README.md

---
title: BirdCLEF 2024
emoji: 👁
colorFrom: yellow
colorTo: pink
sdk: gradio
sdk_version: 4.36.0
app_file: app.py
pinned: false
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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# BirdCLEF 2024: Bird Species Identification from Audio

## Overview

This project is part of the BirdCLEF 2024 competition, focusing on identifying bird species from audio recordings. The
target region is the Western Ghats, a significant biodiversity hotspot in India. The goal is to develop a machine
learning model capable of accurately predicting bird species from audio recordings, with a particular emphasis on
under-studied species.

## Author

This project was created by:

- **Name**: LAGHJAJ ABDELLATIF
- **Email**: [email protected]

## Screenshots

|          **Sketch**          |       **Main App**        |       **Result**        |
|:----------------------------:|:-------------------------:|:-----------------------:|
| ![Sketch](images/sketch.png) | ![Main App](images/1.png) | ![Result](images/2.png) |

## Project Components

1. **Data Collection**
2. **Preprocessing**
3. **Model Training**
4. **Prediction and Visualization**
5. **User Interface**

## Setup Instructions

### Prerequisites

Ensure you have the following installed:

- Python 3.8 or higher
- Required Python packages (specified below)

### Installation

1. Clone the repository:
    ```bash
    git clone https://github.com/ISEF-soft/introduction-to-machine-learning-challenge-abdellatif-laghjaj
    cd introduction-to-machine-learning-challenge-abdellatif-laghjaj
    ```

2. Create and activate a virtual environment:
    ```bash
    python -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
    ```

3. Install the required packages:
    ```bash
    pip install -r requirements.txt
    ```

### Directory Structure

Ensure your project directory is structured as follows:

```
birdclef2024/
│
├── sounds/
│ ├── asbfly.ogg
│ ├── bkwsti.ogg
│ └── comros.ogg
│
|── .gitignore
├── model.joblib
├── data.csv
├── app.py
|── main.ipynb
├── README.md
└── requirements.txt
```

### Files Description

- `sounds/`: Directory containing sample audio files.
- `model.joblib`: Pre-trained machine learning model.
- `data.csv`: CSV file containing bird species metadata.
- `app.py`: Main Python script to run the project.
- `main.ipynb`: Jupyter notebook containing the project code.
- `README.md`: This readme file.
- `requirements.txt`: List of Python packages required.

### Running the Project

1. Ensure you have the correct directory structure and all necessary files.
2. Run the `app.py` script to start the Gradio interface:
    ```bash
    python apy.py
    ```

### Usage

Once the Gradio interface is launched, you can:

- **Upload an audio recording** of a bird to identify the species.
- **Select a sample** from the provided audio files.

The interface will display:

- The **predicted bird species**.
- The **prediction confidence**.
- A **map** showing the location of the recording.
- A **spectrogram** of the audio recording.

## Acknowledgements

This project is based on the BirdCLEF 2024 competition. Special thanks to the organizers and contributors who provided
the dataset and resources.