pdf-to-tts-zh

name	pdf-to-tts-zh
description	Turn a PDF (slides, case reports, handouts) into a single narrated Traditional Chinese audiobook MP3. Rasterize each page to an image, read it, write a natural spoken 繁體中文逐字稿 per page, synthesize each with edge-tts, then concatenate into one mp3. Use when asked to make a Chinese voiceover/audio/podcast/audiobook from a PDF or slide deck, "把 PDF 轉成語音/逐字稿/有聲書", or "用 edge-tts 唸投影片".

pdf-to-tts-zh

Convert any PDF into one continuous Traditional Chinese narration MP3 — page by page.

When to use

• "把這份投影片/PDF 轉成繁中語音 / 有聲書 / podcast"
• "幫每一頁寫逐字稿再用 edge-tts 唸出來，合併成一個 mp3"
• Turning slide decks, case reports, or handouts into an audio walkthrough.

Requirements

Check these are installed before starting; install whatever is missing.

Tool	Purpose	Install
pdftoppm	Rasterize PDF pages to PNG	brew install poppler
edge-tts	Microsoft Edge neural TTS (free, no key)	pip install edge-tts / uv tool install edge-tts
ffmpeg	Concatenate per-page mp3s	brew install ffmpeg

Workflow

Work inside a project directory holding the source PDF (referred to as INPUT.pdf).

1. Rasterize every page

mkdir -p pages audio scripts
pdftoppm -png -r 150 INPUT.pdf pages/page

Produces pages/page-01.png … page-NN.png (zero-padded). 150 dpi is a good legibility/size balance; bump to 200 for dense small text.

2. Read each page and write a 繁中逐字稿

For every PNG, use the Read tool to view the image, then write a natural spoken-Chinese narration to scripts/page-NN.txt (one file per page, plain text).

Writing rules:

• Speak, don't list. Narrate the page like a presenter, not by reading bullets verbatim.
• Keep technical tokens in English — drug names, abbreviations, gene/protein names (e.g. rituximab, MALT, AIHA) stay in English so the voice pronounces them correctly.
• Verbalize numbers/dates in Chinese reading order (e.g. 2026/05/25 → 「2026 年 5 月 25 日」).
• No meta narration — never write 「這一頁顯示」「投影片上」; just deliver the content.
• Section/title pages can be one short sentence; data-heavy pages get a fuller paragraph.
• Plain text only — no markdown, no bullet glyphs.

Scale tip: for long decks, dispatch parallel subagents, each owning a page range, reading its PNGs and writing the scripts/page-NN.txt files. Keep the voice/style instructions identical across agents for a consistent narrator.

3. Synthesize each page with edge-tts

for f in scripts/page-*.txt; do
  n=$(basename "$f" .txt)
  edge-tts --voice zh-TW-HsiaoChenNeural --file "$f" --write-media "audio/$n.mp3"
done

Voice options (all key-free):

• zh-TW-HsiaoChenNeural — Taiwan female (default, warm)
• zh-TW-YunJheNeural — Taiwan male
• zh-CN-XiaoxiaoNeural — Mainland female
• Tune pace with --rate=+10% / --rate=-10%.

List all voices: edge-tts --list-voices | grep zh-

4. Concatenate into one mp3

: > audio/list.txt
for n in $(seq -w 1 $(ls scripts/page-*.txt | wc -l)); do
  echo "file '$PWD/audio/page-$n.mp3'" >> audio/list.txt
done
ffmpeg -y -f concat -safe 0 -i audio/list.txt -c:a libmp3lame -b:a 128k OUTPUT.mp3

Re-encoding (libmp3lame) yields clean joins; -c copy is faster but can click at boundaries if codec params differ between segments.

5. Verify

ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 OUTPUT.mp3

Confirm the file count (ls audio/*.mp3 | wc -l) equals the page count and the duration is sane before delivering OUTPUT.mp3.

Notes

• Intermediate artifacts (pages/, scripts/, audio/) are kept — re-run step 3 after editing any scripts/page-NN.txt, or step 4 only to re-stitch, without redoing the whole pipeline.
• The helper script scripts/build.sh runs steps 1, 3, 4 in one shot once the per-page scripts/page-NN.txt files exist (step 2 still needs a model to read images).