⚡️ Subtitle Translator

Blazing-fast batch subtitle translation for 120+ languages — powered by AI

English · 简体中文

Paste a subtitle file into a general-purpose translator and two things go wrong: the model rewrites your timecodes, and you're doing it one file at a time. Subtitle Translator strips the timing out locally and sends only the dialogue to the engine — the timeline physically cannot be touched — then does a whole season in one drop.

Subtitle Translator is a free, browser-based batch subtitle translation tool for .srt, .ass, .vtt, and .lrc files. With chunked compression and parallel processing it hits ~1 second per episode. Batch-upload a whole season at once, connect to 8 traditional translation APIs (DeepL, Google, Azure, DeepLX, Qwen-MT, TranslateGemma, GTX, Edge) or 27 LLM providers and gateways, and translate into 120+ languages — or into several target languages in a single pass, each exported as its own file. Everything runs locally in your browser; subtitle content and API keys never touch a server. A CLI drives the same engine headlessly when you'd rather script it.

👉 Try it online: https://tools.newzone.top/en/subtitle-translator

Key Features

• Real-Time Translation: Chunked compression + parallel processing → ~1 second per episode (GTX is slightly slower).
• Batch Processing: Drop hundreds of subtitle files at once (a whole season in one go); each file translates and downloads independently with its original filename, and you get an aggregated success/failure summary (e.g. "Exported (3/5)") when the run finishes.
• Multi-Language Output: Translate into multiple target languages in a single pass — each language is exported as its own file with the language code appended (e.g. movie.zh.srt, movie.fr.srt).
• Format Compatibility: Auto-detects .srt, .ass, .vtt, and .lrc. WebVTT NOTE / STYLE / REGION non-cue blocks are correctly skipped (not translated as dialogue). One-click format conversion (SRT ↔ VTT, SRT/VTT → ASS) during translation.
• Bilingual Output: Insert the translation above or below the original; alignment preserved across formats. For SRT / VTT sources you can also export ASS with separate styles for original and translation (Default 70pt white + Secondary 55pt cyan), tweakable in any subtitle editor.
• Context-Aware Translation (LLM only): Sends surrounding lines as context for more coherent dialogue and consistent character voice.
• Structural Separation: Timecodes, cue numbers, ASS headers, and VTT cue IDs are extracted locally — only dialogue text is sent to the engine, so the model can never disrupt your timeline.
• Subtitle Extraction: Strip cues / timing and export clean text (auto-copied to clipboard) for AI summarization, scripts, or content repurposing.
• Unlimited Caching (IndexedDB): All translations cached locally with no browser-storage size limit; refreshing the page doesn't lose translated files.
• 120+ Languages: Translate to/from 120+ languages, with source defaulting to Auto-detect.
• Multi-Locale UI: Powered by next-intl, with full UI translation across 18 languages.
• Command Line: yarn cli runs the same engine, parsers, and cache from a terminal — see Command Line.
• Private by Design: Fully client-side — subtitle content and API keys stay in your browser; LLM requests go directly from your browser to the API endpoint you configure.

Translation APIs

Supports 8 traditional MT APIs and 27 LLM providers and gateways:

Traditional APIs

GTX and Edge need no configuration at all — they are the zero-setup defaults, and each is the other's fallback.

LLM Providers

DeepSeek, OpenAI, Claude, Gemini, Qwen, Moonshot (Kimi), Doubao, Xiaomi MiMo, Zhipu GLM, MiniMax, Baidu ERNIE, Tencent Hunyuan, Mistral, xAI (Grok), Perplexity, Cohere, and YandexGPT.

Gateways

OpenRouter, OpenCode Zen, Groq, SiliconFlow, Atlas Cloud, GitHub Models, Nvidia NIM, Azure OpenAI, LiteLLM, plus any Custom (OpenAI-compatible) endpoint (Ollama / LM Studio / vLLM / Together AI / Fireworks AI etc.).

Providers walled off from browsers by CORS can be routed through an API relay. The built-in relay works out of the box; API Settings → Relay address points every relayed provider at your own deployment of the relay Worker instead.

LLM modes give you:

• Best for: literary works, technical talks, multilingual dialogue
• Customization: configure system / user prompts for a specific translation style
• Temperature Control: adjust AI creativity (0–1 scale)
• Thinking Mode: per-provider toggle for reasoning-capable models

Context-Aware Translation (LLM only)

LLM modes can send surrounding lines as context for each batch, improving dialogue coherence and character-voice consistency.

