openclaw-videotranslate-skill

name	openclaw-videotranslate-skill
description	Translate video subtitles and generate multi-language dubbed audio tracks using OpenClaw's video translation skill with adaptive API scheduling
triggers	["translate video subtitles to another language","add dubbed audio track to video","convert video subtitles from English to Chinese","create multi-language video with TTS dubbing","extract and translate video captions","generate foreign language voiceover for video","translate and dub video content automatically","sync translated audio with video subtitles"]

OpenClaw Video Translation & Dubbing Skill

Skill by ara.so — Hermes Skills collection.

A professional-grade OpenClaw skill for translating video subtitles and generating dubbed audio across multiple languages. Features a 3D adaptive scheduler that handles API rate limits, automatic audio duration alignment, and lossless multi-track video output.

What It Does

This skill processes videos in two modes:

1. Subtitle-only mode: Translates subtitles while keeping original audio, outputs dual-subtitle video
2. Subtitle + Dubbing mode: Translates subtitles AND generates TTS audio in the target language, with automatic timing alignment

Key capabilities:

• Extracts embedded or external subtitles (SRT/VTT)
• Translates using LLM or web translation APIs
• Generates natural-sounding TTS audio with voice mapping
• Automatically stretches/compresses audio to match original timing
• Outputs lossless multi-track MKV videos
• Handles API rate limits with adaptive 3D scheduling (batch size, payload size, concurrency)

Installation

Prerequisites

# Install FFmpeg (required for video processing)
# macOS:
brew install ffmpeg

# Ubuntu/Debian:
sudo apt install ffmpeg

# Windows (use Chocolatey):
choco install ffmpeg

Install the Skill

# Clone the repository
git clone https://github.com/zbjincheng/openclaw-skill-videotranslate.git
cd openclaw-skill-videotranslate

# Install with pip
pip install -e .

# Or with development dependencies
pip install -e ".[dev]"

Environment Setup

# Copy environment template
cp .env.example .env

Edit .env to add your API credentials:

# Translation API (LLM or Web service)
TRANSLATION_ENDPOINT=https://api.openai.com/v1/chat/completions
TRANSLATION_CREDENTIAL=your_api_key_here

# TTS API (for dubbing mode)
TTS_ENDPOINT=https://api.openai.com/v1/audio/speech
TTS_CREDENTIAL=your_api_key_here

Configuration

The skill is configured via manifest.yaml. Here's a complete example:

# manifest.yaml
skill:
  name: translation_dubbing_skill
  version: "1.0.0"

parameters:
  # Required: Input video file
  video_path:
    type: path
    required: true
    description: "Path to source video file"
  
  # Optional: External subtitle file (extracts embedded if omitted)
  subtitle_path:
    type: path
    required: false
    description: "Path to .srt or .vtt subtitle file"
  
  # Required: Source and target languages
  source_language:
    type: string
    required: true
    default: "en"
    description: "Source language code (en, zh-CN, ja, es, fr, de, ko)"
  
  target_language:
    type: string
    required: true
    default: "zh-CN"
    description: "Target language code for translation"
  
  # Required: Processing mode
  processing_mode:
    type: enum
    required: true
    default: "subtitle_and_dubbing"
    
       
       
  
  
  
     
     
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Usage Patterns

Basic Subtitle Translation (No Dubbing)

from translation_dubbing_skill.entry import TranslationDubbingSkill
from translation_dubbing_skill.models import ProcessingMode

# Initialize skill
skill = TranslationDubbingSkill(
    video_path="input.mp4",
    source_language="en",
    target_language="zh-CN",
    processing_mode=ProcessingMode.SUBTITLE_ONLY,
    translation_provider="llm",
    translation_endpoint="https://api.openai.com/v1/chat/completions",
    translation_credential="${TRANSLATION_CREDENTIAL}",
    output_dir="./output"
)

# Run translation
result = skill.execute()
print(f"Output video: {result['output_video']}")
print(f"Subtitle file: {result['output_subtitle']}")

Full Translation + Dubbing

from translation_dubbing_skill.entry import TranslationDubbingSkill
from translation_dubbing_skill.models import ProcessingMode

skill = TranslationDubbingSkill(
    video_path="lecture.mp4",
    subtitle_path="lecture.srt",  # Optional: use external subtitles
    source_language="en",
    target_language="ja",
    processing_mode=ProcessingMode.SUBTITLE_AND_DUBBING,
    
