BirdNET-Gone

July 5, 2026 · View on GitHub

A fork of BirdNET-Go — AI-powered continuous bird detection and identification.



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

Repository Structure

birdnet-gone/
├── cmd/                    # BirdNET-Go CLI commands
├── internal/               # BirdNET-Go Go packages
├── frontend/               # BirdNET-Go Svelte UI
├── docker-compose.yml      # Docker deployment
└── README.md

Installation

Quick Install (BirdNET-Go Core)

For Debian, Ubuntu, and Raspberry Pi OS based systems:

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

Development Setup

For developers who want to contribute or build from source:

See CONTRIBUTING.md for more details.

# Clone the repository
git clone https://github.com/tphakala/birdnet-go.git
cd birdnet-go

# Install Task (if not already installed)
# Linux: sh -c "$(curl --location https://taskfile.dev/install.sh)" -- -d -b /usr/local/bin
# macOS: brew install go-task (assumes Homebrew is installed)

# Setup development environment (Linux apt-based or macOS with homebrew)
task setup-dev

# Build the project
task

# Start development server with hot reload
task dev_server # or "air realtime"

The setup-dev task will automatically install:

  • Go 1.25
  • Node.js LTS
  • Build tools (gcc, git, wget, etc.)
  • golangci-lint (Go linter)
  • air (hot reload for Go)
  • Frontend dependencies and Playwright browsers

Docker Compose

git clone https://github.com/festion/birdnet-gone.git
cd birdnet-gone
docker-compose up -d

This starts BirdNET-Go on port 8080.

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!

Core & Extensions

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

Contributing

Want to contribute? We welcome contributions from the community! 🎉

For comprehensive contributing guidelines, development setup, and workflow documentation, see CONTRIBUTING.md.

Quick Start for Contributors

Experienced developers can get started in 5 minutes:

git clone https://github.com/tphakala/birdnet-go.git && cd birdnet-go
task setup-dev  # One command installs everything (Go, Node.js, tools, git hooks)
air realtime    # Start developing with hot reload

New to the project? The contributing guide includes:

All contributions must follow:

  • ✅ Privacy-by-design principles
  • ✅ Code quality standards (automated via git hooks)
  • ✅ CC BY-NC-SA 4.0 license terms

See CONTRIBUTING.md for complete details.

Data Sources

Taxonomy Data

BirdNET-Go includes embedded taxonomy data derived from the eBird/Clements Checklist:

  • Source: eBird API v2
  • Copyright: © Cornell Lab of Ornithology
  • License: Used under eBird API Terms of Use for non-commercial purposes
  • Attribution: Taxonomy data powered by eBird.org
  • Purpose: Provides fast local genus/family lookups without requiring API calls
  • Coverage: 2,374 genera, 254 families, 11,145 species

For more information about eBird's taxonomy, visit eBird Taxonomy.

License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. See LICENSE for details — BirdNET-Go is for non-commercial use only.

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.