论文标题
redapt：wav2vec 2的适配器编码\\更快，较小的语音翻译而没有质量折衷
RedApt: An Adaptor for wav2vec 2 Encoding \\ Faster and Smaller Speech Translation without Quality Compromise
论文作者
论文摘要
语音翻译（ST）中的预训练的语音变压器具有最新的（SOTA）结果；但是，使用此类编码器在计算上很昂贵。为了改善这一点，我们提出了一个新颖的还原适配器块，它可以无缝集成在任何基于变压器的语音编码体系结构中。在推断时，将预验证的wav2Vec 2语音编码器与重新刺激的速度相结合，记忆降低了33％，较少的失败量减少了24％。令我们的积极惊喜的是，我们带有RedApt的ST模型的表现平均超过了SOTA架构，平均在Mast-C中的8个语言对上的BLEU得分为0.68。
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.
