Translation APIs#

This toolkit integrates 5 translation APIs and 6 mainstream Large Language Model (LLM) interfaces, allowing users to choose the most suitable translation method based on their needs:

Translation API Comparison#

• DeepL: Best for long-form text with natural and fluent output. Does not support web-based API; must be used via local or server-side proxy.
• Google Translate: Offers stable quality, suitable for short phrases and UI content. Supports web-based calls.
• Azure Translate: Supports the most languages, ideal for multilingual translation needs.
• GTX API/Web: Free options suitable for lightweight use. However, they have limited stability and rate caps. For instance, when mrfragger attempted to translate a ~2MB subtitle file (~2 million characters), the GTX API hit its limit after just two translation attempts.

If you require higher speed or quality, you can apply for an API Key here: Google Translate, Azure Translate, DeepL Translate. Refer to the corresponding API Application Guide for instructions.

LLM Translation (AI Language Models)#

In addition to traditional APIs, this tool also supports intelligent translation via various LLMs, including: DeepSeek, OpenAI, Azure OpenAI, Siliconflow, Groq, and customizable Custom LLM options.

• Use Cases: Ideal for content requiring deeper language understanding, such as literary works, technical documents, or multilingual material.
• Highly Customizable: Supports configuration of system and user prompts, allowing for control over translation style, terminology preferences, and more.
• LLM Models: Typically requires specifying the model name provided by the selected service; for Azure OpenAI, enter the deployment name instead.
• Temperature Parameter: Controls creativity vs. stability of the translation output. Higher values yield more diverse and creative results but may reduce accuracy. Lower values ensure stable, consistent output—suitable for formal or technical content.

Local Model Integration Guide#

For users deploying LLMs locally (e.g., via Ollama or LM Studio), the following setup instructions apply. For optimal translation quality, we recommend using models such as qwen2.5-14b-instruct or higher-performance alternatives in your custom configuration.

Sample Default Endpoint URLs#

• Ollama:

http://127.0.0.1:11434/v1/chat/completions

• LM Studio:

http://localhost:61234/v1/chat/completions

CORS Configuration (Cross-Origin Resource Sharing)#

If you're encountering connection failures when accessing your local model from a browser, it may be due to cross-origin policy restrictions. Here’s how to fix it:

• Ollama: Start the service with the following command to allow all origins:

OLLAMA_ORIGINS="*" ollama serve

• LM Studio:

  1. Open the left-hand menu in the app and click the Developer icon.
  2. Go to the local server settings page and click Settings at the top.
  3. Check the Enable CORS box.

  

Once configured, this tool will be able to access your local LLM models successfully. (Special thanks to mrfragger for sharing this setup guide.)

Language Support#

This tool supports translation between over 50 languages, encompassing a broad range of European, Asian, and some African languages. It is suitable for various multilingual content processing scenarios. Supported languages include: English, Chinese, Traditional Chinese, Portuguese, Italian, German, Russian, Spanish, French, Japanese, Korean, Arabic, Turkish, Polish, Ukrainian, Dutch, Greek, Hungarian, Swedish, Danish, Finnish, Czech, Slovak, Bulgarian, Slovenian, Lithuanian, Latvian, Romanian, Estonian, Indonesian, Malay, Hindi, Bengali, Vietnamese, Norwegian, Hebrew, Thai, Filipino (Tagalog), Uzbek, Kyrgyz, Turkmen, Kazakh, Bhojpuri, Kannada, Amharic, Gujarati, Javanese, Persian, Tamil, Swahili, Hausa, Telugu, and Marathi.

For detailed information on supported languages, refer to the official documentation of each service:

• Languages supported by DeepL
• Languages supported by Google Translate
• Languages supported by Azure Translator