Optimizing the Songwriting Process: Genre-Based Lyric Generation Using Deep Learning Models

TL;DR

This paper presents a deep learning approach to generate genre-specific song lyrics, aiming to streamline the songwriting process and produce lyrics that fit different musical genres effectively.

Contribution

It introduces a novel preprocessing method and compares seq2seq and LSTM models for genre-based lyric generation, demonstrating their potential to accelerate songwriting.

Findings

Baseline model achieved higher recall (ROUGE) in lyric generation.

Both models showed similar precision (BLEU) scores.

Generated lyrics were comprehensible and genre-distinctive.

Abstract

The traditional songwriting process is rather complex and this is evident in the time it takes to produce lyrics that fit the genre and form comprehensive verses. Our project aims to simplify this process with deep learning techniques, thus optimizing the songwriting process and enabling an artist to hit their target audience by staying in genre. Using a dataset of 18,000 songs off Spotify, we developed a unique preprocessing format using tokens to parse lyrics into individual verses. These results were used to train a baseline pretrained seq2seq model, and a LSTM-based neural network models according to song genres. We found that generation yielded higher recall (ROUGE) in the baseline model, but similar precision (BLEU) for both models. Qualitatively, we found that many of the lyrical phrases generated by the original model were still comprehensible and discernible between which genres they fit into, despite not necessarily being the exact the same as the true lyrics. Overall, our results yielded that lyric generation can reasonably be sped up to produce genre-based lyrics and aid in hastening the songwriting process.