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TAO/Riva - Punctuation & Capitalization

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Description

This collection contains models and notebooks for Punctuation & Capitalization training and deployment with TAO Toolkit and Riva respectively.

Curator

NVIDIA

Modified

August 25, 2021
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Punctuation and Capitalization Collection

This page contains the information about the Punctuation and Capitalization collection with TAO.

NGC Model Collection: Punctuation and Capitalization ====================================================

Overview --------

Automatic Speech Recognition (ASR) systems typically generate text with no punctuation and capitalization of the words. Besides being hard to read, the ASR output could be an input to named entity recognition, machine translation or text-to-speech models. If the input text has punctuation and words are capitalized correctly, this could potentially boost the performance of such models.

This collection contains end-to-end neural models that solve Punctuation and Capitalization Tasks using the Train Adapt Optimize (TAO) Toolkit.

Available Models ----------------

For instructions on how to use a model, please see its corresponding model card page.

References ----------

  • [Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding."
    arXiv preprint arXiv:1810.04805 (2018).](https://arxiv.org/pdf/1810.04805.pdf)

License -------

License to use these models is covered by the Model EULA. By downloading the model checkpoints, you accept the terms and conditions of these licenses.

Suggested Reading -----------------

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.