  Modern Automatic Speech Recognition (ASR) systems rely on distributed deep
learning to for quick training completion. To enable efficient distributed
training, it is imperative that the training algorithms can converge with a
large mini-batch size. In this work, we discovered that Asynchronous
Decentralized Parallel Stochastic Gradient Descent (ADPSGD) can work with much
larger batch size than commonly used Synchronous SGD (SSGD) algorithm. On
commonly used public SWB-300 and SWB-2000 ASR datasets, ADPSGD can converge
with a batch size 3X as large as the one used in SSGD, thus enable training at
a much larger scale. Further, we proposed a Hierarchical-ADPSGD (H-ADPSGD)
system in which learners on the same computing node construct a super learner
via a fast allreduce implementation, and super learners deploy ADPSGD algorithm
among themselves. On a 64 Nvidia V100 GPU cluster connected via a 100Gb/s
Ethernet network, our system is able to train SWB-2000 to reach a 7.6% WER on
the Hub5-2000 Switchboard (SWB) test-set and a 13.2% WER on the Call-home (CH)
test-set in 5.2 hours. To the best of our knowledge, this is the fastest ASR
training system that attains this level of model accuracy for SWB-2000 task to
be ever reported in the literature.
