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 TAO Toolkit - Punctuation and Capitalization would be the TAO - 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 .riva file
Deployment: Sample workflow to consume the .riva file and deploy it to Riva.

If you are a seasoned Conversation AI developer we recommend installing TAO and referring to the TAO 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 TAO Toolkit inside a virtual environment. The steps to do the same are as follows.

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

TAO Toolkit is a 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-tao\n

To download the jupyter notebook please:

Download the samples using the ngc cli with the following command

ngc registry resource download-version \"nvidia/tao/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 TAO Toolkit Conversational AI, you would be accepting the terms of the Riva 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 TAO Toolkit - Punctuation and Capitalization would be the TAO - 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 `.riva` file\n2. Deployment: Sample workflow to consume the `.riva` file and deploy it to Riva.\n\nIf you are a seasoned Conversation AI developer we recommend installing TAO and referring to the [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/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 TAO Toolkit 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\nTAO Toolkit is a 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-tao\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/tao/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 TAO Toolkit Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)","displayName","Punctuation Capitalization Notebook",["D",1680632542459],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tao/resources/punctuationcapitalization_notebook","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","canGuestDownload","latestVersionSizeInBytes",25521,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[279,280,281,282,283,284,285],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_247":306,"_287":306,"_288":307,"_290":89},{"_247":304,"_287":304,"_288":305,"_290":89},{"_247":302,"_287":302,"_288":303,"_290":89},{"_247":298,"_287":299,"_288":300,"_290":89},{"_247":294,"_287":295,"_288":296,"_290":89},{"_247":292,"_287":292,"_288":293,"_290":89},{"_247":286,"_287":286,"_288":289,"_290":89},"Transfer Learning","value","filter",{"_291":89,"_287":286},"category","field","Punctuation",{"_291":89,"_287":292},"Natural Language Understanding","uscs_natural_language_understanding",{"_291":297,"_287":295},"application","Inference","soln_inference",{"_291":301,"_287":299},"technology","Finetuning",{"_291":89,"_287":302},"Capitalization",{"_291":89,"_287":304},"BERT",{"_291":89,"_287":306},{"_158":1288,"_160":1289},"ngcApps",[1241],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1222":1223,"_1224":1225,"_1226":1227,"_1228":1229,"_1230":319,"_111":345,"_1231":1232,"_1233":60,"_1234":92,"_1235":92,"_1236":1237,"_1238":92,"_1239":1240},"amplitude",{"_1219":1220,"_858":-7,"_1221":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1213":1214,"_1215":1216,"_1217":1218},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789338289599,"1.15.0","defaultSearchSuggestions",{"_1098":425,"_1099":1100,"_1101":1102,"_1103":1104},"announcements",[],"consentState",{"_382":1088,"_1089":92,"_1090":35,"_1091":35,"_1092":1093},"consentConfig",{"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383},"showContactAdmin",1789338289602,[],[],[359,360,361,362,363],{"_49":64,"_51":75,"_53":153,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":71,"_53":153,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":65,"_53":153,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":153,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":153,"_55":-5,"_57":-5,"_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
v1.3Selected	2	37.68 KB	November 23, 2021 UTC
v1.2	2	37.68 KB	August 20, 2021 UTC