WaveGlow: A Flow-based Generative Network for Speech Synthesis

In this paper we propose Wave<PRE_TAG>Glow</POST_TAG>: a flow-based network capable of generating high quality speech from mel-spectrograms. Wave<PRE_TAG>Glow</POST_TAG> combines insights from Glow and WaveNet in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. Wave<PRE_TAG>Glow</POST_TAG> is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our PyTorch implementation produces audio samples at a rate of more than 500 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. All code will be made publicly available online.