  We propose Citrinet - a new end-to-end convolutional Connectionist Temporal
Classification (CTC) based automatic speech recognition (ASR) model. Citrinet
is deep residual neural model which uses 1D time-channel separable convolutions
combined with sub-word encoding and squeeze-and-excitation. The resulting
architecture significantly reduces the gap between non-autoregressive and
sequence-to-sequence and transducer models. We evaluate Citrinet on
LibriSpeech, TED-LIUM2, AISHELL-1 and Multilingual LibriSpeech (MLS) English
speech datasets. Citrinet accuracy on these datasets is close to the best
autoregressive Transducer models.
