Automatic Speech Recognition

ASR, or Automatic Speech Recognition, refers to the problem of getting a program to automatically transcribe spoken language (speech-to-text). Our goal is usually to have a model that minimizes the Word Error Rate (WER) metric when transcribing speech input. In other words, given some audio file (e.g. a WAV file) containing speech.

The best place to get started with TLT - ASR would be the TLT - ASR jupyter notebooks sample enclosed in this sample. This resource has two notebooks included.

1. Training: Sample workflow for training an ASR model and export the model to a .ejrvs file
2. Deployment: Sample workflow to consume the .ejrvs file and deploy it to Jarvis.

If you are a seasoned Conversation AI developer we recommend installing TLT and referring to the TLT documentation for detailed information.

Pre-Requisites

Please make sure to install the following before proceeding further:

• python 3.6.9
• docker-ce > 19.03.5
• docker-API 1.40
• nvidia-container-toolkit > 1.3.0-1
• nvidia-container-runtime > 3.4.0-1
• nvidia-docker2 > 2.5.0-1
• nvidia-driver >= 455.23

Note: A compatible NVIDIA GPU would be required.

Installation

We recommend that you install TLT inside a virtual environment. The steps to do the same are as follows

virtualenv -p python3 <name of venv>
source <name of venv>/bin/activate
pip install jupyter notebook # If you need to run the notebooks

TLT is python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.

pip install nvidia-pyindex
pip install nvidia-tlt

To download the jupyter notebook please:

1. Download the samples using the ngc cli with the following command

ngc registry resource download-version "nvidia/tlt-jarvis/speechtotext_notebook:v1.0"

2. Instantiate the jupyter notebook server

jupyter notebook --ip 0.0.0.0 --allow-root --port 8888

License

By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis license