Trail Echo: An Offline Bird Logger Built on BirdNET

Shreyas — Mon, 12 Oct 2026 06:03:19 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Trail Echo is an offline bird logger. It listens in 60-second chunks, identifies species with the open BirdNET model, throws away chunks where nothing was heard, and gives you a dashboard of every bird you passed once you're back.

The touch-grass idea: the screen is for afterwards. During a walk, the device sits in a bag or pocket and listens, so you can look up at the trees instead of down at an identification app. At the end of the walk you get a species list and a timeline of when each bird was heard. It's for casual walkers and city dwellers who hear birds all the time and never learned their names.

Current status: the live logger is tested with system-audio capture on PC and microphone. Also tested with microphones on phone that connects to the same pipeline after media type conversions (from .m4a to .wav); field audio to be recorded on a phone and run through the same pipeline.

Demo

Code

kilqwe / hacktoberfest-week-1

Offline bird logger built on BirdNET. Hacktoberfest 2026, Week 1: Touch Grass.

Trail Echo

Offline bird logger built on BirdNET It listens in 60-second chunks, identifies species locally, discards chunks with no detections, and shows everything heard in a Streamlit dashboard.

Built for Hacktoberfest 2026, Week 1: Touch Grass. The idea is to leave the device in your pocket during a walk and look at the results afterwards.

How it works

capture audio → BirdNET analysis → CSV per chunk → Streamlit dashboard

Chunks with no detections are deleted automatically (audio and CSV), so storage only grows when something is heard.
All inference runs locally. No audio leaves your machine.

Status

Tested: The microphone successfully captures the sound of the birds (captured using xeno-canto and youtube). Using BirdNET-Analyzer the app can successfully identify the bird species with confidence of >95% Not yet tested: outdoor field recording on a real walk.

Setup

python -m venv .venv
.venv\Scripts\activate          #

…

View on GitHub

How I Built It

Open model: BirdNET (TensorFlow, Cornell Lab of Ornithology and Chemnitz University of Technology), run locally through birdnet-analyzer. The species list it loaded had 6,522 species.
Capture: sounddevice for microphones and WASAPI loopback via PyAudioWPatch; soundfile writes the chunks.
Pipeline: record a chunk, call BirdNET, read the CSV, delete the audio and CSV if there are no detections.
Dashboard: Streamlit and pandas: species table, detections per minute, raw log.

What broke along the way:

BirdNET failed silently because I hid subprocess errors, so the script printed success while writing nothing.
The laptop mic was near-silent, and audio enhancements gated sustained sounds like chirping after about a second.
Some device paths returned garbage values in the billions, so I added level checks.
Storage: 60 s of WAV is about 5 MB, so chunks with no detections are deleted automatically.

Why Does Open Innovation Matter?

A bird logger hears everything around you, including conversations on the trail. Because BirdNET's weights are public, inference runs entirely on my own machine:
no audio leaves the device, nothing needs a signal on a hillside, and there are no per-request fees for hours of recording. I could also read the pipeline and tune it (confidence threshold, chunk length, and later location and week filters) instead of waiting for an API to expose those options. A closed cloud API would have meant uploading hours of ambient audio. One caveat: BirdNET's model is licensed CC BY-NC-SA, so this is a non-commercial project.

DEV Community: Shreyas