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  • Book Overview & Buying Learn OpenAI Whisper
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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

Expanding Applications with Whisper

This chapter will continue our journey into the expansive applications of OpenAI’s Whisper. Here, we delve into how this innovative technology can transform and enhance various applications, from precise transcriptions to creating accessible and searchable content across multiple languages and platforms. We’ll explore techniques for achieving high transcription accuracy in different linguistic environments, integrating Whisper with platforms such as YouTube for multilingual content processing, and optimizing ASR model deployment using tools such as OpenVINO. The chapter also covers using Whisper to make audio and video content more discoverable by converting speech to searchable text and leveraging Whisper with FeedParser to transcribe podcast content for improved SEO. Through hands-on examples and Python notebooks, you’ll gain practical experience in harnessing Whisper’s capabilities to overcome challenges in automated...

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Learn OpenAI Whisper
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