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appendix 2 - common-workflows-transcriptions.md

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common workflows - transcriptions

This appendix provides basic scripts for transcribing audio and video files. Each script can be utilized as a template to build customizable options. The results of the following scripts are extremely accurate though there could be errors based on the quality of the digital files. note: processing batch files of a tested 500 audio files is 00:27:00 hh:mm:ss and 500 video files is 01:32:00 hh:mm:ss on an average Dell PC.

audio transcription workflow

  1. Make sure you have the required libraries installed in your virtual environment. If not, you can install them using the following command:
pip install SpeechRecognition moviepy
  1. Edit the script to set the folder_path variable to the path of the directory containing your audio files and save the script as 'audio_transcription.py'
import os
import speech_recognition as sr
from moviepy.editor import AudioFileClip
from moviepy.editor import VideoFileClip
import pandas as pd

# Initialize the recognizer
r = sr.Recognizer()

# Folder path containing the video files
folder_path = r'Z:\Reagan' #ENTER YOUR PATH TO FILES HERE

# Create an empty list to store the results
results = []

# Create a directory to store the TXT files
output_dir = r'E:\Reagan\transcriptions' #ENTER YOUR PATH TO WHERE YOU WANT TRANSCRIPTIONS HERE
os.makedirs(output_dir, exist_ok=True)

# Iterate through the files in the folder
for filename in os.listdir(folder_path):
    try:
        # Generate the full path of the video file
        video_file = os.path.join(folder_path, filename)

        # Load the video file using moviepy
        video = VideoFileClip(video_file)

        # Extract the audio from the video
        audio: AudioFileClip = video.audio

        # Save the extracted audio as a temporary file
        audio_file = "temp_audio.wav"
        audio.write_audiofile(audio_file)

        # Open the audio file for speech recognition
        with sr.AudioFile(audio_file) as source:
            # Load the audio to memory
            audio_data = r.record(source)

            # Use Google Speech Recognition to transcribe the audio
            transcript = r.recognize_google(audio_data)

            # Append the filename and transcript to the results list
            results.append((filename, transcript))

            # Create a TXT file for each transcript
            output_file = os.path.join(output_dir, os.path.splitext(filename)[0] + ".txt")
            with open(output_file, "w") as f:
                f.write(transcript)

            # Print the transcript
            print(f"Transcript for {filename}: {transcript}")

    except sr.UnknownValueError:
        print(f"Could not transcribe {filename}")
    except sr.RequestError as e:
        print(f"Error occurred during transcription for {filename}: {e}")
    finally:
        # Delete the temporary audio file
        if audio:
            audio.close()

# Check if the lengths of the two arrays are the same
if len(os.listdir(folder_path)) == len(results):
    # Create a pandas DataFrame with the results
    df = pd.DataFrame(results, columns=['Filename', 'Transcript'])

    # Add a column for the file names
    df['File Name'] = [os.path.splitext(filename)[0] + ".txt" for filename in df['Filename']]

    # Save the DataFrame as an Excel file
    output_file = os.path.join(output_dir, "transcripts.xlsx")
    try:
        df.to_excel(output_file, index=False)
        print(f"Transcripts saved to {output_file}")
    except Exception as e:
        print(f"Error occurred while saving transcripts: {e}")
else:
    print("Error: Length mismatch between filenames and transcripts.")
  1. Run the script using the following command:
python audio_transcription.py
  1. The script will transcribe each audio file in the specified directory and save the transcripts to TXT and EXCEL files.

video transcription workflow

  1. Make sure you have the required libraries installed in your virtual environment. If not, you can install them using the following command:
pip install SpeechRecognition moviepy PyQt5 pandas
  1. Edit the script to set the folder_path variable to the path of the directory containing your audio files and save the script as 'video_transcription_app.py'
import os
import speech_recognition as sr
from moviepy.editor import AudioFileClip
from moviepy.editor import VideoFileClip
import pandas as pd

# Initialize the recognizer
r = sr.Recognizer()

# Folder path containing the video files
folder_path = r'Z:\Reagan' #ENTER YOUR PATH TO FILES HERE

# Create an empty list to store the results
results = []

# Create a directory to store the TXT files
output_dir = r'E:\Reagan\transcriptions' #ENTER YOUR PATH TO WHERE YOU WANT TRANSCRIPTIONS HERE
os.makedirs(output_dir, exist_ok=True)

# Iterate through the files in the folder
for filename in os.listdir(folder_path):
    try:
        # Generate the full path of the video file
        video_file = os.path.join(folder_path, filename)

        # Load the video file using moviepy
        video = VideoFileClip(video_file)

        # Extract the audio from the video
        audio: AudioFileClip = video.audio

        # Save the extracted audio as a temporary file
        audio_file = "temp_audio.wav"
        audio.write_audiofile(audio_file)

        # Open the audio file for speech recognition
        with sr.AudioFile(audio_file) as source:
            # Load the audio to memory
            audio_data = r.record(source)

            # Use Google Speech Recognition to transcribe the audio
            transcript = r.recognize_google(audio_data)

            # Append the filename and transcript to the results list
            results.append((filename, transcript))

            # Create a TXT file for each transcript
            output_file = os.path.join(output_dir, os.path.splitext(filename)[0] + ".txt")
            with open(output_file, "w") as f:
                f.write(transcript)

            # Print the transcript
            print(f"Transcript for {filename}: {transcript}")

    except sr.UnknownValueError:
        print(f"Could not transcribe {filename}")
    except sr.RequestError as e:
        print(f"Error occurred during transcription for {filename}: {e}")
    finally:
        # Delete the temporary audio file
        if audio:
            audio.close()

# Check if the lengths of the two arrays are the same
if len(os.listdir(folder_path)) == len(results):
    # Create a pandas DataFrame with the results
    df = pd.DataFrame(results, columns=['Filename', 'Transcript'])

    # Add a column for the file names
    df['File Name'] = [os.path.splitext(filename)[0] + ".txt" for filename in df['Filename']]

    # Save the DataFrame as an Excel file
    output_file = os.path.join(output_dir, "transcripts.xlsx")
    try:
        df.to_excel(output_file, index=False)
        print(f"Transcripts saved to {output_file}")
    except Exception as e:
        print(f"Error occurred while saving transcripts: {e}")
else:
    print("Error: Length mismatch between filenames and transcripts.")
  1. Run the script using the following command:
python video_transcription_app.py
  1. The script will transcribe each video file in the specified directory and save the transcripts to TXT and EXCEL files.

notes

  • Feel free to explore and modify the scripts to suit your specific needs. These basic workflows provide a starting point for audio and video transcription tasks using speech recognition.

  • Make sure to replace audio_transcription.py and video_transcription_app.py with the actual names of your script files.