# Quick Start Guide for Data Center Platforms

NVIDIA Riva Speech Skills supports two architectures, Linux x86_64 and Linux ARM64. These are referred to as **data center** (x86_64) and **embedded** (ARM64). The following instructions are applicable to data center users.  


## Prerequisites

Before using Riva skills, ensure you meet the following prerequisites:

1. You have access and are [logged into](https://ngc.nvidia.com/signin) NVIDIA NGC. For step-by-step instructions, refer to the [NGC Getting Started Guide](https://docs.nvidia.com/ngc/ngc-overview/index.html#registering-activating-ngc-account).

2. You have access to an NVIDIA Volta, NVIDIA Turing, or an NVIDIA Ampere Architecture-based A100 GPU. For more information, refer to the [Support Matrix](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/support-matrix.html).

3. You have Docker installed with support for NVIDIA GPUs. For more information, refer to the [Support Matrix](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/support-matrix.html).


## Getting Started with Riva for Data Center Platforms

1. Download the Riva Quick Start scripts. You can either use the command-line interface or you can download the scripts directly from your browser. Click the **Download** drop-down button in the upper right corner and select:

   - **CLI** - the download command is copied. Ensure you have the [NGC CLI tool](https://ngc.nvidia.com/setup/installers/cli) installed. Once installed, open the command prompt and paste the copied command to start your download.

   - **Broswer (Direct Download)** - the download begins in a location of your choosing. 

2. Initialize and start Riva. The initialization step downloads and prepares Docker images and models. The start script launches the server.

   :::{note}
   This process can take up to an hour on an average internet connection. On the data center platforms, each model is individually optimized for the target GPU after download. 
   :::
      
   *Optional:* Modify the `config.sh` file within the `quickstart` directory with your preferred configuration. Options include which models to retrieve from NGC, where to store them, which GPU to use if more than one is installed on your system (refer to [Local (Docker)](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/deploy-local.html) for more details), and locations of SSL/TLS certificate and key files if using a secure connection.

   ```bash
   cd riva_quickstart_v2.1.0
   ```

   To use a USB device for audio input/output, connect it to the Jetson platform so it gets auto mounted into the container.

   **Initialize and start Riva**
   ```bash
   bash riva_init.sh
   bash riva_start.sh
   ```

3. Start a client container with sample clients for each service.

   ```bash
   bash riva_start_client.sh
   ```
   
4. From inside the client container, try the different services using the provided Jupyter notebooks.

   ```
   jupyter notebook --ip=0.0.0.0 --allow-root --notebook-dir=/work/notebooks
   ```

   If running the Riva Quick Start scripts on a cloud service provider (such as AWS or GCP), ensure that your compute instance has an externally visible IP address. To run the Jupyter notebooks, connect a browser window to the correct port (``8888`` by default) of that external IP address.

5. Shutdown the server when finished. After you've completed these steps and experimented with inferencing, run the ``riva_stop.sh`` script to stop the server.

For more information on how to customize a local deployment, refer to the [Local (Docker)](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/deploy-local.html) section.


## Transcribe Audio Files with Riva

For Automatic Speech Recognition (ASR), run the following commands from inside the Riva client container to perform streaming and offline transcription of audio files. If using SSL/TLS, ensure to include the `--ssl_server_cert /ssl/server.crt` option.

1. For offline recognition, run:

   ```bash
   riva_asr_client --audio_file=/work/wav/en-US_sample.wav
   ```
   
2. For streaming recognition, run:

   ```bash
   riva_streaming_asr_client --audio_file=/work/wav/en-US_sample.wav
   ```


## Synthesize Speech with Riva

From within the Riva client container, run the following command to synthesize the audio files.

```bash
riva_tts_client --voice_name=English-US-Female-1 \
                --text="Hello, this is a speech synthesizer." \
                --audio_file=/work/wav/output.wav
```

The audio files are stored in the `/work/wav` directory.

The streaming API can be tested by using the command-line option `--online=true`. However, there is no difference between both
options with the command-line client since it saves the entire audio to a `.wav` file.


## Riva Collections

The [Riva Collection](https://ngc.nvidia.com/collections/nvidia:riva-api) contains the Riva Speech Server and the Riva Speech Client containers, the Riva Quick Start scripts resources, and then Riva Speech Skills Helm chart.                            

## Suggested Reading

For the latest product documentation, supported hardware and software, and release notes, refer to the [Riva User's Guide](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html).


## License

By downloading and using Riva software, you accept the terms and conditions of this [license](https://developer.nvidia.com/riva/ga/license).

riva_quickstart

Scripts and utilities for getting started with Riva Speech Skills

Riva Skills Quick Start

Description

Publisher

Use Case

Framework

Latest Version

Modified

Compressed Size