Add ASR Evaluation Service
This pull request **introduces 2 alerts** when merging 82fa2be0844042c845af3cf43e7e5e2cf989eab0 into da48c997177f89a9e305e508ab27b15954044814 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-87eabc38d29520a4b3fc1bcfa066c69c4774062f)

**new alerts:**

* 1 for Unnecessary pass
* 1 for Unused import > 
> 
> This pull request **introduces 2 alerts** when merging [82fa2be](https://github.com/NVIDIA/NeMo/commit/82fa2be0844042c845af3cf43e7e5e2cf989eab0) into [da48c99](https://github.com/NVIDIA/NeMo/commit/da48c997177f89a9e305e508ab27b15954044814) - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-87eabc38d29520a4b3fc1bcfa066c69c4774062f)
> 
> **new alerts:**
> 
>     * 1 for Unnecessary pass
> 
>     * 1 for Unused import

Some LGTM warnings to address. This pull request **introduces 2 alerts** when merging 70625bf33217750fe1931d091ca44f4c8d305487 into 532c71b8c632bc9c0fc1096d7630a02da3bee00e - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-5af65e7126afc589c92131f898452b6562e14acf)

**new alerts:**

* 1 for Unnecessary pass
* 1 for Unused import
# Changelog
- Adds Flask based ASR Service with support for gunicorn and docker container
- Enables inference via pretrained checkpoint to evaluate models on audio data that is formatted to 16 KHz and mono-channel wav files.
- Enables upload of trained checkpoints for evaluation of custom models (can be disabled inside `templates/main.html`)

# Usage
## Flask Server

```bash
$ python asr_service.py
```

## Gunicorn Server
By default, the gunicorn server config is loaded from `gunicorn.conf.py`. Command line values will override these arguments.

**NOTE**: Limit the number of workers since each worker will maintain the cache of models in memory.

```bash
# wsgi.py is a thin wrapper over `asr_service.py` to enable the service without debugging mode
$ gunicorn wsgi:app
```

## Docker Server
The app can be built and run easily using docker. 

Build the docker container using -
```bash
$ bash docker_container_build.sh
```

Run the docker container (in detached mode with all available GPUs) using - 
```bash
$ bash docker_container_run.sh
```