Video Tutorial Indexer
Extracts chapter timestamps from tutorial videos using speech-to-text transcription and semantic alignment.
Workflow
- Extract audio from video with ffmpeg (16kHz mono WAV)
- Transcribe with Whisper (base model for speed/accuracy balance)
- Align chapter titles to transcript segments using keyword matching
- Validate structural constraints (monotonicity, range, count)
- Generate JSON output
Usage
pythonimport sys sys.path.insert(0, '/app/environment/skills/evo-video-indexer/scripts') from transcribe import extract_audio, transcribe_audio, save_transcript from align_chapters import align_chapters_to_transcript, validate_chapters, generate_index # Step 1: Extract audio audio_path = extract_audio('/root/tutorial_video.mp4', '/tmp/audio.wav') # Step 2: Transcribe segments = transcribe_audio(audio_path, model_name='base') save_transcript(segments, '/root/transcript_segments.json') # Step 3: Define chapter titles (from task) chapter_titles = [ "What we'll do", "How we'll get there", # ... all chapter titles ... ] # Step 4: Align chapters to transcript chapters = align_chapters_to_transcript(chapter_titles, segments, first_time=0) # Step 5: Validate errors = validate_chapters(chapters, duration=1382, expected_count=29) if errors: print("Validation errors:", errors) # Step 6: Generate output generate_index(chapters, title='In-Depth Floor Plan Tutorial Part 1', duration=1382, output_path='/root/tutorial_index.json')
Key Insights
- Whisper base model is sufficient for clear single-speaker English tutorials
- Chapter alignment requires semantic matching, not just keyword search
- The speaker rarely says exact chapter titles verbatim
- Enforce monotonicity globally across all chapters
- Very short chapters (Save, Break) may occupy only a few seconds
- "Break" and "Continue" pattern: break interrupts a topic, continuation resumes it
- Manual review of transcript is often needed for accurate alignment
- Timestamps should be integers (seconds) for clean output
