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The objective of this work is to give patch-size flexibility to AudioSpectrogram Transformers (AST). Recent advancements in ASTs have shown superiorperformance in various audio-based tasks. However, the performance of standardASTs degrades drastically when evaluated using different patch sizes from thatused during training. As a result, AST models are typically re-trained toaccommodate changes in patch sizes. To overcome this limitation, this paperproposes a training procedure to provide flexibility to standard AST modelswithout architectural changes, allowing them to work with various patch sizesat the inference stage - FlexiAST. This proposed training approach simplyutilizes random patch size selection and resizing of patch and positionalembedding weights. Our experiments show that FlexiAST gives similar performanceto standard AST models while maintaining its evaluation ability at variouspatch sizes on different datasets for audio classification tasks.

FlexiAST: Flexibility is What AST Needs