Efficient Audio Captioning with Encoder-Level Knowledge Distillation
Authors: name: Xuenan Xu, name: Haohe Liu, name: Mengyue Wu, name: Wenwu Wang, name: Mark D. Plumbley

Significant improvement has been achieved in automated audio captioning (AAC)
with recent models. However, these models have become increasingly large as
their performance is enhanced. In this work, we propose a knowledge
distillation (KD) framework for AAC. Our analysis shows that in the
encoder-decoder based AAC models, it is more effective to distill knowledge
into the encoder as compared with the decoder. To this end, we incorporate
encoder-level KD loss into training, in addition to the standard supervised
loss and sequence-level KD loss. We investigate two encoder-level KD methods,
based on mean squared error (MSE) loss and contrastive loss, respectively.
Experimental results demonstrate that contrastive KD is more robust than MSE
KD, exhibiting superior performance in data-scarce situations. By leveraging
audio-only data into training in the KD framework, our student model achieves
competitive performance, with an inference speed that is 19 times
fasterAn online demo is available at
\url{https://huggingface.co/spaces/wsntxxn/efficient_audio_captioning}.