Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models
Nov 03, 2017 (modified: Nov 03, 2017)ICLR 2018 Conference Blind Submissionreaders: everyoneShow 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
Enter your feedback below and we'll get back to you as soon as possible.