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.buildinfo

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+# Sphinx build info version 1
+# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 700970f074d6922cbb969b93b220f545
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/faq.rst.txt

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+FAQ
+===
+
+Creating your own species list
+------------------------------
+
+When editing your own species_list.txt file, make sure to copy species names from the labels file of each model.
+
+You can find label files in the checkpoints folder, e.g., checkpoints/V2.3/BirdNET_GLOBAL_3K_V2.3_Labels.txt.
+
+Species names need to consist of scientific name_common name to be valid.
+
+You can generate a species list for a given location using :ref:`species.py <cli-species>`.

_sources/index.rst.txt

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+BirdNET-Analyzer Documentation
+==============================
+
+Welcome to the BirdNET-Analyzer documentation! This guide provides detailed information on installing, configuring, and using BirdNET-Analyzer.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   installation
+   usage
+   models
+   faq
+   showroom
+
+Introduction
+------------
+
+BirdNET-Analyzer is an open-source tool for analyzing bird sounds using machine learning models. It processes audio recordings and identifies bird species based on their vocalizations.
+
+Citing BirdNET-Analyzer
+-----------------------
+
+Feel free to use BirdNET for your acoustic analyses and research. If you do, please cite as:
+
+.. code-block:: bibtex
+
+   @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}
+   }
+
+License
+-------
+
+This work is licensed under a `Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License <https://creativecommons.org/licenses/by-nc-sa/4.0/>`_.
+
+About
+-----
+
+Developed by the `K. Lisa Yang Center for Conservation Bioacoustics <https://www.birds.cornell.edu/ccb/>`_ at the `Cornell Lab of Ornithology <https://www.birds.cornell.edu/home>`_ in collaboration with `Chemnitz University of Technology <https://www.tu-chemnitz.de/index.html>`_.
+
+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]
+
+We also have a discussion forum on `Reddit <https://www.reddit.com/r/BirdNET_Analyzer/>`_ if you have a general question or just want to chat.
+
+Have a question, remark, or feature request? Please start a new issue thread to let us know. Feel free to submit a pull request.

_sources/installation.rst.txt

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+.. _installation:
+
+Installation
+============
+
+Raven Pro
+---------
+
+If you want to analyze audio files without any additional coding or package install, you can now use `Raven Pro software <https://ravensoundsoftware.com/software/raven-pro/>`_ to run BirdNET models.
+After download, BirdNET is available through the new "Learning detector" feature in Raven Pro.
+| For more information on how to use this feature, please visit the `Raven Pro Knowledge Base <https://ravensoundsoftware.com/article-categories/learning-detector/>`_.
+
+`Download the newest model version here <https://tuc.cloud/index.php/s/2TX59Qda2X92Ppr/download/BirdNET_GLOBAL_6K_V2.4_Model_Raven.zip>`_, extract the zip-file and move the extracted folder to the Raven models folder. On Windows, the models folder is `C:\\Users\\<Your user name>\\Raven Pro 1.6\\Models`. Start Raven Pro and select *BirdNET_GLOBAL_6K_V2.4_Model_Raven* as learning detector.
+
+Python Package
+--------------
+
+The easiest way to setup BirdNET on your machine is to install `birdnetlib <https://joeweiss.github.io/birdnetlib/>`_ or `birdnet <https://pypi.org/project/birdnet/>`_ through pip with:
+
+.. code-block:: bash
+
+   pip install birdnetlib
+
+or
+
+.. code-block:: bash
+
+   pip install birdnet
+
+Please take a look at the `birdnetlib user guide <https://joeweiss.github.io/birdnetlib/#using-birdnet-analyzer>`_ on how to analyze audio with `birdnetlib`. 
+
+When using the `birdnet`-package, you can run BirdNET with:
+
+.. code-block:: python
+
+   from pathlib import Path
+   from birdnet.models import ModelV2M4
+
+   # create model instance for v2.4
+   model = ModelV2M4()
+
+   # predict species within the whole audio file
+   species_in_area = model.predict_species_at_location_and_time(42.5, -76.45, week=4)
+   predictions = model.predict_species_within_audio_file(
+      Path("soundscape.wav"),
+      filter_species=set(species_in_area.keys())
+   )
+
+   # get most probable prediction at time interval 0s-3s
+   prediction, confidence = list(predictions[(0.0, 3.0)].items())[0]
+   print(f"predicted '{prediction}' with a confidence of {confidence:.6f}")
+   # predicted 'Poecile atricapillus_Black-capped Chickadee' with a confidence of 0.814056
+
+| For more examples and documentation, make sure to visit `pypi.org/project/birdnet/ <https://pypi.org/project/birdnet/>`_.
+| For any feature request or questions regarding `birdnet`, please add an issue or PR at `github.com/birdnet-team/birdnet <https://github.com/birdnet-team/birdnet>`_.
+
+Command line installation
+-------------------------
+
+Requires Python 3.10.
+
+Clone the repository
+
+.. code-block:: bash
+
+   git clone https://github.com/kahst/BirdNET-Analyzer.git
+   cd BirdNET-Analyzer
+
+Install the packages
+
+.. code-block:: bash
+
+   pip install -r requirements.txt
+
+Verify the installation
+
+.. code-block:: bash
+
+   python -m birdnet_analyzer.analyze
+
+GUI installation
+----------------
+
+You can download the latest BirdNET-Analyzer installer from our `Releases <https://github.com/kahst/BirdNET-Analyzer/releases/>`_ page. This installer provides an easy setup process for running BirdNET-Analyzer on your system. Make sure to check to select the correct installer for your system.
+
+.. note::
+
+   On Windows, the smartscreen filter might block the installer. In this case, click on "More info" and "Run anyway" to proceed with the installation.
+
+.. note::
+    | NOTE: Installation was only tested on M1 and M2 chips.
+    | Feedback on older Intel CPUs or newer M3 chips is welcome!

