# 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") # # #######################################################################################################################