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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

Technical requirements

As presented in this chapter, you only need a Google account and internet access to run the Whisper AI code in Google Colaboratory. No paid subscription is required to use the free Colab and the GPU version. Those familiar with Python can run this code example in their environment instead of using Colab.

We are using Colab in this chapter as it allows for quick setup and running of the code without installing Python or Whisper locally. The code in this chapter uses the small Whisper model, which works well for testing purposes. In later chapters, we will complete the Whisper installation to utilize more advanced ASR models and techniques.

The code examples from this chapter can be found on GitHub at https://github.com/PacktPublishing/Learn-OpenAI-Whisper/tree/main/Chapter01.

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