Music Genre Classification

Overview

This project focuses on classifying music tracks into various genres using machine learning techniques. By analyzing audio features, the model predicts the genre of a given music track, aiding in music organization and recommendation systems.

Features

• Accurately classifies music tracks into predefined genres.
• Utilizes a trained neural network model (Trained_Model.h5).
• Includes scripts for training (genre_classifier.ipynb) and testing (test_genre.ipynb).

Installation

1. Clone the repository:

   git clone https://github.com/ronnie-allen/Music_Genre_Model.git
   cd Music_Genre_Model

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

   Note: Ensure that the requirements.txt file lists all necessary packages.

Usage

1. Training the Model:

   • Use the genre_classifier.ipynb notebook to train the model on your dataset.
   • Ensure the dataset is placed in the Music_Genre_Dataset directory.

2. Testing the Model:

   • Use the test_genre.ipynb notebook to test the model's performance on new data.
   • Place your test audio files in the Music_Testing directory.

3. Predicting Genre for a New Track:

   • Run the Music_genre.py script with the path to your audio file:

     python Music_genre.py --file_path path_to_your_audio_file
     

Dataset

The project uses a dataset of music tracks categorized by genre. Ensure that your dataset is organized appropriately within the Music_Genre_Dataset directory for training purposes.

Model

The trained model is saved as Trained_Model.h5. You can load this model for predictions or retrain it using the provided training scripts.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Submit a pull request with detailed changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• Librosa for audio processing.
• Keras for building the neural network.
• GTZAN Music Genre Dataset for providing the dataset.

Contact

For any issues or feedback, feel free to open an issue on this repository or contact me directly.

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Happy coding! 🎶