--- name: pdf-to-kindle description: Convert a text-layer PDF (usually one generated by calibre from an ebook) into EPUB/AZW3 for Kindle while preserving italics, bold, chapter headings, sidebars/journal blocks, images and cross-page paragraphs. Use when the user asks to read a PDF book on Kindle or another e-reader, to convert a PDF to EPUB/AZW3/MOBI, or complains that a converted book lost its italics, merged its chapters, or reflows badly on the device. Not for scanned PDFs (no text layer) — those need OCR first. Also covers translating such a book into another language before packing it, delegated to the external book_translator project, and generating a TTS audiobook from it with an integrity check for dropped fragments and a split into per-chapter tagged tracks. --- # PDF to Kindle `ebook-convert book.pdf book.epub` silently destroys formatting: poppler infers style from font names and misses abbreviated ones like `MinionPro-It`, and calibre labels body text as `

` when the most common font is not the body font. Measured on a 399-page novel: 435 italic runs in the PDF became 1 `` tag, and 2817 body paragraphs became `

`. `scripts/pdf2html.py` extracts spans with PyMuPDF, where style comes from span properties, and emits semantic XHTML. calibre is then used only as the XHTML→EPUB packer. ## Two entry points `scripts/pdf2html.py` for PDF, `scripts/epub2html.py` for EPUB. Both emit the same normalized XHTML — one block per line, `
` for code listings — and
everything downstream (translation, packing, audiobook) is identical. Shared
document skeleton and CSS live in `scripts/bookhtml.py`.

**Do not route an EPUB through the PDF path.** Converting EPUB→PDF→XHTML throws
away the semantic markup that is already there and re-derives it from font
sizes. `epub2html.py` needs no font profiling and no threshold tuning: it reads
the spine, drops the nav document, and maps the book's own headings.

Steps 1, 3 and 4 below are PDF-only. For EPUB start at step 2 (only calibre is
needed, not PyMuPDF) and convert with:

`python scripts/epub2html.py book.epub out/book.html`

**Dumps converted from PDF carry no semantics at all** — no headings, no `
`,
just `
` with obfuscated names. The script detects this
(zero headings or zero listings) and rebuilds the structure from the book's own
stylesheet: the three largest font sizes become heading levels, a typewriter or
monospace family becomes `
`, and adjacent listing paragraphs merge back into
one block. A book that carries its own markup never enters this path. Measured on
the dokumen.pub dump of Ousterhout's APoSD: 1833 flat paragraphs became 29
chapters and 58 listings.

It reports which heading level turned out to be the chapter level. In most
EPUBs `
` is the book title and the parts, while chapters are `
` — the
script picks the deepest level that still gives a sane chapter count, because
the translation bridge splits the book on `
`. Measured on Stroustrup's PPP
3rd edition: 7 `
` against 49 `
`, chapters correctly detected as `h2`,
56 chapters out of 656 pages.

## Workflow

1. **Verify a text layer exists.** `pdfinfo` and `pdffonts` on the file. No
   embedded fonts / no extractable text means a scan — stop and say OCR is
   needed; this skill does not apply.
2. **Check tooling.** `ebook-convert` must be on PATH. For PyMuPDF, prefer an
   existing interpreter that has it; otherwise build a throwaway venv in the
   scratchpad — do not install into the system Python:

   ```bash
   python3 -m venv "$SCRATCH/venv" && "$SCRATCH/venv/bin/pip" -q install pymupdf
   ```

3. **Profile the fonts** before converting anything:

   `python scripts/pdf2html.py --fonts book.pdf`

   Read off: the body font (largest character count), the italic variant, the
   heading sizes, and any secondary family used for sidebars, journal entries,
   chat logs or slides.
4. **Tune the thresholds** in `block_kind()` and `style()` to that output. Code
   listings need no tuning — a monospaced span is recognized by the PyMuPDF font
   flag (`flags & 8`), not by font name, and becomes a `
` block. The
   defaults target a 15pt/letter calibre layout: `>= 28` or a display font is a
   title, `>= 24` a chapter/part `h1`, `>= 17` an `h2` subtitle, a secondary
   family is `p.note`. `INDENT_X` (default 88) is the x-coordinate that
   separates an indented first line from a continuation line — verify it
   against the real `x0` values, not by assumption.
5. **Convert to XHTML:**

