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| title: Translation Engine API | |
| emoji: 🌍 | |
| colorFrom: indigo | |
| colorTo: green | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| short_description: Multi-model Translation API powered by CTranslate2 | |
| # 🌍 Translation Engine API | |
| A high-performance, multi-engine Translation API built with **FastAPI** and **Scalar**. | |
| This Space serves local CPU-optimized machine learning models alongside cloud API providers. It is designed to act as the primary translation backend for an upcoming **Django web application**, but functions completely as a **standalone REST API**. | |
| --- | |
| ## Interactive API Documentation | |
| Interactive Scalar documentation is hosted directly on the root endpoint: | |
| 👉 **[Open API Docs & Sandbox](/scalar)** | |
| --- | |
| ## Available Translation Engines | |
| ### 1. Local CPU Models (Offline & Private) | |
| Local models are automatically downloaded, quantized to **INT8 / GGUF** format, and cached in ephemeral storage for fast CPU execution with minimal RAM footprint. | |
| | Model ID | Provider Name | Base Weights | Precision | Target Languages / Features | | |
| | :--- | :--- | :--- | :--- | :--- | | |
| | `nllb` | **NLLB-200 (600M)** | Meta `nllb-200-distilled-600M` | `int8` | Fast general translation (200+ languages) | | |
| | `nllb_1_3b` | **NLLB-200 (1.3B)** | Meta `nllb-200-distilled-1.3B` | `int8` | Higher accuracy general translation | | |
| | `afrinllb` | **AfriNLLB (600M)** | `AfriNLP/AfriNLLB-12enc-12dec-full-ft-kd` | `int8` | Knowledge-distilled for 15+ African languages | | |
| | `madlad` | **MADLAD-400 (3B)** | `google/madlad400-3b-mt` | `int8` | Multilingual translation across 400+ languages | | |
| | `swahili_gemma` | **Swahili Gemma (1B)** | `CraneAILabs/swahili-gemma-1b-GGUF` | `Q4_K_M` | Fine-tuned for English ↔ Swahili translation | | |
| ### 2. Cloud API Engines (Bring-Your-Own-Key) | |
| Cloud API providers allow accessing large language models. You can either configure environment variables on the server or pass your API key dynamically in the JSON payload per request. | |
| | Provider ID | Provider Name | Default Model | Required Client Key | | |
| | :--- | :--- | :--- | :--- | | |
| | `gemini` | **Google Gemini** | `gemini-1.5-flash` | `api_key` (Gemini API Key) | | |
| | `groq` | **Groq LLaMA** | `llama-3.3-70b-versatile` | `api_key` (Groq API Key) | | |
| | `groq_qwen` | **Groq Qwen** | `qwen-2.5-coder-32b` | `api_key` (Groq API Key) | | |
| --- | |
| ## How to Use the API | |
| ### 1. Check Available Engines | |
| ```bash | |
| GET / | |
| ``` | |
| ### 2. Translate via Local Model (e.g., AfriNLLB) | |
| ```bash | |
| POST /translate | |
| Content-Type: application/json | |
| { | |
| "text": "Is it not a beautiful Tuesday morning?", | |
| "source": "eng_Latn", | |
| "target": "swh_Latn", | |
| "engine": "afrinllb" | |
| } | |
| ``` | |
| ### 3. Translate via Cloud API (Passing API Key dynamically) | |
| If no server-side API key is set, clients can supply their own key directly: | |
| ```bash | |
| POST /translate | |
| Content-Type: application/json | |
| { | |
| "provider": "gemini", | |
| "text": "Good morning, how can I help you today?", | |
| "src": "en", | |
| "tgt": "sw", | |
| "api_key": "YOUR_GEMINI_API_KEY_HERE" | |
| } | |
| ``` | |
| --- | |
| ## Environment Variables Configuration | |
| If you are hosting this Space yourself or running it locally, you can set optional environment keys in Space Settings: | |
| ```env | |
| # Optional Cloud Keys (If set, clients do not need to provide their own keys) | |
| GEMINI_API_KEY=your_gemini_key | |
| GROQ_API_KEY=your_groq_key | |
| # CTranslate2 / CPU Threading Controls | |
| OMP_NUM_THREADS=8 | |
| CT2_COMPUTE_TYPE=int8 | |
| ``` | |
| --- | |
| ## 📜 Citations & Credits | |
| This project builds upon open-source research and model checkpoints from the AI community: | |
| ### AfriNLLB | |
| ```bibtex | |
| @inproceedings{moslem-etal-2026-afrinllb, | |
| title = "{A}fri{NLLB}: Efficient Translation Models for African Languages", | |
| author = "Moslem, Yasmin and Wassie, Aman Kassahun and Gizachew, Amanuel", | |
| booktitle = "Proceedings of the Seventh Workshop on African Natural Language Processing (AfricaNLP)", | |
| year = "2026", | |
| publisher = "Association for Computational Linguistics" | |
| } | |
| ``` | |
| ### Swahili Gemma | |
| * Fine-tuned and quantized GGUF weights provided by [Crane AI Labs](https://huggingface.co/CraneAILabs/swahili-gemma-1b-GGUF). | |
| ### Meta NLLB-200 & Google MADLAD-400 | |
| * Meta AI: *No Language Left Behind: Scaling Human-Centered Machine Translation* | |
| * Google Research: *MADLAD-400: A Multilingual Machine Translation Dataset and Model* | |
| ``` |