it out" !). The day they will decide to implement an NLP search similar to this, for up to 100 million tracks [1], they will regret it.

Album reviews is the only "semantically rich" AND "widely used" music description I can think of.

Last.fm has some interesting data on the finer track level (wiki, comments, tags), but it could have been way, way richer

[1] https://www.apple.com/newsroom/2022/10/celebrating-100-milli...

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