feat: update Python processors and add utility scripts
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
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Warren
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scripts/chunk_statistics.py
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scripts/chunk_statistics.py
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#!/opt/bin/python3.11
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"""
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Chunk-based statistics for ASR, Face, and Speaker combinations.
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Generates a comprehensive report of each chunk's content.
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"""
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import json
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import os
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import sys
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UUID = "384b0ff44aaaa1f1"
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BASE_DIR = f"output/{UUID}"
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CHUNK_DURATION = 60 # seconds per chunk
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def load_json(filepath):
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with open(filepath, "r") as f:
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return json.load(f)
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def build_chunk_stats():
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print(f"📊 Building chunk statistics for {UUID}...")
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print(f" Chunk duration: {CHUNK_DURATION}s")
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# Load data
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asr_data = load_json(os.path.join(BASE_DIR, f"{UUID}.asr.json"))
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face_data = load_json(os.path.join(BASE_DIR, f"{UUID}.face_clustered.json"))
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# Get video duration
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segments = asr_data.get("segments", [])
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video_duration = max(seg.get("end", 0) for seg in segments) if segments else 0
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print(f" Video duration: {video_duration:.0f}s ({video_duration / 60:.1f} min)")
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# Build chunk structure
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num_chunks = int(video_duration // CHUNK_DURATION) + 1
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chunks = []
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for i in range(num_chunks):
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chunk_start = i * CHUNK_DURATION
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chunk_end = (i + 1) * CHUNK_DURATION
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chunks.append(
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{
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"chunk_id": i,
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"start": chunk_start,
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"end": chunk_end,
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"asr_count": 0,
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"asr_text_len": 0,
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"face_count": 0,
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"unique_persons": set(),
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"has_speech": False,
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"has_faces": False,
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}
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)
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# Count ASR segments per chunk
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for seg in segments:
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start = seg.get("start", 0)
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end = seg.get("end", 0)
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text = seg.get("text", "")
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# Find overlapping chunks
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chunk_start_idx = int(start // CHUNK_DURATION)
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chunk_end_idx = int(end // CHUNK_DURATION)
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for ci in range(chunk_start_idx, min(chunk_end_idx + 1, len(chunks))):
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chunks[ci]["asr_count"] += 1
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chunks[ci]["asr_text_len"] += len(text)
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chunks[ci]["has_speech"] = True
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# Count faces per chunk
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face_frames = face_data.get("frames", [])
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for frame in face_frames:
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timestamp = frame.get("timestamp", 0)
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faces = frame.get("faces", [])
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chunk_idx = int(timestamp // CHUNK_DURATION)
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if chunk_idx < len(chunks):
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chunks[chunk_idx]["face_count"] += len(faces)
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chunks[chunk_idx]["has_faces"] = len(faces) > 0
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for face in faces:
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pid = face.get("person_id")
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if pid:
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chunks[chunk_idx]["unique_persons"].add(pid)
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# Convert sets to counts for serialization
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for chunk in chunks:
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chunk["unique_person_count"] = len(chunk["unique_persons"])
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chunk["top_persons"] = list(chunk["unique_persons"])[:10] # Top 10
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del chunk["unique_persons"]
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return chunks, video_duration
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def print_summary(chunks):
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print("\n" + "=" * 80)
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print("📈 CHUNK STATISTICS SUMMARY")
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print("=" * 80)
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# Overall stats
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total_asr = sum(c["asr_count"] for c in chunks)
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total_faces = sum(c["face_count"] for c in chunks)
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total_speech_chunks = sum(1 for c in chunks if c["has_speech"])
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total_face_chunks = sum(1 for c in chunks if c["has_faces"])
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chunks_with_both = sum(1 for c in chunks if c["has_speech"] and c["has_faces"])
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chunks_with_neither = sum(
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1 for c in chunks if not c["has_speech"] and not c["has_faces"]
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)
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print(f"\n📊 Overview:")
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print(f" Total chunks: {len(chunks)}")
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print(
