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/fix_asr_text.py

@@ -0,0 +1,114 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Redo ASR word-timestamp mapping correctly.
+Save words first, then map to fine segments with independent scanning.
+"""
+import json, sys, os, time, subprocess, tempfile, shutil
+from faster_whisper import WhisperModel
+
+UUID = "aeed71342a899fe4b4c57b7d41bcb692"
+BASE = "/Users/accusys/momentry/output_dev"
+VIDEO = "/Users/accusys/momentry/var/sftpgo/data/demo/Charade (1963) Cary Grant & Audrey Hepburn \uff5c Comedy Mystery Romance Thriller \uff5c Full Movie.mp4"
+
+print("Load fine segments...")
+fine = json.load(open(f"{BASE}/{UUID}.asrx_fine.json"))
+fine_segs = fine["segments"]
+print(f"{len(fine_segs)} segments")
+
+# Extract full audio
+tmp_dir = tempfile.mkdtemp(prefix="asr_fix_")
+wav_path = os.path.join(tmp_dir, "audio.wav")
+subprocess.run(["ffmpeg", "-y", "-v", "quiet", "-i", VIDEO,
+    "-ar", "16000", "-ac", "1", "-sample_fmt", "s16", wav_path],
+    check=True, capture_output=True, timeout=300)
+
+print("Loading model...")
+model = WhisperModel("small", device="cpu", compute_type="int8")
+
+# Check if words file exists
+words_file = f"{BASE}/{UUID}.words.json"
+if os.path.exists(words_file):
+    print("Loading saved words...")
+    words = json.load(open(words_file))
+else:
+    print("Transcribing with word_timestamps...")
+    t0 = time.time()
+    segments, info = model.transcribe(
+        wav_path, beam_size=5, vad_filter=True,
+        vad_parameters={"min_silence_duration_ms": 500},
+        word_timestamps=True
+    )
+    words = []
+    for seg in segments:
+        if seg.words:
+            for w in seg.words:
+                wt = w.word.strip()
+                if wt:
+                    words.append({"word": wt, "start": w.start, "end": w.end})
+        # Also save segment-level as fallback
+        words.append({"word": seg.text.strip(), "start": seg.start, "end": seg.end, "_seg": True})
+    
+    elapsed = time.time() - t0
+    print(f"  {len(words)} entries in {elapsed:.1f}s")
+    json.dump(words, open(words_file, "w"))
+
+# Separate word-level and segment-level
+word_entries = [w for w in words if not w.get("_seg")]
+seg_entries = [w for w in words if w.get("_seg")]
+print(f"Word-level: {len(word_entries)}, Segment-level: {len(seg_entries)}")
+
+# Map: for each fine segment, find ALL word entries within its time range
+print("Mapping words to segments...")
+assigned = 0
+for si, fs in enumerate(fine_segs):
+    fstart = fs["start_time"]
+    fend = fs["end_time"]
+    
+    seg_words = []
+    # Use word-level entries first (more precise)
+    for w in word_entries:
+        if w["start"] >= fstart and w["end"] <= fend + 0.05:
+            seg_words.append(w["word"])
+        elif w["start"] > fend:
+            break  # words are sorted by time
+    
+    if not seg_words:
+        # Fallback to segment-level
+        for w in seg_entries:
+            if w["start"] >= fstart and w["end"] <= fend + 0.05:
+                seg_words.append(w["word"])
+            elif w["start"] > fend:
+                break
+    
+    text = " ".join(seg_words) if seg_words else ""
+    fs["text"] = text
+    if text:
+        assigned += 1
+    
+    if (si + 1) % 500 == 0:
+        print(f"  {si+1}/{len(fine_segs)}")
+
+print(f"Segments with text: {assigned}/{len(fine_segs)}")
+
+# Fix empty segments: use original ASR text
+asr = json.load(open(f"{BASE}/{UUID}.asr.json"))
+asr_segs = asr["segments"]
+asr_bounds = {(s['start'], s['end']): s['text'] for s in asr_segs}
+
+for fs in fine_segs:
+    if not fs.get('text', '').strip():
+        key = (fs['start_time'], fs['end_time'])
+        if key in asr_bounds:
+            fs['text'] = asr_bounds[key]
+        else:
+            fs['text'] = ""
+
+with_text = sum(1 for fs in fine_segs if fs.get('text','').strip())
+print(f"After fallback: {with_text}/{len(fine_segs)} with text")
+
+# Save
+fine["_asr_meta"]["word_file"] = words_file
+json.dump(fine, open(f"{BASE}/{UUID}.asrx_fine.json", "w"), indent=2)
+print("Saved")
+
+shutil.rmtree(tmp_dir, ignore_errors=True)