Speech Recognition with Python from Udemy

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.

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.
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.

What's inside

Learning objectives

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

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

Syllabus

Introduction

Welcome to the World of Speech Recognition

Course Resources

Course Approach

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Read about what's good

what should give you pause

and possible dealbreakers

Covers cutting-edge techniques like Hidden Markov Models, Neural Networks, and Transformers, which are essential for developing modern speech recognition systems

Explores the end-to-end process of speech recognition, from theoretical foundations to practical implementation, making complex topics accessible and engaging

Teaches how to work with essential libraries like Librosa and implement speech-to-text tools using AI models, including OpenAI's Whisper and Google's Web Speech API

Examines the mechanics behind modern speech recognition systems and how they power real-world applications, such as virtual assistants and smart home devices

Explores the history of speech recognition, the human hearing apparatus, and the behavior of sound waves, providing a deeper understanding of the field

Requires installing Anaconda and setting up a new environment, which may require some familiarity with software installation and environment management

Reviews summary

Practical speech recognition with python

According to learners, this course provides a positive and practical introduction to speech recognition using Python. Students appreciate the emphasis on hands-on projects and the coverage of modern tools and APIs, including OpenAI's Whisper and Google Web Speech API. The course is seen as providing a solid theoretical foundation while remaining accessible. Some feedback suggests that certain sections, particularly theory or advanced topics, could benefit from greater depth for those seeking a more advanced understanding. Overall, it is considered a valuable resource for those looking to integrate speech-to-text functionality into applications or explore the field.

Explanations are easy to follow and understand.

"The instructor explains complex concepts clearly and engagingly."

"Lectures are well-produced and easy to follow."

"I found the teaching style made even difficult topics accessible."

Good grounding in SR fundamentals and AI methods.

"The explanations of fundamental concepts like sound waves and acoustic modeling were clear."

"I appreciated the overview of different AI models from HMMs to Transformers."

"It gives you enough theory to understand *why* things work before jumping into code."

Explores cutting-edge tools like OpenAI Whisper.

"Learning how to use OpenAI's Whisper API was incredibly useful and timely."

"The section on modern techniques and tools like Whisper and Web Speech API is a major plus."

"This course introduced me to the latest advancements in speech recognition technology."

Build real-world speech apps with Python.

"I loved the hands-on coding exercises, they really helped me grasp the concepts."

"The projects using Python libraries like SpeechRecognition were very practical and applicable."

"Finally, a course that shows you how to actually build things, not just theory."

Some seek more advanced or theoretical detail.

"While the introduction is great, I wish some advanced topics were covered in more depth."

"Could use more detail on fine-tuning models or optimization techniques."

"The theory sections felt a little rushed at times; more examples would be helpful."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Speech Recognition with Python with these activities:

Review Fundamentals of Sound

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Reinforce your understanding of sound properties and digital audio conversion. A solid grasp of these concepts is crucial for understanding how speech recognition systems process audio data.

Browse courses on Sound Waves

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Review the properties of sound waves, including frequency, amplitude, and wavelength.
Study the process of analog-to-digital conversion and the key concepts of sample rate, bit depth, and bit rate.
Practice identifying different audio file formats and their characteristics.

Read 'Speech and Language Processing' by Jurafsky and Martin

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Gain a deeper understanding of the theoretical underpinnings of speech recognition. This book provides a comprehensive overview of the field, covering topics such as acoustic modeling, language modeling, and decoding algorithms.

View Speech and Language Processing An Introduction... on Amazon

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Read the chapters related to acoustic modeling and feature extraction.
Study the sections on Hidden Markov Models (HMMs) and their application to speech recognition.
Review the concepts of language modeling and decoding algorithms.

Experiment with Librosa for Audio Processing

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Solidify your understanding of audio processing techniques using Librosa. This hands-on practice will help you become proficient in extracting audio features and visualizing audio data.

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Load audio files using Librosa and explore their properties.
Extract various audio features, such as MFCCs, chroma features, and spectral centroid.
Visualize audio data using spectrograms and waveforms.
Experiment with different audio processing techniques, such as noise reduction and audio segmentation.

Four other activities

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Read 'Deep Learning' by Goodfellow, Bengio, and Courville

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Gain a deeper understanding of the deep learning models used in speech recognition. This book provides a comprehensive overview of the field, covering topics such as CNNs, RNNs, and transformers.

View Deep Learning on Amazon

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Read the chapters related to CNNs, RNNs, and transformers.
Study the sections on sequence modeling and attention mechanisms.
Review the concepts of backpropagation and optimization algorithms.

