Skip to content

README_en.md

Latest commit

 

History

History
211 lines (143 loc) · 13.3 KB

README_en.md

File metadata and controls

211 lines (143 loc) · 13.3 KB

MTranServer

中文 | English | 日本語 | Français | Deutsch

A high-performance offline translation model server with minimal resource requirements - no GPU needed. Average response time of 50ms per request. Supports translation of major languages worldwide.

Note: This model server focuses on offline translation, response speed, cross-platform deployment, and local execution to achieve unlimited free translation. Due to model size and optimization constraints, the translation quality will not match that of large language models. For high-quality translation, consider using online large language model APIs.

v4 has optimized memory usage, further improved speed, and enhanced stability. If you are using an old version, it is recommended to upgrade immediately!

Online Demo

Website TOKEN Other Interface Provider
ipacel.cc __IpacEL_MT_API_TOKEN__ Immersive Translate: https://MTranServer.ipacel.cc/imme?token=__IpacEL_MT_API_TOKEN__ @ApliNi

Thanks to community contributors for providing trial services for users!

Usage Guide

Now supports desktop one-click launch! Supports Windows, Mac, Linux.

Desktop

Manual Download

Download the latest desktop version for your platform from Releases, install and launch directly.

After the desktop app launches, it will create a tray menu to conveniently manage the service.

The program includes a simple UI and online debug documentation.

For detailed usage instructions, jump to Ecosystem Projects

Preview (latest version has updates):

UI

Documentation

Server

It is recommended to use the desktop app or Docker deployment for better performance and convenience. Manual server deployment is for advanced users.

Quick Start

Programmers can start the server directly via command line:

npx mtranserver@latest

npx can be replaced with any package manager you prefer, such as bunx, pnpx, etc.

Important Note:

When translating a language pair for the first time, the server will automatically download the corresponding translation model (unless offline mode is enabled). This process may take some time depending on your network speed and model size. After the model is downloaded, subsequent translation requests will enjoy millisecond-level response speeds. It is recommended to test a translation once before formal use to allow the server to pre-download and load the model. The program is frequently updated. If you encounter problems, please try updating to the latest version.

Quick Install

npm i -g mtranserver@latest

npm can be replaced with any package manager you prefer, such as bun, pnpm, etc.

Then run mtranserver.

Docker Compose Deployment

Create a compose.yml file in an empty directory, with the following content:

services:
  mtranserver:
    image: xxnuo/mtranserver:latest
    container_name: mtranserver
    restart: unless-stopped
    ports:
      - "8989:8989"
    environment:
      - MT_HOST=0.0.0.0
      - MT_PORT=8989
      - MT_OFFLINE=false
      # - MT_API_TOKEN=your_secret_token_here
    volumes:
      - ./models:/app/models
docker pull xxnuo/mtranserver:latest
docker compose up -d

Ecosystem Projects

IDE Plugins

MTranCode Comment Translation Plugin

Supports VS Code, Cursor, Augment, and other VS Code-based IDEs.

Search for MTranCode in the extension marketplace to install the comment translation plugin.

The plugin defaults to calling the server at http://localhost:8989 for comment and code translation. You can adjust it in settings.

This plugin is forked from vscode-comment-translate.

Browser Extension

MTranBrowser

TODO: Under active development.

If you develop a derivative project, feel free to submit a PR. I will add your project to the ecosystem list.

By the way, the project has been published to npm. You can directly call the simple library interface in other programs to implement translation functionality. For specific information, check the TypeScript type definitions.

