Text Normalization: Weighted Finite State Transducer

Model Overview

This model is an OpenFST finite state archive (.far) for use within the opensource Sparrowhawk normalization engine [2]. This model has been created for English (en-US) text.

Model Architecture

This model uses weighted finite-state transducer (WFST) grammars that map strings in written form to strings in spoken form.

Training

The model was constructed using Nemo toolkit as outlined in intro and Text processing

How to Use this Model

To use this model , we can use Riva Skills Quick start guide , it is a starting point to try out Riva models . Information regarding Quick start guide can be found : here.

Input

This model is part of a pipeline for text to speech and accepts text as input.

Output

The output is normalized text which is then passed to the next stage in the text to speech pipeline.

References

[1] NeMo Inverse Text Normalization: From Development To Production

[2] [Google Sparrowhawk] (https://github.com/google/sparrowhawk)

License

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

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This model has been created for English (en-US) text.\n\n### Model Architecture\nThis model uses weighted finite-state transducer (WFST) grammars that map strings in written form to strings in spoken form.\n\n\n### Training\nThe model was constructed using Nemo toolkit as outlined in [intro](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/text_normalization/intro.html) and [Text processing](https://github.com/NVIDIA/NeMo/tree/main/tutorials/text_processing)\n\n### How to Use this Model\nTo use this model , we can use Riva Skills Quick start guide , it is a starting point to try out Riva models . Information regarding Quick start guide can be found : [here](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart).\n\n### Input\nThis model is part of a pipeline for text to speech and accepts text as input.\n\n### Output\nThe output is normalized text which is then passed to the next stage in the text to speech pipeline.\n\n### References\n[1] [NeMo Inverse Text Normalization: From Development To Production](https://arxiv.org/abs/2104.05055)\n\n[2] [Google Sparrowhawk] (https://github.com/google/sparrowhawk)\n\n### License\nBy downloading and using the models and resources packaged with Riva Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)","Riva TTS English Normalization Grammar",["D",1675789460898],"deployable_v1.1","/api/image/nvidia/riva/models/normalization_en_us","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",4076735,"bias","explainability","privacy","safetyAndSecurity",[],[],[],[],[],{"_171":1221,"_173":1222},"ngcApps",[1174],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1155":1156,"_1157":1158,"_1159":1160,"_1161":1162,"_1163":259,"_203":285,"_1164":1165,"_1166":51,"_1167":214,"_1168":214,"_1169":1170,"_1171":214,"_1172":1173},"amplitude",{"_1152":1153,"_799":-7,"_1154":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1146":1147,"_1148":1149,"_1150":1151},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787636906963,"1.12.0","defaultSearchSuggestions",{"_1039":366,"_1040":1041,"_1042":1043,"_1044":54},"announcements",[],"consentState",{"_322":1029,"_1030":214,"_1031":26,"_1032":26,"_1033":1034},"consentConfig",{"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323},"showContactAdmin",[],[],[298,299,300,301,302,303],{"_40":55,"_42":67,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"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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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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