  End-to-end automatic speech recognition (ASR) models are increasingly large
and complex to achieve the best possible accuracy. In this paper, we build an
AutoML system that uses reinforcement learning (RL) to optimize the per-layer
compression ratios when applied to a state-of-the-art attention based
end-to-end ASR model composed of several LSTM layers. We use singular value
decomposition (SVD) low-rank matrix factorization as the compression method.
For our RL-based AutoML system, we focus on practical considerations such as
the choice of the reward/punishment functions, the formation of an effective
search space, and the creation of a representative but small data set for quick
evaluation between search steps. Finally, we present accuracy results on
LibriSpeech of the model compressed by our AutoML system, and we compare it to
manually-compressed models. Our results show that in the absence of retraining
our RL-based search is an effective and practical method to compress a
production-grade ASR system. When retraining is possible, we show that our
AutoML system can select better highly-compressed seed models compared to
manually hand-crafted rank selection, thus allowing for more compression than
previously possible.

Machine Learning;Artificial Intelligence;Audio and Speech Processing;Machine Learning