Subtitle Translation Skill

Translate SRT subtitle files efficiently using LLM APIs with proper cost management.

When to Use

• User wants to translate

  .srt

  subtitle files
• Batch translation of multiple episodes/movies
• Translation between any language pair (default: EN→FR)

Step 0: Ask User for API Provider

Always ask the user which provider to use:

Which API provider do you want to use?

1. **OpenRouter** (Recommended) - Best price/quality, many models
2. **OpenAI** - GPT-4o-mini, reliable but needs credit card
3. **Ollama Local** - Free, runs on your machine (needs GPU)
4. **Ollama Hostinger** - Free, runs on your VPS

For large batches (100+ episodes): OpenRouter or Ollama Hostinger
For small batches (<20 episodes): Any option works

Provider Configuration

https://openrouter.ai/api/v1/chat/completions

https://api.openai.com/v1/chat/completions

http://localhost:11434/api/chat

http://YOUR_VPS_IP:11434/api/chat

Step 1: Choose Model Based on Complexity

Ask user about subtitle complexity:

What type of content are you translating?

A) Simple dialogue (sitcoms, slice-of-life) → Fast/cheap model
B) Standard content (action, drama) → Balanced model
C) Complex content (technical, poetry, wordplay) → Quality model

Model Recommendations (December 2024)

mistral-7b-instruct

mistral:7b

llama-3.3-70b-instruct

gpt-4o-mini

llama3.1:70b

claude-3.5-sonnet

gpt-4o

llama3.1:70b

Low-Resource Servers (VPS < 16GB RAM)

For servers with limited RAM, use lightweight multilingual models:

aya:8b

mistral:7b

gemma2:9b

Aya 8B is recommended for subtitle translation on VPS because:

• Trained specifically for multilingual tasks (100+ languages)
• Low memory footprint (~6GB VRAM/RAM)
• Good quality for dialogue translation
• Free with Ollama

# Install on VPS
ollama pull aya:8b

# Test
ollama run aya:8b "Translate to French: Hello, how are you?"

Live Model Search (OpenRouter)

def search_models(min_context=8000):
    """Search OpenRouter for available models with pricing"""
    r = requests.get("https://openrouter.ai/api/v1/models")
    models = r.json().get("data", [])

    # Filter and sort by price
    suitable = []
    for m in models:
        ctx = m.get("context_length", 0)
        if ctx >= min_context:
            price_in = m.get("pricing", {}).get("prompt", 0)
            price_out = m.get("pricing", {}).get("completion", 0)
            suitable.append({
                "id": m["id"],
                "name": m.get("name", m["id"]),
                "context": ctx,
                "price_in": float(price_in) * 1_000_000,  # Per M tokens
                "price_out": float(price_out) * 1_000_000
            })

    return sorted(suitable, key=lambda x: x["price_in"])[:10]

Key Learnings (Production-Tested)

Cost Reality Check

Always multiply naive estimates by 1.5x due to:

• System prompts repeated per batch
• JSON formatting overhead
• Instruction tokens

Optimal Configuration

BATCH_SIZE = 25          # Subtitles per API request (sweet spot)
DELAY_BETWEEN_REQUESTS = 0.5  # Avoid rate limits
MAX_RETRIES = 3          # Per batch
REQUEST_TIMEOUT = 90     # Seconds

Implementation Steps

1. Parse SRT Format

def parse_srt(content):
    """Parse SRT into list of (index, timing, text) tuples"""
    blocks = []
    current = []
    for line in content.strip().split('\n'):
        if line.strip() == '':
            if current:
                idx = current[0]
                timing = current[1]
                text = '\n'.join(current[2:])
                blocks.append((idx, timing, text))
                current = []
        else:
            current.append(line)
    if current:
        idx = current[0]
        timing = current[1]
        text = '\n'.join(current[2:])
        blocks.append((idx, timing, text))
    return blocks

2. HTML Tag Preservation (Critical)

Never send HTML tags to the LLM - they get corrupted. Strip before, reapply after.

import re

def extract_formatting(text):
    """Extract HTML tags with positions for later restoration"""
    tags = []
    for match in re.finditer(r'<[^>]+>', text):
        tags.append((match.start(), match.end(), match.group()))
    return tags

def strip_html_tags(text):
    """Remove HTML tags for clean translation"""
    return re.sub(r'<[^>]+>', '', text)

def apply_formatting(translated, original_tags):
    """Reapply original HTML structure to translation"""
    if not original_tags:
        return translated
    # Preserve italic tags at start/end
    result = translated
    for start, end, tag in original_tags:
        if tag == '<i>' and not result.startswith('<i>'):
            result = '<i>' + result
        elif tag == '</i>' and not result.endswith('</i>'):
            result = result + '</i>'
    return result

3. Batch Translation with Retry Queue

def translate_batch(texts, source='English', target='French'):
    """Translate batch of texts, return list or None on failure"""
    prompt = f"""Translate these {source} subtitles to {target}.
Return ONLY a JSON array of translated strings, same order.
Keep it natural and conversational.

