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""" |
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Compute mean/min/max for sync-c and sync-d scores from a JSONL file. |
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By default, uses sync["0"][0] to align with export_speakervid_jsonl.py filters. |
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""" |
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import argparse |
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import json |
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import math |
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import sys |
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from typing import Iterable, Tuple, List |
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def iter_sync_pairs(sync_field: dict) -> Iterable[Tuple[float, float]]: |
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for _, items in sync_field.items(): |
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for item in items: |
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if not isinstance(item, list) or len(item) < 2: |
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continue |
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yield float(item[0]), float(item[1]) |
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def get_primary_pair(sync_field: dict, sync_key: str, index: int) -> Tuple[float, float] | None: |
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items = sync_field.get(sync_key) |
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if not isinstance(items, list) or index >= len(items): |
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return None |
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item = items[index] |
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if not isinstance(item, list) or len(item) < 2: |
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return None |
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return float(item[0]), float(item[1]) |
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def main() -> int: |
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parser = argparse.ArgumentParser( |
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description="Compute mean/min/max for sync-c and sync-d from JSONL." |
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) |
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parser.add_argument("path", help="JSONL path.") |
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parser.add_argument( |
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"--sync-key", |
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default="0", |
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help='Sync dict key to read (default: "0").', |
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) |
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parser.add_argument( |
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"--index", |
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type=int, |
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default=0, |
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help="Index within sync list to use per sample (default: 0).", |
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) |
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args = parser.parse_args() |
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path = args.path |
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samples: List[Tuple[float, float, float]] = [] |
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with open(path, "r", encoding="utf-8") as f: |
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for line in f: |
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line = line.strip() |
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if not line: |
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continue |
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obj = json.loads(line) |
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sync_field = obj.get("sync") or {} |
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primary = get_primary_pair(sync_field, args.sync_key, args.index) |
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if primary is None: |
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continue |
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duration = obj.get("duration") |
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if duration is None: |
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duration = 0.0 |
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samples.append((primary[0], primary[1], float(duration))) |
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if not samples: |
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print("No sync-c/sync-d pairs found.") |
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return 1 |
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samples.sort(key=lambda x: x[0], reverse=True) |
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total = len(samples) |
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def summarize(subset: List[Tuple[float, float, float]]) -> str: |
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count = len(subset) |
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sum_c = sum(s[0] for s in subset) |
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sum_d = sum(s[1] for s in subset) |
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min_c = min(s[0] for s in subset) |
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max_c = max(s[0] for s in subset) |
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min_d = min(s[1] for s in subset) |
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max_d = max(s[1] for s in subset) |
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hours = sum(s[2] for s in subset) / 3600.0 |
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mean_c = sum_c / count |
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mean_d = sum_d / count |
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return ( |
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f"sync-c: mean={mean_c:.6f} min={min_c:.6f} max={max_c:.6f}; " |
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f"sync-d: mean={mean_d:.6f} min={min_d:.6f} max={max_d:.6f}; " |
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f"duration_hours={hours:.3f} (n={count})" |
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) |
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print(f"all: {summarize(samples)}") |
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for pct in (10, 20, 30, 40, 50): |
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k = max(1, math.ceil(total * (pct / 100.0))) |
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subset = samples[:k] |
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print(f"top {pct}%: {summarize(subset)}") |
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return 0 |
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if __name__ == "__main__": |
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raise SystemExit(main()) |
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