A desktop app that translates books, subtitles, and documents with AI - local or cloud. Drop a file, pick a language, get the result.

Formats: EPUB, SRT, DOCX, PDF, TXT, PGN

• No size limit. Process documents of any length - from a single page to thousand-page novels. The intelligent chunking system handles unlimited content while preserving context between segments.
• Perfect preservation. Your documents come out exactly as they went in: EPUB formatting, styles, and structure remain intact. SRT timecodes stay perfectly synchronized. Every tag, every timestamp, every formatting detail is preserved.
• Resume anytime. Interrupted translation? Pick up exactly where you left off. The checkpoint system saves progress automatically.
• Reusable writing styles. Extract a style preset from sample books (or write one by hand) and apply it to every chunk for a consistent register, rhythm, and imagery across the whole translation.
• Auto glossary & style. No glossary or preset ready? Pick Auto in the dropdown and the app derives both straight from the document being translated - one extra LLM call each before the job starts, nothing saved.
• PDF support. Text PDFs are translated into a new, reflowed PDF: headings, paragraphs, lists and images are kept, ruled tables are rebuilt as tables, and paragraph colours, background boxes and the font family (serif, sans-serif, monospace) are preserved, but the original page layout is not, and bold or colour inside a sentence is not. Scanned PDFs are not supported (no OCR), multi-column layouts are best effort, and the refinement pass is not available for PDF yet.
• Chess annotations (PGN). Only the comments of annotated games are translated; tags, moves, variations, NAGs and embedded commands such as [%cal] arrows or ChessBase [#] diagrams are kept byte for byte. Moves quoted inside a comment ("after 12.Nf3 ...") must come back unchanged, otherwise that comment is retried and finally left in its original language. Moves keep their English piece letters (Nf3, not Cf3) so every chess program can read them. The output is UTF-8, and the refinement pass is not available for PGN yet.

Translation Quality Benchmarks - Find the best model for your target language.

1. Download the release for your platform (buttons above), or grab it from the releases page.
2. Extract the archive.
3. Run TranslateBook.exe (Windows) or ./TranslateBook (macOS).
4. Open http://localhost:5000 in your browser.

That's it (就这么简单 · これだけ · 이게 다예요). On first launch, you choose a translation provider:

• Free & local - Install Ollama and pull a model (e.g. ollama pull qwen3:14b). Nothing leaves your machine.
• Cloud (often free tier available) - Paste an API key from one of the providers below.

Note: First run creates a TranslateBook_Data folder with your settings.

macOS: On first launch, go to System Settings > Privacy & Security and click Open Anyway.

See docs/PROVIDERS.md for detailed setup instructions.

See docs/TROUBLESHOOTING.md for more solutions.

git clone https://github.com/hydropix/TranslateBooksWithLLMs.git
cd TranslateBookWithLLM
ollama pull qwen3:14b    # Download a model

# Windows
start.bat

# Mac/Linux
chmod +x start.sh && ./start.sh

# Basic (auto-generates "book (Chinese).epub")
python translate.py -i book.epub -sl English -tl Chinese

# With OpenRouter
python translate.py -i book.txt --provider openrouter \
    --openrouter_api_key YOUR_KEY -m anthropic/claude-sonnet-4 -tl French

# With OpenAI
python translate.py -i book.txt --provider openai \
    --openai_api_key YOUR_KEY -m gpt-4o -tl French

# With Gemini
python translate.py -i book.txt --provider gemini \
    --gemini_api_key YOUR_KEY -m gemini-2.0-flash -tl French

# With Mistral
python translate.py -i book.txt --provider mistral \
    --mistral_api_key YOUR_KEY -m mistral-large-latest -tl French

# With DeepSeek
python translate.py -i book.txt --provider deepseek \
    --deepseek_api_key YOUR_KEY -m deepseek-v4-pro -tl French

# With Poe
python translate.py -i book.txt --provider poe \
    --poe_api_key YOUR_KEY -m Claude-Sonnet-4 -tl French

# With NVIDIA NIM
python translate.py -i book.txt --provider nim \
    --nim_api_key YOUR_KEY -m meta/llama-3.1-8b-instruct -tl French

# With local OpenAI-compatible server (llama.cpp, LM Studio, vLLM, etc.)
python translate.py -i book.txt --provider openai \
    --api_endpoint http://localhost:8080/v1/chat/completions -m your-model -tl French

# Provider
LLM_PROVIDER=ollama

# Ollama
API_ENDPOINT=http://localhost:11434/api/generate
DEFAULT_MODEL=qwen3:14b

# API Keys (if using cloud providers)
OPENROUTER_API_KEY=sk-or-v1-...
OPENAI_API_KEY=sk-...
GEMINI_API_KEY=...
MISTRAL_API_KEY=...
DEEPSEEK_API_KEY=...
POE_API_KEY=...
NIM_API_KEY=...

# Performance
REQUEST_TIMEOUT=900
MAX_TOKENS_PER_CHUNK=450     # Token-based chunking (default: 450 tokens)
PARALLEL_TRANSLATIONS=1      # Concurrent chunks (cloud only; Ollama stays at 1)

docker build -t translatebook .
docker run -p 5000:5000 -v $(pwd)/translated_files:/app/translated_files translatebook

One server is one shared workspace - there are no user accounts, so every browser pointed at it sees the same state. A running job's progress panel, paused/interrupted jobs, the completed translation history, and the files in translated_files/ are all shared: another device picks them up live (within a second or two) or on page load. A small set of preferences - source/target language, TTS, text cleanup, bilingual mode, plain-text mode, and the selected custom instruction - is synced too: the server wins at page load, the last change wins after that, and a tab that is already open picks up changes made elsewhere on its next reload.

Two things stay local to the device: if you queue several files, other devices only see the one currently translating, not the rest of the queue (it's held in the browser that built it); and the UI language is per-browser. Provider, model, API keys, and endpoints are never synced - they always come from the server's .env.

If TBL saves you time, you can support its development on Ko-fi:

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License: AGPL-3.0