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arXiv:1711.10433cs.LG

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

Aaron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George van den Driessche, Edward Lockhart, Luis C. Cobo, Florian Stimberg, Norman Casagrande, Dominik Grewe, Seb Noury, Sander Dieleman, Erich Elsen, Nal Kalchbrenner, Heiga Zen, Alex Graves, Helen King, Tom Walters, Dan Belov, Demis Hassabis

Abstract

The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is poorly suited to today's massively parallel computers, and therefore hard to deploy in a real-time production setting. This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no significant difference in quality. The resulting system is capable of generating high-fidelity speech samples at more than 20 times faster than real-time, and is deployed online by Google Assistant, including serving multiple English and Japanese voices.

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cs.LGMachine Learning

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  1. Pocket TTS: A high quality TTS that gives your CPU a voice 1 comment

    • Using neural nets (machine learning) to train TTS voices has been around a long time. [1] (2016 https://arxiv.org/abs/1609.03499) WaveNet: A Generative Model for Raw Audio [2] (2017 https://arxiv.org/abs/1711.10433) Parallel WaveNet: Fast High-Fidelity Speech Synthesis [3] (2021…

      rhdunnView on HNParent context

  2. Did Google Fake Its Big A.I. Demo? 1 comment

    • They have published multiple major TTS papers since the original WaveNet paper in 2016. Including the recent Tacotron results with impressive style control. Tacotron (Mar. 2017): https://arxiv.org/abs/1703.10135 Parallel WaveNet (Nov. 2017): https://arxiv.org/abs/1711.10433…

      nschucherView on HNParent context

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