Quran Recitation Audio Classification

Overview

The Quran Recitation Audio Classification project aims to classify different recitations of the Quran using machine learning techniques. It involves preprocessing Quranic audio data, extracting relevant features, training machine learning models, and deploying a classification system.

Table of Contents

• Overview
• Setup
• Usage
• Expected Output
• License
• Contributing
• Contact

Setup

Prerequisites

• Python (>=3.6)
• Libraries: pandas, librosa, scikit-learn, joblib

Installation

1. Clone the repository:

  git clone https://github.com/your-username/quran-recitation-audio-classification.git
  cd quran-reInstall dependencies:

2. Install dependencies:

pip install -r requirements.txt

Usage

1.Data Preparation:

• Ensure your Quran recitation audio files are organized in a structured format.
• Update config.py or relevant scripts with your dataset paths and configurations.

2.Data Extract:

• download or extract the data from kaggle website or any other website

3.Data Path:

• Example of configuring dataset paths in config.py

DATA_PATH = 'path/to/dataset'

4.Feature Extraction:

• Run feature extraction scripts to preprocess audio data and extract relevant features:

python extract_features.py

5.Model Training:

• Train the classification model using extracted features:

python train_model.py

6.Model Evaluation:

• Evaluate the trained model's performance:

python evaluate_model.py

7.Deployment:

Deploy the model for real-time classification (optional).

Expected Output

• Classification of Quran recitations based on extracted audio features.
• Metrics such as accuracy, precision, recall, and F1-score for model evaluation.

Contributing

• Fork the repository.
• Create a new branch (git checkout -b feature-branch).
• Commit your changes (git commit -am 'Add new feature').
• Push to the branch (git push origin feature-branch).
• Create a new Pull Request.

Contact

For questions or suggestions, please contact usmanazulfiqar2001@gmail.com.

vbnet citation-audio-classification