| """Aggregation, the printed table, and the files a scoring run leaves behind.""" |
|
|
| from __future__ import annotations |
|
|
| import csv |
| import json |
| import statistics |
| from collections import OrderedDict |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
| from .scorers import POLICIES |
|
|
| _AGG_KEYS = ("wer", "wer_strict", "wer_norm", "wer_robust", "ssim", "utmos", |
| "excess_silence") |
|
|
|
|
| def aggregate(rows: list[dict]) -> dict: |
| out: dict = {"n": len(rows)} |
| for key in _AGG_KEYS: |
| vals = [r[key] for r in rows |
| if r.get(key) is not None |
| and not (isinstance(r[key], float) and np.isnan(r[key]))] |
| out[f"{key}_mean"] = float(statistics.mean(vals)) if vals else float("nan") |
| out[f"{key}_median"] = float(statistics.median(vals)) if vals else float("nan") |
| return out |
|
|
|
|
| def _group_by(rows: list[dict], key: str) -> "OrderedDict[str, dict]": |
| buckets: "OrderedDict[str, list[dict]]" = OrderedDict() |
| for r in rows: |
| buckets.setdefault(str(r.get(key, "")), []).append(r) |
| return OrderedDict((k, aggregate(v)) for k, v in sorted(buckets.items())) |
|
|
|
|
| def format_report(title: str, groups: "dict[str, dict]") -> str: |
| """Fixed-width table; one row per group, all three WER policies side by side.""" |
| w = 118 |
| lines = ["=" * w, title, "=" * w, |
| f"{'group':<22}{'n':>5}{'WER strict':>15}{'WER norm':>15}" |
| f"{'WER robust':>15}{'SSIM':>15}{'UTMOS':>15}{'EXCESS-SIL s':>15}", |
| f"{'':<22}{'':>5}" + "".join(f"{'mean/median':>15}" for _ in range(6))] |
| for name, s in groups.items(): |
| lines.append( |
| f"{name:<22}{s['n']:>5}" |
| + "".join(f"{s[f'wer_{p}_mean']:>7.4f}/{s[f'wer_{p}_median']:<7.4f}" |
| for p in POLICIES) |
| + f"{s['ssim_mean']:>7.4f}/{s['ssim_median']:<7.4f}" |
| f"{s['utmos_mean']:>7.4f}/{s['utmos_median']:<7.4f}" |
| f"{s['excess_silence_mean']:>7.4f}/{s['excess_silence_median']:<7.4f}") |
| lines.append("=" * w) |
| return "\n".join(lines) |
|
|
|
|
| def group_report(name: str, rows: list[dict]) -> str: |
| return "\n".join([ |
| format_report(f"ZeroBench-TTS — {name}", |
| {**_group_by(rows, "subset"), "── overall ──": aggregate(rows)}), |
| format_report("by length bucket", _group_by(rows, "length_bucket")), |
| format_report("by voice source", _group_by(rows, "voice_source")), |
| ]) |
|
|
|
|
| def write_outputs(out_dir: Path, name: str, results: list[dict], |
| all_rows: list[dict], args) -> dict: |
| """per_sample.csv + summary.json + report.txt. Returns the summary.""" |
| out_dir.mkdir(parents=True, exist_ok=True) |
|
|
| with (out_dir / "per_sample.csv").open("w", newline="", encoding="utf-8") as f: |
| writer = csv.DictWriter(f, fieldnames=list(results[0].keys())) |
| writer.writeheader() |
| writer.writerows(results) |
|
|
| summary = { |
| "system": name, |
| "benchmark": "zeroweight-ai/ZeroBench-TTS", |
| "n_items": len(all_rows), |
| "n_scored": len(results), |
| "complete": len(results) == len(all_rows), |
| "asr_models": list(getattr(args, "asr", None) or |
| ("openai/whisper-large-v3", "vinai/PhoWhisper-large")), |
| "wer_policies": list(POLICIES), |
| "headline_wer_policy": "robust", |
| "utmos_scored": not getattr(args, "skip_utmos", False), |
| "overall": aggregate(results), |
| "by_subset": _group_by(results, "subset"), |
| "by_length_bucket": _group_by(results, "length_bucket"), |
| "by_voice_source": _group_by(results, "voice_source"), |
| } |
| (out_dir / "summary.json").write_text( |
| json.dumps(summary, indent=2, ensure_ascii=False), encoding="utf-8") |
| (out_dir / "report.txt").write_text(group_report(name, results) + "\n", |
| encoding="utf-8") |
| return summary |
|
|
