transcription.py

import torch
from transformers import pipeline
from logging_config import logger

def run_whisper_transcription(wav_file_path: str, device: str):
    try:
        model_name = "distil-whisper/distil-small.en"
        logger.info(f"Initialising Whisper ASR pipeline with model: {model_name}")
        logger.info(f"Running pipeline on device: {device}")

        asr_pipeline = pipeline(
            "automatic-speech-recognition",
            model=model_name,
            device=0 if device == "cuda" else -1,
            return_timestamps=True
        )
        logger.info("Whisper ASR pipeline initialised.")
        logger.info(f"Starting transcription for file: {wav_file_path}")

        # Perform transcription
        result = asr_pipeline(wav_file_path)
        transcription = result.get("text", "")
        logger.info("Transcription completed successfully.")

        yield transcription # Yield only the transcription string
    except Exception as e:
        err_msg = f"Error during transcription: {str(e)}"
        logger.error(err_msg)
        yield err_msg # Yield only the error message string