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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

Diarizing Speech with WhisperX and NVIDIA’s NeMo

Welcome to Chapter 8, where we will explore the world of speech diarization. While Whisper has proven to be a powerful tool for transcribing speech, there’s another crucial aspect of speech analysis that can significantly enhance its utility – speaker diarization. By augmenting Whisper with the ability to identify and attribute speech segments to different speakers, we open a new realm of possibilities for analyzing multispeaker conversations. This chapter will explore how Whisper can be integrated with cutting-edge diarization techniques to unlock these capabilities.

We will start by exploring the evolution of speaker diarization systems, from the limitations of early approaches to the transformative impact of transformer models. Through practical, hands-on examples, we’ll preprocess audio data, transcribe speech with Whisper, and fine-tune the alignment between transcriptions and the original audio.

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Learn OpenAI Whisper
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