
co-op-translator
Easily generate translations into multiple languages for your project with a single command, powered by Azure AI Services
Stars: 382

Co-op Translator is a tool designed to facilitate communication between team members working on cooperative projects. It allows users to easily translate messages and documents in real-time, enabling seamless collaboration across language barriers. The tool supports multiple languages and provides accurate translations to ensure clear and effective communication within the team. With Co-op Translator, users can improve efficiency, productivity, and teamwork in their cooperative endeavors.
README:
Easily automate the translation of your documentation into multiple languages
[!TIP]
NEW: Now with GitHub Actions support! Automatically translate your documentation when changes are made to your repository. Learn more.
Type | Name |
---|---|
Language Model |
|
Computer Vision |
[!NOTE] If a computer vision service is not available, the co-op translator will switch to Markdown-only mode.
Co-op Translator breaks language barriers by automating multilingual translations for your projects using advanced Large Language Model (LLM) technology and Azure AI Services. Whether through command line or GitHub Actions, it transforms your content to reach global audiences with minimal effort.
With Co-op Translator, you can:
- Command Line: Quickly translate your documentation on-demand
- GitHub Actions: Automatically translate when your repository changes
- Preserve Formatting: Maintain Markdown syntax and structure across languages
- Translate Images: Extract and translate text embedded in images
Here is an example of how Co-op Translator structures the translations and translates both Markdown files and text within images in your project:
Ready to unlock multilingual accessibility? Get started with Co-op Translator today!
- Automated Translations: Translate text into multiple languages effortlessly.
- GitHub Actions Integration: Automate translations as part of your CI/CD pipeline.
- Markdown Preservation: Maintain correct Markdown syntax during translation.
- Image Text Translation: Extract and translate text within images.
- Advanced LLM Technology: Use cutting-edge language models for high-quality translations.
- Easy Integration: Seamlessly integrate with your existing project setup.
- Simplify Localization: Streamline the process of localizing your project for international markets.
English is often considered the universal language of technology, but many developers worldwide are not native English speakers. This can create barriers in accessing and contributing to technical projects.
Co-op Translator aims to break down these language barriers by providing an easy-to-use tool for automating translations. By making technical documentation accessible in multiple languages, we empower developers, students, and researchers globally.
Join us in revolutionizing global communication! Give a β to Co-op Translator on GitHub and help us break down language barriers together. Your support makes a difference!
The process begins with Markdown and image files from your project folder, which are then processed by various AI services:
-
Language Models:
- Azure OpenAI and other supported LLMs translate text from Markdown files. See the supported models and services for more information.
-
Computer Vision Services (optional):
- Azure Computer Vision extracts text from images, which are then translated by the selected language model. If a computer vision service is not available, the process defaults to Markdown-only mode.
The final translated Markdown and image files are saved in the designated translation folder, ready to be used in multiple languages.
Approach | Command Line | GitHub Actions |
---|---|---|
Best for | One-time, manual execution | Automated execution on repository changes |
Automation | Manual trigger | Automatic on repository events |
Setup | Requires local installation | No local setup needed |
Guide | Command Line Guide | GitHub Actions Guide |
[!NOTE] While this tutorial focuses on Azure resources, you can use any supported language model from the supported models and services list.
- Command reference: Learn about all available commands and their options in detail.
- Multi-language support setup: Before starting the translation process, you can add a table in the README linking to the translated versions of your document.
- Supported Languages: Check the list of supported languages and instructions to add new languages.
- Markdown-Only Mode: Learn how to use Co-op Translator in Markdown-only mode.
Below are some officially managed Microsoft projects that utilize the Co-op Translator:
Project Name | Repository Link |
---|---|
Generative AI Agent for Beginners | GitHub |
Generative AI for Beginners .NET | GitHub |
Learn more about Co-op Translator through our presentations (Click the image below to watch on YouTube.):
-
Open at Microsoft: A brief 18-minute introduction and quick guide on how to use Co-op Translator.
-
Microsoft Reactor: A one-hour detailed step-by-step guide covering everything from understanding what Co-op Translator is, setting up the tool, and using it effectively, to a live demo showcasing its capabilities in action.
This project welcomes contributions and suggestions. Interested in contributing to Azure Co-op Translator? Please see our CONTRIBUTING.md for guidelines on how you can help make Co-op Translator more accessible.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoftβs approach to responsible AI is grounded in ourβ―AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.
The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. We also have an interactive Content Safety Studio that allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.
Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using generation quality and risk and safety metrics.
You can evaluate your AI application in your development environment using the prompt flow SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the prompt flow sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Azure AI Studio.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
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