   `python scripts/pdf2html.py book.pdf out/book.html`

6. **Verify before packing** (see Verification). Check the `pre:` counter against
   the real number of listings in the book — a technical book reporting `pre: 0`
   means the mono flag never fired and every listing is about to be reflowed as
   prose. Fix thresholds and re-run until
   the counts are sane. Cheap to iterate; do not skip to packing.
7. **Pack with calibre**, always passing explicit TOC XPaths — without them
   calibre applies its own heuristics and re-breaks the chapters:

   ```bash
   ebook-convert out/book.html "Title.epub" \
     --title="Title" --authors="Author" --language=en \
     --cover=out/images/cover.jpg \
     --level1-toc='//h:h1' --level2-toc='//h:h2' \
     --page-breaks-before='//h:h1' \
     --no-default-epub-cover
   ebook-convert "Title.epub" "Title.azw3"
   ```

   Take title/author from the user or the book's own title page — PDF metadata
   is often an ASIN or a filename.
8. **Deliver both files** and say what each is for: `.epub` for Send to Kindle
   (Amazon converts server-side), `.azw3` for USB copy into `documents/`.
   Delete intermediate artifacts left in the user's directories.

## Verification

Never report success on the converter's own summary line alone. Check:

```bash
python3 - <<'EOF'
import re
t = open('out/book.html').read()
print('italic:', t.count(''), 'bold:', t.count(''))
print('h1:', len(re.findall(r']+>', '', t).count('  '))
EOF
```

- italic count near zero on a novel means `style()` missed the font-name pattern;
- an `h1` count in the hundreds means a size threshold is too low and body text
  is being promoted;
- many double spaces means line joining is off;
- list the extracted headings (`grep -o ']*>.\{0,60\}'`) and read them —
  they become the TOC, and a wrong one is obvious at a glance;
- read one full page of body text and confirm paragraphs merge across page
  breaks and hyphenated words are rejoined.

After packing, confirm the EPUB: `ebook-meta` for metadata, and the `.ncx`
navPoint count for the TOC size.

## Glossary from existing translations

For a book in a series that already has published translations, `scripts/glossary.py`
mines a bilingual glossary so the machine translation does not invent new spellings
for names the reader already knows. Feed it pairs of editions of the *same* volume:

```
python scripts/glossary.py --en vol12.fb2 --ru vol12.ru.fb2 \
                           --en vol15.epub --ru vol15.ru.fb2 --score 0.6
```

Two signals, and both are needed. Position: paragraph indices do not line up
(Russian editions split dialogue, giving 2–3× more paragraphs), so offsets are
measured as a **share of characters**, where the texts track each other closely.
Transliteration: a proper name in Russian is nearly always a transliteration, so
the Cyrillic candidate is romanized and compared to the English term — this is
what turns the output from noise into a usable list.

Two mirrored filters remove the rest of the junk: a candidate whose head word
also appears lowercase in the same text is a sentence-initial common word, not a
name — applied on both sides. Measured on four Dresden Files volumes: 71 pairs,
of which two were wrong.

⚠️ Concept terms (`White Council` → `Белый Совет`, `Spire` → `Копьё`) do **not**
come out of the transliteration path and the positional one alone is too noisy
for them. Extract those by hand and verify by grepping the existing translation.

## Optional stage: translation

Only when the user asks for a translated book. It slots between step 6 and
step 7 — translate the XHTML, then pack the translated file with calibre.

**Code listings are never translated.** The bridge only recognizes `
`, `
`
and `
`; a `
` block is not parsed, and anything unparsed is carried into
the result untouched. Nothing extra is needed to protect code — but this also
means a listing that was misclassified as a paragraph upstream *will* be
translated, which is the real reason step 6 checks the `pre:` counter.

For the same reason `
` is cut out of both language checks. A book that is
40% listings translates correctly and would otherwise fail acceptance, because
the English code drags the Cyrillic share below the threshold.

**One workdir per book.** Chapter files are named `chapter_NNN.json` for every
book, and the external repo skips chapters it has marked done — a reused workdir
silently stitches one book's translation onto another's text. The bridge writes
`bridge_source.json` into the workdir on first run and refuses to start if the
directory belongs to a different book. Re-running the same book is unaffected;
that is the resume path.

Translation is delegated to an external project,
[`vetermanve/book_translator`](https://github.com/vetermanve/book_translator)
(DeepSeek or a local Ollama model). **Never modify that repository** — it is
driven through its documented CLI and file formats only. `scripts/translate.py`
is the bridge: it writes the repo's input format, shells out to
`03_translate_parallel.py --all`, and reads the repo's output format back.

