curbstickle_lw

Aside from “local model”, you might see things like whisper.cpp, llama.cpp, ollama, or “OpenAI compatible endpoint”, or a reference to getting models from HuggingFace (you can think of it like github for llm’s - its more and less, but accurate enough for what we’re talking about here).

whisper.cpp is a c++ implementation of whisper (ASR as mentioned), llama.cpp is a high performance and lightweight tool to support llm/llm inference, ollama is a very user friendly way to do the same, and OpenAI compatible endpoint is just using the protocol for communication, so you can point the client at llama.cpp or similar running on the same machine or another you have access to.

Most things with an extemely tight purpose like this (like listening for some birds singing) will use much leaner models, and may be able to run even from a phone.

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.

A 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!

Thanks, please update the title with [AIP] as well, so people can filter.

Feedback or questions is appropriate. We don’t need to come in abrasive, and you didn’t need chatgpt to ask a followup, so we don’t need to be antagonistic.

Choosy self-hosters choose Debian.

That said, I don’t recommend hosting software at the bare metal level - use VMs or containers. Far easier to manage and isolate.

Please update your post with the appropriate tag [CBH] or [AIT], and if applicable, the AI disclosure. See rules 7&8.

I’m going to go ahead and try to use this comment as a teachable moment.

For one, this comment barely skirts the line to me as acceptable. If you aren’t interested in AI topics, thats what the [AIT] and [AIP] tags are for - for you, as a user, to choose what you are interested in and what you are not.

The first part of your comment, by itself, I would have simply removed. You asked for alternatives, and are being rightfully replied to pointing out that this is all under the subject of automation, which (to me) barely puts this above low effort.

If you don’t want to see AI discussions in the future, use the tags to filter. If I see more comments like this that are barely, and entirely subjectively just above low-effort, the bar is going to raise just a hair and they will be removed, as they will come across like trying to skirt the rules.