Resource-Efficient Separation Transformer
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.
Cite
@article{arxiv.2206.09507,
title = {Resource-Efficient Separation Transformer},
author = {Luca Della Libera and Cem Subakan and Mirco Ravanelli and Samuele Cornell and Frédéric Lepoutre and François Grondin},
journal= {arXiv preprint arXiv:2206.09507},
year = {2024}
}
Comments
Accepted to ICASSP 2024