They can then be imported into the Python file along with the other required modules: import wave, math, contextlib import speech_recognition as sr from moviepy.editor import AudioFileClipįor now, we will hard code the mp4 video file to import and the audio file we will create when converting the video to audio: transcribed_audio_file_name = "transcribed_speech.wav" zoom_video_file_name = "zoom_0.mp4" pip install Wave pip install moviepy pip install SpeechRecognition These can be installed in the standard way using pip. The example uses Python 3.x and PyQt5.įor this we need 3 modules, speech_recognition, wave and moviepy. This assumes the reader has some experience with Python and is familiar with concepts like Object Orientated Programming (OOP). This short blog describes and implements the basic concept as a single script and then shows how we can add a Graphical User Interface (GUI) and use threads to improve performance. Faced with having to transcribe several fairly lengthy interviews (> 1 hour each) carried out online, I decided to create a simple program with a graphical interface to manage this process. Creating a script to do this yourself using Python is also relatively easy. There also exist various software for this task, including free trials and software with various pricing models. Some qualitative researchers also advocate transcribing your own interviews as a way of becoming more familiar with the data. You could of course transcribe your own interviews but this can be a very time consuming and laborious task. Human transcribers remain the gold standard for this sort of work and usually do an excellent job. Often grant funding for such projects will cover transcription costs. Occasionally I have the need to interview participants for various research projects.
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