  Large-scale universal speech models (USM) are already used in production.
However, as the model size grows, the serving cost grows too. Serving cost of
large models is dominated by model size that is why model size reduction is an
important research topic. In this work we are focused on model size reduction
using weights only quantization. We present the weights binarization of USM
Recurrent Neural Network Transducer (RNN-T) and show that its model size can be
reduced by 15.9x times at cost of word error rate (WER) increase by only 1.9%
in comparison to the float32 model. It makes it attractive for practical
applications.
