refactor
This commit is contained in:
Mariano Gabriel
@@ -1,34 +1,19 @@
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#!/usr/bin/env python3
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"""
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Process meeting recordings to extract audio + screen content.
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Combines Whisper transcripts with OCR from screen shares.
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Combines Whisper transcripts with vision analysis or OCR from screen shares.
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"""
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import argparse
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from pathlib import Path
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import sys
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import json
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import logging
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import subprocess
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import shutil
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from meetus.frame_extractor import FrameExtractor
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from meetus.ocr_processor import OCRProcessor
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from meetus.vision_processor import VisionProcessor
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from meetus.transcript_merger import TranscriptMerger
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logger = logging.getLogger(__name__)
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from meetus.workflow import WorkflowConfig, ProcessingWorkflow
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def setup_logging(verbose: bool = False):
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"""
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Configure logging for the application.
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Args:
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verbose: If True, set DEBUG level, otherwise INFO
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"""
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"""Configure logging for the application."""
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level = logging.DEBUG if verbose else logging.INFO
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# Configure root logger
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logging.basicConfig(
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level=level,
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format='%(asctime)s - %(levelname)s - %(message)s',
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@@ -41,58 +26,6 @@ def setup_logging(verbose: bool = False):
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logging.getLogger('paddleocr').setLevel(logging.WARNING)
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def run_whisper(video_path: Path, model: str = "base", output_dir: str = "output") -> Path:
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"""
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Run Whisper transcription on video file.
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Args:
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video_path: Path to video file
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model: Whisper model to use (tiny, base, small, medium, large)
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output_dir: Directory to save output
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Returns:
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Path to generated JSON transcript
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"""
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# Check if whisper is installed
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if not shutil.which("whisper"):
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logger.error("Whisper is not installed. Install it with: pip install openai-whisper")
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sys.exit(1)
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logger.info(f"Running Whisper transcription (model: {model})...")
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logger.info("This may take a few minutes depending on video length...")
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# Run whisper command
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cmd = [
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"whisper",
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str(video_path),
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"--model", model,
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"--output_format", "json",
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"--output_dir", output_dir
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]
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try:
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result = subprocess.run(
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cmd,
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check=True,
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capture_output=True,
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text=True
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)
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# Whisper outputs to <output_dir>/<video_stem>.json
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transcript_path = Path(output_dir) / f"{video_path.stem}.json"
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if transcript_path.exists():
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logger.info(f"✓ Whisper transcription completed: {transcript_path}")
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return transcript_path
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else:
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logger.error("Whisper completed but transcript file not found")
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sys.exit(1)
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except subprocess.CalledProcessError as e:
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logger.error(f"Whisper failed: {e.stderr}")
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sys.exit(1)
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def main():
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parser = argparse.ArgumentParser(
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description="Extract screen content from meeting recordings and merge with transcripts",
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@@ -119,23 +52,23 @@ Examples:
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"""
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)
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# Required arguments
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parser.add_argument(
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'video',
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help='Path to video file'
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)
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# Whisper options
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parser.add_argument(
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'--transcript', '-t',
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help='Path to Whisper transcript (JSON or TXT)',
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default=None
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)
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parser.add_argument(
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'--run-whisper',
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action='store_true',
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help='Run Whisper transcription before processing'
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)
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parser.add_argument(
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'--whisper-model',
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choices=['tiny', 'base', 'small', 'medium', 'large'],
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@@ -143,56 +76,48 @@ Examples:
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default='base'
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)
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# Output options
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parser.add_argument(
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'--output', '-o',
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help='Output file for enhanced transcript (default: output/<video>_enhanced.txt)',
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help='Output file for enhanced transcript (default: auto-generated in output directory)',
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default=None
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)
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parser.add_argument(
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'--output-dir',
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help='Directory for output files (default: output/)',
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help='Base directory for outputs (default: output/)',
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default='output'
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)
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parser.add_argument(
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'--frames-dir',
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help='Directory to save extracted frames (default: frames/)',
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default='frames'
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)
