Self Supervised Audio Spectrogram Transformer (pretrained on AudioSet/Librispeech)
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.
Model description
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.
Usage
The model is pretrained on a massive amount of audio. Please finetune the classifier head before use, as it comes uninitialized.
- Downloads last month
- 14
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.