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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 2 – Incorporating the Common Voice 11 dataset

The Common Voice dataset, spearheaded by Mozilla, represents a pioneering effort in democratizing speech technology through open and diverse speech corpora. A dataset is a structured collection of data where the rows typically represent individual observations or instances, and the columns represent the features or variables of those instances. In the case of Common Voice, each row represents an audio record, and each column represents features or characteristics applicable to the audio record. As an ever-expanding, community-driven initiative across 100+ languages, Common Voice optimally augments multilingual speech recognition systems like Whisper.

Integrating Common Voice data is straightforward with the Hugging Face Datasets library. We load the desired language split in streaming mode to bypass extensive storage requirements and expedite fine-tuning workflows:

from datasets import load_dataset, DatasetDict
common_voice...
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