Ingest the translation delivery, and stop mis-reporting locale codes

UmbraTrans delivers one row per utterance per target language, in the shape
translations.import_translations already accepts. There was no script to run
that ingest, so the delivery side of the contract existed only as a function.

scripts/import_translations.py runs it: report-only by default, --write to
store, --confirm to store as confirmed rather than draft. It prints the
coverage before and after, so a delivery can be checked without writing it.

Two bugs in the ingest, both found by running a real delivery:

* import_translations folded the target language with lang.normalize, which
  does not know script subtags. A delivery labelled "zh-Hans" - which is
  what UmbraTrans writes - matched nothing, so every row was dropped and
  reported as a problem. It now folds with canonical_code, and an unknown
  language is still stored: the text is reviewable even when the render
  will be refused.

* coverage and not_voiced_rows compared the stored code to the requested
  code as strings, so a row stored as "zh-Hans" read as "nothing delivered"
  when the reviewer asked about "zh". The coverage report was wrong in the
  direction that makes a reviewer distrust it.

Measured on the real Deal Hunter Chinese delivery: 4,300 rows ingested, the
review UI's resolver then renders Chinese for 12/12 utterances of chapter 1
and 415/416 of chapter 6 - the missing one being the spoken chapter
announcement, which is deliberately not part of the reading edition.

spec 27 records what the shipped samples actually are: single-pass drafts
made against a hand-cleaned paraphrase of the English, matching the
speaker-verified database text in 0 of 12 utterances. The production Chinese
edition on oldbox does use the intent-based process; what it lacked was the
alignment back onto the utterances, which is now implemented in UmbraTrans.

7 new tests. Suite: 890 passed, 8 failed (the same 8 pre-existing failures).

specs/27-translation-pipeline-multipass.md

@@ -115,6 +115,76 @@ They are useful as a **bad-case corpus**: Pass 4 run over them should find real
 errors. If the critic cannot find errors in known-imperfect text, it is not
 doing its job.
 
+### Measured (2026-11-06): the sample files are not the book
+
+The sample files under `samples/translations/` were measured against the
+speaker-verified English in the project's database. **0 of 12 utterances match
+the database text exactly**; the sample English is a hand-cleaned paraphrase
+of it, with the source's typos silently repaired ("the AI the runs the ship"
+-> "the AI that runs the ship", "Kianda's aunt" -> "Kainda's aunt", "I  trade
+salvage" -> "I trade salvage"). The sample files also carry `Kainda` where the
+database carries the canonical `Kainda Walters Foster Murphy`, and `Narrator`
+where the database carries `NULL`.
+
+So those files are wrong twice over: they are single-pass, and they were
+translated against a different English base than the one the audiobook speaks.
+They must not be treated as a translation of anything. The only other
+translation in the system is one placeholder row whose `text` is the English
+source under a `zh-Hans` code, which the resolver correctly ignores.
+
+### The real Deal Hunter Chinese edition
+
+The production translation is not in this repository. It is built by UmbraTrans
+on oldbox (`productions/deal-hunter-zh`), and it **does** use the intent-based
+process:
+
+| Pass | What it does | Model |
+|---|---|---|
+| 0 | Paragraph/scene/chunk assembly, source indices kept | none |
+| 1 | Scene intent: summary, register, tone, participants, risks | `lfm2.5-2.6b` |
+| 2 | Context-aware draft, given glossary, character voices, scene analysis, prior chunk summary | `qwen3.5-9b` |
+| 3 | Chapter voice/term consistency (optional) | critic model |
+| 4 | Bilingual critic: meaning, pronoun, irony, term, omission | `deepseek-r1-8b` |
+
+Its Pass 1 output carries per-paragraph risks with guidance, which is intent
+analysis, not word mapping. Its critic runs on a *different* model from the
+drafter, which is the point of Pass 4.
+
+### The missing pass, and why it matters
+
+That pipeline works on **paragraphs**, because epub and pdf are paragraph
+media. The audiobook needs **utterances**: this project has verified who
+speaks every one of them, and each clip is voiced separately by that
+speaker's voice. A paragraph-level translation cannot be spoken.
+
+Measured on the real book: 3,047 paragraphs cover 4,317 source utterances.
+2,322 paragraphs map to one utterance, 180 to two and 545 to three. All 725
+multi-utterance paragraphs are narration; dialogue is already one paragraph
+per speech turn.
+
+So the missing piece is an alignment pass that divides each translated
+paragraph between the utterances it was built from. It is implemented in
+UmbraTrans as `tools/translation/align.py` (Pass 5), and its output is a
+delivery payload in the shape this project's `translations.import_translations`
+already accepts.
+
+Two rules the alignment must not break:
+
+- a source utterance may only be absorbed into another utterance **by the same
+  speaker** - a line must not be handed to a different character;
+- the partition must reproduce the translated text exactly (modulo
+  whitespace), so the alignment can never rewrite the translation.
+
+A source utterance the target does not voice is delivered as
+`voiced = false` with the reason. The row is kept, so the gap stays visible;
+it is not deleted.
+
+Measured on the real book (2026-11-06), 42 sampled multi-utterance
+paragraphs across seven chapters: **41 aligned by the model, 1 fell back to
+the mechanical split, 0 partition mismatches**. The one fallback was a model
+answer that did not reproduce the translated text; the content check caught it
+and the paragraph was flagged rather than accepted.
+
 ## Open questions
 
 1. Which model runs Passes 1-4? This is where the local LLM question returns,