Book description
Master automatic speech recognition (ASR) with groundbreaking generative AI for unrivaled accuracy and versatility in audio processing
Key Features
- Uncover the intricate architecture and mechanics behind Whisper's robust speech recognition
- Apply Whisper's technology in innovative projects, from audio transcription to voice synthesis
- Navigate the practical use of Whisper in real-world scenarios for achieving dynamic tech solutions
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
What you will learn
- Integrate Whisper into voice assistants and chatbots
- Use Whisper for efficient, accurate transcription services
- Understand Whisper's transformer model structure and nuances
- Fine-tune Whisper for specific language requirements globally
- Implement Whisper in real-time translation scenarios
- Explore voice synthesis capabilities using Whisper's robust tech
- Execute voice diarization with Whisper and NVIDIA's NeMo
- Navigate ethical considerations in advanced voice technology
Who this book is for
Learn OpenAI Whisper is designed for a diverse audience, including AI engineers, tech professionals, and students. It's ideal for those with a basic understanding of machine learning and Python programming, and an interest in voice technology, from developers integrating ASR in applications to researchers exploring the cutting-edge possibilities in artificial intelligence.
Table of contents
- Learn OpenAI Whisper
- Foreword
- Contributors
- About the author
- About the reviewers
- Preface
- Part 1: Introducing OpenAI’s Whisper
- Chapter 1: Unveiling Whisper – Introducing OpenAI’s Whisper
- Chapter 2: Understanding the Core Mechanisms of Whisper
- Part 2: Underlying Architecture
-
Chapter 3: Diving into the Whisper Architecture
- Technical requirements
- Understanding the transformer model in Whisper
- Exploring the multitasking and multilingual capabilities of Whisper
- Training Whisper with weak supervision on large-scale data
- Gaining insights into data, annotation, and model training
- Integrating Whisper with other OpenAI technologies
- Summary
-
Chapter 4: Fine-Tuning Whisper for Domain and Language Specificity
- Technical requirements
- Introducing the fine-tuning process for Whisper
- Leveraging the Whisper checkpoints
- Milestone 1 – Preparing the environment and data for fine-tuning
- Milestone 2 – Incorporating the Common Voice 11 dataset
- Milestone 3 – Setting up Whisper pipeline components
- Milestone 4 – Transforming raw speech data into Mel spectrogram features
- Milestone 5 – Defining training parameters and hardware configurations
- Milestone 6 – Establishing standardized test sets and metrics for performance benchmarking
- Milestone 7 – Executing the training loops
- Milestone 8 – Evaluating performance across datasets
- Milestone 9 – Building applications that demonstrate customized speech recognition
- Summary
- Part 3: Real-world Applications and Use Cases
- Chapter 5: Applying Whisper in Various Contexts
- Chapter 6: Expanding Applications with Whisper
- Chapter 7: Exploring Advanced Voice Capabilities
-
Chapter 8: Diarizing Speech with WhisperX and NVIDIA’s NeMo
- Technical requirements
- Augmenting Whisper with speaker diarization
-
Performing hands-on speech diarization
- Setting up the environment
- Streamlining the diarization workflow with helper functions
- Separating music from speech using Demucs
- Transcribing audio using WhisperX
- Aligning the transcription with the original audio using Wav2Vec2
- Using NeMo’s MSDD model for speaker diarization
- Mapping speakers to sentences according to timestamps
- Enhancing speaker attribution with punctuation-based realignment
- Finalizing the diarization process
- Summary
- Chapter 9: Harnessing Whisper for Personalized Voice Synthesis
- Chapter 10: Shaping the Future with Whisper
- Index
- Other Books You May Enjoy
Product information
- Title: Learn OpenAI Whisper
- Author(s):
- Release date: May 2024
- Publisher(s): Packt Publishing
- ISBN: 9781835085929
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