Extreme Encoder Output Frame Rate Reduction: Improving Computational
  Latencies of Large End-to-End Models
Rohit Prabhavalkar Zhong Meng Weiran Wang Adam Stooke Xingyu Cai Yanzhang He Arun Narayanan Dongseong Hwang Tara N. Sainath Pedro J. Moreno
The accuracy of end-to-end (E2E) automatic speech recognition (ASR) models
continues to improve as they are scaled to larger sizes, with some now reaching
billions of parameters. Widespread deployment and adoption of these models,
however, requires computationally efficient strategies for decoding. In the
present work, we study one such strategy: applying multiple frame reduction
layers in the encoder to compress encoder outputs into a small number of output
frames. While similar techniques have been investigated in previous work, we
achieve dramatically more reduction than has previously been demonstrated
through the use of multiple funnel reduction layers. Through ablations, we
study the impact of various architectural choices in the encoder to identify
the most effective strategies. We demonstrate that we can generate one encoder
output frame for every 2.56 sec of input speech, without significantly
affecting word error rate on a large-scale voice search task, while improving
encoder and decoder latencies by 48% and 92% respectively, relative to a strong
but computationally expensive baseline.