Speech Recognition: German N-Gram Language Models ==================================================

Model Overview --------------

When deployed, the ASR engine can optionally condition the transcript output on n-gram language models.

Model Architecture ------------------

These models are simple 4-gram language models trained with Kneser-Ney smoothing using KenLM.

Intended Use ------------

Primary use case intended for these models is automatic speech recognition.

Input\nSequence of zero or more words.

Output\nLikelihood of word sequence.

How to Use This Model ---------------------

There are a variety of formats contained within this model archive:

ARPA-formatted Language Models:

riva_de_asr_set_2.0_4gram.arpa
4gram-pruned-0_1_7_9-ru-lm-set-1.0.arpa
de-DE_default_3.0_pnc.arpa (For de-DE Unified model)

KenLM-formatted Binary Language Models

riva_de_asr_set_2.0_4gram.binary
4gram-pruned-0_1_7_9-ru-lm-set-1.0.bin
de-DE_default_3.0_pnc.bin (For de-DE Unified model)

Flashlight Decoder Vocabulary Files

dict_vocab.txt
de-DE_default_3.0_pnc_dict_vocab.txt (For de-DE Unified model)

ARPA and KenLM binary formatted files can be used directly by the CTC CPU Decoder.

Training Information --------------------

Limitations -----------

Currently, TLT cannot train LMs for ASR inference. To train custom LMs for ASR inference,\nuse KenLM and consult the\nRiva Documentation.

License -------

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

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.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat 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. --------------------------------------------------

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To train custom LMs for ASR inference,\nuse KenLM and consult the\n[Riva Documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html).\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 Riva [license](https://developer.nvidia.com/riva/ga/license).\n\n### Ethical AI\n\n# NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat 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. --------------------------------------------------","Riva ASR German LM",["D",1694105856764],"deployable_v3.0","/api/image/nvidia/riva/models/speechtotext_de_de_lm","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",758042135,"bias","explainability","privacy","safetyAndSecurity",[],[249,250,251,252,253,254],[],[],[],{"_89":140,"_125":140,"_126":268,"_128":129},{"_89":265,"_125":265,"_126":266,"_128":129},{"_89":261,"_125":262,"_126":263,"_128":129},{"_89":258,"_125":259,"_126":260,"_128":129},{"_89":131,"_125":131,"_126":257,"_128":129},{"_89":255,"_125":255,"_126":256,"_128":129},"STT",{"_130":129,"_125":255},{"_130":133,"_125":131},"NVIDIA AI","soln_nvidia_ai",{"_130":139,"_125":259},"Language Modeling","uscs_language_modeling",{"_130":264,"_125":262},"application","German",{"_130":267,"_125":265},"language",{"_130":129,"_125":140},{"_171":1244,"_173":1245},"ngcApps",[1197],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1178":1179,"_1180":1181,"_1182":1183,"_1184":1185,"_1186":280,"_203":306,"_1187":1188,"_1189":50,"_1190":214,"_1191":214,"_1192":1193,"_1194":214,"_1195":1196},"amplitude",{"_1175":1176,"_821":-7,"_1177":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1169":1170,"_1171":1172,"_1173":1174},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787885631186,"1.12.0","defaultSearchSuggestions",{"_1061":388,"_1062":1063,"_1064":1065,"_1066":1067},"announcements",[],"consentState",{"_344":1051,"_1052":214,"_1053":26,"_1054":26,"_1055":1056},"consentConfig",{"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345},"showContactAdmin",1787885631189,[],[],[320,321,322,323,324,325],{"_39":54,"_41":64,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":59,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":53,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"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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