Audio processing using Pydub and Google speechRecognition API

# Import necessary libraries
from pydub import AudioSegment
import speech_recognition as sr

# Input audio file to be sliced
audio = AudioSegment.from_wav("1.wav")

'''
Step #1 - Slicing the audio file into smaller chunks.
'''
# Length of the audiofile in milliseconds
n = len(audio)

# Variable to count the number of sliced chunks
counter = 1

# Text file to write the recognized audio
fh = open("recognized.txt", "w+")

# Interval length at which to slice the audio file.
# If length is 22 seconds, and interval is 5 seconds,
# The chunks created will be:
# chunk1 : 0 - 5 seconds
# chunk2 : 5 - 10 seconds
# chunk3 : 10 - 15 seconds
# chunk4 : 15 - 20 seconds
# chunk5 : 20 - 22 seconds
interval = 5 * 1000

# Length of audio to overlap. 
# If length is 22 seconds, and interval is 5 seconds,
# With overlap as 1.5 seconds,
# The chunks created will be:
# chunk1 : 0 - 5 seconds
# chunk2 : 3.5 - 8.5 seconds
# chunk3 : 7 - 12 seconds
# chunk4 : 10.5 - 15.5 seconds
# chunk5 : 14 - 19.5 seconds
# chunk6 : 18 - 22 seconds
overlap = 1.5 * 1000

# Initialize start and end seconds to 0
start = 0
end = 0

# Flag to keep track of end of file.
# When audio reaches its end, flag is set to 1 and we break
flag = 0

# Iterate from 0 to end of the file,
# with increment = interval
for i in range(0, 2 * n, interval):
    
    # During first iteration,
    # start is 0, end is the interval
    if i == 0:
        start = 0
        end = interval

    # All other iterations,
    # start is the previous end - overlap
    # end becomes end + interval
    else:
        start = end - overlap
        end = start + interval 

    # When end becomes greater than the file length,
    # end is set to the file length
    # flag is set to 1 to indicate break.
    if end >= n:
        end = n
        flag = 1

    # Storing audio file from the defined start to end
    chunk = audio[start:end]

    # Filename / Path to store the sliced audio
    filename = 'chunk'+str(counter)+'.wav'

    # Store the sliced audio file to the defined path
    chunk.export(filename, format ="wav")
    # Print information about the current chunk
    print("Processing chunk "+str(counter)+". Start = "
                        +str(start)+" end = "+str(end))

    # Increment counter for the next chunk
    counter = counter + 1
    
    # Slicing of the audio file is done.
    # Skip the below steps if there is some other usage
    # for the sliced audio files.


'''
Step #2 - Recognizing the chunk and writing to a file.
'''

    # Here, Google Speech Recognition is used
    # to take each chunk and recognize the text in it.

    # Specify the audio file to recognize

    AUDIO_FILE = filename

    # Initialize the recognizer
    r = sr.Recognizer()

    # Traverse the audio file and listen to the audio
    with sr.AudioFile(AUDIO_FILE) as source:
        audio_listened = r.listen(source)

    # Try to recognize the listened audio
    # And catch exceptions.
    try:    
        rec = r.recognize_google(audio_listened)
        
        # If recognized, write into the file.
        fh.write(rec+" ")
    
    # If google could not understand the audio
    except sr.UnknownValueError:
        print("Could not understand audio")

    # If the results cannot be requested from Google.
    # Probably an internet connection error.
    except sr.RequestError as e:
        print("Could not request results.")

    # Check for flag.
    # If flag is 1, end of the whole audio reached.
    # Close the file and break.
    if flag == 1:
        fh.close()
        break