  Text-to-Audio (TTA) aims to generate audio that corresponds to the given text
description, playing a crucial role in media production. The text descriptions
in TTA datasets lack rich variations and diversity, resulting in a drop in TTA
model performance when faced with complex text. To address this issue, we
propose a method called Portable Plug-in Prompt Refiner, which utilizes rich
knowledge about textual descriptions inherent in large language models to
effectively enhance the robustness of TTA acoustic models without altering the
acoustic training set. Furthermore, a Chain-of-Thought that mimics human
verification is introduced to enhance the accuracy of audio descriptions,
thereby improving the accuracy of generated content in practical applications.
The experiments show that our method achieves a state-of-the-art Inception
Score (IS) of 8.72, surpassing AudioGen, AudioLDM and Tango.
