# `zerobench_eval` — the official ZeroBench-TTS scorer

Scores **pre-generated wavs**. It never loads, downloads, or runs a TTS model —
you synthesize however you like, this reports the numbers.

```bash
pip install -r zerobench_eval/requirements.txt

python -m zerobench_eval manifest --out manifest.jsonl   # what to synthesize
#   ... your synthesis, one wav per row's `output_wav` ...
python -m zerobench_eval score --wav_dir my_wavs/ --name MyModel
```

## Commands

| command | what it does |
|---|---|
| `manifest` | writes one JSONL row per test item: `text` to say, `ref_audio` to clone, `output_wav` to write |
| `score` | scores a wav directory → `per_sample.csv`, `summary.json`, `report.txt` |
| `rescore` | recomputes WER from saved transcripts — no ASR, no GPU, runs in seconds |

## Layout

`score` looks for `<wav_dir>/<subset>/<voice_id>.wav`, and also accepts a
nested `wav/` folder or flat `<subset>_<voice_id>.wav` / `<id>.wav` names. If
files are missing it tells you which and refuses to report a number, unless you
pass `--allow_missing` (the summary is then flagged `complete: false`).

## Files

| file | contents |
|---|---|
| `scorers.py` | WER / SSIM / UTMOS / silence, self-contained |
| `references.py` | the acceptable-reference expansion — the core of the WER policy |
| `benchmark.py` | locating benchmark data, matching wavs to items |
| `report.py` | aggregation and the printed table |
| `test_references.py` | pins both directions of the WER policy — run it after any edit |

## Notes

* **UTMOSv2 is optional.** WER and SSIM work without it; pass `--skip_utmos`, or
  install it with
  `pip install git+https://github.com/sarulab-speech/UTMOSv2.git`.
* **UTMOS is seeded.** UTMOSv2 ensembles over random crops and is not
  reproducible unseeded (3.05 / 3.03 / 2.96 for the same clip). The RNG is reset
  before every clip so the score is a deterministic function of the audio.
* **Don't change `--asr`** if you want comparable numbers — the default pair is
  part of the benchmark definition.

Full metric definitions and the rationale are in the
[dataset README](../README.md).