Datasets:
File size: 3,884 Bytes
b982e77 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | """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
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