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A RASPBERRY PI NAMES EVERY BIRD IN YOUR GARDEN BY SOUND — OVER 6,000 SPECIES

A Raspberry Pi, a cheap USB microphone and the BirdNET neural network from Cornell Lab turn your garden into a catalogued aviary — no cloud account, no subscription, just a web dashboard that logs every species by song.

by Nachtzuster

FULL CAD BOM FIRMWARE DOCS

ScienceAI

Built withRaspberry Pi

difficulty
●●○○○
time
an evening
license
CC BY-NC-SA 4.0
repo
repo ACTIVE1,138 stars

WHAT YOU’LL NEED

Videos
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COMPAREE VERDICT

BirdNET-Pi is exactly what the title says: the BirdNET bird-sound classifier running entirely on a Raspberry Pi with a cheap USB mic plugged in. The install script is one line, the web dashboard shows a live spectrogram and species confidence scores, and the whole thing runs 24/7 without any cloud dependency. The hardest part is not the software — it is microphone placement. Point it at a road and you will log every car and lawnmower; put it in the wrong spot and you will miss the birds two metres away. The second gotcha is the non-commercial licence, which means this stays a personal project. If you want to learn what actually visits your garden, or contribute detections to citizen science networks like BirdWeather, this is a weekend well spent. If you expect plug-and-play results without touching settings or reviewing false positives in the first few days, set that expectation lower. The single thing most likely to go wrong: underwhelming results because the microphone is in the wrong place.

GOOD TO KNOW

  • —Complete installation script and docs in the repository — works on Raspberry Pi 5, 4B, 400, 3B+ and Zero 2 W (the 3B+ and Zero 2 W need the 64-bit Lite OS).
  • —No printed parts, no soldering — this is a software project with a USB microphone and optional case.
  • —BirdNET model trained by Cornell Lab is included; supports 6,000+ bird species worldwide.
  • —Community fork by Nachtzuster; Patrick McGuire's original repository is archived, so this fork is where updates happen.
  • —Licence is CC BY-NC-SA 4.0 — you cannot sell it or use it commercially.
  • —Reddit and forum support is strong; the install script handles model downloads and the web server automatically.

Parts to buy

6 items

From our check of the build. Exact quantities and part numbers are in the creator’s BOM.

  • Raspberry Pi 3B+ / 4 / 5Find
  • MicroSD card 32GB+Find
  • USB microphone (any cheap lapel or desktop mic works)Find
  • Power supplyFind
  • Ethernet cable or reliable WiFiFind
  • Optional weatherproof case if mounting outdoorsFind

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Can I build this?

Printnothing required
BuyRaspberry Pi 3B+ / 4 / 5, microSD card 32GB+, USB microphone (any cheap lapel or desktop mic works), power supply, Ethernet cable or reliable WiFi, optional weatherproof case if mounting outdoors
ToolsSSH client, web browser, basic Linux command-line comfort (the install script does the heavy lifting but you will need to edit a config file or two)
SkillsYou do not need to understand neural networks, but you do need to be comfortable running an install script over SSH and finding the Pi's IP address on your network. If you have set up a Pi-hole or Octoprint before, this is easier.
TimeOne to two hours for install and initial config, then an afternoon of tuning — adjusting confidence thresholds, reviewing logs, moving the microphone.
Cost$ — dominated by the Raspberry Pi itself; if you already have one, you only need a USB microphone and an SD card.
SafetyNone beyond ordinary electronics care. If mounting outdoors, keep mains power indoors and use a weatherproof enclosure — water and Raspberry Pis do not mix.

Build at your own risk. Projects involve tools, electronics and sometimes mains voltage — follow the creator’s safety notes.

Videos

Community walkthrough by Joyce Lin — not an official project video.

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Gallery

Start here

Navigation into the creator’s own docs — we don’t rewrite the guide, we route you to the source.