    # Translation config
    translation_provider="llm",
    translation_endpoint="https://api.openai.com/v1/chat/completions",
    translation_credential="${TRANSLATION_CREDENTIAL}",
    
    # TTS config
    tts_provider="llm",
    tts_endpoint="https://api.openai.com/v1/audio/speech",
    tts_credential="${TTS_CREDENTIAL}",
    
    output_dir="./output"
)

# Execute with progress tracking
result = skill.execute()

Using External Subtitle Files

# When you already have subtitles extracted
skill = TranslationDubbingSkill(
    video_path="video.mp4",
    subtitle_path="video.srt",  # Provide external .srt or .vtt
    source_language="en",
    target_language="es",
    processing_mode=ProcessingMode.SUBTITLE_ONLY,
    translation_provider="web",
    translation_endpoint="https://translation-api.example.com/translate",
    translation_credential="${WEB_TRANSLATION_KEY}"
)

Custom Scheduler Configuration

# Fine-tune for rate-limited APIs
skill = TranslationDubbingSkill(
    video_path="long_video.mp4",
    source_language="en",
    target_language="zh-CN",
    processing_mode=ProcessingMode.SUBTITLE_AND_DUBBING,
    translation_provider="llm",
    translation_endpoint="${TRANSLATION_ENDPOINT}",
    translation_credential="${TRANSLATION_CREDENTIAL}",
    tts_provider="llm",
    tts_endpoint="${TTS_ENDPOINT}",
    tts_credential="${TTS_CREDENTIAL}",
    
    # Scheduler tuning
    scheduler_config={
        "max_concurrency": 5,          # Lower for strict rate limits
        "initial_batch_size": 10,      # Start with smaller batches
        "max_payload_tokens": 2000,    # Reduce token count per request
        "backoff_multiplier": 3.0,     # More aggressive backoff on 429
        "max_retries": 5
    }
)

Working with Subtitles

Parsing Subtitle Files

from translation_dubbing_skill.subtitle import SubtitleParser

# Parse SRT or VTT
parser = SubtitleParser()
entries = parser.parse("video.srt")

for entry in entries:
    print(f"[{entry.start_time} -> {entry.end_time}]")
    print(f"  {entry.text}")
    print(f"  Speaker: {entry.speaker_id}")

Creating Subtitle Objects

from translation_dubbing_skill.models import SubtitleEntry

entry = SubtitleEntry(
    index=1,
    start_time="00:00:01,500",
    end_time="00:00:04,200",
    text="Hello, welcome to the tutorial.",
    speaker_id="narrator",
    duration_ms=2700
)

Writing Translated Subtitles

from translation_dubbing_skill.subtitle import SubtitleSerializer

# After translation
translated_entries = [
    SubtitleEntry(
        index=1,
        start_time="00:00:01,500",
        end_time="00:00:04,200",
        text="你好,欢迎来到教程。",
        speaker_id="narrator",
        duration_ms=2700
    )
]

serializer = SubtitleSerializer()
serializer.write("output_zh.srt", translated_entries, format="srt")

Translation Providers

LLM Provider (OpenAI-compatible)

from translation_dubbing_skill.providers.translation import LLMTranslationProvider

provider = LLMTranslationProvider(
    endpoint="https://api.openai.com/v1/chat/completions",
    credential="${OPENAI_API_KEY}",
    model="gpt-4o",
    source_language="en",
    target_language="zh-CN"
)

# Translate batch of entries
translated = await provider.translate_batch(subtitle_entries)

The LLM provider:

• Automatically strips <think> reasoning blocks
• Parses JSON responses robustly
• Handles context window overflow with payload slicing
• Supports custom system prompts for translation style

Web Provider (Generic Translation API)

from translation_dubbing_skill.providers.translation import WebTranslationProvider

provider = WebTranslationProvider(
    endpoint="https://api.translator.com/v1/translate",
    credential="${WEB_API_KEY}",
    source_language="en",
    target_language="fr"
)

translated = await provider.translate_batch(subtitle_entries)

TTS Providers

LLM TTS Provider (OpenAI-compatible)

from translation_dubbing_skill.providers.tts import LLMTTSProvider

provider = LLMTTSProvider(
    endpoint="https://api.openai.com/v1/audio/speech",
    credential="${OPENAI_API_KEY}",
    model="tts-1-hd",
    voice_mapping={
        "narrator": "alloy",
        "speaker1": "echo",
        "speaker2": "nova"
    }
)

