Data-Driven Approach to Automated Lyric Generation

Abstract

This project leverages Recurrent Neural Networks
(RNNs) to generate coherent and contextually relevant song
lyrics. The methodology includes extensive text preprocessing and
dataset creation, followed by the construction of a robust model
featuring Embedding, Gated Recurrent Unit (GRU), Dense, and
Dropout layers. The model is compiled and trained using the
Adam optimizer, with checkpointing to monitor and optimize the
training process. Upon successful training on a comprehensive
lyrics dataset, the model is thoroughly evaluated and fine-tuned
to enhance performance. Finally, the model generates new lyrics
from a given seed, showcasing its ability to learn intricate
linguistic patterns and structures, thereby offering a powerful
tool for creative and original lyric composition.