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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

Gaining insights into data, annotation, and model training

Now that we’ve covered Whisper’s semi-supervised training methodology, the next step is to dive deeper into curating optimal data for driving targeted performance gains. While web-scale corpora provide a strong starting point, fine-tuning for niche applications requires customized dataset development.

Keep in mind the concepts we already learned about regarding how transformers process sequences. Traditional sequence-to-sequence models, such as RNNs, process input sequences step by step, which can be time-consuming for long sequences. In contrast, transformers can simultaneously process all words in the input sequence, leading to faster training times. Whisper’s transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, translation, spoken language identification, and voice activity detection. As shown in Figure 3.4, these tasks are...

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