Detection and identification of bird calls

Description

This git repository is made to detect and recognize birds in sound files. It is part of the project of CentraleSupélec's S6.10.06 students.

Installation

The required Python modules are listed in the 'requirement.txt' file. Beware that CUDA necesitates a specific installation and may cause compatibility issues.

Usage

The 'creation_datasets' folder can be used to create the datasets to train and test the AI

To use the AI : put in the input folder a csv file with a column audio_id containing the list of the link to the file you want to test the model on ...

no extension on the audio_id column Note : the csv can have multiple columns, but only the audio_id is considered

To make the training : download and preprocess the dataset : in the terminal, put yourself in the "birdcall-detection" folder. Then type "make prepare" Wait until the download ends. Then you can launch the training with "make train" (still in the terminal). If you have not enough RAM, you'd better train models one by one. To do so, write (still in the terminal of the "birdcall-detection" folder) : python train -m 1 python train -m 2 python train -m 3 python train -m 4

Note : If you want to make your own dataset, in the train/data_training folder, put an "audio_files" folder and a csv with the adequate format : cnt,en,id,length ,class1,audio1.mp3, ,class1,audio2.mp3, ... ,class2,audio4.wav, ...

where audio.* is just the name of the corresponding audio file and *** are characters that needs to be there but are not important

Acknowledgment

A huge thank you to Fred Ngole-Mboula for his help and valuable advice.