# Jasper notebook

## Overview

This notebook provides scripts for you to run Jasper with TRT for inference step by step. You can run inference using either LibriSpeech dataset or your own audio input in .wav format, to generate the corresponding text file for the audio file.

## Requirements

This repository contains a Dockerfile which extends the PyTorch 19.09-py3 NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:

-    NVIDIA Docker
-    PyTorch 19.09-py3 NGC container
-    NVIDIA Turing or Volta based GPU
-    Pretrained Jasper Model Checkpoint

## Quick Start Guide

Running the following scripts will build and launch the container containing all required dependencies for both TensorRT as well as native PyTorch. This is necessary for using inference with TensorRT and can also be used for data download, processing and training of the model.

#### 1. Clone the repository.

```
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/PyTorch/SpeechRecognition/Jasper
```

#### 2. Build the Jasper PyTorch with TRT 6 container:

```
bash trt/scripts/docker/trt_build.sh
```

#### 3. Create directories
Prepare to start a detached session in the NGC container.
Create three directories on your local machine for dataset, checkpoint, and result, respectively, naming "data" "checkpoint" "result":

```
mkdir data checkpoint result
```

#### 4. Download the checkpoint
Download the checkpoint file jasperpyt_fp16 from NGC Model Repository:  
- https://ngc.nvidia.com/catalog/models/nvidia:jasperpyt_fp16

to the directory: _checkpoint_

The Jasper PyTorch container will be launched in the Jupyter notebook. Within the container, the contents of the root repository will be copied to the /workspace/jasper directory.

The /datasets, /checkpoints, /results directories are mounted as volumes and mapped to the corresponding directories "data" "checkpoint" "result" on the host.

#### 5. Copy the notebook to the root

Copy the notebook to the root directory of Jasper:

```
cp notebooks/JasperTRT.ipynb .
```

#### 6. Run the notebook
For running the notebook on your local machine, run:

```
jupyter notebook JasperTRT.ipynb
```

For running the notebook on another machine remotely, run:

```
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`