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status](https://www.r-pkg.org/badges/version/birdnetR)](https://CRAN.R-project.org/package=birdnetR)\n\u003c!-- badges: end --\u003e\n\n`birdnetR` integrates [BirdNET](https://birdnet.cornell.edu/), a state‐of‐the‐art deep learning classifier for automated (bird) sound identification, into an R-workflow.\nThis 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.\n\n`birdnetR` is an R wrapper around the `birdnet` [Python package](https://github.com/birdnet-team/birdnet). 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.\nHowever, it does not include all the advanced features available in the [BirdNET Analyzer](https://github.com/birdnet-team/BirdNET-Analyzer). For advanced applications, such as training custom classifiers and validation, users should use the 'BirdNET Analyzer' directly.\n`birdnetR` is under active development, and changes may affect existing workflows.\n\n\n## Installation\n\nInstall the released version from CRAN:\n\n```r\ninstall.packages(\"birdnetR\")\n```\n\u003cbr\u003e\nor install the development version from GitHub with:\n\n```r\npak::pak(\"birdnet-team/birdnetR\")\n```\n\n\u003cdiv style=\"padding: 15px; margin-bottom: 20px; border: 1px solid #bce8f1; border-radius: 4px; background-color: #d9edf7; color: #31708f;\"\u003e\n\u003cstrong\u003eNote\u003c/strong\u003e\u003cbr\u003e\n 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.\n\u003c/div\u003e\n\n\n## Example use\n\nThis is a simple example using the `tflite` BirdNET model to predict species in an audio file.\n\n```r\n# Load the package\nlibrary(birdnetR)\n\n# Initialize a BirdNET model\nmodel \u003c- birdnet_model_tflite()\n\n# Path to the audio file (replace with your own file path)\naudio_path \u003c- system.file(\"extdata\", \"soundscape.mp3\", package = \"birdnetR\")\n\n# Predict species within the audio file\npredictions \u003c- predict_species_from_audio_file(model, audio_path)\n\n# Get most probable prediction within each time interval\nget_top_prediction(predictions)\n\n```\n\n\n## Citation\n\nFeel free to use `birdnetR` for your acoustic analyses and research. If you do, please cite as:\n\n```bibtex\n@article{kahl2021birdnet,\n  title={BirdNET: A deep learning solution for avian diversity monitoring},\n  author={Kahl, Stefan and Wood, Connor M and Eibl, Maximilian and Klinck, Holger},\n  journal={Ecological Informatics},\n  volume={61},\n  pages={101236},\n  year={2021},\n  publisher={Elsevier}\n}\n```\n\n\n## License\n\n- **Source Code**: The source code for this project is licensed under the [MIT License](https://opensource.org/licenses/MIT).\n- **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)](https://creativecommons.org/licenses/by-nc-sa/4.0/).\n\nPlease ensure you review and adhere to the specific license terms provided with each model. 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