METADATA_DETECTION_IMPROVEMENTS.md•3.5 kB
# MCP SRT Translation - Metadata Detection Improvements
## Problem Solved
The original `detect_conversations` function was returning actual text content along with chunk information, which overwhelmed AI systems with too much information. This made it difficult for AI to make informed translation decisions.
## Solution Implemented
The `detect_conversations` function now returns **metadata only** - no actual text content. This provides AI with essential information for translation decision-making without overwhelming it with text.
## What the Function Now Returns
### Chunk Metadata (per chunk):
- **ID**: Unique chunk identifier
- **Time Range**: Start and end timestamps
- **Duration**: Chunk duration in milliseconds
- **Subtitle Count**: Number of subtitles in chunk
- **Speaker**: Detected speaker (if any)
- **Language Info**:
- Primary language detection (ar/en)
- Confidence score
- Language indicators (script type, numbers, punctuation)
- **Content Type**: dialogue, narration, question, general
- **Complexity**: low, medium, high
- **Translation Priority**: low, medium, high
- **Topic Keywords**: First 3 words for context (not full text)
### File-Level Summary:
- **Total Chunks**: Number of conversation chunks detected
- **Total Duration**: Complete file duration
- **Language Distribution**: Count of chunks by language
- **Speaker Distribution**: Count of chunks by speaker
## Benefits for AI Translation
1. **Informed Decision Making**: AI can assess complexity and priority before translation
2. **Language Detection**: AI knows which chunks are Arabic vs English
3. **Context Awareness**: Topic keywords provide translation context
4. **Efficiency**: No need to process full text for chunk analysis
5. **Scalability**: Works with large files without overwhelming AI
## Example Output
```json
{
"chunkCount": 59,
"totalDuration": 7525000,
"languageDistribution": { "ar": 59 },
"speakerDistribution": { "unknown": 59 },
"chunks": [
{
"id": "chunk-0",
"startTime": "0:0:2",
"endTime": "0:0:13",
"duration": 11000,
"subtitleCount": 2,
"speaker": null,
"languageInfo": {
"primary": "ar",
"confidence": 0.8,
"indicators": ["arabic_script"]
},
"contentType": "general",
"complexity": "medium",
"translationPriority": "low",
"topicKeywords": []
}
]
}
```
## Technical Implementation
### Key Methods Added:
- `extractTopicKeywords()`: Extracts first 3 words only
- `detectLanguageInfo()`: Heuristic language detection
- `detectContentType()`: Classifies content type
- `assessComplexity()`: Evaluates translation complexity
- `assessTranslationPriority()`: Ranks translation priority
- `analyzeLanguageDistribution()`: File-level language analysis
- `analyzeSpeakerDistribution()`: File-level speaker analysis
### Language Detection Logic:
- Arabic script detection: `[\u0600-\u06FF]`
- Latin script detection: `[A-Za-z]`
- Confidence scoring based on script indicators
- Support for mixed-language content
## Usage
The function is now optimized for AI translation workflows:
1. **Detection Phase**: Use `detect_conversations` to get metadata
2. **Analysis Phase**: AI analyzes metadata to plan translation strategy
3. **Translation Phase**: Use `translate_srt` or `translate_chunk` for actual translation
This separation of concerns allows AI to make smarter, more efficient translation decisions while maintaining high translation quality.