Caution
Version 1.0.0 is currently in development and will be a breaking release. Several functions have been renamed or removed. If you are upgrading from 0.x, see the Upgrading from birdnetR 0.x section below.
birdnetR integrates BirdNET, a state‐of‐the‐art deep learning classifier for automated (bird) sound identification, into an R-workflow.
This package will simplify the analysis of (large) audio datasets from bioacoustic projects, allowing researchers to easily apply machine learning techniques—even without a background in computer science.
birdnetR is an R wrapper around the birdnet Python package. It provides the core functionality to analyze audio using the pre-trained 'BirdNET' model or a custom classifier, and to predict bird species occurrence based on location and week of the year.
However, it does not include all the advanced features available in the BirdNET Analyzer. For advanced applications such as training custom classifiers or accessing the broader BirdNET feature set, refer to the BirdNET Analyzer directly.
Install the released version from CRAN:
install.packages("birdnetR")or install the development version from GitHub with:
pak::pak("birdnet-team/birdnetR")Python dependencies are installed on demand, meaning they are installed when you use them for the first time. This will result in longer initial setup.
This is a simple example using BirdNET to predict species in an audio file.
# Load the package
library(birdnetR)
# Load a BirdNET acoustic model
model <- load_birdnet()
# Path to the audio file (replace with your own file path)
audio_path <- system.file("extdata", "soundscape.mp3", package = "birdnetR")
# Predict species within the audio file
predictions <- predict(model, audio_path)
# Convert predictions to a data frame
df <- as.data.frame(predictions)
predict() returns a lightweight Python-backed object — not an R data frame.
Use as.data.frame() when you need the results in R for further analysis.
For file-based pipelines (especially with large datasets), it is more efficient
to skip conversion and write results directly:
predictions <- predict(model, audio_path)
# More efficient for large runs — stays in Python:
write_predictions(predictions, "results.parquet")
# Only when you need results in R:
df <- as.data.frame(predictions)Version 1.0 is a breaking release. The table below maps removed functions to their replacements:
| Removed | Replacement |
|---|---|
birdnet_model_tflite(...) |
load_birdnet(..., backend = "tf", library = "tflite") |
birdnet_model_protobuf(...) |
load_birdnet(..., backend = "pb") |
birdnet_model_meta(...) |
load_birdnet(type = "geo") |
birdnet_model_custom(...) |
load_custom(...) |
predict_species_from_audio_file(model, ...) |
predict(model, files = ...) |
predict_species_at_location_and_time(model, ...) |
predict(model, latitude = ..., longitude = ...) |
labels_path() / read_labels() |
get_species_list(model) |
available_languages() |
supported_languages() |
get_top_prediction() |
Removed; use dplyr or birdnetTools |
See NEWS.md for the full changelog.
birdnetR is part of a set of related tools:
- BirdNET-Analyzer — the upstream desktop/CLI application for custom classifier training and advanced BirdNET features.
- birdnetTools — an R package for post-processing and validating BirdNET predictions: filtering, visualisation, and interactive threshold setting.
Feel free to use birdnetR 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}
}- 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.
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.
BirdNET is a joint effort of partners from academia and industry. Without these partnerships, this project would not have been possible. Thank you!
