docutranslate 1.3.1

文件翻译工具

DocuTranslate

简体中文 / English / 日本語

DocuTranslate is a file translation tool that combines advanced document analysis engines (such as docling and minerU) with large language models (LLMs). It can accurately translate documents in a wide variety of formats.

The new version's architecture adopts Workflow as its core, providing a highly configurable and extensible solution for various types of translation tasks.

• ✅ Supports Multiple Formats: Capable of translating various files such as pdf, docx, xlsx, md, txt, json, epub, srt, and more.
• ✅ Table, Formula, and Code Recognition: Utilizes docling and mineru to recognize and translate tables, formulas, and code frequently found in academic papers.
• ✅ Automatic Glossary Generation: Supports automatic glossary creation to ensure terminology consistency.
• ✅ JSON Translation: Allows specifying values to be translated in JSON using JSONPath (jsonpath-ng syntax) specifications.
• ✅ High-Fidelity Word/Excel Translation: Supports translation of docx and xlsx files (currently does not support doc or xls files) while preserving the original formatting.
• ✅ Multi-AI Platform Support: Compatible with most AI platforms, enabling high-performance concurrent AI translation with customizable prompts.
• ✅ Asynchronous Support: Designed for high-performance scenarios, offering full asynchronous support and service interfaces for parallel task execution.
• ✅ Interactive Web Interface: Provides an out-of-the-box Web UI and RESTful API for easy integration and use.

When translating pdf files, they are first converted to markdown, so the original typesetting will be lost. Users with typesetting requirements should note this.

QQ Discussion Group: 1047781902

UI Interface:

Paper Translation:

Novel Translation:

Integrated Packages

For users who want to get started quickly, we provide integrated packages on GitHub Releases. Simply download, unzip, and enter your AI platform's API key to start using.

• DocuTranslate: The standard version, which uses the online minerU engine to parse documents. Recommended for most users.
• DocuTranslate_full: The full version, which includes the docling local parsing engine. Suitable for offline scenarios or those with higher data privacy requirements.

Installation

Using pip

# Basic installation
pip install docutranslate

# If using the docling local parsing engine
pip install docutranslate[docling]

Using uv

# Initialize the environment
uv init

# Basic installation
uv add docutranslate

# Install docling extension
uv add docutranslate[docling]

Using git

# Initialize the environment
git clone https://github.com/xunbu/docutranslate.git

cd docutranslate

uv sync

Core Concept: Workflow

The core of the new version of DocuTranslate is the Workflow. Each workflow is a complete end-to-end translation pipeline designed for a specific file type. Instead of interacting with large classes as before, you will select and configure the appropriate workflow according to the file type.

The basic usage steps are as follows:

1. Select a Workflow: Choose a workflow based on the input file type (e.g., PDF/Word or TXT). For example, MarkdownBasedWorkflow or TXTWorkflow.
2. Build Configuration: Create a configuration object corresponding to the selected workflow (such as MarkdownBasedWorkflowConfig). This configuration object contains all the necessary sub-configurations, such as:
   • Converter Config: Defines how to convert the original file (e.g., PDF) to Markdown.
   • Translator Config: Defines the LLM to use, API-Key, target language, etc.
   • Exporter Config: Defines specific options for the output format (e.g., HTML).
3. Instantiate the Workflow: Create an instance of the workflow using the configuration object.
4. Execute Translation: Call the workflow's .read_*() method and .translate() / .translate_async() method.
5. Export/Save Results: Call the .export_to_*() method or .save_as_*() method to retrieve or save the translation results.

Available Workflows

Workflow	Application Scenario	Input Format	Output Format	Core Configuration Class
MarkdownBasedWorkflow	Process rich text documents such as PDF, Word, and images. Flow: File -> Markdown -> Translation -> Export.	.pdf, .docx, .md, .png, .jpg, etc.	.md, .zip, .html	MarkdownBasedWorkflowConfig
TXTWorkflow	Process plain text documents. Flow: txt -> Translation -> Export.	.txt and other plain text formats	.txt, .html	TXTWorkflowConfig
JsonWorkflow	Process json files. Flow: json -> Translation -> Export.	.json	.json, .html	JsonWorkflowConfig
DocxWorkflow	Process docx files. Flow: docx -> Translation -> Export.	.docx	.docx, .html	docxWorkflowConfig
XlsxWorkflow	Process xlsx files. Flow: xlsx -> Translation -> Export.	.xlsx	.xlsx, .html	XlsxWorkflowConfig
SrtWorkflow	Process srt files. Flow: srt -> Translation -> Export.	.srt	.srt, .html	SrtWorkflowConfig
EpubWorkflow	Process epub files. Flow: epub -> Translation -> Export.	.epub	.epub, .html	EpubWorkflowConfig
HtmlWorkflow	Process html files. Flow: html -> Translation -> Export.	.html, .htm	.html	HtmlWorkflowConfig

The interactive interface allows export in pdf format.

