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262fa3f | 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 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | #!/usr/bin/env python3
"""Summarize base-vs-latent ASR showcase JSON files."""
from __future__ import annotations
import json
import sys
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional
def _as_float(v: Any) -> Optional[float]:
if v is None:
return None
try:
return float(v)
except Exception:
return None
def _as_int(v: Any) -> int:
try:
return int(v or 0)
except Exception:
return 0
def _fmt_metric(v: Optional[float]) -> str:
if v is None:
return "-"
return f"{v:.6f}"
def _fmt_rel(v: Optional[float]) -> str:
if v is None:
return "-"
return f"{v:+.2f}%"
def _condition_from_name(path: Path) -> str:
stem = path.stem
if stem.endswith("_clean"):
return "clean"
for part in stem.split("_"):
if part.startswith("snr") and part.endswith("db"):
return part
return "unknown"
def _sample_count(rows: Iterable[Dict[str, Any]]) -> int:
return sum(_as_int(row.get("samples_used")) for row in rows)
def load_records(out_dir: Path) -> List[Dict[str, Any]]:
records: List[Dict[str, Any]] = []
for path in sorted(out_dir.glob("*.json")):
try:
payload = json.loads(path.read_text(encoding="utf-8"))
except Exception as exc:
records.append(
{
"json": path.name,
"dataset": path.stem,
"condition": _condition_from_name(path),
"error": str(exc),
}
)
continue
summary = payload.get("summary") or {}
rows = payload.get("rows") or []
base_wer = _as_float(summary.get("base_model_weighted_wer"))
latent_wer = _as_float(summary.get("latent_reasoning_weighted_wer"))
base_cer = _as_float(summary.get("base_model_weighted_cer"))
latent_cer = _as_float(summary.get("latent_reasoning_weighted_cer"))
delta_wer = None if base_wer is None or latent_wer is None else base_wer - latent_wer
delta_cer = None if base_cer is None or latent_cer is None else base_cer - latent_cer
rel_wer = None
if delta_wer is not None and base_wer not in (None, 0.0):
rel_wer = delta_wer / base_wer * 100.0
records.append(
{
"json": path.name,
"dataset": payload.get("dataset_name") or path.stem,
"configs": ",".join(str(c) for c in payload.get("configs") or []),
"condition": _condition_from_name(path),
"samples": _sample_count(rows),
"base_wer": base_wer,
"latent_wer": latent_wer,
"delta_wer": delta_wer,
"rel_wer": rel_wer,
"base_cer": base_cer,
"latent_cer": latent_cer,
"delta_cer": delta_cer,
"error": None,
}
)
return records
def write_report(out_dir: Path, records: List[Dict[str, Any]]) -> Path:
report_path = out_dir / "showcase_report.md"
valid = [r for r in records if not r.get("error")]
wins = sorted(
[r for r in valid if (r.get("delta_wer") or 0.0) > 0.0],
key=lambda r: r.get("delta_wer") or 0.0,
reverse=True,
)
regressions = sorted(
[r for r in valid if (r.get("delta_wer") or 0.0) < 0.0],
key=lambda r: r.get("delta_wer") or 0.0,
)
lines: List[str] = []
lines.append("# LR HuggingFace ASR Showcase Report")
lines.append("")
lines.append(f"- Generated UTC: {datetime.now(timezone.utc).isoformat(timespec='seconds')}")
lines.append(f"- Output directory: `{out_dir}`")
lines.append("- Delta WER is `base_model_wer - latent_reasoning_wer`; positive means LR is better.")
lines.append("")
lines.append("## Best LR Wins")
lines.append("")
lines.append("| Rank | Dataset | Configs | Condition | N | Base WER | LR WER | Delta WER | Relative | Delta CER | JSON |")
lines.append("|---:|---|---|---|---:|---:|---:|---:|---:|---:|---|")
for rank, rec in enumerate(wins[:20], start=1):
lines.append(
"| {rank} | {dataset} | {configs} | {condition} | {samples} | {base} | {lat} | {delta} | {rel} | {dcer} | `{json}` |".format(
rank=rank,
dataset=rec["dataset"],
configs=rec["configs"] or "-",
condition=rec["condition"],
samples=rec["samples"],
base=_fmt_metric(rec["base_wer"]),
lat=_fmt_metric(rec["latent_wer"]),
delta=_fmt_metric(rec["delta_wer"]),
rel=_fmt_rel(rec["rel_wer"]),
dcer=_fmt_metric(rec["delta_cer"]),
json=rec["json"],
)
)
if not wins:
lines.append("| - | - | - | - | - | - | - | - | - | - | - |")
lines.append("")
lines.append("## All Cases")
lines.append("")
lines.append("| Dataset | Configs | Condition | N | Base WER | LR WER | Delta WER | Relative | Base CER | LR CER | Delta CER | JSON |")
lines.append("|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---|")
for rec in sorted(valid, key=lambda r: (r["condition"], r["dataset"], r["configs"])):
lines.append(
"| {dataset} | {configs} | {condition} | {samples} | {base} | {lat} | {delta} | {rel} | {bcer} | {lcer} | {dcer} | `{json}` |".format(
dataset=rec["dataset"],
configs=rec["configs"] or "-",
condition=rec["condition"],
samples=rec["samples"],
base=_fmt_metric(rec["base_wer"]),
lat=_fmt_metric(rec["latent_wer"]),
delta=_fmt_metric(rec["delta_wer"]),
rel=_fmt_rel(rec["rel_wer"]),
bcer=_fmt_metric(rec["base_cer"]),
lcer=_fmt_metric(rec["latent_cer"]),
dcer=_fmt_metric(rec["delta_cer"]),
json=rec["json"],
)
)
lines.append("")
if regressions:
lines.append("## Regressions To Check")
lines.append("")
lines.append("| Dataset | Configs | Condition | N | Delta WER | Relative | JSON |")
lines.append("|---|---|---|---:|---:|---:|---|")
for rec in regressions[:20]:
lines.append(
"| {dataset} | {configs} | {condition} | {samples} | {delta} | {rel} | `{json}` |".format(
dataset=rec["dataset"],
configs=rec["configs"] or "-",
condition=rec["condition"],
samples=rec["samples"],
delta=_fmt_metric(rec["delta_wer"]),
rel=_fmt_rel(rec["rel_wer"]),
json=rec["json"],
)
)
lines.append("")
errors = [r for r in records if r.get("error")]
if errors:
lines.append("## JSON Load Errors")
lines.append("")
for rec in errors:
lines.append(f"- `{rec['json']}`: {rec['error']}")
lines.append("")
report_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
return report_path
def main() -> int:
if len(sys.argv) != 2:
print("usage: summarize_lr_showcase.py OUT_DIR", file=sys.stderr)
return 2
out_dir = Path(sys.argv[1]).expanduser().resolve()
records = load_records(out_dir)
report = write_report(out_dir, records)
wins = sum(1 for r in records if not r.get("error") and (r.get("delta_wer") or 0.0) > 0.0)
losses = sum(1 for r in records if not r.get("error") and (r.get("delta_wer") or 0.0) < 0.0)
print(f"records={len(records)} wins={wins} regressions={losses}")
print(f"report={report}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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