  This paper describes the submission of our end-to-end YiTrans speech
translation system for the IWSLT 2022 offline task, which translates from
English audio to German, Chinese, and Japanese. The YiTrans system is built on
large-scale pre-trained encoder-decoder models. More specifically, we first
design a multi-stage pre-training strategy to build a multi-modality model with
a large amount of labeled and unlabeled data. We then fine-tune the
corresponding components of the model for the downstream speech translation
tasks. Moreover, we make various efforts to improve performance, such as data
filtering, data augmentation, speech segmentation, model ensemble, and so on.
Experimental results show that our YiTrans system obtains a significant
improvement than the strong baseline on three translation directions, and it
achieves +5.2 BLEU improvements over last year's optimal end-to-end system on
tst2021 English-German. Our final submissions rank first on English-German and
English-Chinese end-to-end systems in terms of the automatic evaluation metric.
We make our code and models publicly available.

Computation and Language;Audio and Speech Processing