Starting the Web UI and API Service

For ease of use, DocuTranslate provides a feature-rich web interface and RESTful API.

Starting the Service:

# Start the service, which monitors port 8010 by default
docutranslate -i

# Start with a specified port
docutranslate -i -p 8011

# You can also specify the port using an environment variable
export DOCUTRANSLATE_PORT=8011
docutranslate -i

• Interactive Interface: After starting the service, access http://127.0.0.1:8010 (or the specified port) in your browser.
• API Documentation: The complete API documentation (Swagger UI) is available at http://127.0.0.1:8010/docs.

Usage

Example 1: Translating a PDF File (Using MarkdownBasedWorkflow)

This is the most common use case. Convert the PDF to Markdown using the minerU engine and translate it with an LLM. Here, we use the asynchronous method as an example.

import asyncio
from docutranslate.workflow.md_based_workflow import MarkdownBasedWorkflow, MarkdownBasedWorkflowConfig
from docutranslate.converter.x2md.converter_mineru import ConverterMineruConfig
from docutranslate.translator.ai_translator.md_translator import MDTranslatorConfig
from docutranslate.exporter.md.md2html_exporter import MD2HTMLExporterConfig


async def main():
    # 1. Build translator configuration
    translator_config = MDTranslatorConfig(
        base_url="https://open.bigmodel.cn/api/paas/v4",  # Base URL of the AI platform
        api_key="YOUR_ZHIPU_API_KEY",  # API Key of the AI platform
        model_id="glm-4-air",  # Model ID
        to_lang="English",  # Target language
        chunk_size=3000,  # Text chunk size
        concurrent=10  # Number of concurrent executions
        # glossary_generate_enable=True, # Enable automatic glossary generation
        # glossary_dict={"Jobs":"乔布斯"} # Pass in the glossary
    )

    # 2. Build converter configuration (using minerU)
    converter_config = ConverterMineruConfig(
        mineru_token="YOUR_MINERU_TOKEN",  # Your minerU Token
        formula_ocr=True  # Enable formula recognition
    )

    # 3. Build main workflow configuration
    workflow_config = MarkdownBasedWorkflowConfig(
        convert_engine="mineru",  # Specify the parsing engine
        converter_config=converter_config,  # Pass the converter configuration
        translator_config=translator_config,  # Pass the translator configuration
        html_exporter_config=MD2HTMLExporterConfig(cdn=True)  # HTML export configuration
    )

    # 4. Instantiate the workflow
    workflow = MarkdownBasedWorkflow(config=workflow_config)

    # 5. Load the file and execute translation
    print("Starting file loading and translation...")
    workflow.read_path("path/to/your/document.pdf")
    await workflow.translate_async()
    # Or use the synchronous method
    # workflow.translate()
    print("Translation completed!")

    # 6. Save the results
    workflow.save_as_html(name="translated_document.html")
    workflow.save_as_markdown_zip(name="translated_document.zip")
    workflow.save_as_markdown(name="translated_document.md")  # Markdown with embedded images
    print("Files saved to the ./output folder.")

    # Or directly get the content string
    html_content = workflow.export_to_html()
    html_content = workflow.export_to_markdown()
    # print(html_content)


if __name__ == "__main__":
    asyncio.run(main())

Example 2: Translating TXT Files (Using TXTWorkflow)

For pure text files, the process is simpler as there is no need for document parsing (conversion). Here is an example using the asynchronous method.

import asyncio
from docutranslate.workflow.txt_workflow import TXTWorkflow, TXTWorkflowConfig
from docutranslate.translator.ai_translator.txt_translator import TXTTranslatorConfig
from docutranslate.exporter.txt.txt2html_exporter import TXT2HTMLExporterConfig


async def main():
    # 1. Build the translator configuration
    translator_config = TXTTranslatorConfig(
        base_url="https://api.openai.com/v1/",
        api_key="YOUR_OPENAI_API_KEY",
        model_id="gpt-4o",
        to_lang="中文",
    )

    # 2. Build the main workflow configuration
    workflow_config = TXTWorkflowConfig(
        translator_config=translator_config,
        html_exporter_config=TXT2HTMLExporterConfig(cdn=True)
    )

    # 3. Instantiate the workflow
    workflow = TXTWorkflow(config=workflow_config)

    # 4. Read the file and execute translation
    workflow.read_path("path/to/your/notes.txt")
    await workflow.translate_async()
    # Or use the synchronous method
    # workflow.translate()

