  In this paper we propose WaveGlow: a flow-based network capable of generating
high quality speech from mel-spectrograms. WaveGlow combines insights from Glow
and WaveNet in order to provide fast, efficient and high-quality audio
synthesis, without the need for auto-regression. WaveGlow 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.
