"""ZeroBench-TTS official scorer — pre-generated wavs in, metrics out. This never loads a TTS model. You synthesize the 137 clips however you like, point this at the folder, and it reports WER / SSIM / UTMOS / silence. # 1. what to synthesize python -m zerobench_eval manifest --out manifest.jsonl # 2. ... your own synthesis, writing one wav per row's `output_wav` ... # 3. score python -m zerobench_eval score --wav_dir my_wavs/ --name MyModel Run ``python -m zerobench_eval --help`` for the full flag list. """ from __future__ import annotations import argparse import json import sys from pathlib import Path from .benchmark import find_wavs, load_benchmark, resolve_ref_audio from .report import format_report, group_report, write_outputs from .scorers import DEFAULT_ASR, MetricSuite, load_wav_16k _HERE = Path(__file__).resolve().parent REPO_ID = "zeroweight-ai/ZeroBench-TTS" def _log(msg: str) -> None: print(f"[zerobench] {msg}", flush=True) # ── manifest ────────────────────────────────────────────────────────────────── def cmd_manifest(args: argparse.Namespace) -> None: """Emit exactly what a submission must contain: one row per test item, with the text to say, the reference clip to clone, and the wav path to write.""" rows, root = load_benchmark(args.benchmark) out = Path(args.out) with out.open("w", encoding="utf-8") as f: for r in rows: f.write(json.dumps({ "id": r["id"], "subset": r["subset"], "voice_id": r["voice_id"], "text": r["text"], "lang": r["lang"], "ref_audio": str(resolve_ref_audio(r, root)), "ref_text": r.get("ref_text", ""), "output_wav": f"{r['subset']}/{r['voice_id']}.wav", }, ensure_ascii=False) + "\n") _log(f"wrote {len(rows)} rows -> {out}") _log("Synthesize `text` with `ref_audio` as the voice prompt, save each to " "/, then run: " f"python -m zerobench_eval score --wav_dir ") # ── score ───────────────────────────────────────────────────────────────────── def cmd_score(args: argparse.Namespace) -> None: rows, root = load_benchmark(args.benchmark) if args.subsets: rows = [r for r in rows if r["subset"] in set(args.subsets)] if not rows: raise SystemExit(f"no benchmark items matched (subsets={args.subsets})") wav_dir = Path(args.wav_dir) found, missing = find_wavs(rows, wav_dir) if missing: head = ", ".join(m["id"] for m in missing[:5]) msg = (f"{len(missing)}/{len(rows)} wavs not found under {wav_dir} " f"(e.g. {head}). Expected //.wav — see " f"`python -m zerobench_eval manifest`.") if not args.allow_missing: raise SystemExit(msg + "\nPass --allow_missing to score the rest anyway.") _log("WARNING " + msg) if not found: raise SystemExit("no wavs to score") _log(f"scoring {len(found)}/{len(rows)} items from {wav_dir}") metrics = MetricSuite(device=args.device, asr_models=args.asr or DEFAULT_ASR, skip_utmos=args.skip_utmos) ref_cache: dict[str, "object"] = {} results, t0 = [], __import__("time").time() for i, (row, wav_path) in enumerate(found, 1): ref_path = str(resolve_ref_audio(row, root)) if ref_path not in ref_cache: ref_cache[ref_path] = load_wav_16k(ref_path) scored = metrics.score( pred_wav_16k=load_wav_16k(str(wav_path)), ref_wav_16k=ref_cache[ref_path], text=row["text"], text_normalized=row.get("text_normalized", ""), lang=row["lang"], ) results.append({ "id": row["id"], "subset": row["subset"], "voice_id": row["voice_id"], "voice_source": row.get("voice_source", ""), "lang": row["lang"], "length_bucket": row.get("length_bucket", ""), "text": row["text"], "text_normalized": row.get("text_normalized", ""), **scored, "wav_path": str(wav_path), }) if i % 10 == 0 or i == len(found): _log(f" {i}/{len(found)} last