Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-to-sequence learning. RNNs, however, are inherently sequential models that do not allow parallelization of their computations. Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism. In this paper, we propose the `SepFormer', a novel RNN-free Transformer-based neural network for speech separation. The SepFormer learns short and long-term dependencies with a multi-scale approach that employs transformers. The proposed model matches or overtakes the state-of-the-art (SOTA) performance on the standard WSJ0-2/3mix datasets. It indeed achieves an SI-SNRi of 20.2 dB on WSJ0-2mix matching the SOTA, and an SI-SNRi of 17.6 dB on WSJ0-3mix, a SOTA result. The SepFormer inherits the parallelization advantages of Transformers and achieves a competitive performance even when downsampling the encoded representation by a factor of 8. It is thus significantly faster and it is less memory-demanding than the latest RNN-based systems.

本文介绍了一种基于Transformers、无RNN结构的深度神经网络，即SepFormer，并运用多尺度方法使其实现短时和长时依赖性的学习，从而在语音分离任务中取得了最优结果，并具有较高的计算速度和较小的内存占用。

关注在语音分离中至关重要