Spoken Language Processing in Python

Learn to load, transform, and transcribe human speech from raw audio files in Python.
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Clock4 HoursPlay14 VideosCode53 ExercisesGroup2,064 Learners
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Course Description

We learn to speak far before we learn to read. Even in the digital age, our main method of communication is speech. Spoken Language Processing with Python will help you load, transform and transcribe audio files. You'll start by seeing what raw audio looks like in Python. And then finish by working through an example business use case, transcribing and classifying phone call data.

  1. 1

    Introduction to Spoken Language Processing with Python

    Free
    Audio files are different from most other types of data. Before you can start working with them, they require some preprocessing. In this chapter, you'll learn the first steps to working with speech files by converting two different audio files into soundwaves and comparing them visually.
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  2. 2

    Using the Python SpeechRecognition library

    Speech recognition is still far from perfect. But the SpeechRecognition library provides an easy way to interact with many speech-to-text APIs. In this section, you'll learn how to use the SpeechRecognition library to easily start converting the spoken language in your audio files to text.
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  3. 3

    Manipulating Audio Files with PyDub

    Not all audio files come in the same shape, size or format. Luckily, the PyDub library by James Robert provides tools which you can use to programmatically alter and change different audio file attributes such as frame rate, number of channels, file format and more. In this chapter, you'll learn how to use this helpful library to ensure all of your audio files are in the right shape for transcription.
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  4. 4

    Processing text transcribed from spoken language

    In this chapter, you'll put everything you've learned together by building a speech processing proof of concept project for a technology company, Acme Studios. You'll start by transcribing customer support call phone call audio snippets to text. Then you'll perform sentiment analysis using NLTK, named entity recognition using spaCy and text classification using scikit-learn on the transcribed text.
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In the following tracks
Natural Language Processing
Collaborators
Maggie MatsuiAdrián SotoHillary Green-Lerman
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Daniel Bourke

Machine Learning Engineer and YouTube creator
Machine Learning Engineer who creates YouTube videos and writes about the intersection of health, technology and art.
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