  End-to-end speech-to-text translation models are often initialized with
pre-trained speech encoder and pre-trained text decoder. This leads to a
significant training gap between pre-training and fine-tuning, largely due to
the modality differences between speech outputs from the encoder and text
inputs to the decoder. In this work, we aim to bridge the modality gap between
speech and text to improve translation quality. We propose M-Adapter, a novel
Transformer-based module, to adapt speech representations to text. While
shrinking the speech sequence, M-Adapter produces features desired for
speech-to-text translation via modelling global and local dependencies of a
speech sequence. Our experimental results show that our model outperforms a
strong baseline by up to 1 BLEU score on the Must-C En$\rightarrow$DE
dataset.\footnote{Our code is available at
https://github.com/mingzi151/w2v2-st.}
