Versatile Framework for Song Generation with Prompt-based Control

ACL ARR 2025 February Submission1448 Authors

Abstract:

Song generation focuses on producing controllable high-quality songs based on various prompts.
However, existing methods struggle to generate vocals and accompaniments with prompt-based control and proper alignment.
Additionally, they fall short in supporting various tasks.
To address these challenges, we introduce VersBand, a multi-task song generation framework for synthesizing high-quality, aligned songs with prompt-based control.
VersBand comprises these primary models:
1) VocalBand, a decoupled model, leverages the flow-matching method for generating singing styles, pitches, and mel-spectrograms, allowing fast, high-quality vocal generation with style control.
2) AccompBand, a flow-based transformer model, incorporates the Band-MOE, selecting suitable experts for enhanced quality, alignment, and control.
This model allows for generating controllable, high-quality accompaniments aligned with vocals.
3) Two generation models, LyricBand for lyrics and MelodyBand for melodies, contribute to the comprehensive multi-task song generation system, allowing for extensive control based on multiple prompts.
Experimental results demonstrate that VersBand performs better over baseline models across multiple song generation tasks using objective and subjective metrics.

Paper Type: Long

Research Area: Generation

Research Area Keywords: few-shot generation, model architectures, efficient models, interactive and collaborative generation

Contribution Types: NLP engineering experiment

Languages Studied: Chinese, English

Submission Number: 1448