arXiv:1910.10261eess.AS
QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions
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Abstract
We propose a new end-to-end neural acoustic model for automatic speech recognition. The model is composed of multiple blocks with residual connections between them. Each block consists of one or more modules with 1D time-channel separable convolutional layers, batch normalization, and ReLU layers. It is trained with CTC loss. The proposed network achieves near state-of-the-art accuracy on LibriSpeech and Wall Street Journal, while having fewer parameters than all competing models. We also demonstrate that this model can be effectively fine-tuned on new datasets.
NVIDIA has QuartzNet which contains only 19M weights and achieves 3.9% on test clean without language model and less then 3 with LM. Code (Pytorch): https://github.com/NVIDIA/NeMo Paper: https://arxiv.org/pdf/1910.10261.pdf
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