1. **Check the book actually needs translating.** The script does this first
   and refuses to burn API credits on a book already in the target language:

   `python scripts/translate.py book.html out.html --repo  --workdir  --check-only`

   It reports block/chapter/character counts and the detected source language.
   `--force` overrides the refusal.
2. **Set up the external repo once** (a plain clone; never edit it) and its
   credentials — a `.env` in the working directory with `USE_LOCAL_MODEL=false`
   and `DEEPSEEK_API_KEY=…`, or `USE_LOCAL_MODEL=true` plus `OLLAMA_MODEL` for a
   local model. **Its `requirements.txt` is incomplete** — `pip install openai
   pyyaml python-dotenv` as well. Both `openai` and `pyyaml` are imported at
   runtime and missing from that file; without `pyyaml` every single request
   dies inside `_create_system_prompt` *before* reaching the API, and the tool
   reports "API запросов: 0, ошибок: N" while writing `[UNTRANSLATED]` stubs.
   Write the `.env` under `umask 077`, and never echo the key into logs or
   command output.
3. **Translate:**

   `python scripts/translate.py book.html book.ru.html --repo  --workdir  --workers 12`

   ⚠️ **Not resumable, despite what the external repo claims.** Its
   `progress/translation_progress.json` stays `{"chapters": {}}` and `--all`
   re-translates every chapter, including ones already sitting in
   `translations/`. Measured 2026-08-21 on Stroustrup: a re-run to repair 4
   failed chapters re-did all 29. Budget a full book on every restart, and
   prefer getting one clean run over patching a partial one.

   The one thing that *does* skip work: deleting a chapter from `extracted/`
   before the run. It is then never sent, and `rebuild` keeps the original
   English for it — the right treatment for an index.
**Numbered exercise items come back untranslated.** Measured on Stroustrup
2026-08-21: 182 paragraphs of 10151 (1.8%) shaped `[2] Expanding on what you
have learned…` were echoed back in English. The document-wide Cyrillic check
cannot see this — 1.8% drowns in it — so `rebuild` counts them separately as
"осталось на языке оригинала". If the count is high, add an explicit line to the
prompt that numbered items are prose and must be translated too.

4. **Read the reported counts.** "без перевода" above zero means a chapter came
   back with a different paragraph count and kept its original text; "разметка
   потеряна" counts paragraphs where the model mangled the inline-tag markers
   and the italics were dropped rather than corrupted. Both are expected to be
   near zero — a large number means the translator misbehaved, not that the
   bridge is broken. The script then checks the output's actual language and
   exits non-zero if the text is still the source language or contains
   `[UNTRANSLATED]` stubs — **matching paragraph counts do not prove anything
   was translated**, since the external tool substitutes the original on API
   failure. Do not pack a file that failed this check.
   **Re-running after a failure needs the state cleared:** the external repo's
   `progress/` directory marks those chapters complete and will skip them.
   Delete `/progress`, `/context`, and
   `/translations` before the retry.
5. Pack `book.ru.html` with `--language=ru` and translated `--title`/`--authors`.

How formatting survives a translator that only speaks plain text: inline ``
and `` become `⟦i⟧…⟦/i⟧` markers before the text leaves, and are restored
after. Every paragraph's markers are balance-checked on the way back. Images,
`
`/`
` structure, and block order never leave this side — only the text
of each block round-trips, and blocks are reassembled by index.

`scripts/test_translate.py` covers the marker round-trip, the broken-marker
fallback, language detection, and a full split/rebuild against a faked
translator response — no network, no API key. Run it after touching the bridge.

## Optional stage: audiobook

Also delegated to `book_translator` (`05_create_audiobook.py`, Microsoft
edge-tts — free, needs internet). Two wrappers live in `scripts/audiobook.py`;
the external repo is still never edited.

1. **Strip markup markers first.** The translated JSON still holds the
   `⟦i⟧` markers from the translation stage — TTS would read them aloud:

   `python scripts/audiobook.py prep /translations /translations_tts`

   Point the external script at the *stripped* copy; the original keeps its
   italics for the EPUB.
2. **Synthesize:** `05_create_audiobook.py --translations-dir <…>/translations_tts
   --voice dmitry --rate '+0%'`. Fragments are **one per paragraph** (the
   `--paragraphs-per-group` flag is not used by the loop), named
   `chapter_NNN_intro.mp3` / `chapter_NNN_para_NNNN.mp3` under
   `audiobook/temp_audio/`.
3. **Verify before the temp files are deleted** — `cleanup_temp_files()` wipes
   `temp_audio/`, and after that only the merged file can be checked:

