3e0568b9d1
Add REST endpoints and frontend wiring for per-language project actions:
- POST /api/workspace/{project}/translation/{lang}/queue — queue translation
- POST /api/workspace/{project}/audio/{lang}/queue — queue audio generation
- GET/POST /api/workspace/{project}/format/{lang} — format metadata (EPUB/PDF)
- GET /api/workspace/{project}/review/{lang}/chapters/{chapter} — utterance review
- GET/POST /api/workspace/{project}/front-matter/{lang} — front matter editor
- GET /api/workspace/{project}/chapters/{lang} — chapter list
- GET /api/workspace/{project}/chapter-content/{lang}/{chapter} — chapter text
Frontend fixes:
- Review page shows per-language text (English source, Chinese translation, etc.)
- Non-translated languages show 'Queue translation' button only
- Speaker data from English source matched by paragraph ID
- UmbraAudio column layout: Speaker, Variation, Text, Actions
- Chapter tree in left sidebar: Front matter, Back matter, Body
- Audio Format label at top of sidebar
- Pandoc formatting toolbar for front matter text editing
289 lines
14 KiB
Plaintext
289 lines
14 KiB
Plaintext
DEAL HUNTER — SIMPLIFIED CHINESE TRANSLATION
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STATUS
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Updated: 2026-10-19 13:44 UTC
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Phase: 3 — sample translation (Chapter 1 in progress; front matter re-run queued)
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---------------------------------------------------------------------------
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1. OBJECTIVE
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---------------------------------------------------------------------------
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Translate the English novel Deal Hunter by B. D. Murphy into Simplified
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Chinese. The translation must carry intent, register and character voice, not
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word-for-word substitution. The reading copy is produced as AsciiDoc and as
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an OpenDocument text file (ODT) for review. Front matter, body and back matter
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have different formatting.
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The source is the UmbraAudio controller bundle:
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/home/umbra/audio-read/bundles/dh-bundle/manifest.jsonl
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sha256 21ce568787540cf7164b6ab4028f1caee51996447537022154d1e5768cde87ba
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---------------------------------------------------------------------------
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2. WHAT WAS DONE
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---------------------------------------------------------------------------
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- Read the relevant specifications:
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UmbraAudio specs 27 (multi-pass semantic translation), 25 (speaker audit),
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28 (UI language selection), 29 (accent/voice); UmbraTrans
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docs/plan-translation-pipeline.md.
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- Extracted the book from the bundle into a stable source model:
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1 book, 32 chapter rows, 4,319 utterances, 55 speakers, 66,884 words.
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- Classified matter: 4 front, 27 body, 1 back.
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- Reconstructed 3,047 readable paragraphs. Source utterance indices are kept
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on every paragraph. The spoken "Chapter one, Salvage" production cue and
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the duplicated "Author's Note" heading were removed from the reading text.
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- Built an initial Chinese glossary and style guide (characters, roles,
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places, terms, punctuation, unknown-name policy).
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- Built the multi-pass pipeline:
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Pass 0 paragraph/scene/chunk assembly (local, no LLM)
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Pass 1 scene intent analysis (lfm2.5-2.6b)
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Pass 2 context-aware draft translation (qwen3.5-9b)
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Pass 3 chapter consistency (implemented, optional)
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Pass 4 bilingual critic, different model (deepseek-r1-8b,
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fallback qwen3.5-9b)
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Every call is cached by prompt hash; every analysis, draft and critique is
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a file; a run can be stopped and resumed.
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- Fixed the first prompt iteration's title/proper-name drift:
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Deal Hunter / Hunter / Umbra Audio stay English; no invented full names
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or titles. The front matter must be re-run to pick this up.
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- Fixed a second class of mechanical defects found in the first Chapter 1
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chunks:
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- the drafter no longer sees the "(dialogue; speaker)" labels, so it
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cannot copy them into the Chinese text;
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- a hard-term block (system=星系, sector=星区, jump=跃迁, salvage=打捞,
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bounty=赏金, XO=副舰长 …) blocks the common mistranslations;
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- a deterministic pass strips leaked ids/labels, normalises straight
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quotes to “ ”, pairs unbalanced quotes, removes the spurious quote the
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model wrapped around unquoted narration, and fixes known title forms;
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- stray lowercase English words are detected and repaired in place;
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- the remaining Latin words are recorded per paragraph as warnings
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(proper names go to glossary review, not silent transliteration).
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- Added the QA, names and review tools:
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- qa.py missing paragraphs, metadata leaks, straight/unbalanced
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quotes, double punctuation, title drift, English residue,
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system->系统, proper-name review queue.
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- names.py merges analyst proposed_terms with mid-sentence capitalised
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source names into glossary/candidates.md (no auto-merge).
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- review.py self-contained bilingual HTML review page, issues attached
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to each paragraph, "only flagged" and "hide English" filters.