_sources/models.rst.txt

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+Models
+======
+
+
+V2.4, June 2023
+---------------
+
+* more than 6,000 species worldwide
+* covers frequencies from 0 Hz to 15 kHz with two-channel spectrogram (one for low and one for high frequencies)
+* 0.826 GFLOPs, 50.5 MB as FP32
+* enhanced and optimized metadata model
+* global selection of species (birds and non-birds) with 6,522 classes (incl. 10 non-event classes)
+
+Technical Details
+^^^^^^^^^^^^^^^^^
+
+* 48 kHz sampling rate (we up- and downsample automatically and can deal with artifacts from lower sampling rates)
+* we compute 2 mel spectrograms as input for the convolutional neural network:
+
+    * first one has fmin = 0 Hz and fmax = 3000; nfft = 2048; hop size = 278; 96 mel bins
+    * second one has fmin = 500 Hz and fmax = 15 kHz; nfft = 1024; hop size = 280; 96 mel bins
+
+* both spectrograms have a final resolution of 96x511 pixels
+* raw audio will be normalized between -1 and 1 before spectrogram conversion
+* we use non-linear magnitude scaling as mentioned in `Schlüter 2018 <http://ceur-ws.org/Vol-2125/paper_181.pdf>`_
+* V2.4 uses an EfficienNetB0-like backbone with a final embedding size of 1024
+* See `this comment <https://github.com/kahst/BirdNET-Analyzer/issues/177#issuecomment-1772538736>`_ for more details
+
+Species range model V2.4 - V2, Jan 2024
+---------------------------------------
+
+* updated species range model based on eBird data
+* more accurate (spatial) species range prediction
+* slightly increased long-tail distribution in the temporal resolution 
+* see `this discussion post <https://github.com/kahst/BirdNET-Analyzer/discussions/234>`_ for more details
+
+
+Using older models
+------------------
+
+Older models can also be used as custom classifiers in the GUI or using the `--classifier` argument in the `birdnet_analyzer.analyze` command line interface.
+
+Just download your desired model version and unzip.
+
+| GUI: Select the \*_Model_FP32.tflite file under **Species selection > Custom classifier**
+| CLI: `python -m birdnet_analyzer ... --classifier <path_to_*_Model_FP32.tflite>`
+
+Model Version History
+---------------------
+
+V2.4
+^^^^
+
+- more than 6,000 species worldwide
+- covers frequencies from 0 Hz to 15 kHz with two-channel spectrogram (one for low and one for high frequencies)
+- 0.826 GFLOPs, 50.5 MB as FP32
+- enhanced and optimized metadata model
+- global selection of species (birds and non-birds) with 6,522 classes (incl. 10 non-event classes)
+- Download here: `BirdNET-Analyzer-V2.4.zip <https://drive.google.com/file/d/1ixYBPbZK2Fh1niUQzadE2IWTFZlwATa3>`_
+
+V2.3
+^^^^
+
+- slightly larger (36.4 MB vs. 21.3 MB as FP32) but smaller computational footprint (0.698 vs. 1.31 GFLOPs) than V2.2
