Speech Recognition with Python

with Ivan Manov
4.9/5
(16)

Master speech recognition—the technology that enables machines to understand human speech by converting voice into readable data. Utilize Python speech recognition tools to transcribe audio to text with cutting-edge AI models.

3 hours of content 343 students

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 3 hours of content
  • 61 Interactive exercises
  • 7 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Speech Recognition with Python

A course by Ivan Manov

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 3 hours of content
  • 61 Interactive exercises
  • 7 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

$99.00

Lifetime access

Buy now

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 3 hours of content
  • 61 Interactive exercises
  • 7 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • Master the basics of audio and signal processing to fully grasp speech-to-text technology
  • Understand how machines (and humans) process and interpret speech
  • Transform unstructured audio data into text for actionable insights
  • Explore how advanced deep learning techniques like Transformers can power the speech recognition pipeline
  • Enhance your portfolio with advanced AI skills, utilizing tools like Google Web Speech API and Whisper AI transcription for speech-to-text conversions in Python
  • Utilize the Librosa library for audio processing tasks
  • Implement AI-powered text-to-speech directly in Jupyter Notebook

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

Our Speech Recognition with Python course explores the technology that powers modern voice-activated systems and AI tools like virtual assistants, automated transcription devices, and home devices. We break down the theory behind speech recognition, covering Python audio processing and machine learning aspects in an easy-to-understand format. Along the way, we demonstrate the use of the librosa library, showing you how to perform essential audio processing tasks that are key to preparing sound data for analysis. 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. Additionally, you'll explore the appropriate use of popular speech recognition toolkits like Assembly AI, Meta's Wav2Letter, Mozilla DeepSpeech, and cloud-based solutions, such as Amazon Transcribe and Azure Speech, considering accessibility and costs. This speech recognition course unravels the behind-the-scenes processes that drive speech recognition. We explain how various methodologies operate—from audio feature extraction and noise cleaning to deep learning and transformers. We also cover essential audio concepts, including sound wave properties, analog-to-digital conversion, acoustics fundamentals, and aspects of human hearing. By the end of the course, you'll be fully equipped with the skills to examine the speech recognition technology in greater depth and understand the fundamentals needed to build your own AI-powered model. This course—tailored for data analysts, scientists, audio engineers, AI enthusiasts, and anyone with a curious mind—demonstrates how to convert sound files into structured, text-based outputs for analysis. Whether you’re working with audio data or exploring AI, the Speech Recognition with Python course equips you with the knowledge to effectively transform audio into actionable insights.
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How Do Humans Recognize Speech?

2.1 How Do Humans Recognize Speech?

3 min

Fundamentals of Sound and Sound Waves

2.3 Fundamentals of Sound and Sound Waves

3 min

Interactive Exercises

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

true-false exercise
single-choice exercise
multiple-choice exercise
flashcards exercise
fill-blank-typing exercise
fill-blank-selection exercise
order exercise
categorize exercise

