This is a language model trained for Automatic Speech Recognition. It can be used as a neural rescorer which can rescore the top candidates predicted by an acoustic model.
The model is a large Transformer  trained for language modelling.
NeMo toolkit  was used for training this model. You may find more info on how to train and use neural language models for rescoring of ASR models here: ASR Language Modeling
This collection contains the models trained on the text transcripts of the LibriSpeech train set plus the LibriSpeech language modeling text corpus .
This model has been evaluated on some of the ASR models. You may find the performance of this model on some of the ASR models here.
The model is available for use in the NeMo toolkit , and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
import nemo.collections.nlp as nemo_nlp asr_lm_model = nemo_nlp.models.language_modeling.TransformerLMModel.from_pretrained(model_name="asrlm_en_transformer_large_ls")
You may use this script to evaluate this LM model on an ASR model.
This model accepts text as input.
This model estimates the likelihood of each token conditioned on previous tokens.
Since this model is trained on LibriSpeech text corpus, the performance of this model might may degrade for other domains.