| """Corpus QA reporter — deterministic full re-verification of a committed dataset. |
| |
| Single-label non-autoregressive speech disfluency contract: |
| 1. Category must be CLEAN or DISFLUENCY. |
| 2. All spans must have label "speech disfluency". |
| 3. Zero targets in spans. |
| 4. Clean speech emits spans: []. |
| 5. Roundtrip slice-cut excision must exactly reconstruct clean_text. |
| 6. Entity/Number conservation, pronoun safety, Tier-1 filler free. |
| """ |
|
|
| import argparse |
| import json |
| import re |
| import sys |
| from collections import Counter |
|
|
| sys.path.insert(0, "/opt/vox/sandbox/scripts") |
|
|
| from compiler import verify_roundtrip |
| from dedup import PROTECTED, TIER1, TIER1_ANY, near_dup, norm_tokens |
| from schema import DatasetExample |
|
|
| |
| SAFE_SPAN = PROTECTED |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("dataset") |
| ap.add_argument("--near-dup-jaccard", type=float, default=0.85) |
| ap.add_argument("--max-pairs", type=int, default=200000) |
| ap.add_argument("--report", default=None) |
| args = ap.parse_args() |
|
|
| fails = Counter() |
| examples = {} |
| ids, raws, fps = set(), set(), [] |
| n = 0 |
|
|
| for ln, line in enumerate(open(args.dataset), 1): |
| line = line.strip() |
| if not line: |
| continue |
| n += 1 |
| try: |
| ex = DatasetExample(**{k: v for k, v in json.loads(line).items() |
| if k in DatasetExample.model_fields}) |
| except Exception as e: |
| fails["1_schema"] += 1 |
| examples.setdefault("1_schema", (ln, str(e)[:80])) |
| continue |
|
|
| if ex.category not in {"DISFLUENCY", "CLEAN"}: |
| fails["1_schema"] += 1 |
| examples.setdefault("1_schema", (ln, f"Invalid category: {ex.category}")) |
|
|
| if ex.id in ids: |
| fails["2_id_unique"] += 1 |
| examples.setdefault("2_id_unique", (ln, ex.id)) |
| ids.add(ex.id) |
|
|
| key = ex.raw_text.strip().lower() |
| if key in raws: |
| fails["3_exact_unique"] += 1 |
| raws.add(key) |
| fp = norm_tokens(ex.raw_text) |
| if near_dup(fp, fps, args.near_dup_jaccard): |
| fails["4_near_unique"] += 1 |
| examples.setdefault("4_near_unique", (ln, ex.raw_text[:70])) |
| if len(fps) < args.max_pairs: |
| fps.append(fp) |
|
|
| if ex.raw_text.strip()[-1:] not in ".?!\u0964" or \ |
| ex.clean_text.strip()[-1:] not in ".?!\u0964": |
| fails["10_boundary"] += 1 |
|
|
| if ex.category == "CLEAN": |
| if ex.spans: |
| fails["5_clean_has_spans"] += 1 |
| continue |
|
|
| for s in ex.spans: |
| if s.label != "speech disfluency": |
| fails["1_schema"] += 1 |
| examples.setdefault("1_schema", (ln, f"Invalid span label: {s.label}")) |
|
|
| ok, rec = verify_roundtrip(ex.raw_text, ex.spans, ex.clean_text) |
| if not ok: |
| fails["5_roundtrip"] += 1 |
| examples.setdefault("5_roundtrip", (ln, f"{rec!r} != {ex.clean_text!r}")) |
| continue |
|
|
| for s in ex.spans: |
| t = s.text.strip() |
| if TIER1.match(t) or TIER1_ANY.search(t): |
| fails["8_tier1_free"] += 1 |
| examples.setdefault("8_tier1_free", (ln, t[:40])) |
| if t.lower().strip(",. ") in SAFE_SPAN: |
| fails["9_pronoun_safe"] += 1 |
| examples.setdefault("9_pronoun_safe", (ln, t[:40])) |
|
|
| if TIER1_ANY.search(ex.clean_text): |
| fails["11_clean_not_tier1"] += 1 |
|
|
| cats = Counter() |
| for line in open(args.dataset): |
| try: |
| cats[json.loads(line).get("category", "?")] += 1 |
| except Exception: |
| pass |
|
|
| total = sum(fails.values()) |
| print(f"dataset: {args.dataset}") |
| print(f"examples: {n} categories: {dict(cats)}") |
| if not fails: |
| print("QA REPORT: CLEAN — all deterministic checks pass") |
| else: |
| print(f"QA REPORT: {total} violation(s)") |
| for k in sorted(fails): |
| print(f" {k}: {fails[k]} e.g. {examples.get(k)}") |
|
|
| if args.report: |
| json.dump({"dataset": args.dataset, "examples": n, |
| "categories": dict(cats), "violations": dict(fails), |
| "examples_of_violation": {k: list(v) for k, v in examples.items()}}, |
| open(args.report, "w"), indent=2) |
| sys.exit(1 if fails else 0) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
