299 lines
13 KiB
Python
299 lines
13 KiB
Python
import argparse
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import os
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import sys
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from dotenv import load_dotenv
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# Load environment variables from central .env_files directory
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script_dir = os.path.dirname(os.path.abspath(__file__))
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env_path = os.path.abspath(os.path.join(script_dir, '../../.env_files/.env.aitranscribe'))
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if os.path.exists(env_path):
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load_dotenv(env_path)
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else:
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# Fallback: check local .env
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local_env = os.path.join(script_dir, '.env')
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if os.path.exists(local_env):
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load_dotenv(local_env)
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else:
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# Last resort: just try loading generic (cwd)
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load_dotenv()
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from extractor import extract_audio, embed_subtitles
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from transcriber import transcribe_audio, save_as_srt
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from translator import translate_srt, translate_fallback_free
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from utils import validate_and_repair_srt
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from diarizer import diarize_audio, merge_diarization_with_transcript
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import tracker
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from tracker import JobStatus
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def save_srt_with_speakers(segments, output_path):
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"""Helper to save SRT with speaker labels prepended to text."""
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def format_timestamp(seconds: float):
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whole_seconds = int(seconds)
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milliseconds = int((seconds - whole_seconds) * 1000)
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hours = whole_seconds // 3600
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minutes = (whole_seconds % 3600) // 60
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seconds = whole_seconds % 60
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return f"{hours:02d}:{minutes:02d}:{seconds:02d},{milliseconds:03d}"
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with open(output_path, "w", encoding="utf-8") as f:
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for i, segment in enumerate(segments, start=1):
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start = format_timestamp(segment["start"])
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end = format_timestamp(segment["end"])
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text = segment["text"].strip()
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speaker = segment.get("speaker", "")
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if speaker and speaker != "Unknown":
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text = f"[{speaker}]: {text}"
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f.write(f"{i}\n")
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f.write(f"{start} --> {end}\n")
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f.write(f"{text}\n\n")
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print(f"SRT saved to: {output_path}")
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def process_file(file_path, args, source_lang=None):
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tracker.logger.info(f"=== Processing: {file_path} ===")
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# Initialize Job
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job = tracker.get_job(file_path)
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if job.status == JobStatus.COMPLETED and not args.force:
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tracker.logger.info("Job already completed. Skipping.")
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return
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tracker.update_job_status(file_path, JobStatus.PROCESSING)
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try:
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# 1. Extract Audio
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tracker.update_step(file_path, "step_extract", "processing")
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audio_path = extract_audio(file_path)
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tracker.update_step(file_path, "step_extract", "done")
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# 2. Transcribe (Generate SRT)
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tracker.update_step(file_path, "step_transcribe", "processing")
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transcript_file = os.path.splitext(file_path)[0] + ".srt"
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transcript_exists = os.path.exists(transcript_file) and not args.force
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final_srt_path = transcript_file
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if transcript_exists:
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tracker.logger.info(f"Transcript exists: {transcript_file}. Skipping transcription.")
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with open(transcript_file, "r", encoding="utf-8") as f:
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srt_content = f.read()
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else:
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result = transcribe_audio(audio_path, model_size=args.model, language=source_lang)
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segments = result["segments"]
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if args.diarize:
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hf_token = args.hf_token or os.getenv("HF_TOKEN")
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if hf_token:
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tracker.logger.info("Running Speaker Diarization...")
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diar_segments = diarize_audio(audio_path, hf_token=hf_token)
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if diar_segments:
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segments = merge_diarization_with_transcript(segments, diar_segments)
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tracker.logger.info("Diarization merged into transcript.")
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else:
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tracker.logger.warning("Warning: --diarize requested but HF_TOKEN not provided. Skipping.")
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if args.diarize:
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save_srt_with_speakers(segments, transcript_file)
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else:
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save_as_srt(result, transcript_file)
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validate_and_repair_srt(transcript_file)
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with open(transcript_file, "r", encoding="utf-8") as f:
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srt_content = f.read()
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tracker.update_step(file_path, "step_transcribe", "done")
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# 3. Translate
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tracker.update_step(file_path, "step_translate", "processing")
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base_translated = os.path.splitext(file_path)[0] + f".{args.lang}.srt"
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deep_translated = os.path.splitext(file_path)[0] + f".{args.lang}.deep_translate.srt"
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translated_file = base_translated # Default
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translation_success = False
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method_used = "None"
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if (os.path.exists(base_translated) or os.path.exists(deep_translated)) and not args.force:
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if os.path.exists(deep_translated):
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translated_file = deep_translated
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method_used = "DeepTranslate (Existing)"
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else:
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method_used = "Gemini (Existing)"
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tracker.logger.info(f"Translation exists: {translated_file} ({method_used}). Skipping translation.")
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final_srt_path = translated_file
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translation_success = True
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else:
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if srt_content:
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# Helper functions
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def try_gemini():
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res = translate_srt(srt_content, target_language=args.lang)
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if res:
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with open(base_translated, "w", encoding="utf-8") as f:
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f.write(res)
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return True, base_translated, "Gemini"
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return False, None, None
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def try_deep():
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lang_map = {
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"English": "en", "French": "fr", "Spanish": "es", "German": "de",
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"Italian": "it", "Portuguese": "pt", "Russian": "ru",
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"Japanese": "ja", "Chinese": "zh-CN"
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}
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target_code = lang_map.get(args.lang, "en")
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res = translate_fallback_free(srt_content, target_language=target_code)
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if res:
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with open(deep_translated, "w", encoding="utf-8") as f:
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f.write(res)
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return True, deep_translated, "DeepTranslate"
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return False, None, None
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success = False
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if args.prefer_deep:
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success, path, method = try_deep()
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if not success:
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tracker.logger.info("DeepTranslate failed. Attempting Gemini...")
