Nvidia Corporation

Kevin Shih

Bryan Catanzaro

Andrew J Tao

Wei Ping

Rohan Badlani

Adrian Lancucki

Jose Rafael Valle Gomes Da Costa

Ting-Chun Wang

Animesh Garg

Guilin Liu

Robert Pottorff

Aysegul Dundar

Fitsum A Reda

José Rafael Valle Gomes Da Costa

João Felipe Santos

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing generative text-to-speech models. The techniques include identifying a mapping of speech characteristics (SC) on a target distribution of a latent variable using a non-linear transformation for at least a subset of the SC. Parameters of the non-linear transformation are determined using a neural network that approximates a statistics of the SC with a statistics predicted for the SC based on the identified mapping and the target distribution of the latent variable.

Normalizing flows with neural splines for high-quality speech synthesis

A neural network architecture is disclosed for performing image in-painting using partial convolution operations. The neural network processes an image and a corresponding mask that identifies holes in the image utilizing partial convolution operations, where the mask is used by the partial convolution operation to zero out coefficients of the convolution kernel corresponding to invalid pixel data for the holes. The mask is updated after each partial convolution operation is performed in an encoder section of the neural network. In one embodiment, the neural network is implemented using an encoder-decoder framework with skip links to forward representations of the features at different sections of the encoder to corresponding sections of the decoder.

Image in-painting for irregular holes using partial convolutions

Apparatuses, systems, and techniques to enhance video are disclosed. In at least one embodiment, one or more neural networks are used to create, from a first video, a second video having one or more additional video frames.

Video prediction using one or more neural networks

Generation of synthetic speech from an input text sequence may be difficult when durations of individual phonemes forming the input text sequence are unknown. A predominantly parallel process may model speech rhythm as a separate generative distribution such that phoneme duration may be sampled at inference. Additional information such as pitch or energy may also be sampled to provide improved diversity for synthetic speech generation.

Unsupervised alignment for text to speech synthesis using neural networks

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Santa Clara, CA, United States of America

Kevin Shih