| """tr_norm v1 entry points (DESIGN.md §4). |
| |
| Hypotheses (STT output) are verbalized with `trttsbench.verbalize` into a lattice of spoken alternatives and then |
| normalized with `textnorm.normalize_basic`; references are built from `text_spoken` + `alt_spans`. Both sides end |
| in the same token space, so `trttsbench.score.align(ref, hyp)` compares like with like. |
| """ |
| from __future__ import annotations |
|
|
| from .textnorm import TR_NORM_VERSION, Lattice, build_lattice, tokens |
| from .verbalize import verbalize_lattice |
|
|
| __all__ = ["TR_NORM_VERSION", "normalize_hyp", "normalize_hyp_str", "normalize_ref"] |
|
|
|
|
| def normalize_hyp(text: str) -> Lattice: |
| """Lattice of normalized token sequences for a written (e.g. STT) string; first option = canonical.""" |
| out: Lattice = [] |
| for group in verbalize_lattice(text or ""): |
| opts: list[list[str]] = [] |
| for o in group: |
| t = tokens(" ".join(o) if isinstance(o, (list, tuple)) else str(o)) |
| if t not in opts: |
| opts.append(t) |
| if any(opts): |
| out.append([o for o in opts if o] if all(opts) else opts) |
| merged: Lattice = [] |
| for g in out: |
| if len(g) == 1 and merged and len(merged[-1]) == 1: |
| merged[-1] = [merged[-1][0] + g[0]] |
| else: |
| merged.append(g) |
| return merged |
|
|
|
|
| def normalize_hyp_str(text: str) -> str: |
| """Canonical normalized string of a hypothesis (first option of every group).""" |
| return " ".join(t for g in normalize_hyp(text) for t in g[0]) |
|
|
|
|
| def normalize_ref(text_spoken: str, alt_spans: list[dict] | None = None) -> Lattice: |
| """Reference lattice from the canonical spoken text and its span alternatives.""" |
| return build_lattice(text_spoken, alt_spans) |
|
|
