Speech Recognition With Python

Master Speech Recognition with Python: From Fundamentals to Cutting-Edge AI Applications

What you'll learn

Fundamentals of Speech Recognition

Python for Speech Recognition

Audio Processing Techniques

Advanced AI Algorithms

Building Speech-to-Text Applications

Practical AI Applications

Text-to-Speech Implementation

Requirements

Basic Python Coding Skills

Basic Understanding of Machine Learning

Description

Take the Speech Recognition with Python course and step into the fascinating world of Speech Recognition. Gain the skills to transform spoken language into actionable insights - a crucial skill in the age of AI. This course is your gateway to mastering the technology behind virtual assistants, voice-activated systems, and automated transcription tools. Whether you're an aspiring AI engineer, data scientist, AI developer, or a professional looking to enhance their technical skill set, this course equips you with everything you need to excel in the speech recognition domain.What Will You Learn?The Foundations of Speech Recognition: Explore how audio is transformed into digital data, processed, and converted into text. Build a strong theoretical base, from acoustic modeling to advanced algorithms.Hands-On Python Projects: Use Python’s robust libraries to process, visualize, and transcribe audio files. Learn both online and offline approaches for developing speech-to-text applications.Cutting-Edge Techniques: Dive into Hidden Markov Models, Neural Networks, and Transformers. Understand the mechanics behind modern speech recognition systems and discover how they power real-world applications.Practical Applications: Master the skills to build voice-activated assistants, enhance accessibility, and develop solutions for data-driven decision-making.Why Take This Course?Comprehensive Curriculum: Learn the end-to-end process of speech recognition—from theory to practical implementation—making complex topics accessible and engaging.Expert Instruction: Ivan, your instructor, is a seasoned sound engineer and data scientist passionate about AI. With years of experience in the media and film industries and expertise in AI, he brings a unique blend of creativity and technical insight.Real-World Applications: Understand how speech recognition powers tools like Siri, Google Assistant, and smart home devices, and learn to create similar innovations yourself.Interactive Learning: Follow along with engaging lessons, real-world examples, and practical exercises in Jupyter Notebook.Along the way, we demonstrate the use of the Librosa library, showing you how to perform essential audio processing tasks. You’ll gain hands-on experience as you implement speech-to-text tools using cutting-edge AI models like OpenAI’s Whisper and Google’s Web Speech API with the Python SpeechRecognition library. Additionally, you'll explore the appropriate use of popular speech recognition toolkits like Assembly AI, Meta's Wav2Letter, and Mozilla DeepSpeech, considering accessibility and costs.What Sets This Course Apart?High-Quality Content: Professionally produced lectures with easy-to-follow explanations and animations.Practical Focus: Go beyond theory and build hands-on projects to cement your learning.AI Integration: Learn how speech recognition interacts with broader AI technologies, positioning you as a forward-thinking professional.Supportive Community: Access active Q&A support and a thriving learner community.Who Is This Course For?Data science and AI enthusiasts eager to explore speech recognition technology.Developers looking to integrate speech-to-text functionality into their applications.Professionals seeking to enhance accessibility or automate tasks with voice-driven solutions.Your Future AwaitsThe demand for speech recognition experts is skyrocketing as industries increasingly adopt AI-driven technologies. By enrolling in this course, you’ll not only master a cutting-edge skill but also position yourself for success in a rapidly growing field.This course is backed by a 30-day full money-back guarantee. Take the first step toward a future of endless possibilities—click "Enroll Now" and start your journey into Speech Recognition with Python today!

Overview

Section 1: Introduction

Lecture 1 Welcome to the World of Speech Recognition

Lecture 2 Course Approach

Lecture 3 How It All Started: Formants, Harmonics, and Phonemes

Lecture 4 Development and Evolution

Section 2: Sound and Speech Basics

Lecture 5 How Do Humans Recognize Speech?

Lecture 6 Fundamentals of Sound and Sound Waves

Lecture 7 Properties of Sound Waves

Section 3: Analog to Digital Conversion

Lecture 8 Key Concepts: Sample Rate, Bit Depth, and Bit Rate

Lecture 9 Audio Signal Processing for Machine Learning and AI

Section 4: Audio Feature Extraction for AI Applications

Lecture 10 Time-Domain Audio Features

Lecture 11 Frequency-Domain and Time-Frequency-Domain Audio Features

Lecture 12 Time-Domain Feature Extraction: Framing and Feature Computation

Lecture 13 Frequency-Domain Feature Extraction: Fourier Transform

Section 5: Speech Recognition Mechanics

Lecture 14 Acoustic and Language Modeling

Lecture 15 Hidden Markov Models (HMMs) and Traditional Neural Networks

Lecture 16 Deep Learning Models: CNNs, RNNs, and LSTMs

Lecture 17 Advanced Speech Recognition Systems: Transformers

Lecture 18 Building a Speech Recognition Model Part I

Lecture 19 Building a Speech Recognition Model Part II

Lecture 20 Selecting the Appropriate Speech Recognition Tool

Lecture 21 Expanding Beyond the Tools We've Covered

Section 6: Setting Up the Environment

Lecture 22 Installing Anaconda

Lecture 23 Setting Up a New Environment

Lecture 24 Installing Packages for Speech Recognition

Lecture 25 Importing The Relevant Packages in Jupyter

Section 7: Transcribing Audio with Google Web Speech API

Lecture 26 Audio File Formats for Speech Recognition

Lecture 27 Importing Audio Files in Jupyter Notebook

Lecture 28 The SpeechRecognition Library: Google Web Speech API

Lecture 29 Evaluation Metrics: WER and CER

Lecture 30 Calculating WER and CER in Python

Section 8: Background Noise and Spectrograms

Lecture 31 Understanding Noise in Audio Files

Lecture 32 Creating a Spectrogram with Python

Lecture 33 Dealing with Background Noise

Section 9: Transcribing Audio with OpenAI's Whisper

Lecture 34 Whisper AI: Transformer-based Speech-to-Text

Lecture 35 Homework Assignment

Lecture 36 Transcribing Multiple Audio Files from a Directory

Lecture 37 Saving Audio Transcriptions to CSV for Easy Analysis

Lecture 38 Reversing the Process: AI-Powered Text-to-Speech

Section 10: Final Discussion and Future Directions

Lecture 39 Modern Practices and Applications

Lecture 40 Challenges and Limitations

Lecture 41 The Future of Speech Recognition with AI

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