MelodyCraft: A Prompt-Based Modular AI Framework for Synchronized Lyrics and Instrumental Music Generation

Current artificial intelligence (AI) music generation systems are usually not synchronized with lyrics and melodies, emotional correlativity, or objective assessment criteria. To overcome these weaknesses, this study proposes MelodyCraft, a prompt-based modular AI architecture that combines MusicGen-small to produce instrumental music and a Mixtral transformer to create genre- and emotion-specific lyrics. The fine-tuning of QLoRA-based optimization was performed using 28,000 prompt-lyric pairs with a validation loss of 1.85 and a BLEU score of 0.65, which indicates high lyrical coherence and style fidelity. A spectral analysis of 10 human-composed and 10 AI-generated music of pop, rock, and jazz types showed no statistically significant (p > 0.05) spectral centroid, bandwidth, or roll-o differences among them, which is why it was deemed to have almost human acoustic realism. In addition, a human listening experiment involving 16 subjects found average Mean Opinion Scores (MOS) of 3.9-4.0 in melody quality, lyrical coherency, and emotional connection, creating perceptual parity with human compositions. MelodyCraft is a full-stack multimodal music-generating system that generates semantically, rhythmically, and emotionally consistent music and can serve as a guide for future creative systems assisted by AI.