English Version ---- 中文介绍

Simvoo LTM

Localized Translation LLM for Short Drama and Video Subtitle Localization

---

1. Introduction

Simvoo LTM is an internal multilingual localization translation LLM developed by Simvoo AI. It is designed for short drama subtitles, short-form videos, film dialogue, spoken content, and global content distribution workflows.

Unlike traditional machine translation systems, Simvoo LTM focuses not only on semantic correctness, but also on natural expression, speaker tone, emotional intensity, regional language habits, and long-context consistency.

The core objective of Simvoo LTM is:

To make translated content sound natural to the target audience, not merely correct.

---

2. Model Positioning

Simvoo LTM is a translation-specialized LLM, not a general-purpose chatbot.

The model is mainly designed for the following workflow:

Source Dialogue / Subtitle
        ↓
Context-aware Translation
        ↓
Locale-aware Rewriting
        ↓
Terminology Consistency Check
        ↓
Localized Subtitle Output

Core capabilities include:

---

3. Architecture

The core pipeline of Simvoo LTM consists of the following modules:

3.1 Input Normalization

This module standardizes subtitle and text inputs before model inference.

It handles:

• Subtitle segment cleanup

• Punctuation normalization

• Speaker information formatting

• Special symbol and tag handling

• SRT / VTT / JSON subtitle parsing

This step does not alter the original meaning. It only converts the input into a structured format that is easier for the model to process.

---

3.2 Language & Locale Router

The system identifies the source language, target language, and target locale preference based on task configuration.

Examples:

• en-US: American English

• en-GB: British English

• es-MX: Mexican Spanish

• es-ES: European Spanish

• pt-BR: Brazilian Portuguese

• pt-PT: European Portuguese

• zh-CN: Simplified Chinese

• zh-TW: Traditional Chinese

The Locale Router helps reduce unnatural expressions caused by regional differences within the same language.

---

3.3 Context Builder

Subtitle translation should not be handled sentence by sentence in isolation. Simvoo LTM builds a context window to help the model understand previous and following dialogue, character relationships, and tone shifts.

Context information may include:

• Current subtitle segment

• Surrounding dialogue lines

• Identified speaker names

• Story context

• Previous translation results

• Confirmed terminology and fixed expressions

This module helps reduce:

• Pronoun errors

• Misunderstood character relationships

• Broken emotional continuity

• Inconsistent forms of address

• Contextual translation drift

---

3.4 Translation Core

Translation Core is the main inference module of Simvoo LTM.

The system adapts open-source large language models for translation tasks and enhances them with internal subtitle data, localization samples, and terminology resources.

Currently adapted model families include:

• Qwen series

• LLaMA series

• Experimental Mistral-based variants

The adaptation focuses on:

• Multilingual translation capability

• Subtitle dialogue style

• Short drama tone preservation

• Localization alignment

• Long-context consistency

• Name and terminology consistency

---

3.5 Localization Alignment

Localization Alignment turns a semantically correct translation into a more natural regional expression.

It handles:

• Regional vocabulary differences

• Spoken language patterns

• Forms of address

• Emotional intensity

• Slang and short-form expressions

• Culturally natural rewriting

Example:

Source:
你到底想干什么？

en-US:
What the hell are you trying to do?

en-GB:
What exactly are you trying to do?

es-MX:
¿Qué diablos intentas hacer?

es-ES:
¿Qué se supone que estás haciendo?

---

3.6 Consistency Checker

The Consistency Checker improves stability in batch subtitle translation and long-context tasks.

It checks:

• Character name consistency

• Forms of address

• Named entities

• Terminology matches

• Tone consistency

• Subtitle length

• Possible omissions or over-expansions

This module is typically applied after model generation to reduce manual review costs.

---

4. Supported Languages

Simvoo LTM currently supports 80+ languages and locale variants, with stronger optimization for high-frequency global distribution languages.

4.1 Core Optimized Languages

• English: en-US, en-GB

• Chinese: zh-CN, zh-TW

• Spanish: es-ES, es-MX, es-AR

• Portuguese: pt-BR, pt-PT

• French: fr-FR, fr-CA

• Arabic: ar-EG, ar-Gulf

• Japanese: ja-JP

• Korean: ko-KR

• German: de-DE

• Italian: it-IT

• Vietnamese: vi-VN

• Thai: th-TH

• Indonesian: id-ID

• Turkish: tr-TR

• Russian: ru-RU

4.2 Long-tail Language Support

For long-tail languages, the system relies on base model generalization, parallel corpus enhancement, and rule-based post-processing.

Coverage includes:

• Eastern European languages: Polish, Czech, Romanian, Ukrainian, etc.

• Nordic languages: Swedish, Norwegian, Danish, Finnish, etc.

• South Asian languages: Hindi, Urdu, Bengali, etc.

• Southeast Asian languages: Malay, Filipino, Burmese, etc.

• African regional languages: Swahili, etc.

---

5. Training and Adaptation

Simvoo LTM is not trained from scratch as a general-purpose foundation model. Instead, it is specialized on top of mature open-source base models for translation and localization tasks.

5.1 Base Model Adaptation

The adaptation process includes:

• Instruction format adaptation

• Multilingual translation fine-tuning

• Subtitle corpus enhancement

• Regional expression alignment

• Terminology and character name injection

• Translation style constraint training

5.2 Context-aware Translation Tuning

For subtitle and short drama dialogue scenarios, the model is optimized for context understanding.

Optimization directions include:

• Ellipsis recovery

• Multi-turn dialogue understanding

• Character relationship tracking

• Tone continuity

• Story context preservation

• Long subtitle sequence consistency

5.3 Localization Alignment Training

Localization alignment training helps the model learn more natural expressions for target regions.

