Song Lyric Meaning Generator Using Transformer (Case Study: Drake's Song Lyrics) - Tubagus Alwasi'i; Sam Farisa Chaerul Haviana (Telkom University, 2025)
The limited availability of structured and credible sources that interpret Drake’s song lyrics makes it difficult for listeners to fully grasp the meaning and emotional depth within his music. This study aims to develop a web-based lyric meaning generator capable of automatically interpreting Drake’s lyrics using the Transformer architecture. The system employs the LLaMA 3 model, which is fine-tuned through Low-Rank Adaptation (LoRA) to improve training efficiency while maintaining high semantic accuracy. The curated dataset consists of Drake’s song lyrics, their corresponding interpretations, and metadata such as album and reference sources. Data preprocessing and supervised fine-tuning were conducted using the Hugging Face framework in Google Colab. A gradio-based web application was implemented with a Retrieval-Augmented Generation (RAG) mechanism to enhance contextual relevance during inference. Evaluation metrics, including Semantic Similarity and ROUGE-L, were applied to measure the model’s ability to produce coherent and contextually aligned interpretations. The results demonstrate that the fine-tuned LLaMA 3 model effectively generates accurate lyric meanings while reducing computational cost. Overall, this study highlights the potential of Transformer-based models to bridge the gap between music and natural language understanding, particularly in analyzing metaphorical and emotion-rich song lyrics.