Blog Post: Comparing Speech Recognition APIs

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Deepen your understanding of different speech recognition APIs by comparing their features, accuracy, and cost. Writing a blog post will help you synthesize your knowledge and share it with others.

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Research different speech recognition APIs, such as Google Web Speech API, OpenAI's Whisper, and AssemblyAI.
Compare their features, accuracy, and cost.
Write a blog post summarizing your findings and providing recommendations for different use cases.
Publish your blog post on a platform like Medium or your personal website.

Build a Voice-Controlled Application

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Apply your knowledge of speech recognition to build a practical application. This project will challenge you to integrate speech-to-text functionality into a real-world scenario.

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Choose a project idea, such as a voice-controlled smart home device or a voice-activated assistant.
Implement the speech recognition functionality using a library like SpeechRecognition or an API like OpenAI's Whisper.
Integrate the speech recognition functionality with other components of your application.
Test and refine your application to ensure it works accurately and reliably.

Contribute to an Open Source Speech Recognition Project

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Enhance your skills and contribute to the community by participating in an open-source speech recognition project. This will provide valuable experience in working with real-world codebases and collaborating with other developers.

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Identify an open-source speech recognition project that interests you.
Explore the codebase and identify areas where you can contribute, such as bug fixes, documentation improvements, or new features.
Submit your contributions to the project and participate in code reviews.

Career center

Learners who complete Speech Recognition with Python will develop knowledge and skills that may be useful to these careers:

Speech Recognition Engineer

A Speech Recognition Engineer designs, develops, and implements speech recognition systems. This role involves working with acoustic models, language models, and speech processing algorithms to create applications that accurately transcribe and interpret spoken language. This course helps build a foundation in the core principles of speech recognition, covering topics such as acoustic modeling, Hidden Markov Models, Neural Networks, and Transformers. The hands-on Python projects, especially those using libraries like Librosa and OpenAI's Whisper, provide experience that is highly relevant to the tasks performed by a Speech Recognition Engineer. Furthermore, the course's focus on real-world applications, will prepare you for this role.

See salaries and explore the career path for Speech Recognition Engineer

Artificial Intelligence Developer

An Artificial Intelligence Developer builds AI-powered applications, often including voice-activated systems and virtual assistants. This course provides practical skills in transforming spoken language into actionable insights, understanding the mechanics behind modern speech recognition systems, and implementing speech-to-text tools using cutting-edge AI models. Learning to work with Python libraries like Librosa, SpeechRecognition, OpenAI's Whisper, and Google's Web Speech API provides essential experience for an Artificial Intelligence Developer. The course also covers relevant topics like Hidden Markov Models and neural networks. The course's real-world examples and practical exercises in Jupyter Notebook are helpful.

See salaries and explore the career path for Artificial Intelligence Developer

Natural Language Processing Engineer

A Natural Language Processing Engineer focuses on enabling computers to understand and process human language. This often involves working with speech data, especially in voice-enabled applications. The course is useful because it covers transforming spoken language into text and using Python to process and transcribe audio files. The course's introduction to Hidden Markov Models, neural networks, and Transformers helps to understand the underlying mechanisms of modern speech recognition systems. Gaining familiarity with tools like OpenAI's Whisper and Google's Web Speech API prepares one for many tasks that a Natural Language Processing Engineer performs.

See salaries and explore the career path for Natural Language Processing Engineer

Data Scientist

Data scientists are often involved in analyzing audio data and extracting insights from spoken language. By learning how to transform spoken language into actionable insights, the course helps a Data Scientist to add a valuable skill to their repertoire. The skills of working with Python libraries to process, visualize, and transcribe audio files is a great way to build a foundation for analyzing audio data. The course content on Hidden Markov Models, Neural Networks, and Transformers helps to understand how to derive data from speech. This knowledge is particularly useful for a Data Scientist working in fields like market research or customer service analysis, where understanding spoken feedback is crucial.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models, including those used for speech recognition. This course helps build understanding of the foundations of speech recognition, from acoustic modeling to advanced algorithms. The course's focus on hands-on Python projects, using libraries like Librosa and OpenAI's Whisper, helps to implement speech-to-text applications. Furthermore, the coverage of Hidden Markov Models, Neural Networks, and Transformers ensures that a Machine Learning Engineer can understand and apply these techniques in real-world applications. The course may be useful for a Machine Learning Engineer looking to specialize in speech recognition.

