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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 3 – Setting up Whisper pipeline components

The process of ASR can be broken down into three main parts:

  • Feature extractor: This is the initial step of processing the raw audio inputs. Think of it as preparing the audio files, so the model can easily understand and use them. The feature extractor turns the audio into a format that highlights essential aspects of the sound, such as pitch or volume, which are crucial for the model to recognize different words and sounds.
  • The model: This is the core part of the ASR process. It performs what we call sequence-to-sequence mapping. In simpler terms, it takes the processed audio from the feature extractor and works to convert it into a sequence of text. It’s like translating the language of sounds into the language of text. This part involves complex calculations and patterns to accurately determine what the audio says.
  • Tokenizer: After the model has done its job of mapping the sounds to text, the tokenizer...

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