GitHub - tphakala/birdnet-go: Self-hosted realtime soundscape analyser for birds, bats and other wildlife. Multi-model local AI inference, runs 24/7 on a Raspberry Pi.
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Self-hosted realtime soundscape analyser for birds, bats and other wildlife. Multi-model local AI inference, runs 24/7 on a Raspberry Pi. - tphakala/birdnet-go

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.

  • A_norny_mousse@piefed.zipEnglish
    1·
    1 day ago

    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?

    • pyr0ball@lemmy.dbzer0.comEnglish
      2·
      22 hours ago

      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