An ultra-lightweight local file translation tool based on Large Language Models (LLMs), dedicated to providing an accurate, fast, and extensible translation experience.
- ✅ **Supports Multiple Formats**: Can translate various files such as `pdf`, `docx`, `xlsx`, `md`, `txt`, `json`, `epub`, `srt`, and more. - ✅ **Automatic Glossary Generation**: Supports automatic generation of glossaries to ensure term alignment. - ✅ **PDF Table, Formula, and Code Recognition**: With the `docling` and `mineru` PDF parsing engines, it can recognize and translate tables, formulas, and code frequently found in academic papers. - ✅ **JSON Translation**: Supports specifying the values to be translated in JSON via JSON paths (using `jsonpath-ng` syntax). - ✅ **Word/Excel Format-Preserving Translation**: Supports translating `docx` and `xlsx` files (currently not `doc` or `xls` files) while preserving the original formatting. - ✅ **Multi-AI Platform Support**: Supports most AI platforms, enabling high-performance, concurrent AI translation with custom prompts. - ✅ **Asynchronous Support**: Designed for high-performance scenarios, it offers complete asynchronous support, providing service interfaces for parallel multitasking. - ✅ **LAN and Multi-user Support**: Supports simultaneous use by multiple users on a local area network. - ✅ **Interactive Web Interface**: Provides an out-of-the-box Web UI and RESTful API for easy integration and use. - ✅ **Small-Footprint, Multi-Platform "Lazy" Packages**: Windows and Mac "lazy" packages under 40MB (for versions not using `docling` for local PDF parsing). > When translating `pdf` files, they are first converted to Markdown, which will **lose** the original layout. Users with layout requirements should take note. > QQ Discussion Group: 1047781902 **UI Interface**:  **Thesis Translation**:  **Novel Translation**: ## All-in-One Packages For users who want to get started quickly, we provide all-in-one packages on [GitHub Releases](https://github.com/xunbu/docutranslate/releases). Simply download, unzip, and enter your AI platform API-Key to start using. - **DocuTranslate**: Standard version, uses the online `minerU` engine to parse PDF documents. Choose this version if you don't need local PDF parsing (recommended). - **DocuTranslate_full**: Full version, includes the built-in `docling` local PDF parsing engine. Choose this version if you need local PDF parsing. ## Installation ### Using pip ```bash # Basic installation pip install docutranslate # To use docling for local PDF parsing pip install docutranslate[docling] ``` ### Using uv ```bash # Initialize environment uv init # Basic installation uv add docutranslate # Install docling extension uv add docutranslate[docling] ``` ### Using git ```bash # Initialize environment git clone https://github.com/xunbu/docutranslate.git cd docutranslate uv sync ``` ## Core Concept: Workflow The core of the new DocuTranslate is the **Workflow**. Each workflow is a complete, end-to-end translation pipeline designed specifically for a particular file type. You no longer interact with a monolithic class; instead, you select and configure a suitable workflow based on your file type. **The basic usage process is as follows:** 1. **Select a Workflow**: Choose a workflow based on your input file type (e.g., PDF/Word or TXT), such as `MarkdownBasedWorkflow` or `TXTWorkflow`. 2. **Build the Configuration**: Create the corresponding configuration object for the selected workflow (e.g., `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 which LLM, API-Key, target language, etc., to use. * **Exporter Config**: Defines specific options for the output format (e.g., HTML). 3. **Instantiate the Workflow**: Create a workflow instance using the configuration object. 4. **Execute the Translation**: Call the workflow's `.read_*()` and `.translate()` / `.translate_async()` methods. 5. **Export/Save the Result**: Call the `.export_to_*()` or `.save_as_*()` methods to get or save the translation result. ## Available Workflows | Workflow | Use Case | Input Formats | Output Formats | Core Config Class | |:---|:---|:---|:---|:---| | **`MarkdownBasedWorkflow`** | Processes rich text documents like PDF, Word, images, etc. The process is: `File -> Markdown -> Translate -> Export`. | `.pdf`, `.docx`, `.md`, `.png`, `.jpg`, etc. | `.md`, `.zip`, `.html` | `MarkdownBasedWorkflowConfig` | | **`TXTWorkflow`** | Processes plain text documents. The process is: `txt -> Translate -> Export`. | `.txt` and other plain text formats | `.txt`, `.html` | `TXTWorkflowConfig` | | **`JsonWorkflow`** | Processes JSON files. The process is: `json -> Translate -> Export`. | `.json` | `.json`, `.html` | `JsonWorkflowConfig` | | **`DocxWorkflow`** | Processes docx files. The process is: `docx -> Translate -> Export`. | `.docx` | `.docx`, `.html` | `docxWorkflowConfig` | | **`XlsxWorkflow`** | Processes xlsx files. The process is: `xlsx -> Translate -> Export`. | `.xlsx`, `.csv` | `.xlsx`, `.html` | `XlsxWorkflowConfig` | | **`SrtWorkflow`** | Processes