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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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1
Part 1: Introducing OpenAI’s Whisper
4
Part 2: Underlying Architecture
7
Part 3: Real-world Applications and Use Cases

Exploring the Whisper ASR system

Now that we’ve surveyed the landscape and capabilities of automated speech recognition, it’s time to demystify Whisper’s technical inner workings. This section offers an accessible yet comprehensive overview of the algorithms, data pipelines, and innovations unlocking Whisper’s unprecedented transcription abilities.

We’ll highlight approaches in acoustic modeling, self-supervised pre-training strategies, model architectures, and performance optimizations that set Whisper apart. Collectively, these techniques enable robust real-world speech recognition across languages, environments, and hardware.

While we won’t dig into granular mathematical equations, you’ll develop an intuitive grasp of Whisper’s competitive advantages, such as the following:

  • Handling fuzzy sound-to-symbol mapping with connectionist temporal classification (CTC) acoustic models
  • Incorporating global language...

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