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Learn OpenAI Whisper

You're reading from   Learn OpenAI Whisper Transform your understanding of GenAI through robust and accurate speech processing solutions

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835085929
Length 372 pages
Edition 1st Edition
Concepts
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Author (1):
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Josué R. Batista Josué R. Batista
Author Profile Icon Josué R. Batista
Josué R. Batista
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Table of Contents (16) Chapters Close

Preface 1. Part 1: Introducing OpenAI’s Whisper FREE CHAPTER
2. Chapter 1: Unveiling Whisper – Introducing OpenAI’s Whisper 3. Chapter 2: Understanding the Core Mechanisms of Whisper 4. Part 2: Underlying Architecture
5. Chapter 3: Diving into the Whisper Architecture 6. Chapter 4: Fine-Tuning Whisper for Domain and Language Specificity 7. Part 3: Real-world Applications and Use Cases
8. Chapter 5: Applying Whisper in Various Contexts 9. Chapter 6: Expanding Applications with Whisper 10. Chapter 7: Exploring Advanced Voice Capabilities 11. Chapter 8: Diarizing Speech with WhisperX and NVIDIA’s NeMo 12. Chapter 9: Harnessing Whisper for Personalized Voice Synthesis 13. Chapter 10: Shaping the Future with Whisper 14. Index 15. Other Books You May Enjoy

Milestone 3 – Setting up Whisper pipeline components

The process of ASR can be broken down into three main parts:

  • Feature extractor: This is the initial step of processing the raw audio inputs. Think of it as preparing the audio files, so the model can easily understand and use them. The feature extractor turns the audio into a format that highlights essential aspects of the sound, such as pitch or volume, which are crucial for the model to recognize different words and sounds.
  • The model: This is the core part of the ASR process. It performs what we call sequence-to-sequence mapping. In simpler terms, it takes the processed audio from the feature extractor and works to convert it into a sequence of text. It’s like translating the language of sounds into the language of text. This part involves complex calculations and patterns to accurately determine what the audio says.
  • Tokenizer: After the model has done its job of mapping the sounds to text, the tokenizer...
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