# Generate audio for translated subtitle
audio_bytes = await provider.synthesize(
    text="你好,欢迎来到教程。",
    speaker_id="narrator",
    target_duration_ms=2700  # Auto-stretches to match original
)

Web TTS Provider

from translation_dubbing_skill.providers.tts import WebTTSProvider

provider = WebTTSProvider(
    endpoint="https://tts-api.example.com/synthesize",
    credential="${TTS_API_KEY}",
    voice_mapping={"default": "zh-CN-XiaoxiaoNeural"}
)

audio = await provider.synthesize(
    text="翻译后的文本",
    speaker_id="default",
    target_duration_ms=3000
)

Audio Duration Alignment

The skill automatically aligns TTS audio to match original subtitle timing:

from translation_dubbing_skill.align import AudioAligner

aligner = AudioAligner()

# Stretch or compress audio to exact duration
aligned_audio = aligner.align_duration(
    audio_bytes=tts_output,
    target_duration_ms=2700,
    original_duration_ms=3200  # TTS was 3.2s, needs to be 2.7s
)

Uses time-stretching (not pitch-shifted) to preserve voice quality.

Video Muxing

Creating Multi-Track Output

from translation_dubbing_skill.mux import VideoMuxer

muxer = VideoMuxer()

output_path = muxer.mux_video(
    video_path="original.mp4",
    original_audio_path="original_audio.aac",
    dubbed_audio_path="dubbed_zh.mp3",
    original_subtitle_path="original_en.srt",
    translated_subtitle_path="translated_zh.srt",
    output_path="output_dual_track.mkv",
    source_language="en",
    target_language="zh-CN"
)

Output structure:

• Video: Original video stream (lossless copy)
• Audio Track 1 (default): Dubbed target language
• Audio Track 2: Original source language
• Subtitle Track 1 (default): Translated target language
• Subtitle Track 2: Original source language

Progress Tracking

Monitor Processing Status

from translation_dubbing_skill.progress import ProgressListener

class MyProgressListener(ProgressListener):
    def on_state_change(self, stage: str, current: int, total: int, message: str):
        print(f"[{stage}] {current}/{total} - {message}")
    
    def on_error(self, error: Exception):
        print(f"Error: {error}")
    
    def on_complete(self, result: dict):
        print(f"Complete: {result}")

skill = TranslationDubbingSkill(
    video_path="video.mp4",
    # ... other params
    progress_listener=MyProgressListener()
)

skill.execute()

Error Handling

Common Error Types

from translation_dubbing_skill.errors import (
    SubtitleExtractionError,
    TranslationAPIError,
    TTSAPIError,
    AudioAlignmentError,
    VideoMuxingError,
    RateLimitError
)

try:
    result = skill.execute()
except SubtitleExtractionError as e:
    print(f"Could not extract subtitles: {e}")
except TranslationAPIError as e:
    print(f"Translation failed: {e}")
    print(f"Failed entries: {e.failed_entries}")
except TTSAPIError as e:
    print(f"TTS synthesis failed: {e}")
except RateLimitError as e:
    print(f"Hit API rate limit: {e}")
    print(f"Retry after: {e.retry_after} seconds")
except VideoMuxingError as e:
    print(f"FFmpeg muxing failed: {e}")

Automatic Retry Logic

The 3D scheduler automatically handles:

• HTTP 429 (Rate Limit): Exponential backoff with jitter
• Context Window Overflow: Payload slicing and re-batching
• Network Errors: Configurable retry with backoff

# Scheduler handles retries automatically
skill = TranslationDubbingSkill(
    # ... params
    scheduler_config={
        "max_retries": 5,
        "backoff_multiplier": 2.0,
        "initial_backoff_seconds": 1.0
    }
)

Testing

Run the test suite:

# All tests
pytest

# Unit tests only (fast)
pytest -m "not integration"

# Integration tests (requires API keys)
pytest -m integration

# Specific test file
pytest tests/test_subtitle_parser.py

# With coverage
pytest --cov=src/translation_dubbing_skill

Troubleshooting

FFmpeg not found

# Check if FFmpeg is installed
ffmpeg -version

# If missing, install:
# macOS:
brew install ffmpeg

# Ubuntu:
sudo apt install ffmpeg

API Rate Limits

If you're hitting rate limits frequently:

