Translation APIs#

This tool integrates 5 translation APIs and 6 major Large Language Model (LLM) interfaces, allowing users to choose the appropriate translation method based on their needs.

Translation API Comparison#

• DeepL: Best for long-form text, providing more natural and fluent translations. It does not support a web-based API and requires a local or server-side proxy to be called.
• Google Translate: Offers stable translation quality, suitable for short sentences and interface text, and supports web-based calls.
• Azure Translate: Has the most extensive language support, making it ideal for multilingual translation needs.
• GTX API/Web: Free translation options suitable for lightweight use, but with limited stability and rate limits. For example, when user mrfragger translated a subtitle file of about 2 million characters (~2MB), the GTX API limit was triggered after only two translation runs.

If you have higher requirements for translation speed and quality, you can apply for your own API Key: Google Translate, Azure Translate, DeepL Translate. For the application process, refer to the relevant API application tutorial.

LLM Translation (AI Large Models)#

In addition to traditional translation APIs, this tool also supports calling various LLMs for intelligent translation, including DeepSeek, OpenAI, Azure OpenAI, Siliconflow, Groq, and a freely configurable Custom LLM.

• Use Case: Suitable for content that requires a high degree of language understanding, such as literary works, technical documents, and multilingual materials.
• Highly Customizable: Supports configuring a System Prompt and a User Prompt, allowing for flexible control over translation style, terminology preferences, and more to meet diverse translation needs.
• LLM Model: In general, enter the model name provided by the selected interface. If using Azure OpenAI, you must enter the corresponding deployment name.
• Temperature: Controls the creativity and stability of the translation results. A higher value generates more diverse and creative content but may reduce accuracy. A lower value produces more stable and consistent output, suitable for formal or highly technical scenarios.

Local Model Connection Guide#

For users who wish to deploy large models locally (e.g., using Ollama, LM Studio), you can configure the connection to this tool as follows. To achieve better translation quality, it is recommended to use qwen2.5-14b-instruct or a higher-performance model in your custom configuration.

Default Endpoint Address Examples#

• Ollama:

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

• LM Studio:

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

Cross-Origin Access Configuration (CORS)#

If you encounter a connection failure when calling a locally deployed model from a browser, it may be due to the browser's cross-origin policy. Here is how to resolve it:

• Ollama: You need to start the service with the following command to allow access from all origins:

OLLAMA_ORIGINS="*" ollama serve

• LM Studio:

  1. Open the left-side menu in the software and click the "Developer" icon.
  2. Go to the local server settings page and click "Settings" at the top.
  3. Check the "Enable CORS" checkbox.

After completing the above configuration, the tool will be able to successfully call your local LLM. (Special thanks to mrfragger for sharing this configuration advice.)

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