See salaries and explore the career path for Machine Learning Engineer

Voice User Interface Designer

Voice User Interface Designer creates voice-controlled interfaces for applications and devices. This course helps build the skills to work with voice-activated systems, and the coverage of speech recognition technologies such as Hidden Markov Models helps to design effective and intuitive interfaces. The knowledge of tools like OpenAI's Whisper and Google's Web Speech API may be useful for designing and testing voice interfaces. Overall, this course provides a relevant skillset for anyone interested in shaping the future of human-computer interaction through voice.

See salaries and explore the career path for Voice User Interface Designer

Software Developer

A Software Developer can integrate speech recognition capabilities into various applications. The course helps build skills necessary to develop voice-activated assistants, enhance accessibility, and automate tasks with voice-driven solutions. Learning to use Python's robust libraries to process, visualize, and transcribe audio files is helpful for a developer working on voice-enabled applications. The course helps to understand both online and offline approaches for developing speech-to-text applications. The insights into industry-leading toolkits such as Assembly AI and Mozilla DeepSpeech are useful for a Software Developer looking to add speech recognition features to their projects.

See salaries and explore the career path for Software Developer

Automation Specialist

An Automation Specialist automates tasks and processes, often using voice-driven solutions. The course can help you master the skills to enhance accessibility and automate tasks with voice-driven solutions. The work with Python enables practical applications of automation technologies. This course, which deals with audio and speech, can be useful for automating tasks that rely on translating audio into text. The course's exploration of real-world examples and practical exercises may be helpful for automating tasks.

See salaries and explore the career path for Automation Specialist

Transcription Specialist

A Transcription Specialist converts audio recordings into written text. While traditionally a manual task, AI-powered speech recognition tools are transforming this field. This course helps a Transcription Specialist by providing the skills to leverage these tools, specifically using Python and libraries such as OpenAI's Whisper and Google's Web Speech API. The course's coverage of how audio is transformed into digital data, processed, and converted into text may be useful. Furthermore, understanding evaluation metrics like WER and CER, which are covered in the course, helps improve the accuracy and efficiency of transcription processes.

See salaries and explore the career path for Transcription Specialist

Accessibility Consultant

An Accessibility Consultant advises organizations on how to make their products and services accessible to people with disabilities. Speech recognition technology plays a crucial role in accessibility, allowing users to interact with systems using their voice. This course helps to understand the potential of speech-to-text functionality and how it can be integrated into applications to improve accessibility. The course may be useful when learning to develop voice-driven solutions that address specific accessibility needs. Understanding the mechanics behind modern speech recognition systems helps to advocate for and implement effective accessibility solutions.

See salaries and explore the career path for Accessibility Consultant

UX Researcher

A User Experience Researcher investigates and understands user behavior to inform design decisions. Speech recognition can be a key area of focus, particularly when studying voice interfaces. The course may provide insights into how users interact with voice-activated systems, covering topics such as acoustic modeling, hidden markov models, and neural networks. The focus on practical applications, such as building voice-activated assistants, can help a UX Researcher understand the challenges and opportunities in designing effective voice experiences.

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Acoustic Engineer

An Acoustic Engineer deals with the science and technology of sound and vibration. While this course focuses on the speech recognition aspects, rather than the physics of acoustics, a foundational knowledge of acoustics is certainly relevant. For example, the course covers how audio is transformed into digital data and basic concepts of sound waves. The section on audio feature extraction for AI applications may be helpful. Gaining exposure to speech recognition techniques helps to understand the broader context in which acoustic principles are applied.

See salaries and explore the career path for Acoustic Engineer

Technical Writer

A Technical Writer creates documentation for technical products and services. This course may give you a better understanding of speech recognition technologies. The course may be useful in order to write clear and accurate documentation for users. A fundamental overview of speech recognition can familiarize you with the terminology and concepts needed to write about these technologies effectively.

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Project Manager

A Project Manager oversees technical projects, including those involving speech recognition technologies. The course may be useful in managing projects related to speech recognition, as it provides a broad overview of the field. It introduces topics such as acoustic modeling to transcription tools. The course may give a Project Manager an advantage managing projects that involve speech recognition.

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Quality Assurance Analyst

A Quality Assurance Analyst ensures that products and services meet certain standards of quality. Familiarity with speech recognition technology may be useful for testing and evaluating voice-enabled applications. Understanding the foundations of speech recognition, as covered in the course, may be useful for assessing the accuracy and reliability of speech-to-text systems. The coverage of evaluation metrics like WER and CER, is helpful in performing quality assurance tasks specific to speech recognition.

See salaries and explore the career path for Quality Assurance Analyst

Speech Recognition with Python

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Practical speech recognition with python

Activities

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