Abstract

The Neural Modules Release Notes provides a brief description of the key features, improvements, and known issues for Neural Modules.

1. Nvidia Neural Modules 0.9 Release Notes

Description

Neural Modules is a flexible Python toolkit for building state of the art speech and language deep learning models. It is based on the PyTorch framework.

A “Neural Module” is python code that encapsulates tasks specific to building deep learning networks for speech and language. Inputs and outputs of a module have a “Neural Type” associated with them. Automatic semantic compatibility checking based on these types makes it easy to compose application with modules connected together. NeMo application is a DAG(directed acyclic graph) of connected modules. With the Neural Modules toolkit, a data scientist or deep learning researcher is able to design, build, train or fine tune state of the art speech or language models easily.

Key Features

The key features of Neural Modules are:
  • Core library with built in mixed precision and distributed training support.
  • Collection libraries for ASR(Automatic Speech Recognition), NLP(Natural Language Processing) and TTS(Text to Speech) model development.
  • These collections have several modules(encoders, decoders, data layers etc) that can be used out of the box:
    • Speech recognition modules
    • Natural language processing modules
    • Speech synthesis modules
    • BERT Joint Intent classification and slot filling
    • BERT downstream fine tuning tasks such as Named Entity Recognition
    • Neural Machine Translation and Transformer
  • Pre-trained models for Fine tuning and transfer learning trained on public datasets available on NGC:
    • Jasper and Quartznet
    • Transformer (from attention is all you need paper)
    • Tacotron2 and Waveglow
  • Mandarin Chinese Language Support for Jasper and Quartznet
  • Pre-trained models for fine tuning and transfer learning trained on the Mandarin Chinese ai-shell2 dataset:
    • Jasper and Quartznet
  • Easy to use APIs for building and training neural networks, and mixing and matching components in each collection.
  • Tutorials and examples
  • Export API for exporting speech recognition models for deployment to the Jarvis SDK.
  • Developer documentation available in Mandarin Chinese in addition to English.

Known Issues

  • Neural Modules work best with PyTorch 1.3
  • The toolkit is an early version software

Notices

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