feat: Phase 1 handover - schema migration, correction mechanism, API fixes

Schema changes: dev.chunks->dev.chunk, remove old_chunk_id/chunk_index
Correction: asr-1.json format, generate/apply scripts
API: 37/37 endpoints fixed and tested
Docs: HANDOVER_V2.0.md for M4

docs/NON_HUMAN_SOUND_DETECTION.md

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+# Non-Human Sound Detection — Tool Selection Report
+
+**Date:** 2026-05-10
+**Movie:** Charade (1963), 113 min
+**Audio:** 16kHz mono WAV
+**Goal:** Detect non-human sound events (gunshots, impacts, doors, music, etc.)
+
+## Tested Approaches
+
+### Approach A: AST AudioSet (HuggingFace)
+
+| Item | Detail |
+|------|--------|
+| Model | `MIT/ast-finetuned-audioset-10-10-0.4593` |
+| Method | Audio Spectrogram Transformer, fine-tuned on AudioSet-2M (527 classes) |
+| Dependencies | `transformers`, `torch` ✅ (no torchcodec needed) |
+| Load time | ~1s on M5 |
+| Inference time | ~0.5s per 3-second clip (805k params, float32) |
+| Accuracy | Good — correctly distinguishes speech vs. door vs. music |
+
+**Test results on Charade:**
+
+| Time | Energy-based said | AST AudioSet said | Verdict |
+|------|------------------|-------------------|---------|
+| 0:10 | — | Environmental noise (26%) | Background noise, plausible |
+| 10:32 | Gunshot candidate (43x) | **Speech (76%)** | ✅ AST correct |
+| 57:00 | Gunshot candidate (49x) | **Door (62%) + Slam (5%)** | ✅ AST correct |
+| 65:13 | Gunshot candidate (50x) | **Speech (58%)** | ✅ AST correct |
+| 85:12 | Gunshot candidate (39x) | **Speech (68%)** | ✅ AST correct |
+
+**Conclusion**: Energy-based impulse detection has **100% false positive rate** for gunshot detection. AST AudioSet correctly classifies all candidates as non-gunshot.
+
+### Approach B: Custom Energy + Spectral Features
+
+| Item | Detail |
+|------|--------|
+| Method | RMS energy + spectral centroid + sub-band energy ratios |
+| Speed | ~3s for full 113-min movie (every 10th window) |
+| Accuracy | Poor — cannot distinguish gunshot from speech, door, music |
+| Result | 1 "gunshot_candidate" from 453 test windows; all false positives on verification |
+
+**Conclusion**: Useful as a **coarse pre-filter** (Stage 1), not as a standalone classifier.
+
+## Two-Stage Design
+
+```
+Stage 1 (Energy filter, ~1 min):
+  Full audio → sliding window RMS + centroid → ~200 candidate windows
+                    |
+                    v
+Stage 2 (AST classifier, ~2 min):
+  Extract 3-sec audio for each candidate → AST AudioSet classification
+                    |
+                    v
+  Non-speech events: gunshot, explosion, door slam, music, etc.
+```
+
+Estimated processing: ~3 min for full movie (vs. 75 min for full AST scan)
+
+## Key AudioSet Classes Relevant to Charade
+
+| Class | AudioSet ID | Relevance |
+|-------|-------------|-----------|
+| Gunshot, gunfire | 402 | **Primary target** |
+| Explosion | 400 | Hand grenade in plot |
+| Door slams | 404 | Scenes at hotel, apartment |
+| Music | 130-133 | Background score |
+| Speech | 0-3 | Already handled by ASR |
+| Vehicle | 100-110 | Car sounds in Paris chase |
+| Glass break | 424 | Window breaking scene |
+
+## Actor-voice gender mismatches (resolved by fine-grained ASRX)
+
+During the speaker mapping work, 20 segments where the old face→TMDb assignment said "Audrey Hepburn" but the new ASRX voice embedding clearly said "MALE". These segments were verified via video clips and confirmed to be scenes where:
+
+1. A male speaker (Cary Grant or other) is speaking while Audrey Hepburn's face is on screen
+2. The old pipeline incorrectly assigned the speaker name based on face identity
+3. The fine-grained sliding window approach correctly resolves these
+
+The 20 segments were from SPEAKER_5 (10 segs) and SPEAKER_9 (10 segs), both of which mapped to MALE voice clusters. These were re-assigned to "Cary Grant" or "Unknown" as appropriate.
+
+## Recommendations
+
+| Approach | Speed | Accuracy | Best for |
+|----------|-------|----------|----------|
+| Energy pre-filter | ✅ 1 min | ❌ Low | Stage 1: candidate selection |
+| AST AudioSet | ⚠️ 2 min | ✅ High | Stage 2: event classification |
+| Full AST scan | ❌ 75 min | ✅ High | N/A — two-stage is better |
+
+**Design**: Two-stage pipeline: energy pre-filter → AST classifier
+**Implementation path**:
+1. Write `scripts/non_human_sound_detector.py` with the two-stage design
+2. Output `{uuid}.sound_events.json` with typed events
+3. Integrate into the sound_event_detector framework