- Scan regions can be disabled (Del/Backspace) instead of deleted, shown greyed out
- Resize scan regions by dragging timeline edges or editing table cells
- Grey ghost overlay shows trimmed portions of resized regions
- Ctrl+Z undo for disable, resize, drag, and negative toggle actions
- Fix training stats including scan-exported clips when checkbox unchecked
- Switch classifier to HistGradientBoostingClassifier (multi-threaded)
- Timestamped model saves with latest copy at base path
- Fix next-folder counter not detecting scan export folders
- Each scan area exports to its own numbered clip folder
- Platform-aware HW encoder detection (Linux/Windows/macOS)
- Auto-detect VAAPI render device instead of hardcoding
- Use shutil.move for cross-drive safety on Windows
- Comprehensive README rewrite with scan workflow documentation
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fuse overlapping scan regions before display (merge adjacent 1s-hop windows)
- Hard negatives: mark false positives from scan panel for training feedback
- Toggle with "Add to Negatives" button, red text + red timeline regions
- Stored in dedicated hard_negatives table, always included in training
- Model versioning: auto-backup on retrain, right-click model combo to rollback
- Scan review mode: "Review" toggle hides spread/markers for free navigation
- Scan exports: saved to DB with scan_export flag, no timeline markers
- Training dialog checkbox to optionally include scan exports
- Single group folder per batch with area numbering (clip_042_a1_0.mp4)
- Export scan results: skip negatives, skip regions < 8s, respect spread
- Button shows estimated clip count, updates on spread/fuse/negative changes
- Timeline: reload scan regions on file load, "Clear all markers" context menu
- Default training model changed to HUBERT_XLARGE
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Stash profile and crop_center at export start for async safety
- Scope get_group/delete_group by profile to prevent cross-profile leaks
- Guard auto-negative sampling when no markers exist (prevents flood)
- Wrap ffmpeg subprocess with clean timeout error message
- Fix scan-all panel reload to use stashed profile, not live value
- Remove dead warnings import
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replace librosa with direct ffmpeg subprocess for 10x faster audio loading
- Add ScanResultsPanel with per-model tabs, seek-on-click, delete, and export
- Persist scan results in DB per (filename, profile, model)
- Add model selector dropdown to switch between trained embedding models
- Add "Scan All" button for batch scanning playlist videos
- Support manual negative examples via negative class folder
- Configurable auto-negative margin (default 30s, 0 = disabled)
- Deduplicate nearby training markers (8s min gap)
- Parallel audio loading with ThreadPoolExecutor during training
- Progress callbacks from training for UI status updates
- Cache bypass in scan_video (skip audio loading when embeddings cached)
- Move all caches (models, embeddings, downloads) into project directory
- Add 8cut.sh launcher script with auto venv/conda detection
- Fix 11 bugs across thread safety, signal handling, and state management
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Models now live in <project>/models/ instead of ~/.8cut_models/ so
everything stays self-contained. Updated .gitignore to exclude
models/, .venv/, *.joblib, and *.pt.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fix test_utils.py importing build_annotation_json_path from main
instead of core.annotations (all 59 tests pass now)
- Fix get_training_data double-counting clips at same start_time
in both positive and soft sets — subtract positive from soft
- Add cancel_flag to train_classifier so training can be interrupted
between videos (TrainWorker passes self as cancel_flag)
- Remove orphaned core/export.py (was for deleted server API)
- Remove stale Dockerfile and docker-compose.yml (referenced server)
- Clean up leftover server/__pycache__ and client/ build artifacts
- Add torch to requirements.txt (was only mentioned in comments)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove legacy distance-mode scanning (build_profile, _similarity, etc.)
and hand-crafted intensity features — pipeline is now embedding-only
- Integrate Microsoft BEATs as embedding option alongside wav2vec2/HuBERT
- Add TrainDialog with positive class selector, model picker, video dir
fallback, and live training stats
- Add TrainWorker QThread with cancel support and proper lifecycle cleanup
- Add source_path column to DB for robust source video tracking
- Add get_export_folders/get_training_data/get_training_stats to DB
- Wire source_path in all export DB writes (_on_clip_done, _on_auto_clip_done)
- Cancel scan/train workers in closeEvent to prevent use-after-free crashes
- Add setup_env.sh supporting both conda and python venv (CUDA 12.8)
- Update requirements.txt with all actual dependencies
- Update 8cut_train.py with --positive flag for new DB-driven training
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Root cause of poor discrimination: MFCC[0] (energy) dominated the
feature vector, making cosine similarity see all audio as similar.
Changes:
- Skip MFCC[0], use 12 coefficients instead of 20
- Add delta MFCCs for temporal dynamics
- Add 7-band spectral contrast for tonal vs noise quality
- Switch from cosine similarity to euclidean-distance-based score
- Pre-compute STFT once for whole file (10-20x faster)
- Vectorized sliding window via cumulative sums (no Python loop)
- Lower sample rate 22050→16000 Hz (faster, no quality loss)
- 62-dim feature vector (was 40-dim mean+std of raw MFCCs)
- Default threshold 0.05 (new similarity scale)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Mean-only vectors were too similar across different audio segments,
causing everything to match even at threshold 0.99. Adding std
captures temporal dynamics and makes the similarity scores much
more spread out.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ethanfel