Créer des fonctions d'aide à la transcription

Traitement de la langue parlée en Python

Daniel Bourke

Machine Learning Engineer/YouTube Creator

Explorer les fichiers audio

# Import os module
import os

# Check the folder of audio files
os.listdir("acme_audio_files")

(['call_1.mp3',
  'call_2.mp3',
  'call_3.mp3',
  'call_4.mp3'])

Préparer la preuve de concept

import speech_recognition as sr
from pydub import AudioSegment

# Import call 1 and convert to .wav
call_1 = AudioSegment.from_file("acme_audio_files/call_1.mp3")
call_1.export("acme_audio_files/call_1.wav", format="wav")

# Transcribe call 1
recognizer = sr.Recognizer()
call_1_file = sr.AudioFile("acme_audio_files/call_1.wav")
with call_1_file as source:
    call_1_audio = recognizer.record(call_1_file)
recognizer.recognize_google(call_1_audio)

Fonctions à créer

convert_to_wav() convertit les fichiers non-.wav en fichiers .wav.
show_pydub_stats() affiche les attributs audio d'un fichier .wav.
transcribe_audio() utilise recognize_google() pour transcrire un fichier .wav.

Créer une fonction de conversion de format

# Create function to convert audio file to wav
def convert_to_wav(filename):
  "Takes an audio file of non .wav format and converts to .wav"
  # Import audio file
  audio = AudioSegment.from_file(filename)

  # Create new filename
  new_filename = filename.split(".")[0] + ".wav"

  # Export file as .wav
  audio.export(new_filename, format="wav")
  print(f"Converting {filename} to {new_filename}...")

Utiliser la fonction de conversion de format

convert_to_wav("acme_studios_audio/call_1.mp3")

Converting acme_audio_files/call_1.mp3 to acme_audio_files/call_1.wav...

Créer une fonction d'affichage des attributs

def show_pydub_stats(filename):
  "Returns different audio attributes related to an audio file."
  # Create AudioSegment instance
  audio_segment = AudioSegment.from_file(filename)

  # Print attributes
  print(f"Channels: {audio_segment.channels}")
  print(f"Sample width: {audio_segment.sample_width}")
  print(f"Frame rate (sample rate): {audio_segment.frame_rate}")
  print(f"Frame width: {audio_segment.frame_width}")
  print(f"Length (ms): {len(audio_segment)}")
  print(f"Frame count: {audio_segment.frame_count()}")

Utiliser la fonction d'affichage des attributs

show_pydub_stats("acme_audio_files/call_1.wav")

Channels: 2
Sample width: 2
Frame rate (sample rate): 32000
Frame width: 4
Length (ms): 54888
Frame count: 1756416.0

Créer une fonction de transcription

# Create a function to transcribe audio
def transcribe_audio(filename):
  "Takes a .wav format audio file and transcribes it to text."
  # Setup a recognizer instance
  recognizer = sr.Recognizer()


  # Import the audio file and convert to audio data
  audio_file = sr.AudioFile(filename)
  with audio_file as source:
    audio_data = recognizer.record(audio_file)

  # Return the transcribed text
  return recognizer.recognize_google(audio_data)

Utiliser la fonction de transcription

transcribe_audio("acme_audio_files/call_1.wav")

"hello welcome to Acme studio support line my name is Daniel how can I best help 
you hey Daniel this is John I've recently bought a smart from you guys and I know 
that's not good to hear John let's let's get your cell number and then we 
can we can set up a way to fix it for you one number for 1757 varies how long do 
you reckon this is going to take about an hour now while John we're going to try 
our best hour I will we get the sealing member will start up this support case 
I'm just really really really really I've been trying to contact 34 been put on 
hold more than an hour and half so I'm not really happy I kind of wanna get this 
issue 6 is fossil"

Passons à la pratique !

Traitement de la langue parlée en Python