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+# 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
+To use the AI :
+put in the input folder :
+an "audio_files" folder with the audios and a csv file containing the audios you want to test. The format of the csv is :
+audio_id
+audio1
+audio2
+...
+
+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.
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