Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Speech Recognition with Python

Name: Speech Recognition with Python
Rating: 4.5 (172 reviews)

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

Role Play

Created by365 Careers

Last updated 10/2025

English

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
Open AI's Whisper

Course content

10 sections • 44 lectures • 3h 24m total length

Welcome to the World of Speech Recognition4:50
Fictional Scenario: “The Transcription Tangle”1:11
Course Resources0:03
Course Approach4:19
How It All Started: Formants, Harmonics, and Phonemes3:20
Explore the origins of speech recognition from 1950s Bell Labs experiments, where researchers used spectrograms to study formants, harmonics, and phonemes, which allowed the creation of the Audrey system.
Understanding phonemes
Development and Evolution4:06
Trace the evolution of speech recognition from Audrey to deep learning. Highlight how hidden Markov models and neural networks enable end-to-end systems and popular assistants like Google Assistant and Siri.

Time-Domain Audio Features6:53
Frequency-Domain and Time-Frequency-Domain Audio Features6:19
Explore frequency-domain and time-frequency-domain audio features, including spectral centroid, spectral bandwidth, spectral contrast, and mfccs, and see how they help speech recognition by analyzing tone and energy distribution.
Time-Domain Feature Extraction: Framing and Feature Computation4:43
Learn how time-domain feature extraction turns raw audio into framed samples, computes per-frame features, and aggregates them to support accurate speech recognition with Python tools.
Frequency-Domain Feature Extraction: Fourier Transform4:27
Audio Features

Acoustic and Language Modeling3:49
Explore how acoustic models convert audio features into phonemes and how language models assemble those phonemes into words and sentences within context for accurate speech recognition.
Hidden Markov Models (HMMs) and Traditional Neural Networks6:30
Deep Learning Models: CNNs, RNNs, and LSTMs6:36
Deep Learning Models
Advanced Speech Recognition Systems: Transformers4:52
Transformers
Building a Speech Recognition Model Part I4:26
Building a Speech Recognition Model Part II4:19
Leverage CNNs, RNNs, and transformer architectures to build a robust speech recognizer, split data into training, validation, and test sets, augment data, fine-tune pre-trained models, and post-process.
Selecting the Appropriate Speech Recognition Tool5:48
Help Sarah Navigate the Speech Recognition Maze
Expanding Beyond the Tools We've Covered0:56
Speech Recognition Mechanics

Installing Anaconda2:24
Setting Up a New Environment2:47
Create a new Anaconda environment named speech env with Python 3.9 to isolate libraries for speech recognition, then activate it, install Ipykernel, and add a Jupyter kernel.
Installing Packages for Speech Recognition6:16
Importing The Relevant Packages in Jupyter3:11
Set up the Anaconda environment, launch Jupyter Notebook, and import speech recognition packages—NumPy, Matplotlib, Librosa, Librosa display, soundfile, speech_recognition, Guo, IPython, Whisper, CSV, OS, WAV, and Google text to speech.

Audio File Formats for Speech Recognition7:04
Importing Audio Files in Jupyter Notebook7:47
Import, visualize the waveform, and play an audio file in a Jupyter notebook using Librosa to load the audio and obtain the signal and sample rate.
The SpeechRecognition Library: Google Web Speech API8:32
Explore how the SpeechRecognition library converts audio to text by using the recognizer class and the Google Web Speech API, with practical file handling and transcription steps.
Evaluation Metrics: WER and CER3:11
Calculating WER and CER in Python5:34
Transcribing Audio with Google Web Speech API

Whisper AI: Transformer-based Speech-to-Text7:36
Homework Assignment1:38
Transcribing Multiple Audio Files from a Directory5:22
Saving Audio Transcriptions to CSV for Easy Analysis5:05
Save audio transcriptions to a CSV file to organize data for analysis and model training, with headers, track numbers, and file names for structure.
Reversing the Process: AI-Powered Text-to-Speech3:24
Transcribing Audio with OpenAI's Whisper

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, audio engineer, 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.

Learn to work with essential libraries like Librosa for audio processing and implement speech-to-text tools using cutting-edge AI models, including OpenAI's Whisper and Google's Web Speech API. Get familiar with the Python SpeechRecognition library and explore industry-leading toolkits such as Assembly AI, Meta's Wav2Letter, and Mozilla DeepSpeech, understanding their capabilities, accessibility, and cost considerations.

Dive into fascinating concepts like the human hearing apparatus, the exciting history of speech recognition, and the intricate behavior of sound waves—often overlooked topics that will give you a deeper understanding and set you apart. Learn about digital audio by understanding bit rate, bit depth, and sampling rate.

Listen to real audio and music examples to make learning easier, practical, and fun.

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.
Audio engineers and sound designers interested in modern technologies.
Professionals seeking to enhance accessibility or automate tasks with voice-driven solutions.

Your Future Awaits

The 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!

Who this course is for:

AI Engineers
AI Developers
Data Scientists
Tech Enthusiasts
Audio Engineers

Speech Recognition with Python

What you'll learn

Explore related topics

Course content

Introduction to Speech Recognition for AI6 lectures • 18min

Sound and Speech Basics3 lectures • 13min

Analog to Digital Conversion2 lectures • 10min

Audio Feature Extraction for AI Applications4 lectures • 22min

Speech Recognition Mechanics: From Statistics to Transformers9 lectures • 37min

Setting Up the Environment4 lectures • 15min

Transcribing Audio with Google Web Speech API5 lectures • 32min

Background Noise and Spectrograms3 lectures • 20min

Transcribing Audio with OpenAI's Whisper5 lectures • 23min

Final Discussion and Future Directions4 lectures • 15min

Requirements

Description

Who this course is for: