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.
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.
Input Mel spectrogram of shape (batch x mel_channels x time)
Output Audio of shape (batch x time)
The provided .ejrvs checkpoint can be used, in junction with a Tacotron2 checkpoint, to generate speech via Jarvis. To deploy a TTS service via Jarvis, please refer to the Jarvis documentation
This model is trained on LJSpeech sampled at 22050Hz, and 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.
By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis license
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WaveGlow paper: https://arxiv.org/abs/1811.00002