HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models

Yanqi Zhou, Wei Ping, Sercan Arik, Kainan Peng, Greg Diamos

Feb 15, 2018 (modified: Oct 27, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive models for raw audio waveform generation. As an example, we propose a hybrid model that combines an autoregressive network named WaveNet and a conventional LSTM model to address speech synthesis. Instead of generating one sample per time-step, the proposed HybridNet generates multiple samples per time-step by exploiting the long-term memory utilization property of LSTMs. In the evaluation, when applied to text-to-speech, HybridNet yields state-of-art performance. HybridNet achieves a 3.83 subjective 5-scale mean opinion score on US English, largely outperforming the same size WaveNet in terms of naturalness and provide 2x speed up at inference.
  • TL;DR: It is a hybrid neural architecture to speed-up autoregressive model.
  • Keywords: neural architecture, inference time reduction, hybrid model
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