  This paper describes the submission to the IWSLT 2021 Low-Resource Speech
Translation Shared Task by IMS team. We utilize state-of-the-art models
combined with several data augmentation, multi-task and transfer learning
approaches for the automatic speech recognition (ASR) and machine translation
(MT) steps of our cascaded system. Moreover, we also explore the feasibility of
a full end-to-end speech translation (ST) model in the case of very constrained
amount of ground truth labeled data. Our best system achieves the best
performance among all submitted systems for Congolese Swahili to English and
French with BLEU scores 7.7 and 13.7 respectively, and the second best result
for Coastal Swahili to English with BLEU score 14.9.
