BirdNET-Go

BirdNET-Go is an AI solution for continuous avian monitoring and identification

• 24/7 realtime bird song analysis of soundcard capture, analysis output to log file, SQLite or MySQL
• Utilizes BirdNET AI model trained with more than 6500 bird species
• Local processing, Internet connectivity not required
• Easy to use Web user interface for data visualisation
• Supports over 40 languages for species names
• Advanced features like Deep Detection for improved accuracy and Live Audio Streaming.
• BirdWeather.com API integration
• Realtime log file output can be used as overlay in OBS for bird feeder streams etc.
• Minimal runtime dependencies, BirdNET Tensorflow Lite model is embedded in compiled binary
• Provides endpoint for Prometheus data scraping
• Runs on Windows, Linux and macOS
• Low resource usage, works on Raspberry Pi 3 and equivalent 64-bit single board computers

Installation

Quick install script for Debian, Ubuntu and Raspberry Pi OS based systems:

curl -fsSL https://github.com/tphakala/birdnet-go/raw/main/install.sh -o install.sh
bash ./install.sh

Web Dashboard

For detailed installation instructions, see the installation documentation. For securing your BirdNET-Go installation, see the security documentation. See recommended hardware for optimal performance.

There is more detailed usage documentation at Wiki

Community

Join our Discord server for support, discussions, and updates about BirdNET-Go!

Related Projects

Core & Extensions

• BirdNET-Analyzer - Upstream project providing the BirdNET AI model for bird sound identification
• BirdNET-Go Classifiers - Enhanced BirdNET classifiers including additional species

System Integration

• Cockpit BirdNET-Go - Web-based system management plugin for BirdNET-Go using Cockpit framework

Migration Tools

• BirdNET-Pi2Go - Database conversion tool for migrating from BirdNET-Pi to BirdNET-Go

Hardware Solutions

• BirdNET-Go ESP32 RTSP Microphone - ESP32-based RTSP streaming microphone for remote audio capture
• ESP32 Audio Streamer - Alternative ESP32 RTSP streaming solution for BirdNET-Go audio input

License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Authors

Tomi P. Hakala

Contributions by Hampus Carlsson, Jan Vrska, @twt--, @aster1sk, @hoover67

Please let me know if you are missing from contributors list!

BirdNET AI model by the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology in collaboration with Chemnitz University of Technology. Stefan Kahl, Connor Wood, Maximilian Eibl, Holger Klinck.

BirdNET label translations by Patrick Levin for BirdNET-Pi project by Patrick McGuire.