DeepLearningExamples/Kaldi/SpeechRecognition/notebooks

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Kaldi inference demo

1. Overview

This folder contains two notebooks demonstrating the steps for carrying out inferencing with the Kaldi TRTIS backend server using a Python gRPC client.

• Offline: we will stream pre-recorded .wav files to the inference server and receive the results back.
• Online: we will stream live audio stream from a microphone to the inference server and receive the results back.

2. Quick Start Guide

First, clone the repository:

git clone https://github.com/NVIDIA/DeepLearningExamples.git
cd DeepLearningExamples/Kaldi/SpeechRecognition

Next, build the NVIDIA Kaldi TRTIS container:

scripts/docker/build.sh

Then download the model and some test data set with:

scripts/docker/launch_download.sh

Next, launch the TRTIS container with:

scripts/docker/launch_server.sh

After this step, we should have a TRTIS server ready to serve ASR inference requests.

The next step is to build a TRTIS client container:

docker build -t kaldi_notebook_client -f Dockerfile.notebook .

Start the client container with:

docker run -it --rm --net=host --device /dev/snd:/dev/snd -v $PWD:/Kaldi kaldi_notebook_client

Within the client container, start Jupyter notebook server:

cd /Kaldi
jupyter notebook --ip=0.0.0.0 --allow-root

And navigate a web browser to the IP address or hostname of the host machine at port 8888:

http://[host machine]:8888

Use the token listed in the output from running the jupyter command to log in, for example:

http://[host machine]:8888/?token=aae96ae9387cd28151868fee318c3b3581a2d794f3b25c6b