After 14 years with Plex, I finally moved my video library to Jellyfin. Why rising costs, feature restrictions and digital ownership pushed me towards FOSS.
The Xeon server would be a good bet. Your other machine would be potentially bottleneck for memory (though it meets min spec if the server isn’t doing anything else). There’s a NOAVX docker deployment available, would be slower but should work fine. Just be sure to disable anything associated with lyric detection, it’s an absolute performance nightmare.
I ran it on a Ryzen 5500u mini-PC with 32 GB RAM with the standard deployment with AVX2 support and scaled up to three worker threads. For a collection of 53k tracks it was processing about 100 per hour that way with lyrics/whisper translation enabled, but once I turned that off it was doing 1300-1400 tracks per hour.
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Edit - the 6600T would work too. I found with lyrics disabled, each worker only used between 500MB and 2 GB of RAM. Long as the server isn’t under load while scanning I think that would work, and would be faster for having AVX2 support.
I have audiomuse-ai running its main, complete docker compose script, with all containers, on my 8GB Raspi5, and worker-only containers running on:
An 8GB bhyve VM on my FreeBSD box
An E2-6110 AMD pre-ryzen APU with 16GB of ddr3
A Ryzen 5800x w 32GB RAM
They’ve been running about a week, and I’m a little over a third of the way through
Once the initial analysis is complete, I’ll stop all worker containers and leave it all just running fully on the pi5.
I also created a worker-only addon for the 6600T machine, but as it is already running HAOS and Jellyfin, I was getting a lot of OOM-related failures when it was running.
But I also have 32G of used, eBay bought, ddr4 SODIMMs.coming for it.
Bonus: Most of my homelab is in this. The only things missing are my Sophos running OPNsense, and the raspi5. Oh, and my actual desktop machine.
Pro tip for you, ASR (whisper - lyric detection/transcription) can be kind of bad, but if you have some spare resources, it takes very little to host a local LRCLIB database and clone lrclib.net (they have a GitHub page). This massively speed up lyric analysis for me using the API against a local site instead of getting 429s against lrclib.net or relying on ASR.
Lyrics are the biggest longest part of the scans. My whole collection was like 3+ weeks with lyrics stuff on, but only 2 days with just MusiCNN and CLAP.
That’s cool to know, however the Xeon runs FreeBSD, so I would need to create a VM if it doesn’t work in Linuxulator, (FreeBSD’s Linux compatibility layer, works sorta like wine does)
I have 120k tracks. I like music.
I should do some research this weekend I reckon.
Great, now I have one more thing I gotta do this weekend. Thanks a lot. Lmao.
The Xeon server would be a good bet. Your other machine would be potentially bottleneck for memory (though it meets min spec if the server isn’t doing anything else). There’s a NOAVX docker deployment available, would be slower but should work fine. Just be sure to disable anything associated with lyric detection, it’s an absolute performance nightmare.
I ran it on a Ryzen 5500u mini-PC with 32 GB RAM with the standard deployment with AVX2 support and scaled up to three worker threads. For a collection of 53k tracks it was processing about 100 per hour that way with lyrics/whisper translation enabled, but once I turned that off it was doing 1300-1400 tracks per hour.
——
Edit - the 6600T would work too. I found with lyrics disabled, each worker only used between 500MB and 2 GB of RAM. Long as the server isn’t under load while scanning I think that would work, and would be faster for having AVX2 support.
So.
I did a thing.
I have audiomuse-ai running its main, complete docker compose script, with all containers, on my 8GB Raspi5, and worker-only containers running on:
They’ve been running about a week, and I’m a little over a third of the way through
Once the initial analysis is complete, I’ll stop all worker containers and leave it all just running fully on the pi5.
I also created a worker-only addon for the 6600T machine, but as it is already running HAOS and Jellyfin, I was getting a lot of OOM-related failures when it was running.
But I also have 32G of used, eBay bought, ddr4 SODIMMs.coming for it.
Bonus: Most of my homelab is in this. The only things missing are my Sophos running OPNsense, and the raspi5. Oh, and my actual desktop machine.
Yo, that’s awesome!
Pro tip for you, ASR (whisper - lyric detection/transcription) can be kind of bad, but if you have some spare resources, it takes very little to host a local LRCLIB database and clone lrclib.net (they have a GitHub page). This massively speed up lyric analysis for me using the API against a local site instead of getting 429s against lrclib.net or relying on ASR.
Lyrics are the biggest longest part of the scans. My whole collection was like 3+ weeks with lyrics stuff on, but only 2 days with just MusiCNN and CLAP.
That’s cool to know, however the Xeon runs FreeBSD, so I would need to create a VM if it doesn’t work in Linuxulator, (FreeBSD’s Linux compatibility layer, works sorta like wine does)
I have 120k tracks. I like music.
I should do some research this weekend I reckon.
Great, now I have one more thing I gotta do this weekend. Thanks a lot. Lmao.