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\u003c1.0.0","direct":true,"kind":"runtime","optional":false},{"id":19277147845,"package_name":"tensorflow","ecosystem":"pypi","requirements":"==2.15.1","direct":true,"kind":"runtime","optional":false},{"id":19277147846,"package_name":"resampy","ecosystem":"pypi","requirements":"\u003e=0.4.3, \u003c1.0.0","direct":true,"kind":"runtime","optional":false},{"id":19277147847,"package_name":"soundfile","ecosystem":"pypi","requirements":"\u003e=0.12.1, \u003c1.0.0","direct":true,"kind":"runtime","optional":false},{"id":19277147848,"package_name":"scipy","ecosystem":"pypi","requirements":"\u003e=1.9.3, \u003c2.0.0","direct":true,"kind":"runtime","optional":false},{"id":19277147849,"package_name":"ordered-set","ecosystem":"pypi","requirements":"\u003e=4.1.0, \u003c5.0.0","direct":true,"kind":"runtime","optional":false},{"id":19277147850,"package_name":"tqdm","ecosystem":"pypi","requirements":"\u003e=4.64.1, \u003c5.0.0","direct":true,"kind":"runtime","optional":false},{"id":19277147851,"package_name":"numpy","ecosystem":"pypi","requirements":"\u003e=1.23.5, \u003c2.0.0","direct":true,"kind":"runtime","optional":false}]}],"score":12.849409641877076,"created_at":"2024-11-06T00:07:49.795Z","updated_at":"2026-04-04T13:04:10.976Z","avatar_url":"https://github.com/birdnet-team.png","language":"Python","category":"Biosphere","sub_category":"Avian Monitoring and Analysis","monthly_downloads":2137,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# birdnet\n\n[![PyPI](https://img.shields.io/pypi/v/birdnet.svg)](https://pypi.python.org/pypi/birdnet)\n![PyPI](https://img.shields.io/pypi/pyversions/birdnet.svg)\n[![MIT](https://img.shields.io/github/license/birdnet-team/birdnet.svg)](https://github.com/birdnet-team/birdnet/blob/main/LICENSE.md)\n\nA Python library for identifying bird species by their sounds.\n\nThe 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](https://github.com/birdnet-team/BirdNET-Analyzer). Some features might only be available in the BirdNET Analyzer and not in this package.\n\nPlease note that the project is under active development, so you might encounter changes that could affect your current workflow. We recommend checking for updates regularly.\n\nThe package is also available as an R package at: [birdnetR](https://github.com/birdnet-team/birdnetR).\n\n## Installation\n\n```sh\n# For CPU users\npip install birdnet --user\n\n# For GPU users (NVIDIA GPU driver and CUDA need to be installed in advance)\npip install birdnet[and-cuda] --user\n\n# For edge devices (e.g., Raspberry Pi)\npip install birdnet[litert] --user\n```\n\n## Example usage\n\n### Identify species within an audio file\n\n```py\nfrom pathlib import Path\n\nimport birdnet\nfrom birdnet.utils import get_species_from_file\n\nmodel = birdnet.load(\"acoustic\", \"2.4\", \"tf\", lang=\"en_us\")\n\n# predict only the species from the file\npredictions = model.predict(\n  \"example/soundscape.wav\",\n  custom_species_list=get_species_from_file(Path(\"example/species_list.txt\")),\n)\n\npredictions.to_csv(\"example/predictions.csv\")\n```\n\nThe resulting predictions look like this (excerpt, scores may vary):\n\n\u003cimg src=\"example/scores_preview.png\" alt=\"Preview\" style=\"max-width: 700px; height: auto;\"\u003e\n\nFor a more detailed prediction you can take a look at [example/predictions.csv](example/scores.csv).\n\n### Predict species for a given location and time\n\n```py\nimport birdnet\n\nmodel = birdnet.load(\"geo\", \"2.4\", \"tf\", lang=\"en_us\")\n\npredictions = model.predict(42.5, -76.45, week=4)\n\npredictions.to_csv(\"example/location.csv\")\n```\n\n\u003cimg src=\"example/location_preview.png\" alt=\"Preview\" style=\"max-width: 300px; height: auto;\"\u003e\n\nThe result is at [example/location.csv](example/location.csv).\n\n### Location of Log File\n\nIf something goes wrong, you can find the log file in the following locations:\n\n- Windows: `C:\\Users\\{user}\\AppData\\Local\\Temp\\birdnet.log`\n- Linux/MacOS: `/tmp/birdnet.log`\n\n## File formats\n\nThe audio models support all formats compatible with the SoundFile library (see [here](https://python-soundfile.readthedocs.io/en/0.11.0/#read-write-functions)). 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.\n\n- 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\n- Not supportet at the moment: AAC, M4A, WMA \n\n\n## Model formats and execution details\n\nThis project provides two model formats: Protobuf/Raven and TFLite. Both models are designed to have identical precision up to 2 decimal places, with differences only appearing from the third decimal place onward.\n\n- **TFLite Model**: This model is limited to CPU execution only.\n- **Protobuf Model**: This model can be executed on both GPU and CPU.\n\nEnsure your environment is configured to utilize the appropriate model and available hardware optimally.\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. Note that educational and research purposes are considered non-commercial use cases.\n\n## Citation\n\nFeel free to use birdnet 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## Funding\n\nOur 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.\n\nThe 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.\n\n## Partners\n\nBirdNET is a joint effort of partners from academia and industry.\nWithout these partnerships, this project would not have been possible.\nThank you!\n\n![Our partners](https://tuc.cloud/index.php/s/KSdWfX5CnSRpRgQ/download/box_logos.png)\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/303292","html_url":"https://ost.ecosyste.ms/projects/303292"}