(untitled) (International Journal of Engineering and Computer Science, 2024)
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.