📝 SummarXiv

Abstract

The goal of this paper is to enhance Text-to-Audio generation at inference, focusing on generating realistic audio that precisely aligns with text prompts. Despite the rapid advancements, existing models often fail to achieve a reliable balance between perceptual quality and textual alignment. To address this, we adopt Inference-Time Scaling, a training-free method that improves performance by increasing inference computation. We establish its unexplored application to audio generation and propose a novel multi-reward guidance that equally signifies each component essential in perception. By normalizing each reward value into a common scale and combining them with a weighted summation, the method not only enforces stable guidance but also enables explicit control to reach desired aspects. Moreover, we introduce a new audio-text alignment metric using an audio language model for more robust evaluation. Empirically, our method improves both semantic alignment and perceptual quality, significantly outperforming naive generation and existing reward guidance techniques. Synthesized samples are available on our demo page: https://mm.kaist.ac.kr/projects/score