Convert large wav file to text in python

Question

I already tried this code to convert my large wav file to text

import speech_recognition as sr
r = sr.Recognizer()

hellow=sr.AudioFile('hello_world.wav')
with hellow as source:
    audio = r.record(source)
try:
    s = r.recognize_google(audio)
    print("Text: "+s)
except Exception as e:
    print("Exception: "+str(e))

But it is not converting it accurately, the reason I feel it's the 'US' accent. Please tell me how i can convert whole large wav file accurately.

Answer 1

Google's speech to text is very effective, try the below link,

https://cloud.google.com/speech-to-text/

You can choose the language (English US in your case) and also upload files.

Like @bigdataolddriver commented 100% accuracy is not possible yet, and will be worth millions.

Google speech to text has three types of APIs

Synchronous, Asynchronous and streaming, in which asynchronous allows you to ~480 minutes audio conversion while others will only let you ~1 minute. Following is the sample code to do the conversion.

filepath = "~/audio_wav/"     #Input audio file path
output_filepath = "~/Transcripts/" #Final transcript path
bucketname = "callsaudiofiles" #Name of the bucket created in the step before

# Import libraries
from pydub import AudioSegment
import io
import os
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
import wave
from google.cloud import storage

Speech to text support wav files with LINEAR16 or MULAW encoded audio.

Below is the code to get the frame rate and channel with code.

def frame_rate_channel(audio_file_name):
    with wave.open(audio_file_name, "rb") as wave_file:
        frame_rate = wave_file.getframerate()
        channels = wave_file.getnchannels()
        return frame_rate,channels

and the code below is the does the asynchronous conversion.

def google_transcribe(audio_file_name):

    file_name = filepath + audio_file_name

    # The name of the audio file to transcribe

    frame_rate, channels = frame_rate_channel(file_name)

    if channels > 1:
        stereo_to_mono(file_name)

    bucket_name = bucketname
    source_file_name = filepath + audio_file_name
    destination_blob_name = audio_file_name

    upload_blob(bucket_name, source_file_name, destination_blob_name)

    gcs_uri = 'gs://' + bucketname + '/' + audio_file_name
    transcript = ''

    client = speech.SpeechClient()
    audio = types.RecognitionAudio(uri=gcs_uri)

    config = types.RecognitionConfig(
    encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=frame_rate,
    language_code='en-US')

    # Detects speech in the audio file
    operation = client.long_running_recognize(config, audio)
    response = operation.result(timeout=10000)

    for result in response.results:
        transcript += result.alternatives[0].transcript

    delete_blob(bucket_name, destination_blob_name)
    return transcript

and this is how you write them to file

def write_transcripts(transcript_filename,transcript):
    f= open(output_filepath + transcript_filename,"w+")
    f.write(transcript)
    f.close()

Kindly let me know if you need any further clarifications.