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

scripts/asr_model_benchmark.py

@@ -0,0 +1,83 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Comprehensive ASR Model Selection Benchmark
+Tests 5 models × 2 VAD settings across 3 test clips.
+Output: JSON results + markdown report
+"""
+import json, time, os, gc, sys
+from faster_whisper import WhisperModel
+
+CLIPS = {
+    "A_rapid": {"path": "/tmp/asr_clip_A.mp4", "offset": 1540},
+    "B_normal": {"path": "/tmp/asr_clip_B.mp4", "offset": 600},
+    "C_complex": {"path": "/tmp/asr_clip_C.mp4", "offset": 4400},
+}
+
+MODELS = ["tiny", "base", "small", "medium", "large-v3"]
+VAD_SETTINGS = [200, 500]  # min_silence_duration_ms
+
+RESULTS_FILE = "/tmp/asr_benchmark_results.json"
+
+def run_transcribe(model, clip_path, clip_name, vad_ms):
+    segs = []
+    t0 = time.time()
+    vad_params = {"min_silence_duration_ms": vad_ms}
+    segments, info = model.transcribe(clip_path, beam_size=5, vad_filter=True,
+                                       vad_parameters=vad_params)
+    for seg in segments:
+        segs.append({"start": round(seg.start, 2), "end": round(seg.end, 2),
+                      "text": seg.text.strip()})
+    elapsed = time.time() - t0
+    return segs, info, elapsed
+
+# Load existing results to skip completed
+all_results = {}
+if os.path.exists(RESULTS_FILE):
+    all_results = json.load(open(RESULTS_FILE))
+    print(f"Loaded {sum(len(v) for v in all_results.values())} existing results")
+
+total = len(CLIPS) * len(MODELS) * len(VAD_SETTINGS)
+done = sum(len(v) for v in all_results.values())
+print(f"Total: {total} tests, {done} already done, {total-done} remaining\n")
+
+for clip_name, clip_cfg in CLIPS.items():
+    if clip_name not in all_results:
+        all_results[clip_name] = {}
+    
+    for model_size in MODELS:
+        for vad_ms in VAD_SETTINGS:
+            key = f"{model_size}_vad{vad_ms}"
+            if key in all_results[clip_name]:
+                continue
+
+            print(f"[{clip_name}] {model_size} VAD={vad_ms}ms ...", end=" ", flush=True)
+            t_load = time.time()
+            model = WhisperModel(model_size, device="cpu", compute_type="int8")
+            load_time = time.time() - t_load
+
+            segs, info, trans_time = run_transcribe(model, clip_cfg["path"], clip_name, vad_ms)
+
+            # Total chars
+            total_chars = sum(len(s["text"]) for s in segs)
+            
+            all_results[clip_name][key] = {
+                "model": model_size,
+                "vad_ms": vad_ms,
+                "segments": segs,
+                "segment_count": len(segs),
+                "total_chars": total_chars,
+                "runtime_secs": round(trans_time, 1),
+                "load_time_secs": round(load_time, 1),
+                "language": info.language,
+            }
+            print(f"{len(segs)} segs, {total_chars} chars, {trans_time:.1f}s")
+
+            # Free memory between models
+            del model
+            gc.collect()
+            
+            # Save incrementally
+            json.dump(all_results, open(RESULTS_FILE, "w"))
+
+print("\n=== All tests complete ===")
+print(json.dumps({k: {kk: {kkk: vv for kkk, vv in v.items() if kkk != "segments"} for kk, v in vv.items()} for k, vv in all_results.items()}, indent=2))