Tiny-Sepformer: A Tiny Time-Domain Transformer Network For Speech Separation

The Model Architecture of Tiny-Sepformer

Abstract

Time-domain Transformer neural networks have proven their superiority in speech separation tasks. However, these models usually have a large number of network parameters, thus often encountering the problem of GPU memory explosion. In this paper, we proposed Tiny-Sepformer, a tiny version of Transformer network for speech separation. We presented two techniques to reduce the model parameters and memory consumption{:} (1) Convolution-Attention (CA) block, spliting the vanilla Transformer to two paths, multi-head attention and 1D depthwise separable convolution, (2) parameter sharing, sharing the layer parameters within the CA block. In our experiments, Tiny-Sepformer could greatly reduce the model size, and achieves comparable separation performance with vanilla Sepformer on WSJ0-2/3Mix datasets.

Type
Publication
In 23rd Annual Conference of the International Speech Communication Association
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Jian Luo
Jian Luo
Researcher
Ning Cheng
Ning Cheng
Director