BirdNET-Analyzer and birdnet-go
These are ecosystem siblings where one is the reference implementation (Python-based scientific framework with active maintenance and community adoption) and the other is a third-party reimplementation (Go-based port optimized for realtime processing with different deployment characteristics).
About BirdNET-Analyzer
birdnet-team/BirdNET-Analyzer
BirdNET analyzer for scientific audio data processing.
Leverages deep learning models trained on 6,512+ bird species to automatically detect and classify avian vocalizations in audio files or continuous streams. Provides both command-line and GUI interfaces designed for researchers without CS expertise, with support for batch processing large audio datasets and real-time analysis through Docker containerization. Integrates with Zenodo for model distribution and supports cross-platform deployment on Linux, Windows, and macOS via native installers or Python package management.
About birdnet-go
tphakala/birdnet-go
Realtime BirdNET soundscape analyzer
Embeds the BirdNET TensorFlow Lite model directly in the compiled binary for offline inference across 6500+ species, eliminating internet dependencies. Supports continuous 24/7 soundcard capture with output to SQLite, MySQL, or log files, plus a web dashboard for visualization and Prometheus metrics exposure. Integrates with BirdWeather.com API, RTSP audio streaming, and OBS overlays while maintaining minimal resource overhead on Raspberry Pi and single-board computers.
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