Automatic Subtitle Translation into Other Languages
Imagine: you have a 90-minute movie in SRT format — about 1200 subtitles. Manual translation of each takes 2–3 minutes, totaling 40 hours. An LLM solution does it in 30–60 seconds, but without the right pipeline, the result will have broken timings, lost meaning, and exceeded line length limits. Even the best model makes errors typical of machine translation: lost characters, inconsistent tenses, incorrect slang interpretation. Our pipeline includes automatic post-check that detects and corrects such artifacts. As a result, you get publication-ready subtitles fully synchronized with the original. Let's look at how each stage works.
How LLM Handles Subtitle Length Constraints?
The SubRip format imposes strict requirements on line length, display duration, and reading speed.
| Parameter | Value |
|---|---|
| Max line length | 42 characters (Netflix) or 84 (two lines) |
| Display duration | 1–7 seconds per block |
| Reading speed | ≤17 chars/s (cinema), ≤20 (documentary) |
| Encoding | UTF-8 with BOM |
GPT-4o-mini (the primary tool) is 3× faster than NLLB-200 with comparable quality for European languages. For rare languages, we use NLLB-200 or GPT-4o.
Why Is Context of Neighboring Subtitles Critical?
Without context, the model translates each phrase in isolation — losing dialogue logic. For example, one subtitle says "He's not coming", next says "Why?" Separate translation gives "Он не придёт" and "Почему?" — the connection is preserved, but if subtitles are separated by a pause, the model might interpret "Why?" as the start of a new topic. Grouping by 20–30 subtitles provides enough context for coherent translation. We use dynamic batching: if a group has many short subtitles, batch size increases to 40, reducing API calls and speeding up processing. If you are uncertain about model choice, contact us — we will run a test on 50 subtitles and provide a sample.
What Is Post-Check and How Does It Improve Quality?
After translation, an automatic script runs that checks each subtitle for compliance: line length, reading speed, punctuation presence. If a parameter is exceeded, the model receives a task to shorten the phrase without losing meaning. Additionally, tense consistency and proper names are checked. For critical content, we add manual verification.
In one project with 5000 subtitles for a physics educational course, we encountered systematic line length exceedance for German — average word length in German is 30% longer than in English. We adapted the prompt, instructing the model to use shorter synonyms, and added automatic trimming in post-check. As a result, all subtitles fit within the 84-character limit without loss of meaning.
How to Set Up the Translation Pipeline in 10 Minutes
- Parsing: extract timings and text from SRT/VTT. Check encoding (UTF-8 with BOM).
- Batching: combine subtitles into groups of 20–30 blocks — this is key to context.
- Translation: send the group to LLM with a prompt containing length and speed constraints.
- Post-check: automatically measure line length and reading speed. If exceeded, run auto-shortening.
- Assembly: restore original timings, generate the final file.
Example prompt for group translation
Translate the following subtitles from English to Russian.
Limits:
- Maximum 84 characters per block (2 lines of 42)
- Preserve meaning, adaptation allowed
- Do not use quotation marks if not in original
- Preserve proper names
- Reading speed ≤20 chars/s
Subtitles:
[1] 00:01:00,000 --> 00:01:04,000
Hello, how are you?
[2] 00:01:05,000 --> 00:01:08,000
I am fine, thank you.
Supported Languages and Models
| Language Set | Recommended Model | Reasoning |
|---|---|---|
| Russian, English, European (FR, DE, ES, IT) | GPT-4o-mini | Faster, cheaper, quality >95% BLEU |
| Rare (Swahili, Vietnamese, Hindi) | NLLB-200 | Specialized model for rare languages |
| Critical content | GPT-4o | Maximum quality, but 10× more expensive |
Processing a 90-minute movie (≈1200 subtitles) takes 30–60 seconds with minimal computational cost.
What Our Work Includes
- Analysis of original subtitles (SRT/VTT) for embedded formats, encoding, timing.
- Prompt tuning for language and style (adaptation of jokes, slang).
- Test run on 50 subtitles — you receive a sample.
- Translation of all subtitles with post-validation.
- Generation of ready SRT/VTT files preserving original timing.
- Documentation: work report, list of replaced terms.
Our Experience
We have completed over 50 subtitle processing projects. Certified specialists ensure the models used are up-to-date.
Contact us for a free test translation of 100 subtitles. We will evaluate your file and propose the best solution. Order automatic subtitle translation — get ready SRT/VTT files for 20 languages within 1 business day. Get a consultation on your project — we will select the optimal model for your tasks.