Speech Recognition: Mandarin 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

Sequence of zero or more words.

Output

Likelihood of word sequence.

How to Use this Model

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

ARPA-formatted Language Models:

zh-CN_default_2.0.arpa
zh-CN_default_pnc_6.0.arpa (For zh-CN Unified model)

KenLM-formatted Binary Language Models

zh-CN_default_2.0.bin
zh-CN_default_pnc_6.0.bin (For zh-CN Unified model)

Flashlight Decoder Vocabulary Files

zh-CN_default_2.0_dict_vocab.txt

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

Training

N/A

Limitations

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

License

By downloading and using the models and resources packaged with Riva Conversational AI, you accept 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. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that 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_zh_cn_lm/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/riva/speechtotext_zh_cn_lm:deployable_v6.0_export_v2","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/riva/models/speechtotext_zh_cn_lm/-/version-history","accessCheckResult",{"_187":188,"_189":26},"hasRelatedCollections",true,"context",{"_82":83,"_84":85},1787621086682,[],[],[220,221,222,223,224,225,226],{"_39":54,"_41":64,"_43":56,"_45":57,"_47":165,"_49":50},{"_39":54,"_41":61,"_43":44,"_45":62,"_47":63,"_49":50},{"_39":54,"_41":164,"_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},46,"nvidia/riva/speechtotext_zh_cn_lm","nvidia/riva/models/speechtotext_zh_cn_lm","speechtotext_zh_cn_lm","Base Mandarin 4-gram LM","# Speech Recognition: Mandarin N-Gram Language Models\n\n## Model Overview\n\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 4-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\n## Input\n\nSequence of zero or more words.\n\n## Output\n\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- `zh-CN_default_2.0.arpa`\n- `zh-CN_default_pnc_6.0.arpa` (For zh-CN Unified model)\n\n**KenLM-formatted Binary Language Models**\n- `zh-CN_default_2.0.bin`\n- `zh-CN_default_pnc_6.0.bin` (For zh-CN Unified model)\n\n**Flashlight Decoder Vocabulary Files**\n- `zh-CN_default_2.0_dict_vocab.txt`\n\nARPA and KenLM binary formatted files can be used directly by the CTC CPU Decoder.\n\n## Training\n\nN/A\n\n## Limitations\n\nCurrently, TLT cannot train LMs for ASR inference. To train custom LMs for ASR inference, use 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 Riva Conversational AI, you accept the terms of the Riva [license](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Ethical AI\n\nNVIDIA’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. 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