Curriculum

  • 1. Course Introduction
    4 Lessons 16 Min
    Discover our Speech Recognition with Python course to understand how deeply integrated this technology is in our daily lives. Learn about its origins, evolution, and the advancements that brought us to where we are today.
    Welcome to the World of Speech Recognition
    5 min
    Course Approach
    4 min
    How It All Started: Formants, Harmonics, and Phonemes
    3 min
    Development and Evolution
    4 min
  • 2. Sound and Speech Basics
    3 Lessons 12 Min
    Explore the essential sound and speech concepts needed to master speech recognition. Learn how humans hear and process audio and how this compares to machine audio interpretation. Uncover the mechanics of sound waves and how they transfer energy through a medium, with intriguing Physics concepts explained in a clear and accessible way.
    How Do Humans Recognize Speech?
    3 min
    Fundamentals of Sound and Sound Waves
    3 min
    Properties of Sound Waves
    6 min
  • 3. Analog to Digital Conversion
    2 Lessons 10 Min
    Learn about converting analog signals into digital ones—a necessary step in working with audio data. Explore critical concepts like sample rate, bit depth, and bit rate and understand their connection to the sampling and quantization of a signal. Gain insights into what AI or machine learning engineers need to know about audio signal processing and discover the steps involved in preparing audio data for AI applications and modeling.
    Key Concepts: Sample Rate, Bit Depth, and Bit Rate
    5 min
    Audio Signal Processing for Machine Learning and AI
    5 min
  • 4. Audio Feature Extraction for AI Applications
    4 Lessons 22 Min
    Examine how audio features are extracted from a rough audio signal. Discover how sound characteristics and properties can combine and form features for building speech recognition models. Learn the role of the Fourier transform and its impact on this technology.
    Time-Domain Audio Features
    7 min
    Frequency-Domain and Time-Frequency-Domain Audio Features
    6 min
    Time-Domain Feature Extraction: Framing and Feature Computation
    5 min
    Frequency-Domain Feature Extraction: Fourier Transform
    4 min
  • 5. Speech Recognition Mechanics
    8 Lessons 39 Min
    Understand how speech recognition systems operate—from identifying patterns in sound to predicting words and sentences. Explore the statistical and deep learning methods behind the speech-to-text pipeline, including CNNs, RNNs, LSTMs, and Transformers. Learn the steps to build a speech recognition model and how to select the right tool for your tasks.
    Acoustic and Language Modeling
    4 min
    Hidden Markov Models (HMMs) and Traditional Neural Networks
    7 min
    Deep Learning Models: CNNs, RNNs, and LSTMs
    7 min
    Advanced Speech Recognition Systems: Transformers
    5 min
    Building a Speech Recognition Model Part I
    4 min
    Building a Speech Recognition Model Part II
    4 min
    Selecting the Appropriate Speech Recognition Tool
    6 min
    Expanding Beyond the Tools We've Covered Read now
    2 min
  • 6. Setting Up the Environment
    4 Lessons 17 Min
    Set up your environment for audio machine learning and hands-on speech-to-text implementation. Learn to install Anaconda and Jupyter in a dedicated environment using the correct Python version. Equip your system with essential libraries and tools for audio processing and AI, including Librosa, OpenAI's Whisper, PyTorch, and more.
    Installing Anaconda
    5 min
    Setting up a New Environment
    3 min
    Installing Packages for Speech Recognition
    6 min
    Importing the Relevant Packages in Jupyter Notebook
    3 min
  • 7. Transcribing Audio with Google Web Speech API
    5 Lessons 33 Min
    Master how to distinguish between different audio files and how to load, play, and visualize them in Jupyter Notebook. Dive into Python’s speech_recognition library and discover how it enables access to Google’s API for implementing speech-to-text functionality. Find out how to assess the accuracy of your models or transcriptions by calculating WER (Word Error Rate) and CER (Character Error Rate).
    Audio File Formats for Speech Recognition
    7 min
    Importing Audio Files in Jupyter Notebook
    8 min
    The SpeechRecognition Library: Google Web Speech API
    9 min
    Evaluation Metrics: WER and CER
    3 min
    Calculating WER and CER in Python
    6 min
  • 8. Background Noise and Spectrograms
    3 Lessons 20 Min
    Background noise often disrupts speech recognition systems. Learn to manage and visualize it by extracting spectrograms emphasizing crucial frequencies in audio files.
    Understanding Noise in Audio Files
    4 min
    Creating a Spectrogram with Python
    7 min
    Dealing with Background Noise
    9 min
  • 9. Transcribing Audio with OpenAI's Whisper
    5 Lessons 24 Min
    Dive deep into the speech recognition technology by using OpenAI's Transformer-based Whisper model. Learn to transcribe multiple audio files simultaneously and efficiently store results in a table for comprehensive analysis.
    Whisper AI: Transformer-Based Speech-to-Text
    8 min
    Homework Assignment Read now
    3 min
    Transcribing Multiple Audio Files from a Directory
    5 min
    Saving Audio Transcriptions to CSV for Easy Analysis
    5 min
    Reversing the Process: AI-Powered Text-to-Speech
    3 min
  • 10. Final Discussion and Future Directions
    3 Lessons 12 Min
    Summarize our discussion on AI-driven speech recognition, including emerging trends and modern practices. Explore the potential ethical and technological challenges these systems encounter. Experience the future of speech recognition with exciting insights into innovative topics like edge computing, real-time translation and even how a supermassive black hole can be explored through sonification.
    Modern Practices and Applications
    5 min
    Challenges and Limitations
    3 min
    The Future of Speech Recognition with AI
    4 min
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Topics

Artificial IntelligenceDeep LearningPythonSignal ProcessingTransformersSpectrogramsSound and Speech FundamentalsHidden Markov ModelsSpeech-to-TextText-to-SpeechNeural NetworksSound EngineeringWhisper AIAudio For Machine Learning

Tools & Technologies

python
theory

Course Requirements

  • Basic Understanding of Python
  • Familiarity with Machine Learning and AI

Who Should Take This Course?

Level of difficulty: Intermediate

  • Data professionals expanding skills into Python-based speech-to-text
  • Aspiring data analysts and scientists working with audio data
  • AI and machine learning engineers exploring speech recognition systems
  • Audio enthusiasts interested in sound and AI intersections
  • Developers aiming to incorporate speech recognition in projects
  • Musicians and sound engineers using AI for audio processing and transcription
  • AI researchers

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Ivan Manov

Ivan Manov

Course Creator at

6 Courses

1264 Reviews

14911 Students

Ivan has a background in systems and sound engineering, along with information technologies and communications. In addition, he has professional experience in the media production industry and telecommunications. Ivan believes the value of data is growing every day, and it will soon be the biggest commodity in the world. He describes himself as “forward-looking and visionary”. Besides data analysis, data collection, and Python programming, he is passionate about artificial intelligence, signal processing, sound design, acoustics, and music. He sees these subjects as interconnected, and his work goal is to keep the balance between science and arts.

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What Our Learners Say

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