  Transformers have rapidly overtaken CNN-based architectures as the new
standard in audio classification. Transformer-based models, such as the Audio
Spectrogram Transformers (AST), also inherit the fixed-size input paradigm from
CNNs. However, this leads to performance degradation for ASTs in the inference
when input lengths vary from the training. This paper introduces an approach
that enables the use of variable-length audio inputs with AST models during
both training and inference. By employing sequence packing, our method
ElasticAST, accommodates any audio length during training, thereby offering
flexibility across all lengths and resolutions at the inference. This
flexibility allows ElasticAST to maintain evaluation capabilities at various
lengths or resolutions and achieve similar performance to standard ASTs trained
at specific lengths or resolutions. Moreover, experiments demonstrate
ElasticAST's better performance when trained and evaluated on native-length
audio datasets.
