Data-Driven Approach to Automated Lyric Generation

Abstract

This project leverages Recurrent Neural Networks(RNNs) to generate coherent and contextually relevant songlyrics. The methodology includes extensive text preprocessing anddataset creation, followed by the construction of a robust modelfeaturing Embedding, Gated Recurrent Unit (GRU), Dense, andDropout layers. The model is compiled and trained using theAdam optimizer, with checkpointing to monitor and optimize thetraining process. Upon successful training on a comprehensivelyrics dataset, the model is thoroughly evaluated and fine-tunedto enhance performance. Finally, the model generates new lyricsfrom a given seed, showcasing its ability to learn intricatelinguistic patterns and structures, thereby offering a powerfultool for creative and original lyric composition.