Summary

Creating an NVIDIA Riva ASR service for a new language involves several key steps, including data collection, data preparation, training and validation, and deployment. This process is facilitated by NVIDIA Riva, which provides tools and methodologies to simplify the creation of ASR services. Here, we will delve into the details of this process, highlighting the main ideas and steps involved in making an NVIDIA Riva ASR service for a new language.

Making an ASR Service for a New Language: A Step-by-Step Guide

Introduction

Automatic Speech Recognition (ASR) is a critical component of any speech AI system. With over 6500 spoken languages in use today, most of which lack commercial ASR products, there is a significant need for tools that can help create ASR services for new languages. NVIDIA Riva addresses this need by providing a comprehensive workflow and tools for creating ASR services.

The Riva ASR Workflow

The Riva ASR workflow for a new language is divided into four major phases:

  1. Data Collection: This phase involves gathering a large amount of high-quality transcribed audio data. This data is crucial for training high-quality acoustic models.

  2. Data Preparation: Once the data is collected, it needs to be prepared for training. This includes formatting the data and ensuring it is compatible with the Riva tools.

  3. Training and Validation: In this phase, the acoustic model is trained using the prepared data. The model is then validated to ensure it meets the required accuracy standards.

  4. Riva Deployment: The final phase involves deploying the trained model using Riva. This includes converting the model into a format compatible with Riva and deploying it on a server.

Key Components of an ASR Service

An ASR service consists of several key components:

  • Acoustic Model: This is the most important part of an ASR service. It requires a large amount of data to train and has the largest impact on the overall ASR quality. Riva supports several acoustic models, including QuartzNet, CitriNet, Jasper, and Conformer.

  • Language Model: This is optional but can improve the accuracy of the pipeline. It helps in predicting the next word in a sequence based on the context.

  • Feature Extractor and Decoder: These are readily provided by Riva and do not need to be trained from scratch.

Cross-Language Transfer Learning

Cross-language transfer learning is a technique that can be particularly helpful when training new models for low-resource languages. It is based on the idea that phoneme representation can be shared across different languages. This technique can help boost performance even when a substantial amount of data is available.

Deploying Your Own Models

To deploy your own models on Riva, you need to follow these steps:

  • Use the Riva Quickstart scripts to download the necessary tools and Docker images.
  • Build .riva assets using the nemo2riva command in the servicemaker container.
  • Build RMIR assets using the riva-build tool in the servicemaker container.
  • Deploy the model in .rmir format with riva-deploy.
  • Start the server with riva-start.sh.

Getting Started with Riva

NVIDIA Riva offers comprehensive workflows and tools for new languages, making it a systematic approach to bringing your own language onboard. Whether you are fine-tuning an existing language model for a domain-specific application or implementing one for a brand-new dialect with little or lots of data, Riva provides those capabilities.

Example Use Cases

Riva can be used in various applications, including:

  • Call Centers and Customer Service: Riva delivers minimal latency and a 10x higher inference throughput than other speech recognition technologies, making it ideal for real-time communication in call centers.
  • Smart Devices and Self-Service Kiosks: Riva can be used to build ASR models for smart devices and self-service kiosks, improving user interaction.
  • Education and Enterprise Document Processing: Riva can be used to automate transcription tasks in education and enterprise document processing.

Table: Key Steps in Creating an NVIDIA Riva ASR Service

Step Description
Data Collection Gather high-quality transcribed audio data.
Data Preparation Format data and ensure compatibility with Riva tools.
Training and Validation Train acoustic model and validate its accuracy.
Riva Deployment Convert model into Riva format and deploy on a server.

Table: Key Components of an ASR Service

Component Description
Acoustic Model Most important part of ASR service, requires large amount of data.
Language Model Optional, improves accuracy by predicting next word in sequence.
Feature Extractor and Decoder Provided by Riva, do not need to be trained from scratch.

Conclusion

Creating an NVIDIA Riva ASR service for a new language involves several key steps, including data collection, data preparation, training and validation, and deployment. By following these steps and leveraging the tools and methodologies provided by NVIDIA Riva, you can create high-quality ASR services for new languages. Whether you are working on a domain-specific application or a brand-new dialect, Riva offers the capabilities to bring your own language onboard systematically.