BirdNET-Analyzer

This repo contains BirdNET scripts for processing large amounts of audio data or single audio files. This is the most advanced version of BirdNET for acoustic analyses and we will keep this repository up-to-date with new models and improved interfaces to enable scientists with no CS background to run the analysis.

Feel free to use BirdNET for your acoustic analyses and research. If you do, please cite as:

@article{kahl2021birdnet,
  title={BirdNET: A deep learning solution for avian diversity monitoring},
  author={Kahl, Stefan and Wood, Connor M and Eibl, Maximilian and Klinck, Holger},
  journal={Ecological Informatics},
  volume={61},
  pages={101236},
  year={2021},
  publisher={Elsevier}
}

Documentation

You can access documentation for this project here.

Download

You can download installers for Windows and macOS, and an AppImage or .deb package for Linux, from the releases page. On Windows, once the package is listed in the Windows Package Manager, you can also install it with:

winget install BirdNET-Team.BirdNET-Analyzer

Models can be found on Zenodo.

Installation

Install the package from PyPI (Python 3.11 to 3.13):

pip install birdnet_analyzer

This provides the command line tools birdnet-analyze, birdnet-segments, birdnet-species, birdnet-train and birdnet-evaluate. Additional feature sets are available as extras:

Extra	Adds
embeddings	embedding extraction and similarity search (birdnet-embeddings, birdnet-search)
train	hyperparameter autotuning when training custom classifiers
gui	the graphical user interface (birdnet-gui), including embeddings and train
all	everything above (currently the same set as gui)

pip install "birdnet_analyzer[all]"

For the standalone installers, the Docker image or a source installation, see the installation documentation.

About

Developed by the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology in collaboration with Chemnitz University of Technology.

Go to https://birdnet.cornell.edu to learn more about the project.

Want to use BirdNET to analyze a large dataset? Don’t hesitate to contact us: [email protected]

Have a question, remark, or feature request? Please start a new issue thread to let us know. Feel free to submit a pull request.

License

• Source Code: The source code for this project is licensed under the MIT License.
• Models: The models used in this project are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

Please ensure you review and adhere to the specific license terms provided with each model.

Please note that all educational and research purposes are considered non-commercial use and it is therefore freely permitted to use BirdNET models in any way.

Funding

Our work in the K. Lisa Yang Center for Conservation Bioacoustics is made possible by the generosity of K. Lisa Yang to advance innovative conservation technologies to inspire and inform the conservation of wildlife and habitats.

The development of BirdNET is supported by the German Federal Ministry of Research, Technology and Space (FKZ 01|S22072), the German Federal Ministry for the Environment, Climate Action, Nature Conservation and Nuclear Safety (FKZ 67KI31040E), the German Federal Ministry of Economic Affairs and Energy (FKZ 16KN095550), the Deutsche Bundesstiftung Umwelt (project 39263/01) and the European Social Fund.

Partners

BirdNET is a joint effort of partners from academia and industry. Without these partnerships, this project would not have been possible. Thank you!