Data Science

PyTorch Releases PyTorch Hub, Includes NVIDIA Tacotron2 and WaveGlow Models

At ICML 2019, PyTorch released PyTorch Hub, a repository of pre-trained models designed specifically for reproducible research. PyTorch is one of the most widely used deep learning frameworks by researchers and developers. PyTorch 1.0, announced by Facebook in 2018, is a deep learning framework that powers numerous products and services at scale by merging the best of both worlds – the distributed and native performance found in Caffe2 and the flexibility for rapid development found in the existing PyTorch framework. At a high level, PyTorch is a Python package that provides high level features such as tensor computation with strong GPU acceleration.

PyTorch Hub

PyTorch Hub is a simple API and workflow the provides the basic building blocks for improving machine learning research reproducibility. PyTorch Hub consists of a pre-trained model repository designed specifically to facilitate research reproducibility.  What this means is that researchers in various fields can easily discover each other’s research, leverage it as a baseline and build new cutting edge research from there. Users can discover available models on along with a rich set of metadata, preprocessing steps and the background research. Current Hub contributions from Nvidia include Text-to-Speech models based on Tacotron2 and WaveGlow with more to come.

Current models contributed by Nvidia on PyTorch Hub today:

To access this on Torch Hub, use the command

torch.hub.load(‘nvidia/DeepLearningExamples’, ‘nvidia_tacotron2’, pretrained=True)

torch.hub.load(‘nvidia/DeepLearningExamples’, ‘nvidia_waveglow’, pretrained=True)

NVIDIA strives to bring innovation and flexibility to the deep learning developer community. We are further investing in our engineering efforts and working together to empower and engage with the PyTorch developer community. The full article is available on and you can learn more about all of the pre-built examples built by NVIDIA with state of the art accuracy for various frameworks here.

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