    # 5. Save the result
    workflow.save_as_txt(name="translated_notes.txt")
    print("TXT file saved.")

    # You can also export the translated plain text
    text = workflow.export_to_txt()


if __name__ == "__main__":
    asyncio.run(main())

Example 3: Translating a JSON file (using JsonWorkflow)

Here, we show an example using the asynchronous method. In the json_paths item of JsonTranslatorConfig, you need to specify the JSON paths to be translated (following the jsonpath-ng syntax rules). Only the values matching the JSON paths will be translated.

import asyncio

from docutranslate.exporter.js.json2html_exporter import Json2HTMLExporterConfig
from docutranslate.translator.ai_translator.json_translator import JsonTranslatorConfig
from docutranslate.workflow.json_workflow import JsonWorkflowConfig, JsonWorkflow


async def main():
    # 1. Build the translator configuration
    translator_config = JsonTranslatorConfig(
        base_url="https://api.openai.com/v1/",
        api_key="YOUR_OPENAI_API_KEY",
        model_id="gpt-4o",
        to_lang="Chinese",
        json_paths=["$.*", "$.name"]  # Compliant with the jsonpath-ng path syntax; all values matching the path will be translated
    )

    # 2. Build the main workflow configuration
    workflow_config = JsonWorkflowConfig(
        translator_config=translator_config,
        html_exporter_config=Json2HTMLExporterConfig(cdn=True)
    )

    # 3. Instantiate the workflow
    workflow = JsonWorkflow(config=workflow_config)

    # 4. Read the file and execute translation
    workflow.read_path("path/to/your/notes.json")
    await workflow.translate_async()
    # Or use the synchronous method
    # workflow.translate()

    # 5. Save the results
    workflow.save_as_json(name="translated_notes.json")
    print("The JSON file has been saved.")

    # You can also export the translated json text
    text = workflow.export_to_json()


if __name__ == "__main__":
    asyncio.run(main())

Example 4: Translating a docx File (Using DocxWorkflow)

Here, the asynchronous method is shown as an example.

import asyncio

from docutranslate.exporter.docx.docx2html_exporter import Docx2HTMLExporterConfig
from docutranslate.translator.ai_translator.docx_translator import DocxTranslatorConfig
from docutranslate.workflow.docx_workflow import DocxWorkflowConfig, DocxWorkflow


async def main():
    # 1. Build the translator configuration
    translator_config = DocxTranslatorConfig(
        base_url="https://api.openai.com/v1/",
        api_key="YOUR_OPENAI_API_KEY",
        model_id="gpt-4o",
        to_lang="日本語",
        insert_mode="replace",  # Optional: "replace", "append", "prepend"
        separator="\n",  # Separator used in "append" and "prepend" modes
    )

    # 2. Build the main workflow configuration
    workflow_config = DocxWorkflowConfig(
        translator_config=translator_config,
        html_exporter_config=Docx2HTMLExporterConfig(cdn=True)
    )

    # 3. Instantiate the workflow
    workflow = DocxWorkflow(config=workflow_config)

    # 4. Load the file and execute translation
    workflow.read_path("path/to/your/notes.docx")
    await workflow.translate_async()
    # Or use the synchronous method
    # workflow.translate()

    # 5. Save the result
    workflow.save_as_docx(name="translated_notes.docx")
    print("The docx file has been saved.")

    # You can also export the translated docx as binary
    text_bytes = workflow.export_to_docx()


if __name__ == "__main__":
    asyncio.run(main())

Example 5: Translating an xlsx file (using XlsxWorkflow)

Here, we will use the asynchronous method as an example.

import asyncio

from docutranslate.exporter.xlsx.xlsx2html_exporter import Xlsx2HTMLExporterConfig
from docutranslate.translator.ai_translator.xlsx_translator import XlsxTranslatorConfig
from docutranslate.workflow.xlsx_workflow import XlsxWorkflowConfig, XlsxWorkflow


async def main():
    # 1. Build the translator configuration
    translator_config = XlsxTranslatorConfig(
        base_url="https://api.openai.com/v1/",
        api_key="YOUR_OPENAI_API_KEY",
        model_id="gpt-4o",
        to_lang="日本語",
        insert_mode="replace",  # Optional: "replace", "append", "prepend"
        separator="\n",  # Separator used in "append" and "prepend" modes
    )

    # 2. Build the main workflow configuration
    workflow_config = XlsxWorkflowConfig(
        translator_config=translator_config,
        html_exporter_config=Xlsx2HTMLExporterConfig(cdn=True)
    )