   `python scripts/audiobook.py verify /translations_tts /audiobook`

   It flags chapters missing fragments, zero-byte/undecodable mp3s, and
   chapters whose duration falls short of what their character count predicts.
   The seconds-per-character baseline is the median across chapters, so it
   self-calibrates to whatever voice and `--rate` were used. Exit code is
   non-zero when anything is wrong.
4. **Re-running fills gaps cheaply** — the external script skips any fragment
   file that already exists *and is non-empty*, so delete the bad ones `verify`
   named and run it again. It never re-checks that an existing file is sane,
   which is exactly why step 3 exists.
5. **Split into chapter tracks**, also before the temp files go:

   ```bash
   python scripts/audiobook.py split /translations_tts /audiobook \
     /tracks --album "Название" --author "Автор" --gap 0.3
   ```

   The external stage only ever produces one merged `audiobook_complete.mp3`
   with no chapter marks, which is bad for players. `split` rebuilds per-chapter
   `NNN - Title.mp3` from the same fragments, with ID3 album/artist/track/title
   tags, and refuses a chapter whose fragments are incomplete (`--force`
   overrides). Concatenation is stream-copy, falling back to a re-encode only if
   the mp3 streams don't line up; `--gap` inserts silence between paragraphs,
   generated to match the fragments' own codec parameters so the copy path
   stays viable. Hand the result to the `prepare-audiobooks` skill for covers
   and library layout.

### Prefer local Silero over edge-tts

edge-tts drops fragments silently under load — measured 1 of 49 on one run and
7 of 49 on the next, at *fewer* workers, so it is volume- not concurrency-bound.
`scripts/silero_render.py` replaces the external synthesis stage entirely with a
local model: no network, no dropouts, no `repair` cycle, free.

```bash
pip install torch numpy --index-url https://download.pytorch.org/whl/cpu
curl -O https://models.silero.ai/models/tts/ru/v5_5_ru.pt      # 145 МБ
python scripts/silero_render.py v5_5_ru.pt /translations_tts  \
  --album "Название" --author "Автор" --speaker eugene --tempo 0.87 \
  --skip 0 1 2 3 37
```

**Check `lscpu | grep avx2` before choosing the host.** PyTorch needs AVX2;
without it inference is ~30x slower — measured 3.4x realtime on a Celeron N5095
(SSE4 only) versus 97x on a Ryzen 7 5800H. Use 8 threads, not 16: hyperthreading
loses (97x vs 83x).

`scripts/tts_normalize.py` prepares the text — Latin script is what makes a
Russian voice sound worst, and a full book carries far more of it than a sample
chapter suggests (37 unique tokens in one chapter, 728 across the book). It maps
named entities and acronyms by hand with stress marks, transliterates the rest
by rule, spells out numbers, and drops URLs. It also generates Russian chapter
titles, since translated headings often stay English and the intro fragment
would otherwise read them aloud in Latin.

Voice tempo: SSML `` quantizes to named levels, so percentages
cluster instead of stepping evenly. For fine control use `--tempo`, which
time-stretches with ffmpeg and preserves pitch.

The phonetics stage (`07_extract_terms.py` + `08_generate_phonetics.py`) is
worth running first for a technical book **when using edge-tts**; with
`tts_normalize.py` it is redundant.

**Ordering constraint for the whole stage:** `verify` and `split` both read
`audiobook/temp_audio/`, and the external script's `cleanup_temp_files()`
deletes it right after merging. Run both before that, or the fragments are gone
and only the merged file's total duration can be checked.

`scripts/test_audiobook.py` builds real silent mp3s with ffmpeg and checks that
`verify` catches a missing fragment, an empty file, and passes a clean book.
Needs ffmpeg; no network.

## What the script does

- style from span font names (`-It`, `Italic`, `Bold`, `Semibold`) → ``/``;
- headings by font size, sidebars by font family;
- one PDF block = one paragraph; an unindented block that opens a page is
  appended to the previous page's paragraph;
- trailing hyphens dropped, adjacent `` runs merged, doubled spaces collapsed;
- images written to `images/`, cover rendered from page 1 at 150 dpi;
- absolute positioning is deliberately discarded so text reflows at any font size.

## Limits

- Headers/footers are not stripped — calibre-made PDFs have none. A typeset PDF
  with running heads and page numbers needs blocks filtered by `y` coordinate
  near the margins; add that before trusting the output.
- Multi-column layouts are not handled; block order would need column sorting.
- Thresholds are per-layout constants. Always re-run `--fonts` for a new book.