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- Renderer fixes: Chinese chapter numerals (第一章 打捞), renamed the heading
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styles to avoid LibreOffice built-in collisions, two-character first-line
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indent, cleaner AsciiDoc document header, right-aligned TOC page numbers
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with dot leaders (the tab-stop attributes were in the wrong namespace),
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title-page author line B·D·墨菲, and a review note that points to
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review.html.
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- Critic hardening: model ids contain "/", which was being used literally in
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the cache filename and crashed every critic call; the filename is now
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sanitised. A failed critic (primary and fallback) is recorded as
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"skipped" instead of aborting a long run. Confirmation-only issues, source-
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grammar notes and empty minor suggestions are filtered out.
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- Built the renderer:
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- AsciiDoc book (title, TOC, chapter headings, dialogue roles, scene
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breaks, source comments).
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- Flat ODF -> ODT through LibreOffice, with title page, copyright page,
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TOC, A4 layout, running header, page numbers, distinct front/body/back
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styles.
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- TOC page numbers are populated through UNO; a plain --convert-to
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leaves the index empty.
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- PDF export for quick review.
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- Rendered a 9-page front-matter sample (title page, TOC, review note,
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copyright, author's note, audio preface).
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3. CURRENT METRICS
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---------------------------------------------------------------------------
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Source:
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Chapters 32 (4 front, 27 body, 1 back)
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Utterances 4,319
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Reconstructed 3,047 paragraphs
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Source words 66,884
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Speakers 55
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Translated:
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Front matter 4 chapters / 26 paragraphs (re-run with corrected prompt)
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Chapter 1 24/24 chunks, 232 paragraphs
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Back matter 1 chapter / 3 paragraphs
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Sample total 261 paragraphs, 8,943 Chinese characters
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Glossary:
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Core cast and roles, 9 places, ~60 terms. 204 source name candidates and 14
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analyst term proposals are queued in glossary/candidates.md for approval.
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Render:
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deal-hunter-zh-sample.asciidoc
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deal-hunter-zh-sample.odt
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deal-hunter-zh-sample.pdf (22 pages: title, TOC, review note, copyright,
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author's note, audio preface, Chapter 1, back
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matter, sample end)
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review.html (bilingual side-by-side review page)
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run-manifest.json (hashes, models, counts)
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---------------------------------------------------------------------------
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4. SAMPLE SCOPE
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---------------------------------------------------------------------------
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The sample is intended to prove the whole chain on a meaningful slice:
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- front matter: Sample Track, Opening Track, Copyright, Author's Note
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- body: Chapter 1, Salvage (232 paragraphs, all 24 chunks)
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- back matter: Closing Track
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The sample is complete at the draft/QA/render level. The critic pass covers
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the front/back matter and the first four Chapter 1 chunks; the remaining
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Chapter 1 chunks are deliberately left for the full-book critic pass.
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---------------------------------------------------------------------------
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5. WHAT IS RUNNING
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---------------------------------------------------------------------------
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A background process is running the bounded qwen critic on the sample and
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then re-rendering:
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log: productions/deal-hunter-zh/runs/qwen-critic.log
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mode: critic on front/back matter + first four Chapter 1 chunks
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note: the critic model is qwen3.5-9b for this sample. DeepSeek R1 8B was
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tested and rejected: on Chapter 1 chunk c000 it produced 28 issues,
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almost all duplicates of three contradictory term claims (it wanted
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"236 systems" rendered as 系统 rather than 星系). Qwen is also the
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drafter, so this pass is a consistency check, not an independent
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critic; the full-run critic model is still a decision for the author.
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Do not start a second full-book run until this finishes; the LM Studio server
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serialises requests and two runs would only slow each other down.
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---------------------------------------------------------------------------
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6. BLOCKERS AND DECISIONS
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---------------------------------------------------------------------------
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Blocking a full-book run (answers in docs/Questions.txt):
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Q1 original DOCX for exact paragraphing (default: proceed with heuristic)
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Q3 title treatment (default: 《Deal Hunter》, subtitle 交易猎人)
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Q4 name/rank table (default: current glossary, 公爵夫人)
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Q5 Simplified vs Traditional (default: Simplified, zh-CN)
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Q15 model budget (default: current 9B/2.6B mix for the sample)
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Q16 sample approval before the full run (default: yes)
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Not blocking:
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Q2 reading edition vs audiobook script (default: reading edition)
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Q6-Q8 voice/register/content (defaults recorded)
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Q9-Q11 front/back matter and numbering (defaults recorded)
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Q12-Q14 output and review artefacts (defaults recorded)
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Q17-Q18 data location and glossary additions (defaults recorded)
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Known limitations:
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- Paragraph boundaries are reconstructed, not original. The DOCX would
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settle them.
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- The translation is machine output. It has not had native-speaker review.
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- The 27B model is not loaded; the 9B is the quality/speed compromise.
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- The sample critic is qwen, the same model as the drafter. DeepSeek R1 8B
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was tested and its term judgments were too unreliable to use without a
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human filter.
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- The critic covers only the first four Chapter 1 chunks in the sample.
