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
2026-05-11 07:03:22 +08:00
parent ef894a44ad
commit 39ba5ddf76
147 changed files with 19843 additions and 3053 deletions
@@ -0,0 +1,98 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Step 3: Re-run ASR with word_timestamps on full audio.
+Map words to 4188 fine segments for accurate text.
+"""
+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 ASRX ===")
+fine = json.load(open(f"{BASE}/{UUID}.asrx_fine.json"))
+fine_segs = fine["segments"]
+print(f"Fine segments: {len(fine_segs)}")
+
+print("\n=== Extract audio WAV ===")
+tmp_dir = tempfile.mkdtemp(prefix="asr_step3_")
+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 with word_timestamps...")
+t0 = time.time()
+model = WhisperModel("small", device="cpu", compute_type="int8")
+print(f"  Model loaded in {time.time()-t0:.1f}s")
+
+print("Transcribing with word_timestamps=True...")
+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
+)
+
+# Collect all word-level data
+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})
+    else:
+        words.append({"word": seg.text.strip(), "start": seg.start, "end": seg.end})
+
+elapsed = time.time() - t0
+print(f"  Done in {elapsed:.1f}s, {len(words)} words")
+
+# Map words to fine segments
+print("\n=== Map words to fine segments ===")
+wi = 0
+assigned = 0
+for si, fs in enumerate(fine_segs):
+    fstart = fs["start_time"]
+    fend = fs["end_time"]
+    seg_words = []
+    
+    while wi < len(words):
+        w = words[wi]
+        if w["end"] <= fstart:
+            wi += 1
+            continue
+        if w["start"] >= fend:
+            break
+        seg_words.append(w["word"])
+        wi += 1
+    
+    text = " ".join(seg_words)
+    fs["text"] = text
+    if text:
+        assigned += 1
+
+print(f"  Segments with text: {assigned}/{len(fine_segs)}")
+
+# Show examples
+print("\nSplit segment examples:")
+for fs in fine_segs:
+    # Check if this was split (doesn't match an ASR boundary exactly)
+    is_split = True
+    # We can't easily check here, just show first 10 non-trivial
+    if len(fs.get('text','')) > 10 and is_split:
+        print(f"  [{fs['start_time']:.1f}-{fs['end_time']:.1f}] {fs['speaker_name']:15s} \"{fs['text'][:60]}\"")
+        break  # just one for now
+
+# Count text lengths
+text_lens = [len(fs.get('text','')) for fs in fine_segs]
+print(f"\n  Avg text length: {sum(text_lens)/len(text_lens):.0f} chars")
+print(f"  Empty texts: {sum(1 for l in text_lens if l == 0)}")
+
+# Save
+fine["_asr_meta"] = {"word_timestamps": True, "asr_runtime_secs": round(elapsed, 1)}
+json.dump(fine, open(f"{BASE}/{UUID}.asrx_fine.json", "w"), indent=2)
+print(f"\nSaved")
+
+shutil.rmtree(tmp_dir, ignore_errors=True)