The great success of transformer-based models in natural language processing (NLP) has led to various attempts at adapting these architectures to other domains such as vision and audio. Recent work has shown that transformers can outperform Convolutional Neural Networks (CNNs) on vision and audio tasks. However, one of the main shortcomings of transformer models, compared to the well-established CNNs, is the computational complexity. Compute and memory complexity grow quadratically with the input length. Therefore, there has been extensive work on optimizing transformers, but often at the cost of lower predictive performance. In this work, we propose a novel method to optimize and regularize transformers on audio spectrograms. The proposed models achieve a new state-of-the-art performance on Audioset and can be trained on a single consumer-grade GPU. Furthermore, we propose a transformer model that outperforms CNNs in terms of both performance and training speed.

翻译：以变压器为基础的自然语言处理模型(NLP)取得巨大成功,导致各种尝试将这些结构调整到视觉和音频等其他领域。最近的工作表明,变压器在视觉和音频任务方面能够超过动态神经网络(CNNs),然而,与成熟的CNN相比,变压器模型的主要缺点之一是计算复杂性。计算和记忆复杂性随着输入长度的四倍增长。因此,在优化变压器方面做了大量工作,但往往以低预测性能为代价。在这项工作中,我们提出了一种新颖的方法来优化和规范音频光谱学变压器。拟议模型在音频设置上实现了新的最新状态,并可以在单一的消费者级GPU上得到培训。此外,我们提出了一种在性能和培训速度方面都超过CNN的变压器模型。