Compatible Interfaces

The server provides compatible endpoints for multiple translation plugins:

Endpoint Method Description Supported Plugins
/imme POST Immersive Translation plugin endpoint Immersive Translation
/kiss POST Kiss Translator plugin endpoint Kiss Translator
/deepl POST DeepL API v2 compatible endpoint Clients supporting DeepL API
/deeplx POST DeepLX compatible endpoint Clients supporting DeepLX API
/hcfy POST Selection Translator compatible endpoint Selection Translator
/hcfy POST Selection Translator compatible endpoint Selection Translator
/google/language/translate/v2 POST Google Translate API v2 compatible endpoint Clients supporting Google Translate API
/google/translate_a/single GET Google translate_a/single compatible endpoint Clients supporting Google web translation

Plugin Configuration Guide:

Note:

  • Immersive Translation - Enable Beta features in developer mode in Settings to see Custom API Settings under Translation Services (official tutorial with images). Then increase the Maximum Requests per Second in Custom API Settings to fully utilize server performance. I set Maximum Requests per Second to 512 and Maximum Paragraphs per Request to 1. You can adjust based on your server hardware.

  • Kiss Translator - Scroll down in Settings page to find the custom interface Custom. Similarly, set Maximum Concurrent Requests and Request Interval Time to fully utilize server performance. I set Maximum Concurrent Requests to 100 and Request Interval Time to 1. You can adjust based on your server configuration.

Configure the plugin's custom interface address according to the table below.

Name URL Plugin Setting
Immersive Translation (No Password) http://localhost:8989/imme Custom API Settings - API URL
Immersive Translation (With Password) http://localhost:8989/imme?token=your_token Same as above, change your_token to your MT_API_TOKEN value
Kiss Translator (No Password) http://localhost:8989/kiss Interface Settings - Custom - URL
Kiss Translator (With Password) http://localhost:8989/kiss Same as above, fill KEY with your_token
DeepL Compatible http://localhost:8989/deepl Use DeepL-Auth-Key or Bearer authentication
DeepLX Compatible http://localhost:8989/deeplx Support token parameter or Bearer authentication
Google Compatible http://localhost:8989/google/language/translate/v2 Use key parameter or Bearer authentication
Selection Translator http://localhost:8989/hcfy Support token parameter or Bearer authentication

Regular users can start using the service after setting up the plugin interface address according to the table above.

Command Line Options

./mtranserver [options]

Options:
  -version, -v          Show version information
  -log-level string     Log level (debug, info, warn, error) (default "warn")
  -config-dir string    Configuration directory (default "~/.config/mtran/server")
  -model-dir string     Model directory (default "~/.config/mtran/models")
  -host string          Server host address (default "0.0.0.0")
  -port string          Server port (default "8989")
  -ui                   Enable Web UI (default true)
  -offline              Enable offline mode, disable automatic model download (default false)
  -worker-idle-timeout int  Worker idle timeout in seconds (default 300)
  --download pairs...   Download models for specific language pairs (e.g. --download en_zh zh_en)
  --languages           List all supported language pairs for download

Note: `--download` and `--languages` require network access and do not work in offline mode.

Examples:
  ./mtranserver --host 127.0.0.1 --port 8080
  ./mtranserver --ui --offline
  ./mtranserver -v

Comparison with Similar Projects

Here are some similar projects, you can try them if you have other needs:

Project Name Memory Usage Concurrency Translation Quality Speed Additional Info
NLLB Very High Poor Average Slow Android port RTranslator has many optimizations, but still has high resource usage and is not fast
LibreTranslate Very High Average Average Medium Mid-range CPU processes 3 sentences/s, high-end CPU processes 15-20 sentences/s. Details
OPUS-MT High Average Below Average Fast Performance Tests
Any LLM Extremely High Dynamic Very Good Very Slow High hardware requirements. If you need high concurrency translation, it is recommended to use vllm framework.
MTranServer (This Project) Low High Average Ultra Fast 50ms average response time per request.

Table data is for CPU, English to Chinese scenarios simple testing, not strict testing, non-quantized version comparison, for reference only.

Advanced Configuration Guide

Please refer to API_en.md and the API documentation after startup.

Star History

Star History Chart

Thanks

Bergamot Project for awesome idea of local translation.

Mozilla for the models.