WaveGlow is a neural vocoder model for text-to-speech applications. It is intended as the second part of a two-stage speech synthesis pipeline, with a mel-spectrogram generator such as Tacotron2 as the first stage.
WaveGlow is a Glow-based (alternatively flow-based) model that generates audio conditioned on mel spectrograms. WaveGlow is a reversible neural network. It can be run in two modes: the first mode takes audio and transforms it to samples drawn from a normal distribution, and the second mode takes samples from a normal distribution and transforms it to audio. Both modes are conditioned on a mel spectrogram.
This model can be used to generate most voices in most languages without retraining. We have observed this trained WaveGlow to generate English audio and Mandarin audio.
This model is trained on the LJSpeech dataset sampled at 22050 Hz.
WaveGlow is intended to be used as the second part of a two stage speech synthesis pipeline. WaveGlow takes a mel spectrogram and returns audio.
Mel spectrogram of shape (batch x mel_channels x time)
Audio of shape (batch x time)
WaveGlow paper: https://arxiv.org/abs/1811.00002
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