Automatic Speech Recognition with NVIDIA NeMo

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Overview

This example demonstrates:

• Preparing and preprocessing LibriSpeech data

• Fine-tuning QuartzNet ASR models

• Evaluating Word Error Rate (WER)

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Steps

1

Data Preparation

Prepare and preprocess the LibriSpeech dataset to ensure it is ready for training. Convert the data into the required format compatible with the QuartzNet model.

2

Environment Setup

Configure the environment for the QuartzNet ASR model to enable efficient fine-tuning and evaluation. Ensure all dependencies and tools are installed.

3

Model Fine-tuning

Fine-tune the QuartzNet ASR model on the prepared LibriSpeech data to enhance its transcription capabilities.

4

Evaluation Process

Assess the model's performance by calculating the Word Error Rate (WER) on a test dataset to determine its accuracy.

5

Results Analysis

Analyze the model's predictions and WER results to determine areas of improvement and refine the model if necessary.

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GitHub repository

The repository walks you through how to go through the above steps:

GitHub - valohai/NVIDIA-NeMo-Valohai: This repository contains a complete pipeline for fine-tuning NVIDIA's NeMo QuartzNet model for Automatic Speech Recognition (ASR) using the LibriSpeech dataset, orchestrated with Valohai.GitHub