Getting Started with NVIDIA Riva Speech Recognition

Introduction

In this article, we will delve into how to begin building your own conversational AI applications utilizing NVIDIA Riva. To get started, ensure that you have installed the NVIDIA driver, CUDA, and Docker on your system. Upon successful installation, you can find NVIDIA Riva on the NVIDIA GPU Cloud (NGC). Here, you will also discover various pre-trained models designed for speech recognition, natural language processing, and speech synthesis to help accelerate your development process.

Installation Steps

Installing Riva involves a straightforward four-step process:

1. Download Files: Use the NGC command line interface (CLI) to download the necessary files.

2. Modify Configuration: Update the config.sh file to select the appropriate speech service that aligns with your specific use case and choose the models you wish to run.

3. Initialize Riva Server: Run the Riva initialization script to set up the server.

4. Start Riva Server: Execute the riva_start.sh bash script to launch the server. Once the server has loaded all required models, it will be ready for inference.

Riva also provides client containers that are packaged with sample applications to facilitate your initial setup. You can easily pull and run the client container from NGC by executing the riva_start_client.sh script. Once you have entered the container, you will find folders containing example notebooks, scripts, and sample WAV files for testing.

Example: Speech Recognition with Riva API

To illustrate how the Riva API functions for speech recognition, we will explore a particular example where we stream audio from a WAV file to the Riva server and receive the transcription in return.

In this example, the client code makes a speech recognition inference request to a pre-trained Citrinet model deployed on Riva to generate the transcript. Here’s how it works:

• Import Dependencies: First, import speech protos necessary for using the Python API.

• Specify Settings: Define the chunk size and the path of the WAV file you would like to transcribe.

• Open gRPC Channel: Establish a gRPC channel to the Riva server with the designated configuration settings.

• Send Inference Request: Bundle the configuration into an object and send the inference request to the server using the streaming recognize API call.

• Display Transcription: Finally, the transcription result is printed to the terminal.

With just a few lines of code, you can set up a simple transcription service. The Riva API can be seamlessly integrated into more complex applications, such as chatbots and virtual assistants, at scale.

Ready to dive into the world of NVIDIA Riva? Visit the Riva and NGC links provided in the description for further information and resources.

Keyword

• NVIDIA Riva
• Speech Recognition
• Conversational AI
• NGC
• Pre-trained Models
• Configuration
• gRPC Channel
• Inference Request
• Citrinet Model
• Transcription

FAQ

Q1: What prerequisites do I need before installing NVIDIA Riva?
A1: You need to have the NVIDIA driver, CUDA, and Docker installed on your system.

Q2: Where can I find NVIDIA Riva and pre-trained models?
A2: You can find NVIDIA Riva and various pre-trained models on the NVIDIA GPU Cloud (NGC).

Q3: What are the steps to install NVIDIA Riva?
A3: The installation involves downloading files using NGC CLI, modifying the configuration file, initializing the Riva server, and starting the server using a bash script.

Q4: Can I use Riva for applications other than speech recognition?
A4: Yes, NVIDIA Riva supports natural language processing and speech synthesis as well.

Q5: How can I test the Riva API for speech recognition?
A5: You can stream audio from a WAV file to the Riva server to receive the transcription in return, using a few lines of Python code.