Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers

Abstract

Parameter-efficient transfer learning (PETL) methods have emerged as a solid alternative to the standard full fine-tuning approach. They only train a few extra parameters for each downstream task, without sacrificing performance and dispensing with the issue of storing a copy of the pre-trained model for each task. For audio classification tasks, the Audio Spectrogram Transformer (AST) model shows impressive results. However, surprisingly, how to efficiently adapt it to several downstream tasks has not been tackled before. In this paper, we bridge this gap and present a detailed investigation of common PETL methods for the adaptation of the AST model to audio/speech tasks. Furthermore, we propose a new adapter design that exploits the convolution module of the Conformer model, leading to superior performance over the standard PETL approaches and surpassing or achieving performance parity with full fine-tuning by updating only 0.29% of the parameters. Finally, we provide ablation studies revealing that our proposed adapter: 1) proves to be effective in few-shot efficient transfer learning, 2) attains optimal results regardless of the amount of the allocated parameters, and 3) can be applied to other pre-trained models.