Skip to content

This GitHub repository features a music generation project using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) architecture. Trained on an Irish folk song dataset with ABC notations, the model learns patterns to generate unique musical sequences.

Notifications You must be signed in to change notification settings

SHUBH4M-KUMAR/Generative-Music-using-RNNs

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Music Generation with RNN (LSTM)

This GitHub repository features a music generation project using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) architecture. Trained on an Irish folk song dataset with ABC notations, the model learns patterns to generate unique musical sequences.

Sample Outputs: https://drive.google.com/drive/folders/1Qeh8aRggUsmKpXI0Vc18lO4gmjOHlWy-?usp=drive_link

Usage

  1. Open music_gen.ipynb in Google Colab.
  2. Connect to a GPU runtime for efficient training.
  3. Execute cells sequentially to train the model.

Dependencies

  • Python 3.7
  • TensorFlow
  • Keras
  • abcmidi
  • timidity

Install dependencies with:

pip install -r requirements.txt

Feel free to experiment with the notebook for music generation and contribute to the project. 🎶

Acknowledgments

MIT IntrotoDeeplearning for Datsset and Guidence. Feel free to contribute, report issues, or reach out with any suggestions or improvements. Happy music generation!

About

This GitHub repository features a music generation project using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) architecture. Trained on an Irish folk song dataset with ABC notations, the model learns patterns to generate unique musical sequences.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published