{json.dumps(texts, ensure_ascii=False)}"""

    # API call with retries
    for attempt in range(MAX_RETRIES):
        try:
            response = call_api(prompt)
            return json.loads(response)
        except:
            time.sleep(2 ** attempt)  # Exponential backoff
    return None

4. Failed Batch Queue

Store failed batches for later retry:

def add_failed_batch(file_path, batch_index, blocks_data):
    """Queue failed batch for retry"""
    failed_data = load_failed_batches()
    failed_data["batches"].append({
        "fr_srt": str(file_path),
        "batch_index": batch_index,
        "blocks": blocks_data,
        "retry_count": 0,
        "timestamp": datetime.now().isoformat()
    })
    save_failed_batches(failed_data)

5. Resilient Execution

Always wrap main() in auto-restart:

def run_resilient():
    """Auto-restart on errors (up to 50 times)"""
    max_restarts = 50
    restart_count = 0

    while restart_count < max_restarts:
        try:
            main()
            break
        except KeyboardInterrupt:
            log("Manual stop (Ctrl+C)")
            break
        except Exception as e:
            restart_count += 1
            log(f"ERROR: {e}")
            log(f"Auto-restart {restart_count}/{max_restarts} in 30s...")
            time.sleep(30)

6. Progress Tracking

PROGRESS_FILE = "translation_progress.json"

def load_progress():
    try:
        with open(PROGRESS_FILE) as f:
            return json.load(f)
    except:
        return {"completed": [], "failed": [], "total_cost": 0.0}

def save_progress(state):
    state["last_update"] = datetime.now().isoformat()
    with open(PROGRESS_FILE, 'w') as f:
        json.dump(state, f, indent=2)

API Configuration

OpenRouter (Recommended)

OPENROUTER_API_KEY = "sk-or-v1-..."
MODEL = "meta-llama/llama-3.3-70b-instruct"
API_URL = "https://openrouter.ai/api/v1/chat/completions"

headers = {
    "Authorization": f"Bearer {OPENROUTER_API_KEY}",
    "Content-Type": "application/json"
}

Check Credits

def get_credits():
    r = requests.get("https://openrouter.ai/api/v1/credits",
                     headers={"Authorization": f"Bearer {API_KEY}"})
    data = r.json().get("data", {})
    return data.get("total_credits", 0) - data.get("total_usage", 0)

File Naming Convention

movie.eng.srt

movie.fr.srt

episode.en.srt

episode.fr.srt

Cost Estimation Formula

episodes × 250 subtitles × 50 tokens × 2 (in+out) × price/token × 1.5 (overhead)

Example: 600 episodes EN→FR with Llama 3.3 70B:

• Naive: 600 × 250 × 50 × 2 × $0.20/M = $3.00
• Real: $3.00 × 1.5 = ~$4.50

Common Issues & Solutions

run_resilient()

Skill Chaining

Skills Required Before

• None (can work standalone)

Input Expected

• Directory path containing

  .srt

  files
• Source/target languages (default: EN→FR)
• API provider choice (OpenRouter/OpenAI/Ollama)
• API key (if not Ollama)

Output Produced

• Translated

  .srt

  files with language suffix

• translation_progress.json

  - tracks completed files

• failed_batches.json

  - retry queue

• translation.log

  - detailed logs

Compatible Skills After

• video-transcoding: Burn subtitles into video
• media-organization: Organize translated files

Tools Used

• Write

  (create translation script)

• Bash

  (run translation)

• Read

  (check progress/logs)

• AskUserQuestion

  (provider/model selection)

• WebFetch

  (live model pricing)

Visual Workflow

User: "Translate my subtitles to French"
    ↓
[Ask] Which API provider?
    ├─► OpenRouter (recommended)
    ├─► OpenAI
    ├─► Ollama Local
    └─► Ollama Hostinger
    ↓
[Ask] Content complexity?
    ├─► Simple → mistral-7b ($0.03/M)
    ├─► Standard → llama-3.3-70b ($0.20/M)
    └─► Complex → claude-3.5-sonnet ($9/M)
    ↓
[Calculate] Cost estimate × 1.5
    ↓
[Confirm] "~$X for Y episodes. Proceed?"
    ↓
[Execute] translate_srt.py
    ├─► Parse SRT → Strip HTML → Batch (25/req)
    ├─► Translate → Reapply formatting
    └─► Track progress → Queue failures
    ↓
[Done] X.fr.srt files created

Usage Example

Scenario: Translate 600 One Piece episodes EN→FR

Interaction:

1. Claude asks provider → User: "OpenRouter"
2. Claude asks complexity → User: "Standard (anime)"
3. Claude calculates: 600 × $0.007 × 1.5 = ~$6.30
4. User confirms → Script runs
5. Result: 600

   .fr.srt

   files, actual cost ~$4-7

Output structure:

/anime/onepiece/
├── Episode.001.eng.srt  (original)
├── Episode.001.fr.srt   (NEW)
├── translation_progress.json
├── failed_batches.json
└── translation.log