srt files. The process is: `srt -> Translate -> Export`. | `.srt` | `.srt`, `.html` | `SrtWorkflowConfig` | | **`EpubWorkflow`** | Processes epub files. The process is: `epub -> Translate -> Export`. | `.epub` | `.epub`, `.html` | `EpubWorkflowConfig` | | **`HtmlWorkflow`** | Processes html files. The process is: `html -> Translate -> Export`. | `.html`, `.htm` | `.html` | `HtmlWorkflowConfig` | > In the interactive interface, you can export to PDF format. ## Starting the Web UI and API Service For ease of use, DocuTranslate provides a full-featured Web interface and RESTful API. **Starting the Service:** ```bash # Start the service, listening on port 8010 by default docutranslate -i # Start on a specific port docutranslate -i -p 8011 # You can also specify the port via an environment variable export DOCUTRANSLATE_PORT=8011 docutranslate -i ``` - **Interactive Interface**: After starting the service, please visit `http://127.0.0.1:8010` (or your 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. We will use the `minerU` engine to convert the PDF to Markdown, and then use an LLM for translation. Here is an example using the asynchronous approach. ```python 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 the translator configuration translator_config = MDTranslatorConfig( base_url="https://open.bigmodel.cn/api/paas/v4", # AI platform Base URL api_key="YOUR_ZHIPU_API_KEY", # AI platform API Key model_id="glm-4-air", # Model ID to_lang="English", # Target language chunk_size=3000, # Text chunk size concurrent=10, # Concurrency # glossary_generate_enable=True, # Enable automatic glossary generation # glossary_dict={"Jobs":"乔布斯"} # Pass in a glossary ) # 2. Build the converter configuration (using minerU) converter_config = ConverterMineruConfig( mineru_token="YOUR_MINERU_TOKEN", # Your minerU Token formula_ocr=True # Enable formula recognition ) # 3. Build the main workflow configuration workflow_config = MarkdownBasedWorkflowConfig( convert_engine="mineru", # Specify the parsing engine converter_config=converter_config, # Pass in the converter configuration translator_config=translator_config, # Pass in the translator configuration html_exporter_config=MD2HTMLExporterConfig(cdn=True) # HTML export configuration ) # 4. Instantiate the workflow workflow = MarkdownBasedWorkflow(config=workflow_config) # 5. Read the file and execute the translation print("Starting to read and translate the file...") workflow.read_path("path/to/your/document.pdf") await workflow.translate_async() # Or use the synchronous method # workflow.translate() print("Translation complete!") # 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 have been saved to the ./output folder.") # Or get the content strings directly html_content = workflow.export_to_html() markdown_content = workflow.export_to_markdown() # print(html_content) if __name__ == "__main__": asyncio.run(main()) ``` ### Example 2: Translating a TXT File (using `TXTWorkflow`) For plain text files, the process is simpler as it doesn't require a document parsing (conversion) step. Here is an example using the asynchronous approach. ```python 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="Chinese", ) # 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 the 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 has been 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 is an example using the asynchronous approach. The `json_paths` item in `JsonTranslatorConfig` needs to specify the JSON paths to be translated (satisfying the `jsonpath-ng` syntax). Only values matching the JSON paths will be translated. ```python 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"] # Satisfies jsonpath-ng syntax, values at matching paths 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 the translation workflow.read_path("path/to/your/notes.json") await workflow.translate_async() # Or use the synchronous method # workflow.translate() # 5. Save the result workflow.save_as_json(name="translated_notes.json") print("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 is an example using the asynchronous approach. ```python 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="Chinese", insert_mode="replace", # Options: "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. Read the file and execute the 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("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 a xlsx File (using `XlsxWorkflow`) Here is an example using the asynchronous approach. ```python 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="Chinese", insert_mode="replace", # Options: "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. Read the file and execute the 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("xlsx file has been saved.") # You can also export the translated xlsx as binary text_bytes = workflow.export_to_xlsx() if __name__ == "__main__": asyncio.run(main()) ``` ## Prerequisites