Offered By: IBM

Easy Speech-to-Text with Python

This project explores the multilingual automatic speech recognition (ASR) system and the architecture of signal processing using Python. Today, ASR systems are available through multiple sources, including IBM Watson® Speech to Text, and some publicly available systems through Open AI.

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GPXX0EPMEN

Artificial Intelligence

288 Enrolled
(21)

At a Glance

This project explores the multilingual automatic speech recognition (ASR) system and the architecture of signal processing using Python. Today, ASR systems are available through multiple sources, including IBM Watson® Speech to Text, and some publicly available systems through Open AI.

Why you should do this Guided Project

Let’s say you are a podcast creator, and you want to transcribe your podcast, so that it can be translated into multiple languages or so that hearing impaired people can read your content. Additionally, let’s say you want to improve the discovery of your podcasts through search engine optimization. Transcribing your podcast will enable search engines to index the text, making it easier to find it. 


The purpose of this Guided Project is to introduce you to ASR (automatic speech recognition) system, to understand how the signal processing works, the architecture of a transformer model behind ASR and some examples on how to easily recognize, transcribe and translate some of your audio and video files, using publicly available ASR tool.


A Look at the Project Ahead

After completing this project, you will be able to:  
  • understand how signal processing works
  • load audio file and detect the spoken language
  • transcribe and translate an audio/YouTube file

What You'll Need

You just need a web browser!  Basic Python programming knowledge is recommended but it is not required. 
Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the Cloud IDE and Python runtimes that we offer as part of the IBM Skills Network Labs environment. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.

Estimated Effort

30 min

Level

Intermediate

Skills You Will Learn

Python, Data Science, Machine Learning, Data Analysis, Embeddable AI, PyTorch

Language

English

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Instructors

Profile picture for Svitlana Kramar

Svitlana Kramar

Data Scientist

I’m a passionate data science educator whose goal is to learn by teaching innovative data science tools that can improve our day-to-day tasks and our quality of life. My interests are in Natural Language Processing: text classification, summarization, and generation. Research can take a long time because there are a lot of resources and new opinions posted every day. Having tools to summarize and extract the information can save a lot of time. I hope we can all learn, approve, and apply the data science tools to cut down on the repetitive and tedious tasks, to make more informed decisions in life, to differentiate fake from real, and to open communication spaces to language-diverse or hearing-impaired audiences. The applications are limitless! My personality: I am a foodie and I love cooking and learning different cuisines. I also love travelling and connecting with people by learning a little bit of their language, about their food and music. I hold Data Science and Analytics master’s degree, specializing in Machine Learning, from University of Calgary.

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Contributors

Profile picture for Joseph Santarcangelo

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Profile picture for Roxanne Li

Roxanne Li

Data Scientist at IBM

I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!

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