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# Frame extraction options
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parser.add_argument(
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'--interval',
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type=int,
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help='Extract frame every N seconds (default: 5)',
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default=5
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)
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parser.add_argument(
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'--scene-detection',
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action='store_true',
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help='Use scene detection instead of interval extraction'
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)
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# Analysis options
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parser.add_argument(
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'--ocr-engine',
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choices=['tesseract', 'easyocr', 'paddleocr'],
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help='OCR engine to use (default: tesseract)',
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default='tesseract'
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)
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parser.add_argument(
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'--use-vision',
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action='store_true',
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help='Use local vision model (Ollama) instead of OCR for better context understanding'
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)
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parser.add_argument(
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'--vision-model',
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help='Vision model to use with Ollama (default: llava:13b)',
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default='llava:13b'
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)
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parser.add_argument(
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'--vision-context',
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choices=['meeting', 'dashboard', 'code', 'console'],
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@@ -200,24 +125,22 @@ Examples:
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default='meeting'
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)
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# Processing options
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parser.add_argument(
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'--no-cache',
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action='store_true',
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help='Disable caching - reprocess everything even if outputs exist'
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)
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parser.add_argument(
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'--no-deduplicate',
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action='store_true',
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help='Disable text deduplication'
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)
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parser.add_argument(
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'--extract-only',
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action='store_true',
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help='Only extract frames and OCR, skip transcript merging'
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help='Only extract frames and analyze, skip transcript merging'
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)
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parser.add_argument(
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'--format',
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choices=['detailed', 'compact'],
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@@ -225,6 +148,7 @@ Examples:
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default='detailed'
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)
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# Logging
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parser.add_argument(
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'--verbose', '-v',
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action='store_true',
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@@ -236,166 +160,38 @@ Examples:
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# Setup logging
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setup_logging(args.verbose)
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# Validate video path
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video_path = Path(args.video)
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if not video_path.exists():
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logger.error(f"Video file not found: {args.video}")
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sys.exit(1)
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try:
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# Create workflow configuration
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config = WorkflowConfig(**vars(args))
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# Create output directory
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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# Run processing workflow
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workflow = ProcessingWorkflow(config)
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result = workflow.run()
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# Set default output path
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if args.output is None:
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args.output = str(output_dir / f"{video_path.stem}_enhanced.txt")
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# Print final summary
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print("\n" + "=" * 80)
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print("✓ SUCCESS!")
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print("=" * 80)
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print(f"Output directory: {result['output_dir']}")
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if result.get('enhanced_transcript'):
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print(f"Enhanced transcript ready for AI summarization!")
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print("=" * 80)
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# Define cache paths
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whisper_cache = output_dir / f"{video_path.stem}.json"
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analysis_cache = output_dir / f"{video_path.stem}_{'vision' if args.use_vision else 'ocr'}.json"
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frames_cache_dir = Path(args.frames_dir)
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return 0
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# Check for cached Whisper transcript
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if args.run_whisper:
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if not args.no_cache and whisper_cache.exists():
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logger.info(f"✓ Found cached Whisper transcript: {whisper_cache}")
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args.transcript = str(whisper_cache)
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else:
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logger.info("=" * 80)
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logger.info("STEP 0: Running Whisper Transcription")
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logger.info("=" * 80)
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transcript_path = run_whisper(video_path, args.whisper_model, str(output_dir))
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args.transcript = str(transcript_path)
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logger.info("")
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logger.info("=" * 80)
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logger.info("MEETING PROCESSOR")
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logger.info("=" * 80)
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logger.info(f"Video: {video_path.name}")
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logger.info(f"Analysis: {'Vision Model' if args.use_vision else f'OCR ({args.ocr_engine})'}")
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if args.use_vision:
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logger.info(f"Vision Model: {args.vision_model}")
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logger.info(f"Context: {args.vision_context}")
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logger.info(f"Frame extraction: {'Scene detection' if args.scene_detection else f'Every {args.interval}s'}")
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if args.transcript:
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logger.info(f"Transcript: {args.transcript}")
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logger.info(f"Caching: {'Disabled' if args.no_cache else 'Enabled'}")
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logger.info("=" * 80)
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# Step 1: Extract frames (with caching)
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logger.info("Step 1: Extracting frames from video...")