Training samples include:

• Parallel subtitle corpora

• Human-reviewed translation samples

• Short drama dialogue samples

• Regional expression comparison data

• Terminology and character-name consistency samples

• Online quality review feedback

---

6. Data Scale

Current internal data scale:

The data is mainly used for fine-tuning, preference optimization, terminology alignment, and human evaluation.

To protect data sources and customer content, this document does not disclose detailed corpus sources, sampling rules, or annotation guidelines.

---

7. Version History

Simvoo LTM has completed four major internal iterations.

7.1 Version Notes

v1.0 Prototype

v1.0 focused on validating whether the translation pipeline could handle subtitle tasks reliably.

Completed items:

• Basic subtitle text input

• SRT parsing and output

• Initial validation for Chinese-English and English-Chinese directions

• Basic terminology table

• First version of human evaluation workflow

v2.0 Adaptation

v2.0 introduced task-specific optimization for short drama and short-form video dialogue.

Completed items:

• Context window support

• Short drama dialogue samples

• Enhanced spoken expression

• Initial character name consistency

• Subtitle length control

v3.0 Alignment

v3.0 focused on regional expression differences and human preference alignment.

Completed items:

• Locale-aware routing

• Regional expression differentiation

• Human preference samples

• Stronger performance for Spanish, Portuguese, English, and other core languages

• Translation consistency checks

v4.0 Production

v4.0 focused on production stability and large-scale batch quality.

Completed items:

• Batch subtitle task optimization

• QA feedback loop

• Long-context consistency enhancement

• Improved terminology matching strategy

• More stable inference pipeline and fallback handling

---

8. Training Compute

Simvoo LTM mainly uses parameter-efficient fine-tuning and multi-stage incremental training. It does not perform full pretraining from scratch.

The following table summarizes the training resource consumption across internal versions:

8.1 Total Training Cost

As of v4.0, the cumulative training and alignment compute is approximately:

Total Training Compute: ~3,448 GPU-hours

This includes:

• Multilingual translation fine-tuning

• Subtitle context training

• Localization alignment

• Human preference sample training

• Terminology consistency enhancement

• Multi-round validation regression tests

8.2 Additional Evaluation Compute

In addition to training, model evaluation, batch regression testing, and online sample replay also consume compute resources.

Including training, evaluation, and regression tests, the cumulative compute consumption from v1.0 to v4.0 is approximately:

Total Compute Used: ~4,438 GPU-hours

These figures are internal accounting estimates for describing R&D investment. Detailed training scripts, sampling strategies, hyperparameters, and deployment topology are not disclosed.

---

9. Inference Pipeline

A typical production inference pipeline:

Subtitle / Dialogue Input
        ↓
Input Normalization
        ↓
Language & Locale Routing
        ↓
Context Window Construction
        ↓
Translation LLM Inference
        ↓
Localization Alignment
        ↓
Terminology Consistency Check
        ↓
Subtitle Length Optimization
        ↓
Localized Subtitle Output

---

10. Input and Output

10.1 Input

Simvoo LTM supports:

• Single-sentence text

• Multi-turn dialogue

• SRT subtitles

• VTT subtitles

• JSON subtitle segments

• Batch subtitle tasks

10.2 Output

The system can output:

• Translated text

• SRT subtitles

• VTT subtitles

• Structured JSON results

• Multilingual subtitle packages

Example output:

{
  "source_language": "zh-CN",
  "target_language": "en-US",
  "locale": "en-US",
  "task_type": "subtitle_translation",
  "segments": [
    {
      "index": 1,
      "source": "你到底想干什么？",
      "translation": "What the hell are you trying to do?",
      "confidence": 0.91
    }
  ]
}

---

11. Evaluation

Simvoo LTM is continuously evaluated using the internal benchmark Simvoo-TransBench.

11.1 Evaluation Set

Simvoo-TransBench v2 contains:

• 18,000+ subtitle samples

• 80+ languages and locale variants

• Short drama, short-form video, film dialogue, and spoken content

• Multi-turn dialogue, emotional expression, sarcasm, slang, and ellipsis cases

11.2 Evaluation Dimensions

11.3 Internal Blind Test

In internal blind tests, Simvoo LTM showed more stable performance than the baseline translation pipeline in short-form video and short drama scenarios.

Human preference results indicate that Simvoo LTM improves localized expression quality by approximately 18%–26% over the baseline system in core languages and high-frequency content scenarios.

Full evaluation details are not publicly disclosed because the test set and human evaluation samples contain internal business data.

---

12. Deployment Status

Simvoo LTM is currently a closed-source internal model. Public model weights are not provided.

Supported deployment modes include:

• Internal API service

• Private deployment

• Batch subtitle task processing

• Translation workflow integration

• Human review platform integration

Depending on business requirements, the deployment environment may include:

• Single-model inference service

• Multi-model routing service

• Batch processing queues

• Terminology-enhanced inference

• Human QA feedback loop

---

13. Use Cases

Simvoo LTM is suitable for:

• Short drama subtitle localization

• TikTok / Reels / Shorts multilingual subtitle translation

• YouTube subtitle translation optimization

• Film clip multilingual distribution

• Overseas advertising copy localization

• Enterprise video internationalization

• Batch multilingual subtitle production

---

14. Core Principle

Simvoo LTM is designed to make translations feel natural to the target audience, rather than sounding like machine translation.

Translate meaning. Localize expression.

---

Simvoo AI Vision

Localized Intelligence for Global Video Content