Using Python and Google to Transcribe Audio: A Step-by-Step Guide

In today’s fast-paced world, the ability to quickly transcribe audio into text is invaluable, whether for creating accessible content, searchable archives, or just keeping a record of important meetings. With a little help from Python and Google Speech Recognition, this task becomes not only manageable but efficient.

Google Speech Recognition, a powerful cloud-based service, uses advanced machine learning models to convert spoken language into written text. This tool supports a wide range of languages and dialects, processes audio in real-time, and can be easily integrated into your projects via the API. The Python script provided here guides you step-by-step through the process of transcribing audio files, saving you the time and effort of manual transcription.

Setting Up Your Project

Before diving into the script, ensure that Python is installed on your machine. If you’re new to programming, there are plenty of beginner projects available to help you get started. The script uses the `pydub` and `SpeechRecognition` libraries to manipulate audio files and transcribe them into text.

The process involves loading and converting the audio file into a WAV format, which is then analyzed and transcribed using Google’s Speech Recognition API. The transcribed text is saved into a text file for easy storage and retrieval.

Here’s a glimpse of the Python script that does the heavy lifting:

```python

import speech_recognition as sr

from pydub import AudioSegment

import os

def transcribe_audio(file_name):

try:

# Load the audio file

audio = AudioSegment.from_file(file_name)

except Exception as e:

print(f"Error loading audio file: {e}")

return

try:

# Export to WAV format

wav_file_name = file_name.replace(file_name.split('.')[-1], 'wav')

audio.export(wav_file_name, format="wav")

except Exception as e:

print(f"Error converting to WAV: {e}")

return

try:

# Use speech_recognition to transcribe the audio

recognizer = sr.Recognizer()

with sr.AudioFile(wav_file_name) as source:

audio_data = recognizer.record(source)

transcription = recognizer.recognize_google(audio_data)

print(f"Transcription:\n{transcription}")

# Save transcription to a text file

text_file_name = file_name.replace(file_name.split('.')[-1], 'txt')

with open(text_file_name, 'w') as text_file:

text_file.write(transcription)

print(f"Transcription saved to {text_file_name}")

except sr.UnknownValueError:

print("Sorry, the audio was not clear enough to transcribe.")

except sr.RequestError as e:

print(f"Could not request results from Google Speech Recognition service; {e}")

if __name__ == "__main__":

file_name = input("Enter the audio file name (with extension): ")

if os.path.exists(file_name):

transcribe_audio(file_name)

else:

print(f"File {file_name} does not exist.")

input("Press Enter to exit...")

This simple yet powerful script allows you to automate the transcription process, making it easier to manage large volumes of audio data.

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*Source: [GeekSided](https://geeksided.com/posts/using-python-and-google-to-transcribe-audio-a-step-by-step-guide-01j4s1qc9jwj)*