SepTr: Separable Transformer for Audio Spectrogram Processing @ INTERSPEECH 2022 (official repository)
We propose the Separable Transformer (SepTr), an architecture that employs two transformer blocks in a sequential manner, the first attending to tokens within the same frequency bin, and the second attending to tokens within the same time interval.
The original paper is available at: https://www.isca-speech.org/archive/interspeech_2022/ristea22_interspeech.html
The arxiv version is avalable at: https://arxiv.org/pdf/2203.09581.pdf
This code is released under the CC BY-SA 4.0 license.
Information
Our architecture does not impose a certain axis (time or frequency) for the first transformer block, being flexible in this regard. Without loss of generality, in the above Figure, we illustrate a model that separates the tokens along the time axis first. Our separable transformer block can be repeated L times to increase the depth of the architecture. The final prediction of our model is made by the MLP block.
Implementation
We implemented the model in PyTorch and provide all scripts to run our architecture.
In order to make it work properly, we recommend a python version newer than 3.6
We used the python 3.6.8 version.
Cite us
@inproceedings{Ristea-INTERSPEECH-2022,
title={SepTr: Separable Transformer for Audio Spectrogram Processing},
author={Ristea, Nicolae-Catalin and Ionescu, Radu Tudor and Khan, Fahad Shahbaz},
year={2022},
booktitle={Proceedings of INTERSPEECH},
pages={4103--4107},
doi={10.21437/Interspeech.2022-249}
}