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arXiv · 2206.09507

Resource-Efficient Separation Transformer

Luca Della Libera·Cem Subakan·Mirco Ravanelli·Samuele Cornell·Frédéric Lepoutre·François Grondin
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Abstract

Transformers have recently achieved state-of-the-art performance in speech separation. These models, however, are computationally demanding and require a lot of learnable parameters. This paper explores Transformer-based speech separation with a reduced computational cost. Our main contribution is the development of the Resource-Efficient Separation Transformer (RE-SepFormer), a self-attention-based architecture that reduces the computational burden in two ways. First, it uses non-overlapping blocks in the latent space. Second, it operates on compact latent summaries calculated from each chunk. The RE-SepFormer reaches a competitive performance on the popular WSJ0-2Mix and WHAM! datasets in both causal and non-causal settings. Remarkably, it scales significantly better than the previous Transformer-based architectures in terms of memory and inference time, making it more suitable for processing long mixtures.

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BibTeXRIS

Luca Della Libera, Cem Subakan, Mirco Ravanelli, Samuele Cornell, Frédéric Lepoutre, François Grondin. 2022-06-19. Resource-Efficient Separation Transformer. https://arxiv.org/abs/2206.09507

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