Birdnet-go is a real-time sound analyzer that detects bird species. It can take in audio from microphones. I set mine up to stream audio from my security cameras that I already had installed. There’s even a Home Assistant App, if you’re using it.
I live in a more suburban/semi-urban environment, but was really surprised to so many different bird species.
Specialized models can be made incredibly efficient. The big tech guys are trying to replace humans which is why their crap takes such huge servers to run. Building local-first/only just means you need to specialize the model and the pipeline to match what you’re doing
Thanks.
I understand now that this software does not use one of the big general AI providers but a specialised model (for birds esp.?) that runs locally; that I can get behind.
Are there other keywords in software descriptions I can look for that help me make such distinctions?
LLM and VLM are generally the type of model that needs a ton of resources to run. Birdnet uses an audio classification model, but there are dozens of types of models.
I’m building specialized task-focused pipelines which keep the memory footprint small for self-hosted users, and I’m working on a unified framework too make building other pipelines and applications much easier: https://docs.circuitforge.tech/
If you give me some details on what you’re after I might be able to find you some projects or at least clarify keywords
Edit: an example of how efficient these can get, I have a little ESP32-driven waveshare display running an ONNX wake-word detection model which is absolutely nuts. 4mb psram and a dual-core 240mhz cpu and its only job is handling “hey Mycroft” and establishing a proper speech-to-text connection with my server downstairs