This project focuses on classifying music genres using a neural network model. It demonstrates how to preprocess audio files, extract relevant features, build a neural network architecture, and evaluate the model's performance.
Before running the project, ensure you have the following dependencies installed:
- Python 3.x
numpylibrosajoblibscikit-learntensorflowkeras
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Clone this repository to your local machine using:
bash git clone https://github.com/your-username/music-genre-classification.git cd music-genre-classification
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Organize your music dataset in the following structure:
├── Data │ ├── genres │ ├── genre1 │ ├── song1.wav │ ├── song2.wav │ └── ... │ ├── genre2 │ ├── song1.wav │ ├── song2.wav │ └── ... │ └── ... └── music_genre_model.h5 (Pretrained model)
3.DATASET LINK :- https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification
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Update the
data_directoryin themain.pyfile to point to your dataset directory. -
Run the
main.pyscript to preprocess audio files, train the neural network model, and save it.bash python main.py
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To predict new audio files, update the
new_audio_fileslist in themain.pyfile with the paths to your test audio files. -
Run the
main.pyscript again to load the pretrained model and predict the genres of the new audio files.bash python main.py
The project trains a neural network model to classify music genres based on extracted features from audio files. It prints the predicted genres for the provided test audio files.
This project was inspired by the open-source contributions of the machine learning and audio processing communities.
This project is licensed under the MIT License.
Feel free to modify and extend the project according to your needs. If you find this project helpful, consider giving it a star! If you have any questions or suggestions, please open an issue or a pull request.
Please make sure to customize the paths, descriptions, and any additional details to suit your project and its requirements.