  Pre-trained speech Transformers in speech translation (ST) have facilitated
state-of-the-art (SotA) results; yet, using such encoders is computationally
expensive. To improve this, we present a novel Reducer Adaptor block, RedApt,
that could be seamlessly integrated within any Transformer-based speech
encoding architecture. Integrating the pretrained wav2vec 2 speech encoder with
RedAptbrings 41% speedup, 33% memory reduction with 24% fewer FLOPs at
inference. To our positive surprise, our ST model with RedApt outperforms the
SotA architecture by an average of 0.68 BLEU score on 8 language pairs from
Must-C.

Computation and Language;Artificial Intelligence