Punctuation and Capitalization

Automatic Speech Recognition (ASR) systems typically generate text with no punctuation and capitalization of the words. This tutorial explains how to train a model that will predict punctuation and capitalization for each word in a sentence, thereby making the ASR output more readable and boosting the performance of the named entity recognition, machine translation or text-to-speech models. In this sample, we start by using a pre-trained BERT model. For every word in our training dataset we’re going to predict:

Punctuation mark that should follow the words.
Whether the word should be capitalized.

The best place to get started with TLT - Punctuation and Capitalization would be the TLT - Punctuation and Capitalization jupyter notebook. This resource has two notebooks included.

Training: Sample workflow for training a punctuation capitalization model and export the model to a .ejrvs file
Deployment: Sample workflow to consume the .ejrvs file and deploy it to Jarvis.

If you are a seasoned Conversation AI developer we recommend installing TLT and referring to the TLT documentation for usage information.

Pre-Requisites

Please make sure to install the following before proceeding further:

python 3.6.9
docker-ce > 19.03.5
docker-API 1.40
nvidia-container-toolkit > 1.3.0-1
nvidia-container-runtime > 3.4.0-1
nvidia-docker2 > 2.5.0-1
nvidia-driver >= 455.23

Note: A compatible NVIDIA GPU would be required.

Installation

We recommend that you install TLT inside a virtual environment. The steps to do the same are as follows.

\nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n\">

virtualenv -p python3 <name of venv>\nsource <name of venv>/bin/activate\npip install jupyter notebook # If you need to run the notebooks\n

TLT is python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.

pip install nvidia-pyindex\npip install nvidia-tlt\n

To download the jupyter notebook please:

Download the samples using the ngc cli with the following command

ngc registry resource download-version \"nvidia/tlt-jarvis/punctuationcapitalization_notebook:v1.0\"\n

Instantiate the jupyter notebook server

jupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n

License

By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis license

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This tutorial explains how to train a model that will predict punctuation and capitalization for each word in a sentence, thereby making the ASR output more readable and boosting the performance of the named entity recognition, machine translation or text-to-speech models. In this sample, we start by using a pre-trained BERT model. For every word in our training dataset we’re going to predict:\n\n* Punctuation mark that should follow the words.\n* Whether the word should be capitalized.\n\nThe best place to get started with TLT - Punctuation and Capitalization would be the TLT - Punctuation and Capitalization jupyter notebook. This resource has two notebooks included.\n\n1. Training: Sample workflow for training a punctuation capitalization model and export the model to a `.ejrvs` file\n2. Deployment: Sample workflow to consume the `.ejrvs` file and deploy it to Jarvis.\n\nIf you are a seasoned Conversation AI developer we recommend installing TLT and referring to the [TLT documentation](https://docs.nvidia.com/TLT/tlt-user-guide/text/overview.html) for usage information.\n\n## Pre-Requisites\n\nPlease make sure to install the following before proceeding further:\n\n* python 3.6.9\n* docker-ce > 19.03.5\n* docker-API 1.40\n* nvidia-container-toolkit > 1.3.0-1\n* nvidia-container-runtime > 3.4.0-1\n* nvidia-docker2 > 2.5.0-1\n* nvidia-driver >= 455.23\n\n*Note: A compatible NVIDIA GPU would be required.*\n\n## Installation\n\nWe recommend that you install TLT inside a virtual environment. The steps to do the same are as follows.\n\n```sh\nvirtualenv -p python3 \nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n```\n\nTLT is python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.\n\n```sh\npip install nvidia-pyindex\npip install nvidia-tlt\n```\n\nTo download the jupyter notebook please:\n\n1. Download the samples using the ngc cli with the following command\n\n```sh\nngc registry resource download-version \"nvidia/tlt-jarvis/punctuationcapitalization_notebook:v1.0\"\n```\n\n2. Instantiate the jupyter notebook server\n\n```sh\njupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n```\n\n## License\n\nBy downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis [license](https://developer.nvidia.com/ngc/nvidia-jarvis-license)","displayName","Punctuation Capitalization Notebook",["D",1680631437668],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tlt-jarvis/resources/punctuationcapitalization_notebook","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/TLT-Jarvis.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[277,278,279,280,281,282,283,284,285],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_246":312,"_287":312,"_288":313,"_290":87},{"_246":310,"_287":310,"_288":311,"_290":87},{"_246":307,"_287":308,"_288":309,"_290":87},{"_246":305,"_287":305,"_288":306,"_290":87},{"_246":301,"_287":302,"_288":303,"_290":87},{"_246":298,"_287":299,"_288":300,"_290":87},{"_246":294,"_287":295,"_288":296,"_290":87},{"_246":292,"_287":292,"_288":293,"_290":87},{"_246":286,"_287":286,"_288":289,"_290":87},"Transfer Learning","value","filter",{"_291":87,"_287":286},"category","field","Punctuation",{"_291":87,"_287":292},"Natural Language Understanding","uscs_natural_language_understanding",{"_291":297,"_287":295},"application","Natural Language Processing","uscs_natural_language_processing",{"_291":297,"_287":299},"Inference","soln_inference",{"_291":304,"_287":302},"technology","Finetuning",{"_291":87,"_287":305},"Conversational AI","soln_conversational_ai",{"_291":304,"_287":308},"Capitalization",{"_291":87,"_287":310},"BERT",{"_291":87,"_287":312},{"_157":1293,"_159":1294},"ngcApps",[1246],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1227":1228,"_1229":1230,"_1231":1232,"_1233":1234,"_1235":325,"_109":349,"_1236":1237,"_1238":60,"_1239":90,"_1240":90,"_1241":1242,"_1243":90,"_1244":1245},"amplitude",{"_1224":1225,"_862":-7,"_1226":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1218":1219,"_1220":1221,"_1222":1223},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1102":429,"_1103":1104,"_1105":1106,"_1107":1108},"announcements",[],"consentState",{"_386":1092,"_1093":90,"_1094":35,"_1095":35,"_1096":1097},"consentConfig",{"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387},"showContactAdmin",1785127706334,[],[],[363,364,365,366,367],{"_49":63,"_51":74,"_53":152,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":152,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":152,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":152,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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	Files	Compressed Size		Download
v1.1	2	38.74 KB	June 9, 2021 UTC
v1.0Selected	2	38.7 KB	February 12, 2021 UTC