Spoken Language Processing in Python
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Prepare audio with PyDub, transcribe speech with SpeechRecognition and analyze customer call transcripts for sentiment in Python.
Spoken language processing in Python turns recorded speech into text that a program can analyze. Recorded audio needs preprocessing before transcription, because files differ in frame rate, number of channels and format. You read raw sound waves as numbers, reshape files with PyDub and convert speech to text with the SpeechRecognition library. You then run sentiment analysis, named entity recognition and classification on transcripts of customer support calls.
What You Will Achieve
- Load an audio file in Python, convert its bytes to integers and plot the sound wave over time
- Prepare audio for transcription by changing frame rate, channel count and file format with PyDub
- Transcribe spoken audio to text with SpeechRecognition and cope with difficult recordings
- Build a pipeline that transcribes customer support calls and scores the sentiment, entities and category of each one
Program Overview
Audio Data Foundations in Python
Raw audio is read into Python and turned into numbers, so two recordings can be compared as sound waves.
- Audio data and frequency
- Data types and time stamps
- Visual comparison of two sound waves
Speech-to-Text and Audio Preparation
The SpeechRecognition library connects to speech-to-text APIs, and PyDub reshapes audio file attributes ahead of transcription.
- Choosing an API and using the Recognizer class
- Working with noisy audio and multiple speakers
- Audio parameters, volume and normalization
- Stereo to mono splits, exporting and reformatting
Acme Studios Phone Call Project
A proof of concept for the fictional Acme Studios transcribes customer support calls and analyzes the resulting text.
- Transcription helper functions and PyDub stats
- Sentiment analysis with NLTK
- Named entity recognition and a custom entity in spaCy
- Text classification with scikit-learn
Certificate Awarded (Paid)
Job Outlook
These skills apply wherever recorded speech has to be analyzed as text, such as reviewing customer support phone calls, and in natural language processing work and personal projects that start from audio.
Good for: Python users who want to transcribe recorded speech and analyze the transcripts with the same tools they already use on text.
Keep in mind: The final project is a proof of concept for Acme Studios, a fictional company, built on snippets of customer support calls.
Bottom line: A focused course that pairs PyDub and SpeechRecognition, then carries the transcripts through NLTK, spaCy and scikit-learn analysis.
Frequently Asked Questions
Which libraries handle the audio and the transcripts?
Audio work relies on two libraries: SpeechRecognition, which talks to several speech-to-text APIs, and PyDub, written by James Robert, for changing audio attributes. The closing project adds NLTK for sentiment, spaCy for named entities and scikit-learn for classification.
What is the Acme Studios project?
It is the last chapter's proof of concept for a fictional technology company. You write helper functions that convert and transcribe customer support call snippets, then analyze the text for sentiment, named entities, including a custom entity, and call categories.
Does the course deal with poor-quality recordings?
Yes. The SpeechRecognition chapter works through different kinds of audio, recordings with multiple speakers and noisy audio, and it notes that speech recognition is still far from perfect.
What does the first chapter do with raw audio?
It imports two audio files, converts their sound wave bytes to integers, finds the time stamps for each sample and plots both sound waves so you can compare them visually.
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