    # 3. Instantiate the workflow
    workflow = XlsxWorkflow(config=workflow_config)

    # 4. Load the file and execute translation
    workflow.read_path("path/to/your/notes.xlsx")
    await workflow.translate_async()
    # Or use the synchronous method
    # workflow.translate()

    # 5. Save the result
    workflow.save_as_xlsx(name="translated_notes.xlsx")
    print("The XLSX file has been saved.")

    # You can also export the binary data of the translated XLSX
    text_bytes = workflow.export_to_xlsx()


if __name__ == "__main__":
    asyncio.run(main())

Detailed Explanation of Prerequisites and Settings

1. Obtaining a Large Language Model API Key

The translation function relies on a large language model, and you need to obtain the base_url, api_key, and model_id from the corresponding AI platform.

Recommended models: Volcano Engine's doubao-seed-1-6-250615, doubao-seed-1-6-flash-250715, Zhipu's glm-4-flash, Alibaba Cloud's qwen-plus, qwen-turbo, DeepSeek's deepseek-chat, etc.

Platform Name	Method to Obtain API Key	baseurl
ollama		http://127.0.0.1:11434/v1
lm studio		http://127.0.0.1:1234/v1
openrouter	Click to Obtain	https://openrouter.ai/api/v1
openai	Click to Obtain	https://api.openai.com/v1/
gemini	Click to Obtain	https://generativelanguage.googleapis.com/v1beta/openai/
deepseek	Click to Obtain	https://api.deepseek.com/v1
智譜ai	Click to Obtain	https://open.bigmodel.cn/api/paas/v4
騰訊混元	Click to Obtain	https://api.hunyuan.cloud.tencent.com/v1
阿里云百煉	Click to Obtain	https://dashscope.aliyuncs.com/compatible-mode/v1
火山引擎	Click to Obtain	https://ark.cn-beijing.volces.com/api/v3
硅基流動	Click to Obtain	https://api.siliconflow.cn/v1
DMXAPI	Click to Obtain	https://www.dmxapi.cn/v1

2. Obtaining minerU Token (Online Parsing)

If you select mineru as the document parsing engine (convert_engine="mineru"), you need to apply for a free Token.

1. Visit the minerU official website, register, and apply for the API.
2. Create a new API Token on the API Token management page.

Note: The minerU Token is valid for 14 days. If it expires, please recreate it.

3. Configuring the docling Engine (Local Parsing)

If you select docling as the document parsing engine (convert_engine="docling"), the required models will be downloaded from Hugging Face during the first use.

Solutions for Network Issues:

1. Setting up a Hugging Face Mirror (Recommended):

• Method A (Environment Variable): Set the system environment variable HF_ENDPOINT and restart your IDE or terminal.

   HF_ENDPOINT=https://hf-mirror.com

• Method B (Setting in Code): Add the following code at the beginning of your Python script.

import os

os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'

2. Offline Use (Download Model Packages in Advance):

• Download docling_artifact.zip from GitHub Releases.
• Extract it to your project directory.
• Specify the model path in the configuration:

from docutranslate.converter.x2md.converter_docling import ConverterDoclingConfig

converter_config = ConverterDoclingConfig(
    artifact="./docling_artifact",  # Specify the extracted folder
    code_ocr=True,
    formula_ocr=True
)

FAQ

Q: What should I do if port 8010 is occupied? A: Specify a new port using the -p parameter or set the DOCUTRANSLATE_PORT environment variable.

Q: Is translation of scanned documents supported? A: Yes, it is supported. Please use the mineru parsing engine, which is equipped with powerful OCR capabilities.

Q: Why is it slow on first use? A: When using the docling engine, the model needs to be downloaded from Hugging Face during the first run. To speed up this process, refer to the "Solutions for Network Issues" section above.

Q: How can it be used in an intranet (offline) environment? A: It is completely possible. The following two conditions need to be met:

1. Local Parsing Engine: Use the docling engine and download the model package in advance according to the "Offline Use" guide above.
2. Local LLM: Deploy a language model locally using tools such as Ollama or LM Studio, and enter the base_url of the local model in TranslatorConfig.

Q: How does the caching mechanism work? A: MarkdownBasedWorkflow automatically caches the results of document parsing (conversion from files to Markdown) to avoid wasting time and resources on repeated parsing. The cache is stored in memory by default and records the most recent 10 parsing operations. The number of cached items can be changed via the DOCUTRANSLATE_CACHE_NUM environment variable.

Q: How can I use the software via a proxy? A: The software does not use a proxy by default. Set the DOCUTRANSLATE_PROXY_ENABLED environment variable to true to enable communication via a proxy.

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