birdnet

A Python library for identifying bird species by their sounds.

The library is geared towards providing a robust workflow for ecological data analysis in bioacoustic projects. While it covers essential functionalities, it doesn’t include all the features found in BirdNET-Analyzer, which is available here. Some features might only be available in the BirdNET Analyzer and not in this package.

[!NOTE] This project is under active development, so you might encounter changes that could affect your current workflow. We recommend checking for updates regularly.

The package is also available as an R package at: birdnetR.

Features

• 🐦 Extract classification scores and embeddings from audio recordings — 6,522 species (V2.4) or 11,000+ (V3.0 preview)
• 📍 Predict species presence for a given location and time — 6,522 species (V2.4) or 12,000+ (V3.0)
• 🧠 Utilize your custom-trained acoustic models from BirdNET-Analyzer
• ⚙️ Support for both CPU and GPU execution (including multiple GPUs at the same time)
• 🚀 Multiprocessing support for fast batch analysis of large datasets
• 💾 Low memory footprint and small disk space requirements
• 🎵 Support for various audio file formats (WAV, FLAC, OGG, MP3, etc.)
• 📊 Export results in various output file formats (CSV, Arrow table, Parquet, Numpy, etc.)
• 💻 Cross-platform: Windows, macOS, and Linux
• 🌍 Use multilingual species names (English, German, French, Spanish, etc.)
• ⬇️ Auto-download of all official models
• 🛜 Full offline usage using local (custom) model files

The library is optimized for a minimal memory footprint and maximum scalability, making it suitable for both edge devices and high-performance computing clusters.

Speed benchmarks

For more detailed benchmarks, please refer to the documentation.

Installation

Platform support and Python versions

For details see the official TensorFlow documentation.

Python 3.14

TensorFlow does not yet publish wheels for Python 3.14, so the table above (all TensorFlow-based backends: ProtoBuf, TFLite/LiteRT) is limited to Python 3.11–3.13. On Python 3.14, birdnet installs without TensorFlow and supports the models that have a TensorFlow-free backend: the acoustic 3.0 model and the geo 3.0 model (both via onnx or pt):

pip install birdnet[onnx] --user   # or birdnet[pt]

import birdnet

model = birdnet.load("acoustic", "3.0", "onnx")  # 'pt' also works
predictions = model.predict("example/soundscape.wav")

geo = birdnet.load("geo", "3.0", "onnx")  # 'pt' also works
geo_predictions = geo.predict(42.5, -76.45, week=4)

The TensorFlow-only paths — the tf/pb backends, the acoustic 2.4 and Perch models, and the geo 2.4 model — raise a clear error on Python 3.14. To use them, install birdnet on Python 3.11–3.13. Full 3.14 support will follow once TensorFlow ships Python 3.14 wheels.

Instructions

# For CPU users
pip install birdnet --user

# For GPU users (NVIDIA GPU driver and CUDA need to be installed in advance)
pip install birdnet[and-cuda] --user

The V3.0 models add two additional backends that require optional dependencies:

# PyTorch backend (.pt models)
pip install birdnet[pt] --user

# ONNX backend (.onnx models)
pip install birdnet[onnx] --user

If you encounter issues with audio file reading, please ensure that libsndfile is installed on your system.

• Ubuntu/Debian: sudo apt-get install libsndfile1
• macOS (using Homebrew): brew install libsndfile
• Windows: Download and install the precompiled binaries from the official website, extract them and add the folder to path.

Supported operations, precisions and devices

V2.4

✅ = Supported ❌ = Not supported
*ProtoBuf Raven is only available for custom acoustic models.

V3.0

The V3.0 acoustic and geo models are both available in four backends (TFLite/LiteRT, ProtoBuf, PyTorch and ONNX). The PyTorch backend requires birdnet[pt] and the ONNX backend requires birdnet[onnx].

✅ = Supported ❌ = Not supported

Load a V3.0 model by passing the version and backend, e.g. birdnet.load("acoustic", "3.0", "onnx") or birdnet.load("geo", "3.0", "tf").

Perch V2

Note: Perch can not be run on Intel macOS systems due to compatibility issues with TensorFlow.

Example usage

Identify species within an audio file

import birdnet

model = birdnet.load("acoustic", "2.4", "tf")

predictions = model.predict(
  "example/soundscape.wav",
  # predict only the species from the file
  custom_species_list="example/species_list.txt",
)

predictions.to_csv("example/predictions.csv")

The resulting predictions look like this (excerpt, scores may vary):

For a more detailed prediction you can take a look at example/predictions.csv.

Predict species for a given location and time

import birdnet

model = birdnet.load("geo", "2.4", "tf")

predictions = model.predict(42.5, -76.45, week=4)

predictions.to_csv("example/location.csv")

The resulting predictions look like this (excerpt, scores may vary; sorted alphabetically):

The full result is at example/location.csv.

Location of Log File

If something goes wrong, you can find the log file in the following locations:

• Windows: C:\Users\{user}\AppData\Local\Temp\birdnet.log
• Linux/MacOS: /tmp/birdnet.log

File formats

The audio models support all formats compatible with the SoundFile library (see here). This includes, but is not limited to, WAV, FLAC, OGG, and AIFF. The flexibility of supported formats ensures that the models can handle a wide variety of audio input types, making them adaptable to different use cases and environments.

• Supported: AIFC, AIFF, AU, AVR, CAF, FLAC, HTK, IRCAM, MAT4, MAT5, MP3, MPC2K, NIST, OGG, OPUS, PAF, PVF, RAW, RF64, SD2, SDS, SVX, VOC, W64, WAV, WAVEX, WVE, XI
• Not supported at the moment: AAC, M4A, WMA

Model formats and execution details

Depending on the model version, this project provides up to four model formats: Protobuf/Raven, TFLite, PyTorch and ONNX. All formats are designed to have identical precision up to 2 decimal places, with differences only appearing from the third decimal place onward.

• TFLite Model (tf): Limited to CPU execution only.
• ProtoBuf Model (pb): Can be executed on both GPU and CPU.
• PyTorch Model (pt, V3.0 only): Can be executed on both GPU and CPU. Requires birdnet[pt].
• ONNX Model (onnx, V3.0 only): Can be executed on both GPU and CPU. Requires birdnet[onnx].

Ensure your environment is configured to utilize the appropriate model and available hardware optimally.

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. Note that educational and research purposes are considered non-commercial use cases.

Citation

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}
}

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!