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| 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 | |