AST: Audio Spectrogram Transformer
AST
This repository contains the official implementation (in PyTorch) of the Audio Spectrogram Transformer (AST) proposed in the Interspeech 2021 paper AST: Audio Spectrogram Transformer (Yuan Gong, Yu-An Chung, James Glass).
AST is the first convolution-free, purely attention-based model for audio classification which supports variable length input and can be applied to various tasks. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2. For details, please refer to the paper and the ISCA SIGML talk.
Please have a try! AST can be used with a few lines of code, and we also provide recipes to reproduce the SOTA results on AudioSet, ESC-50, and Speechcommands with almost one click.