Automatic Translation of Subtitles into 20+ Languages Using LLMs

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.

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

1. Parsing: extract timings and text from SRT/VTT. Check encoding (UTF-8 with BOM).
2. Batching: combine subtitles into groups of 20–30 blocks — this is key to context.
3. Translation: send the group to LLM with a prompt containing length and speed constraints.
4. Post-check: automatically measure line length and reading speed. If exceeded, run auto-shortening.
5. Assembly: restore original timings, generate the final file.

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

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.

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.