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
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
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 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.
For now I'm letting that backend access my files directly. The front end can also play YouTube music (free, using the yt-dlp method).
None of this is public yet so right now I don't need anyone's approval.