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# Check if frames already exist
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existing_frames = list(frames_cache_dir.glob(f"{video_path.stem}_*.jpg")) if frames_cache_dir.exists() else []
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if not args.no_cache and existing_frames and len(existing_frames) > 0:
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logger.info(f"✓ Found {len(existing_frames)} cached frames in {args.frames_dir}/")
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# Build frames_info from existing files
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frames_info = []
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for frame_path in sorted(existing_frames):
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# Try to extract timestamp from filename (e.g., video_00001_12.34s.jpg)
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try:
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timestamp_str = frame_path.stem.split('_')[-1].rstrip('s')
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timestamp = float(timestamp_str)
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except:
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timestamp = 0.0
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frames_info.append((str(frame_path), timestamp))
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else:
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extractor = FrameExtractor(str(video_path), args.frames_dir)
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if args.scene_detection:
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frames_info = extractor.extract_scene_changes()
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else:
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frames_info = extractor.extract_by_interval(args.interval)
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if not frames_info:
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logger.error("No frames extracted")
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sys.exit(1)
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logger.info(f"✓ Extracted {len(frames_info)} frames")
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# Step 2: Run analysis on frames (with caching)
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if not args.no_cache and analysis_cache.exists():
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logger.info(f"✓ Found cached analysis results: {analysis_cache}")
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with open(analysis_cache, 'r', encoding='utf-8') as f:
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screen_segments = json.load(f)
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logger.info(f"✓ Loaded {len(screen_segments)} analyzed frames from cache")
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else:
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if args.use_vision:
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# Use vision model
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logger.info("Step 2: Running vision analysis on extracted frames...")
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try:
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vision = VisionProcessor(model=args.vision_model)
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screen_segments = vision.process_frames(
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frames_info,
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context=args.vision_context,
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deduplicate=not args.no_deduplicate
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)
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logger.info(f"✓ Analyzed {len(screen_segments)} frames with vision model")
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except ImportError as e:
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logger.error(f"{e}")
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sys.exit(1)
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else:
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# Use OCR
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logger.info("Step 2: Running OCR on extracted frames...")
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try:
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ocr = OCRProcessor(engine=args.ocr_engine)
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screen_segments = ocr.process_frames(
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frames_info,
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deduplicate=not args.no_deduplicate
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)
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logger.info(f"✓ Processed {len(screen_segments)} frames with OCR")
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except ImportError as e:
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logger.error(f"{e}")
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logger.error(f"To install {args.ocr_engine}:")
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logger.error(f" pip install {args.ocr_engine}")
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sys.exit(1)
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# Save analysis results as JSON
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with open(analysis_cache, 'w', encoding='utf-8') as f:
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json.dump(screen_segments, f, indent=2, ensure_ascii=False)
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logger.info(f"✓ Saved analysis results to: {analysis_cache}")
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if args.extract_only:
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logger.info("Done! (extract-only mode)")
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return
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# Step 3: Merge with transcript (if provided)
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merger = TranscriptMerger()
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if args.transcript:
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logger.info("Step 3: Merging with Whisper transcript...")
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transcript_path = Path(args.transcript)
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if not transcript_path.exists():
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logger.warning(f"Transcript not found: {args.transcript}")
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logger.info("Proceeding with screen content only...")
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audio_segments = []
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else:
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audio_segments = merger.load_whisper_transcript(str(transcript_path))
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logger.info(f"✓ Loaded {len(audio_segments)} audio segments")
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else:
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logger.info("No transcript provided, using screen content only...")
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audio_segments = []
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# Merge and format
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merged = merger.merge_transcripts(audio_segments, screen_segments)
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formatted = merger.format_for_claude(merged, format_style=args.format)
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# Save output
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merger.save_transcript(formatted, args.output)
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logger.info("=" * 80)
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logger.info("✓ PROCESSING COMPLETE!")
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logger.info("=" * 80)
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logger.info(f"Enhanced transcript: {args.output}")
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logger.info(f"OCR data: {ocr_output}")
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logger.info(f"Frames: {args.frames_dir}/")
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logger.info("")
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logger.info("You can now use the enhanced transcript with Claude for summarization!")
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except FileNotFoundError as e:
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logging.error(f"File not found: {e}")
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return 1
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except RuntimeError as e:
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logging.error(f"Processing failed: {e}")
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return 1
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except KeyboardInterrupt:
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logging.warning("\nProcessing interrupted by user")
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return 130
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except Exception as e:
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logging.exception(f"Unexpected error: {e}")
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return 1
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if __name__ == '__main__':
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main()
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sys.exit(main())
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