MTranServer/docs

MTranServer

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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. Waiting for the official release! The dev version is not recommended for upgrade!

Usage Guide

Download the latest version from Releases and start the program in the command line.

MTranServer is mainly for server use, so currently only command line service and Docker deployment are available.

I will improve MTranDesktop for desktop use in the future, contributions are welcome.

After the server starts, the console will output the address of the simple UI and the online documentation address. Below is a preview:

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)

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

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_ENABLE_UI=true
      - MT_OFFLINE=false
      # - MT_API_TOKEN=your_secret_token_here
    volumes:
      - ./models:/app/models

docker pull xxnuo/mtranserver:latest
docker compose up -d

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, the engine startup also requires a few seconds. Once ready, subsequent translation requests will enjoy millisecond-level response times. It's recommended to test a translation before actual use to allow the server to pre-download and load the models.

The program is often updated, if you encounter problems, you can try to update to the latest version.

Translation Plugin Compatible Endpoints

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
/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
/hcfy	POST	Selection Translator compatible endpoint	Selection Translator

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
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.

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 to control translation concurrency through memory and VRAM usage.
MTranServer (This Project)	Low	High	Average	Ultra Fast	50ms average response time per request. v4 has optimized memory usage. Waiting for the official release!

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

Thanks

Bergamot Project for awesome idea of local translation.

Mozilla for the models.