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We propose a new end-to-end neural acoustic model for automatic speechrecognition. The model is composed of multiple blocks with residual connectionsbetween them. Each block consists of one or more modules with 1D time-channelseparable convolutional layers, batch normalization, and ReLU layers. It istrained with CTC loss. The proposed network achieves near state-of-the-artaccuracy on LibriSpeech and Wall Street Journal, while having fewer parametersthan all competing models. We also demonstrate that this model can beeffectively fine-tuned on new datasets.

QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions