Overview

NVIDIA NeMo toolkit supports multiple Automatic Speech Recognition(ASR) models such as Jasper and QuartzNet. Pretrained checkpoints for these models trained on standard datasets can be used immediately, use speech_to_text.py script in the examples directory. In addition, models for ASR sub-tasks such as speech classification are also provided; for example MatchboxNet trained on the Google Speech Commands Dataset is available in speech_to_label.py. Voice Activity Detection is also supported with the same script, by simply changing the config file passed to the script! NeMo also supports training Speech Recognition models with Byte Pair/Word Piece encoding of the corpus, via the speech_to_text_bpe.py example; these models are still under development. In order to simply perform evaluation on a dataset using these models, use the speech_to_text_infer.py example, which shows how to compute WER over the dataset.

Usage

You can instantiate all these models automatically directly from NGC. To do so, start your script with:

import nemo
import nemo.collections.asr as nemo_asr

Then chose what type of model you would like to instantiate. See table below for the list of model base classes. Then use base_class.from_pretrained(...) method. For example:

quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")

Note that you can also list all available models using API by calling base_class.list_available_models(...) method.

You can also download all models' ".nemo" files in the "File Browser" tab and then instantiate those models with base_class.restore_from(PATH_TO_DOTNEMO_FILE) method. In this case, make sure you are matching NeMo and models' versions.

Here is a list of currently available models together with their base classes and short descriptions.