  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.
