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# Audio_Transcription_Lib.py
#########################################
# Transcription Library
# This library is used to perform transcription of audio files.
# Currently, uses faster_whisper for transcription.
#
####################
# Function List
#
# 1. convert_to_wav(video_file_path, offset=0, overwrite=False)
# 2. speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='small.en', vad_filter=False)
#
####################
#
# Import necessary libraries to run solo for testing
import gc
import json
import logging
import os
import queue
import sys
import subprocess
import tempfile
import threading
import time
# DEBUG Imports
#from memory_profiler import profile
#import pyaudio
from faster_whisper import WhisperModel as OriginalWhisperModel
from typing import Optional, Union, List, Dict, Any
#
# Import Local
from App_Function_Libraries.Utils.Utils import load_comprehensive_config
#
#######################################################################################################################
# Function Definitions
#

# Convert video .m4a into .wav using ffmpeg
#   ffmpeg -i "example.mp4" -ar 16000 -ac 1 -c:a pcm_s16le "output.wav"
#       https://www.gyan.dev/ffmpeg/builds/
#


whisper_model_instance = None
config = load_comprehensive_config()
processing_choice = config.get('Processing', 'processing_choice', fallback='cpu')



class WhisperModel(OriginalWhisperModel):
    tldw_dir = os.path.dirname(os.path.dirname(__file__))
    default_download_root = os.path.join(tldw_dir, 'App_Function_Libraries', 'models', 'Whisper')

    valid_model_sizes = [
        "tiny.en", "tiny", "base.en", "base", "small.en", "small", "medium.en", "medium",
        "large-v1", "large-v2", "large-v3", "large", "distil-large-v2", "distil-medium.en",
        "distil-small.en", "distil-large-v3"
    ]

    def __init__(
        self,
        model_size_or_path: str,
        device: str = "auto",
        device_index: Union[int, List[int]] = 0,
        compute_type: str = "default",
        cpu_threads: int = 16,
        num_workers: int = 1,
        download_root: Optional[str] = None,
        local_files_only: bool = False,
        files: Optional[Dict[str, Any]] = None,
        **model_kwargs: Any
    ):
        if download_root is None:
            download_root = self.default_download_root

        os.makedirs(download_root, exist_ok=True)

        # FIXME - validate....
        # Also write an integration test...
        # Check if model_size_or_path is a valid model size
        if model_size_or_path in self.valid_model_sizes:
            # It's a model size, so we'll use the download_root
            model_path = os.path.join(download_root, model_size_or_path)
            if not os.path.isdir(model_path):
                # If it doesn't exist, we'll let the parent class download it
                model_size_or_path = model_size_or_path  # Keep the original model size
            else:
                # If it exists, use the full path
                model_size_or_path = model_path
        else:
            # It's not a valid model size, so assume it's a path
            model_size_or_path = os.path.abspath(model_size_or_path)

        super().__init__(
            model_size_or_path,
            device=device,
            device_index=device_index,
            compute_type=compute_type,
            cpu_threads=cpu_threads,
            num_workers=num_workers,
            download_root=download_root,
            local_files_only=local_files_only,
# Maybe? idk, FIXME
#            files=files,
#            **model_kwargs
        )

def get_whisper_model(model_name, device):
    global whisper_model_instance
    if whisper_model_instance is None:
        logging.info(f"Initializing new WhisperModel with size {model_name} on device {device}")
        whisper_model_instance = WhisperModel(model_name, device=device)
    return whisper_model_instance

# # FIXME: This is a temporary solution.
# # This doesn't clear older models, which means potentially a lot of memory is being used...
# def get_whisper_model(model_name, device):
#     global whisper_model_instance
#     if whisper_model_instance is None:
#         from faster_whisper import WhisperModel
#         logging.info(f"Initializing new WhisperModel with size {model_name} on device {device}")
#
#         # FIXME - add logic to detect if the model is already downloaded
#         # want to first check if the model is already downloaded
#         # if not, download it using the existing logic in 'WhisperModel'
#         # https://github.com/SYSTRAN/faster-whisper/blob/d57c5b40b06e59ec44240d93485a95799548af50/faster_whisper/transcribe.py#L584
#         # Designated path should be `tldw/App_Function_Libraries/models/Whisper/`
#         WhisperModel.download_root = os.path.join(os.path.dirname(__file__), 'models', 'Whisper')
#         os.makedirs(WhisperModel.download_root, exist_ok=True)
#         whisper_model_instance = WhisperModel(model_name, device=device)
#     return whisper_model_instance


# os.system(r'.\Bin\ffmpeg.exe -ss 00:00:00 -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{out_path}"')
#DEBUG
#@profile
def convert_to_wav(video_file_path, offset=0, overwrite=False):
    out_path = os.path.splitext(video_file_path)[0] + ".wav"

    if os.path.exists(out_path) and not overwrite:
        print(f"File '{out_path}' already exists. Skipping conversion.")
        logging.info(f"Skipping conversion as file already exists: {out_path}")
        return out_path
    print("Starting conversion process of .m4a to .WAV")
    out_path = os.path.splitext(video_file_path)[0] + ".wav"

    try:
        if os.name == "nt":
            logging.debug("ffmpeg being ran on windows")

