When deployed, the ASR engine can optionally condition the transcript output on n-gram language models.
These models are simple 3- and 4-gram language models trained with Kneser-Ney smoothing using KenLM.
Primary use case intended for these models is automatic speech recognition.
Sequence of zero or more words.
Likelihood of word sequence.
There are a variety of formats contained within this model archive:
ARPA-formatted Language Models:
nl-BE_default_1.0.arpa
KenLM-formatted Binary Language Models
nl-BE_default_1.0.bin
Flashlight Decoder Vocabulary Files
nl-BE_default_1.0_dict_vocab.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.
The language model provided here is Dutch-only.
Currently, TLT cannot train LMs for ASR inference. To train custom LMs for ASR inference, use KenLM and consult the Riva Documentation.
By downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva license.
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.