  Transformers have emerged as a prominent model framework for audio tagging
(AT), boasting state-of-the-art (SOTA) performance on the widely-used Audioset
dataset. However, their impressive performance often comes at the cost of high
memory usage, slow inference speed, and considerable model delay, rendering
them impractical for real-world AT applications. In this study, we introduce
streaming audio transformers (SAT) that combine the vision transformer (ViT)
architecture with Transformer-Xl-like chunk processing, enabling efficient
processing of long-range audio signals. Our proposed SAT is benchmarked against
other transformer-based SOTA methods, achieving significant improvements in
terms of mean average precision (mAP) at a delay of 2s and 1s, while also
exhibiting significantly lower memory usage and computational overhead.
Checkpoints are publicly available https://github.com/RicherMans/SAT.
