Content-Based Controls for Music Large Language Modeling

Recent years have witnessed a rapid growth of large-scale language models in the domain of music audio. Such models enable end-to-end generation of higher-quality music, and some allow conditioned generation using text descriptions. However, the control power of text controls on music is intrinsically limited, as they can only describe music indirectly through meta-data (such as singers and instruments) or high-level representations (such as genre and emotion). We aim to further equip the models with direct and content-based controls on innate music languages such as pitch, chords and drum track. To this end, we contribute Coco-Mulla, a content-based control method for music large language modeling. It uses a parameter-efficient fine-tuning (PEFT) method tailored for Transformer-based audio models. Experiments show that our approach achieves high-quality music generation with low-resource semi-supervised learning. We fine-tune the model with less than 4% of the orignal parameters on a small dataset with fewer than 300 songs. Moreover, our approach enables effective content-based controls. We illustrate its controllability via chord and rhythm conditions, two of the most salient features of pop music. Furthermore, we show that by combining content-based controls and text descriptions, our system achieves flexible music variation generation and arrangement. Our source codes and demos are available online.