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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

Milestone 6 – Establishing standardized test sets and metrics for performance benchmarking

Now, let’s learn how to check our model’s performance. We’ll use the WER metric, a common way to evaluate speech recognition systems. We’ll load the WER metric from Hugging Face evaluate:

import evaluate
metric = evaluate.load("wer")

Next, we’ll create a function called compute_metrics to calculate the WER:

def compute_metrics(pred):
    # [Code to replace -100, decode predictions and labels, and compute WER]
    return {"wer": wer}

This function fixes our label_ids (where we had replaced padding tokens with -100). Then, it turns both the predicted and label IDs into text strings. Lastly, it calculates the WER between these two.

Loading a pre-trained model checkpoint

We’ll start with a pre-trained Whisper model. This is easy with Hugging Face Transformers:

from transformers...

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