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Speech Synthesis Waveglow

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Description

Universal waveform generator from mel-spectrograms.

Publisher

NVIDIA

Use Case

Riva

Framework

NeMo

Latest Version

deployable_v1.0

Modified

August 26, 2021

Size

326.36 MB

Speech Synthesis: WaveGlow Model Card =====================================

Model Overview --------------

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.

Intended Use ------------

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)

How to Use This Model ---------------------

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

Training Information --------------------

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.

License -------

By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis license

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

Citations and Further Reading =============================

WaveGlow paper: https://arxiv.org/abs/1811.00002