Abstract: TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing ...

Hybrid cloud data management firm Cloudian Inc. today announced the availability of its new PyTorch connector with Remote Direct Memory Access support that delivers erformance improvements for ...

A simple QA model using RNNs to predict answers from custom question-answer pairs. Includes text preprocessing, vocabulary building, and PyTorch training. Ideal for NLP beginners and chatbot ...

Cloud-based and virtualized solutions are set to shake up broadcast audio production. These technologies offer new flexibility, scalability and support for remote workflows, transforming how ...

The vertebrate auditory pathway has been among the most fruitful neural substrates to investigate the function of a number of neural features in light of their computational role. For example, the ...

Abstract: Pytorch_EHR is a codebase enabling fast prototyping of deep learning-based predictive models using electronic health records structured data. Rather than a collection of vertical pipelines ...

Spatial audio has been studied for several decades, but has seen much renewed interest recently due to advances in both software and hardware for capture and playback, and the emergence of ...

Meta is selecting Amazon Web Services as its long-term strategic cloud provider to complement its on-premises infrastructure and round out its integration and PyTorch strategy. The partnership has ...

module: docs Related to our documentation, both in docs/ and docblocks triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module ...

Google LLC’s Android team today added support for a new prototype feature that makes it possible for developers to perform “hardware accelerated inference” on mobile devices using the PyTorch ...

In order to train a PyTorch neural network you must write code to read training data into memory, convert the data to PyTorch tensors, and serve the data up in batches. This task is not trivial and is ...