Variable Scale Pruning for Transformer Model Compression in End-to-End Speech Recognition

Transformer models are being increasingly used in end-to-end speech recognition systems for their performance. However, their substantial size poses challenges for deploying them in real-world applications. These models heavily rely on attention and feedforward layers, with the latter containing a vast number of parameters that significantly contribute to the model's memory footprint. Consequently, it becomes pertinent to consider pruning these layers to reduce the model's size. In this article, our primary focus is on the feedforward layers. We conduct a comprehensive analysis of their parameter count and distribution. Specifically, we examine the weight distribution within each layer and observe how the weight values progress across the transformer model's blocks. Our findings demonstrate a correlation between the depth of the feedforward layers and the magnitude of their weights. Consequently, layers with higher weight values require less pruning. Building upon this insight, we propose a novel pruning algorithm based on variable rates. This approach sets the pruning rate according to the significance and location of each feedforward layer within the network. To evaluate our new pruning method, we conduct experiments on various datasets. The results reveal its superiority over conventional pruning techniques, such as local pruning and global pruning.