  Large speech models are rapidly gaining traction in research community. As a
result, model compression has become an important topic, so that these models
can fit in memory and be served with reduced cost. Practical approaches for
compressing automatic speech recognition (ASR) model use int8 or int4 weight
quantization. In this study, we propose to develop 2-bit ASR models. We explore
the impact of symmetric and asymmetric quantization combined with sub-channel
quantization and clipping on both LibriSpeech dataset and large-scale training
data. We obtain a lossless 2-bit Conformer model with 32% model size reduction
when compared to state of the art 4-bit Conformer model for LibriSpeech. With
the large-scale training data, we obtain a 2-bit Conformer model with over 40%
model size reduction against the 4-bit version at the cost of 17% relative word
error rate degradation
