Music generation using RNNs (Keras)
A Python project for algorithmic music generation using recurrent neural networks.
What is Music generation using RNNs (Keras)?
GRUV is a Python project for algorithmic music generation using recurrent neural networks. It processes raw audio files (MP3, FLAC, WAV) to train deep learning models that can generate new musical compositions. The system converts audio into feature representations, trains RNN models on musical patterns, and produces original music based on learned structures.
Developers and researchers interested in music generation, algorithmic composition, and applying deep learning to audio processing tasks. Particularly suitable for those wanting to experiment with neural networks for creative audio applications.
GRUV offers a complete pipeline for training custom music generation models on personal audio collections, with direct waveform processing that captures more musical nuance than MIDI-based approaches. Its modular design allows for experimentation with different network architectures and generation algorithms.
Overview
GRUV is a Python project for algorithmic music generation.
Use Cases
Best For
- Generating original music from personal audio collections
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