@JensSpahnpasta Sorry meant to reply the other day - also, I'm kinda fascinated how well my Mastodon instance is communicating with this Lemmy thread.

I'm not the dev of AudiomuseAI, just an interested tire kicker.

But like I said, based on what I know about Machine Learning, this could potentially be better at this specific set of human problems (creating playlists, finding similar new artists to maintain the presence of novelty, etc) than trying to draw upon the crowdsourced metadata (meatdata?) of a species of incredibly opinionated apes.

Like for wo examples, we can only assume that the weights will not care about considerations like authenticity as defined by your typical music fan, nor how "commercial" the track is, which has more than once bit me in the ass.

Like for instance, a friend of mine a few years ago played me this record that was perfect mid-70s Heavy Rock of exactly the sort that I like. And another time I heard this other tune on the radio, similarly full of that 70s Overdrive that I love, and was all "holy shit who's that?"

The bands in question were The Osmonds (yes, those Osmonds) and Abba. I would never have looked in their back catalogues for good music that I missed. This thing will do that, in theory, as long as the weights get trained on a given track.

Still to be solved, seems to me, is a repository where people can share their trained weights for a given song; we could save a lot of time that way.

Currently 40% finished initial scanning after 48 hours, by the way. My collection is slightly bigger than 1tb of mostly Flac files.

@aquantumofdonuts I used Plex until Jellyfin reached Minimum Viable Product stage and never went back, and when I used it I did not pay, so I will never have any idea about that 😜

Yeah, I wasn’t trying to say SQLite is universally better. I shouldn’t have said best option, I really meant best default. I don’t think the majority of users are running a central db, most will just spin-up docker compose files for each service and end up with multiple SQL versions running.

I’ve had a lot of problems in the past from software crashes that left sqlite files in a corrupt state

I have had this issue, but it was always easily recoverable. I haven’t had the same issues with backups, although a lot of the software I use that’s running SQLite has a builtin backup feature, then I just backup that directory to a cloud service.

Besides that SQLite really doesn’t play nice with NFS, which is the basis for quite a few cloud storage providers.
Also a good point, I just don’t think the majority of users are using NFS for their DBs.

When an app is using an ORM already, I think they might as well make sure it supports both SQLite and a hosted DB like Postgres

working as expected now. Thanks!
would you prefer feature requests on the github repo or here?

Understood, though it’s still kicking my butt TBH.

Exposing port 3010:3010 seems to get me a connection refused. But I can still connect to port 3443:443. Both ports are set to TCP.

Guess I’m just exhausted, will play around with it more tomorrow. Thanks for your help.

services:
  mixarr:
      container_name: mixarr
      image: ghcr.io/aquantumofdonuts/mixarr:v1.1.0
      ports:
        - 3010:80 # Edit: change 80 to 3000 in order to bypass caddy in the container
        - 3443:443
      volumes:
        - ~/mixarr-data:/data
      environment:
        - SESSION_SECRET=$(openssl rand -hex 32)
        - FRONTEND_URL=https://my-domain:3443/
        - BASE_URL=http://my-domain:3010/
      restart: unless-stopped