Trailside Birder: An Offline AI Companion to Help You Touch Grass

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

What I Built

Trailside Birder is an offline-first bird-call identification companion designed for hikers, birdwatchers, and nature enthusiasts. It helps users reconnect with the outdoors by identifying bird species from real-time audio input without relying on an internet connection. Powered by BirdNET for precise acoustic feature detection and a quantized local open-weight LLM for natural language interactions and ecological summaries, Trailside Birder encourages people to put down their devices, explore trails, and engage deeply with wildlife.

Demo

Trailside Birder | trailside-birder

anshugupta07.github.io

Code

anshugupta07 / trailside-birder

# Trailside Birder An offline bird-call companion built on open models. Record, identify with BirdNET get a short spoken note from a local LLM, and log it to a life list. No internet needed on the trail ## Setup 1. Install Ollama: https://ollama.com, then `ollama pull gemma3:1b` 2. `pip install -r requirements.txt` 3. Optional, for speech: install `espeak-ng` (Linux/Pi: `sudo apt install espeak-ng`; macOS uses `say`) ## Use python trailside.py # record 15 s, identify, speak python trailside.py --voice kid # journal | kid | expert python trailside.py --file bird.wav # analyze an existing recording python trailside.py --model qwen2.5:1.5b # swap the model python trailside.py --lat 23.02 --lon 72.57 --quiet Sightings are saved to `~/.trailside_life_list.json`. ## Website Open `website/index.html` in any browser (single static file). ## Notes - BirdNET model weights are CC BY-NC-SA: non-commercial use only. - BirdNET's command-line flags and CSV columns can change between versions; if…

View on GitHub

How I Built It

Trailside Birder combines lightweight, on-device machine learning with open-source AI models to operate completely offline:

• BirdNET Acoustic Model: Uses localized audio processing to classify avian vocalizations with high accuracy.
• Local Open-Weight LLM: Employs a compressed local LLM running on llama.cpp to generate rich bird descriptions, behavioral insights, and habitat context.
• Offline First Architecture: Built with a lightweight cross-platform framework that caches model weights and maps locally, eliminating cloud dependency.

Why Does Open Innovation Matter?

Open innovation enables true independence from network availability and centralized servers:

• Offline/Zero-Signal Capability: Hikers frequently venture into wilderness areas lacking cellular coverage. Open-weight models and local inference allow intelligent species detection anywhere on the trail.
• Data Privacy & Ownership: Audio recordings and location metadata remain entirely on the user's device, protecting user privacy and preventing unauthorized data harvesting.
• Accessibility & Sustainability: Open models democratize access to advanced ecological tools without subscription fees or API limits.