I am working on my own Youtube Music/Spotify replacement, just so I can ditch the youtube premium on mobile.

Already have $180 ARR prebooked (the money that I used to pay for youtube music), looking forward for more.

if anyone has links for open-source self-hosted spotify/yt music replacement, I would gladly appreciate links

I tried to do something like that here: https://musicdocks.com/

Github: https://github.com/jantznick/youtube-spotify

It essentially uses youtube as the music source, I think I heard somewhere that playing through embedded videos skips ads but I'm not really sure, in all my time testing it I never noticed ads, but I'm also on premium so that may have been why.

by all means critique, I don't know that I have a ton of time left for it and I'm sure there's bugs here and there. I was having issues getting it to autoplay on desktop when the window itself wasn't the active tab. I never really tried it on mobile.

I was trying to get some DB of artist/song info but doing that was proving to be complicated.

I'm working on a recommendation service (which, to me, it's the piece I'm missing when I play my local mp3 collection)

I collect song metadata from various places (genre, instruments, track credits, rating). I also scrape charts by year, genre etc.

Then I run an ETL job on the json data I have downloaded, pre-building queries for extremely fast lookup tables. This gets saved to Duckdb, which is used by my go web ui/api.

It's very early days, and I only spend one or two hours a week on it, but right now it's amazingly useful. It had roughly 80k song metadata. To preview the suggested songs I ended up building a very cut-down YouTube music player, except that the playing song has all the metadata right there, and everything is a link that can take you to the artist, composer, instrument, genre, album etc. It's a great way to "wander through your collection".

Unfortunately this is only useful to me, because I targeted the music I listen to.

Next step is to download lyrics and extract song meaning, keywords etc. Then use MusiCNN, (or CLAP,OpenL3, HTSAT) to extract embeddings. Finally train my own model for nearest-neighbor retrieval based on a mix of metadata, giving the user the ability to tune it on the fly.