  Audio generation has attracted significant attention. Despite remarkable
enhancement in audio quality, existing models overlook diversity evaluation.
This is partially due to the lack of a systematic sound class diversity
framework and a matching dataset. To address these issues, we propose
DiveSound, a novel framework for constructing multimodal datasets with in-class
diversified taxonomy, assisted by large language models. As both textual and
visual information can be utilized to guide diverse generation, DiveSound
leverages multimodal contrastive representations in data construction. Our
framework is highly autonomous and can be easily scaled up. We provide a
textaudio-image aligned diversity dataset whose sound event class tags have an
average of 2.42 subcategories. Text-to-audio experiments on the constructed
dataset show a substantial increase of diversity with the help of the guidance
of visual information.
