Speech Recognition: Citrinet

Model Overview

Citrinet-1024 model which has been trained on the ASR dataset with over 2600 hours of Russian(ru-RU) speech.\nIt utilizes a Google SentencePiece [1] tokenizer with vocabulary size 1024, and transcribes text in Russian alphabet along with spaces.

Model Architecture

Citrinet is a deep residual convolutional neural network architecture that is optimized for Automatic Speech Recognition tasks. There are many variants of the Citrinet family of models, which are further discussed in the paper [2].

Training

The model was trained on various proprietary and open-source datasets. These datasets include variety of accents, domain specific data for various domains, spontaneous speech and dialogue, all of which contribute to the model’s accuracy.\nThis model delivers WER that is better than or comparable to popular alternate Speech to Text solutions for a range of domains and use cases.

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.\nTo use Riva Speech ASR service using this model , document has all the necessary information.

Input

Audio sample that is to be transcribed

Output

This model provides transcribed speech as a string for a given audio sample.

References

[1] Google Sentencepiece Tokenizer

[2] Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition

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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There are many variants of the Citrinet family of models, which are further discussed in the paper [2].\n\n#### Training\n\nThe model was trained on various proprietary and open-source datasets. These datasets include variety of accents, domain specific data for various domains, spontaneous speech and dialogue, all of which contribute to the model’s accuracy.\nThis model delivers WER that is better than or comparable to popular alternate Speech to Text solutions for a range of domains and use cases.\n\n#### How to Use this Model\n\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).\nTo use Riva Speech ASR service using this model , [document](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/service-asr.html) has all the necessary information.\n\n#### Input\n\nAudio sample that is to be transcribed\n\n#### Output\n\nThis model provides transcribed speech as a string for a given audio sample.\n\n#### References\n\n[1] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[2] [Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721)\n\n#### License\n\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 Citrinet ASR Russian",["D",1680637442417],"deployable_v2.0","/api/image/nvidia/riva/models/speechtotext_ru_ru_citrinet","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",240200865,"bias","explainability","privacy","safetyAndSecurity",[],[250,251,252,253,254,255],[],[],[],{"_92":143,"_128":143,"_129":267,"_131":132},{"_92":265,"_128":265,"_129":266,"_131":132},{"_92":262,"_128":263,"_129":264,"_131":132},{"_92":134,"_128":134,"_129":261,"_131":132},{"_92":258,"_128":258,"_129":259,"_131":132},{"_92":256,"_128":256,"_129":257,"_131":132},"STT",{"_133":132,"_128":256},"Russian",{"_133":260,"_128":258},"language",{"_133":136,"_128":134},"NVIDIA AI","soln_nvidia_ai",{"_133":142,"_128":263},"Citrinet",{"_133":132,"_128":265},{"_133":132,"_128":143},{"_171":1243,"_173":1244},"ngcApps",[1196],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1177":1178,"_1179":1180,"_1181":1182,"_1183":1184,"_1185":279,"_203":305,"_1186":1187,"_1188":51,"_1189":214,"_1190":214,"_1191":1192,"_1193":214,"_1194":1195},"amplitude",{"_1174":1175,"_820":-7,"_1176":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1168":1169,"_1170":1171,"_1172":1173},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787560902793,"1.12.0","defaultSearchSuggestions",{"_1060":388,"_1061":1062,"_1063":1064,"_1065":1066},"announcements",[],"consentState",{"_344":1050,"_1051":214,"_1052":26,"_1053":26,"_1054":1055},"consentConfig",{"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345},"showContactAdmin",1787560902794,[],[],[319,320,321,322,323,324],{"_40":55,"_42":66,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":325,"_50":51},4,"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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