Investigating Passive Filter Pruning for Efficient CNN-Transformer Audio Captioning

Although automated audio captioning (AAC) has achieved remarkable performance improvement in recent years, concerns about the complexity of AAC models have drawn little attention from the research community. To reduce the number of model parameters, passive filter pruning has been successfully applied to convolution neural networks (CNNs) in audio classification tasks. However, due to the discrepancy between audio classification and AAC, these pruning methods are not necessarily suitable for captioning. In this work, we investigate the effectiveness of several passive filter pruning approaches on an efficient CNN-Transformer-based AAC architecture. Through extensive experiments, we find that under the same pruning ratio, pruning from the later convolution blocks significantly improves the performance. Utilizing the norm-based pruning method, our pruned model reduces the parameter number by 15% compared to that of the original model while maintaining a similar performance.