  1. 1.Flash the 64-bit Raspberry Pi OS (Trixie, Lite recommended) to a microSD card and enable SSH before first boot.(The install script works on full Raspberry Pi OS too, but Lite is faster and uses less power.)
  2. 2.Run the one-line installer from the README — it pulls the BirdNET model, sets up the web server and starts listening. (The installer takes care of all necessary updates, so you can run it as the very first command after first boot; it writes a log to your home folder.)
  3. 3.Open the web dashboard at the Pi's IP address and review the first hour of detections.(Expect false positives in the first session — planes, wind, dogs. You will tune confidence thresholds in the settings to filter them.)
  4. 4.Move the microphone based on what you see in the logs — closer to feeders, away from roads, out of direct wind.(This is the step that makes or breaks the project. The neural network is excellent; microphone placement is on you.)

Resources

Documentation, files and community threads for this build — we link straight to the original sources and never rehost the creator’s files.

KNOWN ISSUES

  • The USB microphone matters more than you think — check the community list of suggested microphones, and do not seal it inside a plastic case, which muffles everything. Test it outside the case first.
  • Non-commercial licence means you cannot sell dashboards, run this as a paid service or integrate it into a commercial monitoring product.
  • The Pi records 24/7, so give it a stable power supply and a decent SD card; a reliable network only matters for reaching the dashboard and optional uploads.
  • Expect some false positives in the first days from traffic, wind or pets; review the detections and adjust the confidence and sensitivity settings until the log looks right.
  • The BirdNET model is trained globally but weighted toward North America and Europe — rare or regional species may be missed or misidentified.
  • If you mount this outdoors, weather sealing is your problem — the Pi itself is not rated for moisture, and condensation will kill an SD card.

Does this work at night?

Yes — the model includes nocturnal species and owls, and the Pi runs 24/7. Detections during the day are far more common unless you live near an active owl population.

Can I use this to identify calls I recorded on my phone?

It is built for live listening through a USB microphone, and the README does not document analysing recordings from your phone. For one-off recordings, the BirdNET app or the BirdNET-Analyzer command-line tool is the simpler route.

What is the difference between this fork and the original BirdNET-Pi?

The original by Patrick McGuire (mcguirepr89) is archived and no longer updated; Nachtzuster's fork carries on with backup and restore, Bookworm and Trixie support, newer range models and a faster web UI. The README explains how to migrate.

How much bandwidth does this use?

Very little — recognition runs locally on the Pi. Traffic comes from you viewing the dashboard or the live audio stream, from optional features you turn on (BirdWeather uploads, notifications), and from updates you start in Tools > System Controls.

Can I export the data?

Yes — the dashboard includes CSV export, and detections are stored in an SQLite database you can query directly if you want raw access.

Community builds

No community builds yet — be the first, we feature the best ones.

Discussion1

FROM THE COMPAREE TEAM

The model knows 6,000+ species but the quality of your log depends entirely on where you put the microphone — under an eave, next to a feeder, away from the road. Where would you mount yours, and what would you expect to catch first?

CompareeTEAM2mo agoedited

Practical notes from our verification: the Nachtzuster fork is the one with recent commits and active issues; the original mcguirepr89 repository is now archived, and the fork README explains how to migrate from it. Installation is a single curl command on a fresh 64-bit Raspberry Pi OS, and the supported boards are the Pi 5, 4B, 400, 3B+ and Zero 2 W. There is no single official video, so the walkthroughs you will find are community-made and vary in quality. Microphone placement matters as much as the model: a mic sealed inside a plastic case or pointed at a road will log traffic instead of birdsong, so plan an afternoon of moving it around after the install. Note the licence is CC BY-NC-SA 4.0, which rules out building a commercial product on it. Correction (4 October 2026): we re-checked this page line by line against the project's own repository, documentation and videos, and fixed errors in earlier versions.

Nachtzuster

Nachtzuster maintains the actively updated fork of BirdNET-Pi, building on Patrick McGuire's original implementation of the BirdNET neural network for Raspberry Pi. The fork adds backup and restore, a faster web UI, newer range-model support and current Raspberry Pi OS support, with community contributors sending fixes.

GitHub

Star the project on GitHub

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