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success, path, method = try_gemini()
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else:
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success, path, method = try_gemini()
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if not success:
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tracker.logger.warning("Gemini failed. Attempting DeepTranslate...")
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success, path, method = try_deep()
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if success:
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tracker.logger.info(f"Translation saved to: {path} ({method})")
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validate_and_repair_srt(path)
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final_srt_path = path
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translation_success = True
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method_used = method
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else:
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tracker.logger.error("TRANSLATION FAILED.")
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tracker.update_step(file_path, "step_translate", "failed")
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translation_success = False
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if translation_success:
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tracker.update_step(file_path, "step_translate", "done")
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tracker.logger.info(f"Translation Method: {method_used}")
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# 4. Embed Subtitles
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tracker.update_step(file_path, "step_embed", "processing")
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should_embed = args.embed
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if args.embed and not translation_success:
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tracker.logger.warning("SAFETY HALT: Translation failed. Skipping embedding/deletion.")
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should_embed = False
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if should_embed:
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embed_subtitles(file_path, final_srt_path)
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if args.delete_source:
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if args.embed:
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base, ext = os.path.splitext(file_path)
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expected_output = f"{base}.subbed{ext}"
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if os.path.exists(expected_output):
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try:
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os.remove(file_path)
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tracker.logger.info(f"SOURCE DELETED: {file_path}")
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except OSError as e:
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tracker.logger.error(f"Error deleting source: {e}")
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else:
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tracker.logger.error(f"SAFETY ABORT: Output '{expected_output}' not found.")
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else:
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tracker.logger.warning("SAFETY ABORT: Enable --embed to delete source.")
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tracker.update_step(file_path, "step_embed", "done")
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# 5. Cleanup
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if args.cleanup:
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try:
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os.remove(audio_path)
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tracker.logger.info(f"Cleanup: Removed {audio_path}")
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except OSError as e:
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tracker.logger.warning(f"Warning: Could not remove audio: {e}")
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# Mark Complete
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if translation_success:
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tracker.update_job_status(file_path, JobStatus.COMPLETED)
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else:
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tracker.update_job_status(file_path, JobStatus.FAILED, error="Translation failed")
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except Exception as e:
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tracker.logger.exception(f"Job Failed for {file_path}")
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tracker.update_job_status(file_path, JobStatus.FAILED, error=str(e))
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return
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def main():
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parser = argparse.ArgumentParser(description="AI Video Transcriber & Translator")
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# Change nargs='?' to nargs='*' or '+' to support multiple inputs
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parser.add_argument("inputs", nargs='*', help="Path(s) to video file or directory")
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parser.add_argument("--model", default="auto", choices=["auto", "tiny", "base", "small", "medium", "large"], help="Whisper model size (default: auto)")
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parser.add_argument("--lang", default="English", help="Target language for translation (default: English)")
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parser.add_argument("--source-lang", help="Source language of audio. If omitted, prompts user.")
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parser.add_argument("--force", action="store_true", help="Overwrite existing files")
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parser.add_argument("--cleanup", action="store_true", help="Delete temporary .wav file")
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parser.add_argument("--embed", action="store_true", help="Embed subtitles (Soft Subs)")
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parser.add_argument("--diarize", action="store_true", help="Enable speaker diarization")
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parser.add_argument("--hf-token", help="HuggingFace Token")
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parser.add_argument("--delete-source", action="store_true", help="Delete original file after embedding")
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parser.add_argument("--retry-failed", action="store_true", help="Retry FAILED jobs from DB")
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parser.add_argument("--prefer-deep", action="store_true", help="Prefer DeepTranslate (Free) over Gemini")
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args = parser.parse_args()
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if not os.getenv("GEMINI_API_KEY"):
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print("Warning: GEMINI_API_KEY environment variable not set. Translation step will fail.")
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source_lang = args.source_lang
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# Retry Logic
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if args.retry_failed:
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print("Retrying failed jobs from database...")
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failed_files = tracker.get_failed_jobs()
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if not failed_files:
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print("No failed jobs found.")
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return
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if not source_lang:
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print("\n--- Audio Configuration ---")
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user_input = input("Enter source language (e.g. 'French'). Enter for Auto: ").strip()
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source_lang = user_input if user_input else None
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for file_path in failed_files:
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if os.path.exists(file_path):
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process_file(file_path, args, source_lang)
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else:
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print(f"Skipping missing file: {file_path}")
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return
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# Normal Logic
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if not args.inputs:
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parser.print_help()
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sys.exit(1)
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if not source_lang:
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print("\n--- Audio Configuration ---")
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user_input = input("Enter source language (e.g. 'French'). Enter for Auto: ").strip()
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source_lang = user_input if user_input else None
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print(f"Selected: {source_lang if source_lang else 'Auto-detect'}")
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# Process all inputs
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video_extensions = ('.mp4', '.mkv', '.mov', '.avi', '.webm', '.flv', '.wmv', '.m4v')
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for input_path in args.inputs:
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if os.path.isfile(input_path):
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process_file(input_path, args, source_lang)
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elif os.path.isdir(input_path):
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found = False
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for root, dirs, files in os.walk(input_path):
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for file in files:
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if file.lower().endswith(video_extensions):
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found = True
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process_file(os.path.join(root, file), args, source_lang)
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if not found:
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print(f"No video files found in {input_path}")
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else:
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print(f"Error: Invalid input path '{input_path}'")
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if __name__ == "__main__":
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main() |