• Concurrent Lines: max lines translated in parallel (default 20). Too high triggers rate limits.
• Context Lines: lines included per batch as context (default 50). Higher = better coherence but more tokens.

⚠️ Tip: Models under 70B parameters may produce misaligned output. Mainstream online large models (Claude, GPT, DeepSeek, Gemini) are recommended for context mode.

Subtitle Format Support

• Automatic Encoding Detection: jschardet auto-detects UTF-8 / UTF-16 / GBK / Shift-JIS, avoiding garbled output (falls back to UTF-8 if detection fails).
• Filename Preservation: Exported files inherit the original name; multi-language output appends a language code suffix.
• Format Conversion: Convert SRT ↔ VTT and SRT/VTT → ASS during translation — no separate converter needed (identical source/target languages are blocked, so conversion requires a translation pass).

Translation Modes

• Batch Mode (default): drop hundreds of files (a whole season) at once; each file translates independently and auto-downloads, with an aggregated success/failure summary.
• Single-File Mode: instant preview; uploading a new file replaces the current one.

FAQ

Which formats are supported? SRT, ASS, VTT, and LRC. SRT/VTT suit YouTube and HTML5 players; ASS suits Aegisub / anime fansubs (position tags like \an8 auto-restored); LRC suits music lyrics.

Machine translation or LLM? Machine translation (Google, DeepL, Azure, Qwen-MT) is cheap or free but reads flat. LLMs bill per token but produce far more natural dialogue — DeepSeek is the value pick for whole-season batches, Claude Sonnet / GPT give the most natural dialogue, and Gemini's large context handles book-length subtitles.

How do I keep names and proper nouns consistent? Add a glossary in the System Prompt (e.g. "Keep verbatim: iPhone, OpenAI, John Smith") on any LLM engine; all episodes share the same context, so terminology stays consistent across a season.

Do I need "preserve timecodes / line numbers" prompts? No. Timecodes, cue numbers, and headers are extracted locally and re-inserted after translation — the model never sees them. Keep your prompt focused on style, glossary, and tone.

Is it private? Yes. Everything runs client-side: subtitle parsing, translation requests, and caching all happen in your browser. API keys are stored only in local browser storage, and LLM requests go directly from your browser to your configured endpoint.

See the full FAQ in the docs for more.

Command Line

yarn cli translates files headlessly over the same engine as the web app — same parsers, same retry and rate-limit handling, same cache keys. Configure the service once in the browser, hit Export settings, and hand the JSON to the CLI; nothing has to be re-entered.

yarn install   # once

# A whole season into Chinese on the free GTX API — no key, no config.
yarn cli -i s01e01.srt -i s01e02.srt -t zh

# Two targets in one pass, bilingual, using your exported keys/prompts/glossary.
yarn cli -i movie.srt -t zh -t ja --bilingual -s ~/subtitle-settings.json -o out/

# A local model — nothing leaves the machine.
yarn cli -i movie.ass -t zh -m llm --url http://localhost:11434/v1 --model qwen3

# One-off override on top of a settings file.
yarn cli -i movie.vtt -t de -m deepseek --api-key sk-xxx

Output lands beside the input (or in -o <dir>) as movie.zh.srt. Bilingual runs add _bilingual, and for .srt / .vtt sources they default to ASS with separate styles per line — movie.zh_bilingual.ass. Pass --bilingual-format srt to stay in SRT.

Subtitles are not the only input: the same command handles Markdown (.md, .markdown, .mdx — code blocks, links and LaTeX protected by default) and JSON locale files (.json — keys untouched, values translated). yarn cli --list-formats prints the mapping, yarn cli --help the full option list including the Markdown-specific flags.

Runs are resumable: every translated line is cached, so re-running after a Ctrl-C, a rate-limit wall, or a handful of failed lines only pays for what is still missing.

Exit codes: 0 everything translated · 1 finished but some lines soft-failed (kept as source text in the output) or a file failed · 2 bad invocation · 130 cancelled.

Run It Yourself

Node.js >= 24 and Yarn (or npm / pnpm).

git clone https://github.com/rockbenben/subtitle-translator.git
cd subtitle-translator

yarn install
yarn dev        # http://localhost:3000
yarn build      # production build

Documentation & Deployment

For detailed configuration, API setup, and self-hosting instructions, see the Official Documentation.

Quick Deployment: Deploy Guide

Contributing

Contributions are welcome! Feel free to open issues and pull requests.

1. Fork the repo and create a feature branch
2. Run yarn and yarn dev locally
3. Add tests / docs when applicable
4. Submit a PR with a clear description