Speech Recognition: N-Gram Language Models

Model Overview\nWhen deployed, the ASR engine can optionally condition the transcript output on n-gram language models.

Model Architecture

These models are simple n-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:

\n
ARPA-formatted Language Models:
\n
\n
KenLM-formatted Binary Language Models
\n
\n
Flashlight Decoder Vocabulary Files
\n

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

Training Information

Limitations

To train custom LMs for ASR inference,\nuse KenLM and consult the\nRiva Documentation.

License

By downloading and using the models, 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.\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.

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/riva/models/speechtotext_ru_ru_lm/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/riva/speechtotext_ru_ru_lm:deployable_v2.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/riva/models/speechtotext_ru_ru_lm/-/version-history","accessCheckResult",{"_187":188,"_189":26},"hasRelatedCollections",true,"context",{"_83":84,"_85":86},1787601013998,[],[],[220,221,222,223,224,225,226],{"_39":54,"_41":64,"_43":56,"_45":57,"_47":67,"_49":50},{"_39":54,"_41":61,"_43":44,"_45":62,"_47":63,"_49":50},{"_39":54,"_41":165,"_43":44,"_45":62,"_47":227,"_49":50},{"_39":54,"_41":59,"_43":56,"_45":57,"_47":60,"_49":50},{"_39":54,"_41":55,"_43":56,"_45":57,"_47":58,"_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},56,"nvidia/riva/speechtotext_ru_ru_lm","nvidia/riva/models/speechtotext_ru_ru_lm","speechtotext_ru_ru_lm","Base Russian n-gram LM","## Speech Recognition: N-Gram Language Models\n\nModel Overview\nWhen deployed, the ASR engine can optionally condition the transcript output on n-gram language models.\n\n### Model Architecture\n\nThese models are simple n-gram language models trained with Kneser-Ney smoothing using [KenLM](https://github.com/kpu/kenlm).\n\n### Intended Use\n\nPrimary use case intended for these models is automatic speech recognition.\n\nInput\nSequence of zero or more words.\n\nOutput\nLikelihood of word sequence.\n\n### How to Use This Model\n\nThere are a variety of formats contained within this model archive:\n\n- **ARPA-formatted Language Models:**\n\n- **KenLM-formatted Binary Language Models**\n\n- **Flashlight Decoder Vocabulary Files**\n\nARPA and KenLM binary formatted files can be used directly by the CTC CPU Decoder.\n\n### Training Information\n\nNA\n\n### Limitations\n\nTo 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, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license).\n\n### Ethical AI\n\nNVIDIA’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; 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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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