rockbenben/subtitle-translator
Translate a whole season of subtitles in one pass — .srt/.ass/.vtt/.lrc, 120+ languages, 27 LLM providers, timing untouched | 整季字幕一次译完,时轴不动,支持 120+ 语言
What it solves
Translating subtitle files often leads to broken timing or tedious, one-by-one file processing. Standard translators treat subtitle files as plain text, which risks corrupting the timecodes and formatting that keep dialogue synced with video. This tool automates the process, allowing you to translate entire seasons of shows at once while keeping the timing structure perfectly intact.
How it works
This tool runs entirely in your browser or via a command-line interface. It strips out the technical parts of your subtitle file—such as timecodes, cue numbers, and formatting headers—before sending only the dialogue text to your chosen translation engine. Once translated, the dialogue is re-inserted into the original structure. Because it processes files locally, your API keys and subtitle content never leave your machine.
Who it’s for
It is designed for users who need to translate large batches of subtitle files (.srt, .ass, .vtt, .lrc) for personal use, content creation, or media archiving. It is ideal for those who want to use powerful AI models or traditional translation APIs without sacrificing privacy or risking the integrity of their subtitle files.
Highlights
- Batch Processing: Translate hundreds of files at once with parallel processing for high-speed results.
- Format Integrity: Automatically separates dialogue from timing data, ensuring timecodes are never touched or corrupted.
- Flexible Output: Supports bilingual subtitles, multiple target languages in one pass, and automatic format conversion (e.g., SRT to ASS).
- Broad Compatibility: Connects to 8 traditional translation APIs and 27 LLM providers, including options for custom endpoints.
- Private & Local: Operates entirely client-side; your data and API keys stay in your browser or local environment.
- Context-Aware: Uses LLM capabilities to analyze surrounding lines, resulting in more natural, consistent character voices.
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