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Riva ASR English LM

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Description

Base English n-gram LM trained on LibriSpeech, Switchboard and Fisher

Publisher

NVIDIA

Use Case

Riva

Framework

Transfer Learning Toolkit

Latest Version

deployable_v1.0

Modified

August 26, 2021

Size

5.82 GB

Speech Recognition: English 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 3- and 4-gram language models trained with Kneser-Ney smoothing using KenLM.

Intended use

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

How to Use This Model

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

ARPA-formatted Language Models:

-3-gram.pruned.3e-7.arpa

  • mixed_lm-lower.arpa

KenLM-formatted Binary Language Models

  • mixed-lower.binary

Rescoring Language Models

  • G.mixed_lm.3-gram.pruned.3e-7.carpa
  • G.mixed_lm.carpa

FST-formatted Language Models

  • TLG.mixed_lm.3-gram.pruned.3e-7.fst

Vocabulary Files

  • words.mixed_lm.3-gram.pruned.3e-7.fst
  • words.mixed_lm.txt

ARPA and KenLM binary formatted files can be used directly by the CTC CPU Decoder. The GPU decoder uses a FST-formatted language model (derived from the pruned n-gram model) and then optionally uses the carpa-formatted LMs for rescoring.

Input

Sequence of zero or more words.

Output

Likelihood of word sequence.

Training Information

The mixed language model provided here is English-only and is trained on a mix of transcriptions from LibriSpeech, Switchboard and Fisher datasets.

Limitations

Currently, the models distributed with this model resource cannot be trained using the TAO Toolkit. To train custom LMs for ASR inference using the TAO Toolkit please refer to the TAO Toolkit Documentation.

License

By downloading and using the models and resources packaged with TAO Toolkit Conversational AI, you would be accepting the terms of the Jarvis 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.