Self Supervised Audio Spectrogram Transformer (SSAST) model with uninitialized classifier head. It was introduced in the paper SSAST: Self-Supervised Audio Spectrogram Transformer by Gong et al. and first released in this repository.

Disclaimer: The team releasing Audio Spectrogram Transformer did not write a model card for this model.

The Audio Spectrogram Transformer is equivalent to ViT, but applied on audio. Audio is first turned into an image (as a spectrogram), after which a Vision Transformer is applied. The model gets state-of-the-art results on several audio classification benchmarks.