Course
Spoken Language Processing in Python
ПередовойУровень мастерства
Обновлено 08.2024Начать Курс Бесплатно
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PythonData Manipulation4 ч14 videos53 Exercises4,400 XP8,627Свидетельство о достижениях
Пользуется популярностью среди обучающихся в тысячах компаний.
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Learn Speech Recognition and Spoken Language Processing in Python
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 in Python will help you load, transform, and transcribe audio files. You’ll start by seeing what raw audio looks like in Python, and move on to exploring popular libraries and working through an example business use case.Use Python SpeechRecognition and PyDub to Transcribe Audio Files
Python has a number of popular libraries that help you to process spoken language. SpeechRecognition offers you an easy way to integrate with speech-to-text APIs, while PyDub helps you to programmatically alter audio file attributes to get them ready for transcription. Each of these libraries is covered in an in-depth chapter, offering you the opportunity to put theory into practice to cement your knowledge.Practice Speech Transcription with an In-Course Project
The final chapter in this course offers you the opportunity to put everything you’ve learned together by building a speech processing proof of concept for a fictional technology company. You’ll build a system that transcribes phone call audio to text and then performs sentiment analysis to review customer support phone calls.By the end of this course, you’ll have both the knowledge and hands-on experience to put your learning into practice within your job or personal projects.
Предварительные требования
Introduction to Natural Language Processing in PythonSupervised Learning with scikit-learn1
Introduction to Spoken Language Processing with Python
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.
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.
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.
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.
Spoken Language Processing in Python
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