Peupleute

Analysis and identification of bird sounds with Python.

Requirements

• Python 3.9.x

Installing Dependencies

Installation is as simple as:

    pip install -r requirements.txt

Notebook

To browse the notebook just run the following command:

    jupyter notebook

Then open your browser at http://localhost:8888/notebooks/notebooks/birdnet.ipynb

Identification

To identify birds in a record, you can use the birdnet model using:

    cd birdnet && python analyze.py --i '../notebooks/songs/pic-vert.wav'

And then read the result in the generated result.csv file.

Test model

You can test the birdnet model against a bunch of recording of bird songs by following these steps.

First, you have to download some bird songs, you can use the dowload_bird_songs.py for it. Feel free to update the list of birds you want to get records from.

    python dowload_bird_songs.py

Otherwise you can just add manually new songs, following this directory structure:

- dataset
    - audio
        - HouseSparrow
            - song1.mp3
            - song2.mp3
        - CommonStarling
            - song1.mp3
            - song2.mp3

And then test the model against the recordings:

    python test_model.py

NOTE: You should name the bird using the vernacular name or scientific name used in the birdnet/model/labels.txt file.

REST API

To start the server that allows bird songs identification throught a REST API, you have to start the server using:

    fastapi dev ./api/main.py

And then make a POST request on the http://localhost:8080/identify/ passing a record file. You can use http://localhost:8080/docs or Bruno to make the request using an interface.

TODO

• Try using this tensorflow model into a web browser: https://www.tensorflow.org/js/tutorials/conversion/import_saved_model?hl=fr
• Train the model with new species
• Display a Fourier Transform using Matplotlib
• Implement the MFCCs algorithm and test it against the dataset
• Try other models Up to date (and heavier) birdnet model: https://github.com/kahst/BirdNET-Analyzer https://github.com/gojibjib/jibjib-model https://huggingface.co/dima806/bird_sounds_classification https://huggingface.co/dennisjooo/Birds-Classifier-EfficientNetB2