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Learn OpenAI Whisper

Learn OpenAI Whisper

By : Josué R. Batista
4.9 (13)
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Learn OpenAI Whisper

Learn OpenAI Whisper

4.9 (13)
By: Josué R. Batista

Overview of this book

As the field of generative AI evolves, so does the demand for intelligent systems that can understand human speech. Navigating the complexities of automatic speech recognition (ASR) technology is a significant challenge for many professionals. This book offers a comprehensive solution that guides you through OpenAI's advanced ASR system. You’ll begin your journey with Whisper's foundational concepts, gradually progressing to its sophisticated functionalities. Next, you’ll explore the transformer model, understand its multilingual capabilities, and grasp training techniques using weak supervision. The book helps you customize Whisper for different contexts and optimize its performance for specific needs. You’ll also focus on the vast potential of Whisper in real-world scenarios, including its transcription services, voice-based search, and the ability to enhance customer engagement. Advanced chapters delve into voice synthesis and diarization while addressing ethical considerations. By the end of this book, you'll have an understanding of ASR technology and have the skills to implement Whisper. Moreover, Python coding examples will equip you to apply ASR technologies in your projects as well as prepare you to tackle challenges and seize opportunities in the rapidly evolving world of voice recognition and processing.
Table of Contents (16 chapters)
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Free Chapter
1
Part 1: Introducing OpenAI’s Whisper
4
Part 2: Underlying Architecture
7
Part 3: Real-world Applications and Use Cases

Brief history and evolution of ASR technology

The concepts behind automated speech recognition date back to the 1930s, when Bell Laboratories built machines to recognize digits spoken over the telephone. However, widespread commercial adoption of the technology we know today only occurred in the 1990s and 2000s.

After nearly a century of innovation, speech recognition capabilities have advanced enormously thanks to transformative approaches in machine learning and the availability of big data. The accuracy and versatility of ASR continue to progress at a remarkable pace.

The early days – Pattern recognition approaches

The first significant wave of innovation in ASR came during the 1950s at Bell Laboratories. Researchers focused on isolated word recognition using heuristic techniques to match acoustic patterns by examining audio waveforms and identifying distinguishable speech components.

Bell Labs built specialized machines to interpret spoken digit sequences over...

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