31–40 of 40 posts
It's not clear from the blog post, the git page, and most other places if this will run on, even in big-O:
* CPU
* 16GB GPU
* 240GB server (of the type most business can afford)
* Meta/Google/Open AI/Anthropic-style data center
I hope we keep making progress in isolating tracks in music. I love listening to stems of my favorite songs, I find all sorts of neat parts I missed out on. Listening to isolated harmonies is cool too.
For future ML developers: A post like this should include system requirements. It's not clear from the blog post, the git page, and most other places if this will run on, even in big-O: * CPU * 16GB GPU * 240GB server (of the type most business can afford) * Meta/Google/Open AI/Anthropic-style data center
It's seems you need lot's of ram and vram. Reading the issues on github[1], it does not seem many others have had success in using this effectively:
- someone with a 96 Gb VRAM RTX 6000 Pro had cuda oom issues
- someone somehow made it work on a RTX 4090 somehow, but RTF processing time was 12...
- someone with a RTX 5090 managed to use it, but with clips no longer than 20s
It seems utility of the model for hobbyist with consumer grade cards will be low.
[1]: https://github.com/facebookresearch/sam-audio/issues/24
For future ML developers: A post like this should include system requirements. It's not clear from the blog post, the git page, and most other places if this will run on, even in big-O: * CPU * 16GB GPU * 240GB server (of the type most business can afford) * Meta/Google/Open AI/Anthropic-style data center
I use moises frequently for track separation for learning songs. It does pretty dang well. I was shocked that the score of moises is ranked way worse than just about everything else, including lalal.ai, which I also used before buying moises. Perhaps lalal.ai has gotten better since I last tried it.
Maybe I'm totally misinterpreting, but the chart I'm looking at says "Net Win Rate of SAM Audio vs. SoTA Separation (text prompted)", so perhaps a lower number means that the alternative model is better?
I hope we keep making progress in isolating tracks in music. I love listening to stems of my favorite songs, I find all sorts of neat parts I missed out on. Listening to isolated harmonies is cool too.
I wonder if you could assemble a big corpus of individual solo instruments, then permute a cacophonous mix of them. IIRC the main training dataset is comprised of a limited number of real songs. But I think a model trained on real songs might struggle with more "out there" harmonies and mixes.
I hope we keep making progress in isolating tracks in music. I love listening to stems of my favorite songs, I find all sorts of neat parts I missed out on. Listening to isolated harmonies is cool too.
From the papers I've read, the stem separation models all seem to train off what seems like a fairly small dataset that doesn't have great instrument representation. I wonder if you could assemble a big corpus of individual solo instruments, then permute a cacophonous mix of them. IIRC the main training dataset is comprised of a limited number of real songs. But I think a model trained on real songs might struggle wi…
For future ML developers: A post like this should include system requirements. It's not clear from the blog post, the git page, and most other places if this will run on, even in big-O: * CPU * 16GB GPU * 240GB server (of the type most business can afford) * Meta/Google/Open AI/Anthropic-style data center
It realy depends on your runtime environment, but I agree it would be nice to have some references with commonly used setups.
Environments might mean the difference between e.g. 16GB and 24GB, but not 16GB and 160GB.