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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

Considering ethical implications

As ASR technologies such as OpenAI Whisper become more advanced and widely adopted, addressing the ethical considerations and implications associated with their development and deployment is crucial. This section will delve into critical issues such as ensuring fairness, mitigating bias, protecting user privacy and data security, and establishing guidelines and safeguards for their responsible use. By proactively establishing a framework for responsible ASR deployment, we can harness the benefits of these technologies while mitigating potential risks and negative consequences.

Ensuring fairness and mitigating bias in ASR

ASR systems such as Whisper may exhibit performance bias against certain types of speech, such as non-native accents, dialects, age groups, and genders. Studies have shown that these biases can result in higher error rates and unequal user experiences, leading to discrimination against underserved populations. Addressing performance...

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