and Configuration Details ### 1. Get a Large Language Model API Key The translation functionality relies on large language models. You need to obtain a `base_url`, `api_key`, and `model_id` from the respective AI platform. > Recommended models: Volcengine'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 | Get API Key | baseurl | |---|---|---| | ollama | | http://127.0.0.1:11434/v1 | | lm studio | | http://127.0.0.1:1234/v1 | | openrouter | [Click to get](https://openrouter.ai/settings/keys) | https://openrouter.ai/api/v1 | | openai | [Click to get](https://platform.openai.com/api-keys) | https://api.openai.com/v1/ | | gemini | [Click to get](https://aistudio.google.com/u/0/apikey) | https://generativelanguage.googleapis.com/v1beta/openai/ | | deepseek | [Click to get](https://platform.deepseek.com/api_keys) | https://api.deepseek.com/v1 | | Zhipu AI | [Click to get](https://open.bigmodel.cn/usercenter/apikeys) | https://open.bigmodel.cn/api/paas/v4 | | Tencent Hunyuan | [Click to get](https://console.cloud.tencent.com/hunyuan/api-key) | https://api.hunyuan.cloud.tencent.com/v1 | | Alibaba Cloud Bailian | [Click to get](https://bailian.console.aliyun.com/?tab=model#/api-key) | https://dashscope.aliyuncs.com/compatible-mode/v1 | | Volcengine | [Click to get](https://console.volcengine.com/ark/region:ark+cn-beijing/apiKey?apikey=%7B%7D) | https://ark.cn-beijing.volces.com/api/v3 | | SiliconFlow | [Click to get](https://cloud.siliconflow.cn/account/ak) | https://api.siliconflow.cn/v1 | | DMXAPI | [Click to get](https://www.dmxapi.cn/token) | https://www.dmxapi.cn/v1 | ### 2. PDF Parsing Engine (ignore if not translating PDFs) ### 2.1 Get a minerU Token (online PDF parsing, free, recommended) If you choose `mineru` as the document parsing engine (`convert_engine="mineru"`), you need to apply for a free token. 1. Visit the [minerU official website](https://mineru.net/apiManage/docs) to register and apply for an API. 2. Create a new API Token in the [API Token management interface](https://mineru.net/apiManage/token). > **Note**: minerU tokens have a 14-day validity period. Please re-create them after they expire. ### 2.2. docling Engine Configuration (local PDF parsing) If you choose `docling` as the document parsing engine (`convert_engine="docling"`), it will download the required models from Hugging Face on first use. > A better option is to download `docling_artifact.zip` from [GitHub releases](https://github.com/xunbu/docutranslate/releases) and unzip it to your working directory. **Solution for network issues when downloading `docling` models:** 1. **Set 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 (set in code)**: Add the following code at the beginning of your Python script. ```python import os os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' ``` 2. **Offline use (download the model package in advance)**: * Download `docling_artifact.zip` from [GitHub Releases](https://github.com/xunbu/docutranslate/releases). * Unzip it to your project directory. * Specify the model path in the configuration (if the model is not in the same directory as the script): ```python from docutranslate.converter.x2md.converter_docling import ConverterDoclingConfig converter_config = ConverterDoclingConfig( artifact="./docling_artifact", # Point to the unzipped folder code_ocr=True, formula_ocr=True ) ``` ## FAQ **Q: What if port 8010 is occupied?** A: Use the `-p` parameter to specify a new port, or set the `DOCUTRANSLATE_PORT` environment variable. **Q: Does it support translation of scanned PDFs?** A: Yes. Please use the `mineru` parsing engine, which has powerful OCR capabilities. **Q: Why is the first PDF translation so slow?** A: If you are using the `docling` engine, it needs to download models from Hugging Face on its first run. Please refer to the "Network Issues Solution" above to speed up this process. **Q: How to use it in an intranet (offline) environment?** A: It is entirely possible. You need to meet the following conditions: 1. **Local LLM**: Use tools like [Ollama](https://ollama.com/) or [LM Studio](https://lmstudio.ai/) to deploy a language model locally, and fill in the `base_url` of the local model in `TranslatorConfig`. 2. **Local PDF parsing engine** (only needed for parsing PDFs): Use the `docling` engine and follow the "Offline use" instructions above to download the model package in advance. **Q: How does the PDF parsing cache mechanism work?** A: `MarkdownBasedWorkflow` automatically caches the results of document parsing (file to Markdown conversion) to avoid repeated parsing that consumes time and resources. The cache is stored in memory by default and records the last 10 parses. You can modify the cache size using the `DOCUTRANSLATE_CACHE_NUM` environment variable. **Q: How to make the software go through a proxy?** A: The software does not use a proxy by default. You can enable it by setting the environment variable `DOCUTRANSLATE_PROXY_ENABLED` to `true`. ## Star History