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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

Milestone 4 – Transforming raw speech data into Mel spectrogram features

Speech can be considered a one-dimensional array that changes over time, with each point in the array representing the loudness or amplitude of the sound. To understand speech, we need to capture its frequency and acoustic features, which can be done by analyzing the amplitude.

However, speech is a continuous sound stream, and computers can’t handle infinite data. So, we must convert this continuous stream into a series of discrete values by sampling the speech at regular intervals. This sampling is measured in samples per second or Hertz (Hz). The higher the sampling rate, the more accurately it captures the speech, but it also means more data to store every second.

It’s important to ensure that the sampling rate of the audio matches what the speech recognition model expects. If the rates don’t match, it can lead to errors. For example, playing a sound sampled at 16 kHz at 8...

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