NeMo Speech Models

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.