# Reduce concurrency and batch sizes
skill = TranslationDubbingSkill(
    # ... params
    scheduler_config={
        "max_concurrency": 3,        # Lower concurrent requests
        "initial_batch_size": 5,     # Smaller batches
        "max_payload_tokens": 1500   # Smaller payloads
    }
)

Audio Sync Issues

If dubbed audio doesn't align well:

# The aligner uses time-stretching by default
# For better quality, ensure TTS generates close to target duration
from translation_dubbing_skill.align import AudioAligner

aligner = AudioAligner(
    stretch_algorithm="rubberband",  # Higher quality
    quality="high"
)

Subtitle Extraction Fails

# Check if video has embedded subtitles
ffmpeg -i video.mp4

# If no embedded subtitles, provide external file:
skill = TranslationDubbingSkill(
    video_path="video.mp4",
    subtitle_path="video.srt",  # External subtitle
    # ... other params
)

Memory Issues with Large Videos

Process in smaller chunks or reduce concurrency:

skill = TranslationDubbingSkill(
    # ... params
    scheduler_config={
        "max_concurrency": 2,
        "chunk_size": 50  # Process 50 subtitle entries at a time
    }
)

Advanced: Custom Providers

Implementing a Custom Translation Provider

from translation_dubbing_skill.providers.translation import TranslationProvider
from translation_dubbing_skill.models import SubtitleEntry
from typing import List

class MyCustomTranslationProvider(TranslationProvider):
    async def translate_batch(
        self, 
        entries: List[SubtitleEntry]
    ) -> List[SubtitleEntry]:
        translated = []
        for entry in entries:
            # Your custom translation logic
            translated_text = await self._translate(entry.text)
            translated.append(
                SubtitleEntry(
                    index=entry.index,
                    start_time=entry.start_time,
                    end_time=entry.end_time,
                    text=translated_text,
                    speaker_id=entry.speaker_id,
                    duration_ms=entry.duration_ms
                )
            )
        return translated
    
    async def _translate(self, text: str) -> str:
        # Call your translation API
        pass

Implementing a Custom TTS Provider

from translation_dubbing_skill.providers.tts import TTSProvider

class MyCustomTTSProvider(TTSProvider):
    async def synthesize(
        self,
        text: str,
        speaker_id: str,
        target_duration_ms: int
    ) -> bytes:
        # Your TTS logic
        audio_bytes = await self._generate_audio(text, speaker_id)
        
        # Use built-in aligner
        from translation_dubbing_skill.align import AudioAligner
        aligner = AudioAligner()
        aligned = aligner.align_duration(
            audio_bytes,
            target_duration_ms
        )
        return aligned

Command-Line Interface

If the skill provides a CLI wrapper:

# Subtitle-only mode
openclaw-videotranslate \
  --video input.mp4 \
  --source en \
  --target zh-CN \
  --mode subtitle_only \
  --translation-provider llm \
  --translation-endpoint "${TRANSLATION_ENDPOINT}" \
  --translation-credential "${TRANSLATION_CREDENTIAL}" \
  --output ./output

# Full dubbing mode
openclaw-videotranslate \
  --video lecture.mp4 \
  --subtitle lecture.srt \
  --source en \
  --target ja \
  --mode subtitle_and_dubbing \
  --translation-provider llm \
  --translation-endpoint "${TRANSLATION_ENDPOINT}" \
  --translation-credential "${TRANSLATION_CREDENTIAL}" \
  --tts-provider llm \
  --tts-endpoint "${TTS_ENDPOINT}" \
  --tts-credential "${TTS_CREDENTIAL}" \
  --output ./output

Best Practices

1. Use subtitle_only mode first to verify translation quality before dubbing
2. Start with small concurrency (3-5) and increase based on API limits
3. Monitor token usage - adjust max_payload_tokens to avoid context overflow
4. Use external subtitles when possible for better control
5. Test with short videos before processing long content
6. Keep original files - the skill doesn't modify source videos
7. Use environment variables for credentials, never hardcode

Resources

• GitHub: https://github.com/zbjincheng/openclaw-skill-videotranslate
• License: Apache-2.0
• Python Version: 3.11+
• FFmpeg Documentation: https://ffmpeg.org/documentation.html