A lightweight multilingual translation service based on Rust and Bergamot translation engine, compatible with multiple translation frontend APIs.

简体中文

This project originated when I discovered the MTranServer repository, which uses Firefox Translations Models for machine translation and is compatible with APIs like Immersive Translate and Kiss Translator, but found that it wasn't open-sourced yet.

While searching for similar projects, I found Mozilla's translation-service, which works but hasn't been updated for a year and isn't compatible with Immersive Translate or Kiss Translator APIs. Since that project is written in C++ and I'm not very familiar with C++, I rewrote this project in Rust.

• 💪 Written in Rust for excellent performance and low memory footprint
• 🔄 Based on Bergamot Translator engine used in Firefox
• 🧠 Compatible with Firefox Translations Models
• 🔍 Built-in language detection with automatic source language identification
• 🔌 Supports multiple translation API formats:
  • Native API
  • Immersive Translate API
  • Kiss Translator API
  • HCFY API
  • DeepLX API
• 🔑 API key protection support
• 🐳 Docker deployment ready

• Web Framework: Axum
• Translation Engine: Bergamot Translator
• Translation Models: Firefox Translations Models
• Language Detection: Whichlang

Docker is the only recommended deployment method for this service.

# Create models directory
mkdir -p models
# Download your models here
# Pull and start container
docker run -d --name translation-service \
  -p 3000:3000 \
  -v "$(pwd)/models:/app/models" \
  ghcr.io/linguaspark/server:main

docker run -d --name translation-service \
  -p 3000:3000 \
  docker.cnb.cool/aalivexy/translation-service:latest

Note: The English-Chinese model image is about 70MiB, and each worker uses approximately 300MiB+ of memory with low translation latency.

Create a compose.yaml file:

services:
  translation-service:
    image: ghcr.io/linguaspark/server:main
    ports:
      - "3000:3000"
    volumes:
      - ./models:/app/models
    environment:
      API_KEY: "your_api_key"  # Optional, leave empty to disable API key protection
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "/bin/sh", "-c", "echo -e 'GET /health HTTP/1.1\r\nHost: localhost:3000\r\n\r\n' | timeout 5 bash -c 'cat > /dev/tcp/localhost/3000' && echo 'Health check passed'"]
      interval: 30s
      timeout: 10s
      retries: 3

Start the service:

docker compose up -d

If you need to create a custom image with specific language pairs, use this Dockerfile template:

FROM ghcr.io/linguaspark/server:main

COPY ./your-models-directory /app/models

ENV MODELS_DIR=/app/models
ENV NUM_WORKERS=1
ENV IP=0.0.0.0
ENV PORT=3000
ENV RUST_LOG=info

EXPOSE 3000

ENTRYPOINT ["/app/server"]

1. Download pre-trained models from Firefox Translations Models
2. Place them in the models directory with the following structure:

models/
├── enzh/  # Language pair directory name format: "[source language code][target language code]"
│   ├── model.intgemm8.bin  # Translation model
│   ├── model.s2t.bin       # Shortlist file
│   ├── srcvocab.spm        # Source language vocabulary
│   └── trgvocab.spm        # Target language vocabulary
└── zhen/  # Another language pair
    └── ...

The translation service will automatically scan all language pair directories under the models directory and load them. Directory names should follow the [source language][target language] format using ISO 639-1 language codes.

Variable Name	Description	Default Value
MODELS_DIR	Path to models directory	/app/models
NUM_WORKERS	Number of translation worker threads	1
IP	IP address for the service to listen on	127.0.0.1
PORT	Port for the service to listen on	3000
API_KEY	API key (leave empty to disable)	""
RUST_LOG	Log level	info

POST /translate

Request body:

{
  "text": "Hello world",
  "from": "en",  // Optional, omit to auto-detect
  "to": "zh"
}

Response:

{
  "text": "你好世界",
  "from": "en",
  "to": "zh"
}

POST /detect

Request body:

{
  "text": "Hello world"
}

Response:

{
  "language": "en"
}

POST /imme

Request body:

{
  "source_lang": "auto",  // Optional, omit to auto-detect
  "target_lang": "zh",
  "text_list": ["Hello world", "How are you?"]
}

Response:

{
  "translations": [
    {
      "detected_source_lang": "en",
      "text": "你好世界"
    },
    {
      "detected_source_lang": "en",
      "text": "你好吗？"
    }
  ]
}

POST /kiss

Request body:

{
  "text": "Hello world",
  "from": "en",  // Optional, omit to auto-detect
  "to": "zh"
}

Response:

{
  "text": "你好世界",
  "from": "en",
  "to": "zh"
}

POST /hcfy

Request body:

{
  "text": "Hello world",
  "source": "英语",  // Optional, omit to auto-detect
  "destination": ["中文(简体)"]
}

Response:

{
  "text": "Hello world",
  "from": "英语",
  "to": "中文(简体)",
  "result": ["你好世界"]
}

POST /deeplx

Request body:

{
  "text": "Hello world",
  "source_lang": "EN",
  "target_lang": "ZH"
}

Response:

{
  "code": 200,
  "id": 1744646400,
  "data": "你好世界",
  "alternatives": [],
  "source_lang": "EN",
  "target_lang": "ZH",
  "method": "Free"
}

GET /health

Response:

{
  "status": "ok"
}

If the API_KEY environment variable is set, all API requests must provide authentication credentials using one of the following methods:

1. Authorization header: Authorization: Bearer your_api_key
2. Query parameter: ?token=your_api_key

This project is open-sourced under the AGPL-3.0 license.

• Bergamot Translator - Translation engine
• Firefox Translations Models - Translation models
• MTranServer - Inspiration
• Mozilla Translation Service - Reference implementation