  Pre-training and fine-tuning is a paradigm for alleviating the data scarcity
problem in end-to-end speech translation (E2E ST). The commonplace "modality
gap" between speech and text data often leads to inconsistent inputs between
pre-training and fine-tuning. However, we observe that this gap occurs in the
early stages of fine-tuning, but does not have a major impact on the final
performance. On the other hand, we find that there has another gap, which we
call the "capacity gap": high resource tasks (such as ASR and MT) always
require a large model to fit, when the model is reused for a low resource task
(E2E ST), it will get a sub-optimal performance due to the over-fitting. In a
case study, we find that the regularization plays a more important role than
the well-designed modality adaption method, which achieves 29.0 for en-de and
40.3 for en-fr on the MuST-C dataset. Code and models are available at
https://github.com/hannlp/TAB.
