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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

For this chapter, we will leverage Google Colaboratory. We’ll try to secure the best GPU we can afford, with a minimum of 12 GB of GPU memory.

To get a GPU, within Google Colab’s main menu, click Runtime | Change runtime type, then change the Hardware accelerator from None to GPU.

Keep in mind that fine-tuning Whisper will take several hours. Thus, you must monitor your running notebook in Colab regularly.

This chapter teaches you how to fine-tune the Whisper model so that it can recognize speech in multiple languages using tools such as Hugging Face Datasets, Transformers, and the Hugging Face Hub. Check out the Google Colab Python notebook in this book’s GitHub repository (https://github.com/PacktPublishing/Learn-OpenAI-Whisper/tree/main/Chapter04) and try fine-tuning yourself.

The general recommendation is to follow the Colab notebook and upload model checkpoints directly to the Hugging Face Hub while training. The Hub provides...

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