+- larger embedding size (1024 vs 320) than V2.2 (hence the bigger model)
+- enhanced and optimized metadata model
+- global selection of species (birds and non-birds) with 3,337 classes (incl. 10 non-event classes)
+- Download here: `BirdNET-Analyzer-V2.3.zip <https://drive.google.com/file/d/1hhwQBVBngGnEhmqYeDksIW8ZY1FJmwyi>`_
+
+V2.2
+^^^^
+
+- smaller (21.3 MB vs. 29.5 MB as FP32) and faster (1.31 vs 2.03 GFLOPs) than V2.1
+- maintains same accuracy as V2.1 despite more classes
+- global selection of species (birds and non-birds) with 3,337 classes (incl. 10 non-event classes)
+- Download here: `BirdNET-Analyzer-V2.2.zip <https://drive.google.com/file/d/166w8IAkXGKp6ClKb8vaniG1DmOr8Fwem>`_
+
+V2.1
+^^^^
+
+- same model architecture as V2.0
+- extended 2022 training data
+- global selection of species (birds and non-birds) with 2,434 classes (incl. 10 non-event classes)
+- Download here: `BirdNET-Analyzer-V2.1.zip <https://drive.google.com/file/d/15cvPiezn_6H2tQs1FGMVrVdqiwLjLRms>`_
+
+V2.0
+^^^^
+
+- same model design as 1.4 but a bit wider
+- extended 2022 training data
+- global selection of species (birds and non-birds) with 1,328 classes (incl. 10 non-event classes)
+- Download here: `BirdNET-Analyzer-V2.0.zip <https://drive.google.com/file/d/1h2Tbk_29ghNdK62ynrdRWyxT4H1fpFGs>`_
+
+V1.4
+^^^^
+
+- smaller, deeper, faster
+- only 30% of the size of V1.3
+- still linear spectrogram and EfficientNet blocks
+- extended 2021 training data
+- 1,133 birds and non-birds for North America and Europe
+- Download here: `BirdNET-Analyzer-V1.4.zip <https://drive.google.com/file/d/1h14-Y8dOrPr9XCWfIoUjlWMJ9aWyNkKa>`_
+
+V1.3
+^^^^
+
+- Model uses linear frequency scale for spectrograms
+- uses V2 fusion blocks and V1 efficient blocks
+- extended 2021 training data
+- 1,133 birds and non-birds for North America and Europe
+- Download here: `BirdNET-Analyzer-V1.3.zip <https://drive.google.com/file/d/1h0nJzPjyJWbkfPyaWpS332xUwzDOygs9>`_
+
+V1.2
+^^^^
+
+- Model based on EfficientNet V2 blocks
+- uses V2 fusion blocks and V1 efficient blocks
+- extended 2021 training data
+- 1,133 birds and non-birds for North America and Europe
+- Download here: `BirdNET-Analyzer-V1.2.zip <https://drive.google.com/file/d/1h-il_W6t8Tz_XHrRMO1zcp_ThYp9QPLK>`_
+
+V1.1
+^^^^
+
+- Model based on Wide-ResNet (aka "App model")
+- extended 2021 training data
+- 1,133 birds and non-birds for North America and Europe
+- Download here: `BirdNET-Analyzer-V1.1.zip <https://drive.google.com/file/d/1gzpwiCAf2HkfcAmlRq1K9Q0KrDsd5nGP>`_
+
+App Model
+^^^^^^^^^
+
+- Model based on Wide-ResNet
+- ~3,000 species worldwide
+- currently deployed as BirdNET app model
+- Download here: `BirdNET-Analyzer-App-Model.zip <https://drive.google.com/file/d/1gxkxPFlaTYxHFqAODDHYGUX8uEkZDWaL>`_