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- Full-book estimate at the measured rate (~1.6 min/chunk, ~315 chunks) is
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about 8-9 h for the draft, plus 3-5 h for the critic. This is higher than
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the earlier 4-6 h estimate and should be planned for accordingly.
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- The ODT has been checked in LibreOffice only; Word compatibility is
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untested.
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- The glossary candidate list is heuristic (204 entries) and needs manual
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pruning; unapproved names stay English by policy.
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---------------------------------------------------------------------------
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7. NEXT ACTIONS
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1. Finish the bounded qwen critic and re-render (background).
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2. Operator spot-check of the 22-page sample: layout, names, register,
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paragraphing, punctuation, no English residue.
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3. Decide the full-run critic model (qwen consistency check vs DeepSeek with
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human filtering vs a larger model when available).
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4. Answer Q1, Q3-Q5, Q15, Q16; prune glossary/candidates.md and freeze the
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glossary.
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5. If approved, launch the full-book run (~8-9 h at the measured rate), then
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the critic pass, then native review.
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---------------------------------------------------------------------------
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8. FILES
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---------------------------------------------------------------------------
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Docs:
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docs/Questions.txt
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docs/TaskList.txt
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docs/Status.txt
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Tools (tracked):
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tools/translation/book.py source model / paragraph reconstruction
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tools/translation/llm.py LM Studio client, cache, JSON handling
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tools/translation/prompts.py analyst/drafter/critic prompts
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tools/translation/pipeline.py Pass 0-4 orchestration, cleanup, warnings
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tools/translation/render.py AsciiDoc + FODT/ODT/PDF
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tools/translation/qa.py mechanical QA and issue extraction
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tools/translation/names.py glossary candidate collector
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tools/translation/review.py bilingual HTML review page
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tools/translation/README.md tool usage notes
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Production (git-ignored, copyrighted text):
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productions/deal-hunter-zh/source/book.json
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productions/deal-hunter-zh/glossary/glossary.json
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productions/deal-hunter-zh/glossary/candidates.md
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productions/deal-hunter-zh/runs/<chapter>/*.analysis.json
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productions/deal-hunter-zh/runs/<chapter>/*.draft.json
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productions/deal-hunter-zh/runs/<chapter>/*.critic.json
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productions/deal-hunter-zh/runs/<chapter>/translation.json
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productions/deal-hunter-zh/runs/usage.jsonl
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productions/deal-hunter-zh/runs/qa.json
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productions/deal-hunter-zh/output/deal-hunter-zh-sample.*
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productions/deal-hunter-zh/output/review.html
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---------------------------------------------------------------------------
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9. COMMANDS
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Extract:
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python3 tools/translation/book.py \
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--out productions/deal-hunter-zh/source/book.json
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Translate a chapter:
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python3 tools/translation/pipeline.py \
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--book productions/deal-hunter-zh/source/book.json \
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--glossary productions/deal-hunter-zh/glossary/glossary.json \
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--run-dir productions/deal-hunter-zh/runs \
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--chapters ch-6
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Translate the whole book (after approval):
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python3 tools/translation/pipeline.py \
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--book productions/deal-hunter-zh/source/book.json \
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--glossary productions/deal-hunter-zh/glossary/glossary.json \
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--run-dir productions/deal-hunter-zh/runs \
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--chapters all --consistency
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Render:
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python3 tools/translation/render.py \
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--book productions/deal-hunter-zh/source/book.json \
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--run-dir productions/deal-hunter-zh/runs \
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--out-dir productions/deal-hunter-zh/output \
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--sample
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Report:
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python3 tools/translation/pipeline.py \
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--book productions/deal-hunter-zh/source/book.json \
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--glossary productions/deal-hunter-zh/glossary/glossary.json \
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--run-dir productions/deal-hunter-zh/runs --report
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QA, glossary candidates and review page:
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python3 tools/translation/qa.py \
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--book productions/deal-hunter-zh/source/book.json \
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--run-dir productions/deal-hunter-zh/runs \
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--json-out productions/deal-hunter-zh/runs/qa.json
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python3 tools/translation/names.py \
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--book productions/deal-hunter-zh/source/book.json \
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--run-dir productions/deal-hunter-zh/runs \
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--glossary productions/deal-hunter-zh/glossary/glossary.json \
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--out productions/deal-hunter-zh/glossary/candidates.md
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python3 tools/translation/review.py \
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--book productions/deal-hunter-zh/source/book.json \
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--run-dir productions/deal-hunter-zh/runs \
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--out productions/deal-hunter-zh/output/review.html
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---------------------------------------------------------------------------
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10. DECISIONS RECORDED
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---------------------------------------------------------------------------
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- Keep "Deal Hunter" (book title and ship AI), "Hunter" and "Umbra Audio" in
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English.
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- Character names are transliterated by sound, not translated by meaning.
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- Narration is restrained written Chinese; dialogue is colloquial and
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character-specific.
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- Unknown names stay in English until the glossary is approved; no invented
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transliterations.
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- Production data stays out of the public repository.
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