wer={scored['wer_robust']:.3f} " f"(strict {scored['wer_strict']:.3f}) " f"[{__import__('time').time() - t0:.0f}s]") name = args.name or wav_dir.name out_dir = Path(args.out_dir) if args.out_dir else wav_dir.parent / f"{name}_zerobench" summary = write_outputs(out_dir, name, results, rows, args) print("\n" + group_report(name, results)) _log(f"per-sample -> {out_dir / 'per_sample.csv'}") _log(f"summary -> {out_dir / 'summary.json'}") if summary["n_scored"] < len(rows): _log(f"NOTE partial submission: {summary['n_scored']}/{len(rows)} items — " "not comparable to full-benchmark numbers.") # ── rescore ─────────────────────────────────────────────────────────────────── def cmd_rescore(args: argparse.Namespace) -> None: """Recompute WER from saved transcripts — no ASR, no GPU, seconds not minutes. Transcription does not depend on the reference policy, so editing references.py never requires re-running the ASRs. """ import pandas as pd from .scorers import score_all_policies for d in args.run_dirs: d = Path(d) csv_path = d / "per_sample.csv" df = pd.read_csv(csv_path) cols = [c for c in df.columns if c.startswith("transcript_")] if not cols: raise SystemExit(f"{csv_path}: no transcript_* columns") before = df["wer"].mean() new = pd.DataFrame([ score_all_policies( {c[len("transcript_"):]: ("" if pd.isna(r[c]) else str(r[c])) for c in cols}, str(r.text), "" if pd.isna(r.text_normalized) else str(r.text_normalized)) for _, r in df.iterrows()], index=df.index) for c in new.columns: df[c] = new[c] df.to_csv(csv_path, index=False, encoding="utf-8") print(f"[zerobench] {d.name}: WER {before * 100:.2f}% -> {df['wer'].mean() * 100:.2f}%") print(group_report(d.name, df.to_dict("records"))) # ── cli ─────────────────────────────────────────────────────────────────────── def main(argv: "list[str] | None" = None) -> None: p = argparse.ArgumentParser( prog="python -m zerobench_eval", description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) sub = p.add_subparsers(dest="cmd", required=True) def common(sp): sp.add_argument("--benchmark", default=None, help=f"Benchmark dir or metadata.jsonl. Default: this repo if " f"run from a clone, else downloads {REPO_ID} from the Hub.") m = sub.add_parser("manifest", help="write the list of clips to synthesize") common(m) m.add_argument("--out", default="manifest.jsonl") m.set_defaults(func=cmd_manifest) s = sub.add_parser("score", help="score a directory of generated wavs") common(s) s.add_argument("--wav_dir", required=True, help="Directory of generated wavs. Layout /.wav " "(a nested wav/ folder and flat .wav names also work).") s.add_argument("--name", default=None, help="Label for this system in the report.") s.add_argument("--out_dir", default=None) s.add_argument("--subsets", nargs="+", default=None) s.add_argument("--device", default="cuda") s.add_argument("--asr", action="append", default=None, metavar="MODEL_ID", help="Override the ASR set (repeatable). Default is both " "openai/whisper-large-v3 and vinai/PhoWhisper-large, min taken. " "Changing this makes numbers non-comparable to the leaderboard.") s.add_argument("--skip_utmos", action="store_true", help="Skip UTMOSv2 (optional dep); UTMOS is reported as NaN.") s.add_argument("--allow_missing", action="store_true", help="Score a partial submission instead of erroring.") s.set_defaults(func=cmd_score) r = sub.add_parser("rescore", help="recompute WER from saved transcripts (no GPU)") r.add_argument("run_dirs", nargs="+") r.set_defaults(func=cmd_rescore) args = p.parse_args(argv) args.func(args) if __name__ == "__main__": sys.exit(main())