MTranServer

Mini Translation Server Beta Version ⭐️ Please give me a Star

中文 | 日本語 | English

A high-performance offline translation server with minimal resource requirements - runs on CPU with just 1GB memory, no GPU needed. Average response time of 50ms per request. Supports translation of major languages worldwide.

Translation quality comparable to Google Translate.

Note: This model focuses on speed and private deployment on various devices, so the translation quality will not match that of large language models.

For high-quality translation, consider using online large language model APIs.

Comparison with Similar Projects (CPU, English to Chinese)

Project Name	Memory Usage	Concurrency	Translation Quality	Speed	Additional Info
facebook/nllb	Very High	Poor	Average	Slow	Android's RTranslator has optimizations but still has high resource usage and slower speed
LibreTranslate	Very High	Average	Average	Medium	Mid-range CPU: 3 sentences/s, high-end CPU: 15-20 sentences/s. Details
OPUS-MT	High	Average	Below Average	Fast	Performance Benchmarks
Any LLM	Extremely High	Dynamic	Good	Dynamic Very Slow	32B or more parameter models perform well, but require high hardware requirements
MTranServer (This Project)	Low	High	Average	Ultra Fast	50ms average response time per request

The small-parameter quantized versions of the existing large Transformer-based large languagemodels are not considered because actual research and testing have shown that the translation quality is highly unstable, prone to disordered translations, severe hallucinations, and slow speeds. We will test the Diffusion architecture-based language models once they are released.

Note: Non-rigorous testing, non-quantized version comparison, for reference only.

Docker Compose Server Deployment

Currently only supports Docker deployment on amd64 architecture CPUs.

Support for ARM and RISC-V architectures is under development 😳

You can also try it out by installing Docker Desktop on your computer and following the guide below to deploy with Docker Compose.

1. Preparation

Create a folder for configuration files and run the following commands in terminal:

mkdir mtranserver
cd mtranserver
touch config.ini
touch compose.yml
mkdir models

Configuration

1.1 Open config.ini with an editor and write:

CORE_API_TOKEN=your_token

Note: Change your_token to your own password using English letters and numbers.

For internal network use, setting a password is optional. However, for cloud servers, it's strongly recommended to set a password to protect against scanning, attacks, and abuse.

1.2 Open compose.yml with an editor and write:

Note: To change the port, modify the ports value. For example, change to 8990:8989 to map the service port to local port 8990.

services:
  mtranserver:
    image: xxnuo/mtranserver:latest
    container_name: mtranserver
    restart: unless-stopped
    ports:
      - "8989:8989"
    volumes:
      - ./models:/app/models
      - ./config.ini:/app/config.ini

1.3 Optional Step

If you cannot download the image normally in mainland China, you can import the image as follows:

Open Mainland China Download Link (includes Docker image)

Enter the Docker Image Download folder, download the latest image mtranserver.image.tar to your Docker machine.

Open terminal in the download directory and run the following command to import the image:

docker load -i mtranserver.image.tar

Then proceed normally to the next step to download models.

2. Download Models

Mainland China Download Link (includes Docker image) Models are in the Download Models folder

International Download Link

Extract each language's compressed package into the models folder.

Example folder structure with English-Chinese model:

compose.yml
config.ini
models/
├── enzh
│   ├── lex.50.50.enzh.s2t.bin
│   ├── model.enzh.intgemm.alphas.bin
│   └── vocab.enzh.spm

Example with Chinese-English and English-Chinese models:

compose.yml
config.ini
models/
├── enzh
│   ├── lex.50.50.enzh.s2t.bin
│   ├── model.enzh.intgemm.alphas.bin
│   └── vocab.enzh.spm
├── zhen
│   ├── lex.50.50.zhen.t2s.bin
│   ├── model.zhen.intgemm.alphas.bin
│   └── vocab.zhen.spm

Only download the models you need.

Note: For example, Chinese to Japanese translation first translates Chinese to English, then English to Japanese, requiring both zhen and enja models. Other language translations work similarly.

3. Start Service

First, test the service to ensure models are placed correctly, can load normally, and the port isn't occupied.

docker compose up

Example normal output:

[+] Running 2/2
 ✔ Network sample_default  Created  0.1s 
 ✔ Container mtranserver   Created  0.1s 
Attaching to mtranserver
mtranserver  | (2025-03-03 12:49:24) [INFO    ] Using maximum available worker count: 16
mtranserver  | (2025-03-03 12:49:24) [INFO    ] Starting Translation Service
mtranserver  | (2025-03-03 12:49:24) [INFO    ] Service port: 8989
mtranserver  | (2025-03-03 12:49:24) [INFO    ] Worker threads: 16
mtranserver  | Successfully loaded model for language pair: enzh
mtranserver  | (2025-03-03 12:49:24) [INFO    ] Models loaded.
mtranserver  | (2025-03-03 12:49:24) [INFO    ] Using default max parallel translations: 32
mtranserver  | (2025-03-03 12:49:24) [INFO    ] Max parallel translations: 32

Then press Ctrl+C to stop the service, and start it officially:

docker compose up -d

The server will now run in the background.

4. API Documentation

In the following tables, localhost can be replaced with your server address or Docker container name.

The port 8989 can be replaced with the port value you set in compose.yml.

If CORE_API_TOKEN is not set or empty, translation plugins use the API without password.

If CORE_API_TOKEN is set, translation plugins use the API with password.

Replace your_token in the following tables with your CORE_API_TOKEN value from config.ini.

Translation Plugin Interfaces:

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 5000 and Maximum Paragraphs per Request to 10. 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. Note: The first request will be slower because it needs to load the model. Subsequent requests will be faster.

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 CORE_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

Regular users can start using the service after setting up the plugin interface address according to the table above. Skip to "How to Update" below.

Developer APIs:

Base URL: http://localhost:8989

Name	URL	Request Format	Response Format	Auth Header
Service Version	/version	None	None	None
Language Pair List	/models	None	None	Authorization: your_token
Standard Translation	/translate	{"from": "en", "to": "zh", "text": "Hello, world!"}	{"result": "你好，世界！"}	Authorization: your_token
Batch Translation	/translate/batch	{"from": "en", "to": "zh", "texts": ["Hello, world!", "Hello, world!"]}	{"results": ["你好，世界！", "你好，世界！"]}	Authorization: your_token
Health Check	/health	None	{"status": "ok"}	None
Heartbeat Check	/__heartbeat__	None	Ready	None
Load Balancer Heartbeat	/__lbheartbeat__	None	Ready	None

5. How to Update

As this is a beta version of server and models, you may encounter issues. Regular updates are recommended.

Download new models, extract and overwrite the original models folder, then update and restart the server:

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

Other Information

Windows, Mac, and Linux standalone client software version MTranServerCore is under development, please be patient.

You can also try it out by installing Docker Desktop on your computer and following the guide above to deploy with Docker Compose.

The server-side translation inference framework uses the C++-written marian-nmt framework.

Server API source code repository: MTranServerCore (not yet complete, please be patient)

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Contact Me

WeChat: x-xnuo

X: @realxxnuo

Feel free to connect with me to discuss technology and open-source projects!

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