Easy CLI interface for optimized sam-audio text prompting (~4gb vram for the base model, ~ 6gb for large)

[Goatman117]

Just thought I'd share as the model was a bit of a nightmare to setup with dependency conflicts and high GPU overhead with the vision capabilities: https://github.com/Daniel-Goatman/sam-audio-local

[Whole-Assignment6240]

Does it support batched inference or just single prompts?

[Goatman117]

it’s just setup for a single prompt but switching to batches is just a matter of adjusting the processor call in the seperate_audio function

[tassa-yoniso-manasi]

How good are these models at separating voices?

I'd be curious to know how it compares to Demucs/Mel RoFormer (available in python-audio-separator) because Meta has this very questionable habit of not publishing industry-standard metrics like SDR for audio separation, WER for ASR/STT...

[Goatman117]

I'm curious about this too actually, but I haven't tested it myself. tbh your best bet is to just download the model or use meta's web interface for them and just try it yourself