            if sys.platform.startswith('win'):
                ffmpeg_cmd = ".\\Bin\\ffmpeg.exe"
                logging.debug(f"ffmpeg_cmd: {ffmpeg_cmd}")
            else:
                ffmpeg_cmd = 'ffmpeg'  # Assume 'ffmpeg' is in PATH for non-Windows systems

            command = [
                ffmpeg_cmd,  # Assuming the working directory is correctly set where .\Bin exists
                "-ss", "00:00:00",  # Start at the beginning of the video
                "-i", video_file_path,
                "-ar", "16000",  # Audio sample rate
                "-ac", "1",  # Number of audio channels
                "-c:a", "pcm_s16le",  # Audio codec
                out_path
            ]
            try:
                # Redirect stdin from null device to prevent ffmpeg from waiting for input
                with open(os.devnull, 'rb') as null_file:
                    result = subprocess.run(command, stdin=null_file, text=True, capture_output=True)
                if result.returncode == 0:
                    logging.info("FFmpeg executed successfully")
                    logging.debug("FFmpeg output: %s", result.stdout)
                else:
                    logging.error("Error in running FFmpeg")
                    logging.error("FFmpeg stderr: %s", result.stderr)
                    raise RuntimeError(f"FFmpeg error: {result.stderr}")
            except Exception as e:
                logging.error("Error occurred - ffmpeg doesn't like windows")
                raise RuntimeError("ffmpeg failed")
        elif os.name == "posix":
            os.system(f'ffmpeg -ss 00:00:00 -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{out_path}"')
        else:
            raise RuntimeError("Unsupported operating system")
        logging.info("Conversion to WAV completed: %s", out_path)
    except subprocess.CalledProcessError as e:
        logging.error("Error executing FFmpeg command: %s", str(e))
        raise RuntimeError("Error converting video file to WAV")
    except Exception as e:
        logging.error("speech-to-text: Error transcribing audio: %s", str(e))
        return {"error": str(e)}
    gc.collect()
    return out_path


# Transcribe .wav into .segments.json
#DEBUG
#@profile
def speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='medium.en', vad_filter=False, diarize=False):
    global whisper_model_instance, processing_choice
    logging.info('speech-to-text: Loading faster_whisper model: %s', whisper_model)

    time_start = time.time()
    if audio_file_path is None:
        raise ValueError("speech-to-text: No audio file provided")
    logging.info("speech-to-text: Audio file path: %s", audio_file_path)

    try:
        _, file_ending = os.path.splitext(audio_file_path)
        out_file = audio_file_path.replace(file_ending, ".segments.json")
        prettified_out_file = audio_file_path.replace(file_ending, ".segments_pretty.json")
        if os.path.exists(out_file):
            logging.info("speech-to-text: Segments file already exists: %s", out_file)
            with open(out_file) as f:
                global segments
                segments = json.load(f)
            return segments

        logging.info('speech-to-text: Starting transcription...')
        options = dict(language=selected_source_lang, beam_size=5, best_of=5, vad_filter=vad_filter)
        transcribe_options = dict(task="transcribe", **options)
        # use function and config at top of file
        logging.debug("speech-to-text: Using whisper model: %s", whisper_model)
        whisper_model_instance = get_whisper_model(whisper_model, processing_choice)
        segments_raw, info = whisper_model_instance.transcribe(audio_file_path, **transcribe_options)

        segments = []
        for segment_chunk in segments_raw:
            chunk = {
                "Time_Start": segment_chunk.start,
                "Time_End": segment_chunk.end,
                "Text": segment_chunk.text
            }
            logging.debug("Segment: %s", chunk)
            segments.append(chunk)
            # Print to verify its working
            print(f"{segment_chunk.start:.2f}s - {segment_chunk.end:.2f}s | {segment_chunk.text}")

            # Log it as well.
            logging.debug(
                f"Transcribed Segment: {segment_chunk.start:.2f}s - {segment_chunk.end:.2f}s | {segment_chunk.text}")

        if segments:
            segments[0]["Text"] = f"This text was transcribed using whisper model: {whisper_model}\n\n" + segments[0]["Text"]

        if not segments:
            raise RuntimeError("No transcription produced. The audio file may be invalid or empty.")
        logging.info("speech-to-text: Transcription completed in %.2f seconds", time.time() - time_start)

        # Save the segments to a JSON file - prettified and non-prettified
        # FIXME so this is an optional flag to save either the prettified json file or the normal one
        save_json = True
        if save_json:
            logging.info("speech-to-text: Saving segments to JSON file")
            output_data = {'segments': segments}

            logging.info("speech-to-text: Saving prettified JSON to %s", prettified_out_file)
            with open(prettified_out_file, 'w') as f:
                json.dump(output_data, f, indent=2)

            logging.info("speech-to-text: Saving JSON to %s", out_file)
            with open(out_file, 'w') as f:
                json.dump(output_data, f)

        logging.debug(f"speech-to-text: returning {segments[:500]}")
        gc.collect()
        return segments

    except Exception as e:
        logging.error("speech-to-text: Error transcribing audio: %s", str(e))
        raise RuntimeError("speech-to-text: Error transcribing audio")


def record_audio(duration, sample_rate=16000, chunk_size=1024):
    pass
def save_audio_temp(audio_data, sample_rate=16000):
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
        import wave
        wf = wave.open(temp_file.name, 'wb')
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(sample_rate)
        wf.writeframes(audio_data)
        wf.close()
        return temp_file.name

#
#
#######################################################################################################################