speech_to_text.py

filepath = "~/audio_wav/"  #Input audio file path
output_filepath = "`/Transcripts/" #Final transcript path
bucketname = "upload" #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
from google.cloud import speech_v1p1beta1 as speech
from google.cloud.speech_v1p1beta1 import enums
from google.cloud.speech_v1p1beta1 import types


def mp3_to_wav(audio_file_name):
	if audio_file_name.split('.')[1] == 'mp3':    
		sound = AudioSegment.from_mp3(audio_file_name)
		audio_file_name = audio_file_name.split('.')[0] + '.wav'
		sound.export(audio_file_name, format="wav")


def stereo_to_mono(audio_file_name):
	sound = AudioSegment.from_wav(audio_file_name)
	sound = sound.set_channels(1)
	sound.export(audio_file_name, format="wav")


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


def upload_blob(bucket_name, source_file_name, destination_blob_name):
	"""Uploads a file to the bucket."""
	storage_client = storage.Client()
	bucket = storage_client.get_bucket(bucket_name)
	blob = bucket.blob(destination_blob_name)
	blob.upload_from_filename(source_file_name)


def delete_blob(bucket_name, blob_name):
	"""Deletes a blob from the bucket."""
	storage_client = storage.Client()
	bucket = storage_client.get_bucket(bucket_name)
	blob = bucket.blob(blob_name)
	blob.delete()


def google_transcribe(audio_file_name):
	file_name = filepath + audio_file_name
	mp3_to_wav(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


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


def google_transcribe_2(audio_file_name):
	file_name = filepath + audio_file_name
	mp3_to_wav(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',
	enable_speaker_diarization=True,
	diarization_speaker_count=2)
	# Detects speech in the audio file
	operation = client.long_running_recognize(config, audio)
	response = operation.result(timeout=10000)
	result = response.results[-1]
	words_info = result.alternatives[0].words
	tag=1
	speaker=""
	for word_info in words_info:
		if word_info.speaker_tag==tag:
			speaker=speaker+" "+word_info.word
		else:
			transcript += "speaker {}: {}".format(tag,speaker) + '\n'
			tag=word_info.speaker_tag
			speaker=""+word_info.word
	transcript += "speaker {}: {}".format(tag,speaker)
	delete_blob(bucket_name, destination_blob_name)
	return transcript


if __name__ == "__main__":
	# for audio_file_name in os.listdir(filepath):
	audio_file_name = "testfile_mp3.wav"
	transcript = google_transcribe_2(audio_file_name)
	transcript_filename = audio_file_name.split('.')[0] + '.txt'
	write_transcripts(transcript_filename,transcript)