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