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20 hours agoA quick general awareness comment:
A post about a project not by the original creator is not a promo post. A user will not necessarily know the details to provide a disclosure unless a project explicitly has on in their repo.
Requiring it on a project post like this (“Here’s a cool thing I found!”) is not doable.
That said, its clearly an AI project start to finish - the detection wouldn’t really work without it. So I think [AIT] is appropriate here.
@chaospatterns@lemmy.world can you please add the [AIT] tag?
Edit: Thanks!
Audio processing by ai can absolutely happen on a pi.
As an example, whisper.cpp is a high performance speech recognition model, and it does a great job. The biggest version of it as a model requires less than 5GB to live fully in memory, the large model at about 1.5billion parameters, and it works great even CPU only.
The one used most often is small, which has about 244million parameters, and needs about a gig of ram and nothing more.
For a pi 5, usually the base or tiny model are used. Small could be used in most scenarios, but tiny and base are able to run real-time.
So it depends for how local - sometimes its all self contained and can do just fine on a pi, sometimes its local in that it needs an LLM endpoint to hit, but that can be running on another system entirely.
It would really depend on the project for how/where/why for a definition of local.