  Due to the rapid development of computing hardware resources and the dramatic
growth of data, pre-trained models in speech recognition, such as Whisper, have
significantly improved the performance of speech recognition tasks. However,
these models usually have a high computational overhead, making it difficult to
execute effectively on resource-constrained devices. To speed up inference and
reduce model size while maintaining performance, we propose a novel guided
knowledge distillation and quantization for large pre-trained model Whisper.
The student model selects distillation and quantization layers based on
quantization loss and distillation loss, respectively. We compressed
$\text{Whisper}_\text{small}$ to $\text{Whisper}_\text{base}$ and
$\text{Whisper}_\text{tiny}$ levels, making $\text{Whisper}_\text{small}$
5.18x/10.48x smaller, respectively. Moreover, compared to the original
$\text{Whisper}_\text{base}$ and $\text{Whisper}_\text{tiny}$, there is also a
relative character error rate~(CER) reduction of 11.3% and 14.0% for the new
compressed model respectively.
