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Deep Learning with PyTorch Lightning

You're reading from   Deep Learning with PyTorch Lightning Swiftly build high-performance Artificial Intelligence (AI) models using Python

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781800561618
Length 366 pages
Edition 1st Edition
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Authors (2):
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Dheeraj Arremsetty Dheeraj Arremsetty
Author Profile Icon Dheeraj Arremsetty
Dheeraj Arremsetty
Kunal Sawarkar Kunal Sawarkar
Author Profile Icon Kunal Sawarkar
Kunal Sawarkar
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Kickstarting with PyTorch Lightning
2. Chapter 1: PyTorch Lightning Adventure FREE CHAPTER 3. Chapter 2: Getting off the Ground with the First Deep Learning Model 4. Chapter 3: Transfer Learning Using Pre-Trained Models 5. Chapter 4: Ready-to-Cook Models from Lightning Flash 6. Section 2: Solving using PyTorch Lightning
7. Chapter 5: Time Series Models 8. Chapter 6: Deep Generative Models 9. Chapter 7: Semi-Supervised Learning 10. Chapter 8: Self-Supervised Learning 11. Section 3: Advanced Topics
12. Chapter 9: Deploying and Scoring Models 13. Chapter 10: Scaling and Managing Training 14. Other Books You May Enjoy

Automatic speech recognition using Flash

Recognizing speech from an audio file is perhaps one of the most widely used applications of AI. It's part of smartphone speakers such as Alexa, as well as automatically generated captions for video streaming platforms such as YouTube, and also many music platforms. It can detect speech in an audio file and convert it into text. Detection of speech involves various challenges such as speaker modalities, pitch, and pronunciation, as well as dialect and language itself:

Figure 4.6 – A concept of automatic speech recognition

To train a model for Automatic Speech Recognition (ASR), we need a training dataset that is a collection of audio files along with the corresponding text transcription that describes that audio. The more diverse the set of audio files with people from different age groups, ethnicities, dialects, and so on is, the more robust the ASR model will be for the unseen audio files.

In the previous...

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