cloud_speech.py

from google.cloud import speech
from google.cloud import storage
import os
import wave
from pydub import AudioSegment

def get_audio_properties(file_path):
    """
    Get sample rate and number of channels from the WAV file.
    """
    with wave.open(file_path, "rb") as wav_file:
        sample_rate = wav_file.getframerate()
        channels = wav_file.getnchannels()
    return sample_rate, channels

def convert_to_mono(input_path, output_path):
    """Convert video from 2+ channel audio to 1 channel audio for a 
    single detection"""
    audio = AudioSegment.from_wav(input_path)
    mono_audio = audio.set_channels(1)
    mono_audio.export(output_path, format="wav")

def upload_to_gcs(bucket_name, local_file_path, gcs_file_name):
    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(gcs_file_name)
    blob.upload_from_filename(local_file_path)
    return f"gs://{bucket_name}/{gcs_file_name}"

def transcribe_gcs(gcs_uri):
    sample_rate , _ = get_audio_properties('temp_audio.wav')
    client = speech.SpeechClient()
    audio = speech.RecognitionAudio(uri=gcs_uri)
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=sample_rate,
        language_code="en-US",
    )

    operation = client.long_running_recognize(config=config, audio=audio)
    response = operation.result(timeout=600)  # Wait for result

    transcript = "\n".join([result.alternatives[0].transcript for result in response.results])
    return transcript

def get_transcription():
    # Example usage
    bucket_name = "meeting-audio-bucket"
    audio_file = "temp_audio.wav"  # Extracted audio file
    convert_to_mono(audio_file,audio_file)
    gcs_uri = upload_to_gcs(bucket_name, audio_file, "temp_audio.wav")
    transcript = transcribe_gcs(gcs_uri)

    return transcript