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f" Chunks with speech: {total_speech_chunks} ({total_speech_chunks / len(chunks) * 100:.0f}%)"
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)
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print(
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f" Chunks with faces: {total_face_chunks} ({total_face_chunks / len(chunks) * 100:.0f}%)"
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)
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print(
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f" Both speech+faces: {chunks_with_both} ({chunks_with_both / len(chunks) * 100:.0f}%)"
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)
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print(
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f" Neither: {chunks_with_neither} ({chunks_with_neither / len(chunks) * 100:.0f}%)"
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)
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print(f" Total ASR segments: {total_asr}")
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print(f" Total face frames: {total_faces}")
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# Combination breakdown
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print(f"\n🎯 ASR/Face Combination Breakdown:")
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combos = {}
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for c in chunks:
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key = (c["has_speech"], c["has_faces"])
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if key not in combos:
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combos[key] = {"count": 0, "chunk_ids": []}
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combos[key]["count"] += 1
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combos[key]["chunk_ids"].append(c["chunk_id"])
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for (has_speech, has_faces), info in sorted(combos.items()):
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speech_str = "🎤 Speech" if has_speech else " No Speech"
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face_str = "👤 Faces" if has_faces else " No Faces"
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chunk_range = (
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f"{min(info['chunk_ids'])}-{max(info['chunk_ids'])}"
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if len(info["chunk_ids"]) > 1
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else f"{info['chunk_ids'][0]}"
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)
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print(
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f" {speech_str} + {face_str}: {info['count']} chunks (IDs: {chunk_range})"
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)
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# Top chunks by activity
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print(f"\n🔥 Top 10 Most Active Chunks (by ASR+Faces):")
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scored_chunks = []
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for c in chunks:
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score = c["asr_count"] + c["face_count"]
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scored_chunks.append((score, c))
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scored_chunks.sort(key=lambda x: x[0], reverse=True)
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for score, c in scored_chunks[:10]:
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persons = ", ".join(c["top_persons"][:3])
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print(
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f" Chunk {c['chunk_id']:3d} ({c['start']:5d}-{c['end']:5d}s): "
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f"ASR={c['asr_count']:3d}, Faces={c['face_count']:4d}, "
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f"Persons={c['unique_person_count']:2d} ({persons})"
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)
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# Stamp scene chunk
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print(f"\n🔍 Special Interest Chunks:")
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for c in chunks:
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# Stamp scene around 5730s
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if c["start"] <= 5730 <= c["end"]:
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persons = ", ".join(c["top_persons"][:5])
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print(
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f" 🎯 Stamp scene chunk: {c['chunk_id']} ({c['start']}-{c['end']}s)"
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)
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print(
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f" ASR={c['asr_count']}, Faces={c['face_count']}, "
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f"Persons={c['unique_person_count']} ({persons})"
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)
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# Magnifying glass scene around 5727s
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if c["start"] <= 5727 <= c["end"]:
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print(
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f" 🔍 Magnifier scene chunk: {c['chunk_id']} ({c['start']}-{c['end']}s)"
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)
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# Vase scenes
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vase_times = [300, 660, 3720]
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for vt in vase_times:
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for c in chunks:
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if c["start"] <= vt <= c["end"]:
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persons = ", ".join(c["top_persons"][:3])
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print(
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f" 🏺 Vase scene chunk: {c['chunk_id']} ({c['start']}-{c['end']}s)"
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)
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print(
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f" ASR={c['asr_count']}, Faces={c['face_count']}, "
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f"Persons={c['unique_person_count']} ({persons})"
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)
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if __name__ == "__main__":
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chunks, duration = build_chunk_stats()
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print_summary(chunks)
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# Save to file
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output_path = os.path.join(BASE_DIR, "chunk_statistics.json")
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with open(output_path, "w") as f:
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json.dump(
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{
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"uuid": UUID,
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"duration": duration,
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"chunk_duration": CHUNK_DURATION,
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"chunks": chunks,
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},
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f,
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indent=2,
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)
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print(f"\n💾 Saved detailed stats to: {output_path}")
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